Gene Regulatory Networks Research Articles

Decoding the genetic comorbidity network of Alzheimer's disease

Alzheimer's disease (AD) has emerged as the most prevalent and complex neurodegenerative disorder among the elderly population. However, the genetic comorbidity etiology for AD remains poorly understood. In this study, we conducted pleiotropic analysis for 41 AD phenotypic comorbidities, identifying ten genetic comorbidities with 16 pleiotropy genes associated with AD. Through biological functional and network analysis, we elucidated the molecular and functional landscape of AD genetic comorbidities. Furthermore, leveraging the pleiotropic genes and reported biomarkers for AD genetic comorbidities, we identified 50 potential biomarkers for AD diagnosis. Our findings deepen the understanding of the occurrence of AD genetic comorbidities and provide new insights for the search for AD diagnostic markers. Graphical Study pipeline.

Single cell analysis revealed SFRP2 cancer associated fibroblasts drive tumorigenesis in head and neck squamous cell carcinoma

Understanding the mechanisms of invasion and metastasis in head and neck squamous cell carcinoma (HNSCC) is crucial for effective treatment, particularly in metastatic cases. In this study, we analyzed multicenter bulk sequencing and comprehensive single-cell data from 702,446 cells, leading to the identification of a novel subtype of cancer-associated fibroblasts (CAFs), termed Secreted Frizzled-Related Protein2 CAFs (SFRP2_CAFs). These cells, originating from smooth muscle cells, display unique characteristics resembling both myofibroblastic CAFs and inflammatory CAFs, and are linked to poorer survival outcomes in HNSCC patients. Our findings reveal significant interactions between SFRP2_CAFs and SPP1 tumor-associated macrophages, which facilitate tumor invasion and metastasis. Moreover, our research identifies Nuclear factor I/X (NFIX) as a key transcription factor regulating SFRP2_CAFs behavior, confirmed through gene regulatory network analysis and simulation perturbation.

Single-nucleus transcriptional profiling reveals TCF7L2 as a key regulator in adipogenesis in goat skeletal muscle development

Intramuscular adipogenesis plays an important role in muscle development, which determines the quality of goat meat. However, its underlying cellular and molecular mechanisms remain poorly understood. In this study, we provided detailed cellular atlases of goat longissimus dorsi during muscle development at single-nucleus resolution, and identified the subpopulations of fibroblasts/fibro-adipogenic progenitors (FAPs) and muscle satellite cell (MuSC), as well as the differentiation trajectory of FAPs subpopulations. Cellular ligand-receptor interaction analysis revealed enriched BMP and IGF pathways implicated in within-tissue crosstalk centered around FAPs. Through single-nucleus gene regulatory network analysis and in vitro interference verification, we found that TCF7L2 was a critical transcriptional factor (TF) in early adipogenesis in skeletal muscle. Overall, our work reveals the cellular intricacies and diversity of goat longissimus dorsi during muscle development, implementing insights into the critical roles of BMP, IGF pathways and TCF7L2 TF in intramuscular adipogenesis.

The MADS-box gene XAANTAL1 participates in Arabidopsis thaliana primary root growth and columella stem cell patterns in response to ROS, via direct regulation of PEROXIDASE 28 and RETINOBLASTOMA RELATED.

The balance between cell growth, proliferation and differentiation emerges from gene regulatory networks coupled to various signal transduction pathways, including reactive oxygen species (ROS) and transcription factors (TFs), enabling developmental responses to environmental cues. The Arabidopsis thaliana's primary root has become a valuable system for unraveling such networks. Recently, the role of TFs that mediate the ROS's inhibition of primary root growth has begun to be characterized. This study demonstrates that the MADS-box transcription factor XAANTAL1 (XAL1) is an essential regulator of hydrogen peroxide (H2O2) in primary root growth and root stem cell niche identity. Interestingly, our findings suggest that XAL1 acts as a positive regulator of H2O2 concentration in the root meristem by directly regulating genes involved in oxidative stress response, such as PEROXIDASE 28 (PER28). Moreover, we found that XAL1 is necessary for the H2O2-induced inhibition of primary root growth through the negative regulation of peroxidase and catalase activities. Furthermore, XAL1, in conjunction with RETINOBLASTOMA-RELATED (RBR), is essential for positively regulating the differentiation of columella stem cells and for participating in primary root growth inhibition in response to oxidative stress induced by H2O2 treatment.

CRISPR-GEM: A Novel Machine Learning Model for CRISPR Genetic Target Discovery and Evaluation.

