Optimizing data integration improves gene regulatory network inference in Arabidopsis thaliana.

MOTIVATIONS: Gene regulatory networks (GRNs) are traditionally inferred from gene expression profiles monitoring a specific condition or treatment. In the last decade, integrative strategies have successfully emerged to guide GRN inference from gene expression with complementary prior data. However, datasets used as prior information and validation gold standards are often related and limited to a subset of genes. This lack of complete and independent evaluation calls for new criteria to robustly estimate the optimal intensity of prior data integration in the inference process. RESULTS: We address this issue for two regression-based GRN inference models, a weighted random forest (weigthedRF) and a generalized linear model estimated under a weighted LASSO penalty with stability selection (weightedLASSO). These approaches are applied to data from the root response to nitrate induction in Arabidopsis thaliana. For each gene, we measure how the integration of transcription factor binding motifs influences model prediction. We propose a new approach, DIOgene, that uses model prediction error and a simulated null hypothesis in order to optimize data integration strength in a hypothesis-driven, gene-specific manner. This integration scheme reveals a strong diversity of optimal integration intensities between genes, and offers good performance in minimizing prediction error as well as retrieving experimental interactions. Experimental results show that DIOgene compares favorably against state-of-the-art approaches and allows to recover master regulators of nitrate induction. AVAILABILITY AND IMPLEMENTATION: The R code and notebooks demonstrating the use of the proposed approaches are available in the repository https://github.com/OceaneCsn/integrative_GRN_N_induction.

Short tandem repeats are important contributors to silencer elements in T cells.

The action of cis-regulatory elements with either activation or repression functions underpins the precise regulation of gene expression during normal development and cell differentiation. Gene activation by the combined activities of promoters and distal enhancers has been extensively studied in normal and pathological contexts. In sharp contrast, gene repression by cis-acting silencers, defined as genetic elements that negatively regulate gene transcription in a position-independent fashion, is less well understood. Here, we repurpose the STARR-seq approach as a novel high-throughput reporter strategy to quantitatively assess silencer activity in mammals. We assessed silencer activity from DNase hypersensitive I sites in a mouse T cell line. Identified silencers were associated with either repressive or active chromatin marks and enriched for binding motifs of known transcriptional repressors. CRISPR-mediated genomic deletions validated the repressive function of distinct silencers involved in the repression of non-T cell genes and genes regulated during T cell differentiation. Finally, we unravel an association of silencer activity with short tandem repeats, highlighting the role of repetitive elements in silencer activity. Our results provide a general strategy for genome-wide identification and characterization of silencer elements.

Fra-1 regulates its target genes via binding to remote enhancers without exerting major control on chromatin architecture in triple negative breast cancers.

The ubiquitous family of dimeric transcription factors AP-1 is made up of Fos and Jun family proteins. It has long been thought to operate principally at gene promoters and how it controls transcription is still ill-understood. The Fos family protein Fra-1 is overexpressed in triple negative breast cancers (TNBCs) where it contributes to tumor aggressiveness. To address its transcriptional actions in TNBCs, we combined transcriptomics, ChIP-seqs, machine learning and NG Capture-C. Additionally, we studied its Fos family kin Fra-2 also expressed in TNBCs, albeit much less. Consistently with their pleiotropic effects, Fra-1 and Fra-2 up- and downregulate individually, together or redundantly many genes associated with a wide range of biological processes. Target gene regulation is principally due to binding of Fra-1 and Fra-2 at regulatory elements located distantly from cognate promoters where Fra-1 modulates the recruitment of the transcriptional co-regulator p300/CBP and where differences in AP-1 variant motif recognition can underlie preferential Fra-1- or Fra-2 bindings. Our work also shows no major role for Fra-1 in chromatin architecture control at target gene loci, but suggests collaboration between Fra-1-bound and -unbound enhancers within chromatin hubs sometimes including promoters for other Fra-1-regulated genes. Our work impacts our view of AP-1.

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.

