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Objective: Given the importance AI in genomics and its potential impact on human health, the American Medical Informatics Association-Genomics and Translational Biomedical Informatics (GenTBI) Workgroup developed this assessment of factors that can further enable the clinical application of AI in this space., Process: A list of relevant factors was developed through GenTBI workgroup discussions in multiple in-person and online meetings, along with review of pertinent publications. This list was then summarized and reviewed to achieve consensus among the group members., Conclusions: Substantial informatics research and development are needed to fully realize the clinical potential of such technologies. The development of larger datasets is crucial to emulating the success AI is achieving in other domains. It is important that AI methods do not exacerbate existing socio-economic, racial, and ethnic disparities. Genomic data standards are critical to effectively scale such technologies across institutions. With so much uncertainty, complexity and novelty in genomics and medicine, and with an evolving regulatory environment, the current focus should be on using these technologies in an interface with clinicians that emphasizes the value each brings to clinical decision-making., (© The Author(s) 2023. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

In this introduction article, we summarize the 2020 International Conference on Intelligent Biology and Medicine (ICIBM 2020) conference which was held on August 9-10, 2020 (virtual conference). We then briefly describe the nine research articles included in this supplement issue. ICIBM 2020 hosted four scientific sections covering current topics in bioinformatics, computational biology, genomics, biomedical informatics, among others. A total of 75 original manuscripts were submitted to ICIBM 2020. All the papers were under rigorous review (at least three reviewers), and highly ranked manuscripts were selected for oral presentation and supplement issues. This genomics supplement issue included nine manuscripts. These articles cover methods and applications for single cell RNA sequencing, multi-omics data integration for gene regulation, gene fusion detection from long-read RNA sequencing, gene co-expression analysis of metabolic pathways in cancer, integrative genome-wide association studies (GWAS) of subcortical imaging phenotype in Alzheimer's disease, as well as deep learning methods for protein structure prediction, metabolic pathway membership inference, and horizontal gene transfer (HGT) insertion sites prediction.

The 2018 International Conference on Intelligent Biology and Medicine (ICIBM 2018) was held on June 10-12, 2018, in Los Angeles, California, USA. The conference consisted of a total of eleven scientific sessions, four tutorials, one poster session, four keynote talks and four eminent scholar talks, which covered a wild range of aspects of bioinformatics, medical informatics, systems biology and intelligent computing. Here, we summarize nine research articles selected for publishing in BMC Bioinformatics.

Hippocrates was the first to raise awareness of medicine as a science. He asserted the body being a unified whole and emphasized the importance of preventive and predictive medicine, spurring physicians to foster patient collaboration. Recent achievements today have permitted a new approach "P4 medicine" - Predictive, Preventive, Personalized and Participatory - with the aim of depicting an individual's health history and molecular profile in determining the best medical intervention in maintaining or restoring wellbeing. There is a link, which brings together Hippocrates and P4 Medicine. This review will elaborate further this statement, considering the scientific achievements that paved the way for recent medical approaches. Emphasis will be given to the social impact of new diagnostic and therapeutic protocols, considering their costs and their success probabilities.

The human organism presents a complex biological system that has hierarchical organization. The lowest, basal hierarchical level is composed of chemical molecules. The system's structure as "the whole" is maintained by complex interactions of its parts (molecules). New forms and functions, not present in molecules themselves emerge from their orchestrated and coordinated interaction, creating the novel "whole". The "whole" is not a simple summation of its parts. Therefore, on the basis of reductionist analysis and solely on the list of component elements of biological systems it is not possible to predict the future form and function of the "whole". The information included in genes is not directly or proportionally expressed as the phenotype. The phenotype is based on secondary information and arises from the complex interaction of component elements of the system. On a basal (molecular) level, biological systems function according to self-organization rules. No central regulatory device is providing instruction for molecular interaction. Regulatory centers that coordinate the system are dispersed all over the system from molecular level up, keeping it in order and preventing a system to enter the chaos space. Advances in biology and development of new research technologies, as well as bioinformatics, computer sciences and mathematical modeling have enabled research on interaction of cellular macromolecules (proteins, DNA, RNA, etc.) that in a living cell are organized into large dynamic network. Some rules of a structural and functional networks' properties, as well as their organization were discovered. The above research area is regarded as a new academic field termed systems biology. Based on systems biology, many new concepts in medicine, particularly the mechanisms of biogenesis and development of diseases have emerged have potential application for early diagnostics and therapy.

Functional genomics and proteomics involve the simultaneous analysis of hundreds or thousands of expressed genes or proteins and have spawned the modern discipline of computational biology. Novel informatic applications, including sophisticated dimensionality reduction strategies and cancer outlier profile analysis, can distill clinically exploitable biomarkers from enormous experimental datasets. Diagnostic pathologists are now charged with translating the knowledge generated by the "omics" revolution into clinical practice. Food and Drug Administration-approved proprietary testing platforms based on microarray technologies already exist and will expand greatly in the coming years. However, for diagnostic pathology, the greatest promise of the "omics" age resides in the explosion in information technology (IT). IT applications allow for the digitization of histological slides, transforming them into minable data and enabling content-based searching and archiving of histological materials. IT will also allow for the optimization of existing (and often underused) clinical laboratory technologies such as flow cytometry and high-throughput core laboratory functions. The state of pathology practice does not always keep up with the pace of technological advancement. However, to use fully the potential of these emerging technologies for the benefit of patients, pathologists and clinical scientists must embrace the changes and transformational advances that will characterize this new era.

The function of the smooth muscle cells lining the walls of mammalian systemic arteries and arterioles is to regulate the diameter of the vessels to control blood flow and blood pressure. Here, we describe an in silico model, which we call the 'Hernandez-Hernandez model', of electrical and Ca2+ signaling in arterial myocytes based on new experimental data indicating sex-specific differences in male and female arterial myocytes from murine resistance arteries. The model suggests the fundamental ionic mechanisms underlying membrane potential and intracellular Ca2+ signaling during the development of myogenic tone in arterial blood vessels. Although experimental data suggest that KV1.5 channel currents have similar amplitudes, kinetics, and voltage dependencies in male and female myocytes, simulations suggest that the KV1.5 current is the dominant current regulating membrane potential in male myocytes. In female cells, which have larger KV2.1 channel expression and longer time constants for activation than male myocytes, predictions from simulated female myocytes suggest that KV2.1 plays a primary role in the control of membrane potential. Over the physiological range of membrane potentials, the gating of a small number of voltage-gated K+ channels and L-type Ca2+ channels are predicted to drive sex-specific differences in intracellular Ca2+ and excitability. We also show that in an idealized computational model of a vessel, female arterial smooth muscle exhibits heightened sensitivity to commonly used Ca2+ channel blockers compared to male. In summary, we present a new model framework to investigate the potential sex-specific impact of antihypertensive drugs.

