Identification and validation of hub differential genes in pulmonary sarcoidosis.

A total of 138 cDEGs were screened from mediastinal lymph nodes and peripheral whole blood. Among them, 6 hub cDEGs including CTSS, CYBB, FPR2, MNDA, TLR1 and TLR8 with elevated degree and betweenness levels were illustrated in protein-protein interaction network. In comparison to healthy controls, CTSS (1.61 vs. 1.05), CYBB (1.68 vs. 1.07), FPR2 (2.77 vs. 0.96), MNDA (2.14 vs. 1.23), TLR1 (1.56 vs. 1.09), and TLR8 (2.14 vs. 0.98) displayed notably elevated expression levels within pulmonary sarcoidosis PBMC samples (P < 0.0001 for FPR2 and P < 0.05 for others), echoing with prior mRNA microarray findings. The most significant functional pathways were immune response, inflammatory response, plasma membrane and extracellular exosome, with 6 hub cDEGs distributing along these pathways. CTSS, CYBB, FPR2, MNDA, TLR1, and TLR8 could be conducive to improving the diagnostic process and understanding the underlying mechanisms of pulmonary sarcoidosis.

In-silico analysis predicts disruption of normal angiogenesis as a causative factor in osteoporosis pathogenesis.

Angiogenesis-osteogenesis coupling is critical for proper functioning and maintaining the health of bones. Any disruption in this coupling, associated with aging and disease, might lead to loss of bone mass. Osteoporosis (OP) is a debilitating bone metabolic disorder that affects the microarchitecture of bones, gradually leading to fracture. Computational analysis revealed that normal angiogenesis is disrupted during the progression of OP, especially postmenopausal osteoporosis (PMOP). The genes associated with OP and PMOP were retrieved from the DisGeNET database. Hub gene analysis and molecular pathway enrichment were performed via the Cytoscape plugins STRING, MCODE, CytoHubba, ClueGO and the web-based tool Enrichr. Twenty-eight (28) hub genes were identified, eight of which were transcription factors (HIF1A, JUN, TP53, ESR1, MYC, PPARG, RUNX2 and SOX9). Analysis of SNPs associated with hub genes via the gnomAD, I-Mutant2.0, MUpro, ConSurf and COACH servers revealed the substitution F201L in IL6 as the most deleterious. The IL6 protein was modeled in the SWISS-MODEL server and the substitution was analyzed via the YASARA FoldX plugin. A positive ΔΔG (1.936) of the F201L mutant indicates that the mutated structure is less stable than the wild-type structure is. Thirteen hub genes, including IL6 and the enriched molecular pathways were found to be profoundly involved in angiogenesis/endothelial function and immune signaling. Mechanical loading of bones through weight-bearing exercises can activate osteoblasts via mechanotransduction leading to increased bone formation. The present study suggests proper mechanical loading of bone as a preventive strategy for PMOP, by which angiogenesis and the immune status of the bone can be maintained. This in silico analysis could be used to understand the molecular etiology of OP and to develop novel therapeutic approaches.

Identification of function modules in the co-expression protein-protein interaction network of Bombyx mori in response to Beauveria bassiana infection.

Beauveria bassiana (B. bassiana) is a common fungal disease in sericulture. Previous research has primarily focused on investigating genes involved in innate immunity. However, the response of Bombyx mori (B. mori) to B. bassiana requires the coordination of other biological processes in addition to the immune system. We measured protein expression profile of B. mori after inoculating B. bassiana using iTRAQ technology in previous. Here we constructed a co-expression protein-protein interaction network of B. mori in response to B. bassiana infection. Subnetworks and modules were analyzed, and the functions of these modules were annotated. The results revealed the identification of numerous proteins associated with cellular immunity, including those involved in phagosomes, lysosomes, mTOR signaling, sugar metabolism, and the ubiquitin-proteasome pathway. Meanwhile, we observed that the pathways involved in protein synthesis were activated, including pyruvate and purine metabolism, RNA transport, ribosome, protein processing in endoplasmic reticulum, and protein export pathways, during B. bassiana infection. Based on this analysis, we selected six candidate genes (shock protein, ribosome, translocon, actin muscle-type A2, peptidoglycan recognition protein, and collagenase) that were found to be related to the response to B. bassiana. Further verification experiments demonstrated significant changes in their expression levels after inoculation with B. bassiana. These research findings provide new insights into the molecular mechanism of insect immune response to fungal infection.