Probing transcription factor combinatorics in different promoter classes and in enhancers.

BACKGROUND: In eukaryotic cells, transcription factors (TFs) are thought to act in a combinatorial way, by competing and collaborating to regulate common target genes. However, several questions remain regarding the conservation of these combinations among different gene classes, regulatory regions and cell types. RESULTS: We propose a new approach named TFcoop to infer the TF combinations involved in the binding of a target TF in a particular cell type. TFcoop aims to predict the binding sites of the target TF upon the nucleotide content of the sequences and of the binding affinity of all identified cooperating TFs. The set of cooperating TFs and model parameters are learned from ChIP-seq data of the target TF. We used TFcoop to investigate the TF combinations involved in the binding of 106 TFs on 41 cell types and in four regulatory regions: promoters of mRNAs, lncRNAs and pri-miRNAs, and enhancers. We first assess that TFcoop is accurate and outperforms simple PWM methods for predicting TF binding sites. Next, analysis of the learned models sheds light on important properties of TF combinations in different promoter classes and in enhancers. First, we show that combinations governing TF binding on enhancers are more cell-type specific than that governing binding in promoters. Second, for a given TF and cell type, we observe that TF combinations are different between promoters and enhancers, but similar for promoters of mRNAs, lncRNAs and pri-miRNAs. Analysis of the TFs cooperating with the different targets show over-representation of pioneer TFs and a clear preference for TFs with binding motif composition similar to that of the target. Lastly, our models accurately distinguish promoters associated with specific biological processes. CONCLUSIONS: TFcoop appears as an accurate approach for studying TF combinations. Its use on ENCODE and FANTOM data allowed us to discover important properties of human TF combinations in different promoter classes and in enhancers. The R code for learning a TFcoop model and for reproducing the main experiments described in the paper is available in an R Markdown file at address https://gite.lirmm.fr/brehelin/TFcoop .

Probing instructions for expression regulation in gene nucleotide compositions.

Gene expression is orchestrated by distinct regulatory regions to ensure a wide variety of cell types and functions. A challenge is to identify which regulatory regions are active, what are their associated features and how they work together in each cell type. Several approaches have tackled this problem by modeling gene expression based on epigenetic marks, with the ultimate goal of identifying driving regions and associated genomic variations that are clinically relevant in particular in precision medicine. However, these models rely on experimental data, which are limited to specific samples (even often to cell lines) and cannot be generated for all regulators and all patients. In addition, we show here that, although these approaches are accurate in predicting gene expression, inference of TF combinations from this type of models is not straightforward. Furthermore these methods are not designed to capture regulation instructions present at the sequence level, before the binding of regulators or the opening of the chromatin. Here, we probe sequence-level instructions for gene expression and develop a method to explain mRNA levels based solely on nucleotide features. Our method positions nucleotide composition as a critical component of gene expression. Moreover, our approach, able to rank regulatory regions according to their contribution, unveils a strong influence of the gene body sequence, in particular introns. We further provide evidence that the contribution of nucleotide content can be linked to co-regulations associated with genome 3D architecture and to associations of genes within topologically associated domains.

miR-125b controls monocyte adaptation to inflammation through mitochondrial metabolism and dynamics.

Metabolic changes drive monocyte differentiation and fate. Although abnormal mitochondria metabolism and innate immune responses participate in the pathogenesis of many inflammatory disorders, molecular events regulating mitochondrial activity to control life and death in monocytes remain poorly understood. We show here that, in human monocytes, microRNA-125b (miR-125b) attenuates the mitochondrial respiration through the silencing of the BH3-only proapoptotic protein BIK and promotes the elongation of the mitochondrial network through the targeting of the mitochondrial fission process 1 protein MTP18, leading to apoptosis. Proinflammatory activation of monocyte-derived macrophages is associated with a concomitant increase in miR-125b expression and decrease in BIK and MTP18 expression, which lead to reduced oxidative phosphorylation and enhanced mitochondrial fusion. In a chronic inflammatory systemic disorder, CD14+ blood monocytes display reduced miR-125b expression as compared with healthy controls, inversely correlated with BIK and MTP18 messenger RNA expression. Our findings not only identify BIK and MTP18 as novel targets for miR-125b that control mitochondrial metabolism and dynamics, respectively, but also reveal a novel function for miR-125b in regulating metabolic adaptation of monocytes to inflammation. Together, these data unravel new molecular mechanisms for a proapoptotic role of miR-125b in monocytes and identify potential targets for interfering with excessive inflammatory activation of monocytes in inflammatory disorders.

