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bioRxiv2024

Identification of key genes and enriched biological processes in diabetes-associated traumatic brain injury through weighted gene correlation network and bioinformatics analysis

Traumatic brain injury (TBI) is a significant cause of morbidity and mortality worldwide, with long-term neurological and psychological impacts. Recent studies have indicated that diabetes (T1DM) exacerbates the outcomes of TBI, leading to more severe cognitive deficits and increased risk of complications. This study investigated the underlying molecular mechanisms and potential therapeutic targets for T1DM-associated TBI. Four mRNA datasets (GSE4745, GSE125451, GSE173975, and GSE80174) downloaded from GEO repository were used in this study. Using limma, a total of 284 differentially expressed genes (DEGs) were identified in T1DM, of which 11 were upregulated and 9 were downregulated. GSEA showed that these DEGs were significantly enriched in cell communication, lipid metabolic process, and PPAR signaling. A total of 584 DEGs were identified in TBI, of which 186 were upregulated and 9 were downregulated. GSEA showed that these DEGs were mainly enriched in immune response-regulating signaling pathway. WGCNA identified 122 significant genes in TIDM-related modules and 368 significant genes in TBI-related module. GO and KEGG enrichment analysis showed that T1DM module genes were significantly correlated with lipid metabolic process and ribosome biogenesis, while TBI module genes were significantly correlated with inflammation and immune response, including leukocyte mediated immunity, lymphocyte mediated immunity, and cytokine mediated receptor activity. PPI network analysis of T1DM module genes identified 20 hub genes, including 14 ribosomal genes: Rpl23, Rps3a, Rps6, Rpl5, Rpl17, Rps24, Rpl23a, Rps4x, Rpl9, Rps15a14, Rpl30, Rpl31, Rps25, and Rps27a-2. The hub genes were primarily related to ribosome biogenesis and RNA post-transcriptional regulation. PPI network analysis of TBI module genes identified 20 hub genes: Ptprc, Tp53, Stat1, Stat3, Tyrobp, Itgad, Csf1r, Itgb2, Rac2, Icam1, Myd88, Cd44, Vav1, Aif1, C1qa, Laptm5, B2m, Fcer1g, and Lyn. The hub genes were primarily related to inflammatory mediators and immune response. Based on the overlap of T1DM module genes and TBI module genes, Cmklr1, Mgst1, and Plin2, were identified as key genes of T1DM-associated TBI. Functional enrichment analysis showed that they were primarily enriched in the cellular response to hydroperoxide, cytokine-mediated receptor signaling activity, regulation of sequestering of triglyceride, negative regulation of IL-12 production, and positive regulation of macrophage chemotaxis. Using Reactome, Cmklr1, Mgst1, and Plin2 were related to cytokine signaling activity, neutrophil degranulation, and lipid storage, respectively. We concluded that Lipid droplet dysregulation and Neuroinflammation are the potential molecular mechanisms of T1DM-associated TBI. Cmklr1, Mgst1, and Plin2 are positively correlated with T1DM-associated TBI and may be important biomarkers and potential treatment targets for diabetic TBI.

genomics↗

bioRxiv2025

pH-Dependent Membrane Binding Specificity of Synaptogyrins 1-3 with Distinct Isoelectric Points (pI) Identified by Structural Bioinformatics and Molecular Dynamics

Synaptogyrins (SYNGRs) are integral synaptic vesicle proteins that contribute to neurotransmitter release and synaptic plasticity. Alterations in vesicular pH, as observed in ageing and Alzheimers disease, may influence synaptogyrin function, yet the molecular mechanisms remain poorly understood. We compared synaptogyrin-1 (SYNGR1, pI 4.5) and synaptogyrin-3 (SYNGR3, pI 8.4), two structurally similar isoforms with distinct electrostatic properties. Using 50ns all-atom molecular dynamics simulations in realistic lipid bilayers at resting (pH 5.5) and active (pH 7.25) conditions, we examined how vesicular pH modulates protein conformation, membrane binding, and stability. Despite near-identical backbones (RMSD 1.27 [A]), SYNGR1 and SYNGR3 displayed divergent pH-dependent dynamics. Comparative analysis revealed that SYNGR1s resting state closely resembled the active state of SYNGR3, suggesting functional convergence during vesicle recycling. Multivariate amino acid profilling was conducted using homologous residue profiles. Consistent with epistatic potential, ClinVar-reported damaging variants in SYNGR1 clustered within regions of low structural mimicry, whereas SYNGR3 variants localized to conserved regions. These findings identify pH-dependent electrostatic modulation as a determinant of synaptogyrin behaviour and provide a framework for understanding their roles in synaptic vesicle cycling. The distinct conformational and mutational landscapes of SYNGR1 and SYNGR3 highlight potential mechanisms by which pH dysregulation in neurodegeneration may impair synaptic function.

