Transcription Factor Gene Regulatory Networks Research Articles

Identification of shared pathogenetic mechanisms between COVID-19 and IC through bioinformatics and system biology

COVID-19 increased global mortality in 2019. Cystitis became a contributing factor in SARS-CoV-2 and COVID-19 complications. The complex molecular links between cystitis and COVID-19 are unclear. This study investigates COVID-19-associated cystitis (CAC) molecular mechanisms and drug candidates using bioinformatics and systems biology. Obtain the gene expression profiles of IC (GSE11783) and COVID-19 (GSE147507) from the Gene Expression Omnibus (GEO) database. Identified the common differentially expressed genes (DEGs) in both IC and COVID-19, and extracted a number of key genes from this group. Subsequently, conduct Gene Ontology (GO) functional enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis on the DEGs. Additionally, design a protein–protein interaction (PPI) network, a transcription factor gene regulatory network, a TF miRNA regulatory network, and a gene disease association network using the DEGs. Identify and extract hub genes from the PPI network. Then construct Nomogram diagnostic prediction models based on the hub genes. The DSigDB database was used to forecast many potential molecular medicines that are associated with common DEGs. Assess the precision of hub genes and Nomogram models in diagnosing IC and COVID-19 by employing Receiver Operating Characteristic (ROC) curves. The IC dataset (GSE57560) and the COVID-19 dataset (GSE171110) were selected to validate the models' diagnostic accuracy. A grand total of 198 DEGs that overlapped were found and chosen for further research. FCER1G, ITGAM, LCP2, LILRB2, MNDA, SPI1, and TYROBP were screened as the hub genes. The Nomogram model, built using the seven hub genes, demonstrates significant utility as a diagnostic prediction model for both IC and COVID-19. Multiple potential molecular medicines associated with common DEGs have been discovered. These pathways, hub genes, and models may provide new perspectives for future research into mechanisms and guide personalised and effective therapeutics for IC patients infected with COVID-19.

MICA: a multi-omics method to predict gene regulatory networks in early human embryos.

Recent advances in single-cell omics have transformed characterisation of cell types in challenging-to-study biological contexts. In contexts with limited single-cell samples, such as the early human embryo inference of transcription factor-gene regulatory network (GRN) interactions is especially difficult. Here, we assessed application of different linear or non-linear GRN predictions to single-cell simulated and human embryo transcriptome datasets. We also compared how expression normalisation impacts on GRN predictions, finding that transcripts per million reads outperformed alternative methods. GRN inferences were more reproducible using a non-linear method based on mutual information (MI) applied to single-cell transcriptome datasets refined with chromatin accessibility (CA) (called MICA), compared with alternative network prediction methods tested. MICA captures complex non-monotonic dependencies and feedback loops. Using MICA, we generated the first GRN inferences in early human development. MICA predicted co-localisation of the AP-1 transcription factor subunit proto-oncogene JUND and the TFAP2C transcription factor AP-2γ in early human embryos. Overall, our comparative analysis of GRN prediction methods defines a pipeline that can be applied to single-cell multi-omics datasets in especially challenging contexts to infer interactions between transcription factor expression and target gene regulation.

Identification and validation of biomarkers based on cellular senescence in mild cognitive impairment.

