Architecture and dynamics of the abscisic acid gene regulatory network.

The plant hormone abscisic acid (ABA) regulates essential processes in plant development and responsiveness to abiotic and biotic stresses. ABA perception triggers a post-translational signaling cascade that elicits the ABA gene regulatory network (GRN), encompassing hundreds of transcription factors (TFs) and thousands of transcribed genes. To further our knowledge of this GRN, we performed an RNA-seq time series experiment consisting of 14 time points in the 16 h following a one-time ABA treatment of 5-week-old Arabidopsis rosettes. During this time course, ABA rapidly changed transcription levels of 7151 genes, which were partitioned into 44 coexpressed modules that carry out diverse biological functions. We integrated our time-series data with publicly available TF-binding site data, motif data, and RNA-seq data of plants inhibited in translation, and predicted (i) which TFs regulate the different coexpression clusters, (ii) which TFs contribute the most to target gene amplitude, (iii) timing of engagement of different TFs in the ABA GRN, and (iv) hierarchical position of TFs and their targets in the multi-tiered ABA GRN. The ABA GRN was found to be highly interconnected and regulated at different amplitudes and timing by a wide variety of TFs, of which the bZIP family was most prominent, and upregulation of genes encompassed more TFs than downregulation. We validated our network models in silico with additional public TF-binding site data and transcription data of selected TF mutants. Finally, using a drought assay we found that the Trihelix TF GT3a is likely an ABA-induced positive regulator of drought tolerance.

Identification and validation of immunity- and disulfidptosis-related genes signature for predicting prognosis in ovarian cancer.

Background: Ovarian cancer (OC) ranks as the fifth most prevalent neoplasm in women and exhibits an unfavorable prognosis. To improve the OC patient's prognosis, a pioneering risk signature was formulated by amalgamating disulfidptosis-related genes. Methods: A comparative analysis of OC tissues and normal tissues was carried out, and differentially expressed disulfidptosis-related genes (DRGs) were found using the criteria of |log2 (fold change) | > 0.585 and adjusted P-value <0.05. Subsequently, the TCGA training set was utilized to create a prognostic risk signature, which was validated by employing both the TCGA testing set and the GEO dataset. Moreover, the immune cell infiltration, mutational load, response to chemotherapy, and response to immunotherapy were analyzed. To further validate these findings, QRT-PCR analysis was conducted on ovarian tumor cell lines. Results: A risk signature was created using fourteen differentially expressed genes (DEGs) associated with disulfidptosis, enabling the classification of ovarian cancer (OC) patients into high-risk group (HRG) and low-risk group (LRG). The HRG exhibited a lower overall survival (OS) compared to the LRG. In addition, the risk score remained an independent predictor even after incorporating clinical factors. Furthermore, the LRG displayed lower stromal, immune, and estimated scores compared to the HRG, suggesting a possible connection between the risk signature, immune cell infiltration, and mutational load. Finally, the QRT-PCR experiments revealed that eight genes were upregulated in the human OC cell line SKOV3 compared with the human normal OC line IOSE80, while six genes were down-regulated. Conclusions: A fourteen-biomarker signature composed of disulfidptosis-related genes could serve as a valuable risk stratification tool in OC, facilitating the identification of patients who may benefit from individualized treatment and follow-up management.

A pan-cancer analysis of EphA family gene expression and its association with prognosis, tumor microenvironment, and therapeutic targets.

Background: Erythropoietin-producing human hepatocellular (Eph) receptors stand out as the most expansive group of receptor tyrosine kinases (RTKs). Accumulating evidence suggests that within this expansive family, the EphA subset is implicated in driving cancer cell progression, proliferation, invasion, and metastasis, making it a promising target for anticancer treatment. Nonetheless, the extent of EphA family involvement across diverse cancers, along with its intricate interplay with immunity and the tumor microenvironment (TME), remains to be fully illuminated. Methods: The relationships between EphA gene expression and patient survival, immunological subtypes, and TME characteristics were investigated based on The Cancer Genome Atlas (TCGA) database. The analyses employed various R packages. Results: A significant difference in expression was identified for most EphA genes when comparing cancer tissues and non-cancer tissues. These genes independently functioned as prognostic factors spanning multiple cancer types. Moreover, a significant correlation surfaced between EphA gene expression and immune subtypes, except for EphA5, EphA6, and EphA8. EphA3 independently influenced the prognosis of papillary renal cell carcinoma (KIRP). This particular gene exhibited links with immune infiltration subtypes and clinicopathologic parameters, holding promise as a valuable biomarker for predicting prognosis and responsiveness to immunotherapy in patients with KIRP. Conclusion: By meticulously scrutinizing the panorama of EphA genes in a spectrum of cancers, this study supplemented a complete map of the effect of EphA family in Pan-cancer and suggested that EphA family may be a potential target for cancer therapy.

Identification of osteoblastic autophagy-related genes for predicting diagnostic markers in osteoarthritis.

The development of osteoarthritis (OA) involves subchondral bone lesions, but the role of osteoblastic autophagy-related genes (ARGs) in osteoarthritis is unclear. Through integrated analysis of single-cell dataset, Bulk RNA dataset, and 367 ARGs extracted from GeneCards, 40 ARGs were found. By employing multiple machine learning algorithms and PPI networks, three key genes (DDIT3, JUN, and VEGFA) were identified. Then the RF model constructed from these genes indicated great potential as a diagnostic tool. Furthermore, the model's effectiveness in predicting OA has been confirmed through external validation datasets. Moreover, the expression of ARGs was examined in osteoblasts subject to excessive mechanical stress, human and mouse tissues. Finally, the role of ARGs in OA was confirmed through co-culturing explants and osteoblasts. Thus, osteoblastic ARGs could be crucial in OA development, providing potential diagnostic and treatment strategies.

