Large-scale DNA sequencing identifies rare variants associated with Systemic Lupus Erythematosus susceptibility in known risk genes

The identification of rare genetic variants associated to Systemic Lupus Erythematosus (SLE) could also help to understand the pathogenic mechanisms at the basis of the disease. In this study we have analyzed a cohort of 200 Italian SLE patients in order to explore the rare protein-coding variants in five genes (TNFAIP3, STAT4, IL10, TRAF3IP2, and HCP5) already investigated for commons variants found associated in our previous studies. Genomic DNA of 200 SLE patients was sequenced by whole exome sequencing. The identified variants were filtered by frequency and evaluated by in silico predictions. Allelic association analysis was performed with standard Fisher's exact test. Introducing a cutoff at MAF < 0.01, a total of 19 rare variants were identified. Seven of these variants were ultra-rare (MAF < 0.001) and six were absent in the GnomAD database. For TNFAIP3 gene, the variant c.A1939C was observed in 4 SLE patients and it is located in a region enriched in phosphorylation sites and affects the predict affinity of specific kinases. In TRAF3IP2 gene, we observed 5 different rare variants, including the novel variant c.G410A, located in the region that mediates interaction with TRAF6, and therefore a possible risk factor for SLE development. In STAT4 gene, we identified 6 different rare variants. Among these, three missense variants decrease the stability of this protein. Moreover, 3 novel rare variants were detected in 3 SLE patients. In particular, c.A767T variant was predicted as damaging by six prediction tools. Concluding, we have observed that even in genes whose common variability is associated with SLE susceptibility, it is possible to identify rare variants that could have a strong effect in the disease development and could therefore allow a better understanding of the functional domain involved.

A Meta-Analysis Approach to Gene Regulatory Network Inference Identifies Key Regulators of Cardiovascular Diseases

Cardiovascular diseases (CVDs) represent a major concern for global health, whose mechanistic understanding is complicated by a complex interplay between genetic predisposition and environmental factors. Specifically, heart failure (HF), encompassing dilated cardiomyopathy (DC), ischemic cardiomyopathy (ICM), and hypertrophic cardiomyopathy (HCM), is a topic of substantial interest in basic and clinical research. Here, we used a Partial Correlation Coefficient-based algorithm (PCC) within the context of a meta-analysis framework to construct a Gene Regulatory Network (GRN) that identifies key regulators whose activity is perturbed in Heart Failure. By integrating data from multiple independent studies, our approach unveiled crucial regulatory associations between transcription factors (TFs) and structural genes, emphasizing their pivotal roles in regulating metabolic pathways, such as fatty acid metabolism, oxidative stress response, epithelial-to-mesenchymal transition, and coagulation. In addition to known associations, our analysis also identified novel regulators, including the identification of TFs FPM315 and OVOL2, which are implicated in dilated cardiomyopathies, and TEAD1 and TEAD2 in both dilated and ischemic cardiomyopathies. Moreover, we uncovered alterations in adipogenesis and oxidative phosphorylation pathways in hypertrophic cardiomyopathy and discovered a role for IL2 STAT5 signaling in heart failure. Our findings underscore the importance of TF activity in the initiation and progression of cardiac disease, highlighting their potential as pharmacological targets.

NRF2 connects Src tyrosine kinase to ferroptosis resistance in glioblastoma

Glioblastoma is a severe brain tumor characterized by an extremely poor survival rate of patients. Glioblastoma cancer cells escape to standard therapeutic protocols consisting of a combination of ionizing radiation and temozolomide alkylating drugs that trigger DNA damage by rewiring of signaling pathways. In recent years, the up-regulation of factors that counteract ferroptosis has been highlighted as a major driver of cancer resistance to ionizing radiation, although the molecular connection between the activation of oncogenic signaling and the modulation of ferroptosis has not been clarified yet. Here, we provide the first evidence for a molecular connection between the constitutive activation of tyrosine kinases and resistance to ferroptosis. Src tyrosine kinase, a central hub on which deregulated receptor tyrosine kinase signaling converge in cancer, leads to the stabilization and activation of NRF2 pathway, thus promoting resistance to ionizing radiation-induced ferroptosis. These data suggest that the up-regulation of the Src-NRF2 axis may represent a vulnerability for combined strategies that, by targeting ferroptosis resistance, enhance radiation sensitivity in glioblastoma.

Inhibition of exosome biogenesis affects cell motility in heterogeneous sub-populations of paediatric-type diffuse high-grade gliomas

BACKGROUND

Paediatric-type diffuse High-Grade Gliomas (PDHGG) are highly heterogeneous tumours which include distinct cell sub-populations co-existing within the same tumour mass. We have previously shown that primary patient-derived and optical barcoded single-cell-derived clones function as interconnected networks. Here, we investigated the role of exosomes as a route for inter-clonal communication mediating PDHGG migration and invasion.

RESULTS

A comprehensive characterisation of seven optical barcoded single-cell-derived clones obtained from two patient-derived cell lines was performed. These analyses highlighted extensive intra-tumour heterogeneity in terms of genetic and transcriptional profiles between clones as well as marked phenotypic differences including distinctive motility patterns. Live single-cell tracking analysis of 3D migration and invasion assays showed that the single-cell-derived clones display a higher speed and longer travelled distance when in co-culture compared to mono-culture conditions. To determine the role of exosomes in PDHGG inter-clonal cross-talks, we isolated exosomes released by different clones and characterised them in terms of marker expression, size and concentration. We demonstrated that exosomes are actively internalized by the cells and that the inhibition of their biogenesis, using the phospholipase inhibitor GW4689, significantly reduced the cell motility in mono-culture and more prominently when the cells from the clones were in co-culture. Analysis of the exosomal miRNAs, performed with a miRNome PCR panel, identified clone-specific miRNAs and a set of miRNA target genes involved in the regulation of cell motility/invasion/migration. These genes were found differentially expressed in co-culture versus mono-culture conditions and their expression levels were significantly modulated upon inhibition of exosome biogenesis.

CONCLUSIONS

In conclusion, our study highlights for the first time a key role for exosomes in the inter-clonal communication in PDHGG and suggests that interfering with the exosome biogenesis pathway may be a valuable strategy to inhibit cell motility and dissemination for these specific diseases.

Exploring the landscape of tools and resources for the analysis of long non-coding RNAs

In recent years, research on long non-coding RNAs (lncRNAs) has gained considerable attention due to the increasing number of newly identified transcripts. Several characteristics make their functional evaluation challenging, which called for the urgent need to combine molecular biology with other disciplines, including bioinformatics. Indeed, the recent development of computational pipelines and resources has greatly facilitated both the discovery and the mechanisms of action of lncRNAs. In this review, we present a curated collection of the most recent computational resources, which have been categorized into distinct groups: databases and annotation, identification and classification, interaction prediction, and structure prediction. As the repertoire of lncRNAs and their analysis tools continues to expand over the years, standardizing the computational pipelines and improving the existing annotation of lncRNAs will be crucial to facilitate functional genomics studies.

