Transcriptional Gene Regulatory Networks Research Articles

Isoform-level expression of the constitutive androstane receptor (CAR or NR1I3) transcription factor better predicts the mRNA expression of the cytochrome P450s in human liver samples.

Many factors cause inter-person variability in the activity and expression of liver cytochrome P450 (CYP) drug-metabolizing enzymes, leading to variable drug exposure and treatment outcomes. Several liver-enriched transcription factors (TFs) are associated with CYP expression, with estrogen receptor alpha (ESR1) and constitutive androstane receptor (CAR or NR1I3) being the two top factors. ESR1 and NR1I3 undergo extensive alternative splicing that results in numerous splice isoforms, but how these splice isoforms associate with CYP expression is unknown. Here, we quantified 18 NR1I3 splice isoforms and the three most abundant ESR1 isoforms in 260 liver samples derived from African Americans (AA, n=125) and European Americans (EA, n=135). Our results showed variable splice isoform populations in the liver for both NR1I3 and ESR1. Multiple linear regression analyses revealed that, compared to gene-level NR1I3, isoform-level NR1I3 expression better predicted the mRNA expression of most CYPs and three UDP-glucuronosyltransferases (UGTs), while ESR1 isoforms improved predictive models for the UGTs and CYP2D6, but not for most CYPs. Also, different NR1I3 isoforms were associated with different CYPs, and the associations varied depending on sample ancestry. Surprisingly, non-canonical NR1I3 isoforms having retained introns (introns 2 or 6) were abundantly expressed and associated with the expression of most CYPs and UGTs, whereas the reference isoform (NR1I3-205) only associated with CYP2D6. Moreover, NR1I3 isoform diversity increased during the differentiation of induced pluripotent stem cells to hepatocytes, paralleling increasing CYP expression. These results suggest that isoform-level TF expression may help to explain variation in CYP or UGT expression between individuals. Significance Statement We quantified 18 NR1I3 splice isoforms and three ESR1 splice isoforms in 260 liver samples derived from AA and EA donors and found variable NR1I3 and ESR1 splice isoform expression in the liver. Multiple linear regression analysis showed that, compared to gene-level expression, isoform-level expression of NR1I3 and ESR1 better predicted the mRNA expression of some CYPs and UGTs, highlighting the importance of isoform-level analyses to enhance our understanding of gene transcriptional regulatory networks controlling the expression of drug-metabolizing enzymes.

Abstract 151: Single Cell Transcriptional And Epigenetic Map Of Human Coronary Artery Disease In A Multi-ethnic Population

Identifying target genes that are causally linked to coronary artery disease (CAD) remains an elusive problem despite the plethora of CAD genome-wide association study (GWAS) risk loci. Here, we performed single nucleus multiomic (RNA + ATAC) sequencing in 44 human coronary arteries and bulk Hi-C in 16 coronaries from multi-ethnic participants. From sequencing 126,804 nuclei, we identified cell type specific transcriptional signatures, chromatin peaks, and gene regulatory networks through peak to gene linkage analysis. Single-cell quantitative trait loci (QTL) analysis identified cell-specific caQTL’s including cell-type specific caQTLs within CAD GWAS loci. The overlap of CAD GWAS loci with ATAC peaks demonstrated that vascular smooth muscle and endothelial cells harbor the greatest genetic risk. To identify proximal and distal putative target genes for CAD loci in a cell type specific manner, we built a enhancer-gene map by leveraging coronary artery ChiP-seq, cell specific RNA/ATAC-seq, and Hi-C based chromatin contact loops. Using this map, we identified distal target genes linked to CAD GWAS loci. Finally, given the effect of ancestry on CAD GWAS risk interpretation, we leveraged Hi-C data to compare the chromatin architecture in coronary tissue from 8 European Americans vs 8 African Americans. A meta-map, sample level, and local ancestry analysis identified ancestry-specific differences in chromatin looping and topologically associated domains. Collectively, our results provide a cell-type specific transcriptomic and epigenetic map which along with bulk chromatin contacts can be used to interpret and prioritize CAD GWAS candidates and nominate cell specific causal target genes.

