Large Sequence Research Articles

Split intein-mediated protein trans-splicing to express large dystrophins.

Gene replacement using adeno-associated virus (AAV) vectors is a promising therapeutic approach for many diseases1,2. However, this therapeutic modality is challenged by the packaging capacity of AAVs (approximately 4.7 kilobases)3, limiting its application for disorders involving large coding sequences, such as Duchenne muscular dystrophy, with a 14 kilobase messenger RNA. Here we developed a new method for expressing large dystrophins by utilizing the protein trans-splicing mechanism mediated by split inteins. We identified several split intein pairs that efficiently join two or three fragments to generate a large midi-dystrophin or the full-length protein. We show that delivery of two or three AAVs into dystrophic mice results in robust expression of large dystrophins and significant physiological improvements compared with micro-dystrophins. Moreover, using the potent myotropic AAVMYO4, we demonstrate that low total doses (2 × 1013 viral genomes per kg) are sufficient to express large dystrophins in striated muscles body-wide with significant physiological corrections in dystrophic mice. Our data show a clear functional superiority of large dystrophins over micro-dystrophins that are being tested in clinical trials. This method could benefit many patients with Duchenne or Becker muscular dystrophy, regardless of genotype, and could be adapted to numerous other disorders caused by mutations in large genes thatexceed the AAV capacity.

Rare de novo damaging DNA variants are enriched in attention-deficit/hyperactivity disorder and implicate risk genes

Research demonstrates the important role of genetic factors in attention-deficit/hyperactivity disorder (ADHD). DNA sequencing of families provides a powerful approach for identifying de novo (spontaneous) variants, leading to the discovery of hundreds of clinically informative risk genes for other childhood neurodevelopmental disorders. This approach has yet to be extensively leveraged in ADHD. We conduct whole-exome DNA sequencing in 152 families, comprising a child with ADHD and both biological parents, and demonstrate a significant enrichment of rare and ultra-rare de novo gene-damaging mutations in ADHD cases compared to unaffected controls. Combining these results with a large independent case-control DNA sequencing cohort (3206 ADHD cases and 5002 controls), we identify lysine demethylase 5B (KDM5B) as a high-confidence risk gene for ADHD and estimate that 1057 genes contribute to ADHD risk. Using our list of genes harboring ultra-rare de novo damaging variants, we show that these genes overlap with previously reported risk genes for other neuropsychiatric conditions and are enriched in several canonical biological pathways, suggesting early neurodevelopmental underpinnings of ADHD. This work provides insight into the biology of ADHD and demonstrates the discovery potential of DNA sequencing in larger parent-child trio cohorts.

An Atom-Precise Understanding of DNA-Stabilized Silver Nanoclusters.

ConspectusDNA-stabilized silver nanoclusters (AgN-DNAs) are sequence-encoded fluorophores. Like other noble metal nanoclusters, the optical properties of AgN-DNAs are dictated by their atomically precise sizes and shapes. What makes AgN-DNAs unique is that nanocluster size and shape are controlled by nucleobase sequence of the templating DNA oligomer. By choice of DNA sequence, it is possible to synthesize a wide range of AgN-DNAs with diverse emission colors and other intriguing photophysical properties. AgN-DNAs hold significant potential as "programmable" emitters for biological imaging due to their combination of small molecular-like sizes, bright and sequence-tuned fluorescence, low toxicities, and cost-effective synthesis. In particular, the potential to extend AgN-DNAs into the second near-infrared region (NIR-II) is promising for deep tissue imaging, which is a major area of interest for advancing biomedical imaging. Achieving this goal requires a deep understanding of the structure-property relationships that govern AgN-DNAs in order to design AgN-DNA emitters with sizes and geometries that support NIR-II emission.In recent years, major advances have been made in understanding the structure and composition of AgN-DNAs, enabling new insights into the correlation of nanocluster structure and photophysical properties. These advances have hinged on combined innovations in mass characterization and crystallography of compositionally pure AgN-DNAs, together with combinatorial experiments and machine learning-guided design. A combined approach is essential due to the major challenge of growing suitable AgN-DNA crystals for diffraction and to the labor-intensive nature of preparing and solving the molecular formulas of atomically precise AgN-DNAs by mass spectrometry. These approaches alone are not feasibly scaled to explore the large sequence space of DNA oligomer templates for AgN-DNAs.This account describes recent fundamental advances in AgN-DNA science that have been enabled by high throughput synthesis and fluorimetry together with detailed analytical studies of purified AgN-DNAs. First, short introductions to nanocluster chemistry and AgN-DNA basics are presented. Then, we review recent large-scale studies that have screened thousands of DNA templates for AgN-DNAs, leading to discovery of distinct classes of these emitters with unique cluster core compositions and ligand chemistries. In particular, the discovery of a new class of chloride-stabilized AgN-DNAs enabled the first ab initio calculations of AgN-DNA electronic structure and present new approaches to stabilize these emitters in biologically relevant conditions. Near-infrared (NIR) emissive AgN-DNAs are also found to exhibit diverse structures and properties. Finally, we conclude by highlighting recent proof-of-principle demonstrations of NIR AgN-DNAs for targeted fluorescence imaging. Continued efforts may future push AgN-DNAs into the tissue transparency window for fluorescence imaging in the NIR-II tissue transparency window.

