A nonredundant role for T cell-derived interleukin 22 in antibacterial defense of colonic crypts.

Interleukin (IL)-22 is central to immune defense at barrier sites. We examined the contributions of innate lymphoid cell (ILC) and T cell-derived IL-22 during Citrobacter rodentium (C.r) infection using mice that both report Il22 expression and allow lineage-specific deletion. ILC-derived IL-22 activated STAT3 in C.r-colonized surface intestinal epithelial cells (IECs) but only temporally restrained bacterial growth. T cell-derived IL-22 induced a more robust and extensive activation of STAT3 in IECs, including IECs lining colonic crypts, and T cell-specific deficiency of IL-22 led to pathogen invasion of the crypts and increased mortality. This reflected a requirement for T cell-derived IL-22 for the expression of a host-protective transcriptomic program that included AMPs, neutrophil-recruiting chemokines, and mucin-related molecules, and it restricted IFNγ-induced proinflammatory genes. Our findings demonstrate spatiotemporal differences in the production and action of IL-22 by ILCs and T cells during infection and reveal an indispensable role for IL-22-producing T cells in the protection of the intestinal crypts.

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

SUMMARY: In 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.

PAGER-CoV: a comprehensive collection of pathways, annotated gene-lists and gene signatures for coronavirus disease studies.

PAGER-CoV (http://discovery.informatics.uab.edu/PAGER-CoV/) is a new web-based database that can help biomedical researchers interpret coronavirus-related functional genomic study results in the context of curated knowledge of host viral infection, inflammatory response, organ damage, and tissue repair. The new database consists of 11 835 PAGs (Pathways, Annotated gene-lists, or Gene signatures) from 33 public data sources. Through the web user interface, users can search by a query gene or a query term and retrieve significantly matched PAGs with all the curated information. Users can navigate from a PAG of interest to other related PAGs through either shared PAG-to-PAG co-membership relationships or PAG-to-PAG regulatory relationships, totaling 19 996 993. Users can also retrieve enriched PAGs from an input list of COVID-19 functional study result genes, customize the search data sources, and export all results for subsequent offline data analysis. In a case study, we performed a gene set enrichment analysis (GSEA) of a COVID-19 RNA-seq data set from the Gene Expression Omnibus database. Compared with the results using the standard PAGER database, PAGER-CoV allows for more sensitive matching of known immune-related gene signatures. We expect PAGER-CoV to be invaluable for biomedical researchers to find molecular biology mechanisms and tailored therapeutics to treat COVID-19 patients.

BEERE: a web server for biomedical entity expansion, ranking and explorations.

BEERE (Biomedical Entity Expansion, Ranking and Explorations) is a new web-based data analysis tool to help biomedical researchers characterize any input list of genes/proteins, biomedical terms or their combinations, i.e. 'biomedical entities', in the context of existing literature. Specifically, BEERE first aims to help users examine the credibility of known entity-to-entity associative or semantic relationships supported by database or literature references from the user input of a gene/term list. Then, it will help users uncover the relative importance of each entity-a gene or a term-within the user input by computing the ranking scores of all entities. At last, it will help users hypothesize new gene functions or genotype-phenotype associations by an interactive visual interface of constructed global entity relationship network. The output from BEERE includes: a list of the original entities matched with known relationships in databases; any expanded entities that may be generated from the analysis; the ranks and ranking scores reported with statistical significance for each entity; and an interactive graphical display of the gene or term network within data provenance annotations that link to external data sources. The web server is free and open to all users with no login requirement and can be accessed at http://discovery.informatics.uab.edu/beere/.

PAGER 2.0: an update to the pathway, annotated-list and gene-signature electronic repository for Human Network Biology.

