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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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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End-to-end assessment of fecal bacteriome analysis: from sample processing to DNA sequencing and bioinformatics results

Intestinal microbiome, comprising the whole microbiota, their genes and genomes living in the human gut have significant roles in promoting health or disease status. As many studies showed so far, identifying the bacterial components of the microbiome can reveal important biomarkers to help in the disease comprehension to a further adequate treatment. However, the human nature is quite variable considering the genetic components associated with life styles, directly reflecting on the gut microbiome. Thus, it is extremely important to know the populational microbiome background in order to draw conclusions regarding the health and disease conditions. Also, methodological best practices and knowledge about the methods being used are essential for the results quality and applicability with clinical relevance. In this way, we standardized the sample collection and processing methods used for the Probiome assay, a test developed to identify the Brazilian bacteriome from stool samples. EncodeTools Metabarcode pipeline of analysis was developed to obtain the best result from the samples. This pipeline uses the information of amplicon single variants (ASVs) in 100% identical oligotype clusters, and performs a de novo taxonomical assignment based on similarity for unknown sequences. To better comprehend the results obtained in Probiome assays, is essential to know the intestinal bacteriome diversity of Brazilians. Thus, we applied the standardized methods herein developed and began characterizing our populational data to allow a better understanding of the Brazilian bacteriome profiles and how they can be related to other microbiome studies.

microbiology↗

Application of pharmacogenomics and bioinformatics to exemplify the utility of human ex vivo organoculture models in the field of precision medicine.

Here we describe a collaboration between industry, the National Health Service (NHS) and academia that sought to demonstrate how early understanding of both pharmacology and genomics can improve strategies for the development of precision medicines. Diseased tissue ethically acquired from patients suffering from chronic obstructive pulmonary disease (COPD), was used to investigate inter-patient variability in drug efficacy using ex vivo organocultures of fresh lung tissue as the test system. The reduction in inflammatory cytokines in the presence of various test drugs was used as the measure of drug efficacy and the individual patient responses were then matched against genotype and microRNA profiles in an attempt to identify unique predictors of drug responsiveness. Our findings suggest that genetic variation in CYP2E1 and SMAD3 genes may partly explain the observed variation in drug response.

pharmacology and toxicology↗

Bioinformatics analysis of endophytic bacteria related to berberine in the Chinese medicinal plant Coptis teeta Wall.

Plant endophytic microorganisms absorb nutrients and prevent pathogen damage, supporting healthy plant growth. However, relationships between endophytic bacteria of the medicinal plant Coptis teeta Wall. and berberine production remain unclear. Herein, we explored the microbial composition of wild-type (WT) and cultivated Coptis teeta Wall. root, stem and leaf, and endophytic bacteria related to berberine. Microbial characteristics of were analyzed by 16S rDNA sequencing, and berberine in roots was analyzed by high-performance liquid chromatography (HPLC). Proteobacteria, Actinobacteria and Bacteroidetes were the major phyla, and Mycobacterium, Salmonella, Nocardioides, Burkholderia-Paraburkholderia and Rhizobium were the dominant genera. Berberine was positively correlated with total P (TP), total N (TN), total K (TK) and available K (AK) in rhizosphere soil, and with Microbacterium and norank_f_7B-8, whereas TK was positively correlated with Microbacterium, TN, AK and Burkholderia-Paraburkholderia. The findings will support further studies on endophytic bacteria and berberine in Coptis teeta Wall., and may promote berberine production.

microbiology↗

Bioinformatic analysis of brain-specific miRNAs for identification of candidate traumatic brain injury blood biomarkers.

