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Gut metagenomic analysis of gastric cancer patients reveals Akkermansia, Gammaproteobacteria, and Veillonella microbiota as potential non-invasive biomarkers

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Genomics and Informatics 2024年第1期22卷 394-424页

作者： Nath, Anju R. Natarajan, Jeyakumar Data Mining and Text Mining Laboratory Department of Bioinformatics Bharathiar University Coimbatore 641 046 India

The goal of the study was to investigate the changes in the gut microbiota during the advancement of gastric cancer (GC) and identify pertinent taxa associated with the disease. We used a public fecal amplicon gastric cancer dataset from the Sequence Retrieval Archive (SRA), of patients with GC, gastritis, and healthy individuals. We did sequence pre-processing, including quality filtering of the sequences. Then, we performed a diversity analysis, evaluating α- and β-diversity. Next, taxonomic composition analysis was performed and the relative abundances of different taxa at the phylum and genus levels were compared between GC, gastritis, and healthy controls. The obtained results were subsequently subjected to statistical validation. To conclude, metagenomic function prediction was carried out, followed by correlation analysis between the microbiota and KEGG pathways. α analysis revealed a significant difference between male and female categories, while β analysis demonstrated significant distinctions between GC, gastritis, and healthy controls, as well as between sexes within the GC and gastritis groups. The statistically confirmed taxonomic composition analysis highlighted the presence of the microbes Bacteroides and Veillonella. Furthermore, through metagenomic prediction analysis and correlation analysis with pathways, three taxa, namely Akkermansia, Gammaproteobacteria, and Veillonella, were identified as potential biomarkers for GC. Additionally, this study reports, for the first time, the presence of two bacteria, Desulfobacteriota and Synergistota, in GC, necessitating further investigation. Overall, this research sheds light on the potential involvement of gut microbiota in GC pathophysiology;however, additional studies are warranted to explore its functional significance. © The Author(s) 2024.

关键词： Diversity analysis Gastric cancer Gut microbiota Metagenomic analysis QIIME2

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Genotype–phenotype correlation in IBD: unveiling putative inhibitors for IL10RA and IRF5 variants—an in silico analysis

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Journal of Proteins and Proteomics 2024年第3期15卷 509-522页

作者： Evthonis, Treesa Bency Samykannu, Gopinath Natarajan, Jeyakumar Data Mining and Text Mining Laboratory Department of Bioinformatics Bharathiar University Coimbatore India

Despite recent advances, the majority of the susceptibility genes for inflammatory bowel disease (IBD), a chronic inflammatory disorder that affects the gastrointestinal tract and is commonly diagnosed as Crohn disease and ulcerative colitis, are still unknown. Empirical data indicate that the human interleukin-10 receptor subunit alpha (IL10RA) mutation could impact the regulation of the gut inflammatory response, with interferon regulatory factor 5 (IRF5) being a key player in controlling mucosal inflammation in IBD. Notably, aloin, andrographolide, incensole, and curcumin show up as leading contenders that are significantly contributing to anti-inflammation and a natural adjuvant therapy for IBD. Nevertheless, the exact chemical process by which these substances block IL10RA and IRF5 is still unknown. To close this gap, we carried out molecular interaction and molecular dynamics (MD) simulation experiments to understand how synthetic and herbal drugs interact with IL10RA and IRF5 variants. Molecular docking experiments have demonstrated that aloin (−9.028/−8.12 kcal/mol) has the highest binding affinities when compared to mesalazine (−8.22/−8.615 kcal/mol), a synthetic drug. The study discusses the promising inhibitory effects of aloin and mesalazine on IL10RA and IRF5 variants, supported by structural studies and root mean square fluctuation (RMSF) analysis indicating the stability of aloin. Additionally, drug-likeness was evaluated through Pre-ADMET annotation, while pharmacokinetics analysis facilitated the calculation of binding energies. Integrated molecular docking and simulation studies suggest the potential of aloin with IBD variants. The findings demonstrate that analyzing phenotypic correlations aids in understanding IBD variants. Structural analysis of IL10RA and IRF5 proteins indicates how differences lead to functional abnormalities. This is a step toward personalized IBD treatment by multidimensional screening and herbal therapy.

