Machine learning reveals the transcriptional regulatory network and circadian dynamics of Synechococcus elongatus PCC 7942.

Synechococcus elongatus is an important cyanobacterium that serves as a versatile and robust model for studying circadian biology and photosynthetic metabolism. Its transcriptional regulatory network (TRN) is of fundamental interest, as it orchestrates the cell's adaptation to the environment, including its response to sunlight. Despite the previous characterization of constituent parts of the S. elongatus TRN, a comprehensive layout of its topology remains to be established. Here, we decomposed a compendium of 300 high-quality RNA sequencing datasets of the model strain PCC 7942 using independent component analysis. We obtained 57 independently modulated gene sets, or iModulons, that explain 67% of the variance in the transcriptional response and 1) accurately reflect the activity of known transcriptional regulations, 2) capture functional components of photosynthesis, 3) provide hypotheses for regulon structures and functional annotations of poorly characterized genes, and 4) describe the transcriptional shifts under dynamic light conditions. This transcriptome-wide analysis of S. elongatus provides a quantitative reconstruction of the TRN and presents a knowledge base that can guide future investigations. Our systems-level analysis also provides a global TRN structure for S. elongatus PCC 7942.

CD8+ T Cells Utilize Highly Dynamic Enhancer Repertoires and Regulatory Circuitry in Response to Infections.

Differentiation of effector and memory CD8+ T cells is accompanied by extensive changes in the transcriptome and histone modifications at gene promoters; however, the enhancer repertoire and associated gene regulatory networks are poorly defined. Using histone mark chromatin immunoprecipitation coupled with deep sequencing, we mapped the enhancer and super-enhancer landscapes in antigen-specific naive, differentiated effector, and central memory CD8+ T cells during LCMV infection. Epigenomics-based annotation revealed a highly dynamic repertoire of enhancers, which were inherited, de novo activated, decommissioned and re-activated during CD8+ T cell responses. We employed a computational algorithm to pair enhancers with target gene promoters. On average, each enhancer targeted three promoters and each promoter was regulated by two enhancers. By identifying enriched transcription factor motifs in enhancers, we defined transcriptional regulatory circuitry at each CD8+ T cell response stage. These multi-dimensional datasets provide a blueprint for delineating molecular mechanisms underlying functional differentiation of CD8+ T cells.

GraphTGI: an attention-based graph embedding model for predicting TF-target gene interactions.

MOTIVATION: Interaction between transcription factor (TF) and its target genes establishes the knowledge foundation for biological researches in transcriptional regulation, the number of which is, however, still limited by biological techniques. Existing computational methods relevant to the prediction of TF-target interactions are mostly proposed for predicting binding sites, rather than directly predicting the interactions. To this end, we propose here a graph attention-based autoencoder model to predict TF-target gene interactions using the information of the known TF-target gene interaction network combined with two sequential and chemical gene characters, considering that the unobserved interactions between transcription factors and target genes can be predicted by learning the pattern of the known ones. To the best of our knowledge, the proposed model is the first attempt to solve this problem by learning patterns from the known TF-target gene interaction network. RESULTS: In this paper, we formulate the prediction task of TF-target gene interactions as a link prediction problem on a complex knowledge graph and propose a deep learning model called GraphTGI, which is composed of a graph attention-based encoder and a bilinear decoder. We evaluated the prediction performance of the proposed method on a real dataset, and the experimental results show that the proposed model yields outstanding performance with an average AUC value of 0.8864 +/- 0.0057 in the 5-fold cross-validation. It is anticipated that the GraphTGI model can effectively and efficiently predict TF-target gene interactions on a large scale. AVAILABILITY: Python code and the datasets used in our studies are made available at https://github.com/YanghanWu/GraphTGI.

Regulatory networks of senescence-associated gene-transcription factors promote degradation in Moso bamboo shoots.