Looking for Plant microRNAs in Human Blood Samples: Bioinformatics Evidence and Perspectives

Literature has proposed the existence of a cross kingdom regulation (CRK) between human and plants. In this context, microRNAs present in edible plants would be acquired through diet by the consumer's organism and transported via bloodstream to tissues, where they would modulate gene expression. However, the validity of this phenomenon is strongly debated; indeed, some scholars have discussed both the methodologies and the results obtained in previous works. To date, only one study has performed a bioinformatics analysis on small RNA-sequencing data for checking the presence of plant miRNAs (pmiRNAs) in human plasma. For that investigation, the lack of reliable controls, which led to the misidentification of human RNAs as pmiRNAs, has been deeply criticized. Thus, in the present contribution, we aim to demonstrate the existence of pmiRNAs in human blood, adopting a bioinformatics approach characterized by more stringent conditions and filtering. The information obtained from 380 experiments produced in 5 different next generation sequencing (NGS) projects was examined, revealing the presence of 350 circulating pmiRNAs across the analysed data set. Although one of the NGS projects shows results likely to be attributed to sample contamination, the others appear to provide reliable support for the acquisition of pmiRNAs through diet. To infer the potential biological activity of the identified pmiRNAs, we predicted their putative human mRNA targets, finding with great surprise that they appear to be mainly involved in neurogenesis and nervous system development. Unfortunately, no consensus was identified within the sequences of detected pmiRNAs, in order to justify their stability or capability to be preserved in human plasma. We believe that the issue regarding CKR still needs further clarifications, even if the present findings would offer a solid support that this hypothesis is not impossible.

Artificial intelligence methods enhance the discovery of RNA interactions

Understanding how RNAs interact with proteins, RNAs, or other molecules remains a challenge of main interest in biology, given the importance of these complexes in both normal and pathological cellular processes. Since experimental datasets are starting to be available for hundreds of functional interactions between RNAs and other biomolecules, several machine learning and deep learning algorithms have been proposed for predicting RNA-RNA or RNA-protein interactions. However, most of these approaches were evaluated on a single dataset, making performance comparisons difficult. With this review, we aim to summarize recent computational methods, developed in this broad research area, highlighting feature encoding and machine learning strategies adopted. Given the magnitude of the effect that dataset size and quality have on performance, we explored the characteristics of these datasets. Additionally, we discuss multiple approaches to generate datasets of negative examples for training. Finally, we describe the best-performing methods to predict interactions between proteins and specific classes of RNA molecules, such as circular RNAs (circRNAs) and long non-coding RNAs (lncRNAs), and methods to predict RNA-RNA or RNA-RBP interactions independently of the RNA type.

Type I IFNs promote cancer cell stemness by triggering the epigenetic regulator KDM1B

Cancer stem cells (CSCs) are a subpopulation of cancer cells endowed with high tumorigenic, chemoresistant and metastatic potential. Nongenetic mechanisms of acquired resistance are increasingly being discovered, but molecular insights into the evolutionary process of CSCs are limited. Here, we show that type I interferons (IFNs-I) function as molecular hubs of resistance during immunogenic chemotherapy, triggering the epigenetic regulator demethylase 1B (KDM1B) to promote an adaptive, yet reversible, transcriptional rewiring of cancer cells towards stemness and immune escape. Accordingly, KDM1B inhibition prevents the appearance of IFN-I-induced CSCs, both in vitro and in vivo. Notably, IFN-I-induced CSCs are heterogeneous in terms of multidrug resistance, plasticity, invasiveness and immunogenicity. Moreover, in breast cancer (BC) patients receiving anthracycline-based chemotherapy, KDM1B positively correlated with CSC signatures. Our study identifies an IFN-I → KDM1B axis as a potent engine of cancer cell reprogramming, supporting KDM1B targeting as an attractive adjunctive to immunogenic drugs to prevent CSC expansion and increase the long-term benefit of therapy.

PDBe-KB: collaboratively defining the biological context of structural data

The Protein Data Bank in Europe - Knowledge Base (PDBe-KB, https://pdbe-kb.org) is an open collaboration between world-leading specialist data resources contributing functional and biophysical annotations derived from or relevant to the Protein Data Bank (PDB). The goal of PDBe-KB is to place macromolecular structure data in their biological context by developing standardised data exchange formats and integrating functional annotations from the contributing partner resources into a knowledge graph that can provide valuable biological insights. Since we described PDBe-KB in 2019, there have been significant improvements in the variety of available annotation data sets and user functionality. Here, we provide an overview of the consortium, highlighting the addition of annotations such as predicted covalent binders, phosphorylation sites, effects of mutations on the protein structure and energetic local frustration. In addition, we describe a library of reusable web-based visualisation components and introduce new features such as a bulk download data service and a novel superposition service that generates clusters of superposed protein chains weekly for the whole PDB archive.

Dissecting the Genome for Drug Response Prediction

The prediction of the cancer cell lines sensitivity to a specific treatment is one of the current challenges in precision medicine. With omics and pharmacogenomics data being available for over 1000 cancer cell lines, several machine learning and deep learning algorithms have been proposed for drug sensitivity prediction. However, deciding which omics data to use and which computational methods can efficiently incorporate data from different sources is the challenge which several research groups are working on. In this review, we summarize recent advances in the representative computational methods that have been developed in the last 2 years on three public datasets: COSMIC, CCLE, NCI-60. These methods aim to improve the prediction of the cancer cell lines sensitivity to a given treatment by incorporating drug's chemical information in the input or using a priori feature selection. Finally, we discuss the latest published method which aims to improve the prediction of clinical drug response of real patients starting from cancer cell line molecular profiles.

Early life adversity affecting the attachment bond alters ventral tegmental area transcriptomic patterning and behavior almost exclusively in female mice

Early life experiences that affect the attachment bond formation can alter developmental trajectories and result in pathological outcomes in a sex-related manner. However, the molecular basis of sex differences is quite unknown. The dopaminergic system originating from the ventral tegmental area has been proposed to be a key mediator of this process. Here we exploited a murine model of early adversity (Repeated Cross Fostering, RCF) to test how interfering with the attachment bond formation affects the VTA-related functions in a sex-specific manner. Through a comprehensive behavioral screening, within the NiH RDoC framework, and by next-generation RNA-Seq experiments, we analyzed the long-lasting effect of RCF on behavioral and transcriptional profiles related to the VTA, across two different inbred strains of mouse in both sexes. We found that RCF impacted to an extremely greater extent VTA-related behaviors in females than in males and this result mirrored the transcriptional alterations in the VTA that were almost exclusively observed in females. The sexual dimorphism was conserved across two different inbred strains in spite of their divergent long lasting consequences of RCF exposure. Our data suggest that to be female primes a sub-set of genes to respond to early environmental perturbations. This is, to the best of our knowledge, the first evidence of an almost exclusive effect of early life experiences on females, thus mirroring the extremely stronger impact of precocious aversive events reported in clinical studies in women.

