Bioinformatics Databases Research Articles

Alpha5 nicotinic acetylcholine receptor mediated immune escape of lung adenocarcinoma via STAT3/Jab1-PD-L1 signalling

BackgroundImmunotherapy has proven to be an emerging treatment for non-small-cell lung cancer in recent years. Notably, smokers show higher programmed cell death ligand-1 (PD-L1) expression and better responses to PD-1/PD-L1 inhibitors than nonsmokers. Genome-wide association studies show that the CHRNΑ5 encoding α5-nicotinic acetylcholine receptor (α5-nAChR) is especially relevant to lung cancer and nicotine dependence. Jab1 is a key regulatory factor and promotes the stabilization of PD-L1. Our previous study reported that α5-nAChR mediates lung adenocarcinoma (LUAD) epithelial-mesenchymal transition (EMT) and metastasis via STAT3/Jab1. However, the link between α5-nAChR and PD-L1 is unclear in LUAD.MethodsWe used various bioinformatics databases to analyze the expression of related genes and their correlations. Expression and clinicopathologic significance of α5-nAChR and PD-L1 were detected by immunohistochemistry in a tissue microarray. α5-nAChR regulated LUAD cell immune escape by targeting the STAT3/Jab1-PD-L1 signalling by Western-blotting and ChIP in vitro. We used T cell coculture, flow cytometry, ELISA, CCK8 assay and crystal violet staining to detect the expression of regulatory T cell (Tregs), IFN-γ, IL-2 and the ability of T cell-mediated tumour cell killing respectively. IF assays were performed in both cancer cells and tumour xenograft paraffin sections to analyze the protein expression. The in vivo experiments in mouse model were performed to show the α5-nAChR-mediated immune escape via PD-L1 pathway.ResultsThe expression of α5-nAChR was correlated with PD-L1 expression, smoking status and lower survival of LUAD in vivo. In vitro, the expression of α5-nAChR mediated phosphorylated STAT3 (pSTAT3), Jab1 and PD-L1 expression. STAT3 bound to the Jab1 or PD-L1 promoter and mediated PD-L1 expression. Jab1 stabilized PD-L1 expression in LUAD cells. Furthermore, in primary T cell cocultured system, downregulation of α5-nAChR suppressed the function of CD4+CD25+FOXP3+ Tregs, enhanced IFN-γ secretion, and increased T cell-mediated killing of LUAD cells. In the Jurkat T cells and LUAD cells coculture assay, inhibition of α5-nAChR increased IL-2 secretion. In tumour xenograft tissues, α5-nAChR expression was related to PD-L1, Jab1, pSTAT3, CD4 and granzyme B expression (GB).ConclusionsOur results suggest that the novel α5-nAChR/STAT3-Jab1-PD-L1 axis is involved in LUAD immune escape, which could lead to potential therapeutic strategies for cancer immunotherapy.5YyQxALBoHfH59G2YWPT-AVideo abstract

Nine quick tips for pathway enrichment analysis.

Pathway enrichment analysis (PEA) is a computational biology method that identifies biological functions that are overrepresented in a group of genes more than would be expected by chance and ranks these functions by relevance. The relative abundance of genes pertinent to specific pathways is measured through statistical methods, and associated functional pathways are retrieved from online bioinformatics databases. In the last decade, along with the spread of the internet, higher availability of computational resources made PEA software tools easy to access and to use for bioinformatics practitioners worldwide. Although it became easier to use these tools, it also became easier to make mistakes that could generate inflated or misleading results, especially for beginners and inexperienced computational biologists. With this article, we propose nine quick tips to avoid common mistakes and to out a complete, sound, thorough PEA, which can produce relevant and robust results. We describe our nine guidelines in a simple way, so that they can be understood and used by anyone, including students and beginners. Some tips explain what to do before starting a PEA, others are suggestions of how to correctly generate meaningful results, and some final guidelines indicate some useful steps to properly interpret PEA results. Our nine tips can help users perform better pathway enrichment analyses and eventually contribute to a better understanding of current biology.

