Microbiome modeling: a beginner's guide.

Microbiomes, comprised of diverse microbial species and viruses, play pivotal roles in human health, environmental processes, and biotechnological applications and interact with each other, their environment, and hosts via ecological interactions. Our understanding of microbiomes is still limited and hampered by their complexity. A concept improving this understanding is systems biology, which focuses on the holistic description of biological systems utilizing experimental and computational methods. An important set of such experimental methods are metaomics methods which analyze microbiomes and output lists of molecular features. These lists of data are integrated, interpreted, and compiled into computational microbiome models, to predict, optimize, and control microbiome behavior. There exists a gap in understanding between microbiologists and modelers/bioinformaticians, stemming from a lack of interdisciplinary knowledge. This knowledge gap hinders the establishment of computational models in microbiome analysis. This review aims to bridge this gap and is tailored for microbiologists, researchers new to microbiome modeling, and bioinformaticians. To achieve this goal, it provides an interdisciplinary overview of microbiome modeling, starting with fundamental knowledge of microbiomes, metaomics methods, common modeling formalisms, and how models facilitate microbiome control. It concludes with guidelines and repositories for modeling. Each section provides entry-level information, example applications, and important references, serving as a valuable resource for comprehending and navigating the complex landscape of microbiome research and modeling.

A comprehensive multi-omics analysis reveals unique signatures to predict Alzheimer's disease.

Background: Complex disorders, such as Alzheimer's disease (AD), result from the combined influence of multiple biological and environmental factors. The integration of high-throughput data from multiple omics platforms can provide system overviews, improving our understanding of complex biological processes underlying human disease. In this study, integrated data from four omics platforms were used to characterise biological signatures of AD. Method: The study cohort consists of 455 participants (Control:148, Cases:307) from the Religious Orders Study and Memory and Aging Project (ROSMAP). Genotype (SNP), methylation (CpG), RNA and proteomics data were collected, quality-controlled and pre-processed (SNP = 130; CpG = 83; RNA = 91; Proteomics = 119). Using a diagnosis of Mild Cognitive Impairment (MCI)/AD combined as the target phenotype, we first used Partial Least Squares Regression as an unsupervised classification framework to assess the prediction capabilities for each omics dataset individually. We then used a variation of the sparse generalized canonical correlation analysis (sGCCA) to assess predictions of the combined datasets and identify multi-omics signatures characterising each group of participants. Results: Analysing datasets individually we found methylation data provided the best predictions with an accuracy of 0.63 (95%CI = [0.54-0.71]), followed by RNA, 0.61 (95%CI = [0.52-0.69]), SNP, 0.59 (95%CI = [0.51-0.68]) and proteomics, 0.58 (95%CI = [0.51-0.67]). After integration of the four datasets, predictions were dramatically improved with a resulting accuracy of 0.95 (95% CI = [0.89-0.98]). Conclusion: The integration of data from multiple platforms is a powerful approach to explore biological systems and better characterise the biological signatures of AD. The results suggest that integrative methods can identify biomarker panels with improved predictive performance compared to individual platforms alone. Further validation in independent cohorts is required to validate and refine the results presented in this study.

Bioinformatics and systems biology approaches to identify potential common pathogeneses for sarcopenia and osteoarthritis.

Sarcopenia, a geriatric syndrome characterized by progressive loss of muscle mass and strength, and osteoarthritis, a common degenerative joint disease, are both prevalent in elderly individuals. However, the relationship and molecular mechanisms underlying these two diseases have not been fully elucidated. In this study, we screened microarray data from the Gene Expression Omnibus to identify associations between sarcopenia and osteoarthritis. We employed multiple statistical methods and bioinformatics tools to analyze the shared DEGs (differentially expressed genes). Additionally, we identified 8 hub genes through functional enrichment analysis, protein-protein interaction analysis, transcription factor-gene interaction network analysis, and TF-miRNA coregulatory network analysis. We also discovered potential shared pathways between the two diseases, such as transcriptional misregulation in cancer, the FOXO signalling pathway, and endometrial cancer. Furthermore, based on common DEGs, we found that strophanthidin may be an optimal drug for treating sarcopenia and osteoarthritis, as indicated by the Drug Signatures database. Immune infiltration analysis was also performed on the sarcopenia and osteoarthritis datasets. Finally, receiver operating characteristic (ROC) curves were plotted to verify the reliability of our results. Our findings provide a theoretical foundation for future research on the potential common pathogenesis and molecular mechanisms of sarcopenia and osteoarthritis.

