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Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2023

Identification of novel prognostic targets in coronary artery disease and related complications using bioinformatics and next generation sequencing data analysis

Coronary artery disease (CAD) is the most common cardiovascular disorder and the leading cause of heart-related deaths in world. Increasing molecular targets have been discovered for CAD and CAD - related complications prognosis and therapy. However, there is still an urgent need to identify novel biomarkers. Therefore, we evaluated biomarkers that might help the diagnosis and treatment of CAD and CAD - related complications. We searched next generation sequencing (NGS) dataset (GSE202625) and identified differentially expressed genes (DEGs) by comparing CAD and normal control samples using DESeq2. Gene ontology (GO) and pathway enrichment analyses of the DEGs were performed using the g:Profiler online database. The protein-protein interaction (PPI) network was plotted with IMEx interactome and visualized using Cytoscape. Module analysis of the PPI network was done using PEWCC1. MiRNA - hub gene regulatory network and TF - hub gene regulatory network analysis was performed to identify the hub genes, miRNAs and TFs. Receiver operating characteristic (ROC) curve analysis was used to predict the diagnostic effectiveness of the hub genes. A total of 118 DEGs (479 up regulated genes and 479 down regulated genes) were detected. The GO enrichment analysis indicated that the DEGs most significantly enriched in cellular response to stimulus and biosynthetic process. The REACTOME pathway enrichment analysis revealed that the DEGs were most significantly enriched in immune system and eukaryotic translation elongation. PPI network, modules, miRNA - hub gene regulatory network and TF - hub gene regulatory network analysis demonstrated that EGR1, SIRT1, STAT1, LRRK2, HIF1A, CSNK2B, RPS3, RPS2, RPS4X and HDAC11 were the hub genes. On the whole, the findings of this study enhance our understanding of the potential molecular mechanisms of CAD and CAD-related complications, and provide potential targets for further research.

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bioRxiv2023

Uncovering the link between gut microbiome, highly processed food consumption and diet quality through bioinformatics methods

The consumption of highly processed foods, along with other dietary and lifestyle poor habits, has an impact on health by increasing the risk of several non-communicable pathologies, such as diabetes. Gut microbiome composition, in specific, can be modulated by nutrients, deriving in different metabolic outcomes that have an influence on this high disease susceptibility and making it a possible therapeutic target for these comorbidities. In this work, gut microbiome of 60 and 46 individuals from 2 different studies focused, among other aspects, on diet-microbiome interactions, was characterised. By means of differential abundance analyses and supervised machine learning techniques based on random forest, gradient boosting and support vector machines, a set of microbial genera that could be potential biomarkers for the differentiation of individuals with poorer dietary patterns was discovered, after comparing coincidences in these taxa among classifiers and testing them for significant differences. Among these, Dialister, Phocea and Pseudoflavonifractor were suggested to have a role in the way highly processed foods affect health negatively, along with Prevotellaceae NK3B31 group and an undetermined genus from Muribaculaceae in the opposite sense. Furthermore, all the identified genera in this study had already been linked to type 2 diabetes, among which Bacteroides and Pseudoflavonifractor proved to be differentially abundant in groups of individuals with different levels of biomarkers for this disease. Nevertheless, further research via longitudinal studies and experimental validation of these genera should be carried out to confirm the association of these taxa with diet and diabetes.

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bioRxiv2023

Key common genes and pathways in ulcerative colitis and ankylosing spondylitis based on bioinformatics analysis

