Annotated Gene Sets Research Articles

Groundwater resistant gene accumulation in mining-agriculture complex zones: Insights from metagenomic analysis of subterranean mineral and terrestrial agricultural interactions

During the Mining-Agriculture Complex Areas, the mining and agriculture activities could lead to an excessive presence of sulfate content in the regional groundwater. Sulfate exhibits the potential to influence the positive accumulation of RGs, although its mechanisms remain inadequately explored. To address this gap, this study analyzed the RGs buildup mechanisms in the groundwater of the mining-agriculture complex area. Results showed a widespread presence of antibiotic resistance genes (ARGs) and metal resistance genes (MRGs), especially in coal-seams crevice groundwater. And iron and sulfur are primary environmental factors conducive to RGs accumulation through a synergistic interaction. Microbial annotation of gene sets sourced from coal-seams crevice groundwater samples unveiled part of sulfur-metabolizing microorganisms that were hosts of both ARGs and MRGs. Mechanistic insights revealed that iron may stimulates reactive oxygen species (ROS) generation, facilitating RGs accumulation, while adjusting sulfur metabolism and the synthesis of iron-sulfur clusters, thereby augmenting microbial growth which as predominant hosts of RGs, thereby intensifying the buildup of RGs.

A new high-quality genome assembly and annotation for the threatened Florida Scrub-Jay (Aphelocoma coerulescens).

The Florida Scrub-Jay (Aphelocoma coerulescens), a Federally Threatened, cooperatively-breeding bird, is an emerging model system in evolutionary biology and ecology. Extensive individual-based monitoring and genetic sampling for decades has yielded a wealth of data, allowing for the detailed study of social behavior, demography, and population genetics of this natural population. Here, we report a linkage map and a chromosome-level genome assembly and annotation for a female Florida Scrub-Jay made with long-read sequencing technology, chromatin conformation data, and the linkage map. We constructed a linkage map comprising 4,468 SNPs that had 34 linkage groups and a total sex-averaged autosomal genetic map length of 2446.78 cM. The new genome assembly is 1.33 Gb in length, consisting of 33 complete or near-complete autosomes and the sex chromosomes (ZW). This highly contiguous assembly has an NG50 of 68 Mb and a Benchmarking Universal Single-Copy Orthologs (BUSCO) completeness score of 97.1% with respect to the Aves database. The annotated gene set has a BUSCO transcriptome completeness score of 95.5% and 17,964 identified protein-coding genes, 92.5% of which have associated functional annotations. This new, high-quality genome assembly and linkage map of the Florida Scrub-Jay provides valuable tools for future research into the evolutionary dynamics of small, natural populations of conservation concern.

Identifying new cancer genes based on the integration of annotated gene sets via hypergraph neural networks.

Identifying cancer genes remains a significant challenge in cancer genomics research. Annotated gene sets encode functional associations among multiple genes, and cancer genes have been shown to cluster in hallmark signaling pathways and biological processes. The knowledge of annotated gene sets is critical for discovering cancer genes but remains to be fully exploited. Here, we present the DIsease-Specific Hypergraph neural network (DISHyper), a hypergraph-based computational method that integrates the knowledge from multiple types of annotated gene sets to predict cancer genes. First, our benchmark results demonstrate that DISHyper outperforms the existing state-of-the-art methods and highlight the advantages of employing hypergraphs for representing annotated gene sets. Second, we validate the accuracy of DISHyper-predicted cancer genes using functional validation results and multiple independent functional genomics data. Third, our model predicts 44 novel cancer genes, and subsequent analysis shows their significant associations with multiple types of cancers. Overall, our study provides a new perspective for discovering cancer genes and reveals previously undiscovered cancer genes. DISHyper is freely available for download at https://github.com/genemine/DISHyper.

In-depth exploration of the genomic diversity in tea varieties based on a newly constructed pangenome of Camellia sinensis.

