Comparative chemical genomics in Babesia species identifies the alkaline phosphatase PhoD as a determinant of antiparasitic resistance.

Babesiosis is an emerging zoonosis and widely distributed veterinary infection caused by 100+ species of Babesia parasites. The diversity of Babesia parasites and the lack of specific drugs necessitate the discovery of broadly effective antibabesials. Here, we describe a comparative chemogenomics (CCG) pipeline for the identification of conserved targets. CCG relies on parallel in vitro evolution of resistance in independent populations of Babesia spp. (B. bovis and B. divergens). We identified a potent antibabesial, MMV019266, from the Malaria Box, and selected for resistance in two species of Babesia. After sequencing of multiple independently derived lines in the two species, we identified mutations in a membrane-bound metallodependent phosphatase (phoD). In both species, the mutations were found in the phoD-like phosphatase domain. Using reverse genetics, we validated that mutations in bdphoD confer resistance to MMV019266 in B. divergens. We have also demonstrated that BdPhoD localizes to the endomembrane system and partially with the apicoplast. Finally, conditional knockdown and constitutive overexpression of BdPhoD alter the sensitivity to MMV019266 in the parasite. Overexpression of BdPhoD results in increased sensitivity to the compound, while knockdown increases resistance, suggesting BdPhoD is a pro-susceptibility factor. Together, we have generated a robust pipeline for identification of resistance loci and identified BdPhoD as a resistance mechanism in Babesia species.

A rising tide of parasite transcriptomics propels pathogen biology.

Twenty years ago, the first transcriptome of the intraerythrocytic developmental cycle of the malaria parasite Plasmodium falciparum was published in PLOS Biology. Since then, transcriptomics studies have transformed the study of parasite biology.

Comparative single-cell transcriptional atlases of Babesia species reveal conserved and species-specific expression profiles.

Babesia is a genus of apicomplexan parasites that infect red blood cells in vertebrate hosts. Pathology occurs during rapid replication cycles in the asexual blood stage of infection. Current knowledge of Babesia replication cycle progression and regulation is limited and relies mostly on comparative studies with related parasites. Due to limitations in synchronizing Babesia parasites, fine-scale time-course transcriptomic resources are not readily available. Single-cell transcriptomics provides a powerful unbiased alternative for profiling asynchronous cell populations. Here, we applied single-cell RNA sequencing to 3 Babesia species (B. divergens, B. bovis, and B. bigemina). We used analytical approaches and algorithms to map the replication cycle and construct pseudo-synchronized time-course gene expression profiles. We identify clusters of co-expressed genes showing "just-in-time" expression profiles, with gradually cascading peaks throughout asexual development. Moreover, clustering analysis of reconstructed gene curves reveals coordinated timing of peak expression in epigenetic markers and transcription factors. Using a regularized Gaussian graphical model, we reconstructed co-expression networks and identified conserved and species-specific nodes. Motif analysis of a co-expression interactome of AP2 transcription factors identified specific motifs previously reported to play a role in DNA replication in Plasmodium species. Finally, we present an interactive web application to visualize and interactively explore the datasets.

Deep learning trained on hematoxylin and eosin tumor region of Interest predicts HER2 status and trastuzumab treatment response in HER2+ breast cancer.

The current standard of care for many patients with HER2-positive breast cancer is neoadjuvant chemotherapy in combination with anti-HER2 agents, based on HER2 amplification as detected by in situ hybridization (ISH) or protein immunohistochemistry (IHC). However, hematoxylin & eosin (H&E) tumor stains are more commonly available, and accurate prediction of HER2 status and anti-HER2 treatment response from H&E would reduce costs and increase the speed of treatment selection. Computational algorithms for H&E have been effective in predicting a variety of cancer features and clinical outcomes, including moderate success in predicting HER2 status. In this work, we present a novel convolutional neural network (CNN) approach able to predict HER2 status with increased accuracy over prior methods. We trained a CNN classifier on 188 H&E whole slide images (WSIs) manually annotated for tumor Regions of interest (ROIs) by our pathology team. Our classifier achieved an area under the curve (AUC) of 0.90 in cross-validation of slide-level HER2 status and 0.81 on an independent TCGA test set. Within slides, we observed strong agreement between pathologist annotated ROIs and blinded computational predictions of tumor regions / HER2 status. Moreover, we trained our classifier on pre-treatment samples from 187 HER2+ patients that subsequently received trastuzumab therapy. Our classifier achieved an AUC of 0.80 in a five-fold cross validation. Our work provides an H&E-based algorithm that can predict HER2 status and trastuzumab response in breast cancer at an accuracy that may benefit clinical evaluations.

