Analysis Of Genomic Data Research Articles

Simultaneous Occurrence of Hypospadias and Bilateral Cleft Lip and Jaw in a Crossbred Calf: Clinical, Computer Tomographic, and Genomic Characterization.

Congenital abnormalities in animals, including abnormalities of the cleft lip and jaw and hypospadias have been reported in all domesticated species. They are a major concern for breeders due to the increased economic loss they entail. In this article, we described a congenital bilateral cheilognathoschisis (cleft lip and jaw) with campylognathia in association with penile hypospadias and preputial hypoplasia with failure of preputial fusion in a Bos taurus crossbred Piedmontese × Wagyu calf. Clinical examination, computed tomography, and whole genome sequencing were performed to describe and identify a possible cause of the abnormalities. Clinical examination revealed a bilateral cheilognathoschisis of approximately 4 cm in length and 3 cm in width in the widest part, with computer tomography analyses confirming the bilateral absence of the processus nasalis of the incisive bone and the lateral deviation of the processus palatinus towards the left side. Genomic data analyses identified 13 mutations with a high impact on the products of the following overlapped genes: ACVR1, ADGRA2, BHMT2, BMPR1B, CCDC8, CDH1, EGF, F13A1, GSTP1, IRF6, MMP14, MYBPHL, and PHC2 with ADGRA2, EGF, F13A1, GSTP1, and IRF6 having mutations in a homozygous state. The whole genome investigation indicates the involvement of multiple genes in the birth defects observed in this case.

BRGenomics for analyzing high-resolution genomics data in R.

I present here the R/Bioconductor package BRGenomics, which provides fast and flexible methods for post-alignment processing and analysis of high-resolution genomics data within an interactive R environment. Utilizing GenomicRanges and other core Bioconductor packages, BRGenomics provides various methods for data importation and processing, read counting and aggregation, spike-in and batch normalization, re-sampling methods for robust 'metagene' analyses, and various other functions for cleaning and modifying sequencing and annotation data. Simple yet flexible, the included methods are optimized for handling multiple datasets simultaneously, make extensive use of parallel processing, and support multiple strategies for efficiently storing and quantifying different kinds of data, including whole reads, quantitative single-base data, and run-length encoded coverage information. BRGenomics has been used to analyze ATAC-seq, ChIP-seq/ChIP-exo, PRO-seq/PRO-cap, and RNA-seq data; is built to be unobtrusive and maximally compatible with the Bioconductor ecosystem; is extensively tested; and includes complete documentation, examples, and tutorials. BRGenomics is an R package distributed through Bioconductor (https://bioconductor.org/packages/BRGenomics). Full documentation with examples and tutorials are available online (https://mdeber.github.io).

Limobrevibacterium gyesilva gen. nov., sp. nov., Isolated from Forest Soil

An aerobic, oxidase-positive, catalase-negative, non-motile, oval/rod-shaped, and Gram-negative strain RN2-1T was isolated from a Korean forest soil sample. Cells grew in a pH range of 6–9 (optimum range: 7–8), in a temperature range of 20–40 °C (optimum range: 30–35 °C), and in the presence of 0–1% (w/v) NaCl. The major polar lipid profile consisted of phosphatidylglycerol, diphosphatidyl glycerol, phosphatidylethanolamine, phosphatidylcholine, and a group of phospholipids, aminolipids, and polar lipids with unidentified structures. The major quinone was ubiquinone 10. The main fatty acids were summed feature 8 (C18:1 ω7c/C18:1 ω6c), C16:0, Cyclo-C17:0, cyclo C19:0 ω8c, and C18:1 2OH. Phylogenetic similarities based on 16S rDNA sequences showed that strain RN2-1T grouped in the family Acetobacteraceae included the genera Rhodovastum, Acidisoma, Acidisphaera, and Acidibrevibacterium. The aerobic phototrophic bacterium Rhodovastum atsumiense G2-11T was the closest relative to RN2-1T (96.04% sequence similarity). Genomic DNA revealed 68.7% G+C content. Whole genome analysis and phenotypic data of the strain RN2-1T confirmed its distinctive phylogenetic location within the family Acetobacteraceae. Because of these results, strain RN2-1T represents the novel species of a new genus for which the name Limobrevibacterium gyesilva gen. nov., sp. nov. was proposed. The type of strain was strain RN2-1T (KACC 23001 = JCM 35944).

ChroKit: a Shiny-based framework for interactive analysis, visualization and integration of genomic data.