ACBD3 Bioinformatic Analysis and Protein Expression in Breast Cancer Cells.

ACBD3 overexpression has previously been found to correlate with worse prognosis for breast cancer patients and, as an incredibly diverse protein in both function and cellular localisation, ACBD3 may have a larger role in breast cancer than previously thought. This study further investigated ACBD3′s role in breast cancer. Bioinformatic databases were queried to characterise ACBD3 expression and mutation in breast cancer and to investigate how overexpression affects breast cancer patient outcomes. Immunohistochemistry was carried out to examine ACBD3 location within cells and tissue structures. ACBD3 was more highly expressed in breast cancer than in any other cancer or matched normal tissue, and expression over the median level resulted in reduced relapse-free, overall, and distant metastasis-free survival for breast cancer patients as a whole, with some differences observed between subtypes. IHC analysis found that ACBD3 levels varied based on hormone receptor status, indicating that ACBD3 could be a candidate biomarker for poor patient prognosis in breast cancer and may possibly be a biomarker for ER signal reprogramming of precancerous breast tissue.

Bioinformatics and its role in the study of the evolution and probiotic potential of lactic acid bacteria.

Due to their numerous well-established applications in the food industry, there have been many studies regarding the adaptation and evolution of lactic acid bacteria (LAB) in a wide variety of hosts and environments. Progress in sequencing technology and continual decreases in its costs have led to the availability of LAB genome sequence data. Bioinformatics has been central to the extraction of valuable information from these raw genome sequence data. This paper presents the roles of bioinformatics tools and databases in understanding the adaptation and evolution of LAB, as well as the bioinformatics methods used in the initial screening of LAB for probiotic potential. Moreover, the advantages, challenges, and limitations of employing bioinformatics for these purposes are discussed.

Potential roles of synaptotagmin family members in cancers: Recent advances and prospects.

With the continuous development of bioinformatics and public database, more and more genes that play a role in cancers have been discovered. Synaptotagmins (SYTs) are abundant, evolutionarily conserved integral membrane proteins composed of a short N-terminus, a variable linker domain, a single transmembrane domain, and two C2 domains, and they constitute a family of 17 isoforms. The synaptotagmin family members are known to regulate calcium-dependent membrane fusion events. Some SYTs play roles in hormone secretion or neurotransmitter release or both, and much evidence supports SYTs as Ca2+ sensors of exocytosis. Since 5 years ago, an increasing number of studies have found that SYTs also played important roles in the occurrence and development of lung cancer, gastric cancer, colon cancer, and other cancers. Down-regulation of SYTs inhibited cell proliferation, migration, and invasion of cancer cells, but promoted cell apoptosis. Growth of peritoneal nodules is inhibited and survival is prolonged in mice administrated with siSYTs intraperitoneally. Therefore, most studies have found SYTs serve as an oncogene after overexpression and may become potential prognostic biomarkers for multiple cancers. This article provides an overview of recent studies that focus on SYT family members’ roles in cancers and highlights the advances that have been achieved.

The clinical significance of thyroid hormone-responsive in thyroid carcinoma and its potential regulatory pathway.

The study aimed to evaluate the clinical significance of thyroid hormone-responsive (THRSP) and explore its relevant pathways in thyroid carcinoma (THCA).The gene expression data of THRSP were obtained and the prognostic significance of THRSP in THCA was analyzed through various bioinformatics databases. Then, the factors influencing THRSP mRNA expression were explored, and the function of THRSP in predicting the lymph node metastasis (LNM) stage was determined. We further performed the enrichment analysis and constructed a protein–protein interaction (PPI) network to examine potential regulatory pathways associated with THRSP.THRSP gene expression was significantly increased in THCA compared with the normal tissues. High THRSP mRNA expression had a favorable overall survival (OS) in THCA patients (P < .05). Additionally, the mRNA expression of THRSP was related to stage, histological subtype, and methylation among THCA patients (all P < .05). Besides, THRSP served as a potent predictor in discriminating the LNM stage of thyroid cancer patients. According to Kyoto Encyclopedia of Genes and Genomes (KEGG) and gene set enrichment analysis (GSEA) on THRSP-associated genes, THRSP was positively related to metabolic pathways.The upregulation of THRSP predicted a good OS in THCA patients. Furthermore, THRSP might inhibit THCA progression through positive regulation of metabolism-associated pathways.