High-Resolution Small RNAs Landscape Provides Insights into Alkane Adaptation in the Marine Alkane-Degrader Alcanivorax dieselolei B-5

Alkanes are widespread in the ocean, and Alcanivorax is one of the most ubiquitous alkane-degrading bacteria in the marine ecosystem. Small RNAs (sRNAs) are usually at the heart of regulatory pathways, but sRNA-mediated alkane metabolic adaptability still remains largely unknown due to the difficulties of identification. Here, differential RNA sequencing (dRNA-seq) modified with a size selection (~50-nt to 500-nt) strategy was used to generate high-resolution sRNAs profiling in the model species Alcanivorax dieselolei B-5 under alkane (n-hexadecane) and non-alkane (acetate) conditions. As a result, we identified 549 sRNA candidates at single-nucleotide resolution of 5'-ends, 63.4% of which are with transcription start sites (TSSs), and 36.6% of which are with processing sites (PSSs) at the 5'-ends. These sRNAs originate from almost any location in the genome, regardless of intragenic (65.8%), antisense (20.6%) and intergenic (6.2%) regions, and RNase E may function in the maturation of sRNAs. Most sRNAs locally distribute across the 15 reference genomes of Alcanivorax, and only 7.5% of sRNAs are broadly conserved in this genus. Expression responses to the alkane of several core conserved sRNAs, including 6S RNA, M1 RNA and tmRNA, indicate that they may participate in alkane metabolisms and result in more actively global transcription, RNA processing and stresses mitigation. Two novel CsrA-related sRNAs are identified, which may be involved in the translational activation of alkane metabolism-related genes by sequestering the global repressor CsrA. The relationships of sRNAs with the characterized genes of alkane sensing (ompS), chemotaxis (mcp, cheR, cheW2), transporting (ompT1, ompT2, ompT3) and hydroxylation (alkB1, alkB2, almA) were created based on the genome-wide predicted sRNA-mRNA interactions. Overall, the sRNA landscape lays the ground for uncovering cryptic regulations in critical marine bacterium, among which both the core and species-specific sRNAs are implicated in the alkane adaptive metabolisms.

Patch-DCA: improved protein interface prediction by utilizing structural information and clustering DCA scores.

MOTIVATION: Over the past decade, there have been impressive advances in determining the 3D structures of protein complexes. However, there are still many complexes with unknown structures, even when the structures of the individual proteins are known. The advent of protein sequence information provides an opportunity to leverage evolutionary information to enhance the accuracy of protein-protein interface prediction. To this end, several statistical and machine learning methods have been proposed. In particular, direct coupling analysis has recently emerged as a promising approach for identification of protein contact maps from sequential information. However, the ability of these methods to detect protein-protein inter-residue contacts remains relatively limited. RESULTS: In this work, we propose a method to integrate sequential and co-evolution information with structural and functional information to increase the performance of protein-protein interface prediction. Further, we present a post-processing clustering method that improves the average relative F1 score by 70% and 24% and the average relative precision by 80% and 36% in comparison with two state-of-the-art methods, PSICOV and GREMLIN. AVAILABILITY AND IMPLEMENTATION: https://github.com/BioMLBoston/PatchDCA. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Causal Inference Engine: a platform for directional gene set enrichment analysis and inference of active transcriptional regulators.