We developed ChroKit (the Chromatin toolKit), an interactive web-based framework written in R that enables intuitive exploration, multidimensional analyses, and visualization of genomic data from ChIP-Seq, DNAse-Seq or any other NGS experiment that reports the enrichment of aligned reads over genomic regions. This program takes preprocessed NGS data and performs operations on genomic regions of interest, including resetting their boundaries, their annotation based on proximity to genomic features, the association to gene ontologies, and signal enrichment calculations. Genomic regions can be further refined or subsetted by user-defined logical operations and unsupervised classification algorithms. ChroKit generates a full range of plots that are easily manipulated by point and click operations, thus allowing 'on the fly' re-analysis and fast exploration of the data. Working sessions can be exported for reproducibility, accountability, and easy sharing within the bioinformatics community. ChroKit is multiplatform and can be deployed on a server to enhance computational speed and provide simultaneous access by multiple users. ChroKit is a fast and intuitive genomic analysis tool suited for a wide range of users due to its architecture and its user-friendly graphical interface. ChroKit source code is available at https://github.com/ocroci/ChroKit and the Docker image at https://hub.docker.com/r/ocroci/chrokit.

Genomics data analysis via spectral shape and topology.

Mapper, a topological algorithm, is frequently used as an exploratory tool to build a graphical representation of data. This representation can help to gain a better understanding of the intrinsic shape of high-dimensional genomic data and to retain information that may be lost using standard dimension-reduction algorithms. We propose a novel workflow to process and analyze RNA-seq data from tumor and healthy subjects integrating Mapper, differential gene expression, and spectral shape analysis. Precisely, we show that a Gaussian mixture approximation method can be used to produce graphical structures that successfully separate tumor and healthy subjects, and produce two subgroups of tumor subjects. A further analysis using DESeq2, a popular tool for the detection of differentially expressed genes, shows that these two subgroups of tumor cells bear two distinct gene regulations, suggesting two discrete paths for forming lung cancer, which could not be highlighted by other popular clustering methods, including t-distributed stochastic neighbor embedding (t-SNE). Although Mapper shows promise in analyzing high-dimensional data, tools to statistically analyze Mapper graphical structures are limited in the existing literature. In this paper, we develop a scoring method using heat kernel signatures that provides an empirical setting for statistical inferences such as hypothesis testing, sensitivity analysis, and correlation analysis.

Computer-aided analysis of quercetin mechanism of overcoming docetaxel resistance in docetaxel-resistant prostate cancer

BackgroundProstate cancer (PC) is a silent but potent killer among men. In 2018, PC accounted for more than 350, 000 death cases while more than 1.2 million cases were diagnosed. Docetaxel, a chemotherapeutic drug belonging to the taxane family of drugs, is one of the most potent drugs in combating advanced PC. However, PC cells often evolve resistance against the regimen. Hence, necessitating the search for complementary and alternative therapies. Quercetin, a ubiquitous phytocompound with numerous pharmacological properties, has been reported to reverse docetaxel resistance (DR) in docetaxel-resistant prostate cancer (DRPC). Therefore, this study aimed to explore the mechanism via which quercetin reverses DR in DRPC using an integrative functional network and exploratory cancer genomic data analyses. ResultsThe putative targets of quercetin were retrieved from relevant databases, while the differentially expressed genes (DEGs) in docetaxel-resistant prostate cancer (DRPC) were identified by analysing microarray data retrieved from the Gene Expression Omnibus (GEO) database. Subsequently, the protein-protein interaction (PPI) network of the overlapping genes between the DEGs and quercetin targets was retrieved from STRING, while the hub genes, which represent the key interacting genes of the network, were identified using the CytoHubba plug-in of Cytoscape. The hub genes were further subjected to a comprehensive analysis aimed at identifying their contribution to the immune microenvironment and overall survival (OS) of PC patients, while their alterations in PC patients were also revealed. The biological roles played by the hub genes in chemotherapeutic resistance include the positive regulation of developmental process, positive regulation of gene expression, negative regulation of cell death, and epithelial cell differentiation among others. ConclusionFurther analysis revealed epidermal growth factor receptor (EGFR) as the most pertinent target of quercetin in reversing DR in DRPC, while molecular docking simulation revealed an effective interaction between quercetin and EGFR. Ultimately, this study provides a scientific rationale for the further exploration of quercetin as a combinational therapy with docetaxel.

