A universal framework for regulatory element discovery across all genomes and data types

Integrative identification of non-coding regulatory regions driving metastatic prostate cancer

Large-scale sequencing efforts have been undertaken to understand the mutational landscape of the coding genome. However, the vast majority of variants occur within non-coding genomic regions. We designed an integrative computational and experimental framework to identify recurrently mutated non-coding regulatory regions that drive tumor progression. Applying this framework to sequencing data from a large prostate cancer patient cohort revealed a large set of candidate drivers. We used (1) in silico analyses, (2) massively parallel reporter assays, and (3) in vivo CRISPR interference screens to systematically validate metastatic castration-resistant prostate cancer (mCRPC) drivers. One identified enhancer region, GH22I030351, acts on a bidirectional promoter to simultaneously modulate expression of the U2-associated splicing factor SF3A1 and chromosomal protein CCDC157. SF3A1 and CCDC157 promote tumor growth in vivo. We nominated a number of transcription factors, notably SOX6, to regulate expression of SF3A1 and CCDC157. Our integrative approach enables the systematic detection of non-coding regulatory regions that drive human cancers.

pyPAGE: A framework for Addressing biases in gene-set enrichment analysis-A case study on Alzheimer's disease

Inferring the driving regulatory programs from comparative analysis of gene expression data is a cornerstone of systems biology. Many computational frameworks were developed to address this problem, including our iPAGE (information-theoretic Pathway Analysis of Gene Expression) toolset that uses information theory to detect non-random patterns of expression associated with given pathways or regulons. Our recent observations, however, indicate that existing approaches are susceptible to the technical biases that are inherent to most real world annotations. To address this, we have extended our information-theoretic framework to account for specific biases and artifacts in biological networks using the concept of conditional information. To showcase pyPAGE, we performed a comprehensive analysis of regulatory perturbations that underlie the molecular etiology of Alzheimer's disease (AD). pyPAGE successfully recapitulated several known AD-associated gene expression programs. We also discovered several additional regulons whose differential activity is significantly associated with AD. We further explored how these regulators relate to pathological processes in AD through cell-type specific analysis of single cell and spatial gene expression datasets. Our findings showcase the utility of pyPAGE as a precise and reliable biomarker discovery in complex diseases such as Alzheimer's disease.

Regulation of the Drosophila transcriptome by Pumilio and the CCR4-NOT deadenylase complex

The sequence-specific RNA-binding protein Pumilio (Pum) controls Drosophila development; however, the network of mRNAs that it regulates remains incompletely characterized. In this study, we use knockdown and knockout approaches coupled with RNA-seq to measure the impact of Pum on the transcriptome of Drosophila cells in culture. We also use an improved RNA coimmunoprecipitation method to identify Pum-bound mRNAs in Drosophila embryos. Integration of these data sets with the locations of Pum-binding motifs across the transcriptome reveals novel direct Pum target genes involved in neural, muscle, wing, and germ cell development and in cellular proliferation. These genes include components of Wnt, TGF-β, MAPK/ERK, and Notch signaling pathways, DNA replication, and lipid metabolism. We identify the mRNAs regulated by the CCR4-NOT deadenylase complex, a key factor in Pum-mediated repression, and observe concordant regulation of Pum:CCR4-NOT target mRNAs. Computational modeling reveals that Pum binding, binding site number, clustering, and sequence context are important determinants of regulation. In contrast, we show that the responses of direct mRNA targets to Pum-mediated repression are not influenced by the content of optimal synonymous codons. Moreover, contrary to a prevailing model, we do not detect a role for CCR4-NOT in the degradation of mRNAs with low codon optimality. Together, the results of this work provide new insights into the Pum regulatory network and mechanisms and the parameters that influence the efficacy of Pum-mediated regulation.

Artificially designed synthetic promoter for a high level of salt induction using a cis-engineering approach

This work aimed to design a synthetic salt-inducible promoter using a cis-engineering approach. The designed promoter (PS) comprises a minimal promoter sequence for basal-level expression and upstream cis-regulatory elements (CREs) from promoters of salinity-stress-induced genes. The copy number, spacer lengths, and locations of CREs were manually determined based on their occurrence within native promoters. The initial activity profile of the synthesized PS promoter in transiently transformed N. tabacum leaves shows a seven-fold, five-fold, and four-fold increase in reporter GUS activity under salt, drought, and abscisic acid stress, respectively, at the 24-h interval, compared to the constitutive CaMV35S promoter. Analysis of gus expression in stable Arabidopsis transformants showed that the PS promoter induces over a two-fold increase in expression under drought or abscisic acid stress and a five-fold increase under salt stress at 24- and 48-h intervals, compared to the CaMV35S promoter. The promoter PS exhibits higher and more sustained activity under salt, drought, and abscisic acid stress compared to the constitutive CaMV35S.

Revealing the grammar of small RNA secretion using interpretable machine learning

Small non-coding RNAs can be secreted through a variety of mechanisms, including exosomal sorting, in small extracellular vesicles, and within lipoprotein complexes. However, the mechanisms that govern their sorting and secretion are not well understood. Here, we present ExoGRU, a machine learning model that predicts small RNA secretion probabilities from primary RNA sequences. We experimentally validated the performance of this model through ExoGRU-guided mutagenesis and synthetic RNA sequence analysis. Additionally, we used ExoGRU to reveal cis and trans factors that underlie small RNA secretion, including known and novel RNA-binding proteins (RBPs), e.g., YBX1, HNRNPA2B1, and RBM24. We also developed a novel technique called exoCLIP, which reveals the RNA interactome of RBPs within the cell-free space. Together, our results demonstrate the power of machine learning in revealing novel biological mechanisms. In addition to providing deeper insight into small RNA secretion, this knowledge can be leveraged in therapeutic and synthetic biology applications.

Loss of CREBBP and KMT2D cooperate to accelerate lymphomagenesis and shape the lymphoma immune microenvironment

Despite regulating overlapping gene enhancers and pathways, CREBBP and KMT2D mutations recurrently co-occur in germinal center (GC) B cell-derived lymphomas, suggesting potential oncogenic cooperation. Herein, we report that combined haploinsufficiency of Crebbp and Kmt2d induces a more severe mouse lymphoma phenotype (vs either allele alone) and unexpectedly confers an immune evasive microenvironment manifesting as CD8+ T-cell exhaustion and reduced infiltration. This is linked to profound repression of immune synapse genes that mediate crosstalk with T-cells, resulting in aberrant GC B cell fate decisions. From the epigenetic perspective, we observe interaction and mutually dependent binding and function of CREBBP and KMT2D on chromatin. Their combined deficiency preferentially impairs activation of immune synapse-responsive super-enhancers, pointing to a particular dependency for both co-activators at these specialized regulatory elements. Together, our data provide an example where chromatin modifier mutations cooperatively shape and induce an immune-evasive microenvironment to facilitate lymphomagenesis.

