Category Archives: Transhuman News

STK11 loss alters tumor-intrinsic cytokine expression

We knocked-out STK11 in three genetically independent human KRAS-driven LUAD cell lines that normally harbor intact STK11 alleles: NCI-H2009, NCI-H441 and NCI-H1792. STK11 loss was validated by Western Blot analysis (Fig. 1A and Supplemental Data). Based on studies reporting a correlation between Stk11 loss and Il-6 upregulation in mouse models of Kras-driven lung cancers in vivo [12], we compared IL-6 expression between STK11 WT (aka Parent) and matched STK11-KO human LUAD cells using qRT-PCR. Unexpectedly, under standard culture conditions no difference in IL-6 expression was detected between the Parent and STK11-KO cells (Fig. 1B, +Glutamine). However, significant STK11-loss-dependent IL-6 upregulation was observed when cells were cultured under conditions of nutrient stress, achieved via glutamine depletion (Fig. 1B, Glutamine). The rationale for evaluating nutrient stress as a variable was based on evidence that STK11 functions as a nutrient sensor to regulate metabolic homeostasis [19,20,21]. We reasoned STK11 loss might be irrelevant when cells are grown in standard media as nutrients are in excess. Given that tumor microenvironments in vivo are characterized by nutrient stress [22,23,24], we used glutamine depletion to simulate nutrient-deprivation in vitro.

A Western blot analysis confirming knock-out of STK11 (S) in NCI-H2009 and NCI-H441 parent (P) cell lines. B IL-6 mRNA expression in parent versus STK11-KO cell lines grown in standard media (+Glutamine) or glutamine depleted media (-Glutamine). Gene expression normalized to PSMB4. Data presented as meanSD (N=3). C MA plots generated from RNA-seq analysis demonstrate few differentially expressed genes (DEGs) between parent (WT) and STK11-KO cell lines when grown in standard media (+Glutamine; 1100 DEGs for H2009, 928 DEGs for H441). In contrast, the same cells grown in glutamine depleted media exhibit massive increases in DEGs in both cell lines (Glutamine; 7453 DEGs for H2009, 5202 DEGs for H441). D GSEA performed on DEGs from each cell line pair grown in the absence of glutamine identified Cytokine Activity (GO: 0005125) as significantly enriched and positively correlated with STK11 loss. Upregulated genes from the Cytokine Activity list shared across H2009 and H441 STK11 KO cell lines are listed. E, F KEGG Pathway Enrichment Analysis performed on DEGs from H2009 and H441 cell lines comparing parent and STK11-KO cells following glutamine depletion. As expected, pathways related to cytokine signaling were identified. Notably, the Hippo signaling pathway (red box) was significantly enriched in both cell lines. G GSEA performed on DEGs using a curated YAP1 transcriptional signature demonstrates a strong positive correlation with STK11 loss in both cell lines suggesting YAP1 transcriptional activation occurs when cells experience glutamine depletion in the absence of STK11. Upregulated genes from the curated YAP1 signature shared across H2009 and H441 STK11 KO cell lines upon glutamine depletion are listed. ****p<0.0001 was calculated by two-way ANOVA and the Tukey test in (B).

Next, to comprehensively characterize STK11-loss-dependent transcriptional changes, we expanded our analyses and performed whole transcriptome sequencing comparing standard media to glutamine depletion. In standard media, relatively few genes differed between parent and STK11-KO cells (Fig. 1C, +Glutamine; H2009: 1100 DEGs, H441: 928 DEGs). In contrast, when comparing both H2009 and H441 parent lines with their paired STK11-KO lines following glutamine depletion we identified 7453 and 5202 differentially expressed genes (DEGs) respectively (Fig. 1C; Glutamine). This marked STK11-loss-dependent transcriptional impact indicates STK11 plays a critical and generalizable role in regulating transcription in response to nutrient stress. We then performed Gene Set Enrichment Analysis (GSEA) [25] on the DEGs for both H2009 and H441 cell lines and found significant associations between STK11 loss and altered tumor-intrinsic cytokine signaling, specifically upregulation of genes within the Gene Ontology (GO) term Cytokine Activity (GO: 0005125) (Fig. 1D). Of the upregulated genes in this curated list, 9 were shared between the H2009 and H441 cell lines, suggesting overlapping regulatory pathways. Intriguingly, these overlapping genes consist of effectors previously associated with cancer progression, immune evasion, and therapy resistance [26,27,28]. For example, both IL-6 and CXCL8 are reported to be elevated in KRAS-driven STK11-null LUADs and proposed to promote tumor immune evasion [29,30,31]. Similarly, CXCL2 is known to drive neutrophil recruitment, a phenotype associated with cold tumor immune microenvironments [27]. Finally, BMP2 expression is correlated with metastatic burden and STK11 loss in lung cancer and mediates activation of SMAD transcription factors [32, 33], which are known YAP1 binding partners [34].

In addition to GSEA, we also performed pathway enrichment using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database [35]. This approach revealed several significantly enriched networks in STK11-KO cells relative to matched parental lines (Fig. 1E, F). Consistent with prior published reports, both focal adhesion and HIF-1 pathways were over-represented in cells lacking STK11 [36, 37]. In addition, NF-kappa B signaling, TNF signaling, chemokine signaling and HIPPO signaling were significantly enriched in STK11-KO cells. We chose to focus on the HIPPO pathway as STK11 has previously been implicated in HIPPO regulation via direct activation of MARK family kinases and subsequent modulation of YAP1 activity [13]. YAP1-mediated transcriptional activation is controlled in part via cytosolic sequestration; a kinase-dependent process regulated by activation of the HIPPO cascade [16]. Utilizing a curated list of YAP1 transcriptional target genes [13] we repeated GSEA and found a significant positive correlation between STK11 loss and enhanced expression of YAP1 target genes in both the H2009 and H441 cell lines (Fig. 1G).