Genomic and transcriptomic analyses support a silk gland origin of spider venom glands

BackgroundSpiders comprise a hyperdiverse lineage of predators with venom systems, yet the origin of functionally novel spider venom glands remains unclear. Previous studies have hypothesized that spider venom glands originated from salivary glands or evolved from silk-producing glands present in early chelicerates. However, there is insufficient molecular evidence to indicate similarity among them. Here, we provide comparative analyses of genome and transcriptome data from various lineages of spiders and other arthropods to advance our understanding of spider venom gland evolution.ResultsWe generated a chromosome-level genome assembly of a model spider species, the common house spider (Parasteatoda tepidariorum). Module preservation, GO semantic similarity, and differentially upregulated gene similarity analyses demonstrated a lower similarity in gene expressions between the venom glands and salivary glands compared to the silk glands, which questions the validity of the salivary gland origin hypothesis but unexpectedly prefers to support the ancestral silk gland origin hypothesis. The conserved core network in the venom and silk glands was mainly correlated with transcription regulation, protein modification, transport, and signal transduction pathways. At the genetic level, we found that many genes in the venom gland-specific transcription modules show positive selection and upregulated expressions, suggesting that genetic variation plays an important role in the evolution of venom glands.ConclusionsThis research implies the unique origin and evolutionary path of spider venom glands and provides a basis for understanding the diverse molecular characteristics of venom systems.

Abstract NG02: Ordered and selective patterns of cancer genome evolution revealed by lineage tracing of sporadic p53 loss

Abstract Inactivation of the tumor suppressor p53 is associated with molecular features such as genomic instability, polyploidy, and the emergence of heterogeneous tumors that acquire diverse copy number alterations (CNAs). Clinically, p53 loss is associated with aggressive disease, therapeutic resistance, and poor prognosis. However, precisely when specific molecular features are selected following p53 inactivation and their evolutionary contribution to the initiation and progression of heterogenous p53 mutant tumors, remains poorly understood. This is in part because cancer genomes are typically analyzed after tumor development and not as they transverse the benign to malignant spectrum. Here, we develop a mouse model of pancreatic ductal adenocarcinoma (PDAC) that reports sporadic p53 loss in vivo, irrespective of phenotype, enabling the identification, isolation and lineage tracing of incipient cancer cells as they evolve through the benign to malignant transition. Using functional, histopathological, and genomics-based approaches, we find that cells undergoing p53 loss gradually gain malignant potential as they acquire CNAs in highly ordered and selective patterns. Initially, distinct p53-deficient clones emerge and compete via the selective acquisition of recurrent chromosomal deletions in otherwise diploid cells. Following the accumulation of many genomic deletions in a diploid state, a subset of highly rearranged clones undergo genome doubling, entering polyploid, presumably an adaptation to excess loss of gene dosage. In a polyploid state, rampant genomic diversification ensues involving the acquisition of additional deletions and, now, widespread chromosomal gains and amplifications resulting in a heterogenous malignant tumor. Despite vast heterogeneity, chromosomal deletions are selectively fixed as truncal, homogenous events in the tumor hierarchy. Retrospective analysis of whole genome, targeted-panel, and single-cell sequencing data of human PDAC corroborate these patterns in human disease. p53 was coined “guardian of the genome” 30 years ago upon the discovery that its inactivation prevents proliferative arrest following DNA damage, leading to genomic instability. However, the evolutionary relationship between p53 inactivation, associated genomic features, and the emergence of p53 tumors remains poorly understood. Leveraging a unique lineage tracing mouse model, we find that p53 inactivation results in a set of predictable evolutionary rules that are highly ordered and deterministic. These specific rules suggest stage dependent contributions of CNA events to malignant transformation and point to new strategies to target lethal, p53-deficient tumors. Citation Format: Timour Baslan. Ordered and selective patterns of cancer genome evolution revealed by lineage tracing of sporadic p53 loss. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr NG02.

Abstract 1172: Oncogenic context shapes the fitness landscape of tumor suppression

