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bioRxiv2022

Empirical study on software and process quality in bioinformatics tools

Software quality in computational tools impacts research output in a variety of scientific disciplines. Biology is one of these fields, especially for High Throughput Sequencing (HTS) data, such tools play an important role. This study therefore characterises the overall quality of a selection of tools which are frequently part of HTS pipelines, as well as analyses the maintainability and process quality of a selection of HTS alignment tools. Our findings highlight the most pressing issues, and point to software engineering best practices developed for the improvement of maintenance and process quality. To help future research, we share the tooling for the static code analysis with SonarCloud which we used to collect data on the maintainability of different alignment tools. The results of the analysis show that the maintainability level is generally high but trends towards increasing technical debt over time. We also observed that the development activities on alignment tools are generally driven by very few developers and are not utilising modern tooling to their advantage. Based on these observations, we recommend actions to improve both maintainability and process quality in open source alignment tools. Those actions include improvements in tooling like the use of linters as well as better documentation of architecture and features. We encourage developers to use these tools in order to ease future maintenance efforts, increase user experience, support reproducibility, and ultimately increase the quality of research through increasing the quality of research software tools.

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bioRxiv2022

Differential gene expression profiling reveals potential biomarkers and pharmacological compounds against SARS-CoV-2: insights from machine learning and bioinformatics approaches

SARS-CoV-2 continues to spread and evolve worldwide, despite intense efforts to develop multiple vaccines and therapeutic options against COVID-19. Moreover, the precise role of SARS-CoV-2 in the pathophysiology of the nasopharyngeal tract (NT) is still unfathomable. Therefore, we used the machine learning methods to analyze 22 RNA-seq datasets from COVID-19 patients (n=8), recovered individuals (n=7), and healthy individuals (n=7) to find disease-related differentially expressed genes (DEGs). In comparison to healthy controls, we found 1960 and 153 DEG signatures in COVID-19 patients and recovered individuals, respectively. We compared dysregulated DEGs to detect critical pathways and gene ontology (GO) connected to COVID-19 comorbidities. In COVID-19 patients, the DEG- miRNA and DEG-transcription factors (TFs) interactions network analysis revealed that E2F1, MAX, EGR1, YY1, and SRF were the most highly expressed TFs, whereas hsa-miR-19b, hsa-miR-495, hsa-miR-340, hsa-miR-101, and hsa-miR-19a were the overexpressed miRNAs. Three chemical agents (Valproic Acid, Alfatoxin B1, and Cyclosporine) were abundant in COVID-19 patients and recovered individuals. Mental retardation, mental deficit, intellectual disability, muscle hypotonia, micrognathism, and cleft palate were the significant diseases associated with COVID-19 by sharing DEGs. Finally, we detected DEGs impacted by SARS-CoV-2 infection and mediated by TFs and miRNA expression, indicating that SARS-CoV-2 infection may contribute to various comorbidities. These pathogenetic findings can provide some crucial insights into the complex interplay between COVID-19 and the recovery stage and support its importance in the therapeutic development strategy to combat against COVID-19 pandemic. IMPORTANCEDespite it has now been over two years since the beginning of the COVID-19 pandemic, many crucial questions about SARS-CoV-2 infection and the different COVID-19 symptoms it causes remain unresolved. An intriguing question about COVID-19 is how SARS-CoV-2 interplays with the host during infection and how SARS-CoV-2 infection can cause so many disease symptoms. Our analysis of three different datasets (COVID-19, recovered, and healthy) revealed significantly higher DEGs in COVID-19 patients than recovered humans and healthy controls. Some of these DEGs were found to be co-expressed in both COVID-19 patients. They recovered humans supporting the notion that DEGs level is directly correlated with the viral load, disease progression, and different comorbidities. The protein-protein interaction consisting of 24 nodes and 72 edges recognized eight hub-nodes as potential hub-proteins (i.e., RPL4, RPS4X, RPL19, RPS12, RPL19, EIF3E, MT-CYB, and MT-ATP6). Protein-chemical interaction analysis identified three chemical agents (e.g., Valproic Acid, Alfatoxin B1, and Cyclosporine) enriched in COVID-19 patients and recovered individuals. Mental retardation, mental deficiency, intellectual disability, muscle hypotonia, micrognathism, and cleft palate were the significant diseases associated with COVID-19 by sharing DEGs.