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.

Inflammation primes the murine kidney for recovery by activating AZIN1 adenosine-to-inosine editing.

The progression of kidney disease varies among individuals, but a general methodology to quantify disease timelines is lacking. Particularly challenging is the task of determining the potential for recovery from acute kidney injury following various insults. Here, we report that quantitation of post-transcriptional adenosine-to-inosine (A-to-I) RNA editing offers a distinct genome-wide signature, enabling the delineation of disease trajectories in the kidney. A well-defined murine model of endotoxemia permitted the identification of the origin and extent of A-to-I editing, along with temporally discrete signatures of double-stranded RNA stress and Adenosine Deaminase isoform switching. We found that A-to-I editing of Antizyme Inhibitor 1 (AZIN1), a positive regulator of polyamine biosynthesis, serves as a particularly useful temporal landmark during endotoxemia. Our data indicate that AZIN1 A-to-I editing, triggered by preceding inflammation, primes the kidney and activates endogenous recovery mechanisms. By comparing genetically modified human cell lines and mice locked in either A-to-I edited or uneditable states, we uncovered that AZIN1 A-to-I editing not only enhances polyamine biosynthesis but also engages glycolysis and nicotinamide biosynthesis to drive the recovery phenotype. Our findings implicate that quantifying AZIN1 A-to-I editing could potentially identify individuals who have transitioned to an endogenous recovery phase. This phase would reflect their past inflammation and indicate their potential for future recovery.

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.

Elucidating immune-related gene transcriptional programs via factorization of large-scale RNA-profiles.

Recent developments in immunotherapy, including immune checkpoint blockade (ICB) and adoptive cell therapy (ACT), have encountered challenges such as immune-related adverse events and resistance, especially in solid tumors. To advance the field, a deeper understanding of the molecular mechanisms behind treatment responses and resistance is essential. However, the lack of functionally characterized immune-related gene sets has limited data-driven immunological research. To address this gap, we adopted non-negative matrix factorization on 83 human bulk RNA sequencing (RNA-seq) datasets and constructed 28 immune-specific gene sets. After rigorous immunologist-led manual annotations and orthogonal validations across immunological contexts and functional omics data, we demonstrated that these gene sets can be applied to refine pan-cancer immune subtypes, improve ICB response prediction and functionally annotate spatial transcriptomic data. These functional gene sets, informing diverse immune states, will advance our understanding of immunology and cancer research.

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.

Construction of m6A-Related Gene Prediction Model and Subtype Analysis in Non-Obstructive Azoospermia Based on Bioinformatics.

PURPOSE: Non-obstructive azoospermia (NOA) is a severe and common cause of male infertility. Currently, the most reliable predictor of sperm retrieval success in NOA is histopathology, but preoperative testicular biopsy often increases the difficulty of sperm retrieval surgery. This study aims to explore the characteristics of N6-methyladenosine (m6A) modification in NOA patients and investigate the potential biomarkers and molecular mechanisms for pathological diagnosis and treatment of NOA using m6A-related genes. METHODS: NOA-related datasets were downloaded from the GEO database. Based on the results of LASSO regression analysis, a prediction model was established from differentially expressed m6A-related genes, and the predictive performance of the model was evaluated using ROC curves. Cluster analysis was performed based on differentially expressed m6A-related genes to evaluate the differences in different m6A modification patterns in terms of differentially expressed genes (DEGs), biological features, and immune features. RESULTS: There were significant differences in eight m6A-related genes between NOA samples and healthy controls. The ROC curves showed excellent predictive performance for the diagnostic models constructed with ALKBH5 and FTO. DEGs of two m6A modification subtypes indicated the influence of m6A-related genes in the biological processes of mitosis and meiosis in NOA patients, and there were significant immune differences between the two subtypes. CONCLUSION: The NOA pathological diagnostic models constructed with FTO and ALKBH5 have good predictive ability. We have identified two different m6A modification subtypes, which may help predict sperm retrieval success rate and treatment selection in NOA patients.

Unveiling the therapeutic prospects of IFNW1 and IFNA21: insights into glioma pathogenesis and clinical significance.

Glioma, a type of brain tumor, poses significant challenges due to its heterogeneous nature and limited treatment options. Interferon-related genes (IRGs) have emerged as potential players in glioma pathogenesis, yet their expression patterns and clinical implications remain to be fully elucidated. We conducted a comprehensive analysis to investigate the expression patterns and functional enrichment of IRGs in glioma. This involved constructing protein-protein interaction networks, heatmap analysis, survival curve plotting, diagnostic and prognostic assessments, differential expression analysis across glioma subgroups, GSVA, immune infiltration analysis, and drug sensitivity analysis. Our analysis revealed distinct expression patterns and functional enrichment of IRGs in glioma. Notably, IFNW1 and IFNA21 were markedly downregulated in glioma tissues compared to normal tissues, and higher expression levels were associated with improved overall survival and disease-specific survival. Furthermore, these genes showed diagnostic capabilities in distinguishing glioma tissues from normal tissues and were significantly downregulated in higher-grade and more aggressive gliomas. Differential expression analysis across glioma subgroups highlighted the association of IFNW1 and IFNA21 expression with key pathways and biological processes, including metabolic reprogramming and immune regulation. Immune infiltration analysis revealed their influence on immune cell composition in the tumor microenvironment. Additionally, elevated expression levels were associated with increased resistance to chemotherapeutic agents. Our findings underscore the potential of IFNW1 and IFNA21 as diagnostic biomarkers and prognostic indicators in glioma. Their roles in modulating glioma progression, immune response, and drug sensitivity highlight their significance as potential therapeutic targets. These results contribute to a deeper understanding of glioma biology and may inform the development of personalized treatment strategies for glioma patients.

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.

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.