Plasma proteomics for risk prediction of Alzheimer's disease in the general population.

We aimed to develop and validate a protein risk score for predicting Alzheimer's disease (AD) and compare its performance with a validated clinical risk model (Cognitive Health and Dementia Risk Index for AD [CogDrisk-AD]) and apolipoprotein E (APOE) genotypes. The development cohort, consisting of 35,547 participants from England in the UK Biobank, was randomly divided into a 7:3 training-testing ratio. The validation cohort included 4667 participants from Scotland and Wales in the UK Biobank. In the training set, an AD protein risk score was constructed using 31 proteins out of 2911 proteins. In the testing set, the AD protein risk score had a C-index of 0.867 (95% CI, 0.828, 0.906) for AD prediction, followed by CogDrisk-AD risk factors (C-index, 0.856; 95% CI, 0.823, 0.889), and APOE genotypes (C-index, 0.705; 95% CI, 0.660, 0.750). Adding the AD protein risk score to CogDrisk-AD risk factors (C-index increase, 0.050; 95% CI, 0.008, 0.093) significantly improved the predictive performance for AD. However, adding CogDrisk-AD risk factors (C-index increase, 0.040; 95% CI, -0.007, 0.086) or APOE genotypes (C-index increase, 0.000; 95% CI, -0.054, 0.055) to the AD protein risk score did not significantly improve the predictive performance for AD. The top 10 proteins with the highest coefficients in the AD protein risk score contributed most of the predictive power for AD risk. These results were verified in the external validation cohort. EGFR, GFAP, and CHGA were identified as key proteins within the protein network. Our result suggests that the AD protein risk score demonstrated a good predictive performance for AD risk.

Exploration on cold adaptation of Antarctic lichen via detection of positive selection genes.

Lichen as mutualistic symbiosis is the dominant organism in various extreme terrestrial environment on Earth, however, the mechanisms of their adaptation to extreme habitats have not been fully elucidated. In this study, we chose the Antarctic dominant lichen species Usnea aurantiacoatra to generate a high-quality genome, carried out phylogenetic analysis using maximum likelihood and identify genes under positive selection. We performed functional enrichment analysis on the positively selected genes (PSGs) and found that most of the PSGs focused on transmembrane transporter activity and vacuole components. This suggest that the genes related to energy storage and transport in Antarctic U. aurantiacoatra were affected by environmental pressure. Inside of the 86 PSGs screened, two protein interaction networks were identified, which were RNA helicase related proteins and regulator of G-protein signaling related proteins. The regulator of the G-protein signaling gene (UaRGS1) was chosen to perform further verification by the lichen genetic manipulation system Umbilicaria muhlenbergii. Given that the absence of UmRgs1 resulted in elevated lethality to cold shock, the role for UaRgs1 in Antarctic U. aurantiacoatra resistance to cold can be inferred. The investigation of lichen adaptation to extreme environments at the molecular level will be opened up.

Sex-dependent differences in the ability of nicotine to modulate discrimination learning and cognitive flexibility in mice.

Nicotine, an addictive compound found in tobacco, functions as an agonist of nicotinic acetylcholine receptors (nAChRs) in the brain. Interestingly, nicotine has been reported to act as a cognitive enhancer in both human subjects and experimental animals. However, its effects in animal studies have not always been consistent, and sex differences have been identified in the effects of nicotine on several behaviors. Specifically, the role that sex plays in modulating the effects of nicotine on discrimination learning and cognitive flexibility in rodents is still unclear. Here, we evaluated sex-dependent differences in the effect of daily nicotine intraperitoneal (i.p.) administration at various doses (0.125, 0.25, and 0.5 mg/kg) on visual discrimination (VD) learning and reversal (VDR) learning in mice. In male mice, 0.5 mg/kg nicotine significantly improved performance in the VDR, but not the VD, task, while 0.5 mg/kg nicotine significantly worsened performance in the VD, but not VDR task in female mice. Furthermore, 0.25 mg/kg nicotine significantly worsened performance in the VD and VDR task only in female mice. Next, to investigate the cellular mechanisms that underlie the sex difference in the effects of nicotine on cognition, transcriptomic analyses were performed focusing on the medial prefrontal cortex tissue samples from male and female mice that had received continuous administration of nicotine for 3 or 18 days. As a result of pathway enrichment analysis and protein-protein interaction analysis using gene sets of differentially expressed genes, decreased expression of postsynaptic-related genes in males and increased expression of innate immunity-related genes in females were identified as possible molecular mechanisms related to sex differences in the effects of nicotine on cognition in discrimination learning and cognitive flexibility. Our result suggests that nicotine modulates cognitive function in a sex-dependent manner by alternating the expression of specific gene sets in the medial prefrontal cortex.