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bioRxiv2022

Identification of Key Genes and Biological Pathways in Bipolar Disorder by Bioinformatics and Next Generation Sequencing Data Analysis

Bipolar disorder (BD), also known as psychiatric disorder, affects millions of people all over the world. The aim of this investigation was to screen and verify hub genes involved in BD as well as to explore potential molecular mechanisms. The next generation sequencing (NGS) dataset GSE124326 was downloaded from the Gene Expression Omnibus (GEO) database, which contained 480 samples, including 240 BD and 240 normal controls. Differentially expressed genes (DEGs) were filtered and subjected to gene ontology (GO) and pathway enrichment analyses. A protein-protein interaction (PPI) network and module analysis were used to identify hub genes. The miRNA-hub gene regulatory network and TF-hub gene regulatory network were also constructed. Receiver operating characteristic curve (ROC) analysis was used to validate hub genes. In this work, 957 DEGs, including 477 up regulated and 480 down regulated, were obtained from NGS data. The GO and pathway enrichment analyses of the DEGs showed that the up regulated genes were enriched in the neutrophil degranulation, immune system, transport, cytoplasm and enzyme regulator activity, and the down regulated genes were enriched in extracellular matrix organization, diseases of metabolism, multicellular organismal process, cell periphery and metal ion binding. Finally, through analyzing the PPI network, miRNA-hub gene regulatory network and TF-hub gene regulatory network, we screened hub genes UBB, UBE2D1, TUBA1A, RPL11, RPS24, NOTCH3, CAV1, CNBD2, CCNA1 and MYH11 by the Cytoscape software. The current investigation illustrates a characteristic NGS data in BD, which might contribute to the interpretation of the progression of BD and provide novel biomarkers and therapeutic targets for BD.

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bioRxiv2022

A Bioinformatics Approach To Reveal Common Genes And Molecular Pathways Shared By Cutaneous Melanoma and Uveal Melanoma.

BackgroundMelanomas are highly aggressive in nature and are known for metastasis and death. Melanocytes that give rise to melanomas are neural crest progenitor-cells. Our research is primarily concerned with Uveal Melanoma (UM) and Cutaneous Melanoma (CM). Although they both share same melanocytic origin, but the biology of their respective is different. AimThe aim of our study is to recognize the common differentially expressed genes (DEGs) between UM and CM. MethodologyThe gene expression profile was downloaded from the GEO and analyzed by GEO2R for identification of DEGs. By applying DAVID, GO and KEGG, pathway enrichment analysis was performed. PPI of these DEGs was analyzed using STRING and visualized through Cytoscape and MCODE. Further, we utilized HPA and GEPIA to obtain Kaplan-Meier Graph for survival analysis in order to assess the prognostic value of hub genes. ResultsWe examined the UM and CM datasets and discovered three common upregulated and eight common downregulated DEGs for both the melanomas based on computational analysis. HMGCS1 and ELOVL5 were shown to be enriched in a variety of altered molecular pathways and pathways in cancer. Overexpression of HMGCS1 and ELOVL5 were linked to poor prognosis in CM. ConclusionOn computational evaluation, we found that HMGCS1 and ELOVL5 were upregulated in both of these melanomas. Enrichment analysis showed that these genes are involved in cancer metabolism pathway and associated with poor prognosis in CM. However, the molecular study of these genes in UM is limited. Therefore, a better understanding of the cancer metabolism pathways should be carried to pave the way for clinical benefits.

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bioRxiv2022

SARS-CoV-2 Helicase might interfere with cellular nonsense-mediated RNA decay, insights from a bioinformatics study

Unraveling molecular interactions between viral proteins and host cells is key to understanding the pathogenesis of viral diseases. We hypothesized that potential sequence and structural similarities between SARS-CoV2 proteins and proteins of infected cells might influence host cell biology and antiviral defense. Comparing the proteins of SARS-CoV-2 with human and mammalian proteins revealed sequence and structural similarities between viral helicase with human UPF1. The latter is a protein that is involved in nonsense mediated RNA decay (NMD), an mRNA surveillance pathway which also acts as a cellular defense mechanism against viruses. Protein sequence similarities were also observed between viral nsp3 and human Poly ADP-ribose polymerase (PARP) family of proteins. Gene set enrichment analysis on transcriptomic data derived from SARS-CoV-2 positive samples illustrated the enrichment of genes belonging to the NMD pathway compared with control samples. Moreover, comparing transcriptomic data from SARS-CoV2-infected samples with transcriptomic data derived from UPF1 knockout cells demonstrated a significant overlap between datasets. These findings suggest that helicase/UPF1 sequence and structural similarity might have the ability to interfere with the NMD pathway with pathogenic and immunological implications.