Regulation of the Drosophila transcriptome by Pumilio and CCR4-NOT deadenylase

The sequence-specific RNA-binding protein Pumilio controls development of Drosophila; however, the network of mRNAs that it regulates remains incompletely characterized. In this study, we utilize knockdown and knockout approaches coupled with RNA-Seq to measure the impact of Pumilio on the transcriptome of Drosophila cells. We also used an improved RNA co-immunoprecipitation method to identify Pumilio bound mRNAs in Drosophila embryos. Integration of these datasets with the content of Pumilio binding motifs across the transcriptome revealed novel direct Pumilio target genes involved in neural, muscle, wing, and germ cell development, and cellular proliferation. These genes include components of Wnt, TGF-beta, MAPK/ERK, and Notch signaling pathways, DNA replication, and lipid metabolism. Additionally, we identified the mRNAs regulated by the CCR4-NOT deadenylase complex, a key factor in Pumilio-mediated repression, and observed concordant regulation of Pumilio:CCR4-NOT target mRNAs. Computational modeling revealed that Pumilio binding, binding site number, density, and sequence context are important determinants of regulation. Moreover, the content of optimal synonymous codons in target mRNAs exhibits a striking functional relationship to Pumilio and CCR4-NOT regulation, indicating that the inherent translation efficiency and stability of the mRNA modulates their response to these trans-acting regulatory factors. Together, the results of this work provide new insights into the Pumilio regulatory network and mechanisms, and the parameters that influence the efficacy of Pumilio-mediated regulation.

Integrative identification of non-coding regulatory regions driving metastatic prostate cancer

Large-scale sequencing efforts of thousands of tumor samples have been undertaken to understand the mutational landscape of the coding genome. However, the vast majority of germline and somatic variants occur within non-coding portions of the genome. These genomic regions do not directly encode for specific proteins, but can play key roles in cancer progression, for example by driving aberrant gene expression control. Here, we designed an integrative computational and experimental framework to identify recurrently mutated non-coding regulatory regions that drive tumor progression. Application of this approach to whole-genome sequencing (WGS) data from a large cohort of metastatic castration-resistant prostate cancer (mCRPC) revealed a large set of recurrently mutated regions. We used (i) in silico prioritization of functional non-coding mutations, (ii) massively parallel reporter assays, and (iii) in vivo CRISPR-interference (CRISPRi) screens in xenografted mice to systematically identify and validate driver regulatory regions that drive mCRPC. We discovered that one of these enhancer regions, GH22I030351, acts on a bidirectional promoter to simultaneously modulate expression of U2-associated splicing factor SF3A1 and chromosomal protein CCDC157. We found that both SF3A1 and CCDC157 are promoters of tumor growth in xenograft models of prostate cancer. We nominated a number of transcription factors, including SOX6, to be responsible for higher expression of SF3A1 and CCDC157. Collectively, we have established and confirmed an integrative computational and experimental approach that enables the systematic detection of non-coding regulatory regions that drive the progression of human cancers.

Systematic assessment of prognostic molecular features across cancers

Precision oncology promises accurate prediction of disease trajectories by utilizing molecular features of tumors. We present a systematic analysis of the prognostic potential of diverse molecular features across large cancer cohorts. We find that the mRNA expression of biologically coherent sets of genes (modules) is substantially more predictive of patient survival than single-locus genomic and transcriptomic aberrations. Extending our analysis beyond existing curated gene modules, we find a large novel class of highly prognostic DNA/RNA cis-regulatory modules associated with dynamic gene expression within cancers. Remarkably, in more than 82% of cancers, modules substantially improve survival stratification compared with conventional clinical factors and prominent genomic aberrations. The prognostic potential of cancer modules generalizes to external cohorts better than conventionally used single-gene features. Finally, a machine-learning framework demonstrates the combined predictive power of multiple modules, yielding prognostic models that perform substantially better than existing histopathological and clinical factors in common use.

Cooperative super-enhancer inactivation caused by heterozygous loss of CREBBP and KMT2D skews B cell fate decisions and yields T cell-depleted lymphomas

Mutations affecting enhancer chromatin regulators CREBBP and KMT2D are highly co-occurrent in germinal center (GC)-derived lymphomas and other tumors, even though regulating similar pathways. Herein, we report that combined haploinsufficiency of Crebbp and Kmt2d (C+K) indeed accelerated lymphomagenesis. C+K haploinsufficiency induced GC hyperplasia by altering cell fate decisions, skewing B cells away from memory and plasma cell differentiation. C+K deficiency particularly impaired enhancer activation for immune synapse genes involved in exiting the GC reaction. This effect was especially severe at super-enhancers for immunoregulatory and differentiation genes. Mechanistically, CREBBP and KMT2D formed a complex, were highly co-localized on chromatin, and were required for each-other's stable recruitment to enhancers. Notably, C+K lymphomas in mice and humans manifested significantly reduced CD8 + T-cell abundance. Hence, deficiency of C+K cooperatively induced an immune evasive phenotype due at least in part to failure to activate key immune synapse super-enhancers, associated with altered immune cell fate decisions.

SIGNIFICANCE

Although CREBBP and KMT2D have similar enhancer regulatory functions, they are paradoxically co-mutated in lymphomas. We show that their combined loss causes specific disruption of super-enhancers driving immune synapse genes. Importantly, this leads to reduction of CD8 cells in lymphomas, linking super-enhancer function to immune surveillance, with implications for immunotherapy resistance.

Regulation of eIF4E guides a unique translational program to control erythroid maturation

Translation control is essential in balancing hematopoietic precursors and differentiation; however, the mechanisms underlying this program are poorly understood. We found that the activity of the major cap-binding protein eIF4E is unexpectedly regulated in a dynamic manner throughout erythropoiesis that is uncoupled from global protein synthesis rates. Moreover, eIF4E activity directs erythroid maturation, and increased eIF4E expression maintains cells in an early erythroid state associated with a translation program driving the expression of PTPN6 and Igf2bp1. A cytosine-enriched motif in the 5' untranslated region is important for eIF4E-mediated translation specificity. Therefore, selective translation of key target genes necessary for the maintenance of early erythroid states by eIF4E highlights a unique mechanism used by hematopoietic precursors to rapidly elicit erythropoietic maturation upon need.

Direct and indirect gene repression by the ecdysone cascade during mosquito reproductive cycle

Hematophagous Aedes aegypti mosquitoes spread devastating viral diseases. Upon blood feeding, a steroid hormone, 20-hydroxyecdysone (20E), initiates a reproductive program during which thousands of genes are differentially expressed. While 20E-mediated gene activation is well known, repressive action by this hormone remains poorly understood. Using bioinformatics and molecular biological approaches, we have identified the mechanisms of 20E-dependent direct and indirect transcriptional repression by the ecdysone receptor (EcR). While indirect repression involves E74, EcR binds to an ecdysone response element different from those utilized in 20E-mediated gene activation to exert direct repressive action. Moreover, liganded EcR recruits a corepressor Mi2, initiating chromatin compaction. This study advances our understanding of the 20E-EcR repression mechanism and could lead to improved vector control approaches.

Hematophagous mosquitoes transmit devastating human diseases. Reproduction of these mosquitoes is cyclical, with each egg maturation period supported by a blood meal. Previously, we have shown that in the female mosquito Aedes aegypti, nearly half of all genes are differentially expressed during the postblood meal reproductive period in the fat body, an insect analog of mammalian liver and adipose tissue. This work aims to decipher how transcription networks govern these genes. Bioinformatics tools found 89 putative transcription factor binding sites (TFBSs) on the cis-regulatory regions of more than 1,400 differentially expressed genes. Putative transcription factors that may bind to these TFBSs were identified and used for the construction of temporally coordinated regulatory networks. Further molecular biology analyses have uncovered mechanisms of direct and indirect negative transcriptional regulation by the steroid hormone 20-hydroxyecdysone (20E) through the ecdysone receptor (EcR). Genes within the two groups, early genes and late mid-genes, have distinctly different expression profiles. However, both groups of genes show lower expression at the high titers of 20E and are down-regulated by the 20E/EcR cascade by different molecular mechanisms. Transcriptional repression of early genes is indirect and involves the classic 20E pathway with ecdysone-induced protein E74 functioning as a repressor. Late mid-genes are repressed directly by EcR that recognizes and binds a previously unreported DNA element, different from those utilized in the 20E-mediated gene activation, within the regulatory regions of its target genes and recruits Mi2 that acts as a corepressor, initiating chromatin condensation.