Integrative Gene-set, Network and Pathway Analysis (GNPA) is a powerful data analysis approach developed to help interpret high-throughput omics data. In PAGER 1.0, we demonstrated that researchers can gain unbiased and reproducible biological insights with the introduction of PAGs (Pathways, Annotated-lists and Gene-signatures) as the basic data representation elements. In PAGER 2.0, we improve the utility of integrative GNPA by significantly expanding the coverage of PAGs and PAG-to-PAG relationships in the database, defining a new metric to quantify PAG data qualities, and developing new software features to simplify online integrative GNPA. Specifically, we included 84 282 PAGs spanning 24 different data sources that cover human diseases, published gene-expression signatures, drug-gene, miRNA-gene interactions, pathways and tissue-specific gene expressions. We introduced a new normalized Cohesion Coefficient (nCoCo) score to assess the biological relevance of genes inside a PAG, and RP-score to rank genes and assign gene-specific weights inside a PAG. The companion web interface contains numerous features to help users query and navigate the database content. The database content can be freely downloaded and is compatible with third-party Gene Set Enrichment Analysis tools. We expect PAGER 2.0 to become a major resource in integrative GNPA. PAGER 2.0 is available at http://discovery.informatics.uab.edu/PAGER/.

Repositioning drugs by targeting network modules: a Parkinson's disease case study.

BACKGROUND: Much effort has been devoted to the discovery of specific mechanisms between drugs and single targets to date. However, as biological systems maintain homeostasis at the level of functional networks robustly controlling the internal environment, such networks commonly contain multiple redundant mechanisms designed to counteract loss or perturbation of a single member of the network. As such, investigation of therapeutics that target dysregulated pathways or processes, rather than single targets, may identify agents that function at a level of the biological organization more relevant to the pathology of complex diseases such as Parkinson's Disease (PD). Genome-wide association studies (GWAS) in PD have identified common variants underlying disease susceptibility, while gene expression microarray data provide genome-wide transcriptional profiles. These genomic studies can illustrate upstream perturbations causing the dysfunction in signaling pathways and downstream biochemical mechanisms leading to the PD phenotype. We hypothesize that drugs acting at the level of a gene expression module specific to PD can overcome the lack of efficacy associated with targeting a single gene in polygenic diseases. Thus, this approach represents a promising new direction for module-based drug discovery in human diseases such as PD. RESULTS: We built a framework that integrates GWAS data with gene co-expression modules from tissues representing three brain regions-the frontal gyrus, the lateral substantia, and the medial substantia in PD patients. Using weighted gene correlation network analysis (WGCNA) software package in R, we conducted enrichment analysis of data from a GWAS of PD. This led to the identification of two over-represented PD-specific gene co-expression network modules: the Brown Module (Br) containing 449 genes and the Turquoise module (T) containing 905 genes. Further enrichment analysis identified four functional pathways within the Br module (cellular respiration, intracellular transport, energy coupled proton transport against the electrochemical gradient, and microtubule-based movement), and one functional pathway within the T module (M-phase). Next, we utilized drug-protein regulatory relationship databases (DMAP) and developed a Drug Effect Sum Score (DESS) to evaluate all candidate drugs that might restore gene expression to normal level across the Br and T modules. Among the drugs with the 12 highest DESS scores, 5 had been reported as potential treatments for PD and 6 hold potential repositioning applications. CONCLUSION: In this study, we present a systems pharmacology framework which draws on genetic data from GWAS and gene expression microarray data to reposition drugs for PD. Our innovative approach integrates gene co-expression modules with biomolecular interaction network analysis to identify network modules critical to the PD pathway and disease mechanism. We quantify the positive effects of drugs in a DESS score that is based on known drug-target activity profiles. Our results illustrate that this modular approach is promising for repositioning drugs for use in polygenic diseases such as PD, and is capable of addressing challenges of the hindered gene target in drug repositioning approaches to date.

PAGER: constructing PAGs and new PAG-PAG relationships for network biology.