Deciphering the shared mechanisms of Gegen Qinlian Decoction in treating type 2 diabetes and ulcerative colitis via bioinformatics and machine learning.

Background: Although previous clinical studies and animal experiments have demonstrated the efficacy of Gegen Qinlian Decoction (GQD) in treating Type 2 Diabetes Mellitus (T2DM) and Ulcerative Colitis (UC), the underlying mechanisms of its therapeutic effects remain elusive. Purpose: This study aims to investigate the shared pathogenic mechanisms between T2DM and UC and elucidate the mechanisms through which GQD modulates these diseases using bioinformatics approaches. Methods: Data for this study were sourced from the Gene Expression Omnibus (GEO) database. Targets of GQD were identified using PharmMapper and SwissTargetPrediction, while targets associated with T2DM and UC were compiled from the DrugBank, GeneCards, Therapeutic Target Database (TTD), DisGeNET databases, and differentially expressed genes (DEGs). Our analysis encompassed six approaches: weighted gene co-expression network analysis (WGCNA), immune infiltration analysis, single-cell sequencing analysis, machine learning, DEG analysis, and network pharmacology. Results: Through GO and KEGG analysis of weighted gene co-expression network analysis (WGCNA) modular genes and DEGs intersection, we found that the co-morbidity between T2DM and UC is primarily associated with immune-inflammatory pathways, including IL-17, TNF, chemokine, and toll-like receptor signaling pathways. Immune infiltration analysis supported these findings. Three distinct machine learning studies identified IGFBP3 as a biomarker for GQD in treating T2DM, while BACE2, EPHB4, and EPHA2 emerged as biomarkers for GQD in UC treatment. Network pharmacology revealed that GQD treatment for T2DM and UC mainly targets immune-inflammatory pathways like Toll-like receptor, IL-17, TNF, MAPK, and PI3K-Akt signaling pathways. Conclusion: This study provides insights into the shared pathogenesis of T2DM and UC and clarifies the regulatory mechanisms of GQD on these conditions. It also proposes novel targets and therapeutic strategies for individuals suffering from T2DM and UC.

Bioinformatics proficiency among African students.

Bioinformatics, the interdisciplinary field that combines biology, computer science, and data analysis, plays a pivotal role in advancing our understanding of life sciences. In the African context, where the diversity of biological resources and healthcare challenges is substantial, fostering bioinformatics literacy and proficiency among students is important. This perspective provides an overview of the state of bioinformatics literacy among African students, highlighting the significance, challenges, and potential solutions in addressing this critical educational gap. It proposes various strategies to enhance bioinformatics literacy among African students. These include expanding educational resources, fostering collaboration between institutions, and engaging students in research projects. By addressing the current challenges and implementing comprehensive strategies, African students can harness the power of bioinformatics to contribute to innovative solutions in healthcare, agriculture, and biodiversity conservation, ultimately advancing the continent's scientific capabilities and improving the quality of life for her people. In conclusion, promoting bioinformatics literacy among African students is imperative for the continent's scientific development and advancing frontiers of biological research.

Comprehensive identification and expression analysis of FAR1/FHY3 genes under drought stress in maize (Zea mays L.).