STK11 has previously been proposed to indirectly modulate the HIPPO/YAP1 axis via MARK activation, ultimately promoting YAP1 sequestration and degradation [13]. We therefore hypothesized that STK11 loss would result in increased YAP1 protein due to enhanced protein stabilization (Fig. 2A). Western blot analysis comparing whole cell extracts from parent and STK11-KO LUAD cell lines support this assertion, showing a ~2-fold increase in relative YAP1 abundance (Fig. 2B), an observation supported by prior studies in mice [13]. Interestingly, this difference occurs only at the protein level, as YAP1 transcript levels remain unchanged, supporting our hypothesis that STK11 loss results in YAP1 protein stabilization (Fig. 2C). Nuclear and cytosolic fractionation analyses further demonstrate that increased YAP1 protein levels are not isolated to either compartment but increased throughout cells lacking STK11. Upon glutamine deprivation, we observed increased YAP1 nuclear translocation in both parent and STK11-null cells, though the increase was more pronounced in the STK11-null cells (Fig. 2D). This data supports an STK11-dependent impact on global YAP1 protein abundance, including nuclear localization, which we posit drives changes in YAP1-mediated gene expression (Figs. 1G and 2A).

A We posit STK11, either directly or indirectly, contributes to YAP1 cytoplasmic sequestration and degradation. If true, STK11 loss should lead to enhanced YAP1 protein accumulation and potentially increased transcriptional activity. B Western blot analysis targeting YAP1 in whole cell extracts (WCE) from H2009 parent (P) versus H2009 STK11 KO (S) cells results in a ~2-fold increase in YAP1 protein. Data presented as meanSD (N=4). C YAP1 qRT-PCR analysis argues the difference in YAP1 protein abundance is not due to enhanced YAP1 gene expression. Data presented as meanSD (N=3). D Western blot analysis performed on nuclear and cytoplasmic fractions isolated from H2009 parent (P) or STK11 KO (S) cells support the whole cell extract data showing enhanced YAP1 protein abundance in the absence of STK11. Nuclear fraction data presented as meanSD (N=4). Cytoplasmic fraction data presented as meanSD (N=5). *p<0.0332, **p<0.0021, ***p<0.0002 was calculated by Students t Test (B, C) or two-way ANOVA and Tukey test in (D).

To validate our pathway analyses we reasoned we could inhibit STK11-loss-dependent cytokine induction following glutamine depletion by blocking the downstream signaling networks responsible. To examine the role of YAP1 in driving this phenotype, we engineered STK11/YAP1 double knockouts in both H2009 and H441 LUAD cell lines (Fig. 3A). Our data demonstrate significantly less IL-6, CXCL8 and CXCL2 expression in the STK11/YAP1 double KO lines compared with STK11-KO lines following glutamine depletion (Fig. 3B). Importantly, these changes were mirrored by levels of secreted IL-6 and CXCL8 protein levels measured by ELISA (Fig. 3C). YAP1-KO alone had no impact on expression of these cytokines, regardless of glutamine availability, demonstrating the necessity of STK11 loss in producing this phenotype (Fig. 3B).

A Western blot analysis confirming knockout of YAP1 (Y) in NCI-H2009 and NCI-H441 parent (P) and STK11-KO (S) cell lines. The STK11/YAP1 double knockout lines are abbreviated as SY. B IL-6, CXCL8, and CXCL2 qRT-PCR analysis demonstrates that upon glutamine depletion, the STK11-loss-dependent induction is blunted by the absence of YAP1. Expression normalized to PSMB4, and data presented as meanSD (N=3). C IL6 and CXCL8 ELISAs performed on conditioned media from H2009 cell lines. Data presented as meanSD (N=3). D qRT-PCR analysis of IL-6, CXCL8, and CXCL2 on cells treated with 1.5mM verteporfin (VP) vs vehicle. Expression normalized to PSMB4, and data presented as meanSD (N=3). *p<0.0332, **p<0.0021, ***p<0.0002, ****p<0.0001 was calculated by two-way ANOVA and Tukey test in (B, C) or three-way ANOVA and Tukey test in (D).

After establishing YAP1 functions downstream of STK11 and is at least in part responsible for the increased cytokine expression occurring in STK11-KO cells following glutamine depletion, we next sought to phenocopy YAP1 KO via pharmacologic antagonism of YAP1 with verteporfin (VP) [38]. One mechanism by which VP is known to alter YAP1 activity occurs via physically disrupting the interaction between YAP1 and members of the TEAD transcription factor family [38]. Our data clearly show that the STK11-loss-dependent upregulation of IL-6 and CXCL8 upon glutamine depletion is blunted by VP treatment (Fig. 3D). Interestingly, this affect does not extend to CXCL2 (Fig. 3D). Together these results support CXCL8 and IL-6 expression are likely regulated, at least in part, by YAP1/TEAD interactions. The fact that CXCL2 expression is reduced upon YAP1 genetic ablation, but not VP treatment, was unexpected and suggests YAP1s impact on CXCL2 expression may be independent of TEAD. YAP1 is known to interact with many transcription factors, including SMAD family members and the b-catenin/TBX5 complex [34]. We think it likely that YAP1s impact on CXCL2 expression relies on a transcription factor other than a TEAD family member, which is why genetic ablation of YAP1 results in altered expression, whereas TEAD dissociation with VP does not. Whether this definitively explains the discrepancy in our CXCL2 data awaits further investigation but remains a favored hypothesis.

To define the transcriptome-wide impact of YAP1 KO in STK11 deficient cells, we performed RNA-seq on H2009 cells following 24h in either standard or glutamine depleted media. In standard media, few genes differed between STK11-KO and STK11/YAP1 double KO cells (Fig. 4A, +Glutamine; 733 DEGs). Compared with the H2009 parent line, similar numbers of DEGs were detected in the STK11/YAP1 double KO as were seen in the STK11 KO when grown in the absence of glutamine (Fig. 4A, Glutamine; 7698 DEGs vs Fig. 1C, Glutamine; 7453 DEGs).