Effect of SARS-CoV-2 infection on asthma patients.

BackgroundSARS-CoV-2 causes coronavirus disease 2019 (COVID-19), a new coronavirus pneumonia, and containing such an international pandemic catastrophe remains exceedingly difficult. Asthma is a severe chronic inflammatory airway disease that is becoming more common around the world. However, the link between asthma and COVID-19 remains unknown. Through bioinformatics analysis, this study attempted to understand the molecular pathways and discover potential medicines for treating COVID-19 and asthma.MethodsTo investigate the relationship between SARS-CoV-2 and asthma patients, a transcriptome analysis was used to discover shared pathways and molecular signatures in asthma and COVID-19. Here, two RNA-seq data (GSE147507 and GSE74986) from the Gene Expression Omnibus were used to detect differentially expressed genes (DEGs) in asthma and COVID-19 patients to find the shared pathways and the potential drug candidates.ResultsThere were 66 DEGs in all that were classified as common DEGs. Using a protein-protein interaction (PPI) network created using various bioinformatics techniques, five hub genes were found. We found that asthma has some shared links with the progression of COVID-19. Additionally, protein-drug interactions with common DEGs were also identified in the datasets.ConclusionWe investigated possible links between COVID-19 and asthma using bioinformatics databases, which might be useful in treating COVID-19 patients. More studies on populations affected by these diseases are needed to elucidate the molecular mechanism behind their association.

A reappraisal of microbiome dysbiosis during experimental periodontitis.

Periodontitis is a chronic inflammatory disease associated with the presence of dysbiotic microbial communities. Several studies interrogating periodontitis pathogenesis have utilized the murine ligature-induced periodontitis (LIP) model and have further examined the ligature-associated microbiome relying on 16S rRNA-based sequencing techniques. However, it is often very challenging to compare microbial profiles across studies due to important differences in bioinformatic processing and databases used for taxonomic assignment. Thus, our study aim was to reanalyze microbiome sequencing datasets from studies utilizing the LIP model through a standardized bioinformatic analysis pipeline, generating a comprehensive overview of microbial dysbiosis during experimental periodontitis.We conducted a reanalysis of 16S rDNA gene sequencing datasets from nine published studies utilizing the LIP model. Reads were grouped according to the hypervariable region of the 16S rDNA gene amplified (V1-V3 and V4), preprocessed, binned into operational taxonomic units and classified utilizing relevant databases. Alpha- and beta-diversity analyses were conducted, along with relative abundance profiling of microbial communities. Our findings revealed similar microbial richness and diversity across studies and determined shifts in microbial community structure determined by periodontitis induction and study of origin. Clear variations in the relative abundance of bacterial taxa were observed starting on day 5 after ligation and onward, consistent with a distinct microbial composition during health and experimental periodontitis. We also uncovered differentially represented bacterial taxa across studies, dominating periodontal health and LIP-associated communities. Collectively, this reanalysis provides a unified overview of microbial dysbiosis during the LIP model, providing new insights that aim to inform further studies dedicated to unraveling oral host-microbial interactions.

Genome-wide association study identifies Sjögren’s risk loci with functional implications in immune and glandular cells

Sjögren’s disease is a complex autoimmune disease with twelve established susceptibility loci. This genome-wide association study (GWAS) identifies ten novel genome-wide significant (GWS) regions in Sjögren’s cases of European ancestry: CD247, NAB1, PTTG1-MIR146A, PRDM1-ATG5, TNFAIP3, XKR6, MAPT-CRHR1, RPTOR-CHMP6-BAIAP6, TYK2, SYNGR1. Polygenic risk scores yield predictability (AUROC = 0.71) and relative risk of 12.08. Interrogation of bioinformatics databases refine the associations, define local regulatory networks of GWS SNPs from the 95% credible set, and expand the implicated gene list to >40. Many GWS SNPs are eQTLs for genes within topologically associated domains in immune cells and/or eQTLs in the main target tissue, salivary glands.