Mild cognitive impairment (MCI), a syndrome defined as decline of cognitive function greater than expected for an individual's age and education level, occurs in up to 22.7% of elderly patients in United States, causing the heavy psychological and economic burdens to families and society. Cellular senescence (CS) is a stress response that accompanies permanent cell-cycle arrest, which has been reported to be a fundamental pathological mechanism of many age-related diseases. This study aims to explore biomarkers and potential therapeutic targets in MCI based on CS. The mRNA expression profiles of peripheral blood samples from patients in MCI and non-MCI group were download from gene expression omnibus (GEO) database (GSE63060 for training and GSE18309 for external validation), CS-related genes were obtained from CellAge database. Weighted gene co-expression network analysis (WGCNA) was conducted to discover the key relationships behind the co-expression modules. The differentially expressed CS-related genes would be obtained through overlapping among the above datasets. Then, pathway and GO enrichment analyses were performed to further elucidate the mechanism of MCI. The protein-protein interaction network was used to extract hub genes and the logistic regression was performed to distinguish the MCI patients from controls. The hub gene-drug network, hub gene-miRNA network as well as transcription factor-gene regulatory network were used to analyze potential therapeutic targets for MCI. Eight CS-related genes were identified as key gene signatures in MCI group, which were mainly enriched in the regulation of response to DNA damage stimulus, Sin3 complex and transcription corepressor activity. The receiver operating characteristic curves of logistic regression diagnostic model were constructed and presented great diagnostic value in both training and validation set. Eight CS-related hub genes - SMARCA4, GAPDH, SMARCB1, RUNX1, SRC, TRIM28, TXN, and PRPF19 - serve as candidate biomarkers for MCI and display the excellent diagnostic value. Furthermore, we also provide a theoretical basis for targeted therapy against MCI through the above hub genes.

Sensitivity Analysis of Genome-Scale Metabolic Flux Prediction.

TRIMER, Transcription Regulation Integrated with MEtabolic Regulation, is a genome-scale modeling pipeline targeting at metabolic engineering applications. Using TRIMER, regulated metabolic reactions can be effectively predicted by integrative modeling of metabolic reactions with a transcription factor-gene regulatory network (TRN), which is modeled through a Bayesian network (BN). In this article, we focus on sensitivity analysis of metabolic flux prediction for uncertainty quantification of BN structures for TRN modeling in TRIMER. We propose a computational strategy to construct the uncertainty class of TRN models based on the inferred regulatory order uncertainty given transcriptomic expression data. With that, we analyze the prediction sensitivity of the TRIMER pipeline for the metabolite yields of interest. The obtained sensitivity analyses can guide optimal experimental design (OED) to help acquire new data that can enhance TRN modeling and achieve specific metabolic engineering objectives, including metabolite yield alterations. We have performed small- and large-scale simulated experiments, demonstrating the effectiveness of our developed sensitivity analysis strategy for BN structure learning to quantify the edge importance in terms of metabolic flux prediction uncertainty reduction and its potential to effectively guide OED.

Comparative co-expression analysis of RNA-Seq transcriptome revealing key genes, miRNA and transcription factor in distinct metabolic pathways in diabetic nerve, eye, and kidney disease.

Diabetes and its related complications are associated with long term damage and failure of various organ systems. The microvascular complications of diabetes considered in this study are diabetic retinopathy, diabetic neuropathy, and diabetic nephropathy. The aim is to identify the weighted co-expressed and differentially expressed genes (DEGs), major pathways, and their miRNA, transcription factors (TFs) and drugs interacting in all the three conditions. The primary goal is to identify vital DEGs in all the three conditions. The overlapped five genes (AKT1, NFKB1, MAPK3, PDPK1, and TNF) from the DEGs and the co-expressed genes were defined as key genes, which differentially expressed in all the three cases. Then the protein-protein interaction network and gene set linkage analysis (GSLA) of key genes was performed. GSLA, gene ontology, and pathway enrichment analysis of the key genes elucidates nine major pathways in diabetes. Subsequently, we constructed the miRNA-gene and transcription factor-gene regulatory network of the five gene of interest in the nine major pathways were studied. hsa-mir-34a-5p, a major miRNA that interacted with all the five genes. RELA, FOXO3, PDX1, and SREBF1 were the TFs interacting with the major five gene of interest. Finally, drug-gene interaction network elucidates five potential drugs to treat the genes of interest. This research reveals biomarker genes, miRNA, TFs, and therapeutic drugs in the key signaling pathways, which may help us, understand the processes of all three secondary microvascular problems and aid in disease detection and management.

