Exome sequencing as a first-tier test for copy number variant detection: retrospective evaluation and prospective screening in 2418 cases.

BACKGROUND: Despite the availability of whole exome (WES) and genome sequencing (WGS), chromosomal microarray (CMA) remains the first-line diagnostic test in most rare disorders diagnostic workup, looking for copy number variations (CNVs), with a diagnostic yield of 10%-20%. The question of the equivalence of CMA and WES in CNV calling is an organisational and economic question, especially when ordering a WGS after a negative CMA and/or WES. METHODS: This study measures the equivalence between CMA and GATK4 exome sequencing depth of coverage method in detecting coding CNVs on a retrospective cohort of 615 unrelated individuals. A prospective detection of WES-CNV on a cohort of 2418 unrelated individuals, including the 615 individuals from the validation cohort, was performed. RESULTS: On the retrospective validation cohort, every CNV detectable by the method (ie, a CNV with at least one exon not in a dark zone) was accurately called (64/64 events). In the prospective cohort, 32 diagnoses were performed among the 2418 individuals with CNVs ranging from 704 bp to aneuploidy. An incidental finding was reported. The overall increase in diagnostic yield was of 1.7%, varying from 1.2% in individuals with multiple congenital anomalies to 1.9% in individuals with chronic kidney failure. CONCLUSION: Combining single-nucleotide variant (SNV) and CNV detection increases the suitability of exome sequencing as a first-tier diagnostic test for suspected rare Mendelian disorders. Before considering the prescription of a WGS after a negative WES, a careful reanalysis with updated CNV calling and SNV annotation should be considered.

SuperFly: a comparative database for quantified spatio-temporal gene expression patterns in early dipteran embryos.

We present SuperFly (http://superfly.crg.eu), a relational database for quantified spatio-temporal expression data of segmentation genes during early development in different species of dipteran insects (flies, midges and mosquitoes). SuperFly has a special focus on emerging non-drosophilid model systems. The database currently includes data of high spatio-temporal resolution for three species: the vinegar fly Drosophila melanogaster, the scuttle fly Megaselia abdita and the moth midge Clogmia albipunctata. At this point, SuperFly covers up to 9 genes and 16 time points per species, with a total of 1823 individual embryos. It provides an intuitive web interface, enabling the user to query and access original embryo images, quantified expression profiles, extracted positions of expression boundaries and integrated datasets, plus metadata and intermediate processing steps. SuperFly is a valuable new resource for the quantitative comparative study of gene expression patterns across dipteran species. Moreover, it provides an interesting test set for systems biologists interested in fitting mathematical gene network models to data. Both of these aspects are essential ingredients for progress toward a more quantitative and mechanistic understanding of developmental evolution.

Pharmacological assessment defines Leishmania donovani casein kinase 1 as a drug target and reveals important functions in parasite viability and intracellular infection.

Protein kinase inhibitors have emerged as new drugs in various therapeutic areas, including leishmaniasis, an important parasitic disease. Members of the Leishmania casein kinase 1 (CK1) family represent promising therapeutic targets. Leishmania casein kinase 1 isoform 2 (CK1.2) has been identified as an exokinase capable of phosphorylating host proteins, thus exerting a potential immune-suppressive action on infected host cells. Moreover, its inhibition reduces promastigote growth. Despite these important properties, its requirement for intracellular infection and its chemical validation as a therapeutic target in the disease-relevant amastigote stage remain to be established. In this study, we used a multidisciplinary approach combining bioinformatics, biochemical, and pharmacological analyses with a macrophage infection assay to characterize and define Leishmania CK1.2 as a valid drug target. We show that recombinant and transgenic Leishmania CK1.2 (i) can phosphorylate CK1-specific substrates, (ii) is sensitive to temperature, and (iii) is susceptible to CK1-specific inhibitors. CK1.2 is constitutively expressed at both the promastigote insect stage and the vertebrate amastigote stage. We further demonstrated that reduction of CK1 activity by specific inhibitors, such as D4476, blocks promastigote growth, strongly compromises axenic amastigote viability, and decreases the number of intracellular Leishmania donovani and L. amazonensis amastigotes in infected macrophages. These results underline the potential role of CK1 kinases in intracellular survival. The identification of differences in structure and inhibition profiles compared to those of mammalian CK1 kinases opens new opportunities for Leishmania CK1.2 antileishmanial drug development. Our report provides the first chemical validation of Leishmania CK1 protein kinases, required for amastigote intracellular survival, as therapeutic targets.

T-Coffee: a web server for the multiple sequence alignment of protein and RNA sequences using structural information and homology extension.

