Category Archives: Genome

Modelling random DNA fragmentation

To begin our study, we required a model that would accurately reflect the properties of a stochastically fragmented DNA sample. The odds that a region targeted by a PCR assay will be interrupted by a DNA breakage in randomly fragmented DNA depend on the length of the region and the size of the fragments. These odds are effectively determined by establishing two adjacent fragment-sized sliding windows (wherein the end of one fragment is the start of another) and calculating the number of times a region is fully within the first fragment window, compared to the number of times the region is situated within both windows (Fig.1).

Diagram depicting example calculation of the proportion of intact copies of a target region (4bp) given a single specified fragment length (6bp). This calculation can be viewed as the probability that a region will not be cleaved at any point along its length if a genome were broken into equal length fragments. The fragment-sized Window 1 sliding across this region depicts all possible fragmentation states for this region. The intact proportion is calculated as the number of states where the region remains entirely within the fragment window over the total number of possible fragmentation states. Window 2 demonstrates that all possible states are represented at the point before the region fully exits Window 1, as these states are then repeated in this adjacent window.

This model is represented in Eq.(1), which determines the probability that a region of DNA will remain unbroken for a given fragment length:

$${text{proportion}};{text{intact}} = frac{{{text{f }}{-}{text{ r }} + { }1}}{{text{f}}},$$

(1)

where r is the length of the DNA region and f is the length at which the DNA is fragmented. However, DNA samples do not fragment at a single length but rather as a distribution, and by incorporating size distribution profiles, which contain the concentration of DNA at each fragment length, the proportion of intact target regions within a fragmented DNA sample can be calculated, as detailed in Eq.(2):

$${text{proportion}};{text{intact }} = frac{{mathop sum nolimits_{{f = r{ }}}^{n} frac{{{text{f }}{-}{text{ r }} + { }1}}{{text{f }}}{ }C_{f} }}{{mathop sum nolimits_{{f = m{ }}}^{n} C_{f} }},$$

(2)

where n is the length of the longest fragment within the sample, m is the length of the shortest fragment, and Cf is the concentration of each fragment length (i.e., pg/l).

We next sought to design qPCR and ddPCR assays that could be used to interrogate DNA fragmentation. A major focus of this assay design was to incorporate design elements that would enable the assays to be used on clinical cancer samples, as these samples are some of the most common types to undergo stochastic fragmentation. However, cancer samples are also prone to chromosomal amplifications and deletions within the genome16,17,18, and PCR assays that intersected with frequently amplified/deleted regions would result in inaccurate measures of concentration when these copy number aberrations (CNAs) occurred (i.e., the concentration of a region that is unique in the human reference genome is assumed to correspond to the overall number of genome copies within the measured sample). To control for this, we undertook an analysis to determine the regions of the human genome that were least affected by CNAs. CNA data that had been tested for statically significant gain or loss was retrieved from the Catalogue of Somatic Mutations in Cancer (COSMIC release v78)19,20. This data was filtered to exclude cell line samples and samples missing total copy number or minor allele values. Only 27 of the 10,637 samples remaining after this filtering were not derived from The Cancer Genome Atlas (TCGA) data21. We, therefore, opted to exclusively use these 10,610 TCGA samples to better ensure a dataset with experimental and analytical consistency in determining copy number changes (S1 Table).

After filtering out regions that were not covered by Affymetrix copy number probes (e.g., centromeres) the only regions completely devoid of CNAs were telomeric and likely artefactual. Outside of telomeres the minimum CNA region contained 5 samples. To determine a reasonable threshold for low copy number variation that might provide us with enough region space to meet the requirements of our assay design, we calculated the number of samples with CNAs in commonly used copy number reference genes. We found that the Human TaqMan Copy Number Reference Assays targeting RNase P and TERT offered by Applied Biosystems had CNAs in 61 and 360 of the total 10,610 samples, respectively, and the well-established standard reference gene RPP30 had CNAs in 23 samples. Based on this we set a threshold at the bottom 10th percentile of regions, excluding those where greater than 34 samples had significant copy number variation (Fig.2A). After applying this filter, we were left with 621 megabases across 858 non-contiguous regions on 22 chromosomes.

Design and performance of PCR assays against copy-neutral regions in the genome. (A) Circos plot depicting the percent of samples that undergo copy number aberration (CNA) in cancer. Chromosomes are shown in the outermost ring and include an overlay of cytogenetic Giemsa banding and centromeres marked with a red band. The second outermost ring shows the 946,615 Affymetrix Genome-Wide Human SNP Array 6.0 copy number probes used for the detection of CNAs by the TCGA. The final layer is a histogram displaying the number of samples that underwent statistically significant CNA (either loss or gain) within each region. Each grid line represents 1% of the 10,610 total samples. Universal assays were designed to target regions in the bottom 10th percentile of CNAs, excluding regions that are not covered by the Affymetrix CNV probes (e.g., centromeres). Less than 35 of the 10,610 samples (<0.33%) have CNAs in these regions, represented on the histogram as a dotted white line (above which regions were excluded). (B, C) Standard curves estimating amplification efficiencies of universal quantitation assays in 4-plex qPCR on gDNA (B) and bisulfite-converted DNA (C). Curves are artificially offset for better visualisation. E=efficiency.

We next designed a single-tube 4-plex quantitative PCR assay targeting these CNA neutral regions, which included a variety of design considerations to maximize the utility of the assay and minimize confounding effects. First, each assay would target a separate chromosome to minimize inaccurate quantification due to the remote possibility that one of the chromosomes, or at least a large portion, may be affected by CNAs. Given the size and number of regions, the second design consideration was identifying assay regions that would be unaffected by bisulfite conversion treatment, since the bisulfite conversion process is used to examine DNA methylation and is a common application in cancer genomics but also leads to substantial sample fragmentation and loss. To address this design consideration the CNA neutral regions were further analysed to identify primer and probe regions that were cytosine-free and would, therefore, be unaffected by the bisulfite conversion process. Notably, use of the assays on bisulfite material requires an extra step in qPCR data analysis to correct for the fact that only one DNA strand is quantified, resulting in a positive shift of 1 cycle threshold when compared to the unconverted genomic DNA (gDNA) counterpart.

The third design criterion was to enable assessment of the degree of sample fragmentation using this 4-plex assay. To achieve this, two of the assays were designed to be 125bp in length, and two were designed to be 175bp long. By taking the ratio of concentrations for the long to short assays, a quantitative metric for sample fragmentation can be imputed for any sample.

Finally, we sought to establish the combination of fluorescent probe chemistries that would enable successful multiplexing quantitation using either standard qPCR or ddPCR. In qPCR four different probe fluorophores (FAM, HEX, Cy5 and Texas Red) were used, whereas ddPCR 4-plex was achieved using a method developed by Dobnik et al. (2016)22 that uses two FAM probes and two HEX probes and varies probe concentrations to alter the resulting levels of fluorescence amplitude, allowing for the detection of two targets per fluorescence channel (S1 and S2 Figs).

After all these design criteria were successfully implemented, we next undertook experiments to verify the amplification fidelity and efficiency of each of the four assays. The fidelity of the assays was established by performing standard PCR and qPCR on a variety of sample types (buffy coat DNA, cfDNA, and bisulfite-converted DNA) and analysing the PCR products by standard DNA gel electrophoresis to confirm that only a single PCR amplicon was produced in singleplex (S3 Fig), and that multiplex assays produced only two bands of the expected sizes (S4 and S5 Figs). Next, the amplification efficiencies of all assays were determined using LinRegPCR window-of-linearity analysis23, and standard titration curves; this was done for all four amplicons in both singleplex and multiplex configurations, for both genomic and bisulfite-converted DNA, using both fluorescent dye and PrimeTime qPCR probes in multiple fluorophore configurations (Fig.2B,C, Table 1). Notably, all assays demonstrated>90% amplification efficiency across all conditions, indicating robust performance. Primer and probe sequences can be found in S2 Table.

The [long]/[short] ratios of any two target region lengths can be determined by applying the following equation to fragment size distribution data (Eq.3):

$${text{[long]/[short]}} = frac{{mathop sum nolimits_{{f = b{ }}}^{n} frac{{{text{f }}{-}{text{ b }} + { }1}}{{text{f }}}{ }C_{f} }}{{mathop sum nolimits_{f = s}^{n} frac{{{text{f }}{-}{text{ s }} + { }1}}{{text{f }}}{ }C_{f} }},$$

(3)

where b is the length of the longer region and s is the length of the shorter region.

To evaluate how well our model of stochastic fragmentation fit with experimental results we compared [175bp]/[125bp] ddPCR and qPCR ratios with those derived using Eq.(3) on Agilent 2100 Bioanalyzer fragment size concentration data. This analysis was performed on seven levels of increasing fragmentation induced by the ultrasonication of pooled buffy coat gDNA. The ddPCR and qPCR [175bp]/[125bp] ratios of our sonicated samples both showed high goodness-of-fit for ratios derived using Eq.(3), with R-square values of 0.995 and 0.989 for ddPCR and qPCR, respectively (Fig.3A,B).

Modelling and quantifying randomly fragmented DNA. (A) Table showing DNA samples sonicated to different fragment lengths, their fragment distribution profiles in electropherogram and pseudo-gel form, and comparison between the theoretically (Eq.3 applied to Bioanalyzer data) and experimentally determined [175bp]/[125bp] differential amplicon length ratios. The full unedited pseudo-gel image for these sonicated samples can be found in S6 Fig. (B) Line graph plotting the [175bp]/[125bp] ratios determined by qPCR, ddPCR, and our mathematical model applied to fragment size distribution data (Bioanalyzer) on differentially fragmented DNA samples. (C) Comparison of nucleic acid quantification methods on fragmented DNA. PCR data points are averages of the two universal assays for each amplicon length per well. Fluorometric (Qubit) and absorbance (Nanodrop) spectroscopy measurements were made on each sample on three separate occasions. Spectroscopy concertation measurements are depicted in ng/l and PCR as copies/l. Axes are scaled so that 1 copy=3.5pg.

Quantification of DNA samples affects all subsequent experimental steps and can lead to costly experimental failures if this step is not performed accurately. Therefore, to further extend our study we next compared the effects of fragmentation on nucleic acid quantification techniques using our sonicated DNA samples, referred to here by their peak (modal) fragment sizes: 150, 195, 283, 694, 828, 1082, and 1504bp.