COTAN: scRNA-seq data analysis based on gene co-expression

Estimating the co-expression of cell identity factors in single-cell is crucial. Due to the low efficiency of scRNA-seq methodologies, sensitive computational approaches are critical to accurately infer transcription profiles in a cell population. We introduce COTAN, a statistical and computational method, to analyze the co-expression of gene pairs at single cell level, providing the foundation for single-cell gene interactome analysis. The basic idea is studying the zero UMI counts’ distribution instead of focusing on positive counts; this is done with a generalized contingency tables framework. COTAN can assess the correlated or anti-correlated expression of gene pairs, providing a new correlation index with an approximate p-value for the associated test of independence. COTAN can evaluate whether single genes are differentially expressed, scoring them with a newly defined global differentiation index. Similarly to correlation network analysis, it provides ways to plot and cluster genes according to their co-expression pattern with other genes, effectively helping the study of gene interactions, becoming a new tool to identify cell-identity markers. We assayed COTAN on two neural development datasets with very promising results. COTAN is an R package that complements the traditional single cell RNA-seq analysis and it is available at https://github.com/seriph78/COTAN.

BRIO: a web server for RNA sequence and structure motif scan

The interaction between RNA and RNA-binding proteins (RBPs) has a key role in the regulation of gene expression, in RNA stability, and in many other biological processes. RBPs accomplish these functions by binding target RNA molecules through specific sequence and structure motifs. The identification of these binding motifs is therefore fundamental to improve our knowledge of the cellular processes and how they are regulated. Here, we present BRIO (BEAM RNA Interaction mOtifs), a new web server designed for the identification of sequence and structure RNA-binding motifs in one or more RNA molecules of interest. BRIO enables the user to scan over 2508 sequence motifs and 2296 secondary structure motifs identified in Homo sapiens and Mus musculus, in three different types of experiments (PAR-CLIP, eCLIP, HITS). The motifs are associated with the binding of 186 RBPs and 69 protein domains. The web server is freely available at http://brio.bio.uniroma2.it.

Motif Discovery from CLIP Experiments

RNA primary and secondary motif discovery is an important step in the annotation and characterization of unknown interaction dynamics between RNAs and RNA-Binding Proteins, and several methods have been developed to meet the need of fast and efficient discovery of interaction motifs. Recent advances have increased the amount of data produced by experimental assays and there is no available method suitable for the analysis of all type of results. Here we present a simple workflow to help choosing the more appropriate method, depending on the starting situation, among the three algorithms that best cover the landscape of approaches. A detailed analysis is presented to highlight the need for different algorithms in different working settings. In conclusion, the proposed workflow depends on the nature of the starting data and on the availability of RNA annotations.

A eutherian-specific microRNA controls the translation of Satb2 in a model of cortical differentiation

Cerebral cortical development is controlled by key transcription factors that specify the neuronal identities in the different layers. The mechanisms controlling their expression in distinct cells are only partially known. We investigated the expression and stability of Tbr1, Bcl11b, Fezf2, Satb2, and Cux1 mRNAs in single developing mouse cortical cells. We observe that Satb2 mRNA appears much earlier than its protein and in a set of cells broader than expected, suggesting an initial inhibition of its translation, subsequently released during development. Mechanistically, Satb2 3'UTR modulates protein translation of GFP reporters during mouse corticogenesis. We select miR-541, a eutherian-specific miRNA, and miR-92a/b as the best candidates responsible for SATB2 inhibition, being strongly expressed in early and reduced in late progenitor cells. Their inactivation triggers robust and premature SATB2 translation in both mouse and human cortical cells. Our findings indicate RNA interference as a major mechanism in timing cortical cell identities.

Relative Information Gain: Shannon entropy-based measure of the relative structural conservation in RNA alignments

Structural characterization of RNAs is a dynamic field, offering many modelling possibilities. RNA secondary structure models are usually characterized by an encoding that depicts structural information of the molecule through string representations or graphs. In this work, we provide a generalization of the BEAR encoding (a context-aware structural encoding we previously developed) by expanding the set of alignments used for the construction of substitution matrices and then applying it to secondary structure encodings ranging from fine-grained to more coarse-grained representations. We also introduce a re-interpretation of the Shannon Information applied on RNA alignments, proposing a new scoring metric, the Relative Information Gain (RIG). The RIG score is available for any position in an alignment, showing how different levels of detail encoded in the RNA representation can contribute differently to convey structural information. The approaches presented in this study can be used alongside state-of-the-art tools to synergistically gain insights into the structural elements that RNAs and RNA families are composed of. This additional information could potentially contribute to their improvement or increase the degree of confidence in the secondary structure of families and any set of aligned RNAs.

COVID-19 and Genetic Variants of Protein Involved in the SARS-CoV-2 Entry into the Host Cells

The recent global COVID-19 public health emergency is caused by SARS-CoV-2 infections and can manifest extremely variable clinical symptoms. Host human genetic variability could influence susceptibility and response to infection. It is known that ACE2 acts as a receptor for this pathogen, but the viral entry into the target cell also depends on other proteins. The aim of this study was to investigate the variability of genes coding for these proteins involved in the SARS-CoV-2 entry into the cells. We analyzed 131 COVID-19 patients by exome sequencing and examined the genetic variants of TMPRSS2, PCSK3, DPP4, and BSG genes. In total we identified seventeen variants. In PCSK3 gene, we observed a missense variant (c.893G>A) statistically more frequent compared to the EUR GnomAD reference population and a missense mutation (c.1906A>G) not found in the GnomAD database. In TMPRSS2 gene, we observed a significant difference in the frequency of c.331G>A, c.23G>T, and c.589G>A variant alleles in COVID-19 patients, compared to the corresponding allelic frequency in GnomAD. Genetic variants in these genes could influence the entry of the SARS-CoV-2. These data also support the hypothesis that host genetic variability may contribute to the variability in infection susceptibility and severity.

A community proposal to integrate structural bioinformatics activities in ELIXIR (3D-Bioinfo Community)

Structural bioinformatics provides the scientific methods and tools to analyse, archive, validate, and present the biomolecular structure data generated by the structural biology community. It also provides an important link with the genomics community, as structural bioinformaticians also use the extensive sequence data to predict protein structures and their functional sites. A very broad and active community of structural bioinformaticians exists across Europe, and 3D-Bioinfo will establish formal platforms to address their needs and better integrate their activities and initiatives. Our mission will be to strengthen the ties with the structural biology research communities in Europe covering life sciences, as well as chemistry and physics and to bridge the gap between these researchers in order to fully realize the potential of structural bioinformatics. Our Community will also undertake dedicated educational, training and outreach efforts to facilitate this, bringing new insights and thus facilitating the development of much needed innovative applications e.g. for human health, drug and protein design. Our combined efforts will be of critical importance to keep the European research efforts competitive in this respect.