Bamboo cultivation, particularly Moso bamboo (Phyllostachys edulis), holds significant economic importance in various regions worldwide. Bamboo shoot degradation (BSD) severely affects productivity and economic viability. However, despite its agricultural consequences, the molecular mechanisms underlying BSD remain unclear. Consequently, we explored the dynamic changes of BSD through anatomy, physiology and the transcriptome. Our findings reveal ruptured protoxylem cells, reduced cell wall thickness and the accumulation of sucrose and reactive oxygen species (ROS) during BSD. Transcriptomic analysis underscored the importance of genes related to plant hormone signal transduction, sugar metabolism and ROS homoeostasis in this process. Furthermore, BSD appears to be driven by the coexpression regulatory network of senescence-associated gene transcription factors (SAG-TFs), specifically PeSAG39, PeWRKY22 and PeWRKY75, primarily located in the protoxylem of vascular bundles. Yeast one-hybrid and dual-luciferase assays demonstrated that PeWRKY22 and PeWRKY75 activate PeSAG39 expression by binding to its promoter. This study advanced our understanding of the molecular regulatory mechanisms governing BSD, offering a valuable reference for enhancing Moso bamboo forest productivity.

Increased production of isobutanol from xylose through metabolic engineering of Saccharomyces cerevisiae overexpressing transcription factor Znf1 and exogenous genes.

Only trace amount of isobutanol is produced by the native Saccharomyces cerevisiae via degradation of amino acids. Despite several attempts using engineered yeast strains expressing exogenous genes, catabolite repression of glucose must be maintained together with high activity of downstream enzymes, involving iron-sulfur assimilation and isobutanol production. Here, we examined novel roles of nonfermentable carbon transcription factor Znf1 in isobutanol production during xylose utilization. RNA-seq analysis showed that Znf1 activates genes in valine biosynthesis, Ehrlich pathway and iron-sulfur assimilation while coupled deletion or downregulated expression of BUD21 further increased isobutanol biosynthesis from xylose. Overexpression of ZNF1 and xylose-reductase/dehydrogenase (XR-XDH) variants, a xylose-specific sugar transporter, xylulokinase, and enzymes of isobutanol pathway in the engineered S. cerevisiae pho13gre3Δ strain resulted in the superb ZNXISO strain, capable of producing high levels of isobutanol from xylose. The isobutanol titer of 14.809 ± 0.400 g/L was achieved, following addition of 0.05 g/L FeSO4.7H2O in 5 L bioreactor. It corresponded to 155.88 mg/g xylose consumed and + 264.75% improvement in isobutanol yield. This work highlights a new regulatory control of alternative carbon sources by Znf1 on various metabolic pathways. Importantly, we provide a foundational step toward more sustainable production of advanced biofuels from the second most abundant carbon source xylose.

Lung Cancer Gene Regulatory Network of Transcription Factors Related to the Hallmarks of Cancer.

The transcriptomic analysis of microarray and RNA-Seq datasets followed our own bioinformatic pipeline to identify a transcriptional regulatory network of lung cancer. Twenty-six transcription factors are dysregulated and co-expressed in most of the lung cancer and pulmonary arterial hypertension datasets, which makes them the most frequently dysregulated transcription factors. Co-expression, gene regulatory, coregulatory, and transcriptional regulatory networks, along with fibration symmetries, were constructed to identify common connection patterns, alignments, main regulators, and target genes in order to analyze transcription factor complex formation, as well as its synchronized co-expression patterns in every type of lung cancer. The regulatory function of the most frequently dysregulated transcription factors over lung cancer deregulated genes was validated with ChEA3 enrichment analysis. A Kaplan-Meier plotter analysis linked the dysregulation of the top transcription factors with lung cancer patients' survival. Our results indicate that lung cancer has unique and common deregulated genes and transcription factors with pulmonary arterial hypertension, co-expressed and regulated in a coordinated and cooperative manner by the transcriptional regulatory network that might be associated with critical biological processes and signaling pathways related to the acquisition of the hallmarks of cancer, making them potentially relevant tumor biomarkers for lung cancer early diagnosis and targets for the development of personalized therapies against lung cancer.

etiBsu1209: A comprehensive multiscale metabolic model for Bacillus subtilis.