Molecular topography of an entire nervous system

We have produced gene expression profiles of all 302 neurons of the C. elegans nervous system that match the single-cell resolution of its anatomy and wiring diagram. Our results suggest that individual neuron classes can be solely identified by combinatorial expression of specific gene families. For example, each neuron class expresses distinct codes of ∼23 neuropeptide genes and ∼36 neuropeptide receptors, delineating a complex and expansive "wireless" signaling network. To demonstrate the utility of this comprehensive gene expression catalog, we used computational approaches to (1) identify cis-regulatory elements for neuron-specific gene expression and (2) reveal adhesion proteins with potential roles in process placement and synaptic specificity. Our expression data are available at https://cengen.org and can be interrogated at the web application CengenApp. We expect that this neuron-specific directory of gene expression will spur investigations of underlying mechanisms that define anatomy, connectivity, and function throughout the C. elegans nervous system.

Using Network-Based Machine Learning to Predict Transcription Factors Involved in Drought Resistance

Gene regulatory networks underpin stress response pathways in plants. However, parsing these networks to prioritize key genes underlying a particular trait is challenging. Here, we have built the Gene Regulation and Association Network (GRAiN) of rice (Oryza sativa). GRAiN is an interactive query-based web-platform that allows users to study functional relationships between transcription factors (TFs) and genetic modules underlying abiotic-stress responses. We built GRAiN by applying a combination of different network inference algorithms to publicly available gene expression data. We propose a supervised machine learning framework that complements GRAiN in prioritizing genes that regulate stress signal transduction and modulate gene expression under drought conditions. Our framework converts intricate network connectivity patterns of 2160 TFs into a single drought score. We observed that TFs with the highest drought scores define the functional, structural, and evolutionary characteristics of drought resistance in rice. Our approach accurately predicted the function of OsbHLH148 TF, which we validated using in vitro protein-DNA binding assays and mRNA sequencing loss-of-function mutants grown under control and drought stress conditions. Our network and the complementary machine learning strategy lends itself to predicting key regulatory genes underlying other agricultural traits and will assist in the genetic engineering of desirable rice varieties.

In silico analysis of the transcriptional regulatory logic of neuronal identity specification throughout the C. elegans nervous system

The generation of the enormous diversity of neuronal cell types in a differentiating nervous system entails the activation of neuron type-specific gene batteries. To examine the regulatory logic that controls the expression of neuron type-specific gene batteries, we interrogate single cell expression profiles of all 118 neuron classes of the Caenorhabditis elegans nervous system for the presence of DNA binding motifs of 136 neuronally expressed C. elegans transcription factors. Using a phylogenetic footprinting pipeline, we identify cis-regulatory motif enrichments among neuron class-specific gene batteries and we identify cognate transcription factors for 117 of the 118 neuron classes. In addition to predicting novel regulators of neuronal identities, our nervous system-wide analysis at single cell resolution supports the hypothesis that many transcription factors directly co-regulate the cohort of effector genes that define a neuron type, thereby corroborating the concept of so-called terminal selectors of neuronal identity. Our analysis provides a blueprint for how individual components of an entire nervous system are genetically specified.

Dynamic landscape of protein occupancy across the Escherichia coli chromosome

Free-living bacteria adapt to environmental change by reprogramming gene expression through precise interactions of hundreds of DNA-binding proteins. A predictive understanding of bacterial physiology requires us to globally monitor all such protein-DNA interactions across a range of environmental and genetic perturbations. Here, we show that such global observations are possible using an optimized version of in vivo protein occupancy display technology (in vivo protein occupancy display-high resolution, IPOD-HR) and present a pilot application to Escherichia coli. We observe that the E. coli protein-DNA interactome organizes into 2 distinct prototypic features: (1) highly dynamic condition-dependent transcription factor (TF) occupancy; and (2) robust kilobase scale occupancy by nucleoid factors, forming silencing domains analogous to eukaryotic heterochromatin. We show that occupancy dynamics across a range of conditions can rapidly reveal the global transcriptional regulatory organization of a bacterium. Beyond discovery of previously hidden regulatory logic, we show that these observations can be utilized to computationally determine sequence specificity models for the majority of active TFs. Our study demonstrates that global observations of protein occupancy combined with statistical inference can rapidly and systematically reveal the transcriptional regulatory and structural features of a bacterial genome. This capacity is particularly crucial for non-model bacteria that are not amenable to routine genetic manipulation.

The PfAP2-G2 transcription factor is a critical regulator of gametocyte maturation

Differentiation from asexual blood stages to mature sexual gametocytes is required for the transmission of malaria parasites. Here, we report that the ApiAP2 transcription factor, PfAP2-G2 (PF3D7_1408200) plays a critical role in the maturation of Plasmodium falciparum gametocytes. PfAP2-G2 binds to the promoters of a wide array of genes that are expressed at many stages of the parasite life cycle. Interestingly, we also find binding of PfAP2-G2 within the gene body of almost 3,000 genes, which strongly correlates with the location of H3K36me3 and several other histone modifications as well as Heterochromatin Protein 1 (HP1), suggesting that occupancy of PfAP2-G2 in gene bodies may serve as an alternative regulatory mechanism. Disruption of pfap2-g2 does not impact asexual development, but the majority of sexual parasites are unable to mature beyond stage III gametocytes. The absence of pfap2-g2 leads to overexpression of 28% of the genes bound by PfAP2-G2 and none of the PfAP2-G2 bound genes are downregulated, suggesting that it is a repressor. We also find that PfAP2-G2 interacts with chromatin remodeling proteins, a microrchidia (MORC) protein, and another ApiAP2 protein (PF3D7_1139300). Overall our data demonstrate that PfAP2-G2 establishes an essential gametocyte maturation program in association with other chromatin-related proteins.

Identification of long regulatory elements in the genome of Plasmodium falciparum and other eukaryotes

Long regulatory elements (LREs), such as CpG islands, polydA:dT tracts or AU-rich elements, are thought to play key roles in gene regulation but, as opposed to conventional binding sites of transcription factors, few methods have been proposed to formally and automatically characterize them. We present here a computational approach named DExTER (Domain Exploration To Explain gene Regulation) dedicated to the identification of candidate LREs (cLREs) and apply it to the analysis of the genomes of P. falciparum and other eukaryotes. Our analyses show that all tested genomes contain several cLREs that are somewhat conserved along evolution, and that gene expression can be predicted with surprising accuracy on the basis of these long regions only. Regulation by cLREs exhibits very different behaviours depending on species and conditions. In P. falciparum and other Apicomplexan organisms as well as in Dictyostelium discoideum, the process appears highly dynamic, with different cLREs involved at different phases of the life cycle. For multicellular organisms, the same cLREs are involved in all tissues, but a dynamic behavior is observed along embryonic development stages. In P. falciparum, whose genome is known to be strongly depleted of transcription factors, cLREs are predictive of expression with an accuracy above 70%, and our analyses show that they are associated with both transcriptional and post-transcriptional regulation signals. Moreover, we assessed the biological relevance of one LRE discovered by DExTER in P. falciparum using an in vivo reporter assay. The source code (python) of DExTER is available at https://gite.lirmm.fr/menichelli/DExTER.