In this article, we described a new database framework to perform integrative "gene-set, network, and pathway analysis" (GNPA). In this framework, we integrated heterogeneous data on pathways, annotated list, and gene-sets (PAGs) into a PAG electronic repository (PAGER). PAGs in the PAGER database are organized into P-type, A-type and G-type PAGs with a three-letter-code standard naming convention. The PAGER database currently compiles 44 313 genes from 5 species including human, 38 663 PAGs, 324 830 gene-gene relationships and two types of 3 174 323 PAG-PAG regulatory relationships-co-membership based and regulatory relationship based. To help users assess each PAG's biological relevance, we developed a cohesion measure called Cohesion Coefficient (CoCo), which is capable of disambiguating between biologically significant PAGs and random PAGs with an area-under-curve performance of 0.98. PAGER database was set up to help users to search and retrieve PAGs from its online web interface. PAGER enable advanced users to build PAG-PAG regulatory networks that provide complementary biological insights not found in gene set analysis or individual gene network analysis. We provide a case study using cancer functional genomics data sets to demonstrate how integrative GNPA help improve network biology data coverage and therefore biological interpretability. The PAGER database can be accessible openly at http://discovery.informatics.iupui.edu/PAGER/.

DMAP: a connectivity map database to enable identification of novel drug repositioning candidates.

BACKGROUND: Drug repositioning is a cost-efficient and time-saving process to drug development compared to traditional techniques. A systematic method to drug repositioning is to identify candidate drug's gene expression profiles on target disease models and determine how similar these profiles are to approved drugs. Databases such as the CMAP have been developed recently to help with systematic drug repositioning. METHODS: To overcome the limitation of connectivity maps on data coverage, we constructed a comprehensive in silico drug-protein connectivity map called DMAP, which contains directed drug-to-protein effects and effect scores. The drug-to-protein effect scores are compiled from all database entries between the drug and protein have been previously observed and provide a confidence measure on the quality of such drug-to-protein effects. RESULTS: In DMAP, we have compiled the direct effects between 24,121 PubChem Compound ID (CID), which were mapped from 289,571 chemical entities recognized from public literature, and 5,196 reviewed Uniprot proteins. DMAP compiles a total of 438,004 chemical-to-protein effect relationships. Compared to CMAP, DMAP shows an increase of 221 folds in the number of chemicals and 1.92 fold in the number of ATC codes. Furthermore, by overlapping DMAP chemicals with the approved drugs with known indications from the TTD database and literature, we obtained 982 drugs and 622 diseases; meanwhile, we only obtained 394 drugs with known indication from CMAP. To validate the feasibility of applying new DMAP for systematic drug repositioning, we compared the performance of DMAP and the well-known CMAP database on two popular computational techniques: drug-drug-similarity-based method with leave-one-out validation and Kolmogorov-Smirnov scoring based method. In drug-drug-similarity-based method, the drug repositioning prediction using DMAP achieved an Area-Under-Curve (AUC) score of 0.82, compared with that using CMAP, AUC = 0.64. For Kolmogorov-Smirnov scoring based method, with DMAP, we were able to retrieve several drug indications which could not be retrieved using CMAP. DMAP data can be queried using the existing C2MAP server or downloaded freely at: http://bio.informatics.iupui.edu/cmaps CONCLUSIONS: Reliable measurements of how drug affect disease-related proteins are critical to ongoing drug development in the genome medicine era. We demonstrated that DMAP can help drug development professionals assess drug-to-protein relationship data and improve chances of success for systematic drug repositioning efforts.

SLDR: a computational technique to identify novel genetic regulatory relationships.

We developed a new computational technique called Step-Level Differential Response (SLDR) to identify genetic regulatory relationships. Our technique takes advantages of functional genomics data for the same species under different perturbation conditions, therefore complementary to current popular computational techniques. It can particularly identify "rare" activation/inhibition relationship events that can be difficult to find in experimental results. In SLDR, we model each candidate target gene as being controlled by N binary-state regulators that lead to ≤2N observable states ("step-levels") for the target. We applied SLDR to the study of the GEO microarray data set GSE25644, which consists of 158 different mutant S. cerevisiae gene expressional profiles. For each target gene t, we first clustered ordered samples into various clusters, each approximating an observable step-level of t to screen out the "de-centric" target. Then, we ordered each gene x as a candidate regulator and aligned t to x for the purpose of examining the step-level correlations between low expression set of x (Ro) and high expression set of x (Rh) from the regulator x to t, by finding max f(t, x): |Ro-Rh| over all candidate × in the genome for each t. We therefore obtained activation and inhibitions events from different combinations of Ro and Rh. Furthermore, we developed criteria for filtering out less-confident regulators, estimated the number of regulators for each target t, and evaluated identified top-ranking regulator-target relationship. Our results can be cross-validated with the Yeast Fitness database. SLDR is also computationally efficient with o(N²) complexity. In summary, we believe SLDR can be applied to the mining of functional genomics big data for future network biology and network medicine applications.