Abstract Cancer progression is a quintessential example of a walk on an adaptive fitness landscape, with tumor growth depending on the cooperation of multiple driver mutations. While sequencing of tens of thousands of clinical samples has revealed a vast set of cancer drivers, far less is known about the interactions of oncogene-tumor suppressor pairs. Due to patient-level selection bias, confounding biological factors and limited sample sizes, a map of these interactions cannot be generated from human data alone and, instead, requires direct perturbational experiments and functional genomics approaches. Here, we model and quantify tumors using an autochthonous mouse platform and Tubaseq, which integrates barcoded lentiviral-sgRNA/Cre vectors and high-throughput barcode sequencing to uncover the number of neoplastic cells in each tumor of each genotype. Across four oncogenic contexts—KRAS G12D, KRAS G12C, BRAF V600E, and EGFR L858R—we analyzed >10,000,000 tumors to estimate the tumorigenesis potential of the oncogene alone as well as the effect of inactivating 28 tumor suppressor genes. We discovered that despite KRAS G12D producing >10x the tumors as G12C, tumor suppressor inactivations provide similar growth effects on both backgrounds. In contrast, although the intrinsic abilities of BRAF V600E and EGFR L858R to drive tumor development fall within the range of the KRAS variants, tumor suppressive effects are categorically different in the context of each oncogene. Many tumor suppressors show clear sign epistasis with the oncogenes, whereby inactivation is advantageous in one context and neutral or deleterious in another. Inactivation of some of the strongest tumor suppressors (e.g., Lkb1, Setd2, and Kmt2d) in KRAS-driven tumors strongly decreases tumor growth in the presence of oncogenic EGFR. While some of these epistatic effects are consistent with a textbook understanding of the RAS pathway, most cannot be predicted based on the linear oncogenic EGFR → KRAS → BRAF pathway model. Analyses of clinical genomics data from AACR Project GENIE confirm that high rates of passenger mutations in KRAS- and BRAF-driven lung tumors, among other factors, prevent the discovery of these interactions from human data alone. However, for EGFR-mutant lung cancers, which are less confounded by high mutational burden, the rates of coincident tumor suppressor mutation are highly correlated with tumor growth effects in our in vivo model. Thus, we find via causal experiments that the landscape of tumor suppression is highly dependent on oncogenic context, with a minority of tumor suppressive effects robust to changes in the oncogene. These findings suggest that the utility of a specific cancer mutation as a prognostic or predictive biomarker of patient outcomes will be dependent on coincident mutations in the tumor and highlight the utility of high-throughput, quantitative autochthonous mouse models in advancing our understanding of cancer biology. Citation Format: Lily M. Blair, Joseph M. Juan, Lafia Sebastian, Vy B. Tran, Wensheng Nie, Gregory D. Wall, Mehmet Gerceker, Ian K. Lai, Edwin A. Apilado, Gabriel Grenot, David Amar, Giorgia Foggetti, Mariana Do Carmo, Zeynep Ugur, Debbie Deng, Alex Chenchik, Maria Paz Zafra, Lukas E. Dow, Katerina Politi, Jonathan J. MacQuitty, Dmitri A. Petrov, Monte M. Winslow, Michael J. Rosen, Ian P. Winters. Oncogenic context shapes the fitness landscape of tumor suppression [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 1172.

Abstract 775: Integrative genomic and transcriptomic profiling of myeloma precursors in a prospective longitudinal observational study (ORIGIN)

Abstract Background: Monoclonal gammopathy of undetermined significance (MGUS) and smoldering myeloma (SMM) are myeloma precursors (MPD). A better understanding of the oncogenic/immune programs underlying progression may reveal exploitable targets for patients (pts) at high risk of progression and aid new intervention strategies. We designed a longitudinal Observational prospective Research study In monoclonal Gammopathies leadINg to myeloma (ORIGIN) to collect well-annotated, longitudinal samples (sps) for integrative genomic/transcriptomic analysis. Methods: Pts were accrued meeting MGUS/SMM IMWG definition. BM sps were partitioned into CD138-positive and -negative compartments and DNAs/RNAs extracted for bulk RNA and whole-exome sequencing (WES). Matched germline controls were included for somatic mutation and DNA copy number analysis. A number of tools and algorithms were applied, and the data were thoroughly analyzed and integrated. Results: From 12/2015-03/2022, 249 pts were accrued and 205 eligible (99 MGUS/106 SMM). Median follow up time is 33 months (6-49 months). As of 11/2022, 21 pts progressed (prog) on ORIGIN study (14% 15/106 SMM, 3% 3/99 prog to SMM from MGUS and 1% 3/205 to AL amyloidosis). To increase analytic power, genomic data of 166 SMM baseline sps from SMM pts on treatment studies were added. Altogether, genomic data of 496 sps were analyzed. Briefly, WES was performed on 170 sps from 150 pts and RNA-Seq was performed on enriched tumor/TME cells in 326 sps from 187 pts. Relative to MGUS, we observed higher mutation load in SMM and more frequent somatic mutations in known drivers of myeloma: KRAS (14% vs. 2%), NRAS (7% vs. 2%) leading to a shorter time to myeloma as well as genes recurrently mutated in other cancers: FOXO3 (7% vs 0%). We observed increased aneuploidy levels in SMM vs MGUS and in prog vs. non-prog including both gains and losses of multiple chromosomes. Transcriptome analysis of CD138+ cells revealed clearly distinct expression profiles between SMM and MGUS, and between prog and non-prog. The pathway activity of oxidative phosphorylation and MYC was significantly increased in SMM vs. MGUS and in prog vs. non-prog, suggesting their potential roles in driving progression. We noted a greater degree of heterogeneity in TME cell compositions in SMM vs MGUS sps and in prog vs. non-prog: ie, SMM and prog sps formed multiple distinct clusters exhibiting differential abundance of cytotoxic T cells, neutrophils, fibroblasts and myeloid cells. Conclusions: Together, our integrative analysis of genomic and transcriptome data in ORIGIN demonstrates extensive changes in both CD138+ cells and the immune microenvironment and co-evolution of the ecosystems during disease progression. This study is an invaluable resource and lays the molecular foundation for the community for future biomarker and target discovery. Citation Format: Minghao Dang, Chutima Kunacheewa, Hans C. Lee, Krina K. Patel, Sheeba K. Thomas, Melody Becnel, Donna M. Weber, Pei Lin, Zuzana Berkova, Mei Huang, David A. Berrios, Hima Bansal, Andrew Futreal, Cristhiam Rojas Hernandez, Vahid Afshar-Khargan, Michael Kroll, Peter Kuhn, Robert Z. Orlowski, Linghua Wang, Elisabet E. Manasanch. Integrative genomic and transcriptomic profiling of myeloma precursors in a prospective longitudinal observational study (ORIGIN) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 775.