Inference of active regulatory mechanisms underlying specific molecular and environmental perturbations is essential for understanding cellular response. The success of inference algorithms relies on the quality and coverage of the underlying network of regulator-gene interactions. Several commercial platforms provide large and manually curated regulatory networks and functionality to perform inference on these networks. Adaptation of such platforms for open-source academic applications has been hindered by the lack of availability of accurate, high-coverage networks of regulatory interactions and integration of efficient causal inference algorithms. In this work, we present CIE, an integrated platform for causal inference of active regulatory mechanisms form differential gene expression data. Using a regularized Gaussian Graphical Model, we construct a transcriptional regulatory network by integrating publicly available ChIP-seq experiments with gene-expression data from tissue-specific RNA-seq experiments. Our GGM approach identifies high confidence transcription factor (TF)-gene interactions and annotates the interactions with information on mode of regulation (activation vs. repression). Benchmarks against manually curated databases of TF-gene interactions show that our method can accurately detect mode of regulation. We demonstrate the ability of our platform to identify active transcriptional regulators by using controlled in vitro overexpression and stem-cell differentiation studies and utilize our method to investigate transcriptional mechanisms of fibroblast phenotypic plasticity.

ModEx: A text mining system for extracting mode of regulation of transcription factor-gene regulatory interaction.

BACKGROUND: Transcription factors (TFs) are proteins that are fundamental to transcription and regulation of gene expression. Each TF may regulate multiple genes and each gene may be regulated by multiple TFs. TFs can act as either activator or repressor of gene expression. This complex network of interactions between TFs and genes underlies many developmental and biological processes and is implicated in several human diseases such as cancer. Hence deciphering the network of TF-gene interactions with information on mode of regulation (activation vs. repression) is an important step toward understanding the regulatory pathways that underlie complex traits. There are many experimental, computational, and manually curated databases of TF-gene interactions. In particular, high-throughput ChIP-Seq datasets provide a large-scale map or transcriptional regulatory interactions. However, these interactions are not annotated with information on context and mode of regulation. Such information is crucial to gain a global picture of gene regulatory mechanisms and can aid in developing machine learning models for applications such as biomarker discovery, prediction of response to therapy, and precision medicine. METHODS: In this work, we introduce a text-mining system to annotate ChIP-Seq derived interaction with such meta data through mining PubMed articles. We evaluate the performance of our system using gold standard small scale manually curated databases. RESULTS: Our results show that the method is able to accurately extract mode of regulation with F-score 0.77 on TRRUST curated interaction and F-score 0.96 on intersection of TRUSST and ChIP-network. We provide a HTTP REST API for our code to facilitate usage. Availibility: Source code and datasets are available for download on GitHub: https://github.com/samanfrm/modex.

Stochastic Modeling of Gene Regulation by Noncoding Small RNAs in the Strong Interaction Limit.

Noncoding small RNAs (sRNAs) are known to play a key role in regulating diverse cellular processes, and their dysregulation is linked to various diseases such as cancer. Such diseases are also marked by phenotypic heterogeneity, which is often driven by the intrinsic stochasticity of gene expression. Correspondingly, there is significant interest in developing quantitative models focusing on the interplay between stochastic gene expression and regulation by sRNAs. We consider the canonical model of regulation of stochastic gene expression by sRNAs, wherein interaction between constitutively expressed sRNAs and mRNAs leads to stoichiometric mutual degradation. The exact solution of this model is analytically intractable given the nonlinear interaction term between sRNAs and mRNAs, and theoretical approaches typically invoke the mean-field approximation. However, mean-field results are inaccurate in the limit of strong interactions and low abundances; thus, alternative theoretical approaches are needed. In this work, we obtain analytical results for the canonical model of regulation of stochastic gene expression by sRNAs in the strong interaction limit. We derive analytical results for the steady-state generating function of the joint distribution of mRNAs and sRNAs in the limit of strong interactions and use the results derived to obtain analytical expressions characterizing the corresponding protein steady-state distribution. The results obtained can serve as building blocks for the analysis of genetic circuits involving sRNAs and provide new insights into the role of sRNAs in regulating stochastic gene expression in the limit of strong interactions.