Mitophagy-mediated molecular subtypes depict the hallmarks of the tumour metabolism and guide precision chemotherapy in pancreatic adenocarcinoma.

Background: Mitophagy is closely related to cancer initiation and progression. However, heterogeneity with reference to mitophagy remains unexplored in pancreatic adenocarcinoma (PAAD). Materials and methods: We used Reactome database to download the mitophagy-related, glycolysis-related and cholesterol biosynthesis-related signaling pathways. Unsupervised clustering using the “ConsensusClusterPlus” R package was performed to identify molecular subtypes related to mitophagy and metabolism. Prognosis-related mitophagy regulators were identified by univariate Cox regression analysis. Receiver operating characteristics (ROC) and Kaplan-Meier (K-M) survival analyses were used to assess the diagnostic and prognostic role of the hub genes and prognosis risk model. Weighted gene co-expression network analysis (WGCNA) was utilized for screening the mitophagy subtype-related hub genes. Metascape was utilized to carry out functional enrichment analysis. The “glmnet” R package was utilised for LASSO, and the “e1071” R package was utilised for SVM. Chemotherapeutic drug sensitivity was estimated using the R package “pRRophetic” and Genomics of Drug Sensitivity in Cancer (GDSC) database. The nomogram was established by the “rms” R package. Results: Three distinct mitophagy subtypes (low, high and intermediate) of PAAD were identified based on the landscape of mitophagy regulators. The high mitophagy subtype had the worst prognosis, highest mRNA expression-based stemness index scores and most hypoxic environment compared to the other subtypes. Additionally, glycolysis and cholesterol biosynthesis were significantly elevated. Three mitophagy subtype-specific gene signatures (CAST, CCDC6, and ERLIN1) were extracted using WGCNA and machine learning. Moreover, PAAD tumours were insensitive to Erlotinib, Sunitinib and Imatinib in the high mitophagy subtype and high CAST, CCDC6, and ERLIN1 expressed subtypes. Furthermore, CAST, CCDC6, and ERLIN1 affected immune cell infiltration (M1 and CD8Tcm), resulting in the altered prognosis of patients with PAAD. A nomogram was constructed to screen patients with the low mitophagy subtype, which showed a higher sensitivity to chemotherapeutic agents. Conclusion: Based on various bioinformatics tools and databases, the PAAD heterogeneity regarding mitophagy was systematically examined. Three different PAAD subtypes having different outcomes, metabolism patterns and chemosensitivity were observed. Moreover, three novel biomarkers that are closely associated with mitophagy and have the potential to guide individualised treatment regimens in PAAD were obtained.

Computational Drug Discovery in Patients with Endometriosis-Induced Infertility via Text Mining and Biomedical Databases

Background: Endometriosis (EMT) is the most common benign gynecological disease among women of reproductive age, causing infertility and seriously affects women’s physical and mental health. However, the current treatment was not always effective. This study was designed to use publicly available data to identify drugs targeting the relevant gene with EMT-induced-infertility using computational tools. Methods: EMT and infertility genes were determined by text mining, and the GeneCodis program was used to analyzed gene ontology of the intersection of the two gene sets. A string database was used to analyze the protein-protein interaction network. The Drug-Gene Interaction database is queried for the rich gene set belonging to the identified pathways to find drug candidates that can be used in EMT-induced infertility. Results: Our analysis identified 550 genes common to both the EMT and infertility by text mining. Gene enrichment analysis and protein-protein interaction analysis found 39 genes potentially targetable by a total of 49 drugs that could be formulated for application, which have not been used in EMT-induced infertility. Conclusions: The findings from the present analysis can facilitate the Identification of existing drugs that have the potential of topical administration to improve EMT-induced infertility and present tremendous opportunities to study novel targets pharmacology using in silico text mining and pathway analysis tools. However, all the results were based on online bioinformatics databases, and as such require validation experiments. And some of the drugs highlighted as possibly relevant may be toxic and as such safely data is required before any experiments are undertaken in humans.