Associations between ulcerative colitis (UC) and ankylosing spondylitis (AS) have been observed in multiple studies, but the common etiology of UC and AS remain unknown. Thus, the current research was conducted to investigate the shared genes and relevant mechanisms in UC and AS. GSE87466 and GSE25101 datasets were used to identify DEGs involved in UC and AS, respectively. The clusterProfiler R package was utilized to detect the biological processes of DEGs in UC and AS. The performance of common DEGs in distinguishing UC or AS samples from control ones were evaluated by ROC curves. The miRWalk, Cistrome and TransmiR database were utilized to construct the network of TF-miRNA-diagnostic biomarker. GSEA method and CTD database were used to investigate the common KEGG pathways shared by UC and AS. In addition, CTD database was also used to detect the interaction score between diagnostic biomarkers and diseases associated with UC or AS. Moreover, prospective diagnostic biomarker-targeting drugs were identified using the DGIdb database. A total of 20 common DEGs were obtained by analyzing data in GSE97466 and GSE25101 datasets. ROC curves revealed that GMFG, GNG11, CLEC4D, CMTM2, VAMP5, S100A8, S100A12 and DGKQ may serve as diagnostic biomarkers for the individuals with AS and UC. A network of TF-miRNA-diagnostic biomarker, composed of 212 nodes and 721 edges, was constructed and visualized by Cytoscape software. Toll-like receptor signaling pathway, antigen processing and presentation, allograft rejection, viral myocarditis, pathways in cancer, graft versus host disease and natural killer cell mediated cytotoxicity were identified as common pathways in UC and AS. For the first time, our study identified 8 common key genes and 7 common pathways in UC and AS. These findings may help to clarify the relationship between UC and AS, and provide guidance in the diagnosis and treatment of UC and AS patients. Author SummaryAnkylosing spondylitis (AS) and Ulcerative colitis (UC) are two types of autoimmune diseases that often co-occur. The simultaneous onset of both diseases often results in severe clinical manifestations and limited therapeutic efficacy. So there is an urgent need to gain a deeper understanding of the causes of UC and AS in order to develop more effective treatment strategies. In this study, we explored the genes and pathways commonly involved in two diseases, constructed a transcriptional network, and further investigated potential drugs. The discoveries could potentially offer insights into the connection between UC and AS, and assist in identifying and managing patients with UC and AS.

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bioRxiv2023

RAMZIS: a bioinformatic toolkit for rigorous assessment of the alterations to glycoprotein structure that occur during biological processes

MotivationGlycosylation elaborates the structures and functions of glycoproteins; glycoproteins are common post-translationally modified proteins and are heterogeneous and non-deterministically synthesized as an evolutionarily driven mechanism that elaborates the functions of glycosylated gene products. While glycoproteins account for approximately half of all proteins, their macro- and micro-heterogeneity requires specialized proteomics data analysis methods as a given glycosite can be divided into several glycosylated forms, each of which must be quantified. Sampling of heterogeneous glycopeptides is limited by mass spectrometer speed and sensitivity, resulting in missing values. In conjunction with the low sample size inherent to glycoproteomics, this necessitated specialized statistical metrics to identify if observed changes in glycopeptide abundances are biologically significant or due to data quality limitations. ResultsWe developed an R package, Relative Assessment of m/z Identifications by Similarity (RAMZIS), that uses similarity metrics to guide biomedical researchers to a more rigorous interpretation of glycoproteomics data. RAMZIS uses contextual similarity to assess the quality of mass spectral data and generates graphical output that demonstrates the likelihood of finding biologically significant differences in glycosylation abundance dataset. Investigators can assess dataset quality, holistically differentiate glycosites, and identify which glycopeptides are responsible for glycosylation pattern expression change. Herein RAMZIS approach is validated by theoretical cases and by a proof-of-concept application. RAMZIS enables comparison between datasets too stochastic, small, or sparse for interpolation while acknowledging these issues in its assessment. Using our tool, researchers will be able to rigorously define the role of glycosylation and the changes that occur during biological processes. Availabilityhttps://github.com/WillHackett22/RAMZIS ContactJoseph Zaia, Boston University Medical Campus, 670 Albany St., rm 509, Boston, MA 02118 USA, (e) jzaia@bu.edu, (v) 1-617-358-2429 Supplementary informationSupplementary data are available

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bioRxiv2023

Identification of differentially expressed genes and enriched pathways in inflammatory bowel disease using bioinformatics and next generation sequencing data analysis

Inflammatory bowel disease (IBD) is the most common chronic digestive disorders and inflammation in the gastrointestinal tract globally that is characterized by episodes of abdominal pain, diarrhea, bloody stools and weight loss. However, the pathophysiologic mechanisms of IBD have not been thoroughly investigated. To explore potential targets for treatment of IBD, we reorganized and analyzed next generation sequencing (NGS) dataset (GSE186507). The R package DESeq2 tool was used to screen for differentially expressed genes (DEGs) between IBD and normal control samples. We used the g:Profiler database to perform Gene Ontology (GO) enrichment analysis and the REACTOME for pathway enrichment analysis. Protein-protein interaction (PPI) network construction and module analysis were performed to elucidate molecular mechanisms of DEGs and screen hub genes. Then miRNA-hub gene regulatory network and TF-hub gene regulatory network of these hub genes were visualized by Cytoscape. We also validated the identified hub genes via receiver operating characteristic (ROC) curve analysis. A total of 957 DEGs (478 up regulated genes and 479 down regulated genes) were detected in NGS dataset. And they were mainly enriched in the terms of multicellular organismal process, response to stimulus, GPCR ligand binding and immune system. Based on the data of PPI network, miRNA-hub gene regulatory network and TF-hub gene regulatory network the top hub genes were ranked, including IL7R, ERBB2, SMAD1, RPS26, TLE1, HNF4A, CDKN1A, SRPK1, H3C12 and SFN. In conclusion, the identified DEGs, particularly the hub genes, strengthen the understanding of the development and progression of IDB, and certain novel genes might be used as candidate target molecules to diagnose, monitor and treat IDB.