Objective(s): MicroRNAs, which are micro-coordinators of gene expression, have been recently investigated as a potential treatment for cancer. The study used computational techniques to identify microRNAs that could target a set of genes simultaneously. Due to their multi-target-directed nature, microRNAs have the potential to impact multiple key pathways and their pathogenic cross-talk. Materials and Methods: We identified microRNAs that target a prostate cancer-associated gene set using integrated bioinformatics analyses and experimental validation. The candidate gene set included genes targeted by clinically approved prostate cancer medications. We used STRING, GO, and KEGG web tools to confirm gene-gene interactions and their clinical significance. Then, we employed integrated predicted and validated bioinformatics approaches to retrieve hsa-miR-124-3p, 16-5p, and 27a-3p as the top three relevant microRNAs. KEGG and DIANA-miRPath showed the related pathways for the candidate genes and microRNAsResults: The Real-time PCR results showed that miR-16-5p simultaneously down-regulated all genes significantly except for PIK3CA/CB in LNCaP; miR-27a-3p simultaneously down-regulated all genes significantly, excluding MET in LNCaP and PIK3CA in PC-3; and miR-124-3p could not down-regulate significantly PIK3CB, MET, and FGFR4 in LNCaP and FGFR4 in PC-3. Finally, we used a cell cycle assay to show significant G0/G1 arrest by transfecting miR-124-3p in LNCaP and miR-16-5p in both cell lines. Conclusion: Our findings suggest that this novel approach may have therapeutic benefits and these predicted microRNAs could effectively target the candidate genes.

Summary: Background: Melanoma is a heterogeneous cancer influenced by the plasticity of melanoma cells and their dynamic adaptations to microenvironmental cues. Melanoma cells transition between well-defined transcriptional cell states that impact treatment response and resistance. Methods: In this study, we applied single-cell RNA sequencing to interrogate the molecular features of immunotherapy-naive and immunotherapy-resistant melanoma tumours in response to ex vivo BRAF/MEK inhibitor treatment. Findings: We confirm the presence of four distinct melanoma cell states - melanocytic, transitory, neural-crest like and undifferentiated, and identify enrichment of neural crest-like and undifferentiated melanoma cells in immunotherapy-resistant tumours. Furthermore, we introduce an integrated computational approach to identify subsets of responding and nonresponding melanoma cells within the transcriptional cell states. Interpretation: Nonresponding melanoma cells are identified in all transcriptional cell states and are predisposed to BRAF/MEK inhibitor resistance due to pro-inflammatory IL6 and TNFɑ signalling. Our study provides a framework to study treatment response within distinct melanoma cell states and indicate that tumour-intrinsic pro-inflammatory signalling contributes to BRAF/MEK inhibitor resistance. Funding: This work was supported by Macquarie University, Melanoma Institute Australia, and the National Health and Medical Research Council of Australia (NHMRC; grant 2012860, 2028055).

Background Global public health is seriously threatened by the escalating issue of antimicrobial resistance (AMR). Antimicrobial peptides (AMPs), pivotal components of the innate immune system, have emerged as a potent solution to AMR due to their therapeutic potential. Employing computational methodologies for the prompt recognition of these antimicrobial peptides indeed unlocks fresh perspectives, thereby potentially revolutionizing antimicrobial drug development. Methods In this study, we have developed a model named as deepAMPNet. This model, which leverages graph neural networks, excels at the swift identification of AMPs. It employs structures of antimicrobial peptides predicted by AlphaFold2, encodes residue-level features through a bi-directional long short-term memory (Bi-LSTM) protein language model, and constructs adjacency matrices anchored on amino acids’ contact maps. Results In a comparative study with other state-of-the-art AMP predictors on two external independent test datasets, deepAMPNet outperformed in accuracy. Furthermore, in terms of commonly accepted evaluation matrices such as AUC, Mcc, sensitivity, and specificity, deepAMPNet achieved the highest or highly comparable performances against other predictors. Conclusion deepAMPNet interweaves both structural and sequence information of AMPs, stands as a high-performance identification model that propels the evolution and design in antimicrobial peptide pharmaceuticals. The data and code utilized in this study can be accessed at https://github.com/Iseeu233/deepAMPNet.

Biomedical science, which initially required only conventional research in the laboratory, currently involves information technology and has created bioinformatics in its development. Bioinformatics, a branch of biology, quantitatively analyzes information within biological macromolecules using software. Contemporary applications of bioinformatics have advanced biotechnological, medical, and pharmaceutical practices. Among the established applications of bioinformatics is diagnosing lung diseases using the genome-wide association study (GWAS) technique. Owing to sequencing technology and rapid computational methods, this technique is applied to analyze the link between genes with essential traits in the population, thus mapping the target genes to diagnose and treat diseases. The lung diseases diagnosed using GWAS include the responsible locus in asthma, chronic obstructive pulmonary disease (COPD), and lung cancer. Moreover, it can identify the treatment for COPD and suggest a new locus in lung cancer. Advancing the current gene-mapping technology demands genotype and phenotype data to study disease-linked genomes. Currently, bioinformatics is barely known and receives little attention in Indonesia. However, it can grow rapidly through open-source basis data and cross-disciplinary collaboration.

Abstract Multi-omics approaches have been successfully applied to investigate pregnancy and health outcomes at a molecular and genetic level in several studies. As omics technologies advance, research areas are open to study further. Here we discuss overall trends and examples of successfully using omics technologies and techniques (e.g., genomics, proteomics, metabolomics, and metagenomics) to investigate the molecular epidemiology of pregnancy. In addition, we outline omics applications and study characteristics of pregnancy for understanding fundamental biology, causal health, and physiological relationships, risk and prediction modeling, diagnostics, and correlations.

We develop a thermodynamic model describing the binding of RNA binding proteins (RBP) to oligomers in vitro. We apply expectation-maximization to infer the specificity of RBPs, represented as position-specific weight matrices (PWMs), by maximizing the likelihood of RNA Bind’n Seq data from the ENCODE project. Analyzing these public data we find sequence motifs that can partly explain the data for more than half of the studied 111 RBPs, and for 48 of the proteins these motifs are consistent with the known specificity. Our code is publicly available, facilitating analysis of RBP binding data.