A biological network-based regularized artificial neural network model for robust phenotype prediction from gene expression data.

BACKGROUND: Stratification of patient subpopulations that respond favorably to treatment or experience and adverse reaction is an essential step toward development of new personalized therapies and diagnostics. It is currently feasible to generate omic-scale biological measurements for all patients in a study, providing an opportunity for machine learning models to identify molecular markers for disease diagnosis and progression. However, the high variability of genetic background in human populations hampers the reproducibility of omic-scale markers. In this paper, we develop a biological network-based regularized artificial neural network model for prediction of phenotype from transcriptomic measurements in clinical trials. To improve model sparsity and the overall reproducibility of the model, we incorporate regularization for simultaneous shrinkage of gene sets based on active upstream regulatory mechanisms into the model. RESULTS: We benchmark our method against various regression, support vector machines and artificial neural network models and demonstrate the ability of our method in predicting the clinical outcomes using clinical trial data on acute rejection in kidney transplantation and response to Infliximab in ulcerative colitis. We show that integration of prior biological knowledge into the classification as developed in this paper, significantly improves the robustness and generalizability of predictions to independent datasets. We provide a Java code of our algorithm along with a parsed version of the STRING DB database. CONCLUSION: In summary, we present a method for prediction of clinical phenotypes using baseline genome-wide expression data that makes use of prior biological knowledge on gene-regulatory interactions in order to increase robustness and reproducibility of omic-scale markers. The integrated group-wise regularization methods increases the interpretability of biological signatures and gives stable performance estimates across independent test sets.

Prediction of bacterial small RNAs in the RsmA (CsrA) and ToxT pathways: a machine learning approach.

BACKGROUND: Small RNAs (sRNAs) constitute an important class of post-transcriptional regulators that control critical cellular processes in bacteria. Recent research using high-throughput transcriptomic approaches has led to a dramatic increase in the discovery of bacterial sRNAs. However, it is generally believed that the currently identified sRNAs constitute a limited subset of the bacterial sRNA repertoire. In several cases, sRNAs belonging to a specific class are already known and the challenge is to identify additional sRNAs belonging to the same class. In such cases, machine-learning approaches can be used to predict novel sRNAs in a given class. METHODS: In this work, we develop novel bioinformatics approaches that integrate sequence and structure-based features to train machine-learning models for the discovery of bacterial sRNAs. We show that features derived from recurrent structural motifs in the ensemble of low energy secondary structures can distinguish the RNA classes with high accuracy. RESULTS: We apply this approach to predict new members in two broad classes of bacterial small RNAs: 1) sRNAs that bind to the RNA-binding protein RsmA/CsrA in diverse bacterial species and 2) sRNAs regulated by the master regulator of virulence, ToxT, in Vibrio cholerae. CONCLUSION: The involvement of sRNAs in bacterial adaptation to changing environments is an increasingly recurring theme in current research in microbiology. It is likely that future research, combining experimental and computational approaches, will discover many more examples of sRNAs as components of critical regulatory pathways in bacteria. We have developed a novel approach for prediction of small RNA regulators in important bacterial pathways. This approach can be applied to specific classes of sRNAs for which several members have been identified and the challenge is to identify additional sRNAs.

Interpreting transcriptional changes using causal graphs: new methods and their practical utility on public networks.