BackgroundDetection of brain-specific miRNAs in the peripheral blood could serve as a surrogate marker of traumatic brain injury (TBI). Here, we systematically identified brain-enriched miRNAs, and tested their utility for use as TBI biomarkers in the acute phase of care. MethodsPublically-available microarray data generated from 31 postmortem human tissues was used to rank 1,364 miRNAs in terms of their degree of brain-specific expression. Levels of the top five ranked miRNAs were then prospectively measured in serum samples collected from 10 TBI patients at hospital admission, as well as from 10 controls. ResultsThe top five miRNAs identified in our analysis (miR-137, miR-219a-5p, miR-128-3p, miR-124-3p, and miR-138-5p) exhibited 31 to 74-fold higher expression in brain relative to other tissues. Furthermore, their levels were elevated in serum from TBI patients compared to controls, and were collectively able to discriminate between groups with 90% sensitivity and 80% specificity. Subsequent informatic pathway analysis revealed that their target transcripts were significantly enriched for components of signaling pathways which are active in peripheral organs such as the heart. ConclusionsThe five candidate miRNAs identified in this study have promise as blood biomarkers of TBI, and could also be molecular contributors to systemic physiologic changes commonly observed post-injury. A FINAL PEER REVIEWED VERSION OF THIS ARTICLE HAS BEEN PUBLISHED IN BRAIN INJURY AT THE FOLLOWING DOI: 10.1080/02699052.2020.1764102 There are some notable differences between the analysis presented in this preprint and our final peer-reviewed article. There was a single tissue sample originating from spinal cord that we had classified as a non-brain tissue in our original analysis outlined in this preprint. Because the composition of spinal cord and brain are highly similar in terms of gene expression, classifying this sample as a non-brain tissue dramatically reduced the levels of brain enrichment observed in the analysis. Because brain and spinal cord are molecularly highly similar, but technically distinct anatomical structures, we simply decided to exclude this sample from our final analysis published in Brain Injury to avoid confounds. The top 5 miRNAs identified in our original analysis still fell within the top 7 of this final analysis. In addition, the final analysis identified two additional miRNAs which could be candidate biomarkers based on levels of brain enrichment. The final article published in Brain Injury also reports an additional confirmatory tissue specificity analysis performed in a second independent dataset, as well as additional analysis examining the brain specificity of several notable previously proposed miRNA TBI biomarkers, which is not described in this preprint.

neuroscience↗

Functional Unknomics of the SAR11 clade using bioinformatics approaches

A substantial fraction of the genes in marine bacteria lack detectable sequence similarity to genes with known functions. These functionally uncharacterized genes--collectively referred to as the "unknome"--represent a largely unexplored genetic repertoire harboring insights into marine bacterial ecology. In this study, we explored the function of the unknome of SAR11 clade, the most abundant bacterial lineage in the ocean, with a particular focus on genes that provide insight into their ecology. Based on the COG classification, approximately 58% of SAR11 ortholog groups were classified as unknome. Among the SAR11 unknome, we successfully inferred the functions of 69 ortholog groups that are conserved in SAR11 clade by protein structure similarity searches and genomic context analyses. These ortholog groups include putative transporter components, supporting the current ecological understanding that SAR11 clade is specialized in substrate uptake to adapt to oligotrophic marine environments. Furthermore, structural analysis indicated that the DUF2237-containing protein, enriched in marine environments, has potential interactions with purine nucleotide-containing compounds. This may suggest the existence of unique nucleotide utilization mechanisms in marine bacteria. In addition, we found candidates of virus defense systems within the unknome, which demonstrates that diverse defense systems are present at least in one-third of the cultured SAR11 strains. The conservation of these viral defense systems, even within streamlined SAR11 genomes, suggests that they confer significant ecological advantages. Our exploration provided insights into the genetic basis of bottom-up processes (adaptation to oligotrophic environments) and top-down processes (antiviral defense strategy) contributing to ecological success of SAR11.

microbiology↗

Archaeogenomic and Bioinformatic Analysis of the Columbus Lineage: Evidence from the Counts of Gelves.

The geographical and familial origins of Christopher Columbus have remained a subject of intense historiographical debate for over five centuries. Despite numerous hypotheses, empirical genetic evidence capable of resolving his ancestral history or place of birth has been absent from the literature until now. This study presents the third stage of the first forensic genetic analysis performed on skeletal remains belonging to several direct descendants of Columbus, spanning the 16th to 18th centuries. By applying Massively Parallel Sequencing (MPS) to analyse autosomal, X- and Y- chromosome DNA markers, and integrating the results with multidisciplinary evidence from historical, genealogical, archaeological, and anthropological research implicated in this project, the identification of several individuals founded in the Crypt of Santa Maria de Gracia located in Gelves (Sevilla, Spain) has been achieved. The analysis of their biological relatedness enabled the reconstruction of kinship networks among the individuals interred in the crypt, which, when interpreted in the context of documented genealogical lineages, provides indirect but consistent evidence pointing toward the debated origin of the discoverer.

genetics↗

Integrated bioinformatics and single-cell analysis identifies vascular aging-related hub genes and immune drivers in atherosclerosis.

