Fourteen Yale faculty elected to American Academy of Arts & Sciences – Yale News

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Fourteen Yale faculty members who work across a range of disciplines were among the 252 accomplished individuals elected to the American Academy of Arts & Sciences last week.

Those elected are extraordinary people who help solve the worlds most urgent challenges, create meaning through art, and contribute to the common good, said the academy in announcing the new members, who include artists, scholars, scientists, and leaders in the public, nonprofit, and private sectors.

We are honoring the excellence of these individuals, celebrating what they have achieved so far, and imagining what they will continue to accomplish, said David Oxtoby, president of the American Academy of Arts & Sciences. This past year has been replete with evidence of how things can get worse; this is an opportunity to illuminate the importance of art, ideas, knowledge, and leadership that can make a better world.

The academy was founded in 1780 by John Adams, John Hancock, and others who believed the new republic should honor exceptionally accomplished individuals and engage them in advancing the public good.

The new members from Yale are:

Dirk Bergemann, the Douglass and Marion Campbell Professor of Economics and professor of computer science, whose research is focused on game theory, contract theory, venture capital, and market design. He has made important contributions to the theory of mechanism design and has pioneered work on consumer behavior and dynamic pricing structures.

Ronald Breaker, Sterling Professor of Molecular, Cellular, and Developmental Biology and professor of molecular biophysics and biochemistry, who conducts research on the advanced functions of nucleic acids, including ribozyme reaction mechanisms, molecular switch technology, next-generation biosensors, and catalytic DNA engineering. His lab established the first proofs that metabolites are directly bound by messenger RNA elements called riboswitches, among other important discoveries.

Nancy Brown, the Jean and David W. Wallace Dean of the Yale School of Medicine and C.N.H. Long Professor of Internal Medicine, who is committed to medical education and mentorship.

Her own research has defined the molecular mechanisms through which commonly prescribed blood pressure and diabetes drugs affect the risk of cardiovascular and kidney disease, and in her clinical practice, she has treated patients with resistant and secondary forms of hypertension.

Hui Cao, the John C. Malone Professor of Applied Physics, whose research focuses on understanding and controlling quantum optical processes in nanostructures. Her work involves nanofabrication, material characterization, optical measurement with high spatial, spectral, and temporal resolution, and numerical simulation.

BJ Casey, professor of psychology, who is considered a world leader in human neuroimaging and its use in typical and atypical development. She uses brain imaging to examine developmental transitions across the life span, especially during adolescence. She heads the Fundamentals of Adolescent Brain Lab, and is a member of the Justice Collaboratory at the Yale Law School and the Interdepartmental Neuroscience Program.

Valerie Hansen, the Stanley Woodward Professor of History, whose scholarly expertise is on China before 1600, Chinese religious and legal history, and the history of the Silk Road. She most recently authored The Year 1000: When Explorers Connected the World and Globalization Began.

Arthur L. Horwich, Sterling Professor of Genetics and professor of pediatrics, a pioneer in the field of molecular chaperones and their role in protein folding in the cell and in neurodegeneration. His discoveries have advanced an understanding of the relevance of protein misfolding in diseases such as Alzheimers.

Gregory Huber, the Forst Family Professor of Political Science and chair of the political science department, who studies American politics and political economy. He is interested in understanding how interactions among the mass public and elites, political institutions, and policies explain important outcomes.

Akiko Iwasaki, the Waldemar Von Zedtwitz Professor Immunobiology and Molecular, Cellular, and Developmental Biology, and professor of epidemiology (infectious diseases), whose research focuses on the mechanisms of immune defense against viruses at the mucosal surfaces. Most recently, she has advanced understanding of SARS-CoV-2 and virus mutations.

Marcia K. Johnson, Sterling Professor Emeritus of Psychology, whose work has focused on memory and cognition, especially how complex memories are created, memory disorders, and the relation between emotion and cognition. She directs the Memory and Cognition Lab at Yale, which also studies cognition changes associated with aging.

Frederick J. Sigworth, professor of cellular and molecular physiology and biomedical engineering and of molecular biophysics and biochemistry, whose research centers on the structure and function of ion channels, which are central to many physiological processes. His laboratory is developing new computational and experimental methods for imaging membrane proteins in membranes.

Daniel A. Spielman, Sterling Professor of Computer Science and professor of statistics and data science and of mathematics, whose broad research interests include the development of fast algorithms for large computational problems often found in machine learning, scientific computing, and optimization. He was awarded a MacArthur Fellowship for this work and most recently won the Held Prize for helping solve a theoretical problem that mathematicians had been working on for decades.

Kathryn Tanner, the Frederick Marquand Professor of Systematic Theology, whose research relates the history of Christian thought to contemporary issues of theological concern using social, cultural, and feminist theory. One of her contributions was to illuminate the role that Christian faith and practice can have on the global economic system.

Ebonya L. Washington, the Samuel C. Park Jr. Professor of Economics, who specializes in public finance and political economy with research interests in the interplay of race, gender, and political representation. She also studies behavioral motivations and consequences of political participation and the processes through which low-income Americans meet their financial needs.

Joining the Yale faculty members as new members are such noted individuals as neurosurgeon and CNN medical correspondent Sanjay Gupta; playwright, screenwriter, and actor Suzan-Lori Parks; songwriter and performer Robbie Robertson; atmospheric scientist Anne Thompson; and media entrepreneur and philanthropist Oprah Winfrey. Benjamin Franklin was elected a member in 1781, and since then other honorees have included Alexander Hamilton, Ralph Waldo Emerson, Charles Darwin, Margaret Mead, Martin Luther King Jr., Anthony Fauci, Antonin Scalia, and Anna Deavere Smith.

The list of all new members is available on the academys website.

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Fourteen Yale faculty elected to American Academy of Arts & Sciences - Yale News

Targeting oncoproteins with a positive selection assay for protein degraders – Science Advances

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RESULTS AND DISCUSSION

To develop a positive selection assay for protein degraders, we made a bicistronic lentivirus encoding (i) a POI fused to a modified version of deoxycytidine kinase (hereafter called DCK*) that converts the non-natural nucleoside 2-bromovinyldeoxyuridine (BVdU) into a poison (4) and (ii) green fluorescent protein (GFP). We reasoned that GFP could be used to mark reporter-positive cells, to FACS (fluorescence-activated cell sorting) sort for cells with the desired reporter mRNA levels, and to count cells in multiwell plate assays. In our initial proof-of-concept experiments, we used this virus to create 293FT cells expressing the IMiD target IKZF1 (1, 2, 5) fused to DCK* and compared them to cells expressing unfused DCK* or unfused IKZF1 (Fig. 1, A and B). As expected, the IMiD pomalidomide (POM) down-regulated DCK*-IKFZ1 and IKZF1 but not DCK* (Fig. 1B). We also confirmed that 293FT cells expressing DCK*-IKZF1 or unfused DCK* were more sensitive to BVdU than 293FT cells expressing IKZF1 alone or infected with an empty vector (EV) (Fig. 1C). The increased BVdU sensitivity of the DCK* cells relative to the DCK*-IKZF1 cells is likely explained by the higher protein levels of DCK* compared to DCK*-IKZF1 (Fig. 1B). Similar results were observed with cells expressing DCK*-K-Ras (G12V), DCK*-Cyclin D1, DCK*-FOXP3, and DCK*-MYC, indicating that DCK* remains active when fused to a variety of proteins (fig. S1). POM increased the BVdU median effective concentration (EC50) of cells expressing DCK*-IKZF1 but not of cells expressing DCK* (Fig. 1D).

(A) Vector schematic. DCK*, variant deoxycytidine kinase with Ser74Glu, Arg104Met, and Asp133Ala substitutions; V5, V5 epitope tag; GGS, Gly-Gly-Ser spacer; IRES, internal ribosomal entry site. (B) Immunoblot analysis of 293FT cells infected with the lentiviral vectors depicted in (A) and then treated with 1 or 10 M POM, as indicated by the triangles, for 24 hours. (C and D) Relative survival of 293FT cells infected with the lentiviral vectors depicted in (A) and then treated with the indicated concentrations of BVdU for 4 days. In (D), cells were also treated with 1 M (POM) starting 24 hours before BVdU was added. n = 3 biological replicates. (E and F) Number of GFP-positive 293FT cells infected to produce DCK* (E) or DCK*-IKZF1 (F) using the vectors in depicted in (A) and then treated with indicated concentrations of POM and BVdU in 384-well plate format. POM was added 24 hours before treatment with BVdU for 4 days. n = 4 biological replicates. (G) Immunoblot analyses of cells treated as in (E) and (F). (H) Fluorescence data of 384-well plate containing 293FT cells expressing DCK*-IKZF1 treated with DMSO (columns 1 to 11 and 24) or 1 M POM (columns 12 to 23), followed 24 hours later by the addition of 100 M BVdU for 4 days (columns 1 to 24).

Next, we seeded either the DCK*-IKZF1 cells or DCK* cells in 384-well plates and treated the wells with increasing amounts of POM or with dimethyl sulfoxide (DMSO). We added BVdU 24 hours later and measured cell viability 4 days thereafter by measuring the number of GFP-positive objects per well. POM again promoted the survival of the DCK*-IKZF1 cells, but not the DCK* cells, over a range of POM and BVdU concentrations (Fig. 1, E to G). In anticipation of using this assay for a high-throughput screen, we next seeded the DCK*-IKZF1 cells in 384-well plates and treated half the wells with POM and half the wells with DMSO, followed 24 hours later by BVdU (Fig. 1H). Measuring GFP-positive objects 4 days later produced a favorable Z value (0.7) for this assay.

Encouraged by these findings, we did a pilot screen with 293FT cells expressing DCK*-IKZF1 or unfused DCK* grown in 384-well plates and a library of ~2000 bioactive compounds, which included lenalidomide (LEN) and POM (Fig. 2, A to C). Each well received a different compound at a concentration of approximately 10 M by pin transfer, followed the next day by BVdU. BVdU was added at 100 M to the DCK*-IKZF1 cells and at 10 M to the DCK* cells to achieve comparable cell killing despite the higher levels of DCK* relative to DCK*-IKZF1 (fig. S2). Four days thereafter, the GFP fluorescence for each well was measured and converted to a z score based on the GFP fluorescence values for the other wells on its plate. LEN and POM scored positively (z > 2) in the DCK*-IKZF1 screen but not the DCK* screen (Fig. 2, B to E). Some compounds promoted the survival of both DCK* cells and the DCK*-IKZF1 cells, including compounds that interfere with BVdU uptake (e.g., dipyridamole) (6, 7) or incorporation into DNA (e.g., thymidine) (compare Fig. 2, B and C). Such assay positives could be largely eliminated by subtracting the DCK* z score for each chemical from its DCK*-IKZF1 z score (Fig. 2F). For comparative purposes, we also did a screen with the same 2000 bioactive compound collection using 293FT cells expressing a bicistronic mRNA encoding (i) an IKZF1Firefly luciferase (Fluc) fusion and (ii) Renilla luciferase (Rluc), using a decrease in the Fluc/Rluc ratio to identify IKZF1 degraders (Fig. 2, G to I, and fig. S3) (2). As expected for such a down assay, this screen underperformed the DCK*-IKZF1 up screen with respect to both signal to noise and the number of false positives, which included compounds that inhibit Cap-dependent translation (e.g., VX-11e or BIX02565) (810). Compounds that nonselectively inhibit transcription, translation, or protein folding would predictably be especially problematic for Fluc fusions with shorter half-lives than the Rluc internal control. Notably, the transcriptional inhibitor actinomycin D and the translational inhibitor cycloheximide did not promote the survival of the DCK*-IKZF1 cells at any concentration tested (fig. S4).

(A) Scheme for positive selection protein degradation assay. (B and C) Representative fluorescence data of 384-well plates containing 293FT cells expressing DCK* (B) or DCK*-IKZF1 (C) treated with compounds in the Selleck BioActive Library (one compound per well), followed 24 hours later by the addition of BVdU at the EC85 (10 and 100 M, respectively) for 4 days. BVdU was omitted in column 1. Columns 23 and 24 contained 10 M POM and 12.5 M dipyridamole (DiP), respectively. Library wells containing POM and DiP are indicated by the red and white arrows, respectively. (D and E) Z-distribution of GFP fluorescence of DCK* cells (D) and DCK*-IKZF1 cells (E) screened with the full Selleck BioActive Library. LEN and POM are indicted by the blue circle and red triangle, respectively. n = 2 biological replicates. (F) Corrected z scores obtained by subtracting z scores in (D) from z scores in (E). (G) Scheme for negative selection screening using the dual-luciferase reporter assay. (H and I) Z scores of Fluc/Rluc ratio of 293FT IKZF1-Fluc-IRES-Rluc cells after screening with the Selleck BioActive Library for 8 hours (H) or 4 days (I). n = 2 biological replicates.

As one way to minimize false positives, we seeded 384-well plates with a 1:1 mixture of 293FT cells expressing either (i) DCK*-IKZF1 and GFP or (ii) DCK* and TdTomato (Fig. 3A). Both POM and dipyridamole increased the number of GFP-positive cells, but dipyridamole was readily identified as a false positive by examining the TdTomato fluorescence channel (Fig. 3B). We then repeated these experiments in 384-well plate format, exposing the cells to 10 different concentrations of a small library of approximately 100 analogs of POM that we had synthesized, which included the known IKZF1 degraders LEN, POM, and avadomide (MI-2-65) (11) and several uncharacterized IMiD-like molecules from the literature (12) (Fig. 3C and tables S1 and S2). This library was generated to test whether our assay could correctly identify the known IKZF1 degraders and identify additional IKZF1 degraders made by alternative diversification of the aryl moiety of POM. LEN, POM, and avadomide all scored in our assay (Fig. 3C). In addition, several previously uncharacterized compounds, including MI-2-61 and MI-2-197, appeared to be at least as potent as POM in this screen and in confirmatory immunoblot assays (Fig. 3, C to F, and fig. S5). Our screen also correctly classified compounds that did not down-regulate IKZF1 in immunoblot assays, including some (e.g., MI-2-192 and MI-2-118) that still bound to cereblon in biochemical assays (fig. S5).

(A) Scheme for in-well GFP/TdTomato competition assay. 293FT cells were infected to produce DCK*-IKZF1 and GFP or DCK* and TdTomato using bicistronic vectors analogous to those depicted in Fig. 1A. (B) Top: Heatmap of the fold change (relative to treatment with DMSO) of GFP fluorescence of a 1:1 mixture of GFP-positive DCK*-IKZF1 and TdTomato-positive DCK* cells treated with 3.125, 6.25, 12.5, or 25 M POM or dipyridamole or with vehicle (DMSO) and followed 1 day later by the addition of 100 M BVdU for 4 days. Bottom: Heatmap of the fold change (relative to treatment with DMSO) of the ratio of GFP fluorescence to TdTomato fluorescence of the cells treated in (A). n = 2 biological replicates. (C) Heatmap of the fold change (relative to treatment with DMSO) of the ratio of GFP to TdTomato fluorescence of a 1:1 mixture of GFP-positive DCK*-IKZF1 and TdTomato-positive DCK* cells treated with 1.3 nM, 3.8 nM, 11.4 nM, 34 nM, 102 nM, 310 nM, 920 nM, 2.78 M, 8.33 M, and 25 M of the indicated IMiDs, as indicated by the triangles, or with vehicle (DMSO), and followed 1 day later by the addition of 100 M BVdU for 4 days. n = 2 biological replicates. (D) Immunoblot analysis of 293FT cells lentivirally transduced to express IKZF1-V5 and treated with the indicated IMiD derivatives for 24 hours using the same concentration range as in (C). (E) Structures of POM and IMiD MI-2-61. (F) Quantification of immunoblot data in (D); n = 2 biological replicates.

To begin looking for non-IMiD IKZF1 degraders, we screened ~546 metabolic inhibitors and anticancer drugs at 10 different concentrations using the DCK*-IKZF1 293FT cells in 384-well plate format (tables S3 and S4) (13). In parallel, we counterscreened against unfused DCK* cells. Spautin-1 (14), like POM, promoted the survival of the DCK*-IKZF1 cells, but not the DCK* cells, in a dose-dependent manner (Fig. 4, A to D). We confirmed that Spautin-1 down-regulated DCK*-IKZF1 and V5-tagged exogenous IKZF1 but not DCK* (Fig. 4E). IKZF1-V5 was among the 100 most down-regulated proteins after 24 hours of Spautin-1 treatment, as determined by quantitative mass spectrometry proteomics (fig. S6 and table S5). Until the direct target of Spautin-1 linked to IKZF1 turnover is known, it is impossible to know how many of these changes in protein abundance are direct versus indirect and on-target versus off-target. Notably, Spautin-1, unlike POM, down-regulated IKZF1 in cells lacking cereblon (Fig. 4F).

(A) Chemical structure of Spautin-1. (B) GFP fluorescence of DCK*-IKZF1 and DCK* 293FT cells treated with ranolazine, Spautin-1, and resveratrol at concentrations of 25 M, 8.33 M, 2.78 M, 920 nM, 310 nM, 102 nM, 34 nM, 11.4 nM, 3.8 nM, and 1.3 nM, as indicated by the triangle, followed 24 hours later by the addition of BVdU at the EC85. Shown for comparison are cells treated with POM (10 M) or dipyridamole (DiP) (12.5 M) before adding BVdU. n = 2 biological replicates. (C and D) Quantification of GFP fluorescence from (B) for Spautin-1 (C) and for an analogous titration with POM (D). (E) Immunoblot analysis of 293FT cells infected with lentiviruses as in Fig. 1A and treated with the indicated concentrations of Spautin-1 for 24 hours. (F) Immunoblot analysis of isogenic 293FT CRBN +/+ and CRBN / cells transduced to express IKZF1-V5 and treated with the indicated concentrations of Spautin-1 or POM (1 M) for 24 hours. (G) Immunoblot analysis of 293FT cells stably expressing IKZF1-V5 and simultaneously treated with MLN7243 (1 M), MLN4924 (1 M), MG132 (1 M), Spautin-1 (10 M), or POM (1 M) for 24 hours as indicated. (H and I) Immunoblot (H) and RT-qPCR (I) analysis of KMS11 multiple myeloma cells treated with indicated concentrations of Spautin-1 or POM (1 M) for 24 hours. n = 3 biological replicates.

Spautin-1 reportedly suppresses autophagy by inhibiting the USP10 and USP13 deubiquitinases (14). IKZF1 protein levels were not decreased after small interfering RNAmediated down-regulation of USP10, alone or in combination with USP13 (fig. S7A), and Spautin-1s ability to down-regulate IKZF1 was not altered when one or both of these proteins were suppressed (fig. S7, B and C). Moreover, Spautin-1 down-regulated IKZF1 in 293FT cells in which autophagy was disabled by CRISPR-Cas9mediated disruption of ATG7, Beclin1, or FIP200 (fig. S8).

In contrast, down-regulation of IKZF1 by Spautin-1 was blocked by compounds that inhibit either the E1 ubiquitin activating enzyme or the proteasome (Fig. 4G). Down-regulation of IKZF1 by Spautin-1 was not, however, blocked by an inhibitor of neddylation, which is required for cullin-dependent ubiquitin ligases [e.g., the cereblon-containing ubiquitin E3 ligase that is coopted by the IMiDs (1, 2, 5)] (Fig. 4G). Down-regulation of exogenous IKZF1 by Spautin-1 requires the IKZF1 N-terminal region containing IKZF1s first zinc finger domain (ZF1) but not the IKZF1 zinc finger domain (ZF2) targeted by the IMiDs (fig. S9, A and B) (15, 16). The down-regulation of the N terminus of IKZF1 was similarly blocked by compounds that inhibit either the E1 ubiquitin activating enzyme or the proteasome but not by inhibitors of neddylation (fig. S9C). Preliminary structure-activity relationship studies identified both active and inactive Spautin-1 derivatives (fig. S10), suggesting that down-regulation of IKZF1 by Spautin-1 reflects a specific protein-binding event and that Spautin-1s potency and specificity can be optimized further.

The experiments described above implied that Spautin-1 posttranscriptionally regulates IKZF1. Nonetheless, Spautin-1 also suppressed exogenous IKZF1 mRNA levels in 293FT cells (fig. S11). However, Spautin-1 suppressed endogenous IKZF1 protein levels in KMS11 and L363 myeloma cells at concentrations that minimally suppressed IKZF1 mRNA levels (Fig. 4, H and I, and fig. S12, A to C). Spautin-1 did not down-regulate IKZF1 in all myeloma cells tested (fig. S12, D and E). The biochemical basis for this variability is not clear.

Notably, down-regulation of IKZF1 by Spautin-1 occurs much more slowly than with IMiDs, suggesting that its effect on IKZF1 is indirect (fig. S13). Nonetheless, its ability to score in a positive selection assay, as well as its inability to down-regulate IKZF1 in some myeloma lines, suggests that it is not broadly toxic at concentrations that down-regulate IKZF1. We are currently seeking the direct Spautin-1 target linked to IKZF1 turnover using genetic and biochemical tools.

One advantage of positive selection assays is their enablement of pooled screens. Our positive selection assay, however, uses a suicide gene. Some suicide genes cause bystander killing that could confound their use in pooled screens. In pilot studies, however, we confirmed that DCK*-IKZF1 cells rapidly outgrew DCK* cells in cocultures treated with IMiDs and BVdU (fig. S14A) and that the DCK* single guide RNA (sgRNA) was rapidly and specifically enriched relative to the control sgRNA in Cas9-positive 293FT cells expressing either DCK*-IKZF1 or DCK*-FOXP3 and then treated with BVdU (fig. S14, B and C). Therefore, bystander killing is negligible in this system.

To begin to address the general utility of our methodology, as well as its ability to function in a pooled format, we next did experiments with ASCL1 in place of IKZF1. ASCL1 is an undruggable lineage-specific transcription factor that is required for survival in many small cell lung cancers (SCLCs) and neuroblastomas (1719). We made Jurkat T cells that express Cas9 and either (i) DCK*, (ii) the neural/neuroendocrine lineagespecific transcription factor ASCL1, (iii) DCK*-ASCL1, or (iv) ASCL1-DCK* (Fig. 5A and fig. S15A). Jurkat cells were chosen because they are easily grown and expanded in suspension cultures. ASCL1-DCK* was chosen for further study because we could not generate cells producing high levels of DCK*-ASCL1 (fig. S15A). We first confirmed that ASCL1-DCK* expression sensitized Jurkat cells to BVdU and that this was partially reversed after down-regulating the fusion with ASCL1 sgRNAs (Fig. 5, A and B, and fig. S16, A and B). Cas9 expression was also slightly attenuated in the ASCL1-DCK* cells over time for unclear reasons (Fig. 5A). Nonetheless, these cells efficiently edited a GFP-based reporter of Cas9 activity within 10 days of receiving a GFP sgRNA (fig. S15, B and C). Next, we infected the ASCL1-DCK* and DCK* cells with a lentiviral sgRNA library targeting 788 genes (seven sgRNAs per gene) that encode druggable proteins (table S6). Ten days later (to allow time for gene editing), the cells were split and grown in the presence of 200 or 500 M BVdU for an additional 2 weeks (fig. S16C). We then determined sgRNA abundance by next-generation sequencing of genomic DNA and analyzed relative enrichment of sgRNAs compared to the time point before BVdU treatment (fig. S16D). We identified multiple sgRNAs against CDK2 that were markedly enriched at both BVdU concentrations in the ASCL1-DCK* cells but not the DCK* cells (Fig. 5C, fig. S16, D and E, and table S7).

(A) Immunoblot analysis of Jurkat cells first infected to express Cas9 and then superinfected to express exogenous ASCL1, DCK*, or the ASCL1-DCK* fusion. NCI-H69 cells are included as a benchmark for ASCL1 endogenous expression. (B) Growth inhibition (%), based on viable cell numbers relative to untreated controls, of the indicated cell lines from (A) treated with BVdU for 6 days. n = 2 biological replicates. (C) Hypergeometric analysis of BVdU positive selection CRISPR-Cas9 screen on day 25 relative to day 10 (early time point before BVdU treatment) of ASCL1-DCK* Cas9 Jurkat cells treated with 500 M BVdU. n = 2 biological replicates. (D) Quantification of fold change in mCherry:BFP ratio after 18 days of 500 M BVdU or DMSO (0) treatment of ASCL1-DCK* Cas9 Jurkat cells expressing the indicated sgRNAs and mCherry or a nontargeting control sgRNA and blue fluorescent protein (BFP) (initially mixed 1:3). n = 3 biological replicates. (E) Immunoblot and (F) RT-qPCR analysis of ASCL1-DCK* Cas9 Jurkat cells superinfected to express the indicated sgRNAs. n = 4 biological replicates. (G) Immunoblot analysis of Jurkat cells first infected with a lentivirus to stably express exogenous ASCL1, then infected with Dox-inducible (DOX-On) sgRNA-resistant CDK2 wild-type (WT) or CDK2 kinase-dead (KD) mutant, and lastly superinfected with a CDK2 or nontargeting sgRNA. Following superinfection with the sgRNA lentiviruses, cells were grown in DOX to maintain exogenous CDK2 expression. n = 4 biological replicates. Exo, exogenous CDK2; Endo, endogenous CDK2. Error bars represent SD. ns, nonsignificant; *P < 0.05; ***P < 0.001; ****P < 0.0001.

In validation studies, ASCL1-DCK* Jurkat cells expressing CDK2 sgRNAs outcompeted ASCL1-DCK* cells expressing control sgRNAs in the presence of BVdU but not in the presence of DMSO (Fig. 5D and fig. S16F). CDK2 sgRNAs also posttranscriptionally down-regulated ASCL1-DCK* protein levels in the Jurkat cells (Fig. 5, E and F) and endogenous, unfused, ASCL1 in human SCLC lines (NCI-H1876 and NCI-H2081) (Fig. 6, A and B, and fig. S18, A and B). Down-regulation of exogenous ASCL1 in Jurkat cells treated with a CDK2 sgRNA was rescued by an sgRNA-resistant CDK2 complementary DNA (cDNA) encoding wild-type CDK2 but not kinase-dead CDK2 (Fig. 5G). The kinase-dead CDK2 was, however, produced at slightly lower levels, presumably because it is less stable or because of its known dominant-negative effects due to cyclin sequestration (2022).

