Bioinformatics Methods Research Articles

Detecting T-cell clonal expansions and quantifying clone survival using deep profiling of immune repertoires.

An individual's T-cell repertoire constantly changes under the influence of external and internal factors. Cells that do not receive a stimulatory signal die, while those that encounter and recognize a pathogen or receive a co-stimulatory signal divide, resulting in clonal expansions. T-cell clones can be traced by monitoring the presence of their unique T-cell receptor (TCR) sequence, which is assembled de novo through a process known as V(D)J rearrangement. Tracking T cells can provide valuable insights into the survival of cells after hematopoietic stem cell transplantation (HSCT) or cancer treatment response and can indicate the induction of protective immunity by vaccination. In this study, we report a bioinformatic method for quantifying the T-cell repertoire dynamics from TCR sequencing data. We demonstrate its utility by measuring the T-cell repertoire stability in healthy donors, by quantifying the effect of donor lymphocyte infusion (DLI), and by tracking the fate of the different T-cell subsets in HSCT patients and the expansion of pathogen-specific clones in vaccinated individuals.

Immune protection of three serine protease inhibitors vaccine in mice against Rhipicephalus sanguineus

Bioactive molecules in tick saliva are considered to be key to successful feeding and further the transmission of tick-borne pathogens. Problems such as pathogen transmission and animal weight loss result in tick infestation can cause tremendous economic losses to the livestock industry. Therefore, the development of a universal tick vaccine is urgently needed. In this paper, three serine protease inhibitor (serpin) proteins RMS-3, L7LRK7 and L7LTU1 were analyzed with bioinformatics methods. Subsequently the proteins were expressed and purified, and inoculated into Kunming mice for immune protection analysis. The amino acid sequence similarities between RMS-3, L7LRK7 and L7LTU1 were up to 90% in Rhipicephalus sanguineus. The recombinant RMS-3 + L7LRK7 + L7LTU1 showed anticoagulant reaction function and could inhibit the activity of CD4+ lymphocytes, when inoculated into Kunming mice. Additionally, After the immunized mice were challenged with Rhipicephalus sanguineus, the percentage of larvae and nymphs that were fully engorged dropped to 40.87% (P < 0.05) and 87.68% (P > 0.05) in the RmS-3 + L7LRK7 immune group, 49.57% (P < 0.01) and 52.06% (P < 0.05) in the RmS-3 + L7LTU1 group, and 45.22% (P < 0.05) and 60.28% (P < 0.05) in the RmS-3 + L7LRK7 + L7LTU1 immune group, in comparison with the control group. These data indicate that RmS-3 + L7LRK7 + L7LTU1 has good immune protection and has the potential to be developed into a vaccine against the larvae and nymphs of R. sanguineus.

Effect of CHST11, a novel biomarker, on the biological functionalities of clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is a common malignant tumor, and the role of carbohydrate sulfotransferase 11 (CHST11) in this cancer remains unclear. Here, by using bioinformatics methods, we comprehensively analyzed the relationship between CHST11 and clinical significance, immune infiltration, functional enrichment, m6A methylation, and protein–protein interaction networks. We found that CHST11 expression was significantly higher in ccRCC samples than in normal tissues. Additionally, CHST11 levels correlated with the clinicopathological features of ccRCC patients and functioned as a prognostic factor for patient survival. Functional analysis revealed the involvement of CHST11 in metabolic pathways. Immune infiltration and m6A methylation analysis suggested the association of CHST11 with immune cell abundance in the tumor microenvironment and specific methylation patterns in ccRCC. The in vitro analysis of the clinical samples and ccRCC cell lines demonstrated that the overexpression of CHST11 promotes ccRCC cell proliferation, migration, and invasion, while its suppression has the opposite effect. Thus, CHST11 may play a remarkable role in the occurrence and progression of ccRCC. Functionally, CHST11 promotes the aggressiveness of ccRCC cells. These findings provide insights into the role of CHST11 in ccRCC progression.Registry and the Registration No. of the study/trial: No. 2021K034.