Inference of differential key regulatory networks and mechanistic drug repurposing candidates from scRNA-seq data with SCANet.

The reconstruction of small key regulatory networks that explain the differences in the development of cell (sub)types from single-cell RNA sequencing is a yet unresolved computational problem. To this end, we have developed SCANet, an all-in-one package for single-cell profiling that covers the whole differential mechanotyping workflow, from inference of trait/cell-type-specific gene co-expression modules, driver gene detection, and transcriptional gene regulatory network reconstruction to mechanistic drug repurposing candidate prediction. To illustrate the power of SCANet, we examined data from two studies. First, we identify the drivers of the mechanotype of a cytokine storm associated with increased mortality in patients with acute respiratory illness. Secondly, we find 20 drugs for eight potential pharmacological targets in cellular driver mechanisms in the intestinal stem cells of obese mice. SCANet is a free, open-source, and user-friendly Python package that can be seamlessly integrated into single-cell-based systems medicine research and mechanistic drug discovery.

Inference of cell type-specific gene regulatory networks on cell lineages from single cell omic datasets

Cell type-specific gene expression patterns are outputs of transcriptional gene regulatory networks (GRNs) that connect transcription factors and signaling proteins to target genes. Single-cell technologies such as single cell RNA-sequencing (scRNA-seq) and single cell Assay for Transposase-Accessible Chromatin using sequencing (scATAC-seq), can examine cell-type specific gene regulation at unprecedented detail. However, current approaches to infer cell type-specific GRNs are limited in their ability to integrate scRNA-seq and scATAC-seq measurements and to model network dynamics on a cell lineage. To address this challenge, we have developed single-cell Multi-Task Network Inference (scMTNI), a multi-task learning framework to infer the GRN for each cell type on a lineage from scRNA-seq and scATAC-seq data. Using simulated and real datasets, we show that scMTNI is a broadly applicable framework for linear and branching lineages that accurately infers GRN dynamics and identifies key regulators of fate transitions for diverse processes such as cellular reprogramming and differentiation.

Single-cell analysis of megakaryopoiesis in peripheral CD34+ cells: insights into ETV6-related thrombocytopenia

BackgroundGermline mutations in the ETV6 transcription factor gene are responsible for familial thrombocytopenia and leukemia predisposition syndrome. Although previous studies have shown that ETV6 plays an important role in megakaryocyte (MK) maturation and platelet formation, the mechanisms by which ETV6 dysfunction promotes thrombocytopenia remain unclear. ObjectivesTo decipher the transcriptional mechanisms and gene regulatory network linking ETV6 germline mutations and thrombocytopenia. MethodsPresuming that ETV6 mutations result in selective effects at a particular cell stage, we applied single-cell RNA sequencing to understand gene expression changes during megakaryopoiesis in peripheral CD34+ cells from healthy controls and patients with ETV6-related thrombocytopenia. ResultsAnalysis of gene expression and regulon activity revealed distinct clusters partitioned into 7 major cell stages: hematopoietic stem/progenitor cells, common-myeloid progenitors (CMPs), MK-primed CMPs, granulocyte-monocyte progenitors, MK-erythroid progenitors (MEPs), progenitor MKs/mature MKs, and platelet-like particles. We observed a differentiation trajectory in which MEPs developed directly from hematopoietic stem/progenitor cells and bypassed the CMP stage. ETV6 deficiency led to the development of aberrant cells as early as the MEP stage, which intensified at the progenitor MK/mature MK stage, with a highly deregulated core “ribosome biogenesis” pathway. Indeed, increased translation levels have been documented in patient CD34+-derived MKs with overexpression of ribosomal protein S6 and phosphorylated ribosomal protein S6 in both CD34+-derived MKs and platelets. Treatment of patient MKs with the ribosomal biogenesis inhibitor CX-5461 resulted in an increase in platelet-like particles. ConclusionThese findings provide novel insight into both megakaryopoiesis and the link among ETV6, translation, and platelet production.