Here we highlight the major European contributions to the field of structural bioinformatics, the most pressing challenges remaining and how Europe-wide interactions, enabled by ELIXIR and its platforms, will help in addressing these challenges and in coordinating structural bioinformatics resources across Europe. In particular, we present recent activities and future plans to consolidate an ELIXIR 3D-Bioinfo Community in structural bioinformatics and propose means to develop better links across the community. These include building new consortia, organising workshops to establish data standards and seeking community agreement on benchmark data sets and strategies. We also highlight existing and planned collaborations with other ELIXIR Communities and other European infrastructures, such as the structural biology community supported by Instruct-ERIC, with whom we have synergies and overlapping common interests.

PDBe-KB: a community-driven resource for structural and functional annotations

The Protein Data Bank in Europe-Knowledge Base (PDBe-KB, https://pdbe-kb.org) is a community-driven, collaborative resource for literature-derived, manually curated and computationally predicted structural and functional annotations of macromolecular structure data, contained in the Protein Data Bank (PDB). The goal of PDBe-KB is two-fold: (i) to increase the visibility and reduce the fragmentation of annotations contributed by specialist data resources, and to make these data more findable, accessible, interoperable and reusable (FAIR) and (ii) to place macromolecular structure data in their biological context, thus facilitating their use by the broader scientific community in fundamental and applied research. Here, we describe the guidelines of this collaborative effort, the current status of contributed data, and the PDBe-KB infrastructure, which includes the data exchange format, the deposition system for added value annotations, the distributable database containing the assembled data, and programmatic access endpoints. We also describe a series of novel web-pages-the PDBe-KB aggregated views of structure data-which combine information on macromolecular structures from many PDB entries. We have recently released the first set of pages in this series, which provide an overview of available structural and functional information for a protein of interest, referenced by a UniProtKB accession.

Discovering sequence and structure landscapes in RNA interaction motifs

RNA molecules are able to bind proteins, DNA and other small or long RNAs using information at primary, secondary or tertiary structure level. Recent techniques that use cross-linking and immunoprecipitation of RNAs can detect these interactions and, if followed by high-throughput sequencing, molecules can be analysed to find recurrent elements shared by interactors, such as sequence and/or structure motifs. Many tools are able to find sequence motifs from lists of target RNAs, while others focus on structure using different approaches to find specific interaction elements. In this work, we make a systematic analysis of RBP-RNA and RNA-RNA datasets to better characterize the interaction landscape with information about multi-motifs on the same RNAs. To achieve this goal, we updated our BEAM algorithm to combine both sequence and structure information to create pairs of patterns that model motifs of interaction. This algorithm was applied to several RNA binding proteins and ncRNAs interactors, confirming already known motifs and discovering new ones. This landscape analysis on interaction variability reflects the diversity of target recognition and underlines that often both primary and secondary structure are involved in molecular recognition.

Kinome-wide identification of phosphorylation networks in eukaryotic proteomes

Motivation

Signaling and metabolic pathways are finely regulated by a network of protein phosphorylation events. Unraveling the nature of this intricate network, composed of kinases, target proteins and their interactions, is therefore of crucial importance. Although thousands of kinase-specific phosphorylations (KsP) have been annotated in model organisms their kinase-target network is far from being complete, with less studied organisms lagging behind.

Results

In this work, we achieved an automated and accurate identification of kinase domains, inferring the residues that most likely contribute to peptide specificity. We integrated this information with the target peptides of known human KsP to predict kinase-specific interactions in other eukaryotes through a deep neural network, outperforming similar methods. We analyzed the differential conservation of kinase specificity among eukaryotes revealing the high conservation of the specificity of tyrosine kinases. With this approach we discovered 1590 novel KsP of potential clinical relevance in the human proteome.

Availability and implementation

http://akid.bio.uniroma2.it

Supplementary information

Supplementary data are available at Bioinformatics online.

Modeling cancer drug response through drug-specific informative genes

Recent advances in pharmacogenomics have generated a wealth of data of different types whose analysis have helped in the identification of signatures of different cellular sensitivity/resistance responses to hundreds of chemical compounds. Among the different data types, gene expression has proven to be the more successful for the inference of drug response in cancer cell lines. Although effective, the whole transcriptome can introduce noise in the predictive models, since specific mechanisms are required for different drugs and these realistically involve only part of the proteins encoded in the genome. We analyzed the pharmacogenomics data of 961 cell lines tested with 265 anti-cancer drugs and developed different machine learning approaches for dissecting the genome systematically and predict drug responses using both drug-unspecific and drug-specific genes. These methodologies reach better response predictions for the vast majority of the screened drugs using tens to few hundreds genes specific to each drug instead of the whole genome, thus allowing a better understanding and interpretation of drug-specific response mechanisms which are not necessarily restricted to the drug known targets.

BEAM web server: a tool for structural RNA motif discovery

Motivation

RNA structural motif finding is a relevant problem that becomes computationally hard when working on high-throughput data (e.g. eCLIP, PAR-CLIP), often represented by thousands of RNA molecules. Currently, the BEAM server is the only web tool capable to handle tens of thousands of RNA in input with a motif discovery procedure that is only limited by the current secondary structure prediction accuracies.

Results

The recently developed method BEAM (BEAr Motifs finder) can analyze tens of thousands of RNA molecules and identify RNA secondary structure motifs associated to a measure of their statistical significance. BEAM is extremely fast thanks to the BEAR encoding that transforms each RNA secondary structure in a string of characters. BEAM also exploits the evolutionary knowledge contained in a substitution matrix of secondary structure elements, extracted from the RFAM database of families of homologous RNAs. The BEAM web server has been designed to streamline data pre-processing by automatically handling folding and encoding of RNA sequences, giving users a choice for the preferred folding program. The server provides an intuitive and informative results page with the list of secondary structure motifs identified, the logo of each motif, its significance, graphic representation and information about its position in the RNA molecules sharing it.

Availability and implementation

The web server is freely available at http://beam.uniroma2.it/ and it is implemented in NodeJS and Python with all major browsers supported.

Supplementary information

Supplementary data are available at Bioinformatics online.

c-MYC inhibition impairs hypoxia response in glioblastoma multiforme

The c-MYC oncoprotein is a DNA binding transcription factor that enhances the expression of many active genes. c-MYC transcriptional signatures vary according to the transcriptional program defined in each cell type during differentiation. Little is known on the involvement of c-MYC in regulation of gene expression programs that are induced by extracellular cues such as a changing microenvironment. Here we demonstrate that inhibition of c-MYC in glioblastoma multiforme cells blunts hypoxia-dependent glycolytic reprogramming and mitochondria fragmentation in hypoxia. This happens because c-MYC inhibition alters the cell transcriptional response to hypoxia and finely tunes the expression of a subset of Hypoxia Inducible Factor 1-regulated genes. We also show that genes whose expression in hypoxia is affected by c-MYC inhibition are able to distinguish the Proneural subtype of glioblastoma multiforme, thus potentially providing a molecular signature for this class of tumors that are the least tractable among glioblastomas.