Tea, one of the most widely consumed beverages globally, exhibits remarkable genomic diversity in its underlying flavour and health-related compounds. In this study, we present the construction and analysis of a tea pangenome comprising a total of 11 genomes, with a focus on three newly sequenced genomes comprising the purple-leaved assamica cultivar "Zijuan", the temperature-sensitive sinensis cultivar "Anjibaicha" and the wild accession "L618" whose assemblies exhibited excellent quality scores as they profited from latest sequencing technologies. Our analysis incorporates a detailed investigation of transposon complement across the tea pangenome, revealing shared patterns of transposon distribution among the studied genomes and improved transposon resolution with long read technologies, as shown by long terminal repeat (LTR) Assembly Index analysis. Furthermore, our study encompasses a gene-centric exploration of the pangenome, exploring the genomic landscape of the catechin pathway with our study, providing insights on copy number alterations and gene-centric variants, especially for Anthocyanidin synthases. We constructed a gene-centric pangenome by structurally and functionally annotating all available genomes using an identical pipeline, which both increased gene completeness and allowed for a high functional annotation rate. This improved and consistently annotated gene set will allow for a better comparison between tea genomes. We used this improved pangenome to capture the core and dispensable gene repertoire, elucidating the functional diversity present within the tea species. This pangenome resource might serve as a valuable resource for understanding the fundamental genetic basis of traits such as flavour, stress tolerance, and disease resistance, with implications for tea breeding programmes.

Transcriptomic point of departure determination: a comparison of distribution-based and gene set-based approaches.

A key step in assessing the potential human and environmental health risks of industrial and agricultural chemicals is to determine the toxicity point of departure (POD), which is the highest dose level that causes no adverse effect. Transcriptomic POD (tPOD) values have been suggested to accurately estimate toxicity POD values. One step in the most common approach for tPOD determination involves mapping genes to annotated gene sets, a process that might lead to substantial information loss particularly in species with poor gene annotation. Alternatively, methods that calculate tPOD values directly from the distribution of individual gene POD values omit this mapping step. Using rat transcriptome data for 79 molecules obtained from Open TG-GATEs (Toxicogenomics Project Genomics Assisted Toxicity Evaluation System), the hypothesis was tested that methods based on the distribution of all individual gene POD values will give a similar tPOD value to that obtained via the gene set-based method. Gene set-based tPOD values using four different gene set structures were compared to tPOD values from five different individual gene distribution methods. Results revealed a high tPOD concordance for all methods tested, especially for molecules with at least 300 dose-responsive probesets: for 90% of those molecules, the tPOD values from all methods were within 4-fold of each other. In addition, random gene sets based upon the structure of biological knowledge-derived gene sets produced tPOD values with a median absolute fold change of 1.3-1.4 when compared to the original biological knowledge-derived gene set counterparts, suggesting that little biological information is used in the gene set-based tPOD generation approach. These findings indicate using individual gene distributions to calculate a tPOD is a viable and parsimonious alternative to using gene sets. Importantly, individual gene distribution-based tPOD methods do not require knowledge of biological organization and can be applied to any species including those with poorly annotated gene sets.

Abstract PO2-13-07: SMARCA2 compensation of SMARCA4 loss mediates adaptive resistance to neoadjuvant systemic therapy in triple-negative breast cancer: paired transcriptomic analysis of pre- and post-treatment samples from NeoSTOP and NeoPACT

