Annalisa Marsico

PROmiRNA: a new miRNA promoter recognition method uncovers the complex regulation of intronic miRNAs

The regulation of intragenic miRNAs by their own intronic promoters is one of the open problems of miRNA biogenesis. Here, we describe PROmiRNA, a new approach for miRNA promoter annotation based on a semi-supervised statistical model trained on deepCAGE data and sequence features. We validate our results with existing annotation, PolII occupancy data and read coverage from RNA-seq data. Compared to previous methods PROmiRNA increases the detection rate of intronic promoters by 30%, allowing us to perform a large-scale analysis of their genomic features, as well as elucidate their contribution to tissue-specific regulation. PROmiRNA can be downloaded from http://promirna.molgen.mpg.de.

MeMotif: a database of linear motifs in α-helical transmembrane proteins

Membrane proteins are important for many processes in the cell and used as main drug targets. The increasing number of high-resolution structures available makes for the first time a characterization of local structural and functional motifs in α-helical transmembrane proteins possible. MeMotif (http://projects.biotec.tu-dresden.de/memotif) is a database and wiki which collects more than 2000 known and novel computationally predicted linear motifs in α-helical transmembrane proteins. Motifs are fully described in terms of several structural and functional features and editable. Motifs contained in MeMotif can be used in different biological applications, from the identification of biochemically important functional residues which are candidates for mutagenesis experiments to the improvement of tools for transmembrane protein modeling.

A novel pattern recognition algorithm to classify membrane protein unfolding pathways with high-throughput single-molecule force spectroscopy

MOTIVATION Misfolding of membrane proteins plays an important role in many human diseases such as retinitis pigmentosa, hereditary deafness and diabetes insipidus. Little is known about membrane proteins as there are only very few high-resolution structures. Single-molecule force spectroscopy is a novel technique, which measures the force necessary to pull a protein out of a membrane. Such force curves contain valuable information on the protein structure, conformation, and inter- and intra-molecular forces. High-throughput force spectroscopy experiments generate hundreds of force curves including spurious ones and good curves, which correspond to different unfolding pathways. Manual analysis of these data is a bottleneck and source of inconsistent and subjective annotation. RESULTS We propose a novel algorithm for the identification of spurious curves and curves representing different unfolding pathways. Our algorithm proceeds in three stages: first, we reduce noise in the curves by applying dimension reduction; second, we align the curves with dynamic programming and compute pairwise distances and third, we cluster the curves based on these distances. We apply our method to a hand-curated dataset of 135 force curves of bacteriorhodopsin mutant P50A. Our algorithm achieves a success rate of 81% distinguishing spurious from good curves and a success rate of 76% classifying unfolding pathways. As a result, we discuss five different unfolding pathways of bacteriorhodopsin including three main unfolding events and several minor ones. Finally, we link folding barriers to the degree of conservation of residues. Overall, the algorithm tackles the force spectroscopy bottleneck and leads to more consistent and reproducible results paving the way for high-throughput analysis of structural features of membrane proteins.

A long non-coding RNA links calreticulin-mediated immunogenic cell removal to RB1 transcription.

A subset of promoters bidirectionally expresses long non-coding RNAs (ncRNAs) of unknown function and protein-coding genes (PCGs) in parallel. Here, we define a set of 1107 highly conserved human bidirectional promoters that mediate the linked expression of long ncRNAs and PCGs. Depletion of the long ncRNA expressed from the RB1 promoter, ncRNA-RB1, reveals regulatory effects different from the RB1-controlled transcriptional program. ncRNA-RB1 positively regulates the expression of calreticulin (CALR) that in response to certain therapeutic interventions can translocate from the endoplasmic reticulum to the cell surface, hence activating anticancer immune responses. Knockdown of ncRNA-RB1 in tumor cells reduced expression of CALR, impaired the translocation of the protein to the cell surface upon treatment with anthracylines and consequently inhibited the cellular uptake by macrophages. In conclusion, co-transcription of ncRNA-RB1 and RB1 provides a positive link between the expression of the two tumor suppressors RB1 and the immune-relevant CALR protein. This regulatory interplay exemplifies disease-relevant co-regulation of two distinct gene products, in which loss of expression of one oncosuppressor protein entails the abolition of additional tumor-inhibitory mechanisms.