Gene co-expression network analysis of Trypanosoma brucei in tsetse fly vector

Background

Trypanosoma brucei species are motile protozoan parasites that are cyclically transmitted by tsetse fly (genus Glossina) causing human sleeping sickness and nagana in livestock in sub-Saharan Africa. African trypanosomes display digenetic life cycle stages in the tsetse fly vector and in their mammalian host. Experimental work on insect-stage trypanosomes is challenging because of the difficulty in setting up successful in vitro cultures. Therefore, there is limited knowledge on the trypanosome biology during its development in the tsetse fly. Consequently, this limits the development of new strategies for blocking parasite transmission in the tsetse fly.

Methods

In this study, RNA-Seq data of insect-stage trypanosomes were used to construct a T. brucei gene co-expression network using the weighted gene co-expression analysis (WGCNA) method. The study identified significant enriched modules for genes that play key roles during the parasite’s development in tsetse fly. Furthermore, potential 3′ untranslated region (UTR) regulatory elements for genes that clustered in the same module were identified using the Finding Informative Regulatory Elements (FIRE) tool.

Results

A fraction of gene modules (12 out of 27 modules) in the constructed network were found to be enriched in functional roles associated with the cell division, protein biosynthesis, mitochondrion, and cell surface. Additionally, 12 hub genes encoding proteins such as RNA-binding protein 6 (RBP6), arginine kinase 1 (AK1), brucei alanine-rich protein (BARP), among others, were identified for the 12 significantly enriched gene modules. In addition, the potential regulatory elements located in the 3′ untranslated regions of genes within the same module were predicted.

Conclusions

The constructed gene co-expression network provides a useful resource for network-based data mining to identify candidate genes for functional studies. This will enhance understanding of the molecular mechanisms that underlie important biological processes during parasite’s development in tsetse fly. Ultimately, these findings will be key in the identification of potential molecular targets for disease control.

Using Network-Based Machine Learning to Predict Transcription Factors Involved in Drought Resistance

Gene regulatory networks underpin stress response pathways in plants. However, parsing these networks to prioritize key genes underlying a particular trait is challenging. Here, we have built the Gene Regulation and Association Network (GRAiN) of rice (Oryza sativa). GRAiN is an interactive query-based web-platform that allows users to study functional relationships between transcription factors (TFs) and genetic modules underlying abiotic-stress responses. We built GRAiN by applying a combination of different network inference algorithms to publicly available gene expression data. We propose a supervised machine learning framework that complements GRAiN in prioritizing genes that regulate stress signal transduction and modulate gene expression under drought conditions. Our framework converts intricate network connectivity patterns of 2160 TFs into a single drought score. We observed that TFs with the highest drought scores define the functional, structural, and evolutionary characteristics of drought resistance in rice. Our approach accurately predicted the function of OsbHLH148 TF, which we validated using in vitro protein-DNA binding assays and mRNA sequencing loss-of-function mutants grown under control and drought stress conditions. Our network and the complementary machine learning strategy lends itself to predicting key regulatory genes underlying other agricultural traits and will assist in the genetic engineering of desirable rice varieties.

Regulatory and evolutionary adaptation of yeast to acute lethal ethanol stress

The yeast Saccharomyces cerevisiae has been the subject of many studies aimed at understanding mechanisms of adaptation to environmental stresses. Most of these studies have focused on adaptation to sub-lethal stresses, upon which a stereotypic transcriptional program called the environmental stress response (ESR) is activated. However, the genetic and regulatory factors that underlie the adaptation and survival of yeast cells to stresses that cross the lethality threshold have not been systematically studied. Here, we utilized a combination of gene expression profiling, deletion-library fitness profiling, and experimental evolution to systematically explore adaptation of S. cerevisiae to acute exposure to threshold lethal ethanol concentrations—a stress with important biotechnological implications. We found that yeast cells activate a rapid transcriptional reprogramming process that is likely adaptive in terms of post-stress survival. We also utilized repeated cycles of lethal ethanol exposure to evolve yeast strains with substantially higher ethanol tolerance and survival. Importantly, these strains displayed bulk growth-rates that were indistinguishable from the parental wild-type strain. Remarkably, these hyper-ethanol tolerant strains had reprogrammed their pre-stress gene expression states to match the likely adaptive post-stress response in the wild-type strain. Our studies reveal critical determinants of yeast survival to lethal ethanol stress and highlight potentially general principles that may underlie evolutionary adaptation to lethal stresses in general.

Principles of mRNA control by human PUM proteins elucidated from multimodal experiments and integrative data analysis

The human PUF-family proteins, PUM1 and PUM2, posttranscriptionally regulate gene expression by binding to a PUM recognition element (PRE) in the 3'-UTR of target mRNAs. Hundreds of PUM1/2 targets have been identified from changes in steady-state RNA levels; however, prior studies could not differentiate between the contributions of changes in transcription and RNA decay rates. We applied metabolic labeling to measure changes in RNA turnover in response to depletion of PUM1/2, showing that human PUM proteins regulate expression almost exclusively by changing RNA stability. We also applied an in vitro selection workflow to precisely identify the binding preferences of PUM1 and PUM2. By integrating our results with prior knowledge, we developed a "rulebook" of key contextual features that differentiate functional versus nonfunctional PREs, allowing us to train machine learning models that accurately predict the functional regulation of RNA targets by the human PUM proteins.

Statistical modelling of bacterial promoter sequences for regulatory motif discovery with the help of transcriptome data: application to Listeria monocytogenes

Automatic de novo identification of the main regulons of a bacterium from genome and transcriptome data remains a challenge. To address this task, we propose a statistical model that can use information on exact positions of the transcription start sites and condition-dependent expression profiles. The central idea of this model is to improve the probabilistic representation of the promoter DNA sequences by incorporating covariates summarizing expression profiles (e.g. coordinates in projection spaces or hierarchical clustering trees). A dedicated trans-dimensional Markov chain Monte Carlo algorithm adjusts the width and palindromic properties of the corresponding position-weight matrices, the number of parameters to describe exact position relative to the transcription start site, and chooses the expression covariates relevant for each motif. All parameters are estimated simultaneously, for many motifs and many expression covariates. The method is applied to a dataset of transcription start sites and expression profiles available for Listeria monocytogenes. The results validate the approach and provide a new global view of the transcription regulatory network of this important pathogen. Remarkably, a previously unreported motif is found in promoter regions of ribosomal protein genes, suggesting a role in the regulation of growth.

SArKS: de novo discovery of gene expression regulatory motif sites and domains by suffix array kernel smoothing

MOTIVATION

We set out to develop an algorithm that can mine differential gene expression data to identify candidate cell type-specific DNA regulatory sequences. Differential expression is usually quantified as a continuous score-fold-change, test-statistic, P-value-comparing biological classes. Unlike existing approaches, our de novo strategy, termed SArKS, applies non-parametric kernel smoothing to uncover promoter motif sites that correlate with elevated differential expression scores. SArKS detects motif k-mers by smoothing sequence scores over sequence similarity. A second round of smoothing over spatial proximity reveals multi-motif domains (MMDs). Discovered motif sites can then be merged or extended based on adjacency within MMDs. False positive rates are estimated and controlled by permutation testing.