Gene expression variability across cells and species shapes the relationship between renal resident macrophages and infiltrated macrophages

BackgroundTwo main subclasses of macrophages are found in almost all solid tissues: embryo-derived resident tissue macrophages and bone marrow-derived infiltrated macrophages. These macrophage subtypes show transcriptional and functional divergence, and the programs that have shaped the evolution of renal macrophages and related signaling pathways remain poorly understood. To clarify these processes, we performed data analysis based on single-cell transcriptional profiling of renal tissue-resident and infiltrated macrophages in human, mouse and rat.ResultsIn this study, we (i) characterized the transcriptional divergence among species and (ii) illustrated variability in expression among cells of each subtype and (iii) compared the gene regulation network and (iv) ligand-receptor pairs in human and mouse. Using single-cell transcriptomics, we mapped the promoter architecture during homeostasis.ConclusionsTranscriptionally divergent genes, such as the differentially TF-encoding genes expressed in resident and infiltrated macrophages across the three species, vary among cells and include distinct promoter structures. The gene regulatory network in infiltrated macrophages shows comparatively better species-wide consistency than resident macrophages. The conserved transcriptional gene regulatory network in infiltrated macrophages among species is uniquely enriched in pathways related to kinases, and TFs associated with largely conserved regulons among species are uniquely enriched in kinase-related pathways.

De-novo reconstruction and identification of transcriptional gene regulatory network modules differentiating single-cell clusters.

Single-cell RNA sequencing (scRNA-seq) technology provides an unprecedented opportunity to understand gene functions and interactions at single-cell resolution. While computational tools for scRNA-seq data analysis to decipher differential gene expression profiles and differential pathway expression exist, we still lack methods to learn differential regulatory disease mechanisms directly from the single-cell data. Here, we provide a new methodology, named DiNiro, to unravel such mechanisms de novo and report them as small, easily interpretable transcriptional regulatory network modules. We demonstrate that DiNiro is able to uncover novel, relevant, and deep mechanistic models that not just predict but explain differential cellular gene expression programs. DiNiro is available at https://exbio.wzw.tum.de/diniro/.

Identifying strengths and weaknesses of methods for computational network inference from single-cell RNA-seq data.

Single-cell RNA-sequencing (scRNA-seq) offers unparalleled insight into the transcriptional programs of different cellular states by measuring the transcriptome of thousands of individual cells. An emerging problem in the analysis of scRNA-seq is the inference of transcriptional gene regulatory networks and a number of methods with different learning frameworks have been developed to address this problem. Here, we present an expanded benchmarking study of eleven recent network inference methods on seven published scRNA-seq datasets in human, mouse, and yeast considering different types of gold standard networks and evaluation metrics. We evaluate methods based on their computing requirements as well as on their ability to recover the network structure. We find that, while most methods have a modest recovery of experimentally derived interactions based on global metrics such as Area Under the Precision Recall curve, methods are able to capture targets of regulators that are relevant to the system under study. Among the top performing methods that use only expression were SCENIC, PIDC, MERLIN or Correlation. Addition of prior biological knowledge and the estimation of transcription factor activities resulted in the best overall performance with the Inferelator and MERLIN methods that use prior knowledge outperforming methods that use expression alone. We found that imputation for network inference did not improve network inference accuracy and could be detrimental. Comparisons of inferred networks for comparable bulk conditions showed that the networks inferred from scRNA-seq datasets are often better or at par with the networks inferred from bulk datasets. Our analysis should be beneficial in selecting methods for network inference. At the same time, this highlights the need for improved methods and better gold standards for regulatory network inference from scRNAseq datasets.

Central gene transcriptional regulatory networks shaping monocyte development in bone marrow.

