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Homology-directed repair (HDR) induced by site specific DNA double-strand breaks with CRISPR-Cas9 is a precision gene editing approach that occurs at low frequency in comparison to indel forming non-homologous end joining (NHEJ). In order to obtain high HDR percentages in mammalian cells, we engineered a Cas9 protein fused to a monoavidin domain to bind biotinylated donor DNA. In addition, we used the cationic polymer, polyethylenimine, to deliver Cas9-donor DNA complexes into cells. Improved HDR percentages of up to 90% in three loci tested (CXCR4, EMX1, and TLR) in standard HEK293T cells were observed. Our results suggest that donor DNA biotinylation and Cas9-donor conjugation in addition to delivery influence HDR efficiency.

Cells are highly asymmetrical, a feature that relies on the sorting of molecular constituents, including proteins, lipids, and nucleic acids, to distinct subcellular locales. The localization of RNA molecules is an important layer of gene regulation required to modulate localized cellular activities, although its global prevalence remains unclear. We combine biochemical cell fractionation with RNA-sequencing (CeFra-seq) analysis to assess the prevalence and conservation of RNA asymmetric distribution on a transcriptome-wide scale in Drosophila and human cells. This approach reveals that the majority (∼80%) of cellular RNA species are asymmetrically distributed, whether considering coding or noncoding transcript populations, in patterns that are broadly conserved evolutionarily. Notably, a large number of Drosophila and human long noncoding RNAs and circular RNAs display enriched levels within specific cytoplasmic compartments, suggesting that these RNAs fulfill extra-nuclear functions. Moreover, fraction-specific mRNA populations exhibit distinctive sequence characteristics. Comparative analysis of mRNA fractionation profiles with that of their encoded proteins reveals a general lack of correlation in subcellular distribution, marked by strong cases of asymmetry. However, coincident distribution profiles are observed for mRNA/protein pairs related to a variety of functional protein modules, suggesting complex regulatory inputs of RNA localization to cellular organization., (© 2018 Benoit Bouvrette et al.; Published by Cold Spring Harbor Laboratory Press for the RNA Society.)

Three-dimensional genome organization is an important higher order transcription regulation mechanism that can be studied with the chromosome conformation capture techniques. Here, we combined chromatin organization analysis by chromosome conformation capture-carbon copy, computational modeling and epigenomics to achieve the first integrated view, through time, of a connection between chromatin state and its architecture. We used this approach to examine the chromatin dynamics of the HoxA cluster in a human myeloid leukemia cell line at various stages of differentiation. We found that cellular differentiation involves a transient activation of the 5'-end HoxA genes coinciding with a loss of contacts throughout the cluster, and by specific silencing at the 3'-end with H3K27 methylation. The 3D modeling of the data revealed an extensive reorganization of the cluster between the two previously reported topologically associated domains in differentiated cells. Our results support a model whereby silencing by polycomb group proteins and reconfiguration of CTCF interactions at a topologically associated domain boundary participate in changing the HoxA cluster topology, which compartmentalizes the genes following differentiation.

MicroRNAs (miRNAs) are short RNA species derived from hairpin-forming miRNA precursors (pre-miRNA) and acting as key posttranscriptional regulators. Most computational tools labeled as miRNA predictors are in fact pre-miRNA predictors and provide no information about the putative miRNA location within the pre-miRNA. Sequence and structural features that determine the location of the miRNA, and the extent to which these properties vary from species to species, are poorly understood. We have developed miRdup, a computational predictor for the identification of the most likely miRNA location within a given pre-miRNA or the validation of a candidate miRNA. MiRdup is based on a random forest classifier trained with experimentally validated miRNAs from miRbase, with features that characterize the miRNA-miRNA* duplex. Because we observed that miRNAs have sequence and structural properties that differ between species, mostly in terms of duplex stability, we trained various clade-specific miRdup models and obtained increased accuracy. MiRdup self-trains on the most recent version of miRbase and is easy to use. Combined with existing pre-miRNA predictors, it will be valuable for both de novo mapping of miRNAs and filtering of large sets of candidate miRNAs obtained from transcriptome sequencing projects. MiRdup is open source under the GPLv3 and available at http://www.cs.mcgill.ca/∼blanchem/mirdup/.