Identification of a Putative Binding Site for Negatively Charged Surfaces in the Fibronectin Type II Domain of Human Factor XII

Monoclonal antibodies directed against functional sites of proteins provide useful tools for structure-function studies. Here we describe a mAb, KOK5, directed against the heavy chain region of human coagulation factor XII (FXII), which inhibits kaolin-induced clotting activity by preventing the binding of FXII to kaolin. Furthermore, mAb KOK5 enhances FXII susceptibility for cleavage by kallikrein and supports FXII autoactivation. Hence, mAb KOK5 likely is directed against the binding site of FXII for negatively charged surfaces. Screening of two phage-displayed random peptide libraries with mAb KOK5 selected phages that could be grouped on the basis of two amino acid consensus sequences: A) FXFQTPXW and B) HQ/LCTHR/KKC. Sequence A contains two motifs: one shares homology with FXII amino acid residues 30-33 (FPFQ), the second one with residues 57-60 (TPNF); both amino acid stretches belonging to the fibronectin type II domain of FXII. Sequence B also reveals homology with part of the fibronectin type II domain, i.e. the stretch 40-47 (HKCTHKGR). A three-dimensional model of FXII residues 28-65, obtained by homology modeling, indicated that the three amino acid stretches 30-33, 40-47 and 57-60 are close to each other and accessible for the solvent, i.e. in a form available for interaction with the monoclonal antibody, suggesting that mAb KOK5 recognizes a discontinuous epitope on the fibronectin type II domain of FXII. Peptides corresponding to FXII sequences 29-37 (FXII29-37) or 39-47 (FXII39-47), were synthesized and tested for the capability to inhibit FXII binding to negatively charged surfaces. Peptide FXII39-47 inhibited the binding of labeled FXII to kaolin and effectively prevented both dextran sulfate-and kaolin-induced activation of the contact system in plasma. Hence, we suggest that the fibronectin type II domain of FXII, in particular residues 39 to 47, contribute to the binding site of FXII for negatively charged surfaces.

Structural Defects of Laminin β3 N-terminus Underlie Junctional Epidermolysis Bullosa with Altered Granulation Tissue Response

Mutations in the laminin-332 (α3Aβ3γ2) genes cause junctional epidermolysis bullosa (JEB), a recessively inherited disease characterized by blistering and altered wound repair. In addition, specific mutations that affect the N-terminus of the α3A chain cause a JEB-related non-blistering condition characterized by chronic production of granulation tissue, suggesting a critical role of this region in epithelial-mesenchymal communication. We report here a 9-year-old patient with JEB with a few long-standing skin ulcers with prominent granulation tissue in the absence of active blistering. He bears a homozygous missense mutation, p.Gly254Asp, within the first laminin epidermal growth factor-like (LE) repeat of the β3 short arm. We show that p.Gly254Asp causes mis-folding of the LE motif, leading to reduced secretion of laminin-332 and structural alterations of the cutaneous basement membrane zone. These findings demonstrate, in a patient in vivo, that the β3 short arm is also involved in the outcome of the granulation tissue response.

A novel method for the identification of conserved structural patterns in RNA: From small scale to high-throughput applications

Functional RNA regions are often related to recurrent secondary structure patterns (or motifs), which can exert their role in several different ways, particularly in dictating the interaction with RNA-binding proteins, and acting in the regulation of a large number of cellular processes. Among the available motif-finding tools, the majority focuses on sequence patterns, sometimes including secondary structure as additional constraints to improve their performance. Nonetheless, secondary structures motifs may be concurrent to their sequence counterparts or even encode a stronger functional signal. Current methods for searching structural motifs generally require long pipelines and/or high computational efforts or previously aligned sequences. Here, we present BEAM (BEAr Motif finder), a novel method for structural motif discovery from a set of unaligned RNAs, taking advantage of a recently developed encoding for RNA secondary structure named BEAR (Brand nEw Alphabet for RNAs) and of evolutionary substitution rates of secondary structure elements. Tested in a varied set of scenarios, from small- to large-scale, BEAM is successful in retrieving structural motifs even in highly noisy data sets, such as those that can arise in CLIP-Seq or other high-throughput experiments.

Genome-wide methylation analysis demonstrates that 5-aza-2-deoxycytidine treatment does not cause random DNA demethylation in fragile X syndrome cells

Background

Fragile X syndrome (FXS) is caused by CGG expansion over 200 repeats at the 5′ UTR of the FMR1 gene and subsequent DNA methylation of both the expanded sequence and the CpGs of the promoter region. This epigenetic change causes transcriptional silencing of the gene. We have previously demonstrated that 5-aza-2-deoxycytidine (5-azadC) treatment of FXS lymphoblastoid cell lines reactivates the FMR1 gene, concomitant with CpG sites demethylation, increased acetylation of histones H3 and H4 and methylation of lysine 4 on histone 3.

Results

In order to check the specificity of the 5-azadC-induced DNA demethylation, now we performed bisulphite sequencing of the entire methylation boundary upstream the FMR1 promoter region, which is preserved in control wild-type cells. We did not observe any modification of the methylation boundary after treatment. Furthermore, methylation analysis by MS-MLPA of PWS/AS and BWS/SRS loci demonstrated that 5-azadC treatment has no demethylating effect on these regions. Genome-wide methylation analysis through Infinium 450K (Illumina) showed no significant enrichment of specific GO terms in differentially methylated regions after 5-azadC treatment. We also observed that reactivation of FMR1 transcription lasts up to a month after a 7-day treatment and that maximum levels of transcription are reached at 10–15 days after last administration of 5-azadC.

Conclusions

Taken together, these data demonstrate that the demethylating effect of 5-azadC on genomic DNA is not random, but rather restricted to specific regions, if not exclusively to the FMR1 promoter. Moreover, we showed that 5-azadC has a long-lasting reactivating effect on the mutant FMR1 gene.

Electronic supplementary material

The online version of this article (doi:10.1186/s13072-016-0060-x) contains supplementary material, which is available to authorized users.

Preface: BITS2014, the annual meeting of the Italian Society of Bioinformatics

This Preface introduces the content of the BioMed Central journal Supplements related to BITS2014 meeting, held in Rome, Italy, from the 26th to the 28th of February, 2014.

Tools and data services registry: a community effort to document bioinformatics resources

Life sciences are yielding huge data sets that underpin scientific discoveries fundamental to improvement in human health, agriculture and the environment. In support of these discoveries, a plethora of databases and tools are deployed, in technically complex and diverse implementations, across a spectrum of scientific disciplines. The corpus of documentation of these resources is fragmented across the Web, with much redundancy, and has lacked a common standard of information. The outcome is that scientists must often struggle to find, understand, compare and use the best resources for the task at hand.