IκBα kinase inhibitor BAY 11-7082 promotes anti-tumor effect in RAS-driven cancers

BackgroundOncogenic mutations in the RAS gene are associated with uncontrolled cell growth, a hallmark feature contributing to tumorigenesis. While diverse therapeutic strategies have been diligently applied to treat RAS-mutant cancers, successful targeting of the RAS gene remains a persistent challenge in the field of cancer therapy. In our study, we discover a promising avenue for addressing this challenge.MethodsIn this study, we tested the viability of several cell lines carrying oncogenic NRAS, KRAS, and HRAS mutations upon treatment with IkappaBalpha (IκBα) inhibitor BAY 11-7082. We performed both cell culture-based viability assay and in vivo subcutaneous xenograft-based assay to confirm the growth inhibitory effect of BAY 11-7082. We also performed large RNA sequencing analysis to identify differentially regulated genes and pathways in the context of oncogenic NRAS, KRAS, and HRAS mutations upon treatment with BAY 11-7082.ResultsWe demonstrate that oncogenic NRAS, KRAS, and HRAS activate the expression of IκBα kinase. BAY 11-7082, an inhibitor of IκBα kinase, attenuates the growth of NRAS, KRAS, and HRAS mutant cancer cells in cell culture and in mouse model. Mechanistically, BAY 11-7082 inhibitor treatment leads to suppression of the PI3K-AKT signaling pathway and activation of apoptosis in all RAS mutant cell lines. Additionally, we find that BAY 11-7082 treatment results in the downregulation of different biological pathways depending upon the type of RAS protein that may also contribute to tumor growth inhibition.ConclusionOur study identifies BAY 11-7082 to be an efficacious inhibitor for treating RAS oncogene (HRAS, KRAS, and NRAS) mutant cancer cells. This finding provides new therapeutic opportunity for effective treatment of RAS-mutant cancers.

Copy number variation introduced by a massive mobile element facilitates global thermal adaptation in a fungal wheat pathogen

Copy number variation (CNV) can drive rapid evolution in changing environments. In microbial pathogens, such adaptation is a key factor underpinning epidemics and colonization of new niches. However, the genomic determinants of such adaptation remain poorly understood. Here, we systematically investigate CNVs in a large genome sequencing dataset spanning a worldwide collection of 1104 genomes from the major wheat pathogen Zymoseptoria tritici. We found overall strong purifying selection acting on most CNVs. Genomic defense mechanisms likely accelerated gene loss over episodes of continental colonization. Local adaptation along climatic gradients was likely facilitated by CNVs affecting secondary metabolite production and gene loss in general. One of the strongest loci for climatic adaptation is a highly conserved gene of the NAD-dependent Sirtuin family. The Sirtuin CNV locus localizes to an ~68-kb Starship mobile element unique to the species carrying genes highly expressed during plant infection. The element has likely lost the ability to transpose, demonstrating how the ongoing domestication of cargo-carrying selfish elements can contribute to selectable variation within populations. Our work highlights how standing variation in gene copy numbers at the global scale can be a major factor driving climatic and metabolic adaptation in microbial species.