BACKGROUND: Inference of active regulatory cascades under specific molecular and environmental perturbations is a recurring task in transcriptional data analysis. Commercial tools based on large, manually curated networks of causal relationships offering such functionality have been used in thousands of articles in the biomedical literature. The adoption and extension of such methods in the academic community has been hampered by the lack of freely available, efficient algorithms and an accompanying demonstration of their applicability using current public networks. RESULTS: In this article, we propose a new statistical method that will infer likely upstream regulators based on observed patterns of up- and down-regulated transcripts. The method is suitable for use with public interaction networks with a mix of signed and unsigned causal edges. It subsumes and extends two previously published approaches and we provide a novel algorithmic method for efficient statistical inference. Notably, we demonstrate the feasibility of using the approach to generate biological insights given current public networks in the context of controlled in-vitro overexpression experiments, stem-cell differentiation data and animal disease models. We also provide an efficient implementation of our method in the R package QuaternaryProd available to download from Bioconductor. CONCLUSIONS: In this work, we have closed an important gap in utilizing causal networks to analyze differentially expressed genes. Our proposed Quaternary test statistic incorporates all available evidence on the potential relevance of an upstream regulator. The new approach broadens the use of these types of statistics for highly curated signed networks in which ambiguities arise but also enables the use of networks with unsigned edges. We design and implement a novel computational method that can efficiently estimate p-values for upstream regulators in current biological settings. We demonstrate the ready applicability of the implemented method to analyze differentially expressed genes using the publicly available networks.

Robust clinical outcome prediction based on Bayesian analysis of transcriptional profiles and prior causal networks.

MOTIVATION: Understanding and predicting an individual's response in a clinical trial is the key to better treatments and cost-: effective medicine. Over the coming years, more and more large-scale omics datasets will become available to characterize patients with complex and heterogeneous diseases at a molecular level. Unfortunately, genetic, phenotypical and environmental variation is much higher in a human trial population than currently modeled or measured in most animal studies. In our experience, this high variability can lead to failure of trained predictors in independent studies and undermines the credibility and utility of promising high-dimensional datasets. METHODS: We propose a method that utilizes patient-level genome-wide expression data in conjunction with causal networks based on prior knowledge. Our approach determines a differential expression profile for each patient and uses a Bayesian approach to infer corresponding upstream regulators. These regulators and their corresponding posterior probabilities of activity are used in a regularized regression framework to predict response. RESULTS: We validated our approach using two clinically relevant phenotypes, namely acute rejection in kidney transplantation and response to Infliximab in ulcerative colitis. To demonstrate pitfalls in translating trained predictors across independent trials, we analyze performance characteristics of our approach as well as alternative feature sets in the regression on two independent datasets for each phenotype. We show that the proposed approach is able to successfully incorporate causal prior knowledge to give robust performance estimates.

Molecular causes of transcriptional response: a Bayesian prior knowledge approach.

MOTIVATION: The abundance of many transcripts changes significantly in response to a variety of molecular and environmental perturbations. A key question in this setting is as follows: what intermediate molecular perturbations gave rise to the observed transcriptional changes? Regulatory programs are not exclusively governed by transcriptional changes but also by protein abundance and post-translational modifications making direct causal inference from data difficult. However, biomedical research over the last decades has uncovered a plethora of causal signaling cascades that can be used to identify good candidates explaining a specific set of transcriptional changes. METHODS: We take a Bayesian approach to integrate gene expression profiling with a causal graph of molecular interactions constructed from prior biological knowledge. In addition, we define the biological context of a specific interaction by the corresponding Medical Subject Headings terms. The Bayesian network can be queried to suggest upstream regulators that can be causally linked to the altered expression profile. RESULTS: Our approach will treat candidate regulators in the right biological context preferentially, enables hierarchical exploration of resulting hypotheses and takes the complete network of causal relationships into account to arrive at the best set of upstream regulators. We demonstrate the power of our method on distinct biological datasets, namely response to dexamethasone treatment, stem cell differentiation and a neuropathic pain model. In all cases relevant biological insights could be validated. AVAILABILITY AND IMPLEMENTATION: Source code for the method is available upon request.

Beta-Sitosterol Alters Collagen Distribution in Prostate Fibroblasts.

