TF-COMB - discovering grammar of transcription factor binding sites

BestCRM: An Exhaustive Search for Optimal Cis-Regulatory Modules in Promoters Accelerated by the Multidimensional Hash Function

The concept of cis-regulatory modules located in gene promoters represents today's vision of the organization of gene transcriptional regulation. Such modules are a combination of two or more single, short DNA motifs. The bioinformatic identification of such modules belongs to so-called NP-hard problems with extreme computational complexity, and therefore, simplifications, assumptions, and heuristics are usually deployed to tackle the problem. In practice, this requires, first, many parameters to be set before the search, and second, it leads to the identification of locally optimal results. Here, a novel method is presented, aimed at identifying the cis-regulatory elements in gene promoters based on an exhaustive search of all the feasible modules' configurations. All required parameters are automatically estimated using positive and negative datasets. To be computationally efficient, the search is accelerated using a multidimensional hash function, allowing the search to complete in a few hours on a regular laptop (for example, a CPU Intel i7, 3.2 GH, 32 Gb RAM). Tests on an established benchmark and real data show better performance of BestCRM compared to the available methods according to several metrics like specificity, sensitivity, AUC, etc. A great practical advantage of the method is its minimum number of input parameters-apart from positive and negative promoters, only a desired level of module presence in promoters is required.

Genome-wide DNase I-hypersensitive site assay reveals distinct genomic distributions and functional features of open chromatin in autopolyploid sugarcane

The characterization of cis-regulatory DNA elements (CREs) is essential for deciphering the regulation of gene expression in eukaryotes. Although there have been endeavors to identify CREs in plants, the properties of CREs in polyploid genomes are still largely unknown. Here, we conducted the genome-wide identification of DNase I-hypersensitive sites (DHSs) in leaf and stem tissues of the auto-octoploid species Saccharum officinarum. We revealed that DHSs showed highly similar distributions in the genomes of these two S. officinarum tissues. Notably, we observed that approximately 74% of DHSs were located in distal intergenic regions, suggesting considerable differences in the abundance of distal CREs between S. officinarum and other plants. Leaf- and stem-dependent transcriptional regulatory networks were also developed by mining the binding motifs of transcription factors (TFs) from tissue-specific DHSs. Four TEOSINTE BRANCHED 1, CYCLOIDEA, and PCF1 (TCP) TFs (TCP2, TCP4, TCP7, and TCP14) and two ethylene-responsive factors (ERFs) (ERF109 and ERF03) showed strong causal connections with short binding distances from each other, pointing to their possible roles in the regulatory networks of leaf and stem development. Through functional validation in transiently transgenic protoplasts, we isolate a set of tissue-specific promoters. Overall, the DHS maps presented here offer a global view of the potential transcriptional regulatory elements in polyploid sugarcane and can be expected to serve as a valuable resource for both transcriptional network elucidation and genome editing in sugarcane breeding.

Transcription factor condensates and signaling driven transcription

Transcription Factor (TF) condensates are a heterogenous mix of RNA, DNA, and multiple co-factor proteins capable of modulating the transcriptional response of the cell. The dynamic nature and the spatial location of TF-condensates in the 3D nuclear space is believed to provide a fast response, which is on the same pace as the signaling cascade and yet ever-so-specific in the crowded environment of the nucleus. However, the current understanding of how TF-condensates can achieve these feet so quickly and efficiently is still unclear. In this review, we draw parallels with other protein condensates and share our speculations on how the nucleus uses these TF-condensates to achieve high transcriptional specificity and fidelity. We discuss the various constituents of TF-condensates, their properties, and the known and unknown functions of TF-condensates with a particular focus on steroid signaling-induced transcriptional programs.

Computational tools for inferring transcription factor activity

Transcription factors (TFs) are essential players in orchestrating the regulatory landscape in cells. Still, their exact modes of action and dependencies on other regulatory aspects remain elusive. Since TFs act cell type-specific and each TF has its own characteristics, untangling their regulatory interactions from an experimental point of view is laborious and convoluted. Thus, there is an ongoing development of computational tools that estimate transcription factor activity (TFA) from a variety of data modalities, either based on a mapping of TFs to their putative target genes or in a genome-wide, gene-unspecific fashion. These tools can help to gain insights into TF regulation and to prioritize candidates for experimental validation. We want to give an overview of available computational tools that estimate TFA, illustrate examples of their application, debate common result validation strategies, and discuss assumptions and concomitant limitations.