Integrated bioinformatics analysis identified leucine rich repeat containing 15 and secreted phosphoprotein 1 as hub genes for calcific aortic valve disease and osteoarthritis.

Calcific aortic valve disease (CAVD) and osteoarthritis (OA) are common diseases in the ageing population and share similar pathogenesis, especially in inflammation. This study aims to discover potential diagnostic and therapeutic targets in patients with CAVD and OA. Three CAVD datasets and one OA dataset were obtained from the Gene Expression Omnibus database. We used bioinformatics methods to search for key genes and immune infiltration, and established a ceRNA network. Immunohistochemical staining was performed to verify the expression of candidate genes in human and mice aortic valve tissues. Two key genes obtained, leucine rich repeat containing 15 (LRRC15) and secreted phosphoprotein 1 (SPP1), were further screened using machine learning and verified in human and mice aortic valve tissues. Compared to normal tissues, the infiltration of immune cells in CAVD tissues was significantly higher, and the expressions of LRRC15 and SPP1 were positively correlated with immune cells infiltration. Moreover, the ceRNA network showed extensive regulatory interactions based on LRRC15 and SPP1. The authors' findings identified LRRC15 and SPP1 as hub genes in immunological mechanisms during CAVD and OA initiation and progression, as well as potential targets for drug development.

Characterization of the Fatty Acyl-CoA Reductase (FAR) Gene Family and Its Response to Abiotic Stress in Rice (Oryza sativa L.).

Fatty acyl-CoA reductase (FAR) is an important NADPH-dependent enzyme that can produce primary alcohol from fatty acyl-CoA or fatty acyl-carrier proteins as substrates. It plays a pivotal role in plant growth, development, and stress resistance. Herein, we performed genome-wide identification and expression analysis of FAR members in rice using bioinformatics methods. A total of eight OsFAR genes were identified, and the OsFARs were comprehensively analyzed in terms of phylogenetic relationships, duplication events, protein motifs, etc. The cis-elements of the OsFARs were predicted to respond to growth and development, light, hormones, and abiotic stresses. Gene ontology annotation analysis revealed that OsFAR proteins participate in biological processes as fatty acyl-CoA reductase during lipid metabolism. Numerous microRNA target sites were present in OsFARs mRNAs. The expression analysis showed that OsFARs were expressed at different levels during different developmental periods and in various tissues. Furthermore, the expression levels of OsFARs were altered under abiotic stresses, suggesting that FARs may be involved in abiotic stress tolerance in rice. The findings presented here serve as a solid basis for further exploring the functions of OsFARs.

PM2.5, component cause of severe metabolically abnormal obesity: An in silico, observational and analytical study

Obesity is currently one of the most alarming pathological conditions due to the progressive increase in its prevalence. In the last decade, it has been associated with fine particulate matter suspended in the air (PM2.5). The purpose of this study was to explore the mechanistic interaction of PM2.5 with a high-fat diet (HFD) through the differential regulation of transcriptional signatures, aiming to identify the association of these particles with metabolically abnormal obesity. The research design was observational, using bioinformatic methods and an explanatory approach based on Rothman's causal model. We propose three new transcriptional signatures in murine adipose tissue. The sum of transcriptional differences between the group exposed to an HFD and PM2.5, compared to the control group, were 0.851, 0.265, and −0.047 (p > 0.05). The HFD group increased body mass by 20% with two positive biomarkers of metabolic impact. The group exposed to PM2.5 maintained a similar weight to the control group but exhibited three positive biomarkers. Enriched biological pathways (p < 0.05) included PPAR signaling, small molecule transport, adipogenesis genes, cytokine-cytokine receptor interaction, and HIF-1 signaling. Transcriptional regulation predictions revealed CpG islands and common transcription factors. We propose three new transcriptional signatures: FAT-PM2.5-CEJUS, FAT-PM2.5-UP, and FAT-PM2.5-DN, whose transcriptional regulation profile in adipocytes was statistically similar by dietary intake and HFD and exposure to PM2.5 in mice; suggesting a mechanistic interaction between both factors. However, HFD-exposed murines developed moderate metabolically abnormal obesity, and PM2.5-exposed murines developed severe abnormal metabolism without obesity. Therefore, in Rothman's terms, it is concluded that HFD is a sufficient cause of the development of obesity, and PM2.5 is a component cause of severe abnormal metabolism of obesity. These signatures would be integrated into a systemic biological process that would induce transcriptional regulation in trans, activating obesogenic biological pathways, restricting lipid mobilization pathways, decreasing adaptive thermogenesis and angiogenesis, and altering vascular tone thus inducing a severe metabolically abnormal obesity.