Uncovering Hidden Members and Functions of the Soil Microbiome Using De Novo Metaproteomics

Metaproteomics has been increasingly utilized for high-throughput characterization of proteins in complex environments and has been demonstrated to provide insights into microbial composition and functional roles. However, significant challenges remain in metaproteomic data analysis, including creation of a sample-specific protein sequence database. A well-matched database is a requirement for successful metaproteomics analysis, and the accuracy and sensitivity of PSM identification algorithms suffer when the database is incomplete or contains extraneous sequences. When matched DNA sequencing data of the sample is unavailable or incomplete, creating the proteome database that accurately represents the organisms in the sample is a challenge. Here, we leverage a de novo peptide sequencing approach to identify the sample composition directly from metaproteomic data. First, we created a deep learning model, Kaiko, to predict the peptide sequences from mass spectrometry data and trained it on 5 million peptide–spectrum matches from 55 phylogenetically diverse bacteria. After training, Kaiko successfully identified organisms from soil isolates and synthetic communities directly from proteomics data. Finally, we created a pipeline for metaproteome database generation using Kaiko. We tested the pipeline on native soils collected in Kansas, showing that the de novo sequencing model can be employed as an alternative and complementary method to construct the sample-specific protein database instead of relying on (un)matched metagenomes. Our pipeline identified all highly abundant taxa from 16S rRNA sequencing of the soil samples and uncovered several additional species which were strongly represented only in proteomic data.

Construction of circRNA-Mediated Immune-Related ceRNA Network and Identification of Circulating circRNAs as Diagnostic Biomarkers in Acute Ischemic Stroke.

Background and PurposeAccumulating evidence suggests that circular RNAs (circRNAs) are involved in immune and inflammatory processes after acute ischemic stroke (AIS). However, the roles of circRNA-mediated competing endogenous RNA (ceRNA) in modulating immune inflammation of AIS have not yet been determined. This study aimed to construct a circRNA-mediated immune-related ceRNA network and identify novel circRNAs in AIS.MethodsMicroarray data were downloaded from the GEO database and further analysed by R software. Then, we constructed a circRNA-mediated ceRNA network based on interaction information from the bioinformatics database. A topological property analysis of the ceRNA network was conducted to screen novel circRNAs. Finally, we further applied quantitative real-time polymerase chain reaction (qRT-PCR) to two independent sets.ResultsWe constructed an AIS immune-related ceRNA (AISIRC) network containing immune-related genes (IRGs), miRNAs, and circRNAs. Additionally, we extracted the subnetwork from the AISIRC network and screened six immune-related circRNAs. After identification and validation, we finally confirmed that plasma levels of circPTP4A2 and circTLK2 were significantly increased in AIS patients compared with both healthy control subjects (HCs) and transient ischemic attack (TIA) patients. Logistic regression and receiver-operating characteristic (ROC) curve analyses demonstrated that these two circRNAs may function as predictive and discriminative biomarkers for AIS. We also confirmed that plasma levels of circPTP4A2 were elevated in TIA patients compared with HCs and might be an independent risk factor for predicting TIA. Longitudinal analysis of circRNA expression up to 90 days after AIS indicated that the ability of circPTP4A2 and circTLK2 to monitor AIS dynamics was highly desirable.ConclusionIn summary, the circRNA-mediated immune-related ceRNA network was successfully constructed, and two circulating circRNAs (circPTP4A2 and circTLK2) improved sensitivity for the diagnosis of AIS and could be considered diagnostic biomarkers.

