Transcription Factor Knockout Research Articles

Bayesian inference of sample-specific coexpression networks.

Gene regulatory networks (GRNs) are effective tools for inferring complex interactions between molecules that regulate biological processes and hence can provide insights into drivers of biological systems. Inferring coexpression networks is a critical element of GRN inference, as the correlation between expression patterns may indicate that genes are coregulated by common factors. However, methods that estimate coexpression networks generally derive an aggregate network representing the mean regulatory properties of the population and so fail to fully capture population heterogeneity. Bayesian optimized networks obtained by assimilating omic data (BONOBO) is a scalable Bayesian model for deriving individual sample-specific coexpression matrices that recognizes variations in molecular interactions across individuals. For each sample, BONOBO assumes a Gaussian distribution on the log-transformed centered gene expression and a conjugate prior distribution on the sample-specific coexpression matrix constructed from all other samples in the data. Combining the sample-specific gene coexpression with the prior distribution, BONOBO yields a closed-form solution for the posterior distribution of the sample-specific coexpression matrices, thus allowing the analysis of large data sets. We demonstrate BONOBO's utility in several contexts, including analyzing gene regulation in yeast transcription factor knockout studies, the prognostic significance of miRNA-mRNA interaction in human breast cancer subtypes, and sex differences in gene regulation within human thyroid tissue. We find that BONOBO outperforms other methods that have been used for sample-specific coexpression network inference and provides insight into individual differences in the drivers of biological processes.

Utilization of ferulic acid in Aspergillus niger requires the transcription factor FarA and a newly identified Far-like protein (FarD) that lacks the canonical Zn(II)2Cys6 domain.

The feruloyl esterase B gene (faeB) is specifically induced by hydroxycinnamic acids (e.g. ferulic acid, caffeic acid and coumaric acid) but the transcriptional regulation network involved in faeB induction and ferulic acid metabolism has only been partially addressed. To identify transcription factors involved in ferulic acid metabolism we constructed and screened a transcription factor knockout library of 239 Aspergillus niger strains for mutants unable to utilize ferulic acid as a carbon source. The ΔfarA transcription factor mutant, already known to be involved in fatty acid metabolism, could not utilize ferulic acid and other hydroxycinnamic acids. In addition to screening the transcription factor mutant collection, a forward genetic screen was performed to isolate mutants unable to express faeB. For this screen a PfaeB-amdS and PfaeB-lux613 dual reporter strain was engineered. The rationale of the screen is that in this reporter strain ferulic acid induces amdS (acetamidase) expression via the faeB promoter resulting in lethality on fluoro-acetamide. Conidia of this reporter strain were UV-mutagenized and plated on fluoro-acetamide medium in the presence of ferulic acid. Mutants unable to induce faeB are expected to be fluoro-acetamide resistant and can be positively selected for. Using this screen, six fluoro-acetamide resistant mutants were obtained and phenotypically characterized. Three mutants had a phenotype identical to the farA mutant and sequencing the farA gene in these mutants indeed showed mutations in FarA which resulted in inability to growth on ferulic acid as well as on short and long chain fatty acids. The growth phenotype of the other three mutants was similar to the farA mutants in terms of the inability to grow on ferulic acid, but these mutants grew normally on short and long chain fatty acids. The genomes of these three mutants were sequenced and allelic mutations in one particular gene (NRRL3_09145) were found. The protein encoded by NRRL3_09145 shows similarity to the FarA and FarB transcription factors. However, whereas FarA and FarB contain both the Zn(II)2Cys6 domain and a fungal-specific transcription factor domain, the protein encoded by NRRL3_09145 (FarD) lacks the canonical Zn(II)2Cys6 domain and possesses only the fungal specific transcription factor domain.

Analysis of Neutrophil Morphology and Function Under Genetic Perturbation of Transcription Factors In Vitro.

Hoxb8 cells are immortalized myeloid progenitors that maintain their multipotent potential and can be differentiated into neutrophils. Genetic modification of Hoxb8 cells can be used as a model system for the functional analysis of regulators of neutrophil maturation and effector functions, such as transcription factors. Here we describe the generation of transcription factor (TF) knockout Hoxb8 cell lines in vitro with the lentivirus (lenti)CRISPR-Cas 9 technique. After their differentiation into neutrophils, the study of their maturation profile, morphology, and effector functions, including NETosis, phagocytosis, and ROS production, is described.