Atherosclerosis (AS) is a chronic inflammatory disease closely linked to vascular senescence, yet the specific molecular mechanisms connecting aging processes to AS pathogenesis remain incompletely understood. This study integrated transcriptomic data from GEO datasets (GSE100927 and GSE43292) to identify vascular aging-related differentially expressed genes (VARDEGs). Following batch effect correction, 28 VARDEGs were screened and subjected to functional enrichment, protein-protein interaction (PPI) network analysis, and immune infiltration assessment. Seven hub genes (MMP9, APOE, TNF, ICAM1, PPARG, CYBA, and NCF2) were identified and experimentally validated via qRT-PCR, confirming their significant upregulation in AS samples. Receiver operating characteristic (ROC) analysis demonstrated high diagnostic accuracy for six of these genes (AUC > 0.7), with TNF exhibiting superior performance. Immune infiltration analysis revealed profound alterations in 28 immune cell types, particularly monocytes and T cells, which correlated strongly with hub gene expression. Furthermore, single-cell RNA sequencing analysis (GSE184073) localized the expression of core genes predominantly to monocytes and T cells, highlighting TNF overexpression in T cells as a potential critical driver. Finally, molecular docking simulations suggested that curcumin exhibits strong binding affinity to these hub genes, particularly PPARG, providing a mechanistic basis for its therapeutic potential. Collectively, this study elucidates the landscape of vascular aging-related genes in AS, identifies novel diagnostic biomarkers, and proposes potential therapeutic targets involving immune modulation and natural compounds.

biochemistry↗

Mutational and bioinformatic analysis of the binding site for the ribonucleotide reductase-specific transcriptional repressor NrdR

The prevalent transcriptional repressor NrdR binds to highly conserved prokaryotic sequences in the promoter regions of operons encoding the essential enzyme ribonucleotide reductase. The NrdR binding sites consist of two partially palindromic 16 bp sequences (NrdR boxes) separated by a 15-16 bp linker sequence. We have assessed the requirement of both boxes for binding, the propensity of different NrdRs to bind to heterologous binding sites, and that the linker sequence is only limited to length and not sequence conservation. As we have observed several deviations from the conserved sequences of the NrdR boxes, we here test the conservation requirements of individual basepairs in the NrdR boxes using a synthetic DNA fragment (Synt DNA) to which the NrdR proteins from the actinomycete Streptomyces coelicolor and the gammaproteobacterium Escherichia coli bind equally well as to their homologous binding sites. By introducing isolated mutations to Synt DNA and testing the binding capacity of NrdR from S. coelicolor and E. coli we expand our understanding of what criteria are needed to build a functional binding site for the NrdR repressor.

molecular biology↗

Digital Kennison: A bioinformatics pipeline for rapid mapping of sequences to the Drosophila melanogaster Y chromosome

The Drosophila melanogaster Y chromosome is currently known to contain 13 single-copy protein-coding genes, six of which are essential for male fertility, as well as several non-coding genes and abundant repetitive DNA. Localization of Y-linked sequences has traditionally relied on labor-intensive crosses using Kennisons translocation strains, which map Y-linked loci by generating flies deficient for each of the six Y-chromosome fertility regions (ks-1, ks-2, kl-1, kl-2, kl-3, and kl-5). Here we present Digital Kennison, a computational pipeline that recasts this classical mapping strategy as a sequence-based analysis. The pipeline queries eight genomic databases derived from Kennisons strains using BLAST and read coverage, assigning sequences to fertility regions with a calibrated confidence score. We benchmarked the method on 60 Y-linked sequences spanning all six regions, including single-copy protein-coding genes, Mst77Y family members, non-coding RNAs, and the centromere. Digital Kennison achieved 97% precision while resolving challenging cases, including boundary-spanning genes (PRY and Ppr-Y), fragmented Mst77Y copies, and FDY, which has a closely related autosomal paralog. Beyond validating known localizations, the pipeline localized the unmapped gene CG41561 to the kl-1region and reassigned the transcript CR40629-RC from the kl-2 region to kl-5. It also localized 7 of 16 recently transferred Y-linked sequences described by Tobler et al. (2017), including 4 with high confidence. Applied to 904 small R6 scaffolds, Digital Kennison assigned 75% to fertility regions, including five currently annotated as autosomal-pericentromeric. Digital Kennison reduces sequence localization from weeks of genetic crosses to minutes of computation while preserving the power of classical translocation mapping. Article summaryThe Drosophila melanogaster Y chromosome is difficult to study because it consists largely of repetitive, non-recombining DNA. Researchers have traditionally mapped Y-linked genes using slow, labor-intensive genetic crosses. Here we introduce Digital Kennison, a computational pipeline that replicates this classical mapping strategy using DNA sequence data instead of live flies. By comparing a query sequence against genomic databases built from fly strains, the pipeline assigns it to one of six Y-chromosome regions and reports a confidence score. Tested on 60 known sequences, it achieved 97% precision, corrected an annotation error, and mapped previously unplaced sequences in minutes rather than weeks.

genomics↗

Bioinformatic Characterization of Regulated IRE1a-Dependent Decay (RIDD) in Heart Failure

