Computational Analysis of T-Cell Receptor Repertoire Workflow: From T-Cell Isolation to Bioinformatics Analysis.

The T-cell receptor (TCR) is the key molecule involved in the adaptive immune response. It is generated by the V(D)J recombination, responsible of the enormous diversity of the TCR repertoire, a crucial feature determining the individual capability to response to antigens and to build immunological memory. A pivotal role in the recognition of antigen is played by the hypervariable complementarity-determining region 3 (CDR3) of the V-beta chain of TCR. Investigating the CDR3 supports the understanding of the adaptive immune system dynamics in physiological processes, such as immune aging, and in disease, especially autoimmune disorders in which T cells are main actors. High-throughput sequencing (HTS) paved the way for a great progress in the investigation of TCR repertoire, enhancing the read depth in the process of library generation of sequencing and the number of samples that can be analyzed simultaneously. Therefore, the leverage of big datasets stressed the need to develop computational approach, by bioinformatics, to unravel the characteristics of the TCR repertoire.

Application of a Computational Metabolomics Workflow for the Diagnosis of Inborn Errors of Metabolism in a Laboratory Setting.

Inborn errors of metabolism constitute a set of hereditary diseases that impose severe medical and physical challenges in the affected individual, in particular, for the pediatric patient population. Timely diagnosis is crucial for these patients, as any delay could result in irreversible health damage, underscoring the importance of early initiation of personalized treatment. Current routine diagnostic screening for inborn errors of metabolism relies on various targeted analyses of established biomarkers. However, this approach is time-consuming, focuses on a limited number of tests (based on clinical information) with a relatively small number of biomarkers, and does not facilitate the identification of new markers. In contrast, untargeted metabolomics-based screening offers a more efficient diagnostic solution, by assessing thousands of metabolites across multiple metabolic pathways in a single test. This not only saves time but also conserves resources for clinicians, the diagnostic laboratory, and for patients.This chapter describes the computational workflow of our "Next Generation Metabolic Screening" approach, which is a metabolomics-based method that is currently applied at the Translational Metabolic Laboratory of the Radboud University Medical Center (the Netherlands) for the diagnosis of inborn errors of metabolism.

Metabolic and protein expression responses of Shewanella baltica in golden pomfret broths to slightly acidic electrolysed water.

Shewanella baltica is a specific spoilage organism of golden pomfret. This study aims to explore the antibacterial mechanism of slightly acidic electrolysed water (SAEW) against S. baltica (strains ABa4, ABe2 and BBe1) in golden pomfret broths by metabolomics, proteomics and bioinformatics analyses. S. baltica was decreased by at least 3.94 log CFU/mL after SAEW treatment, and strain ABa4 had the highest resistance. Under SAEW stress, amino acids and organic acids in S. baltica decreased, and nucleotide related compounds degraded. Furthermore, 100 differentially expressed proteins (DEPs) were identified. Most DEPs of strains ABe2 and BBe1 were down-regulated, while some DEPs of strain ABa4 were up-regulated, especially those oxidative stress related proteins. These results suggest that the modes of SAEW against S. baltica can be traced to the inhibition of amino acid, carbon, nucleotide and sulphur metabolisms, and the loss of functional proteins for temperature regulation, translation, motility and protein folding.

Hi-C/3C-seq Data Analysis for Prokaryotic Genomes with HiC-Pro.

Hi-C and 3C-seq are powerful tools to study the 3D genomes of bacteria and archaea, whose small cell sizes and growth conditions are often intractable to detailed microscopic analysis. However, the circularity of prokaryotic genomes requires a number of tricks for Hi-C/3C-seq data analysis. Here, I provide a practical guide to use the HiC-Pro pipeline for Hi-C/3C-seq data obtained from prokaryotes.

Read Mapping for Hi-C Analysis.