Chromatin interaction-aware gene regulatory modeling with graph attention networks.

Linking distal enhancers to genes and modeling their impact on target gene expression are longstanding unresolved problems in regulatory genomics and critical for interpreting noncoding genetic variation. Here, we present a new deep learning approach called GraphReg that exploits 3D interactions from chromosome conformation capture assays to predict gene expression from 1D epigenomic data or genomic DNA sequence. By using graph attention networks to exploit the connectivity of distal elements up to 2 Mb away in the genome, GraphReg more faithfully models gene regulation and more accurately predicts gene expression levels than the state-of-the-art deep learning methods for this task. Feature attribution used with GraphReg accurately identifies functional enhancers of genes, as validated by CRISPRi-FlowFISH and TAP-seq assays, outperforming both convolutional neural networks (CNNs) and the recently proposed activity-by-contact model. Sequence-based GraphReg also accurately predicts direct transcription factor (TF) targets as validated by CRISPRi TF knockout experiments via in silico ablation of TF binding motifs. GraphReg therefore represents an important advance in modeling the regulatory impact of epigenomic and sequence elements.

Transcription factor regulatory modules provide the molecular mechanisms for functional redundancy observed among transcription factors in yeast

BackgroundCurrent technologies for understanding the transcriptional reprogramming in cells include the transcription factor (TF) chromatin immunoprecipitation (ChIP) experiments and the TF knockout experiments. The ChIP experiments show the binding targets of TFs against which the antibody directs while the knockout techniques find the regulatory gene targets of the knocked-out TFs. However, it was shown that these two complementary results contain few common targets. Researchers have used the concept of TF functional redundancy to explain the low overlap between these two techniques. But the detailed molecular mechanisms behind TF functional redundancy remain unknown. Without knowing the possible molecular mechanisms, it is hard for biologists to fully unravel the cause of TF functional redundancy.ResultsTo mine out the molecular mechanisms, a novel algorithm to extract TF regulatory modules that help explain the observed TF functional redundancy effect was devised and proposed in this research. The method first searched for candidate TF sets from the TF binding data. Then based on these candidate sets the method utilized the modified Steiner Tree construction algorithm to construct the possible TF regulatory modules from protein-protein interaction data and finally filtered out the noise-induced results by using confidence tests. The mined-out regulatory modules were shown to correlate to the concept of functional redundancy and provided testable hypotheses of the molecular mechanisms behind functional redundancy. And the biological significance of the mined-out results was demonstrated in three different biological aspects: ontology enrichment, protein interaction prevalence and expression coherence. About 23.5% of the mined-out TF regulatory modules were literature-verified. Finally, the biological applicability of the proposed method was shown in one detailed example of a verified TF regulatory module for pheromone response and filamentous growth in yeast.ConclusionIn this research, a novel method that mined out the potential TF regulatory modules which elucidate the functional redundancy observed among TFs is proposed. The extracted TF regulatory modules not only correlate the molecular mechanisms to the observed functional redundancy among TFs, but also show biological significance in inferring TF functional binding target genes. The results provide testable hypotheses for biologists to further design subsequent research and experiments.