Genome-scale metabolic models (GEMs) have been widely used to guide the computational design of microbial cell factories, and to date, seven GEMs have been reported for Bacillus subtilis, a model gram-positive microorganism widely used in bioproduction of functional nutraceuticals and food ingredients. However, none of them are widely used because they often lead to erroneous predictions due to their low predictive power and lack of information on regulatory mechanisms. In this work, we constructed a new version of GEM for B. subtilis (iBsu1209), which contains 1209 genes, 1595 metabolites, and 1948 reactions. We applied machine learning to fill gaps, which formed a relatively complete metabolic network able to predict with high accuracy (89.3%) the growth of 1209 mutants under 12 different culture conditions. In addition, we developed a visualization and code-free software, Model Tool, for multiconstraints model reconstruction and analysis. We used this software to construct etiBsu1209, a multiscale model that integrates enzymatic constraints, thermodynamic constraints, and transcriptional regulatory networks. Furthermore, we used etiBsu1209 to guide a metabolic engineering strategy (knocking out fabI and yfkN genes) for the overproduction of nutraceutical menaquinone-7, and the titer increased to 153.94 mg/L, 2.2-times that of the parental strain. To the best of our knowledge, etiBsu1209 is the first comprehensive multiscale model for B. subtilis and can serve as a solid basis for rational computational design of B. subtilis cell factories for bioproduction.

Revealing 29 sets of independently modulated genes in Staphylococcus aureus, their regulators, and role in key physiological response.

The ability of Staphylococcus aureus to infect many different tissue sites is enabled, in part, by its transcriptional regulatory network (TRN) that coordinates its gene expression to respond to different environments. We elucidated the organization and activity of this TRN by applying independent component analysis to a compendium of 108 RNA-sequencing expression profiles from two S. aureus clinical strains (TCH1516 and LAC). ICA decomposed the S. aureus transcriptome into 29 independently modulated sets of genes (i-modulons) that revealed: 1) High confidence associations between 21 i-modulons and known regulators; 2) an association between an i-modulon and σS, whose regulatory role was previously undefined; 3) the regulatory organization of 65 virulence factors in the form of three i-modulons associated with AgrR, SaeR, and Vim-3; 4) the roles of three key transcription factors (CodY, Fur, and CcpA) in coordinating the metabolic and regulatory networks; and 5) a low-dimensional representation, involving the function of few transcription factors of changes in gene expression between two laboratory media (RPMI, cation adjust Mueller Hinton broth) and two physiological media (blood and serum). This representation of the TRN covers 842 genes representing 76% of the variance in gene expression that provides a quantitative reconstruction of transcriptional modules in S. aureus, and a platform enabling its full elucidation.

Comparative physiological and transcriptomic analyses reveal key regulatory networks and potential hub genes controlling peanut chilling tolerance.

The unclear molecular mechanism by which peanuts adapt to chilling stress limits progress in molecular breeding for peanut chilling tolerance. Here, the physiological and transcriptional differences between two genotypes with contrasting tolerance under chilling stress were compared. The inhibition of photosynthesis mainly caused by stomatal factors was a common response of peanut seedlings to chilling stress. Chilling-tolerant genotypes could inhibit the accumulation of ROS to adapt to chilling stress, and enhanced activities of CAT and APX were major causes of lower H2O2 content. The results of a conjoint analysis of physiological indices and the RNA-Seq database by WGCNA indicated that the genes in key modules were significantly enriched in pathways related to the oxidation-reduction process. Hub genes encoding RLK, CAT, MYC4, AOS, GST, PP2C, UPL5 and ZFP8 were likely to positively regulate peanut chilling tolerance, but hub genes encoding PAO, NAC2 and NAC72 were likely to negatively regulate peanut chilling tolerance.