biochemistry↗

bioRxiv2025

THE UNCHARTED ROLE OF ERAP2 IN AGING: A BIOINFORMATICS PERSPECTIVE

BackgroundAging is associated with significant changes in the immune system, leading to increased susceptibility to infections, reduced vaccine efficacy, and a higher risk of autoimmune diseases. However, the molecular mechanisms driving these immunological alterations remain incompletely understood. In this study, we performed differential gene expression analysis (DGEA) to identify genes implicated in immune aging and explored their functional significance through enrichment analysis. MethodsUsing publicly available transcriptomic data from aged versus young individuals, we conducted DGEA to identify significantly dysregulated genes. Functional enrichment analyses, including Gene Ontology (GO) and Reactome pathway analysis, were performed to uncover biological processes and pathways enriched in the differentially expressed genes. We specifically focused on ERAP2, a gene found to be differentially expressed in aging, and examined its expression across various tissues. Additionally, we reviewed existing literature to assess ERAP2s role in antigen presentation and immune regulation. ResultsOur analysis revealed significant enrichment of pathways related to antigen processing and presentation, peptide metabolism, and immune system regulation. ERAP2, an aminopeptidase involved in trimming peptides for MHC class I presentation, was highly associated with these processes. Interestingly, no direct KEGG pathway annotation was found for ERAP2, suggesting a potential gap in functional characterization. However, Reactome analysis confirmed its involvement in class I MHC-mediated antigen processing and immune system pathways, aligning with its known immunological role. The expression analysis across multiple tissues showed ERAP2 to be widely expressed in immune-related tissues, further supporting its relevance. ConclusionOur findings highlight the potentially underexplored role of ERAP2 in immune aging. The lack of KEGG pathway annotation suggests that its function in aging and immune modulation may not be fully characterized, presenting an opportunity for further research. Given its role in antigen presentation, ERAP2 may represent a critical factor in the declining immune function observed in aging individuals. Further experimental validation is required to establish its mechanistic involvement and potential as a therapeutic target for age-related immune dysfunction.

immunology↗

bioRxiv2025

Bioinformatics-Driven Identification and Prioritization of PTSD Targets Based on Published Multi-omic Data

Efforts in recent years to uncover neurobiological mechanisms underlying post-traumatic stress disorder (PTSD) have yielded an expanding candidate pool of targets from genomic and transcriptomic data. However, not all candidates are disease-causing, related to pathological mechanisms, clinically relevant, nor druggable by conventional means. An effective method to systematically identify and prioritize high-confidence, high-impact targets in the central nervous system (CNS) is required to de-risk resource-intensive experimental validation of disease mechanisms and accelerate the development of novel treatments. Here, we describe methods and implementation of a novel 3-phased, biologically rationalized, and quantitative prioritization strategy to identify and rank PTSD-associated targets based on confidence of association to PTSD and estimated CNS-relevant pathogenicity. Phase 1 was designed to identify and advance targets confidently associated with PTSD through their expression in CNS tissues. Putative targets derived from 29 transcriptomic and genomic analyses of PTSD were evaluated for: 1. Replication in independent cohorts, 2. Observation of differential expression in PTSD CNS tissues, and 3. Demonstration of consistent direction of effect. This strategy resulted in 177 targets that passed criteria for advancement. Phase 1-selected targets were enriched for PTSD relevant traits including irritability, emotional symptoms, and insomnia (FDR <0.05). Phase 2 advanced targets with additional evidence of association to pathological CNS phenotypes. DisGeNET gene-disease association scores were applied to each Phase 1-selected target to assign a confidence score indicating that a target was associated with CNS-relevant pathology using criteria for moderate or strong evidence of CNS disease association. Phase 2 advanced 55 of the 177 (31.1%) targets. The Phase 2 target pool was enriched for CNS phenotypic abnormalities (FDR<0.05). Finally, Phase 3 enabled target prioritization by annotating targets with a composite pathogenicity score. Components of the pathogenicity score included metrics derived from drug trial databases, predicted loss-of-function intolerance, and connectivity within a protein-protein interaction network defined by PTSD-associated targets. The resulting 55 targets were ultimately prioritized by the sum of Phase 2 and Phase 3 scores, where top-ranked targets had strong evidence to support both association with PTSD in brain and high pathogenicity estimates in a CNS-relevant context. Biologically, top-ranked targets implicate transmitter systems (GABA, histamine, and estrogen), structural regulation of neurites, and protein homeostasis. Future work will be required to experimentally validate the utility of the high priority PTSD targets we identified as well as to demonstrate the general applicability of this methodology. Ultimately, we anticipate that the three phased approach will enable efficient de-risking of PTSD and other poorly understood CNS disorders.

