Decidual-tissue-resident memory T cells protect against nonprimary human cytomegalovirus infection at the maternal-fetal interface.

Congenital cytomegalovirus (cCMV) is the most common intrauterine infection, leading to infant neurodevelopmental disabilities. An improved knowledge of correlates of protection against cCMV is needed to guide prevention strategies. Here, we employ an ex vivo model of human CMV (HCMV) infection in decidual tissues of women with and without preconception immunity against CMV, recapitulating nonprimary vs. primary infection at the authentic maternofetal transmission site. We show that decidual tissues of women with preconception immunity against CMV exhibit intrinsic resistance to HCMV, mounting a rapid activation of tissue-resident memory CD8+ and CD4+ T cells upon HCMV reinfection. We further reveal the role of HCMV-specific decidual-tissue-resident CD8+ T cells in local protection against nonprimary HCMV infection. The findings could inform the development of a vaccine against cCMV and provide insights for further studies of the integrity of immune defense against HCMV and other pathogens at the human maternal-fetal interface.

A dual role of RBM42 in modulating splicing and translation of CDKN1A/p21 during DNA damage response.

p53-mediated cell cycle arrest during DNA damage is dependent on the induction of p21 protein, encoded by the CDKN1A gene. p21 inhibits cyclin-dependent kinases required for cell cycle progression to guarantee accurate repair of DNA lesions. Hence, fine-tuning of p21 levels is crucial to preserve genomic stability. Currently, the multilayered regulation of p21 levels during DNA damage is not fully understood. Herein, we identify the human RNA binding motif protein 42 (RBM42) as a regulator of p21 levels during DNA damage. Genome-wide transcriptome and interactome analysis reveals that RBM42 alters the expression of p53-regulated genes during DNA damage. Specifically, we demonstrate that RBM42 facilitates CDKN1A splicing by counteracting the splicing inhibitory effect of RBM4 protein. Unexpectedly, we also show that RBM42, underpins translation of various splicing targets, including CDKN1A. Concordantly, transcriptome-wide mapping of RBM42-RNA interactions using eCLIP further substantiates the dual function of RBM42 in regulating splicing and translation of its target genes, including CDKN1A. Collectively, our data show that RBM42 couples splicing and translation machineries to fine-tune gene expression during DNA damage response.

LIS1 RNA-binding orchestrates the mechanosensitive properties of embryonic stem cells in AGO2-dependent and independent ways.

Lissencephaly-1 (LIS1) is associated with neurodevelopmental diseases and is known to regulate the molecular motor cytoplasmic dynein activity. Here we show that LIS1 is essential for the viability of mouse embryonic stem cells (mESCs), and it governs the physical properties of these cells. LIS1 dosage substantially affects gene expression, and we uncovered an unexpected interaction of LIS1 with RNA and RNA-binding proteins, most prominently the Argonaute complex. We demonstrate that LIS1 overexpression partially rescued the extracellular matrix (ECM) expression and mechanosensitive genes conferring stiffness to Argonaute null mESCs. Collectively, our data transforms the current perspective on the roles of LIS1 in post-transcriptional regulation underlying development and mechanosensitive processes.

Proteasome gene expression is controlled by the coordinated functions of multiple transcription factors.

Proteasome activity is crucial for cellular integrity, but how tissues adjust proteasome content in response to catabolic stimuli is uncertain. Here, we demonstrate that transcriptional coordination by multiple transcription factors is required to increase proteasome content and activate proteolysis in catabolic states. Using denervated mouse muscle as a model system for accelerated proteolysis in vivo , we reveal that a two-phase transcriptional program activates genes encoding proteasome subunits and assembly chaperones to boost an increase in proteasome content. Initially, gene induction is necessary to maintain basal proteasome levels, and in a more delayed phase (7-10 d after denervation) it stimulates proteasome assembly to meet cellular demand for excessive proteolysis. Intriguingly, the transcription factors PAX4 and α-PAL NRF-1 control the expression of proteasome among other genes in a combinatorial manner, driving cellular adaptation to muscle denervation. Consequently, PAX4 and α-PAL NRF-1 represent new therapeutic targets to inhibit proteolysis in catabolic diseases (e.g. type-2 diabetes, cancer).