The association between polypharmacy and health-related quality of life among older adults with prostate cancer.

INTRODUCTION: Older adults with prostate cancer (PC) are at risk of polypharmacy, which further complicates disease management and health-related quality of life (HRQoL). This study evaluated the association between polypharmacy and HRQoL among Medicare beneficiaries with PC. MATERIALS AND METHODS: This observational, retrospective study analyzed data from the Surveillance, Epidemiology, and End Results (SEER) Medicare Health Outcomes Survey (MHOS) data resource. Beneficiaries aged ≥65 and enrolled in Medicare Advantage Organizations were included if they had a PC diagnosis and continuously enrolled in Part D for 12 months prior to the completion of MHOS. Polypharmacy was determined based on the unique number of concurrent Part D prescriptions during 12 months before survey: no polypharmacy (NP, n = 0-4), polypharmacy (PP, n = 5-9), and excessive polypharmacy (EPP, n ≥ 10). HRQoL was assessed using the Physical and Mental Component Summary T-scores (PCS and MCS, respectively) in MHOS. ANOVA and Pearson's Chi-Square tests were performed to assess variances between polypharmacy and continuous/categorical variables. Multivariate linear regression models with generalized estimating equations were used to assess the association between polypharmacy and HRQoL. The severely impaired HRQoL cohort was identified based on normalized z-scores of PCS and MCS. Odds ratios were calculated to prioritize drug-drug and class-class pairs associated with patients with severely impaired HRQoL. RESULTS: Data from 16,573 beneficiaries (24,126 records) showed that 44.4% had PP and 10.1% had EPP. Beneficiaries with PP and EPP had significantly lower mean PCS and MCS scores compared to those without polypharmacy (p < 0.001). After adjusting for covariates, beneficiaries with EPP had clinically significantly lower PCS (adjusted marginal difference: -8.47 [-9.00, -7.94]) and MCS (adjusted marginal difference: -4.32 [-4.89, -3.75]) compared to the NP group. Top-ranked drug-drug pairs like tiotropium bromide and oxycodone/acetaminophen exhibited significant associations with HRQoL decline. Analysis of class-class pairs highlighted (1) corticosteroid hormone receptor agonists and opioid agonists and (2) benzodiazepines and adrenergic beta2-agonists as having significant associations with HRQoL decline. DISCUSSION: Polypharmacy exhibits a significant association with HRQoL declines among older adults with PC.

PAGER-scFGA: unveiling cell functions and molecular mechanisms in cell trajectories through single-cell functional genomics analysis.