Abstract 6635: Lipid and glycolipid antigen presentation faults as a mechanism of resistance to immune checkpoint therapy in poorly immunogenic melanomas

Abstract Introduction: Cold tumors are characterized by lacking infiltration of antitumor T cells. These tumors are universally refractory to immune checkpoint therapies (ICT). While poor T cell infiltration has been partly ascribed to low tumor mutational burden (TMB), TMB doesn’t necessarily exclude immunogenic responses nor ensure a response to ICT. Mechanisms of T cell exclusion in tumors with sufficient TMB may be caused by molecular failure of the antigen processing and presentation (APP), which impacts T cell generation. It was previously described that loss of heterozygosity (LOH) of an essential component of the MHC class I molecule, the beta-2-microglobulin (B2M), is associated with low antigen presentation, tumor T cell infiltration, and ICT resistance in melanoma. Here we provide further insight into APP and ICT response in melanoma. Methods: Transcriptomic analysis and visualization of genomic data from the GDC-TCGA skin cutaneous melanoma (SKCM) study were performed using the Xena platform. Four anti-PD1 metastatic melanoma cohorts were normalized with transcripts per million units. STRING protein-protein interaction analysis was performed in the human database with 11 nodes and 39 edges. Nanostring transcriptomic data from metastatic uveal melanoma (mUM) was revisited from our previous publications for Spearman’s correlational and group analysis of APP signatures amongst ICT responders and non-responders. Results: In an expanded analysis of 4 normalized anti-PD1 treated metastatic melanoma cohorts (n=272), we confirmed that transcriptomic levels of B2M are significantly reduced in non-responders. Moreover, STRING protein-protein interaction analysis of B2M revealed CD1D as a potential molecular partner of B2M. We, therefore, observed that CD1D transcriptomic expression is not only reduced in anti-PD1 poor responders but is a survival prognosticator in primary tumors from the GDC-TCGA study. Interestingly, CD1D expression is also significantly reduced in poorly immunogenic mUM tumors, a cancer type regarded as universally refractory to ICT. Conclusions: These findings suggest that APP faults in ICT-resistant tumors are not restricted to faults in peptide presentation to T lymphocytes (via B2M) but may also impact the presentation of tumor-associated lipid and glycolipid antigens via CD1d, which can impact antitumor responses of natural killer T cells (NKT). Adjuvant therapies that restore MHC-I and CD1d-mediated antigen presentation may unleash the response to ICT via NKT. Understanding CD1d expression faults locally and systemically may provide valuable prognostication information likely to impact how cancer is treated by optimally selecting patients for ICT and validating novel therapeutic targets for immunotherapy development. Citation Format: Carlos Rogerio Figueiredo. Lipid and glycolipid antigen presentation faults as a mechanism of resistance to immune checkpoint therapy in poorly immunogenic melanomas [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6635.