Inositol-requiring enzyme 1a (IRE1a) is a canonical signaling factor in the unfolded protein response (UPR). In addition to this essential role (which prevents the accumulation of misfolded proteins in the endoplasmic reticulum), the endoribonuclease activity of IRE1a targets multiple mRNAs for degradation through a process called Regulated IRE1a-Dependent Decay (RIDD). The products of over 50 genes have been identified as RIDD targets; however, the biological significance of this process remains underexplored. Using publicly available datasets, we examined the fate of 27 well-characterized RIDD targets in the septal wall of heart failure patients, and in mice subject to pressure overload-induced heart failure. We show that decreased mRNA abundance from these RIDD substrate genes - an outcome consistent with RIDD induction - is commonly observed in heart failure.

cell biology↗

Deciphering Novel Transcriptional Wiring in Colorectal Cancer: An Integrative Bioinformatic and Experimental Study

Colorectal cancer remains among the most lethal malignancies worldwide, and the proliferative programme that sustains it has proved to be a challenging target, particularly with acceptable selectivity. Herein, we combined stage-resolved transcriptomic analysis with experimental testing in colorectal cancer cells to inquire whether small molecules, in particular melatonin, act on that programme. The comparison of stage II, III and IV colorectal tumours with normal tissue identified 410 genes upregulated at every stage as a core set, dominated by cell-cycle, spindle-assembly and chromosome-segregation functions. Twenty hub genes were extracted from the corresponding protein interaction network, thirteen of which were required for viability across 59 colorectal cancer cell lines in genome-wide CRISPR screening data. Target-set enrichment nominated E2F4, FOXM1, SIN3A and both DNA-binding subunits of NF-Y as upstream regulators. NF-YA and NF-YB were distinctive in one respect: their annotated targets include BUB1 and CCNA2 but exclude NCAPG, yielding a testable prediction. Our experimental results showed melatonin reduces SW480 viability with an IC of 2.63 mM and lowers BUB1 and CCNA2 expression in different manners of concentration-dependency, while NCAPG remains unchanged. Melatonin treatment arrests cells in G1 phase, causes a drastic fall in the cycling S-phase fraction, impairs the migration and proliferation phenotype, and rises apoptosis moderately. We also found {beta}2-microglobulin to be an unsuitable normalization reference gene for CRC research due to changes upon treatment. Selective repression of two NF-Y targets with sparing of a non-target is consistent with reduced NF-Y-dependent transcription, though occupancy and subunit-level evidence are to be established.

cell biology↗

Uncovering the Multifaceted Role of the Kinetochore Scaffold 1 (KNL1) in Hepatocellular Carcinoma: Integrative Bioinformatics Analysis and CRISPR-Cas9 Experimental Study

Hepatocellular carcinoma (HCC) is a highly aggressive malignancy characterized by pronounced heterogeneity, chromosomal instability, and limited therapeutic options. Kinetochore scaffold 1 (KNL1), a core mitotic checkpoint component, is implicated in multiple cancers, but its role in HCC remains unclear. Here, we integrated bulk and single-cell transcriptomic analyses with functional in vitro validation to elucidate the biological and clinical significance of KNL1 in HCC. KNL1 was markedly overexpressed in HCC tumors, particularly those harbouring TP53 mutations, and correlated with increased tumor break load, suggesting a link to structural chromosomal instability. Weighted gene co-expression network analysis (WGCNA) and hallmark gene set enrichment identified a KNL1-associated G2M checkpoint network, in which KNL1 ranked among the top 10 hub genes. A KNL1-G2M network score, stratified patients into high- and low-score groups, with high scores correlates with poorer overall survival, increased proliferation, and higher chromosomal instability. Immune deconvolution (CIBERSORTx) revealed that high-score tumors were enriched in T-regulatory cells and M0 macrophages, and had elevated TIDE and exclusion scores, indicating potential resistance to immune checkpoint blockade. Single-cell RNA-seq confirmed preferential activation of the KNL1-G2M module in malignant hepatocytes and highlighted increased extracellular matrix remodeling and immunomodulatory interactions. CRISPR-Cas9-mediated KNL1 downregulation in HepG2 cells induced modest G2/M arrest, suppressed proliferation, reduced survivin and Bcl2 expression, and produced ultrastructural changes consistent with early apoptosis. Collectively, our results implicate KNL1 and its associated G2/M network in tumor progression-related processes, including chromosomal instability, proliferative activity, and immune modulation in HCC. While further mechanistic and clinical studies are required, KNL1 and its associated network may represent a biologically relevant vulnerability and a potential biomarker of aggressive disease behavior in hepatocellular carcinoma.