neuroscience↗

bioRxiv2025

Genomic and Bioinformatic Insights into Enterococcus faecalis from Retail Meats in Nigeria

BackgroundEnterococcus faecalis (E. faecalis) is a commensal and opportunistic pathogen increasingly recognized for its antimicrobial resistance (AMR) and zoonotic potential. This study employs whole-genome sequencing (WGS) to characterize E. faecalis isolates from retail meat samples, focusing on antimicrobial resistance genes (ARGs), virulence determinants, mobile genetic elements, and phylogenomic relationships. Materials and MethodsFifty raw meat samples, including chicken (n=18), beef (n=17), and turkey (n=15), were collected from retail markets in Akungba-Akoko, Nigeria. E. faecalis isolates were identified using standard microbiological methods and subjected to antimicrobial susceptibility testing were further analysed using WGS. ResultsTen E. faecalis isolates were recovered, with the highest prevalence in chicken (n=6), followed by beef (n=2) and turkey (n=2). All isolates were resistant to clindamycin, erythromycin, and tetracycline. Frequent ARGs included aac(6)-aph(2), ant(6)-Ia, lsa(A), erm(B), tet(M), and tet(L). Plasmid replicons rep9c and repUS43 showed ST-specific associations with ST477 and ST16, respectively. MGEs such as IS3, IS6, IS256, and IS1380 co-localized with ARGs and virulence determinants. Phylogenomic analysis revealed two major lineages, with ST477 distributed across meat types and ST16 restricted to chicken. Comparative genomic analysis with publicly available African E. faecalis isolates revealed distinct clonal lineages and geographic clustering across the continent. ConclusionThe co-occurrence of multidrug resistance, virulence factors, and MGEs in foodborne E. faecalis poses a public health concern due to the risk of horizontal gene transfer and zoonotic spread. These findings underscore the need for genomic surveillance and antimicrobial stewardship in food systems, particularly in low- and middle-income countries.

genomics↗

bioRxiv2025

Key genes and pathways in asparagine metabolism in Alzheimers Disease: a bioinformatics approach

BackgroundAsparagine (Asn) metabolism is essential for maintaining cellular homeostasis and supporting neuronal energy demands. Recent studies have suggested its dysregulation may contribute to Alzheimers disease (AD) pathogenesis; however, the specific genes and regulatory mechanisms involved remain incompletely understood. MethodsFour publicly available microarray datasets (GSE5281, GSE29378, GSE36980, and GSE138260) were utilized to investigate genes with differential expression between control and AD samples. Asparagine metabolism-related genes (AMGs) were retrieved from the GeneCards database, and their intersection with DEGs yielded candidate asparagine metabolism-related differentially expressed genes (AMG-DEGs). Functional enrichment analysis (Gene Set Enrichment Analysis, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes), protein-protein interaction (PPI) network analysis, and centrality scoring identified hub genes. Regulatory mechanisms were investigated through construction of competing endogenous RNA and transcription factor networks. Potential therapeutic compounds were predicted via drug-gene enrichment and evaluated using molecular docking simulations. ResultsThirty-nine AMG-DEGs were identified and found to be enriched in neurodevelopmental, synaptic transmission, and inflammatory signaling pathways. PPI analysis and centrality screening revealed seven hub genes (HPRT1, GAD2, TUBB3, GFAP, CD44, CCL2, and NFKBIA). Regulatory network analysis highlighted specific miRNAs, long non-coding RNAs, and transcription factors involved in their modulation. Drug screening and docking identified Bathocuproine disulfonate, DL-Mevalonic acid, and Phenethyl isothiocyanate as promising compounds with strong binding affinities to hub proteins. ConclusionThis study comprehensively maps the dysregulation of asparagine metabolism in Alzheimers disease and reveals a set of hub genes and regulatory elements potentially involved in disease progression. The predicted therapeutic compounds provide a foundation for further experimental validation and may contribute to the development of novel metabolism-targeted strategies for AD treatment.