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bioRxiv2023

Exploring phytoconstituents of Juglans regia to treat cervical cancer using integrative Bioinformatics

Cervical cancer is the fourth most common malignancy among women, which also turns out to be the most common cause of death in women worldwide. Medicinal plants have traditionally been used to treat various diseases and disorders. The current study utilizes the molecular docking technique to investigate the anticancer potential of Juglans regia phytoconstituents against cervical cancer target proteins. This study includes the microarray dataset analysis of GSE63678 from the NCBI Gene Expression Omnibus database to identify differentially expressed genes. Furthermore, network biology approaches were employed to construct protein-protein interaction of differentially expressed genes. Next, the computation of topological parameters utilizing Cytohubba renders the top five hub genes (IGF1, FGF2, ESR1, MYL9, and MYH11). In addition, Juglans regia phytocompounds mined from the IMPPAT database were subjected to molecular docking analysis against identified hub genes. The application of molecular dynamics simulation validated the stability of prioritized docked complexes with minimum binding energy.

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bioRxiv2023

Transcriptomic Enrichment of Ferroptosis-Related Gene ACSL4 in Advanced Hepatic Fibrosis/Cirrhosis: Bioinformatics Analysis and Experimental validation

BackgroundLiver fibrosis is a critical part of the clinical process of liver disease that progresses to cirrhosis and even liver cancer, and effective treatment and clinical biomarkers are urgently needed to manage liver fibrosis. Ferroptosis, a notable biological phenomenon that has received attention because of the role it performs in liver fibrosis. The objective of this research is in order to identify key ferroptosis genes related to advanced liver fibrosis/cirrhosis. MethodsGene expression differences were analyzed in liver fibrosis liver tissue of hepatitis B virus(HBV)infection patients, non-alcoholic steatohepatitis (NASH) patients and alcoholic hepatitis (AH) patients to obtain overlapping ferroptosis-related genes that are significantly up-regulated. A multigroup comparison was performed to evaluate the diagnostic clinical importance of ferroptosis-related genes of patients in differential degrees of liver fibrosis, and confirmed via gene expression trend analysis. The differential expression of candidate ferroptosis-related genes through classical carbon tetrachloride (CCl4) induced advanced liver fibrosis mice model were validated by real-time quantitative PCR (qPCR). Correlation analysis was conducted to tentatively identify the connections between hepatic ferroptosis-related genes and key genes participating in functional pathways relevant to liver fibrosis. ResultsWe screened and obtained 10 genes related to ferroptosis, all of which were significantly up-regulated in liver tissue from liver fibrosis patients of different etiologies, and identified acyl-CoA synthetase long chain family member 4 (ACSL4) was transcriptomic enriched in patients with HBV infection, NASH, AH-associated advanced liver fibrosis and cirrhotic tissue adjacent to hepatocellular carcinoma (HCC). In CCl4 induced advanced liver fibrosis mice model, the hepatic ACSL4 expression was significantly up-regulated when compared to normal controls. In our study, we also suggest a significant association between ACSL4 and representative genes in liver fibrosis-related pathway. ConclusionWe found that ACSL4 mRNA can effectively differentiate the severity of liver fibrosis, suggesting its potential clinical diagnostic value in patients with liver fibrosis regardless of its etiology. ACSL4 may be a promising biomarker, which deserves further research.