Background: Understanding how cells and tissues respond to stress factors and perturbations during disease processes is crucial for developing effective prevention, diagnosis, and treatment strategies. Single-cell RNA sequencing (scRNA-seq) enables high-resolution identification of cells and exploration of cell heterogeneity, shedding light on cell differentiation/maturation and functional differences. Recent advancements in multimodal sequencing technologies have focused on improving access to cell-specific subgroups for functional genomics analysis. To facilitate the functional annotation of cell groups and characterization of molecular mechanisms underlying cell trajectories, we introduce the Pathways, Annotated Gene Lists, and Gene Signatures Electronic Repository for Single-Cell Functional Genomics Analysis (PAGER-scFGA). Results: We have developed PAGER-scFGA, which integrates cell functional annotations and gene-set enrichment analysis into popular single-cell analysis pipelines such as Scanpy. Using differentially expressed genes (DEGs) from pairwise cell clusters, PAGER-scFGA infers cell functions through the enrichment of potential cell-marker genesets. Moreover, PAGER-scFGA provides pathways, annotated gene lists, and gene signatures (PAGs) enriched in specific cell subsets with tissue compositions and continuous transitions along cell trajectories. Additionally, PAGER-scFGA enables the construction of a gene subcellular map based on DEGs and allows examination of the gene functional compartments (GFCs) underlying cell maturation/differentiation. In a real-world case study of mouse natural killer (mNK) cells, PAGER-scFGA revealed two major stages of natural killer (NK) cells and three trajectories from the precursor stage to NK T-like mature stage within blood, spleen, and bone marrow tissues. As the trajectories progress to later stages, the DEGs exhibit greater divergence and variability. However, the DEGs in different trajectories still interact within a network during NK cell maturation. Notably, PAGER-scFGA unveiled cell cytotoxicity, exocytosis, and the response to interleukin (IL) signaling pathways and associated network models during the progression from precursor NK cells to mature NK cells. Conclusion: PAGER-scFGA enables in-depth exploration of functional insights and presents a comprehensive knowledge map of gene networks and GFCs, which can be utilized for future studies and hypothesis generation. It is expected to become an indispensable tool for inferring cell functions and detecting molecular mechanisms within cell trajectories in single-cell studies. The web app (accessible at https://au-singlecell.streamlit.app/) is publicly available.

Body Weight Correlates with Molecular Variances in Patients with Cancer.

Overweight and obesity are identified by a high body mass index (BMI) and carry significant health risks due to associated comorbidities. Although epidemiologic data connect overweight/obesity with 13 cancer types, a better understanding of the molecular mechanisms underlying this correlation is needed to improve prevention and treatment strategies. In this study, we conducted a comprehensive analysis of molecular differences between overweight or obese patients and normal weight patients across 14 different cancer types from The Cancer Genome Atlas. Using the propensity score weighting algorithm to control for confounding factors, obesity-specific mutational features were identified, such as higher mutation burden in rectal cancer and biased mutational signatures in other cancers. Differentially expressed genes (DEG) in tumors from patients with overweight/obesity were predominantly upregulated and enriched in inflammatory and hormone-related pathways. These DEGs were significantly associated with survival rates in various cancer types, highlighting the impact of elevated body fat on gene expression profiles and clinical outcomes in patients with cancer. Interestingly, while high BMI seemed to have a negative impact on most cancer types, the normal weight-biased mutational and gene expression patterns indicated overweight/obesity may be beneficial in endometrial cancer, suggesting the presence of an "obesity paradox" in this context. Body fat also significantly impacted the tumor microenvironment by modulating immune cell infiltration, underscoring the importance of understanding the interplay between weight and immune response in cancer progression. Together, this study systematically elucidates the molecular differences corresponding to body weight in multiple cancer types, offering potentially critical insights for developing precision therapy for patients with cancer. SIGNIFICANCE: Elucidation of the complex interplay between body weight and the molecular landscape of cancer could potentially guide tailored therapies and improve patient management amid the global obesity crisis.

Explorative Discovery of Gene Signatures and Clinotypes in Glioblastoma Cancer Through GeneTerrain Knowledge Map Representation.

This study introduces the GeneTerrain Knowledge Map Representation (GTKM), a novel method for visualizing gene expression data in cancer research. GTKM leverages protein-protein interactions to graphically display differentially expressed genes (DEGs) on a 2-dimensional contour plot, offering a more nuanced understanding of gene interactions and expression patterns compared to traditional heatmap methods. The research demonstrates GTKM's utility through four case studies on glioblastoma (GBM) datasets, focusing on survival analysis, subtype identification, IDH1 mutation analysis, and drug sensitivities of different tumor cell lines. Additionally, a prototype website has been developed to showcase these findings, indicating the method's adaptability for various cancer types. The study reveals that GTKM effectively identifies gene patterns associated with different clinical outcomes in GBM, and its profiles enable the identification of sub-gene signature patterns crucial for predicting survival. The methodology promises significant advancements in precision medicine, providing a powerful tool for understanding complex gene interactions and identifying potential therapeutic targets in cancer treatment.