Proteomic analysis of cerebrospinal fluid of amyotrophic lateral sclerosis patients in the presence of autologous bone marrow derived mesenchymal stem cells.

BACKGROUND: Amyotrophic lateral sclerosis (ALS) is a fatal and rapidly progressive motoneuron degenerative disorder. There are still no drugs capable of slowing disease evolution or improving life quality of ALS patients. Thus, autologous stem cell therapy has emerged as an alternative treatment regime to be investigated in clinical ALS. METHOD: Using Proteomics and Protein-Protein Interaction Network analyses combined with bioinformatics, the possible cellular mechanisms and molecular targets related to mesenchymal stem cells (MSCs, 1 × 106 cells/kg, intrathecally in the lumbar region of the spine) were investigated in cerebrospinal fluid (CSF) of ALS patients who received intrathecal infusions of autologous bone marrow-derived MSCs thirty days after cell therapy. Data are available via ProteomeXchange with identifier PXD053129. RESULTS: Proteomics revealed 220 deregulated proteins in CSF of ALS subjects treated with MSCs compared to CSF collected from the same patients prior to MSCs infusion. Bioinformatics enriched analyses highlighted events of Extracellular matrix and Cell adhesion molecules as well as related key targets APOA1, APOE, APP, C4A, C5, FGA, FGB, FGG and PLG in the CSF of cell treated ALS subjects. CONCLUSIONS: Extracellular matrix and cell adhesion molecules as well as their related highlighted components have emerged as key targets of autologous MSCs in CSF of ALS patients. TRIAL REGISTRATION: Clinicaltrial.gov identifier NCT0291768. Registered 28 September 2016.

Seasonal piRNA Expression Profile Changes in the Testes of Plateau Zokor (Eospalax baileyi).

Seasonal reproduction is a mammalian behavior that has developed over an extended evolutionary period and requires animals to respond to external environmental changes to facilitate reproduction. In this study, we investigated the role of PIWI-interacting RNA (piRNA) in the seasonal reproduction of plateau zokors (Eospalax baileyi). piRNA expression profiles in plateau zokor testes during both breeding and non-breeding seasons were examined. The piRNAs had a distinctive ping-pong signature and ranged from 27 to 32 nt with a peak at 30 nt. Testicular piRNAs predominantly aligned to specific genomic regions, including repeat and gene regions. Analysis of the piRNA-mRNA interaction network and functional enrichment of differentially expressed piRNAs targeting mRNAs revealed their association with testicular development and spermatogenesis. Significantly, PIWIL4 is an mRNA gene that interacts with piRNA and exhibits high expression levels within the testes during the non-breeding phase. This study provides a foundation to improve our understanding of piRNA regulatory mechanisms during testicular development and spermatogenesis in seasonally reproducing animals and, specifically, in the plateau zokor.

A Systems Biology Approach to the Pathogenesis of Celiac Disease: Identification of Potential Protective and Promoting Mechanisms.

Background: Celiac disease (CeD) is an autoimmune enteropathy triggered by dietary gluten. Almost 90% of CeD patients have HLA-DQ2 or -DQ8 haplotypes. As a high proportion of first-degree relatives (FDRs) of CeD patients have the same haplotype, it is assumed that they are at a higher risk of disease development than the general population. Nevertheless, the prevalence of CeD among FDRs is considerably low (7.5%). Materials and Methods: In order to figure out this discrepancy, a microarray dataset of intestinal mucosal biopsies of CeD patients, FDRs, and control groups was reanalyzed, and a protein-protein interaction network was constructed. Results: Principal component analysis showed that CeD and FDR groups are far away in terms of gene expression. Comparing differentially expressed genes of both networks demonstrated inverse expression of some genes mainly related to cell cycle mechanisms. Moreover, analysis of the modular structures of up- and downregulated gene networks determined activation of protein degradation mechanisms and inhibition of ribosome-related protein synthesis in celiac patients with an upside-down pattern in FDRs. Conclusions: The top-down systems biology approach determined some regulatory pathways with inverse function in CeD and FDR groups. These genes and molecular mechanisms could be a matter of investigation as potential druggable targets or prognostic markers in CeD.