The development of monocytes in bone marrow is a complex process with multiple steps. We used RNA-seq data to analyze the transcriptome profiles in developing stages of monocytes, including hematopoietic stem cells (HSCs), common myeloid progenitors (CMPs), granulocyte–monocyte progenitors (GMPs), and monocytes. We found that genes related to potassium and other cation transmembrane activities and ion binding were upregulated during the differentiation of HSCs into CMPs. Protein transport and membrane surface functional molecules were significantly upregulated in the GMP stage. The CD42RAC and proteasome pathways are significantly upregulated during the development of HSCs into monocytes. Transcription factors Ank1, Runx2, Hmga2, Klf1, Nfia, and Bmyc were upregulated during the differentiation of HSCs into CMPs; Gfi1 and Hmgn2 were highly expressed during the differentiation of CMPs into GMPs; Seventeen transcription factors including Foxo1, Cdkn2d, Foxo3, Ep300, Pias1, Nfkb1, Creb1, Bcl6, Ppp3cb, Stat5b, Nfatc4, Mef2a, Stat6, Ifnar2, Irf7, Irf5, and Cebpb were identified as potentially involved in the development of GMPs into monocytes in mice and humans. In metabolism pathway regulation, HSCs have high glucose, lipid, and nucleic acid metabolism activities; CMPs mainly up regulate the TCA cycle related genes; and GMPs have extremely active metabolisms, with significantly elevated pentose phosphate pathway, TCA cycle, histidine metabolism, and purine metabolism. In the monocyte phase, the tricarboxylic acid (TCA) cycle is reduced, and the anaerobic glycolysis process becomes dominated. Overall, our studies offer the kinetics and maps of gene transcriptional expressions and cell metabolisms during monocyte development in bone marrow.

Deciphering transcriptome alterations in bone marrow hematopoiesis at single-cell resolution in immune thrombocytopenia

Immune thrombocytopenia (ITP) is an autoimmune disorder, in which megakaryocyte dysfunction caused by an autoimmune reaction can lead to thrombocytopenia, although the underlying mechanisms remain unclear. Here, we performed single-cell transcriptome profiling of bone marrow CD34+ hematopoietic stem and progenitor cells (HSPCs) to determine defects in megakaryopoiesis in ITP. Gene expression, cell-cell interactions, and transcriptional regulatory networks varied in HSPCs of ITP, particularly in immune cell progenitors. Differentially expressed gene (DEG) analysis indicated that there was an impaired megakaryopoiesis of ITP. Flow cytometry confirmed that the number of CD9+ and HES1+ cells from Lin−CD34+CD45RA− HSPCs decreased in ITP. Liquid culture assays demonstrated that CD9+Lin−CD34+CD45RA− HSPCs tended to differentiate into megakaryocytes; however, this tendency was not observed in ITP patients and more erythrocytes were produced. The percentage of megakaryocytes differentiated from CD9+Lin−CD34+CD45RA− HSPCs was 3-fold higher than that of the CD9− counterparts from healthy controls (HCs), whereas, in ITP patients, the percentage decreased to only 1/4th of that in the HCs and was comparable to that from the CD9− HSPCs. Additionally, when co-cultured with pre-B cells from ITP patients, the differentiation of CD9+Lin−CD34+CD45RA− HSPCs toward the megakaryopoietic lineage was impaired. Further analysis revealed that megakaryocytic progenitors (MkP) can be divided into seven subclusters with different gene expression patterns and functions. The ITP-associated DEGs were MkP subtype-specific, with most DEGs concentrated in the subcluster possessing dual functions of immunomodulation and platelet generation. This study comprehensively dissects defective hematopoiesis and provides novel insights regarding the pathogenesis of ITP.