Beyond ‘Community Craft Specialisation’ in Prehistoric Mainland Southeast Asian Metallurgy: Copper Smelting Behaviours at Khao Sai On, Nil Kham Haeng, Non Pa Wai, Phromthin Tai, and Tha Kae in Central Thailand

Archaeology in Lopburi Province has been under long-term development since the 1970s and has led to the discovery of Khao Wang Prachan Valley (KWPV) as one of three currently known primary copper production (mining and smelting) sites in Southeast Asia. However, since the 1980s, archaeometallurgical research has focused mainly on the two sites of Non Pa Wai (NPW) and Nil Kham Haeng (NKH), dated from the Neolithic (ca. 1800 B.C.) to Early Iron Age (ca. 2300 B.C.), that are known to have large sequences of archaeological material linked to copper production. Although other sites with evidence of copper production are known in this restricted geographical area, their assemblages have not previously been subjected to archaeometric study. This article discusses the results of archaeometallurgical analyses of slags from Khao Sai On (KSO), Tha Kae (TK), and Phromthin Tai (PTT) with sequences dated during the Middle-Late Iron Age. Slag analysis reveals that different smelting behaviours were occurring at these sites. One heterogenous smelting behaviour at Khao Sai On was probably linked to a smaller community practicing metallurgy among other activities, while Phromthin Tai shows more homogeneous production linked to a larger settlement. Although these three sites are located within 26 km of each other, smelters were probably exploiting different local copper deposits around KWPV. The results seem to support a community based type of production where production behaviours are linked to local geology, technical competence, and intensity of exploitation.

Overview and Evolution of Insect Fibroin Heavy Chain (FibH).

The FibH gene, crucial for silk spinning in insects, encodes a protein that significantly influences silk fiber mechanics. Due to its large size and repetitive sequences, limited known sequences of insect FibH impede comprehensive understanding. Here, we analyzed 114 complete FibH gene sequences from Lepidoptera (71 moths, 24 butterflies) and 13 Trichoptera, revealing single-copy FibH in most species, with 2-3 copies in Hesperinae and Heteropterinae (subfamily of skippers). All FibH genes are structured with two exons and one intron (39-45 bp), with the second exon being notably longer. Moths exhibit higher GC content in FibH compared to butterflies and Trichoptera. The FibH composition varies among species, with moths and butterflies favoring Ala, Gly, Ser, Pro, Gln, and Asn, while Trichoptera FibH is enriched in Gly, Ser, and Arg, and has less Ala. Unique to Trichoptera FibH are Tyr, Val, Arg, and Trp, whereas Lepidoptera FibH is marked by polyAla (polyalanine), polySer (polyserine), and the hexapeptide GAGSGA. A phylogenetic analysis suggests that Lepidoptera FibH evolved from Trichoptera, with skipper FibH evolving from Papilionoidea. This study substantially expands the FibH repertoire, providing a foundation for the development of artificial silk.

The genomic landscape of Ménière's disease: a path to endolymphatic hydrops

BackgroundMénière's disease (MD) is a disorder of the inner ear that causes episodic bouts of severe dizziness, roaring tinnitus, and fluctuating hearing loss. To date, no targeted therapy exists. As such, we have undertaken a large whole genome sequencing study on carefully phenotyped unilateral MD patients with the goal of gene/pathway discovery and a move towards targeted intervention. This study was a retrospective review of patients with a history of Ménière's disease. Genomic DNA, acquired from saliva samples, was purified and subjected to whole genome sequencing.ResultsStringent variant calling, performed on 511 samples passing quality checks, followed by gene-based filtering by recurrence and proximity in molecular interaction networks, led to 481 high priority MD genes. These high priority genes, including MPHOSPH8, MYO18A, TRIOBP, OTOGL, TNC, and MYO6, were previously implicated in hearing loss, balance, and cochlear function, and were significantly enriched in common variant studies of hearing loss. Validation in an independent MD cohort confirmed 82 recurrent genes. Pathway analysis pointed to cell–cell adhesion, extracellular matrix, and cellular energy maintenance as key mediators of MD. Furthermore, the MD-prioritized genes were highly expressed in human inner ear hair cells and dark/vestibular cells, and were differentially expressed in a mouse model of hearing loss.ConclusionBy enabling the development of model systems that may lead to targeted therapies and MD screening panels, the genes and variants identified in this study will inform diagnosis and treatment of MD.