Background: FAR1/FHY3 transcription factors are derived from transposase, which play important roles in light signal transduction, growth and development, and response to stress by regulating downstream gene expression. Although many FAR1/FHY3 members have been identified in various species, the FAR1/FHY3 genes in maize are not well characterized and their function in drought are unknown. Method: The FAR1/FHY3 family in the maize genome was identified using PlantTFDB, Pfam, Smart, and NCBI-CDD websites. In order to investigate the evolution and functions of FAR1 genes in maize, the information of protein sequences, chromosome localization, subcellular localization, conserved motifs, evolutionary relationships and tissue expression patterns were analyzed by bioinformatics, and the expression patterns under drought stress were detected by quantitative real-time polymerase chain reaction (qRT-PCR). Results: A total of 24 ZmFAR members in maize genome, which can be divided into five subfamilies, with large differences in protein and gene structures among subfamilies. The promoter regions of ZmFARs contain abundant abiotic stress-responsive and hormone-respovensive cis-elements. Among them, drought-responsive cis-elements are quite abundant. ZmFARs were expressed in all tissues detected, but the expression level varies widely. The expression of ZmFARs were mostly down-regulated in primary roots, seminal roots, lateral roots, and mesocotyls under water deficit. Most ZmFARs were down-regulated in root after PEG-simulated drought stress. Conclusions: We performed a genome-wide and systematic identification of FAR1/FHY3 genes in maize. And most ZmFARs were down-regulated in root after drought stress. These results indicate that FAR1/FHY3 transcription factors have important roles in drought stress response, which can lay a foundation for further analysis of the functions of ZmFARs in response to drought stress.

Single Cell Transcriptome Analysis During Development in Dictyostelium.

Dictyostelium represents a stripped-down model for understanding how cells make decisions during development. The complete life cycle takes around a day and the fully differentiated structure is composed of only two major cell types. With this apparent reduction in "complexity," single cell transcriptomics has proven to be a valuable tool in defining the features of developmental transitions and cell fate separation events, even providing causal information on how mechanisms of gene expression can feed into cell decision-making. These scientific outputs have been strongly facilitated by the ease of non-disruptive single cell isolation-allowing access to more physiological measures of transcript levels. In addition, the limited number of cell states during development allows the use of more straightforward analysis tools for handling the ensuing large datasets, which provides enhanced confidence in inferences made from the data. In this chapter, we will outline the approaches we have used for handling Dictyostelium single cell transcriptomic data, illustrating how these approaches have contributed to our understanding of cell decision-making during development.

Protocol for the construction and functional profiling of metagenome-assembled genomes for microbiome analyses.

Constructing metagenome-assembled genomes (MAGs) from complex metagenomic samples involves a series of bioinformatics operations, each requiring deep bioinformatics knowledge. Here, we present a protocol for constructing MAGs and conducting functional profiling to address biological questions. We describe steps for system configuration, data downloads, read processing, removal of human DNA contamination, metagenomic assembly, and statistical quality assessment of the final assembly. Additionally, we detail procedures for the construction and refinement of MAGs, as well as the functional profiling of MAGs.

A graph neural network approach for predicting drug susceptibility in the human microbiome.

Recent studies have illuminated the critical role of the human microbiome in maintaining health and influencing the pharmacological responses of drugs. Clinical trials, encompassing approximately 150 drugs, have unveiled interactions with the gastrointestinal microbiome, resulting in the conversion of these drugs into inactive metabolites. It is imperative to explore the field of pharmacomicrobiomics during the early stages of drug discovery, prior to clinical trials. To achieve this, the utilization of machine learning and deep learning models is highly desirable. In this study, we have proposed graph-based neural network models, namely GCN, GAT, and GINCOV models, utilizing the SMILES dataset of drug microbiome. Our primary objective was to classify the susceptibility of drugs to depletion by gut microbiota. Our results indicate that the GINCOV surpassed the other models, achieving impressive performance metrics, with an accuracy of 93% on the test dataset. This proposed Graph Neural Network (GNN) model offers a rapid and efficient method for screening drugs susceptible to gut microbiota depletion and also encourages the improvement of patient-specific dosage responses and formulations.

Resistin as a potential diagnostic biomarker for sepsis: insights from DIA and ELISA analyses.