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bioRxiv2022

Identification of hub genes and key pathways in pediatric Crohn's disease using next generation sequencing and bioinformatics analysis

Pediatric Crohn Disease (CD) also known as inflammatory bowel diseases, affects millions of people all over the world. The aim of this investigation is to identify the key genes in CD and uncover their potential functions. We downloaded the next generation sequencing (NGS) dataset GSE101794 from the Gene Expression Omnibus (GEO) database. The NGS dataset GSE101794 was used to screen differentially expressed genes (DEGs) between samples from patients with CD and healthy controls. Gene ontology (GO) and REACTOME pathway enrichment analyses were applied for the DEGs. Subsequently, a protein - protein interaction (PPI) network, modules, miRNA- hub gene regulatory network and TF - hub gene regulatory network were constructed to identify hub genes, miRNAs and TFs. Receiver operating characteristic curve (ROC) analysis was applied to validate the hub genes. A total of 957 DEGs were identified, including 478 up regulated genes and 479 down regulated genes. GO and REACTOME results suggested that several Go terms and pathways are involved in response to stimulus, extracellular region, signaling receptor binding, small molecule metabolic process, membrane, transporter activity, immune system and biological oxidations. The top centrality hub genes MDFI, MNDA, FBXO6, TFRC, STAT1, DPP4, MME, SLC39A4, APOA1 and TMEM25 were screened out as the critical genes among the DEGs from the PPI network, modules, miRNA-hub gene regulatory network and TF-hub gene regulatory network. This investigation identified key genes and signal pathways, which might help us improve our understanding of the molecular mechanisms of CD and identify some novel therapeutic targets for CD.

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bioRxiv2022

Bioinformatics analysis in identifying significantly expressed immune related genes in influenza A virus infection

This study is done to identify significantly expressed genes of influenza A virus infection in human, facilitated by the idea of checking for the presence of immune related genes in these significantly expressed genes, followed by the analysis of their potential mechanism and pathway. Considering the usage of microarray dataset, the gene expression profile GSE66597 was downloaded from the gene expression omnibus(GEO) database, and then a subsequent generation of differentially expressed genes(DEGs) using the GEO2R tool. A total of 1107 genes was generated with 703 upregulated and 404 downregulated genes. The protein-protein interaction (PPI) network was constructed using string and Cytoscape software which resulted in generating the core genes, hub genes, and bottleneck genes. Subsequent Venn diagram analysis finally gave the six candidate genes CRP, CCL5, IL17A, STAT1, CD34, SPI1, subjected to gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) for enrichment and pathway analysis respectively. Out of the initial six candidate genes, four of these genes CRP, CCL5, IL17A, are immune related, and initiated particularly by the presence of influenza A virus.

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bioRxiv2022

UltraSEQ: a universal bioinformatic platform for information-based clinical metagenomics and beyond

Applied metagenomics is a powerful emerging capability enabling untargeted detection of pathogens, and its application in clinical diagnostics promises to alleviate the limitations of current targeted assays. While metagenomics offers a hypothesis-free approach to identify any pathogen, including unculturable and potentially novel pathogens, its application in clinical diagnostics has so far been limited by workflow-specific requirements, computational constraints, and lengthy expert review requirements. To address these challenges, we developed UltraSEQ, a first-of its kind metagenomics-based clinical diagnostics and biosurveillance tool that is accurate and scalable. Here we present results for evaluation of our novel UltraSEQ pipeline using an in silico synthesized metagenome, mock microbial community datasets, and publicly available clinical datasets from samples of different infection types, and both short-read and long-read sequencing data. Our results show that UltraSEQ successfully detected all expected species across the tree of life in the in silico sample and detected all 10 bacterial and fungal species in the mock microbial community dataset. For clinical datasets, even without requiring dataset-specific configuration settings changes, background sample subtraction, or prior sample information, UltraSEQ achieved an overall accuracy of 91%. Further, we demonstrated UltraSEQs ability to provide accurate antibiotic resistance and virulence factor genotypes that are consistent with phenotypic results. Taken together, the above results demonstrates that the UltraSEQ platform offers a transformative approach to microbial and metagenomic sample characterization, employing a biologically informed detection logic, deep metadata, and a flexible system architecture for classification and characterization of taxonomic origin, gene function, and user-defined functions, including disease-causing infection.