FUNCTIONAL DISSECTION OF TRANSCRIPTION FACTOR NETWORKS GOVERNING HAEMATOPOIETIC PROGENITOR STATES

Maintenance of the adult haematopoietic system requires carefully balanced cell fate decisions at the level of multipotent progenitor cells. These decisions are controlled by complex transcription factor (TF) networks. While some TF-target connections have been experimentally established, they are mostly inferred from correlations of either gene expression (RNA-Seq) or genome-wide binding events (ChIP-Seq). This limits our understanding and prevents informed manipulation of cell states. To address these issues, we used CRISPR/Cas9 system and small scale RNA-Seq to experimentally assay gene regulatory functions of 38 TFs in the Hoxb8-FL cellular system, which captures in vitro a lymphoid-primed multipotent progenitor (LMPP) like state. Our RNA-Seq analysis following each TF knockout revealed >15,000 functional activating/inhibitory relationships across >7000 genes. Network analysis augmented with genome-wide TF binding information predicted direct interactions, regulatory elements and putative new circuits maintaining the multipotent state. Among many others, this included a novel interplay between co-existing Ebf1 and Gata3 regulating expression of lymphoid and B-cell specific genes. To gain further insights into how TFs regulate cell state transitions, we developed a new computational method that visualises perturbation effects in the context of single cell expression landscapes. Importantly, this avoids using externally curated ontology databases and instead directly employs human or mouse expression data. In the current study, the analysis revealed how each TF is linked with diverse differentiation programs. In addition to revealing known functions, we also identified previously unrecognized associations, such as Meis1 suppression of the monocytic programme. Studies are underway to validate new circuits and their influence on differentiation in primary cells.

Transcription Factors Indirectly Regulate Genes through Nuclear Colocalization.

Various types of data, including genomic sequences, transcription factor (TF) knockout data, TF-DNA interaction and expression profiles, have been used to decipher TF regulatory mechanisms. However, most of the genes affected by knockout of a particular TF are not bound by that factor. Here, I showed that this interesting result can be partially explained by considering the nuclear positioning of TF knockout affected genes and TF bound genes. I found that a statistically significant number of TF knockout affected genes show nuclear colocalization with genes bound by the corresponding TF. Although these TF knockout affected genes are not directly bound by the corresponding TF; the TF tend to be in the same cellular component with the TFs that directly bind these genes. TF knockout affected genes show co-expression and tend to be involved in the same biological process with the spatially adjacent genes that are bound by the corresponding TF. These results demonstrate that TFs can regulate genes through nuclear colocalization without direct DNA binding, complementing the conventional view that TFs directly bind DNA to regulate genes. My findings will have implications in understanding TF regulatory mechanisms.

An information theoretic treatment of sequence-to-expression modeling.

Studying a gene’s regulatory mechanisms is a tedious process that involves identification of candidate regulators by transcription factor (TF) knockout or over-expression experiments, delineation of enhancers by reporter assays, and demonstration of direct TF influence by site mutagenesis, among other approaches. Such experiments are often chosen based on the biologist’s intuition, from several testable hypotheses. We pursue the goal of making this process systematic by using ideas from information theory to reason about experiments in gene regulation, in the hope of ultimately enabling rigorous experiment design strategies. For this, we make use of a state-of-the-art mathematical model of gene expression, which provides a way to formalize our current knowledge of cis- as well as trans- regulatory mechanisms of a gene. Ambiguities in such knowledge can be expressed as uncertainties in the model, which we capture formally by building an ensemble of plausible models that fit the existing data and defining a probability distribution over the ensemble. We then characterize the impact of a new experiment on our understanding of the gene’s regulation based on how the ensemble of plausible models and its probability distribution changes when challenged with results from that experiment. This allows us to assess the ‘value’ of the experiment retroactively as the reduction in entropy of the distribution (information gain) resulting from the experiment’s results. We fully formalize this novel approach to reasoning about gene regulation experiments and use it to evaluate a variety of perturbation experiments on two developmental genes of D. melanogaster. We also provide objective and ‘biologist-friendly’ descriptions of the information gained from each such experiment. The rigorously defined information theoretic approaches presented here can be used in the future to formulate systematic strategies for experiment design pertaining to studies of gene regulatory mechanisms.