Bioinformatics-based dynamics of cuproptosis -related indicators in experimental silicosis

Pneumoconiosis is one of the most serious occupational diseases worldwide. Silicosis due to prolonged inhalation of free silica dust during occupational activities is one of the main types. Cuproptosis is a newly discovered mode of programmed cell death characterized by the accumulation of free copper in the cell, which ultimately leads to cell death. Increased copper in the serum of silicosis patients, suggests that the development of silicosis is accompanied by changes in copper metabolism, but whether cuproptosis is involved in the progression of silicosis is actually to be determined. To test this hypothesis, we screened the genetic changes in patients with idiopathic fibrosis by bioinformatics methods and predicted and functionally annotated the cuproptosis-related genes among them. Subsequently, we established a mouse silicosis model and detected the concentration of copper ions and the activity of ceruloplasmin (CP) in serum, as well as changes of the concentration of copper and cuproptosis related genes in mouse lung tissues. We identified 9 cuproptosis-related genes among the differential genes in patients with IPF at different times and the tissue-specific expression levels of ferredoxin 1 (FDX1) and Lipoyl synthase (LIAS) proteins. Furthermore, serum CP activity and copper ion levels in silicosis mice were elevated on days 7th and 56th after silica exposure. The expression of CP in mouse lung tissue elevated at all stages after silica exposure. The mRNA level of FDX1 decreased on days 7th and 56th, and the protein level remained in accordance with the mRNA level on day 56th. LIAS and Dihydrolipoamide dehydrogenase (DLD) levels were downregulated at all times after silica exposure. In addition, Heatshockprotein70 (HSP70) expression was increased on day 56. In brief, our results demonstrate that there may be cellular cuproptosis during the development of experimental silicosis in mice and show synchronization with enhanced copper loading in mice.

EEPD1 is identified as a predictor of prognosis and immune microenvironment through pan-cancer analysis and related to progression of colorectal cancer

BackgroundEEPD1 is vital in homologous recombination, while its role in cancer remains unclear. MethodsWe performed multiple pan-cancer analyses of EEPD1 with bioinformatics methods, such as gene expression, gene alterations, Prognosis and enrichment analysis, tumor microenvironment, immune cell infiltration, TMB, MSI, immunotherapy, co-expression of genes, and drug resistance. Finally, RT-qPCR, EdU, and transwell assays helped investigate the impact of EEPD1 on CRC cells. ResultsEEPD1 was dysregulated and correlated with bad prognosis in several cancers. GSVA and GSEA revealed that EEPD1 was primarily associated with the "WNT_BETA_CATENIN_SIGNALING," "ribonucleoprotein complex biogenesis," "Ribosome," and "rRNA processing." The infiltration of CD8+ T cells, MAIT cells, iTreg cells, NK cells, Tc cells, Tex cells, Tfh cells, and Th1 cells were negatively correlated with EEPD1 expression. Additionally, EEPD1 is significantly associated with TMB and MSI in COAD, while enhanced CRC cell proliferation and migration. ConclusionsEEPD1 was dysregulated in human cancers and correlated with various cancer patient prognoses. The dysregulated EEPD1 expression can affect tumor-infiltrating immune cells and immunotherapy response. Therefore, EEPD1 could act as an oncogene associated with immune cell infiltration in CRC.