SCancerRNA: Expression at the Single-cell Level and Interaction Resource of Non-coding RNA Biomarkers for Cancers.

Non-coding RNAs (ncRNAs) participate in multiple biological processes associated with cancers as tumor suppressors or oncogenic drivers. Due to their high stability in plasma, urine, and many other fluids, ncRNAs have the potential to serve as key biomarkers for early diagnosis and screening of cancers. During cancer progression, tumor heterogeneity plays a crucial role, and it is particularly important to understand the gene expression patterns of individual cells. With the development of single-cell RNA sequencing (scRNA-seq) technologies, uncovering gene expression in different cell types for human cancers has become feasible by profiling transcriptomes at the cellular level. However, a well-organized and comprehensive online resource that provides access to the expression of genes corresponding to ncRNA biomarkers in different cell types at the single-cell level is not available yet. Therefore, we developed the SCancerRNA database to summarize experimentally supported data on long ncRNA, microRNA, PIWI-interacting RNA, small nucleolar RNA, and circular RNA biomarkers, as well as data on their differential expression at the cellular level. Furthermore, we collected biological functions and clinical applications of biomarkers to facilitate the application of ncRNA biomarkers to cancer diagnosis, as well as the monitoring of progression and targeted therapies. SCancerRNA also allows users to explore interaction networks of different types of ncRNAs, and build computational models in the future. SCancerRNA is freely accessible at http://www.scancerrna.com/BioMarker.

Plant virus community structuring is shaped by habitat heterogeneity and traits for host plant resource utilisation.

Host plants provide resources critical to viruses and the spatial structuring of plant communities affects the niches available for colonisation and disease emergence. However, large gaps remain in the understanding of mechanisms that govern plant-virus disease ecology across heterogeneous plant assemblages. We combine high-throughput sequencing, network, and metacommunity approaches to test whether habitat heterogeneity in plant community composition corresponded with virus resource utilisation traits of transmission mode and host range. A majority of viruses exhibited habitat specificity, with communities connected by key generalist viruses and potential host reservoirs. There was an association between habitat heterogeneity and virus community structuring, and between virus community structuring and resource utilisation traits of host range and transmission. The relationship between virus species distributions and virus trait responses to habitat heterogeneity was scale-dependent, being stronger at finer (site) than larger (habitat) spatial scales. Results indicate that habitat heterogeneity has a part in plant virus community assembly, and virus community structuring corresponds to virus trait responses that vary with the scale of observation. Distinctions in virus communities caused by plant resource compartmentalisation can be used to track trait responses of viruses to hosts important in forecasting disease emergence.

Long-term grazing reduces soil fungal network complexity but enhances plant-soil microbe network connectivity in a semi-arid grassland.

Grazing plays a significant role in shaping both aboveground vegetation and belowground microbial communities in arid and semi-arid grasslands, which in turn affects ecosystem functions and sustainability. Therefore, it was essential to implement effective grazing management practices to preserve ecological balance and support sustainable development in these delicate environments. To optimize the traditional continuous grazing policy, we conducted a 10-year seasonal grazing experiment with five treatments in a typical grassland in northern China: no grazing (NG), continuous summer grazing (CG), and three seasonal grazing treatments (G57 in May and July, G68 in June and August, and G79 in July and September). Our study found that although grazing reduced plant community biomass, G68 treatment maintained high plant height and community diversity (P < 0.05). Grazing did not affect soil bacterial and archaeal alpha diversity, but CG treatment reduced soil fungal diversity (P < 0.05). CG reduced the archaeal network's vertices (which represent microbial taxa, OTUs) and connections (ecological interactions between taxa), but seasonal grazing increased its complexity. Furthermore, grazing did not change bacterial networks but enhanced cross-domain interactions (relationships between different biological groups) of plant-soil fungi and plant-soil archaea. Overall, we used the Mantel test to find that soil microbial diversity was positively correlated with soil physicochemical properties rather than plant community characteristics after grazing. These findings are beneficial for the optimization of sustainable grassland management policies and the protection of plant and soil biodiversity.