microRNAs and Personalized Medicine: Evaluating Their Potential as Cancer Biomarkers.

microRNA deregulations are often, if not invariably, associated with human malignancies, including cancers. Though most of these deregulations may not be functionally implicated in tumorigenesis, the fact that microRNA expression can be monitored in a variety of human specimens, including biological fluids, supports studies aimed at characterizing microRNA signatures able to detect various cancers (diagnosis), predict their outcome (prognosis), monitor their treatment (theranosis), and adapt therapy to a patient (precision medicine). Here, we review and discuss pros and cons of microRNA-based approaches that can support their exploitation as cancer biomarkers.

Comparison of different extraction techniques to profile microRNAs from human sera and peripheral blood mononuclear cells.

BACKGROUND: microRNAs (miRNAs) play crucial roles in major biological processes and their deregulations are often associated with human malignancies. As such, they represent appealing candidates as targets of innovative therapies. Another interesting aspect of their biology is that they are present in various biological fluids where, advantageously, they appear to be very stable. A plethora of studies have now reported their potential as biomarkers that can be used in diagnosis, prognosis and/or theranostic issues. However, the application of circulating miRNAs in clinical practices still requires the identification of highly efficient, robust and reproducible methods for their isolation from biological samples.In that context, we performed an independent cross-comparison of three commercially available RNA extraction kits for miRNAs isolation from human blood samples (Qiagen and Norgen kits as well as the new NucleoSpin miRNAs Plasma kit from Macherey-Nagel). miRNAs were further profiled using the Taqman Low Density Array technology. RESULTS: We found that, although these 3 kits had equal performances in extracting miRNAs from peripheral blood mononuclear cells, the Macherey-Nagel kit presented several advantages when isolating miRNAs from sera. Besides, our results have indicated that, depending on the quantity of the biological samples used, the extraction procedure directly impacted on the G/C composition of the miRNAs detected. CONCLUSION: Overall, our study contributes to the definition of a reliable framework for profiling circulating miRNAs.

Retroviral GAG proteins recruit AGO2 on viral RNAs without affecting RNA accumulation and translation.

Cellular micro(mi)RNAs are able to recognize viral RNAs through imperfect micro-homologies. Similar to the miRNA-mediated repression of cellular translation, this recognition is thought to tether the RNAi machinery, in particular Argonaute 2 (AGO2) on viral messengers and eventually to modulate virus replication. Here, we unveil another pathway by which AGO2 can interact with retroviral mRNAs. We show that AGO2 interacts with the retroviral Group Specific Antigen (GAG) core proteins and preferentially binds unspliced RNAs through the RNA packaging sequences without affecting RNA stability or eliciting translation repression. Using RNAi experiments, we provide evidences that these interactions, observed with both the human immunodeficiency virus 1 (HIV-1) and the primate foamy virus 1 (PFV-1), are required for retroviral replication. Taken together, our results place AGO2 at the core of the retroviral life cycle and reveal original AGO2 functions that are not related to miRNAs and translation repression.

TFscope: systematic analysis of the sequence features involved in the binding preferences of transcription factors.

Characterizing the binding preferences of transcription factors (TFs) in different cell types and conditions is key to understand how they orchestrate gene expression. Here, we develop TFscope, a machine learning approach that identifies sequence features explaining the binding differences observed between two ChIP-seq experiments targeting either the same TF in two conditions or two TFs with similar motifs (paralogous TFs). TFscope systematically investigates differences in the core motif, nucleotide environment and co-factor motifs, and provides the contribution of each key feature in the two experiments. TFscope was applied to > 305 ChIP-seq pairs, and several examples are discussed.