neuroscience↗

bioRxiv2025

Bioinformatics classification of the MgtE Mg2+ channel and de novo protein design for the stabilization of its novel subclass

MgtE channels play crucial roles in Mg{superscript 2} homeostasis and are implicated in bacterial survival under antibiotic exposure. Previous structural and biophysical studies have predominantly focused on the Thermus thermophilus MgtE, leaving the structural and mechanistic diversity of MgtE family proteins largely unexplored. In this study, using a genome mining approach, we identified diverse MgtE homologs, including a novel subclass termed the "mini-N type," which lacks the canonical cytoplasmic N and CBS domains but possesses a unique small N-like domain. Despite extensive expression screening, mini-N type homologs could not be stably purified. To address this issue, we designed a series of de novo proteins and determined their crystal structures. A selected de novo protein was fused to a mini-N type MgtE, enabling successful purification and preliminary cryo-EM imaging. Our findings demonstrate that de novo designed protein fusions can serve as powerful tools for stabilizing and purifying otherwise unstable membrane proteins, opening new avenues for the structural and functional studies of otherwise inaccessible membrane proteins.

biophysics↗

bioRxiv2025

A Bioinformatic Analysis of BAG Protein Interactors and Pathways in Alzheimers and Parkinsons Disease

Alzheimers disease (AD) and Parkinsons disease (PD) are the two most common neurodegenerative disorders. While the symptoms and general etiology may be different, these two diseases share significant common features in terms of their disease pathogenesis. Within the scope of neurodegenerative disorders, the Bcl-2 associated athanogene (BAG) family proteins and associated interactors have been a key area of focus. The BAG family is a group of proteins that contain at least one evolutionarily conserved BAG domain. Despite this similarity, their interactions and functions can vary widely. So far, research has predominantly scrutinized individual BAG proteins, rather than explore potential cooperative actions among family members. Some BAG family members may function together thereby indicating potential interactions within this family. Although connections among BAG members have been observed, their role in neurodegenerative disorders, such as AD and PD, remains largely uncharacterized. This mini review explores the common pathways, intersections, and differences within these interactions as well as their link to AD and PD. Using computational techniques to mine transcriptomic data, several groupings of pathways that these BAG family members are involved in were identified in the context of AD and PD. Understanding these pathways and their relationships may uncover potential gaps in current research and help identify novel therapeutic targets for the treatment of these neurodegenerative diseases. Significance statementAlthough distinct diseases, Alzheimers disease and Parkinsons disease share common features such as protein aggregation and mitochondrial dysfunction. Members of the BAG family of proteins have been implicated in the pathogenesis of both diseases. Computational techniques were used to mine transcriptomic data of Alzheimers and Parkinsons disease cases to identify common pathways. BAG protein interactors, common to all family members, were analyzed in the context of these common pathways for Alzheimers and Parkinsons disease. These analyses provide insights into the pathways mediated by these BAG protein interactors that are likely at the intersection of Alzheimers disease and Parkinsons disease pathologies.

neuroscience↗

bioRxiv2025

Toward a model of uORF-mediated translational control: An integrated bioinformatic and experimental approach