Herbal supplements containing several types of plant sterols, vitamins, and minerals, are marketed for prostate health. In the majority of these supplements, the most abundant plant sterol is saw palmetto extract or its' principal component, beta-sitosterol. In terms of prostate health, previous work almost exclusively focused on the effects of beta-sitosterol on prostatic epithelium, with little attention paid to the effects on prostatic stroma. This omission is a concern, as the abnormal accumulation of collagen, or fibrosis, of the prostatic stroma has been identified as a factor contributing to lower urinary tract symptoms and dysfunction in aging men. To address whether beta-sitosterol may be promoting prostatic fibrosis, immortalized and primary prostate stromal fibroblasts were subjected to immunoblotting, immunofluorescence, qRT-PCR, ELISA, and image quantitation and analysis techniques to elucidate the effects of beta-sitosterol on cell viability and collagen expression and cellular localization. The results of these studies show that beta-sitosterol is nontoxic to prostatic fibroblasts and does not stimulate collagen production by these cells. However, beta-sitosterol alters collagen distribution and sequesters collagen within prostatic fibroblasts, likely in an age-dependent manner. This is a significant finding as prostate health supplements are used predominantly by middle aged and older men who may, then, be affected disproportionately by these effects.

Single cell expression and chromatin accessibility of the Toxoplasma gondii lytic cycle identifies AP2XII-8 as an essential ribosome regulon driver.

Sequential lytic cycles driven by cascading transcriptional waves underlie pathogenesis in the apicomplexan parasite Toxoplasma gondii. This parasite's unique division by internal budding, short cell cycle, and jumbled up classically defined cell cycle stages have restrained in-depth transcriptional program analysis. Here, unbiased transcriptome and chromatin accessibility maps throughout the lytic cell cycle are established at the single-cell level. Correlated pseudo-timeline assemblies of expression and chromatin profiles maps transcriptional versus chromatin level transition points promoting the cell division cycle. Sequential clustering analysis identifies functionally related gene groups promoting cell cycle progression. Promoter DNA motif mapping reveals patterns of combinatorial regulation. Pseudo-time trajectory analysis reveals transcriptional bursts at different cell cycle points. The dominant burst in G1 is driven largely by transcription factor AP2XII-8, which engages a conserved DNA motif, and promotes the expression of 44 ribosomal proteins encoding regulon. Overall, the study provides integrated, multi-level insights into apicomplexan transcriptional regulation.

Drug resistant pancreatic cancer cells exhibit altered biophysical interactions with stromal fibroblasts in imaging studies of 3D co-culture models.

Interactions between tumor and stromal cells are well known to play a prominent roles in progression of pancreatic ductal adenocarcinoma (PDAC). As knowledge of stromal crosstalk in PDAC has evolved, it has become clear that cancer associated fibroblasts can play both tumor promoting and tumor suppressive roles through a combination of paracrine crosstalk and juxtacrine interactions involving direct physical contact. Another major contributor to dismal survival statistics for PDAC is development of resistance to chemotherapy drugs. Though less is known about how the acquisition of chemoresistance impacts upon tumor-stromal crosstalk. Here, we use 3D co-culture geometries to recapitulate juxtacrine interactions between epithelial and stromal cells. In particular, extracellular matrix (ECM) overlay cultures in which stromal cells (pancreatic stellate cells, or normal human fibroblasts) are placed adjacent to PDAC cells (PANC1), result in direct heterotypic cell adhesions accompanied by dramatic fibroblast contractility which leads to highly condensed macroscopic multicellular aggregates as detected using particle image velocimetry (PIV) analysis to quantify cell velocities over the course of time lapse movie sequences. To investigate how drug resistance impacts these juxtacrine interactions we contrast cultures in which PANC1 are substituted with a drug resistant subline (PANC1-OR) previously established in our lab. We find that heterotypic cell-cell interactions are highly suppressed in drug-resistant cells relative to the parental PANC1 cells. To investigate further we conduct RNA-seq and bioinformatics analysis to identify differential gene expression in PANC1 and PANC1-OR, which shows that negative regulation of cell adhesion molecules, consistent with increased epithelial mesenchymal transition (EMT), is also consistent with loss of hetrotypic cell-cell contact necessary for the contractile behavior observed in drug naïve cultures. Overall these findings elucidate the role of drug-resistance in inhibiting an avenue of stromal crosstalk which is associated with tumor suppression and also help to establish cell culture conditions useful for further mechanistic investigation.