Hi-C is a popular ligation-based technique to detect 3D physical chromosome structure within the nucleus using cross-linking and next-generation sequencing. As an unbiased genome-wide assay based on chromosome conformation capture, it provides rich insights into chromosome structure, dynamic chromosome folding and interactions, and the regulatory state of a cell. Bioinformatics analyses of Hi-C data require dedicated protocols as most genome alignment tools assume that both paired-end reads will map to the same chromosome, resulting in large two-dimensional matrices as processed data. Here, we outline the necessary steps to generate high-quality aligned Hi-C data by separately mapping each read while correcting for biases from restriction enzyme digests. We introduce our own custom open-source pipeline, which enables users to select an aligner of their choosing with high accuracy and performance. This enables users to generate high-resolution datasets with fast turnaround and fewer unmapped reads. Finally, we discuss recent innovations in experimental techniques, bioinformatics techniques, and their applications in clinical testing for diagnostics.

CWL-Based Analysis Pipeline for Hi-C Data: From FASTQ Files to Matrices.

Over a decade has passed since the development of the Hi-C method for genome-wide analysis of 3D genome organization. Hi-C utilizes next-generation sequencing (NGS) technology to generate large-scale chromatin interaction data, which has accumulated across a diverse range of species and cell types, particularly in eukaryotes. There is thus a growing need to streamline the process of Hi-C data analysis to utilize these data sets effectively. Hi-C generates data that are much larger compared to other NGS techniques such as chromatin immunoprecipitation sequencing (ChIP-seq) or RNA-seq, making the data reanalysis process computationally expensive. In an effort to bridge this resource gap, the 4D Nucleome (4DN) Data Portal has reanalyzed approximately 600 Hi-C data sets, allowing users to access and utilize the analyzed data. In this chapter, we provide detailed instructions for the implementation of the common workflow language (CWL)-based Hi-C analysis pipeline adopted by the 4DN Data Portal ecosystem. This reproducible and portable pipeline generates standard Hi-C contact matrices in formats such as .hic or .mcool from FASTQ files. It enables users to output their own Hi-C data in the same format as those registered in the 4DN Data portal, facilitating comparative analysis using data registered in the portal. Our custom-made scripts are available on GitHub at https://github.com/kuzobuta/4dn_cwl_pipeline .

Extracting Chromosome Structural Information as One-Dimensional Metrics and Integrating Them with Epigenomics.

Hi-C is a powerful method for obtaining genome-wide chromosomal structural information. The typical Hi-C analysis utilizes a two-dimensional (2D) contact matrix, which poses challenges for quantitative comparisons, visualizations, and integrations across multiple datasets. Here, we present a protocol for extracting one-dimensional (1D) features from chromosome structure data by HiC1Dmetrics. Leveraging these 1D features enables integrated analysis of Hi-C and epigenomic data.

UBE2T is a diagnostic and prognostic biomarker for endometrial cancer.

BACKGROUND: Endometrial cancer (UCEC) is one of the most common malignant tumors in gynecology, and early diagnosis is crucial for its treatment. Currently, there is a lack of early screening tests specific to UCEC, and treatment advances are limited. It is crucial to identify more sensitive biomarkers for screening, diagnosis, and predicting UCEC. Previous studies have shown that UBE2T is involved in the development of various tumors such as breast cancer and liver cancer, but research on the role of UBE2T in UCEC is limited. METHODS: Using data from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and UALCAN databases, we analyzed the differential expression of UBE2T mRNA and protein in endometrial cancer (UCEC), along with its clinical relevance. A total of 113 clinical samples were collected, and immunohistochemistry and Western blot analysis were employed to validate bioinformatics analysis results. Volcano plots were generated using UBE2T and its differentially expressed genes, and a protein-protein interaction (PPI) network was constructed. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), gene set enrichment analysis (GSEA), and immune infiltration analysis were used to predict the functional role of UBE2T in UCEC progression. Correlation between UBE2T expression and patient survival was analyzed using TCGA data, and Kaplan-Meier survival curves were plotted. RESULTS: UBE2T is significantly overexpressed in UCEC and correlates with poor prognosis. Its overexpression is closely associated with mitosis, cell cycle regulation, and histological grade in UCEC patients. CONCLUSION: UBE2T is highly expressed in UCEC and suppresses anti-tumor immune responses in UCEC patients. It serves as a key participant in UCEC progression, associated with a range of adverse outcomes, and holds potential as a clinical diagnostic and prognostic biomarker.