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bioRxiv2022

A Bioinformatics Analysis Reveals that the Transcription Levels of Cell Surface Receptor Encoding Genes are Associated with the Drug Response of Breast Cancer Cells and the Drug Off-Target Effects

Breast cancer is characterised by varied responses to different anticancer therapies, which may provoke several different off-target effects. We hypothesise that for drugs that target cell surface receptors (CSRs), the different responses of tumours and the adverse events produced by these drugs may be attributed to variations in the transcriptional landscapes of CSRs in both breast tumours and healthy tissues. Here, we use data from various sources to compare the CSR transcriptional landscapes of breast tumours and a range of different non-diseased human tissues. We demonstrate an association between the responses to drug perturbation of breast cancer cell lines and the transcription levels of their targeted CSRs. Furthermore, we reveal important differences in the CSR transcriptional landscapes of primary breast tumour subtypes and the CSR transcriptional landscapes of breast cancer cell lines, which will likely impact the accuracy of drug response predictions. Finally, applying clinical trial data, we expose a link between the expression levels of CSR genes in healthy tissues and adverse reactions of patients to anticancer drugs. Altogether, this approach allows for the isolation of the most suitable CSR target(s) among the expressed transcripts, solely based on the measured dose-responses of cell lines to small molecules, the CSR transcriptional landscape in health patient tissues, and reported adverse responses of patients to drugs targeting CSRs.

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bioRxiv2022

Bioinformatic-based identification of 3 immune-related genes for prognostic in breast invasive carcinoma

Breast invasive carcinoma (BRCA) is the leading cause of cancer deaths in female worldwide. Immune cell infiltration is considered to be crucial factor for the success or failure of BRCA therapy. As we all known, early diagnosis of breast cancer can greatly improve the survival rate of patients. Hence, to screen effective biomarkers for breast cancer immunotherapy might be extremely important. In this research, we identified an immune-related, three-gene biomarkers for prognosis of BRCA. We obtained altogether 192 differentially expressed genes (DEGs) from GEO datasets (GSE22820, GSE36295) and GEPIA database, followed by the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. We screened 3 immune-related candidate biomarkers composing by CXCL2, CXCL9 and RBP7 that improves survival prediction outcome in BRCA. Kaplan-Meier analysis was conducted to analyze the patient survival based on 3 genes. In addition, we found that selected biomarkers were closely connected with infiltration levels of different tumor immune cells. Moreover, principal component analysis showed three biomarkers could effectively distinguish tumor samples from normal samples. In summary, these findings suggested that CXCL2, CXCL9 and RBP7 are viable prognostic and diagnostic biomarkers and provide new ideas for immunotherapy solutions of BRCA in the future.

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bioRxiv2022

Some Mendelian Disorders could be fixed with a pill? A Structural Bioinformatic investigation