Phylogenetic analysis of Ellipsomyxa species (Myxosporea) and description of Ellipsomyxa gordeyi n. sp. from the gall bladder of mullets (Mugiliformes: Mugilidae) in Nha Trang Bay of the East Sea, Vietnam

The coastal waters of Vietnam are home to a wide diversity of fishes, but the parasite diversity of these potential hosts is much less well characterized. To begin addressing this knowledge gap, we carried out surveys of myxozoan parasites in fishes collected from Nha Trang Bay in Vietnam's East Sea in 2018–2019. Mugilid fishes were collected in March–April 2018, January–February 2019, and November–December 2019, and examined for myxozoans. Myxospores consistent with those of the genus Ellipsomyxa were found in the gall bladder of four mullet species, and we thoroughly characterized those from Planiliza melinoptera. Myxospores were elliptoid and devoid of striation, with a distinct sinuous suture line. Polar capsules were pyriform and oriented toward the poles of the spore. Morphological features were compared to nominal species and this species from Vietnam was distinct. Phylogenetic analysis based on partial small subunit rDNA sequence revealed that broadly, Ellipsomyxa species split into three phylogenetic lineages, and although in some branches there are groupings by host family, habitat or locality, there are no clear phylogenetic patterns. The new species we encountered in P. melinoptera had a close sister relationship with Ellipsomyxa adlardi, with both species part of a larger subclade within the Ellipsomyxa lineage. Despite this phylogenetic similarity, these species were morphologically distinct, and partial large subunit DNA sequences were only 93% similar to each other. A combination of the morphological characteristics and molecular data suggest that this is an undescribed species and we propose the name Ellipsomyxa gordeyi n. sp.

MRCat: A Novel CatBoost Predictor for the Binary Classification of mRNA Subcellular Localization by Fusing Large Language Model Representation and Sequence Features.

The subcellular localization of messenger RNAs (mRNAs) is a pivotal aspect of biomolecules, tightly linked to gene regulation and protein synthesis, and offers innovative insights into disease diagnosis and drug development in the field of biomedicine. Several computational methods have been proposed to predict the subcellular localization of mRNAs within cells. However, there remains a deficiency in the accuracy of these predictions. In this study, we propose an mRCat predictor based on the gradient boosting tree algorithm specifically to predict whether mRNAs are localized in the nucleus or in the cytoplasm. This predictor firstly uses large language models to thoroughly explore hidden information within sequences and then integrates traditional sequence features to collectively characterize mRNA gene sequences. Finally, it employs CatBoost as the base classifier for predicting the subcellular localization of mRNAs. The experimental validation on an independent test set demonstrates that mRCat obtained accuracy of 0.761, F1 score of 0.710, MCC of 0.511, and AUROC of 0.751. The results indicate that our method has higher accuracy and robustness compared to other state-of-the-art methods. It is anticipated to offer deep insights for biomolecular research.

Simultaneous enhancement of multiple functional properties using evolution-informed protein design

A major challenge in protein design is to augment existing functional proteins with multiple property enhancements. Altering several properties likely necessitates numerous primary sequence changes, and novel methods are needed to accurately predict combinations of mutations that maintain or enhance function. Models of sequence co-variation (e.g., EVcouplings), which leverage extensive information about various protein properties and activities from homologous protein sequences, have proven effective for many applications including structure determination and mutation effect prediction. We apply EVcouplings to computationally design variants of the model protein TEM-1 β-lactamase. Nearly all the 14 experimentally characterized designs were functional, including one with 84 mutations from the nearest natural homolog. The designs also had large increases in thermostability, increased activity on multiple substrates, and nearly identical structure to the wild type enzyme. This study highlights the efficacy of evolutionary models in guiding large sequence alterations to generate functional diversity for protein design applications.

Multiple targets, germline BRCA1 mutation and HRD in a lung cancer patient: Molecular considerations and treatment decision-making.

Several biomarkers are currently available to address targeted treatments in cancer patients, with lung malignancies representing one of the best examples. We report the case of a patient affected by advanced non-small cell lung cancer with an uncommon histology and a complex biology. The use of a large next-generation sequencing (NGS) NGS panel allowed us to identify an extremely rare BRAF mutation (V600Q), a MET amplification, a high tumor mutational burden, a germline pathogenetic BRCA1 mutation and a homologous recombination deficiency through RAD51 assay. The treatment decision was driven by the abundance of molecular information. This case highlights that an attentive and critical evaluation of molecular reports is key for the tailoring of treatment algorithms at the patient-level scale.