HT-smFISH: a cost-effective and flexible workflow for high-throughput single-molecule RNA imaging.

The ability to visualize RNA in its native subcellular environment by using single-molecule fluorescence in situ hybridization (smFISH) has reshaped our understanding of gene expression and cellular functions. A major hindrance of smFISH is the difficulty to perform systematic experiments in medium- or high-throughput formats, principally because of the high cost of generating the individual fluorescent probe sets. Here, we present high-throughput smFISH (HT-smFISH), a simple and cost-efficient method for imaging hundreds to thousands of single endogenous RNA molecules in 96-well plates. HT-smFISH uses RNA probes transcribed in vitro from a large pool of unlabeled oligonucleotides. This allows the generation of individual probes for many RNA species, replacing commercial DNA probe sets. HT-smFISH thus reduces costs per targeted RNA compared with many smFISH methods and is easily scalable and flexible in design. We provide a protocol that combines oligo pool design, probe set generation, optimized hybridization conditions and guidelines for image acquisition and analysis. The pipeline requires knowledge of standard molecular biology tools, cell culture and fluorescence microscopy. It is achievable in ~20 d. In brief, HT-smFISH is tailored for medium- to high-throughput screens that image RNAs at single-molecule sensitivity.

MIR@NT@N: a framework integrating transcription factors, microRNAs and their targets to identify sub-network motifs in a meta-regulation network model.

BACKGROUND: To understand biological processes and diseases, it is crucial to unravel the concerted interplay of transcription factors (TFs), microRNAs (miRNAs) and their targets within regulatory networks and fundamental sub-networks. An integrative computational resource generating a comprehensive view of these regulatory molecular interactions at a genome-wide scale would be of great interest to biologists, but is not available to date. RESULTS: To identify and analyze molecular interaction networks, we developed MIR@NT@N, an integrative approach based on a meta-regulation network model and a large-scale database. MIR@NT@N uses a graph-based approach to predict novel molecular actors across multiple regulatory processes (i.e. TFs acting on protein-coding or miRNA genes, or miRNAs acting on messenger RNAs). Exploiting these predictions, the user can generate networks and further analyze them to identify sub-networks, including motifs such as feedback and feedforward loops (FBL and FFL). In addition, networks can be built from lists of molecular actors with an a priori role in a given biological process to predict novel and unanticipated interactions. Analyses can be contextualized and filtered by integrating additional information such as microarray expression data. All results, including generated graphs, can be visualized, saved and exported into various formats. MIR@NT@N performances have been evaluated using published data and then applied to the regulatory program underlying epithelium to mesenchyme transition (EMT), an evolutionary-conserved process which is implicated in embryonic development and disease. CONCLUSIONS: MIR@NT@N is an effective computational approach to identify novel molecular regulations and to predict gene regulatory networks and sub-networks including conserved motifs within a given biological context. Taking advantage of the M@IA environment, MIR@NT@N is a user-friendly web resource freely available at http://mironton.uni.lu which will be updated on a regular basis.

Transcriptional repression of microRNA genes by PML-RARA increases expression of key cancer proteins in acute promyelocytic leukemia.

Micro(mi)RNAs are small noncoding RNAs that orchestrate many key aspects of cell physiology and their deregulation is often linked to distinct diseases including cancer. Here, we studied the contribution of miRNAs in a well-characterized human myeloid leukemia, acute promyelocytic leukemia (APL), targeted by retinoic acid and trioxide arsenic therapy. We identified several miRNAs transcriptionally repressed by the APL-associated PML-RAR oncogene which are released after treatment with all-trans retinoic acid. These coregulated miRNAs were found to control, in a coordinated manner, crucial pathways linked to leukemogenesis, such as HOX proteins and cell adhesion molecules whose expressions are thereby repressed by the chemotherapy. Thus, APL appears linked to transcriptional perturbation of miRNA genes, and clinical protocols able to successfully eradicate cancer cells may do so by restoring miRNA expression. The identification of abnormal miRNA biogenesis in cancer may therefore provide novel biomarkers and therapeutic targets in myeloid leukemias.