A MA-Plots generated from RNA-seq data contrast the number of differentially expressed genes in H2009 cell lines upon glutamine depletion. As expected, few DEGs are identified between STK11 KO and STK11/YAP1 double KO cells when cultured with glutamine (+Glutamine; 733 DEGs). A similar number of DEGs were detected in the STK11/YAP1 double KO compared with the parent line when grown in glutamine depleted media (Glutamine, 7698 DEGs) as were seen in the STK11 KO (Fig. 1C, Glutamine; 7453 DEGs). When the STK11/YAP1 double KO cells are compared directly with STK11 KO cells in the absence of glutamine, 4167 DEGs are detected. B K-means clustering of all mapped transcripts highlights genes that are induced upon glutamine depletion in STK11 KO cells, but whose induction is blunted in STK11/YAP1 double KO cells (Cluster 2, Red vs Orange). This group represents candidate YAP1-transcriptional targets. C GSEA performed on DEGs identified between STK11 KO and STK11/YAP1 double KO cells using the curated YAP1 signature gene list results in a strong negative correlation indicating reduced expression in the absence of YAP1. K-means clustering of the YAP1 gene signature supports this assertion (Cluster 1, Red vs Blue). Dot plot visualization of the 17 genes shared between H2009 and H441 cells (Fig. 1G) indicates the magnitude of expression blunting that occurs in the absence of YAP1. D GSEA performed on DEGs identified between STK11 KO and STK11/YAP1 double KO cells using the gene ontology cytokine activity list demonstrates no significant correlation, in line with a blunted response due to YAP1 loss. K-means clustering of the cytokine activity signature supports this assertion (Cluster 1, Red vs Blue). Dot plot visualization of the 9 genes shared between H2009 and H441 cells (Fig. 1D) indicates the magnitude of expression blunting that occurs in the absence of YAP1. E Proposed model linking the tumor-intrinsic role of an STK11/YAP1 axis with altered transcriptional profiles in KRAS-driven, STK11-null LUADs that promote a cold tumor immune microenvironment, potentiating anti-PD-1 therapy resistance. Our data support targeting YAP1 as a strategy to foster a hot tumor immune microenvironment, thereby sensitizing patients to anti-PD-1 therapy. ***p<0.0001 reflects the padj values attained by the Wald test and corrected for multiple testing using the Benjamini and Hochberg method within DESeq2.

However, when the STK11/YAP1 double KO cells are compared directly with STK11 KO cells in the absence of glutamine, 4167 DEGs were detected (Fig. 4A, Glutamine; 4167 DEGs). If YAP1 loss had no impact, we would predict no DEGs identified between these two conditions. The DEGs detected represent genes that still change upon glutamine depletion, but the magnitude of that change is significantly reduced in the absence of YAP1 indicating these genes are candidates for YAP1-mediated regulation. K-means clustering of genes differentially expressed between STK11-KO and STK11/YAP1 double KO cells revealed a large group of genes that, while still induced by glutamine depletion, were repressed relative to the induction observed in STK11-null/YAP1-competent cells (Fig. 4B; cluster 2, Red v Orange). GSEA performed on DEGs identified between H2009 STK11-KO and STK11/YAP1 double KO cells using the previously described curated YAP1 gene signature demonstrated a significant negative correlation, indicating gene repression in STK11/YAP1 double KO cells relative to STK11-KO/YAP1-intact cells (Fig. 4C). Specifically, 102 genes within the curated YAP1 signature exhibited reduced expression upon YAP1 ablation in STK11-KO cells, highlighted by dot plot analysis of the 17 genes identified in Fig. 1G, which show overlap in gene induction between H2009 and H441 cells upon STK11 ablation (Fig. 4C). We posit those genes demonstrating significant reduction in expression are regulated in part by YAP1. We also performed GSEA using the cytokine activity signature (GO: 0005125) previously described (Fig. 1D) and observed repression of 35 member genes upon YAP1 ablation in STK11-KO cells (Fig. 4D). Again, dot plot analysis highlights repression of a subset of these genes following YAP1 deletion in H2009 cells lacking STK11 (Fig. 4D). Taken together, these data support YAP1 antagonism as a strategy to curb expression of key genes, including immunomodulatory cytokines, in KRAS-driven STK11-null LUADs. We speculate a similar response in vivo would aid in transitioning immunologically cold tumor immune microenvironments to hot, potentiating the effectiveness of checkpoint inhibitor therapies such as anti-PD-1 monoclonal antibodies (Fig. 4E).

Read the original here:
STK11 loss leads to YAP1-mediated transcriptional activation in ... - Nature.com

CRISPR-broad framework

We developed a procedural pipeline for detecting gRNAs and implemented this in Python as a standalone application (Fig.1a). For speeding up gRNA selection, we employed multithreading and used big data Python module Pandas. This allowed splitting millions of short sequences for mapping and processing large numbers of uncompressed alignments. The different steps and options of CRISPR-broad are implemented in seven different modules (in Python with Pandas and PyRanges packages) to avoid re-performing steps that are computationally demanding. Multiple options for user input are available (Fig.1b).

Modules and features of CRISPR-broad. (a) Working scheme of the CRISPR-broad tool. Several steps in this pipeline are multithreaded. The input is a multiFASTA genome file and each step can be individually executed. Indexing and mapping steps are time limiting and can be performed separately. The output of this pipeline is a ranked list of gRNAs in text format. (b) The different modules in execution of CRISPR-broad, their features and applicability as well as the respective options for user input are shown. The different options for running the individual modules are described in detail at https://github.com/AlagurajVeluchamy/CRISPR-broad.

Running CRISPR-broad on the C. elegans genome (target window size 50kb), we obtained 5,734,064 candidate gRNAs with the Cas9 PAM pattern NGG at the 3-end and flanked by 20 nt at the 5-end. We allowed a range of mismatches from 0 to 3 to map to the C. elegans genome assembly Ce235 using the end-to-end all alignment option in bowtie2. The large pairwise alignment was parsed for indels and matches to calculate a ranking score. About 18% of these candidate gRNAs were mapped to multiple sites. We further filtered entries that were aligned to less than five genomic loci. Our analysis resulted in 27,858 gRNAs (five hits in the selected window) that could target 6421 unique 50kb regions (Supplementary Fig.2a).

Next, we scanned the human genome (target window size 500kb) and filtered candidate gRNAs with a cutoff of 50% GC. This resulted in around 120 million gRNAs. We mapped these sequences with a range of mismatches from zero to three and maximum hits of 10,000. The multi-mapped positions were verified for PAM sequences at their 3-end and pooled. We processed candidate gRNAs further that had at least five hits in the genome. This combined filtering resulted in 2,413,602 (0.6%) gRNAs that target 1,678,629 windows (Supplementary Fig.2b). The targetable windows with minimally five loci for a unique gRNA of the C. elegans and H. sapiens genomes were spread throughout the different chromosomes (Supplementary Fig.2b). The aggregate gRNA score pattern distribution for both sample genomes showed that although off-targets are high (negative score), a significant number of high scoring regions in these genomes are available for gRNA targeting (Fig.2a). Irrespective of genome size or sequence content, the aggregate score decreased with the number of off-targets thereby validating the score-based selection of gRNAs (Fig.2b,c).