Advances in bioinformatic approaches to tardigrade phylogeny.

The quest to discover the evolutionary relationships of organisms is an evolving, long-time topic of research. Such research gave rise to many different taxonomic databases and various definitions of systematic groups. One such group is the phylum Tardigrada. Tardigrades are an important field of study because of their biotechnological potential as well as their complex biological processes, which have the potential to answer questions about animal evolution. The evolutionary relationships within the phyla are subject to rigorous research, and new data is added to the literature constantly. For these studies, a widespread technique is the use of bioinformatic approaches in order to put forward concrete phylogenetic evidence. Bioinformatics is a field of computational biology that interprets large amounts of data in order to compute and demonstrate results. It is widely used not only for phylogeny but also for various different types of analyses and has been growing as a field since its foundation. This review discusses the different aspects, advantages, and methods of the use of bioinformatics in tardigrade phylogeny. It aims to put forward a defining picture of how the bioinformatic methods prove useful for providing phylogenetic results and elaborate on future perspectives.

Integrative approach to assessing bioactivity from hempseed protein isolate extracted and dehydrated by different methods: Synergising in silico prediction and in vitro validation.

This study demonstrated a comprehensive workflow combining in silico screening and prediction with in vitro validation to investigate the bioactivity of hempseed protein isolate (HPI) extracted and dehydrated using different methods. By adopting an in silico approach, 13 major proteins of HPI were hydrolysed by 20 selected enzymes, leading to the prediction of 20 potential bioactivities. With papain hydrolysis, dipeptidyl peptidase-IV (DPP4) and angiotensin-converting enzyme (ACE) inhibitory activities emerged as having the highest potential. In vitro experiments confirmed these predictions, with DPP4 and ACE inhibitory activities displaying IC50 values of 0.32-0.42 mg/mL and 6.8-9.17 µg/mL, respectively. A strong correlation (r2 = 0.96) was observed between in vitro protein inhibitory results and in silico predicted data. This study demonstrated an effective integrative approach for predicting bioactive peptides in food protein, providing valuable guidance on its processing to create value-added products.

Identification of novel hub genes and immune infiltration in atopic dermatitis using integrated bioinformatics analysis.

The aim of this study was to identify key genes and investigate the immunological mechanisms of atopic dermatitis (AD) at the molecular level via bioinformatics analysis. Gene expression profiles (GSE32924, GSE107361, GSE121212, and GSE230200) were obtained for screening common differentially expressed genes (co-DEGs) from the gene expression omnibus database. Functional enrichment analysis, protein-protein interaction network and module construction, and identification of common hub genes were performed. Hub genes were validated using receiver operating characteristic curve analysis based on GSE130588 and GSE16161. NetworkAnalyst was used to detect microRNAs (miRNAs) and transcription factors (TFs) associated with the hub genes. The immune cell infiltration was analyzed using the CIBERSORT algorithm to further analyze the correlation between hub genes and immune cells. A total of 146 co-DEGs were obtained, showing significant enrichment in cytokine-cytokine receptor interaction and JAK-STAT signaling pathway. Seven hub genes were identified by Cytoscape and validated with external datasets. Subsequent prediction of miRNAs and TFs targeting these hub genes revealed their regulatory roles. Analysis of immune cell infiltration and correlation revealed a significant positive correlation between CCL22 expression and the number of dendritic cells activated. The identified hub genes represent potential diagnostic and therapeutic targets in the immunological pathogenesis of AD.