Abstract Objectives/Rationale: Triple-negative breast cancer (TNBC) patients with residual disease (RD) after neoadjuvant systemic therapy (NAST) are at high risk for disease recurrence and death. While multiple prognostic biomarkers associated with response to NAST have been identified, adaptive resistance mechanisms to NAST remain poorly understood. Identifying and characterizing adaptive resistance mechanisms can illuminate novel therapeutic approaches and improve outcomes for TNBC patients with RD. Methods: Total RNA was isolated from pre-treatment and paired surgical specimens (for patients with RD) from TNBC patients treated with chemotherapy on the NeoSTOP (NCT02413320) or chemoimmunotherapy on the NeoPACT (NCT03639948) neoadjuvant trials and was subjected to RNA exome sequencing. Comparative marker selection analysis was performed by computing the two-sided paired samples t-test for each gene followed by pre-ranked gene set enrichment analysis (GSEA) amongst 23,797 annotated gene sets. Gene sets with false-discovery rate (FDR) corrected P values < 0.001 were determined to be significant. Results: Pre-treatment sequencing data were available for N=200 patients and the overall pathologic complete response (pCR) rate was 56.5%. Paired pre- and post-treatment sequencing data were available for N=58 patients with RD (N=27 from NeoSTOP and N=31 from NeoPACT). 165/23,797 gene sets were significantly enriched in post-treatment compared to pre-treatment samples. SHEN_SMARCA2_TARGETS_UP (M29) was among the top five significantly enriched gene set and was selected for further analysis based on the known role of the SWI/SNF complex in cell survival and the availability of clinically viable SMARCA2 degraders. Pre-treatment SMARCA4 expression was associated with pCR, with lower median expression noted in patients with RD compared to patients who achieved pCR (P=0.04). Pre-treatment SMARCA2 expression was not associated with pCR (P=0.48). On paired sample analysis there was pronounced downregulation of SMARCA4 in post-treatment compared to pre-treatment samples in all patients (mean Z=-1.46, P< 0.0001), in patients treated with chemotherapy (mean Z=-1.65, P< 0.0001), and in patients treated with chemoimmunotherapy (mean Z=-1.28, P< 0.0001). Although there was no difference in SMARCA2 expression between paired pre- and post-treatment samples (mean Z=0.03, P=0.83), there was enrichment of SMARCA2 target expression on GSEA analysis (NES=2.63, FDR q< 0.00001) indicating that SMARCA2 activity is compensating for therapy-induced SMARCA4 loss. Conclusions: We identified 165 gene sets that were significantly enriched in paired post-treatment compared to pre-treatment TNBC samples. Altered activity of the SMARCA2 subunit of the SWI/SNF chromatin remodeling complex was identified as one of the top hits. SMARCA2 and SMARCA4 are mutually exclusive subunits of SWI/SNF, and inactivation of both SMARCA2 and SMARCA4 is synthetic lethal. We observed an association of lower pre-treatment SMARCA4 expression with resistance to NAST and pronounced therapy-induced downregulation of SMARCA4 and induction of SMARCA2 target genes in post-treatment compared to pre-treatment samples, suggesting that SMARCA2 compensates for adaptive SMARCA4 loss. SMARCA2 degraders are in early-phase clinical trials and are known to induce synthetic lethality in SMARCA4-deficient cells. Our results identify adaptive loss of SMARCA4 and synthetic lethal targeting of compensatory SMARCA2 as an attractive therapeutic target in TNBC patients with RD after neoadjuvant chemotherapy or chemoimmunotherapy. Citation Format: Shane Stecklein, Rachel Yoder, Joshua Staley, Zachary Schmitt, Anne O'Dea, Lauren Nye, Deepti Satelli, Gregory Crane, Rashna Madan, Maura O'Neil, Andrew Godwin, Harsh Pathak, Qamar Khan, Joyce O'Shaughnessy, Priyanka Sharma. SMARCA2 compensation of SMARCA4 loss mediates adaptive resistance to neoadjuvant systemic therapy in triple-negative breast cancer: paired transcriptomic analysis of pre- and post-treatment samples from NeoSTOP and NeoPACT [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO2-13-07.

Abstract 2343: Adapting gene set enrichment analysis to single cell data

Abstract Gene Set Enrichment Analysis (GSEA) is a method for quantifying the activation of pathways and processes in gene expression data. GSEA works by ranking genes and testing if genes in annotated gene sets representing molecular pathways are overrepresented at the top or bottom of the ranked list. Standard GSEA assumes a gene-by-sample matrix with samples falling in two phenotype class labels and ranks genes based on correlation of expression and sample class labels. Single sample (ssGSEA) uses a similar approach to enrichment scoring but ranks genes in a single sample based on their expression. There has been recent interest in using the ssGSEA modality to measure pathway activity in individual cells from single cell RNA-sequencing (scRNA-seq) data. However, we have observed that ssGSEA scores in scRNA-seq cells are prone to uncertainty due to the characteristic sparsity of scRNA-seq data. This is because sparse data leads to large groups of tied genes, that is, genes with equal expression and subsequently equivalent positions in the ranked list. Because ssGSEA, which was designed for bulk gene expression technologies which yield much less sparse data, breaks ties arbitrarily, these ties can lead to variability in enrichment scores that is purely algorithmic and not a result of the underlying biology. To address this issue, we performed benchmarking to characterize the stability of scores using different enrichment scoring statistics, normalization methods, and aggregation of similar single cells. We have implemented the best practices identified using this analysis as Single Cell GSEA (scGSEA), a software tool which we will make freely available to researchers as open-source software to enable robust gene set enrichment scoring in scRNA-seq data. Citation Format: Alexander T. Wenzel, John Jun, Jill P. Mesirov. Adapting gene set enrichment analysis to single cell data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2343.