It has been recently suggested that amino acid replacements with Gly can modify the shape of protein surfaces and, hence, protein dynamics and functions. We have browsed ClinVar, the database of all the reported variants of clinical relevance, to identify all the proteins having missense X/Gly mutations that determine Mendelian disorders. We have found 959 benign and 875 pathogenic X/Gly substitutions. Pathogenicity origins were initially searched in the distribution profiles of replaced amino acids. These profiles indicate that Mendelian disorders including Gly-replacements arise mainly from substitutions of amino acids bearing bulky hydrophobic side chains, thus reducing protein core stability. In the case mutated proteins were structurally defined, we could give a deeper insight into pathogenicity mechanisms, checking whether Gly-mutations altered protein shapes, modifying water surface dynamics and, hence, the physiological protein-protein interaction processes. In several cases, indeed, we have found that pathological Gly-mutants present additional surface pockets, suggesting that the new pockets could be the target of a pharmacological strategy for Mendelian disorder remediation. HighlightsO_LIClinVar has been scanned to find signals for pathogenicity due to X/Gly mutations C_LIO_LIPathogenicity origins of X/Gly replacements have been structurally analyzed C_LIO_LIX/Gly mutations can create protein surface pockets with binding capabilities C_LIO_LIGly-formed new protein binding sites can be the target for Mendelian disorder cures C_LIO_LIAI procedures will expand the search for structural damages due to X/Gly mutations C_LI Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=52 SRC="FIGDIR/small/513070v1_ufig1.gif" ALT="Figure 1"> View larger version (26K): org.highwire.dtl.DTLVardef@13b4d9borg.highwire.dtl.DTLVardef@6404faorg.highwire.dtl.DTLVardef@82c69forg.highwire.dtl.DTLVardef@1bdff58_HPS_FORMAT_FIGEXP M_FIG C_FIG

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bioRxiv2022

A Bioinformatic Study of the Distribution of Mn Oxidation Proteins in Sequenced Bacterial Genomes

1.BackgroundWhile many species of bacteria have been identified that can convert soluble, reduced manganese (Mn+2) into insoluble, oxidized Mn+4 oxides, the mechanisms these bacteria employ and their distribution throughout the bacterial domain are less well understood. One of the best characterized MnOB is the gamma-proteobacterium Pseudomonas putida GB-1, which uses three distinct proteins (PpMnxG, McoA and MopA) to oxidize Mn+2. The best characterized Mn oxidase enzyme is the MnxG homolog of Bacillus sp. PL-12 (BaMnxG), which appears to be the only Mn oxidase in this species. MofA, found in Leptothrix discophora sp SS-1 is an additional putative Mn oxidase. ResultsBy querying publicly available databases of bacterial genome sequences for homologs to these Mn oxidase proteins, it was possible to determine the distribution of the proteins within bacteria. The overwhelming majority of homologs were found in just three phyla: proteobacteria, actinobacteria and firmicutes. These data do not preclude the possibility of novel Mn oxidase mechanisms in other as yet uncharacterized groups of bacteria. Each of the homologs had a statistically significant probability of being present as the solo Mn oxidase in a genome. When genomes did have more than one oxidase, they were present in the same combinations as in P. putida GB-1. ConclusionsThese results do not support the initial hypothesis that multiple enzymes are required to complete the two-electron oxidation of Mn+2 to Mn+4. Alternatively, the various Mn oxidase enzymes may be optimized to function under different environmental conditions; organisms like P. putida GB-1 may need to oxidize Mn at different temperatures, nutritional states or oxygen conditions.

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bioRxiv2022

Bioinformatic methods for stratification of obese patients and identification of cancer susceptibility biomarkers based on the analysis of the gut microbiome

Obesity has an impact on health by increasing the risk of various diseases. However, these risks might also depend on the metabolic health status, as it seems that metabolically healthy obese subjects are under a reduced risk of suffering comorbidities such as colorectal cancer. The gut microbiome has an effect on obesity and metabolic disorders through several integration pathways, making it a potential therapeutic target for these diseases. In this study, we characterized the gut microbiota of 356 obese and non-obese European individuals with different comorbidities associated with obesity. Using approaches based on supervised machine learning and network biology, we found a set of biomarkers of interest for differentiating metabolically healthy from unhealthy subjects. Then, we performed a linear discriminant analysis of effect size on a population of 1593 colorectal cancer, adenoma and control subjects assembled by the COST Action ML4Microbiome to investigate their role in colorectal cancer risk. Four of our biomarkers appeared in both approaches, suggesting their possible role in colorectal cancer development, prognosis and follow up: Clostridium leptum, Gordonibacter pamelaeae, Eggerthella lenta and Collinsella intestinalis. Further research via longitudinal studies or experimental validation of these microbial species would be necessary to confirm this association.