Aggregate gRNA score distribution for two model organisms. (a) The aggregate gRNA score ranging from 1 to +1 for two datasets is shown in a density plot. Aggregate gRNA score with up to 10k off-target (OT) settings in C. elegans (b) and H. sapiens (c).

Inter-bin distance defines the gap between two target regions and hence illustrates the density of target windows. Analysis of this parameter between different gRNA candidates with or without off-targets revealed that gRNA distribution is not biased over different chromosomes (Fig.3a). Finding potential gRNAs was further supported by increase in window size and by selecting gRNA that are multi-targeting (Fig.3b).

Distribution of gRNAs along the chromosomes of C. elegans and H. sapiens. (a) gRNA sequences clustered in small intervals are evident from this analysis on distribution of gRNA hits. Inter-bin distances of multi-hit gRNA sequences with and without off-target. The distances of gRNA hits are shown in bp (in equal bin size). Note the difference in the distribution of gRNAs with or without off-targets for C. elegans and H. sapiens due to the different repetitiveness of the two genomes. (b) Boxplot showing the relationship between size and number of target bins in the genome of C. elegans. Off-target hits represent the sum of gRNA hits that fall outside all the multiple target windows. W, window size; N, number of target windows.

Typical unique sgRNA selection involves reducing off-target hits on multiple genomic regions and finding a unique target sequence. Tandem duplications in the genome are one cause of off-target effects. CRISPR-broad uses these duplication events in detecting gRNAs in bins (a large genomic region). Larger window sizes could reduce the potential off- target effect of gRNAs in our tool. This was evident from the number of on- and off-target hits (Fig.3b).

Each sgRNA has N total hits in the genome, T hits in the target window and O hits in the off-targets (region outside/different from the 50/500kb target window). When analyzing the C. elegans and H. sapiens genomes, there was no correlation between N and O (Fig.4a,b). The 50kb and 500kb windows showed a vast number of on-targets compared to off-targets, revealing a wide range of selectable regions. Indeed, on-target regions could be identified that showed a high number of gRNA loci with zero off-targets. This included a pericentromeric region of human chromosome 1, which has 272 gRNAs loci with no apparent off-targets (Supplementary Fig.3a). Similarly, in C. elegans analysis with a window size of 10kb revealed a region on the X chromosome (chrX:73517361kb) where at least 1000 loci could be found for one gRNA (Supplementary Fig.3b). The candidate target regions identified in both, C. elegans and H. sapiens were not limited to functionally annotated repetitive regions (e.g. telomeres, satellites) that could be directly targeted by classical gRNA design tools such as CHOPCHOP (Supplementary Fig.3c,d).

Relationship of on-target and off-target sites for each gRNA. Multi-hit alignment with short read aligner was performed for each gRNA. Number of hits within the selected window and off-target windows were enumerated from the alignment. (a) Off-target distribution in comparison to the number of on-target hits in C. elegans (50kb window). (b) off-target distribution in comparison to the number of on-target hits in H. sapiens (500kb window). (c) Off-targets predicted by CasOFFinder compared to the CRISPR-broad scoring system in C. elegans. (d) CRISPR-broad score for gRNAs in H. sapiens compared to off-targets predicted by CasOFFinder. The number of off-targets predicted for individual gRNAs is anticorrelated to our CRISPR-broad scoring system.

Global comparison of the CRISPR-broad scores derived from analyzing the C. elegans and H. sapiens genomes to the results of an independent, state-of-the-art off-target scanning tool for individual gRNAs (CasOffinder), indicated that these are higher for gRNAs that were identified to have a lower number of predicted off-targets (Fig.4c,d). This supported the notion that our scoring method is relevant for selection of multi-targeting gRNAs.

We calculated cumulative scores for the gRNAs matching to selected loci and including a penalty score in case off-targets were found. These scores range from 1 to +1. In both genomes analyzed, C. elegans and H. sapiens we observed a bias towards the extreme values on both sides of the aggregate gRNA score, i.e. many gRNAs are either good candidates for multi-targeting with many hits and no off-targeting (aggregate gRNA score close to +1) or are showing many off-target hits and mismatching (aggregate gRNA score of close to 1) (Fig.2a). The very high negative aggregate gRNA scores observed are reflection of repetitive elements such as Alu sequences, LINE-1 retrotransposons, MIR, and human endogenous retroviruses (HERVs), which represent 55% of the human genome, occurring in multiple copies27. Similarly, in the C. elegans genome MITE sequence repeats might elevate the number of off-targets28. These off-targets are correlated to the aggregate gRNA score (Fig.2b,c).

sgRNA efficiency has been correlated with the GC content of the nucleotide sequence29. We explored whether the GC content feature impacted the number of available gRNAs (with significant number of on-target hits and lower off-target hits). The aggregate gRNA scores (gRNA scores of each window) varied highly from the GC-contents of the sequences (Fig.5). This indicated that CRISPR-broad scans a wide range of gRNAs that may have different levels of repetitive nucleotide sequences. The repetitive elements may be AT-rich and gRNA selection based on gRNA score is not limited by GC content.

gRNA score correlation to GC composition of the 23 nucleotides gRNA sequence. (a, b) Sequence composition as dinucleotide frequencies were calculated. The gRNA score (range from 2 to +1) and the GC content are depicted in the density plot. Aggregate gRNA score and repetition of sequence (off-target) are independent of the sequence composition. Many candidate gRNAs with high aggregate gRNA score that corresponds to candidate target windows are available for varied GC content.

To elucidate the effects of user-defined bin size and number of distinct gRNA combinations, we scanned the C. elegans genome with two window sizes of 1kb and 200kb and targeting window numbers of 3 and 10. As expected, the number of off-targets decreased with increasing target window sizes and the number of target regions (Fig.3b). Our analysis showed that with different bin sizes and using multiple gRNA, a wide range of regions can be selected for targeting with singular gRNAs.