Generalized weighted permutation entropy analysis of SPEI index in Serbia as a proxy of corn production

AbstractIn this work we analyse yearly standardized precipitation evapotranspiration index for accumulation timescale of 6 months (SPEI‐6) for August which has been identified as a strong proxy for corn production in Serbia. By applying recently proposed method generalized weighted permutation entropy (GWPE), which provides the information about predictability of large/small fluctuations in nonstationary time series, on SPEI index calculated from high‐resolution gridded dataset for the period 1951–2022, we found that the small fluctuation sequences of consecutive 4‐year SPEI values are the most predictable (indicated by lowest entropy values). The order of large fluctuation sequences is less predictable, while the order of sequences considering all magnitudes of fluctuations are the least predictable (indicated by highest entropy values). We also analysed 4‐month SPEI‐6 sequences for May, June, July and August (during the growing season of corn) along the years under study and found that they display lower GWPE values and thus are more predictable than yearly SPEI‐6 August sequences (small fluctuations displaying lower entropy values than large fluctuations). Regarding spatial distributions, in both cases SPEI‐6 sequences show similar pattern for large fluctuations: higher predictability (lower entropy values) in northern and eastern part of Serbia and lower predictability (higher entropy values) in southern and western part. For small fluctuations spatial distribution of GWPE values indicates that yearly SPEI‐6 August series are more predictable in the western part of the country while May to August monthly SPEI‐6 sequences are more predictable in the eastern part.

Optimal object stitching and video condensation-aided improved YOLOv5-oriented anomaly object detection framework

Recent advances in intelligent surveillance to boost public security have increased interest in anomaly location and identification among computer vision researchers in congested situations. Unrestricted video monitoring is required due to its extensive use in private and public spheres. Particularly, research on finding anomalies in surveillance videos has drawn much interest. Video condensation is an efficient method for quickly scanning and retrieving lengthy surveillance films. Reordering the video events in the spatial or temporal domain seeks to condense large video sequences into their equivalent compact video representation. Additionally, if a delay occurs in anomaly detection, it leads to time consumption for rectifying it. Therefore, the core aim of this task is to reduce the time consumption of detecting the instance of video anomaly detection faster by introducing the optimal object stitching through the deep learning-based video condensation concept. The steps include video condensation and deep learning-based anomaly detection. Initially, the necessary video for anomaly detection is gathered from the datasets. The gathered video is converted into frames and is used for object detection in each frame using Improved YOLOv5 (I-YOLOv5). Then, the optimal frame selection is performed using the hybrid Fitness of Wild Geese and Path Finder (FWGPF) for stitching the detected objects into the selected optimal frames, thus compressing the relevant frames. In the second phase, the condensed (compressed) frames are utilized for determining the anomaly frames with the support of Parameter Tuning in Dilated MobileNet (PTDM), and further, the anomaly objects are detected on the classified condensed frames based on the I-YOLOv5. Here, the tuning of constraint is done in the anomaly detection phase by the same hybrid FWGPF to reduce the time consumption. The experimental analysis of the developed model is performed with baseline algorithms and detection techniques to confirm the developed model’s efficiency.

Identification of novel driver risk genes in CNV loci associated with neurodevelopmental disorders

Copy number variants (CNVs) are genome-wide structural variations involving the duplication or deletion of large nucleotide sequences. While these types of variations can be commonly found in humans, large and rare CNVs are known to contribute to the development of various neurodevelopmental disorders (NDDs), including autism spectrum disorder (ASD). Nevertheless, given that these NDD-risk CNVs cover broad regions of the genome, it is particularly challenging to pinpoint the critical gene(s) responsible for the manifestation of the phenotype. In this study, we performed a meta-analysis of CNV data from 11,614 patients with NDDs and 4,031 controls from SFARI database to identify 41 NDD-risk CNV loci, including 24 novel regions. We also found evidence for dosage-sensitive genes within these regions being significantly enriched for known NDD-risk genes and pathways. In addition, a significant proportion of these genes was found to i) converge in protein-protein interaction networks; ii) be among most expressed genes in the brain across all developmental stages; and iii) be hit by deletions that are significantly over-transmitted to individuals with ASD within multiplex ASD families from the iHART cohort. Finally, we conducted a burden analysis using 4,281 NDD cases from Decipher and iHART cohorts, and 2,504 neurotypical controls from 1,000 Genomes and iHART, that resulted in the validation of the association of 162 dosage sensitive genes driving risk for NDDs, including 22 novel NDD-risk genes. Importantly, most NDD-risk CNV loci entail multiple NDD-risk genes in agreement with a polygenic model associated with the majority of NDD cases.