Thyroid Hemiagenesis in a Thyroiditis Prone Mouse Strain

Background: Thyroid hemiagenesis, a rare congenital condition detected by ultrasound screening of the neck, is usually not manifested clinically in humans. This condition has been reported in mice with hypothyroidism associated with induced deficiency in paired box 8 and NK2 homeobox 1, sonic hedgehog, or T-box 1. Unexpectedly, we observed thyroid hemiagenesis in NOD.H2^h4 mice, an unusual strain that spontaneously develops iodide enhanced thyroid autoimmunity but remains euthyroid. Objectives and Methods: First, to compare mice with thyroid hemiagenesis versus bilobed littermates for serum T4, autoantibodies to thyroglobulin (ELISA) and thyroid peroxidase (TPO; flow cytometry with eukaryotic cells expressing mouse TPO), gross anatomy, and thyroid histology; second, to estimate the percentage of mice with thyroid hemiagenesis in the NOD.H2^h4 mice we have studied over 6 years. Results: Thyroid hemiagenesis was observed in 3 of 1,025 NOD.H2^h4 mice (2 females, 1 male; 0.3%). Two instances of hemiagenesis were in wild-type females and one in a transgenic male expressing the human TSHR A-subunit in the thyroid. Two mice had very large unilobed glands, as in some human cases with this condition. Thyroid lymphocytic infiltration, serum T4, and the levels of thyroid autoantibodies were similar in mice with thyroid hemiagenesis and bilobed littermates. Conclusions: Unlike hypothyroidism associated with hemiagenesis in transcription factor knockout mice, hemiagenesis in euthyroid NOD.H2^h4 mice occurs spontaneously and is phenotypically similar to that occasionally observed in humans.

Goblet Cells Contribute to Ocular Surface Immune Tolerance-Implications for Dry Eye Disease.

Conjunctival goblet cell (GC) loss in dry eye is associated with ocular surface inflammation. This study investigated if conjunctival GCs contribute to ocular surface immune tolerance. Antigens applied to the ocular surface, imaged by confocal microscopy, passed into the conjunctival stroma through goblet cell associated passages (GAPs) in wild type C57BL/6 (WT), while ovalbumin (OVA) was retained in the epithelium of SAM pointed domain containing ETS transcription factor (Spdef) knockout mice (Spdef−/−) that lack GCs and are a novel model of dry eye. Stimulated GC degranulation increased antigen binding to GC mucins. Induction of tolerance to topically applied OVA measured by cutaneous delayed type hypersensitivity (DTH) was observed in WT, but not Spdef−/−. OTII CD4+ T cells primed by dendritic cells (DCs) from the conjunctival draining lymph nodes of Spdef−/− had greater IFN-γ production and lower Foxp3 positivity than those primed by WT DCs. These findings indicate that conjunctival GCs contribute to ocular surface immune tolerance by modulating antigen distribution and antigen specific immune response. GC loss may contribute to the abrogation of ocular surface immune tolerance that is observed in dry eye.

A prior-based integrative framework for functional transcriptional regulatory network inference.

Transcriptional regulatory networks specify regulatory proteins controlling the context-specific expression levels of genes. Inference of genome-wide regulatory networks is central to understanding gene regulation, but remains an open challenge. Expression-based network inference is among the most popular methods to infer regulatory networks, however, networks inferred from such methods have low overlap with experimentally derived (e.g. ChIP-chip and transcription factor (TF) knockouts) networks. Currently we have a limited understanding of this discrepancy. To address this gap, we first develop a regulatory network inference algorithm, based on probabilistic graphical models, to integrate expression with auxiliary datasets supporting a regulatory edge. Second, we comprehensively analyze our and other state-of-the-art methods on different expression perturbation datasets. Networks inferred by integrating sequence-specific motifs with expression have substantially greater agreement with experimentally derived networks, while remaining more predictive of expression than motif-based networks. Our analysis suggests natural genetic variation as the most informative perturbation for network inference, and, identifies core TFs whose targets are predictable from expression. Multiple reasons make the identification of targets of other TFs difficult, including network architecture and insufficient variation of TF mRNA level. Finally, we demonstrate the utility of our inference algorithm to infer stress-specific regulatory networks and for regulator prioritization.

CLIP-GENE: a web service of the condition specific context-laid integrative analysis for gene prioritization in mouse TF knockout experiments.