PURPOSE: The primary objective of this investigation is to systematically screen and identify differentially expressed proteins (DEPs) within the plasma of individuals afflicted with sepsis. This endeavor employs both Data-Independent Acquisition (DIA) and enzyme-linked immunosorbent assay (ELISA) methodologies. The overarching goal is to furnish accessible and precise serum biomarkers conducive to the diagnostic discernment of sepsis. METHOD: The study encompasses 53 sepsis patients admitted to the Affiliated Hospital of Southwest Medical University between January 2019 and December 2020, alongside a control cohort consisting of 16 individuals devoid of sepsis pathology. Subsequently, a subset comprising 10 randomly selected subjects from the control group and 22 from the sepsis group undergoes quantitative proteomic analysis via DIA. The acquired data undergoes Gene Ontology (GO) and Kyoto Encyclopedia of Genes (KEGG) analyses, facilitating the construction of a Protein-Protein Interaction (PPI) network to discern potential markers. Validation of core proteins is then accomplished through ELISA. Comparative analysis between the normal and sepsis groups ensues, characterized by Receiver Operating Characteristic (ROC) curve construction to evaluate diagnostic efficacy. RESULT: A total of 187 DEPs were identified through bioinformatic methodologies. Examination reveals their predominant involvement in biological processes such as wound healing, coagulation, and blood coagulation. Functional pathway analysis further elucidates their engagement in the complement pathway and malaria. Resistin emerges as a candidate plasma biomarker, subsequently validated through ELISA. Notably, the protein exhibits significantly elevated levels in the serum of sepsis patients compared to the normal control group. ROC curve analysis underscores the robust diagnostic capacity of these biomarkers for sepsis. CONCLUSION: Data-Independent Acquisition (DIA) and Enzyme-Linked Immunosorbent Assay (ELISA) show increased Resistin levels in sepsis patients, suggesting diagnostic potential, warranting further research.

Identification of immune-associated biomarkers of diabetes nephropathy tubulointerstitial injury based on machine learning: a bioinformatics multi-chip integrated analysis.