One overlooked aspect of DNA fragmentation is that it results in fewer adjacent base pairs for fluorescent DNA dyes to intercalate when dye-based fluorometric methods are used. Thus predictably, and as other studies have noted1,2, the mean DNA concentration measured by fluorescence spectroscopy (Qubit 2.0) decreased with increasing fragmentation (p<0.001; one-way ANOVA), with untreated gDNA measuring at 50.40ng/l (SD=0.72), and the most fragmented sample (150bp) at 35.27ng/l (SD=2.14), which calculate to 14,400 (SD=206) and 10,100 (SD=613) genome copies, respectively, assuming 1 genome weighs 3.5pg based on the following formula:

$$begin{aligned} {text{Amount}} left( {{text{pg}}} right) & = frac{{{text{length }}left( {{text{bp}}} right)*{text{pg}}/{text{g}}*{text{weight}};{text{of}};{text{bp}} left( {{text{g}}/{text{mole}}/{text{bp}}} right)*{text{copies }} left( {{text{molecules}}} right)}}{{{text{Avogadro's}};{text{number }} left( {{text{molecules}}/{text{mole}}} right)}} \ Amount left( {pg} right) & = frac{{3,234,830,000* 10^{12} *650*1}}{{6.022*10^{23} }} \ end{aligned}$$

(4)

For absorption spectroscopy (Nanodrop 1000), the mean measurement for intact gDNA was 68.40ng/l (SD=1.97), which calculates to 19,600 (SD=563) genome copies. Although there was no dose-dependent trend towards decreasing concentration with increasing fragmentation, a one-way ANOVA did show a significant difference in concentration (p<0.001), and a Tukey's HSD test found the concentration of intact gDNA to be significantly higher than all seven levels of fragmentation (p<0.001). The highest mean concentration measured for the fragmented gDNA was 63.10ng/l (SD=0.79; 150bp) and the lowest was 57.43ng/l (SD=0.32; 283bp), which calculate to 18,100 (SD=226) and 16,400 (SD=92) genome copies, respectively.

Both qPCR and ddPCR measured substantial downward trends in concentration with increasing fragmentation (Fig.3C). This decline in amplifiable copies with increasing fragmentation reflects an increasing number of breakages in the targeted regions, the magnitude of decline being greater for the 175bp amplicon as longer target regions are more likely to be cleaved. ddPCR on the intact gDNA measured 18,984 (SD=765) and 19,058 (SD=608) mean copies for the two 125bp assays and 18,905 (SD=308) and 19,306 (SD=246) for the two 175bp assays.

The mean absorbance spectroscopy estimate for the number of genome copies in our intact gDNA sample was only 2.8% greater than the combined mean of the four ddPCR assays (M=19,063, SD=150). Whereas, the mean number of genome copies estimate for fluorescence spectroscopy was 25% lower, suggesting this method also underestimated intact, not just fragmented, DNA concentration. Our results, therefore, show that absorbance spectroscopy is the most accurate method for quantifying overall nucleic acid concentration, regardless of the degree of fragmentation. However, this technique lacks sensitivity and becomes increasingly inaccurate at the lower end of its analytical range (15ng/l)24. Absorbance spectroscopy is also highly susceptible to reporting falsely high concentrations due to protein contamination and/or phenolic compounds that absorb UV. PCR-based quantification is highly sensitive and most accurately measures the amount of amplifiable DNA at the amplicon length used. Our universal multiplex assay and accompanying online tool Fragment Calculator, which we detail in the following section, extends this ability to estimate the amount of amplifiable DNA of any given region length, while also providing an estimate of overall concentration when working with human genomic or bisulfite-converted DNA.

In addition to describing the fragmentation of the sample, the dual 175 and 125bp assays, combined with representative DNA samples, can also be leveraged to estimate the concentration of any other sized DNA region. To better enable this we designed the Fragment Calculator online tool to provide a more quantitative and actionable estimate of fragmentation (www.primer-suite.com/fragcalc). This tool uses measured 175bp and 125bp concentrations and the [175bp]/[125bp] ratio to estimate the average fragment length of a genomic or bisulfite-converted human DNA sample, the total number of genome copies in a measured sample, as well as the number of amplifiable (unbroken) instances of a DNA region of any length. This tool uses the fragment size distribution data of our seven sonicated DNA samples with average fragment lengths of 254, 291, 428, 493, 590, 745, and 1274bp, a highly fragmented FFPE DNA sample with an average fragment length of 92bp to represent the lower bounds of random fragmentation, and four gDNA samples with average fragment lengths of 6714, 15,422, 34,625 and 41,496bp for the upper bounds (S1 File).

The number of intact copies of an input DNA region length is estimated by taking the two [175bp]/[125bp] ratios from our representative fragment size distribution data that an input [175bp]/[125bp] ratio falls between (x1,x2), calculating the corresponding [125bp]/[input size] ratios using Eq.(3) on these size distribution data (y1, y2), determining the slope between these points to estimate the [125bp]/[input size] ratio corresponding to the input [175bp]/[125bp] ratio, and dividing the 125bp concentration by this ratio. For example, if the concentration measured for a fragmented DNA sample is 1000 copies for the 125bp amplicon and 700 copies for the 175bp amplicon, the input [175bp]/[125bp] ratio is 0.7, which falls between the [175bp]/[125bp] ratios of the 291bp (0.669) and 428bp (0.778) reference samples. To estimate the concentration of a 50bp region, for example, the corresponding [125bp]/[50bp] ratios determined using Eq.(3) are 0.585 and 0.707, for the 291bp and 428bp reference samples, respectively. The 50bp concentration is then calculated using the following linear equation:

$${text{y}} = mx + y_{0} ,$$

(5)

$$m = frac{{y_{2} - y_{1} }}{{x_{2} - x_{1} }}$$

$$m = frac{{0.707 - 0.585{ }}}{0.778 - 0.669}$$

$$[125{text{bp}}]/[50{text{bp}}] = 1.119*0.7 - 0.164,$$

$$[50{text{bp}}] = frac{{[125{text{bp}}]}}{{[125{text{bp}}]/[50{text{bp}}]}},$$

$$[50{text{bp}}] = frac{{1000 ;{text{copies}}}}{0.619},$$

$$[50{text{bp}}] = 1615 ;{text{copies}},$$

where m is the slope and y0 is the y-intercept. The number of genome copies is also estimated using this same method by dividing the input 125bp concentration by the [125bp]/[1bp] ratio. Similarly, the average fragment length is estimated using the [175bp]/[125bp] ratios from our fragment size distribution data (x1,x2) and their corresponding average fragment lengths (y1, y2) (Fig.4).

Fragment Calculator online tool with example inputs. The concentrations measured by the two amplicon sizes of our universal quantitative PCR 4-plex assay (125bp and 175bp) can be used to estimate the total concentration (i.e., the number of genomic copies), average fragment length of the sample, and the concentration of intact copies of any input region size.

Importantly, Fragment Calculator assumes fragment distributions for the samples being estimated to be similar to those of our representative samples. However, in our experience working with these assays, we have found FFPE samples do not behave like untreated DNA samples. The [175bp]/[125bp] ratio for FFPE samples is generally much lower than the ratio calculated from the size distributions of these samples using Eq.(3). This reveals that there is generally less amplifiable DNA in FFPE samples than their size distribution profiles suggest, which we hypothesise is likely due to a combination of single-stranded breaks and incomplete reversal of DNA crosslinking. Our assays are, therefore, a better indicator of the amount of amplifiable FFPE treated DNA than fragment size distribution data from microfluidic capillary electrophoresis instruments like the Agilent 2100 Bioanalyzer.

Further complicating this, however, is evidence that even regions of the same length can have substantially different concentrations of amplifiable FFPE treated DNA. Some of our routine quality control and quantification analyses of FFPE treated samples have revealed vast differences in the number of copies measured by the two 125bp assays, and these differences are consistent among numerous FFPE samples (S2 File). Despite assays having the same length amplicons, differences in the number of amplifiable copies are likely to occur at high degrees of fragmentation, for instance, due to differences in binding efficiencies among primers when their target regions are truncated. Indeed, we regularly observe statistically significant differences in the number of copies measured by assays of the same size in highly fragmented pooled buffy coat gDNA samples subjected to ultrasonication, some examples of which are forthcoming. However, these differences are relatively small in magnitude and may be due to sequence-specific biases in sonication-induced scission25,26. We hypothesise that the much greater differences we observe in FFPE samples may emerge due to differences in the degree to which crosslinking is reversed among regions, as well as potential differences in their susceptibility to DNA breakage. These differences may reflect an underlying nucleosome footprint given that formaldehyde cross-linking is more efficient in nucleosome-bound DNA, as evidenced by the FAIRE-Seq (Formaldehyde-Assisted Isolation of Regulatory Elements) technique27.

Since PCR-based assays that target both genomic and bisulfite-converted DNA provide more accurate measures of bisulfite conversion recovery than other quantification techniques28, we next assessed the performance and utility of our universal multiplex assay to compare the recovery and degree of fragmentation of three commonly used commercial bisulfite conversion kits (MethylEasy Exceed, EZ DNA Methylation-Gold, and EZ DNA Methylation-Lightning) across three starting concentrations (500, 50 and 5ng) using high molecular weight (HMW) gDNA.

A three-way ANOVA on the qPCR results found significant effects of starting concentration (p<0.001), assay (p<0.001), and conversion kit (p<0.001) on recovery (Fig.5A). Additionally, a significant interaction was found between starting concentration and kit (p<0.001), resulting from an increase in recovery with decreasing concentration in MethylEasy Exceed but a decrease in EZ DNA Methylation-Gold and EZ DNA Methylation-Lightning. Trends were similar for the 125bp and 175bp assays, except in MethylEasy Xceed where the proportional increase in mean recovery between 50 and 5ng was greater in 125bp assays (22%, SD=12 vs. 32%, SD=5) compared to the 175bp assays (16%, SD=10 vs. 20%, SD=5; Fig.5B). As for fragmentation, a two-way ANOVA found a significant effect of conversion kit on the [175bp]/[125bp] ratio (p<0.001), no significant effect of starting concentration (p=0.251), but a significant interaction between kit and concentration (p=0.027) arising from a decrease in the [175bp]/[125bp] ratio of MethylEasy Xceed with decreasing starting concentration.

Universal assay comparisons of DNA recovery and fragmentation by bisulfite conversion kits. (A) Recovery and fragmentation across different starting concentrations as measured by universal quantitation assays in 4-plex qPCR. (B) Plots comparing the recovery and fragmentation trends from qPCR data across decreasing starting concentrations. Recovery data points are averages of the two universal assays for each amplicon length and these values were divided to determine the [175bp]/[125bp] ratios. (C) Recovery and fragmentation measured by ddPCR 4-plex. Also includes results from in-house bisulfite protocol. (A, C) Each conversion was conducted in six replicates per concentration for each kit. [175bp]/[125bp] fragmentation ratios were calculated by dividing the average copies of the two 175bp assays by the average of copies the two 125bp assays. Error bars represent one standard deviation.

Due to the low starting concentration and recovery of the 5ng samples, we did not have enough sample left for ddPCR analysis and therefore only ran the 500ng and 50ng samples. In addition to the three commercial kits, we also included our in-house bisulfite conversion protocol in these ddPCR comparisons (Fig.5C). A three-way ANOVA showed similar results to the qPCR analysis, with significant effects of starting concentration (p<0.001), assay (p<0.001), and conversion kit (p<0.001) on recovery, and a significant interaction between kit and concentration (p=0.001). Similar to qPCR, this interaction resulted from declines in the mean recovery of similar proportions between 500 and 50ng in all kits except MethylEasy Xceed, which showed a mild increase (13%, SD=4 vs. 16%, SD=11). A two-way ANOVA found a slight statistically significant difference in the [175bp]/[125bp] ratios among conversion kits (p=0.033), which a Tukey's HSD test showed resulted from a significant difference (p=0.048) between DNA Methylation-Lightning (M=0.83, SD=0.05) and MethylEasy Xceed (M=0.75, SD=0.07). Our in-house method and DNA Methylation-Gold had mean ratios of 0.77 (SD=0.04) and 0.81 (SD=0.07), respectively. To estimate the absolute nucleic acid recovery and average fragment size after bisulfite conversion we used our Fragment Calculator tool on combined qPCR and ddPCR results (Table 2).