The dispersion of a gRNA within a bin is depending on the number of hits and this increases with the number of mismatches (03). Nevertheless, most hits for gRNAs were unique with no mismatches. This is revealed from sgRNA mismatch analysis of the whole genome of C elegans and a random selection of 10,000 sgRNA in H. sapiens (Fig.6a and Supplementary Fig.5a). Also, these mismatches were independent of the position within a bin (Supplementary Fig.4). Further, the dispersion of individual gRNAs did not correlate with the aggregate gRNA score in both C. elegans and H. sapiens. In C. elegans most gRNAs with higher standard deviation from the mid position of the bin showed lower aggregate gRNA scores (Fig.6b). Also, in H. sapiens, the standard deviation was not correlated to the gRNA score but was associated with a varied range of gRNA scores (Supplementary Fig.5b). This difference is because the H. sapiens genome is large and has more multi-targetable regions compared to the C. elegans genome. In both cases, a substantial number of gRNAs of varied standard deviation and with no off-targets could be selected.

Assessment of displacement of gRNAs within on-target windows. (a) CRISPR-broad was used to scan for potential gRNAs with different levels of mismatches, since earlier reports have shown that the efficiency of gRNAs are limited by the number of mismatches. Mismatch levels and number of on-target hits for gRNAs of individual 50kb windows in C. elegans are shown. Mismatch levels are set in the range from 0 to 3. Many selectable gRNAs and their corresponding target windows are available even at a mismatch level of 0. (b) Hexbin plot showing the relationship between aggregate gRNA score and dispersion. Standard deviation (dispersion) was calculated from the position of the gRNA hits within a target window. The aggregate gRNA score ranges from negative to positive values. Higher values of standard deviation correspond to higher distribution of gRNA within a target window. Standard deviation and gRNA score were calculated using 500kb windows in H. sapiens.

Using PyRanges, we created intervals of user-defined size that are overlapping with gRNA candidates containing the Cas9 PAM pattern (3-NGG-5). Since this step is computationally intensive, we have implemented options to narrow down the search with minimum and maximum number of hits for a target window.

Analysis of the annotation of regions of the C. elegans and H. sapiens genomes that can be targeted by multi-targeting gRNAs indicated that a broad range of features including genes and gene regulatory elements are available for selection. The range of annotated, targetable regions for each genome could be further significantly increased when combining gRNA searches for different genome-targeting systems that use different PAM sequences (Supplementary Fig.6).

To test CRISPR-broad we resorted to a previously described method of painting genome regions by targeting dCas9 fused to green fluorescent protein (GFP). Singular gRNAs targeting more than 100 directly repeated sequences within telomeres or pericentromeres identified by classical gRNA design tools has enabled mapping of these functional chromosome elements in cellular context4,5,6. Using CRISPR-broad we identified a singular gRNA targeting a 317kb region on human chromosome 19 at 19p13.2 with 86 hits (Fig.7a). Human U2OS transfected with a plasmid expressing dCas9-3XGFP together with a plasmid expressing the identified sgRNA showed two or 4 dots of accumulated green fluorescence in the nucleus in agreement with a 2n (G1- and S-phase) or 4n (G2-phase) chromosome content. In contrast and as described before4,5,6, dCas9-3XGFP in the absence of specific gRNA-mediated targeting displayed nucleolar background staining in the cell nucleus (Fig.7b). The results indicated that CRISPR-broad can identify large genomic regions for efficient targeting of dCas9 apart from simple and obvious repetitive elements of the genome.

Targeting of a broad region of the genome using a singular gRNA designed by CRIPSR-broad. (a) Scheme depicting a 317kb region on human chromosome 19 that can be targeted by a sgRNA at 86 locations. (b) Fluorescence imaging of U2OS cells transfected with a plasmid expressing dCas9-3XGFP together with a plasmid expressing the sgRNA targeting the region depicted in (A) (top) or the corresponding empty vector (bottom). Focal enrichment of GFP inside the nucleus is marked by arrows. Note that due to the different cell cycle stages two (2n chromosome content, G1- , S-phases) or four (4n chromosome content, G2-phase) labeled spots are expected. Scale bar represents 20m. Details on the selection of the presented cells and images can be found in Supplementary Fig.8.

To assess the wider application and potential of CRISPR-broad, we compared the results of the test runs on the C. elegans and H. sapiens genomes using the single Cas9 PAM with annotated (epi-)genetic features using the ENCODE and modENCODE datasets. We found the multi-targetable windows defined by CRISPR-broad overlapping with the features transcription factor binding sites (ChIP-seq peak regions), histone modification region (ChIP-seq peaks), annotated transposable elements in the genome and sites of DNA methylation (WGBS: methylated CpG sites). The fact that the fraction of each of these sites that could be targeted by multi-targeting gRNAs (number of features overlapped to a gRNA window of 5kb/total number of features) is substantial (Supplementary Fig.7) indicated that CRISPR-broad could be useful in various strategies of epigenome editing.

CRISPR-broad was developed in Python and the source code is available at https://github.com/AlagurajVeluchamy/CRISPR-broad. CRISPR-broad runs in seven independent modules with multiple options for user input (Fig.1b). The limiting steps are mapping the gRNAs to the genome and obtaining all hits. We tested the performance of the tool on a Linux workstation with 3040 threads computed for genome sizes of 103Mb (C. elegans) and 3.2Gb (H. sapiens) (Table 1). With an increase in genome size and in the allowed number of mismatches, the run time increased. The gRNA sequences, aggregate gRNA scores, GC content, number of on- and off-target hits, optimal on-target window of pre- selected size, and co-ordinates of each hit are compiled and exported in a tab-delimited text (Supplementary Table 2).

Go here to see the original:
CRISPR-broad: combined design of multi-targeting gRNAs and ... - Nature.com

Researchers at Duke University have adapted CRISPR technologies for high-throughput screening of gene function in human immune cells and discovered that a single master regulator of the genome can be used to reprogram a network of thousands of genes in T cells and greatly enhance cancer cell killing.

The master regulator is called BATF3 and is one of several genes that the researchers identified and tested for improving T-cell therapies. These targets, and the methods developed to identify, test and manipulate them, could make any of the T cell cancer therapies currently in use and under development more potent. Combined with other advances, the platform could also enable generalized, off-the-shelf versions of the therapy and expansion into other disease areas such as autoimmune disorders.

The results appear online November 9 in the journal Nature Genetics.