In vitro modeling of CD8+ T cell exhaustion enables CRISPR screening to reveal a role for BHLHE40

Identifying molecular mechanisms of exhausted CD8 T cells (Tex) is a key goal of improving immunotherapy of cancer and other diseases. However, high-throughput interrogation of in vivo Tex can be costly and inefficient. In vitro models of Tex are easily customizable and quickly generate high cellular yield, enabling CRISPR screening and other high-throughput assays. We established an in vitro model of chronic stimulation and benchmarked key phenotypic, functional, transcriptional, and epigenetic features against bona fide in vivo Tex. We leveraged this model of in vitro chronic stimulation in combination with CRISPR screening to identify transcriptional regulators of T cell exhaustion. This approach identified several transcription factors, including BHLHE40. In vitro and in vivo validation defined a role for BHLHE40 in regulating a key differentiation checkpoint between progenitor and intermediate Tex subsets. By developing and benchmarking an in vitro model of Tex, then applying high-throughput CRISPR screening, we demonstrate the utility of mechanistically annotated in vitro models of Tex.

Gene regulatory network inference in soybean upon infection by Phytophthora sojae

Phytophthora sojae is a soil-borne oomycete and the causal agent of Phytophthora root and stem rot (PRR) in soybean (Glycine max [L.] Merrill). Yield losses attributed to P. sojae are devastating in disease-conducive environments, with global estimates surpassing 1.1 million tonnes annually. Historically, management of PRR has entailed host genetic resistance (both vertical and horizontal) complemented by disease-suppressive cultural practices (e.g., oomicide application). However, the vast expansion of complex and/or diverse P. sojae pathotypes necessitates developing novel technologies to attenuate PRR in field environments. Therefore, the objective of the present study was to couple high-throughput sequencing data and deep learning to elucidate molecular features in soybean following infection by P. sojae. In doing so, we generated transcriptomes to identify differentially expressed genes (DEGs) during compatible and incompatible interactions with P. sojae and a mock inoculation. The expression data were then used to select two defense-related transcription factors (TFs) belonging to WRKY and RAV families. DNA Affinity Purification and sequencing (DAP-seq) data were obtained for each TF, providing putative DNA binding sites in the soybean genome. These bound sites were used to train Deep Neural Networks with convolutional and recurrent layers to predict new target sites of WRKY and RAV family members in the DEG set. Moreover, we leveraged publicly available Arabidopsis (Arabidopsis thaliana) DAP-seq data for five TF families enriched in our transcriptome analysis to train similar models. These Arabidopsis data-based models were used for cross-species TF binding site prediction on soybean. Finally, we created a gene regulatory network depicting TF-target gene interactions that orchestrate an immune response against P. sojae. Information herein provides novel insight into molecular plant-pathogen interaction and may prove useful in developing soybean cultivars with more durable resistance to P. sojae.

In Vitro Modeling of CD8 T Cell Exhaustion Enables CRISPR Screening to Reveal a Role for BHLHE40

Identifying novel molecular mechanisms of exhausted CD8 T cells (T ex ) is a key goal of improving immunotherapy of cancer and other diseases. However, high-throughput interrogation of in vivo T ex can be costly and inefficient. In vitro models of T ex are easily customizable and quickly generate high cellular yield, offering an opportunity to perform CRISPR screening and other high-throughput assays. We established an in vitro model of chronic stimulation and benchmarked key phenotypic, functional, transcriptional, and epigenetic features against bona fide in vivo T ex . We leveraged this model of in vitro chronic stimulation in combination with pooled CRISPR screening to uncover transcriptional regulators of T cell exhaustion. This approach identified several transcription factors, including BHLHE40. In vitro and in vivo validation defined a role for BHLHE40 in regulating a key differentiation checkpoint between progenitor and intermediate subsets of T ex . By developing and benchmarking an in vitro model of T ex , we demonstrate the utility of mechanistically annotated in vitro models of T ex , in combination with high-throughput approaches, as a discovery pipeline to uncover novel T ex biology.

Strand Asymmetries Across Genomic Processes

Across biological systems, a number of genomic processes, including transcription, replication, DNA repair, and transcription factor binding, display intrinsic directionalities. These directionalities are reflected in the asymmetric distribution of nucleotides, motifs, genes, transposon integration sites, and other functional elements across the two complementary strands. Strand asymmetries, including GC skews and mutational biases, have shaped the nucleotide composition of diverse organisms. The investigation of strand asymmetries often serves as a method to understand underlying biological mechanisms, including protein binding preferences, transcription factor interactions, retrotransposition, DNA damage and repair preferences, transcription-replication collisions, and mutagenesis mechanisms. Research into this subject also enables the identification of functional genomic sites, such as replication origins and transcription start sites. Improvements in our ability to detect and quantify DNA strand asymmetries will provide insights into diverse functionalities of the genome, the contribution of different mutational mechanisms in germline and somatic mutagenesis, and our knowledge of genome instability and evolution, which all have significant clinical implications in human disease, including cancer. In this review, we describe key developments that have been made across the field of genomic strand asymmetries, as well as the discovery of associated mechanisms.