RBPmap: A Tool for Mapping and Predicting the Binding Sites of RNA-Binding Proteins Considering the Motif Environment.

RNA-binding proteins (RBPs) play a key role in post-transcriptional regulation via binding to coding and non-coding RNAs. Recent development in experimental technologies, aimed to identify the targets of RBPs, has significantly broadened our knowledge on protein-RNA interactions. However, for many RBPs in many organisms and cell types, experimental RNA-binding data is not available. In this chapter we describe a computational approach, named RBPmap, available as a web service via http://rbpmap.technion.ac.il/ and as a stand-alone version for download. RBPmap was designed for mapping and predicting the binding sites of any RBP within a nucleic acid sequence, given the availability of an experimentally defined binding motif of the RBP. The algorithm searches for a sub-sequence that significantly matches the RBP motif, considering the clustering propensity of other weak matches within the motif environment. Here, we present different applications of RBPmap for discovering the involvement of RBPs and their targets in a variety of cellular processes, in health and disease states. Finally, we demonstrate the performance of RBPmap in predicting the binding targets of RBPs in large-scale RNA-binding data, reinforcing the strength of the tool in distinguishing cognate binding sites from weak motifs.

Uncovering the RNA-binding protein landscape in the pluripotency network of human embryonic stem cells.

Embryonic stem cell (ESC) self-renewal and cell fate decisions are driven by a broad array of molecular signals. While transcriptional regulators have been extensively studied in human ESCs (hESCs), the extent to which RNA-binding proteins (RBPs) contribute to human pluripotency remains unclear. Here, we carry out a proteome-wide screen and identify 810 proteins that bind RNA in hESCs. We reveal that RBPs are preferentially expressed in hESCs and dynamically regulated during early stem cell differentiation. Notably, many RBPs are affected by knockdown of OCT4, a master regulator of pluripotency, several dozen of which are directly targeted by this factor. Using cross-linking and immunoprecipitation (CLIP-seq), we find that the pluripotency-associated STAT3 and OCT4 transcription factors interact with RNA in hESCs and confirm the binding of STAT3 to the conserved NORAD long-noncoding RNA. Our findings indicate that RBPs have a more widespread role in human pluripotency than previously appreciated.

Evaluation of COVID-19 RT-qPCR Test in Multi sample Pools.

BACKGROUND: The recent emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) led to a current pandemic of unprecedented scale. Although diagnostic tests are fundamental to the ability to detect and respond, overwhelmed healthcare systems are already experiencing shortages of reagents associated with this test, calling for a lean immediately applicable protocol. METHODS: RNA extracts of positive samples were tested for the presence of SARS-CoV-2 using reverse transcription quantitative polymerase chain reaction, alone or in pools of different sizes (2-, 4-, 8-, 16-, 32-, and 64-sample pools) with negative samples. Transport media of additional 3 positive samples were also tested when mixed with transport media of negative samples in pools of 8. RESULTS: A single positive sample can be detected in pools of up to 32 samples, using the standard kits and protocols, with an estimated false negative rate of 10%. Detection of positive samples diluted in even up to 64 samples may also be attainable, although this may require additional amplification cycles. Single positive samples can be detected when pooling either after or prior to RNA extraction. CONCLUSIONS: As it uses the standard protocols, reagents, and equipment, this pooling method can be applied immediately in current clinical testing laboratories. We hope that such implementation of a pool test for coronavirus disease 2019 would allow expanding current screening capacities, thereby enabling the expansion of detection in the community, as well as in close organic groups, such as hospital departments, army units, or factory shifts.

Regulation of alternative splicing by p300-mediated acetylation of splicing factors.