Abstract 5148: Immune landscapes and interactions in human fallopian tubes and their implications for early high-grade serous ovarian cancers

Abstract The five-year survival rate of ovarian cancer (OC) dramatically rises from below 40% to 90% with patients who are diagnosed early (stage I), when the disease restricts to the ovary. Thus, the ultimate goals of our research are to learn how to detect ovarian cancer at its earliest stages of development and thereby improve patient survival and to learn how the immune microenvironment contributes to the development of OC. Achieving these goals requires us to better characterize the earliest steps in ovarian carcinogenesis. The human fallopian tube harbors the cell-of-origin for most high-grade serous 'ovarian' cancers (HGSOCs), but its cellular composition, particularly the immune component, is poorly characterized. Emerging evidence from several research groups using molecular sequencing technologies of fallopian tube epithelium strongly support this hypothesis. We recently described our use of single-cell transcriptome technology (scRNA-Seq) to profile the heterogeneity of non-malignant FT and define a novel transcriptional gene regulatory network of epithelial differentiation and tumorigenesis in the human FT. Here we present an in-depth characterization of the immune cell atlas for human fallopian tubes, which has not been reported. Our unpublished analyses of our FT dataset and the publicly available HGSOC scRNA-Seq dataset demonstrate a significant difference in the immune cell landscape. There are limited macrophages and a high abundance of monocytes in non-malignant FT and adjacent normal ovaries compared to those in HGSOC tumors. This finding leads us to hypothesize that the macrophage-to-monocyte ratio and their interactions with other immune cell types in human fallopian tubes create a permissive microenvironment allowing for the development of HGSOC and can provide a biomarker for early cancer detection. To test if those myeloid cells could be a marker for early HGSOC, we performed scRNA-seq on FT from women carrying mutant alleles of BRCA1 or BRCA2 who are at high risk of HGSOC. Intriguingly, the ratio of monocytes to macrophages in the FTs from half of these high-risk individuals is more like that found in HGSOC than in normal FT. Furthermore, computational analyses on myeloid cell types and their interactions with different epithelial cells, stromal fibroblast cells, and T and NK cells revealed the extended contribution of the non-epithelial microenvironment in FT, potentially driving early HGSOC development and progression. This cellular and molecular compendium of the human fallopian tube immune landscapes in cancer-free and cancer-high-risk women is expected to advance our understanding of the earliest stages of fallopian epithelial neoplasia. Citation Format: Josh Brand, Marcela Harro, Forough Abbasi, Xianzhi Lin, Matthew Siedhoff, Li Andrew, Bobbie J. Rimel, Fabiola Medeiros, Kate Lawrenson, Huy Q. Dinh. Immune landscapes and interactions in human fallopian tubes and their implications for early high-grade serous ovarian cancers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5148.

Mettl14-Mediated m6A Modification Is Essential for Germinal Center B Cell Response.

The germinal center (GC) response is essential for generating memory B and long-lived Ab-secreting plasma cells during the T cell-dependent immune response. In the GC, signals via the BCR and CD40 collaboratively promote the proliferation and positive selection of GC B cells expressing BCRs with high affinities for specific Ags. Although a complex gene transcriptional regulatory network is known to control the GC response, it remains elusive how the positive selection of GC B cells is modulated posttranscriptionally. In this study, we show that methyltransferase like 14 (Mettl14)-mediated methylation of adenosines at the position N 6 of mRNA (N 6-methyladenosine [m6A]) is essential for the GC B cell response in mice. Ablation of Mettl14 in B cells leads to compromised GC B cell proliferation and a defective Ab response. Interestingly, we unravel that Mettl14-mediated m6A regulates the expression of genes critical for positive selection and cell cycle regulation of GC B cells in a Ythdf2-dependent but Myc-independent manner. Furthermore, our study reveals that Mettl14-mediated m6A modification promotes mRNA decay of negative immune regulators, such as Lax1 and Tipe2, to upregulate genes requisite for GC B cell positive selection and proliferation. Thus, our findings suggest that Mettl14-mediated m6A modification plays an essential role in the GC B cell response.