Objective To investigate the expression of COL6A1 in sarcoma tissue and its significance based on bioinformatics analysis. Methods The GEO database was used to download the GSE16088, GSE21122, and GSE49972 datasets to analyze the expression level of COL6A1-3. The Kaplan-Meier survival curves were used to analyze the impact of the expression level of COL6A1-3 on the prognosis of patients with sarcoma. A Spearman correlation analysis was performed using GSVA package to investigate the correlation between the expression level of COL6A1 and immune cell infiltration. The LinkedOmics database was used to analyze the genes co-expressed with COL6A1, and the ggstatsplot package of R software was used to investigate the correlation between COL6A1 and COL6A2. The ClusterProfiler package of R software was used to perform GO functional enrichment analysis and KEGG pathway analysis of the genes co-expressed with COL6A1. The GSEA analysis was used to investigate the important kinase, miRNA, and transcription factor targets of the genes co-expressed with COL6A1. Results Compared with normal tissue, there was a significant increase in the expression level of COL6A1 in osteosarcoma, soft tissue sarcoma, and clear cell sarcoma of the kidney. The survival analysis showed that the high expression of COL6A1 was associated with low overall survival rate and di-sease-specific survival rate (χ2=6.37,4.57,P

We herein introduce voyAGEr, an online graphical interface to explore age-related gene expression alterations in 49 human tissues. voyAGEr offers a visualisation and statistical toolkit for the finding and functional exploration of sex- and tissue-specific transcriptomic changes with age. In its conception, we developed a novel bioinformatics pipeline leveraging RNA sequencing data, from the GTEx project, encompassing more than 900 individuals. voyAGEr reveals transcriptomic signatures of the known asynchronous ageing between tissues, allowing the observation of tissue-specific age periods of major transcriptional changes, associated with alterations in different biological pathways, cellular composition, and disease conditions. Notably, voyAGEr was created to assist researchers with no expertise in bioinformatics, providing a supportive framework for elaborating, testing and refining their hypotheses on the molecular nature of human ageing and its association with pathologies, thereby also aiding in the discovery of novel therapeutic targets. voyAGEr is freely available at https://compbio.imm.medicina.ulisboa.pt/app/voyAGEr.

Identification of genes whose expression increases or decreases with age is central to understanding the mechanisms behind aging. Recent scRNA-seq studies have shown that changes in single-cell expression profiles with aging are complex and diverse. In this study, we introduce a novel workflow to detect changes in the distribution of arbitrary monotonic age-related changes in single-cell expression profiles. Since single-cell gene expression profiles can be analyzed as probability distributions, our approach uses information theory to quantify the differences between distributions and employs distance matrices for association analysis. We tested this technique on simulated data and confirmed that potential parameter changes could be detected in a set of probability distributions. Application of the technique to a public scRNA-seq dataset demonstrated its potential utility as a straightforward screening method for identifying aging-related cellular features.

Antimicrobial resistance genes (ARG) are commonly found on acquired mobile genetic elements (MGEs) such as plasmids or transposons. Understanding the spread of resistance genes associated with mobile elements (mARGs) across different hosts and environments requires linking ARGs to the existing mobile reservoir within bacterial communities. However, reconstructing mARGs in metagenomic data from diverse ecosystems poses computational challenges, including genome fragment reconstruction (assembly), high-throughput annotation of MGEs, and identification of their association with ARGs. Recently, several bioinformatics tools have been developed to identify assembled fragments of plasmids, phages, and insertion sequence (IS) elements in metagenomic data. These methods can help in understanding the dissemination of mARGs. To streamline the process of identifying mARGs in multiple samples, we combined these tools in an automated high-throughput open-source pipeline, MetaMobilePicker, that identifies ARGs associated with plasmids, IS elements and phages, starting from short metagenomic sequencing reads. This pipeline was used to identify these three elements on a simplified simulated metagenome dataset, comprising whole genome sequences from seven clinically relevant bacterial species containing 55 ARGs, nine plasmids and five phages. The results demonstrated moderate precision for the identification of plasmids (0.57) and phages (0.71), and moderate sensitivity of identification of IS elements (0.58) and ARGs (0.70). In this study, we aim to assess the main causes of this moderate performance of the MGE prediction tools in a comprehensive manner. We conducted a systematic benchmark, considering metagenomic read coverage, contig length cutoffs and investigating the performance of the classification algorithms. Our analysis revealed that the metagenomic assembly process is the primary bottleneck when linking ARGs to identified MGEs in short-read metagenomics sequencing experiments rather than ARGs and MGEs identification by the different tools.

Phylogenetic comparative methods comprise the general endeavor of using an estimated phylogenetic tree (or set of trees) to make secondary inferences: about trait evolution, diversification dynamics, biogeography, community ecology, and a wide range of other phenomena or processes. Over the past ten years or so, the phytools R package has grown to become an important research tool for phylogenetic comparative analysis. phytools is a diverse contributed R library now consisting of hundreds of different functions covering a variety of methods and purposes in phylogenetic biology. As of the time of writing, phytools included functionality for fitting models of trait evolution, for reconstructing ancestral states, for studying diversification on trees, and for visualizing phylogenies, comparative data, and fitted models, as well numerous other tasks related to phylogenetic biology. Here, I describe some significant features of and recent updates to phytools, while also illustrating several popular workflows of the phytools computational software.

Objective To prepare a whole-cell vaccine for murine Lewis lung carcinoma (LLC), and to investigate the role of this vaccine in enhancing the anti-LLC ability of immune system in C57BL/6N mice. Methods TCGA database was used to analyze the changes in the infiltration level of immune cells within human lung adenocarcinoma (LUAD) tissue in the context of MYC amplification, and the optimal duration of ultraviolet irradiation was explored for the preparation of LLC whole-cell vaccine. A mouse model was used to assess the effect of LLC whole-cell vaccine with targeted inhibition of Myc on resistance to LLC. Results In human LUAD tissue samples, compared with the low N-MYC expression group, the high N-MYC expression group had a significant reduction in the infiltration abundance of activated CD4+ memory T cells and activated NK cells (W=28 233,27 990,P

Abstract Background The crosstalk between cancer and the tumour immune microenvironment (TIME) has attracted significant interest in the latest years because of its impact on cancer evolution and response to treatment. Despite this, cancer-specific tumour-TIME interactions and their mechanistic insights are still poorly understood. Methods Here, we compute the significant interactions occurring between cancer-specific genetic drivers and five anti- and pro-tumour TIME features in 32 cancer types using Lasso regularised ordinal regression. Focusing on head and neck squamous cancer (HNSC), we rebuild the functional networks linking specific TIME driver alterations to the TIME state they associate with. Results The 477 TIME drivers that we identify are multifunctional genes whose alterations are selected early in cancer evolution and recur across and within cancer types. Tumour suppressors and oncogenes have an opposite effect on the TIME and the overall anti-tumour TIME driver burden is predictive of response to immunotherapy. TIME driver alterations predict the immune profiles of HNSC molecular subtypes, and perturbations in keratinization, apoptosis and interferon signalling underpin specific driver-TIME interactions. Conclusions Overall, our study delivers a comprehensive resource of TIME drivers, gives mechanistic insights into their immune-regulatory role, and provides an additional framework for patient prioritisation to immunotherapy. The full list of TIME drivers and associated properties are available at http://www.network-cancer-genes.org .