Splicing of precursor mRNA (pre-mRNA) is an important regulatory step in gene expression. Recent evidence points to a regulatory role of chromatin-related proteins in alternative splicing regulation. Using an unbiased approach, we have identified the acetyltransferase p300 as a key chromatin-related regulator of alternative splicing. p300 promotes genome-wide exon inclusion in both a transcription-dependent and -independent manner. Using CD44 as a paradigm, we found that p300 regulates alternative splicing by modulating the binding of splicing factors to pre-mRNA. Using a tethering strategy, we found that binding of p300 to the CD44 promoter region promotes CD44v exon inclusion independently of RNAPII transcriptional elongation rate. Promoter-bound p300 regulates alternative splicing by acetylating splicing factors, leading to exclusion of hnRNP M from CD44 pre-mRNA and activation of Sam68. p300-mediated CD44 alternative splicing reduces cell motility and promotes epithelial features. Our findings reveal a chromatin-related mechanism of alternative splicing regulation and demonstrate its impact on cellular function.

Adaptation to sub-optimal hosts is a driver of viral diversification in the ocean.

Cyanophages of the Myoviridae family include generalist viruses capable of infecting a wide range of hosts including those from different cyanobacterial genera. While the influence of phages on host evolution has been studied previously, it is not known how the infection of distinct hosts influences the evolution of cyanophage populations. Here, using an experimental evolution approach, we investigated the adaptation of multiple cyanophage populations to distinct cyanobacterial hosts. We show that when infecting an "optimal" host, whose infection is the most efficient, phage populations accumulated only a few mutations. However, when infecting "sub-optimal" hosts, different mutations spread in the phage populations, leading to rapid diversification into distinct subpopulations. Based on our results, we propose a model demonstrating how shifts in microbial abundance, which lead to infection of "sub-optimal" hosts, act as a driver for rapid diversification of viral populations.

Ribonucleoprotein particles: advances and challenges in computational methods.

RNA-binding proteins (RBPs) interact with RNA to form Ribonucleoprotein Particles (RNPs). The interaction between RBPs and their RNA partners are traditionally thought to be mediated by highly conserved RNA-binding domains (RBDs). Recently, high-throughput studies led to the discovery of hundreds of novel proteins and domains, of which many do not follow the classical definition of RNA-binding. Despite technological innovations, experimental screenings are currently limited to the detection of specific types of RNPs, underscoring the importance of computational methods for predicting novel RBPs and RNA interacting residues and interfaces. Here, we discuss major challenges in computational prediction of RBPs and RBDs and outline new strategies to circumvent current limitations of experimental techniques.

SMARTIV: combined sequence and structure de-novo motif discovery for in-vivo RNA binding data.

Gene expression regulation is highly dependent on binding of RNA-binding proteins (RBPs) to their RNA targets. Growing evidence supports the notion that both RNA primary sequence and its local secondary structure play a role in specific Protein-RNA recognition and binding. Despite the great advance in high-throughput experimental methods for identifying sequence targets of RBPs, predicting the specific sequence and structure binding preferences of RBPs remains a major challenge. We present a novel webserver, SMARTIV, designed for discovering and visualizing combined RNA sequence and structure motifs from high-throughput RNA-binding data, generated from in-vivo experiments. The uniqueness of SMARTIV is that it predicts motifs from enriched k-mers that combine information from ranked RNA sequences and their predicted secondary structure, obtained using various folding methods. Consequently, SMARTIV generates Position Weight Matrices (PWMs) in a combined sequence and structure alphabet with assigned P-values. SMARTIV concisely represents the sequence and structure motif content as a single graphical logo, which is informative and easy for visual perception. SMARTIV was examined extensively on a variety of high-throughput binding experiments for RBPs from different families, generated from different technologies, showing consistent and accurate results. Finally, SMARTIV is a user-friendly webserver, highly efficient in run-time and freely accessible via http://smartiv.technion.ac.il/.

A Novel Geometry-Based Approach to Infer Protein Interface Similarity.

The protein interface is key to understand protein function, providing a vital insight on how proteins interact with each other and with other molecules. Over the years, many computational methods to compare protein structures were developed, yet evaluating interface similarity remains a very difficult task. Here, we present PatchBag - a geometry based method for efficient comparison of protein surfaces and interfaces. PatchBag is a Bag-Of-Words approach, which represents complex objects as vectors, enabling to search interface similarity in a highly efficient manner. Using a novel framework for evaluating interface similarity, we show that PatchBag performance is comparable to state-of-the-art alignment-based structural comparison methods. The great advantage of PatchBag is that it does not rely on sequence or fold information, thus enabling to detect similarities between interfaces in unrelated proteins. We propose that PatchBag can contribute to reveal novel evolutionary and functional relationships between protein interfaces.