TReNCo: Topologically associating domain (TAD) aware regulatory network construction

Introduction: There has long been a desire to understand, describe, and model gene regulatory networks controlling numerous biologically meaningful processes like differentiation. Despite many notable improvements to models over the years, many models do not accurately capture subtle biological and chemical characteristics of the cell such as high-order chromatin domains of the chromosomes. Methods: Topologically Associated Domains (TAD) are one of these genomic regions that are enriched for contacts within themselves. Here we present TAD-aware Regulatory Network Construction or TReNCo, a memory-lean method utilizing epigenetic marks of enhancer and promoter activity, and gene expression to create context-specific transcription factor-gene regulatory networks. TReNCo utilizes common assays, ChIP-seq, RNA-seq, and TAD boundaries as a hard cutoff, instead of distance based, to efficiently create context-specific TF-gene regulatory networks. Results: We used TReNCo to define the enhancer landscape and identify transcription factors (TFs) that drive the cardiac development of the mouse. Conclusion: Our results show that we are able to build specialized adjacency regulatory network graphs containing biologically relevant connections and time dependent dynamics.

Changes in gene expression of adipose tissue CD14+ cells in patients with Type 2 diabetes mellitus and their relationship with environmental factors.

To study the gene expression of adipose tissue CD14+ cells in patients with Type 2 diabetes mellitus (T2DM) based on chip data, screen differentially expressed genes, and analyze their relationship with the environmental factors. The data of GSE54350 were obtained from the public database of gene expression profiling. The data were pre-processed by Network Analyst, String 11.0, Cytoscape 3.7.1, and other analytical software. The differentially expressed genes were analyzed by gene ontology biological function and kyoto encycopedia of genes and genomes (KEGG) signaling pathway to establish differential gene protein interaction network, transcription factor-gene regulatory network, microRNA-gene regulatory network, environmental factors-gene regulatory network, and other interaction systems. The gene expression pattern of CD14+ cells in adipose tissue of obese T2DM patients was significantly different from that of obese non-T2DM patients. There were 19 differentially expressed genes with up-regulation. The differentially expressed genes were mainly involved in ARP2/3 complex regulation of actin cytoskeleton, positively associated with biological processes such as protein complex assembly, and involved in the phagocytic Fcγ receptor signaling pathways and receptor family signaling pathways. The protein-protein interaction networks showed that TNF was the core protein node. The microRNA-gene regulatory network showed that hsa-mir-124-3p interacted with differentially expressed genes; TNF, KYNU, RCAN1 and other related genes all interacted with environmental factors. The gene expression of adipose tissue CD14+ cells are significantly changed in obese T2DM patients. TNF may play an important role in the process of obesity affecting the immune status of T2DM patients. Multiple microRNAs, transcription factors, and environmental factors also play a role in the above process. This study provides new material and new ideas for further exploration of the impact of obesity on T2DM patients.

Abstract A22: Kras drives changes in acinar-specific gene regulatory networks in early pancreatic neoplasia in conjunction with Bmi1