Whole-transcriptome analyses of ovine lung microvascular endothelial cells infected with bluetongue virus.

Bluetongue virus (BTV) infection induces profound and intricate changes in the transcriptional profile of the host to facilitate its survival and replication. However, there have been no whole-transcriptome studies on ovine lung microvascular endothelial cells (OLMECs) infected with BTV. In this study, we comprehensively analysed the whole-transcriptome sequences of BTV-1 serotype-infected and mock-infected OLMECs and subsequently performed bioinformatics differential analysis. Our analysis revealed 1215 differentially expressed mRNA transcripts, 82 differentially expressed long noncoding RNAs (lncRNAs) transcripts, 63 differentially expressed microRNAs (miRNAs) transcripts, and 42 differentially expressed circular RNAs (circRNAs) transcripts. Annotation from Gene Ontology, enrichment from the Kyoto Encyclopedia of Genes and Genomes, and construction of endogenous competing RNA network analysis revealed that the differentially expressed RNAs primarily participated in viral sensing and signal transduction pathways, antiviral and immune responses, inflammation, and extracellular matrix (ECM)-related pathways. Furthermore, protein‒protein interaction network analysis revealed that BTV may regulate the conformation of ECM receptor proteins and change their biological activity through a series of complex mechanisms. Finally, on the basis of real-time fluorescence quantitative polymerase chain reaction results, the expression trends of the differentially expressed RNA were consistent with the whole-transcriptome sequencing data, such as downregulation of the expression of COL4A1, ITGA8, ITGB5, and TNC and upregulation of the expression of CXCL10, RNASEL, IRF3, IRF7, and IFIHI. This study provides a novel perspective for further investigations of the mechanism of the ECM in the BTV-host interactome and the pathogenesis of lung microvascular endothelial cells.

Design Glutamate Dehydrogenase for Nonaqueous System by Motifs Reassembly and Interaction Network Analysis.

Glutamate dehydrogenases (GDH) serve as the key regulated enzyme that links protein and carbohydrate metabolism. Combined with motif reassembly and mutation, novel GDHs were designed. Motif reassembly of thermophilic GDH and malate dehydrogenase aims to overcome stability and activity tradeoff in nonaqueous systems. Structural compatibility and dynamic cooperation of the designed AaDHs were studied by molecular dynamics simulation. Furthermore, multipoint mutations improved its catalytic activity for unnatural substrates. Amino acid interaction network analysis indicated that the high density of hydrogen-bonded salt bridges is beneficial to the stability. Finally, the experimental verification determines the kinetics of AaDHs in a nonaqueous system. The activity of Aa05 was increased by 1.78-fold with ionic liquid [EMIM]BF4. This study presents the strategy of a combination of rigid motif assembly and mutations of active sites for robust dehydrogenases with high activity in the nonaqueous system, which overcomes the activity-stability tradeoff effect.

Integrative analysis reveals novel insights into juvenile idiopathic arthritis pathogenesis and shared molecular pathways with associated traits.

Background: Juvenile idiopathic arthritis (JIA) is an autoimmune joint disease that frequently co-occurs with other complex phenotypes, including cancers and other autoimmune diseases. Despite the identification of numerous risk variants through genome-wide association studies (GWAS), the affected genes, their connection to JIA pathogenesis, and their role in the development of associated traits remain unclear. This study aims to address these gaps by elucidating the gene-regulatory mechanisms underlying JIA pathogenesis and exploring its potential role in the emergence of associated traits. Methods: A two-sample Mendelian Randomization (MR) analysis was conducted to identify blood-expressed genes causally linked to JIA. A curated protein interaction network was subsequently used to identify sets of single-nucleotide polymorphisms (i.e., spatial eQTL SNPs) that regulate the expression of JIA causal genes and their protein interaction partners. These SNPs were cross-referenced against the GWAS catalog to identify statistically enriched traits associated with JIA. Results: The two-sample MR analysis identified 52 genes whose expression changes in the blood are putatively causal for JIA. These genes (e.g., HLA, LTA, LTB, IL6ST) participate in a range of immune-related pathways (e.g., antigen presentation, cytokine signalling) and demonstrate cell type-specific regulatory patterns across different immune cell types (e.g., PPP1R11 in CD4+ T cells). The spatial eQTLs that regulate JIA causal genes and their interaction partners were statistically enriched for GWAS SNPs linked with 95 other traits, including both known and novel JIA-associated traits. This integrative analysis identified genes whose dysregulation may explain the links between JIA and associated traits, such as autoimmune/inflammatory diseases (genes at 6p22.1 locus), Hodgkin lymphoma (genes at 6p21.3 [FKBPL, PBX2, AGER]), and chronic lymphocytic leukemia (BAK1). Conclusion: Our approach provides a significant advance in understanding the genetic architecture of JIA and associated traits. The results suggest that the burden of associated traits may differ among JIA patients, influenced by their combined genetic risk across different clusters of traits. Future experimental validation of the identified connections could pave the way for refined patient stratification, the discovery of new biomarkers, and shared therapeutic targets.