Sp1 induced gene TIMP1 is related to immune cell infiltration in glioblastoma

Tumor immune microenvironment exerts a profound effect on the population of infiltrating immune cells. Tissue inhibitor of matrix metalloproteinase 1 (TIMP1) is frequently overexpressed in a variety of cells, particularly during inflammation and tissue injury. However, its function in cancer and immunity remains enigmatic. In this study, we find that TIMP1 is substantially up-regulated during tumorigenesis through analyzing cancer bioinformatics databases, which is further confirmed by IHC tissue microarrays of clinical samples. The TIMP1 level is significantly increased in lymphocytes infiltrating the tumors and correlated with cancer progression, particularly in GBM. Notably, we find that the transcriptional factor Sp1 binds to the promoter of TIMP1 and triggers its expression in GBM. Together, our findings suggest that the Sp1-TIMP1 axis can be a potent biomarker for evaluating immune cell infiltration at the tumor sites and therefore, the malignant progression of GBM.

In-silico functional and structural annotation of hypothetical protein from Klebsiella pneumonia: A potential drug target

Klebsiella pneumonia is known to cause several nosocomial infections in immunocompromised patients. It has developed resistance against a broad range of presently available antibiotics, resulting in high mortality rates in patients and declared an urgent threat. Therefore, exploration of possible novel drug targets against this opportunistic bacteria needs to be undertaken. In the present study, we performed an extensive in-silico analysis for functional and structural annotation and characterized HP CP995_08280 from K. pneumonia as a drug target and aimed to identify potent drug candidates. The functional and structural studies using several bioinformatics tools and databases predicted that HP CP995_08280 is a cytosolic protein that belongs to the β-lactamase family and shares structural similarity with FmtA protein from Staphylococcus aureus (PDB ID: 5ZH8). The structure of HP CP995_08280 was successfully modeled followed by structure-based virtual screening, docking, molecular dynamics, and Molecular mechanic/Poisson–Boltzmann surface area (MMPBSA) were performed to identify the potential compounds. We have found five potent antibacterial molecules, namely BDD 24083171, BDD 24085737, BDE 25098678, BDE 33638819, and BDE 33672484, which exhibited high binding affinity (>−7.5 kcal/mol) and were stabilized by hydrogen bonding and hydrophobic interactions with active site residues (Ser42, Lys45, Tyr126, and Asp128) of protein. Molecular dynamics and MMPBSA revealed that HP CP995_08280 – ligand(s) complexes were less dynamic and more stable than native HP CP995_08280. Hence, the present study may serve as a potential lead for developing inhibitors against drug-resistant Klebsiella pneumonia.

Development of a Novel Sphingolipid Signaling Pathway-Related Risk Assessment Model to Predict Prognosis in Kidney Renal Clear Cell Carcinoma.

This study aimed to explore underlying mechanisms by which sphingolipid-related genes play a role in kidney renal clear cell carcinoma (KIRC) and construct a new prognosis-related risk model. We used a variety of bioinformatics methods and databases to complete our exploration. Based on the TCGA database, we used multiple R-based extension packages for data transformation, processing, and statistical analyses. First, on analyzing the CNV, SNV, and mRNA expression of 29 sphingolipid-related genes in various types of cancers, we found that the vast majority were protective in KIRC. Subsequently, we performed cluster analysis of patients with KIRC using sphingolipid-related genes and successfully classified them into the following three clusters with significant prognostic differences: Cluster 1, Cluster 2, and Cluster 3. We performed differential analyses of transcription factor activity, drug sensitivity, immune cell infiltration, and classical oncogenes to elucidate the unique roles of sphingolipid-related genes in cancer, especially KIRC, and provide a reference for clinical treatment. After analyzing the risk rates of sphingolipid-related genes in KIRC, we successfully established a risk model composed of seven genes using LASSO regression analysis, including SPHK1, CERS5, PLPP1, SGMS1, SGMS2, SERINC1, and KDSR. Previous studies have suggested that these genes play important biological roles in sphingolipid metabolism. ROC curve analysis results showed that the risk model provided good prediction accuracy. Based on this risk model, we successfully classified patients with KIRC into high- and low-risk groups with significant prognostic differences. In addition, we performed correlation analyses combined with clinicopathological data and found a significant correlation between the risk model and patient’s M, T, stage, grade, and fustat. Finally, we developed a nomogram that predicted the 5-, 7-, and 10-year survival in patients with KIRC. The model we constructed had strong predictive ability. In conclusion, we believe that this study provides valuable data and clues for future studies on sphingolipid-related genes in KIRC.