CRISPR gene editing strategies are shaping cell therapies through precise and tunable control over gene expression. However, limitations in safely delivering high quantities of CRISPR machinery demand careful target gene selection to achieve reliable therapeutic effects. Informed target gene selection requires a thorough understanding of the involvement of target genes in gene regulatory networks (GRNs) and thus their impact on cell phenotype. Effective decoding of these complex networks has been achieved using machine learning models, but current techniques are limited to single cell types and focus mainly on transcription factors, limiting their applicability to CRISPR strategies. To address this, we present CRISPR-GEM, a multilayer perceptron (MLP) based synthetic GRN constructed to accurately predict the downstream effects of CRISPR gene editing. First, input and output nodes are identified as differentially expressed genes between defined experimental and target cell/tissue types, respectively. Then, MLP training learns regulatory relationships in a black-box approach allowing accurate prediction of output gene expression using only input gene expression. Finally, CRISPR-mimetic perturbations are made to each input gene individually, and the resulting model predictions are compared to those for the target group to score and assess each input gene as a CRISPR candidate. The top scoring genes provided by CRISPR-GEM therefore best modulate experimental group GRNs to motivate transcriptomic shifts toward a target group phenotype. This machine learning model is the first of its kind for predicting optimal CRISPR target genes and serves as a powerful tool for enhanced CRISPR strategies across a range of cell therapies.

Landscape of evolutionary arms races between transposable elements and KRAB-ZFP family

Transposable elements (TEs) are mobile parasitic sequences that have expanded within the host genome. It has been hypothesized that host organisms have expanded the Krüppel-associated box-containing zinc finger proteins (KRAB-ZFPs), which epigenetically suppress TEs, to counteract disorderly TE transpositions. This process is referred to as the evolutionary arms race. However, the extent to which this evolutionary arms race occurred across various TE families remains unclear. In the present study, we systematically explored the evolutionary arms race between TE families and human KRAB-ZFPs using public ChIP-seq data. We discovered and characterized new instances of evolutionary arms races with KRAB-ZFPs in endogenous retroviruses. Furthermore, we found that the regulatory landscape shaped by this arms race contributed to the gene regulatory networks. In summary, our results provide insight into the impact of the evolutionary arms race on TE families, the KRAB-ZFP family, and host gene regulatory networks.

Investigating Full-Length circRNA Transcripts to Reveal circRNAMediated Regulation of Competing Endogenous RNAs in Gastric Cancer

Background: Circular RNAs (circRNAs) play important regulatory roles in the progression of gastric cancer (GC), but the exact mechanisms governing their regulation remain incompletely understood. Prior studies typically used back-spliced junctions (BSJs) to represent a range of circRNA isoforms, overlooking the prevalence of alternative splicing (AS) events within circRNAs, which could lead to unreliable or even incorrect conclusions in subsequent analyses, hindering our comprehension of the specific functions of circRNAs in GC. Objective: This study aimed to explore the potential functional roles of the dysregulated circRNA transcripts in GC and provide new biomarkers and effective novel therapeutic strategies for GC treatment. Methods: RNA-seq data with rRNA depletion and RNase R treatment was employed to characterize the expression profiles of circRNAs in GC, and RNA-seq data only with rRNA depletion was employed to identify differentially expressed mRNAs in GC. Based on the full-sequence information and accurate isoform-level quantification of circRNA transcripts calculated by the CircAST tool, we performed a series of bioinformatic analyses. A circRNA-miRNA-hub gene regulatory network was constructed to reveal the circRNA-mediated regulation of competing endogenous RNAs in GC, and then the protein-protein interaction (PPI) network was built to identify hub genes. Results: A total of 18,398 circular transcripts were successfully reconstructed in the samples. Herein, 351 upregulated and 177 downregulated circRNA transcripts were identified. Functional enrichment analysis revealed that their parental genes were strongly associated with GC. After several screening steps, 19 dysregulated circRNA transcripts, 40 related miRNAs, and 65 target genes (mRNAs) were selected to construct the ceRNA network. Through PPI analysis, five hub genes (COL5A2, PDGFRB, SPARC, COL1A2, and COL4A1) were excavated. All these hub genes may play vital roles in gastric cancer cell proliferation and invasion. Conclusion: Our study revealed a comprehensive profile of full-length circRNA transcripts in GC, which could provide potential prognostic biomarkers and targets for GC treatment. The results would be helpful for further studies on the biological roles of circRNAs in GC and offer new mechanistic insights into the pathogenesis of GC.