Genome-Wide Identification, Expression, and Protein Analysis of CKX and IPT Gene Families in Radish (Raphanus sativus L.) Reveal Their Involvement in Clubroot Resistance.

Cytokinins (CKs) are a group of phytohormones that are involved in plant growth, development, and disease resistance. The isopentenyl transferase (IPT) and cytokinin oxidase/dehydrogenase (CKX) families comprise key enzymes controlling CK biosynthesis and degradation. However, an integrated analysis of these two gene families in radish has not yet been explored. In this study, 13 RsIPT and 12 RsCKX genes were identified and characterized, most of which had four copies in Brassica napus and two copies in radish and other diploid Brassica species. Promoter analysis indicated that the genes contained at least one phytohormone or defense and stress responsiveness cis-acting element. RsIPTs and RsCKXs were expanded through segmental duplication. Moreover, strong purifying selection drove the evolution of the two gene families. The expression of the RsIPT and RsCKX genes distinctly showed diversity in different tissues and developmental stages of the root. Expression profiling showed that RsCKX1-1/1-2/1-3 was significantly upregulated in club-resistant materials during primary infection, suggesting their vital function in clubroot resistance. The interaction network of CKX proteins with similar 3D structures also reflected the important role of RsCKX genes in disease resistance. This study provides a foundation for further functional study on the IPT and CKX genes for clubroot resistance improvement in Raphanus.

Valuable Contributions and Lessons Learned from Proteomics and Metabolomics Studies of COVID-19.

The COVID-19 pandemic caused by the SARS-CoV-2 virus infected more than 775,686,716 humans and was responsible for the death of more than 7,054,093 individuals. COVID-19 has taught us that the development of vaccines, repurposing of drugs, and understanding the mechanism of a disease can be done within a short time. The COVID-19 proteomics and metabolomics has contributed to its diagnosis, understanding of its progression, host-virus interaction, disease mechanism, and also in the search of suitable anti-COVID therapeutics. Mass spectrometry based proteomics was used to find the potential biomarkers of different stages of COVID-19 including severe and nonsevere cases in the blood serum. Notably, protein-protein interaction techniques to understand host-virus interactions were also significantly useful. The single-cell proteomics studies were carried out to ascertain the changes in immune cell composition and its activation in mild COVID-19 patients versus severe COVID-19 patients using whole-blood and peripheral-blood mononuclear cells. Modern technologies were helpful to deal with the pandemic; however, there is still scope for further development. Further, attempts were made to understand the protein-protein, metabolite-metabolite, and protein-metabolite interactomes, derived from proteins and metabolite fingerprints of COVID-19 patients by reanalysis of COVID-19 public mass spectrometry based proteomics and metabolomics studies. Further, some of these interactions were supported by the literature as validations in the COVID-19 studies.

Identification of the Shared Gene Signatures Between Alzheimer's Disease and Diabetes-Associated Cognitive Dysfunction by Bioinformatics Analysis Combined with Biological Experiment.