Abstract 2066: Visualization and analysis of cancer genomics data using UCSC Xena

Abstract UCSC Xena (http://xena.ucsc.edu/) is a web-based visual integration and exploration tool for multi-omic data and associated clinical and phenotypic annotations. Researchers can easily view and explore public data, their own private data, or both using the Xena Browser. Private data are kept on the researcher's computer and are never uploaded to our public servers. We support Mac, Windows, and Linux. Questions Xena can help you answer: * Is overexpression of this gene associated with lower/higher survival? * What genes are differentially expressed between these two groups of samples? * What is the relationship between mutation, copy number, expression, etc for this gene? Xena showcases seminal cancer genomics datasets from TCGA, the Pan-Cancer Atlas, GDC, PCAWG, ICGC, and more; a total of more than 1500 datasets across 50 cancer types. We support virtually any type of functional genomics data: SNPs, INDELs, copy number variation, gene expression, ATAC-seq, DNA methylation, exon-, transcript-, miRNA-, lncRNA-expression and structural variants. We also support clinical data such as phenotype information, subtype classifications and biomarkers. All of our data is available for download via python or R APIs, or using our URL links. Our signature Visual Spreadsheet view shows multiple data types side-by-side enabling discovery of correlations across and within genes and genomic regions. We also have dynamic Kaplan-Meier survival analysis, powerful filtering and subgrouping, differential gene expression analysis, charts, statistical analyses, genomic signatures, and the ability to generate URLs to live views. We link out to the UCSC Genome Browser as well as MuPIT/CRAVAT and TumorMap. New features include: * Genome-wide differential gene expression analysis * Select samples directly from the screen for filtering and creating subgroups * Violin plots on any numerical data * Loading of Microsoft Excel files Our beta prototype site for visualizing single-cell data delivers million-cell-scale multi-omics data for interactive visualization in a web browser. Contact us for access to our beta prototype site. If you use us please cite our publication in Nature Biotechnology: https://www.nature.com/articles/s41587-020-0546-8 Citation Format: Mary Goldman, Brian Craft, Jingchun Zhu, David Haussler. Visualization and analysis of cancer genomics data using UCSC Xena [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2066.

Proteome and genome integration analysis of obesity.

The prevalence of obesity has increased worldwide in recent decades. Genetic factors are now known to play a substantial role in the predisposition to obesity and may contribute up to 70% of the risk for obesity. Technological advancements during the last decades have allowed the identification of many hundreds of genetic markers associated with obesity. However, the transformation of current genetic variant-obesity associations into biological knowledge has been proven challenging. Genomics and proteomics are complementary fields, as proteomics extends functional analyses. Integrating genomic and proteomic data can help to bridge a gap in knowledge regarding genetic variant-obesity associations and to identify new drug targets for the treatment of obesity. We provide an overview of the published papers on the integrated analysis of proteomic and genomic data in obesity and summarize four mainstream strategies: overlap, colocalization, Mendelian randomization, and proteome-wide association studies. The integrated analyses identified many obesity-associated proteins, such as leptin, follistatin, and adenylate cyclase 3. Despite great progress, integrative studies focusing on obesity are still limited. There is an increased demand for large prospective cohort studies to identify and validate findings, and further apply these findings to the prevention, intervention, and treatment of obesity. In addition, we also discuss several other potential integration methods.

An improvised nature-inspired algorithm enfolded broad learning system for disease classification

Deep analysis of genomic data reveals that many deadly diseases are generated due to genetic mutation. To make the health care system more robust, a machine learning researcher’s prime intention is to classify the genomic data more efficiently within less time. As the genomic data suffers from the malediction of excessive dimensionality, the selection of the notable genes is always a big challenge for the researcher. The selection of prominent genomic key attributes by any nature-inspired learning algorithm always remains a non-deterministic polynomial-time (NP-Hard) problem. Therefore, there is always a scope to apply new algorithms. In this projected work, an improvised sine–cosine hybridized Monarch Butterfly Optimization (SC-MBO) algorithm, is embedded with the Broad Learning System (BLS), which is defined as SC-MBO-BLS, for choosing the most significant genes and classifying the genomic data simultaneously. Initially, Kernel-based Fisher Score (K-FS) is applied to select notable genes. Then, the selected genes further undergoes for execution using the SC-MBO-BLS model. To prove the effectiveness of the suggested model, ten cancerous genomic data are considered. Here, several performance evaluators (i.e., precision, MCC, sensitivity, Kappa, F-score, and specificity) are applied for unbiased comparison. This presented model is compared with SC-MBO wrapped Multilayer Perceptron (SC-MBO-MLP), SC-MBO wrapped Extreme Learning Machine (SC-MBO-ELM), and SC-MBO wrapped Kernel Extreme Learning Machine (SC-MBO- KELM) and yields the highest accuracy in ten datasets such as 100%, 98.4%, 99%, 99.6%, 100, 97.2%, 100%, 100%, 98.6%, 99.5% in Leukemia, Colon tumor, Breast cancer, Ovarian cancer, Lymphoma-3, MLL, ALL-AML-3, SRBCT, ALL-AML-4 and Lung cancer respectively. Further, the existing twenty standard models are taken for comparison with the suggested model. Additionally, to assess the presented model, a statistical method i.e., Analysis of variance (ANOVA) is considered. As per the above quantitative and qualitative estimation, it is deduced that the suggested SC-MBO-BLS approach outclasses other considering models.