In vivo perturb-seq of cancer and microenvironment cells dissects oncologic drivers and radiotherapy responses in glioblastoma.

Genetic perturbation screens with single-cell readouts have enabled rich phenotyping of gene function and regulatory networks. These approaches have been challenging in vivo, especially in adult disease models such as cancer, which include mixtures of malignant and microenvironment cells. Glioblastoma (GBM) is a fatal cancer, and methods of systematically interrogating gene function and therapeutic targets in vivo, especially in combination with standard of care treatment such as radiotherapy, are lacking. Here, we iteratively develop a multiplex in vivo perturb-seq CRISPRi platform for single-cell genetic screens in cancer and tumor microenvironment cells that leverages intracranial convection enhanced delivery of sgRNA libraries into mouse models of GBM. Our platform enables potent silencing of drivers of in vivo growth and tumor maintenance as well as genes that sensitize GBM to radiotherapy. We find radiotherapy rewires transcriptional responses to genetic perturbations in an in vivo-dependent manner, revealing heterogenous patterns of treatment sensitization or resistance in GBM. Furthermore, we demonstrate targeting of genes that function in the tumor microenvironment, enabling alterations of ligand-receptor interactions between immune and stromal cells following in vivo CRISPRi perturbations that can affect tumor cell phagocytosis. In sum, we demonstrate the utility of multiplexed perturb-seq for in vivo single-cell dissection of adult cancer and normal tissue biology across multiple cell types in the context of therapeutic intervention, a platform with potential for broad application.

Transcriptional network dynamics in early T cell development.

The rate at which cells enter the T cell pathway depends not only on the immigration of hematopoietic precursors into the strong Notch signaling environment of the thymus but also on the kinetics with which each individual precursor cell reaches T-lineage commitment once it arrives. Notch triggers a complex, multistep gene regulatory network in the cells in which the steps are stereotyped but the transition speeds between steps are variable. Progenitor-associated transcription factors delay T-lineage differentiation even while Notch-induced transcription factors within the same cells push differentiation forward. Progress depends on regulator cross-repression, on breaching chromatin barriers, and on shifting, competitive collaborations between stage-specific and stably expressed transcription factors, as reviewed here.

Light and temperature signals are integrated through a phyB-dependent gene regulatory network in rice.

Angiosperms are the most dominant land plant flora and have colonised most of the terrestrial habitats, thriving in different environmental conditions, among which light and temperature play a crucial role. In the eudicot Arabidopsis thaliana, light and temperature are integrated into a phytochrome B (phyB)-dependent signalling network that regulates development. However, whether this signal integration controls the development in other angiosperm lineages and if phyB is a conserved hub of this integratory network in angiosperms is unclear. We used a combination of phylogenetic, phenotypic, and transcriptomic analyses to understand the phyB-dependent light and temperature integratory network in the monocot Oryza sativa and infer its conservation in angiosperms. Here, we show that light and temperature co-regulate rice growth through a phyB-dependent regulatory network that shares conserved features between O. sativa and A. thaliana. Despite the conservation of the components of this regulatory network, the transcriptional regulation between the components has changed qualitatively since monocots and eudicots diverged (~192-145 MYA). The evolutionary flexibility of this integratory network might underlie the successful adaptation of plants to diverse ecological niches. Furthermore, our findings provide promising candidate genes whose activity and expression can be fine-tuned to improve plant growth and productivity in a warming planet.

Development and Validation of an AI-Driven System for Automatic Literature Analysis and Molecular Regulatory Network Construction.

Decoding gene regulatory networks is essential for understanding the mechanisms underlying many complex diseases. GENET is developed, an automated system designed to extract and visualize extensive molecular relationships from published biomedical literature. Using natural language processing, entities and relations are identified from a randomly selected set of 1788 scientific articles, and visualized in a filterable knowledge graph. The performance of GENET is evaluated and compared with existing methods. The named entity recognition model has achieved an overall precision of 94.23% (4835/5131; 93.56-94.84%), recall of 97.72% (4835/4948; 97.27-98.10%), and an F1 score of 95.94%. The relation extraction model has demonstrated an overall precision of 91.63% (2593/2830; 90.55-92.59%), recall of 89.17% (2593/2908; 87.99-90.25%), and an F1 score of 90.38%. GENET significantly outperforms existing methods in extracting molecular relationships (P<0.001). Additionally, GENET has successfully predicted WNT family member 4 regulates insulin-like growth factor 2 via signal transducer and activator of transcription 3 in colon cancer. With RNA sequencing data and multiple immunofluorescence, the authenticity of this prediction is validated, supporting the promising feasibility of GENET.