The RNA polymerase II (RNAP II)-associated protein (RPAP) 2 has been discovered through its association with various subunits of RNAP II in affinity purification coupled with mass spectrometry experiments. Here, we show that RPAP2 is a mainly cytoplasmic protein that shuttles between the cytoplasm and the nucleus. RPAP2 shuttling is tightly coupled with nuclear import of RNAP II, as RPAP2 silencing provokes abnormal accumulation of RNAP II in the cytoplasmic space. Most notably, RPAP4/GPN1 silencing provokes the retention of RPAP2 in the nucleus. Our results support a model in which RPAP2 enters the nucleus in association with RNAP II and returns to the cytoplasm in association with the GTPase GPN1/RPAP4. Although binding of RNAP II to RPAP2 is mediated by an N-terminal domain (amino acids 1-170) that contains a nuclear retention domain, and binding of RPAP4/GPN1 to RPAP2 occurs through a C-terminal domain (amino acids 156-612) that has a dominant cytoplasmic localization domain. In conjunction with previously published data, our results have important implications, as they indicate that RPAP2 controls gene expression by two distinct mechanisms, one that targets RNAP II activity during transcription and the other that controls availability of RNAP II in the nucleus.

More than a simple carrier of the genetic information, messenger RNA (mRNA) coding regions can also harbor functional elements that evolved to control different post-transcriptional processes, such as mRNA splicing, localization and translation. Functional elements in RNA molecules are often encoded by secondary structure elements. In this aticle, we introduce Structural Profile Assignment of RNA Coding Sequences (SPARCS), an efficient method to analyze the (secondary) structure profile of protein-coding regions in mRNAs. First, we develop a novel algorithm that enables us to sample uniformly the sequence landscape preserving the dinucleotide frequency and the encoded amino acid sequence of the input mRNA. Then, we use this algorithm to generate a set of artificial sequences that is used to estimate the Z-score of classical structural metrics such as the sum of base pairing probabilities and the base pairing entropy. Finally, we use these metrics to predict structured and unstructured regions in the input mRNA sequence. We applied our methods to study the structural profile of the ASH1 genes and recovered key structural elements. A web server implementing this discovery pipeline is available at http://csb.cs.mcgill.ca/sparcs together with the source code of the sampling algorithm.

Affinity purification combined with tandem mass spectrometry (AP-MS/MS) is a well-established method used to discover interaction partners for a given protein of interest. Because most AP-MS/MS approaches are performed using the soluble fraction of whole cell extracts (WCE), information about the cellular compartments where the interactions occur is lost. More importantly, classical AP-MS/MS often fails to identify interactions that take place in the nonsoluble fraction of the cell, for example, on the chromatin or membranes; consequently, protein complexes that are less soluble are underrepresented. In this paper, we introduce a method called multiple cell compartment AP-MS/MS (MCC-AP-MS/MS), which identifies the interactions of a protein independently in three fractions of the cell: the cytoplasm, the nucleoplasm, and the chromatin. We show that this fractionation improves the sensitivity of the method when compared to the classical affinity purification procedure using soluble WCE while keeping a very high specificity. Using three proteins known to localize in various cell compartments as baits, the CDK9 subunit of transcription elongation factor P-TEFb, the RNA polymerase II (RNAP II)-associated protein 4 (RPAP4), and the largest subunit of RNAP II, POLR2A, we show that MCC-AP-MS/MS reproducibly yields fraction-specific interactions. Finally, we demonstrate that this improvement in sensitivity leads to the discovery of novel interactions of RNAP II carboxyl-terminal domain (CTD) interacting domain (CID) proteins with POLR2A.

Background: The "small phylogeny" problem consists in inferring ancestral genomes associated with each internal node of a phylogenetic tree of a set of extant species. Existing methods can be grouped into two main categories: the distance-based methods aiming at minimizing a total branch length, and the synteny-based (or mapping) methods that first predict a collection of relations between ancestral markers in term of "synteny", and then assemble this collection into a set of Contiguous Ancestral Regions (CARs). The predicted CARs are likely to be more reliable as they are more directly deduced from observed conservations in extant species. However the challenge is to end up with a completely assembled genome., Results: We develop a new synteny-based method that is flexible enough to handle a model of evolution involving whole genome duplication events, in addition to rearrangements, gene insertions, and losses. Ancestral relationships between markers are defined in term of Gapped Adjacencies, i.e. pairs of markers separated by up to a given number of markers. It improves on a previous restricted to direct adjacencies, which revealed a high accuracy for adjacency prediction, but with the drawback of being overly conservative, i.e. of generating a large number of CARs. Applying our algorithm on various simulated data sets reveals good performance as we usually end up with a completely assembled genome, while keeping a low error rate., Availability: All source code is available at http://www.iro.umontreal.ca/~mabrouk.