FinaleToolkit: Accelerating Cell-Free DNA Fragmentation Analysis with a High-Speed Computational Toolkit.

Cell-free DNA (cfDNA) fragmentation pattern represents a promising non-invasive biomarker for disease diagnosis and prognosis. Numerous fragmentation features, such as end motif and window protection score (WPS), have been characterized in cfDNA genomic sequencing. However, the analytical tools developed in these studies are often not released to the liquid biopsy community or are inefficient for genome-wide analysis in large datasets. To address this gap, we have developed FinaleToolkit, a fast and memory efficient Python package designed to generate comprehensive fragmentation features from large cfDNA genomic sequencing data. For instance, FinaleToolkit can generate genome-wide WPS features from a ~100X cfDNA whole-genome sequencing (WGS) dataset in 1.2 hours using 16 CPU cores, offering up to a ~50-fold increase in processing speed compared to original implementations in the same dataset. We have benchmarked FinaleToolkit against original studies or implementations where possible, confirming its efficacy. Furthermore, FinaleToolkit enabled the genome-wide analysis of fragmentation patterns over arbitrary genomic intervals, significantly boosting the performance for cancer early detection. FinaleToolkit is open source and thoroughly documented with both command line interface and Python application programming interface (API) to facilitate its widespread adoption and use within the research community: https://github.com/epifluidlab/FinaleToolkit.

An analysis of likely germline events by tumor tissue testing on large somatic panels.

e22524 Background: The increase in large panel testing has facilitated the detection of numerous events, including those in genes associated with inherited cancer. Further, large panel sequencing may uncover variants in these genes in non-inherited cancers. Hence the frequency of pathogenic/likely pathogenic (P/LP) events in some common germline genes was studied across cancers in the Indian population. Methods: A statistical model was used to derive a method to label variants as likely germline. Variants in hereditary breast and ovarian cancer (HBOC) and mismatch repair (MMR) genes from 1553 cases sequenced at our referral laboratory on the TruSight Oncology 500 panel (TSO500) were analyzed for their variant allele frequency (VAF) distribution. A 3 component Gaussian mixture model (intended to separate somatic, germline heterozygous, homozygous variants) was fit to this VAF distribution and a cutoff was determined such that the area under the curve of the somatic component above the cutoff was below 0.01. This gave a threshold value of 40% VAF for determining likely germline variants (LGVs). Data from 1713 TSO500 cases was then analyzed to identify LGVs. Results: The dataset of 1713 cases comprised 20.67% NSCLC, 12.61% breast, 12.49% ovarian and 10.5% colorectal cancer and < 10% of other cancer types including 2.39% pancreatic and 2.04% prostate cancer. 23.8% of ovarian, 17.14% of prostate, 12.5% of breast and 12.2% of pancreatic cancer cases had variants in BRCA1/BRCA2. Among these, 18.69% of ovarian, 17.14% of prostate, 8.33% of breast and 7.32% of pancreatic cancers had LGVs. LGVs in BRCA1/2 were also found in 3.33% of colorectal and 1.13% of NSCLC. The BRCA1/BRCA2 LGV ratio was found to be 3.0 in ovarian, 1.0 in breast and 2.0 in pancreatic but 0.2 in prostate and colorectal and 0.33 in NSCLC cancer. Adding other HBOC genes (ATM, BRCA1/2, BRIP1, CHEK2, PALB2, RAD51C, RAD51D) increased the LGV frequency to 22.43% of ovarian, 22.86% of prostate, 11.11% of breast, 9.76% of pancreatic, 4.44% of colorectal and 2.26% of NSCLC. MMR gene LGVs were seen in 1.23% of all cancers mainly in 10.71% of endometrial and 4.44% of colorectal cancers. Unlike the HBOC genes, MMR gene variants were not observed across multiple cancers and did not show a predominantly germline distribution (somatic/LGV 1:1). In contrast, the background P/LP frequency calculated from gnomAD v4 South Asian data (excluding those with conflicting labels) was 0.74% for BRCA1/2, 1.82% for the extended HBOC list and 0.5% for the MMR genes. Conclusions: Somatic testing was used to identify LGVs and determine the frequencies of HBOC and MMR genes in cancers in the Indian population. Somatic testing also identified LGVs in multiple tissues at frequencies above the background P/LP frequency for this population, suggesting a possible role for these genes in other cancers. This information underscores the need for further study and may be useful to define a clinically relevant subset of patients.