Pathways to selective vulnerability in neuromodulatory subcortical structures during the early pathogenic stages of Alzheimer’s disease

AbstractBackgroundThe two main catecholaminergic members of the isodendritic core nuclei are the substantia nigra (SN) and the locus coeruleus (LC). Despite cytoarchitectural and neurochemical similarities between the two nuclei, the LC is significantly more vulnerable to early degenerative changes in tauopathies like Alzheimer’s disease (AD) as compared to the SN. Here, we seek to use the gradient of vulnerability in these catecholaminergic nuclei to identify pathways associated with vulnerability in early AD pathological stages.MethodThis cohort includes individuals with no neuropathologic entities and Alzheimer’s disease proteinopathic stages. Individuals with TDP‐43, Lewy Body, or Chronic Traumatic Encephalopathy pathologic entities were excluded. The LC and SN was dissected from frozen human brainstem tissue using macroscopic anatomic features. To compare the LC and SN, only cases below Braak III with sufficient RNA from both nuclei were included. The Nanostring nCounter platform was used to probe specific pathways related to neurodegenerative processes. DBH and SLC6A3 served as confirmatory readouts of accurate anatomic sampling of the LC and SN. Differential gene expression analysis was used to evaluate patterns of gene expression that differentiate the two nuclei. Annotated gene sets were evaluated using global significance scores (GSS), a representation of cumulative t‐statistics for relevant genes.ResultThe LC and SN were collected from 26 cases (Figure 1) eligible for this analysis. Accurate dissections were confirmed by differential expression of DBH (p = 1.38e‐9) and SLC6A3 (p = 4.5e‐16). Among the differentially expressed genes (Figure 2), genes associated with microglia activation (GSS = 2.35), oxidative stress (GSS = 2.17), and lipid metabolism (GSS = 2.00) significantly differentiated the LC and SN.ConclusionThe LC and SN serve as examples of differentially vulnerable nuclei at the earliest neuropathologic stages of AD. Cell intrinsic factors, physiological demands, vascular contributions, and exposure to extrinsic factors like toxins are thought to contribute to the LC’s early vulnerability. Here, we show that neuroinflammation and oxidative stress pathways are likely central to differential vulnerability at the earliest stages of Alzheimer’s disease and may represent targets for intervention in prodromal stages.

A Gene Set-Integrated Approach for Predicting Disease-Associated Genes.

It is important to identify disease-associated genes for studying the pathogenic mechanism of complex diseases. Recently, models for disease gene prediction are dominantly based on molecular expression data and networks, including gene expression, protein expression, co-expression networks, protein-protein interaction networks, etc. One limitation of these methods is that they do not consider the knowledge of annotated gene sets representing known pathways or functionally-related sets of genes. In this study, we propose a new approach to predict disease-associated genes by integrating annotated gene sets data from the Molecular Signature Database (MSigDB). It first represents and integrates the different types of annotated gene sets in the MSigDB database in the form of the signal matrix. It then uses the signal matrix as the gene feature to train the disease gene prediction model. We compare our method with existing methods in predicting genes for five complex diseases. The results show that our method is superior to other methods. Further, we perform a case study on autism spectrum disorder (ASD). We find that ASD predictions are associated with ASD based on the statistical analysis of biological networks and independent ASD studies. The source code, prediction results and datasets are publicly available on https://github.com/genemine/GSI.git.