Abstract The initial stages of pancreatic ductal adenocarcinoma (PDA) involve an oncogenic Kras-driven metaplastic transition of acinar cells to a more duct-like phenotype referred to as acinar to ductal metaplasia (ADM). The exact mechanism by which Kras reprograms acinar cells to a more duct-like cell at the genomic level is not completely understood. Developmental transcription factor (TF) networks regulate and maintain the fate of cells in other contexts and rely on appropriate chromatin context for their activity. Deletion of Bmi1, a component of the Polycomb Repressor Complex 1, completely represses Kras-driven ADM and neoplasia in genetically engineered mouse models of PDA. Thus, we hypothesized that Kras reprograms the acinar fate TF network to promote PDA in a Bmi1-dependent epigenetic context. We used a genetically engineered mouse model of early pancreatic neoplasia bearing an acinar cell-specific tamoxifen inducible Cre-recombinase under the elastase promoter (Ela-CreER) combined with the Kras G12D oncogenic allele (Kras−LSL-G12D/+). We added the conditional Bmi1 knockout allele (Bmi1−fl) and the Rosa26 tdTomato reporter to generate Ela-CreER, Kras−LSL-G12D/+, Bmi1−fl/fl, R26 tdTomato mice and their litter mate controls with and without KrasG12D and Bmi1. We activated the Cre-recombinase in 6- to 8-week-old mice with 5 daily tamoxifen gavages (4mg/day) prior to induction of acute pancreatitis by intraperitoneal caerulein administration (eight doses per day for two days) one week after the first tamoxifen dose. We used FACS to sort tdTomato+ cells one week after pancreatitis induction and analyzed RNA expression levels of 26 TFs, previously reported to play a role in pancreatic development, homeostasis and neoplasia. Expression levels of three lineage markers (amylase, elastase and cytokeratin 19) were also measured by quantitative RT-PCR. Tamoxifen gavage induced high tdTomato expression in the acinar compartment of Ela-CreER+ mice. RT-qPCR of tdTomato+ cells indicated a loss of acinar specific TFs in mice expressing mutant Kras one week after induction of pancreatitis. This is consistent with cells undergoing ADM. Genetic ablation of Bmi1 in the presence of mutant Kras restored elastase and amylase expression at the mRNA level. At the morphologic level, loss of Bmi1 also led to recovery of acinar cells from ADM and correlated with an increase in expression of three key acinar-specific fate TFs, Mist1, Hnf1a and Nr5a2. The mRNA expression levels of other acinar TFs like Pdx1 and Ptf1a/p48 did not recover with loss of Bmi1 and mutant Kras expression. Together, our data suggest that oncogenic Kras-driven ADM is controlled by changes in master TF gene regulatory networks. Bmi1 deletion leads to partial reprogramming of these networks to allow acinar cells to resist Kras-driven oncogenesis. Citation Format: Joyce K. Thompson, Jack Blumberg, Osama Alkahili, Howard C. Crawford, Marina Pasca di Magliano, Filip Bednar. Kras drives changes in acinar-specific gene regulatory networks in early pancreatic neoplasia in conjunction with Bmi1 [abstract]. In: Proceedings of the AACR Special Conference on Targeting RAS-Driven Cancers; 2018 Dec 9-12; San Diego, CA. Philadelphia (PA): AACR; Mol Cancer Res 2020;18(5_Suppl):Abstract nr A22.

Abstract C56: Bmi1 is widely expressed in acini and regulates Kras-driven transcription factor networks in early pancreatic neoplasia