OCT4 impedes cell fate redirection by the melanocyte lineage master regulator MITF in mouse ESCs.

Ectopic expression of lineage master regulators induces transdifferentiation. Whether cell fate transitions can be induced during various developmental stages has not been systemically examined. Here we discover that amongst different developmental stages, mouse embryonic stem cells (mESCs) are resistant to cell fate conversion induced by the melanocyte lineage master regulator MITF. By generating a transgenic system we exhibit that in mESCs, the pluripotency master regulator Oct4, counteracts pro-differentiation induced by Mitf by physical interference with MITF transcriptional activity. We further demonstrate that mESCs must be released from Oct4-maintained pluripotency prior to ectopically induced differentiation. Moreover, Oct4 induction in various differentiated cells represses their lineage identity in vivo. Alongside, chromatin architecture combined with ChIP-seq analysis suggest that Oct4 competes with various lineage master regulators for binding promoters and enhancers. Our analysis reveals pluripotency and transdifferentiation regulatory principles and could open new opportunities in the field of regenerative medicine.

A combined sequence and structure based method for discovering enriched motifs in RNA from in vivo binding data.

RNA binding proteins (RBPs) play an important role in regulating many processes in the cell. RBPs often recognize their RNA targets in a specific manner. In addition to the RNA primary sequence, the structure of the RNA has been shown to play a central role in RNA recognition by RBPs. In recent years, many experimental approaches, both in vitro and in vivo, were developed and employed to identify and characterize RBP targets and extract their binding specificities. In vivo binding techniques, such as CrossLinking and ImmunoPrecipitation (CLIP)-based methods, enable the characterization of protein binding sites on RNA targets. However, these methods do not provide information regarding the structural preferences of the protein. While methods to obtain the structure of RNA are available, inferring both the sequence and the structure preferences of RBPs remains a challenge. Here we present SMARTIV, a novel computational tool for discovering combined sequence and structure binding motifs from in vivo RNA binding data relying on the sequences of the target sites, the ranking of their binding scores and their predicted secondary structure. The combined motifs are provided in a unified representation that is informative and easy for visual perception. We tested the method on CLIP-seq data from different platforms for a variety of RBPs. Overall, we show that our results are highly consistent with known binding motifs of RBPs, offering additional information on their structural preferences.

Mutual enrichment in aggregated ranked lists with applications to gene expression regulation.

MOTIVATION: It is often the case in biological measurement data that results are given as a ranked list of quantities-for example, differential expression (DE) of genes as inferred from microarrays or RNA-seq. Recent years brought considerable progress in statistical tools for enrichment analysis in ranked lists. Several tools are now available that allow users to break the fixed set paradigm in assessing statistical enrichment of sets of genes. Continuing with the example, these tools identify factors that may be associated with measured differential expression. A drawback of existing tools is their focus on identifying single factors associated with the observed or measured ranks, failing to address relationships between these factors. For example, a scenario in which genes targeted by multiple miRNAs play a central role in the DE signal but the effect of each single miRNA is too subtle to be detected, as shown in our results. RESULTS: We propose statistical and algorithmic approaches for selecting a sub-collection of factors that can be aggregated into one ranked list that is heuristically most associated with an input ranked list (pivot). We examine performance on simulated data and apply our approach to cancer datasets. We find small sub-collections of miRNA that are statistically associated with gene DE in several types of cancer, suggesting miRNA cooperativity in driving disease related processes. Many of our findings are consistent with known roles of miRNAs in cancer, while others suggest previously unknown roles for certain miRNAs. AVAILABILITY AND IMPLEMENTATION: Code and instructions for our algorithmic framework, MULSEA, are in: https://github.com/YakhiniGroup/MULSEAContact:dalia.cohn@gmail.com SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

How motif environment influences transcription factor search dynamics: Finding a needle in a haystack.