Machine learning on large scale perturbation screens for SARS-CoV-2 host factors identifies β-catenin/CBP inhibitor PRI-724 as a potent antiviral.

Expanding antiviral treatment options against SARS-CoV-2 remains crucial as the virus evolves under selection pressure which already led to the emergence of several drug resistant strains. Broad spectrum host-directed antivirals (HDA) are promising therapeutic options, however the robust identification of relevant host factors by CRISPR/Cas9 or RNA interference screens remains challenging due to low consistency in the resulting hits. To address this issue, we employed machine learning, based on experimental data from several knockout screens and a drug screen. We trained classifiers using genes essential for virus life cycle obtained from the knockout screens. The machines based their predictions on features describing cellular localization, protein domains, annotated gene sets from Gene Ontology, gene and protein sequences, and experimental data from proteomics, phospho-proteomics, protein interaction and transcriptomic profiles of SARS-CoV-2 infected cells. The models reached a remarkable performance suggesting patterns of intrinsic data consistency. The predicted HDF were enriched in sets of genes particularly encoding development, morphogenesis, and neural processes. Focusing on development and morphogenesis-associated gene sets, we found β-catenin to be central and selected PRI-724, a canonical β-catenin/CBP disruptor, as a potential HDA. PRI-724 limited infection with SARS-CoV-2 variants, SARS-CoV-1, MERS-CoV and IAV in different cell line models. We detected a concentration-dependent reduction in cytopathic effects, viral RNA replication, and infectious virus production in SARS-CoV-2 and SARS-CoV-1-infected cells. Independent of virus infection, PRI-724 treatment caused cell cycle deregulation which substantiates its potential as a broad spectrum antiviral. Our proposed machine learning concept supports focusing and accelerating the discovery of host dependency factors and identification of potential host-directed antivirals.

Enrichr-KG: bridging enrichment analysis across multiple libraries.

Gene and protein set enrichment analysis is a critical step in the analysis of data collected from omics experiments. Enrichr is a popular gene set enrichment analysis web-server search engine that contains hundreds of thousands of annotated gene sets. While Enrichr has been useful in providing enrichment analysis with many gene set libraries from different categories, integrating enrichment results across libraries and domains of knowledge can further hypothesis generation. To this end, Enrichr-KG is a knowledge graph database and a web-server application that combines selected gene set libraries from Enrichr for integrative enrichment analysis and visualization. The enrichment results are presented as subgraphs made of nodes and links that connect genes to their enriched terms. In addition, users of Enrichr-KG can add gene-gene links, as well as predicted genesto the subgraphs. This graphical representation of cross-library results with enriched and predicted genes can illuminate hidden associations between genes and annotated enriched terms from across datasets and resources. Enrichr-KG currently serves 26 gene set libraries from different categories that include transcription, pathways, ontologies, diseases/drugs, and cell types. To demonstrate the utility of Enrichr-KG we provide several case studies. Enrichr-KG is freely available at: https://maayanlab.cloud/enrichr-kg.

Redefining normal breast cell populations using long noncoding RNAs.

Single-cell RNAseq has allowed unprecedented insight into gene expression across different cell populations in normal tissue and disease states. However, almost all studies rely on annotated gene sets to capture gene expression levels and sequencing reads that do not align to known genes are discarded. Here, we discover thousands of long noncoding RNAs (lncRNAs) expressed in human mammary epithelial cells and analyze their expression in individual cells of the normal breast. We show that lncRNA expression alone can discriminate between luminal and basal cell types and define subpopulations of both compartments. Clustering cells based on lncRNA expression identified additional basal subpopulations, compared to clustering based on annotated gene expression, suggesting that lncRNAs can provide an additional layer of information to better distinguish breast cell subpopulations. In contrast, these breast-specific lncRNAs poorly distinguish brain cell populations, highlighting the need to annotate tissue-specific lncRNAs prior to expression analyses. We also identified a panel of 100 breast lncRNAs that could discern breast cancer subtypes better than protein-coding markers. Overall, our results suggest that lncRNAs are an unexplored resource for new biomarker and therapeutic target discovery in the normal breast and breast cancer subtypes.