Abstract Rare acinar cells expressing Bmi1, a component of the Polycomb Repressor Complex 1 (PRC1), represent a reservoir of cells contributing to pancreatic repair during injury and stress. This is marked by acinar-ductal metaplasia (ADM), a transition of acinar cells to a more duct-like phenotype, which is seen in pancreatitis and early stages of Kras-driven pancreatic neoplasia. The mechanism behind Kras-driven ADM at the genomic level is not completely understood. Developmental transcription factor (TF) networks regulate and maintain the fate of cells and rely on appropriate chromatin context for their activity. Deletion of Bmi1 represses Kras-driven ADM and neoplasia in genetically engineered mouse models (GEMMs) of PDA. Thus, we hypothesized that Kras reprograms the acinar fate TF network to promote PDA in a Bmi1-dependent epigenetic context. We used GEMMs to study the expression of Bmi1 in the acinar compartment and its role in early pancreatic neoplasia. We combined an acinar cell-specific tamoxifen-inducible Cre-recombinase under the elastase promoter (Ela-CreER) with the Kras G12D oncogenic allele and the conditional Bmi1 knockout allele and the Rosa26 tdTomato reporter to generate Ela-CreER, KrasLSL-G12D/+, Bmi1, R26 tdTomato mice and their littermate controls. We also generated Ela-CreER, Bmi1GFP, R26tdTomato and Bmi1-CreER, R26 tdTomato mice to quantify the fraction of acinar cells expressing Bmi1. We activated the Cre-recombinase in 6- to 8-week-old Ela-CreER mice with 5 tamoxifen gavages (4mg/day) prior to induction of acute pancreatitis by intraperitoneal caerulein administration (8 hourly injections x 2 days) one week after the first tamoxifen dose. We FACS sorted tdTomato+ cells from untreated mice (0 hr) and one week (168 hr) after pancreatitis induction and analyzed RNA levels of 8 TFs, reported to play a role in pancreatic development, homeostasis, and neoplasia. Expression levels of two lineage markers (amylase and elastase) were also measured by RT-qPCR. Lineage tracing in Bmi1-CreER and Ela-CreER, Bmi1GFP mice revealed expression of Bmi1 in vast majority of acinar cells. RT-qPCR of tdTomato+ cells in the Ela-CreER models indicated a loss of acinar-specific TFs in mice expressing mutant Kras one week after induction of pancreatitis consistent with cells undergoing ADM. Genetic ablation of Bmi1 in the presence of mutant Kras restored elastase and amylase mRNA levels. At the morphologic level, loss of Bmi1 also led to recovery of acinar cells from ADM and correlated with an increase in RNA expression of three key acinar-specific fate TFs—Mist1, Hnf1a, and Nr5a2. The mRNA levels of other key acinar TFs—Pdx1 and Ptf1a/p48—did not recover with the loss of Bmi1 in the presence of KrasG12D. Together, our data suggest that Bmi1 is expressed in the majority of acinar cells and regulates oncogenic Kras-driven ADM by altering master TF gene regulatory networks. Its deletion leads to partial reprogramming of these networks to allow acinar cells to resist Kras-driven oncogenesis. Citation Format: Joyce K. Thompson, Emily Wu, Osama Alkhalili, Howard C. Crawford, Marina Pasca di Magliano, Filip Bednar. Bmi1 is widely expressed in acini and regulates Kras-driven transcription factor networks in early pancreatic neoplasia [abstract]. In: Proceedings of the AACR Special Conference on Pancreatic Cancer: Advances in Science and Clinical Care; 2019 Sept 6-9; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2019;79(24 Suppl):Abstract nr C56.

Molecular mechanisms of autophagic memory in pathogenic T cells in human arthritis

T-cell resilience is critical to the immune pathogenesis of human autoimmune arthritis. Autophagy is essential for memory T cell generation and associated with pathogenesis in rheumatoid arthritis (RA). Our aim here was to delineate the role and molecular mechanism of autophagy in resilience and persistence of pathogenic T cells from autoimmune arthritis.We demonstrated “Autophagic memory” as elevated autophagy levels in CD4+ memory T cells compared to CD4+ naive T cells and in Jurkat Human T cell line trained with starvation stress. We then showed increased levels of autophagy in pathogenic CD4+ T cells subsets from autoimmune arthritis patients. Using RNA-sequencing, transcription factor gene regulatory network and methylation analyses we identified MYC as a key regulator of autophagic memory. We validated MYC levels using qPCR and further demonstrated that inhibiting MYC increased autophagy. The present study proposes the novel concept of autophagic memory and suggests that autophagic memory confers metabolic advantage to pathogenic T cells from arthritis and supports its resilience and long term survival. Particularly, suppression of MYC imparted the heightened autophagy levels in pathogenic T cells. These studies have a direct translational valency as they identify autophagy and its metabolic controllers as a novel therapeutic target.

Gene expression profiling and construction of a putative gene regulatory network of bladder cancer tumor-initiating cells.