Upstream open reading frames (uORFs) are short translated regions that occur in the 5{square} untranslated regions (5{square} UTRs) of mRNA transcripts where they primarily serve to repress expression translation of the downstream primary open reading frame (pORF). Their widespread presence across mammalian transcriptomes suggests an important role in shaping the proteome, although the mechanistic basis of their regulatory effects remain incompletely understood. Here we present an integrated experimental and computational investigation into the features that govern uORF-mediated translation control. Using high-resolution proteomics data from 29 healthy human tissues and machine learning-based simulations, we have systematically dissected how features including uORF length, amino acid composition, start codon position, stop codon position, and Kozak context influence repressive activity, and performed experimental validation using reporter gene constructs. We also investigated how multiple uORFs within a single 5{square} UTR can interact in synergistic or antagonistic ways, with the potential to produce counterintuitive effects on pORF translation. From these studies, we present a model of uORF function, suggesting a hierarchy of uORF feature importance, and proposing that a combination of uORF translation initiation probability, ribosome recycling rate, intercistronic ternary complex recharging requirements, and ribosome stalling mechanisms underlie uORF repressive activity. Together, these studies provide a comprehensive view of the molecular logic underlying uORF activity, offering new insights into their endogenous and highlighting their potential as targets for drug development.

biochemistry↗

bioRxiv2025

Development of Advanced Bioinformatic Profiles to Improve the Detection and Functional Understanding of Fungal Acid Phosphatases

We have retrieved approximately 9,000 protein sequences annotated as fungal acid phosphatase or phytase from the UniProtKB database. After stringent quality filtering, a curated dataset comprising 3,058 high-confidence sequences was assembled. Phylogenetic analysis revealed that these enzymes segregate into eight distinct clades, representing distinct groups of fungal acid phosphatases, purple acid phosphatases, phytases and groups containing both phytases and acid phosphatases annotations. Based on this classification, we have developed three representative protein profiles referred to as Pfr-A-Fungal_phos, Pfr-B-Fungal_phos and Pfr-C-Fungal_phos, each designed to capture the phylogenetic and functional of the enzyme families. The specificity and breadth of these profiles were validated through comprehensive heat-map analyses. When deployed on public protein and metagenomic databases, these profiles demonstrated high sensitivity and specificity, enabling the identification of hundreds of previously uncharacterized fungal proteins. These proteins spanned a broad taxonomic distribution, with notable prevalence in the Ascomycota and Basidiomycota phyla. Collectively, these findings highlight the utility of the newly developed profiles to uncover a novel and taxonomically widespread family of fungal enzymes. This work provided valuable insights into the evolutionary diversity and ecological significance of fungal enzymes involved in phosphorous cycling.

microbiology↗

bioRxiv2025

Multiscale Hyperbolic Embedding for Cell Hierarchies in Large-Scale Bioinformatics Data

The rapid expansion of biological and computational datasets demands scalable methods that support both visualization and quantitative interpretation. Hyperbolic embeddings are well suited to represent hierarchical structure, but existing approaches are limited by fixed curvature assumptions or poor scalability to large datasets. We introduce MuH-MDS, a multiscale hyperbolic multidimensional scaling algorithm that employs an adi-abatic optimization strategy: local positions are iteratively refined while cluster centroids are temporarily fixed. This strategy accelerates computation by 103 and enables scaling to datasets with over 80,000 samples. Applied to diverse benchmarks, including C. elegans embryogenesis scRNA-seq data, MuH-MDS uncovers intrinsic hierarchical organization and improves both pseudotime inference and lineage reconstruction relative to UMAP and other standard methods. In contrast to UMAP and t-SNE, which prioritize local neighborhoods at the expense of global coherence and metric fidelity, MuH-MDS pre-serves both local detail and global hierarchy, providing a metrically faithful framework for multiscale analysis of complex biological systems.

biophysics↗

bioRxiv2018

Bioinformatic characterisation of the effector repertoire of the strawberry pathogen Phytophthora cactorum

The oomycete pathogen Phytophthora cactorum causes crown rot, a major disease of cultivated strawberry. We report the draft genome of P. cactorum isolate 10300, cultured from symptomatic Fragaria x ananassa tissue. Our analysis revealed that there are a large number of genes encoding putative secreted effectors in the genome, including nearly 200 RxLR domain containing effectors, 77 Crinklers (CRN) grouped into 38 families and numerous apoplastic effectors, such as phytotoxins (PcF proteins) and necrosis inducing proteins. As in other Phytophthora species, the genomic environment of many RxLR and CRN genes differed from core eukaryotic genes, a hallmark of the two-speed genome. We found genes homologous to known Phytophthora infestans avirulence genes including Avr1, Avr3b, Avr4, Avrblb1 and AvrSmira2 indicating effector sequence conservation between Phytophthora species of Clade 1A and 1C. The reported P. cactorum genome sequence and associated annotations represent a comprehensive resource for avirulence gene discovery in other Phytophthora species from Clade 1 and will facilitate effector informed breeding strategies in other crops.