Overexpression of SEZ6L2 and Immune Infiltration in Cancer Based on Gene Image Diagnosis.

BACKGROUND: With the rapid advancement of optical image diagnostic technology, researchers are delving into the potential applications in the field of cancer diagnosis and treatment. The exact link between the SEZ6L2 gene and cancer immune infiltration remains elusive. MATERIALS AND METHODS: This study aims to investigate the relationship between SEZ6L2 gene overexpression and cancer immune infiltration using optical image diagnostic technology, thereby presenting novel insights for enhancing cancer diagnosis and treatment strategies. Tissue samples obtained from cancer patients were meticulously analyzed to quantitatively assess the expression of the SEZ6L2 gene through light image diagnostic technology. Additionally, immunohistochemical techniques were employed to assess the nature and quantity of immune infiltrating cells within the cancerous tissues. RESULTS: The enrichment pathways were found to include complement activation, circulating immunoglobulin mediated humoral immune response, protein activation cascade, immunoglobulin complex, and immunoglobulin. In addition, the expression of SEZ6L2 is closely related to the infiltration level of tumor infiltrating immune cells (TIICs), and there is a potential relationship between the expression of SEZ6L2 and different marker genes of TIIC. CONCLUSION: Increased SEZ6L2 mRNA expression in breast invasive carcinoma was significantly associated with negative prognosis and immune invasion. SEZ6L2 may be a novel prognostic biomarker and a potential immunotherapeutic target in BRCA.

Investigating the mechanism of METTL16-dependent m6A modification regulating the SAMD11 protein signaling pathway to inhibit thyroid cancer phenotypes.

Despite substantial progress in the research and treatment of thyroid cancer, many areas in the molecular mechanisms remain to be explored. This study aims to comprehensively and deeply investigate the key role and potential molecular mechanisms of RNA methyltransferase METTL16 in the development and progression of thyroid cancer. Firstly, through bioinformatics analysis of tumor databases, we examined the correlation between METTL16 expression levels and patient prognosis. Subsequently, immunofluorescence experiments on clinical patient tissue microarrays were conducted to validate these findings. We also compared the nucleic acid and protein expression levels of METTL16 in different cell lines. By integrating bioinformatics analysis of public databases, laboratory molecular biology experiments, and comprehensive data analysis, we revealed the high expression of METTL16 in clinical tissues and thyroid cancer cells, and confirmed its role in regulating the biological characteristics of cell proliferation, migration, and invasion in thyroid cancer through in vitro and in vivo experiments. Additionally, we identified SAMD11 as a target gene of METTL16 and further validated its importance and potential regulatory pathways in thyroid cancer.

Insight into the structure-activity relationship of thermal hysteresis activity of cod collagen peptides through peptidomics and bioinformatics approaches.

To elucidate the correlation between variations in thermal hysteresis activity (THA) and the physicochemical properties and structure, antifreeze peptides (AFPs) of isolated fractions (CCP-1 and CCP-2) were characterized on based peptidomics and bioinformatics. The results revealed a positive correlation between the THA of cod collagen antifreeze peptide (CCAFP) and peptide chain length, isoelectric point, and hydrophobic amino acid content. Notably, the THA of CCP-1, which has higher alkaline amino acid content, was 2.60 °C at a concentration of 10 mg/mL, significantly higher than CCP (1.90 °C) and CCP-2 (2.27 °C). Glycine, proline, and valine were the vital amino acids to the formation of hydrogen bonds. Conversely, aspartic and glutamic acids at terminal regions of AFPs tended to introduce kinks in their structures. This distortion reduced binding sites for ice crystals, thereby decreasing their THA, providing a theory for understanding the physicochemical properties and structure of AFPs that influence their THA.

Unveiling shared diagnostic biomarkers and molecular mechanisms between T2DM and sepsis: Insights from bioinformatics to experimental assays.