Colorectal cancer (CRC) is the third most prevalent cancer with the second-highest mortality rate worldwide. microRNAs(miRNAs) of cancer-derived exosomes have shown promising diagnosis potential. Recent studies have shown themetastatic potential of a specific group of microRNAs called metastasis. Therefore, down-regulation of miRNAs at thetranscriptional level can reduce metastasis probability. The aim of this bioinformatics research is targeting of miRNAsprecursors using CRISPR-C2c2 (Cas13a) technique. The C2c2 (Cas13a) enzyme structure was downloaded fromthe RCSB database, the sequence miRNAs and their precursors were collected from miRbase. The crRNAs weredesigned and evaluated for their specificity by using CRISPR-RT server. The modeling 3D structure of the designedcrRNA was performed by RNAComposer server. Finally, HDOCK server was used to perform molecular docking toevaluate docked molecules' energy level and position. The crRNAs designed for miR-1280, miR-206, miR-195, miR-371a, miR-34a, miR-27a, miR-224, miR-99b, miR-877, miR-495 and miR-384 that showed high structural similaritywith the situation observed in normal and appropriate orientation was obtained. Despite high specificity, the correctorientation was not established in the case of crRNAs that designed to target miR-145, miR-378a, miR-199a, miR-320a and miR-543. The predicted interactions between crRNAs and Cas13a enzyme showed that crRNAs have astrong potential to inhibit metastasis. Therefore, crRNAs may be considered as an effective anticancer agent for furtherresearch in drug development.

Abstract Background Transcriptional classification has been used to stratify colorectal cancer (CRC) into molecular subtypes with distinct biological and clinical features. However, it is not clear whether such subtypes represent discrete, mutually exclusive entities or molecular/phenotypic states with potential overlap. Therefore, we focused on the CRC Intrinsic Subtype (CRIS) classifier and evaluated whether assigning multiple CRIS subtypes to the same sample provides additional clinically and biologically relevant information. Methods A multi-label version of the CRIS classifier (multiCRIS) was applied to newly generated RNA-seq profiles from 606 CRC patient-derived xenografts (PDXs), together with human CRC bulk and single-cell RNA-seq datasets. Biological and clinical associations of single- and multi-label CRIS were compared. Finally, a machine learning-based multi-label CRIS predictor (ML2CRIS) was developed for single-sample classification. Results Surprisingly, about half of the CRC cases could be significantly assigned to more than one CRIS subtype. Single-cell RNA-seq analysis revealed that multiple CRIS membership can be a consequence of the concomitant presence of cells of different CRIS class or, less frequently, of cells with hybrid phenotype. Multi-label assignments were found to improve prediction of CRC prognosis and response to treatment. Finally, the ML2CRIS classifier was validated for retaining the same biological and clinical associations also in the context of single-sample classification. Conclusions These results show that CRIS subtypes retain their biological and clinical features even when concomitantly assigned to the same CRC sample. This approach could be potentially extended to other cancer types and classification systems.

Objective To investigate the differentially expressed genes (DEGs) in the substantia nigra of mice with Parkinson’s disease (PD) induced by 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) based on bioinformatics, as well as their role in the pathological and progression of PD. Methods GEO database was used to obtain the gene expression microarray data in the substantia nigra of mice in the normal group and the PD model group, and the bioinformatics method was used to screen for DEGs in the substantia nigra between the normal group and the PD model. Metascape tool was used to perform GO functional enrichment analysis and KEGG pathway enrichment analysis. Quantitative real-time PCR was used to measure the mRNA expression levels of key functional genes regulating neuronal apoptosis and prion transmission. Results There were 174 upregulated DEGs and 173 downregulated DEGs in the substantia nigra of mice with MPTP-induced PD (P0.26). The upregulated genes were significantly enriched in the pathways regulating prion transmission, neuronal development, iron homeostasis, and export across the plasma membrane, while the downregulated genes were significantly enriched in the pathways regulating synaptic vesicle release, transsynaptic complex transmission, neuronal apoptosis, and actin in cytoskeleton. Compared with the normal group, the PD model group had significant reductions in the expressions of Bdnf and Fbxw7 in the substantia nigra of mice (t=2.25,2.39,P0.05). Conclusion There are significant changes in the key functional genes Bdnf and Fbxw7 that regulate neuronal apoptosis in PD model, which lays a foundation for further clarifying their role in the pathological progression of PD.

Introduction: Gastric cancer (GC) is the fifth most diagnosed neoplasia and the third leading cause of cancer-related deaths. A substantial number of patients exhibit an advanced GC stage once diagnosed. Therefore, the search for biomarkers contributes to the improvement and development of therapies. Objective: This study aimed to identify potential GC biomarkers making use of in silico tools. Methods: Gastric tissue microarray data available in Gene Expression Omnibus and The Cancer Genome Atlas Program was extracted. We applied statistical tests in the search for differentially expressed genes between tumoral and non-tumoral adjacent tissue samples. The selected genes were submitted to an in-house tool for analyses of functional enrichment, survival rate, histological and molecular classifications, and clinical follow-up data. A decision tree analysis was performed to evaluate the predictive power of the potential biomarkers. Results: In total, 39 differentially expressed genes were found, mostly involved in extracellular structure organization, extracellular matrix organization, and angiogenesis. The genes SLC7A8, LY6E, and SIDT2 showed potential as diagnostic biomarkers considering the differential expression results coupled with the high predictive power of the decision tree models. Moreover, GC samples showed lower SLC7A8 and SIDT2 expression, whereas LY6E was higher. SIDT2 demonstrated a potential prognostic role for the diffuse type of GC, given the higher patient survival rate for lower gene expression. Conclusion: Our study outlines novel biomarkers for GC that may have a key role in tumor progression. Nevertheless, complementary in vitro analyses are still needed to further support their potential.

Biomedical data are usually analyzed at the univariate level, focused on a single primary outcome measure to provide insight into systems biology, complex disease states, and precision medicine opportunities. More broadly, these complex biological and disease states can be detected as common factors emerging from the relationships among measured variables using multivariate approaches. 'Syndromics' refers to an analytical framework for measuring disease states using principal component analysis and related multivariate statistics as primary tools for extracting underlying disease patterns. A key part of the syndromic workflow is the interpretation, the visualization, and the study of robustness of the main components that characterize the disease space. We present a new software package, syndRomics, an open-source R package with utility for component visualization, interpretation, and stability for syndromic analysis. We document the implementation of syndRomics and illustrate the use of the package in case studies of neurological trauma data.