Network Analysis of Dysregulated Immune Response to COVID-19 mRNA Vaccination in Hemodialysis Patients

Introduction: End-stage renal disease (ESRD) results in immune dysfunction that is characterized by both systemic inflammation and immune incompetence, leading to impaired responses to vaccination. Methods: To unravel the complex regulatory immune interplay in ESRD, we performed the network-based transcriptomic profiling of ESRD patients on maintenance hemodialysis (HD) and matched healthy controls (HCs) who received the two-dose regimen of the COVID-19 mRNA vaccine BNT162b2. Results: Co-expression networks based on blood transcription modules (BTMs) of genes differentially expressed between the HD and HC groups revealed co-expression patterns that were highly similar between the two groups but weaker in magnitude in the HD compared to HC subjects. These networks also showed weakened coregulation between BTMs within the dendritic cell (DC) family as well as with other BTM families involved with innate immunity. The gene regulatory networks of the most enriched BTMs, likewise, highlighted weakened targeting by transcription factors of key genes implicated in DC, natural killer (NK) cell, and T cell activation and function. The computational deconvolution of immune cell populations further bolstered these findings with discrepant proportions of conventional DC subtypes, NK T cells, and CD8+ T cells in HD subjects relative to HCs. Conclusion: Altogether, our results indicate that constitutive inflammation in ESRD compromises the activation of DCs and NK cells, and, ultimately, their mediation of downstream lymphocytes, leading to a delayed but intact immune response to mRNA vaccination.

Roles of noncoding RNA in allergic rhinitis.

Allergic rhinitis (AR) is one of the most common respiratory noninfectious diseases and chronic inflammatory diseases, the incidence of which has been increasing in recent years. The main pathological characteristics of AR are repeated inflammation, airway hyperreactivity, mucus hypersecretion, and reversible airway obstruction due to inflammatory cell response. AR occurrence is associated with various factors, including those of genetic and environmental origins. Noncoding RNAs (ncRNAs) are a group of RNA molecules that cannot be converted into polypeptides. The three main categories of ncRNAs include microRNAs (miRNAs), long ncRNAs (lncRNAs), and circular RNAs (circRNAs). NcRNAs play a crucial role in controlling gene expression and contribute to the development of numerous human diseases. Articles are selected based on Pubmed's literature review and the author's personal knowledge. The largest and highest quality studies were included. The search selection is not standardized. Several recent studies have indicated the relationship of ncRNAs with the development of respiratory allergic diseases. NcRNAs, including miRNAs, lncRNAs, and circRNAs, are important gene expression regulatory factors. We review the expression and function of ncRNAs in AR, their role as disease biomarkers, and their prospective applicability in future research and clinically. We also discuss interactions between ncRNAs and their influence on AR comprehensively, these interactions are essential for determining the underlying pathological mechanisms further and discovering new drug therapeutic targets. NcRNAs can be used as biomarkers for early AR diagnosis, disease surveillance and prognosis assessment. Various categories of ncRNAs play distinct yet interconnected roles and actively contribute to intricate gene regulatory networks. They are also therapeutic targets and biomarkers in other allergic diseases. This article demonstrates ncRNAs have a wide range of applications in AR treatment. The database covers three key areas: miRNAs, lncRNAs, and circRNAs. Additionally, potential avenues for future research to facilitate the practical application of ncRNAs as therapeutic targets and biomarkers will be explore. With further research and technological development, ncRNAs may provide additional innovative, effective solutions for AR treatment.

12554 Human Sex Determination: SRY Length Regulates Its Cellular Stability And Hence The Robustness Of Testis Differentiation