Human bladder cancer tumors have been shown to contain a subpopulation of cells with stem-like characteristics that may trigger tumor growth, recurrence, and metastasis. These cells, known as tumor-initiating cells (TICs), would be effective diagnostic tools and valuable therapeutic targets. Here, we report the isolation of TICs from seven bladder cancer cell lines and show that TICs from different sources vary on their ability to form tumorspheres in vitro and generate xenografts in vivo, which suggest they are remarkably heterogeneous. We used the Affymetrix PrimeView™ Human Gene Expression Array to analyze gene expression profiles of bladder TICs, which may help understand their tumorigenic capacities and develop novel treatments specifically targeted toward these cells. We then constructed a transcription factor-gene regulatory network that includes three key transcription factors that are involved in cell survival, differentiation, proliferation, and apoptosis. We validated our findings by analyzing mRNA expression of the key genes in this network in 24 clinical tissues. Our results suggest that this transcription factor-gene regulatory network could be useful in the development of clinical diagnostic tools and therapy approaches for bladder cancer.

Identification of Key Transcription Factors Associated with Lung Squamous Cell Carcinoma.

BackgroundLung squamous cell carcinoma (lung SCC) is a common type of lung cancer, but its mechanism of pathogenesis is unclear. The aim of this study was to identify key transcription factors in lung SCC and elucidate its mechanism.Material/MethodsSix published microarray datasets of lung SCC were downloaded from Gene Expression Omnibus (GEO) for integrated bioinformatics analysis. Significance analysis of microarrays was used to identify differentially expressed genes (DEGs) between lung SCC and normal controls. The biological functions and signaling pathways of DEGs were mapped in the Gene Otology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway database, respectively. A transcription factor gene regulatory network was used to obtain insights into the functions of DEGs.ResultsA total of 1,011 genes, including 539 upregulated genes and 462 downregulated genes, were filtered as DEGs between lung SCC and normal controls. DEGs were significantly enriched in cell cycle, DNA replication, p53 signaling pathway, pathways in cancer, adherens junction, and cell adhesion molecules signaling pathways. There were 57 transcription factors identified, which were used to construct a regulatory network. The network consisted of 736 interactions between 49 transcription factors and 486 DEGs. NFIC, BRCA1, and NFATC2 were the top 3 transcription factors that had the highest connectivity with DEGs and that regulated 83, 82, and 75 DEGs in the network, respectively.ConclusionsNFIC, BRCA1, and NFATC2 might be the key transcription factors in the development of lung SCC by regulating the genes involved in cell cycle and DNA replication pathways.

MicroRNA and Transcription Factor Gene Regulatory Network Analysis Reveals Key Regulatory Elements Associated with Prostate Cancer Progression.

Technological and methodological advances in multi-omics data generation and integration approaches help elucidate genetic features of complex biological traits and diseases such as prostate cancer. Due to its heterogeneity, the identification of key functional components involved in the regulation and progression of prostate cancer is a methodological challenge. In this study, we identified key regulatory interactions responsible for primary to metastasis transitions in prostate cancer using network inference approaches by integrating patient derived transcriptomic and miRomics data into gene/miRNA/transcription factor regulatory networks. One such network was derived for each of the clinical states of prostate cancer based on differentially expressed and significantly correlated gene, miRNA and TF pairs from the patient data. We identified key elements of each network using a network analysis approach and validated our results using patient survival analysis. We observed that HOXD10, BCL2 and PGR are the most important factors affected in primary prostate samples, whereas, in the metastatic state, STAT3, JUN and JUNB are playing a central role. Benefiting integrative networks our analysis suggests that some of these molecules were targeted by several overexpressed miRNAs which may have a major effect on the dysregulation of these molecules. For example, in the metastatic tumors five miRNAs (miR-671-5p, miR-665, miR-663, miR-512-3p and miR-371-5p) are mainly responsible for the dysregulation of STAT3 and hence can provide an opportunity for early detection of metastasis and development of alternative therapeutic approaches. Our findings deliver new details on key functional components in prostate cancer progression and provide opportunities for the development of alternative therapeutic approaches.