(A) Immunoblot and (B) RT-qPCR analysis of the NCI-H1876 SCLC cell line that endogenously expresses ASCL1 infected to express the indicated sgRNAs. n = 3 biological replicates. (C and E) Immunoblot and (D and F) RT-qPCR analysis of NCI-H1876 human SCLC cells (C and D) and 97-2 mouse SCLC cells (E and F) after treatment with the CDK2 PROTAC degraders (TMX-2138 and TMX-2172) or the indicated negative controls, all used at 500 nM for either 36 hours (C and D) or 8 hours (E and F). Neg Deg, negative control degrader ZXH-7035. n = 3 biological replicates. (G) Immunoblot analysis and (H) quantification of ASCL1 protein levels in 97-2 cells first treated with the CDK2 PROTAC degrader or negative control (500 nM) for 4 hours and then treated with cycloheximide (CHX) (150 g/ml) for the indicated times. S.E., short exposure; L.E., long exposure. n = 4 biological replicates. In all experiments, error bars represent SD except in (H), where error bars represent SEM. *P < 0.05; ***P < 0.001; ****P < 0.0001.

CDK2 has been well recognized as a potential anticancer target. The development of selective small-molecule CDK2 inhibitors, however, has been hampered by their off-target effects on other CDK family members, especially the broadly essential kinase CDK1. We verified that well-established CDK2 inhibitor dinaciclib (23) down-regulated both ASCL1 protein and mRNA levels (fig. S17, A to D), potentially due to its polypharmacological activity on both CDK2 and other CDKs such as CDK9 (23, 24). We obtained, however, two small-molecule CDK2 degraders (TMX-2138 and TMX-2172) that more selectively target CDK2 through recruitment of cereblon (25). Both of these compounds down-regulated ASCL1 protein levels in both human (NCI-H1876 and NCI-H1092) and mouse (97-2 and 188) SCLC lines (Fig. 6, C to F, and fig. S18, C to F). For unclear reasons, ASCL1 was down-regulated more rapidly in the mouse lines than in the human lines. We focused on TMX-2172 because TMX-2138 also suppressed ASCL1 mRNA levels in the mouse cells (Fig. 6F and fig. S18F). TMX-2172 decreased the half-life of ASCL1 protein (Fig. 6, G and H), consistent with posttranscriptional regulation of ASCL1 by CDK2.

We conducted our screens in IKZF1-independent 293FT cells rather than IKZF1-dependent myeloma cells and in ASCL1-independent Jurkat cells rather than in ASCL1-dependent SCLC cells in an attempt to preserve positive selection. It is possible, however, that some degradation mechanisms will be highly context dependent and restricted to the therapeutic target cell of interest. We also anticipate that some DCK* fusion proteins will not be functional due to steric or conformational effects. This might be remedied by fusing DCK* to the alternative POI terminus (N-terminus versus C-terminus), by exploring different linkers, or using alternative suicide proteins.

IMiDs are important multiple myeloma drugs, but loss of cereblon has emerged as an important mechanism of IMiD resistance (2628). Identification of Spautin-1s mechanism of action could eventually lead to drugs for circumventing this problem.

ASCL1 is a sequence-specific DNA binding transcription factor that would classically be deemed undruggable and serves as a lineage addiction oncoprotein in neural crestderived tumors, such as SCLCs and neuroblastomas (1719, 29). Genetic studies in Xenopus indicate that CDK2 regulates ASCL1 function and that ASCL1 contains multiple potential CDK2 phosphorylation sites that prevent it from inducing neuronal differentiation (30, 31). CDK2 is a potential dependency in some neuroblastomas (3234). CDK2 and N-MYC drive the accumulation of phosphorylated ASCL1 in undifferentiated neuroblastomas (31). Conversely, loss of CDK2 activity, such as through retinoic acidmediated induction of p27 or small-molecule inhibitors, is associated with neuroblastoma differentiation and decreased tumor formation (3237). It will be important to determine how, mechanistically, CDK2 regulates ASCL1 turnover. In particular, we have not yet shown that the regulation of ASCL1 by CDK2 is direct. Nonetheless, our study provides further support for CDK2 as a potential therapeutic target in SCLC and neuroblastoma.

The discovery that the IMiDs reprogram the cereblon ubiquitin E3 ligase for therapeutic benefit has galvanized interest in identifying compounds that can degrade, directly and indirectly, otherwise undruggable proteins. Sometimes, one can engineer heterobifunctional degrader molecules consisting of a POI-binding moiety, a linker, and a ubiquitin-ligase recruitment moiety (38). This approach requires a ligand with suitable binding affinity for the POI, and identifying a successful linker often requires multiple iterations of trial and error. Moreover, this approach fails to harness the many other ways a chemical could directly or indirectly degrade a protein, such as by inhibiting a deubiquitinating enzyme, displacing an interacting protein, or altering protein folding or subcellular localization. A trivial way to down-regulate proteins, especially those with naturally rapid turnovers, is to poison transcription or translation. The screening methodology described here should facilitate the characterization of designer degraders as well as enable mechanism-agnostic searches for compounds and targets that regulate the abundance of previously undruggable proteins.

293FT cells were originally obtained from the American Type Culture Collection (ATCC). 293AD cells were from Cell Biolabs. 293FT CRBN / cells were made by CRISPR-Cas9 editing (see below). 293FT and 293AD cells were maintained in Dulbeccos minimum essential medium (DMEM) supplemented with 10% fetal bovine serum (FBS), penicillin (100 U/ml), and streptomycin (100 g/ml). KMS11, KMS34, MM.1S, and L363 human multiple myeloma cells [gift of K. Anderson (Dana-Farber Cancer Institute)] and Jurkat cells (obtained from ATCC in September 2016) were maintained in RPMI medium supplemented with 10% FBS, penicillin (100 U/ml), and streptomycin (100 g/ml). NCI-H1876 (obtained in November 2016), NCI-H1092 (obtained in November 2018), and NCI-H2081 (obtained in November 2018) were obtained from ATCC. NCI-H1876, NCI-H1092, and NCI-H2081 cells were maintained in DMEM/F12 media supplemented with HITES [10 nM hydrocortisone (Sigma-Aldrich, #H0135), insulin (0.01 mg/ml), human transferrin (0.0055 mg/ml), sodium selenite (0.005 g/ml) (ITS, Gemini, #400-145), and 10 nM -estradiol (Sigma-Aldrich, #E2257)] and 5% FBS. The cell lines 188 and 97-2 were isolated from genetically engineered SCLC mouse tumors (see below for description of cell line generation) and maintained in RPMI 1640 media supplemented with HITES and 10% FBS. All cells were grown at 37C in the presence of 5% CO2. Fresh aliquots of cells were thawed every 4 to 6 months.

The following compounds were purchased: POM (Selleck, #S1567), LEN (Selleck, #S1029), MG132 (N-carbobenzyloxy-l-leucyl-l-leucyl-l-leucinal; Thermo Fisher Scientific, #47479020MG), MLN4924 (Active Biochem, #A-1139), MLN7243 (Thermo Fisher Scientific, #NC1129906), Spautin-1 (BioTechne; #5197/10), cycloheximide (VWR, #97064-724), BVdU (Chem-Impex International Inc., catalog no. 27735), actinomycin D (Thermo Fisher Scientific, #11805017), and dinaciclib (Selleck, #S2768).

CDK2 degraders. Synthesis and characterization of the small-molecule CDK2 degraders TMX-2138 and TMX-2172 and the negative degrader ZXH-7035 (structurally similar to the CDK2 binding region of TMX-2138 and TMX-2172 but lacking the cereblon recruiting element) are described previously (25).

293FT cells stably transduced with bicistronic lentiviruses expressing (i) a fusion between DCK* and the POI and (ii) GFP were seeded at a density of 0.25 106 cells/ml in 25 ml of media in a 15-cm dish (Corning, 353025). Two days later, the cells were counted and resuspended in media to a concentration of 10 106 cells/ml. The sample was passed through a mesh strainer (Thermo Fisher Scientific, #352235). The GFP fluorescence of the cells was analyzed by FACS using a Fortessa Aria II instrument. The brightest 1% of cells were collected in an Eppendorff tube, replated in a six-well dish, and expanded. This process was repeated three to four more times to isolate cells expressing the desired GFP levels.

Jurkat cells were first transduced with PLL3.7-Cas9-IRES-Neo. Neomycin-resistant cells with confirmed Cas9 expression were then superinfected with pLX304-ASCL1-DCK*-IRES-GFP or pLX304-DCK*-IRES-GFP, and transduced cells were selected with blasticidin. The blasticidin-resistant cells were then prepared for FACS sorting as above. In total, the brightest 1% of cells were FACS-sorted three times to isolate cells expressing the desired GFP levels. Jurkat cells expressing Cas9 and DCK*-FOXP3 were made in an analogous manner.

Cell pellets were lysed in a modified EBC lysis buffer [50 mM tris-Cl (pH 8.0), 250 mM NaCl, 0.5% NP-40, and 5 mM EDTA] supplemented with a protease inhibitor cocktail (cOmplete, Roche Applied Science, #11836153001). Whole-cell extracts were quantified using the Bradford protein assay. For experiments with 293FT cells, 10 g of protein per sample was boiled after adding 3 sample buffer (6.7% SDS, 33% glycerol, 300 mM dithiothreitol, and bromophenol blue) to a final concentration of 1; resolved by SDSpolyacrylamide gel electrophoresis (PAGE) using either 12.5% SDS-PAGE, Mini-Protean TGX 4 to 15% gels (Bio-Rad, #456-1086), or Criterion TGX gels (Bio-Rad, #5671085); semi-dry transferred onto nitrocellulose membranes; blocked in 5% milk in tris-buffered saline with 0.1% Tween 20 (TBS-T) for 1 hour; and probed with the indicated primary antibodies overnight at 4C. Membranes were then washed three times in TBS-T, probed with the indicated horseradish peroxidaseconjugated secondary antibodies for 1 hour at room temperature, and washed three times in TBS-T. Bound antibodies were detected with enhanced chemiluminescence Western blotting detection reagents [Immobilon (Thermo Fisher Scientific, #WBKLS0500) or SuperSignal West Pico (Thermo Fisher Scientific, #PI34078)]. The primary antibodies and dilutions used were as follows: rabbit anti-IKZF1 (Cell Signaling Technology, #5443S) at 1:1000, rabbit anti-V5 (Bethyl Laboratories, #A190-120A) at 1:1000, rabbit anti-DCK (Abcam, #151966) at 1:2000, rabbit anti-ASCL1 (Abcam, #ab211327) at 1:1000, rabbit anti-CDK2 (Cell Signaling Technology, #2546S) at 1:1000, mouse anti-P62 (Abcam, #ab56416) at 1:1000, rabbit antiLC3-I and LC3-II (Cell Signaling Technology, #3868S) at 1:1000, rabbit anti-ATG7L (Cell Signaling Technology, #8558S) at 1:1000, rabbit anti-Beclin1 (Cell Signaling Technology, #3495S) at 1:1000, rabbit anti-FIP200 (Cell Signaling Technology, #12436S) at 1:1000, rabbit -phospho-RB1 S795 (Cell Signaling Technology, #9301P) at 1:1000, mouse -RB1 4H1 (Cell Signaling Technology, #9309S) at 1:1000, mouse anti-actin (Sigma-Aldrich; clone AC-15, #A3854) at 1:25,000, mouse anti-Cas9 (Cell Signaling Technology, #14697) at 1:1000, mouse anti-vinculin (Sigma-Aldrich; #V9131) at 1:10,000, and mouse anti-actin (Cell Signaling Technology, #3700S) at 1:10,000. The secondary antibodies and dilutions used were goat anti-mouse (Pierce) at 1:10,000 and goat anti-rabbit (Pierce) at 1:5000.

A total of 750,000 293FT IKZF1-V5 cells per well were seeded in six-well dishes in a volume of 2 ml. On the next day, drugs to be added were diluted from a 10 mM stock (stored at 20C) into 0.5 ml of media before being added to the cells. The final volume in each well was then made up to 3 ml by adding a second drug in 0.5 ml or adding 0.5 ml of drug-free media.

Myeloma cells were seeded in 10-cm plates at a density of 0.75 106 cells/ml in a total volume of 8 ml. On the next day, the desired drug was diluted from a 10 mM stock (stored at 20C) into 1 ml of media, which was added to the intended well to achieve the desired final concentration. The final volume in each well was then made up to 10 ml by adding a second drug in 1 ml or adding 1 ml of drug-free media. After 24 hours, the cells were harvested for analysis.

293FT cells stably transduced with bicistronic lentiviruses encoding (i) IKZF1, DCK*, or DCK*-IKZF1 (IKZF1 cells, DCK* cells, and DCK*-IKZF1 cells, respectively) and (ii) GFP, as well as corresponding EV control cells, were seeded into six-well plates at 20,000 cells per well in 2.5 ml of media. The next day, 1 M BVdU dissolved in DMSO was diluted into media to prepare 6 stock solutions of BVdU at concentrations of 6 mM, 600 M, 60 M, 6 M, and 600 nM. For each stock solution, DMSO concentration was adjusted to a final concentration of 0.6%. Each well in the six-well dish received 0.5 ml of a 6 stock solution of BVdU to achieve final concentrations of 1 mM, 100 M, 10 M, 1 M, and 100 nM, respectively. A total of 0.5 ml of media with 0.6% DMSO was added to the sixth well as a control. Four days later, the cells were collected and counted using a Vi-Cell XR cell counter.

293FT cells were seeded as above at a density of 20,000 cells per well in 2 ml of media. A stock solution of 10 mM POM in DMSO was diluted into media to prepare a 6 M stock solution of POM. Cells received 0.5 ml of the 6 M POM stock solution to achieve an eventual final concentration of 1 M or 0.5 ml of control media. The next day, BVdU was added as described above, and cell proliferation was analyzed as above.

Jurkat cells expressing Cas9 and either ASCL1, ASCL1-DCK*, or DCK* alone were plated at 0.05 106 cells/ml per well in a 12-well plate and treated with increasing concentrations of BVdU (0, 1, 10, 100, 200, or 500 M). Six days later, the cells were counted using a Vi-Cell XR cell counter. For ASCL1 sgRNA rescue experiments, Jurkat cells expressing Cas9 and ASCL1-DCK* cells were superinfected with pLentiGuide-Purobased lentiviruses expressing sgRNAs targeting ASCL1 or a nontargeting sgRNA (sgCTRL). The cells were selected with puromycin, and expression of ASCL1 was analyzed by immunoblot analysis. The cells were then subjected to the BVdU assay as described above.

293FT cells stably transduced with bicistronic lentiviruses encoding (i) DCK*, DCK*-IKZF1, DCK*-K-RAS (G12V), DCK*-Cyclin D1, DCK*-PAX5, DCK*-FOXP3, and DCK*-MYC and (ii) GFP, as well as corresponding EV control cells, were seeded into 384-well plates (Corning, #3764) at 200 cells per well in 30 l of media. The next day, 1 M BVdU dissolved in DMSO was diluted into media to prepare 4 stock solutions of BVdU at concentrations of 4 mM, 2 mM, 1 mM, 400 M, 200 M, 100 M, 40 M, 20 M, 4 M, and 400 nM. On each plate, 10 l of each stock concentration of BVdU was added to two columns (32 wells) to achieve final concentrations of 1 mM, 500 M, 250 M, 100 M, 50 M, 25 M, 10 M, 5 M, 1 M, and 100 nM. Ten microliters of control media was added to four columns. Four days later, the cells were analyzed using an Acumen laser scanning cytometer (TTP Biosciences). GFP fluorescence was quantified by defining the metric GFP-positive object to identify GFP-positive cells while excluding debris or cell fragments.

Determination of Z. DCK* and DCK*-IKZF1 cells were seeded into 384-well plates (Corning, #3764) in 30 l of media at a density of 200 cells per well and allowed to adhere overnight. For each plate, an HPD300 dispenser (Hewlett-Packard) was used to add 4 nl of POM to a final concentration of 1 M to half the wells. An equal volume of DMSO was added to the other half of the plate. The next day, BVdU was added to the entire plate at a concentration of 10 M for the plate of DCK* cells and 100 M for the plate of DCK*-IKZF1 cells. Four days later, the cells were analyzed using an Acumen laser scanning cytometer (TTP Biosciences). The number of GFP-positive objects in each well was measured, and a Z statistic was calculated comparing the POM-treated wells to the DMSO-treated wells.

High-throughput chemical library screening. DCK* and DCK*-IKZF1 293FT cells were seeded into 384-well plates (Corning, #3764) at a density of 200 cells per well in a volume of 30 l of media. A custom-built Seiko Compound Transfer Robot was used to pin transfer 100 nl per well of small-molecule stock solutions from the wells of a drug library plate to the wells of assay plate, such that each well of the assay plate received a unique small molecule. An HPD300 non-contact dispenser (Hewlett-Packard) was used to dispense 100 nl of POM and dipyridamole into columns 23 and 24 and to add 100 nl of DMSO to columns 1 and 2. The final concentrations of POM and dipyridamole were 10 M and 12.5 M, respectively. The next day, 10 l of BVdU stock solution was added to columns 2 to 24 of each of the DCK-IKZF1 and DCK* assay plates, respectively. The concentration of the BVdU stock solution was calculated to achieve the desired final concentration of BVdU (10 M in DCK* assay plates and 100 M in DCK*-IKZF1 assay plates) in the well.

After 4 days, the GFP fluorescence of each assay plates was quantified using an Acumen scanning laser cytometer. For each plate, the average and SD of the GFP fluorescence of wells in columns 3 to 22 were calculated. For each well on an assay plate, the GFP fluorescence was converted to a z score using the formula: z(well) = [GFP (well) GFP (plate)] / GFP (plate), where GFP (plate) is the mean GFP fluorescence for that plate and GFP (plate) is the SD for that plate.

High-throughput chemical library screening (in-well competition assay). DCK*-IKZF1 (GFP) and DCK* (Td) cells were mixed together in a 1:1 ratio and then seeded into 384-well plates (Corning, #3764) at a density of 400 cells per well in 30 l of media. Pin transfer from IMiD derivative library plates and dispensation of POM and dipyridamole were performed as described above. The next day, 10 l of BVdU stock solution was added to columns 2 to 24 of each plate to achieve a final concentration of 100 M. After 4 days, the GFP and TdTomato fluorescence of each assay plate was quantified using an Acumen scanning laser cytometer. For each well, the ratio of GFP/tdTomato fluorescence was calculated and normalized to the values in the well that received DMSO and BVdU. The resulting values were converted to a heatmap using Morpheus (Broad Institute).

Determination of Z. 293FT IKZF1-Fluc cells were seeded into 96-well plates at a density of 2000 cells per well in a volume of 50 l of media and incubated overnight at 37C. The next day, an additional 50-l media and POM (final concentration of 2 M) was added to 30 wells of the plate (rows B to G, columns 2 to 6). Control media containing DMSO was added to 30 wells of the plate (rows B to G, columns 7 to 11). A Dual-Glo assay (Promega) was performed by first aspirating all media from the tissue culture plates. Twenty-five microliters of a 1:1 dilution of Dual-Glo luciferase assay reagent in phosphate-buffered saline (PBS) was added to wells and incubated for 10 min. Luminescent signal was measured with a plate reader. Stop & Glo reagent (12.5 l) was then added to the wells, incubated for 10 min, and luminescent signal was measured. The average Fluc/Rluc ratio for cells treated with DMSO and POM was calculated, and a Z statistic was calculated.

High-throughput library screening using Fluc/Rluc readout. IKZF1-Fluc assay plates were generated by plating 293FT IKZF1-Fluc cells into 384-well plates. For the 8-hour treatment arm, cells were plated at a density of 4000 cells per well. A custom-built Seiko Compound Transfer Robot was used to pin transfer 100 nl per well of small molecule from the drug library plate to the assay plate, such that each well of the assay plates received a unique small molecule. After 8 hours, the plates were shaken out and blotted on clean paper towels to remove the media. A Thermo Multidrop Combi was used to dispense 20 l of a 1:1 dilution of Dual-Glo luciferase reagent, and the plates were shaken for 10 min. Firefly luciferase signal was quantified using an EnVision plate reader. A Thermo Multidrop Combi was used to dispense 10 l of Dual-Glo Stop + Glo reagent, and the plates were shaken for 10 min. Renilla luciferase signal was quantified using an EnVision plate reader. For each plate, the ratios of the Firefly/Renilla luciferase signals were converted to a Z-distribution as outlined above. For the 4-day treatment arm, the experiment was performed in an analogous manner, but the cells were plated at a density of 200 cells per well and were incubated for 4 days before analysis.

Gene-targeting sgRNAs and appropriate controls were designed using the rule set described at the Genetic Perturbation Program (GPP) portal (http://portals.broadinstitute.org/gpp/public). Oligonucleotides were flanked by polymerase chain reaction (PCR) primer sites, and PCR was used to amplify DNA using NEBNext kits. The PCR products were purified using Qiagen PCR cleanup kits and cloned into pXPR_BRD003 using Golden Gate cloning reactions. Pooled libraries were amplified using electrocompetent Stbl4 cells. Viruses were generated as outlined at the GPP portal. The sgRNA library (CP1080, M-AB34) was custom-designed to target cancer-relevant druggable genes. It consisted of 5566 sgRNAs targeting 788 genes (7 sgRNAs targeting each gene) and 300 nontargeting sgRNAs as controls (table S6).

Jurkat cells that had been infected with PLL3.7-Cas9-IRES-Neo and subsequently maintained in G418 were then superinfected with pLX304 ASCL1-DCK*-V5-IRES-GFP or pLX304 DCK*-V5-IRES-GFP and placed under blasticidin selection. Blasticidin-resistant cells were sorted for GFP expression (top 1%) three times by FACS. Protein abundance of ASCL1-DCK* or DCK* alone was confirmed by immunoblot analysis, and functionality of ASCL1-DCK* or DCK* alone was determined using BVdU sensitivity and rescue experiments with sgRNAs targeting ASCL1. Cas9 expression was confirmed by immunoblot analysis, and Cas9 activity was confirmed using a Cas9 GFP reporter [pXPR_011 (Addgene, #59702)] (39) that showed near maximal editing 10 days after infection.

On day 0, ASCL1-DCK* and DCK* cells expressing Cas9 were expanded and then counted. For each line, 2.2 107 cells (4000 cells per sgRNA) were pelleted and resuspended at 2 106 cells/ml in media supplemented with polybrene (8 g/ml) and infected at a multiplicity of infection (MOI) of ~0.3 with the sgRNA druggable library (CP1080, M-AB34) described above. The cells mixed with polybrene and virus were then plated in 1-ml aliquots onto 12-well plates and centrifuged at 434g for 2 hours at 30C. Sixteen hours later (day 1), the cells were collected, pooled, and centrifuged to remove the virus and polybrene, and the cell pellet was resuspended in complete media at 2 105 cells/ml and plated into nontissue culturetreated t175 flasks. The cells were then cultured for 48 hours before being placed under puromycin (1 g/ml) drug selection at 4 105 cells/ml.

A parallel experiment was performed on day 3 to determine the MOI. To do this, the cells infected with the sgRNA library and mock-infected cells were plated at 4 105 cells/ml in the presence or absence of puromycin. After 72 hours (day 6), cells were counted using the Vi-Cell XR cell counter, and the MOI was calculated (which ranged from 0.2 to 0.3 for each replicate) using the following equation: (# of puromycin-resistant cells infected with the sgRNA library / # total cells surviving without puromycin after infection with the sgRNA library) (# of puromycin-resistant mock-infected cells / # total mock-infected cells).

On day 6 after MOI determination, puromycin-resistant cells were pooled, collected, and counted, and 1 108 cells were replated at a concentration of 4 105 cells/ml in complete media containing puromycin (1 g/ml). The remaining cells were discarded. On day 8, again, the puromycin-resistant cells were pooled, collected, and counted, and 1 108 cells were replated at a concentration of 4 105 cells/ml in complete media containing puromycin (1 g/ml).

On day 10, puromycin-resistant cells were pooled, collected, and counted. A total of 2 107 cells were collected and washed in PBS, and the cell pellets were frozen for genomic DNA isolation for the initial time point before BVdU selection. Then, 2 107 cells were resuspended in complete media (now without puromycin) containing either 200 or 500 M BVdU at a final concentration of 5 104 cells/ml and plated into t175-cm flasks. Thus, at least 1000 cells per sgRNA were introduced into BVdU selection.

On day 15, cells treated with 200 or 500 M BVdU were collected and counted. A total of 10 106 cells from each arm of the screen were then resuspended in complete media containing either 200 or 500 M BVdU at a final concentration of 5 104 cells/ml and plated into t175-cm flasks. The remaining cells were centrifuged and washed in PBS, and the cell pellets were frozen. Again, at least 1000 cells per sgRNA were maintained under BVdU selection.

On day 20, cells treated with 200 or 500 M BVdU were collected and counted. A total of 10 106 cells from each arm of the screen were then resuspended in complete media containing either 200 or 500 M BVdU at a final concentration of 5 104 cells/ml and plated into t175-cm flasks. If available, the remaining cells were centrifuged and washed in PBS, and the cell pellets were frozen. Again, at least 1000 cells per sgRNA were maintained under BVdU selection.

On day 25, all remaining cells were collected and counted. The remaining cells were divided in aliquots of 6 106 cells (which corresponds to 1000 cells per sgRNA) and washed in PBS, and the cell pellets were frozen for genomic DNA isolation for the final time point after BVdU selection. The screen was performed in two biological replicates.

Following completion of the screen, genomic DNA was isolated using a Qiagen Genomic DNA midi prep kit (catalog no. 51185) according to the manufacturers protocol. Raw Illumina reads were normalized between samples using log2[(sgRNA reads/total reads for sample) 1 106 + 1]. The initial time point data (day 10) were then subtracted from the end time point after BVdU selection (day 25) to determine the relative enrichment of each individual sgRNA after BVdU treatment using hypergeometric analysis and the STARS algorithm. A q value cutoff of <0.25 was used to call hits. The averaged data from two biological replicates were used for all analyses.