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bioRxiv2022

Identification of Key Genes and Underlying Mechanisms in Cystic fibrosis and its Associated Complications Based on Bioinformatics Analysis of Next Generation Sequencing Data Analysis

Cystic fibrosis (CF) is one of the inherited autosomal recessive disorders with very complicated pathogenesis. Identifying the molecular signatures and specific biomarkers of CF might provide novel clues for CF and and CF associated complications prognosis and targeted therapy. Based on the nexgeneration sequencing (NGS) dataset GSE136371 downloaded from the Gene Expression Omnibus (GEO) database, the differentially expressed genes (DEGs) between CF samples and normal controls were identified by using DESeq2 bioconductor package of R software. Gene ontology (GO) and REACTOME pathway enrichment analyses were applied for the DEGs. Then protein-protein interaction (PPI) network of these DEGs was visualized by Cytoscape with IMEx interactome database. The most significant module from the PPI network was selected for GO and pathway enrichment analysis. Subsequently, a miRNA-hub gene regulatory network and TF-hub gene regulatory network were constructed to identify hub genes, miRNAs and TFs. Finally, receiver operating characteristic curve (ROC) analysis was established to validate these hub genes. Total of 917 DEGs were identified between CF and normal control samples in GSE136371 dataset, including 479 up regulated and 438 down regulated genes. The most enriched DEGs in GO and pathway enrichment analysis were mainly associated with response to stimulus, regulation of cellular process, Neutrophil degranulation and rRNA processing. PPI network, module analysis, miRNA-hub gene regulatory network and TF-hub gene regulatory network predicted ten hub genes (FN1, UBE2D1, SRPK1, MAPK14, CEBPB, HSP90AB1, HSPA8, XRCC6, NCL and PARP1). In conclusion, the DEGs, relative pathways and hub genes identified in the present study might aid in understanding of the molecular mechanisms underlying CF and CF associated complications progression and provide potential molecular targets and biomarkers for CF and CF associated complications.

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bioRxiv2023

Exploration of the Link Between COVID-19 and Alcoholic Hepatitis from the Perspective of Bioinformatics and Systems Biology

ObjectiveSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been suggested to purpose threats to health of mankind. Alcoholic hepatitis (AH) is a life-threatening acute and chronic liver failure that takes place in sufferers who drink excessively. During the epidemic, AH has an increasing incidence of severe illness and mortality. However, for these two diseases, the intrinsic relationship of molecular pathogenesis, as well as common therapeutic strategies are still poorly understood. MethodsThe transcriptome of the COVID-19 and AH has been compared to obtain the altered genes and hub genes were screened out through protein-protein interaction (PPI) network analysis. Via gene ontology (GO), pathway enrichment and transcription regulator analysis, a deeper appreciation of the interplay mechanism between hub genes were established. ResultsWith 181 common differentially expressed genes (DEGs) of AH and COVID-19 were obtained, 10 hub genes were captured. Follow-up studies located that these 10 genes typically mediated the diseases occurrence by regulating the activities of the immune system. Other results suggest that the common pathways of the two ailments are enriched in regulating the function of immune cells and the release of immune molecules. ConclusionThis study reveals the common pathogenesis of COVID-19 and AH and assist to discover necessary therapeutic targets to combat the ongoing pandemic induced via SARS-CoV-2 infection and acquire promising remedy strategies for the two diseases.

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bioRxiv2023

Study on the expression of RAD51 in non-small cell lung cancer based on bioinformatics

ObjectiveRAD51 is a DNA repair protein, which participates in the resistance of tumor cells to radiotherapy/chemotherapy and reduces the therapeutic effect. Based on the research status of RAD51 at home and abroad and the analysis of online databases, the purpose of this study was to explore the relationship between RAD51 expression and clinical patient survival and prognosis. It is expected to provide a new theoretical basis for the clinical treatment of lung cancer patients, help identify new molecular markers, and provide new targets for the biological therapy of lung cancer patients. Methodsthe RNA Seq data of NSCLC in TCGA database were downloaded, and the expression of RAD51 gene in NSCLC and normal tissues were analyzed by R studio software. Clinical correlation analysis revealed its correlation with the clinicopathological characteristics of non-small cell lung cancer. Survival analysis was used to evaluate the relationship between the expression level and the prognosis of patients. CIBERSORT and TIMER were used to evaluate the correlation between the expression level of CIBERSORT and immune cells in the tumor microenvironment. The protein expression level of RAD51 in non-small cell lung cancer was evaluated by HPA. ResultsRAD51 was highly expressed in lung cancer (p<0.05), which was significantly associated with poor prognosis of lung adenocarcinoma patients (p=0.0026), but not with lung squamous cell carcinoma (p=0.76). The expression level of RAD51 mRNA was associated with different pathological stages of lung adenocarcinoma (p=0.000528), but not with different pathological stages of lung squamous cell carcinoma (p=0.326). RAD51 was positively correlated with the expression of TP53, BRAF, EGFR, MYC, PD-L1, and KRAS (p<0.001). In lung adenocarcinoma, lung cancer cells were positively correlated with CD4+memory T cells, CD8+T cells, and M1 macrophages (p<0.001). In lung squamous cell carcinoma, tumor cells were positively correlated with M1 macrophages (p<0.05), but not with CD4+T memory cells, CD8+T cells, M2 macrophages, and Tregs cells (p>0.05). The HPA database indicated that RAD51 protein was positive in non-small cell lung cancer. ConclusionRAD51 is highly expressed in non-small cell lung cancer and is associated with poor prognosis. RAD51 can be used as a biomarker related to the prognosis of non-small cell lung cancer and is expected to become a target for the diagnosis and treatment of non-small cell lung cancer.