Here we present a community-driven curation effort, supported by ELIXIR—the European infrastructure for biological information—that aspires to a comprehensive and consistent registry of information about bioinformatics resources. The sustainable upkeep of this Tools and Data Services Registry is assured by a curation effort driven by and tailored to local needs, and shared amongst a network of engaged partners.

As of November 2015, the registry includes 1785 resources, with depositions from 126 individual registrations including 52 institutional providers and 74 individuals. With community support, the registry can become a standard for dissemination of information about bioinformatics resources: we welcome everyone to join us in this common endeavour. The registry is freely available at https://bio.tools.

Kinome-wide decoding of network-attacking mutations rewiring cancer signaling

Cancer cells acquire pathological phenotypes through accumulation of mutations that perturb signaling networks. However, global analysis of these events is currently limited. Here, we identify six types of network-attacking mutations (NAMs), including changes in kinase and SH2 modulation, network rewiring, and the genesis and extinction of phosphorylation sites. We developed a computational platform (ReKINect) to identify NAMs and systematically interpreted the exomes and quantitative (phospho-)proteomes of five ovarian cancer cell lines and the global cancer genome repository. We identified and experimentally validated several NAMs, including PKCγ M501I and PKD1 D665N, which encode specificity switches analogous to the appearance of kinases de novo within the kinome. We discover mutant molecular logic gates, a drift toward phospho-threonine signaling, weakening of phosphorylation motifs, and kinase-inactivating hotspots in cancer. Our method pinpoints functional NAMs, scales with the complexity of cancer genomes and cell signaling, and may enhance our capability to therapeutically target tumor-specific networks.

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Mutations perturbing signaling networks are systematically classified and interpreted

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Several such functional mutations are identified in cancer and experimentally validated

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The results suggest that a single point mutant can have profound signaling effects

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Systematic interpretation of genomic data may assist future precision-medicine efforts

Revealing protein-lncRNA interaction

Long non-coding RNAs (lncRNAs) are associated to a plethora of cellular functions, most of which require the interaction with one or more RNA-binding proteins (RBPs); similarly, RBPs are often able to bind a large number of different RNAs. The currently available knowledge is already drawing an intricate network of interactions, whose deregulation is frequently associated to pathological states. Several different techniques were developed in the past years to obtain protein–RNA binding data in a high-throughput fashion. In parallel, in silico inference methods were developed for the accurate computational prediction of the interaction of RBP–lncRNA pairs. The field is growing rapidly, and it is foreseeable that in the near future, the protein–lncRNA interaction network will rise, offering essential clues for a better understanding of lncRNA cellular mechanisms and their disease-associated perturbations.

Web-Beagle: a web server for the alignment of RNA secondary structures

Web-Beagle (http://beagle.bio.uniroma2.it) is a web server for the pairwise global or local alignment of RNA secondary structures. The server exploits a new encoding for RNA secondary structure and a substitution matrix of RNA structural elements to perform RNA structural alignments. The web server allows the user to compute up to 10 000 alignments in a single run, taking as input sets of RNA sequences and structures or primary sequences alone. In the latter case, the server computes the secondary structure prediction for the RNAs on-the-fly using RNAfold (free energy minimization). The user can also compare a set of input RNAs to one of five pre-compiled RNA datasets including lncRNAs and 3′ UTRs. All types of comparison produce in output the pairwise alignments along with structural similarity and statistical significance measures for each resulting alignment. A graphical color-coded representation of the alignments allows the user to easily identify structural similarities between RNAs. Web-Beagle can be used for finding structurally related regions in two or more RNAs, for the identification of homologous regions or for functional annotation. Benchmark tests show that Web-Beagle has lower computational complexity, running time and better performances than other available methods.

Exploiting holistic approaches to model specificity in protein phosphorylation

Phosphate plays a chemically unique role in shaping cellular signaling of all current living systems, especially eukaryotes. Protein phosphorylation has been studied at several levels, from the near-site context, both in sequence and structure, to the crowded cellular environment, and ultimately to the systems-level perspective. Despite the tremendous advances in mass spectrometry and efforts dedicated to the development of ad hoc highly sophisticated methods, phosphorylation site inference and associated kinase identification are still unresolved problems in kinome biology. The sequence and structure of the substrate near-site context are not sufficient alone to model the in vivo phosphorylation rules, and they should be integrated with orthogonal information in all possible applications. Here we provide an overview of the different contexts that contribute to protein phosphorylation, discussing their potential impact in phosphorylation site annotation and in predicting kinase-substrate specificity.

Computational methods for analysis and inference of kinase/inhibitor relationships

The central role of kinases in virtually all signal transduction networks is the driving motivation for the development of compounds modulating their activity. ATP-mimetic inhibitors are essential tools for elucidating signaling pathways and are emerging as promising therapeutic agents. However, off-target ligand binding and complex and sometimes unexpected kinase/inhibitor relationships can occur for seemingly unrelated kinases, stressing that computational approaches are needed for learning the interaction determinants and for the inference of the effect of small compounds on a given kinase. Recently published high-throughput profiling studies assessed the effects of thousands of small compound inhibitors, covering a substantial portion of the kinome. This wealth of data paved the road for computational resources and methods that can offer a major contribution in understanding the reasons of the inhibition, helping in the rational design of more specific molecules, in the in silico prediction of inhibition for those neglected kinases for which no systematic analysis has been carried yet, in the selection of novel inhibitors with desired selectivity, and offering novel avenues of personalized therapies.

A novel approach to represent and compare RNA secondary structures

Structural information is crucial in ribonucleic acid (RNA) analysis and functional annotation; nevertheless, how to include such structural data is still a debated problem. Dot-bracket notation is the most common and simple representation for RNA secondary structures but its simplicity leads also to ambiguity requiring further processing steps to dissolve. Here we present BEAR (Brand nEw Alphabet for RNA), a new context-aware structural encoding represented by a string of characters. Each character in BEAR encodes for a specific secondary structure element (loop, stem, bulge and internal loop) with specific length. Furthermore, exploiting this informative and yet simple encoding in multiple alignments of related RNAs, we captured how much structural variation is tolerated in RNA families and convert it into transition rates among secondary structure elements. This allowed us to compute a substitution matrix for secondary structure elements called MBR (Matrix of BEAR-encoded RNA secondary structures), of which we tested the ability in aligning RNA secondary structures. We propose BEAR and the MBR as powerful resources for the RNA secondary structure analysis, comparison and classification, motif finding and phylogeny.