Snyder et al. (2016)11 identified nucleosome protection peaks using deep sequencing of pooled cfDNA samples. Implicit in these analyses is the fact that nucleosome position correlated with the enrichment of fragments at specific locations, which could only occur if nucleosome positions were at least somewhat conserved among people. However, it was unclear the extent to which these peaks might shift between individuals. If little movement occurs and peaks are instead universally conserved, this would have important implications for assay design. Targeting such peaks would maximise an assays sensitivity in cfDNA while failing to consider nucleosome protection could severely reduce sensitivity.

Snyder et al. (2016)11 calculated a Windowed Protection Score (WPS) for each nucleotide position within the mappable human genome by summing the number of sequenced 120180bp cfDNA fragments that wholly overlap a centred 120bp window and subtracting the number that truncate within this window. Peaks in nucleosome-mediated protection were then called by identifying contiguous regions of elevated WPS. Using the nucleosome protection peaks determined for the pooled healthy sample CH01, we designed two cfDNA assays targeting nucleosome protection peaks that could also be used for bisulfite-converted DNA material: a 95bp assay targeting chromosome 2 (cfUQ02) with an above-average WPS of 108 and maximum distance of 62bp from the local maxima, and a 100bp assay targeting chromosome 11 (cfUQ11) with a below-average WPS of 30 and a maximum distance of 56bp. The mean WPS of the nearly 13 million peaks identified in the CH01 sample is 63.7 (SD=41.4). We also designed several staggered PCR assays of varying lengths to flank each of these regions.

15 cfDNA samples isolated from the blood plasma of breast cancer patients were profiled using dye-based ddPCR to compare the number of amplifiable copies of our universal cfDNA assays along with these staggered assays. We observed that some samples displayed substantial differences in amplifiable copies among assays whereas others did not and that this appeared to coincide with the technique used for cfDNA isolation. We measured the fragmentation profiles of these samples and found 6 displayed characteristic~166 peaks with no sign of HMW contamination, which we thus classified as true cfDNA (Fig.6A), 6 had little to no cfDNA peak and were reclassified as contaminating HMW DNA (Fig.6B), and 3 had strong cfDNA peaks but also possible or likely contamination by HMW DNA and were excluded from analysis (S7 Fig). Although high levels of HMW DNA can occur in cfDNA due to non-apoptotic cell death (e.g., necrosis), we suspect the source in these samples was instead the result of poor plasma separation and extraction. Regardless of its source, we only expect to find nucleosome-mediated patterns of fragmentation in the DNA of apoptosed cells, and HMW DNA is likely to obscure these patterns.

Effects of amplicon length and distance from nucleosome protection peak on intact copies in cfDNA. (AB) Electropherograms and pseudo-gel images from Agilent 2100 Bioanalyzer with a High Sensitivity DNA Chip (2100 Expert version B.02.10.SI764). DNA samples are from plasma of breast cancer patients, except sample labelled sDNA which is a pooled buffy coat gDNA sample sonicated and gel-purified to produce a similar fragment size distribution to cfDNA. Samples classified as true cfDNA samples (A) were isolated using our in-house phenolchloroform method (14) and QIAamp Circulating Nucleic Acid Kit with EconoSpin All-In-One Mini Spin Columns (Epoch Life Sciences) instead of columns supplied with the kit (56). Samples classified as contaminating buffy coat DNA (B) were isolated using QIAamp Circulating Nucleic Acid Kit (711) and in-house phenolchloroform method (12). (CE) Plots of ratio to mean copies (assay/sample mean) against amplicon length (C) and against distance from nucleosome protection peak (D) for assays targeting chr11 nucleosome protection peak locus, and against distance from nucleosome protection peak for assays targeting chr2 locus (E). Box and whisker plots are centred above corresponding amplicon positions for each assay, along with cell line data of nucleosome signal (K652 and GM12878) from Kundaje et al. (2012)29 and nucleosome protection peak position (blue tick) from Snyder et al. (2016)11, adjoined by characteristic 146bp nucleosomal DNA length (blue) and 10bp linker DNA (red). Sample numbers for each sample type are gDNA=15, buffy coat DNA=6, breast cancer cfDNA=6, and sonicated DNA=1 (four technical replicates). (F-G) Box and whisker plots for chr11 102bp/56bp and chr2 142bp/62bp differential distance from protection peak copy count ratios (F), as well as the ratio to mean copies across the two loci for cfDNA samples (G). Sample numbers for each sample type are gDNA=20, colon cancer cfDNA=34, brain cancer cfDNA=10, and sonicated DNA=1 (four technical replicates). All four amplicons are 100bp in length. Letters above or below box and whisker plots (DG) represent homogenous subsets determined by post hoc Tukeys HSD analyses (=0.05) of one-way ANOVAs (p values on plots). The bottom line of each box represents the 25th percentile, top line the 75th percentile, and thick middle line the median. Whiskers extend up to a maximum of 1.5 times the height of the box. Any values that fall outside this range are classified as outliers (circles). Values that are greater than 3 times the height of the box are classified as extreme outliers (asterisks).

To normalise among samples of the same category the concentration measured for each assay was divided by the mean concentration of all assays within each region (chr11 or chr2), giving a ratio to mean copies (assay/sample mean). For ddPCR on HMW gDNA, all assays specific for unique regions should measure the same number of copies within the same sample. Therefore, the ratio of copies measured for a single assay to the mean copies of all assays should be 1:1 for intact gDNA, regardless of proximity to nucleosome peaks. Consistent with this, a one-way ANOVA on samples classified as contaminating HMW DNA found no statistical difference in ratio to mean copies among assays in the chr11 (p=0.100) and chr2 (p=0.239) regions. HMW gDNA samples extracted from the blood of 15 healthy individuals were also used as negative controls and similarly showed little variation in ratio to mean copies among assays. No significant difference was found among assays in the chr2 region (p=0.084). However, a significant difference was detected in the chr11 region (p=0.004), resulting from a minor effect of amplicon length on the number of amplifiable copies (Fig.6C). A similar trend appears to exist in the contaminating HMW DNA; however, its effects likely did not reach statistical significance due to the smaller sample size (6 vs. 15).

In contrast, the ratio to mean copies for cfDNA decreased with increasing distance from the nucleosome peak, with the highest ratio for each region being our universal cfDNA assays (cfUQ11 and cfUQ2). However, given that cfDNA is highly fragmented, differential amplicons sizes are likely to result in differences in the number of amplifiable copies, therefore confounding the effects of nucleosome protection. To control for this we used ultrasonication and gel purification to produce a blood pooled gDNA sample with a similar level of fragmentation as cfDNA, which we measured in four technical replicates for each assay to compare the effects of random fragmentation on the number of amplifiable copies. In the chr11 region, which had the greatest variance in amplicon size among assays, similar ratios were observed in the sonicated DNA and cfDNA for each assay tested (Fig.6D). A two-way ANOVA comparing these two sample types found a significant difference among assays (p<0.001) but no statistically significant interaction between sample type and assay, signifying that only the cfDNA level of fragmentation, and not nucleosome protection, was affecting the number of amplifiable copies (p=0.637). These results show that even small differences in amplicon length can have a significant impact on the number of amplifiable copies at such high levels of fragmentation but proximity to the nucleosome protection peak is likely providing little to no differential protection within this region.

Conversely, the assays targeting the chr2 region were far less variable in length and showed little difference in ratio to mean copies in the sonicated DNA, especially when compared to the cfDNA. A one-way ANOVA on the sonicated samples within this region did find significant differences in concentration ratios among assays (p=0.001); however, the magnitudes of these differences were small, they did not track with differences in amplicon length, and they appear to result from a positional effect, perhaps resulting from a sequence-specific bias in fragmentation within this region. Unlike the chr11 assays, the ratio to mean copies for the chr2 assays tracked the distance from the nucleosome peak in cfDNA, rather than the amplicon length. A two-way ANOVA comparing the sonicated and cfDNA samples found a significant difference among assays (p<0.001) as well as a significant interaction between assay and sample (p<0.001), which supports cfDNA having an effect on the number of amplifiable copies in this region beyond that caused by its level of fragmentation on differently sized amplicons (Fig.6E). Notably, a one-way ANOVA on the cfDNA samples showed no significant difference (p=0.495) in ratio to mean copies (M=1.00, SD=0.10 vs. M=0.97, SD=0.13) for the two assays with the most similar maximum distances from the nucleosome peak (92 and 99bp) and only 1bp difference in length (100 vs. 99bp.). Whereas, the 50bp distance (92 vs. 142bp) separating the two 100bp amplicons resulted in a significant decrease (M=1.00, SD=0.10 vs. M=0.78, SD=0.07; p<0.001), and the 95bp universal cfDNA assay with the smallest maximum distance from the nucleosome peak (62bp) had a significantly higher ratio (M=1.25, SD=0.06) than each of the other three assays (p<0.001; Tukeys HSD). Despite HMW contamination, the three samples with substantial cfDNA size peaks excluded from this analysis also revealed differences in copies among assays that match a nucleosome-mediated fragmentation pattern in the chr2 region (S3 File).

To further explore and confirm these results we designed probes for one flanking assay per region (in addition to the probes already designed for the cfUQ11 and cfUQ02 universal cfDNA assays), selecting those with the greatest difference between the sonicated and cfDNA samples. Where necessary, the forward or reverse primer for each assay was redesigned to normalise all amplicons to 100bp while maintaining the same maximum distance from the nucleosome peak. We ran these assays in duplex ddPCR on cfDNA samples extracted from the blood plasma of 34 patients with colorectal cancer and 10 patients with brain cancer, as well as gDNA samples from the blood of 20 healthy donors and four technical replicates of the sonicated gDNA. We then calculated the ratio of copies for the assay furthest to the assay closest to the nucleosome peak (chr11=[102bp]/[56bp] and chr2=[142bp]/[62bp]) and compared the four sample types for each region. For the chr11 region, a one-way ANOVA found no significant difference between the ratios of the colorectal (M=0.95, SD=0.11) or brain cancer (M=0.98, SD=0.11) cfDNA, gDNA (M=1.00, SD=0.06), or sonicated DNA (M=1.07, SD=0.03) samples (p=0.081). Although not significant, these differences tended towards a slight nucleosome-mediated protective effect (Fig.6F).