Independent Component Analysis Reveals the Transcriptional Regulatory Modules in Bradyrhizobium diazoefficiens USDA110.

The dynamic adaptation of bacteria to environmental changes is achieved through the coordinated expression of many genes, which constitutes a transcriptional regulatory network (TRN). Bradyrhizobium diazoefficiens USDA110 is an important model strain for the study of symbiotic nitrogen fixation (SNF), and its SNF ability largely depends on the TRN. In this study, independent component analysis was applied to 226 high-quality gene expression profiles of B. diazoefficiens USDA110 microarray datasets, from which 64 iModulons were identified. Using these iModulons and their condition-specific activity levels, we (1) provided new insights into the connection between the FixLJ-FixK2-FixK1 regulatory cascade and quorum sensing, (2) discovered the independence of the FixLJ-FixK2-FixK1 and NifA/RpoN regulatory cascades in response to oxygen, (3) identified the FixLJ-FixK2 cascade as a mediator connecting the FixK2-2 iModulon and the Phenylalanine iModulon, (4) described the differential activation of iModulons in B. diazoefficiens USDA110 under different environmental conditions, and (5) proposed a notion of active-TRN based on the changes in iModulon activity to better illustrate the relationship between gene regulation and environmental condition. In sum, this research offered an iModulon-based TRN for B. diazoefficiens USDA110, which formed a foundation for comprehensively understanding the intricate transcriptional regulation during SNF.

Host Transcriptional Regulatory Genes and Microbiome Networks Crosstalk through Immune Receptors Establishing Normal and Tumor Multiomics Metafirm of the Oral-Gut-Lung Axis.

The microbiome has shown a correlation with the diet and lifestyle of each population in health and disease, the ability to communicate at the cellular level with the host through innate and adaptative immune receptors, and therefore an important role in modulating inflammatory process related to the establishment and progression of cancer. The oral cavity is one of the most important interaction windows between the human body and the environment, allowing the entry of an important number of microorganisms and their passage across the gastrointestinal tract and lungs. In this review, the contribution of the microbiome network to the establishment of systemic diseases like cancer is analyzed through their synergistic interactions and bidirectional crosstalk in the oral-gut-lung axis as well as its communication with the host cells. Moreover, the impact of the characteristic microbiota of each population in the formation of the multiomics molecular metafirm of the oral-gut-lung axis is also analyzed through state-of-the-art sequencing techniques, which allow a global study of the molecular processes involved of the flow of the microbiota environmental signals through cancer-related cells and its relationship with the establishment of the transcription factor network responsible for the control of regulatory processes involved with tumorigenesis.

Genome-wide strategies identify downstream target genes of chick connective tissue-associated transcription factors.

Connective tissues support organs and play crucial roles in development, homeostasis and fibrosis, yet our understanding of their formation is still limited. To gain insight into the molecular mechanisms of connective tissue specification, we selected five zinc-finger transcription factors - OSR1, OSR2, EGR1, KLF2 and KLF4 - based on their expression patterns and/or known involvement in connective tissue subtype differentiation. RNA-seq and ChIP-seq profiling of chick limb micromass cultures revealed a set of common genes regulated by all five transcription factors, which we describe as a connective tissue core expression set. This common core was enriched with genes associated with axon guidance and myofibroblast signature, including fibrosis-related genes. In addition, each transcription factor regulated a specific set of signalling molecules and extracellular matrix components. This suggests a concept whereby local molecular niches can be created by the expression of specific transcription factors impinging on the specification of local microenvironments. The regulatory network established here identifies common and distinct molecular signatures of limb connective tissue subtypes, provides novel insight into the signalling pathways governing connective tissue specification, and serves as a resource for connective tissue development.

Deciphering transcriptional regulators of banana fruit ripening by regulatory network analysis.