Androgen receptor splice variants drive castration-resistant prostate cancer metastasis by activating distinct transcriptional programs.

One critical mechanism through which prostate cancer (PCa) adapts to treatments targeting androgen receptor (AR) signaling is the emergence of ligand-binding domain-truncated and constitutively active AR splice variants, particularly AR-V7. While AR-V7 has been intensively studied, its ability to activate distinct biological functions compared with the full-length AR (AR-FL), and its role in regulating the metastatic progression of castration-resistant PCa (CRPC), remain unclear. Our study found that, under castrated conditions, AR-V7 strongly induced osteoblastic bone lesions, a response not observed with AR-FL overexpression. Through combined ChIP-seq, ATAC-seq, and RNA-seq analyses, we demonstrated that AR-V7 uniquely accesses the androgen-responsive elements in compact chromatin regions, activating a distinct transcription program. This program was highly enriched for genes involved in epithelial-mesenchymal transition and metastasis. Notably, we discovered that SOX9, a critical metastasis driver gene, was a direct target and downstream effector of AR-V7. Its protein expression was dramatically upregulated in AR-V7-induced bone lesions. Moreover, we found that Ser81 phosphorylation enhanced AR-V7's pro-metastasis function by selectively altering its specific transcription program. Blocking this phosphorylation with CDK9 inhibitors impaired the AR-V7-mediated metastasis program. Overall, our study has provided molecular insights into the role of AR splice variants in driving the metastatic progression of CRPC.

A Bayesian Noisy Logic Model for Inference of Transcription Factor Activity from Single Cell and Bulk Transcriptomic Data.

The advent of high-throughput sequencing has made it possible to measure the expression of genes at relatively low cost. However, direct measurement of regulatory mechanisms, such as Transcription Factor (TF) activity is still not readily feasible in a high-throughput manner. Consequently, there is a need for computational approaches that can reliably estimate regulator activity from observable gene expression data. In this work, we present a noisy Boolean logic Bayesian model for TF activity inference from differential gene expression data and causal graphs. Our approach provides a flexible framework to incorporate biologically motivated TF-gene regulation logic models. Using simulations and controlled over-expression experiments in cell cultures, we demonstrate that our method can accurately identify TF activity. Moreover, we apply our method to bulk and single cell transcriptomics measurements to investigate transcriptional regulation of fibroblast phenotypic plasticity. Finally, to facilitate usage, we provide user-friendly software packages and a web-interface to query TF activity from user input differential gene expression data: https://umbibio.math.umb.edu/nlbayes/.

A Bayesian noisy logic model for inference of transcription factor activity from single cell and bulk transcriptomic data.

The advent of high-throughput sequencing has made it possible to measure the expression of genes at relatively low cost. However, direct measurement of regulatory mechanisms, such as transcription factor (TF) activity is still not readily feasible in a high-throughput manner. Consequently, there is a need for computational approaches that can reliably estimate regulator activity from observable gene expression data. In this work, we present a noisy Boolean logic Bayesian model for TF activity inference from differential gene expression data and causal graphs. Our approach provides a flexible framework to incorporate biologically motivated TF-gene regulation logic models. Using simulations and controlled over-expression experiments in cell cultures, we demonstrate that our method can accurately identify TF activity. Moreover, we apply our method to bulk and single cell transcriptomics measurements to investigate transcriptional regulation of fibroblast phenotypic plasticity. Finally, to facilitate usage, we provide user-friendly software packages and a web-interface to query TF activity from user input differential gene expression data: https://umbibio.math.umb.edu/nlbayes/.