biochemistry↗

Lessons Learned: Recommendations for Establishing Critical Periodic Scientific Benchmarking

The dependence of life scientists on software has steadily grown in recent years. For many tasks, researchers have to decide which of the available bioinformatics software are more suitable for their specific needs. Additionally researchers should be able to objectively select the software that provides the highest accuracy, the best efficiency and the highest level of reproducibility when integrated in their research projects.\n\nCritical benchmarking of bioinformatics methods, tools and web services is therefore an essential community service, as well as a critical component of reproducibility efforts. Unbiased and objective evaluations are challenging to set up and can only be effective when built and implemented around community driven efforts, as demonstrated by the many ongoing community challenges in bioinformatics that followed the success of CASP. Community challenges bring the combined benefits of intense collaboration, transparency and standard harmonization. Only open systems for the continuous evaluation of methods offer a perfect complement to community challenges, offering to larger communities of users that could extend far beyond the community of developers, a window to the developments status that they can use for their specific projects. We understand by continuous evaluation systems as those services which are always available and periodically update their data and/or metrics according to a predefined schedule keeping in mind that the performance has to be always seen in terms of each research domain.\n\nWe argue here that technology is now mature to bring community driven benchmarking efforts to a higher level that should allow effective interoperability of benchmarks across related methods. New technological developments allow overcoming the limitations of the first experiences on online benchmarking e.g. EVA. We therefore describe OpenEBench, a novel infra-structure designed to establish a continuous automated benchmarking system for bioinformatics methods, tools and web services.\n\nOpenEBench is being developed so as to cater for the needs of the bioinformatics community, especially software developers who need an objective and quantitative way to inform their decisions as well as the larger community of end-users, in their search for unbiased and up-to-date evaluation of bioinformatics methods. As such OpenEBench should soon become a central place for bioinformatics software developers, community-driven benchmarking initiatives, researchers using bioinformatics methods, and funders interested in the result of methods evaluation.

bioinformatics↗

AmyCo: the Amyloidoses Collection

AmyCo: the Amyloidoses CollectionAmyloid fibrils are formed when soluble proteins misfold into highly ordered insoluble fibrillar aggregates and affect various organs and tissues. The deposition of amyloid fibrils is the main hallmark of a group of disorders, called amyloidoses. Curiously, fibril deposition has been also recorded as a complication in a number of other pathological conditions, including well-known neurodegenerative or endocrine diseases. To date, amyloidoses are roughly classified, owing to their tremendous heterogeneity. In this work, we introduce AmyCo, a freely available collection of amyloidoses and clinical disorders related to amyloid deposition. AmyCo classifies 74 diseases associated with amyloid deposition into two distinct categories, namely 1) amyloidosis and 2) clinical conditions associated with amyloidosis. Each database entry is annotated with the major protein component (causative protein), other components of amyloid deposits and affected tissues or organs. Database entries are also supplemented with appropriate detailed annotation and are referenced to ICD-10, MeSH, OMIM, PubMed, AmyPro and UniProtKB databases. To our knowledge, AmyCo is the largest repository containing information about amyloidoses and diseases related to amyloid deposition. The AmyCo web interface is available at http://bioinformatics.biol.uoa.gr/amyco.\n\nKaterina C. Nastou is a Ph.D. student in Bioinformatics, at the Department of Biology of the National and Kapodistrian University of Athens. She is currently working on computational analysis of membrane and amyloidogenic proteins as part of her Ph.D. thesis. Her research focuses on the study of protein-protein interactions and the visualization and analysis of biological networks for these protein families, on the computational prediction of protein structure and function and the design and development of biological databases.\n\nGeorgia I. Nasi is a Ph.D. student in Biophysics, at the Department of Biology of the National and Kapodistrian University of Athens and a second year student in the Bioinformatics Masters Program, at the same department. She is currently conducting her Masters thesis on the computational analysis and visualization of the interaction network of amyloidoses and proteins associated with these disorders. Her research for her Ph.D. focuses on biophysical and computational analysis of amyloidogenic proteins and peptide-analogues associated with amyloidoses.\n\nDr. Paraskevi L. Tsiolaki is a Biologist with an MSc in Bioinformatics and a PhD in Molecular Biophysics. She is currently working as a postdoctoral fellow in Dr V. Iconomidous group, Assist. Prof. at the National and Kapodistrian University of Athens and her research interests focus on Molecular Biophysics and Structural Biology. Her current research efforts have been directed towards identifying the structural characteristics that underlie the self-assembly mechanisms,governing amyloidogenicity. More specifically she works on the structure and self-assembly of different amyloidogenic proteins or amyloidogenic peptide-analogues, implicated with amyloidoses, utilizing biophysical and biochemical techniques. She is also working on the computational and structural analysis of the anomalous type of protein-protein interactions in protein aggregation, with particular focus on the development of novel therapeutic intervention strategies.\n\nDr.Zoi Litou works as a Special Laboratory Teaching Staff in \"Bioinformatics-Biophysics\" at the Section of Cell Biology and Biophysics, Department of Biology, National &Kapodistrian University of Athens. She has a PhD in Bioinformatics. She is currently working on computational analysis of membrane proteins focusing on the automated recognition and classification of single-spanning membrane proteins, CWPs, GPCRs and Ion channels. Biological Network Analysis, Prediction algorithms, Algorithm Visualization techniques in Bioinformatics, High throughput sequencing analysis and visualization, Clustering Analysis, Knowledge discovery, management and representation, Data integration, Chemoinformatics, Pharmacogenomics, Text Mining in Bioinformatics, Personalized Medicine, Parallel programming.\n\nDr. Vassiliki A. Iconomidou is an Assistant Professor of Structural Biology/Molecular Biophysics and a group leader of Biophysics and Bioinformatics Lab at the Department of Biology of the National and Kapodistrian University of Athens. Her research interests include: 1) Structural and self-assembly studies of fibrous proteins, which form extracellular, proteinaceous structures of physiological importance like lepidopteran, dipteran and fish chorions and arthropod cuticle, 2) Structural and self-assembly studies of silkmoth chorion peptide-analogues as novel self-assembled polymers with amyloid properties, aiming at the construction of novel biomaterials with extraordinary physical properties, 3) Experimental studiesof the role of a great variety of amyloidogenic ( aggregation-prone) peptides, predicted by our AMYLPRED prediction algorithm, in several widespread and also rare pathological amyloidoses. She had been visiting European Molecular Biology Laboratory (EMBL Heidelberg) for more than ten years, conducting research on molecular self-assembly focusing especially on functional, protective and pathological amyloids and amyloidoses, and she was there when she published the first article on natural protective amyloids. She is the author of 41 publications and 6 book chapters which focus mostly on functional and pathological amyloid studies.