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Advancing brain tumor diagnosis: Deep siamese convolutional neural network as a superior model for MRI classification

Brain-X

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Brain-X 2025年第2期3卷

作者： Murugesan, Gowtham Nagendran, Pavithra Natarajan, Jeyakumar Data Mining and Text Mining Laboratory Department of Bioinformatics Bharathiar University Tamil Nadu Coimbatore India

The timely detection and precise classification of brain tumors using techniques such as magnetic resonance imaging (MRI) are imperative for optimizing treatment strategies and improving patient outcomes. This study evaluated five state-of-the-art classification models to determine the optimal model for brain tumor classification and diagnosis using MRI. We utilized 3064 T1-weighted contrast-enhanced brain MRI images that included gliomas, pituitary tumors, and meningiomas. Our analysis employed five advanced classification model categories: machine learning classifiers, deep learning-based pre-trained models, convolutional neural networks (CNNs), hyperparameter-tuned deep CNNs, and deep siamese CNNs (DeepSCNNs). The performance of these models was assessed using several metrics, such as accuracy, precision, sensitivity, recall, and F1-score, to ensure a comprehensive evaluation of their classification capabilities. DeepSCNN exhibited remarkable classification performance, attaining exceptional precision and recall values, with an overall F1-score of 0.96. DeepSCNN consistently outperformed the other models in terms of F1-score and robustness, setting a new standard for brain tumor classification. The superior accuracy of DeepSCNN across all classification tasks underscores its potential as a tool for precise and efficient brain tumor classification. This advance may significantly contribute to improved patient outcomes in neuro-oncology diagnostics, offering insight and guidance for future studies. © 2025 The Author(s). Brain-X published by John Wiley & Sons Australia, Ltd on behalf of Ainuohui Medical Technology.

关键词： brain tumor classification models DeepSCNN model performance

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Tree based models for classification of membrane and secreted proteins in heart

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Journal of Proteins and Proteomics 2024年第2期15卷 147-157页

作者： Charles, Sona Subeesh, A. Natarajan, Jeyakumar Data Mining and Text Mining Laboratory Department of Bioinformatics Bharathiar University Coimbatore India Division of Crop Improvement and Biotechnology ICAR-Indian Institute of Spices Research Calicut India Agricultural Mechanization Division ICAR- Central Institute of Agricultural Engineering Bhopal India

Computational differentiation of membrane and secreted proteins is one of the challenging and interesting topics in bioinformatics. It is a laborious as well as time-consuming task to experimentally differentiate between membrane and secreted proteins. In this study, we used tree-based classifiers such as decision trees, random forest, light gradient boosting machine, gradient boosting decision tree and extreme gradient boosting trees using sequence-based descriptors, viz. amino acid composition, dipeptide composition, conjoint triads, composition/transition/distribution and pseudo amino acid composition in the prediction scheme to enhance the predictive power of algorithms. RF on CTD was able to better discriminate the classes in the classification problems secreted versus non-secreted and secreted versus membrane proteins while in membrane versus non-membrane. Feature selection using mutual information considerably increased the prediction accuracy of the models. Multiclass models to distinguish membrane protein and secreted proteins from other proteins in the heart were enhanced by the addition of protein interaction network-based features, with highest accuracy being displayed by XgBoost. It is expected that the models developed using tree-based algorithms will be useful for classification and annotation proteins with known sequences.

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Literature Based Discovery (LBD): Towards Hypothesis Generation and Knowledge Discovery in Biomedical text mining

arXiv

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arXiv 2023年

作者： Bhasuran, Balu Murugesan, Gurusamy Natarajan, Jeyakumar DRDO-BU Center for Life Sciences Bharathiar University Campus TamilNadu Coimbatore India Data mining and Text mining Laboratory Department of Bioinformatics Bharathiar University TamilNadu Coimbatore India

Biomedical knowledge is growing in an astounding pace with a majority of this knowledge is represented as scientific publications. text mining tools and methods represents automatic approaches for extracting hidden patterns and trends from this semi structured and unstructured data. In Biomedical text mining, Literature Based Discovery (LBD) is the process of automatically discovering novel associations between medical terms otherwise mentioned in disjoint literature sets. LBD approaches proven to be successfully reducing the discovery time of potential associations that are hidden in the vast amount of scientific literature. The process focuses on creating concept profiles for medical terms such as a disease or symptom and connecting it with a drug and treatment based on the statistical significance of the shared profiles. This knowledge discovery approach introduced in 1989 still remains as a core task in text mining. Currently the ABC principle based two approaches namely open discovery and closed discovery are mostly explored in LBD process. This review starts with general introduction about text mining followed by biomedical text mining and introduces various literature resources such as MEDLINE, UMLS, MESH, and SemMedDB. This is followed by brief introduction of the core ABC principle and its associated two approaches open discovery and closed discovery in LBD process. This review also discusses the deep learning applications in LBD by reviewing the role of transformer models and neural networks based LBD models and its future aspects. Finally, reviews the key biomedical discoveries generated through LBD approaches in biomedicine and conclude with the current limitations and future directions of LBD. © 2023, CC BY.