RESULTS

We applied SArKS to published gene expression data representing distinct neocortical neuron classes in Mus musculus and interneuron developmental states in Homo sapiens. When benchmarked against several existing algorithms using a cross-validation procedure, SArKS identified larger motif sets that formed the basis for regression models with higher correlative power.

AVAILABILITY AND IMPLEMENTATION

https://github.com/denniscwylie/sarks.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Refining the transcriptome of the human malaria parasite Plasmodium falciparum using amplification-free RNA-seq

Background

Plasmodium parasites undergo several major developmental transitions during their complex lifecycle, which are enabled by precisely ordered gene expression programs. Transcriptomes from the 48-h blood stages of the major human malaria parasite Plasmodium falciparum have been described using cDNA microarrays and RNA-seq, but these assays have not always performed well within non-coding regions, where the AT-content is often 90–95%.

Results

We developed a directional, amplification-free RNA-seq protocol (DAFT-seq) to reduce bias against AT-rich cDNA, which we have applied to three strains of P. falciparum (3D7, HB3 and IT). While strain-specific differences were detected, overall there is strong conservation between the transcriptional profiles. For the 3D7 reference strain, transcription was detected from 89% of the genome, with over 78% of the genome transcribed into mRNAs. We also find that transcription from bidirectional promoters frequently results in non-coding, antisense transcripts. These datasets allowed us to refine the 5′ and 3′ untranslated regions (UTRs), which can be variable, long (> 1000 nt), and often overlap those of adjacent transcripts.

Conclusions

The approaches applied in this study allow a refined description of the transcriptional landscape of P. falciparum and demonstrate that very little of the densely packed P. falciparum genome is inactive or redundant. By capturing the 5′ and 3′ ends of mRNAs, we reveal both constant and dynamic use of transcriptional start sites across the intraerythrocytic developmental cycle that will be useful in guiding the definition of regulatory regions for use in future experimental gene expression studies.

RBMS1 Suppresses Colon Cancer Metastasis through Targeted Stabilization of Its mRNA Regulon

Identifying master regulators that drive pathologic gene expression is a key challenge in precision oncology. Here, we have developed an analytic framework, named PRADA, that identifies oncogenic RNA-binding proteins through the systematic detection of coordinated changes in their target regulons. Application of this approach to data collected from clinical samples, patient-derived xenografts, and cell line models of colon cancer metastasis revealed the RNA-binding protein RBMS1 as a suppressor of colon cancer progression. We observed that silencing RBMS1 results in increased metastatic capacity in xenograft mouse models, and that restoring its expression blunts metastatic liver colonization. We have found that RBMS1 functions as a posttranscriptional regulator of RNA stability by directly binding its target mRNAs. Together, our findings establish a role for RBMS1 as a previously unknown regulator of RNA stability and as a suppressor of colon cancer metastasis with clinical utility for risk stratification of patients. SIGNIFICANCE: By applying a new analytic approach to transcriptomic data from clinical samples and models of colon cancer progression, we have identified RBMS1 as a suppressor of metastasis and as a post-transcriptional regulator of RNA stability. Notably, RBMS1 silencing and downregulation of its targets are negatively associated with patient survival.See related commentary by Carter, p. 1261.This article is highlighted in the In This Issue feature, p. 1241.

Comprehensive Genome-wide Perturbations via CRISPR Adaptation Reveal Complex Genetics of Antibiotic Sensitivity

Genome-wide CRISPR screens enable systematic interrogation of gene function. However, guide RNA libraries are costly to synthesize, and their limited diversity compromises the sensitivity of CRISPR screens. Using the Streptococcus pyogenes CRISPR-Cas adaptation machinery, we developed CRISPR adaptation-mediated library manufacturing (CALM), which turns bacterial cells into "factories" for generating hundreds of thousands of crRNAs covering 95% of all targetable genomic sites. With an average gene targeted by more than 100 distinct crRNAs, these highly comprehensive CRISPRi libraries produced varying degrees of transcriptional repression critical for uncovering novel antibiotic resistance determinants. Furthermore, by iterating CRISPR adaptation, we rapidly generated dual-crRNA libraries representing more than 100,000 dual-gene perturbations. The polarized nature of spacer adaptation revealed the historical contingency in the stepwise acquisition of genetic perturbations leading to increasing antibiotic resistance. CALM circumvents the expense, labor, and time required for synthesis and cloning of gRNAs, allowing generation of CRISPRi libraries in wild-type bacteria refractory to routine genetic manipulation.

Hierarchical transcriptional control regulates Plasmodium falciparum sexual differentiation

BACKGROUND

Malaria pathogenesis relies on sexual gametocyte forms of the malaria parasite to be transmitted between the infected human and the mosquito host but the molecular mechanisms controlling gametocytogenesis remains poorly understood. Here we provide a high-resolution transcriptome of Plasmodium falciparum as it commits to and develops through gametocytogenesis.

RESULTS

The gametocyte-associated transcriptome is significantly different from that of the asexual parasites, with dynamic gene expression shifts characterizing early, intermediate and late-stage gametocyte development and results in differential timing for sex-specific transcripts. The transcriptional dynamics suggest strict transcriptional control during gametocytogenesis in P. falciparum, which we propose is mediated by putative regulators including epigenetic mechanisms (driving active repression of proliferation-associated processes) and a cascade-like expression of ApiAP2 transcription factors.

CONCLUSIONS

The gametocyte transcriptome serves as the blueprint for sexual differentiation and will be a rich resource for future functional studies on this critical stage of Plasmodium development, as the intraerythrocytic transcriptome has been for our understanding of the asexual cycle.

Attenuated Codon Optimality Contributes to Neural-Specific mRNA Decay in Drosophila

Tissue-specific mRNA stability is important for cell fate and physiology, but the mechanisms involved are not fully understood. We found that zygotic mRNA stability in Drosophila correlates with codon content: optimal codons are enriched in stable transcripts associated with metabolic functions like translation, while non-optimal codons are enriched in unstable transcripts, including those associated with neural development. Bioinformatic analyses and reporter assays revealed that similar codons stabilize or destabilize mRNAs in the nervous system and other tissues, but the link between codon content and stability is attenuated in the nervous system. We confirmed that optimal codons are decoded by abundant tRNAs while non-optimal codons are decoded by less abundant tRNAs in embryos and in the nervous system. We conclude that codon optimality is a general determinant of zygotic mRNA stability, and attenuation of codon optimality allows trans-acting factors to exert greater influence over mRNA decay in the nervous system.

Burow et al. report that codon optimality is a general determinant of zygotic mRNA stability in Drosophila embryos, but the link between codons and stability is weak in the nervous system. Bioinformatics, reporter transcript assays, and tRNA quantitation show that the attenuation of codon optimality establishes neuralspecific mRNA decay.