PascalX: a Python library for GWAS gene and pathway enrichment tests.

'PascalX' is a Python library providing fast and accurate tools for mapping SNP-wise GWAS summary statistics. Specifically, it allows for scoring genes and annotated gene sets for enrichment signals based on data from, both, single GWAS and pairs of GWAS. The gene scores take into account the correlation pattern between SNPs. They are based on the cumulative density function of a linear combination of χ2 distributed random variables, which can be calculated either approximately or exactly to high precision. Acceleration via multithreading and GPU is supported. The code of PascalX is fully open source and well suited as a base for method development in the GWAS enrichment test context. The source code is available at https://github.com/BergmannLab/PascalX and archived under doi://10.5281/zenodo.4429922. A user manual with usage examples is available at https://bergmannlab.github.io/PascalX/.

Abstract 6568: Extended support for model organisms: The next frontier for the molecular signatures database

Abstract In 2005, we introduced the Gene Set Enrichment Analysis (GSEA) method to enable the identification and estimation of significance of activated biological pathways and processes in molecular data. Serving a community of over 300,000 registered users, and contributing scientific analysis to more than 30,000 publications, GSEA has become ubiquitous in gene expression analysis and particularly in cancer research. The power of GSEA relies, in a large part, on the Molecular Signatures Database (MSigDB) that provides tens of thousands of expertly curated and annotated gene sets representing cellular processes, canonical pathways, response signatures, etc., derived from prior studies, public data, and curated pathway databases. Here we describe an important recent addition to the GSEA/MSigDB resource. Historically, the GSEA-MSigDB resource has focused specifically on human data, offering guidance and support for analysis of model organism data through limited provision of orthology mapping files. In recognition of the importance of the mouse as a model organism for cancer research, we recently significantly expanded our support for mouse data in two ways. (1) We introduced Mouse MSigDB with a total of about 15,000 sets that are provided in the native mouse gene symbol space and are derived from published mouse datasets and mouse-focused resources. This allows GSEA analysis of mouse datasets without the need for orthology conversion. (2) For investigators who wish to use MSigDB’s human gene sets to analyze mouse data, or vice versa, we also implemented a new procedure for orthology mapping gene identifiers in an input dataset to match the identifiers of the gene sets. The new mapping considers the particular requirements of gene set enrichment analysis, and incorporates high confidence gene ortholog data from the Alliance of Genome Resources and ortholog-match refinement utilizing Ensembl’s ortholog datasets. Together, these new additions offer a substantial step forward for making mouse a first-class citizen of the GSEA-MSigDB ecosystem. The new Mouse MSigDB, and the full GSEA-MSigDB resource, is available at gsea-msigdb.org. Citation Format: Anthony S. Castanza, Jill M. Recla, David Eby, Alexander T. Wenzel, Helga Thorvaldsdottir, Carol J. Bult, Jill P. Mesirov. Extended support for model organisms: The next frontier for the molecular signatures database [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 6568.

Mining Potential Drug Targets and Constructing Diagnostic Models for Heart Failure Based on miRNA-mRNA Networks.

Heart failure (HF) is a globally prevalent cardiovascular disease, but effective drug targets and diagnostic models are still lacking. This study was designed to investigate effective drug targets and diagnostic models for HF in terms of miRNA targets, hoping to contribute to the understanding and treatment of HF. Using HF miRNA and gene expression profile data from the GEO database, we analyzed differentially expressed miRNAs/gene identification in HF using Limma and predicted miRNA targets by the online TargetScan database. Subsequently, gene set enrichment analysis and annotation were performed using WebGestaltR package. Protein-protein interactions were identified using the STRING database. The proximity of drugs to treat HF was also calculated and predicted for potential target therapeutic drug. In addition, further drug identification was performed by molecular docking. Finally, diagnostic models were constructed based on differential miRNAs. The GEO dataset was used to screen 66 differentially expressed miRNAs, incorporating 56 downregulated miRNAs and 10 upregulated miRNAs. The JAK-STAT signaling pathway, MAPK signaling pathway, p53 signaling pathway, Prolactin signaling pathway, and TGF-beta signaling pathway were enriched, as shown by KEGG enrichment analysis on the target genes. In addition, we found that 83 genes were upregulated and 92 genes were downregulated in HF patients vs. healthy individuals. Based on the inflammation-related score, hypoxia-related score, and energy metabolism-related score, we identified key miRNA-mRNA pairs and constructed an interaction network. Following that, TAP1, which had the highest expression and network connectivity in acute HF with crystal and molecular docking studies, was selected as a key candidate gene in the network. And the compound DB04847 was selected to produce a large number of favorable interactions with TAP1 protein. Finally, we constructed two diagnostic models based on the differential miRNAs hsa-miR-6785-5p and hsa-miR-4443. In conclusion, we identified TAP1, a key candidate gene in the diagnosis and treatment of HF, and determined that compound DB04847 is highly likely to be a potential inhibitor of TAP1. The TAP1 gene was also found to be regulated by hsa-miR-6785-5p and hsa-miR-4443, and a diagnostic model was constructed. This provides a new promising direction to improve the diagnosis, prognosis, and treatment outcome and guide more effective immunotherapy strategies of HF.