MotivationTranscriptome data from the gene knockout experiment in mouse is widely used to investigate functions of genes and relationship to phenotypes. When a gene is knocked out, it is important to identify which genes are affected by the knockout gene. Existing methods, including differentially expressed gene (DEG) methods, can be used for the analysis. However, existing methods require cutoff values to select candidate genes, which can produce either too many false positives or false negatives. This hurdle can be addressed either by improving the accuracy of gene selection or by providing a method to rank candidate genes effectively, or both. Prioritization of candidate genes should consider the goals or context of the knockout experiment. As of now, there are no tools designed for both selecting and prioritizing genes from the mouse knockout data. Hence, the necessity of a new tool arises.ResultsIn this study, we present CLIP-GENE, a web service that selects gene markers by utilizing differentially expressed genes, mouse transcription factor (TF) network, and single nucleotide variant information. Then, protein-protein interaction network and literature information are utilized to find genes that are relevant to the phenotypic differences. One of the novel features is to allow researchers to specify their contexts or hypotheses in a set of keywords to rank genes according to the contexts that the user specify. We believe that CLIP-GENE will be useful in characterizing functions of TFs in mouse experiments.Availability http://epigenomics.snu.ac.kr/CLIP-GENE ReviewersThis article was reviewed by Dr. Lee and Dr. Pongor.Electronic supplementary materialThe online version of this article (doi:10.1186/s13062-016-0158-x) contains supplementary material, which is available to authorized users.

Functional redundancy of transcription factors explains why most binding targets of a transcription factor are not affected when the transcription factor is knocked out.

BackgroundBiologists are puzzled by the extremely low percentage (3%) of the binding targets of a yeast transcription factor (TF) affected when the TF is knocked out, a phenomenon observed by comparing the TF binding dataset and TF knockout effect dataset.ResultsThis study gives a plausible biological explanation of this counterintuitive phenomenon. Our analyses find that TFs with high functional redundancy show significantly lower percentage than do TFs with low functional redundancy. This suggests that functional redundancy may lead to one TF compensating for another, thus masking the TF knockout effect on the binding targets of the knocked-out TF. In addition, we show that seven classes of genes (lowly expressed genes, TATA box-less genes, genes containing a nucleosome-free region immediately upstream of the transcriptional start site (TSS), genes with low transcriptional plasticity, genes with a low number of bound TFs, genes with a low number of TFBSs, and genes with a short average distance of TFBSs to the TSS) are insensitive to the knockout of their promoter-binding TFs, providing clues for finding other biological explanations of the surprisingly low percentage of the binding targets of a TF affected when the TF is knocked out.ConclusionsThis study shows that one property of TFs (functional redundancy) and seven properties of genes (expression level, TATA box, nucleosome, transcriptional plasticity, the number of bound TFs, the number of TFBSs, and the average distance of TFBSs to the TSS) may be useful for explaining a counterintuitive phenomenon: most binding targets of a yeast transcription factor are not affected when the transcription factor is knocked out.

Decoupling of evolutionary changes in transcription factor binding and gene expression in mammals.

To understand the evolutionary dynamics between transcription factor (TF) binding and gene expression in mammals, we compared transcriptional output and the binding intensities for three tissue-specific TFs in livers from four closely related mouse species. For each transcription factor, TF-dependent genes and the TF binding sites most likely to influence mRNA expression were identified by comparing mRNA expression levels between wild-type and TF knockout mice. Independent evolution was observed genome-wide between the rate of change in TF binding and the rate of change in mRNA expression across taxa, with the exception of a small number of TF-dependent genes. We also found that binding intensities are preferentially conserved near genes whose expression is dependent on the TF, and the conservation is shared among binding peaks in close proximity to each other near the TSS. Expression of TF-dependent genes typically showed an increased sensitivity to changes in binding levels as measured by mRNA abundance. Taken together, these results highlight a significant tolerance to evolutionary changes in TF binding intensity in mammalian transcriptional networks and suggest that some TF-dependent genes may be largely regulated by a single TF across evolution.

Optimization of transcription factor binding map accuracy utilizing knockout-mouse models.

Genome-wide assessment of protein–DNA interaction by chromatin immunoprecipitation followed by massive parallel sequencing (ChIP-seq) is a key technology for studying transcription factor (TF) localization and regulation of gene expression. Signal-to-noise-ratio and signal specificity in ChIP-seq studies depend on many variables, including antibody affinity and specificity. Thus far, efforts to improve antibody reagents for ChIP-seq experiments have focused mainly on generating higher quality antibodies. Here we introduce KOIN (knockout implemented normalization) as a novel strategy to increase signal specificity and reduce noise by using TF knockout mice as a critical control for ChIP-seq data experiments. Additionally, KOIN can identify ‘hyper ChIPable regions’ as another source of false-positive signals. As the use of the KOIN algorithm reduces false-positive results and thereby prevents misinterpretation of ChIP-seq data, it should be considered as the gold standard for future ChIP-seq analyses, particularly when developing ChIP-assays with novel antibody reagents.