NGS panels' capture of biomarkers associated with targeted therapies in clinical trials.

e13508 Background: ASCO and NCCN guidelines strongly encourage participation in clinical trials though participation rates remain low. Although up to 60% of trials require biomarker information as a prerequisite to participation, guidelines are silent on biomarker testing to assess eligibility. To help inform policy development, we analyzed commercially available NGS panels to assess the capture rate of the specific gene variants required for targeted therapies in clinical trials. Methods: Publicly available gene variant information on 12 high volume NGS tissue and plasma commercial assays representative of panel size was accessed. The JAX-CKB database was queried to identify genomic prerequisites in active phase I-III clinical trials; mRNA and protein expression biomarkers were excluded. We used unpaired t-testing to compare the ability of large versus small panels to detect variants required for targeted treatments. (Somatic gene variant annotations and related content have been powered by The Jackson Laboratory Clinical Knowledgebase (JAX-CKBTM)). Results: Overall, 185 genes were linked to eligibility for targeted therapies in clinical trials (Table). Large panel assays (>80 genes) had a higher average capture rate compared to smaller panels, (84% vs. 46%, p value < 0.000002). Similarly, capture rates in select cancers were higher in larger panels: lung (85% vs 59%), breast (92% vs 54%), prostate (86% vs 34%), pancreatic (84% vs 54%), colorectal (88% vs 52%). All results were statistically significant (p < 0.05). Smaller panels were more likely to exclude large genes (e.g. BRCA), genome-wide signatures (e.g. TMB), fusions, and copy number variants. Conclusions: NGS-based panels with more than 80 genes had higher capture rates for biomarkers required for clinical trials of targeted therapies. Clinicians searching for all relevant treatment options for patients should include large genomic sequencing panels to maximize identification of potential clinical trial eligibility. Professional societies and policy makers interested in enhancing clinical trial enrollment rates should consider the panel size-related variability in capture rates of genetic variants when developing recommendations. [Table: see text]

Complete genome sequences of Cytobacillus sp., Domibacillus sp., Enterobacter sp., Neisseria sp., Pseudomonas sp., and Streptococcus sp. strains from human clinical infections collected at diagnostic units in 2020.

Members of Bacillota and Pseudomonadota phyla are frequently considered bacterial infectious agents in humans. As part of a large sequencing project of clinically relevant pathogens, we hybrid-assembled complete genomes of Cytobacillus, Domibacillus, Enterobacter, Neisseria, Pseudomonas, and Streptococcus species isolated from clinical specimens.

A novel clinical metaproteomics workflow enables bioinformatic analysis of host-microbe dynamics in disease.

Clinical metaproteomics has immense potential to offer functional insights into the microbiome and its contributions to human disease. However, there are numerous challenges in the metaproteomic analysis of clinical samples, including handling of very large protein sequence databases for sensitive and accurate peptide and protein identification from mass spectrometry data, as well as taxonomic and functional annotation of quantified peptides and proteins to enable interpretation of results. To address these challenges, we have developed a novel clinical metaproteomics workflow that provides customized bioinformatic identification, verification, quantification, and taxonomic and functional annotation. This bioinformatic workflow is implemented in the Galaxy ecosystem and has been used to characterize diverse clinical sample types, such as nasopharyngeal swabs and bronchoalveolar lavage fluid. Here, we demonstrate its effectiveness and availability for use by the research community via analysis of residual fluid from cervical swabs.