Abstract Disclosure: M.A. Weiss: None. The abundance of transcription factors (TFs) mediated by the rates of degradation are subjected to be a robust to an appropriate level. This regulation via the proteasome is largely controlled by the stability of individual proteins and then could determine the direction of a gene-regulatory network. Insight is obtained through studies of bistable genetic circuits mediated by initiating transcription factors. A model is provided by SRY, a Y-encoded TF that initiates testicular differentiation. Known functions in human SRY (204 residues) majorly cluster in its high mobility group (HMG) box whereas the functions of the N- and C-terminal non-box segments are not well characterized. Here, we have used cell-based and mouse transgenic studies to measure the transcriptional threshold of SRY regulating the balance between development and dysgenesis. Our findings demonstrate a threshold length in the C-terminal domain of human SRY that determines the protein’s proteosome-enforced half-life. In a pre-Sertoli cell model, truncation of SRY resulted in the reduction of intracellular concentration and twofold attenuation of the male-specific GRN. Expression of the 1-164 fragment of human SRY in CRISPR-Cas9-edited XX mice failed to drive male differentiation whereas the 1-200 of SRY initiated male GRN development. This study provides insight into the robustness of human SRY and illustrates a powerful strategy to link biochemical properties in cultured cells and in vivo developmental outcomes. Our study reveals a checkpoint in a key TF initiating a sex-specific GRN, functioning as an experimental “control knob” in development. Our approach probes molecular determinants of cell fate and so promises to extend structure-function studies of SRY to the flanking and relatively obscure non-box domains. This result implies the balance between robustness and evolvability in metazoan is a game of numbers of initial transcription factor in the networks. Presentation: 6/1/2024

Systems Biology Methods via Genome-Wide RNA Sequences to Investigate Pathogenic Mechanisms for Identifying Biomarkers and Constructing a DNN-Based Drug–Target Interaction Model to Predict Potential Molecular Drugs for Treating Atopic Dermatitis

This study aimed to construct genome-wide genetic and epigenetic networks (GWGENs) of atopic dermatitis (AD) and healthy controls through systems biology methods based on genome-wide microarray data. Subsequently, the core GWGENs of AD and healthy controls were extracted from their real GWGENs by the principal network projection (PNP) method for Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway annotation. Then, we identified the abnormal signaling pathways by comparing the core signaling pathways of AD and healthy controls to investigate the pathogenesis of AD. Then, IL-1β, GATA3, Akt, and NF-κB were selected as biomarkers for their important roles in the abnormal regulation of downstream genes, leading to cellular dysfunctions in AD patients. Next, a deep neural network (DNN)-based drug–target interaction (DTI) model was pre-trained on DTI databases to predict molecular drugs that interact with these biomarkers. Finally, we screened the candidate molecular drugs based on drug toxicity, sensitivity, and regulatory ability as drug design specifications to select potential molecular drugs for these biomarkers to treat AD, including metformin, allantoin, and U-0126, which have shown potential for therapeutic treatment by regulating abnormal immune responses and restoring the pathogenic signaling pathways of AD.

The AP2 transcription factor BARE RECEPTACLE regulates floral organogenesis via auxin pathways in woodland strawberry.

During flower development, different floral organs are formed to ensure fertilization and fruit set. Although the genetic networks underlying flower development are increasingly well understood, less is known about the mechanistic basis in different species. Here, we identified a mutant of woodland strawberry (Fragaria vesca), bare receptacle (bre), which produces flowers with greatly reduced carpels and other floral organs. Genetic analysis revealed that BRE encodes an APETALA2 (AP2) transcription factor. BRE was highly expressed in floral meristems and floral organ primordia. BRE could directly bind the GCC-box motif in the YUCCA (YUC) auxin biosynthesis genes FveYUC4 and FveYUC2 and promote their expression. The yuc4 mutant had fewer floral organs, and the bre yuc4 double mutant had similar numbers of petals and carpels to bre. Auxin homeostasis and distribution were severely disrupted in bre. Although auxin application or FveYUC4 overexpression did not rescue the bre phenotypes, bre was hypersensitive to treatment with the polar auxin transport inhibitor N-1-naphthylphthalamic acid (NPA). In addition, BRE was able to directly bind and regulate the expression of five other auxin pathway genes. Overall, these results demonstrate that BRE is required for floral organogenesis, particularly carpel initiation, and acts through the auxin pathway in strawberry.

A Next-Generation Combinatorial Genomic Strategy Scans Genomic Loci Governing Heat Stress Tolerance in Chickpea.

In the wake of rising earth temperature, chickpea crop production is haunted by the productivity crisis. Chickpea, a cool season legume manifests tolerance in several agro-physiological level, which is complex quantitative in nature, and regulated by multiple genes and genetic networks. Understanding the molecular genetic basis of this tolerance and identifying key regulators can leverage chickpea breeding against heat stress. This study employed a genomics-assisted breeding strategy utilizing multi-locus GWAS to identify 10 key genomic regions linked to traits contributing to heat stress tolerance in chickpea. These loci subsequently delineated few key candidates and hub regulatory genes, such as RAD23b, CIPK25, AAE19, CK1 and WRKY40, through integrated genomics, transcriptomics and interactive analyses. The differential transcript accumulation of these identified candidates in contrasting chickpea accessions suggests their potential role in heat stress tolerance. Differential ROS accumulation along with their scavengers' transcript abundance aligning with the expression of identified candidates in the contrasting chickpea accessions persuade their regulatory significance. Additionally, their functional significance is ascertained by heterologous expression and subsequent heat stress screening. The high confidence genomic loci and the superior genes and natural alleles delineated here has great potential for swift genomic interventions to enhance heat resilience and yield stability in chickpea.