The m6A methyltransferase METTL5 promotes neutrophil extracellular trap network release to regulate hepatocellular carcinoma progression.

Hepatocellular carcinoma (HCC) is one of the most common malignant tumors worldwide, it has a poor prognosis due to its highly invasive and metastatic nature. Consequently, identifying effective prognostic markers and potential therapeutic targets has been extensively investigated. METTL5, an 18S rRNA methyltransferase, is abnormally high in HCC. But its biological function and prognostic significance in HCC remain largely unelucidated. This study aimed to investigate the role of METTL5 in HCC progression, and elucidate its possible molecular mechanisms in HCC via transcriptome sequencing, providing new insights for identifying new HCC prognostic markers and therapeutic targets. The METTL5 expression in HCC and paracancerous tissues was analyzed using HCC immunohistochemical microarrays and bioinformatic retrieval methods to correlate METTL5 with clinicopathological features and survival prognosis. We constructed a METTL5 knockdown hepatocellular carcinoma cell line model and an animal model to determine the effect of METTL5 on hepatocellular carcinoma progression. Subsequently, RNA sequencing was performed to analyze the molecular mechanism of METTL5 in HCC based on the sequencing results, and relevant experiments were performed to verify it. We found that METTL5 expression was elevated in hepatocellular carcinoma tissues and correlated with poor patient prognosis, and in the analysis of clinicopathological features showed a correlation with TNM staging. In hepatocellular carcinoma cell lines with knockdown of METTL5, the malignant biological behavior was significantly reduced both invitro and invivo. Based on the sequencing results as well as the results of GO functional enrichment analysis and KEGG pathway enrichment analysis, we found that METTL5 could promote the generation and release of neutrophil extracellular capture network (NETs) and might further accelerate the progression of HCC. The m6A methyltransferase METTL5 is overexpressed in hepatocellular carcinoma (HCC) and correlates with poor prognosis. METTL5 accelerates malignant progression of HCC by promoting generation and release of the neutrophil extracellular traps (NETs) network, providing new insights for clinical biomarkers and immunotherapeutic targets in HCC prognosis.

Exacerbating effects of circadian rhythm disruption on the systemic lupus erythematosus

ObjectiveCircadian rhythm disruption (CRD) has been associated with inflammation and immune disorders, but its role in SLE progression is unclear. We aimed to investigate the impact of circadian rhythms on...

A Bioinformatics Method for Identification of Human Proteases Active against Viral Envelope Glycoproteins: A Case Study on the SARS-CoV-2 Spike Protein

A Bioinformatics Method for Identification of Human Proteases Active against Viral Envelope Glycoproteins: A Case Study on the SARS-CoV-2 Spike Protein

Identification of Drug Targets and Agents Associated with Ferroptosis-related Osteoporosis through Integrated Network Pharmacology and Molecular Docking Technology.

Osteoporosis is a systemic bone disease characterized by progressive reduction of bone mineral density and degradation of trabecular bone microstructure. Iron metabolism plays an important role in bone; its imbalance leads to abnormal lipid oxidation in cells, hence ferroptosis. In osteoporosis, however, the exact mechanism of ferroptosis has not been fully elucidated. The main objective of this project was to identify potential drug target proteins and agents for the treatment of ferroptosis-related osteoporosis. In the current study, we investigated the differences in gene expression of bone marrow mesenchymal stem cells between osteoporosis patients and normal individuals using bioinformatics methods to obtain ferroptosis-related genes. We could predict their protein structure based on the artificial intelligence database of AlphaFold, and their target drugs and binding sites with the network pharmacology and molecular docking technology. We identified five genes that were highly associated with osteoporosis, such as TP53, EGFR, TGFB1, SOX2 and MAPK14, which, we believe, can be taken as the potential markers and targets for the diagnosis and treatment of osteoporosis. Furthermore, we observed that these five genes were highly targeted by resveratrol to exert a therapeutic effect on ferroptosis-related osteoporosis. We examined the relationship between ferroptosis and osteoporosis based on bioinformatics and network pharmacology, presenting a promising direction to the pursuit of the exact molecular mechanism of osteoporosis so that a new target can be discovered for the treatment of osteoporosis.