[The democratic side of science-fiction]. / Le côté démocratique de la science-fiction.

Suspicion towards technological advances has progressively grown during the xx(th) century. However, in the XXI(st) century, reading the NBIC (nanotechnology, biotechnology, information technology and cognitive science) report of the National Science Foundation, we can note that science has caught up with science fiction. These changes in public mentality on one side and in scientific capacities on the other argue for an evolution of the debate on sciences. The recent example of the national debate on nanotechnology in France has clearly shown that the public is no longer waiting for additional sources of scientific knowledge but rather waiting for the recognition of its authority to participate in the definition of the national R&D priority and associated scientific strategies. This is all the more legitimate that these strategies will have profound impact on the future of our societies and therefore cannot be decided only by scientists. Hence, it is crucial to identify innovative tools promoting debate on sciences and their technological spin-off. Here, we contend that science fiction has major assets that could face this challenge and facilitate the dialogue between sciences and society.

A choreography of centrosomal mRNAs reveals a conserved localization mechanism involving active polysome transport.

Local translation allows for a spatial control of gene expression. Here, we use high-throughput smFISH to screen centrosomal protein-coding genes, and we describe 8 human mRNAs accumulating at centrosomes. These mRNAs localize at different stages during cell cycle with a remarkable choreography, indicating a finely regulated translational program at centrosomes. Interestingly, drug treatments and reporter analyses reveal a common translation-dependent localization mechanism requiring the nascent protein. Using ASPM and NUMA1 as models, single mRNA and polysome imaging reveals active movements of endogenous polysomes towards the centrosome at the onset of mitosis, when these mRNAs start localizing. ASPM polysomes associate with microtubules and localize by either motor-driven transport or microtubule pulling. Remarkably, the Drosophila orthologs of the human centrosomal mRNAs also localize to centrosomes and also require translation. These data identify a conserved family of centrosomal mRNAs that localize by active polysome transport mediated by nascent proteins.

Discovery of widespread transcription initiation at microsatellites predictable by sequence-based deep neural network.

Using the Cap Analysis of Gene Expression (CAGE) technology, the FANTOM5 consortium provided one of the most comprehensive maps of transcription start sites (TSSs) in several species. Strikingly, ~72% of them could not be assigned to a specific gene and initiate at unconventional regions, outside promoters or enhancers. Here, we probe these unassigned TSSs and show that, in all species studied, a significant fraction of CAGE peaks initiate at microsatellites, also called short tandem repeats (STRs). To confirm this transcription, we develop Cap Trap RNA-seq, a technology which combines cap trapping and long read MinION sequencing. We train sequence-based deep learning models able to predict CAGE signal at STRs with high accuracy. These models unveil the importance of STR surrounding sequences not only to distinguish STR classes, but also to predict the level of transcription initiation. Importantly, genetic variants linked to human diseases are preferentially found at STRs with high transcription initiation level, supporting the biological and clinical relevance of transcription initiation at STRs. Together, our results extend the repertoire of non-coding transcription associated with DNA tandem repeats and complexify STR polymorphism.

Human Enhancers Harboring Specific Sequence Composition, Activity, and Genome Organization Are Linked to the Immune Response.

The FANTOM5 consortium recently characterized 65,423 human enhancers from 1829 cell and tissue samples using the Cap Analysis of Gene Expression technology. We showed that the guanine and cytosine content at enhancer regions distinguishes two classes of enhancers harboring distinct DNA structural properties at flanking regions. A functional analysis of their predicted gene targets highlighted one class of enhancers as significantly enriched for associations with immune response genes. Moreover, these enhancers were specifically enriched for regulatory motifs recognized by transcription factors involved in immune response. We observed that enhancers enriched for links to immune response genes were more cell-type specific, preferentially activated upon bacterial infection, and with specific response activity. Looking at chromatin capture data, we found that the two classes of enhancers were lying in distinct topologically associating domains and chromatin loops. Our results suggest that specific nucleotide compositions encode for classes of enhancers that are functionally distinct and specifically organized in the human genome.