MicroRNAs provide negative feedback and stability in gene regulatory network models of cell-state transitions

The development of multicellular organisms occurs through a series of cell state transitions controlled by gene regulatory networks. Central to these networks are transcription factors (TFs) which bind enhancers and activate the expression of other genes, some of which are also TFs. Gene regulatory networks (GRN) connect TFs and enhancers in a nonlinear circuit capable of producing complex behavior such as bifurcations between stable cell states. Our dynamic network modelling of the Embryonic Stem Cell (ESC) to Definitive Endoderm (DE) transition requires an as yet unknown negative feedback mechanism for stability. Here, we show that cell state specific microRNAs (miRNAs) can provide this negative feedback by inactivating other cell lineage determining TFs (ESC or DE) during the transition. Our model provides a mechanism to maintain stable cell states without requiring a large set of cell-type-specific repressive TFs, of which there are fewer known examples than activators. In support of this model, we use computational models and analyze gene and miRNA expression and chromatin accessibility data from human cell lines to detect enhancers activating the miRNAs consistent with our network model. Our analysis highlights the interplay between TFs and miRNAs during ESC to DE transition and proposes a novel model for gene regulation.

Predicting p53-dependent cell transitions from thermodynamic models.

A cell's fate involves transitions among its various states, each defined by a distinct gene expression profile governed by the topology of gene regulatory networks, which are affected by 3D genome organization. Here, we develop thermodynamic models to determine the fate of a malignant cell as governed by the tumor suppressor p53 signaling network, taking into account long-range chromatin interactions in the mean-field approximation. The tumor suppressor p53 responds to stress by selectively triggering one of the potential transcription programs that influence many layers of cell signaling. These range from p53 phosphorylation to modulation of its DNA binding affinity, phase separation phenomena, and internal connectivity among cell fate genes. We use the minimum free energy of the system as a fundamental property of biological networks that influences the connection between the gene network topology and the state of the cell. We constructed models based on network topology and equilibrium thermodynamics. Our modeling shows that the binding of phosphorylated p53 to promoters of target genes can have properties of a first order phase transition. We apply our model to cancer cell lines ranging from breast cancer (MCF-7), colon cancer (HCT116), and leukemia (K562), with each one characterized by a specific network topology that determines the cell fate. Our results clarify the biological relevance of these mechanisms and suggest that they represent flexible network designs for switching between developmental decisions.

In-depth investigation of the complex pathophysiological mechanisms between diabetes and ischemic stroke through gene expression and regulatory network analysis

This study explores the intricate relationship between diabetes and ischemic stroke (IS) through gene expression analysis and regulatory network investigation to identify potential biomarkers and therapeutic targets. Using datasets from the Gene Expression Omnibus (GEO) database, differential gene analysis was conducted on GSE43950 (diabetes) and GSE16561 (IS), revealing overlapping differentially expressed genes (DEGs). Functional enrichment analysis, Protein-Protein Interaction (PPI) network construction, and hub gene identification were performed, followed by validation in independent datasets (GSE156035 and GSE58294). The analysis identified 307 upregulated and 156 downregulated overlapping DEGs with significant enrichment in GO and KEGG pathways. Key hub genes (TLR2, TLR4, HDAC1, ITGAM) were identified through a PPI network (257 nodes, 456 interactions), with their roles in immune and inflammatory responses highlighted through GeneMANIA analysis. TRRUST-based transcription factor enrichment analysis revealed regulatory links involving RELA, SPI1, STAT3, and SP1. Differential expression analysis confirmed that RELA and SPI1 were upregulated in diabetes, while SPI1, STAT3, and SP1 were linked to IS. These transcription factors are involved in regulating immunity and inflammation, providing insights into the molecular mechanisms underlying diabetes-IS comorbidity. This bioinformatics-driven approach offers new understanding of the gene interactions and pathways involved, paving the way for potential therapeutic targets.