293FT cells stably transduced with bicistronic lentiviruses encoding (i) DCK*-IKZF1 and GFP and (ii) DCK* and TdTomato were mixed together at a ratio of 1:99. Pooled cells were plated at a density of 20,000 cells per well of a six-well plate and in a total volume of 2 ml of media. Cells received 0.5 ml of the 6 M POM stock solution to achieve an eventual final concentration of 1 M or 0.5 ml of control media. The next day, the cells received 0.5 ml of 600 M BVdU stock solution or 0.5 ml of control media. Cells were collected for FACS analysis on days 0, 3, 6, 10, and 14. After each time point, cells were reseeded at 20,000 cells per well and treated with fresh BVdU (or DMSO).

DCK*-IKZF1 293FT cells were infected with a mixture of two lentiviruses encoding Cas9 and either (i) sgDCK and mCherry or (ii) sgCTRL and BFP (blue fluorescent protein). The two lentiviruses were mixed together such that the ratio of mCherry-positive to BFP-positive cells after infection and puromycin selection was 1:99. An analogous experiment was set up using a lentivirus encoding sgCTRL and mCherry. The pool of infected cells was plated at 20,000 cells per well in a six-well plate and then cultured in media containing either 100 M BVdU or DMSO for 21 days. Cells were collected for FACS analysis on days 1, 6, 18, and 33. After each time point, cells were reseeded at 20,000 cells per well and treated with fresh BVdU.

Jurkat cells that had been stably infected to express Cas9 and DCK*-FOXP3 were superinfected with lentivirus encoding either (i) sgDCK and mCherry or (ii) sgCTRL and BFP. These cells were mixed together and analyzed by FACS to achieve a final ratio of mCherry-positive to BFP-positive cells of 1:99. The pool of infected cells was plated at 40,000 cells/ml in a six-well plate and then cultured in media containing either 100 M BVdU or DMSO for 14 days. Cells were collected for FACS analysis on days 6 and 14.

The Jurkat cells expressing Cas9 and ASCL1-DCK* that were used for the CRISPR-Cas9 screen described above were superinfected with lentiviruses encoding sgRNAs targeting CDK2, ASCL1, or a nontargeting sgRNA as a control, the fluorescent protein mCherry and a puromycin resistance gene or with a lentivirus encoding a nontargeting sgRNA as a control, and the fluorescent protein BFP and a puromycin resistance gene (see schema in fig. S16F). The cells were selected with puromycin. mCherry puromycin-resistant cells were then mixed with BFP puromycin-resistant cells at a 1:3 ratio as determined by FACS analysis. The mixed cells were plated at 5 104 cells/ml and then cultured in media containing 500 M BVdU or DMSO (0) for 18 days. FACS analysis was performed every 6 days. After each FACS analysis, fresh BVdU was added, and the density of the cells was adjusted to 5 104 cells/ml with fresh media.

Cells were counted using a Vi-Cell XR cell counter and were plated at a concentration of 4 105 cells/ml per well for NCI-H1092, 188, and 97-2 SCLC cell lines or at 1 106 cells/ml per well for the NCI-H1876 SCLC cell line in six-well plates. Cells were then treated with the CDK2 degraders TMX-2138, TMX-2172, or the negative degrader ZXH-7035 (Neg Deg) at 500 nM for 36 hours for NCI-H1092 and NCI-H1876 human SCLC cell lines or 8 hours for 188 and 97-2 mouse SCLC cell lines. For half-life time determination with cycloheximide, 97-2 cells were treated with CDK2 degraders for 4 hours before the addition of cycloheximide at 150 g/ml. Cells were harvested at the indicated times after addition of cycloheximide.

293FT cells were seeded in six-well plates at a density of 750,000 cells per well in 2.5 ml of media per well. The next day, the cells were treated with the indicated concentrations of Spautin-1, POM, or DMSO for 24 hours. Multiple myeloma cells were seeded in 10-cm plates at a density of 0.75 106 cells/ml in a total volume of 9 ml of media. The next day, the cells were treated with the indicated concentrations of Spautin-1, POM, or DMSO. RNA was extracted using an RNeasy mini kit (Qiagen, #74106) according to the manufacturers instructions. RNA concentration was determined using the NanoDrop 8000 (Thermo Fisher Scientific). cDNA was generated by reverse transcription using the AffinityScript qPCR (quantitative PCR) cDNA Synthesis kit (Agilent, 600559) according to the manufacturers instructions. qPCR was performed using the LightCycler 480 (Roche) with the LightCycler 480 Probes Master Kit (Roche) and TaqMan probes (Thermo Fisher Scientific) according to the manufacturers instructions. The Ct values for each probe were then normalized to the Ct value of ACTB for that sample. The data from each experiment were then normalized to the control to determine the relative fold change in mRNA expression. The following TaqMan probes were used: Hs00958474_m1 (IKZF1 human), ASCL1 human Hs04187546_g1 for detection of endogenous ASCL1, ASCL1 human Hs05000540_s1 for detection of the exogenous ASCL1-DCK* fusion, ACTB human Hs01060665_m1, Ascl1 mouse Mm03058063_m1, and Actb mouse Mm00607939_s1. All quantitative calculations were performed using the 2Ct method using Beta Actin (ACTB) as a reference gene.

For the positive selection small-molecule screen, GFP fluorescence for each well was normalized to untreated wells. For each library drug, normalized GFP fluorescence was plotted as a function of library drug concentration. Each drug treatment was performed in duplicate. Data were analyzed and plotted using GraphPad Prism v6, median inhibitory concentration (IC50) values were determined using the log (inhibitor) versus response -- Variable slope (four parameters) analysis module, and area under the curve (AUC) values were determined using the AUC analysis module (13). For the positive selection CRISPR-Cas9 BVdU resistance screen, the relative fold enrichment of each individual sgRNA after BVdU treatment was calculated using both Broad Institutes hypergeometric analysis and the STARS algorithm to determine a rank list of candidate ASCL1 stabilizer genes ranked by q value, where statistical significance is q < 0.25.

For all other experiments, statistical significance was calculated using unpaired, two-tailed Students t test. P values were considered statistically significant if the P value was <0.05. For all figures, *P < 0.05, **P < 0.01, ***P < 0.001, and ****P < 0.0001. Error bars represent SD unless otherwise indicated.

Acknowledgments: We thank the members of the Kaelin and Oser laboratories for helpful discussions. Special thanks to the ICCB (Longwood) at HMS for assistance with small-molecule screens, to W. Gao for generation of adenoviral vectors used for recombination cloning, and D. Hong for generation of SCLC mouse cell lines. Funding: W.G.K. is supported by an NIH R35 grant and is an HHMI investigator. M.G.O. is supported by a Damon Runyon Cancer Research Foundation Clinical Investigator Award and an NCI/NIH KO8 grant (no. K08CA222657). V.K. is supported by an American Society of Hematology Research Training Award and T32 NIH Training Grant CA009172. J.A.P. is funded by an NIGMS grant R01 GM132129. E.S.F. is funded by NCI R01CA2144608. N.S.G. is funded by NIH R01 CA214608-03. M.I. is supported by an Internationalisation Fellowship from the Carlsberg Foundation. C.J.O. is supported by an NIH/NCI Pathway to Independence Award (R00CA190861). Author contributions: V.K., L.D., and B.L.L. performed experiments and, together with W.G.K. and M.G.O., designed experiments, analyzed data, and assembled and wrote the manuscript. J.A.M. helped design experiments. A.C.W. and A.H.S. performed experiments. M.I. designed and synthesized the IMiD library. J.P. and C.J.O. measured CRBN binding and cellular activity of candidate IMiDs; J.B. supervised these experiments. I.S.H. and J.E.E. constructed the Ludwig anticancer and antimetabolite libraries and helped analyze data from the screen. E.D., X.L., and S.J.B. synthesized and characterized Spautin-1 derivatives. J.A.P. performed TMT global proteomic profiling of Spautin-1. S.P.G. supervised these experiments. K.A.D. and E.S.F. analyzed TMT proteomic data. K.J.B. determined the half-lives of luciferase fusion proteins and the Z of the dual-luciferase system. J.G.D. helped analyze data from the CRISPR screen. M.T., T.Z., and N.S.G. helped generate and validate CDK2 degraders. Competing interests: W.G.K. has financial interests in Lilly Pharmaceuticals, Fibrogen, Agios Pharmaceuticals, Cedilla Therapeutics, Nextech Invest, Tango Therapeutics, and Tracon Pharmaceuticals. N.S.G. is a founder, science advisory board member, and equity holder in Gatekeeper, Syros, Petra, C4, B2S, Aduro, and Soltego (board member). E.S.F. is a founder, scientific advisory board (SAB) member, and equity holder of Civetta Therapeutics, Jengu Therapeutics (board member), and Neomorph Inc. E.S.F. is an equity holder of C4 Therapeutics. E.S.F. consults or has consulted for Novartis, AbbVie, Astellas, Deerfield, EcoR1, and Pfizer. The Fischer laboratory receives or has received research funding from Novartis, Deerfield, and Astellas. The Gray laboratory receives or has received research funding from Novartis, Takeda, Astellas, Taiho, Janssen, Kinogen, Voronoi, Her2llc, Deerfield, and Sanofi. M.G.O. has sponsored research agreements with Lilly Pharmaceuticals and Takeda Pharmaceuticals. V.K. has consulted for Cedilla Therapeutics. S.J.B. is on the SAB of Adenoid Cystic Carcinoma Foundation. J.B. is an employee, executive, and shareholder of Novartis AG (Basel, Switzerland). J.G.D. consults for Agios, Foghorn Therapeutics, Maze Therapeutics, Merck, and Pfizer; J.G.D. consults for and has equity in Tango Therapeutics. J.G.D.s interests were reviewed and are managed by the Broad Institute in accordance with its conflict of interest policies. I.S.H. is a consultant for ONO Pharmaceuticals (USA). V.K. and W.G.K. are inventors on a patent application on positive selection assays to identify protein degraders, which was filed by the Dana-Farber Cancer Institute (U.S. patent application number 16/332,921, filed on 13 March 2019 and published on 1 August 2019). N.S.G., M.T., and T.Z. are named inventors on patent applications covering Cdlk2 degraders described in the paper, and which were filed by the Dana Farber Cancer Institute (U.S. Provisional Application No. 62/829,302, filed April 4, 2019 and U.S. Provisional Application No: 62/981,334, filed February 25, 2020). The authors declare that they have no other competing interests. Data and materials availability: All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Materials. Additional data related to this paper may be requested from the authors. All plasmids are available from the authors.

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Targeting oncoproteins with a positive selection assay for protein degraders - Science Advances

Immune cell shuttle for precise delivery of nanotherapeutics for heart disease and cancer – Science Advances

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Abstract

The delivery of therapeutics through the circulatory system is one of the least arduous and less invasive interventions; however, this approach is hampered by low vascular density or permeability. In this study, by exploiting the ability of monocytes to actively penetrate into diseased sites, we designed aptamer-based lipid nanovectors that actively bind onto the surface of monocytes and are released upon reaching the diseased sites. Our method was thoroughly assessed through treating two of the top causes of death in the world, cardiac ischemia-reperfusion injury and pancreatic ductal adenocarcinoma with or without liver metastasis, and showed a significant increase in survival and healing with no toxicity to the liver and kidneys in either case, indicating the success and ubiquity of our platform. We believe that this system provides a new therapeutic method, which can potentially be adapted to treat a myriad of diseases that involve monocyte recruitment in their pathophysiology.

Hypovascularity in pancreatic ductal adenocarcinoma (PDAC) (1) and reduced blood supply to the heart following ischemic myocardial injury mean that sole reliance on drug delivery through the circulatory system is ineffective under these conditions; therefore, if this method is to be used to achieve efficient delivery of drugs to target locations, then augmentation will be needed. Vascular permeability has been used as a method of passive drug delivery (2); however, studies have shown that this phenomenon occurs only transiently in the heart and the available time window is not long enough for meaningful delivery of therapeutics (3, 4). This makes low vascular permeability a bottleneck that greatly hampers drug efficacy and deliverability. Therefore, a drug delivery platform capable of leaving the circulatory system, regardless of vascular permeability, and infiltrating deep into the disease site is attractive.

Recruitment of immune cells, such as monocytes, takes place as a natural response to a change in the physiological environment. The role of monocytes varies. In the tumor microenvironment, as a cancer-related inflammatory response, they are constantly recruited and are capable of infiltrating into the tumor site (5, 6), while after myocardial injury, splenic monocytes are recruited and are capable of infiltrating into the heart to help heal the myocardium (7, 8). Inspired by this phenomenon, we designed a lipid nanoparticle (LNP)based drug delivery platform with an active targeting scaffold that acts as a vehicle and is capable of selectively attaching onto the surface of circulating monocytes in the blood stream, moving with them, and extravasating together with them into the diseased site.

The body consists of a myriad type of cells, and targeting a specific cell type is therefore challenging. One possible way to achieve this is to use a cell-specific ligand as the targeting scaffold. As an example, several studies have reported nanoparticles carrying macrophage-specific ligands in their cargo as therapeutics. These nanoparticles were able to deliver the ligands into the macrophages resulting in their activation (9). Although this kind of ligands can potentially be used as a targeting scaffold, we chose not to use them, as we only aim to attach our nanoparticles on the monocyte surface without activating them. Furthermore, some of the ligands may not be monocyte specific and may also target endothelial cells (10, 11), resulting in unwanted off-target accumulation. Taking these into consideration, we avoided using ligands as the targeting scaffold and we opted to use aptamers instead.

Aptamers are synthetic short, single-stranded DNA or RNA oligonucleotides used as biotechnological tools and therapeutic agents. They can be designed to have high affinities toward specific proteins through their folding into tertiary structures (12). The idea of using oligonucleotides to target proteins emerged in the early 1990s, and since then, aptamers have been widely applied in many fields, including food safety, environmental monitoring, clinical diagnosis, and therapy (12). With the development of cell systematic evolution of ligands by exponential enrichment (Cell-SELEX), it has become possible to design and select aptamers with high affinities toward specific cells types, such as monocytes, while avoiding unwanted bindings to endothelial cells (13). In this study, we took advantage of this advanced technique to select a specific monocyte-targeting aptamer and integrated it with our LNP as an active-targeting scaffold to produce a high-affinity monocyte-targeting drug delivery vehicle.

Several studies have described a similar strategy whereby the bodys own cells were used to carry nanoparticles to diseased sites. T cells carrying nanoparticles loaded with a topoisomerase inhibitor ligand SN-38 were reported to reduce tumor burden in mice with disseminated lymphoma (14). LNPs carrying tumor necrosis factorrelated apoptosis-inducing ligand were able to attach onto the surface of leukocytes and kill colorectal and prostate cancer cells, as well as circulating tumor cells in mice (15). Furthermore, by hitchhiking on the surface of red blood cells, nanogels carrying reteplase, a thrombolytic enzyme, ameliorated pulmonary embolism in mice (16). Our strategy, on the other hand, makes use of monocyte recruitment to the diseased site. We hypothesize that because the recruitment is an active process, it ensures that the nanoparticle and its cargo can reach the site it is intended. We also hypothesize that our monocyte-targeting drug delivery platform is versatile and can be used to treat myocardial ischemia-reperfusion (IR) injury and pancreatic cancer, two very different deadly diseases, which involve the monocyte recruitment phenomena that we harness in our strategy.

IOX2, a potent and selective hypoxia-inducible factor (HIF)1 prolyl hydroxylase2 inhibitor, is capable of preventing proteasome-mediated degradation of HIF-1 (17, 18). The HIF-1 protective effect of IOX2 not only contributes to the reduction of apoptosis but also enhances the transcription responses of HIF-1 (19, 20). Gemcitabine is a common chemotherapeutic agent for pancreatic cancer. It is a deoxycytidine analog capable of inhibiting the DNA replication in cancer cells and causing cell death (21). We encapsulated both of these drugs separately into our delivery vehicle, and by doing so, we were able to successfully ameliorate IR injury (using IOX2-loaded nanoparticles) and reduce tumor burden in PDAC mice (using gemcitabine-loaded nanoparticles). Moreover, unlike other bio-based materials, our aptamer-based scaffold is not patient specific, synthetic, and can be chemically modified, which are highly advantageous traits in the clinical setting.

As our delivery of therapeutics to disease sites relies on the recruitment of monocytes, we first examined the most efficient time point for delivery by constructing monocyte recruitment profiles to the injured heart and tumor site using IR (Fig. 1, A to C) and PDAC (Fig. 1, D to F) models of transgenic CCR2RFP/+ mice, respectively. We observed an increase in the number of recruited monocytes following IR injury and PDAC model establishments, which reached a maximum at day 4 after IR injury (Fig. 1B) and day 7 after KPC (KrasG12D, p53fl/fl, Pdx1-Cre) tumor cell transplantation (Fig. 1E). Furthermore, the number of circulating monocytes after IR injury and KPC tumor cell transplantation showed significant difference until 5 hours and day 14, respectively (figs. S1 and S2). Recruitment of monocytes to the IR heart was further confirmed by fluorescence-based intravital microscopy of the heart, whereby CCR2RFP/+ monocytes were observed (Fig. 1C). In the PDAC model, transplantation success and recruitment of monocytes were further confirmed by fluorescence-based intravital microscopy, whereby green fluorescent protein (GFP)+ KPC cells and red fluorescent protein (RFP)+ CCR2 monocytes were clearly observed at the injection site (Fig. 1F).

(A) The in vivo imaging system (IVIS) revealed CCR2RFP/+ cell recruitment to the injured heart after IR. (B) IVIS quantification of the CCR2RFP/+ recruitment to the injured heart after IR. (C) Recruitment of CCR2RFP/+ cells in the injured heart after IR under an intravital microscope. (D) Representative IVIS images of CCR2RFP/+ monocyte recruitment in a mouse orthotopic pancreatic cancer (PDAC) model. The mouse KPC cells were luciferase and GFP double transgenic. (E) IVIS quantification of CCR2RFP/+ monocyte recruitment in the tumor site. (F) CCR2RFP/+ recruitment in the PDAC model under an intravital microscope. (G) Schematic illustration of the aptamer-based LNP delivery approach in the mouse cardiac IR and PDAC models via circulating monocytes. (H) Flow cytometric analysis of the specificity of J10 aptamer to monocyte cell lines RAW264.7 and J774A.1, as well as mouse endothelial cell line SVEC. The S2 aptamer was a random ordering of the J10 aptamer sequence. (I) Flow cytometry showed ex vivo targeting of Cy5-labeled J10 aptamer against mouse monocytes. (J) In vivo targeting of J10 aptamerdecorated quantum dots QD655 to circulating CCR2RFP/+ and CX3CR1GFP/+ monocytes via intravital imaging. (K) Polymerase chain reaction (PCR) analysis of J10 aptamer accumulation in the infarct area after cardiac IR. GAPDH, glyceraldehyde-3-phosphate dehydrogenase. One-way analysis of variance (ANOVA) with a Tukey adjustment was used to analyze data in (B) and (I). Two-way ANOVA with a Tukey adjustment was used for data analysis in (E) and (H). Unpaired Students t test was used to analyze data in (K). *P < 0.05, **P < 0.01, ***P < 0.001, and ****P < 0.0001. Scale bars, 100 m (C and F) and 20 m (K).

Aiming to produce a nanoplatform capable of binding to monocytes, we used nontoxic liposome-based nanoparticles coated with aptamers as a targeting scaffold, which are envisioned to be capable of infiltrating into the injured myocardium and pancreatic tumor site along with the monocyte (Fig. 1G). Aptamer candidates were chosen through the SELEX process against two monocyte/macrophage cell lines, RAW264.7 and J774A.1, for positive selection and the murine endothelial cell line, SVEC, for negative selection. Aptamers specific to both monocyte cell lines but not to SVEC were amplified through polymerase chain reaction (PCR). Following several rounds of SELEX, we identified aptamer J10 as the best candidate. The sequence of J10 was then scrambled to yield a control aptamer, S2 (fig. S3, A to E). The structures of both aptamers were predicted by Mfold software (22) (fig. S3, F and G). We then thoroughly investigated the capability of both aptamers to bind selectively to monocytes in vitro, in vivo, and ex vivo. Binding assays with Cy5-labeled aptamers confirmed that J10, but not S2, was capable of binding selectively to mouse monocyte cell lines (RAW264.7 and J774A.1) in vitro (Fig. 1H) and circulating myeloid (CD45+ CD11b+) cells ex vivo (Fig. 1I). Moreover, using intravital imaging to visualize the binding between circulating monocytes and QD655-labeled J10 (Fig. 1J and movies S1 to S4) clearly demonstrated that J10 selectively bound to monocytes. In vivo, intravenous injection of J10 and S2 aptamers revealed more J10 aptamer accumulated in the hearts with IR compared to S2 (Fig. 1K). J10 aptamer also has a higher binding affinity toward human monocyte cell lines THP-1 and U937, but not human endothelial cell line HUVEC, compared with S2 (fig. S4). All of these results supported our hypothesis that J10-labeled scaffold is capable of attaching selectively onto monocyte surface, which we then exploit to target the diseased sites.

After we successfully identified J10 as the candidate for monocyte-targeting drug delivery platform, we then endeavored to use it as an active-targeting scaffold on the nanoparticles for the treatment of IR injury. LNPs were synthesized using a thiolated linker DNA that can readily conjugate to maleimide-containing DSPE-PEG (1, 2-distearoyl-Sn-glycero-3-phosphoethanolamine-N-[methoxy (polyethylene glycol)-2000]). The resulting DSPE-PEGlinker lipid was capable of hybridization with the aptamers (J10 or S2) to give the final monocyte-targeting LNP end product. Optimal aptamer density was determined through optimization of the molar ratio of linker:lipid, which was found to be 0.3%. A higher ratio, which translates to a higher density, did not result in a higher binding affinity to monocytes (fig. S5). Following self-assembly and encapsulation of the intended drugs (IOX2 or gemcitabine), aptamers could be decorated on the LNP surface through hybridization without conformational changes during the process (Fig. 2A) (23). Because of the complexity of the structure, mass spectrometry measurement was performed after each synthesis step to confirm the success of the synthesis and expected mass/charge ratio value was obtained for each step (fig. S6). Cryoelectron microscopy (cryo-EM) and high performance liquid chromatography (HPLC) analysis were performed to confirm successful encapsulation of IOX2 (Fig. 2, B and C). As expected, measurement of size and zeta potential showed that attachment of the aptamers increased the size and the negativity of the zeta potential following aptamer attachment (tables S1 and S2).

(A) Step-wise synthesis of aptamer-conjugated LNPs encapsulated with IOX2. (B) Aptamer-IOX2-LNPs under a cryo-EM. Yellow arrowheads indicate precipitation of IOX2, and red arrows indicate the conjugated aptamers. Scale bars, 100 nm. (C) HPLC chromatogram of IOX2-LNPs. (D) In vitro binding affinity of aptamer-IOX2-LNPs to mouse monocyte cell lines J774A.1 and RAW264.7, as well as mouse endothelial cell line SVEC. mAU, intensity of absorbance (in milli-absorbance units); RT, retention time; ns, not significant. (E) IVIS imaging of aptamer-IOX2-LNPs accumulation in the injured heart. The particles were labeled with the DiD lipophilic cyanine dyes. (F) Quantitative analysis of DiD-labeled aptamer-IOX2-LNPs in the injured heart using IVIS. ROI, region of interest. (G) Accumulation of aptamer-IOX2-LNPs in the infarct area under an intravital microscope. The aptamer-LNPs were labeled with the DiD lipophilic cyanine dyes. Scale bars, 100 m. (H) Biodistribution of aptamer-IOX2-LNPs in organs. One-way ANOVA with a Tukey adjustment was used to analyze data in (F). Two-way ANOVA with a Tukey adjustment was used to analyze the data in (D) and (H). ****P < 0.0001, **P < 0.01, and *P < 0.05.

Following the success of obtaining aptamer-LNPs, we examined the interaction between the LNPs and monocytes. Time-lapse live cell imaging taken over the course of 90 min of incubation between S2 and J10 aptamers with the monocyte cell line RAW264.7 showed that although some of nanoparticles were internalized, most of them remained on the surface, which is expected. More J10-LNPs were also observed on the surface of monocytes compared to S2, which further supports our finding that J10 is a better monocyte-targeting aptamer (fig. S7A and movies S5 and S6). We also investigated whether the attachment of aptamer-LNPs affected monocyte function. We profiled the cytokines [interleukin-1 (IL-1), IL-6, IL-10, monocyte chemoattractant protein-1 (MCP-1), and transforming growth factor] of LNP-, J10-, and J10-LNPtreated RAW264.7 monocyte cell line using quantitative PCR. The results showed no changes in the levels of these cytokines, indicating that the nanoparticles did not affect the function of or cause adverse side effects to the monocytes (fig. S7B).

Having successfully encapsulated IOX2 in the J10-decorated nanoparticles, we then examined the ability of J10-IOX2-LNPs to bind to monocytes in vitro and to use monocytes to target IR hearts in vivo. Flow cytometry analysis using DiD [The far-red fluorescent dye DiD (1,1-Dioctadecyl-3,3,3,3-Tetramethylindodicarbocyanine Perchlorate)]labeled J10- and S2-LNPs revealed that the binding of J10-decorated LNPs to monocytes was more effective than S2-LNPs and nondecorated LNPs, with minimal binding to endothelial cells in vitro (Fig. 2D). For the in vivo study, in vivo imaging system (IVIS) analysis showed a significant increase in fluorescence for DiD-labeled J10-IOX2-LNPs compared to phosphate-buffered saline (PBS) (background) and DiD-labeled S2-IOX2-LNPs, indicative of successful targeting of J10-decorated nanoparticles to the injured hearts (Fig. 2, E and F). Intravital imaging further confirmed higher J10-IOX2-LNPs accumulation in the infarct area, suggesting that the nanoparticles successfully reached the intended site (Fig. 2G). Biodistribution study of IOX2-loaded S2- and J10-LNPs (Fig. 2H) showed a significant increase in IOX2 retention in the heart for J10-LNPs 4 hours after injection, indicating that our J10 aptamer drug delivery system successfully increased drug delivery to the heart. To confirm that J10-IOX2-LNPs delivered the IOX2 cargo by hitchhiking on the surface of monocytes, we depleted the circulating monocytes in IR mice using clodronate liposomes (24) and injected the nanoparticles. Complete blood count confirmed the success of monocyte depletion (fig. S8A), while quantification of IOX2 content in the heart showed significant decrease in clodronate-treated mice (fig. S8B). This result proved that our J10 drug delivery platform hitchhiked on the surface of monocytes to reach the injured heart.