Conversely, a one-way ANOVA found a significant difference (p<0.001) among sample types for the chr2 region. A post hoc Tukey HSD test showed this difference was due to a drop in the [142bp]/[62bp] ratio in cfDNA, with gDNA (M=1.00, SD=0.09) and sonicated DNA (M=1.00, SD=0.03) being placed in one homogenous subset, and colorectal (M=0.67, SD=0.10) and brain cancer (M=0.62, SD=0.12) cfDNA placed in another (p<0.001). These results strongly reinforce our previous findings, showing that, unlike the chr11 nucleosome peak, the chr2 peak provides substantial and consistent protection from fragmentation among individuals. Furthermore, comparison across these two regions revealed that the stronger chr2 protection peak resulted not only in greater protection than the weaker chr11 peak but greater degradation in the adjacent valley (Fig.6G). A two-way ANOVA found significant differences (p<0.001) in the ratio to mean copies between the four assays, and no significant interaction (p=0.189) between the colorectal and brain cancer samples, indicating that the differences between assays were similar for these two cohorts. A Tukey HSD test showed significant differences between all four assays, with the chr2:142bp (M=0.81, SD=0.09), chr11:102bp (M=0.95, SD=0.06), chr11:56bp (M=1.00, SD=0.08), and chr2:62bp (M=1.24, SD=0.10) assays each being placed into separate homogenous subsets (=0.025). These results are consistent with cfDNA protection peaks being the result of nucleosome occupancy. As predicted, the protection peak with a low WPS provided weaker but more even protection within its occupied region and the peak with a high WPS provided greater but more narrow protection, thus validating the WPS metric that Snyder et al. (2016)11 applied in their analyses.

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Modelling clinical DNA fragmentation in the development of universal PCR-based assays for bisulfite-converted, formalin-fixed and cell-free DNA sample...

Scientists have developed a new technique to reveal the hidden human genome.

Numerous short RNA sequences that code for microproteins and peptides have been identified, providing new opportunities for the study of diseases and the development of drugs.

Researchers from Duke-NUS Medical School and their collaborators have discovered thousands of previously unknown DNA sequences in the human genome that code for microproteins and peptides that could be critical for human health and disease.

Much of what we understand about the known two per cent of the genome that codes for proteins comes from looking for long strands of protein-coding nucleotide sequences, or long open reading frames, explained computational biologist Dr Sonia Chothani, a research fellow with Duke-NUS Cardiovascular and Metabolic Disorders (CVMD) Programme and first author of the study. Recently, however, scientists have discovered small open reading frames (smORFs) that can also be translated from RNA into small peptides, which have roles in DNA repair, muscle formation and genetic regulation.

Scientists have been seeking to identify smORFs and the tiny peptides they code for since smORF disruption can cause disease. However, the currently available techniques are quite limited.

Much of the current datasets do not provide information that is detailed enough to identify smORFs in RNA, added Dr Chothani. The majority also comes from analyses of immortalised human cells that are propagatedsometimes for decadesto study cell physiology, function and disease. However, these cell lines arent always accurate representations of human physiology.

Chothani and her colleagues from Singapore, Germany, the United Kingdom, and Australia present an approach they created to address these challenges in a recentstudy published in Molecular Cell. They scoured existing ribosome profiling datasets for short strands of RNA with periodic three-base sections that covered more than 60% of the RNAs length. They then performed their own RNA sequencing and Ribosome profiling to establish a combined data set of six kinds of cells and five types of tissue derived from hundreds of patients.

Analyses of these data identified nearly 8,000 smORFs. Interestingly, they were highly specific to the tissues that they were found in, meaning that these smORFs may perform a function specific to their environment. The team also identified 603 microproteins coded by some of these smORFs.

The genome is littered with smORFs, said Assistant Professor Owen Rackham, senior author of the study from the CVMD Programme. Our comprehensive and spatially resolved map of human smORFs highlights overlooked functional components of the genome, pinpoints new players in health and disease and provides a resource for the scientific community as a platform to accelerate discoveries.

Professor Patrick Casey, Senior Vice-Dean of Research at Duke-NUS, said, With the healthcare system evolving to not only treat diseases but also prevent them, identifying potential new targets for disease research and drug development could open avenues to new solutions. This research by Dr Chothani and her team, published as a resource for the scientific community, brings important insights to the field.

Reference: A high-resolution map of human RNA translation by Sonia P. Chothani, Eleonora Adami, Anissa A. Widjaja, Sarah R. Langley, Sivakumar Viswanathan, Chee Jian Pua, Nevin Tham Zhihao, Nathan Harmston, Giuseppe DAgostino, Nicola Whiffin, Wang Mao, John F. Ouyang, Wei Wen Lim, Shiqi Lim, Cheryl Q.E. Lee, Alexandra Grubman, Joseph Chen, J.P. Kovalik, Karl Tryggvason, Jose M. Polo, Lena Ho, Stuart A. Cook, Owen J.L. Rackham and Sebastian Schafer, 15 July 2022, Molecular Cell.DOI: 10.1016/j.molcel.2022.06.023

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Revealing the Hidden Genome: Unknown DNA Sequences Identified That May Be Critical to Human Health - SciTechDaily

Mental illness is a growing public health problem. In 2019, an estimated 1 in 8 people around the world were affected by mental disorders like depression, schizophrenia or bipolar disorder. While scientists have long known that many of these disorders run in families, their genetic basis isnt entirely clear. One reason why is that the majority of existing genetic data used in research is overwhelmingly from white people.

In 2003, the Human Genome Project generated the first reference genome of human DNA from a combination of samples donated by upstate New Yorkers, all of whom were of European ancestry. Researchers across many biomedical fields still use this reference genome in their work. But it doesnt provide a complete picture of human genetics. Someone with a different genetic ancestry will have a number of variations in their DNA that arent captured by the reference sequence.

When most of the worlds ancestries are not represented in genomic data sets, studies wont be able to provide a true representation of how diseases manifest across all of humanity. Despite this, ancestral diversity in genetic analyses hasnt improved in the two decades since the Human Genome Project announced its first results. As of June 2021, over 80% of genetic studies have been conducted on people of European descent. Less than 2% have included people of African descent, even though these individuals have the most genetic variation of all human populations.

To uncover the genetic factors driving mental illness, I, Sinad Chapman and our colleagues at the Broad Institute of MIT and Harvard have partnered with collaborators around the world to launch Stanley Global, an initiative that seeks to collect a more diverse range of genetic samples from beyond the U.S. and Northern Europe, and train the next generation of researchers around the world. Not only does the genetic data lack diversity, but so do the tools and techniques scientists use to sequence and analyze human genomes. So we are implementing a new sequencing technology that addresses the inadequacies of previous approaches that dont account for the genetic diversity of global populations.

To study the genetics of psychiatric conditions, researchers use data from genome-wide association studies that compare the genetic variations between people with and without a particular disease. However, these data sets are mostly based on people of European ancestry, largely because research infrastructure and funding for large-scale genetics studies, and the scientists conducting these studies, have historically been concentrated in Europe and the United States.

One way to close this gap is to sequence genetic data from diverse populations. My colleagues and I are working in close partnership with geneticists, statisticians and epidemiologists in 14 countries across four continents to study the DNA of tens of thousands of people of African, Asian and Latino ancestries who are affected by mental illness. We work together to recruit participants and collect DNA samples that are sequenced at the Broad Institute in Massachusetts and shared with all partners for analysis.

Prioritizing the voices and priorities of local communities and scientists is foundational to our work. All partners have joint ownership of the project, including decision-making and sample and data ownership and control. To do this, we build relationships and trust with the local communities we are studying and the local university leaders and scientists with whom we are partnering. We work to understand local cultures and practices, and adapt our collection methods to ensure study participants are comfortable. For example, because there are different cultural sensitivities around providing saliva and blood samples, we have adapted our practices by location to ensure study participants are comfortable.

We also freely share knowledge and materials with our partners. There is a two-way exchange of information between the Broad Institute and local teams on study progress and results, enabling continual learning, teaching and unity between teams. We strive to meet each other where we are by exchanging practices and training scientists to support the development of locally grown and locally led research programs.

Our collaboration with African research groups provides a prime example of our model. For example, our African research colleagues are co-leaders on the grants that fund the lab equipment, scientists and other staff for projects based at their study sites. And we help to support the next generation of African geneticists and bioinformaticians through a dedicated training program.

Collecting samples from more diverse populations is only half of the challenge.

Existing genomic sequencing and analysis technologies do not adequately capture genetic variation across populations from around the world. Thats because these technologies were designed to detect genetic variations based on reference DNA from people of European ancestry, and they reduce accuracy when analyzing sequences that arent derived from the reference genome. When these tools are applied to genetic data from other populations, they fail to detect much of the rich variation in their genomes. This can lead researchers to miss out on important biomedical discoveries.

To address this issue, we developed an approach to genome sequencing that can detect more genetic variation from populations around the world. It works by sequencing the exome the less than 2% of the genome that codes for proteins in high detail, as well as sequencing the 98% of the genome that does not code for proteins in less detail.

This combined approach reduces the trade-offs geneticists often have to make in sequencing projects. High-depth whole genome sequencing, which reads through the entire genome multiple times to get detailed data, is too costly to do on a large number of DNA samples. While low-coverage sequencing reduces costs by reading smaller segments of the genome, it may miss some important genetic variation. With our new technology, geneticists can get the best of both worlds: sequencing the exome in depth maximizes the likelihood of pinpointing specific genes that play a role in mental illness, while sequencing the whole genome less in depth allows researchers to process large numbers of whole genomes more cost-effectively.

Our hope is that this new technology will allow researchers to sequence large sample sizes from a diverse range of ancestries to capture the full breadth of genetic variation. With a better understanding of the genetics of mental illness, clinicians and researchers will be better equipped to develop new treatments that work for everyone.

Genomic sequencing opened a new era of personalized medicine, which promises to deliver treatments tailored to each individual person. This can be done only if the genetic variations of all ancestries are represented in the data sets that researchers use to make new discoveries about disease and develop treatments.

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Uncovering the genetic basis of mental illness requires data and tools that aren't just based on white people this international team is collecting...

A Newfound Link Between Cancer and Aging?

A new study in 2022 reveals a thought-provoking relationship between how long animals live and how quickly their genetic codes mutate.

Cancer is a product of time and mutations, and so researchers investigated its onset and impact within 16 unique mammals. A new perspective on DNA mutation broadens our understanding of aging and cancer developmentand how we might be able to control it.

Cancer is the uncontrolled growth of cells. It is not a pathogen that infects the body, but a normal body process gone wrong.

Cells divide and multiply in our bodies all the time. Sometimes, during DNA replication, tiny mistakes (called mutations) appear randomly within the genetic code. Our bodies have mechanisms to correct these errors, and for much of our youth we remain strong and healthy as a result of these corrective measures.

However, these protections weaken as we age. Developing cancer becomes more likely as mutations slip past our defenses and continue to multiply. The longer we live, the more mutations we carry, and the likelihood of them manifesting into cancer increases.

Since mutations can occur randomly, biologists expect larger lifeforms (those with more cells) to have greater chances of developing cancer than smaller lifeforms.

Strangely, no association exists.

It is one of biologys biggest mysteries as to why massive creatures like whales or elephants rarely seem to experience cancer. This is called Petos Paradox. Even stranger: some smaller creatures, like the naked mole rat, are completely resistant to cancer.