A Viroinformatics Study: B-Cell Polytope Mapping of Envelope Protein to Develop Vaccine Candidate against Four DENV Serotype

Nowadays, dengue virus (DENV) is still become a global problem, even though the virus infection issues have reached half of the population in some countries each year. DENV belongs to the enveloped virus with positive-sense single-stranded RNA (+ssRNA) genus Flavivirus and belongs to the Flaviviridae family. DENV has structural proteins which consist of the envelope protein (E), capsid (C), and membrane (M). There are four serotypes of this virus which are DENV-1, 2, 3, and 4. These four serotypes are transmitted to humans through Aedes sp. The development of this vaccine is still in progress and the challenge of this DENV vaccine candidate design is to overcome the heterotypic infection and the expansion of coverage protection to all virus serotypes. This research uses design simulation for vaccine candidates using B cell epitope in all DENV’s serotypes envelope to trigger the antibody formation through bioinformatics method that consists of protein modeling, immunogenicity, toxicity, and immune stimulation. DENV envelope protein was predicted to have polytope that can be recognized by B cells and act as an antigen, have low similarity with the composing sequence of cell surface receptors on the body, and non-toxic, and then can trigger the population increase of B cell and IgM antibody production with high avidity to neutralize four of the DENV serotypes. We recommend the B cell polytype which consists of A, C, E, and G peptides be examined by the wet-lab approach.

Genome-Wide Analysis of the LBD Gene Family in Melon and Expression Analysis in Response to Wilt Disease Infection.

LBD transcription factors are a class of transcription factors that regulate the formation of lateral organs, establish boundaries, and control secondary metabolism in plants. In this study, we identified 37 melon LBD transcription factors using bioinformatics methods and analyzed their basic information, chromosomal location, collinearity, evolutionary tree, gene structure, and expression patterns. The results showed that the genes were unevenly distributed across the 13 chromosomes of melon plants, with tandem repeats appearing on chromosomes 11 and 12. These 37 transcription factors can be divided into two major categories, Class I and Class II, and seven subfamilies: Ia, Ib, Ic, Id, Ie, IIa, and IIb. Of the 37 included transcription factors, 25 genes each contained between one to three introns, while the other 12 genes did not contain introns. Through cis-acting element analysis, we identified response elements such as salicylic acid, MeJA, abscisic acid, and auxin, gibberellic acid, as well as light response, stress response, and MYB-specific binding sites. Expression pattern analysis showed that genes in the IIb subfamilies play important roles in the growth and development of various organs in melon plants. Expression analysis found that the majority of melon LBD genes were significantly upregulated after infection with wilt disease, with the strongest response observed in the stem.

Discovery and characterization of l-DOPA 2,3-dioxygenase from Streptomyces hygroscopicus jingganensis