bioinformatics↗

Bringing TB genomics to the clinic: A comprehensive pipeline to predict antimicrobial susceptibility from genomic data, validated and accredited to ISO standards.

BackgroundWhole genome sequencing (WGS) is increasingly contributing to the clinical management of tuberculosis. Whilst the availability of bioinformatic tools for analysis and clinical reporting of Mycobacterium tuberculosis sequence data is improving, However, there remains a need for accessible, flexible bioinformatic tools that can be easily tailored for clinical reporting needs in different settings and are suitable for accreditation to international standards. MethodsWe developed tbtAMR, a flexible yet comprehensive tool for analysis of Mycobacterium tuberculosis genomic data, including inference of phenotypic susceptibility and lineage calling. Validation was undertaken using local and publicly-available real-world data (phenotype and genotype) and synthetic genomic data to determine the appropriate quality control metrics and extensively validate the pipeline for clinical use. FindingstbtAMR accurately predicted lineages and phenotypic susceptibility for first- and second-line drugs, with equivalent computational and predictive performance compared to other bioinformatics tools currently available. tbtAMR is flexible with modifiable criteria to tailor results to users needs. InterpretationThe tbtAMR tool is suitable for use in clinical and public health microbiology laboratory settings, and can be tailored to specific local needs by non-programmers. We have accredited this tool to ISO standards in our laboratory, and it has been implemented for routine reporting of AMR from genomic sequence data in a clinically relevant timeframe. Reporting templates, validation methods and datasets are provided to offer a pathway for laboratories to adopt and seek their own accreditation for this critical test, to improve the management of tuberculosis globally. FundingVictorian Government Department of Health; Australian National Health and Medical Research Council and Medical Research Futures Fund. Research in contextO_ST_ABSEvidence before this studyC_ST_ABSWe searched PubMed for studies using the search terms: "Mycobacterium tuberculosis", "clinical", "bioinformatics", "genomics", "drug resistance (OR antimicrobial)", published prior to 31st July 2024 without language restrictions (n=258). We considered all studies from this search that used genomics to infer likely drug-resistance in M. tuberculosis. Many of these studies highlight the challenges in making meaningful interpretations from genomic data for the purpose of inferring AMR for clinical applications. Despite the development of bioinformatics tools and compilation of catalogues of resistance conferring mutations, few of these studies directly address the challenges specific to implementation of a whole genome sequencing (WGS) and bioinformatics pipelines to deliver validated and accredited results for use in clinical applications and none provide sustainable solutions. Added value of this studyTo the best of our knowledge, this study is the first to detail practical solutions to challenges to validating and accrediting the routine inference of AMR from WGS data for clinical applications for treatment of M. tuberculosis. We developed, validated and accredited a bioinformatics tool, tbtAMR, which is robust and flexible, using a data-driven approach to inferring resistance, allowing for simple customisation of behaviour on a large collection of in-house data supplemented with publicly available data. Furthermore, we have made this pipeline and the accompanying data available for use by the wider public health community to aid others in implementing similar programs. Consultation with infectious disease clinicians, microbiologists and epidemiologists has allowed us to develop a robust workflow, that encompasses sequencing, analysis, interpretation, reporting, discrepancy resolution and ongoing verification. Implications of all the available evidenceThis study addressed the specific needs of reporting genomic AMR for M. tuberculosis in a clinical and public health laboratory. Furthermore, this work makes available a pipeline and data for laboratories to utilise to implement genomic AMR for M. tuberculosis. Through the use and further development of publicly available and open-source bioinformatics software, we have demonstrated the feasibility of clinical reporting of genomic AMR results for M. tuberculosis in a low-incidence high-income setting. We believe that this work lays the foundation for making reporting of genomic AMR for M. tuberculosis more accessible to public health and reference laboratories in general.