Blockage of PD-L1 by FERMT3-mediated Wnt/β-catenin signalling regulates chemoresistance and immune evasion of colorectal cancer cells.

Colorectal cancer (CRC) constitutes a major public health problem because of the high rate of morbidity and mortality. Chemotherapy and immunotherapy are the major and promising strategies for cancer patients including CRC; nevertheless, chemoresistance and immune escape limit the final efficacy of the above approaches. FERMT3 has proven to exert a critical role in the immune system and has contradictive effects on cancer progression. In this study, bioinformatics database analysis and clinical specimen detection both corroborated the downregulation of FERMT3 in CRC tissues and cells. Of interest, overexpression of FERMT3 suppressed CRC cell invasion and sensitized cells to 5-fluorouracil (5-FU) by reducing cell viability and increasing cell apoptosis and caspase 3 activity. Noticeably, FERMT3 upregulation enhanced natural killer (NK) cells activation by increasing secretions of interferon γ (IFN-γ) and tumour necrosis factor α (TNF-α) when NK cells were co-cultured with CRC cells. Importantly, upregulation of FERMT3 promoted NK cell-mediated killing of CRC cells. Mechanically, FERMT3 inhibited the aberrant activation of Wnt/β-catenin signalling and the subsequent programmed death-ligand 1 (PD-L1) expression in CRC cells. Moreover, knockdown of PD-L1 suppressed CRC cell invasion, 5-FU resistance and NK cells-mediated tumour killing. Additionally, reactivating the Wnt/β-catenin signalling with a specific WNT agonist CAS 853220-52-7 overturned the efficacy of FERMT3 overexpression against CRC cell invasion, 5-FU chemoresistance and cell susceptibility to NK cell-mediated cytotoxicity. Therefore, the current findings substantiate that FERMT3 elevation may attenuate CRC cell chemoresistance and NK cell-mediated immune response to tumour cells by inhibiting Wnt/β-catenin-PD-L1 signalling. Therefore, FERMT3 elevation may be a promising therapeutic approach to overcome chemoresistance and immune evasion in CRC.

Systematic Functional Annotation Workflow for Insects.

Simple SummaryThe functions of all genes encoded in the genome should be studied for genome editing. Genome editing technology can speed up insect research for the functional analysis of genes. Our knowledge about the functional information of genes is still incomplete currently, while the genome sequencing of an organism can be completed. The functional information has been annotated based solely on the information that has been obtained from the results of previous biological research. However, this information will be important in determining the target genes for genome editing. In particular, it is very important that this information is in machine-readable form because computer programs mainly parse this information for the understanding of biological systems. In this paper, we describe a workflow-based method for annotating gene functions in insects that makes use of transcribed sequence information as well as reference genome and protein sequence databases. Using the developed workflow, we annotated the functional information of the Japanese stick insect and silkworm, including gene expression as well as sequence analysis. The functional annotation information obtained by the workflow will greatly expand the possibilities of entomological research using genome editing.Next-generation sequencing has revolutionized entomological study, rendering it possible to analyze the genomes and transcriptomes of non-model insects. However, use of this technology is often limited to obtaining the nucleotide sequences of target or related genes, with many of the acquired sequences remaining unused because other available sequences are not sufficiently annotated. To address this issue, we have developed a functional annotation workflow for transcriptome-sequenced insects to determine transcript descriptions, which represents a significant improvement over the previous method (functional annotation pipeline for insects). The developed workflow attempts to annotate not only the protein sequences obtained from transcriptome analysis but also the ncRNA sequences obtained simultaneously. In addition, the workflow integrates the expression-level information obtained from transcriptome sequencing for application as functional annotation information. Using the workflow, functional annotation was performed on the sequences obtained from transcriptome sequencing of the stick insect (Entoria okinawaensis) and silkworm (Bombyx mori), yielding richer functional annotation information than that obtained in our previous study. The improved workflow allows the more comprehensive exploitation of transcriptome data and is applicable to other insects because the workflow has been openly developed on GitHub.