Pum2 Shapes the Transcriptome in Developing Axons through Retention of Target mRNAs in the Cell Body

Localized protein synthesis is fundamental for neuronal development, maintenance, and function. Transcriptomes in axons and soma are distinct, but the mechanisms governing the composition of axonal transcriptomes and their developmental regulation are only partially understood. We found that the binding motif for the RNA-binding proteins Pumilio 1 and 2 (Pum1 and Pum2) is underrepresented in transcriptomes of developing axons. Introduction of Pumilio-binding elements (PBEs) into mRNAs containing a β-actin zipcode prevented axonal localization and translation. Pum2 is restricted to the soma of developing neurons, and Pum2 knockdown or blocking its binding to mRNA caused the appearance and translation of PBE-containing mRNAs in axons. Pum2-deficient neurons exhibited axonal growth and branching defects in vivo and impaired axon regeneration in vitro. These results reveal that Pum2 shapes axonal transcriptomes by preventing the transport of PBE-containing mRNAs into axons, and they identify somatic mRNAs retention as a mechanism for the temporal control of intra-axonal protein synthesis.

What functional genomics has taught us about transcriptional regulation in malaria parasites

Malaria parasites are characterized by a complex life cycle that is accompanied by dynamic gene expression patterns. The factors and mechanisms that regulate gene expression in these parasites have been searched for even before the advent of next generation sequencing technologies. Functional genomics approaches have substantially boosted this area of research and have yielded significant insights into the interplay between epigenetic, transcriptional and post-transcriptional mechanisms. Recently, considerable progress has been made in identifying sequence-specific transcription factors and DNA-encoded regulatory elements. Here, we review the insights obtained from these efforts including the characterization of core promoters, the involvement of sequence-specific transcription factors in life cycle progression and the mapping of gene regulatory elements. Furthermore, we discuss recent developments in the field of functional genomics and how they might contribute to further characterization of this complex gene regulatory network.

Characterization of the accessible genome in the human malaria parasite Plasmodium falciparum

Human malaria is a devastating disease and a major cause of poverty in resource-limited countries. To develop and adapt within hosts Plasmodium falciparum undergoes drastic switches in gene expression. To identify regulatory regions in the parasite genome, we performed genome-wide profiling of chromatin accessibility in two culture-adapted isogenic subclones at four developmental stages during the intraerythrocytic cycle by using the Assay for Transposase-Accessible Chromatin by sequencing (ATAC-seq). Tn5 transposase hypersensitivity sites (THSSs) localize preferentially at transcriptional start sites (TSSs). Chromatin accessibility by ATAC-seq is predictive of active transcription and of the levels of histone marks H3K9ac and H3K4me3. Our assay allows the identification of novel regulatory regions including TSS and enhancer-like elements. We show that the dynamics in the accessible chromatin profile matches temporal transcription during development. Motif analysis of stage-specific ATAC-seq sites predicts the in vivo binding sites and function of multiple ApiAP2 transcription factors. At last, the alternative expression states of some clonally variant genes (CVGs), including eba, phist, var and clag genes, associate with a differential ATAC-seq signal at their promoters. Altogether, this study identifies genome-wide regulatory regions likely to play an essential function in the developmental transitions and in CVG expression in P. falciparum.

Genome wide analysis of 3' UTR sequence elements and proteins regulating mRNA stability during maternal-to-zygotic transition in zebrafish

Posttranscriptional regulation plays a crucial role in shaping gene expression. During the maternal-to-zygotic transition (MZT), thousands of maternal transcripts are regulated. However, how different cis-elements and trans-factors are integrated to determine mRNA stability remains poorly understood. Here, we show that most transcripts are under combinatorial regulation by multiple decay pathways during zebrafish MZT. By using a massively parallel reporter assay, we identified cis-regulatory sequences in the 3' UTR, including U-rich motifs that are associated with increased mRNA stability. In contrast, miR-430 target sequences, UAUUUAUU AU-rich elements (ARE), CCUC, and CUGC elements emerged as destabilizing motifs, with miR-430 and AREs causing mRNA deadenylation upon genome activation. We identified trans-factors by profiling RNA-protein interactions and found that poly(U)-binding proteins are preferentially associated with 3' UTR sequences and stabilizing motifs. We show that this activity is antagonized by C-rich motifs and correlated with protein binding. Finally, we integrated these regulatory motifs into a machine learning model that predicts reporter mRNA stability in vivo.

A Quantitative and Predictive Model for RNA Binding by Human Pumilio Proteins

High-throughput methodologies have enabled routine generation of RNA target sets and sequence motifs for RNA-binding proteins (RBPs). Nevertheless, quantitative approaches are needed to capture the landscape of RNA-RBP interactions responsible for cellular regulation. We have used the RNA-MaP platform to directly measure equilibrium binding for thousands of designed RNAs and to construct a predictive model for RNA recognition by the human Pumilio proteins PUM1 and PUM2. Despite prior findings of linear sequence motifs, our measurements revealed widespread residue flipping and instances of positional coupling. Application of our thermodynamic model to published in vivo crosslinking data reveals quantitative agreement between predicted affinities and in vivo occupancies. Our analyses suggest a thermodynamically driven, continuous Pumilio-binding landscape that is negligibly affected by RNA structure or kinetic factors, such as displacement by ribosomes. This work provides a quantitative foundation for dissecting the cellular behavior of RBPs and cellular features that impact their occupancies.

Massively Parallel Assays and Quantitative Sequence-Function Relationships

Over the last decade, a rich variety of massively parallel assays have revolutionized our understanding of how biological sequences encode quantitative molecular phenotypes. These assays include deep mutational scanning, high-throughput SELEX, and massively parallel reporter assays. Here, we review these experimental methods and how the data they produce can be used to quantitatively model sequence-function relationships. In doing so, we touch on a diverse range of topics, including the identification of clinically relevant genomic variants, the modeling of transcription factor binding to DNA, the functional and evolutionary landscapes of proteins, and cis-regulatory mechanisms in both transcription and mRNA splicing. We further describe a unified conceptual framework and a core set of mathematical modeling strategies that studies in these diverse areas can make use of. Finally, we highlight key aspects of experimental design and mathematical modeling that are important for the results of such studies to be interpretable and reproducible.

A Quantitative and Predictive Model for RNA Binding by Human Pumilio Proteins

High-throughput methodologies have enabled routine generation of RNA target sets and sequence motifs for RNA-binding proteins (RBPs). Nevertheless, quantitative approaches are needed to capture the landscape of RNA-RBP interactions responsible for cellular regulation. We have used the RNA-MaP platform to directly measure equilibrium binding for thousands of designed RNAs and to construct a predictive model for RNA recognition by the human Pumilio proteins PUM1 and PUM2. Despite prior findings of linear sequence motifs, our measurements revealed widespread residue flipping and instances of positional coupling. Application of our thermodynamic model to published in vivo crosslinking data reveals quantitative agreement between predicted affinities and in vivo occupancies. Our analyses suggest a thermodynamically driven, continuous Pumilio-binding landscape that is negligibly affected by RNA structure or kinetic factors, such as displacement by ribosomes. This work provides a quantitative foundation for dissecting the cellular behavior of RBPs and cellular features that impact their occupancies.

Mechanical Force Induces Phosphorylation-Mediated Signaling that Underlies Tissue Response and Robustness in Xenopus Embryos

Mechanical forces are essential drivers of numerous biological processes, notably during development. Although it is well recognized that cells sense and adapt to mechanical forces, the signal transduction pathways that underlie mechanosensing have remained elusive. Here, we investigate the impact of mechanical centrifugation force on phosphorylation-mediated signaling in Xenopus embryos. By monitoring temporal phosphoproteome and proteome alterations in response to force, we discover and validate elevated phosphorylation on focal adhesion and tight junction components, leading to several mechanistic insights into mechanosensing and tissue restoration. First, we determine changes in kinase activity profiles during mechanoresponse, identifying the activation of basophilic kinases. Pathway interrogation using kinase inhibitor treatment uncovers a crosstalk between the focal adhesion kinase (FAK) and protein kinase C (PKC) in mechanoresponse. Second, we find LIM domain 7 protein (Lmo7) as upregulated upon centrifugation, contributing to mechanoresponse. Third, we discover that mechanical compression force induces a mesenchymal-to-epithelial transition (MET)-like phenotype.