A Proteome-wide Domain-centric Perspective on Protein Phosphorylation

Phosphorylation is a widespread post-translational modification that modulates the function of a large number of proteins. Here we show that a significant proportion of all the domains in the human proteome is significantly enriched or depleted in phosphorylation events. A substantial improvement in phosphosites prediction is achieved by leveraging this observation, which has not been tapped by existing methods. Phosphorylation sites are often not shared between multiple occurrences of the same domain in the proteome, even when the phosphoacceptor residue is conserved. This is partly because of different functional constraints acting on the same domain in different protein contexts. Moreover, by augmenting domain alignments with structural information, we were able to provide direct evidence that phosphosites in protein-protein interfaces need not be positionally conserved, likely because they can modulate interactions simply by sitting in the same general surface area.

Regulation dynamics of Leishmania differentiation: deconvoluting signals and identifying phosphorylation trends

Leishmania are obligatory intracellular parasitic protozoa that cause a wide range of diseases in humans, cycling between extracellular promastigotes in the mid-gut of sand flies and intracellular amastigotes in the phagolysosomes of mammalian macrophages. Although many of the molecular mechanisms of development inside macrophages remain a mystery, the development of a host-free system that simulates phagolysosome conditions (37 °C and pH 5.5) has provided new insights into these processes. The time course of promastigote-to-amastigote differentiation can be divided into four morphologically distinct phases: I, signal perception (0-5 h after exposure); II, movement cessation and aggregation (5-10 h); III, amastigote morphogenesis (10-24 h); and IV, maturation (24-120 h). Transcriptomic and proteomic analyses have indicated that differentiation is a coordinated process that results in adaptation to life inside phagolysosomes. Recent phosphoproteomic analysis revealed extensive differences in phosphorylation between promastigotes and amastigotes and identified stage-specific phosphorylation motifs. We hypothesized that the differentiation signal activates a phosphorylation pathway that initiates Leishmania transformation, and here we used isobaric tags for relative and absolute quantitation to interrogate the dynamics of changes in the phosphorylation profile during Leishmania donovani promastigote-to-amastigote differentiation. Analysis of 163 phosphopeptides (from 106 proteins) revealed six distinct kinetic profiles; with increases in phosphorylation predominated during phases I and III, whereas phases II and IV were characterized by greater dephosphorylation. Several proteins (including a protein kinase) were phosphorylated in phase I after exposure to the complete differentiation signal (i.e. signal-specific; 37 °C and pH 5.5), but not after either of the physical parameters separately. Several other protein kinases (including regulatory subunits) and phosphatases also showed changes in phosphorylation during differentiation. This work constitutes the first genome-scale interrogation of phosphorylation dynamics in a parasitic protozoa, revealing the outline of a signaling pathway during Leishmania differentiation. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium (identifier PXD000671). Data can be viewed using ProteinPilot™ software.

Role of CTCF protein in regulating FMR1 locus transcription

Fragile X syndrome (FXS), the leading cause of inherited intellectual disability, is caused by epigenetic silencing of the FMR1 gene, through expansion and methylation of a CGG triplet repeat (methylated full mutation). An antisense transcript (FMR1-AS1), starting from both promoter and intron 2 of the FMR1 gene, was demonstrated in transcriptionally active alleles, but not in silent FXS alleles. Moreover, a DNA methylation boundary, which is lost in FXS, was recently identified upstream of the FMR1 gene. Several nuclear proteins bind to this region, like the insulator protein CTCF. Here we demonstrate for the first time that rare unmethylated full mutation (UFM) alleles present the same boundary described in wild type (WT) alleles and that CTCF binds to this region, as well as to the FMR1 gene promoter, exon 1 and intron 2 binding sites. Contrariwise, DNA methylation prevents CTCF binding to FXS alleles. Drug-induced CpGs demethylation does not restore this binding. CTCF knock-down experiments clearly established that CTCF does not act as insulator at the active FMR1 locus, despite the presence of a CGG expansion. CTCF depletion induces heterochromatinic histone configuration of the FMR1 locus and results in reduction of FMR1 transcription, which however is not accompanied by spreading of DNA methylation towards the FMR1 promoter. CTCF depletion is also associated with FMR1-AS1 mRNA reduction. Antisense RNA, like sense transcript, is upregulated in UFM and absent in FXS cells and its splicing is correlated to that of the FMR1-mRNA. We conclude that CTCF has a complex role in regulating FMR1 expression, probably through the organization of chromatin loops between sense/antisense transcriptional regulatory regions, as suggested by bioinformatics analysis.

Fragile X syndrome is the most common cause of inherited intellectual disability, accounting for about 1∶3000 males and 1∶4000 females. It is caused by a dynamic mutation of FMR1, a gene mapping on the X chromosome and containing a CGG repeat in its promoter region. Expansion of this unstable sequence beyond 200 repeats (full mutation) is followed by DNA methylation and histone changes, leading to the transcriptional inactivation of FMR1 and to the lack of the FMRP protein. Recently, an antisense transcript (FMR1-AS1) spanning the CGG repeats and a region of transition of DNA methylation (boundary) located upstream of the CGG repeats have been identified in transcriptional active FMR1 alleles. Several nuclear proteins bound to the methylation boundary have been described, such as the zinc-finger protein CTCF, the first known insulator in mammals. This protein is an important transcriptional regulator of genes harboring trinucleotide repeats and it is mostly active in chromatin organization. For the first time, we have investigated the role of CTCF protein in the transcriptional regulation of the FMR1 gene. Our results define a complex role for CTCF acting through chromatin organization of the FMR1 locus.

DBATE: database of alternative transcripts expression

The use of high-throughput RNA sequencing technology (RNA-seq) allows whole transcriptome analysis, providing an unbiased and unabridged view of alternative transcript expression. Coupling splicing variant-specific expression with its functional inference is still an open and difficult issue for which we created the DataBase of Alternative Transcripts Expression (DBATE), a web-based repository storing expression values and functional annotation of alternative splicing variants. We processed 13 large RNA-seq panels from human healthy tissues and in disease conditions, reporting expression levels and functional annotations gathered and integrated from different sources for each splicing variant, using a variant-specific annotation transfer pipeline. The possibility to perform complex queries by cross-referencing different functional annotations permits the retrieval of desired subsets of splicing variant expression values that can be visualized in several ways, from simple to more informative. DBATE is intended as a novel tool to help appreciate how, and possibly why, the transcriptome expression is shaped.

Database URL:http://bioinformatica.uniroma2.it/DBATE/.

Alternative splicing tends to avoid partial removals of protein-protein interaction sites

BACKGROUND

Anecdotal evidence of the involvement of alternative splicing (AS) in the regulation of protein-protein interactions has been reported by several studies. AS events have been shown to significantly occur in regions where a protein interaction domain or a short linear motif is present. Several AS variants show partial or complete loss of interface residues, suggesting that AS can play a major role in the interaction regulation by selectively targeting the protein binding sites. In the present study we performed a statistical analysis of the alternative splicing of a non-redundant dataset of human protein-protein interfaces known at molecular level to determine the importance of this way of modulation of protein-protein interactions through AS.