Identification of a circulating microRNAs biomarker panel for non-invasive diagnosis of coronary artery disease: case–control study

BackgroundCirculating microRNAs (miRNAs) are considered a hot spot of research that can be employed for monitoring and/or diagnostic purposes in coronary artery disease (CAD). Since different disease features might be reflected on altered profiles or plasma miRNAs concentrations, a combination of miRNAs can provide more reliable non-invasive biomarkers for CAD.Subjects and methodsWe investigated a panel of 14-miRNAs selected using bioinformatics databases and current literature searching for miRNAs involved in CAD using quantitative real-time PCR technique in 73 CAD patients compared to 73 controls followed by function and pathway enrichment analysis for the 14-miRNAs.ResultsOur results revealed three out of the 14 circulating miRNAs understudy; miRNAs miR133a, miR155 and miR208a were downregulated. While 11 miRNAs were up-regulated in a descending order from highest fold change to lowest: miR-182, miR-145, miR-21, miR-126, miR-200b, miR-146A, miR-205, miR-135b, miR-196b, miR-140b and, miR-223. The ROC curve analysis indicated that miR-145, miR-182, miR-133a and, miR-205 were excellent biomarkers with the highest AUCs as biomarkers in CAD. All miRNAs under study except miR-208 revealed a statistically significant relation with dyslipidemia. MiR-126 and miR-155 showed significance with BMI grade, while only miR-133a showed significance with the obese patients in general. MiR-135b and miR-140b showed a significant correlation with the Wall Motion Severity Index. Pathway enrichment analysis for the miRNAS understudy revealed pathways relevant to the fatty acid biosynthesis, ECM-receptor interaction, proteoglycans in cancer, and adherens junction.ConclusionThe results of this study identified a differentially expressed circulating miRNAs signature that can discriminate CAD patients from normal subjects. These results provide new insights into the significant role of miRNAs expression associated with CAD pathogenesis.

Aberrantly methylated-differentially expressed genes and related pathways in cholangiocarcinoma.

This study aimed to explore aberrantly methylated-differentially expressed genes and related pathways in cholangiocarcinoma (CCA).The mRNA expression data (GSE26566) and methylation profiling data (GSE44965) were collected from the Gene Expression Omnibus (GEO) Datasets. Differentially expressed genes and differentially methylated genes were identified using GEO2R. Gene ontology analysis and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were performed using clusterprofiler in R. MCODE clustering tool was used to screen modules of the protein–protein interaction network in Cytoscape. Related pathways of hub gene by using gene set enrichment analysis.Eighty-one hypermethylated, lowly expressed genes (Hyper-LGs) and 76 hypomethylated, highly expressed genes (Hypo-HGs) were identified in this study. Hyper-LGs were enriched in ion channel binding and transcription factor activity, which was associated with Mineral absorption and Cell adhesion molecules. Hypo-HGs were enriched in cysteine-type endopeptidase activity, which was associated with Sphingolipid signaling pathway and T cell receptor signaling pathway. Based on protein–protein interaction networks, MYC and VWF were identified as hub genes for Hyper-LGs, and no hub genes for Hypo-HGs.This study found methylated-differentially expressed genes and signaling pathways that are connected with the CCA by using a series of bioinformatics databases and tools. MYC and VWF act as hub genes of CCA, which can be used as biomarkers based on aberrant methylation for the accurate diagnosis and treatment of CCA.