Structural fragment clustering reveals novel structural and functional motifs in α-helical transmembrane proteins

BackgroundA large proportion of an organisms genome encodes for membrane proteins. Membrane proteins are important for many cellular processes, and several diseases can be linked to mutations in them. With the tremendous growth of sequence data, there is an increasing need to reliably identify membrane proteins from sequence, to functionally annotate them, and to correctly predict their topology.ResultsWe introduce a technique called structural fragment clustering, which learns sequential motifs from 3D structural fragments. From over 500,000 fragments, we obtain 213 statistically significant, non-redundant, and novel motifs that are highly specific to α-helical transmembrane proteins. From these 213 motifs, 58 of them were assigned to function and checked in the scientific literature for a biological assessment. Seventy percent of the motifs are found in co-factor, ligand, and ion binding sites, 30% at protein interaction interfaces, and 12% bind specific lipids such as glycerol or cardiolipins. The vast majority of motifs (94%) appear across evolutionarily unrelated families, highlighting the modularity of functional design in membrane proteins. We describe three novel motifs in detail: (1) a dimer interface motif found in voltage-gated chloride channels, (2) a proton transfer motif found in heme-copper oxidases, and (3) a convergently evolved interface helix motif found in an aspartate symporter, a serine protease, and cytochrome b.ConclusionsOur findings suggest that functional modules exist in membrane proteins, and that they occur in completely different evolutionary contexts and cover different binding sites. Structural fragment clustering allows us to link sequence motifs to function through clusters of structural fragments. The sequence motifs can be applied to identify and characterize membrane proteins in novel genomes.

Zebrafish Expression Ontology of Gene Sets (ZEOGS): a tool to analyze enrichment of zebrafish anatomical terms in large gene sets

The zebrafish (Danio rerio) is an established model organism for developmental and biomedical research. It is frequently used for high-throughput functional genomics experiments, such as genome-wide gene expression measurements, to systematically analyze molecular mechanisms. However, the use of whole embryos or larvae in such experiments leads to a loss of the spatial information. To address this problem, we have developed a tool called Zebrafish Expression Ontology of Gene Sets (ZEOGS) to assess the enrichment of anatomical terms in large gene sets. ZEOGS uses gene expression pattern data from several sources: first, in situ hybridization experiments from the Zebrafish Model Organism Database (ZFIN); second, it uses the Zebrafish Anatomical Ontology, a controlled vocabulary that describes connected anatomical structures; and third, the available connections between expression patterns and anatomical terms contained in ZFIN. Upon input of a gene set, ZEOGS determines which anatomical structures are overrepresented in the input gene set. ZEOGS allows one for the first time to look at groups of genes and to describe them in terms of shared anatomical structures. To establish ZEOGS, we first tested it on random gene selections and on two public microarray datasets with known tissue-specific gene expression changes. These tests showed that ZEOGS could reliably identify the tissues affected, whereas only very few enriched terms to none were found in the random gene sets. Next we applied ZEOGS to microarray datasets of 24 and 72 h postfertilization zebrafish embryos treated with beclomethasone, a potent glucocorticoid. This analysis resulted in the identification of several anatomical terms related to glucocorticoid-responsive tissues, some of which were stage-specific. Our studies highlight the ability of ZEOGS to extract spatial information from datasets derived from whole embryos, indicating that ZEOGS could be a useful tool to automatically analyze gene expression pattern features of any large zebrafish gene set.

ssHMM: extracting intuitive sequence-structure motifs from high-throughput RNA-binding protein data

Abstract RNA-binding proteins (RBPs) play an important role in RNA post-transcriptional regulation and recognize target RNAs via sequence-structure motifs. The extent to which RNA structure influences protein binding in the presence or absence of a sequence motif is still poorly understood. Existing RNA motif finders either take the structure of the RNA only partially into account, or employ models which are not directly interpretable as sequence-structure motifs. We developed ssHMM, an RNA motif finder based on a hidden Markov model (HMM) and Gibbs sampling which fully captures the relationship between RNA sequence and secondary structure preference of a given RBP. Compared to previous methods which output separate logos for sequence and structure, it directly produces a combined sequence-structure motif when trained on a large set of sequences. ssHMM’s model is visualized intuitively as a graph and facilitates biological interpretation. ssHMM can be used to find novel bona fide sequence-structure motifs of uncharacterized RBPs, such as the one presented here for the YY1 protein. ssHMM reaches a high motif recovery rate on synthetic data, it recovers known RBP motifs from CLIP-Seq data, and scales linearly on the input size, being considerably faster than MEMERIS and RNAcontext on large datasets while being on par with GraphProt. It is freely available on Github and as a Docker image.