Septic patients with T2DM were prone to prolonged recovery and unfavorable prognoses. Thus, this study aimed to pinpoint potential genes related to sepsis with T2DM and develop a predictive model for the disease. The candidate genes were screened using protein-protein interaction networks (PPI) and machine learning algorithms. The nomogram and receiver operating characteristic curve were developed to assess the diagnostic efficiency of the biomarkers. The relationship between sepsis and immune cells was analyzed using the CIBERSORT algorithm. The biomarkers were validated by qPCR and western blotting in basic experiments, and differences in organ damage in mice were studied. Three genes (MMP8, CD177, and S100A12) were identified using PPI and machine learning algorithms, demonstrating strong predictive capabilities. These biomarkers presented significant differences in gene expression patterns between diseased and healthy conditions. Additionally, the expression levels of biomarkers in mouse models and blood samples were consistent with the findings of the bioinformatics analysis. The study elucidated the common molecular mechanisms associated with the pathogenesis of T2DM and sepsis and developed a gene signature-based prediction model for sepsis. These findings provide new targets for the diagnosis and intervention of sepsis complicated with T2DM.

Spatial Transcriptomics Unravel the Tissue Complexity of Oral Pathogenesis.

Spatial transcriptomics (ST) is a cutting-edge methodology that enables the simultaneous profiling of global gene expression and spatial information within histological tissue sections. Traditional transcriptomic methods lack the spatial resolution required to sufficiently examine the complex interrelationships between cellular regions in diseased and healthy tissue states. We review the general workflows for ST, from specimen processing to ST data analysis and interpretations of the ST dataset using visualizations and cell deconvolution approaches. We show how recent studies used ST to explore the development or pathogenesis of specific craniofacial regions, including the cranium, palate, salivary glands, tongue, floor of mouth, oropharynx, and periodontium. Analyses of cranial suture patency and palatal fusion during development using ST identified spatial patterns of bone morphogenetic protein in sutures and osteogenic differentiation pathways in the palate, in addition to the discovery of several genes expressed at critical locations during craniofacial development. ST of salivary glands from patients with Sjögren's disease revealed co-localization of autoimmune antigens with ductal cells and a subpopulation of acinar cells that was specifically depleted by the dysregulated autoimmune response. ST of head and neck lesions, such as premalignant leukoplakia progressing to established oral squamous cell carcinomas, oral cancers with perineural invasions, and oropharyngeal lesions associated with HPV infection spatially profiled the complex tumor microenvironment, showing functionally important gene signatures of tumor cell differentiation, invasion, and nontumor cell dysregulation within patient biopsies. ST also enabled the localization of periodontal disease-associated gene expression signatures within gingival tissues, including genes involved in inflammation, and the discovery of a fibroblast subtype mediating the transition between innate and adaptive immune responses in periodontitis. The increased use of ST, especially in conjunction with single-cell analyses, promises to improve our understandings of craniofacial development and pathogenesis at unprecedented tissue-level resolution in both space and time.

Transipedia.org: k-mer-based exploration of large RNA sequencing datasets and application to cancer data.

Indexing techniques relying on k-mers have proven effective in searching for RNA sequences across thousands of RNA-seq libraries, but without enabling direct RNA quantification. We show here that arbitrary RNA sequences can be quantified in seconds through their decomposition into k-mers, with a precision akin to that of conventional RNA quantification methods. Using an index of the Cancer Cell Line Encyclopedia (CCLE) collection consisting of 1019 RNA-seq samples, we show that k-mer indexing offers a powerful means to reveal non-reference sequences, and variant RNAs induced by specific gene alterations, for instance in splicing factors.

A comprehensive analysis of genes associated with hypoxia and cuproptosis in pulmonary arterial hypertension using machine learning methods and immune infiltration analysis: AHR is a key gene in the cuproptosis process.