This phenomenon motivates researchers to look into the genetics of naked mole rats and whales. And while weve discovered that special genetic bonuses (like extra tumor-suppressing genes) benefit these creatures, a pattern for cancer rates across all other species is still poorly understood.

Researchers at the Wellcome Sanger Institute report the first study to look at how mutation rates compare with animal lifespans.

Mutation rates are simply the speed at which species beget mutations. Mammals with shorter lifespans have average mutation rates that are very fast. A mouse undergoes nearly 800 mutations in each of its four short years on Earth. Mammals with longer lifespans have average mutation rates that are much slower. In humans (average lifespan of roughly 84 years), it comes to fewer than 50 mutations per year.

The study also compares the number of mutations at time of death with other traits, like body mass and lifespan. For example, a giraffe has roughly 40,000 times more cells than a mouse. Or a human lives 90 times longer than a mouse. What surprised researchers was that the number of mutations at time of death differed only by a factor of three.

Such small differentiation suggests there may be a total number of mutations a species can collect before it dies. Since the mammals reached this number at different speeds, finding ways to control the rate of mutations may help stall cancer development, set back aging, and prolong life.

The findings in this study ignite new questions for understanding cancer.

Confirming that mutation rate and lifespan are strongly correlated needs comparison to lifeforms beyond mammals, like fishes, birds, and even plants.

It will also be necessary to understand what factors control mutation rates. The answer to this likely lies within the complexities of DNA. Geneticists and oncologists are continuing to investigate genetic curiosities like tumor-suppressing genes and how they might impact mutation rates.

Aging is likely to be a confluence of many issues, like epigenetic changes or telomere shortening, but if mutations are involved then there may be hopes of slowing genetic damageor even reversing it.

While just a first step, linking mutation rates to lifespan is a reframing of our understanding of cancer development, and it may open doors to new strategies and therapies for treating cancer or taming the number of health-related concerns that come with aging.

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Explainer: The Basics of DNA and Genetic Systems - Visual Capitalist

DetailsCategory: DNA RNA and CellsPublished on Saturday, 17 September 2022 10:29Hits: 269

CAMBRIDGE, MA, USA I September 16, 2022 I Intellia Therapeutics, Inc. (NASDAQ:NTLA), a leading clinical-stage genome editing company focused on developing potentially curative therapeutics leveraging CRISPR-based technologies, today announced positive interim results from an ongoing Phase 1/2 clinical study of NTLA-2002, its second in vivo genome editing candidate. NTLA-2002 is a systemically administered CRISPR candidate being developed for hereditary angioedema (HAE) and is designed to knock out the KLKB1 gene in liver cells, thereby reducing the production of kallikrein protein. Uncontrolled activity of kallikrein is responsible for the overproduction of bradykinin, which leads to the recurring, debilitating and potentially fatal swelling attacks that occur in people living with HAE. The interim data were shared today in an oral presentation at the 2022 Bradykinin Symposium held in Berlin, Germany.

The data presented are from the initial six adult patients with HAE in the ongoing dose-escalation study with a data cut-off date of July 27, 2022. Single doses of 25 mg (n=3) and 75 mg (n=3) of NTLA-2002 were administered via intravenous infusion, and changes from baseline values of plasma kallikrein protein were measured for each patient. Administration of NTLA-2002 led to dose-dependent reductions in plasma kallikrein and achieved maximal reductions by week eight, with mean reductions of 65% and 92% in the 25 mg and 75 mg dose cohorts, respectively. Furthermore, these reductions were sustained through at least 16 weeks in the 25 mg cohort and eight weeks in the 75 mg cohort for which complete cohort biomarker data were available.

In addition to plasma kallikrein levels, HAE attack rates are also being measured in the study, with the first analysis occurring at the end of the pre-specified 16-week primary observation period. To date, all three patients in the 25 mg dose cohort have reached the end of this initial observation period. Patients in this group had a baseline HAE attack rate ranging from 1.1 to 7.2 attacks per month, as confirmed by the investigator. Treatment with a single dose of 25 mg of NTLA-2002 resulted in a mean reduction in HAE attacks of 91% throughout the 16-week observation period. Additionally, two of the three patients have not had a single HAE attack since treatment, and all three patients have been attack free since week 10 (follow-up through weeks 24 - 32). Patients in the 75 mg cohort have not completed the primary 16-week observation period. Attack-rate data for this cohort will be presented at the American College of Allergy, Asthma & Immunology (ACAAI) Annual Scientific Meeting, November 10 14 in Louisville, Kentucky.

Prophylaxis medications are permitted in the Phase 1 part of the study. Two of the three patients in the 25 mg cohort were actively receiving prophylaxis therapy prior to administration of NTLA-2002. For these two patients, the study protocol permitted investigators to withdraw the patients prophylaxis therapy after completion of the 16-week primary observation period. This treatment approach was implemented for the two applicable patients in this cohort, and neither patient has had an HAE attack since discontinuing their prophylaxis therapy through the latest follow-up.

These initial data represent a significant milestone for both Intellia and people around the world suffering from genetic diseases, such as HAE, said Intellia President and Chief Executive Officer John Leonard, M.D. We are strongly encouraged by the greater than 90% reduction in HAE attacks observed in the 25 mg dose cohort, as these interim results support our belief that a single dose of NTLA-2002 has the potential to permanently prevent the debilitating swelling attacks associated with HAE. Additionally, todays announcement continues to validate our genome editing approach and the modular platform we have built. This is now the second time in history clinical data have been generated suggesting we can precisely edit target cells within the human body to potentially treat genetic diseases with a single, systemic administration of a CRISPR-based therapy. We plan to move as quickly and judiciously as possible on behalf of people living with HAE and a number of additional genetic diseases in the months and years ahead.

At both dose levels, NTLA-2002 was generally well-tolerated, and the majority of adverse events were mild in severity. The most frequent adverse events were infusion-related reactions, which were mostly Grade 1 and resolved within one day. There have been no dose-limiting toxicities, no serious adverse events and no adverse events of Grade 3 or higher observed to date. No clinically significant laboratory abnormalities were observed, including any significant elevation in liver enzymes.

Many people living with HAE continue to experience breakthrough attacks despite currently available treatments and often find the burden of untreated attacks, frequent infusions or injections to be tremendously disruptive to their lives, said Hilary Longhurst, M.D., Ph.D., Faculty of Medical and Health Sciences, University of Auckland, New Zealand, and the trials principal investigator in New Zealand. These early data support NTLA-2002 as a potential one-time treatment capable of producing profound reductions in HAE attacks. While the clinical data are still emerging, I am highly optimistic that NTLA-2002 could become a new treatment option for the HAE community.

Based on the interim data presented today, Intellia selected a third dose of 50 mg to be evaluated in the ongoing dose-escalation portion of the Phase 1/2 study. Dosing at this level has recently completed, and Intellia expects to select up to two doses to further evaluate in the Phase 2, placebo-controlled, dose-expansion portion of the study, which is expected to begin in the first half of 2023. Intellia anticipates expanding country and site participation, including U.S. clinical sites, as part of the Phase 2 study.

Intellia Therapeutics Investor Event and Webcast Information Intellia will host a live webcast today, Friday, September 16, 2022, at 8:00 a.m. ET, to provide a clinical update from its in vivo portfolio, during which the company will review the presented clinical data at the 2022 Bradykinin Symposium alongside interim results from NTLA-2001. To join the webcast, please visit this link, or the Events and Presentations page of the Investors & Media section of the companys website at http://www.intelliatx.com. A replay of the webcast will be available on Intellias website for at least 30 days following the call.

About the NTLA-2002 Clinical ProgramIntellias multi-national Phase 1/2 study is evaluating the safety, tolerability, pharmacokinetics and pharmacodynamics of NTLA-2002 in adults with Type I or Type II hereditary angioedema (HAE). This includes the measurement of plasma kallikrein protein levels and activity as determined by HAE attack rate measures. The Phase 1 portion of the study is an open-label, single-ascending dose design used to identify up to two dose levels of NTLA-2002 that will be further evaluated in the randomized, placebo-controlled Phase 2 portion of the study. This Phase 1/2 study will identify the dose of NTLA-2002 for use in future studies.Visit clinicaltrials.gov (NCT05120830) for more details.

About NTLA-2002

Based on Nobel Prize-winning CRISPR/Cas9 technology, NTLA-2002 is the first single-dose investigational treatment being explored in clinical trials for the potential to continuously reduce kallikrein activity and prevent attacks in people living with hereditary angioedema (HAE). NTLA-2002 is a wholly owned investigational CRISPR therapeutic candidate designed to inactivate thekallikrein B1 (KLKB1) gene, which encodes for prekallikrein, the kallikrein precursor protein. NTLA-2002 is Intelliassecond investigational CRISPR therapeutic candidate to be administered systemically, by intravenous infusion, to edit disease-causing genes inside the human body with a single dose of treatment. Intellias proprietary non-viral platform deploys lipid nanoparticles to deliver to the liver a two-partgenome editingsystem: guide RNAspecific to the disease-causing gene and messenger RNAthat encodes the Cas9 enzyme, which together carry out the precision editing.

About Hereditary Angioedema

Hereditary angioedema (HAE) is a rare, genetic disorder characterized by severe, recurring and unpredictable inflammatory attacks in various organs and tissues of the body, which can be painful, debilitating and life-threatening. It is estimated that one in 50,000 people are affected by HAE, and current treatment options often include life-long therapies, which may require chronic intravenous (IV) or subcutaneous (SC) administration as often as twice per week, or daily oral administration to ensure constant pathway suppression for disease control. Despite chronic administration, breakthrough attacks still occur. Kallikrein inhibition is a clinically validated strategy for the preventive treatment of HAE attacks.

About Intellia TherapeuticsIntellia Therapeutics, a leading clinical-stage genome editing company, is developing novel, potentially curative therapeutics leveraging CRISPR-based technologies. To fully realize the transformative potential of CRISPR-based technologies, Intellia is pursuing two primary approaches. The companys in vivo programs use intravenously administered CRISPR as the therapy, in which proprietary delivery technology enables highly precise editing of disease-causing genes directly within specific target tissues. Intellias ex vivo programs use CRISPR to create the therapy by using engineered human cells to treat cancer and autoimmune diseases. Intellias deep scientific, technical and clinical development experience, along with its robust intellectual property portfolio, have enabled the company to take a leadership role in harnessing the full potential of genome editing to create new classes of genetic medicine. Learn more at intelliatx.com. Follow us on Twitter@intelliatx.

SOURCE: Intellia Therapeutics

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Intellia Therapeutics Announces Positive Interim Clinical Data for its Second Systemically Delivered Investigational CRISPR Candidate, NTLA-2002 for...

WALTHAM, Mass.--(BUSINESS WIRE)--Pretzel Therapeutics, a biotechnology company harnessing the intricacies of mitochondrial biology to develop groundbreaking therapies, launched today with a $72.5 million Series A financing to pioneer novel therapies to modulate mitochondrial function. The financing was led by ARCH Venture Partners and Mubadala Capital with participating investors HealthCap, Cambridge Innovation Capital, Cambridge Enterprise, Angelini Ventures, GV, Invus, Eir Ventures, GU Ventures, and Karolinska Institutet Holding.