Biomedical data are usually analyzed at the univariate level, focused on a single primary outcome measure to provide insight into systems biology, complex disease states, and precision medicine opportunities. More broadly, these complex biological and disease states can be detected as common factors emerging from the relationships among measured variables using multivariate approaches. 'Syndromics' refers to an analytical framework for measuring disease states using principal component analysis and related multivariate statistics as primary tools for extracting underlying disease patterns. A key part of the syndromic workflow is the interpretation, the visualization, and the study of robustness of the main components that characterize the disease space. We present a new software package, syndRomics, an open-source R package with utility for component visualization, interpretation, and stability for syndromic analysis. We document the implementation of syndRomics and illustrate the use of the package in case studies of neurological trauma data.

BackgroundPredicting neurological recovery after spinal cord injury (SCI) is challenging. Using topological data analysis, we have previously shown that mean arterial pressure (MAP) during SCI surgery predicts long-term functional recovery in rodent models, motivating the present multicenter study in patients.MethodsIntra-operative monitoring records and neurological outcome data were extracted (n = 118 patients). We built a similarity network of patients from a low-dimensional space embedded using a non-linear algorithm, Isomap, and ensured topological extraction using persistent homology metrics. Confirmatory analysis was conducted through regression methods.ResultsNetwork analysis suggested that time outside of an optimum MAP range (hypotension or hypertension) during surgery was associated with lower likelihood of neurological recovery at hospital discharge. Logistic and LASSO (least absolute shrinkage and selection operator) regression confirmed these findings, revealing an optimal MAP range of 76-[104-117] mmHg associated with neurological recovery.ConclusionsWe show that deviation from this optimal MAP range during SCI surgery predicts lower probability of neurological recovery and suggest new targets for therapeutic intervention.FundingNIH/NINDS: R01NS088475 (ARF); R01NS122888 (ARF); UH3NS106899 (ARF); Department of Veterans Affairs: 1I01RX002245 (ARF), I01RX002787 (ARF); Wings for Life Foundation (ATE, ARF); Craig H. Neilsen Foundation (ARF); and DOD: SC150198 (MSB); SC190233 (MSB).

Abstract Background Diabetic nephropathy (DN) is a main cause of chronic renal failure. Despite decades of extensive study, the molecular mechanisms underlying diabetic tubulointerstitial injury remain unclear. We aim to identify key transcription factor genes involved in diabetic tubulointerstitial injury. Methods A microarray dataset (GSE30122) from Gene Expression Omnibus (GEO) was downloaded. A total of 38 transcription factor genes based on 166 differentially expressed genes (DEGs) were identified by UCSC_TFBS. Results The regulatory network showed connections between the top 10 transcription factors and their target DEGs. Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of targeted DEGs indicated that extracellular space, extracellular exosome, cell surface and complement and coagulation cascades were most significantly enriched. Utilizing Nephroseq v5 online platform, the mRNA expression pattern analysis of transcription factor genes demonstrated that mRNA expression of CDC5, CEBPA, FAC1, HFH1, IRF1, NFE2 and TGIF1 increased in renal tubulointerstitium of DN patients compared with normal controls while that of CEBPB and FOXO4 decreased in renal tubulointerstitium of DN patients compared with normal controls. Correlation analysis between mRNA expression of transcription factor genes in renal tubulointerstitium and clinical features showed that AP1, BACH1, CDC5, FAC1, FOXD1, FOXJ2, FOXO1, FOXO4, HFH1, IRF1, POU3F2, SOX5, SOX9, RSRFC4, S8 and TGIF1 may be related to diabetic tubulointerstitial injury. Conclusions (1) CDC5, FAC1, FOXO4, HFH1, IRF1 and TGIF1 may be key transcription factor genes. (2)Transcription factors involved in diabetic tubulointerstitial injury may become prospective targets for diagnosis and treatment of DN.

Objectives Nicotine is an ingredient of tobacco, and exposure to nicotine increases the risks of various cancers, including oral cancer. Previous studies have focused on the addictive properties of nicotine, but its carcinogenic mechanism has rarely been studied. We aimed to explore the key genes in the process through which nicotine promotes the occurrence and development of oral cancer via data mining and experimental verification. Methods This study involved three parts. First, key genes related to nicotine-related oral cancer were screened through data mining; second, the expression and clinical significance of a key gene in oral cancer tissues were verified by bioinformatics. Finally, the expression and clinical significance of the key gene in oral cancer were histologically investigated, and the effects of its expression on cell proliferation, invasion, and drug resistance were cytologically assessed. Results SERPINE1 was identified as the key gene, which was upregulated in nicotine-treated oral cells and may be an independent prognostic factor for oral cancer. SERPINE1 was enriched in various pathways, such as the tumor necrosis factor and apelin pathways, and was related to the infiltration of macrophages, CD4+T cells, and CD8+T cells. Overexpression of SERPINE1 was associated with N staging and may be involved in hypoxia, angiogenesis, and metastasis. Knockdown of SERPINE1 in oral cancer cells resulted in weakened cell proliferation and invasion ability and increased sensitivity to bleomycin and docetaxel. Conclusion This study revealed SERPINE1 as a key gene for nicotine-related oral cancer, indicating that SERPINE1 may be a novel prognostic indicator and therapeutic target for oral carcinoma.

Abstract Background Aging affects the incidence of diseases such as cancer and dementia, so the development of biomarkers for aging is an important research topic in medical science. While such biomarkers have been mainly identified based on the assumption of a linear relationship between phenotypic parameters, including molecular markers, and chronological age, numerous nonlinear changes between markers and aging have been identified. However, the overall landscape of the patterns in nonlinear changes that exist in aging is unknown. Result We propose a novel computational method, Data-driven Identification and Classification of Nonlinear Aging Patterns (DICNAP), that is based on functional data analysis to identify biomarkers for aging and potential patterns of change during aging in a data-driven manner. We applied the proposed method to large-scale, public DNA methylation data to explore the potential patterns of age-related changes in methylation intensity. The results showed that not only linear, but also nonlinear changes in DNA methylation patterns exist. A monotonous demethylation pattern during aging, with its rate decreasing at around age 60, was identified as the candidate stable nonlinear pattern. We also analyzed the age-related changes in methylation variability. The results showed that the variability of methylation intensity tends to increase with age at age-associated sites. The representative variability pattern is a monotonically increasing pattern that accelerates after middle age. Conclusion DICNAP was able to identify the potential patterns of the changes in the landscape of DNA methylation during aging. It contributes to an improvement in our theoretical understanding of the aging process.