T-cell therapy is a decade-old approach to treating cancer. More recent versions involve reprogramming the immune system's primary soldiers to seek and destroy cancerous cells that they might otherwise overlook. Many companies are working to enhance the technology, mostly through the use of genetic engineering techniques that instruct the T cells how to identify cancerous cells and make them more effective at destroying them.

There are currently six FDA-approved T-cell therapies for specific leukemias, lymphomas and multiple myeloma. Their approaches, however, do not currently fare well when applied to solid tumors, although there are hints of success in certain studies. Solid tumors often present large physical barriers for the T cells to overcome, and the sheer number and density of cancer cells presenting targets can lead to "T-cell exhaustion," wearing the attackers out to the point that they are not able to mount an antitumor response.

"In some cases, T-cell therapy works like a miracle drug, but in most others, it hardly works at all," said Charles Gersbach, the John W. Strohbehn Distinguished Professor of Biomedical Engineering at Duke. "We are looking for generic solutions that can make these cells better across the board by reprogramming their gene regulation software, rather than rewriting or damaging their genetic hardware. This demonstration is a crucial step toward overcoming a major hurdle to getting T-cell therapy to work in more patients across a greater range of cancer types."

Gersbach and his laboratory have spent the past several years developing a method that uses a version of the gene-editing technology CRISPR-Cas9 to explore and modulate genes without cutting them. Instead, it makes changes to the structures that package and store the DNA, affecting the activity level of the accompanying genes.

Sean McCutcheon, a PhD candidate working in Gersbach's lab and lead author of the study, focused on regions of this 'dark genome' that change as T cells transition between states, such as functional versus exhausted. He identified 120 genes that encode "master regulators," which are responsible for the activity levels of many other genes. Using the CRISPR platform, he dialed the activity levels of these targets both up and down to see how they affected other known markers of T cell function.

While several promising candidates emerged, one of the most promising was a gene called BATF3. When McCutcheon subsequently delivered BATF3 directly to the T cells, there were thousands of tweaks to the packaging structure of the T cells' DNA, and this correlated with increased potency and resistance to exhaustion.

"A known barrier to using T cells to fight cancer is that they tend to get 'tired' over time and lose their ability to kill cancer cells," McCutcheon said. "We're identifying manipulations that make T cells stronger and more resilient by mimicking naturally occurring cell states that work well in clinical products."

The researchers put BATF3 through a battery of tests. The most interesting results came when they overexpressed BATF3 in T cells programmed to attack human breast cancer tumors in a mouse model. While the standard-of-care T-cell therapy struggled to slow tumor growth, the exact same dose of T cells engineered with BATF3 completely eradicated the tumors.

While the results with BATF3 are exciting to Gersbach, McCutcheon and the rest of the group, they are even more enthusiastic about the general success of the methodology to identify and modulate master regulators to improve therapeutic performance, which they have been developing for the better part of a decade. They can now readily profile master regulators of T cell fitness using any T cell source or cancer model and under various experimental conditions that mimic the clinical setting.

For example, in the last part of this study, McCutcheon screened T cells, with or without BATF3, while using CRISPR to remove every other master regulator of gene expression -- more than 1,600 regulators in total. This led to the discovery of a whole new set of factors that could be targeted alone or in combination with BATF3 to increase the potency of T-cell therapy.

"This study focused in depth on one particular target identified by these CRISPR screens, but now that Sean and the team have the whole discovery engine up and running, we can do this over and over again for different models and tumor types," Gersbach said. "This study suggests many strategies for applying this approach to enhance T-cell therapy, from using a patient's own T cells to having a bank of generalized T cells for a wide variety of cancers. We hope that these technologies can be generally applicable across all strategies."

This research was supported by the National Institutes of Health (U01AI146356, UM1HG012053, UM1HG009428, RM1HG011123), the National Science Foundation (EFMA-1830957), the Paul G. Allen Frontiers, the Open Philanthropy Project, and the Duke-Coulter Translational Partnership.

Read the original:
Master regulator of the dark genome greatly improves cancer T-cell ... - Science Daily

CAMBRIDGE, Mass., Nov. 13, 2023 (GLOBE NEWSWIRE) -- Omega Therapeutics, Inc. (Nasdaq: OMGA) (Omega), a clinical-stage biotechnology company pioneering the development of a new class of programmable epigenomic mRNA medicines, today announced the presentation of new preclinical data from two different programs that demonstrated sustained upregulation of gene expression and coordinated pre-transcriptional downregulation of multiple genes in models of liver fibrosis and inflammation, respectively, at the American Association for the Study of Liver Diseases (AASLD) The Liver Meeting 2023, taking place in Boston, Massachusetts, November 10 14.

Genetic medicines have made tremendous progress towards precise downregulation of gene expression. However, to extend their reach, we need to bidirectionally control the expression of multiple genes simultaneously, said Thomas McCauley, Ph.D., Chief Scientific Officer of Omega Therapeutics. We believe that these new data demonstrate the power of our programmable epigenomic mRNA development candidates to control gene expression with unmatched flexibility. To our knowledge, these are the first results to show how site-specific epigenomic modulation can durably upregulate the expression of a master liver regeneration gene. Additionally, a second poster highlights our ability to multiplex gene regulation with a single construct to control a cluster of inflammatory chemokines. These exciting results highlight the progress we have made and possible applications of our approach in multiple liver diseases.

Poster 3444-A: Induction of Hepatocyte Nuclear Factor 4 alpha (HNF4) using novel epigenomic controllers

Key Findings

Poster 2621-A: Targeting CXCL9/CXCL10/CXCL11 using novel epigenomic controllers for the treatment of inflammatory liver disease

Key Findings:

These posters are available on the Omega website at https://omegatherapeutics.com/science/publications.

About Omega Therapeutics Omega Therapeutics is a clinical-stage biotechnology company pioneering the development of a new class of programmable epigenomic mRNA medicines to treat or cure a broad range of diseases. By pre-transcriptionally modulating gene expression, Omegas approach enables controlled epigenomic modulation of nearly all human genes, including historically undruggable and difficult-to-treat targets, without altering native nucleic acid sequences. Founded in 2017 by Flagship Pioneering following breakthrough research by world-renowned experts in the field of epigenetics, Omega is led by a seasoned and accomplished leadership team with a track record of innovation and operational excellence. The Company is committed to revolutionizing genomic medicine and has a diverse pipeline of therapeutic candidates derived from its OMEGA platform spanning oncology, regenerative medicine, multigenic diseases including immunology, and select monogenic diseases.