We are excited to pioneer a new era in the treatment of diseases related to mitochondrial dysfunction. The expertise we have assembled and the platform technologies we have created will allow new inroads into treating both rare genetic diseases as well as common diseases of aging, said Jay Parrish, Ph.D., Chairman of the Board and Chief Executive Officer of Pretzel. Were proud to be backed by an outstanding investor syndicate, with a Series A financing that will allow us to prosecute preclinical development across our pipeline and continue to build out our talented team.

Pretzel is advancing a first-of-its-kind platform to modulate mitochondrial biology, with a vast range of potential applications across rare and common disorders, said Alaa Halawa, MBA, Partner and Head of the U.S. Ventures business at Mubadala Capital. As investors focused on partnering early with companies that will positively impact patients lives, were proud to co-lead the companys Series A financing and to partner with Pretzel on their journey to build the worlds leading center of excellence addressing diseases of mitochondrial dysfunction.

Dysfunctional mitochondria are involved in more than 50 diseases. The most severe of these are broadly termed mitochondrial diseases, a group of rare genetic conditions which affect individuals of all ages. Mitochondrial dysfunction also plays an important role in more common diseases, including aging-related disorders such as Alzheimers and Parkinsons diseases. In addition, modulating mitochondrial biology presents a potential approach to the treatment of diseases not directly caused by mitochondrial dysfunction, for instance cancer and metabolic diseases.

Pretzels platform encompasses three primary technologies to modulate mitochondrial function: Genome correction, genome expression modulation, and mitochondrial quality control. The companys genome correction therapeutics will utilize specialized gene-editing tools to reduce mutated mitochondrial DNA and increase the levels of healthy mitochondrial DNA. Genome expression modulation will be accomplished using small molecules that act on the enzymes involved in mitochondrial DNA replication, transcription, and translation. Finally, mitochondrial quality control will be targeted using small molecules that modulate mitochondrias built-in quality control system.

Founders and Team

Pretzels founders include three leading academics in the field of mitochondrial biology. Claes Gustafsson, M.D., Ph.D., is professor of medical biochemistry at the University of Gothenburg and an expert in mitochondrial gene expression. Michal Minczuk, Ph.D., is a Group Leader and MRC Investigator at the MRC Mitochondrial Biology Unit, University of Cambridge and an expert in mitochondrial genome engineering. Nils-Gran Larsson, M.D., Ph.D., is professor of mitochondrial genetics at the Department of Medical Biochemistry and Biophysics at Karolinska Institutet who has published over 150 articles on mitochondrial biology.

In addition to Drs. Gustafsson, Minczuk, and Larsson, founders Gabriel Martinez, Ph.D. and Paul Thurk, Ph.D., played a formative role in the companys creation based on their deep biotechnology industry expertise. Finally, the company recognizes the contributions of Gunther Kern, Ph.D., MBA; Jeremy Green, Ph.D.; and Christina Trojel-Hansen, Ph.D., to the formation of Pretzel.

Mitochondria have historically been a challenging cellular organelle to target therapeutically, in part because mitochondrial diseases are extremely diverse, both genetically and phenotypically, but also due to the distinctive characteristics of mitochondrial genome function. However, scientific understanding of mitochondrial biology has greatly advanced in recent years, allowing new insights into their role in many prevalent diseases, as well as how they can be therapeutically targeted, said Claes Gustafsson. Its gratifying to form Pretzel to translate these insights into therapies that could meaningfully improve peoples lives.

Pretzel is led by accomplished experts in drug discovery, drug development, and company foundation, and is advised by a Board of Directors and Scientific Advisory Board with deep scientific and industry expertise.

Pretzels leadership team is comprised of:

The companys Board of Directors is comprised of:

The companys Scientific Advisory Board is comprised of:

About Pretzel Therapeutics

Pretzel Therapeutics is a biotechnology company harnessing the intricacies of mitochondrial biology to develop groundbreaking therapies. Pretzel was founded by leading academic experts in mitochondrial biology and is backed by a world-class investor syndicate. The company is headquartered in Waltham, MA and has research facilities in Gothenburg, Sweden. For more information, visit http://www.pretzeltx.com.

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Pretzel Therapeutics Launches With $72.5 Million Series A Financing to Pioneer Mitochondrial Therapies - Business Wire

The actinomycete DEM30355 was isolated from a soil sample, collected from the El Tatio geyser field within an arid part of the Atacama Desert in Chile17. Strain DEM30355 was recovered in the genus Amycolatopsis, based on 16S rRNA analysis, forming a subgroup with Amycolatopsis vancoresmycina DSM 44592T and Amycolatopsis bullii SF27T (see ESI). The genus Amycolatopsis contains 94 species and four subspecies encompassing both extremophiles and producers of bioactive secondary metabolites, including the clinically used vancomycin and rifamycin antibiotics19,20. Preliminary bioactivity screening showed that extracts of Amycolatopsis sp. DEM30355 displayed promising antibiotic activity against B. subtilis, thus we decided to examine the genome of Amycolatopsis sp. DEM30355 for novel biosynthetic potential. Purified genomic DNA from Amycolatopsis sp. DEM30355 was analysed using both PacBio and Illumina sequencing technologies and genome assembly was performed using the combined datasets to give a 9.6Mb draft genome, in 13 contigs. The draft genome of Amycolatopsis sp. DEM30355 was examined using the secondary metabolite analysis software AntiSMASH 6.0.121. Of the 31 biosynthetic gene clusters (BGCs) detected, a PKS cluster was identified showing moderate overall similarity (81%) to that which encodes for rishirilides A and B22,23,24,25,26. These compounds are anthracenone polyketides, originally isolated from Streptomyces rishiriensis OFR-1056, with no reported antibiotic activity. Rishirilide B has been shown to be a moderately potent inhibitor of 2-macroglobulin, glutathione S-transferase and asparaginyl-tRNA synthetase, whilst little is known about the biological role of rishirilide A (Fig.1)18,27,28.

Top ()-Rishirilide A (1) and (+)-rishirilide B (2). Relative stereochemistry of ()-1 and absolute stereochemistry of (+)-2 shown. Bottom. Structure of ()-tatiomicin (3) as derived from NMR and SCXRD experiments. Key COSY (red) and HMBC (blue) correlations shown. Absolute stereochemistry as shown by both vibrational circular dichroism (VCD) and single-crystal X-ray diffraction (SCXRD) resonant scattering experiments. Structural differences of rishirilide A and tatiomicin are highlighted (magenta).

Further inspection of the BGC from Amycolatopsis sp. DEM30355 revealed a highly altered gene synteny (see ESI), compared to the rishirilide BGC, along with the presence of several new genes: one postulated to be involved in PKS biosynthesis (tatS1), two encoding methyltransferases (tatM1 and tatM2), two encoding cyclases (tatC4 and tatC5) and one cytochrome p450 oxidoreductase (tatO11) (Fig.2). Due to the significant variation in the genetic make-up of the BGC, we postulated that it may code for the production of an as yet undiscovered polyketide and as such we set about attempting to identify this molecule from the metabolome of Amycolatopsis sp. DEM30355.

Organization of the tatiomicin BGC. Genes coding for polyketide biosynthesis (red; tatS=starter unit biosynthesis, tatK=chain biosynthesis), polyketide modification (blue; tatO=oxidoreductases, tatC=cyclases, tatM=methyltransferases), regulation (yellow; tatR), transport (green, tatT) and others (grey; tatP=phosphorylase, black; genes not assigned to the tatiomicin BGC based on homology to the rishirilide BGC and proposed biosynthetic pathway)).

Preliminary analysis of the fermentation supernatant of Amycolatopsis sp. DEM30355 by HPLC-HRMS showed the presence of a large number of secondary metabolites, in keeping with the predicted number of BGCs, including a compound with activity against Gram-positive bacteria (MW of 402Da, m/z=403 [M+H]+, m/z=425 [M+Na]+, ()-tatiomicin (3)). Fermentation of Amycolatopsis sp. DEM30355, removal of the biomass, extraction of the supernatant and bioactivity guided fractionation by multiple chromatography steps resulted in a fraction which retained antimicrobial activity and contained two closely related compounds. HRMS analysis suggested that these compounds were stereoisomers of each other, the major compound showing m/z=425.1221 [M+Na]+ corresponding to a molecular formula of C21H22O8 for both molecules (see ESI).

Structural determination of the major component was initially performed by NMR, which provided the majority of molecular connectivity with the exception of the ordering of the three contiguous quaternary centres at the C-3, C-4 and C-4a positions. Structural assignment was completed via single-crystal X-ray diffraction (XRD) analysis, revealing a highly oxygenated anthracenone polyketide, structurally consistent with the BGC of interest, which we named ()-tatiomicin (3) (Fig.1)29.

NMR and HPLC experiments demonstrated that the minor compound was the C-2 epimer, capable of equilibrating with ()-(3) under acidic conditions (see ESI).

Determination of the absolute stereochemistry of ()-3 was undertaken in parallel via vibrational and electronic circular dichroism spectroscopies and additional single-crystal X-ray diffraction (SCXRD) experiments.

Absolute configuration determination by vibrational circular dichroism (VCD) was based on a comparison of experimental and computationally predicted spectra, taking into account the presence of two epimers of ()-3. Conformational analysis (see ESI), removal of redundant geometries and final optimization at the B3LYP/6311++G(d,p) level allowed Boltzmann-weighted VCD spectra for both epimers of ()-3 to be constructed. The final predicted spectrum was obtained by applying a 3:1 ratio to account for the experimentally analysed mixture of epimers. Numerical analysis was used to establish agreement between experiment and theory, the neighbourhood similarity values (IR=92.0, VCD=71.2, ESI=57.8) suggesting an absolute stereochemical assignment of (2S,3S,4R,4aR,10R) (Fig.3 and ESI)30. The assignment was supported through similar electronic circular dichroism (ECD) experiments; however, in this case correlation between experiment and prediction was weaker (see ESI).

Experimental IR (top) and VCD spectra (bottom) of ()-tatiomicin 3 (CDCl3) with predicted spectra obtained at the B3LYP/PCM/6311++G(d,p) level of theory. VCD: Solid line=(2R,3R,4S,4aS,10S), dashed line=(2S,3S,4R,4aR,10R). Spectra have been frequency scaled Black line (=0.987) to yield maximal similarity grey line between the computed and experimental VCD spectra.

A suitable, albeit small, single-crystal of tatiomicin (3) was grown via slow evaporation from a benzene solution. Due to the crystals dimensions, diffraction data were collected at beam line I19 at the Diamond Light Source using synchrotron radiation at standard operating wavelength (=0.6889), providing a data set of sufficient quality to allow for structural confirmation. ()-Tatiomicin (3) crystallized as an H-bonded dimer in the unit cell (Z=2) along with a single molecule of solvent (benzene). To validate the absolute stereochemical assignment a further single-crystal X-ray diffraction experiment was undertaken at I19, employing non-typical, longer wavelength synchrotron radiation (=1.4879) to enhance resonant scattering contributions (also known inappropriately as anomalous dispersion). The absolute-structure (Flack) parameter (0.05(6)) was insignificantly different from zero and with a small standard uncertainty, indicating the correct absolute configuration in the refined (2S,3S,4R,4aR,10R) structure (see ESI)29. Interestingly, following extensive stereochemical debate and several reported total syntheses, the absolute stereochemistry of the congeneric (+)-rishirilide B (2) was recently revised (2S,3S,4S), matching that of ()-(3) over the three common stereocentres, suggesting a similar biosynthetic pathway for both sets of natural products (Fig.4)31,32,33,34,35.