genomics↗

Genome-scale enzymatic reaction prediction by variational graph autoencoders

BackgroundEnzymatic reaction networks are crucial to explore the mechanistic function of metabolites and proteins in biological systems and understanding the etiology of diseases and potential target for drug discovery. The increasing number of metabolic reactions allows the development of deep learning-based methods to discover new enzymatic reactions, which will expand the landscape of existing enzymatic reaction networks to investigate the disrupted metabolisms in diseases. ResultsIn this study, we propose the MPI-VGAE framework to predict metabolite-protein interactions (MPI) in a genome-scale heterogeneous enzymatic reaction network across ten organisms with thousands of enzymatic reactions. We improved the Variational Graph Autoencoders (VGAE) model to incorporate both molecular features of metabolites and proteins as well as neighboring features to achieve the best predictive performance of MPI. The MPI-VGAE framework showed robust performance in the reconstruction of hundreds of metabolic pathways and five functional enzymatic reaction networks. The MPI-VGAE framework was also applied to a homogenous metabolic reaction network and achieved as high performance as other state-of-art methods. Furthermore, the MPI-VGAE framework could be implemented to reconstruct the disease-specific MPI network based on hundreds of disrupted metabolites and proteins in Alzheimers disease and colorectal cancer, respectively. A substantial number of new potential enzymatic reactions were predicted and validated by molecular docking. These results highlight the potential of the MPI-VGAE framework for the discovery of novel disease-related enzymatic reactions and drug targets in real-world applications. Data availability and implementationThe MPI-VGAE framework and datasets are publicly accessible on GitHub https://github.com/mmetalab/mpi-vgae. Author BiographiesCheng Wang received his Ph.D. in Chemistry from The Ohio State Univesity, USA. He is currently a Assistant Professor in School of Public Health at Shandong University, China. His research interests include bioinformatics, machine learning-based approach with applications to biomedical networks. Chuang Yuan is a research assistant at Shandong University. He obtained the MS degree in Biology at the University of Science and Technology of China. His research interests include biochemistry & molecular biology, cell biology, biomedicine, bioinformatics, and computational biology. Yahui Wang is a PhD student in Department of Chemistry at Washington University in St. Louis. Her research interests include biochemistry, mass spectrometry-based metabolomics, and cancer metabolism. Ranran Chen is a master graduate student in School of Public Health at University of Shandong, China. Yuying Shi is a master graduate student in School of Public Health at University of Shandong, China. Gary J. Patti is the Michael and Tana Powell Professor at Washington University in St. Louis, where he holds appointments in the Department of Chemisrty and the Department of Medicine. He is also the Senior Director of the Center for Metabolomics and Isotope Tracing at Washington University. His research interests include metabolomics, bioinformatics, high-throughput mass spectrometry, environmental health, cancer, and aging. Leyi Wei received his Ph.D. in Computer Science from Xiamen University, China. He is currently a Professor in School of Software at Shandong University, China. His research interests include machine learning and its applications to bioinformatics. Qingzhen Hou received his Ph.D. in the Centre for Integrative Bioinformatics VU (IBIVU) from Vrije Universiteit Amsterdam, the Netherlands. Since 2020, He has serveved as the head of Bioinformatics Center in National Institute of Health Data Science of China and Assistant Professor in School of Public Health, Shandong University, China. His areas of research are bioinformatics and computational biophysics. Key pointsO_LIGenome-scale heterogeneous networks of metabolite-protein interaction (MPI) based on thousands of enzymatic reactions across ten organisms were constructed semi-automatically. C_LIO_LIAn enzymatic reaction prediction method called Metabolite-Protein Interaction Variational Graph Autoencoders (MPI-VGAE) was developed and optimized to achieve higher performance compared with existing machine learning methods by using both molecular features of metabolites and proteins. C_LIO_LIMPI-VGAE is broadly useful for applications involving the reconstruction of metabolic pathways, functional enzymatic reaction networks, and homogenous networks (e.g., metabolic reaction networks). C_LIO_LIBy implementing MPI-VGAE to Alzheimers disease and colorectal cancer, we obtained several novel disease-related protein-metabolite reactions with biological meanings. Moreover, we further investigated the reasonable binding details of protein-metabolite interactions using molecular docking approaches which provided useful information for disease mechanism and drug design. C_LI