For more information, visit omegatherapeutics.com, or follow us on X (formerly Twitter) and LinkedIn.

Forward-Looking Statements This press release contains forward-looking statements within the meaning of the Private Securities Litigation Reform Act of 1995. All statements contained in this press release that do not relate to matters of historical fact should be considered forward-looking statements, including without limitation statements regarding the broad potential of precision epigenomic control, the potential of the Companys pipeline of therapeutic candidates, and upcoming events and presentations. These statements are neither promises nor guarantees, but involve known and unknown risks, uncertainties and other important factors that may cause our actual results, performance or achievements to be materially different from any future results, performance or achievements expressed or implied by the forward-looking statements, including, but not limited to, the following: the novel technology on which our product candidates are based makes it difficult to predict the time and cost of preclinical and clinical development and subsequently obtaining regulatory approval, if at all; the substantial development and regulatory risks associated with epigenomic controllers due to the novel and unprecedented nature of this new category of medicines; our limited operating history; the incurrence of significant losses and the fact that we expect to continue to incur significant additional losses for the foreseeable future; our need for substantial additional financing; our investments in research and development efforts that further enhance the OMEGA platform, and their impact on our results; uncertainty regarding preclinical development, especially for a new class of medicines such as epigenomic controllers; potential delays in and unforeseen costs arising from our clinical trials; the fact that our product candidates may be associated with serious adverse events, undesirable side effects or have other properties that could halt their regulatory development, prevent their regulatory approval, limit their commercial potential, or result in significant negative consequences; the impact of increased demand for the manufacture of mRNA and LNP based vaccines to treat COVID-19 on our development plans; difficulties manufacturing the novel technology on which our epigenomic controller candidates are based; our ability to adapt to rapid and significant technological change; our reliance on third parties for the manufacture of materials; our ability to successfully acquire and establish our own manufacturing facilities and infrastructure; our reliance on a limited number of suppliers for lipid excipients used in our product candidates; our ability to advance our product candidates to clinical development; and our ability to obtain, maintain, enforce and adequately protect our intellectual property rights. These and other important factors discussed under the caption Risk Factors in our Quarterly Report on Form 10-Q for the quarter ended September 30, 2023, and our other filings with the SEC, could cause actual results to differ materially from those indicated by the forward-looking statements made in this press release. Any such forward-looking statements represent managements estimates as of the date of this press release. While we may elect to update such forward-looking statements at some point in the future, we disclaim any obligation to do so, even if subsequent events cause our views to change.

Here is the original post:
Omega Therapeutics Showcases Bidirectional and Multiplexed ... - BioSpace

Nov. 15 is internationally recognized as a day to raise awareness for 15q neurodevelopmental disorders, which is an overarching categorization for three distinct conditions: Angelman syndrome, Dup15q syndrome and PraderWilli syndrome. Each condition is unique, yet all are caused by a change or mutation in the 15th chromosome.

The National Library of Medicine estimates that Angelman syndrome affects one in every 20,000 people and PraderWilli syndrome affects one in every 10,000 to 30,000. Dup15q has an unknown frequency, and it is estimated that it could be as high as one in every 5,000 people.

Human male karyotype after G-banding. Chromosome 15 highlighted.

PraderWilli syndrome is caused by a genetic mutation of undefined mechanisms. Current research suggests that this mutation causes an inability to express paternal genes; this may be due to a child inheriting two maternal chromosomes (as opposed to one maternal and one paternal copy), a defect in the paternal gene that prevents proper expression, or a complete lack of paternal genes on the chromosome.

Typically, symptom presentation begins around 2 years of age, and disease presentation includes hyperphagia, poor responsiveness, underdevelopment and hypotonia. Hyperphagia often leads to obesity, which means that individuals with the syndrome maintain a higher risk of experiencing obesity-related complications such as Type II diabetes, high blood pressure, elevated cholesterol and heart disease. Additionally, decreased hormone production due to underdevelopment may lead to complications including sterility and osteoporosis.

The critical region for PraderWilli Syndrome is located at chromosome 15(q11-13), in which exon 1 is a key region for imprinting. In PWS, exon 1 is maternally imprinted, meaning that paternal allele expression does not occur. Genetically, this can be caused by one of three things: deletion of a critical region, uniparental disomy or an imprinting center defect.

Deletions: Deletions causing PWS are categorized as either large, small or microdeletions. Large deletions are often between 4 to 6 Mb and occur in one of two regions. Deletions extending from the D15S541 region to the D15S12 region are often considered class I, while those ranging from D15S543 to D15S12 are categorized as class II. These are collectively the most common type of deletion observed in PWS, and both ultimately inhibit the functions of the imprinting center. Small deletions occur within a smaller range, and microdeletions often occur in areas such as an SNRPN gene exon 1 deletion, which is also known as an imprinting center mutation or defect.

Imprinting center defect: ICD is a subset of deletions most commonly known for deletion of imprinting region exon 1. At the moment, the only available method of detection for an ICD is through DNA testing.

Uniparental disomy: UPD is primarily characterized by a lack of paternal input. In one of the most common forms of the condition, chromosome 15 displays only maternal copies. This condition is often referred to as UPDmat and occurs through a mechanism known as trisomy rescue, in which a combination of a meiotic and somatic abnormal events results in either a heterodisomy or an isodisomy.

Angelman syndrome is often caused by issues with the ubiquitin ligase UBE3A gene located on chromosome 15q. This condition commonly occurs due to activation of only the maternal copy of the gene, with either a missing or defective paternal gene; however, it can also be caused by the inheritance of two paternal genes.

Symptoms of AS can first appear around six to 12 months of age and can include difficulty walking, lack of speech and seizures, among others. There is currently no known cure for the condition.

Current research focuses on the development of mouse models as a way to better understand the development and progression of AS. Humans and mice have similar UBE3A loci, which makes the mouse a viable preclinical model preceding the production of potential translational therapeutics.

Dup15q Syndrome, also referred to as maternal 15q duplication syndrome, is a condition caused by the presence of at least one extra chromosome 15 region (15q11.2-q13.1). Dup15q typically only occurs when the duplicate copy is maternally inherited.