BACKGROUND: Diabetic nephropathy (DN) is a major microvascular complication of diabetes and has become the leading cause of end-stage renal disease worldwide. A considerable number of DN patients have experienced irreversible end-stage renal disease progression due to the inability to diagnose the disease early. Therefore, reliable biomarkers that are helpful for early diagnosis and treatment are identified. The migration of immune cells to the kidney is considered to be a key step in the progression of DN-related vascular injury. Therefore, finding markers in this process may be more helpful for the early diagnosis and progression prediction of DN. METHODS: The gene chip data were retrieved from the GEO database using the search term ' diabetic nephropathy '. The ' limma ' software package was used to identify differentially expressed genes (DEGs) between DN and control samples. Gene set enrichment analysis (GSEA) was performed on genes obtained from the molecular characteristic database (MSigDB. The R package 'WGCNA' was used to identify gene modules associated with tubulointerstitial injury in DN, and it was crossed with immune-related DEGs to identify target genes. Gene ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed on differentially expressed genes using the 'ClusterProfiler' software package in R. Three methods, least absolute shrinkage and selection operator (LASSO), support vector machine recursive feature elimination (SVM-RFE) and random forest (RF), were used to select immune-related biomarkers for diagnosis. We retrieved the tubulointerstitial dataset from the Nephroseq database to construct an external validation dataset. Unsupervised clustering analysis of the expression levels of immune-related biomarkers was performed using the 'ConsensusClusterPlus 'R software package. The urine of patients who visited Dongzhimen Hospital of Beijing University of Chinese Medicine from September 2021 to March 2023 was collected, and Elisa was used to detect the mRNA expression level of immune-related biomarkers in urine. Pearson correlation analysis was used to detect the effect of immune-related biomarker expression on renal function in DN patients. RESULTS: Four microarray datasets from the GEO database are included in the analysis : GSE30122, GSE47185, GSE99340 and GSE104954. These datasets included 63 DN patients and 55 healthy controls. A total of 9415 genes were detected in the data set. We found 153 differentially expressed immune-related genes, of which 112 genes were up-regulated, 41 genes were down-regulated, and 119 overlapping genes were identified. GO analysis showed that they were involved in various biological processes including leukocyte-mediated immunity. KEGG analysis showed that these target genes were mainly involved in the formation of phagosomes in Staphylococcus aureus infection. Among these 119 overlapping genes, machine learning results identified AGR2, CCR2, CEBPD, CISH, CX3CR1, DEFB1 and FSTL1 as potential tubulointerstitial immune-related biomarkers. External validation suggested that the above markers showed diagnostic efficacy in distinguishing DN patients from healthy controls. Clinical studies have shown that the expression of AGR2, CX3CR1 and FSTL1 in urine samples of DN patients is negatively correlated with GFR, the expression of CX3CR1 and FSTL1 in urine samples of DN is positively correlated with serum creatinine, while the expression of DEFB1 in urine samples of DN is negatively correlated with serum creatinine. In addition, the expression of CX3CR1 in DN urine samples was positively correlated with proteinuria, while the expression of DEFB1 in DN urine samples was negatively correlated with proteinuria. Finally, according to the level of proteinuria, DN patients were divided into nephrotic proteinuria group (n = 24) and subrenal proteinuria group. There were significant differences in urinary AGR2, CCR2 and DEFB1 between the two groups by unpaired t test (P < 0.05). CONCLUSIONS: Our study provides new insights into the role of immune-related biomarkers in DN tubulointerstitial injury and provides potential targets for early diagnosis and treatment of DN patients. Seven different genes ( AGR2, CCR2, CEBPD, CISH, CX3CR1, DEFB1, FSTL1 ), as promising sensitive biomarkers, may affect the progression of DN by regulating immune inflammatory response. However, further comprehensive studies are needed to fully understand their exact molecular mechanisms and functional pathways in DN.

Comprehensive transcriptomic analysis identifies SLC25A4 as a key predictor of prognosis in osteosarcoma.

Background: Osteosarcoma (OS) is highly malignant and prone to local infiltration and distant metastasis. Due to the poor outcomes of OS patients, the study aimed to identify differentially expressed genes (DEGs) in OS and explore their role in the carcinogenesis and progression of OS. Methods: RNA sequencing was performed to identify DEGs in OS. The functions of the DEGs in OS were investigated using bioinformatics analysis, and DEG expression was verified using RT-qPCR and Western blotting. The role of SLC25A4 was evaluated using gene set enrichment analysis (GSEA) and then investigated using functional assays in OS cells. Results: In all, 8353 DEGs were screened. GO and KEGG enrichment analyses indicated these DEGs showed strong enrichment in the calcium signaling pathway and pathways in cancer. Moreover, the Kaplan-Meier survival analysis showed ten hub genes were related to the outcomes of OS patients. Both SLC25A4 transcript and protein expression were significantly reduced in OS, and GSEA suggested that SLC25A4 was associated with cell cycle, apoptosis and inflammation. SLC25A4-overexpressing OS cells exhibited suppressed proliferation, migration, invasion and enhanced apoptosis. Conclusion: SLC25A4 was found to be significantly downregulated in OS patients, which was associated with poor prognosis. Modulation of SLC25A4 expression levels may be beneficial in OS treatment.

Proceedings of the EuBIC-MS developers meeting 2023.

The 2023 European Bioinformatics Community for Mass Spectrometry (EuBIC-MS) Developers Meeting was held from January 15th to January 20th, 2023, in Congressi Stefano Franscin at Monte Verità in Ticino, Switzerland. The participants were scientists and developers working in computational mass spectrometry (MS), metabolomics, and proteomics. The 5-day program was split between introductory keynote lectures and parallel hackathon sessions focusing on "Artificial Intelligence in proteomics" to stimulate future directions in the MS-driven omics areas. During the latter, the participants developed bioinformatics tools and resources addressing outstanding needs in the community. The hackathons allowed less experienced participants to learn from more advanced computational MS experts and actively contribute to highly relevant research projects. We successfully produced several new tools applicable to the proteomics community by improving data analysis and facilitating future research.