Displacement ellipsoid plot of the molecular structure of ()-tatiomicin (3), absolute stereochemistry as shown determined by resonant scatteringthe dimer molecular structure (Flack parameter=0.05(6)). Displacement ellipsoids shown at 50% probability level.

To verify that the BGC previously identified does indeed encode the biosynthetic pathway for tatiomicin (3), a high molecular-weight P1 artificial chromosome (PAC) library was obtained, consisting of 2,688 clones with an average insert size of 138kb which contained resistant markers for kanamycin (for E. coli) and thiostreptone (for S. coelicolor). The PAC library was screened by PCR, using four primer pairs for the putative BGC. A single PAC clone was identified with the required PCR profile, which was then transferred into E. coli strain ET12567/pR9604 (dam- dcm-), the plasmid was subsequently transferred into S. coelicolor M1152 via conjugation. Exconjugants containing the plasmid integrated on the chromosome were selected for resistance to thiostrepton. Ninety-six putatively identified exconjugants were arrayed into 24 well plates and screened for the production of tatiomicin (3) by TLC, with detection based on the characteristic fluorescence upon UV irradiation at 365nm. Based on these screening parameters, S. coelicolor M1152::tat was identified as a producer of tatiomicin (3) (see ESI).

Growth of S. coelicolor M1152::tat was examined on solid medium, the agar was extracted (EtOAc) and analysed by LCMS alongside similar fermentation extracts from both the parent strain M1152, Amycolatopsis sp. DEM30355 and a tatiomicin (3) standard. An LCMS peak corresponding to tatiomicin (3) was observed in the extract from S. coelicolor M1152::tat but was absent in that of the parent strain M1152 (Fig.6).

Tatiomicin (3) was subsequently isolated from the fermentation of S. coelicolor M1152::tat in liquid medium (GYMG), as demonstrated by HRMS ([M+H]+=403.1403), with a production level in the heterologous host estimated at 0.57mg/L, confirming the identity of the tatiomicin BGC (Fig.5).

Detection of tatiomicin from the fermentation of heterologous host S. coelicolor M1152::tat. Top) Extracted ion chromatogram (EIC) based on m/z=827.25. S. coelicolor M1152 (purple), S. coelicolor M1152::tat (blue), Amycolatopsis sp. DEM30355 (black) and tatiomicin standard (red). Bottom) MS spectrum of tatiomicin (3) purified from the heterologous host S. coelicolor M1152::tat.

Based on a comparison between the tatiomicin and rishirilide BGCs22,23,24,25,26 we propose the following biosynthetic pathway operates for the assembly of tatiomicin (3) (See ESI). The modular type I polyketide synthase TatS1 is likely responsible for the biosynthesis of the polyketide starter unit, cis-crotonyl-ACP, which is then elongated via the attachment of eight malonyl-CoA by minimal PKS enzymes TatK1, TatK2, and TatK3. TatC1, TatC2, TatC3 and TatO10 show close homology to rishirilide cyclases RslC1, RslC2, and RslC3 and C9-ketoreductase RslO10, respectively. Thus, TatC1 and TatO10 likely act together to form the A ring of tatiomicin (3), whilst TatC2 and TatC3 catalyse the formation of the B and C rings. Tailoring of the polyketide core likely involves oxidation of the C ring by TatO4, and installation of the C ring epoxide by flavin mononucleotide (FMN)-dependent monooxygenase TatO1 together with a putative flavin reductase, TatO2. Opening of the epoxide is proposed to be mediated by NADPH:acceptor oxidoreductase TatO5, followed by the key BaeyerVilliger oxidation/rearrangement controlled by TatO9 and finally reduction of the B ring ketone by ketoreductase TatO8.

Three additional tailoring enzymes are present in the BGC of tatiomicin (3) for which no homologues are present in that of rishirilide, TatO11, TatM1 and TatM2. TatO11 is a cytochrome p450 oxidoreductase, likely responsible for oxidation of the A ring to the hydroquinone form, followed by double methylation by the two methyl transferases TatM1 and TatM2 to yield the completed molecule (Fig.6).

Top) Proposed pathway for the biosynthesis of ()-tatiomicin (3) based on homology with the biosynthetic gene cluster for the rishirilides. Enzymes shown in red have no direct congener in the rishirilide BGC and their biosynthetic role is hypothesised, based on BLAST analysis. Bottom) comparison of the rishirilide and (-) tatiomicin gene cluter based on BLAST analysis. (red; tatS=starter unit biosynthesis, tatK or rslK=chain biosynthesis), polyketide modification (blue; tatO or rslO=oxidoreductases, tatC or rslO=cyclases), regulation (yellow; tatR or rslR), transport (green, tatT or rslT) and others (grey; tatP or rslP=phosphorylase; tatM=methyltransferases, black; genes not assigned to the tatiomicin BGC based on homology to the rishirilide BGC and proposed biosynthetic pathway)).

The enzymes TatC4 and TatC5, which are not present in the rishirilide cluster, encode for a dehydrogenase and a monooxygenase and are located in the centre of the biosynthetic gene cluster. The tatiomicin BGC contains all orthologous genes responsible for the synthesis of rishirilide. The function of these additional genes is therefore not immediate obvious and might be a result of evolutionary divergence.

()-Tatiomicin (3) showed no detectable antimicrobial activity (MIC>64g/mL) against ten Gram-negative bacteria and two eukaryotic microorganisms (Candida spp.) (see ESI). However, antibacterial activity was observed against a sub-set of Gram-positive bacteria (MIC=48g/mL), namely Staphylococcus and Streptococcus species. Due to the interest in developing new antibiotics against drug-resistant Staphylococcus infections, we further evaluated ()-3 against a panel of MRSA clinical isolates, including twenty-four EMRSA-15 and EMRSA-16 strains (the main causative agents of nosocomial epidemic MRSA bacteraemia in the UK, with resistance to penicillin, ciprofloxacin and erythromycin)36, and twelve MRSA strains isolated from Belgian, Finnish, French and German hospitals (see SI). In all cases antibiotic activity was maintained (MIC=48g/mL), suggesting that ()-3 does not operate via a mode-of-action previously encountered by these strains, prompting us towards further investigation.

Elucidation of the mode-of-action (MOA) for a new antibacterial agent is a significant experimental challenge. The characterization of resistance mutations can be informative, however all attempts to isolate Bacillus subtilis mutants resistant to ()-tatiomicin (3) proved unsuccessful (see ESI). Also, no positive responses were seen with a panel of B. subtilis strains containing lacZ reporter genes used to indicate common antibacterial mechanisms of action, including: fatty acid synthesis (fabHA), DNA damage (105 prophage induction), RNA polymerase (RNAP) inhibition (helD), cell wall damage (ypuA), gyrase inhibition (gyrA), and cell envelope stress (liaI)) (see ESI)37,38,39.

Due to the presence of an, albeit electron-rich, ,-unsaturated carbonyl moiety, we postulated that the observed biological activity of ()-tatiomicin (3) may involve the covalent modification of thiol-containing enzymes through a conjugate or Michael addition of the thiol to the ,-unsaturated carbonyl. Thus, ()-tatiomicin (3) was reacted with L-cysteine hydrochloride, L-cysteine methyl ester hydrochloride and a short thiol-containing peptide (LcrV (271291)) as an enzyme proxy, under biologically relevant conditions. In all cases thiol adducts could be detected by LCMS, suggesting that ()-tatiomicin (3) may have biologically relevant Michael acceptor activity (see ESI).

To gain further insight into a potential mode-of-action, we undertook a bacterial cytological profiling experiment in which antibacterial induced changes in the morphology of test bacteria are compared to those induced by known mode-of-action antibacterials40,41. B. subtilis 168CA-CRW419 expresses two fusion proteins, HbsU-GFP and WALP23-mCherry, allowing simultaneous visualization of both the chromosomal DNA and the bacterial cell membrane by fluorescence microscopy. The cytoplasmic membrane was unaffected unlike in the control compound nisin, which forms large pores in the membrane42. Interestingly, treatment with ()-tatiomicin (3) induced chromosome decondensation in B. subtilis 168CA-CRW419, similar to the effects elicited by the RNAP inhibitor rifampicin (Fig.7).

Single-cell analysis of chromosome and membrane integrity. Phase contrast (top panels) and fluorescence microscopy of B. subtilis cells treated with various antibiotics (indicated above). DNA was visualized with an HsbU-GFP fusion (middle panels) and the cytoplasmic membrane with a WALP23-mCherry fusion (bottom panels).

The combination of the negative result observed with the helD reporter strain, cell lysis after prolonged incubation with the compound and the inability to create resistant mutants suggest that direct RNAP inhibition is unlikely. We therefore attempted to examine the integrity of the cytoplasmic membrane using the voltage sensitive dye DiSC3(5). This dye accumulates in well-energised cells in the cytoplasmic membrane15,43 but is released upon depolarisation of the membrane, and this release can be measured by fluorescence microscopy. DiSC3(5) is used in parallel with Sytox Green, a membrane-impermeable DNA stain used as a reporter for pore formation44. Upon addition of nisin, which forms large pores in the B. subtilis membrane42, both a loss of DiSC3(5) and uptake of Sytox Green was observed. In contrast gramicidin, which forms small cation-specific channels45, showed loss of DiSC3(5) without Sytox Green staining. Treatment with ()-tatiomicin (3) showed a similar effect to that of gramicidin, i.e. loss of DiSC3(5) without Sytox Green staining. Hence tatiomicin probably acts to dissipate the membrane potential without the formation of large pores (Fig.8).

Single-cell measurement of membrane potential and permeability. Phase contrast (top panels) and fluorescence microscopy of B. subtilis cells stained with the voltage-sensitive dye DiSC3(5) (middle panels) and the membrane permeability indicator Sytox Green (bottom panels) in the presence and absence of 32 g/mL of tatiomicin. As positive controls, the cells were treated with 5 g/mL of gramicidin (membrane depolarisation without pore formation) and 10 M nisin (membrane depolarisation through pore formation). Cellular DiSC3(5) and Sytox Green fluorescence values were quantified for cells treated with tatiomicin (32 g/mL), gramicidin (5 g/mL), and nisin (10 M) (see SI).

In an attempt to ascertain whether the observed loss of membrane potential is a downstream effect or occurs at the same time as chromosome depolarisation we performed a time-course experiment using DiSC3(5) in combination with a HsbU-GFP fusion to assess chromosome decondensation with images taken every two minutes. This showed that the loss of membrane potential occurred simultaneously with the chromosome decondensation, between 2 to 4min, suggesting that they are closely linked events (Fig.9).