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bioRxiv2023

Bioinformatic identification of ClpI, a distinct class of Clp unfoldases in Actinomycetota

All clades of bacteria possess Hsp100/Clp family unfoldase enzymes that contribute to aspects of protein quality control. In Actinomycetota, these include ClpB, which functions as an independent chaperone and disaggregase, and ClpC, which cooperates with the ClpP1P2 peptidase to carry out regulated proteolysis of client proteins. We initially sought to algorithmically catalog Clp unfoldase orthologs from Actinomycetota into ClpB and ClpC categories. In the process, we uncovered a phylogenetically distinct third group of double-ringed Clp enzymes, which we term ClpI. ClpI enzymes are architecturally similar to ClpB and ClpC, with intact ATPase modules and motifs associated with substrate unfolding and translation. While ClpI possess an M-domain similar in length to that of ClpC, their N-terminal domain is more variable than the strongly conserved N-terminal domain of ClpC. Surprisingly, we identified separate sets of ClpI sequences that possess or lack the LGF-motifs required for stable assembly with ClpP1P2. In species where they occur, we suggest that ClpI enzymes provides additional pathways and points of regulatory control over protein quality control programs, supplementing the conserved roles of ClpB and ClpC.

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bioRxiv2023

An integrated bioinformatics approach to identify candidate biomarkers and the evaluation of drugs for pheochromocytoma

ObjectivesIdentification of the most potential druggable targets and drugs for treating pheochromocytoma by understanding the molecular mechanisms in the progression of pheochromocytoma and discovering additional biomarkers for early diagnosis. MethodsThe differential gene expression in microarray data was analyzed using CRAN packages such as Geoquery, Affy, Limma, Tidyverse, GOchord, etc., and functional and pathway enrichment was performed through the David database, Further PPI network is constructed by retrieving the Protein partners were retrieved from the STRING database. The top 3 ranked gene products from cytohubba were docked with the ligand molecules, which are all in phase III trials. The complex with the highest binding affinity was simulated using molecular dynamics simulations for 100ns, PCA and FEL analysis were performed to determine the stability of the complex at each time interval additionally miRNA and TRFs were predicted for the prominent genes. ResultsAs a result of functional enrichment, most of the 97 differentially expressed genes (DEGs) are involved in cancer-causing pathways. Pheochromocytomas were associated with CDC42, VEGFA, PIK3R1, ITGAV, and LAMB-1, among prominent genes among the ten hub genes. Simulated results verified the effectiveness of targeting VEGFA with Lenvatinib. ConclusionsWe found that differentially expressed genes in the interactome and hub genes with high correlations could serve as biomarkers, along with their miRNAs and TRFs. By targeting VEGFA as a drug target, Lenvatinib displays a high affinity for VEGFA, as it interacts with other genes through homodimerization, activation, phosphorylation, and dephosphorylation. It also appears to interact with the VEGFA protein via hydrogen bonds. In conclusion, this study suggests that Lenvatinib might be a promising anticancer agent that could treat pheochromocytoma, and VEGF-A, CDC42, PIK3R1, as well as several predicted miRNAs, such as miR-224 and miR-221, might be biomarkers for the early diagnosis.

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