Progress based on a multi-omics research strategy in the biosynthesis and modernization of active ingredients of Herpetospermum pedunculosum seeds.

Herpetospermum pedunculosum seeds also known as Herpetospermum caudigerum Wall. is the mature seed of the Herpetospermum pedunculosum(Ser.) C. B. Clarke,Cucurbitaceae. Modern pharmacological studies have shown that H. pedunculosum has hepatoprotective, anti-inflammatory, anti-gout and antibacterial pharmacological activities. The biologically active chemical components include lignin compounds such as Herpetin, Herpetetrone, Herpetoriol and so on. The natural product displays considerable skeletal diversity and structural complexity, offering significant opportunities for novel drug discovery. Based on the multi-omics research strategy and the 'gene-protein-metabolite' research framework, the biosynthetic pathway of terpenoids and lignans in H. pedunculosum has has been elucidated at multiple levels. These approaches provide comprehensive genetic information for cloning and identification of pertinent enzyme genes. Furthermore, the application of multi-omics integrative approaches provides a scientific means to elucidate entire secondary metabolic pathways. We investigated the biosynthetic pathways of lignin and terpene components in H. pedunculosum and conducted bioinformatics analysis of the crucial enzyme genes involved in the biosynthetic process using genomic and transcriptomic data. We identified candidate genes for six key enzymes in the biosynthetic pathway. This review reports on the current literature on pharmacological investigations of H. pedunculosum, proposing its potential as an antidiabetic agent. Moreover, we conclude, for the first time, the identification of key enzyme genes potentially involved in the biosynthesis of active compounds in H. pedunculosum. This review provides a scientific foundation for the discovery of novel therapeutic agents from natural sources.

Bioinformatic prediction of the stereoselectivity of modular polyketide synthase: an update of the sequence motifs in ketoreductase domain.

Polyketides are a major class of natural products, including bioactive medicines such as erythromycin and rapamycin. They are often rich in stereocenters biosynthesized by the ketoreductase (KR) domain within the polyketide synthase (PKS) assembly line. Previous studies have identified conserved motifs in KR sequences that enable the bioinformatic prediction of product stereochemistry. However, the reliability and applicability of these prediction methods have not been thoroughly assessed. In this study, we conducted a comprehensive bioinformatic analysis of 1,762 KR sequences from cis-AT PKSs to reevaluate the residues involved in conferring stereoselectivity. Our findings indicate that the previously identified fingerprint motifs remain valid for KRs in ß-modules from actinobacteria, but their reliability diminishes for KRs from other module types or taxonomic origins. Additionally, we have identified several new motifs that exhibit a strong correlation with the stereochemical outcomes of KRs. These updated fingerprint motifs for stereochemical prediction not only enhance our understanding of the enzymatic mechanisms governing stereocontrol but also facilitate accurate stereochemical prediction and genome mining of polyketides derived from modular cis-AT PKSs.

Bioinformatics and system biology approach to identify potential common pathogenesis for COVID-19 infection and sarcopenia.