Pyrrolizidine alkaloids (PAs) are one of the largest distributed classes of toxins in nature. They have a wide range of toxicity, such as hepatotoxicity, pulmonary toxicity, neuronal toxicity, and carcinogenesis. Yet, biological targets responsible for these effects are not well addressed. Using methods of computational biology for target identification, we tested more than 200 PAs. We used a machine-learning approach that applies structural similarity for target identification, ChemMapper, and SwissTargetPrediction. The predicted targets with high probabilities were muscarinic acetylcholine receptor M1. The predicted interactions between these two targets and PAs were further studied by molecular docking-based binding energies using AutoDock and VinaLC, which revealed good binding affinities. The PAs are bound to the same binding pocket as pirenzepine, a known M1 antagonist. These results were confirmed by in vitro assays showing that PAs increased the levels of intracellular calcium. We conclude that PAs are potential acetylcholine receptor M1 antagonists. This elucidates for the first time the serious neuro-oncological toxicities exerted by PA consumption.

Introduction: Recent experiments provide evidence that diluting the blood plasma restores the plasma environment to a more youthful level at least partially restoring the health of organs and tissues throughout the body. Objectives: We analyze whether a dynamical-systems model representing the plasma constituents could support the optimization process and help determine the appropriate dilution level, frequency and any simultaneous plasma infusions to achieve the most favorable outcome. Methods: We use a combination of a gradient descent, a simulated annealing and a genetic algorithm to find a population of models that fit illustrative data. Results: We analyze this population and present distributions of the model parameters and include a collection of plots of the dilution process for illustrative models. Conclusions: We find that, if we had more data to disambiguate the models, these dynmical-systems models could give useful predictions for plasma-dilution/addition experiments as well as support the interpretation of their results.

Abstract Background Effective treatment is needed for advanced, inoperable, or chemotherapy-resistant cervical cancer patients. Immunotherapy has become a new treatment modality for cervical cancer patients, and there is an urgent need to identify additional targets for cervical cancer immunotherapy. Methods In this study the core gene, RGS1, which affects immune status and the FIGO stage of cervical cancer patients was identified by WGCNA analysis and differential analysis using TCGA database. 10 related genes interacting with RGS1 were identified using PPI network, and the functional and immune correlations were analyzed. Based on the expression of RGS1 and related genes, the consensus clustering method was used to divide CESC patients into two groups (group 1, high expression of RGS1; group 2, low expression of RGS1). Then, the functional enrichment analysis was used to search for the functional differences in differentially expressed genes (DEGs) between group 1 and group 2. Immune infiltration analysis was performed using ESTIMATE, CIBERSORT, and ssGSEA, and the differences in expression of immune checkpoint inhibitors (ICIs) targets were assessed between the two groups. We investigated the effect of RGS1 on the clinical relevance of CESC patients, and experimentally verified the differences in RGS1 expression between cervical cancer patient tissues and normal cervical tissues, the role of RGS1 in cell function, and the effect on tumor growth in tumor-bearing mice. Results We found that RGS1 was associated with CD4, GNAI3, RGS2, GNAO1, GNAI2, RGS20, GNAZ, GNAI1, HLA-DRA and HLA-DRB1, especially CD4 and RGS2. Functional enrichment of DEGs was associated with T cell activation. Compared with group 2, group 1 had stronger immune infiltration and higher ICI target expression. RGS1 had higher expression in cervical cancer tissues than normal tissues, especially in HPV-E6 positive cancer tissues. In cervical cancer cell lines, knockdown of RGS1 can inhibited cell proliferation, migration, invasion, and tumor growth in nude mice and promoted apoptosis. Conclusions RGS1, as an oncogenic gene of cervical cancer, affects the immune microenvironment of patients with cervical cancer and may be a target of immunotherapy.

Precise organization of growing structures is a fundamental process in developmental biology. In plants, radial growth is mediated by the cambium, a stem cell niche continuously producing wood (xylem) and bast (phloem) in a strictly bidirectional manner. While this process contributes large parts to terrestrial biomass, cambium dynamics eludes direct experimental access due to obstacles in live-cell imaging. Here, we present a cell-based computational model visualizing cambium activity and integrating the function of central cambium regulators. Performing iterative comparisons of plant and model anatomies, we conclude that the receptor-like kinase PXY and its ligand CLE41 are part of a minimal framework sufficient for instructing tissue organization. By integrating tissue-specific cell wall stiffness values, we moreover probe the influence of physical constraints on tissue geometry. Our model highlights the role of intercellular communication within the cambium and shows that a limited number of factors are sufficient to create radial growth by bidirectional tissue production.

In the era of Big Data, data collection underpins biological research more than ever before. In many cases, this can be as time-consuming as the analysis itself. It requires downloading multiple public databases with various data structures, and in general, spending days preparing the data before answering any biological questions. Here, we introduce Sherlock, an open-source, cloud-based big data platform (https://earlham-sherlock.github.io/) to solve this problem. Sherlock provides a gap-filling way for computational biologists to store, convert, query, share and generate biology data while ultimately streamlining bioinformatics data management. The Sherlock platform offers a simple interface to leverage big data technologies, such as Docker and PrestoDB. Sherlock is designed to enable users to analyze, process, query and extract information from extremely complex and large data sets. Furthermore, Sherlock can handle different structured data (interaction, localization, or genomic sequence) from several sources and convert them to a common optimized storage format, for example, the Optimized Row Columnar (ORC). This format facilitates Sherlock’s ability to quickly and efficiently execute distributed analytical queries on extremely large data files and share datasets between teams. The Sherlock platform is freely available on GitHub, and contains specific loader scripts for structured data sources of genomics, interaction and expression databases. With these loader scripts, users can easily and quickly create and work with specific file formats, such as JavaScript Object Notation (JSON) or ORC. For computational biology and large-scale bioinformatics projects, Sherlock provides an open-source platform empowering data management, analytics, integration and collaboration through modern big data technologies.