DEMA: a distance-bounded energy-field minimization algorithm to model and layout biomolecular networks with quantitative features.

SummaryIn biology, graph layout algorithms can reveal comprehensive biological contexts by visually positioning graph nodes in their relevant neighborhoods. A layout software algorithm/engine commonly takes a set of nodes and edges and produces layout coordinates of nodes according to edge constraints. However, current layout engines normally do not consider node, edge or node-set properties during layout and only curate these properties after the layout is created. Here, we propose a new layout algorithm, distance-bounded energy-field minimization algorithm (DEMA), to natively consider various biological factors, i.e., the strength of gene-to-gene association, the gene’s relative contribution weight and the functional groups of genes, to enhance the interpretation of complex network graphs. In DEMA, we introduce a parameterized energy model where nodes are repelled by the network topology and attracted by a few biological factors, i.e., interaction coefficient, effect coefficient and fold change of gene expression. We generalize these factors as gene weights, protein–protein interaction weights, gene-to-gene correlations and the gene set annotations—four parameterized functional properties used in DEMA. Moreover, DEMA considers further attraction/repulsion/grouping coefficient to enable different preferences in generating network views. Applying DEMA, we performed two case studies using genetic data in autism spectrum disorder and Alzheimer’s disease, respectively, for gene candidate discovery. Furthermore, we implement our algorithm as a plugin to Cytoscape, an open-source software platform for visualizing networks; hence, it is convenient. Our software and demo can be freely accessed at http://discovery.informatics.uab.edu/dema.Supplementary information Supplementary data are available at Bioinformatics online.

Abstract 5032: Data driven refinement of gene signatures for enrichment analysis and cell state characterization

Abstract In the study of human disease, the use of gene expression data has been crucial to the functional characterization of changes in molecular pathway activity and for identifying targets for novel treatments. However, the interpretation of this data is often complicated by its high dimensionality and the difficulty of identifying biological signals within a list of differentially expressed genes. Gene Set Enrichment Analysis (GSEA) is a community standard method for identifying pathway enrichment in gene expression data by testing whether a set of genes whose expression would indicate the activity of a specific process or phenotype are coordinately up- or downregulated more than would be expected by chance. As GSEA relies on high quality gene sets with coordinately regulated member genes, we maintain the Molecular Signatures Database (MSigDB) which contains 9 collections of curated, annotated gene sets representing different biological pathways and processes. Over time, we have observed that some of the MSigDB gene sets, especially those that are manually curated or defined in a very specific biological context, may not provide a sensitive and specific enough co-regulation signature for their corresponding phenotype. In response, we have created a data-driven, matrix-factorization-based refinement method to build more sensitive and specific gene sets. This method incorporates large-scale datasets from multiple sources including compendia such as the Cancer Dependency Map as well as curated protein-protein interaction networks. We will present the initial results of this refinement method as well as our ongoing work which will yield a new collection of refined gene sets that will be made freely available in MSigDB for use with GSEA and many other applications. Citation Format: Alexander T. Wenzel, Pablo Tamayo, Jill P. Mesirov. Data driven refinement of gene signatures for enrichment analysis and cell state characterization [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5032.