Differential long non-coding RNA expression profiles in human oocytes and cumulus cells.

Progress in assisted reproductive technologies strongly relies on understanding the regulation of the dialogue between oocyte and cumulus cells (CCs). Little is known about the role of long non-coding RNAs (lncRNAs) in the human cumulus-oocyte complex (COC). To this aim, publicly available RNA-sequencing data were analyzed to identify lncRNAs that were abundant in metaphase II (MII) oocytes (BCAR4, C3orf56, TUNAR, OOEP-AS1, CASC18, and LINC01118) and CCs (NEAT1, MALAT1, ANXA2P2, MEG3, IL6STP1, and VIM-AS1). These data were validated by RT-qPCR analysis using independent oocytes and CC samples. The functions of the identified lncRNAs were then predicted by constructing lncRNA-mRNA co-expression networks. This analysis suggested that MII oocyte lncRNAs could be involved in chromatin remodeling, cell pluripotency and in driving early embryonic development. CC lncRNAs were co-expressed with genes involved in apoptosis and extracellular matrix-related functions. A bioinformatic analysis of RNA-sequencing data to identify CC lncRNAs that are affected by maternal age showed that lncRNAs with age-related altered expression in CCs are essential for oocyte growth. This comprehensive analysis of lncRNAs expressed in human MII oocytes and CCs could provide biomarkers of oocyte quality for the development of non-invasive tests to identify embryos with high developmental potential.

miRNA-30 Family Members Inhibit Breast Cancer Invasion, Osteomimicry, and Bone Destruction by Directly Targeting Multiple Bone Metastasis-Associated Genes.

miRNAs are master regulators of gene expression that play key roles in cancer metastasis. During bone metastasis, metastatic tumor cells must rewire their biology and express genes that are normally expressed by bone cells (a process called osteomimicry), which endow tumor cells with full competence for outgrowth in the bone marrow. Here, we establish miR-30 family members miR-30a, miR-30b, miR-30c, miR-30d, and miR-30e as suppressors of breast cancer bone metastasis that regulate multiple pathways, including osteomimicry. Low expression of miR-30 in primary tumors from patients with breast cancer were associated with poor relapse-free survival. In addition, estrogen receptor (ER)-negative/progesterone receptor (PR)-negative breast cancer cells expressed lower miR-30 levels than their ER/PR-positive counterparts. Overexpression of miR-30 in ER/PR-negative breast cancer cells resulted in the reduction of bone metastasis burden in vivoIn vitro, miR-30 did not affect tumor cell proliferation, but did inhibit tumor cell invasion. Furthermore, overexpression of miR-30 restored bone homeostasis by reversing the effects of tumor cell-conditioned medium on osteoclastogenesis and osteoblastogenesis. A number of genes associated with osteoclastogenesis stimulation (IL8, IL11), osteoblastogenesis inhibition (DKK-1), tumor cell osteomimicry (RUNX2, CDH11), and invasiveness (CTGF, ITGA5, ITGB3) were identified as targets for repression by miR-30. Among these genes, silencing CDH11 or ITGA5 in ER-/PR-negative breast cancer cells recapitulated inhibitory effects of miR-30 on skeletal tumor burden in vivo Overall, our findings provide evidence that miR-30 family members employ multiple mechanisms to impede breast cancer bone metastasis and may represent attractive targets for therapeutic intervention.Significance: These findings suggest miR-30 family members may serve as an effective means to therapeutically attenuate metastasis in triple-negative breast cancer. Cancer Res; 78(18); 5259-73. ©2018 AACR.