Abstract 988: A mouse model gene expression database reveals E2Fs as key regulators of breast cancer metastasis

Abstract Metastasis is primarily responsible for the mortality associated with breast cancer. Despite this, the molecular underpinnings of cancer progression are poorly understood. By integrating bioinformatics with traditional genetics, we have addressed the hypothesis that genetic events mediating breast cancer metastasis can be elucidated from mouse models. To test this hypothesis, we assembled a gene expression database of over 1100 mouse breast cancers from 23 different models. Using this database, E2F transcription factors were predicted to be strikingly activated in metastatic mouse models. Transgenic MMTV-Neu mice were interbred with E2F transcription factor knockouts to test this hypothesis. Tumor latency was increased significantly with the loss of any of the activator E2Fs in the MMTV-Neu mice. Strikingly, there was also a significant reduction in the extent of pulmonary metastasis with loss of either E2F1 or E2F2. This metastasis reduction was shown to be a cell autonomous effect through a transplant experiment. Moreover, CD-31 staining revealed vasculature alterations with E2F loss. These data led to the hypothesis that E2F transcription factors were critical in mediating metastasis. Highly metastatic MMTV-PyMT transgenic mice also had predicted E2F transcription factor activation. Genetically testing this prediction, we noted that PyMT transgenic mice lacking E2F1 or E2F2 recapitulated the MMTV-Neu results with a vast reduction in metastasis. Using a tail vein injection we noted defects in extravasation with E2F1 or E2F2 loss. To determine the mechanism by which E2Fs regulated metastatic progression, we examined gene expression profiles of MMTV-Neu and PyMT tumors in both control and E2F knockout backgrounds. This analysis identified a cohort of genes with reduced expression in the non-metastatic E2F knockout breast cancers relative to the metastatic wild type E2F controls. Importantly, genes identified in the mouse model system are co-amplified in human HER2+ breast cancer but are not part of the HER2 amplicon. This amplicon is present in over 30% of HER2+ human breast cancers and is inversely associated with metastasis free survival. In addition to examining the amplicon genes in HER2+ human breast cancer, we have also examined predicted E2F activity using genomic signatures. This revealed four common combinations of E2F activity in HER2+ breast cancer. Importantly, low E2F1 and high E2F2 in the same sample was associated with far better relapse free survival than the opposite E2F pattern. These data indicate that the effects and the mechanisms uncovered in the mouse models are present in HER2+ human breast cancer. Taken together, by integrating bioinformatics with mouse models of breast cancer we have demonstrated that the E2F transcription factors play a pivotal role in progression from primary breast cancer to metastasis. Citation Format: Daniel Hollern, John Rennhack, Eran Andrechek. A mouse model gene expression database reveals E2Fs as key regulators of breast cancer metastasis. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 988. doi:10.1158/1538-7445.AM2014-988

The role of CD4+ T cell subsets in the clearance of Pneumocystis murina pneumonia in mice (MPF6P.734)

Abstract Pneumocystis (PC) Pneumonia remains a serious complication in the immuno-compromised host. The host defense against PC is critically dependent upon CD4+ T cells. The role of specific T-cell subsets that mediate host resistance against PC infection remains unclear. We use STAT transcription factor knockout (KO) mice to determine the role of Th1, Th2, and Th17 cells in PC infection. After 28 days infection with PC, serum, lung tissue, and mediastinal lymph node (LN) were collected. LN and lung tissue cells were measured for cytokines by ELISpot and intracellular staining. Total lung RNA was measured for PC burden by RT-qPCR. Cytokines were assayed from cell culture supernatant. Anti-PC antibody was measured by ELISA.Stat4, Stat6 single KO, and Stat4/6 double KO mice all were resistant to PC compared to the Rag2/gc KO mice. Stat4 was required for a Th1 response and Stat6 was required for a Th2 response. STAT3fl/fl/CD4Cre+ mice had partial susceptibility of PC. Stat3 expression was required in CD4+ T cells for PC specific Th17 responses. STAT4/6 double KO crossed to STAT3fl/fl/CD4Cre+ (Triple KO) mice were highly susceptible to PC infection compare to single and double KO mice. Individual Stat4 and 6 and double mutants clear PC. STAT3fl/fl/CD4Cre+ conferred partial susceptibility to PC. Triple KO were as susceptible for PC as Rag2/gc KO mice. These data support the hypothesis that PC is a true opportunist and that various T-cell subsets can confer protection against infection.