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bioRxiv2023

Bioinformatic Surveillance Leads to Discovery of Two Novel Putative Bunyaviruses Associated with Black Soldier Fly

The black soldier fly (Hermetia illucens, BSF) has emerged as an industrial insect of high promise because of its ability to convert organic waste into nutritious feedstock, making it an environmentally sustainable alternative protein source. As global interest rises, rearing efforts are also upscaled, which is highly conducive to pathogen transmission. Viral epidemics have stifled mass-rearing efforts of other insects of economic importance, such as crickets, silkworms, and honeybees, but little is known about the viruses that associate with BSF. Although it is thought that BSF are unusually resistant to pathogens because of their expansive antimicrobial gene repertoire, surveillance techniques could be useful to identify emerging pathogens and common BSF microbes. In this study, we used high-throughput sequencing data to survey BSF larvae and frass samples, and we identified two novel bunyavirus-like sequences. Our phylogenetic analyses grouped one in the family Nairoviridae, and the other with two unclassified bunyaviruses. We describe these putative novel viruses as BSF Nairovirus-like 1 and BSF uncharacterized bunyavirus-like 1. We identified candidate segments for the full BSF Nairovirus-like 1 genome using a technique based on transcript co-occurrence, and only a partial genome for BSF uncharacterized bunyavirus-like 1. These results emphasize the value of routine BSF colony surveillance and add to the number of viruses associated with BSF.

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bioRxiv2023

A Template for Translational Bioinformatics: Facilitating Multimodal Data Analyses in Preclinical Models of Neurological Injury

BackgroundPediatric neurological injury and disease is a critical public health issue due to increasing rates of survival from primary injuries (e.g., cardiac arrest, traumatic brain injury) and a lack of monitoring technologies and therapeutics for the treatment of secondary neurological injury. Translational, preclinical research facilitates the development of solutions to address this growing issue but is hindered by a lack of available data frameworks and standards for the management, processing, and analysis of multimodal data sets. MethodsHere, we present a generalizable data framework that was implemented for large animal research at the Childrens Hospital of Philadelphia to address this technological gap. The presented framework culminates in an interactive dashboard for exploratory analysis and filtered data set download. ResultsCompared with existing clinical and preclinical data management solutions, the presented framework accommodates heterogeneous data types (single measure, repeated measures, time series, and imaging), integrates data sets across various experimental models, and facilitates dynamic visualization of integrated data sets. We present a use case of this framework for predictive model development for intra-arrest prediction of cardiopulmonary resuscitation outcome. ConclusionsThe described preclinical data framework may serve as a template to aid in data management efforts in other translational research labs that generate heterogeneous data sets and require a dynamic platform that can easily evolve alongside their research.

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bioRxiv2023

Identification of five characteristic ferroptosis-related genes as novel biomarkers for ischemic stroke using bioinformatic and machine learning strategies

Ferroptosis is crucial in neuronal cell death, associated with various neurological disorders. However, the role of ferroptosis-related genes (FRGs) in ischaemic stroke (IS) has yet to be well elucidated. We downloaded IS-related gene information and FRGs from the Gene Expression Omnibus (GEO) and Ferritin databases (FerrDb). 22 IS-related DE-FRGs were obtained. Functional enrichment analysis showed that these genes involve multiple regulatory pathways related to IS pathogenesis, including redox, amino acid metabolism, and cell cycle. Subsequently, DUOX2, MDM2, EGFR, MAP3K14, and TRIM46 were identified as core marker genes among the 22 DE-FRGs by lasso and SVM-RFE algorithms. The construction of the nomogram using the five marker genes had excellent diagnostic value. In addition, CIBERSORT analysis showed that changes in the immune microenvironment of IS patients might be associated with TRIM46, MDM2, and DUOX2. In addition, a total of 66 drugs targeting two characteristic FRGs were obtained. The ceRNA networks revealed complex regulatory relationships based on characteristic FRGs. These findings provide new insights into the diagnosis and treatment of IS.

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bioRxiv2023

Bioinformatics and next generation data analysis reveals the potential role of inflammation in sepsis and its associated complications