Sarcopenia is a condition characterized by age-related loss of muscle mass and strength. Increasing evidence suggests that patients with sarcopenia have higher rates of coronavirus 2019 (COVID-19) infection and poorer post-infection outcomes. However, the exact mechanism and connections between the two is unknown. In this study, we used high-throughput data from the GEO database for sarcopenia (GSE111016) and COVID-19 (GSE171110) to identify common differentially expressed genes (DEGs). We conducted GO and KEGG pathway analyses, as well as PPI network analysis on these DEGs. Using seven algorithms from the Cytoscape plug-in cytoHubba, we identified 15 common hub genes. Further analyses included enrichment, PPI interaction, TF-gene and miRNA-gene regulatory networks, gene-disease associations, and drug prediction. Additionally, we evaluated immune cell infiltration with CIBERSORT and assessed the diagnostic accuracy of hub genes for sarcopenia and COVID-19 using ROC curves. In total, we identified 66 DEGs (34 up-regulated and 32 down-regulated) and 15 hub genes associated with sarcopenia and COVID-19. GO and KEGG analyses revealed functions and pathways between the two diseases. TF-genes and TF-miRNA regulatory network suggest that FOXOC1 and hsa-mir-155-5p may be identified as key regulators, while gene-disease analysis showed strong correlations with hub genes in schizophrenia and bipolar disorder. Immune infiltration showed a correlation between the degree of immune infiltration and the level of infiltration of different immune cell subpopulations of hub genes in different datasets. The ROC curves for ALDH1L2 and KLF5 genes demonstrated their potential as diagnostic markers for both sarcopenia and COVID-19. This study suggests that sarcopenia and COVID-19 may share pathogenic pathways, and these pathways and hub genes offer new targets and strategies for early diagnosis, effective treatment, and tailored therapies for sarcopenia patients with COVID-19.

The association between vitamin D and the progression of diabetic nephropathy: insights into potential mechanisms.

Aims: Vitamin D deficiency (VDD) is prevalent in the population, with inadequate intake, impaired absorption and metabolism as the main causative factors. VDD increases the risk of developing chronic diseases such as type 2 diabetes mellitus (T2DM) and diabetic nephropathy (DN), but the molecular mechanisms underlying this phenomenon are not known. The aim of this study was to investigate the association and potential mechanisms of vitamin D levels with the progression of DN by analyzing general clinical data and using bioinformatics methods. Methods: The study included 567 diabetes mellitus type 2 (T2DM) patients from the Rocket Force Characteristic Medical Center as the case group and 221 healthy examinees as the normal control group. T2DM patients were categorized into T2DM, early diabetic nephropathy (EDN), and advanced diabetic nephropathy (ADN) based on the progression of diabetic nephropathy. The renal RNA-seq and scRNA-seq data of patients with DN were mined from public databases, and the differential expression of vitamin D-related genes in normal-EDN-ADN was analyzed by bioinformatics method, protein interaction network was constructed, immune infiltration was evaluated, single cell map was drawn, and potential mechanisms of VD and DN interaction were explored. Results: Chi-square test showed that vitamin D level was significantly negatively correlated with DN progression (p < 0.001). Bioinformatics showed that the expression of vitamin D-related cytochrome P450 family genes was down-regulated, and TLR4 and other related inflammatory genes were abnormally up-regulated with the progression of DN. Vitamin D metabolism disturbance up-regulate "Nf-Kappa B signaling pathway," B cell receptor signaling pathway and other immune regulation and insulin resistance related pathways, and inhibit a variety of metabolic pathways. In addition, vitamin D metabolism disturbance are strongly associated with the development of diabetic cardiomyopathy and several neurological disease complications. Conclusion: VDD or vitamin D metabolism disturbance is positively associated with the severity of renal injury. The mechanisms may involve abnormal regulation of the immune system by vitamin D metabolism disturbance, metabolic suppression, upregulation of insulin resistance and inflammatory signalling pathways.

How does the age of control individuals hinder the identification of target genes for Huntington's disease?