Fruit ripening is a critical phase in the production and marketing of fruits. Previous studies have indicated that fruit ripening is a highly coordinated process, mainly regulated at the transcriptional level, in which transcription factors play essential roles. Thus, identifying key transcription factors regulating fruit ripening as well as their associated regulatory networks promises to contribute to a better understanding of fruit ripening. In this study, temporal gene expression analyses were performed to investigate banana fruit ripening with the aim to discern the global architecture of gene regulatory networks underlying fruit ripening. Eight time points were profiled covering dynamic changes of phenotypes, the associated physiology and levels of known ripening marker genes. Combining results from a weighted gene co-expression network analysis (WGCNA) as well as cis-motif analysis and supported by EMSA, Y1H, tobacco-, banana-transactivation experimental results, the regulatory network of banana fruit ripening was constructed, from which 25 transcription factors were identified as prime candidates to regulate the ripening process by modulating different ripening-related pathways. Our study presents the first global view of the gene regulatory network involved in banana fruit ripening, which may provide the basis for a targeted manipulation of fruit ripening to attain higher banana and loss-reduced banana commercialization.

Transcriptional control of parturition: insights from gene regulation studies in the myometrium.

The onset of labour is a culmination of a series of highly coordinated and preparatory physiological events that take place throughout the gestational period. In order to produce the associated contractions needed for foetal delivery, smooth muscle cells in the muscular layer of the uterus (i.e. myometrium) undergo a transition from quiescent to contractile phenotypes. Here, we present the current understanding of the roles transcription factors play in critical labour-associated gene expression changes as part of the molecular mechanistic basis for this transition. Consideration is given to both transcription factors that have been well-studied in a myometrial context, i.e. activator protein 1, progesterone receptors, oestrogen receptors, and nuclear factor kappa B, as well as additional transcription factors whose gestational event-driving contributions have been demonstrated more recently. These transcription factors may form pregnancy- and labour-associated transcriptional regulatory networks in the myometrium to modulate the timing of labour onset. A more thorough understanding of the transcription factor-mediated, labour-promoting regulatory pathways holds promise for the development of new therapeutic treatments that can be used for the prevention of preterm labour in at-risk women.

GenHITS: A network science approach to driver gene detection in human regulatory network using gene's influence evaluation.

Cancer is among the diseases causing death, in which, cells uncontrollably grow and reproduce beyond the cell regulatory mechanism. In this disease, some genes are initiators of abnormalities and then transmit them to other genes through protein interactions. Accordingly, these genes are known as cancer driver genes (CDGs). In this regard, several methods have been previously developed for identifying cancer driver genes. Most of these methods are computational-based, which use the concept of mutation to predict CDGs. In this research, a method has been proposed for identifying CDGs in the transcription regulatory network using the concept of influence diffusion and by modifying the Hyperlink-Induced Topic Search algorithm based on the diffusion concept. Due to the type of these networks and the processes of abnormality progression in cells and the formation of cancerous tumors, high-influence genes can be the most likely considered as the driver genes. Therefore, we can use the influence diffusion concept as an acceptable theory to identify these genes. Recently, a method has been proposed to detect CDGs with the concept of the influence maximization. One of the challenges in these types of networks is finding the power of regulatory interaction between genes. Moreover, we have proposed a novel method to calculate the weight of regulatory interactions, based on the concept of diffusion. The performance of the proposed method was compared with other seventeen computational and network tools. Correspondingly, three cancer types were used as benchmarks as follows: breast invasive carcinoma (BRCA), Colon adenocarcinoma (COAD), and lung squamous cell carcinoma (LUSC). In addition, to determine the accuracy of the detected drivers using each method, CGC (Cancer Gene Census) and Mut-driver gene lists were utilized as gold standard. The results show that GenHITS performs better compared to the most of the other computational and network methods. Besides, it is also able to identify genes that have been identified by none of the other methods yet.