Sepsis is the leading systemic inflammatory response syndrome in worldwide, yet relatively little is known about the genes and signaling pathways involved in sepsis progression. The current investigation aimed to elucidate potential key candidate genes and pathways in sepsis and its associated complications. Next generation sequencing (NGS) dataset (GSE185263) was downloaded from the Gene Expression Omnibus (GEO) database, which included data from 348 sepsis samples and 44 normal control samples. Differentially expressed genes (DEGs) were identified using t-tests in the DESeq2 R package. Next, we made use of the g:Profiler to analyze gene ontology (GO) and REACTOME pathway. Then protein-protein interaction (PPI) of these DEGs was visualized by Cytoscape with Search Tool for the Retrieval of Interacting Genes (STRING). Furthermore, we constructed miRNA-hub gene regulatory network and TF-hub gene regulatory network among hub genes utilizing miRNet and NetworkAnalyst online databases tool and Cytoscape software. Finally, we performed receiver operating characteristic (ROC) curve analysis of hub genes through the pROC package in R statistical software. In total, 958 DEGs were identified, of which 479 were up regulated and 479 were down regulated. GO and REACTOME results showed that DEGs mainly enriched in regulation of cellular process, response to stimulus, extracellular matrix organization and immune system. The hub genes of PRKN, KIT, FGFR2, GATA3, ERBB3, CDK1, PPARG, H2BC5, H4C4 and CDC20 might be associated with sepsis and its associated complications. Predicted miRNAs (e.g., hsa-mir-548ad-5p and hsa-mir-2113) and TFs (e.g., YAP1 and TBX5) were found to be significantly correlated with sepsis and its associated complications. In conclusion, the DEGs, relative pathways, hub genes, miRNA and TFs identified in the current investigation might help in understanding of the molecular mechanisms underlying sepsis and its associated complications progression and provide potential molecular targets and biomarkers for sepsis and its associated complications.

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bioRxiv2023

Racial Disparities in the Genetic Landscape of Acute Myeloid Leukaemia from The Cancer Genome Atlas: Insights from a Bioinformatics Analysis

Acute myeloid leukaemia (AML) is a heterogeneous disease with complex pathogenesis that affects hematopoietic stem cells. Ethnic and racial disparities have been reported to affect treatment and survival outcomes in AML patients. Here, we analysed clinical and transcriptomic data from The Cancer Genome Atlas (TCGA) to investigate potential differences in the genetic landscape of AML between African and European individuals. We found several differentially expressed mRNA transcripts between the AML of Africans and Europeans. Notably, AML in African patients exhibited enrichment for several pathways, including signalling by G-protein-coupled receptors, oncostatin M, and codeine and morphine metabolism. In contrast, AML in European patients showed enrichment for pathways related to the glial cell-line derived neurotrophic factor/rearranged during transfection signalling axis, gamma-aminobutyric acid receptor activation, and ligand-gated ion transport channels. Additionally, kinase enrichment analysis identified shared and distinct kinases in AML among Africans and Europeans: Africans showed an enrichment of cyclin-dependent kinases, while Europeans exhibited an enrichment of ULK2, CSNK2B, and CAMK1. Our study highlights the potential importance of considering race when evaluating the genetic landscape of AML, which may improve treatment strategies for this disease.

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bioRxiv2023

Identification of candidate biomarkers and pathways associated with multiple sclerosis using bioinformatics and next generation sequencing data analysis

Multiple sclerosis (MS) is an autoinflammatory disease that might lead to severe disability. The diagnosis of MS is defined due to the urgency for biomarkers with both reliability and efficiency. Demyelination of axons are deeply involved in the pathogenesis of MS. Our study aims to identify the underlying molecular mechanism and screening for related biomarkers and signaling pathways. We obtained next generation sequencing (NGS) dataset (GSE138614) from the GEO database. Differentially expressed genes (DEGs) were screened by the DESeq2 package in R bioconductor with considering specific criteria. Gene Ontology (GO) enrichment analysis, REACTOME pathway enrichment analysis were performed; a protein-protein interaction (PPI) network was constructed; significant modules were analyzed and hub genes were identified by Human Integrated Protein-Protein Interaction rEference (HiPPIE). Subsequently, miRNA-hub gene regulatory network, TF-hub gene regulatory network and drug-hub gene interaction network were built by Cytoscape to predict the underlying microRNAs (miRNAs), transcription factors (TFs) and drugs associated with hub genes. Receiver operating characteristic (ROC) curves analysis was performed to calculate diagnostic value of hub genes. Finally, we performed molecular docking study for prediction of drug molecules against protein targets. A total of 959 DEGs (479 up-regulated and 480 down-regulated genes) were identified in the MS samples and compared with normal control samples. The DEGs were predominantly enriched in an ensemble of genes encoding the immune system process, developmental process, immune system and regulation of cholesterol biosynthesis by SREBP (SREBF). A PPI network was obtained through HiPPIE analysis, and the results were imported into Cytoscape software. The DEGs were sequenced by the Network Analyzer plug-in by various calculation methods, and 10 hub genes (LCK, PYHIN1, SLAMF1, DOK2, TAB2, CFTR, RHOB, LMNA, EGLN3 and ERBB3) were finally selected. Based on the miRNA-hub gene regulatory network and TF-hub gene regulatory network construction, miRNAs including hsa-mir-6794-3p, hsa-mir-3689a-3p, hsa-mir-4651, hsa-mir-548q, BRCA1, HNF4A, TFAP2C and NR2F1 were determined to be potential key biomarkers. Drug-hub gene interaction network constructed from DrugBank, which identified targeted therapeutic drugs (Palivizumab, Cu-Bicyclam, Lumacaftor and Zonisamide) for the hub genes. From molecular docking study we showed good drug - protein bind affinity and amino acid interactions. This study identified novel biomarkers for MS and established a reliable diagnostic model as well as predicted novel drug molecules. The transcriptional changes identified may help to reveal the pathogenesis and molecular mechanisms of MS.