bioinformatics↗

AIChatBio: An Artificial Intelligence Chatbot Model for Biological Knowledge Retrieval and Biomacromolecule Design

Conversational agents for bioinformatics data analysis and interpretation remain largely inaccessible to the broader biological research community. This gap is especially pronounced in the current Generative AI era, which demands a paradigm shift in how researchers interact with computational tools. There is a pressing need to bridge well-established biological infrastructures and databases with the capabilities of Generative AI to democratize access to bioinformatics insights. In this study, we present an integrated framework that connects the robust bioinformatics resources of the National Center for Biotechnology Information (NCBI) with Generative AI through a novel Artificial Intelligence Chatbot Model for Biological Knowledge Retrieval and Biomacromolecule Design, AIChatBio. This operational model positions Generative AI as an intelligent information hub. User interactions with the chatbot are enriched by real-time data retrieval from web portal of the biological databases hosted at NCBI, which are then translated into structured inquiries toward the web applications of NCBI and bioinformatics analysis tools. These inquiries are directed toward bioinformatics analysis tools to perform tasks such as sequence alignment and primer design. Additionally, the outputs generated by these tools are interpreted by the chatbot, allowing users to gain meaningful insights without requiring deep technical expertise in bioinformatics. To demonstrate the feasibility of this approach, we developed a prototype implementation that integrates PCR primer design using Primer-BLAST [1], literature interpretation via PubMed for general topics, and the LitVar2 for SNPs associated topics [23]. This system was built using TypeScript and the ChatGPT API combining the bioinformatics web applications from NCBI, and its source code is publicly available via GitHub and the Chrome extension is available at Chrome Web Store. Our work highlights the potential of Generative AI to transform biological data analysis workflows, making them more intuitive, accessible, and scalable for researchers across disciplines.

bioinformatics↗

A Course-Undergraduate Research Experience (CURE) to explore the effect of structural variants on gene expression in C. elegans balancers

Bioinformatics, a discipline at the crossroads of Biology and Computational Sciences, also referred to as Computational Biology, is nowadays widely spread in research programs. However, implementing any Bioinformatics projects requires the ability to comprehend biological concepts and apply computational approaches, and rare are the undergraduate programs offering such multi-disciplinary training. In addition, understanding the dynamic between Biology research projects and Bioinformatics analyses is challenging with no real-life experience. Course-based undergraduate research experience (CURE) courses are innovative programs that allow more students to acquire research experience and provide the perfect setting to introduce students to applied bioinformatics. As a part of the Bachelor of Health Sciences of the Cumming School of Medicine at the University of Calgary (Canada), a CURE applied bioinformatics was implemented in the Winter of 2023 to 2025. Students investigated the effect of structural variants (SVs, genetic variants larger than 50 bp) on gene expression in the model organism Caenorhabditis elegans (a hermaphrodite 1-mm long roundworm). The students detected and characterized SVs by analyzing genome and transcriptome sequencing data of C. elegans strains called balancers, as they are known to carry large genomic variations balancing regions of the genome by limiting recombination and allowing maintenance of lethal mutations. They used Galaxy, a public web-based supercomputing resource, but also a local High-Performance computing system, and R, to report different effects of SVs on gene expression and splicing. Students research explained the molecular mechanism behind the uncoordinated phenotype caused by the reciprocal translocation eT1(III;V) and uncovered unexpected effects on gene expression on an understudied gene. We evaluated the courses impact on student learning journeys and showed that the CURE favored students understanding of the Bioinformatics field and fostered their research interest. We provide here guidelines to facilitate the CURE implementations to improve access for undergraduate students to bioinformatics research experiences.

bioinformatics↗