pathology↗

bioRxiv2018

Functional phenomics: An emerging field integrating high-throughput phenotyping, physiology, and bioinformatics

HighlightFunctional phenomics is an emerging field in plant biology that relies on high-throughput phenotyping, big data analytics, controlled manipulative experiments, and simulation modelling to increase understanding of plant physiology.\n\nAbstractThe emergence of functional phenomics signifies the rebirth of physiology as a 21st century science through the use of advanced sensing technologies and big data analytics. Functional phenomics highlights the importance of phenotyping beyond only identifying genetic regions because a significant knowledge gap remains in understanding which plant properties will influence ecosystem services beneficial to human welfare. Here, a general approach for the theory and practice of functional phenomics is outlined including exploring the phene concept as a unit of phenotype. The functional phenomics pipeline is proposed as a general method for conceptualizing, measuring, and validating utility of plant phenes. The functional phenomics pipeline begins with ideotype development. Second, a phenotyping platform is developed to maximize the throughput of phene measurements. A mapping population is screened measuring target phenes and indicators of plant performance such as yield and nutrient composition. Traditional forward genetics allows genetic mapping, while functional phenomics links phenes to plant performance. Based on these data, genotypes with contrasting phenotypes can be selected for smaller yet more intensive experiments to understand phene-environment interactions in depth. Simulation modeling can be used to understand the phenotypes and all stages of the pipeline feed back to ideotype and phenotyping platform development.

plant biology↗

bioRxiv2018

Expression profile analysis of circular RNAs in essential hypertension by microarray and bioinformatics.

Circular RNAs (circRNAs), widely found in human cells, are involved in disease and play an important role in progression. To determine whether circRNAs are related in essential hypertension (EH), we analyzed the expression profile of circRNAs and miRNAs in 5 EH and 5 healthy controls cases which were screened by microarray. Through microarray data and public data analysis, differently expressed transcripts were divided into modules, and circRNAs were functionally annotated by miRNAs. The expression of two circRNAs, has_circ_0037909 and has_circ_0105015, were validated in EH by qRT-PCR, which may be associated with EH. Further analysis showed that two circRNAs might through immune system by up-regulation circRNAs and down-regulation expression. These circRNAs biological functions need to be further validated.

genetics↗

bioRxiv2018

Characterization of emetic and diarrheal Bacillus cereus strains from a 2016 foodborne outbreak using whole-genome sequencing: addressing the microbiological, epidemiological, and bioinformatic challenges

The Bacillus cereus group comprises multiple species capable of causing emetic or diarrheal foodborne illness. Despite being responsible for tens of thousands of illnesses each year in the U.S. alone, whole-genome sequencing (WGS) has not been routinely employed to characterize B. cereus group isolates from foodborne outbreaks. Here, we describe the first WGS-based characterization of isolates linked to an outbreak caused by members of the B. cereus group. In conjunction with a 2016 outbreak traced to a supplier of refried beans served by a fast food restaurant chain in upstate New York, a total of 33 B. cereus group strains were obtained from human cases (n =7) and food samples (n = 26). Emetic (n = 30) and diarrheal (n = 3) isolates were most closely related to B. paranthracis (clade III) and B. cereus sensu stricto (clade IV), respectively. WGS indicated that the 30 emetic isolates (24 and 6 from food and humans, respectively) were closely-related and formed a well-supported clade relative to publicly-available emetic clade III genomes with an identical sequence type (ST 26). When compared to publicly-available emetic clade III ST 26 B. cereus group genomes, the 30 emetic clade III isolates from this outbreak differed from each other by a mean of 8.3 to 11.9 core single nucleotide polymorphisms (SNPs), while differing from publicly-available genomes by a mean of 301.7 to 528.0 core SNPs, depending on the SNP calling methodology used. Using a WST-1 cell proliferation assay, the strains isolated from this outbreak had only mild detrimental effects on HeLa cell metabolic activity compared to reference diarrheal strain B. cereus ATCC 14579. Based on both WGS and epidemiological data, we hypothesize that the outbreak was a single source outbreak caused by emetic clade III B. cereus belonging to the B. paranthracis species. In addition to showcasing how WGS can be used to characterize B. cereus group strains linked to a foodborne outbreak, we also discuss potential microbiological and epidemiological challenges presented by B. cereus group outbreaks, and we offer recommendations for analyzing WGS data from the isolates associated with them.