Principles of microRNA Regulation Revealed Through Modeling microRNA Expression Quantitative Trait Loci.

Extensive work has been dedicated to study mechanisms of microRNA-mediated gene regulation. However, the transcriptional regulation of microRNAs themselves is far less well understood, due to difficulties determining the transcription start sites of transient primary transcripts. This challenge can be addressed using expression quantitative trait loci (eQTLs) whose regulatory effects represent a natural source of perturbation of cis-regulatory elements. Here we used previously published cis-microRNA-eQTL data for the human GM12878 cell line, promoter predictions, and other functional annotations to determine the relationship between functional elements and microRNA regulation. We built a logistic regression model that classifies microRNA/SNP pairs into eQTLs or non-eQTLs with 85% accuracy; shows microRNA-eQTL enrichment for microRNA precursors, promoters, enhancers, and transcription factor binding sites; and depletion for repressed chromatin. Interestingly, although there is a large overlap between microRNA eQTLs and messenger RNA eQTLs of host genes, 74% of these shared eQTLs affect microRNA and host expression independently. Considering microRNA-only eQTLs we find a significant enrichment for intronic promoters, validating the existence of alternative promoters for intragenic microRNAs. Finally, in line with the GM12878 cell line derived from B cells, we find genome-wide association (GWA) variants associated to blood-related traits more likely to be microRNA eQTLs than random GWA and non-GWA variants, aiding the interpretation of GWA results.

pysster: classification of biological sequences by learning sequence and structure motifs with convolutional neural networks

Abstract Summary Convolutional neural networks (CNNs) have been shown to perform exceptionally well in a variety of tasks, including biological sequence classification. Available implementations, however, are usually optimized for a particular task and difficult to reuse. To enable researchers to utilize these networks more easily, we implemented pysster, a Python package for training CNNs on biological sequence data. Sequences are classified by learning sequence and structure motifs and the package offers an automated hyper-parameter optimization procedure and options to visualize learned motifs along with information about their positional and class enrichment. The package runs seamlessly on CPU and GPU and provides a simple interface to train and evaluate a network with a handful lines of code. Using an RNA A-to-I editing dataset and cross-linking immunoprecipitation (CLIP)-seq binding site sequences, we demonstrate that pysster classifies sequences with higher accuracy than previous methods, such as GraphProt or ssHMM, and is able to recover known sequence and structure motifs. Availability and implementation pysster is freely available at https://github.com/budach/pysster. Supplementary information Supplementary data are available at Bioinformatics online.

Genome-wide Chromatin Profiling of Legionella pneumophila-Infected Human Macrophages Reveals Activation of the Probacterial Host Factor TNFAIP2.

BACKGROUND Legionella pneumophila is a causative agent of severe pneumonia. Infection leads to a broad host cell response, as evident, for example, on the transcriptional level. Chromatin modifications, which control gene expression, play a central role in the transcriptional response to L. pneumophila METHODS We infected human-blood-derived macrophages (BDMs) with L. pneumophila and used chromatin immunoprecipitation followed by sequencing to screen for gene promoters with the activating histone 4 acetylation mark. RESULTS We found the promoter of tumor necrosis factor α-induced protein 2 (TNFAIP2) to be acetylated at histone H4. This factor has not been characterized in the pathology of L. pneumophila TNFAIP2 messenger RNA and protein were upregulated in response to L. pneumophila infection of human-BDMs and human alveolar epithelial (A549) cells. We showed that L. pneumophila-induced TNFAIP2 expression is dependent on the NF-κB transcription factor. Importantly, knock down of TNFAIP2 led to reduced intracellular replication of L. pneumophila Corby in A549 cells. CONCLUSIONS Taken together, genome-wide chromatin analysis of L. pneumophila-infected macrophages demonstrated induction of TNFAIP2, a NF-κB-dependent factor relevant for bacterial replication.