Some common characteristics of Dup15q syndrome include hypotonia, intellectual disabilities, autism spectrum disorder and epilepsy. Currently, treatment options for the condition are limited to treatment of specific symptoms and surveillance, as well as genetic and prenatal testing to monitor development throughout pregnancy.

There are two forms of Dup15q syndrome that are commonly recognized:

Maternal isodicentric chromosome 15: In this form of Dup15q syndrome, two extra maternal copies of 15q11.2-q13.1 are present, which results in tetrasomy. This form of the condition accounts for approximately 60% to 80% of Dup15q syndrome diagnoses.

Maternal interstitial duplication: This subset of the condition is characterized by the presence of one additional copy of 15q11.2-q13.1, resulting in trisomy for this region of chromosome 15. Maternal interstitial duplication is less common than isodicentric chromosome 15 and accounts for only about 20% to 40% of diagnoses.

The 15q chromosome contains several regions known as segmental duplications, which have a higher susceptibility to rearrangement and thus mutation. Several genes of interest that may contribute to disease progression have been observed in this region, including UBE3A, GABRB3, GABRA5, GABRG3, and HERC2.

UBE3A: This is the same gene that is affected in Angelman syndrome. However, in Dup15q syndrome, it is suspected to play a role in symptoms including intellectual disability, anxiety and a reduced threshold for seizures, which is likely due to an increase in neuronal expression of the gene.

GABRB3, GABRA5 and GABRG3: These genes encode for receptor subunits of the GABAa receptor, a ligand-gated ion channel that plays a major role in synaptic transmission throughout the central nervous system. It has been suggested that this gene may therefore play a role in seizures during Dup15q symptom presentation.

HERC2: This gene is an E3 ubiquitin ligase, part of a subset of genes responsible for interaction with E2 ubiquitin enzymes and a corresponding target protein in order to accomplish transfer of ubiquitin from E2 to the protein. Current literature suggests that pathogenic variants of this gene may lead to neurodevelopmental complications, which is supported by the increased neuronal expression of HERC2 in individuals with Dup15q syndrome.

Treatment of PWS often includes approaches such as establishing nutritional supplementation for infants, human growth hormone treatment, sex hormone treatment, implementation of behavioral therapies and provision of mental health resources, among other possibilities. While there is not one streamlined approach for treating PWS, there is a repertoire of available resources that aid in early detection and improved quality of life.

AS does not have one specific treatment method. Therapeutic approaches focus primarily on addressing specific side effects of the condition, including seizures, anxiety and gastrointestinal complications, among others. This typically involves implementation of behavioral, dietary, physical or occupational therapies, as well as consultation with physicians to identify additional effective symptom management options.

Similar to both PWS and AS, Dup15q therapeutic approaches are highly diversified depending on clinical manifestation and disease progression. Employment of advice and care from clinical professionals is highly emphasized, and resulting care is individualized depending on the specific needs of each patient.

While there is still much to be discovered about 15q conditions, there are various resources available for those experiencing these conditions, as well as those who simply want to gain a deeper understanding of current clinical progressions toward treatment of PWS, AS and Dup15q syndrome.

This page on the Eunice Kennedy Shriver National Institute for Child Health and Human Development has resources for patients, researchers and physicians about PWS. It includes information about patient advocacy groups, foundations that support elimination of PWS, and organizations that offer support to patients and family members experiencing PWS diagnosis.

The Angelman Syndrome Foundation offers information about behavioral resources, counseling and familial support. It also has a podcast, which provides education about the condition.

The Dup15q Alliance is an organization that provides information about current research, clinical trials and even volunteer opportunities to foster a greater, more widespread understanding of Dup15q syndrome.

Continued here:
Today is International 15q Day - ASBMB Today

A research team from the University of Cologne, in collaboration with colleagues from the Leibniz Institute for Food Systems Biology in Freising, has discovered a receptor for bitter taste in twelve different cartilaginous fish (sharks and rays). The receptor belongs to the so-called taste receptors type 2 (T2R), which also make humans perceive bitter and potentially toxic foods. Until now, it was assumed that such receptors only occur in bony vertebrates. The work was published under the title A singular shark bitter taste receptor provides insights into the evolution of bitter taste perception in the renowned journal Proceedings of the National Academy of Sciences (PNAS).

In the past, molecular research has had limited information on sharks, as their genomes are often relatively large. Therefore, sequencing is often more complex and takes longer than with many other animals. However, the techniques are more advanced nowadays, providing ever more information on the gene sequences of many cartilaginous fishes. This enabled the neurobiologists lecturer (Privatdozent) Dr Maik Behrens and Tatjana Lang from the Leibniz Institute for Food Systems Biology and Professor Dr Sigrun Korsching at the Institute of Genetics of the University of Cologne to specifically search for bitter taste receptors in cartilaginous fish.

Twelve out of seventeen cartilaginous fish genomes studied contained genes for the taste receptors type 2, with only one T2R gene present in each species. The researchers named this single gene T2R1. The fact that only a single T2R gene was found suggests that it is the original form of these bitter taste receptors, which was not altered by gene duplication and subsequent different specialization of the resulting receptors.

'These findings give us new insights into the evolution of these receptors: We can look back almost 500 million years on the molecular and functional origin of an entire family of bitter taste receptors. Because that is how old the last common ancestor of cartilage and bony fish is,'says Sigrun Korsching. The authors have also introduced the T2R1 gene of the bamboo shark (C. plagiosum) and the catshark (S. canicula) into immortalized cell lines. The results showed that both sharks can taste bitter substances also perceived by humans, such as colchicine or bile acid. A screening of ninety-four human bitter substances identified eleven substances that could also activate the sharks receptors. Some of these eleven substances also activate the bitter taste receptors of the living fossil coelacanth (Latimeria chalumnae), an ancient species of bony fish, as the authors have shown in a previous study. Sigrun Korsching summarizes that 'the extent to which this function has been conserved is astonishing, i.e. through the entire evolution of vertebrates.'

Proceedings of the National Academy of Sciences

A singular shark bitter taste receptor provides insights into the evolution of bitter taste perception

13-Nov-2023

Disclaimer: AAAS and EurekAlert! are not responsible for the accuracy of news releases posted to EurekAlert! by contributing institutions or for the use of any information through the EurekAlert system.

Read the rest here:
Evolution of taste: Sharks were already able to perceive bitter ... - EurekAlert