Identification of protein-protein interactions (PPI) by affinity purification (AP) coupled with tandem mass spectrometry (AP-MS/MS) produces large data sets with high rates of false positives. This is in part because of contamination at the AP level (due to gel contamination, nonspecific binding to the TAP columns in the context of tandem affinity purification, insufficient purification, etc.). In this paper, we introduce a Bayesian approach to identify false-positive PPIs involving contaminants in AP-MS/MS experiments. Specifically, we propose a confidence assessment algorithm (called Decontaminator) that builds a model of contaminants using a small number of representative control experiments. It then uses this model to determine whether the Mascot score of a putative prey is significantly larger than what was observed in control experiments and assigns it a p-value and a false discovery rate. We show that our method identifies contaminants better than previously used approaches and results in a set of PPIs with a larger overlap with databases of known PPIs. Our approach will thus allow improved accuracy in PPI identification while reducing the number of control experiments required.

The "Chromosome Conformation Capture" (3C) and 3C-related technologies are used to measure physical contacts between DNA segments at high resolution in vivo. 3C studies indicate that genomes are likely organized into dynamic networks of physical contacts between genes and regulatory DNA elements. These interactions are mediated by proteins and are important for the regulation of genes. For these reasons, mapping physical connectivity networks with 3C-related approaches will be essential to fully understand how genes are regulated. The 3C-Carbon Copy (5C) technology can be used to measure chromatin contacts genome-scale within (cis) or between (trans) chromosomes. Although unquestionably powerful, this approach can be challenging to implement without proper understanding and application of publicly available bioinformatics tools. This chapter explains how 5C studies are performed and describes stepwise how to use currently available bioinformatics tools for experimental design, data analysis, and interpretation.

The ancestral sequence reconstruction problem asks to predict the DNA or protein sequence of an ancestral species, given the sequences of extant species. Such reconstructions are fundamental to comparative genomics, as they provide information about extant genomes and the process of evolution that gave rise to them. Arguably the best method for ancestral reconstruction is maximum likelihood estimation. Many effective algorithms for accurately computing the most likely ancestral sequence have been proposed. We consider the less-studied problem of computing the expected reconstruction error of a maximum likelihood reconstruction, given the phylogenetic tree and model of evolution, but not the extant sequences. This situation can arise, for example, when deciding which genomes to sequence for a reconstruction project given a gene-tree phylogeny (The Taxon Selection Problem). In most applications, the reconstruction error is necessarily very small, making Monte Carlo simulations very inefficient for accurate estimation. We present the first practical algorithm for this problem and demonstrate how it can be used to quickly and accurately estimate the reconstruction accuracy. We then use our method as a kernel in a heuristic algorithm for the taxon selection problem. The implementation is available at http://www.mcb.mcgill.ca/ blanchem/mlerror.

Background: The availability of newly sequenced vertebrate genomes, along with more efficient and accurate alignment algorithms, have enabled the expansion of the field of comparative genomics. Large-scale genome rearrangement events modify the order of genes and non-coding conserved regions on chromosomes. While certain large genomic regions have remained intact over much of vertebrate evolution, others appear to be hotspots for genomic breakpoints. The cause of the non-uniformity of breakpoints that occurred during vertebrate evolution is poorly understood., Results: We describe a machine learning method to distinguish genomic regions where breakpoints would be expected to have deleterious effects (called breakpoint-refractory regions) from those where they are expected to be neutral (called breakpoint-susceptible regions). Our predictor is trained using breakpoints that took place along the human lineage since amniote divergence. Based on our predictions, refractory and susceptible regions have very distinctive features. Refractory regions are significantly enriched for conserved non-coding elements as well as for genes involved in development, whereas susceptible regions are enriched for housekeeping genes, likely to have simpler transcriptional regulation., Conclusion: We postulate that long-range transcriptional regulation strongly influences chromosome break fixation. In many regions, the fitness cost of altering the spatial association between long-range regulatory regions and their target genes may be so high that rearrangements are not allowed. Consequently, only a limited, identifiable fraction of the genome is susceptible to genome rearrangements.