The largest natural reservoir of untapped carbon can be found in the cell-wall strengthening, plant woody-tissue polymer, lignin - a polymer of catechols or 1,2-dihydroxybenzene monomers. The catecholic carbon of lignin could be valorized into feedstocks and natural products by way of catabolic and biosynthetic transformations, including the oxygen-dependent cleavage reaction of extradiol dioxygenase (EDX) enzymes. The EDX l-DOPA 2,3-dioxygenase was first discovered as part of a biosynthetic gene cluster to the natural product antibiotic, lincomycin, and also contributes to the biosyntheses of anthramycin, sibiromycin, tomaymycin, porothramycin and hormaomycin. Using these l-DOPA 2,3-dioxygenases as a starting point, we searched sequence space in order to identify new sources of dioxygenase driven natural product diversity. A “vicinal-oxygen-chelate (VOC) family protein” from Streptomyces hygroscopicus jingganensis was identified using bioinformatic methods and biochemically investigated for dioxygenase activity against a suite of natural and synthetic catechols. Steady-state oxygen consumption assays were used to screen and identify substrates, and a steady-state kinetic model of oxygen consumption was developed to evaluate activity of the S. hygroscopicus jingganensis VOC-family-protein with respect to activity of l-DOPA 2,3-dioxygenases from Streptomyces lincolnensis and Streptomyces sclerotialus. Lastly, these data were integrated with steady-state kinetic methods to observe the formation of the EDX cleavage product with UV–visible spectroscopy. The genomic context and enzymatic activity of the S. hygroscopicus jingganensis VOC family protein are consistent with a l-DOPA 2,3-dioxygenase contained within a cryptic biosynthetic pathway.

The Dysregulated circRNAs in Hepatocellular Carcinoma Were Identified Through Circular RNA Sequencing and Bioinformatics Analysis

Background: The research evidence suggests that dysregulated circRNAs are closely associated with the progression of HCC and can serve as diagnostic markers and therapeutic targets for HCC. However, the status of differentially expressed circRNAs in HCC cancerous tissues and adjacent non-cancerous tissues remains unclear. Methods: Firstly, circRNA-Seq was employed to screen for dysregulated circRNAs in HCC tissues and adjacent non-cancerous tissues. Secondly, differential expression of circRNAs in the GSE97332 dataset was identified using bioinformatics methods. Lastly, the intersection of circRNAs that were upregulated or downregulated in both the circRNA-Seq data and the GSE97332 dataset was taken. Results:The results of the circRNA-Seq showed that there are a total of 2477 differentially expressed circRNAs between HCC tissues and adjacent non-cancerous tissues, with 1567 upregulated and 910 downregulated. We identified 869 differentially expressed circRNAs in the GSE97332 dataset, with 421 upregulated and 448 downregulated. Additionally, we took the intersection of circRNAs that were upregulated or downregulated in both the circRNA-seq dataset and the GSE97332 dataset, ultimately obtaining 13 and 6 circRNAs, respectively. Conclusion: This study identified dysregulated circRNAs in HCC through circRNA-seq and analysis of the dataset GSE97332, which offer new options for the discovery of diagnostic markers and therapeutic targets for HCC.

Investigating the important proteins in causing West Nile virus infection in retinal pigment cells

Introduction: West Nile virus is an infection transmitted through blood-sucking arthropods. Ocular manifestations are one of the characteristics of West Nile virus infection. They occur mostly in connection with severe neurological disease. In this study، using bioinformatics analysis، we investigated and identified the genes in the pathways related to West Nile virus infection in the retina.&#x0D; Methods: In this study, using bioinformatics methods and referring to the GEO dataset, the appropriate dataset was selected for analysis. This dataset included gene expression profiles in West Nile virus infection with retinal pigment cells. Gene clusters with high and low expression were categorized. For more accurate data evaluation, databases such as Enrichr، STRING and Network analyst were used. The candidate genes were isolated and their protein relationship was also measured using cytoescape software version 3.7.1. The obtained values with P-Value˂0.05 were selected as significant data.&#x0D; Results: The number of 830 genes with high expression and 500 genes with low expression was involved in the development pathways of West Nile virus infection in the eye. Apoptosis، necroptosis، AMPK and MAPK autophagy pathways were clearly observed. After evaluating the relationship between protein networks، RIPK1، STAT1/3 and JAK2 increased expression and GSK3B، MAPK8 and TSC2 genes had decreased expression.&#x0D; Conclusion: The present study showed that important proteins and genes played a major role in strengthening West Nile virus inflammation in the retina, among which RIPK1,TSC2, STAT1 and JAK2 showed a more prominent role in this pathway.

Long non-coding RNA variability in porcine skeletal muscle.