Abstract Background Amyotrophic lateral sclerosis (ALS) is a complex, late-onset, neurodegenerative disease with a genetic contribution to disease liability. Genome-wide association studies (GWAS) have identified ten risk loci to date, including the TNIP1/GPX3 locus on chromosome five. Given association analysis data alone cannot determine the most plausible risk gene for this locus, we undertook a comprehensive suite of in silico, in vivo and in vitro studies to address this. Methods The Functional Mapping and Annotation (FUMA) pipeline and five tools (conditional and joint analysis (GCTA-COJO), Stratified Linkage Disequilibrium Score Regression (S-LDSC), Polygenic Priority Scoring (PoPS), Summary-based Mendelian Randomisation (SMR-HEIDI) and transcriptome-wide association study (TWAS) analyses) were used to perform bioinformatic integration of GWAS data (N cases = 20,806, N controls = 59,804) with ‘omics reference datasets including the blood (eQTLgen consortium N = 31,684) and brain (N = 2581). This was followed up by specific expression studies in ALS case-control cohorts (microarray N total = 942, protein N total = 300) and gene knockdown (KD) studies of human neuronal iPSC cells and zebrafish-morpholinos (MO). Results SMR analyses implicated both TNIP1 and GPX3 (p < 1.15 × 10−6), but there was no simple SNP/expression relationship. Integrating multiple datasets using PoPS supported GPX3 but not TNIP1. In vivo expression analyses from blood in ALS cases identified that lower GPX3 expression correlated with a more progressed disease (ALS functional rating score, p = 5.5 × 10−3, adjusted R 2 = 0.042, B effect = 27.4 ± 13.3 ng/ml/ALSFRS unit) with microarray and protein data suggesting lower expression with risk allele (recessive model p = 0.06, p = 0.02 respectively). Validation in vivo indicated gpx3 KD caused significant motor deficits in zebrafish-MO (mean difference vs. control ± 95% CI, vs. control, swim distance = 112 ± 28 mm, time = 1.29 ± 0.59 s, speed = 32.0 ± 2.53 mm/s, respectively, p for all

MicroRNAs (miRNAs) can participate in airway remodeling by regulating immune molecule expression. Here, we aimed to identify the differential miRNAs involved in airway remodeling. Airway remodeling was induced by ovalbumin in female BALB/C mice. The differentially expressed miRNAs were screened with microarray. GO (Gene Ontology) and KEGG enrichment analysis was performed. The miRNA target gene network and miRNA target pathway network were constructed. Verification with real-time PCR and Western blot was performed. We identified 63 differentially expressed miRNAs (50 up-regulated and 13 down-regulated) in the lungs of ovalbumin-induced airway remodeling mice. Real-time PCR confirmed that 3 miRNAs (mmu-miR-1931, mmu-miR-712-5p, and mmu-miR-770-5p) were significantly up-regulated, and 4 miRNAs (mmu-miR-128-3p, mmu-miR-182-5p, mmu-miR-130b-3p, and mmu-miR-20b-5p) were significantly down-regulated. The miRNA target gene network analysis identified key mRNAs in the airway remodeling, such as Tnrc6b (trinucleotide repeat containing adaptor 6B), Sesn3 (sestrin 3), Baz2a (bromodomain adjacent to zinc finger domain 2a), and Cux1 (cut like homeobox 1). The miRNA target pathway network showed that the signal pathways such as MAPK (mitogen-activated protein kinase), PI3K/Akt (phosphoinositide 3-Kinase/protein kinase B), p53 (protein 53), and mTOR (mammalian target of rapamycin) were closely related to airway remodeling in asthma. Collectively, differential miRNAs involved in airway remodeling (such as mmu-miR-1931, mmu-miR-712-5p, mmu-miR-770-5p, mmu-miR-128-3p mmu-miR-182-5p, and mmu-miR-130b-3p) as well as their target genes (such as Tnrc6b, Sesn3, Baz2a, and Cux1) and pathways (such as MAPK, PI3K/Akt, p53, mTOR pathways) have been identified. Our findings may help to further understand the pathogenesis of airway remodeling.

Abstract Background The Wnt signaling pathway is core to the growth of bladder tumors. Epithelial-to-mesenchymal transition (EMT) is significant for bladder tumor metastasis. Nevertheless, the relationship between the Wnt signaling pathway, outcomes of bladder cancer (BLCA), and the specific mechanisms driving immune infiltration have not been studied. Methods We obtained Wnt pathway-related gene mRNA and clinicopathological data from the Cancer Genome Atlas (TCGA). We obtained 34 genes that were greatly correlated with outcome using univariate Cox regression analysis and conducted a completely randomized data t-test to perform clinical staging. According to the single-sample gene set enrichment analysis (ssGSEA), the weighted correlation network analysis (WGCNA) was applied to identify relevant biological functions. Various subtypes were identified using consensus cluster analysis. Univariate Cox regression and least absolute shrinkage sum selection operator–Cox regression algorithm analysis were conducted on TCGA and Gene Expression Omnibus data to identify risk characteristics. The Kaplan–Meier method and receiver running feature curves were adopted to calculate overall survival. Single-sample gene set enrichment analysis (ssGSEA) was adopted for the assessment of the degree of immune infiltration. Then, we demonstrated the relationship between PPP2CB and EMT function in two cell lines. Results Thirty-four Wnt signaling pathway-related genes were risk factors for BLCA outcome, and their expression levels differed by clinical stage. The co-expression of WGCNA showed the relationship between the Wnt signaling pathway and biological functions and was closely associated with EMT. We divided BLCA patients into two subtypes using consensus clustering. Survival curves and clinical analysis showed that the Wnt pathway enriched group had worse outcomes. The Wnt signature showed the significance of the outcome for MAPK10, PPP2CB, and RAC3. Based on these genes, the degree of immune infiltration was evaluated. Cell function experiments suggested that PPP2CB drives the proliferation and migration of BLCA cells. Conclusion We found that Wnt signaling pathway-related genes can be used as prognostic risk factors for BLCA, and the Wnt signaling pathway is a cancer-promoting signaling pathway associated with EMT. We identified three critical genes: MAPK10, RAC3, and PPP2CB. The genes in these three Wnt signaling pathways are associated with tumor cell EMT and immune cell infiltration. The most important finding was that these three genes were independent prognostic factors for BLCA.

In the era of Big Data, data collection underpins biological research more than ever before. In many cases, this can be as time-consuming as the analysis itself. It requires downloading multiple public databases with various data structures, and in general, spending days preparing the data before answering any biological questions. Here, we introduce Sherlock, an open-source, cloud-based big data platform (https://earlham-sherlock.github.io/) to solve this problem. Sherlock provides a gap-filling way for computational biologists to store, convert, query, share and generate biology data while ultimately streamlining bioinformatics data management. The Sherlock platform offers a simple interface to leverage big data technologies, such as Docker and PrestoDB. Sherlock is designed to enable users to analyze, process, query and extract information from extremely complex and large data sets. Furthermore, Sherlock can handle different structured data (interaction, localization, or genomic sequence) from several sources and convert them to a common optimized storage format, for example, the Optimized Row Columnar (ORC). This format facilitates Sherlock’s ability to quickly and efficiently execute distributed analytical queries on extremely large data files and share datasets between teams. The Sherlock platform is freely available on GitHub, and contains specific loader scripts for structured data sources of genomics, interaction and expression databases. With these loader scripts, users can easily and quickly create and work with specific file formats, such as JavaScript Object Notation (JSON) or ORC. For computational biology and large-scale bioinformatics projects, Sherlock provides an open-source platform empowering data management, analytics, integration and collaboration through modern big data technologies.