Tumor-Associated Macrophage Targeting of Nanomedicines in Cancer Therapy.

The tumor microenvironment (TME) is pivotal in tumor growth and metastasis, aligning with the "Seed and Soil" theory. Within the TME, tumor-associated macrophages (TAMs) play a central role, profoundly influencing tumor progression. Strategies targeting TAMs have surfaced as potential therapeutic avenues, encompassing interventions to block TAM recruitment, eliminate TAMs, reprogram M2 TAMs, or bolster their phagocytic capabilities via specific pathways. Nanomaterials including inorganic materials, organic materials for small molecules and large molecules stand at the forefront, presenting significant opportunities for precise targeting and modulation of TAMs to enhance therapeutic efficacy in cancer treatment. This review provides an overview of the progress in designing nanoparticles for interacting with and influencing the TAMs as a significant strategy in cancer therapy. This comprehensive review presents the role of TAMs in the TME and various targeting strategies as a promising frontier in the ever-evolving field of cancer therapy. The current trends and challenges associated with TAM-based therapy in cancer are presented.

PAGER Web APP: An Interactive, Online Gene Set and Network Interpretation Tool for Functional Genomics.

Functional genomics studies have helped researchers annotate differentially expressed gene lists, extract gene expression signatures, and identify biological pathways from omics profiling experiments conducted on biological samples. The current geneset, network, and pathway analysis (GNPA) web servers, e.g., DAVID, EnrichR, WebGestaltR, or PAGER, do not allow automated integrative functional genomic downstream analysis. In this study, we developed a new web-based interactive application, "PAGER Web APP", which supports online R scripting of integrative GNPA. In a case study of melanoma drug resistance, we showed that the new PAGER Web APP enabled us to discover highly relevant pathways and network modules, leading to novel biological insights. We also compared PAGER Web APP's pathway analysis results retrieved among PAGER, EnrichR, and WebGestaltR to show its advantages in integrative GNPA. The interactive online web APP is publicly accessible from the link, https://aimed-lab.shinyapps.io/PAGERwebapp/.

WINNER: A network biology tool for biomolecular characterization and prioritization.

Background and contribution: In network biology, molecular functions can be characterized by network-based inference, or "guilt-by-associations." PageRank-like tools have been applied in the study of biomolecular interaction networks to obtain further the relative significance of all molecules in the network. However, there is a great deal of inherent noise in widely accessible data sets for gene-to-gene associations or protein-protein interactions. How to develop robust tests to expand, filter, and rank molecular entities in disease-specific networks remains an ad hoc data analysis process. Results: We describe a new biomolecular characterization and prioritization tool called Weighted In-Network Node Expansion and Ranking (WINNER). It takes the input of any molecular interaction network data and generates an optionally expanded network with all the nodes ranked according to their relevance to one another in the network. To help users assess the robustness of results, WINNER provides two different types of statistics. The first type is a node-expansion p-value, which helps evaluate the statistical significance of adding "non-seed" molecules to the original biomolecular interaction network consisting of "seed" molecules and molecular interactions. The second type is a node-ranking p-value, which helps evaluate the relative statistical significance of the contribution of each node to the overall network architecture. We validated the robustness of WINNER in ranking top molecules by spiking noises in several network permutation experiments. We have found that node degree-preservation randomization of the gene network produced normally distributed ranking scores, which outperform those made with other gene network randomization techniques. Furthermore, we validated that a more significant proportion of the WINNER-ranked genes was associated with disease biology than existing methods such as PageRank. We demonstrated the performance of WINNER with a few case studies, including Alzheimer's disease, breast cancer, myocardial infarctions, and Triple negative breast cancer (TNBC). In all these case studies, the expanded and top-ranked genes identified by WINNER reveal disease biology more significantly than those identified by other gene prioritizing software tools, including Ingenuity Pathway Analysis (IPA) and DiAMOND. Conclusion: WINNER ranking strongly correlates to other ranking methods when the network covers sufficient node and edge information, indicating a high network quality. WINNER users can use this new tool to robustly evaluate a list of candidate genes, proteins, or metabolites produced from high-throughput biology experiments, as long as there is available gene/protein/metabolic network information.