FTO controls reversible m6Am RNA methylation during snRNA biogenesis

Small nuclear RNAs (snRNAs) are core spliceosome components and mediate pre-mRNA splicing. Here we show that snRNAs contain a regulated and reversible nucleotide modification causing them to exist as two different methyl isoforms, m₁ and m₂, reflecting the methylation state of the adenosine adjacent to the snRNA cap. We find that snRNA biogenesis involves the formation of an initial m₁ isoform with a single-methylated adenosine (2'-O-methyladenosine, Am), which is then converted to a dimethylated m₂ isoform (N⁶,2'-O-dimethyladenosine, m⁶Am). The relative m₁ and m₂ isoform levels are determined by the RNA demethylase FTO, which selectively demethylates the m₂ isoform. We show FTO is inhibited by the oncometabolite D-2-hydroxyglutarate, resulting in increased m₂-snRNA levels. Furthermore, cells that exhibit high m₂-snRNA levels show altered patterns of alternative splicing. Together, these data reveal that FTO controls a previously unknown central step of snRNA processing involving reversible methylation, and suggest that epitranscriptomic information in snRNA may influence mRNA splicing.

Cancer cells exploit an orphan RNA to drive metastatic progression

In this study we performed a systematic search to identify breast cancer-specific small non-coding RNAs, which we have collectively termed orphan non-coding RNAs (oncRNAs). We subsequently discovered that one of these oncRNAs, which originates from the 3’ end of TERC, acts as a regulator of gene expression and is a robust promoter of breast cancer metastasis. This oncRNA, which we have named T3p, exerts its pro-metastatic effects by acting as an inhibitor of RISC complex activity and increasing the expression of the pro-metastatic genes NUPR1 and PANX2. Furthermore, we have shown that oncRNAs are present in cancer cell-derived extracellular vesicles, raising the possibility that these circulating oncRNAs may also play a role in non-cell autonomous disease pathogenesis. Additionally, these circulating oncRNAs present a novel avenue for cancer fingerprinting using liquid biopsies.

Triciribine increases LDLR expression and LDL uptake through stabilization of LDLR mRNA

Low-density lipoprotein receptor (LDLR) is a key regulator of the metabolism of plasma low-density lipoprotein cholesterol (LDL-C), the elevated levels of which are associated with an increased risk of cardiovascular disease. Therefore, enhancing LDLR expression represents a potent treatment strategy for hypercholesterolemia. Here, we report that in cultured human hepatoma cells, triciribine, a highly selective AKT inhibitor, increases the stability of LDLR mRNA, an event that translates into upregulation of cell-surface LDLR levels and induction of cellular LDL uptake. This effect of triciribine requires ERK activity and is partially dependent on the intervening sequence between the AU-rich elements ARE3 and ARE4 in LDLR 3′UTR. We also show that triciribine downregulates the expression of PCSK9 mRNA and blunts the secretion of its protein. Notably, triciribine was found to potentiate the effect of mevastatin on LDLR protein levels and activity. We also show that primary human hepatocytes respond to triciribine by increasing the expression of LDLR. Furthermore, a pilot experiment with mice revealed that a two-weeks treatment with triciribine significantly induced the hepatic expression of LDLR protein. These results identify triciribine as a novel LDLR-elevating agent and warrant further examination of its potential as a hypocholesterolemic drug either as monotherapy or in combination with statins.

Specific covalent inhibition of MALT1 paracaspase suppresses B cell lymphoma growth

The paracaspase MALT1 plays an essential role in activated B cell-like diffuse large B cell lymphoma (ABC DLBCL) downstream of B cell and TLR pathway genes mutated in these tumors. Although MALT1 is considered a compelling therapeutic target, the development of tractable and specific MALT1 protease inhibitors has thus far been elusive. Here, we developed a target engagement assay that provides a quantitative readout for specific MALT1-inhibitory effects in living cells. This enabled a structure-guided medicinal chemistry effort culminating in the discovery of pharmacologically tractable, irreversible substrate-mimetic compounds that bind the MALT1 active site. We confirmed that MALT1 targeting with compound 3 is effective at suppressing ABC DLBCL cells in vitro and in vivo. We show that a reduction in serum IL-10 levels exquisitely correlates with the drug pharmacokinetics and degree of MALT1 inhibition in vitro and in vivo and could constitute a useful pharmacodynamic biomarker to evaluate these compounds in clinical trials. Compound 3 revealed insights into the biology of MALT1 in ABC DLBCL, such as the role of MALT1 in driving JAK/STAT signaling and suppressing the type I IFN response and MHC class II expression, suggesting that MALT1 inhibition could prime lymphomas for immune recognition by cytotoxic immune cells.

Genome-wide real-time in vivo transcriptional dynamics during Plasmodium falciparum blood-stage development

Genome-wide analysis of transcription in the malaria parasite Plasmodium falciparum has revealed robust variation in steady-state mRNA abundance throughout the 48-h intraerythrocytic developmental cycle (IDC), suggesting that this process is highly dynamic and tightly regulated. Here, we utilize rapid 4-thiouracil (4-TU) incorporation via pyrimidine salvage to specifically label, capture, and quantify newly-synthesized RNA transcripts at every hour throughout the IDC. This high-resolution global analysis of the transcriptome captures the timing and rate of transcription for each newly synthesized mRNA in vivo, revealing active transcription throughout all IDC stages. Using a statistical model to predict the mRNA dynamics contributing to the total mRNA abundance at each timepoint, we find varying degrees of transcription and stabilization for each mRNA corresponding to developmental transitions. Finally, our results provide new insight into co-regulation of mRNAs throughout the IDC through regulatory DNA sequence motifs, thereby expanding our understanding of P. falciparum mRNA dynamics.

Transcriptomic analysis often doesn’t differentiate between newly synthesized and stabilized mRNAs. Using rapid 4-thiouracil incorporation, Painter et al. here define genome-wide active transcription throughout Plasmodium blood-stage developmental stages and identify associated regulatory DNA sequence motifs.

DiNAMO: highly sensitive DNA motif discovery in high-throughput sequencing data

Background

Discovering over-represented approximate motifs in DNA sequences is an essential part of bioinformatics. This topic has been studied extensively because of the increasing number of potential applications. However, it remains a difficult challenge, especially with the huge quantity of data generated by high throughput sequencing technologies. To overcome this problem, existing tools use greedy algorithms and probabilistic approaches to find motifs in reasonable time. Nevertheless these approaches lack sensitivity and have difficulties coping with rare and subtle motifs.

Results

We developed DiNAMO (for DNA MOtif), a new software based on an exhaustive and efficient algorithm for IUPAC motif discovery. We evaluated DiNAMO on synthetic and real datasets with two different applications, namely ChIP-seq peaks and Systematic Sequencing Error analysis. DiNAMO proves to compare favorably with other existing methods and is robust to noise.

Conclusions

We shown that DiNAMO software can serve as a tool to search for degenerate motifs in an exact manner using IUPAC models. DiNAMO can be used in scanning mode with sliding windows or in fixed position mode, which makes it suitable for numerous potential applications.