Focal adhesion is associated with lithium response in bipolar disorder: evidence from a network-based multi-omics analysis

Lithium (Li) is one of the most effective drugs for treating bipolar disorder (BD), however, there is presently no way to predict response to guide treatment. The aim of this study is to identify functional genes and pathways that distinguish BD Li responders (LR) from BD Li non-responders (NR). An initial Pharmacogenomics of Bipolar Disorder study (PGBD) GWAS of lithium response did not provide any significant results. As a result, we then employed network-based integrative analysis of transcriptomic and genomic data. In transcriptomic study of iPSC-derived neurons, 41 significantly differentially expressed (DE) genes were identified in LR vs NR regardless of lithium exposure. In the PGBD, post-GWAS gene prioritization using the GWA-boosting (GWAB) approach identified 1119 candidate genes. Following DE-derived network propagation, there was a highly significant overlap of genes between the top 500- and top 2000-proximal gene networks and the GWAB gene list (Phypergeometric = 1.28E–09 and 4.10E–18, respectively). Functional enrichment analyses of the top 500 proximal network genes identified focal adhesion and the extracellular matrix (ECM) as the most significant functions. Our findings suggest that the difference between LR and NR was a much greater effect than that of lithium. The direct impact of dysregulation of focal adhesion on axon guidance and neuronal circuits could underpin mechanisms of response to lithium, as well as underlying BD. It also highlights the power of integrative multi-omics analysis of transcriptomic and genomic profiling to gain molecular insights into lithium response in BD.

A retrospective analysis of phosphatase catalytic subunit gene variants in patients with rare disorders identifies novel candidate neurodevelopmental disease genes.

Rare genetic disorders represent some of the most severe and life-limiting conditions that constitute a considerable burden on global healthcare systems and societies. Most individuals affected by rare disorders remain undiagnosed, highlighting the unmet need for improved disease gene discovery and novel variant interpretation. Aberrant (de) phosphorylation can have profound pathological consequences underpinning many disease processes. Numerous phosphatases and associated proteins have been identified as disease genes, with many more likely to have gone undiscovered thus far. To begin to address these issues, we have performed a systematic survey of de novo variants amongst 189 genes encoding phosphatase catalytic subunits found in rare disease patients recruited to the 100,000 Genomes Project (100kGP), the largest national sequencing project of its kind in the United Kingdom. We found that 49% of phosphatases were found to carry de novo mutation(s) in this cohort. Only 25% of these phosphatases have been previously linked to genetic disorders. A gene-to-patient approach matching variants to phenotypic data identified 9 novel candidate rare-disease genes: PTPRD, PTPRG, PTPRT, PTPRU, PTPRZ1, MTMR3, GAK, TPTE2, PTPN18. As the number of patients undergoing whole genome sequencing increases and information sharing improves, we anticipate that reiterative analysis of genomic and phenotypic data will continue to identify candidate phosphatase disease genes for functional validation. This is the first step towards delineating the aetiology of rare genetic disorders associated with altered phosphatase function, leading to new biological insights and improved clinical outcomes for the affected individuals and their families.

Description of a Novel Fish Pathogen, Plesiomonas shigelloides subsp. oncorhynchi, Isolated from Rainbow Trout (Oncorhynchus mykiss): First Genome Analysis and Comparative Genomics

Plesiomonas shigelloides is the only species in its genus, and has zoonotic importance due to the serious implications resulting from the consumption of contaminated seafood. This is the first report on the genomic features of the whole-genome sequence (WGS) of P. shigelloides strain V-78, recovered from diseased rainbow trout, Oncorhynchus mykiss. The genome of P. shigelloides V-78 consists of 4,478,098 base pairs (bp), which encode 3730 proteins, and has a G + C content of 51.1%. The bioinformatics analysis of WGS of V-78 confirmed the presence of 121 tRNA genes and 42 rRNA genes (15 genes for 5S rRNA, 13 genes for 16S rRNA, and 14 genes for 23S rRNA). Comprehensive genome analyses revealed that the strain encodes for secondary metabolites, antimicrobial resistance, and virulence genes. The strain V-78 has 31 known antibiotic resistance models, which encode many antimicrobial resistances. In addition, strain V-78 has 42 different virulence genes, such as adhesion, secretion system, and motility. The digital DNA–DNA hybridization value against P. shigelloides NCTC 10360 was 74.2%, while the average nucleotide identity value was 97.1%. Based on the scrutinized analysis of genomic data, strain V-78 should be considered a novel subspecies of P. shigelloides, for which Plesiomonas shigelloides subsp. oncorhynchi is proposed.