The Same but Different: Worms Reveal the Pervasiveness of Developmental System Drift

The Same but Different: Worms Reveal the Pervasiveness of Developmental System Drift

Metabolic Constraint-Based Refinement of Transcriptional Regulatory Networks

There is a strong need for computational frameworks that integrate different biological processes and data-types to unravel cellular regulation. Current efforts to reconstruct transcriptional regulatory networks (TRNs) focus primarily on proximal data such as gene co-expression and transcription factor (TF) binding. While such approaches enable rapid reconstruction of TRNs, the overwhelming combinatorics of possible networks limits identification of mechanistic regulatory interactions. Utilizing growth phenotypes and systems-level constraints to inform regulatory network reconstruction is an unmet challenge. We present our approach Gene Expression and Metabolism Integrated for Network Inference (GEMINI) that links a compendium of candidate regulatory interactions with the metabolic network to predict their systems-level effect on growth phenotypes. We then compare predictions with experimental phenotype data to select phenotype-consistent regulatory interactions. GEMINI makes use of the observation that only a small fraction of regulatory network states are compatible with a viable metabolic network, and outputs a regulatory network that is simultaneously consistent with the input genome-scale metabolic network model, gene expression data, and TF knockout phenotypes. GEMINI preferentially recalls gold-standard interactions (p-value = 10−172), significantly better than using gene expression alone. We applied GEMINI to create an integrated metabolic-regulatory network model for Saccharomyces cerevisiae involving 25,000 regulatory interactions controlling 1597 metabolic reactions. The model quantitatively predicts TF knockout phenotypes in new conditions (p-value = 10−14) and revealed potential condition-specific regulatory mechanisms. Our results suggest that a metabolic constraint-based approach can be successfully used to help reconstruct TRNs from high-throughput data, and highlights the potential of using a biochemically-detailed mechanistic framework to integrate and reconcile inconsistencies across different data-types. The algorithm and associated data are available at https://sourceforge.net/projects/gemini-data/

Inferring functional transcription factor-gene binding pairs by integrating transcription factor binding data with transcription factor knockout data

BackgroundChromatin immunoprecipitation (ChIP) experiments are now the most comprehensive experimental approaches for mapping the binding of transcription factors (TFs) to their target genes. However, ChIP data alone is insufficient for identifying functional binding target genes of TFs for two reasons. First, there is an inherent high false positive/negative rate in ChIP-chip or ChIP-seq experiments. Second, binding signals in the ChIP data do not necessarily imply functionality.MethodsIt is known that ChIP-chip data and TF knockout (TFKO) data reveal complementary information on gene regulation. While ChIP-chip data can provide TF-gene binding pairs, TFKO data can provide TF-gene regulation pairs. Therefore, we propose a novel network approach for identifying functional TF-gene binding pairs by integrating the ChIP-chip data with the TFKO data. In our method, a TF-gene binding pair from the ChIP-chip data is regarded to be functional if it also has high confident curated TFKO TF-gene regulatory relation or deduced hypostatic TF-gene regulatory relation.Results and conclusionsWe first validated our method on a gathered ground truth set. Then we applied our method to the ChIP-chip data to identify functional TF-gene binding pairs. The biological significance of our identified functional TF-gene binding pairs was shown by assessing their functional enrichment, the prevalence of protein-protein interaction, and expression coherence. Our results outperformed the results of three existing methods across all measures. And our identified functional targets of TFs also showed statistical significance over the randomly assigned TF-gene pairs. We also showed that our method is dataset independent and can apply to ChIP-seq data and the E. coli genome. Finally, we provided an example showing the biological applicability of our notion.