Recently, numerous studies including various tissues have been carried out on long non-coding RNAs (lncRNAs), but still, its variability has not yet been fully understood. In this study, we characterised the inter-individual variability of lncRNAs in pigs, in the context of number, length and expression. Transcriptomes collected from muscle tissue belonging to six Polish Landrace boars (PL1-PL6), including half-brothers (PL1-PL3), were investigated using bioinformatics (lncRNA identification and functional analysis) and statistical (lncRNA variability) methods. The number of lncRNA ranged from 1289 to 3500 per animal, and the total number of common lncRNAs among all boars was 232. The number, length and expression of lncRNAs significantly varied between individuals, and no consistent pattern has been found between pairs of half-brothers. In detail, PL5 exhibits lower expression than the others, while PL4 has significantly higher expression than PL2-PL3 and PL5-PL6. Noteworthy, comparing the inter-individual variability of lncRNA and mRNA expression, they exhibited concordant patterns. The enrichment analysis for common lncRNA target genes determined a variety of biological processes that play fundamental roles in cell biology, and they were mostly related to whole-body homeostasis maintenance, energy and protein synthesis as well as dynamics of multiple nucleoprotein complexes. The high variability of lncRNA landscape in the porcine genome has been revealed in this study. The inter-individual differences have been found in the context of three aspects: the number, length and expression of lncRNAs, which contribute to a better understanding of its complex nature.

CoT: a transformer-based method for inferring tumor clonal copy number substructure from scDNA-seq data.

Single-cell DNA sequencing (scDNA-seq) has been an effective means to unscramble intra-tumor heterogeneity, while joint inference of tumor clones and their respective copy number profiles remains a challenging task due to the noisy nature of scDNA-seq data. We introduce a new bioinformatics method called CoT for deciphering clonal copy number substructure. The backbone of CoT is a Copy number Transformer autoencoder that leverages multi-head attention mechanism to explore correlations between different genomic regions, and thus capture global features to create latent embeddings for the cells. CoT makes it convenient to first infer cell subpopulations based on the learned embeddings, and then estimate single-cell copy numbers through joint analysis of read counts data for the cells belonging to the same cluster. This exploitation of clonal substructure information in copy number analysis helps to alleviate the effect of read counts non-uniformity, and yield robust estimations of the tumor copy numbers. Performance evaluation on synthetic and real datasets showcases that CoT outperforms the state of the arts, and is highly useful for deciphering clonal copy number substructure.

An in-silico analysis of OGT gene association with diabetes mellitus

O-GlcNAcylation is a nutrient-sensing post-translational modification process. This cycling process involves two primary proteins: the O-linked N-acetylglucosamine transferase (OGT) catalysing the addition, and the glycoside hydrolase OGA (O-GlcNAcase) catalysing the removal of the O-GlCNAc moiety on nucleocytoplasmic proteins. This process is necessary for various critical cellular functions. The O-linked N-acetylglucosamine transferase (OGT) gene produces the OGT protein. Several studies have shown the overexpression of this protein to have biological implications in metabolic diseases like cancer and diabetes mellitus (DM). This study retrieved 159 SNPs with clinical significance from the SNPs database. We probed the functional effects, stability profile, and evolutionary conservation of these to determine their fit for this research. We then identified 7 SNPs (G103R, N196K, Y228H, R250C, G341V, L367F, and C845S) with predicted deleterious effects across the four tools used (PhD-SNPs, SNPs&Go, PROVEAN, and PolyPhen2). Proceeding with this, we used ROBETTA, a homology modelling tool, to model the proteins with these point mutations and carried out a structural bioinformatics method– molecular docking– using the Glide model of the Schrodinger Maestro suite. We used a previously reported inhibitor of OGT, OSMI-1, as the ligand for these mutated protein models. As a result, very good binding affinities and interactions were observed between this ligand and the active site residues within 4Å of OGT. We conclude that these mutation points may be used for further downstream analysis as drug targets for treating diabetes mellitus.