Human transcriptional gene regulatory network compiled from 14 data resources

The transcriptional regulatory network (TRN) in a cell orchestrates spatio-temporal expression of genes to generate cellular responses for maintenance, reproduction, development and survival of the cell and its hosting organism. Transcription factors (TF) regulate the expression of their target genes (TG) and are the fundamental units of TRN. Several databases have been developed to catalogue human TRN based on low- and high-throughput experimental and computational studies considering their importance in understanding cellular physiology. However, literature lacks their comparative assessment on the strengths and weaknesses. We compared over 2.2 million regulatory pairs between 1379 TF and 22,518 TG from 14 resources. Our study reveals that the TF and TG were common across data resources but not their regulatory pairs. TF and TG of the regulatory pairs showed weak expression correlation, significant gene ontology overlap, co-citations in PubMed and low numbers of TF-TG pairs representing transcriptional repression relationships. We assigned each TF-TG regulatory pair a combined confidence score reflecting its reliability based on its presence in multiple databases. The assembled TRN contains 2,246,598 TF-TG pairs, of which, 44,284 with information on TF's activating or repressing effects on their TG and is available upon request. This study brings the information about transcriptional regulation scattered over the literature and databases at one place in the form of one of the most comprehensive and complete human TRN assembled to date. It will be a valuable resource for benchmarking TRN prediction tools, and to the scientific community working in functional genomics, gene expression and regulation analysis.

Regulation of Chromatin Organization in Cell Stemness: The Emerging Role of Long Non-coding RNAs.

Chromatin is organized as chromosome territories in the nucleus of an interphase cell. The cell-type- and cell-state-specific organization of chromatin including the location, volume, compaction level, and spatial arrangement of chromosome territories are the major determinants of genome function. In addition, in response to different signaling stimuli and regulatory cues, it is the dynamic adaptation of chromatin structure that establishes and organizes transcriptional programs. It is known that varying levels of stemness are defined by gene regulatory networks. Accordingly, chromatin is the main milieu to host the transcriptional programs and gene regulatory networks responsible for the stemness status of a cell. In this review, our current understanding of the spatial organization of chromatin and the ways by which it defines stemness are discussed. In particular, the role of lncRNAs that regulate and affect chromatin organization and stemness properties are delineated. These roles can be categorized into the topics of specific binding to and epigenetic regulation of the promoter of pluripotency genes, their interaction with transcription factors, coordinating the intra- and inter-chromosomal looping of pluripotency-related genes, and their RNA-independent functions. This review brings together the results of studies that have begun to clarify the emerging roles of lncRNAs in the regulation of chromatin organization adapted for stemness and cancer plasticity.

An Integrated Database of Small RNAs and Their Interplay With Transcriptional Gene Regulatory Networks in Corynebacteria.

Small RNAs (sRNAs) are one of the key players in the post-transcriptional regulation of bacterial gene expression. These molecules, together with transcription factors, form regulatory networks and greatly influence the bacterial regulatory landscape. Little is known concerning sRNAs and their influence on the regulatory machinery in the genus Corynebacterium, despite its medical, veterinary and biotechnological importance. Here, we expand corynebacterial regulatory knowledge by integrating sRNAs and their regulatory interactions into the transcriptional regulatory networks of six corynebacterial species, covering four human and animal pathogens, and integrate this data into the CoryneRegNet database. To this end, we predicted sRNAs to regulate 754 genes, including 206 transcription factors, in corynebacterial gene regulatory networks. Amongst them, the sRNA Cd-NCTC13129-sRNA-2 is predicted to directly regulate ydfH, which indirectly regulates 66 genes, including the global regulator glxR in C. diphtheriae. All of the sRNA-enriched regulatory networks of the genus Corynebacterium have been made publicly available in the newest release of CoryneRegNet(www.exbio.wzw.tum.de/coryneregnet/) to aid in providing valuable insights and to guide future experiments.