Objectives. Sensitization to specific inhalant allergens is a major risk factor for the development of atopic diseases, which impose a major socioeconomic burden and significantly diminish quality of life. However, patterns of inhalant allergic sensitization have yet to be precisely described. Therefore, to enhance the understanding of aeroallergens, we performed a cluster analysis of inhalant allergic sensitization using a computational model. Methods. Skin prick data were collected from 7,504 individuals. A positive skin prick response was defined as an allergen-to-histamine wheal ratio ≥1. To identify the clustering of inhalant allergic sensitization, we performed computational analysis using the four-parameter unified-Richards model. Results. Hierarchical cluster analysis grouped inhalant allergens into three clusters based on the Davies-Bouldin index (0.528): cluster 1 (Dermatophagoides pteronyssinus and Dermatophagoides farinae), cluster 2 (mugwort, cockroach, oak, birch, cat, and dog), and cluster 3 (Alternaria tenus, ragweed, Candida albicans, Kentucky grass, and meadow grass). Computational modeling revealed that each allergen cluster had a different trajectory over the lifespan. Cluster 1 showed a high level (>50%) of sensitization at an early age (before 19 years), followed by a sharp decrease in sensitization. Cluster 2 showed a moderate level (10%–20%) of sensitization before 29 years of age, followed by a steady decrease in sensitization. However, cluster 3 revealed a low level (

Abstract Background The ongoing COVID-19 pandemic has created an urgency to identify novel vaccine targets for protective immunity against SARS-CoV-2. Early reports identify protective roles for both humoral and cell-mediated immunity for SARS-CoV-2. Methods We leveraged our bioinformatics binding prediction tools for human leukocyte antigen (HLA)-I and HLA-II alleles that were developed using mass spectrometry-based profiling of individual HLA-I and HLA-II alleles to predict peptide binding to diverse allele sets. We applied these binding predictors to viral genomes from the Coronaviridae family and specifically focused on T cell epitopes from SARS-CoV-2 proteins. We assayed a subset of these epitopes in a T cell induction assay for their ability to elicit CD8+ T cell responses. Results We first validated HLA-I and HLA-II predictions on Coronaviridae family epitopes deposited in the Virus Pathogen Database and Analysis Resource (ViPR) database. We then utilized our HLA-I and HLA-II predictors to identify 11,897 HLA-I and 8046 HLA-II candidate peptides which were highly ranked for binding across 13 open reading frames (ORFs) of SARS-CoV-2. These peptides are predicted to provide over 99% allele coverage for the US, European, and Asian populations. From our SARS-CoV-2-predicted peptide-HLA-I allele pairs, 374 pairs identically matched what was previously reported in the ViPR database, originating from other coronaviruses with identical sequences. Of these pairs, 333 (89%) had a positive HLA binding assay result, reinforcing the validity of our predictions. We then demonstrated that a subset of these highly predicted epitopes were immunogenic based on their recognition by specific CD8+ T cells in healthy human donor peripheral blood mononuclear cells (PBMCs). Finally, we characterized the expression of SARS-CoV-2 proteins in virally infected cells to prioritize those which could be potential targets for T cell immunity. Conclusions Using our bioinformatics platform, we identify multiple putative epitopes that are potential targets for CD4+ and CD8+ T cells, whose HLA binding properties cover nearly the entire population. We also confirm that our binding predictors can predict epitopes eliciting CD8+ T cell responses from multiple SARS-CoV-2 proteins. Protein expression and population HLA allele coverage, combined with the ability to identify T cell epitopes, should be considered in SARS-CoV-2 vaccine design strategies and immune monitoring.

Abstract Background The recent global pandemic has placed a high priority on identifying drugs to prevent or lessen clinical infection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), caused by Coronavirus disease-2019 (COVID-19). Methods We applied two computational approaches to identify potential therapeutics. First, we sought to identify existing FDA approved drugs that could block coronaviruses from entering cells by binding to ACE2 or TMPRSS2 using a high-throughput AI-based binding affinity prediction platform. Second, we sought to identify FDA approved drugs that could attenuate the gene expression patterns induced by coronaviruses, using our Disease Cancelling Technology (DCT) platform. Results Top results for ACE2 binding iincluded several ACE inhibitors, a beta-lactam antibiotic, two antiviral agents (Fosamprenavir and Emricasan) and glutathione. The platform also assessed specificity for ACE2 over ACE1, important for avoiding counterregulatory effects. Further studies are needed to weigh the benefit of blocking virus entry against potential counterregulatory effects and possible protective effects of ACE2. However, the data herein suggest readily available drugs that warrant experimental evaluation to assess potential benefit. DCT was run on an animal model of SARS-CoV, and ranked compounds by their ability to induce gene expression signals that counteract disease-associated signals. Top hits included Vitamin E, ruxolitinib, and glutamine. Glutathione and its precursor glutamine were highly ranked by two independent methods, suggesting both warrant further investigation for potential benefit against SARS-CoV-2. Conclusions While these findings are not yet ready for clinical translation, this report highlights the potential use of two bioinformatics technologies to rapidly discover existing therapeutic agents that warrant further investigation for established and emerging disease processes.

Objective(s): Alginates play a key role in mucoid Pseudomonas aeruginosa colonization, biofilm formation, and driving out of cationic antibiotics. P. aeruginosa alginate lyase (AlgL) is a periplasmic enzyme that is necessary for alginate synthesis and secretion. It also has a role in depolymerization of alginates. Using AlgLs in cystic fibrosis patients along with antibiotics enhances bacterial killing and host healing. In this study, we investigated the different biochemical properties of a newly isolated AlgL from P. aeruginosa S21 to complete the databank of AlgLsMaterials and Methods: The enzyme was extracted from the periplasmic space of the bacteria by the heat shock method. Using the TBA method, the enzyme activity and biochemical properties were assessed. The mutability of P. aeruginosa S21 AlgL to increase its thermal stability was investigated. The most favorable mutations were studied computationally. The molecular dynamics simulation (MDS) package GROMACS was used for determining the effect of S34R mutation on enzyme’s thermal stability.Results: Data showed that this enzyme has the best activity at 37 °C and pH 7.5 and it can degrade mannuronate blocks, guluronate blocks, and sodium alginate. After 7 hr at 80 °C, 45% of the enzyme activity was retained. This enzyme needed 15 min to completely degrade accessible sodium alginate. Tris buffer, pH 8.5 and Britton-Robinson buffer, pH 7.0 were the preferable buffers for the enzyme activity. MDS of native and mutated enzymes showed desirable results.Conclusion: P. aeruginosa S21 AlgL can be used in medical and industrial applications to degrade alginates.