The therapeutic effect of IOX2-loaded nanoparticles was then examined in a murine model of myocardial IR injury. The mice were injected with three doses of S2- and J10-IOX2-LNPs at 5 hours, 1 day, and 2 days after IR injury (Fig. 3A). These time points were optimal for therapy because injections at 5 hours or 5 days after IR injury resulted in a similar IOX2 accumulation level (fig. S9). Because IOX2 prevents the degradation of HIF-1, which is up-regulated early after IR injury, early injection time points were chosen for the efficacy trial. Furthermore, because the enhanced permeability and retention effect diminishes after 24 hours (3), the fact that accumulation of IOX2 remained similar at 5 hours and 5 days further suggests that nanoparticle delivery was achieved by hitchhiking on the monocyte surface. This is also supported by our monocyte recruitment and circulating monocyte profiles (Fig. 1 and fig. S1), where the monocyte levels remained high within these time points. We then aimed to understand the drug release profile, by performing biodistribution studies of IOX2 in J10-IOX2-LNPtreated IR mice (fig. S10). The nanoparticle injection was performed 5 hours after IR injury, and the organs were collected at different time points (5 hours, 1 day, and 4 days) after injection. We found that accumulation of IOX2 was at the highest at day 1 after injection and decreased at day 4. This suggests that the body started to eliminate the nanoparticles and the drugs after 24 hours after administration.

(A) Experimental design for in vivo functional evaluation of aptamer-IOX2-LNPs in the mouse cardiac IR injury model. (B) The protein levels of HIF-1 after aptamer-IOX2-LNP treatment. (C) Terminal deoxynucleotidyl transferasemediated deoxyuridine triphosphate nick end labeling (TUNEL) assay for detection of apoptosis in the injured heart after aptamer-IOX2-LNP treatment. The apoptotic index was defined as of the percentage of TUNEL+ cells in a field examined. DAPI, 4,6-diamidino-2-phenylindole; CTnl, cardiac troponin I. (D) Staining for -smooth muscle actin (-SMA) and isolectin IB4 (IB4) to examine the effects of J10-IOX2-LNPs on angiogenesis in the injured heart. WGA, wheat germ agglutinin. (E and F) Quantification of -SMA+ (E) and IB4+ (F) vessels in the injured heart after aptamer-IOX2-LNP treatment. G) The effects of aptamer-IOX2-LNPs on cardiac fibrosis on day 21 after IR injury. (H) Quantification of cardiac fibrosis after aptamer-IOX2-LNP treatment. LV, left ventricle. (I to P) The effects of aptamer-IOX2-LNPs on the heart function 21 days after IR injury, including ejection fraction (EF) (I), fraction shortening (FS) (J), end-systolic volume (ESV) (K), end-diastolic volume (EDV) (L), dP/dt maximum (dP/dt max) (M), dP/dt minimum (dP/dt min) (N), ESPVR (end-systolic pressure-volume relationship) (O), and EDPVR (end-diastolic pressure-volume relationship) (P). (Q) The effects of aptamer-IOX2-LNPs on the survival rate of a mouse cardiac IR model. One-way ANOVA with a Tukey adjustment was used for data analysis. The Kaplan-Meier method and the log-rank (Mantel-Cox) tests were used for construction and analysis of the survival curves in (Q). *P < 0.05, **P < 0.01, ***P < 0.001, and ****P < 0.0001.

Following the injection of S2- and J10-IOX2-LNPs, the hearts were collected for analysis. Western blot analysis showed that J10-IOX2-LNP treatment retained the HIF-1 protein level in the heart, which indicates that IOX2 successfully reached the heart and prevented the degradation of HIF-1. This, in turn, is indicative of a cardioprotective effect (Fig. 3B). On the other hand, terminal deoxynucleotidyl transferasemediated deoxyuridine triphosphate nick end labeling (TUNEL) assay showed reduced number of apoptotic cells, demonstrating that our treatment prevented cardiomyocyte loss (Fig. 3C). In addition, J10-IOX2-LNP treatment also augmented angiogenesis, which was shown by the increased staining of -smooth muscle actin (-SMA) for vessels and isolectin B4 (IB4) for capillaries (Fig. 3, D to F). Trichrome staining of three levels of the heart on day 21 after IR injury showed that the J10-IOX2-LNP group had a significant reduction in infarct size compared to the controls, demonstrating better healing of the myocardium (Fig. 3, G and H). The results thus far indicated a better cardiac performance, which we then proved through echocardiography and cardiac catheterization experiments, which revealed that the J10-IOX2-LNP group showed significant improvement in all cardiac parameters in comparison to the control groups at day 21 (Fig. 3, I to P, and fig. S11).

To ensure the safety of our platform, we examined the hepatotoxicity [aspartate aminotransferase (AST), alanine aminotransferase (ALT) and alkaline phosphatase (ALP)] and nephrotoxicity [blood urea nitrogen (BUN) and CREA] of J10-IOX2-LNPs through serum analysis, all of which fell within the level of healthy animals (fig. S12, A to E). Histology analysis of the liver and kidneys was also performed, which showed no abnormalities (fig. S12F). All of these results combined showed that in the murine model of myocardial IR injury, our nanoparticles successfully targeted the injured hearts, resulting in improved cardiac functions, reduced infarct size, augmented angiogenesis, and, overall, prolonged survival of the mice (Fig. 3Q) without causing adverse side effects to the liver, kidneys, and monocytes.

Following the success of IR injury treatment with our J10 aptamer delivery platform, we then continued our investigation using this platform to treat PDAC mice. Gemcitabine, a drug used for pancreatic cancer treatment, was encapsulated into the nanoparticles using a passive loading method (table S3). The encapsulation success was confirmed by cryo-EM and by exploiting the presence of nuclear magnetic resonance (NMR)active 19F nuclei in gemcitabine using 19F NMR spectroscopy (2), as well as by HPLC (Fig. 4, A to C). A cytotoxicity assay confirmed that the gemcitabine toxicity to the tumor cells was retained following encapsulation (Fig. 4D).

(A) The aptamer-Gem-LNPs under a cryo-EM. Yellow arrowheads indicate precipitation of gemcitabine, and red arrows indicate the conjugated aptamers. Scale bars, 100 nm. (B) Representative 19F NMR spectrum of free and liposome-encapsulated gemcitabine. ppm, parts per million. (C) Representative HPLC chromatogram of free and liposome-encapsulated gemcitabine. (D) Cytotoxicity of free and liposome-encapsulated gemcitabine to cultured mouse pancreatic cancer (KPC) cell line. IC50, median inhibitory concentration. (E) In vitro targeting specificity of aptamer-Gem-LNPs against mouse monocyte and endothelial cell lines using flow cytometry. RAW264.7 and J774A.1 are the mouse monocyte cell lines; SVEC is a mouse endothelial cell line. (F to H) In vivo binding specificity of aptamer-Gem-LNPs to (F) monocytes, (G) lymphocytes, and (H) granulocytes. (I) Accumulation of aptamer-Gem-LNPs in mouse orthotopic pancreatic tumor determined with IVIS. The aptamer-Gem-LNPs were labeled with the DiD lipophilic cyanine dyes. (J) Quantification of gemcitabine accumulation in the mouse orthotopic pancreatic cancer using 19F NMR. Two-way ANOVA with a Tukey adjustment was used for data analysis in (E), and mixed-effects analysis was used to analyze data in (J). One-way ANOVA with a Tukey adjustment was used for data analysis in (F) to (I). *P < 0.05, **P < 0.01, and ***P < 0.001.

Having successfully encapsulated gemcitabine, we then examined the ability of J10-gemcitabine-LNPs (J10-Gem-LNPs) to selectively bind to monocytes in vitro and to deliver the cargo into the tumor site in vivo. Flow cytometry analysis using DiD-labeled J10- and S2-LNPs revealed that J10-Gem-LNPs were able to bind to monocytes more efficiently than to S2-LNPs and nondecorated LNPs, with minimal binding to endothelial cells in vitro (Fig. 4E). This was also confirmed in vivo through flow cytometry, whereby a preferential binding to monocytes but not to lymphocytes or granulocytes was observed (Fig. 4, F to H, and figs. S13 to S15). IVIS analysis of excised tumor showed that after 24 hours of LNP administration, the highest accumulation of nanoparticles was found in J10 group (Fig. 4I). We then aimed to understand the release profile of gemcitabine, by quantifying the amount of gemcitabine in PDAC mice at different time points (6, 24, and 48 hours) after injection (Fig. 4J). Comparison of gemcitabine content between S2 and J10 groups showed a significant accumulation at 24 hours and a modest accumulation at 48 hours for J10 group. This suggests that the body started to eliminate the nanoparticles and the drugs after 24 hours after administration, which is in agreement with the release profile of J10-IOX2-LNPs in IR hearts. All of these findings indicate that gemcitabine-loaded nanoparticles were able to target the tumor, with J10-decorated nanoparticles having the highest efficacy. To confirm that J10-Gem-LNPs delivered the gemcitabine cargo by hitchhiking on the surface of monocytes, we repeated the circulating monocyte depletion experiment in PDAC mice using clodronate liposomes and injected the nanoparticles. Complete blood count confirmed the success of monocyte depletion (fig. S8C), while quantification of gemcitabine content in the tumor showed a significant decrease in clodronate-treated mice (fig. S8D). This result proved that our J10 drug delivery platform hitchhiked on the surface of monocytes to reach the tumor site. Last, we investigated the effects of accumulated concentration of gemcitabine on monocytes, which showed that monocyte viability was not affected, suggesting no adverse side effects (fig. S16).

The therapeutic consequence of increased accumulation of gemcitabine-loaded nanoparticles was assessed in a murine PDAC model (Fig. 5A). TUNEL assay and proliferation assay using Ki67 showed that treatment with J10-Gem-LNPs significantly increased tumor cell apoptosis and decreased tumor cell proliferation, respectively, compared to S2-Gem-LNPs (Fig. 5, B and C), indicating that the treatment successfully hampered the growth of the tumor. This was then confirmed by IVIS and functional magnetic resonance imaging (fMRI) monitoring, which showed greater tumor growth suppression in the J10 group, in agreement with the tumor weight at the day of death (Fig. 5, D to F). Furthermore, treatment of gemcitabine-loaded nanoparticles did not affect the body weight (Fig. 5G), and serum chemistry assessment for hepatotoxicity (AST, ALT, and ALP) and nephrotoxicity (BUN and CREA) showed no adverse effects in both liver and kidney functions (fig. S17), which overall indicates the safety of J10-Gem-LNPs. All of these results combined showed that in the murine model of PDAC, our nanoparticles successfully targeted the tumor site, resulting in increased tumor cell apoptosis, reduced tumor cell proliferation and growth, and, overall, prolonged survival of the mice (Fig. 5H).

(A) Experimental design for the functional evaluation of aptamer-Gem-LNPs in a mouse orthotopic pancreatic cancer model. (B) J10-Gem-LNPs caused apoptosis of pancreatic tumor cells in vivo. The apoptotic index was determined with TUNEL assay. Scale bars, 20 m. (C) J10-Gem-LNPs reduced proliferation of pancreatic tumor cells in vivo. The proliferation index was determined by the ratio of Ki67+ cells. Scale bars, 20 m. (D) J10-Gem-LNPs reduced pancreatic tumor size on day 29 after treatment. The pancreatic tumor sizes were determined with IVIS to detect the luciferase activity of the mouse KPC cell line. (E) The J10-Gem-LNPs reduced pancreatic tumor size under MRI. (F) Quantification of orthotopic pancreatic tumor size harvested from mice treated with PBS, gemcitabine, Gem-LNPs, S2-Gem-LNPs, and J10-Gem-LNPs. (G) The effects of aptamer-Gem-LNPs on the body weight of the mouse orthotopic pancreatic cancer model. (H) J10-Gem-LNPs improved the survival rate of the mouse orthotopic pancreatic cancer model. (I) Effects of aptamer-Gem-LNPs on liver metastatic tumor volume under MRI. (J) Effects of aptamer-Gem-LNPs on the size of liver metastatic tumor on day 32 after treatment using IVIS. (K) Effects of aptamer-Gem-LNPs on the survival rate of mouse with liver metastatic tumors. Data in (B), (C), and (I) were analyzed with unpaired Students t test. One-way ANOVA with a Tukey adjustment was used for data analysis in (D) to (F) and (J). The data in (G) were analyzed with the two-way ANOVA with a Tukey adjustment. The survival curves in (H) and (K) were constructed with the Kaplan-Meier method and analyzed with the log-rank (Mantel-Cox) test. *P < 0.05, **P < 0.01, and ***P < 0.001.

As one of the most common metastatic site for pancreatic cancer is the liver, we further examined the therapeutic efficacy of our nanoparticles using a murine model of pancreatic cancer with liver metastasis (25). The progression of the metastatic tumor growth on the liver was similarly suppressed in the J10 group, as shown by fMRI and IVIS measurements (Fig. 5, I and J). Ultimately, we found that the J10-Gem-LNP platform was also capable of targeting liver metastasis, resulting in increased survival of the mice (Fig. 5K), which is in agreement to the results we obtained for the IR and PDAC models.

Previously, we have developed an injectable nanogel and reloadable targeted nanoparticles to improve the treatment of ischemic diseases such as myocardial infarction and hind limb ischemia (26, 27). However, both strategies are too invasive. Methods that rely solely on the ability of the drugs or drug-loaded nanoparticles to extravasate from the circulation into diseased sites are vastly limited by the availability and permeability of the blood vessels surrounding the sites. Although the method developed in our study also relies on the circulatory system to some extent, the drug-loaded nanoparticles were able to leave the blood stream and penetrate into the diseased site. With this strategy, we were able to successfully increase the therapeutic efficacy of drugs used in treating both IR injury and PDAC, a result that otherwise could not have been achieved.

Our aptamer-based LNP targeting system can be synthesized and is not patient specific. This eliminates the necessity to freshly prepare targeting scaffolds and, in a clinical setting, enables the treatment of patients who are in need of immediate administration of therapeutics. We have shown that our aptamer is capable of selectively binding to both murine and human monocyte cell lines (Fig. 1I and fig. S4), although the binding to human monocytes is not as strong as that to murine monocytes. This is expected, because we performed the SELEX procedure using murine monocyte cell lines, taking into account the difference between human and murine monocytes; this disparity is to be expected. Our findings have shown that circulating monocytes can be used as a shuttle bus for drug delivery using the appropriate aptamer-based targeting scaffold. Aptamers that can bind selectively to human monocytes with good affinity can be developed by following our approach using human monocytes to produce human monocyte-specific aptamers and be used for translational medicine purposes.

We have shown that our aptamer-based targeting vehicle was able to treat myocardial IR injury; however, we are limited by the monocyte recruitment time point and the number of circulating monocytes, which are at their optimum 4 days after injury (Fig. 1B and fig. S1). This time point is not early enough for the delivery of early cardioprotective therapeutics, which should ideally be administered a few hours after the IR episode. Nevertheless, delivery of therapeutics that prevents the heart from suffering further damage can be successfully achieved using our delivery method.

Using the same delivery vehicle and strategy, we assessed the therapeutic efficacy of our method in the treatment of PDAC. PDAC is known to exhibit hypovascularity, which makes treatments with reliance on the circulatory system challenging and ineffective (28). Fortunately, the development of PDAC involves the recruitment of monocytes in its pathogenesis (29), which is the basis of our therapeutic strategy. Therefore, although our aptamer-based delivery method also relies on the circulatory system to reach the tumor site, the ability of the drug-loaded nanoparticles to attach to monocytes, leave the blood vessel, and penetrate through the dense stromal extracellular matrix along with the monocytes increased the efficiency of drug delivery. This was validated by the increased amount of gemcitabine that successfully reached the tumor site, reduced tumor size and weight, and prolonged survival rate. Nevertheless, clinically, it is difficult to determine how inflammatory the tumor is at the time of treatment and if the treatment remains effective if given when the tumors are smaller (earlier) or larger (later). More studies involving the in vivo delivery kinetics will be required to further elucidate the therapeutic time window of this drug delivery system.

Last, our drug delivery system is potentially useful for the treatment of pancreatic cancer with liver metastasis. Before the formation of metastasis, monocytes are recruited to the liver (30, 31), to support the growth and proliferation of the invading tumor cells, in the end resulting in metastasis. Our delivery system was also assessed for treating liver metastasis, and we have shown that it was also able to reduce the metastatic tumor volume and prolong the survival of the mice suffering from pancreatic cancer with liver metastasis.

Our delivery system has a lot of advantages. It can potentially be used to deliver a wide variety of therapeutics such as small interfering RNA, modified RNA, antisense oligonucleotides, and protein drugs. It can also be used as a drug delivery platform for other diseases that involve monocyte recruitment in their pathophysiology. Furthermore, it is easy to manufacture and is not patient specific, which can potentially be useful for translational purposes. The only shortcoming of our study is that we only treated the mice for a short period of time, and although we managed to improve the overall condition and survival of the mice, we did not cure them. Prolonged treatment using our delivery platform may improve the overall outcome, and therefore, future longer-term studies are warranted.

Male 8- to 10-week-old wild-type C57BL/6 J mice, weighing approximately 25 g, were used for all experiments, unless otherwise stated. All mice were purchased from BioLASCO or National Laboratory Animal Center, Taiwan. Mice were housed in a 12-hour day/night cycle with unlimited access to food and water. Homozygous B6.129(Cg)-Ccr2tm2.1Ifc/J (CCR2RFP/RFP) and B6.129P2(Cg)-Cx3cr1tm1Litt/J (CX3CR1GFP/GFP) mice were purchased from the Jackson laboratory, USA. Heterozygous CCR2RFP/+ and CX3CR1GFP/+ mice were generated from Institute of Biomedical Sciences, Academia Sinica, Taiwan. For both intravital imaging and monocyte profiling, 6- to 8-week-old CCR2RFP/+ mice were used, while 10- to 12-week-old CX3CR1GFP/+ mice were used for intravital imaging. All mouse experiments have been approved by Academia Sinica Institutional Animal Care and Use Committee.

Mice (8 to 10 weeks old) were anesthetized with Zoletil 50 (80 mg/kg; Virbac) and Rompun (3.5 mg/kg; Bayer) and given O2 via a tracheal tube on a 37C heating pad. The heart was accessed via left thoracotomy between the third and fourth ribs. The left anterior descending coronary artery was temporarily ligated with sutures 7-0 polypropylene through polyethylene-10 tubing for 45 min. Subsequently, polyethylene-10 tubing was removed to induce myocardial IR injury. The success of the surgery was evaluated by echocardiography on the following day.

For orthotopic tumor implantation, 5 105 live KPC cells suspended in 20 l of sterile PBS were administered to 6- to 8-week-old C57BL/6 J mice by intrapancreatic injection around 2 to 3 mm from the pancreas tail. For the PDAC liver metastasis model, injection of KPC cells was performed on day 10 after orthotopic implantation by injection of 5 105 live KPC cells suspended in 10 l of sterile PBS into the portal vein using a Hamilton syringe.

Lipid film (total mass, 35 mg) was prepared in a round-bottom flask by dissolving 1,2-distearoyl-sn-glycero-3-phosphocholine (DSPC), cholesterol, and DSPE-PEG2000 in chloroform and DSPE-PEG2000 linker and DiD in methanol (molar ratio, 45:50:0.047:0.003:0.005). Solvent was removed under reduced pressure at room temperature, and the lipid film was lyophilized overnight.

IOX2-LNPs was prepared following a previously reported method (32). Briefly, the dry film was hydrated with 1 ml of internal buffer (200 mM calcium acetate) to form multilayer vehicles (MLVs). After the thin film was completely dissolved, the size and lamilarity of MLV were reduced by 10 freeze-thaw cycles under vacuum using liquid nitrogen and a 65C water bath. It was then sonicated using a probe sonicator in total for 2 min through a series of 2-s sonication and 10-s pause. Following this, liposome solution was extruded through a 0.1-m polycarbonate membrane 20 times at 65C to obtain around 100-nm small unilamilar vehicle linkerLNP. Calcium acetate was removed using Sepharose CL-4B size exclusion column to establish the liposome cross membrane gradient. Then, IOX2 was incubated with liposome in a drug to a lipid molar ratio of 0.4 at 65C for 30 min. Unencapsulated IOX2 was removed by Sepharose CL-4B size exclusion column with PBS as the mobile phase. Linker-IOX2-LNPs were then hybridized with J10 and S2 aptamers separately through overnight incubation at 4C (linker:aptamer, 1:2.5). Free aptamer was removed by Sepharose CL-4B size exclusion column with PBS as the mobile phase.

For fabrication of Gem-LNPs, the dry film was hydrated by 1 ml of gemcitabine in PBS solution (75 mg/ml) to form MLV linkerGem-LNP. After the dry film was completely dissolved, the size of MLV was reduced by 10 freeze-thaw cycles under vacuum using liquid nitrogen and a 65C water bath. Linker-Gem-LNP was sonicated using a probe sonicator in total for 2 min through a series of 2-s sonication and 10-s pause. Linker-Gem-LNP was then extruded through a 0.1-m polycarbonate membrane 20 times at 65C and stored overnight at 4C. Linker-Gem-LNPs were purified using a Sepharose CL-4B size exclusion column with PBS as the mobile phase. Pure linker-Gem-LNPs were then hybridized with J10 and S2 aptamers separately through overnight incubation at 4C (aptamers:linker, 2.5:1), followed by purification using a Sepharose CL-4B size exclusion column with PBS as the mobile phase.

Following the encapsulation, the drug concentration was measured to be 0.0625 mg per mg/ml of lipid and 0.186 mg per mg/ml of lipid for IOX2 and gemcitabine, respectively. The dosages used for the in vivo experiments are 0.7 mg of IOX2/kg for three injections and 1.66 mg of gemcitabine/kg for three injections.

The multiphoton intravital imaging was performed following a published procedure (33). All animals were anesthetized by 1.5% isoflurane (Minrad) during the experiment. Injection of 100 l of 5 mM S2-IOX2-LNP and J10-IOX2-LNP was administered to IR day 1 CCR2RFP/+ mice for an hour, and then the infarct area was visualized by a multiphoton microscope (FVMPE-RS, Olympus). Because the fluorescence of DiD-labeled IOX2-LNP was quenched within seconds under multiphoton imaging, QD655s (20 l; Invitrogen) modified with S2 or J10 were injected to CCR2RFP/+ and CX3CR1GFP/+ mice to visualize J10-QD655stagged monocytes passing through the blood vessel.

GraphPad Prism 8 was used for all statistical analysis and graph generation. Statistical tests are described in the figure legends. For group analysis, one-way or two-way analysis of variance (ANOVA) with Tukeys multiple comparison tests was used. For survival analysis, deaths were recorded and used to generate Kaplan-Meier survival curves, which were compared using Mantel-Cox log-rank tests. IVIS images of tumor luminescence and nanoparticle fluorescence were quantified using Living Image 3.1 software. For tumor size quantification, MRI images were processed in Avizo using the measure tool. 19F NMR spectra acquisition was performed on Bruker TopSpin 2.1 and processed on Bruker TopSpin 2.1 or 4.0.2. Adjustments to immunofluorescence image brightness and contrast were made to improve visual clarity and were applied equally to all images within a series. Figures were assembled in Adobe Illustrator.

Acknowledgments: We would like to thank the aptamer core facility in the Institute of Biomedical Sciences (IBMS), Academia Sinica for Cell-SELEX assistance. We would also like to thank the IBMS Flow Cytometry Core facility for flow cytometry analysis and Y.-H. Chen and IBMS Animal Core staff for animal experiments. We thank Academia Sinica High-Field NMR Center (HFNMRC) for technical support. We also thank J.-H. Lin, P.-J. Lin, and S.-C. Ruan DVM for assistance with the animal experiments. Funding: This work was supported by the Ministry of Science and Technology, Taiwan (MOST 108-2319-B-001-004, 108-2321-B-001-017, and 108-3111-Y-001-053), the National Health Research Institutes grant EX109-10907SI and the Academia Sinica Program for Translational Innovation of Biopharmaceutical Development-Technology Supporting Platform Axis (AS-KPQ-106-TSPA), the Thematic Research Program (AS-107-TP-L12), and the Summit Research Program (MOST 107-0210-01-19-01). HFNMRC is funded by the Academia Sinica Core Facility and Innovative Instrument Project (AS-CFII-108-112). Author contributions: S.-S.H. and K.-J.L. designed and performed experiments and contributed to data analysis, manuscript, and figure preparation. H.-C.C. contributed to the data analysis, discussion, and figure design. R.P.P. performed experiments and contributed to the discussion and manuscript preparation. C.-H.H., O.K.C., S.-C.H., and C.Y.B. performed experiments. C.-B.J. and X.-E.Y. contributed to the IOX2-liposome fabrication. D.-Y.C. and C.W.K. performed the intravital imaging. T.-C.C. established the orthotopic pancreatic cancer model. L.-L.C. drew the schematic illustration. J.J.L. and T.J.K. contributed to the discussion. P.C. managed the intravital imaging. Y.-W.T. contributed to the discussion of PDAC experiments. H.-M.L. managed the liposome fabrication and characterization. P.C.-H.H supervised and managed the project. Competing interests: T.J.K. serves as a consultant for Fujifilm Cellular Dynamics Incorporated. P.C.-H.H., S.-S.H., K.-J.L., and H.-C.C. have patent provisional applications (US 2020/63030674 and US 2020/63030555) related to the use of aptamer-based drug delivery for treatment of heart diseases and cancer. The patent provisional applications were filed by Academia Sinica. The authors declare that they have no other competing interests. Data and materials availability: All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Materials. For patent and tech transfer concerns, the raw and analyzed datasets generated during the study are available for research purposes from the corresponding author on reasonable request. Additional data related to this paper may be requested from the authors.