Single-cell transcriptomics dissects hematopoietic cell destruction and T-cell engagement in aplastic anemia

AbstractAplastic anemia (AA) is a T cell–mediated autoimmune disorder of the hematopoietic system manifested by severe depletion of the hematopoietic stem and progenitor cells (HSPCs). Nonetheless, our understanding of the complex relationship between HSPCs and T cells is still obscure, mainly limited by techniques and the sparsity of HSPCs in the context of bone marrow failure. Here we performed single-cell transcriptome analysis of residual HSPCs and T cells to identify the molecular players from patients with AA. We observed that residual HSPCs in AA exhibited lineage-specific alterations in gene expression and transcriptional regulatory networks, indicating a selective disruption of distinct lineage-committed progenitor pools. In particular, HSPCs displayed frequently altered alternative splicing events and skewed patterns of polyadenylation in transcripts related to DNA damage and repair, suggesting a likely role in AA progression to myelodysplastic syndromes. We further identified cell type–specific ligand-receptor interactions as potential mediators for ongoing HSPCs destruction by T cells. By tracking patients after immunosuppressive therapy (IST), we showed that hematopoiesis remission was incomplete accompanied by IST insensitive interactions between HSPCs and T cells as well as sustained abnormal transcription state. These data collectively constitute the transcriptomic landscape of disrupted hematopoiesis in AA at single-cell resolution, providing new insights into the molecular interactions of engaged T cells with residual HSPCs and render novel therapeutic opportunities for AA.

Role of integrated noise in pathway-specific signal propagation in feed-forward loops

Cells impose optimal noise control mechanism in diverse situations to cope with distinct environmental cues. Sometimes, it is desirable for the cell to utilize fluctuations for noise-driven processes. In other cases, noise can be harmful to the cell to show optimal fitness. It is, therefore, important to unravel the noise propagation mechanism inside the cell. Such noise controlling mechanism is accomplished by using gene transcription regulatory networks. One such gene regulatory network is feed-forward loop, having three regulatory nodes S, X and Y. Here, we consider the most abundant type 1 of coherent and incoherent feed-forward loops with both OR and AND logic functions, forming four different architectures. In OR logic function, the functions representing S and X act additively for the regulation of Y, while in AND logic function, the same functions (S and X) act multiplicatively for the regulation of Y. Measurement of susceptibility of the signal at output Y is done using elasticity of each regulation in FFLs. Using susceptibility, we demonstrate the nature of pathway integration by which one-step and two-step pathways get overlapped. The integration type is competitive for motifs having OR gate, while it is noncompetitive for the same with AND gate. The pathway integration property explains the output noise behavior of the motifs properly but cannot infer about the mechanism by which the upstream noise propagates to output. To account this, the total output noise is decomposed, which results in integrated noise as an additional noise source along with pathway-specific noise components. The integrated noise is found to appear as a consequence of integration between the pathways and has different functional characteristics explaining noise amplification and noise attenuation property of coherent and incoherent feed-forward loops, respectively. The noise decomposition also quantifies the contribution of different noise sources toward total noise. Finally, the noise propagation is being tuned as a function of input signal noise and its time scale of fluctuations, which shows considerable intrinsic noise strength and relatively slow relaxation time scale causes a higher degree of noise propagation in FFLs.

DeltaNeTS+: elucidating the mechanism of drugs and diseases using gene expression and transcriptional regulatory networks