Several studies have compared the transcriptome across various brain regions in Huntington's disease (HD) gene-positive and neurologically normal individuals to identify potential differentially expressed genes (DEGs) that could be pharmaceutical or prognostic targets for HD. Despite adhering to technical recommendations for optimal RNA-Seq analysis, none of the genes identified as upregulated in these studies have yet demonstrated success as prognostic or therapeutic targets for HD. Earlier studies included samples from neurologically normal individuals older than the HD gene-positive group. Considering the gradual transcriptional changes induced by aging in the brain, we posited that utilizing samples from older controls could result in the misidentification of DEGs. To validate our hypothesis, we reanalyzed 146 samples from this study, accessible on the SRA database, and employed Propensity Score Matching (PSM) to create a "virtual" control group with a statistically comparable age distribution to the HD gene-positive group. Our study underscores the adverse impact of using neurologically normal individuals over 75 as controls in gene differential expression analysis, resulting in false positives and negatives. We conclusively demonstrate that using such old controls leads to the misidentification of DEGs, detrimentally affecting the discovery of potential pharmaceutical and prognostic markers. This underscores the pivotal role of considering the age of control samples in RNA-Seq analysis and emphasizes its inclusion in evaluating best practices for such investigations. Although our primary focus is HD, our findings suggest that judiciously selecting age-appropriate control samples can significantly improve best practices in differential expression analysis.

Genomic characterization revealing the high rate of tet(X4)-positive Escherichia coli in animals associated with successful genetic elements.

Introduction: The rapid spread of plasmid-mediated tet(X4) conferring high tigecycline resistance poses a significant threat to public health. Escherichia coli as the most common pathogen which carries tet(X4) has been widely disseminated in China. Thus, comprehensive investigations are required to understand the mechanism of transmission of tet(X4)-positive E. coli. Methods: In this study, a total of 775 nonduplicate samples were collected in Guangdong, China from 2019 to 2020. We screened for tet(X4)-positive E. coli by PCR amplification and species identification. Furthermore, we analyzed the phylogenetics and genetic context of tet(X4)-positive E. coli through whole-genome sequencing and long-reads sequencing. Results: Overall, 146 (18.84%) tet(X4)-positive E. coli were isolated, comprising 2 isolates from humans and 144 isolates from pigs. The majority of tet(X4)-positive E. coli exhibited resistance to multiple antibiotics but all of them were susceptible to amikacin and colistin. Phylogenetic analysis showed that ST877, ST871, and ST195 emerged as the predominant sequence types in tet(X4)-positive E. coli. Further analysis revealed various genetic environments associated with the horizontal transfer of tet(X4). Notably, a 100-kbp large fragment insertion was discovered downstream of tet(X4), containing a replicon and a 40-kbp gene cluster for the bacterial type IV secretion system. Discussion: The high colonization rate of tet(X4)-positive E. coli in animals suggests that colonization as a key factor in its dissemination to humans. Diverse genetic context may contribute to the transfer of tet(X4). Our findings underline the urgent need for controlling the spread of plasmid-mediated tigecycline resistance.

Systematic analysis of the expression profiles and prognostic significance of the MED gene family in renal clear cell carcinoma.

The mediator complex (MED) family is a contributing factor in the regulation of transcription and proliferation of cells, and is closely associated with the development of various types of cancer. However, the significance of the expression levels and prognostic value of MED genes in kidney renal clear cell carcinoma (KIRC) have rarely been reported. The present study analyzed the expression and prognostic potential of MED genes in KIRC. The Search Tool for the Retrieval of Interacting Genes/Proteins was used to construct the protein-protein interaction network (PPI), the Assistant for Clinical Bioinformatics database was used to perform correlation analysis, GEPIA 2 was utilized to draw the Kaplan-Meier plot and analyze prognostic significance and the Tumor Immune Estimation Resource was used to assess the association of MED genes with the infiltration of immune cells in patients with KIRC. A total of 30 MED genes were identified, and among these genes, 11 were selected for the creation of a prognostic gene signature based on the results of a LASSO Cox regression analysis. Furthermore, according to univariate and multivariate analyses, MED7, MED16, MED21, MED25 and MED29 may be valuable independent predictive biomarkers for the prognosis of individuals with KIRC. Furthermore, there were significant differences in the expression levels of MED7, MED21 and MED25 in KIRC among different tumor grades. Additionally, patients with KIRC with high transcription levels of MED7, MED21 and MED29 had considerably longer overall survival times. The expression levels of MED genes were also linked to the infiltration of several immune cells. Overall, MED genes may have potential significance in predicting the prognosis of patients with KIRC.