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Immune cell shuttle for precise delivery of nanotherapeutics for heart disease and cancer - Science Advances

Synthetic Biology Startup Acquires AI Platform To Disrupt The Drug Industry – Forbes

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Sean McClain, Co-Founder and CEO of AbSci.

There has been a lot of recent attention on the challenges of delivering COVID-19 vaccines. But there are also challenges in making them. For some of the newer options like those from Johnson & Johnson and Oxford-AstraZeneca, the modified cells used in vaccine production are struggling under the scale of demand. But synthetic biology company AbScis recent acquisition of the artificial intelligence platform, Denovium, could help mitigate this type of challenge in the future.

Unlike mRNA vaccines, the Johnson & Johnson/Oxford-AstraZeneca class of vaccines rely on a type of virus called adenovirus which is known to cause colds in chimpanzees. To address COVID-19, the adenovirus is genetically altered to express the SARS-CoV-2 spike protein which is what ultimately triggers the bodys immune response. Like mRNA vaccines, adenovirus-based vaccines train the body to recognize and fight COVID-19, foregoing the need to inject a person with a weakened version of SARS-CoV-2.

But producing enough adenovirus cells has been a challenge. To make vaccine doses, large volumes of altered adenovirus are produced by replicating cells in bioreactors. But, the scale of production can also cause the cells to weaken. This can result in a reduced output of adenovirus copies. So while these new vaccines may represent a breakthrough in adenovirus-based therapeutics, the process also highlights some critical roadblocks.

One major issue is that drug discovery and drug manufacturing are often disconnected from one another. Drug discovery typically starts with screeningthe process of finding a set of compounds out of 100,000 combinations that can best neutralize a targeted weak point of a disease. But when a promising protein is identified, it often turns out to be difficult to scale effectively.

Once a therapeutic compound is identified, researchers must then determine if it works well with a group of similar cells called a cell-line. By inserting the compound into the cellswhich then divide and multiply in a bioreactorthe cells act like factories to produce greater volumes of the compound of choice. But, as in the case with adenovirus-producing cells, not all cells can maintain their functions at large volumes. If the protein compound doesnt work well in a scalable cell-line, researchers often have to go back to the drawing board to find a new compound and start again.

Many in the biopharma space are aware of this inefficient process. The synthetic biology company AbSci has spent years developing a platform solution that streamlines the workflow. [Our platform] is simultaneously a drug discovery and manufacturing platform that allows you to discover your drug and the cell line that can manufacture [it], says AbSci CEO, Sean McClain. Were finally uniting drug discovery and manufacturing the first time.

AbSci refers to their core process as their Protein Printing platform, not because it uses ink and paper to make proteins but as an analogy for ease and speed. The first technology [in our platform] is our SoluPro E. coli strain. It has been highly engineered to be more mammalian-like to be able to produce mammalian-like proteins that E. coli wasn't previously capable of doing, says McClain. AbSci also uses what the company calls a folding solution to precisely tailor how proteins fold and therefore function.

Imad Ajjawi, Co-Founder and CBO of Denovium

To find the most effective protein, AbSci alters its folding solutions to create as many protein varieties as possible, often to the order of 10s of millions. The more protein types available, which AbSci refers to as libraries, the higher the likelihood of success. But this also creates a challenge: so many options, but which to choose?

To address this, AbSci recently acquired artificial intelligence company, Denovium. By integrating Denoviums AI platform, AbSci can improve its data analysis via AI models. From there, the company can take the best candidates and find the most effective cell-line to produce the chosen compounds at scale. McClain explains that traditional drug discovery and manufacturing typically takes years. But AbScis platform can take that timeline down to weeks. Were actually able to manufacture [therapeutics] because the dirty secret in pharma is that so many drugs get shelved because [pharma companies] can't actually manufacture them, says McClain.

For McClain, acquiring Denovium is a big step forward for AbScis discovery process. Its going to change the paradigm. Its really a perfect marriage of both data and AI technology. If you don't have good data feeding into your AI model, it's worthless. But if you don't have an AI technology, you can't mine [the data] and get all the benefits, says McClain.

Denoviums co-founder and CBO, Imad Ajjawi, also sees the new collaboration as a significant opportunity. It's really exciting to be a part of AbSci because they have all the data, billions of points that the deep learning engine can now analyze, says Ajjawi. AbScis acquisition also comes on the heels of the companys $65 million Series E in late 2020.

Upgrading the union of biology and AI is important for advancing synthetic biology innovation. But the true potential beneficiaries of this advanced discovery platform are those in need of novel drug options.

AbScis main goal as a company is to bring therapeutics to market more quickly. This technology's impact on healthcare is profound because more drugs and biologics can now enter patients' hands faster, says McClain.

McClain believes that AbScis technology will help speed the process of clinically testing new medications. Faster clinical trial turnarounds could increase the number of drugs approved to address a range of diseases. This could be most impactful for patients with rare or difficult to treat conditions as drug discovery is often prioritized based on how long it takes to find a scalable cell-line.

But though AbSci is working to accelerate drug discovery, the process still takes time. Right now, we have six drugs that are in preclinical or clinical trials. And one of them is actually in phase three. So we could have an improved product here in the next couple of years, says McClain.

As Absci and Denovium finalize their technology integrations, McClain is also looking ahead to build as many partnerships as possible. The more partnerships we do, the more patients were able to affect that at the end of the day, says McClain.

In line with that goal, AbSci today announced a continuation of its partnership with Astellas and Xyphos. AbSci will take on screening and identifying an optimal cell-line for a leading variant of Xyphos MicAbody, a bispecific antibody-like adaptor molecule used in the company's immuno-oncology program.

McClain expects more partnership announcements will follow in the first quarter of 2021. We have some really exciting partnerships that are going to be coming out over this next quarter that I think speak to the [range] of the types of disease states we're working on and the breadth of how the technology can be used within biopharma, says McClain.

Im the founder of SynBioBeta, and some of the companies that I write about are sponsors of the SynBioBeta conference and weekly digest, including AbSci. Thank you to Fiona Mischel and Vinit Parekh for additional research and reporting in this article.

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Synthetic Biology Startup Acquires AI Platform To Disrupt The Drug Industry - Forbes

Programming in the pandemic – Perforce: In open source, crowd is a positive – ComputerWeekly.com

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The Computer Weekly Developer Network examines the impact of Covid-19 (Coronavirus) on the software application development community.

With only a proportion of developers classified as key workers (where their responsibilities perhaps included the operations-side of keeping mission-critical and life-critical systems up and online), the majority of programmers will have been forced to work remotely, often in solitude.

So how have the fallout effects of this played out?

This post comes from Justin Reock in his role as chief evangelist for open source software (OSS) & Application Programming Interface (API) management at Perforce Software.

Reock reflects upon the use of open source platforms, languages and related technologie in general in light of the Covid-19 global crisis and writes as follows

On the whole, I would argue that open source software has been invaluable during the pandemic.

Crowd-sourced software initiatives and hackathons, protein-folding peer-to-peer networks and foundation sponsorship have all been in play throughout the contagion and many of these initiatives continue forwards.

GitHub has shown us that commits held steady or even increased suggesting (if it is fair to measure that in terms of raw commits without considering quality) that developer productivity has held steady or even gone up.

For many developers, having a shared project and sense of community during a very isolating time for humanity has been uplifting and good for their spirits. Its a reminder that coding together is in fact a social activity, no different than any other collaborative and creative endeavour.

Perhaps the biggest impact and fallout from this whole period of experiences (for programmers, operations staff and the wider software engineering community) will be the acceleration of transformation and DevOps initiatives within businesses.

So many have witnessed the resilience of businesses that have already undergone the DevOps transition (and even watched their profits soar) as we moved to online ordering, contactless delivery and more.

The CI/CD part of the DevOps makeover has always been about dealing with constant change.The mantra of releases are hard, so release often embraces the notion that change is difficult, so organisations should make themselves really good at dealing with it. That meant when the pandemic hit, the seams of our global digital twin were tested. Companies that were capable of quickly refactoring to online experiences, digital goods and other conveniences have now become essential to carrying on a reasonable quality of life in the physical world.

It is one thing to expect the unexpected, and it is quite another to design systems that thrive in unexpected conditions.Whatever requisite effort may need to be invested to achieve DevOps maturity in an organisation, the positive impact it can have to business longevity is now indisputable.

However, especially in segments of the industry that are highly collaborative such as gaming, quality and deadlines have suffered drastically and development teams have blamed it squarely on moving to a remote work model.

NOTE: As a software change management specialist, Perforce has a particularly acute proximity with and close understanding of how games programmers work.

Even enabling employees to work from home was a challenge, as the hardware supply chain which we rely on to deliver our webcams, tablets, and laptops and other tech gear suffered major disruptions: so, all in all, there is no question that organisations, including open source communities, which had already taken steps towards transformation and remote work were able to continue operations smoothly, though not completely without impact.

That said, the overall industry picture is not all rosy, with many segments that rely heavily on peer collaboration taking a hit in quality and productivity.

We hope, of course, for brighter future times for all.

Reock: Commit to commit dear developers, you know you want to.

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Programming in the pandemic - Perforce: In open source, crowd is a positive - ComputerWeekly.com

Six faculty elected to National Academy of Sciences – Stanford Today – Stanford University News

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Six Stanford University researchers are among the 120 newly elected members of the National Academy of Sciences. Scientists are elected to the NAS by their peers.

The six Stanford faculty members newly elected to the National Academy of Sciences. (Image credit: Andrew Brodhead)

The new members from Stanford are Savas Dimopoulos, the Hamamoto Family Professor and professor of physics in the School of Humanities and Sciences; Daniel Freedman, a visiting professor at theStanford Institute for Theoretical Physics (SITP) and professor of applied mathematics and theoretical physics, emeritus, at MIT; Judith Frydman, professor of biology and the Donald Kennedy Chair in the School of Humanities and Sciences, and professor of genetics in the Stanford School of Medicine; Kathryn A. Kam Moler, vice provost and dean of research, and the Marvin Chodorow Professor and professor of applied physics and of physics in the School of Humanities and Sciences; Tirin Moore, professor of neurobiology in the Stanford School of Medicine; and John Rickford, professor of linguistics and the J.E. Wallace Sterling Professor in the Humanities, emeritus, in the School of Humanities and Sciences.

Savas Dimopoulos collaborates on a number of experiments that use the dramatic advances in atom interferometry to do fundamental physics. These include testing Einsteins theory of general relativity to fifteen decimal precision, atom neutrality to thirty decimals, and looking for modifications of quantum mechanics. He is also designing an atom-interferometric gravity-wave detector that will allow us to look at the universe with gravity waves instead of light.

Daniel Freedmans research is in quantum field theory, quantum gravity and string theory with an emphasis on the role of supersymmetry. Freedman, along with physicists Sergio Ferrara and Peter van Nieuwenhuizen, developed the theory of supergravity. A combination of the principles of supersymmetry and general relatively, supergravity is a deeply influential blueprint for unifying all of natures fundamental interactions.

Judith Frydman uses a multidisciplinary approach to address fundamental questions about protein folding and degradation, and molecular chaperones, which help facilitate protein folding. In addition, this work aims to define how impairment of cellular folding and quality control are linked to disease, including cancer and neurodegenerative diseases, and examine whether reengineering chaperone networks can provide therapeutic strategies.

Kam Molers research involves developing new tools to measure magnetic properties of quantum materials and devices on micron length-scales. These tools can then be used to investigate fundamental materials physics, superconducting devices and exotic Josephson effects a phenomenon in superconductors that shows promise for quantum computing.

Tirin Moore studies the activity of single neurons and populations of neurons in areas of the brain that relate to visual and motor functions. His lab explores the consequences of changes in that activity and aims to develop innovative approaches to fundamental problems in systems and circuit-level neuroscience.

John Rickfords research and teaching are focused on sociolinguistics the relation between linguistic variation and change and social structure. He is especially interested in the relation between language and ethnicity, social class and style, language variation and change, pidgin and creole languages, African American Vernacular English, and the applications of linguistics to educational problems.

The academy is a private, nonprofit institution that was created in 1863 to advise the nation on issues related to science and technology. Scholars are elected in recognition of their outstanding contributions to research. This years election brings the total of active academy members to 2,461.

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Six faculty elected to National Academy of Sciences - Stanford Today - Stanford University News

Gene expression signatures of target tissues in type 1 diabetes, lupus erythematosus, multiple sclerosis, and rheumatoid arthritis – Science Advances

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Abstract

Autoimmune diseases are typically studied with a focus on the immune system, and less attention is paid to responses of target tissues exposed to the immune assault. We presently evaluated, based on available RNA sequencing data, whether inflammation induces similar molecular signatures at the target tissues in type 1 diabetes, systemic lupus erythematosus, multiple sclerosis, and rheumatoid arthritis. We identified confluent signatures, many related to interferon signaling, indicating pathways that may be targeted for therapy, and observed a high (>80%) expression of candidate genes for the different diseases at the target tissue level. These observations suggest that future research on autoimmune diseases should focus on both the immune system and the target tissues, and on their dialog. Discovering similar disease-specific signatures may allow the identification of key pathways that could be targeted for therapy, including the repurposing of drugs already in clinical use for other diseases.

The incidence of autoimmune diseases is increasing on a worldwide basis, and the prevalence of some of the most severe autoimmune diseases, i.e., type 1 diabetes (T1D), systemic lupus erythematosus (SLE), multiple sclerosis (MS), and rheumatoid arthritis (RA), has reached levels of prevalence ranging from 0.5 to 5% in different regions of the world (1). There is no cure for these autoimmune diseases, which are characterized by the activation of the immune system against self-antigens. This is, in most cases, orchestrated by autoreactive B and T cells that trigger and drive tissue destruction in the context of local inflammation (25). While the immune targets of T1D, SLE, MS, and RA are distinct, they share several similar elements, including common variants that pattern disease risk, local inflammation with contribution by innate immunity, and downstream mechanisms mediating target tissue damage. In addition, disease courses are characterized by periods of aggressive autoimmune assaults followed by periods of decreased inflammation and partial recovery of the affected tissues (3, 611). Endoplasmic reticulum stress (1215), reactive oxygen species (1619), and inflammatory cytokines, such as interleukin-1 (IL-1) and interferons (IFNs), are also shared mediators of tissue damage in these pathologies (2023).

Despite these common features, autoimmune disorders are traditionally studied independently and with a focus on the immune system rather than on the target tissues. There is increasing evidence that the target tissues of these diseases are not innocent bystanders of the autoimmune attack but participate in a deleterious dialog with the immune system that contributes to their own demise, as shown by our group and others in the case of T1D [reviewed in (3, 24, 25)]. Furthermore, in T1D, several of the risk genes for the disease seem to act at the target tissue levelin this case, pancreatic cellsregulating the responses to danger signals, the dialog with the immune system, and apoptosis (20, 25, 26). Against this background, we hypothesize that key inflammation-induced mechanisms, potentially shared between T1D, SLE, MS, and RA, may drive similar molecular signatures at the target tissue level. Discovering these similar (or, in some cases, divergent) disease-specific signatures may allow the identification of key pathways that could be targeted for therapy, including the repurposing of drugs already in clinical use for other diseases.

To test this hypothesis, we obtained RNA sequencing (RNA-seq) datasets from pancreatic cells from controls or individuals affected by T1D (27), from kidney cells from controls or individuals affects by SLE (28), from optic chiasm from controls or individuals affected by MS (29), and from joint tissue from controls or individuals affected by RA (30). In some cases, we also compared these datasets against our own datasets of cytokine-treated human cells (31). These unique data were mined to identify similar and dissimilar gene signatures and to search for drugs that may potentially revert the observed signatures. Furthermore, we searched for the expression of candidate genes for the different autoimmune diseases at the target tissue level and the signaling pathways downstream of these candidate genes.

These studies indicate major common gene expression changes at the target tissues of the four autoimmune disease evaluated, many of them downstream of types I and II IFNs, and massive expression of candidate genes (>80% in all cases). These findings support the importance of studying the target tissue of autoimmune diseases, in dialog with the immune system, to better understand the genetics and natural history of these devastating diseases.

The metadata of the tissue donors evaluated in the present analysis are shown in Table 1. The number of samples is proportional to the accessibility of the target tissues, with the highest number of samples available for joint tissue in RA. The age and sex of the patients reflect the natural history of the different diseases, with younger patients in the T1D group and a higher proportion of female patients in the MS and SLE groups. Sex information was obtained from the original metadata and, when not available, was inferred using chromosomal marker information present in the transcriptome (see Materials and Methods). Of note, while some of the samples used for RNA-seq were obtained following fluorescence-activated cell sorting (FACS) purification (e.g., pancreatic cells) (27), other samples comprised a mixture of target cells and infiltrating immune cells. Determination of the leukocyte marker CD45 expression in the different samples indicated a trend for higher presence of immune-derived cells among samples obtained in T1D, MS, and RA, but not in SLE (table S1). This contribution by immune cells was, however, modest. For instance, while in the cell preparation the number of transcripts per million (TPM) for CD45 in the patient group was 16.4 (mean), the TPM values for the following cell markers were as follows: INS (Insulin), 125.359; Sodium/potassium-transporting ATPase gamma chain (FXYD2a), 65; GCK (Glucokinase), 20; Homeobox protein Nkx-2.2 (NKX2-2), 28; Synaptotagmin 4 (SYT4), 36; Neurogenic Differenciation 1 (NEUROD1), 27; Homeobox protein Nkx-6.1 (NKX6-1), 27; and MAF BZIP Transcription Factor B (MAFB), 23, indicating that the observed responses are driven, at least in part, by the constitutive cells of the target tissues. Of note, proinflammatory cytokines decrease the expression of several of the cell markers (3, 20, 32) described above.

RNA-seq data from four studies of target tissues in autoimmune diseases were retrieved from the Gene Expression Omnibus (GEO) portal (https://ncbi.nlm.nih.gov/geo/), reanalyzed, and quantified with Salmon using GENCODE 31 as the reference. N/A, data nonavailable. For the sex column: M, male; F, female.

In the T1D and SLE datasets, but not in the MS and RA ones, there was a trend for more up-regulated than down-regulated genes in the target tissues, which was particularly marked in the T1D dataset, with more than twofold higher number of up-regulated genes as compared with the down-regulated ones (Fig. 1A). Of note, because of a statistically significant difference in the age of patients with RA and their respective controls, we have included age as an independent variable when determining the differentially expressed genes in the joint tissue samples (see Materials and Methods).

(A) Number of protein-coding genes differentially expressed in four autoimmune diseases. Each RNA-seq data set was quantified with Salmon using GENCODE 31 as the reference. Differential expression was assessed with DESeq2. The numbers within the bars represent the protein-coding genes with |fold change| >1.5 and an adjusted P value <0.05. RNA-seq sample numbers (n) are as follows: T1D (n = 4 for patients, n = 10 for controls), SLE (n = 20 for patients, n = 7 for controls), MS (n = 5 for patients, n = 5 for controls), and RA (n = 56 for patients, n = 28 for controls). Results for the RA samples were adjusted by age as an independent variable. (B to E) Gene set enrichment analysis (GSEA) of T1D (B), SLE (C), MS (D), and RA (E) target tissues. After quantification using Salmon and differential expression with DESeq2, genes were ranked according to their fold change. Then, the fGSEA algorithm (76) was used along with the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Reactome databases to determine significantly modified pathways. Bars in red and blue represent, respectively, a positive and negative enrichment in the associated pathway. The x axis shows the normalized enrichment score (NES) of the fGSEA analysis, and the y axis the enriched pathways. The numbers at the end of the signaling pathway names represent, respectively, (i) the number of genes present in the leading edge of the GSEA analysis and (ii) the total number of genes present in the gene set.

Enrichment analysis of these disease-modified genes (Fig. 1, B to E) indicated similarities and differences between the different autoimmune diseases. Thus, both T1D and SLE have several up-regulated IFN-related pathways among the top up-regulated ones (Fig. 1, B and C); IFN pathways were also detected as enriched for MS and RA, but not among the 20 top ones [e.g., MS: IFN- signaling normalized enrichment score (NES) = 2.26 (P adj. < 0.007); RA, IFN- signaling NES = 2.64 (P adj. < 0.004)]. This similar enrichment in IFN-related genes can also explain the appearance of SLE as the top up-regulated pathway in T1D (Fig. 1B). Up-regulated pathways related to antigen presentation or antigen-related activation of immune cells were present for the four diseases (Fig. 1, B to E), in line with their autoimmune nature, while complement cascades were preeminent in MS (Fig. 1D) and RA (Fig. 1E), but less so in T1D and SLE. To evaluate whether these observed IFN-induced signatures originate, at least in part, from nonimmune cells in the target tissues, we reanalyzed available single-cell(sc)/nucleus(sn)RNA-seq data focusing on nonimmune cells in affected tissues in T1D [pancreatic cells (33)], SLE [kidney epithelial cells (34)], MS [brain neurons (35)], and RA [synovial fibroblasts (36)] (fig. S1A), confirming that there is a significant IFN signature in the target of the four autoimmune diseases as measured by an IFN response score, defined as the average expression of known IFN-stimulated genes (ISGs; see Materials and Methods) (34, 37).

The down-regulated pathways tended to be more disease specific and related to the dysfunction of the target organ. Thus, for T1D, there was down-regulation of pathways involved in integration of energy metabolism, a key step for insulin release, and in regulation of gene expression in cells, which reflects the down-regulation of several transcription factors (TFs) critical for the maintenance of cell phenotype and function (e.g., PDX1 and MAFA) (38) (Fig. 1B), while in RA, there was a decrease in collagen chain trimerization, an important step for proper collagen folding (Fig. 1E) (39). Moreover, down-regulation of pathways involved in lipid metabolism was enriched in MS samples (Fig. 1D). Supporting that, disruption of lipid metabolism in oligodendrocytes compromises the lipid-rich myelin production/regeneration, a hallmark of MS, both in in vitro studies (40) and in samples obtained from individuals with MS (41).

Gene set enrichment analysis (GSEA) of the sc/snRNA-seq data of nonimmune cells from the four autoimmune diseases (fig. S1, B to E) confirmed several up-regulated pathways in common, including IFN signaling (present for all diseases, although not always among the top 20 shown), T1D (which appears in three of the four diseases), allograft rejection, etc. As observed in the bulk RNA-seq analysis, there were less similarities between diseases regarding the down-regulated pathways.

We also analyzed the intersection between significantly up- and down-regulated genes of the bulk RNA-seq of the four diseases using another criterion, namely, considering genes as significantly modified if they presented a false discovery rate <0.10 without a fold change threshold (fig. S2, A and B). This showed a higher similarity among up- than down-regulated genes, but there were few genes in common between the four diseases. On the basis of a hypergeometric test to search for gene set enrichment for the cases where there were >50 genes in common between two and three diseases, we identified IFN signaling, antigen processing, and presentation and cytokine signaling, among others. It was, however, difficult to find common pathways among the down-regulated genes. A limitation of this approach is that we can only analyze genes that pass a fixed statistical cutoff, which makes the results very susceptible to the number of samples studied, as presently observed for the higher intersection between RA (a disease with a much higher number of samples) and the other autoimmune diseases. This type of analysis must thus be redone as more samples become available for the different diseases.

To obtain more detailed information on the (dis)similarities between the different autoimmune diseases, avoiding the pitfalls mentioned above for threshold-based analysis, we performed the rank-rank hypergeometric overlap (RRHO) analysis (Fig. 2) (42), a genome-wide approach that compares two equally ranked datasets using a threshold-free algorithm (see Materials and Methods). The main similarities between the diseases were observed among up-regulated genes, while there was no major intersection of commonly down-regulated genes between datasets (Fig. 2). This finding is in line with the above-described observation that down-regulated genes tended to be target-tissue related (Fig. 1, B to E). cells in T1D, in particular, showed a strong correlation with regard to up-regulated genes with SLE, RA, and MS (Fig. 2). The functional enrichment analysis of these up-regulated overlapping pathways showed concordance for both types I and II IFN signaling for nearly all disease pairs (Fig. 3). Pathways related to neutrophil degranulation were highly up-regulated when comparing MS against T1D (Fig. 3B), SLE (Fig. 3D), or RA (Fig. 3F); this pathway also appeared highly in common between T1D and RA (Fig. 3C).

(A) Genes were ranked by their fold change from the most down- to the most up-regulated ones and then submitted to the RRHO algorithm. The level map colors display the adjusted log P values of the overlap (the P values were adjusted using the Benjamini and Yekutieli method) between genes up-regulated in both datasets (bottom left quadrant), down-regulated in both (top right quadrant), up-regulated in the left-hand pathology and down in the bottom part (top left quadrant), and down in the left-hand pathology and up-regulated in the bottom part pathology (bottom right quadrant). (B) The panel displays the number of genes significantly overlapping in each pairwise analysis (A). NS, not significant quadrant.

(A to F) Genes significantly overlapping between different pairs of autoimmune diseases in the RRHO analysis (Fig. 2B) were selected for enrichment analysis using the clusterProfiler tool with the Reactome database. The top 20 gene sets are represented according to their adjusted P values (Benjamini and Hochberg correction) and their gene ratio (no. of modified genes/total gene set size). Diseases were analyzed in pairs. Enrichment analysis of genes significantly up-regulated in the target tissues of both (A) T1D and SLE, (B) T1D and MS, (C) T1D and RA, (D) SLE and MS, (E) SLE and RA, and (F) MS and RA.