Availability

https://github.com/bonsai-team/DiNAMO.

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2215-1) contains supplementary material, which is available to authorized users.

Systematic identification of factors mediating accelerated mRNA degradation in response to changes in environmental nitrogen

Cellular responses to changing environments frequently involve rapid reprogramming of the transcriptome. Regulated changes in mRNA degradation rates can accelerate reprogramming by clearing or stabilizing extant transcripts. Here, we measured mRNA stability using 4-thiouracil labeling in the budding yeast Saccharomyces cerevisiae during a nitrogen upshift and found that 78 mRNAs are subject to destabilization. These transcripts include Nitrogen Catabolite Repression (NCR) and carbon metabolism mRNAs, suggesting that mRNA destabilization is a mechanism for targeted reprogramming of the transcriptome. To explore the molecular basis of destabilization we implemented a SortSeq approach to screen the pooled deletion collection library for trans factors that mediate rapid GAP1 mRNA repression. We combined low-input multiplexed Barcode sequencing with branched-DNA single-molecule mRNA FISH and Fluorescence-activated cell sorting (BFF) to identify the Lsm1-7p/Pat1p complex and general mRNA decay machinery as important for GAP1 mRNA clearance. We also find that the decapping modulators EDC3 and SCD6, translation factor eIF4G2, and the 5' UTR of GAP1 are factors that mediate rapid repression of GAP1 mRNA, suggesting that translational control may impact the post-transcriptional fate of mRNAs in response to environmental changes.

Unconventional function of an Achaete-Scute homolog as a terminal selector of nociceptive neuron identity

Proneural genes are among the most early-acting genes in nervous system development, instructing blast cells to commit to a neuronal fate. Drosophila Atonal and Achaete-Scute complex (AS-C) genes, as well as their vertebrate orthologs, are basic helix-loop-helix (bHLH) transcription factors with such proneural activity. We show here that a C. elegans AS-C homolog, hlh-4, functions in a fundamentally different manner. In the embryonic, larval, and adult nervous systems, hlh-4 is expressed exclusively in a single nociceptive neuron class, ADL, and its expression in ADL is maintained via transcriptional autoregulation throughout the life of the animal. However, in hlh-4 null mutants, the ADL neuron is generated and still appears neuronal in overall morphology and expression of panneuronal and pansensory features. Rather than acting as a proneural gene, we find that hlh-4 is required for the ADL neuron to function properly, to adopt its correct morphology, to express its unusually large repertoire of olfactory receptor-encoding genes, and to express other known features of terminal ADL identity, including neurotransmitter phenotype, neuropeptides, ion channels, and electrical synapse proteins. hlh-4 is sufficient to induce ADL identity features upon ectopic expression in other neuron types. The expression of ADL terminal identity features is directly controlled by HLH-4 via a phylogenetically conserved E-box motif, which, through bioinformatic analysis, we find to constitute a predictive feature of ADL-expressed terminal identity markers. The lineage that produces the ADL neuron was previously shown to require the conventional, transient proneural activity of another AS-C homolog, hlh-14, demonstrating sequential activities of distinct AS-C-type bHLH genes in neuronal specification. Taken together, we have defined here an unconventional function of an AS-C-type bHLH gene as a terminal selector of neuronal identity and we speculate that such function could be reflective of an ancestral function of an "ur-" bHLH gene.

Chromatin Accessibility-Based Characterization of the Gene Regulatory Network Underlying Plasmodium falciparum Blood-Stage Development

Underlying the development of malaria parasites within erythrocytes and the resulting pathogenicity is a hardwired program that secures proper timing of gene transcription and production of functionally relevant proteins. How stage-specific gene expression is orchestrated in vivo remains unclear. Here, using the assay for transposase accessible chromatin sequencing (ATAC-seq), we identified ∼4,000 regulatory regions in P. falciparum intraerythrocytic stages. The vast majority of these sites are located within 2 kb upstream of transcribed genes and their chromatin accessibility pattern correlates positively with abundance of the respective mRNA transcript. Importantly, these regions are sufficient to drive stage-specific reporter gene expression and DNA motifs enriched in stage-specific sets of regulatory regions interact with members of the P. falciparum AP2 transcription factor family. Collectively, this study provides initial insights into the in vivo gene regulatory network of P. falciparum intraerythrocytic stages and should serve as a valuable resource for future studies.

Systematic and synthetic approaches to rewire regulatory networks

Microbial gene regulatory networks are composed of cis- and trans-components that in concert act to control essential and adaptive cellular functions. Regulatory components and interactions evolve to adopt new configurations through mutations and network rewiring events, resulting in novel phenotypes that may benefit the cell. Advances in high-throughput DNA synthesis and sequencing have enabled the development of new tools and approaches to better characterize and perturb various elements of regulatory networks. Here, we highlight key recent approaches to systematically dissect the sequence space of cis-regulatory elements and trans-regulators as well as their inter-connections. These efforts yield fundamental insights into the architecture, robustness, and dynamics of gene regulation and provide models and design principles for building synthetic regulatory networks for a variety of practical applications.

Identification of a Druggable Pathway Controlling Glioblastoma Invasiveness

Diffuse and uncontrollable brain invasion is a hallmark of glioblastoma (GBM), but its mechanism is understood poorly. We developed a 3D ex vivo organotypic model to study GBM invasion. We demonstrate that invading GBM cells upregulate a network of extracellular matrix (ECM) components, including multiple collagens, whose expression correlates strongly with grade and clinical outcome. We identify interferon regulatory factor 3 (IRF3) as a transcriptional repressor of ECM factors and show that IRF3 acts as a suppressor of GBM invasion. Therapeutic activation of IRF3 by inhibiting casein kinase 2 (CK2)-a negative regulator of IRF3-downregulated the expression of ECM factors and suppressed GBM invasion in ex vivo and in vivo models across a panel of patient-derived GBM cell lines representative of the main molecular GBM subtypes. Our data provide mechanistic insight into the invasive capacity of GBM tumors and identify a potential therapy to inhibit GBM invasion.

Identification of diverse target RNAs that are functionally regulated by human Pumilio proteins

Human Pumilio proteins, PUM1 and PUM2, are sequence specific RNA-binding proteins that regulate protein expression. We used RNA-seq, rigorous statistical testing and an experimentally derived fold change cut-off to identify nearly 1000 target RNAs-including mRNAs and non-coding RNAs-that are functionally regulated by PUMs. Bioinformatic analysis defined a PUM Response Element (PRE) that was significantly enriched in transcripts that increased in abundance and matches the PUM RNA-binding consensus. We created a computational model that incorporates PRE position and frequency within an RNA relative to the magnitude of regulation. The model reveals significant correlation of PUM regulation with PREs in 3' untranslated regions (UTRs), coding sequences and non-coding RNAs, but not 5' UTRs. To define direct, high confidence PUM targets, we cross-referenced PUM-regulated RNAs with all PRE-containing RNAs and experimentally defined PUM-bound RNAs. The results define nearly 300 direct targets that include both PUM-repressed and, surprisingly, PUM-activated target RNAs. Annotation enrichment analysis reveal that PUMs regulate genes from multiple signaling pathways and developmental and neurological processes. Moreover, PUM target mRNAs impinge on human disease genes linked to cancer, neurological disorders and cardiovascular disease. These discoveries pave the way for determining how the PUM-dependent regulatory network impacts biological functions and disease states.