Rare Germline Variants Are Associated with Rapid Biochemical Recurrence After Radical Prostate Cancer Treatment: A Pan Prostate Cancer Group Study

BackgroundGermline variants explain more than a third of prostate cancer (PrCa) risk, but very few associations have been identified between heritable factors and clinical progression. ObjectiveTo find rare germline variants that predict time to biochemical recurrence (BCR) after radical treatment in men with PrCa and understand the genetic factors associated with such progression. Design, setting, and participantsWhole-genome sequencing data from blood DNA were analysed for 850 PrCa patients with radical treatment from the Pan Prostate Cancer Group (PPCG) consortium from the UK, Canada, Germany, Australia, and France. Findings were validated using 383 patients from The Cancer Genome Atlas (TCGA) dataset. Outcome measurements and statistical analysisA total of 15,822 rare (MAF <1%) predicted-deleterious coding germline mutations were identified. Optimal multifactor and univariate Cox regression models were built to predict time to BCR after radical treatment, using germline variants grouped by functionally annotated gene sets. Models were tested for robustness using bootstrap resampling. Results and limitationsOptimal Cox regression multifactor models showed that rare predicted-deleterious germline variants in “Hallmark” gene sets were consistently associated with altered time to BCR. Three gene sets had a statistically significant association with risk-elevated outcome when modelling all samples: PI3K/AKT/mTOR, Inflammatory response, and KRAS signalling (up). PI3K/AKT/mTOR and KRAS signalling (up) were also associated among patients with higher-grade cancer, as were Pancreas-beta cells, TNFA signalling via NKFB, and Hypoxia, the latter of which was validated in the independent TCGA dataset. ConclusionsWe demonstrate for the first time that rare deleterious coding germline variants robustly associate with time to BCR after radical treatment, including cohort-independent validation. Our findings suggest that germline testing at diagnosis could aid clinical decisions by stratifying patients for differential clinical management. Patient summaryProstate cancer patients with particular genetic mutations have a higher chance of relapsing after initial radical treatment, potentially providing opportunities to identify patients who might need additional treatments earlier.

Predicted mouse interactome and network-based interpretation of differentially expressed genes.

The house mouse or Mus musculus has become a premier mammalian model for genetic research due to its genetic and physiological similarities to humans. It brought mechanistic insights into numerous human diseases and has been routinely used to assess drug efficiency and toxicity, as well as to predict patient responses. To facilitate molecular mechanism studies in mouse, we present the Mouse Interactome Database (MID, Version 1), which includes 155,887 putative functional associations between mouse protein-coding genes inferred from functional association evidence integrated from 9 public databases. These putative functional associations are expected to cover 19.32% of all mouse protein interactions, and 26.02% of these function associations may represent protein interactions. On top of MID, we developed a gene set linkage analysis (GSLA) web tool to annotate potential functional impacts from observed differentially expressed genes. Two case studies show that the MID/GSLA system provided precise and informative annotations that other widely used gene set annotation tools, such as PANTHER and DAVID, did not. Both MID and GSLA are accessible through the website http://mouse.biomedtzc.cn.

BlitzGSEA: efficient computation of gene set enrichment analysis through gamma distribution approximation.

The identification of pathways and biological processes from differential gene expression is central for interpretation of data collected by transcriptomics assays. Gene set enrichment analysis (GSEA) is the most commonly used algorithm to calculate the significance of the relevancy of an annotated gene set with a differential expression signature. To compute significance, GSEA implements permutation tests which are slow and inaccurate for comparing many differential expression signatures to thousands of annotated gene sets. Here, we present blitzGSEA, an algorithm that is based on the same running sum statistic as GSEA, but instead of performing permutations, blitzGSEA approximates the enrichment score probabilities based on Gamma distributions. blitzGSEA achieves significant improvement in performance compared with prior GSEA implementations, while approximating small P-values more accurately. The data, a python package, together with all source code, and a detailed user guide are available from GitHub at: https://github.com/MaayanLab/blitzgsea. Supplementary data are available at Bioinformatics online.