Motivation: The computational identification of transcription factor binding sites is a major challenge in bioinformatics and an important complement to experimental approaches., Results: We describe a novel, exact discriminative seeding DNA motif discovery algorithm designed for fast and reliable prediction of cis-regulatory elements in eukaryotic promoters. The algorithm is tested on biological benchmark data and shown to perform equally or better than other motif discovery tools. The algorithm is applied to the analysis of plant tissue-specific promoter sequences and successfully identifies key regulatory elements.

Orphan nuclear receptor ERRalpha (NR3B1) is recognized as a key regulator of mitochondrial biogenesis, but it is not known whether ERRalpha and other ERR isoforms play a broader role in cardiac energetics and function. We used genome-wide location analysis and expression profiling to appraise the role of ERRalpha and gamma (NR3B3) in the adult heart. Our data indicate that the two receptors, acting as nonobligatory heterodimers, target a common set of promoters involved in the uptake of energy substrates, production and transport of ATP across the mitochondrial membranes, and intracellular fuel sensing, as well as Ca(2+) handling and contractile work. Motif-finding algorithms assisted by functional studies indicated that ERR target promoters are enriched for NRF-1, CREB, and STAT3 binding sites. Our study thus reveals that the ERRs orchestrate a comprehensive cardiac transcriptional program and further suggests that modulation of ERR activities could be used to manage cardiomyopathies.

Background: In vertebrates, a large part of gene transcriptional regulation is operated by cis-regulatory modules. These modules are believed to be regulating much of the tissue-specificity of gene expression., Result: We develop a Bayesian network approach for identifying cis-regulatory modules likely to regulate tissue-specific expression. The network integrates predicted transcription factor binding site information, transcription factor expression data, and target gene expression data. At its core is a regression tree modeling the effect of combinations of transcription factors bound to a module. A new unsupervised EM-like algorithm is developed to learn the parameters of the network, including the regression tree structure., Conclusion: Our approach is shown to accurately identify known human liver and erythroid-specific modules. When applied to the prediction of tissue-specific modules in 10 different tissues, the network predicts a number of important transcription factor combinations whose concerted binding is associated to specific expression.

Our understanding of mechanisms by which the expression of IFN-gamma is regulated is limited. Herein, we identify two evolutionarily conserved noncoding sequence elements (IFNgCNS1 and IFNg CNS2) located approximately 5 kb upstream and approximately 18 kb downstream of the initiation codon of the murine Ifng gene. When linked to the murine Ifng gene (-3.4 to +5.6 kb) and transiently transfected into EL-4 cells, these elements clearly enhanced IFN-gamma expression in response to ionomycin and phorbol 12-myristate 13-acetate and weakly enhanced expression in response to T-bet. A DNase I hypersensitive site and extragenic transcripts at IFNgCNS2 correlated positively with the capacity of primary T cell subsets to produce IFN-gamma. Transcriptionally favorable histone modifications in the Ifng promoter, intronic regions, IFNgCNS2, and, although less pronounced, IFNgCNS1 increased as naïve T cells differentiated into IFN-gamma-producing effector CD8+ and T helper (TH) 1 T cells, but not into TH2 T cells. Like IFN-gamma expression, these histone modifications were T-bet-dependent in CD4+ cells, but not CD8+ T cells. These findings define two distal regulatory elements associated with T cell subset-specific IFN-gamma expression.

Functional RNA molecules typically have structural patterns that are highly conserved in evolution. Here we present an algorithmic method for multiple alignment of RNAs, taking into consideration both structural similarity and sequence identity. Furthermore, our window-sized comparative analysis corrects the misaligned structure within a distance threshold and identifies the conserved substructures. Based on this new algorithm, StructMiner outperforms existing approaches, which ignore structure information for the alignment and lack the effective means to adjust the misalignments in the analysis phase. In addition, StructMiner is efficient in terms of CPU time and memory usage, making it suitable for structural analysis of very long sequences.