Identification of Transcription Factor-Gene Regulatory Network in Acute Myocardial Infarction.

Acute myocardial infarction (AMI) is a common disease with serious mortality and morbidity, worldwide. The present study aimed to identify differentially expressed genes (DEGs) and construct a transcription factor-gene regulatory network to further study the early diagnosis of AMI. The integrated analysis of publicly available Gene Expression Omnibus datasets of AMI was performed. Differentially expressed genes were identified between AMI and normal blood samples. Gene Ontology enrichment analyses, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, and the transcription factor-gene regulatory network were used to obtain insights into the functions of DEGs. Quantitative real-time polymerase chain reactions (qRT-PCR) were performed to validate the expression level of DEGs. A total of 2,502 DEGs, including 917 up-regulated genes and 1,585 down-regulated genes, were identified between AMI and normal blood samples by integrating four expression profiles of AMI. Differentially expressed genes were significantly enriched in pathways including complement and coagulation cascades, Staphylococcus aureus infection, and cell adhesion molecules. Transcription factors were screened and performed to construct the regulatory network. The transcription factor-gene regulatory network consisted of 871 interactions between 80 transcription factors and 716 DEGs. ETS homologous factor (EHF) was one of transcription factors that had high connectivity with DEGs and regulated CACNB4 in the network. Verification by qRT-PCR revealed that EHF, KRT6A and DSG3 were significantly up-regulated, while CACNG4 was significantly down-regulated in AMI. Furthermore, CACNG6, CACNB4 and CLDN18 had a tendency to be down-regulated, and CALML3 had a tendency to be up-regulated in AMI. The identification of important differentially expressed transcription factors and genes in the development of AMI would be the groundwork for the early diagnosis and early intervention of AMI.

Time-Series Transcriptomics Reveals That AGAMOUS-LIKE22 Affects Primary Metabolism and Developmental Processes in Drought-Stressed Arabidopsis.

In Arabidopsis thaliana, changes in metabolism and gene expression drive increased drought tolerance and initiate diverse drought avoidance and escape responses. To address regulatory processes that link these responses, we set out to identify genes that govern early responses to drought. To do this, a high-resolution time series transcriptomics data set was produced, coupled with detailed physiological and metabolic analyses of plants subjected to a slow transition from well-watered to drought conditions. A total of 1815 drought-responsive differentially expressed genes were identified. The early changes in gene expression coincided with a drop in carbon assimilation, and only in the late stages with an increase in foliar abscisic acid content. To identify gene regulatory networks (GRNs) mediating the transition between the early and late stages of drought, we used Bayesian network modeling of differentially expressed transcription factor (TF) genes. This approach identified AGAMOUS-LIKE22 (AGL22), as key hub gene in a TF GRN. It has previously been shown that AGL22 is involved in the transition from vegetative state to flowering but here we show that AGL22 expression influences steady state photosynthetic rates and lifetime water use. This suggests that AGL22 uniquely regulates a transcriptional network during drought stress, linking changes in primary metabolism and the initiation of stress responses.

Application of Biostatistics and Bioinformatics Tools to Identify Putative Transcription Factor-Gene Regulatory Network of Ankylosing Spondylitis and Sarcoidosis

Transcription factors and corresponding cis-regulatory elements are considered key components in gene regulation. We combined biostatistics and bioinformatics tools to streamline identification of putative transcription factor-gene regulatory networks unique for two immune-mediated diseases, ankylosing spondylitis and sarcoidosis. After identifying differentially expressed genes from microarrays, we employed tightCluster to find tight clusters of potentially co-regulated genes. By subsequently applying bioinformatics tools to search for common cis-regulatory elements, putative transcription factor-gene regulatory networks were found. Recognition of these networks by applying this methodology could pave the way for new insights into disease pathogenesis.