Single-cell measurement of chromosome decondensation and membrane potential in a time course experiment in the presence of tatiomicin (32 g/mL). Phase contrast (top panels), fluorescence microscopy of B. subtilis HsbUGFP (chromosome marker) (middle panel) and stained with the voltage sensitive dye DiSC3(5) bottom panel. Cellular DiSC3(5) fluorescence values where quantified over time. The bar chart depicts the fluorescent intensity values of individual cells (> 30) (see SI).

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Discovery, isolation, heterologous expression and mode-of-action studies of the antibiotic polyketide tatiomicin from Amycolatopsis sp. DEM30355 |...

Bursts in microRNA (miRNA) diversity often line up with sudden increases in morphological complexity, especially in the context of the nervous system. In a 2022 bioRxiv preprint, researchers uncovered an miRNA repertoire expansion (orange) in the ancestor of coleoid cephalopodsthe group that includes squids and octopuses, generally thought to be more intelligent than any other invertebrateson par with ones seen in the ancestors of vertebrates (blue) and placental mammals (green).

The term noncoding RNA is a catch-all for sequences in the genome that are transcribed but typically not translated. These molecules, which account for the majority of the transcribed sequences in the genome, are now thought to play key roles in brain evolution and function. Noncoding RNAs can be classified based on their size, structure, location, or function, with dozens of different kinds described to date. Here are four types of noncoding RNA frequently studied in brain tissues.

Long noncoding RNAs (lncRNAs) are generally described as any noncoding RNAs greater than 200 nucleotides in length. Because of their variable size and composition, they can have complex shapes and perform a variety of cellular activities, though most lncRNAs await functional investigation.

Example: The human and chimpanzee versions of a lncRNA called HAR1 differ by 18 nucleotides, which impacts the molecules secondary structure. The human version is predicted to be more stable, but exactly how that translates into differences in brain form or function isnt yet clear.

MicroRNAs (miRNAs) are small noncoding RNAs of just ~2026 nucleotides (teal) that are cleaved from larger precursors. Their most well-described function is the regulation of gene expression via binding to messenger RNAs, where they generally inhibit translation and, therefore, reduce the amount of protein produced from a given gene.

Example: Overexpression of miRNA-124 leads to Alzheimers-like pathologies in mice, and elevated levels of the miRNA are found in the brains of people who died from the disease.

As the name suggests, circular RNAs (circRNAs) are noncoding RNAs with joined ends, creating a more stable, circular molecule. Many questions remain as to the functions of circRNAs, but some are known to bind miRNAs, likely acting as sponges to modulate the miRNAs translation-suppressing effects.

Example:The circRNA CDR1-AS fine tunes neuronal development in humans, binding microRNAs (teal) highly expressed in secretory neurons that regulate developmental gene expression.

Transfer RNAs primary job is to shuttle amino acids to growing peptide chains during translation. In the brain specifically, theres emerging evidence that modifications to tRNAs play important roles in neuronal health and disease. Furthermore,tRNA fragmentssmall chunks from tRNA breakdownseem to have their own functions, including in neurodegeneration.

Example: When researchers exposed Drosophilaneuron cultures to synthetic tRFGln-CTG(teal)a fragment of the tRNA for glutaminethe cells swelled and died, suggesting the fragment could play a role in neuronal necrosis.

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Infographic: Noncoding RNA in the Brain - The Scientist

Mon 05-09-2022 20:18 PM

ABU DHABI, 5th September, 2022 (WAM) -- Zayed Higher Organisation for People of Determination (ZHO) organised today the third stage of examining the Emirati Genome Programme for people of determination and their families in cooperation with G42 Health Care.

This comes as part of the ZHO's initiatives to benefit from advanced scientific research in the age of innovation and to follow scientific methods in providing care and rehabilitation programmes and therapy sessions. A number of people of determination and their families were received at the ZHO's headquarters for necessary check-ups, with the aim of creating a healthier Emirati society, and strengthening therapy plans via a smart system of a distinctive level of treatment, prevention, monitoring and prediction of diseases and epidemics.

Abdullah Abdul Ali Al Humaidan, ZHO's Secretary-General, stated that ZHO is cooperating with G42 Health Care in carrying out the Emirati Genome Programme for people of determination and their families as mapping the reference genome for people of determination and their families by studying the full genetic sequence through the latest technologies and providing a genetic database for use in the medical and diagnostic field contributes to the development of disability codes in an attempt to curb the occurrence of disability, and then with the aim of providing a distinctive treatment for them.

He added that the UAE pays great attention to all groups of society, especially people of determination. Based on its supportive plans, qualitative initiatives and national strategies, it constantly works to improve the services provided to these groups, and to facilitate appropriate means, to enjoy welfare and prosperity in their homeland. They also obtain all means of care from all state institutions. We are keen, in ZHO, to provide proactive services for them and their families.

Al Humaidan thanked all families and people of determination who took the initiative to volunteer in order to contribute with ZHO to the success of the programme, to undergo the required check-ups and provide blood samples.

WAM/Khoder Nashar/Amjad Saleh

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ZHO rolls out third phase of Emirati Genome Programme for people of determination and their families - Emirates News Agency

Presentations will include evidence on how genomic signatures and next-generation sequencing can improve the care of patients with cancer

SAN DIEGO, Sept. 5, 2022 /PRNewswire/ -- Illumina, Inc. (NASDAQ: ILMN), a global leader in DNA sequencing and array-based technologies, today announced the upcoming presentationof multipleoncology research abstracts atthis year's European Society of Medical Oncology (ESMO) annual meeting, September 913 in Paris. The abstractsunderscore the value of next-generation sequencing (NGS) and Illumina's oncology assays for cancer research and patient care.

"For oncologists, the priority for each patient is to provide the best course of therapy as early as possible," said Dr. Phil Febbo, chief medical officer of Illumina. "There is growing evidence that using comprehensive genomic profiling that includes pan-cancer and immunotherapy biomarkers can identify the greatest number of patients who most benefit from targeted therapy."

Illumina's mission in oncology is to help save lives by enabling personalized cancer care through genomics, workingwith customers in both centralized and in-house pathology labs to perform comprehensive genomic profiling (CGP) of tissue and liquid biopsies to match patients to the rapidly growing catalogue of targeted drugs and immunotherapies.

CGP is an NGS approach using a single test to assess hundreds of genesincluding relevant cancer biomarkers, as established in medical guidelines and clinical trialsfor tumor therapy guidance. Pathologists and oncologists are increasingly adopting CGP to enhance their abilities to identify actionable biomarkers, which can better match patients with precision therapies and clinical trials.Studies show that patients who receive a genomic match with biomarker-driven targeted therapies or immunotherapies experience improved clinical outcomes.

Abstracts accepted for ESMO Congress 2022 One way of gaining insight into the tumor genome is to identify the mutations used in the evaluation of homologous recombination deficiency (HRD). HRD status has emerged as an important biomarker in tumors that have high levels of DNA damage, such as ovarian cancer. However, to enable genomic profiling of ovarian cancer tumors with indications for PARP inhibitors and predict response to these treatments, labs must reliably assess HRD status in those tumors. Combining HRD assessment with asingle, comprehensive assay, such as the TruSight Oncology 500 (TSO 500) HRD research assay, to assess a wide range of biomarkers provides results more quickly than iterative testing and provides a high-resolution, comprehensive view of the tumor genome. These two abstracts highlight how labs are combining CGP and HRD to offer a powerful test:

Analytical performance of a next-generation sequencing (NGS) assay kit for assessing homologous recombination deficiency (HRD) from solid tumor samples (925P): This is a poster presentation on a study completed in collaboration with Merck and Myriad Genetics. The data verifies the performance of the TSO 500, showing its high analytical sensitivity and specificity in detecting BRCA1 and BRCA2 variants (5%10% limit of detection) and its accurate genomic instability score (GIS) for HRD status assessment in ovarian cancer. The results show high (>95%) concordance in BRCA variant calling and that the GIS were highly concordant (R = 0.98) to the reference method assay. This assay provides labs with an efficient, in-house solution using just one workflow and a limited sample size, which will aid clinical research and broaden access to clinical trials for potential precision therapies.

High concordance of different molecular assays in the determination of HRD-associated GIS in high grade epithelial ovarian cancer (572P):This abstract presents data resulting from collaboration between Illumina and the Institute of Pathology at the Technical University of Munich, which included use of the TSO 500 HRD assay.

Large-cohort studies show that CGPhas the potential to identify relevant genetic alterations in up to 90% of samples. Illumina is committed to enabling CGP and enhancing research critical to realizing precision medicine in oncology, and collaborations are vital in increasing the evidence of the clinical utility of CGP. Three studies being presented at this year's meeting build on this evidence base:

Tissue and liquid biopsy utilization in advanced NSCLC in a large community US practice (1162P): This is a poster presentation from a real-world utility study between Illumina and Florida Cancer Specialists & Research Institute. In advanced non-small-cell lung cancer (NSCLC), itis critical to havebiomarker testingresults early.The results showed that42% of stage IV NSCLC patients received both liquid and tissue testingandhad results in hand prior to first-line treatment more often (89.4%) than those who had just a tissue (67%) or liquid (72.5%) tumor test. This is the first time that a large US community practice, where most oncology patients are seen, has shown data onhow using both tissue and liquid biopsy testing enables faster turnaround time, guideline-concordant biomarker testing, and biomarker testing results prior to first-line therapy decisions.

The IMPRESS-Norway trial: Improving public cancer care by implementing precision cancer medicine in Norway (503P):This abstract presents data resulting from the Illumina-supported national trial across Norwegian hospitals to show the potential and value of CGP in cancer therapy selection testing.

Exploring different strategies to incorporate germline analysis in tumor whole genome sequencing(1673P):This abstract presents data from the Illumina-supported Dutch WIDE study (short for "whole-genome sequencing implementation in the standarddiagnostics forevery cancer patient"), done through a collaboration between theHartwigMedical Foundation, the Netherlands Cancer Institute, and the University Medical Center Utrecht academic hospital.

Recently announced CGP collaborationsIllumina continues to expand its broad portfolio of oncology partnerships with industry leaders, aimed at advancing cancer diagnostics and precision medicine. Most recently, Illumina launched the research-use-only TSO 500 HRD test, codeveloped with Merck, to unlock deeper insights about the tumor genome. Earlier this year, in collaboration with Bayer, Illumina launched the first companion diagnostic claim for the TruSight Oncology Comprehensive (EU) test enabling targeted therapy with Bayer's Vitrakvi (larotrectinib) for patients with NTRK fusion cancer. Additionally, Illumina and Allegheny Health Network launched a collaborationto evaluate the impact of in-house CGP to enhance patient care.

About IlluminaIllumina is improving human health by unlocking the power of the genome. Our focus on innovation has established us as a global leader in DNA sequencing and array-based technologies, serving customers in the research, clinical, and applied markets. Our products are used for applications in the life sciences, oncology, reproductive health, agriculture, and other emerging segments. To learn more,visitillumina.comand connect with us onTwitter,Facebook,LinkedIn,Instagram, andYouTube.

Investors:Salli SchwartzUS: +01 858 291 6421IR@illumina.com

Media:Adi Raval US: +01 202 629 8172PR@illumina.com

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