关键词： data mining

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Identification of effect of gene mutations related to telomere maintenance in gastric cancer patients

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Medicine in Omics 2024年 10卷

作者： Anju R. Nath Jeyakumar Natarajan Data Mining and Text Mining Laboratory Department of Bioinformatics Bharathiar University Coimbatore 641 046 India

Background Mutations in gastric cancer may remain harmless despite mutations that can turn oncogenic. Although omics technologies have supplied a clear view of mutations and driver genes in cancers, the pathology of gastric cancer still is unknown. Objective This study aims to understand mutations of genes that relate to telomere maintenance in gastric cancer. Methods We used transcriptomic and proteomic data from normal and gastric cancer patients to decide the relationship between genes and proteins. We ranked the gene-protein pairs with the Spearman correlation and subjected the differently ranked genes for functional analysis. We performed the co-expression analysis to select the genes from functional analysis. Then we assessed the selected genes for mutations and thereafter neoantigen analysis. Results We identified 20 genes through differential ranking based on Spearman correlation of gene-protein pairs. Among these, ERCC1, MRE11A, RAD50, TP53BP1, and YWHAE were linked to critical functions like DNA repair, apoptosis, and stress response. Gene prioritization focused on these five genes, and from them through gene co-expression, RAD50, TP53BP1, and YWHAE were selected. Mutational analysis revealed prevalent C > T and G > A mutations. These genes were associated with elderly patients, specific histologic grades, and types of gastric cancer. Importantly, these mutations generated neoantigens with potential for immunotherapeutic targeting. Conclusion Our extensive research shows the complex genetic makeup of gastric cancer and its potential effects on diagnosis and treatment. The identified genes and the mutations linked to them offer important information on the molecular processes behind gastric cancer progression. These results set the foundation for additional research and immunotherapeutic applications in the field of gastric cancer.

关键词： Gastric Cancer Genomics Proteomics Mutation Analysis Neoantigen Analysis

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Discovering mycobacterial lectins as potential drug targets and vaccine candidates for tuberculosis treatment: a theoretical approach

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Journal of proteins and proteomics 2021年第2期12卷 93-104页

作者： Shobana Sundar Lokesh Thangamani Shanmughavel Piramanayagam Jeyakumar Natarajan Computational Biology Laboratory Department of Bioinformatics Bharathiar University Coimbatore Tamilnadu India. Data Mining and Text Mining Laboratory Department of Bioinformatics Bharathiar University Coimbatore Tamilnadu India.

M. tuberculosis proliferates within the macrophages during infection and they are bounded by carbohydrates in the cell wall, called lectins. Despite their surface localization, the studies on exact functions of lectins are unexplored. Hence, in our study, using insilico approaches, 11 potential lectins of Mtb was explored as potential drug targets and vaccine candidates. Initially, a gene interaction network was constructed for the 11 potential lectins and identified its functional partners. A gene ontology analysis was also performed for the 11 mycobacterial lectins along with its functional partners and found most of the proteins are present in the extracellular region of the bacterium and belongs to the PE/PPE family of proteins. Further, molecular docking studies were performed for two of the potential lectins (Rv2075c and Rv1917c). A novel series of quinoxalinone and fucoidan derivatives have been made to dock against these selected lectins. Molecular docking study reveals that quinoxalinone derivatives showed better affinity against Rv2075c, whereas fucoidan derivatives have good binding affinity against Rv1917c. Moreover, the mycobacterial lectins can interact with the host and they are considered as potential vaccine candidates. Hence, immunoinformatics study was carried out for all the 11 potential lectins. B-cell and T-cell binding epitopes were predicted using insilico tools. Further, an immunodominant epitope 1062SIPAIPLSVEV1072 of Rv1917c was identified, which was predicted to bind B-cell and most of the MHC alleles. Thus, the study has explored that mycobacterial lectins could be potentially used as drug targets and vaccine candidates for tuberculosis treatment.

关键词： Gene network analysis Immunoinformatics Molecular docking Mycobacterial lectins Mycobacterium tuberculosis

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Screening of FDA-approved compound library identifies potential small-molecule inhibitors of SARS-CoV-2 non-structural proteins NSP1, NSP4, NSP6 and NSP13: molecular modeling and molecular dynamics studies

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Journal of proteins and proteomics 2021年第3期12卷 161-175页

作者： Shobana Sundar Lokesh Thangamani Shanmughavel Piramanayagam Chandrasekar Narayanan Rahul Natarajan Aiswarya Kanagaraj Sekar Jeyakumar Natarajan Computational Biology Lab Department of Bioinformatics Bharathiar University Coimbatore India. Department of Computational and Data Sciences Indian Institute of Science Bangalore Karnataka India. Data Mining and Text Mining Laboratory Department of Bioinformatics Bharathiar University Coimbatore Tamil Nadu India.