Background: The connection between diabetes-associated cognitive dysfunction (DACD) and Alzheimer's disease (AD) has been shown in several observational studies. However, it remains controversial as to how the two related. Objective: To explore shared genes and pathways between DACD and AD using bioinformatics analysis combined with biological experiment. Methods: We analyzed GEO microarray data to identify DEGs in AD and type 2 diabetes mellitus (T2DM) induced-DACD datasets. Weighted gene co-expression network analysis was used to find modules, while R packages identified overlapping genes. A robust protein-protein interaction network was constructed, and hub genes were identified with Gene ontology enrichment and Kyoto Encyclopedia of Genome and Genome pathway analyses. HT22 cells were cultured under high glucose and amyloid-ß 25-35 (Aß25-35) conditions to establish DACD and AD models. Quantitative polymerase chain reaction with reverse transcription verification analysis was then performed on intersection genes. Results: Three modules each in AD and T2DM induced-DACD were identified as the most relevant and 10 hub genes were screened, with analysis revealing enrichment in pathways such as synaptic vesicle cycle and GABAergic synapse. Through biological experimentation verification, 6 key genes were identified. Conclusions: This study is the first to use bioinformatics tools to uncover the genetic link between AD and DACD. GAD1, UCHL1, GAP43, CARNS1, TAGLN3, and SH3GL2 were identified as key genes connecting AD and DACD. These findings offer new insights into the diseases' pathogenesis and potential diagnostic and therapeutic targets.

Unraveling the molecular pathogenesis of Type 2 Diabetes and its impact on female infertility: A bioinformatics and systems biology approach.

Type 2 diabetes mellitus (T2D) has been linked with female infertility (FI). Nevertheless, our understanding of the molecular hallmarks and underlying mechanisms remains elusive. This research article aimed to find the hub genes, pathways, transcription factors, and miRNA involved. For this study, softwares like cytoscape, string, Enrichr, FFL loop, etc., were utilized. This research article employed differentially expressed genes (DEGs) to identify multiple biological targets to understand the association between T2D and female infertility (FI). Between T2D and FI, we found 3869 differentially expressed genes. We have also analyzed different pathways like thyroid hormone signaling pathways, AGE-RAGE signaling pathways in diabetic complications and ubiquitin-mediated proteolysis through pathway analysis. Moreover, hub genes MED17, PRKCG, THRA, FOXO1, NCOA2, PLCG2, COL1A1, CXCL8, PRPF19, ANAPC5, UBE2I, XIAP and KEAP1 have been identified. Additionally, these hub genes were subjected to identify the miRNA-mRNA regulation network specific to T2D-associated female infertility. In the FFL study (Feed Forward Loop), transcription factor (SP1, NFKB1, RELA and FOX01), miRNA (has-mir-7-5p, has-let-7a-5p, hsa-mir-16-5p, hsa-mir-155-5p, has-mir-122-5p, has-let-7b-5p, has-mir-124-3p, has-mir-34a-5p, has-mir-130a-3p, has-let-7i-5p, and hsa-mir-27a-3p) and six genes (XIAP, THRA, NCOA2, MED17, FOXO1, and COL1A1) among the thirteen key genes were recognized as regulator and inhibitor. Our analysis reveals that these genes can serve as a significant biomarker for female infertility linked with Type 2 Diabetes, through the prioritization of candidate genes. This study gives us insight into the molecular and cellular mechanism of T2D-associated FI. This finding helps in developing novel therapeutic approaches and will improve efficacy and reduce side effects of the treatment. This research requires further experimental investigation of the principal targets.

Network-based prediction of anti-cancer drug combinations.

Drug combinations have emerged as a promising therapeutic approach in cancer treatment, aimed at overcoming drug resistance and improving the efficacy of monotherapy regimens. However, identifying effective drug combinations has traditionally been time-consuming and often dependent on chance discoveries. Therefore, there is an urgent need to explore alternative strategies to support experimental research. In this study, we propose network-based prediction models to identify potential drug combinations for 11 types of cancer. Our approach involves extracting 55,299 associations from literature and constructing human protein interactomes for each cancer type. To predict drug combinations, we measure the proximity of drug-drug relationships within the network and employ a correlation clustering framework to detect functional communities. Finally, we identify 61,754 drug combinations. Furthermore, we analyze the network configurations specific to different cancer types and identify 30 key genes and 21 pathways. The performance of these models is subsequently assessed through in vitro assays, which exhibit a significant level of agreement. These findings represent a valuable contribution to the development of network-based drug combination design strategies, presenting potential solutions to overcome drug resistance and enhance cancer treatment outcomes.