An in vivo model of glioblastoma radiation resistance identifies long noncoding RNAs and targetable kinases.

Key molecular regulators of acquired radiation resistance in recurrent glioblastoma (GBM) are largely unknown, with a dearth of accurate preclinical models. To address this, we generated 8 GBM patient-derived xenograft (PDX) models of acquired radiation therapy-selected (RTS) resistance compared with same-patient, treatment-naive (radiation-sensitive, unselected; RTU) PDXs. These likely unique models mimic the longitudinal evolution of patient recurrent tumors following serial radiation therapy. Indeed, while whole-exome sequencing showed retention of major genomic alterations in the RTS lines, we did detect a chromosome 12q14 amplification that was associated with clinical GBM recurrence in 2 RTS models. A potentially novel bioinformatics pipeline was applied to analyze phenotypic, transcriptomic, and kinomic alterations, which identified long noncoding RNAs (lncRNAs) and targetable, PDX-specific kinases. We observed differential transcriptional enrichment of DNA damage repair pathways in our RTS models, which correlated with several lncRNAs. Global kinomic profiling separated RTU and RTS models, but pairwise analyses indicated that there are multiple molecular routes to acquired radiation resistance. RTS model-specific kinases were identified and targeted with clinically relevant small molecule inhibitors. This cohort of in vivo RTS patient-derived models will enable future preclinical therapeutic testing to help overcome the treatment resistance seen in patients with GBM.

Biological Network Mining.

In this book chapter, we introduce a pipeline to mine significant biomedical entities (or bioentities) in biological networks. Our focus is on prioritizing both bioentities themselves and the associations between bioentities in order to reveal their biological functions. We will introduce three tools BEERE, WIPER, and PAGER 2.0 that can be used together for network analysis and function interpretation: (1) BEERE is a network analysis tool for "Biomedical Entity Expansion, Ranking and Explorations," (2) WIPER is an entity-to-entity association ranking tool, and (3) PAGER 2.0 is a service for gene enrichment analysis.

Corrigendum: Statistical Enrichment Analysis of Samples: A General-Purpose Tool to Annotate Metadata Neighborhoods of Biological Samples.

[This corrects the article DOI: 10.3389/fdata.2021.725276.].

Statistical Enrichment Analysis of Samples: A General-Purpose Tool to Annotate Metadata Neighborhoods of Biological Samples.

Unsupervised learning techniques, such as clustering and embedding, have been increasingly popular to cluster biomedical samples from high-dimensional biomedical data. Extracting clinical data or sample meta-data shared in common among biomedical samples of a given biological condition remains a major challenge. Here, we describe a powerful analytical method called Statistical Enrichment Analysis of Samples (SEAS) for interpreting clustered or embedded sample data from omics studies. The method derives its power by focusing on sample sets, i.e., groups of biological samples that were constructed for various purposes, e.g., manual curation of samples sharing specific characteristics or automated clusters generated by embedding sample omic profiles from multi-dimensional omics space. The samples in the sample set share common clinical measurements, which we refer to as "clinotypes," such as age group, gender, treatment status, or survival days. We demonstrate how SEAS yields insights into biological data sets using glioblastoma (GBM) samples. Notably, when analyzing the combined The Cancer Genome Atlas (TCGA)-patient-derived xenograft (PDX) data, SEAS allows approximating the different clinical outcomes of radiotherapy-treated PDX samples, which has not been solved by other tools. The result shows that SEAS may support the clinical decision. The SEAS tool is publicly available as a freely available software package at https://aimed-lab.shinyapps.io/SEAS/.