Genome-wide identification and expression profiling analysis of the trihelix gene family and response of PgGT1 under abiotic stresses in Platycodon grandiflorus

The trihelix gene family plays an important role in plant growth and abiotic stress responses. Through the analysis of genomic and transcriptome data, 35 trihelix family members were identified for the first time in Platycodon grandiflorus; they were classified into five subfamilies: GT-1, GT-2, SH4, GTγ, and SIP1. The gene structure, conserved motifs and evolutionary relationships were analyzed. Prediction of physicochemical properties of the 35 trihelix proteins founded, the number of amino acid molecules is between 93 and 960, theoretical isoelectric point is between 4.24 and 9.94, molecular weight is between 9829.77 and 107435.38, 4 proteins among them were stable, and all GRAVY is negative. The full-length cDNA sequence of the PgGT1 gene of the GT-1 subfamily was cloned by PCR. It is a 1165 bp ORF encoding a 387 amino acid protein, with a molecular weight of 43.54 kDa. The predicted subcellular localization of the protein in the nucleus was experimentally verified. After being treated with NaCl, PEG6000, MeJA, ABA, IAA, SA, and ethephon, the expression of PgGT1 gene showed an up-regulated trend except for the roots treated with NaCl and ABA. This study laid a bioinformatics foundation for the research of trihelix gene family and the cultivation of excellent germplasm of P. grandiflorus.

Computer-aided genomic data analysis of drug-resistant Neisseria gonorrhoeae for the Identification of alternative therapeutic targets.

Neisseria gonorrhoeae is an emerging multidrug resistance pathogen that causes sexually transmitted infections in men and women. The N. gonorrhoeae has demonstrated an emerging antimicrobial resistance against reported antibiotics, hence fetching the attention of researchers to address this problem. The present in-silico study aimed to find putative novel drug and vaccine targets against N. gonorrhoeae infection by the application of bioinformatics approaches. Core genes set of 69N. gonorrhoeae strains was acquired from complete genome sequences. The essential and non-homologous metabolic pathway proteins of N. gonorrhoeae were identified. Moreover, different bioinformatics databases were used for the downstream analysis. The DrugBank database scanning identified 12 novel drug targets in the prioritized list. They were preferred as drug targets against this bacterium. A viable vaccine is unavailable so far against N. gonorrhoeae infection. In the current study, two outer-membrane proteins were prioritized as vaccine candidates via reverse vaccinology approach. The top lead B and T-cells overlapped epitopes were utilized to generate a chimeric vaccine construct combined with immune-modulating adjuvants, linkers, and PADRE sequences. The top ranked prioritized vaccine construct (V7) showed stable molecular interaction with human immune cell receptors as inferred during the molecular docking and MD simulation analyses. Considerable response for immune cells was interpreted by in-silico immune studies. Additional tentative validation is required to ensure the effectiveness of the prioritized vaccine construct against N. gonorrhoeae infection. The identified proteins can be used for further rational drug and vaccine designing to develop potential therapeutic entities against the multi-drug resistant N. gonorrhoeae.

Disease progression based feature screening for ultrahigh-dimensional survival-associated biomarkers.

The increased availability of ultrahigh-dimensional biomarker data and the high demand of identifying biomarkers importantly related to survival outcomes made feature screening methods commonplace in the analysis of cancer genome data. When survival outcomes include endpoints of overall survival (OS) and time-to-progression (TTP), a high concordance is typically found in both endpoints in cancer studies, namely, patients' OS would most likely be extended when tumour progression is delayed. Existing screening procedures are often performed on a single survival endpoint only and may result in biased selection of features for OS in ignorance of disease progression. We propose a novel feature screening method by incorporating information of TTP into the selection of important biomarker predictors for more accurate inference of OS subsequent to disease progression. The proposal is based on the rank of correlation between individual features and the conditional distribution of OS given observations of TTP. It is advantageous for its flexible model nature, which requires no marginal model assumption for each endpoint, and its minimal computational cost for implementation. Theoretical results show its ranking consistency, sure screening and false rate control properties. Simulation results demonstrate that the proposed screener leads to more accurate feature selection than the method without considering the prior observations of disease progression. An application to breast cancer genome data illustrates its practical utility and facilitates disease classification using selected biomarker predictors.