microbiology↗

bioRxiv2018

Bioinformatic analysis reveals mechanisms underlying parasitoid venom evolution and function

Introduction Introduction Methods Results and Discussion Conclusions References Parasitoid wasps are a numerous and diverse group of insects that obligately infect other arthropod species. These wasps lay their eggs either on the surface or within the body cavity of their hosts, and the resulting offspring exploit the hosts resources to complete their development [1]. Many parasitoids also introduce venom gland derived proteins or polydnaviruses into the host during infection. These factors act through a variety of mechanisms to manipulate host biology in order to increase the fitness of the developing parasitoid offspring [2-4]. Many hosts mount immune responses to parasitoid infection, and accordingly, parasitoids have evolved multiple venom genes that encode immunom ...

genomics↗

bioRxiv2018

Bioinformatic analysis of long-lasting transcriptional and translational changes in the basolateral amygdala following acute stress

Stress profoundly impacts the brain and increases the risk of developing a psychiatric disorder. The brains response to stress is mediated by a number of pathways that affect gene expression and protein function throughout the cell. Understanding how stress achieves such dramatic effects on the brain requires an understanding of the brains stress response pathways. The majority of studies focused on molecular changes have employed repeated or chronic stress paradigms to assess the long-term consequences of stress and have not taken an integrative genomic and/or proteomic approach. Here, we determined the lasting impact of a single stressful event (restraint) on the broad molecular profile of the basolateral amygdala complex (BLC), a key brain region mediating emotion, memory and stress. Molecular profiling performed thirty days post-restraint consisted of small RNA sequencing, RNA sequencing and quantitative mass spectrometry and identified long-lasting changes in microRNA (miRNA), messenger RNA (mRNA) and proteins. Alignment of the three datasets further delineated the regulation of stress-specific pathways which were validated by qPCR and Western Blot analysis. From this analysis, mir-29a-5p was identified as a putative regulator of stress-induced adaptations in the BLC. Further, a number of predicted mir-29a-5p targets are regulated at the mRNA and protein level. The concerted and long-lasting disruption of multiple molecular pathways in the amygdala by a single stress event is expected to be sufficient to alter behavioral responses to a wide array of future experiences, including exposure to additional stressors.

neuroscience↗

bioRxiv2019

Identification of differentially expressed genes in hepatocellular carcinoma by integrated bioinformatic analysis

Hepatocellular carcinoma is one of the most common tumors in the world and has a high mortality rate. This study elucidates the mechanism of hepatocellular carcinoma- (HCC) related development. The HCC gene expression profile (GSE54238, GSE84004) was downloaded from Gene Expression Omnibus for comprehensive analysis. A total of 359 genes were identified, of which 195 were upregulated and 164 were downregulated. Analysis of the condensed results showed that \"extracellular allotrope\" is a substantially enriched term. \"Cell cycle\", \"metabolic pathway\" and \"DNA replication\" are three significantly enriched Kyoto Encyclopedia of Genes and Genomespathways. Subsequently, a protein-protein interaction network was constructed. The most important module in the protein-protein interaction network was selected for path enrichment analysis. The results showed that CCNA2, PLK1, CDC20, UBE2C and AURKA were identified as central genes, and the expression of these five hub genes in liver cancer was significantly increased in The Cancer Genome Atlas. Univariate regression analysis was also performed to show that the overall survival and disease-free survival of patients in the high expression group were longer than in the expression group. In addition, genes in important modules are mainly involved in \"cell cycle\", \"DNA replication\" and \"oocyte meiosis\" signaling pathways. Finally, through upstream miRNA analysis, mir-300 and mir-381-3p were found to coregulate CCNA2, AURKA and UBE2C. These results provide a set of targets that can help researchers to further elucidate the underlying mechanism of liver cancer.

genetics↗