RESULTS

Using a Cochran-Mantel-Haenszel chi-square test we demonstrated that the alternative splicing-mediated partial removal of both heterodimeric and homodimeric binding sites occurs at lower frequencies than expected, and this holds true even if we consider only those isoforms whose sequence is less different from that of the canonical protein and which therefore allow to selectively regulate functional regions of the protein. On the other hand, large removals of the binding site are not significantly prevented, possibly because they are associated to drastic structural changes of the protein. The observed protection of the binding sites from AS is not preferentially directed towards putative hot spot interface residues, and is widespread to all protein functional classes.

CONCLUSIONS

Our findings indicate that protein-protein binding sites are generally protected from alternative splicing-mediated partial removals. However, some cases in which the binding site is selectively removed exist, and here we discuss one of them.

webPDBinder: a server for the identification of ligand binding sites on protein structures

The webPDBinder (http://pdbinder.bio.uniroma2.it/PDBinder) is a web server for the identification of small ligand-binding sites in a protein structure. webPDBinder searches a protein structure against a library of known binding sites and a collection of control non-binding pockets. The number of similarities identified with the residues in the two sets is then used to derive a propensity value for each residue of the query protein associated to the likelihood that the residue is part of a ligand binding site. The predicted binding residues can be further refined using conservation scores derived from the multiple alignment of the PFAM protein family. webPDBinder correctly identifies residues belonging to the binding site in 77% of the cases and is able to identify binding pockets starting from holo or apo structures with comparable performances. This is important for all the real world cases where the query protein has been crystallized without a ligand and is also difficult to obtain clear similarities with bound pockets from holo pocket libraries. The input is either a PDB code or a user-submitted structure. The output is a list of predicted binding pocket residues with propensity and conservation values both in text and graphical format.

Nucleos: a web server for the identification of nucleotide-binding sites in protein structures

Nucleos is a web server for the identification of nucleotide-binding sites in protein structures. Nucleos compares the structure of a query protein against a set of known template 3D binding sites representing nucleotide modules, namely the nucleobase, carbohydrate and phosphate. Structural features, clustering and conservation are used to filter and score the predictions. The predicted nucleotide modules are then joined to build whole nucleotide-binding sites, which are ranked by their score. The server takes as input either the PDB code of the query protein structure or a user-submitted structure in PDB format. The output of Nucleos is composed of ranked lists of predicted nucleotide-binding sites divided by nucleotide type (e.g. ATP-like). For each ranked prediction, Nucleos provides detailed information about the score, the template structure and the structural match for each nucleotide module composing the nucleotide-binding site. The predictions on the query structure and the template-binding sites can be viewed directly on the web through a graphical applet. In 98% of the cases, the modules composing correct predictions belong to proteins with no homology relationship between each other, meaning that the identification of brand-new nucleotide-binding sites is possible using information from non-homologous proteins. Nucleos is available at http://nucleos.bio.uniroma2.it/nucleos/.

Enrichment of Leishmania donovani ATP-binding proteins using a staurosporine capture compound

Trypanosomatid parasites of the genus Leishmania cause severe human diseases collectively termed leishmaniasis. Parasite ATP-binding proteins have emerged as potent targets for chemotherapeutic intervention. However, many parasite-specific ATP-binding proteins may escape current efforts in drug target identification, validation and deconvolution due to the lack of sequence conservation and functional annotation of these proteins in early branching eukaryotic trypanosomatids. Here, we selectively enriched for ATP-binding proteins from Leishmania donovani axenic promastigote and amastigote total protein extracts utilizing a Capture Compound™ (CC) linked to the ATP-competitive inhibitor staurosporine. As judged by in-gel kinase activity assay and competitive inhibition with free staurosporine, the CC specifically enriched for parasite phosphotransferases. Comparative nanoLC-MS(n) analysis identified 70 captured proteins, including 24 conserved protein kinases, and 32 hypothetical proteins with potential ATP-binding function. We identified conserved signature sequence motifs characteristic for staurosporine-binding protein kinases, and identified the hypothetical proteins LinJ.20.0280 and LinJ.09.1630 as novel ATP-binding proteins. Thus, functional enrichment procedures such as described here, combined with bio-informatics analyses and activity assays, provide powerful tools for the discovery of parasite-specific ATP-binding proteins that escape homology-based identification, which can be subsequently targeted for pharmacological intervention.

BIOLOGICAL SIGNIFICANCE

Functional enrichment using a Capture Compound™ linked to the ATP-competitive inhibitor staurosporine provides a powerful new tool for the discovery of parasite-specific ATP-binding proteins that escape homology-based identification, which can be subsequently targeted for pharmacological intervention.

Bioinformatics in Italy: BITS2012, the ninth annual meeting of the Italian Society of Bioinformatics

The BITS2012 meeting, held in Catania on May 2-4, 2012, brought together almost 100 Italian researchers working in the field of Bioinformatics, as well as students in the same or related disciplines. About 90 original research works were presented either as oral communication or as posters, representing a landscape of Italian current research in bioinformatics.

This preface provides a brief overview of the meeting and introduces the manuscripts that were accepted for publication in this supplement, after a strict and careful peer-review by an International board of referees.

Mapping the human phosphatome on growth pathways

Phosphatases control cell growth by a variety of mechanisms. A novel strategy is presented that combines multiparametric analysis of cell perturbations with logic modeling to achieve a detailed mapping of human phosphatase function on growth pathways.

siRNA-mediated downregulation of 298 phosphatase and phosphatase-related genes coupled to automated microscopy was used to characterize their impact on key growth pathways.

In parallel, a literature-derived signed directed network was derived and optimized by training with experimental data.

The resulting logic-based growth model was used to infer the cell state upon perturbation of each signaling node and compare it with the profiles obtained upon phosphatase perturbation.

Mapping of 67% of the protein phosphatase onto the growth model shows that phosphatases are key modulators of growth pathways and affect cell-cycle progression.

This novel approach is general and enables to efficiently map proteins onto complex pathways.

Large-scale siRNA screenings allow linking the function of poorly characterized genes to phenotypic readouts. According to this strategy, genes are associated with a function of interest if the alteration of their expression perturbs the phenotypic readouts. However, given the intricacy of the cell regulatory network, the mapping procedure is low resolution and the resulting models provide little mechanistic insights. We have developed a new strategy that combines multiparametric analysis of cell perturbation with logic modeling to achieve a more detailed functional mapping of human genes onto complex pathways. A literature-derived optimized model is used to infer the cell activation state following upregulation or downregulation of the model entities. By matching this signature with the experimental profile obtained in the high-throughput siRNA screening it is possible to infer the target of each protein, thus defining its ‘entry point' in the network. By this novel approach, 41 phosphatases that affect key growth pathways were identified and mapped onto a human epithelial cell-specific growth model, thus providing insights into the mechanisms underlying their function.