COVID-19, the current global pandemic has caused immense damage to human lives and the global economy. It is instigated by the SARS-CoV-2 virus and there is an immediate need for the identification of effective drugs against this deadly virus. SARS-CoV-2 genome codes for four structural proteins, sixteen non-structural proteins (NSPs) and several accessory proteins for its survival inside the host cells. In the present study, through in silico approaches, we aim to identify compounds that are effective against the four NSPs namely, NSP1, NSP4, NSP6 and NSP13 of SARS-CoV-2. The selection criteria of these four NSP proteins are they are least explored and potential targets. First, we have modeled the 3D structures of these proteins using homology modeling methods. Further, through molecular docking studies, we have screened the FDA-approved compounds against these modeled proteins and reported their docking scores. To gain dynamic insights, molecular dynamics studies have also been carried out for the best scored ligand against the NSPs. This study can further pave way for exposing more number of compounds against these proteins and enhance COVID-19 treatment.

关键词： COVID-19 FDA-approved compounds Molecular docking Molecular dynamics NSPs

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Molecular interaction and MD-simulations: investigation of Sizofiran as a promising anti-cancer agent targeting eIF4E in colorectal cancer

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In silico pharmacology 2024年第1期12卷 33页

作者： Gopinath Samykannu Nandhini Mariyappan Jeyakumar Natarajan Data Mining and Text Mining Laboratory Department of Bioinformatics Bharathiar University Coimbatore TamilNadu India. Molecular Modelling and Designing Laboratory Department of Physics Bharathiar University Coimbatore TamilNadu India.

CRC has a major global health impact due to high mortality rates. CRC shows high expression of eukaryotic translation initiation factor (eIF4E) protein, the rapid development of lung, bladder, colon, prostate, breast, head, and neck cancer is attributed to the dysregulation of eIF4E making an important target for treatment. Targeting eIF4E-mediated translation is a promising anti-cancer strategy. Many organic compounds that inhibit eIF4E are being studied clinically. The compound Sizofiran has emerged as a promising eIF4E inhibitor candidate, but its exact mechanism of action is unclear. In an effort to close this discrepancy by clarifying the mechanism of the interactions between phytochemical substances and eIF4E, molecular docking and dynamics studies were conducted. Molecular docking studies found Sizofiran (− 12.513 kcal/mol) has the most affinity eIF4E binding energy out of 93 phytochemicals, 5 current drugs, and 4 known inhibitors. This positions it as a top eIF4E inhibitor candidate. An alignment of eIF4E protein sequences from multiple pathogens revealed that the glutamate103 interacting residues are evolutionarily conserved across the different eIF4E proteins. Further insights from 100 ns of MD simulations supported Sizofiran having superior stability and eIF4E inhibition compared to reference compounds. Designed Sizofiran-related compounds showed better activity than the current drugs such as Camptosar, Sorafenib, Regorafenib, Doxorubicin, and Kenpaullone, indicating strong potential to suppress CRC progression by targeting eIF4E. This research aims to significantly aid development of improved eIF4E-targeting drugs for cancer treatment. Showing the Graphical abstract of the complete study.

关键词： Colorectal cancer MD simulation Phytochemical Sizofiran eIF4E

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Transcriptomic insights into heart development and cardiovascular diseases

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Medicine in Omics 2024年 10卷

作者： Sona Charles Jeyakumar Natarajan Data Mining and Text Mining Laboratory Department of Bioinformatics Bharathiar University Coimbatore Tamil Nadu India Crop Improvement and Biotechnology Division ICAR-Indian Institute of Spices Research Calicut Kerala India

The heart is a complex organ that starts to function by the end of the third week of gestation. Cardiovascular diseases attribute to the leading causes of death globally. The explosion of technological advances and computational tools for the analysis of next generation sequencing data has opened new avenues for comprehending the underlying regulatory mechanisms of complex disorders. The transcriptomic and genomic aspects of genes, non-coding RNAs and transcription factors have largely assisted understanding cardiogenesis and cardiovascular disease. The widespread application of transcriptomic methods has assisted in the screening of huge number of differentially expressed non-coding RNAs and their projected role as therapeutic targets and biomarkers. Transcriptomic profiling, including techniques such as RNA sequencing (RNA-Seq) has enabled the comprehensive characterization of gene expression patterns at various stages of embryonic and postnatal heart development. In the context of cardiac diseases, transcriptomic analyses have provided unprecedented insights into the molecular alterations associated with conditions such as myocardial infarction, heart failure, arrhythmias, and congenital heart defects. This review presents an overview of recent advancements in transcriptomic research aimed at unraveling the intricate genetic and molecular networks governing heart development, as well as in cardiac diseases. In this review, we attempted to systematically review the transcriptomic experiments performed to identify significant genes, miRNAs and lncRNAs with biomarkers therapeutic potential, meta -analysis of multiple samples, and ceRNA networks in heart diseases.

关键词： Heart Non-coding RNAs Transcriptomics ceRNA Meta-analysis Cardiovascular diseases

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