BackgroundKnowledge on the molecular targets of diseases and drugs is crucial for elucidating disease pathogenesis and mechanism of action of drugs, and for driving drug discovery and treatment formulation. In this regard, high-throughput gene transcriptional profiling has become a leading technology, generating whole-genome data on the transcriptional alterations caused by diseases or drug compounds. However, identifying direct gene targets, especially in the background of indirect (downstream) effects, based on differential gene expressions is difficult due to the complexity of gene regulatory network governing the gene transcriptional processes.ResultsIn this work, we developed a network analysis method, called DeltaNeTS+, for inferring direct gene targets of drugs and diseases from gene transcriptional profiles. DeltaNeTS+ uses a gene regulatory network model to identify direct perturbations to the transcription of genes using gene expression data. Importantly, DeltaNeTS+ is able to combine both steady-state and time-course expression profiles, as well as leverage information on the gene network structure. We demonstrated the power of DeltaNeTS+ in predicting gene targets using gene expression data in complex organisms, including Caenorhabditis elegans and human cell lines (T-cell and Calu-3). More specifically, in an application to time-course gene expression profiles of influenza A H1N1 (swine flu) and H5N1 (avian flu) infection, DeltaNeTS+ shed light on the key differences of dynamic cellular perturbations caused by the two influenza strains.ConclusionDeltaNeTS+ is a powerful network analysis tool for inferring gene targets from gene expression profiles. As demonstrated in the case studies, by incorporating available information on gene network structure, DeltaNeTS+ produces accurate predictions of direct gene targets from a small sample size (~ 10 s). Integrating static and dynamic expression data with transcriptional network structure extracted from genomic information, as enabled by DeltaNeTS+, is crucial toward personalized medicine, where treatments can be tailored to individual patients. DeltaNeTS+ can be freely downloaded from http://www.github.com/cabsel/deltanetsplus.

A comprehensive transcription factor and DNA-binding motif resource for the construction of gene regulatory networks in Botrytis cinerea and Trichoderma atroviride

Botrytis cinerea and Trichoderma atroviride are two relevant fungi in agricultural systems. To gain insights into these organisms’ transcriptional gene regulatory networks (GRNs), we generated a manually curated transcription factor (TF) dataset for each of them, followed by a GRN inference utilizing available sequence motifs describing DNA-binding specificity and global gene expression data. As a proof of concept of the usefulness of this resource to pinpoint key transcriptional regulators, we employed publicly available transcriptomics data and a newly generated dual RNA-seq dataset to build context-specific Botrytis and Trichoderma GRNs under two different biological paradigms: exposure to continuous light and Botrytis-Trichoderma confrontation assays. Network analysis of fungal responses to constant light revealed striking differences in the transcriptional landscape of both fungi. On the other hand, we found that the confrontation of both microorganisms elicited a distinct set of differentially expressed genes with changes in T. atroviride exceeding those in B. cinerea. Using our regulatory network data, we were able to determine, in both fungi, central TFs involved in this interaction response, including TFs controlling a large set of extracellular peptidases in the biocontrol agent T. atroviride. In summary, our work provides a comprehensive catalog of transcription factors and regulatory interactions for both organisms. This catalog can now serve as a basis for generating novel hypotheses on transcriptional regulatory circuits in different experimental contexts.

Dynamic Modeling of Transcriptional Gene Regulatory Networks.

Diverse cellular phenotypes are determined by groups of transcription factors (TFs) and other regulators that influence each others' gene expression, forming transcriptional gene regulatory networks (GRNs). In many biological contexts, especially in development and associated diseases, the expression of the genes in GRNs is not static but evolves in time. Modeling the dynamics of GRN state is an important approach for understanding diverse cellular phenomena such as cell-fate specification, pluripotency and cell-fate reprogramming, oncogenesis, and tissue regeneration. In this protocol, we describe how to model GRNs using a data-driven dynamic modeling methodology, gene circuits. Gene circuits do not require knowledge of the GRN topology and connectivity but instead learn them from training data, making them very general and applicable to diverse biological contexts. We utilize the MATLAB-based gene circuit modeling software Fast Inference of Gene Regulation (FIGR) for training the model on quantitative gene expression data and simulating the GRN. We describe all the steps in the modeling life cycle, from formulating the model, training the model using FIGR, simulating the GRN, to analyzing and interpreting the model output. This protocol highlights these steps with the example of a dynamical model of the gap gene GRN involved in Drosophila segmentation and includes example MATLAB statements for each step.