We next investigated the potential TFs controlling the observed interdisease similarities. For this purpose, we evaluated the enrichment of TF binding site motifs in the promoter region of up-regulated genes from the pairwise analysis of autoimmune diseases (fig. S3). In line with the predominance of IFN-related pathways observed in Fig. 3, there was a high prevalence of common binding site motifs for IFN-induced TFs, including IFN-stimulated response element (ISRE), IFN regulatory factor 1 (IRF1), and IRF2, particularly when comparing T1D versus SLE (fig. S3A) and T1D versus RA (fig. S3C). To examine whether these TFs are expressed by constitutive cells of the target tissues, we have reevaluated the TF expression in nonimmune cells present in sc/snRNA-seq of the target tissues from the four autoimmune diseases. Since the presently available methods for sc/snRNA-seq only detect on average 1000 to 5000 genes per cell (43), which is 75 to 80% lower than the total number of genes identified by bulk cell RNA-seq (>20,000 genes), we selected for this analysis the top 10 TFs presenting the highest expression in the affected target tissues. By this approach, we observed that the majority of these TFs are expressed by nonimmune cells from the target tissues (fig. S3G). In agreement with this observation, we have previously shown that exposure of the human cell line EndoC-H1 to INF leads to the activation of several of the same TFs identified, including signal transducer and activator of transcription 1 (STAT1), STAT2, STAT3, IRF1, and IRF9 (31, 48).

To assess whether a putative in vivo type I IFN signaling in the context of different autoimmune diseases activates similar pathways in the target tissues, we compared gene expression of primary human islets (31) and skin keratinocytes (44) exposed in vitro to IFN- for 8 and 6 hours, respectively (fig. S4). There were approximately 40% differentially expressed genes in common between these two tissues (fig. S4A), leading to the induction of pathways such as IFN signaling and antigen presentation/processing (fig. S4B) that were similar to the pathways observed in target tissues from patients affected by T1D (Fig. 1B and fig. S5) or SLE (Fig. 1C and fig. S5).

It is noteworthy that when comparing SLE versus T1D and SLE versus RA (Fig. 2, A and B), there were a large number of genes up-regulated in one disease but down-regulated in the other. A more detailed analysis of these oppositely regulated genes (fig. S6) indicated that neutrophil degranulation and signaling by RHO GTPases (guanosine triphosphatases) were among the most enriched gene sets. A similar observation was made regarding SLE versus RA, where neutrophil degranulation was also the most represented gene set. This apparent disagreement between genes regulating neutrophil degranulation in SLE and other autoimmune diseases may reflect the presence of two distinct populations of neutrophils in patients with SLE that have functional differences in pathways controlling chemotaxis, phagocytosis, and degranulation (45). Other dissimilarities include the anti-inflammatory IL-10 signaling and groups related to the regulation of the dialog between immune and resident cells, such as immunoregulatory interaction between a lymphoid and nonlymphoid cell and PD-1 (programmed cell death protein 1) signaling.

The availability of the above-described datasets allowed us to mine the overlapping genes in the target tissues of the different autoimmune diseases to search for common therapeutic targets, with the potential to find drugs to be repurposed (Fig. 4). As a proof of concept, we identified dihydrofolate reductase inhibitors as a potential therapeutic target for several pairs of autoimmune diseases (Fig. 4, B to D and F), and methotrexate, a member of this class, is already routinely used for the treatment of different autoimmune diseases, including RA (46) and SLE (47). Bromodomain inhibitors were also observed as common perturbagens between T1D and SLE (Fig. 4A) and SLE versus RA (Fig. 4E). This is in line with our recent observations that two of these bromodomain inhibitors, JQ1 and I-BET-151, protect human cells against the deleterious effects of IFN- (31). There were additional interesting candidates, some with a profile covering multiple diseases, such as phosphoinositide 3-kinase (PI3K) (T1D versus SLE, SLE versus RA, and MS versus RA) and janus kinase (JAK) inhibitors (SLE versus RA and MS versus RA), while others acting on specific pairs of diseases, namely, bile acids (T1D versus MS) and fibroblast growth factor receptor (FGFR) inhibitors (SLE versus MS) (Fig. 4). Of note, clinical trials are currently evaluating the effects of the bile acid tauroursodeoxycholic acid (TUDCA) in patients with recent-onset T1D (ClinicalTrials.gov, NCT02218619) and MS (ClinicalTrials.gov, NCT03423121).

(A to F) After determining statistically which genes were overlapped in pairs of autoimmune diseases from the RRHO analysis (Fig. 2), the top 150 overlapping genes were submitted to the Connectivity Map database to identify perturbagen classes driving an opposite signature (negative tau score) to the one present in the target tissues of the four autoimmune diseases. Only classes with a median tau score <80 were considered. (A to F) Perturbagen classes driving down the genomic signatures of up-regulated genes. The same methodology and conditions have been applied for subsequent analysis: (A) T1D and SLE, (B) T1D and MS, (C) T1D and RA, (D) SLE and MS, (E) SLE and RA, and (F) MS and RA. EGFR, epidermal growth factor receptor. LOF, Loss of Function; GOS, Gain of Function; IAP, inhibitor of Apoptosis; FGFR, Fibroblast Growth Factor Receptor; MDM, Murine Double Minute; HIF, Hypoxia Inducible Factor; BCL, B-Cell Lymphoma.

We have previously shown that isolated human pancreatic islets express a large number of risk genes for T1D (20, 24, 26, 48), and we presently examined whether this is also the case for the target tissues in other autoimmune diseases (table S2). Confirming our previous findings, 81% of risk genes for T1D were expressed in human cells; similar findings were observed for the target tissues for SLE (92%), MS (83%), and RA (88%). The autoimmune assault changed the expression of >65% of these candidate genes for joint tissue RA (table S2), but the number of disease-induced and significantly modified genes was much smaller for the other autoimmune diseases, probably because of limited statistical power associated to the number of samples analyzed (>80 samples studied in the case of RA and between 10 and 27 for the other diseases). The list of risk genes expressed in the target tissues is available in data file S1. An overview of these candidate genes and their coexpression in different autoimmune diseases is provided in Fig. 5. Genes related to antigen presentation [human lymphocyte antigen (HLA)DQB1 and HLA-DRB1] and to type I IFN signaling (TYK2) are present in all target tissues for the four autoimmune diseases. Reactome (49) analysis of risk genes in T1D (data file S2) identified ILs and IFN signaling as important pathways. IFN signaling also appears pro-eminently for kidney tissue in SLE, optic chiasm in MS, and joint tissue in RA (data file S2), but there are also clusters related to defense against the autoimmune assault, including PD-1 (for all diseases) and IL-10 signaling (for SLE and MS only); PD-1PDL1 (programmed death ligand 1) is probably also an important defense mechanism of human cells in T1D (50).

Venn diagram representing risk genes identified in GWAS studies in target tissues for T1D, SLE, MS, and RA. For each disease, the risk genes were extracted from the GWAS Catalog (www.ebi.ac.uk/gwas/) and selected as described in Materials and Methods. In brief, each list was curated according to their relationship to the disease, and only genes with a P value <0.5 108 for their SNP-trait relationship were kept. Last, an intersection between the four lists was performed and represented as a Venn diagram. Numbers in the diagram represent the numbers of genes present in each subgroup, and genes overlapping between diseases are displayed by their HGNC symbols. A gene was considered as expressed if it presents a mean TPM > 0.5 in either the patient or control group. N/A, not applicable (no gene in common).

To evaluate whether the observed candidate genes are expressed in nonimmune cells from the target tissues studied, we have used a similar approach as done for the TF analysis (fig. S3G) and revised sc/snRNA-seq data from nonimmune cells in affected tissues in T1D (33), SLE [kidney epithelial cells (34)], MS [brain neurons (35)], and RA [synovial fibroblasts (36)]. This confirmed that >80% of the top 50 risk genes are expressed by the target cells (fig. S1, F to I). Of note, the present limitations of the scRNA-seq method regarding the number of genes detected (commented upon above) may explain why less candidate genes are observed in single cells (fig. S1, F to I) than in whole tissue or FACS-sorted bulk cells (data file S1).

In the present study, we tested the hypothesis that target tissues from four different autoimmune diseases, namely, T1D, SLE, MS, and RA, engage in a dialog with the invading immune cells that leaves molecular footprints. These footprints may share similarities, as local inflammation is a common outcome of these diseases, and point to common mechanisms that can be targeted by therapy.

The analysis of the gene expression patterns of the target tissues in the different diseases showed up-regulation of type I and II IFNrelated pathways, which is in line with observations made in the peripheral blood of individuals with T1D (51), SLE (52, 53), MS (54), and RA (55). These descriptive similarities were confirmed by comparing the ranking of the up-regulated genes via RRHO, a method that allows the comparison between differentially expressed genes in control and diseased tissue from two different diseases, outlining the similarities and/or dissimilarities between the modified genes in both diseases. Here, we observed clear but different degrees of overlap between the diseases mostly regarding the up-regulated expression patterns. In support of the robustness of the present findings, these similarities were present despite the fact that the original RNA-seq data were obtained by different research teams, using different extraction and sequencing processes, and that there were major differences between the studies regarding age and sex of the patients and respective controls (many of these differences were inherent to the diseases studied, e.g., SLE is more common in females).

The observed similarities in pathway activation between target tissues were translated into the identification of several classes of drugs that could potentially be used to treat more than one autoimmune disease (Fig. 4). Among them, JAK inhibitors, which act downstream of the types I and II IFN receptors by blocking activation of the kinases JAK1 and JAK2, are of particular interest. These inhibitors were recently approved for the treatment of RA (56) and had promising results in a phase 2 clinical trial of patients with SLE (57). In line with this, JAK inhibitors prevent the proinflammatory and proapoptotic effects of IFN- on human pancreatic cells (31) and revert established insulitis in diabetes-prone NOD (nonobese diabetic) mice (58). Another class of drugs presently identified for potential use in several autoimmune diseases are the PI3K inhibitors. These drugs target a family of lipid kinases that phosphorylate phosphoinositides from cell membranes, modulating cellular processes such as cell growth, metabolism, and immune responses. In agreement with our analysis, inhibitors of the PI3K isoforms and have beneficial effects in animal models of MS (59), SLE (60), and RA (61). PI3K inhibitors, however, may have opposite effects on different tissues. Thus, PI3K inhibitors exacerbate inflammatory responses in the airways and gut, tissues often exposed to pathogens, leading to severe cases of pneumonitis and colitis (62). This indicates that selection of potential new therapeutic agents needs to consider also the specific characteristics of the target tissue(s). This is in agreement with our present observations of tissue-specific down-regulated pathways in different diseases, such as pathways related to maintenance of the cell phenotype in T1D, or down-regulation of pathways involved in collagen folding in joint tissues from RA.

There have been previous attempts to perform individual drug repurposing on these pathologies [e.g., (63, 64)]. Our present study attempts to expand this approach, potentially leading to drug repurposing for multiple autoimmune diseases, for instance, in the case of JAK inhibitors. Repurposing already-studied drugs provides the benefits of having their pharmacodynamic and pharmacokinetic profiles already well studied, which considerably reduces the bench-to-bedside time frame (65), and helping the treating physicians to survey for previously detected side effects.

More than 80% of candidate genes for which a single-nucleotide polymorphism (SNP)trait link has been deemed significant are expressed in the target tissues of the different autoimmune diseases studied. This is in line with our previous observations in T1D (20, 26, 48), where these candidate genes probably regulate cell responses to danger signals, such as viral infections, and the signal transduction of type I IFNs (23). The fact that similar observations are now made in the target tissues of SLE, MS, and RA (present data) suggests that future studies in these diseases should also consider the impact of candidate genes acting at the target tissue level. Of note, and to detect eQTL (Expression quantitative trait loci) in target tissues, it may be necessary to expose them to relevant stimuli, such as proinflammatory cytokines in the case of T1D (26).

The present observations, showing the expression of candidate genes in the target tissues of autoimmune diseases, may contribute to explain why certain people have different innate immune responses at the tissue level to seemingly similar triggers (such as viral infections or other danger signals), leading to different outcomes, e.g., progressive tissue damage or resolution of inflammation and return to homeostasis. For instance, diverse polymorphisms in candidate genes for T1D may contribute to disease at the cell level by regulating antiviral responses, innate immunity, activation of apoptosis, and, at least for a few of them, cell phenotype (24, 25, 66).

The candidate genes presently observed as overlapping between target tissues of two or more diseases are mostly related to inflammatory mediators, particularly the signal transduction of IFNs, suggesting that similarities between these diseases are dependent, at least in part, on the genetically mediated regulation of local immune responses. These findings may have therapeutic implications. For instance, one of the candidate genes in common between all the four autoimmune diseases is TYK2, a key component of the JAK-STAT signaling pathway. TYK2 inhibitors are already in phase 3 clinical trial for another autoimmune disease, psoriasis (67), and two different TYK inhibitors protect human cells against the deleterious effects of IFN- (68). Targeting IFN pathways at an early step of its signal transduction may not be, however, a sufficiently specific approach, and the role of IFNs may vary according to the stage of disease and the genetic background of the affected individuals. The success of IFN-blocking therapies in human SLE and other rheumatic diseases remains to be proven (69).

The data generated in the present study contribute to a better understanding of the communication between the immune system and the target tissues in T1D, SLE, MS, and RA, and strengthen the putative implication of the target tissues in these autoimmune diseases. These findings also indicate a role for similar candidate genes expressed in target tissues of two or more diseases and indicate potential new therapeutic agents to target key similar pathways. As a whole, these observations suggest that future research on the genetics and pathogenesis of autoimmune diseases should focus on both the immune system and their target tissues and on their dialog.

The studys first limitation relates to the scarcity of RNA-seq data for target tissues in autoimmune diseases, particularly in the cases where these tissues are difficult to access, such as in T1D or MS. This decreases the power of the analysis and may bias the data in favor of diseases where a larger number of samples were available (e.g., RA). Another issue is the stage of the disease, as the impact of the immune system on the target tissues may differ in the early and late phases of the disease [for instance, in the case of T1D, innate rather than adaptive immunity may have a major role at earlier stages (3, 25, 70)]. Unfortunately, and because of the scarcity of samples in, for instance, T1D or MS, this stage issue is difficult to address. It is noteworthy that despite these limitations, it was still possible to obtain clear conclusions from the available data.

Another potential limitation is that immune cells are present in the target tissue preparations analyzed (although there was a statistically significant increase in the expression of the immune marker CD45 only in T1D and RA), which may affect the gene expression pathways described above. The facts that (i) an IFN signature is present in nonimmune cells of the diseased tissues analyzed and these nonimmune cells express several candidate genes for the diseases studied (fig. S1); (ii) at least in the case of a pure human cell line, EndoC-H1 cells, exposure to IFN- induces a gene signature that is similar to that observed in cells obtained from patients affected by T1D (31); and (iii) histological analysis of pancreatic islets from patients with T1D show expression of HLA class I (ABC) (71), HLA-E (31), PDL1 (50), CXCL10 (72), and STAT1 (71) in pancreatic cells, taken as a whole, suggest that at least part of the observed gene signatures originate from the target tissues and cannot be explained by the immune infiltration alone. Future follow-up studies based on direct histological staining of the specific cells involved are required to define the exact contribution of immune and nonimmune cells in the affected target tissues.

For each dataset, control and patient target tissue gene expressions were quantified using Salmon version 0.13.2 (73) with parameters --seqBias gcBias --validateMappings. GENCODE version 31 (GRCh38) (74) was chosen as the reference genome and has been indexed with the default k-mer values. Differential expression was performed with DESeq2 version 1.24.0 (75). For each gene included in DESeq2s model, a log2 fold change was computed and a Wald test statistic was assessed with a P value and an adjusted P value. In this study, we consider a gene as differentially expressed when |fold change| >1.50 and adjusted P value <0.05. Since there was a statistical difference in the age between patients with RA and controls, for this particular dataset, we have taken age as an independent variable in the general linear model performed by DESeq2. To introduce age as a confounding factor in the analysis, we performed a binning on the ages and assigned each donor a group, respectively: 10 to 29, 30 to 49, 50 to 69, and >70 years old. All the other parameters of the DESeq2 analysis were the same as for the others target tissues.

We have obtained the expression matrices containing the processed reads from transcriptome studies of the following target tissues: (i) scRNA-seq from cryo-banked islets obtained from three donors with T1D and three controls matched for body mass index, age, sex, and storage time, performed using the SmartSeq-2 protocol as described in (33) and accessible under the Gene Expression Omnibus (GEO) number GSE124742; (ii) scRNA-seq from kidney biopsies from 24 patients with LN and 10 control samples acquired from living donor kidney biopsies using a modified CEL-Seq2 protocol as described in (34) and accessible in the ImmPort repository (accession code SDY997); (iii) scRNA-seq from snap-frozen brain tissue blocks obtained at autopsies from 10 patients with MS (1 primary progressive MS, 9 secondary progressive MS) and 9 nonaffected individuals processed using the 10x Genomics Single-Cell 3 system as described in (35) and accessible on Sequence Read Archive (SRA; accession number PRJNA544731); and (iv) scRNA-seq of synovial tissues from ultrasound-guided biopsies or joint replacements of 36 patients with RA and 15 patients with osteoarthritis, as reference controls, using the CEL-Seq2 protocol as described in (34) and available at ImmPort (accession code SDY998). After that, we normalized the gene expression levels by transforming the counts to log2(CPM + 1) (counts per million).

For the purpose of reproducibility, we have kept the same cell identity classification defined in the original sc/snRNA-seq study (3336). To represent nonimmune cells on the target tissues, we have selected (i) in T1D, the cells isolated from pancreatic islets; (ii) in SLE, all the kidney epithelial cells from the kidney biopsy; (iii) in MS, all the cells from different clusters of brain neurons; and (iv) in RA, all the cells from the fibroblast clusters of joint synovial tissues.

For most, but not all, target tissues, sex information was available in the metadata on the GEO website. To compensate for this lack of information, we inferred the sex based on the expression of 40 genes exclusively coded on the Y chromosome and the female-expressed XIST (X-inactive specific transcript) (data file S1). We created a machine learning model on the basis of a linear discriminant analysis algorithm that we trained on the expression of both controls and patient expression matrices in RA. The training was supervised with the sex described in the metadata as the desired outcome. We then tried our model to predict the sex of patients on different target tissues (i.e., T1D and MS) where the outcome was known, according to their metadata, which provided only one prediction different from the expected outcome (96% accuracy). This allowed us to estimate the sex ratio in the studies missing this information in the available metadata.

Risk genes associated with each disease were identified using genome-wide association study (GWAS) catalog (www.ebi.ac.uk/gwas/; consulted January 2020). The candidate genes were selected on the basis of the following criteria: (i) T1D, SLE, MA, and RA as the disease/trait evaluated by the study; (ii) a P value of <0.5 108 for the lead SNP; (iii) selecting the reported genes linked to the lead SNP described by the original study; and (iv) expression of the reported genes in the target tissue (TPM > 0.5). An overlap between the four lists of genes was then performed and represented as a Venn diagram.

To evaluate for the presence of types I and II IFN signatures on the target tissues of the four autoimmune diseases, we have calculated for each cell from the sc/snRNA-seq an ISG score. This ISG score was calculated as the average expression of known ISGs listed on data file S1. The statistical difference between groups was determined using a two-tailed Mann-Whitney U test.

To compare the genomic signatures of the target tissues of the four autoimmune diseases, we used an RRHO (42) mapping, an unbiased method to uncover the concordances and discordances between two similarly ranked lists. Briefly, for a pair of diseases, the full list of genes is ranked according to their fold change from the most down-regulated to the most up-regulated gene. Then, an intersection of shared genes is performed, and the analysis of the ranking order of genes is performed with a hypergeometric test.

The visual output of this analysis is an RRHO level map (Fig. 2A), where the hypergeometric P value for enrichment of k overlapping genes is calculated for all possible threshold pairs for each experiment, generating a matrix where the indices are the current rank in each experiment. P values for each test are then log transformed and reported on a heatmap to display the degrees of similarities according to four quadrants representing the concordance or the discordance in gene ranking in the two differential expression analysis (e.g., up-regulated in one disease and down-regulated in the other).

The functional enrichment analysis was based on results from the differential expression analysis. Genes from bulk RNA-seq data were preranked according to the Wald test statistic of the differential expression results from DESeq2. For sc/snRNA-seq data, we filtered out genes that were expressed in less than 10% of all cells to minimize the dropout impact on the overall gene expression. The remaining genes were then preranked according to the log2 fold change of the differential expression results from DESeq2. We used fGSEA (76) along with the Kyoto Encyclopedia of Genes and Genomes (KEGG) (77) and Reactome (49) databases as the references to determine which pathways were positively or negatively enriched in the target tissue of each disease. Default parameters were used, except for the number of permutations (10,000) for the most accurate P values. For bulk RNA-seq data, results with an adjusted P value <0.05 (Benjamini-Hochberg correction) were then sorted according to their NES. For sc/snRNA-seq data, results with an adjusted P value <0.15 (Benjamini-Hochberg correction) were then sorted according to their NES.

To determine the functional enrichment in genes up-regulated in pairs of diseases, we used a hypergeometric test included in the clusterProfiler package (78) on the genes overlapping significantly in the RRHO mapping. The Reactome (49) database was used as the reference for the gene sets. Default parameters were used, and P values were adjusted with the Benjamini-Hochberg correction.

Genes differentially expressed with an adjusted P value <0.10 (Benjamini-Hochberg correction) were selected. The gene lists of all diseases were then overlapped and represented as a Venn diagram of up- or down-regulated genes. In case of an overlap of >50 genes, the gene list was processed using a hypergeometric test with the Reactome database as the reference. Defaults parameters were used, and P values were adjusted with the Benjamini-Hochberg correction.

Motif discovery for TF binding site in the promoter regions of up-regulated genes was done using the script findMotifs.pl from the HOMER (79) tools suite with parameters -start -2000 -end 2000. The promoter regions were considered as 2000 base pairs from the gene transcription start site. Known TF binding site motifs uncovered and included in the study have a P value <0.05.

For each RRHO analysis result, we picked the top 150 up-regulated genes shared between two diseases and processed this list with the Connectivity Map dataset (80) using the cloud-based CLUE software platform (https://clue.io). This allowed us to query the database for compounds that are driving down the input genomic signatures, revealing potential drugs that could be repurposed to treat one or more diseases. We focused then on perturbagen classes that displayed a negative median tau score and retained as potential drug candidates only classes with a median tau score <80.

TPM values are given according to their means SD. Results considered as significant in this study have a P value (or an adjusted P value when applicable) <0.05. For gene expression, we considered that a gene is differentially expressed if |fold change| >1.5 and adjusted P value <0.05, unless explicitly stated.

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Gene expression signatures of target tissues in type 1 diabetes, lupus erythematosus, multiple sclerosis, and rheumatoid arthritis - Science Advances

TYME Granted U.S. Patent Claims Covering Use of TYME-19 to Treat COVID-19 Infections – Business Wire

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BEDMINSTER, N.J.--(BUSINESS WIRE)--Tyme Technologies, Inc. (NASDAQ: TYME), an emerging biotechnology company developing cancer metabolism-based therapies (CMBTs), announced that it has received notification that the United States Patent and Trademark Office has granted additional patent claims related to the Companys metabolomic technology platform. The patent, U.S. Patent No. 10,905,698, is directed to methods for treating COVID-19.

Unlike immune therapies that depend upon the structure of the external virus coat of COVID-19 where the therapy directs its attack, we believe TYME-19 is agnostic to this structure and any mutations to the viral coat. Like other TYME agents, TYME-19 affects cellular metabolism. It constrains viral replication after a virus has inserted its genetic blueprint into an infected cell by inhibiting the ability of the virus to use the cells synthetic apparatus to make viral proteins and lipids. As a result, we believe that TYME-19 diminishes the ability of COVID-19 to hijack an infected cell. TYME intends to initiate the appropriate clinical trials to substantiate the safety and efficacy of TYME-19.

TYME-19 is an investigational compound that is not approved in the U.S. for any disease indication.

About TYME-19

TYME-19 is an oral synthetic member of the bile acid family that the Company also uses in its anticancer compound, TYME-18. Because of its expertise in metabolic therapies, the Company was able to identify TYME-19 as a potent, well characterized antiviral bile acid and has performed preclinical experiments establishing effectiveness against COVID-19. Bile acids have primarily been used for liver disease; however, like all steroids, they are messenger molecules that modulate a number of diverse critical cellular regulators. Bile acids modulate lipid and glucose metabolism and can remediate dysregulated protein folding, with potentially therapeutic effects on cardiovascular, neurologic, immune, and other metabolic systems. Some agents in this class also have antiviral properties. In preclinical testing, TYME-19 repeatedly prevented COVID-19 viral replication without attributable cytotoxicity to the treated cells. Previous preclinical research has also shown select bile acids like TYME-19 have had broad antiviral activity.

About Tyme Technologies

Tyme Technologies, Inc., is an emerging biotechnology company developing cancer therapeutics that are intended to be broadly effective across tumor types and have low toxicity profiles. Unlike targeted therapies that attempt to regulate specific mutations within cancer, the Companys therapeutic approach is designed to take advantage of a cancer cells innate metabolic weaknesses to compromise its defenses, leading to cell death through oxidative stress and exposure to the bodys natural immune system.

With the development of TYME-18 and TYME-19, the Company believes that it is also emerging as a leader in the development of bile acids as potential therapies for cancer and COVID-19. For more information, visit http://www.tymeinc.com. Follow us on social media: Facebook, LinkedIn, Twitter, YouTube and Instagram.