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bioRxiv2024

Unveiling the role of Tdark genes in genetic diseases and phenotypes through bioinformatics-based functional enrichment and network analyses

In recent years, there has been growing interest in understanding the role of dark genes in genetic diseases and phenotypes. Despite their lack of functional characterisation, dark genes account for a significant portion of the human genome and are believed to play a role in regulating gene expression and cellular processes. We investigated the role of dark genes in genetic diseases and phenotypes by conducting integrative network analyses and functional enrichment studies across multiple large-scale molecular datasets. Our investigation revealed a predominant association of both dark and light genes with psoriasis. Furthermore, we found that the transcription factors UBTF and NFE2L2 are potential regulators of both dark and light genes associated with tuberculosis. In contrast, the transcription factors SUZ12 and TP63 are potential regulators of both dark and light genes associated with interstitial cystitis. Further network analysis of dark genes, including CALHM6, HCP5, PRRG4, DDX60L and RASA2, revealed a notably high weighted degree of association with genetic diseases and phenotypes. Moreover, our analysis revealed that numerous genetic diseases and phenotypes, including psoriasis, pick disease, tuberculosis, ulcerative colitis, interstitial cystitis, and Crohns disease, exhibited shared gene linkages. Additionally, we conducted a protein-protein interaction analysis to reveal 16 dark genes that encode hub proteins, including R3HDM2, RPUSD4, FASTKD5, and MRPL15, that could play a role in many genetic diseases and phenotypes, and are widely expressed across body tissues. Our findings contribute to the understanding of the genetic basis of diseases and provide potential therapeutic targets for future research. Identifying dysregulated dark genes in disease states can lead to new strategies for prevention, diagnosis, and treatment, thus advancing our understanding of disease mechanisms.

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bioRxiv2024

Multiple Sclerosis Stages and their Differentially Expressed Genes: A Bioinformatics Analysis

Multiple Sclerosis (MS) is an inflammatory, chronic, autoimmune, and demyelinating disease of the central nervous system. MS is a heterogeneous disease with three main clinical forms, affecting the progression and therefore the treatment of the disease. Thus, finding key genes and microRNAs (miRNA) associated with MS stages and analyzing their interactions is important to better understand the molecular mechanism underlying the occurrence and the evolution of MS. Based on publicly available datasets of mRNA and miRNA expression profiles, differentially expressed genes (DEGs) and differentially expressed miRNAs (DEMs) between patients with different stages of MS and healthy controls and between relapsing and remitting phases of RRMS were determined using Deseq2 and GEO2R tools. We then analyzed miRNA-mRNA regulatory interactions and gene ontology for the DEGs. Based on miRNA-mRNA regulatory interactions, we identified potential biomarkers of RRMS, 13 upregulated miRNA regulators of 30 downregulated genes and 17 downregulated miRNA regulators of 32 upregulated genes. We also identified 9 downregulated miRNA regulators of 12 upregulated genes as potential biomarkers of SPMS. Our study findings highlight some key protein-coding genes and miRNAs that are involved in the occurrence and evolution of MS.