Background: Pulmonary arterial hypertension (PAH) is a serious condition characterized by elevated pulmonary artery pressure, leading to right heart failure and increased mortality. This study investigates the link between PAH and genes associated with hypoxia and cuproptosis. Methods: We utilized expression profiles and single-cell RNA-seq data of PAH from the GEO database and genecad. Genes related to cuproptosis and hypoxia were identified. After normalizing the data, differential gene expression was analyzed between PAH and control groups. We performed clustering analyses on cuproptosis-related genes and constructed a weighted gene co-expression network (WGCNA) to identify key genes linked to cuproptosis subtype scores. KEGG, GO, and DO enrichment analyses were conducted for hypoxia-related genes, and a protein-protein interaction (PPI) network was created using STRING. Immune cell composition differences were examined between groups. SingleR and Seurat were used for scRNA-seq data analysis, with PCA and t-SNE for dimensionality reduction. We analyzed hub gene expression across single-cell clusters and built a diagnostic model using LASSO and random forest, optimizing parameters with 10-fold cross-validation. A total of 113 combinations of 12 machine learning algorithms were employed to evaluate model accuracy. GSEA was utilized for pathway enrichment analysis of AHR and FAS, and a Nomogram was created to assess risk impact. We also analyzed the correlation between key genes and immune cell types using Spearman correlation. Results: We identified several diagnostic genes for PAH linked to hypoxia and cuproptosis. PPI networks illustrated relationships among these hub genes, with immune infiltration analysis highlighting associations with monocytes, macrophages, and CD8 T cells. The genes AHR, FAS, and FGF2 emerged as key markers, forming a robust diagnostic model (NaiveBayes) with an AUC of 0.9. Conclusion: AHR, FAS, and FGF2 were identified as potential biomarkers for PAH, influencing cell proliferation and inflammatory responses, thereby offering new insights for PAH prevention and treatment.

Anoikis in prostate cancer bone metastasis gene signatures and therapeutic implications.

Background: Bone metastasis from prostate cancer severely impacts patient outcomes and quality of life. Anoikis, a form of programmed cell death triggered by the loss of cell-matrix interactions, plays a critical role in cancer progression. However, its precise relationship with prostate cancer-induced bone metastasis remains unclear. This study aims to elucidate this relationship, focusing on anoikis-related gene signatures, molecular pathways, and therapeutic implications. Methods: We used the TCGA-PRAD dataset for training, with MSKCC and GSE70769 as validation cohorts. To evaluate immunotherapy efficacy, we examined IMvigor 210 and GSE91016 datasets, and GSE137829 provided single-cell insights into prostate cancer. Specific anoikis-related genes (ARGs) were identified, and Random Survival Forest analysis and multivariate Cox regression were employed to develop anoikis-linked features. The 'clustanoikisProfilanoikis' and 'GSEA' packages were used to explore potential ARG-related pathways. Results: Analyzing 553 samples from TCGA, 231 from MSKCC, 94 from GSE70769, and single-cell data from 6 prostate cancer patients (GSE137829), we constructed a prognostic model based on 9 ARGs. GSVA revealed upregulation of carcinogenic pathways, including epithelial-mesenchymal transition, E2F targets, and angiogenesis, with downregulation of metabolic pathways. Significant differences in somatic mutations were observed between cohorts, with a positive correlation between anoikis scores and tumor mutational burden (TMB). Immune landscape analysis suggested high-risk patients might benefit more from chemotherapy than immunotherapy based on their risk score. Single-cell analysis indicated overactivation of carcinogenic pathways in the high anoikis score group. Conclusion: This study elucidates the complex interplay between anoikis and bone metastasis in prostate cancer. Our findings highlight the critical role of anoikis in metastatic progression, enhancing the understanding of key biomarkers and molecular dynamics. The identified anoikis-related gene signatures and disrupted pathways offer promising avenues for predictive and therapeutic strategies in prostate cancer management.