Maize endosperm development.

Recent breakthroughs in transcriptome analysis and gene characterization have provided valuable resources and information about the maize endosperm developmental program. The high temporal-resolution transcriptome analysis has yielded unprecedented access to information about the genetic control of seed development. Detailed spatial transcriptome analysis using laser-capture microdissection has revealed the expression patterns of specific populations of genes in the four major endosperm compartments: the basal endosperm transfer layer (BETL), aleurone layer (AL), starchy endosperm (SE), and embryo-surrounding region (ESR). Although the overall picture of the transcriptional regulatory network of endosperm development remains fragmentary, there have been some exciting advances, such as the identification of OPAQUE11 (O11) as a central hub of the maize endosperm regulatory network connecting endosperm development, nutrient metabolism, and stress responses, and the discovery that the endosperm adjacent to scutellum (EAS) serves as a dynamic interface for endosperm-embryo crosstalk. In addition, several genes that function in BETL development, AL differentiation, and the endosperm cell cycle have been identified, such as ZmSWEET4c, Thk1, and Dek15, respectively. Here, we focus on current advances in understanding the molecular factors involved in BETL, AL, SE, ESR, and EAS development, including the specific transcriptional regulatory networks that function in each compartment during endosperm development.

Microarray data analysis reveals gene expression changes in response to ionizing radiation in MCF7 human breast cancer cells.

BACKGROUND: The aim of this study was to identify potential therapeutic target genes for breast cancer (BC) by the investigation of gene expression changes after ionizing radiation (IR) in BC cells. Gene expression profile GSE21748, including BC cell line MCF-7 samples at different time points after IR treatment, were downloaded from Gene Expression Omnibus. Differentially expressed genes (DEGs) were identified in different time points following IR compared with cell samples before IR, respectively. Gene ontology functions and The Kyoto Encyclopedia of Genes and Genomes pathways of the overlapping DEGs were enriched using DAVID. Transcription factor (TFs)-encoding genes were identified from the overlapping DEGs, followed by construction of transcriptional regulatory network and co-expression network. RESULTS: A total of 864 overlapping DEGs were identified, which were significantly enriched in regulation of cell proliferation and apoptosis, and cell cycle process. We found that FOXD1, STAT6, XBP1, STAT2, LMO2, TFAP4, STAT3, STAT1 were hub nodes in the transcriptional regulatory network of the overlapping DEGs. The co-expression network of target genes regulated by STAT3, STAT1, STAT6 and STAT2 included some key genes such as BCL2L1. CONCLUSION: STAT1, STAT2, STAT3, STAT6, XBP1, BCL2L1, CYB5D2, ESCO2, and PARP2 were significantly affected by IR and they may be used as therapeutic gene targets in the treatment of BC.

Multiple transcription factors co-regulate the Mycobacterium tuberculosis adaptation response to vitamin C.

BACKGROUND: Latent tuberculosis infection is attributed in part to the existence of Mycobacterium tuberculosis in a persistent non-replicating dormant state that is associated with tolerance to host defence mechanisms and antibiotics. We have recently reported that vitamin C treatment of M. tuberculosis triggers the rapid development of bacterial dormancy. Temporal genome-wide transcriptome analysis has revealed that vitamin C-induced dormancy is associated with a large-scale modulation of gene expression in M. tuberculosis. RESULTS: An updated transcriptional regulatory network of M.tuberculosis (Mtb-TRN) consisting of 178 regulators and 3432 target genes was constructed. The temporal transcriptome data generated in response to vitamin C was overlaid on the Mtb-TRN (vitamin C Mtb-TRN) to derive insights into the transcriptional regulatory features in vitamin C-adapted bacteria. Statistical analysis using Fisher's exact test predicted that 56 regulators play a central role in modulating genes which are involved in growth, respiration, metabolism and repair functions. Rv0348, DevR, MprA and RegX3 participate in a core temporal regulatory response during 0.25 h to 8 h of vitamin C treatment. Temporal network analysis further revealed Rv0348 to be the most prominent hub regulator with maximum interactions in the vitamin C Mtb-TRN. Experimental analysis revealed that Rv0348 and DevR proteins interact with each other, and this interaction results in an enhanced binding of DevR to its target promoter. These findings, together with the enhanced expression of devR and Rv0348 transcriptional regulators, indicate a second-level regulation of target genes through transcription factor- transcription factor interactions. CONCLUSIONS: Temporal regulatory analysis of the vitamin C Mtb-TRN revealed that there is involvement of multiple regulators during bacterial adaptation to dormancy. Our findings suggest that Rv0348 is a prominent hub regulator in the vitamin C model and large-scale modulation of gene expression is achieved through interactions of Rv0348 with other transcriptional regulators.