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bioRxiv2024

RhizoBindingSites v2.0 is a bioinformatic database of DNA motifs potentially involved in transcriptional regulation deduced from sites of the itself genome

RhizoBindingSites is a de novo depurified database of conserved DNA motifs potentially involved in the transcriptional regulation of the Rhizobium, Sinorhizobium, Bradyrhizobium, Azorhizobium, and Mesorhizobium representative symbiotic species, deduced from the upstream regulatory sequences of orthologous genes (O-matrices) from the Rhizobiales taxon. The sites collected with O-matrices per gene per genome from RhizoBindingSites were used to deduce matrices using the dyad-Regulatory Sequence Analysis Tool (RSAT) method, giving rise to novel S-matrices for the construction of the RizoBindingSites v2.0 database. A comparison of the S-matrix logos showed a greater frequency and/or re-definition of specific-position nucleotides found in the O-matrices. Moreover, S-matrices were better at detecting genes in the genome and there was a greater number of transcription factors (TFs) in the vicinity than O-matrices, corresponding to a more significant genomic coverage for S-matrices. The homology between the matrices of TFs from a genome showed inter-regulation between the clustered TFs. In addition, matrices of AraC, ArsR, GntR, and LysR ortholog TFs showed different motifs, suggesting distinct regulation. Benchmarking showed 72%, 68%, and 81% of common genes per regulon for O-matrices and approximately 14% less common genes with S-matrices of Rhizobium etli CFN42, R. leguminosarum bv. viciae 3841, and Sinorhizobium meliloti 1021. These data were deposited in RhizoBindingSites and the RhizoBindingSites v2.0 database (http://rhizobindingsites.ccg.unam.mx/).

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bioRxiv2024

Resistance Gene Association and Inference Network (ReGAIN): A Bioinformatics Pipeline for Assessing Probabilistic Co-Occurrence Between Resistance Genes in Bacterial Pathogens

The rampant rise of multidrug resistant (MDR) bacterial pathogens poses a severe health threat, necessitating innovative tools to unravel the complex genetic underpinnings of antimicrobial resistance. Despite significant strides in developing genomic tools for detecting resistance genes, a gap remains in analyzing organism-specific patterns of resistance gene co-occurrence. Addressing this deficiency, we developed the Resistance Gene Association and Inference Network (ReGAIN), a novel web-based and command line genomic platform that uses Bayesian network structure learning to identify and map resistance gene networks in bacterial pathogens. ReGAIN not only detects resistance genes using well- established methods, but also elucidates their complex interplay, critical for understanding MDR phenotypes. Focusing on ESKAPE pathogens, ReGAIN yielded a queryable database for investigating resistance gene co-occurrence, enriching resistome analyses, and providing new insights into the dynamics of antimicrobial resistance. Furthermore, the versatility of ReGAIN extends beyond antibiotic resistance genes to include assessment of co-occurrence patterns among heavy metal resistance and virulence determinants, providing a comprehensive overview of key gene relationships impacting both disease progression and treatment outcomes. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=190 SRC="FIGDIR/small/582197v1_ufig1.gif" ALT="Figure 1"> View larger version (53K): org.highwire.dtl.DTLVardef@158a667org.highwire.dtl.DTLVardef@114c965org.highwire.dtl.DTLVardef@1b24504org.highwire.dtl.DTLVardef@d112af_HPS_FORMAT_FIGEXP M_FIG C_FIG

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bioRxiv2024

Bioinformatic analysis of human ZPR1 gene pathogenic exome mutation

Advanced sequencing technologies enable rapid detection of sequence variants, aiming to uncover the molecular foundations of human genetic disorders. The challenge lies in interpreting the influence of new exome variants that lead to diverse phenotypes. Our study introduces a detailed, multi-tiered method for assessing the impact of novel variants, particularly focusing on the zinc finger protein 1 (ZPR1) gene. Herein, we employed a combination of variant effect predictors, protein stability analyses, and the American College of Medical Genetics and Association of Molecular Pathology (ACMG/AMP) guidelines. Our structural analysis pinpoints specific amino acid residues in the ZPR1 zinc finger domains that are sensitive to changes, distinguishing between benign and disease-causing coding variants using rigorous in silico tools. We examined 223 germline ZPR1 exome variants, uncovering significant ethnic disparities in the frequency of heterozygous harmful ZPR1 variants, ranging from 0.04% in the Ashkenazi Jewish population to 0.34% in African/African Americans. Additionally, the discovery of three homozygous carriers in European and South Asian groups suggests a higher occurrence of ZPR1 variants in these demographics, meriting further exploration. This research provides insights into the prevalence and implications of amino acid substitutions in the ZPR1 protein.

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