Transcription factors (TFs) have to find their binding sites, which are distributed throughout the genome. Facilitated diffusion is currently the most widely accepted model for this search process. Based on this model the TF alternates between one-dimensional sliding along the DNA, and three-dimensional bulk diffusion. In this view, the non-specific associations between the proteins and the DNA play a major role in the search dynamics. However, little is known about how the DNA properties around the motif contribute to the search. Accumulating evidence showing that TF binding sites are embedded within a unique environment, specific to each TF, leads to the hypothesis that the search process is facilitated by favorable DNA features that help to improve the search efficiency. Here, we review the field and present the hypothesis that TF-DNA recognition is dictated not only by the motif, but is also influenced by the environment in which the motif resides.

BindUP: a web server for non-homology-based prediction of DNA and RNA binding proteins.

Gene expression is a multi-step process involving many layers of regulation. The main regulators of the pathway are DNA and RNA binding proteins. While over the years, a large number of DNA and RNA binding proteins have been identified and extensively studied, it is still expected that many other proteins, some with yet another known function, are awaiting to be discovered. Here we present a new web server, BindUP, freely accessible through the website http://bindup.technion.ac.il/, for predicting DNA and RNA binding proteins using a non-homology-based approach. Our method is based on the electrostatic features of the protein surface and other general properties of the protein. BindUP predicts nucleic acid binding function given the proteins three-dimensional structure or a structural model. Additionally, BindUP provides information on the largest electrostatic surface patches, visualized on the server. The server was tested on several datasets of DNA and RNA binding proteins, including proteins which do not possess DNA or RNA binding domains and have no similarity to known nucleic acid binding proteins, achieving very high accuracy. BindUP is applicable in either single or batch modes and can be applied for testing hundreds of proteins simultaneously in a highly efficient manner.

A widespread role of the motif environment in transcription factor binding across diverse protein families.

Transcriptional regulation requires the binding of transcription factors (TFs) to short sequence-specific DNA motifs, usually located at the gene regulatory regions. Interestingly, based on a vast amount of data accumulated from genomic assays, it has been shown that only a small fraction of all potential binding sites containing the consensus motif of a given TF actually bind the protein. Recent in vitro binding assays, which exclude the effects of the cellular environment, also demonstrate selective TF binding. An intriguing conjecture is that the surroundings of cognate binding sites have unique characteristics that distinguish them from other sequences containing a similar motif that are not bound by the TF. To test this hypothesis, we conducted a comprehensive analysis of the sequence and DNA shape features surrounding the core-binding sites of 239 and 56 TFs extracted from in vitro HT-SELEX binding assays and in vivo ChIP-seq data, respectively. Comparing the nucleotide content of the regions around the TF-bound sites to the counterpart unbound regions containing the same consensus motifs revealed significant differences that extend far beyond the core-binding site. Specifically, the environment of the bound motifs demonstrated unique sequence compositions, DNA shape features, and overall high similarity to the core-binding motif. Notably, the regions around the binding sites of TFs that belong to the same TF families exhibited similar features, with high agreement between the in vitro and in vivo data sets. We propose that these unique features assist in guiding TFs to their cognate binding sites.

The role of RNA conformation in RNA-protein recognition.

Interactions between protein and RNA play a key role in many biological processes in the gene expression pathway. Those interactions are mediated through a variety of RNA-binding protein domains, among them the highly abundant RNA recognition motif (RRM). Here we studied protein-RNA complexes from different RNA binding domain families solved by NMR and x-ray crystallography. Characterizing the structural properties of the RNA at the binding interfaces revealed an unexpected number of nucleotides with unusual RNA conformations, specifically found in RNA-RRM complexes. Moreover, we observed that the RNA nucleotides that are directly involved in interactions with the RRM domains, via hydrogen bonds and hydrophobic contacts, are significantly enriched with unique RNA conformations. Further examination of the sequences binding the RRM domain showed a preference for G nucleotides in syn conformation to precede or to follow U nucleotides in the anti-conformation, and U nucleotides in C2' endo conformation to precede U and G nucleotides possessing the more common C3' endo conformation. These findings imply a possible mode of RNA recognition by the RRM domains which enables the recognition of a wide variety of different RNA sequences and shapes. Overall, this study suggests an additional way by which the RRM domain recognizes its RNA target, involving a conformational readout.