Norepinephrine triggers an immediate-early regulatory network response in primary human white adipocytes.

BACKGROUND: Norepinephrine (NE) signaling has a key role in white adipose tissue (WAT) functions, including lipolysis, free fatty acid liberation and, under certain conditions, conversion of white into brite (brown-in-white) adipocytes. However, acute effects of NE stimulation have not been described at the transcriptional network level. RESULTS: We used RNA-seq to uncover a broad transcriptional response. The inference of protein-protein and protein-DNA interaction networks allowed us to identify a set of immediate-early genes (IEGs) with high betweenness, validating our approach and suggesting a hierarchical control of transcriptional regulation. In addition, we identified a transcriptional regulatory network with IEGs as master regulators, including HSF1 and NFIL3 as novel NE-induced IEG candidates. Moreover, a functional enrichment analysis and gene clustering into functional modules suggest a crosstalk between metabolic, signaling, and immune responses. CONCLUSIONS: Altogether, our network biology approach explores for the first time the immediate-early systems level response of human adipocytes to acute sympathetic activation, thereby providing a first network basis of early cell fate programs and crosstalks between metabolic and transcriptional networks required for proper WAT function.

Microarray analysis to identify the similarities and differences of pathogenesis between aortic occlusive disease and abdominal aortic aneurysm.

Objectives Expression profile of GSE57691 was analyzed to identify the similarities and differences between aortic occlusive disease and abdominal aortic aneurysm. Methods The expression profile of GSE57691 was downloaded from Gene Expression Omnibus database, including 20 small abdominal aortic aneurysm samples, 29 large abdominal aortic aneurysm samples, 9 aortic occlusive disease samples, and 10 control samples. Using the limma package in R, the differentially expressed genes were screened. Followed by enrichment analysis was performed for the differentially expressed genes using database for annotation, visualization, and integrated discovery online tool. Based on string online tool and Cytoscape software, protein-protein interaction network and module analyses were carried out. Moreover, integrated TF platform database and Cytoscape software were used for constructing transcriptional regulatory networks. Results As a result, 1757, 354, and 396 differentially expressed genes separately were identified in aortic occlusive disease, large abdominal aortic aneurysm, and small abdominal aortic aneurysm samples. UBB was significantly enriched in proteolysis related pathways with a high degree in three groups. SPARCL1 was another gene shared by these groups and regulated by NFIA, which had a high degree in transcriptional regulatory network. ACTB, a significant upregulated gene in abdominal aortic aneurysm samples, could be regulated by CLIC4, which was significantly enriched in cell motions. ACLY and NFIB were separately identified in aortic occlusive disease and small abdominal aortic aneurysm samples, and separately enriched in lipid metabolism and negative regulation of cell proliferation. Conclusions The downregulated UBB, NFIA, and SPARCL1 might play key roles in both aortic occlusive disease and abdominal aortic aneurysm, while the upregulated ACTB might only involve in abdominal aortic aneurysm. ACLY and NFIB were specifically involved in aortic occlusive disease and small abdominal aortic aneurysm separately.