Forward-Looking Statements/Disclosure Notice

In addition to historical information, this press release contains forward-looking statements under the Private Securities Litigation Reform Act that involve substantial risks and uncertainties. Such forward-looking statements within this press release include, without limitation, statements regarding our drug candidates (including SM-88 and TYME- 18) and their clinical potential and non-toxic safety profiles, our drug development plans and strategies, ongoing and planned preclinical or clinical trials, including the proposed TYME-19 proof-of-concept study, preliminary data results and the therapeutic design and mechanisms of our drug candidates. The words believes, expects, hopes, may, will, plan, intends, estimates, could, should, would, continue, seeks, anticipates, and similar expressions (including their use in the negative) are intended to identify forward-looking statements. Forward-looking statements can also be identified by discussions of future matters such as: the effect of the novel coronavirus (COVID-19) pandemic and the associated economic downturn and impacts on the Company's ongoing clinical trials and ability to analyze data from those trials; the cost of development and potential commercialization of our lead drug candidate and of other new products; expected releases of interim or final data from our clinical trials; possible collaborations; and the timing, scope, status, objectives and strategy of our ongoing and planned trials; the success of management transitions; and other statements that are not historical. The forward-looking statements contained in this press release are based on managements current expectations and projections which are subject to uncertainty, risks and changes in circumstances that are difficult to predict and many of which are outside of our control. These statements involve known and unknown risks, uncertainties and other factors which may cause the Companys actual results, performance or achievements to be materially different from any historical results and future results, performance or achievements expressed or implied by the forward-looking statements. These risks and uncertainties include but are not limited to: the severity, duration, and economic and operational impact of the COVID-19 pandemic; that the information is of a preliminary nature and may be subject to change; uncertainties inherent in the cost and outcomes of research and development, including the cost and availability of acceptable-quality clinical supply, and in the ability to achieve adequate start and completion dates, as well as uncertainties in clinical trial design and patient enrollment, dropout or discontinuation rates; the possibility of unfavorable study results, including unfavorable new clinical data and additional analyses of existing data; risks associated with early, initial data, including the risk that the final data from any clinical trials may differ from prior or preliminary study data; final results of additional clinical trials that may be different from the preliminary data analysis and may not support further clinical development; that past reported data are not necessarily predictive of future patient or clinical data outcomes; whether and when any applications or other submissions for SM-88 may be filed with regulatory authorities; whether and when regulatory authorities may approve any applications or submissions; decisions by regulatory authorities regarding labeling and other matters that could affect commercial availability of SM-88; the ability of TYME and its collaborators to develop and realize collaborative synergies; competitive developments; and the factors described in the section captioned Risk Factors of TYMEs Annual Report on Form 10-K filed with the U.S. Securities and Exchange Commission on May 22, 2020, as well as subsequent reports we file from time to time with the U.S. Securities and Exchange Commission available at http://www.sec.gov.

The information contained in this press release is as of its release date and TYME assumes no obligation to update forward-looking statements contained in this release as a result of future events or developments.

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TYME Granted U.S. Patent Claims Covering Use of TYME-19 to Treat COVID-19 Infections - Business Wire

AI Solving Real-world Problems and AI Ethics Among Top Trends for 2021, According to Oxylabs’ AI and ML Advisory Board – insideBIGDATA

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Data science, machine learning, and AI experts highlight the top AI and ML trends they expect to shape the data science industry in 2021

The ongoing impact of Covid-19 is still affecting organizations nearly a year since the pandemic began, with business leaders continuing to leverage technology in order to navigate the crisis. According to Oxylabs dedicated AI and ML advisory board, some of the most important trends in 2021 will include the increased use of ethical AI for diversity, accountability, and model explainability, alongside increased instances of AI solving challenging real-world problems.

Oxylabs advisory board comprises the leading figures in the machine learning, AI, and data science industries and its members outline what they believe are the most important data science predictions for the year ahead:

Firstly, Pujaa Rajan, Machine Learning Engineer at Stripe, USA Ambassador at Women in AI andGoogleDeveloper MLExpert, believes COVID-19 will instigate a renewed enthusiasm for the application of edge AI in the healthcare industry and the use of ethical AI:

Covid-19 defined 2020 and although development in healthcare has historically been slower than other industries due to regulation this year will see a focus on edge AI in the healthcare industry and other industries. This will lead to the ability to run ML models locally, and tiny ML, resulting in smaller sized ML models that fit on smaller devices like phones. Businesses will focus on these specific, technical areas because they are related to data privacy and security, which the general public and government increasingly care about.Model explainability and interpretability is a space that the government, healthcare companies and finance companies are all actively exploring because of technical curiosity and business motivations. Many leaders will also finally prioritise AI ethics, diversity, inclusion, model explainability, and model interpretability after public outrage at many bad, biased, and unethical applications of AI. On the other hand, the biggest AI news last year was OpenAIs GPT-3, so I expect continued innovation in large NLP models. Software and hardware are like yin and yang. Since the larger models will need more efficient hardware, neural network accelerators will be a hot space.

Ali Chaudhry, PhD researcher, Artificial Intelligence atUCL, sees AI as having have more of a contribution in solving challenging real-world problems in 2021:

I think there will be more focus on fairness, transparency, accountability and explainability in AI systems this year, hence, we can expect more regulations from governments around the globe. We will also see AIs contribution in solving more challenging real-world problems, similar to the protein folding problem that was recently solved by AI. In terms of AI techniques that are set to emerge, there will be more real-world applications of Reinforcement Learning (RL) algorithms and RL will also retain its top position in academia.

Another prediction comes from Gautam Kedia, Machine Learning Engineering Manager at Stripe, ex-Applied Scientist Lead at Microsoft, previously Head of Applied ML at Lyft. He considers how AI-generated content could finally become mainstream across multiple sectors:

AI-generated content will become mainstream and in the next few years, I expect truly generative models to be producing logos, short stories, stock images, voiceovers and workouts, DALL-E is just a start and I believe this content will gradually start to pass the Turing Test. Self-driving cars will also take another step forward and I expect Waymo to start a taxi service directly competing with Uber & Lyft. Tesla will also release the much-awaited Full Self Driving computer.

Finally, Jonas Kubilius, AI researcher, Marie Skodowska-Curie Alumnus, and Co-Founder of Three Thirds is optimistic about the implementation of AI in healthcare but also has fears that AI investment may suffer:

Im certainly optimistic about AI-driven solutions making a greater impact in the healthcare sector and drug discovery, however, my only concern is the economic impact of the global COVID-19 pandemic. It may well be that there is a slowdown of investments in AI-driven solutions and research labs, forcing companies to justify any investments they make and focus very clearly on problems where AI brings a clear added value. With ever increasing pressure on governments and organisations to take action in regard to climate change, I expect to see more AI-driven solutions being leveraged in this field. Particularly in the areas that could benefit from the optimisation of manufacturing and logistics processes to reduce the impact they have on the environment.

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Malaysia: Leveraging On Digitalisation Trends – The ASEAN Post

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Analysts and pundits didnt foresee COVID-19 coming in 2020 and that the virus would accelerate the digitalisation trend a seismic or tectonic shift in its own right resulting from the fragmentation of physical processes and the emphases on a low-touch economy as part of compliance to the standard operating procedures (SOP) to break and contain the transmission of the virus.

Not all digitalisation trends are precipitated (in the sense of having their momentum accelerated) by the unprecedented spread of COVID-19 though, as some would have been in the works for years and the breakthroughs only came this year. Likewise, digitalisation trends for 2021 would also reflect similar developments. That is, COVID-19 would have been the impetus and catalyst in contradistinction from cause for the rise of some digitalisation trends whilst others would have already been pursued beforehand.

Lets take a look at some of the digital lessons from 2020 as well as look ahead to 2021.

Cloud Kitchens

COVID-19 has encouraged and enhanced the use of cloud services for physical operations such as in cloud kitchens. What this means is that cooking and delivery services could be centralised rather than having disparate collection points such as various restaurants. The underlying purpose is that dining-in (front-of-house) areas are removed from the overall business process thus saving on costs labour/manpower, operational, overheads, dining assets, etc.

In Malaysia in particular and the region in general, online food delivery businesses such as GrabFood (through Grab e-Kitchen) and FoodPanda have been leveraging on the cloud kitchen concept due to high demand and cost effectiveness. The cloud kitchen trend which came to the fore in 2020 is expected to grow and expand in the Klang Valley in tandem with the overall growth and explosion of e-commerce in the country.

Theres also the trend of hyperconverged infrastructure/technology (HCI) whereby businesses and enterprises can save costs and physical space too. Data management and cloud specialist Nutanix defines HCI as the combination of common datacentre hardware using locally attached storage resources with intelligent software to create flexible building blocks that replace legacy infrastructure consisting of separate servers, storage networks, and storage arrays.

International Data Corporation (IDC), a leading information and communications technology (ICT) market intelligence firm, has predicted that the HCI market will grow to US$7.64 billion in 2021. In Malaysia, local logistics and express carrier giant Gdex has adopted Nutanix Hybrid Cloud to keep up with demands in e-commerce for scalability and business-to-consumer (B2C) operations.

Augmented Reality / Virtual Reality

And then, we have augmented reality (AR)/virtual reality (VR) which is making its presence felt in Malaysias tourism sector. Again, COVID-19 has resulted in partial lockdowns or movement control order (MCO) in Malaysias case, which has massively impacted its tourism sector which is the countrys third major export and foreign exchange earner.

AR/VR is a digital gateway and portal to the on-site tourism experience. Used for marketing and promotional purposes, it allows potential on-site tourists to enjoy an audio-visual sampling of the full package on offer the real world, tactual experience. All one needs to access the virtual experience is a smartphone, laptop, tablet or personal computer (PC).

Moving forward, the Artificial Intelligence of Things (AIoT) which is basically the combination of artificial intelligence (AI) and the Internet of Things (IoT) is making rapid headway. According to futurist Bernard Marr, IoT devices such as sensors, universal remote controllers, and biometric scanners can be likened to a digital nervous system with AI serving as the brain.

When AI is added to the IoT it means that those devices can analyse data and make decisions and act on that data without involvement by humans, explains Marr.

With the advent of 5G technology and smart cities, AIoT is expected to emerge in the near future as part of the new norm in our homes.

Protein Folding

While not exactly a digitalisation trend, the online journal Nature on 30 November reported that after years of pain-staking efforts, an AI called AlphaFold developed by Google offshoot DeepMind has achieved a gargantuan leap in computational biology, namely by determining a proteins 3D shape from its amino-acid sequence or what is popularly known as protein folding where structure is function (an axiom of molecular biology).

As proteins are the building-blocks of life, unravelling their molecular structure would yield insights into the mysteries of life so that finding treatments and cures for intractable diseases such as Parkinsons, producing viral drugs for COVID-19 or identifying suitable enzymes that biodegrade industrial waste, would be possible.

According to the DeepMind website, AlphaFold was taught (via deep learning) by reproducing the sequences and structures of around 100,000 known proteins. Come 2021, we could expect to herald the beginning of a new chapter related to many scientific and industrial applications which hopefully extends to agriculture and food production, air pollution control (carbon capture and storage) and water treatment, among others.

Connected to the AI breakthrough in predicting protein folding is, of course, quantum computingthat represents the leap from bits (binary 0 or 1) to qubits (0 & 1 at the same time) based on quantum physics and mechanics (of the simultaneity-duality of supposition and entanglement). For now, quantum computing can be deployed for complex tasks such as predicting the 3D shape of protein folding and structure.

Blockchain

As for blockchain or distributed ledger technology (DLT), it is fast making a mark in supply chain management (SCM) with the strategic collaboration between public and private sectors. In Malaysia, the use of blockchain by the Royal Malaysian Customs Department (RMCD) will ease and facilitate import-export transactions of private sector stakeholders (shipping/logistics and traders).

Specifically, the TradeLens platform jointly developed by AP Moller-Maersk and IBM is based on the Collaboration Application Programming Interface (API) concept which ensures that all logistics activities such as haulage, warehousing, shipping and freight forwarding at both, domestic and international levels, can now be wholly integrated.

Notwithstanding, will quantum supremacy which Google had claimed to achieve finally constrain the full potential of blockchain technology? According to Deloitte, someone with an operational quantum computer who has access to the public key (public address) could then falsify the transaction signature known as hashing which is an encryption mechanism (in the form of a cryptographic function) serving as proof of work that is linkable to another block of transaction data (hence forming a blockchain) and therefore hack to gain entry to the private key (i.e., for the purpose of decryption of the signature). Be that as it may, quantum computing could also easily be deployed in blockchain technology to fend off would-be hackers or rogue miners.

Autonomous Driving

And not least, robotic process automation (RPA) is increasingly being used in fintech (financial technology). In its Fintech and Digital Banking 2025 Asia Pacific report, IDC stated that financial liberalisation, drive towards cost-reduction, intense competition from counterparts as well as P2P (peer-to-peer) players, wafer-thin net interest margins, etc. are catalysing banks to further automate, e.g., through RPA software that enables computers to process manual workloads of business processes more efficiently and effectively (such as triggering error-free responses).

Finally, autonomous driving will soon be an in-thing in Malaysia as it is in other parts of the world, not least across the Causeway (in Singapore). Software by eMooVit Technology, a local start-up specialising in driverless agnostic vehicle software for urban environment routes can be used in different applications such as first/last-mile transportation, logistics and utility solutions.

On 23 December last year, eMoovit was reported to be the first company to use Malaysias first self-driving vehicle testing route as announced by Futurise, a wholly-owned subsidiary of technology hub enabler, Cyberview. As reported in the local media, the seven-kilometre Cyberjaya Malaysia Autonomous Vehicle (MyAV) Testing Route was jointly developed by Futurise and the Ministry of Transport (MoT) under the National Regulatory Sandbox (NRS) initiative for the development of autonomous or self-driving vehicles.

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Malaysia: Leveraging On Digitalisation Trends - The ASEAN Post

Comparative modelling unravels the structural features of eukaryotic TCTP implicated in its multifunctional properties: an in silico approach -…

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This article was originally published here

J Mol Model. 2021 Jan 7;27(2):20. doi: 10.1007/s00894-020-04630-y.

ABSTRACT

Comparative modelling helps compare the structure and functions of a given protein, to track the path of its origin and evolution and also guide in structure-based drug discovery. Presently, this has been applied for modelling the tertiary structure of highly conserved eukaryotic TCTP (translationally controlled tumour protein) which is involved in a plethora of functions during growth and development and also acts as a biomarker for many cancers like lung, breast, and prostate cancer. The modelled TCTP structures of different organisms belonging to the eukaryotic group showed similar spatial arrangement of structural units except loops and similar patterns of root mean square deviation (RMSD), root mean square fluctuation, and radius of gyration (Rg) inspected through molecular dynamics simulations. Essential dynamics (ED) analyses revealed different domains that exhibited different motions for the assistance in its multifunctional properties. Construction of a free-energy landscape (FEL) based on Rg versus RMSD was employed to characterize the folding behaviours of structures and observe that all proteins had nearly similar conformation and topologies, indicating common thermodynamic/kinetic pathways. A physico-chemical interaction study demonstrated the helices and sheets were well stabilized with ample amounts of bonding compared to turns or loops and charged residues were more accessible to solvent molecules. Hence, the current study reveals the important structural features of TCTP that aid in diverse functions in a wide range of organisms, thus extending our knowledge of TCTP and also providing a venue for designing the potent inhibitors against it.

PMID:33410974 | DOI:10.1007/s00894-020-04630-y

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Comparative modelling unravels the structural features of eukaryotic TCTP implicated in its multifunctional properties: an in silico approach -...

Global Transfection Reagent and Equipment Market: In-Depth Market Research and Trends Analysis till 2030 KSU | The Sentinel Newspaper – KSU | The…

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The global transfection reagents and equipment market accounted for 1793.0 Million in 2020 and is estimated to be US$ 931.3 Million by 2029 and is anticipated to register a CAGR of 7.5%.

The report Global Transfection Reagent and Equipment Market, By Product (Reagents and Equipment), By Method (Biochemical Methods, Physical Methods, and Viral Methods), By Application (Biomedical Research, Gene Expression Studies, Cancer Research, Transgenic Models, Protein Production, and Therapeutic Delivery), By End User (Academics & Research Institutes and Pharmaceutical & Biotechnology Companies), and By Region (North America, Europe, Asia Pacific, Latin America, and the Middle East & Africa) Trends, Analysis and Forecast till 2029.

Get Sample Copy of This Report @ https://www.prophecymarketinsights.com/market_insight/Insight/request-sample/4486

Key Highlights:

In July 2020, Polyplus-transfection(R) SA, the leading biotechnology company that supports the gene and cell therapy market by supplying innovative transfection solutions, announced the launch of the industrys first GMP-compliant residual test for its PEIpro (R) product portfolio, transfection reagents designed for process development, pre-clinical, clinical and commercial lentivirus and adeno-associated virus (AAV) production for cell & gene therapies.In March 2020, Thermo Fisher, the largest maker of scientific tools, announced its plan to produce up to 5 million of a new test to detect the novel coronavirus that causes Covid-19.Analyst View:

Large number of application along with fast results

The cells which are transiently transfected takes around 24 96 hours post transfection and the mRNA is expressed within minutes after transfection that means it takes very less time to show the results. The main application of transfection reagent and equipment is used in studying the effect of gene expression, gene products, gene silencing and largely producing recombinant proteins. Highly increasing application like gene expression studies, protein production, transgenic models, therapeutic delivery, cancer research and biomedical research is expected to foster the transfection reagents and equipment market. Transfection of mammalian cells is mostly effective in the production of recombinant protein with proper folding and post-translational modification.

Technological advancements in transfection technology

The transfection reagents and equipment market has seen diverse technological achievements in equipment as well as reagents, to address the demands of biotechnology and researchers and biopharmaceutical associations. Increasing demand of synthetic genes, R & D investment and research activities, emerging economics drives the target market. For instance, in July 2020, Poly plus-transfection(R) SA, the leading biotechnology company that supports the gene and cell therapy market by supplying innovative transfection solutions, announced the launch of the industrys first GMP-compliant residual test for its PEIpro (R) product portfolio, transfection reagents designed for process development, pre-clinical, clinical and commercial lentivirus and adeno-associated virus (AAV) production for cell & gene therapies. Transfection promotes the process of introducing genetic material into eukaryotic cell to enable the production or expression of proteins using cells machinery.

Key Market Insights from the report:

The global transfection reagents and equipment market accounted for 1793.0 Million in 2020 and is estimated to be US$ 931.3 Million by 2029 and is anticipated to register a CAGR of 7.5%. The market report has been segmented on the basis of product, method, application, end user, and region.

Depending upon product, the reagents segment is projected to grow at highest CAGR over the forecast period. The reagents provide a well-structured overview of significant innovations, discoveries coupled with the technological advancements that occur in the global industry.Depending upon the method, the biochemical segment is projected to register highest share of the market in 2019. Biochemical segment has various applications in cell research, target validation and drug discovery, and technological advances such as synthetic genes, whose demand has been growing, thus anticipated to contribute to the growth of the market.In terms of application, cancer research projected to witness highest CAGR over the forecast period.By end-user, pharmaceutical & biotechnology companies segment estimated for highest share in 2019 due to strategic framework to boost the growth journey, actionable results to meet all the business priorities.By region, North America region contributes to the largest share in the global transfection reagents and equipment market due to due to rising prevalence of various cancers such as cervical cancer, breast cancer, colon cancer and prostate cancer. Further, the government of North America is highly increasing their investments in the biological research, genomics research and in the production of therapeutic proteins, which in turn boosts growth of the target market in this region.

To know the upcoming trends and insights prevalent in this market, click the link below:

https://www.prophecymarketinsights.com/market_insight/Global Transfection Reagents and Equipment Market-4486

Competitive Landscape:

The prominent player operating in the global transfection reagents and equipment market includes Thermo Fisher Scientific, Promega Corporation, Lonza, QIAGEN, F. Hoffmann-La Roche Ltd., and Bio-Rad Laboratories, Merck KGaA, OriGene Technologies, MaxCyte, Polyplus-transfection SA.

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Pandemic-related research initiative receives strong campus response – University of Wisconsin-Madison

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The high volume of applications submitted to a recent Office of the Vice Chancellor for Research and Graduate Education initiative underscores the serious impact that the COVID-19 pandemic had on research at the University of WisconsinMadison last spring.

The OVCRGE received 110 applications for the Pandemic-Affected Research Continuation Initiative and will support 70. Funded projects come from across campus and represent each of the four research divisions.

Last spring, some researchers were faced with spending down their existing funds while the pandemic limited certain on-site research activities. This included face-to-face human subjects research, research travel and most research activities conducted in-person in university research facilities.

The PARCI supports projects that are now facing a shortage of funds to complete those activities, and is helping to replace critical and time-sensitive research supplies and resources lost due to pandemic-related restrictions. The awards vary, up to $50,000.

We heard many stories about how research progress and funding were impacted by the closure of labs, field work suspension and limitations to other research activities, says Steve Ackerman, vice chancellor for research and graduate education. We knew there was a need for this initiative, even as research activity has successfully restarted on campus.

For example, chemistry professor Tina Wangs research efforts, delayed by the pandemic and resulting campus closures, are being supported by PARCI funding. Her lab is working to develop and use new methods for research in chemical biology, exploring the interplay between protein folding and function, and development of robust sensors and gene circuits. Dysfunctional protein folding is a hallmark of a number of diseases, most notably neurodegenerative disorders.

Michael Cahill, professor of comparative biosciences, received funding to support his work with animal models and to continue funding a graduate student. Cahills research focuses on understanding how gene-based alterations identified in schizophrenia, major depressive disorder and autism spectrum disorders influence neuronal morphology and function.

PARCI is also supporting Dan Vimont, professor of atmospheric and oceanic sciences and co-director of the Nelson Institute Center for Climatic Research along with others at the center to help them more fully resume research into climate variability and climate change, interactions between weather and climate, and global and regional impacts of climate change.

While this initiative will help CCR maintain our pursuit of the Wisconsin Idea through world-class research and outreach on the causes and impacts of climate change, it does more, Vimont says. In addition to recognizing the importance of our colleagues for what they do, it also recognizes the importance of who they are: parents, spouses and family members who are also world-class scientists. As we face what we expect will be a challenging time for the university and for research funding, this is welcome help to our center and to our scientists.

Due to COVID-19, the OVCRGE also has extended end dates on other OVCRGE research-related funding affected by the pandemic and considered reallocations from existing budget line items.

COVID-19 also has had an impact personally on researchers, including faculty members, postdocs, technicians and graduate students. It has affected their educational progress, their career development and their work-life balance. For graduate students and early-career scientists, the disruptions have made it increasingly challenging for them to complete necessary research and to advance their careers.

In response, the Graduate School also recently sponsored a program to support PhD and MFA students facing pandemic graduation delays. The Dissertation Completion Emergency Fellowships program provides one-semester fellowships for students whose graduation has been unavoidably delayed by pandemic-related restrictions who cannot be supported through normal program appointments or endowment funds in Spring 2021 but who now expect to graduate by August 2021. Thirty-nine fellowships are being funded through the DCEF program.

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Pandemic-related research initiative receives strong campus response - University of Wisconsin-Madison

Protein Folding Breakthrough: Evolution or Design …

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Image: Detail from the structure of myoglobin, by AzaToth, Public domain, via Wikimedia Commons.

If U.S. engineers built a spaceship with hyperdrive, and a foreign country managed to reverse-engineer it and figure out how it works, who should get the credit? What is the bigger accomplishment: reverse-engineering a futuristic craft, or designing one from scratch?

DeepMind is a leader in artificial intelligence (AI). Its geniuses managed to beat humans at the popular name Go using its AlphaGo algorithm. Its AI systems have now reached 90 percent success at predicting how a protein will fold. Ablog post from DeepMindexplains why this is a big deal:

In his acceptance speech for the 1972 Nobel Prize in Chemistry, Christian Anfinsen famously postulated that,in theory, a proteins amino acid sequence should fully determine its structure. This hypothesis sparked a five decade questto be able tocomputationally predict a proteins 3D structure based solely on its 1D amino acid sequenceas a complementary alternative to these expensive and time consuming experimental methods.A major challenge, however, is that the number of ways a protein could theoretically fold before settling into its final 3D structure is astronomical.In 1969 Cyrus Levinthal noted that itwould take longer than the age of the known universeto enumerate all possible configurations of a typical protein by brute force calculation Levinthal estimated 10^300 possible conformations for a typical protein.Yet in nature,proteins fold spontaneously, some within milliseconds a dichotomy sometimes referred to as Levinthals paradox.[Emphasis added.]

Reverse-engineering a hyperdrive looks simple by comparison. In 1994, a professor started a contest for AI specialists named CASP: Critical Assessment of [protein] Structure Prediction. Every two years, contestants try to predict a proteins fold from its amino acid sequence alone, without knowing the fold in advance. Before now, scores achieved 20 to 40 on the Global Distance Test (GDT), a measure of the distance between predicted amino acid positionsversusactual biological positions. DeepMind achieved an average score of 60 with AlphaFold in 2018. They increased it enormously this year to 92.4. The blog entry pictures how closely the predicted fold matches the actual fold for two cases. They appear to overlap very closely.

This computational work representsa stunning advanceon the protein-folding problem, a 50-year-old grand challenge in biology. It hasoccurred decades before many people in the field would have predicted. It will be exciting to see the many ways in which it will fundamentally change biological research.

Achieving this success drew inspiration from biology, physics, and machine learning, along with leading experts in protein structure. The team constructed a neural network to approach the challenge, solving small clusters of amino acids then using deep learning methods to explore how they might join up. Even so, the CASP contest uses relatively simple proteins called domains. AlphaFold has more trouble figuring out proteins that interact.Nature Newssays,

The network alsostruggles to model individual structures in protein complexes, or groups, wherebyinteractionswith other proteins distort their shapes.

Nevertheless, the success represents a gigantic leap that will change everything, Ewen Callaway writes. In what ways? John Moult, a professor at the University of Maryland who co-founded CASP, explains inThe Scientist,

This will change medicine. It will change research. It will change bioengineering. It will change everything, Andrei Lupas, an evolutionary biologist at the Max Planck Institute for Developmental Biology in Germany who helped judge the contest, tellsNature, adding that AlphaFold took only 30 minutes to produce the structure of a protein his lab had been trying to figure out for 10 years.

Writing inScienceMagazine, Robert F. Service adds,

Knowing those shapes helps researchersdevise drugsthat can lodge in proteins crevices. And being able to synthesize proteins with a desired structure couldspeed development of enzymes to make biofuels and degrade waste plastic.

This is great news, and rightly applauded. But we need to remember that this folding problem that has baffled humans for 50 years is solved rapidly in living cells at every moment. Levinthal noted that proteins routinely fold spontaneously, some within milliseconds inside the cell. A few need help from chaperones to find their native fold, but many go directly from 1D amino acid sequence to 3D functional protein.

Thats not all. The cell has repair enzymes, too, that can dismantle improperly folded proteins and fix them or replace them if they are irreparable. In our hyperdrive spacecraft analogy, it is like having robots able to detect failed components, pull them out, fix them, and re-insert them. How does a cell without eyes and brains do this? Think of the sophistication of algorithms able to perform such operations!

The concept ofSearchbears heavily in intelligent design theory. If the search problem is complicated enough, succeeding requires additional information beyond what blind search can achieve in the time available. Finding a marked atom in a galaxy, for instance, would take far longer than the age of the universe to succeed. William Dembski proved in his bookNo Free Lunchthat no evolutionary algorithm is superior to blind search, unless auxiliary information is provided. The catch-22, though, is that the searcher needs to search through all possible sources of the auxiliary information to know which one is correct. Dembski likened it to finding a buried treasure by digging at random on an island. That kind of blind search is highly unlikely to succeed if the island is big enough. If the searcher is handed a map, he could go directly to the spot with that auxiliary information. All well and good, like in the movieIts a Mad Mad Mad Mad World, where the information provided was accurate from somebody who knew.

Evolutionary algorithms, though, having no foresight, could generate a billion treasure maps, only one of which might be correct. The search problem then switches to finding the correct map out of billions of treasure maps. If a book on the shelf tells you which map is the correct one (more auxiliary information), how does the searcher know? The searcher would have to check a billion books offering random answers with that information, only one of which might be correct. Each added piece of auxiliary information must be checked out by another search. Thats why no evolutionary algorithm is superior to blind search. The only way to speed to the buried treasure is to trust a source that knows and test the information by digging there. That information must come ultimately from a mind someone who knows the right answer.

Returning to the protein folding problem, we have seen that the search space for protein folds is vast beyond comprehension, like an island as big as the universe. Observing cells routinely folding proteins quickly and accurately, one can conclude therefore that a mind was behind the information. That conclusion is certified by watching AI experts using their minds to reverse engineer protein folds. AI is notinventingsequences that will fold; it is trying to figure out how a given sequence will produce an observed functional fold. Inventing a foldde novois the harder problem (Stephen Meyer discusses functional folds inSignature in the Cell, pp. 99ff.)

Does evolution come into the story at all? Some of the workers in the CASP contest are evolutionary biologists. Thinking that some proteins evolved into other proteins through mutation and natural selection, they believe that similar proteins are connected by common ancestry. This belief, they feel, can allow them to evolve new proteins with similar folds. Callaway says, Some applications, such as the evolutionary analysis of proteins, are set to flourish because the tsunami of available genomic data might now be reliably translated into structures. That is intelligent design, however, not Darwinian evolution; take the word evolutionary out of evolutionary analysis, otherwise it is an oxymoron. There are limits to the amount of variation a fold can endure and preserve function. (Douglas Axe discusses these limits inUndeniable, p. 80ff and 180-182. See coverage byEvolution Newshereandhereabout simplistic evolutionary approaches to solving the folding problem, and why they miss the mark.)

If DeepMinds AlphaFold algorithm succeeds in designing new enzymes, it will be through intelligent design, not blind search. It will build on the information in working proteins, extending that knowledge by design. Materialistic evolutionary processes have no such foresight.

In short, DeepMinds achievement is laudable, but the real prize goes to the designer of protein systems: their encoding in DNA, their translation in the ribosome, their spontaneous (sometimes chaperone-assisted) folding, their functions, their interactions, and their repair mechanisms. All those get perfect scores when not harmed by random mutations that degrade information. AI has not even begun to imitate those capabilities. Any higher scores through AI in the future will be attained by intelligent design, not evolution. The news only underscores the superior knowledge built into the molecular basis of life.

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Protein Folding Breakthrough: Evolution or Design ...

Elizabeth Komives: Why scientists are calling a recent breakthrough on protein structures a ‘game-changer’ – The San Diego Union-Tribune

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Every other year, scientists hold a contest to see whose computer program can best predict a proteins three-dimensional structure just by looking at the sequence of its amino acid building blocks. This years contest was won by AlphaFold, an artificial intelligence network developed by the Google offshoot DeepMind. Its predictions came so close to the actual protein structures that many scientists have declared AlphaFold a game-changer, going so far as to say the protein folding problem is now solved.

So why is knowing the 3D shape of a protein so important? Every function in our bodies from digestion to growth is carried out by proteins. We need to know how those proteins are shaped so we can better understand how they work normally, as well as what goes wrong with them in various diseases. If we can see a proteins structure, that can also help us know how best to manipulate it with therapeutic drugs.

That brings us to the protein folding problem: Its very difficult to predict a proteins shape just by looking at its sequence of amino acids.

In 2001, the human genome the entire sequence of our DNA was published. This was a great advance because it gave us the blueprint of what we are made of. However, interpreting that blueprint into all the functions that go on in our bodies is not a solved problem at all. Our cells first translate that DNA blueprint into 20 different amino acids, then link them together into proteins.

Finally, proteins usually need to fold up into unique structures before they can carry out their specific functions.

Predicting protein structure is a challenge in part because any random string of amino acids likely will not fold into a unique shape. As an amino acid string collapses on itself, the side chains of those 20 different amino acids interact in ways that may be favorable or unfavorable. There may be many different favorable ways to fold and the protein becomes frustrated it cant figure out which way is best. Natural proteins have evolved their unique shapes because as they collapse, theres really only one favorable way to go. Even knowing this, its still difficult to predict, just from the sequence, which amino acids will make the most favorable interactions.

Lots of work by hundreds of scientists contributed to AlphaFolds success. A key insight was made in the 1980s by researchers who realized that favorable interactions help proteins avoid frustration as they fold. Then, to train artificial intelligence systems like AlphaFolds, we had to know all the possible amino acid interactions that can be made, and to do that it was necessary to solve a lot of protein structures experimentally. Scientists including many at UC San Diego do that in a laborious process that involves crystallizing the proteins and capturing their native structures by X-ray. Working backward, they can then see what interactions the amino acids are making.

After the human genome was solved, the National Institutes of Health, knowing that the DNA sequence didnt really give us answers about all the functions in our bodies, funded the Structural Genomics Initiative. The goal was to increase the numbers of known protein structures in a central reference database, the Protein Data Bank, which is headquartered at UC San Diego and Rutgers University. Since 2000, many more protein structures have been solved experimentally, giving scientists the data they so badly needed for their predictions and giving AlphaFold the missing pieces of data it needed to optimize its algorithm and improve upon past attempts to win the contest.

As is often the case in science, AlphaFold stands on the shoulders of hundreds of scientists who have experimentally determined protein structures and who have worked out the theoretical understanding of how to extract and balance the physical, geometrical and fragment information from known folded structures.

Komives, Ph.D., is a distinguished professor of chemistry and biochemistry at UC San Diego. She lives in Clairemont.

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Elizabeth Komives: Why scientists are calling a recent breakthrough on protein structures a 'game-changer' - The San Diego Union-Tribune

Behind the screens of AlphaFold | Opinion – Chemistry World

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Not so long ago, a list of holy grails of chemistry like that recently compiled by Chemistry World might very probably have included solving the protein folding problem. It was widely believed that the ability to predict the structure of a protein from just its amino-acid sequence would be of immense value to the life sciences.

At the start of December, many media headlines announced what appeared to be the realisation of that goal. The artificial-intelligence company DeepMind has shown that their AlphaFold deep-learning algorithm can predict many protein structures from their sequence with an atomic-scale precision often comparable to that obtained from the best crystallographic analyses. It has been hailed as a major breakthrough. It will change everything, evolutionary biologist Andrei Lupas told Nature, while structural biologist Janet Thornton said the advance will really help us to understand how human beings operate and function. Some reports would have us believe cures for diseases such as Alzheimers (which stems from protein misfolding) are now just around the corner.

But such assertions have been contested. Some biochemists pointed out that the accuracy of prediction was not always so impressive and is in general unlikely to be accepted without experimental corroboration from, say, crystallography, NMR studies or cryo-electron microscopy. While the majority of predicted structures were within experimental resolution, one cant tell a priori which are and which arent so you need experiments to check. Also, its still not yet clear that the accuracy meets whats needed for, say, finding drug candidates that might bind to the proteins active site to block its function.

Others take issue with the notion that the method solves the protein folding problem at all. Since the pioneering work of Christian Anfinsen in the 1950s, it has been known that unravelled (denatured) protein molecules may regain their native conformation spontaneously, implying that the peptide sequence alone encodes the rules for correct folding. The challenge was to find those rules and predict the folding path.

AlphaFold has not done this. It says nothing about the mechanism of folding, but just predicts the structure using standard machine learning. It finds correlations between sequence and structure by being trained on the 170,000 or so known structures in the Protein Data Base: the algorithm doesnt so much solve the protein-folding problem as evade it. How it reasons from sequence to structure remains a black box.

If some see this as cheating, that doesnt much matter for practical purposes. It will surely be valuable to deduce even a good guess at the structure from just the sequence. From that we can often make inferences about the proteins function and the chemical mechanism of its mode of action. And good enough predictions can be a useful starting point for refinement with crystallographic data.

But the idea that the protein-folding problem holds the key to understanding how gene sequences dictate cell function looks less compelling than it did a few decades ago. We know the real picture is much more complicated, for many reasons.

Theres more to enzyme action than correct folding. Many proteins are chemically modified after being translated on the ribosome: parts of the peptide chain may be crosslinked, and non-amino-acid groups such as porphyrins or metal ions are incorporated. Besides, knowing the structure doesnt by itself tell you the function. Sometimes this can be deduced by analogy, or rather, homology: proteins with similar folds may have similar functions. But thats not invariably true: proteins with very similar structures can behave in chemically very different ways, while very different folds can achieve similar transformations. There is no unique structure-function relationship.

Whats more, designing a ligand for a protein can be challenging even if you know its structure very accurately, partly because we dont know all the rules of recognition some depend, for example, on fine details of solvation at the active site. And for drug discovery the biggest hurdles are typically upstream from the identification of a potential molecular target not least because it often proves to be the wrong target.

In any case, the picture in which protein function is determined by a unique and static crystal structure is known now to be far too simplistic. The dynamics might be crucial. Ligand binding typically involves some flexibility and adaptation at the active site but more generally, the emerging view of protein function invokes the ensemble of conformations accessible to it: the statistical populations and occupancy times of the different dynamic states it can reach. Whats more, many proteins dont have well-defined folded conformations at all, but contain intrinsically disordered, floppy parts of the peptide chain. Thats not nature being sloppy: the disorder and resulting flexibility seems to be functional. AI approaches may well identify which sequences are likely to be disordered, but that alone wont help to understand their behaviour.

Finally, any deep-learning system is only competent within the bounds of its training set. We dont know the size of the human proteome, but some estimates say that only around 5% of all human proteins have been crystallised and their structure determined. So the training data are likely to be biased towards the structures that are relatively easy to solve. Some researchers think there could be a systematic repertoire of protein structures that we just dont know about.

None of this is to diminish the achievement of AlphaFold and indeed we can anticipate that AI approaches might help tackle some of these caveats too. The real point is that we have long ago had to abandon the simple notion that the cells secrets are digitally encoded in any molecular sequence.

Not so long ago, a list of holy grails of chemistry like that recently compiled by Chemistry World might have included solving the protein folding problem. It was widely believed that the ability to predict the structure of a protein from just its amino-acid sequence would be of immense value to the life sciences.

At the start of December, many media headlines announced what appeared to be the realisation of that goal. The artificial-intelligence company DeepMind has shown that their AlphaFold deep-learning algorithm can predict many protein structures from their sequence with an atomic-scale precision often comparable to that obtained from the best crystallographic analyses. It will change everything, evolutionary biologist Andrei Lupas told Nature, while structural biologist Janet Thornton said the advance will really help us to understand how human beings operate and function. Some reports would have us believe cures for diseases such as Alzheimers (which stems from protein misfolding) are now just around the corner.

But such assertions have been contested. Some biochemists pointed out that the accuracy of prediction was not always so impressive and is in general unlikely to be accepted without experimental corroboration from, say, crystallography, NMR studies or cryo-electron microscopy. Also, its still not yet clear that the accuracy meets whats needed for, say, finding drug candidates that might bind to the proteins active site to block its function.

Others take issue with the notion that the method solves the protein folding problem at all. Since the pioneering work of Christian Anfinsen in the 1950s, it has been known that unravelled (denatured) protein molecules may regain their native conformation spontaneously, implying that the peptide sequence alone encodes the rules for correct folding. The challenge was to find those rules and predict the folding path.

AlphaFold has not done this. It says nothing about the mechanism of folding; how it reasons from sequence to structure remains a black box.

If some see this as cheating, that doesnt much matter for practical purposes. It will surely be valuable to deduce even a good guess at the structure from just the sequence. From that we can often make inferences about the proteins function and the chemical mechanism of its mode of action. And good enough predictions can be a useful starting point for refinement with crystallographic data.

But theres more to enzyme action than correct folding. Many proteins are chemically modified after being translated on the ribosome: parts of the peptide chain may be crosslinked, and non-amino-acid groups such as porphyrins or metal ions are incorporated. Besides, knowing the structure doesnt by itself tell you the function: proteins with very similar structures can behave in chemically very different ways, while very different folds can achieve similar transformations. There is no unique structure-function relationship.

Whats more, designing a ligand for a protein can be challenging even if you know its structure very accurately, partly because we dont know all the rules of recognition some depend, for example, on fine details of solvation at the active site. And for drug discovery the biggest hurdles are typically upstream from the identification of a potential molecular target not least because it often proves to be the wrong target.

In any case, the picture in which protein function is determined by a unique and static crystal structure is far too simplistic. The dynamics might be crucial. Ligand binding typically involves some flexibility and adaptation at the active site but more generally, the emerging view of protein function invokes the ensemble of conformations accessible to it: the statistical populations and occupancy times of the different dynamic states it can reach. Whats more, many proteins dont have well-defined folded conformations at all, but contain intrinsically disordered, floppy parts of the peptide chain. Thats not nature being sloppy: the disorder and resulting flexibility seems to be functional. AI approaches may well identify which sequences are likely to be disordered, but that alone wont help to understand their behaviour.

Finally, any deep-learning system is only competent within the bounds of its training set. Some estimates say that only around 5% of all human proteins have been crystallised and their structure determined. So the training data are likely to be biased towards the structures that are relatively easy to solve. Some researchers think there could be a systematic repertoire of protein structures that we just dont know about.

None of this is to diminish the achievement of AlphaFold and indeed we can anticipate that AI approaches might help tackle some of these caveats too. The real point is that we have long ago had to abandon the simple notion that the cells secrets are digitally encoded in any molecular sequence.

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Behind the screens of AlphaFold | Opinion - Chemistry World

A|I: The AI Times Sounding the alarm – BetaKit

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The AI Times is a weekly newsletter covering the biggest AI, machine learning, big data, and automation news from around the globe. If you want to read A|I before anyone else, make sure to subscribe using the form at the bottom of this page.

Kevin Magee (Microsoft), Christopher Salvatore (Cybersecure Catalyst), and Tahseen Shabab (Penfield) explore how Canada can build a cybersecurity ecosystem.

At Hustle Fund, were convinced that Canada is positioned well to produce some of the largest, category-defining companies on the planet. Were eager to fund these companies, and excited to partner with Hockeystick to identify these opportunities! Eric Bahn (General Partner)

Since launch, Hockeystick has made over 6000 funder recommendations to Canadian startups. Learn how startups are using technology to meet funders around the world.

DeepMind said that its system, called AlphaFold, had solved what is known as the protein folding problem.

Gebru is known for coauthoring a groundbreaking paper that showed facial recognition to be less accurate at identifying women and people of color, which means its use can end up discriminating against them.

The merger with Star Peak will give Stem an estimated $608 million in gross proceeds to invest in its burgeoning smart grid technology which helps support green forms of energy.

Scale, which charges based on the amount of data it processes for customers, has seen major growth by working with DoorDash.

Startups raised a total of $349.5 million in Q3 2020, a 135 percent increase for the Waterloo Region.

If signed into law, Massachusetts would become the first state to fully ban the technology, following bans barring the use of facial recognition in police body cameras and other, more limited city-specific bans on the tech.

A growing group of lawyers are uncovering, navigating, and fighting the automated systems that deny the poor housing, jobs, and basic services.

A 12,000-person survey found that workers around the globe are looking to AI-powered digital assistants and chatbots to cope with mental health during the pandemic.

In one Southern California city, flying drones with artificial intelligence are aiding investigations while presenting new civil rights questions.

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A|I: The AI Times Sounding the alarm - BetaKit

Predictions: The AI Challenges of 2021 – Marketscreener.com

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The overall theme of Splunk's four-part 2021 Predictions report is the rapid acceleration of digital transformation, driven by the specific event of the COVID-19 pandemic, and the momentum of data technologies that have brought us into a true Data Age. Nowhere is that acceleration going to be more transformative than around the application of artificial intelligence and machine learning.

AI/ML was a hot topic before 2020 disrupted everything, and over the course of the pandemic, adoption has increased. We've seen it particularly in terms of security use cases, but security is far from the only arena. Already, it seems like artificial intelligence is everywhere. John Sabino, our chief customer officer, notes in the report that every software vendor is claiming AI/ML as a secret sauce in its solutions, and there's a danger of fatigue as AI/ML becomes something everyone talks about, but no one ever quite sees.

Despite that, meaningful applications of machine learning in particular are already common. We see machine learning having an impact in everything from how recruiters parse stacks of resumes to how businesses analyze subtle trends in customer behavior; from improving user experience with everything from how web pages are served and products are recommended to intelligent chat features. And developments go far beyond business. Deep learning techniques produced a recent breakthrough in protein folding, which has applications in developing effective medical treatments, using enzymes to break down industrial waste, and more. It represents a considerable advance in AI development.

As we see machine learning adopted by more organizations, for more purposes, there are three innovations that I am keeping an eye out for in the near future:

The Emerging Technology Predictions report goes deeper into these topics, and other AI/ML predictions, including a stellar use case in medical research. It also covers 5G, AR/VR, blockchain and more. These are technologies that are going to reshape our world, and it's fascinating to look ahead even as the future is unfolding.

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Predictions: The AI Challenges of 2021 - Marketscreener.com

What are proteins and why do they fold? – DW (English)

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The proteins in our bodies are easily confused with the proteinin food.There are similarities and links between the two for example, both consist of amino acids.

But, when scientists talk about proteins in biology, they are talking about tiny butcomplex molecules that perform a huge range of functions at a cellular level, keeping us healthy and functioning as a whole.

Scientists will often talk about proteins "folding" and say that when they fold properly, we're OK. The way they fold determines their shape, or 3D structure, and that determines their function.

But, when proteins fail to fold properly, they malfunction, leaving us susceptible to potentially life-threatening conditions.

We don't fully understand why: why proteins fold and how, and why it doesn't always work out.

When proteins go wrong: 'Lewy bodies' or protein deposits in neurons can lead to Parkinson's cisease

The whole thing has been bugging biologists for 50 or 60 years, with three questions summarized as the "protein-folding problem."

It appears that that final question has now been answered, at least in part.

An artificial intelligence systemknown as AlphaFold can apparently predict the structure of proteins.

AlphaFold is a descendant of AlphaGo a gaming AI that beat human GO champion Lee Sedol in 2016. GO is a game like chess but tougher to the power of 10.

DeepMind,the company behind AlphaFold, is calling it a "major scientific advance."

To be fair, it's not the first time that scientists have reported they have used computer modeling to predict the structure of proteins;they have done that for a decade or more.

Perhaps it's the scale that AI brings to the field the ability to do more, faster. DeepMind say they hope to sequence the human proteome soon, the same way that scientists sequenced the human genome and gave us all our knowledge about DNA.

But why do it? What is it about proteins that makes them so important for life?

Well, predicting protein structure may help scientists predict your health for instance, the kinds of cancer you may or may not be at risk of developing.

Proteins are indeed vital for life they are like mechanical components, such ascogs in a watch or strings and keys in a piano.

Proteins form when amino acids connect in a chain. And that chain "folds" into a 3D structure. When it fails to fold, it forms a veritable mess a sticky lump of dysfunctional nothing.

Proteins can lend strength to muscle cells, or form neurons in the brain.The US National Institutes of Health lists five main groups of proteins and their functions:

There can be between 20,000 and 100,000 unique types of proteins within a human cell. They form out of an average of 300 amino acids, sometimes referred to as protein building blocks. Each is a mix of the 22 differentknown amino acids.

Those amino acids are chained together, and the sequence, or order, of that chain determines how the protein folds upon itselfand, ultimately, its function.

Protein-folding can be a process of hit-and-miss. It's a four-part process that usually begins with twobasic folds.

Healthy proteins depend on a specific sequence of amino acids and how the molecule 'folds' and coils

First, parts of a protein chain coil up into what areknown as "alpha helices."

Then, other parts or regions of the protein form "beta sheets," which look a bit like the improvised paper fans we make on a hot summer's day.

In steps three and four, you get more complex shapes. The two basic structures combine into tubes and other shapes that resemble propellers, horseshoes or jelly rolls. And that gives them their function.

Tube or tunnel-like proteins, for instance, can act as an express route for traffic to flow in and out of cells. There are "coiled coils" that move like snakes to enable a function in DNA clearly, it takes all types in the human body.

Successful protein folding depends on a number of things, such as temperature, sufficient space in a celland, it is said, even electrical and magnetic fields.

Temperature and acidity (pH values) in a cell, for instance, can affect the stability of a protein its ability to hold its shape and therefore perform its correct function.

Chaperone proteins can assist other proteins while folding and help mitigate bad folding. But it doesn't always work.

Misfolded proteins are thought to contribute to a range of neurological diseases, including Alzheimer's, Parkinson's andHuntington's diseaseand ALS.

It's thought that when a protein fails to foldand perform a specific function, known as "loss of function," that specific job just doesn't get done.

As a result, cells can get tired for instance, when a protein isn't there to give them the energy they need and eventually they get sick.

Researchers have been trying to understand why some proteins misfold more than others, why chaperones sometimes fail to help, and why exactly misfolded proteins cause the diseases they are believed to cause.

Who knows? DeepMind's AlphaFold may help scientists answer these questions a lot faster now. Or throw up even more questions to answer.

Bugs can be tasty. So why is it that we don't we eat more of them? There are plenty of reasons to do so: insects are easy to raise and consume fewer resources than cows, sheep or pigs. They dont need pastures, they multiply quickly and they don't produce greenhouse gasses.

Water bugs, scorpions, cockroaches - on a stick or fried to accompany beer: these are delicacies in Asia, and healthy ones at that. Insects, especially larvae, are an energy and protein bomb. One hundred grams of termites, for example, have 610 calories - more than chocolate! Add to that 38 grams of protein and 46 grams of fat.

Insects are full of unsaturated fatty acids, iron, vitamins and minerals says the UNS Food and Agriculture Organisation (FAO). The organization wants to increase the popularity of insect recipes around the world.

In many countries around the world, insects have long been a popular treat, especially in parts of Asia, Africa and Latin America. Mopane caterpillars, like the ones shown here, are a delicacy in southern Africa. They're typically boiled, roasted or grilled.

Even international fine cuisine features insects. And in Mexican restaurants, worms with guacamole are a popular snack. Meanwhile, new restaurants in Germany are starting to pop up that offer grasshoppers, meal worms and caterpillars to foodies with a taste for adventure.

In Europe and America, beetles, grubs, locusts and other creepy crawlers are usually met with a yuck! The thought of eating deep-fried tarantulas, a popular treat in Cambodia, is met with great disgust. But is there a good reason for that response?

Fine food specialists Terre Exotique (Exotic Earth) offer a grilled grasshopper snack. The French company currently sells the crunchy critters online via special order. A 30-gram jar goes for $11.50 (9 euros).

There are about 1,000 edible insect varieties in the world. Bees are one of them. They're a sustainable source of nutrition, full of protein and vitamins - and tasty for the most part. The world needs to discover this delicacy, says the UN's Food and Agriculture Organization.

In 2012, researchers used ecological criteria to monitor mealworm production at an insect farm in the Netherlands. The result? For the production of one kilogram of edible protein, worm farms use less energy and much less space than dairy or beef farms.

Even in Germany, insects used to be eaten in abundance. May beetle soup was popular until the mid-1900s. The taste has been described as reminiscent of crab soup. In addition, beetles were sugared or candied, then sold in pastry shops.

French start-up Ynsect is cooking up plans to offer ground up mealworms as a cost-effective feed for animals like fish, chicken and pigs. This could benefit the European market, where 70 percent of animal feed is imported.

Author: Lori Herber

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What are proteins and why do they fold? - DW (English)

Recently on the Kottke Ride Home Podcast – kottke.org

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The Kottke Ride Home podcast has been humming away since August and host Jackson Bird has been sharing some great stuff lately. From todays show comes this New York magazine piece by David Wallace-Wells about the stunning speed with which the Covid-19 vaccine was developed:

You may be surprised to learn that of the trio of long-awaited coronavirus vaccines, the most promising, Modernas mRNA-1273, which reported a 94.5 percent efficacy rate on November 16, had been designed by January 13. This was just two days after the genetic sequence had been made public in an act of scientific and humanitarian generosity that resulted in Chinas Yong-Zhen Zhangs being temporarily forced out of his lab. In Massachusetts, the Moderna vaccine design took all of one weekend. It was completed before China had even acknowledged that the disease could be transmitted from human to human, more than a week before the first confirmed coronavirus case in the United States. By the time the first American death was announced a month later, the vaccine had already been manufactured and shipped to the National Institutes of Health for the beginning of its Phase I clinical trial.

Mondays show featured the intrigue behind the discovery of a real life treasure:

And if you look back to last week, Jackson clued us in to Radiooooo (The Musical Time Machine), Tetris championships, Chinas Change 5 mission to the Moon, and DeepMinds AI breakthrough in protein folding.

If any or all of that sounds interesting to you, you can subscribe to Kottke Ride Home right here or in your favorite podcast app.

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Recently on the Kottke Ride Home Podcast - kottke.org