This article introduces a new interface for T-Coffee, a consistency-based multiple sequence alignment program. This interface provides an easy and intuitive access to the most popular functionality of the package. These include the default T-Coffee mode for protein and nucleic acid sequences, the M-Coffee mode that allows combining the output of any other aligners, and template-based modes of T-Coffee that deliver high accuracy alignments while using structural or homology derived templates. These three available template modes are Expresso for the alignment of protein with a known 3D-Structure, R-Coffee to align RNA sequences with conserved secondary structures and PSI-Coffee to accurately align distantly related sequences using homology extension. The new server benefits from recent improvements of the T-Coffee algorithm and can align up to 150 sequences as long as 10,000 residues and is available from both http://www.tcoffee.org and its main mirror http://tcoffee.crg.cat.

Accurate multiple sequence alignment of transmembrane proteins with PSI-Coffee.

BACKGROUND: Transmembrane proteins (TMPs) constitute about 20~30% of all protein coding genes. The relative lack of experimental structure has so far made it hard to develop specific alignment methods and the current state of the art (PRALINE™) only manages to recapitulate 50% of the positions in the reference alignments available from the BAliBASE2-ref7. METHODS: We show how homology extension can be adapted and combined with a consistency based approach in order to significantly improve the multiple sequence alignment of alpha-helical TMPs. TM-Coffee is a special mode of PSI-Coffee able to efficiently align TMPs, while using a reduced reference database for homology extension. RESULTS: Our benchmarking on BAliBASE2-ref7 alpha-helical TMPs shows a significant improvement over the most accurate methods such as MSAProbs, Kalign, PROMALS, MAFFT, ProbCons and PRALINE™. We also estimated the influence of the database used for homology extension and show that highly non-redundant UniRef databases can be used to obtain similar results at a significantly reduced computational cost over full protein databases. TM-Coffee is part of the T-Coffee package, a web server is also available from http://tcoffee.crg.cat/tmcoffee and a freeware open source code can be downloaded from http://www.tcoffee.org/Packages/Stable/Latest.

STRIKE: evaluation of protein MSAs using a single 3D structure.

MOTIVATION: Evaluating alternative multiple protein sequence alignments is an important unsolved problem in Biology. The most accurate way of doing this is to use structural information. Unfortunately, most methods require at least two structures to be embedded in the alignment, a condition rarely met when dealing with standard datasets. RESULT: We developed STRIKE, a method that determines the relative accuracy of two alternative alignments of the same sequences using a single structure. We validated our methodology on three commonly used reference datasets (BAliBASE, Homestrad and Prefab). Given two alignments, STRIKE manages to identify the most accurate one in 70% of the cases on average. This figure increases to 79% when considering very challenging datasets like the RV11 category of BAliBASE. This discrimination capacity is significantly higher than that reported for other metrics such as Contact Accepted mutation or Blosum. We show that this increased performance results both from a refined definition of the contacts and from the use of an improved contact substitution score. CONTACT: cedric.notredame@crg.eu AVAILABILITY: STRIKE is an open source freeware available from www.tcoffee.org SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Can molecular dynamics simulations help in discriminating correct from erroneous protein 3D models?

BACKGROUND: Recent approaches for predicting the three-dimensional (3D) structure of proteins such as de novo or fold recognition methods mostly rely on simplified energy potential functions and a reduced representation of the polypeptide chain. These simplifications facilitate the exploration of the protein conformational space but do not permit to capture entirely the subtle relationship that exists between the amino acid sequence and its native structure. It has been proposed that physics-based energy functions together with techniques for sampling the conformational space, e.g., Monte Carlo or molecular dynamics (MD) simulations, are better suited to the task of modelling proteins at higher resolutions than those of models obtained with the former type of methods. In this study we monitor different protein structural properties along MD trajectories to discriminate correct from erroneous models. These models are based on the sequence-structure alignments provided by our fold recognition method, FROST. We define correct models as being built from alignments of sequences with structures similar to their native structures and erroneous models from alignments of sequences with structures unrelated to their native structures. RESULTS: For three test sequences whose native structures belong to the all-alpha, all-beta and alphabeta classes we built a set of models intended to cover the whole spectrum: from a perfect model, i.e., the native structure, to a very poor model, i.e., a random alignment of the test sequence with a structure belonging to another structural class, including several intermediate models based on fold recognition alignments. We submitted these models to 11 ns of MD simulations at three different temperatures. We monitored along the corresponding trajectories the mean of the Root-Mean-Square deviations (RMSd) with respect to the initial conformation, the RMSd fluctuations, the number of conformation clusters, the evolution of secondary structures and the surface area of residues. None of these criteria alone is 100% efficient in discriminating correct from erroneous models. The mean RMSd, RMSd fluctuations, secondary structure and clustering of conformations show some false positives whereas the residue surface area criterion shows false negatives. However if we consider these criteria in combination it is straightforward to discriminate the two types of models. CONCLUSION: The ability of discriminating correct from erroneous models allows us to improve the specificity and sensitivity of our fold recognition method for a number of ambiguous cases.

Protein secondary structure assignment revisited: a detailed analysis of different assignment methods.

BACKGROUND: A number of methods are now available to perform automatic assignment of periodic secondary structures from atomic coordinates, based on different characteristics of the secondary structures. In general these methods exhibit a broad consensus as to the location of most helix and strand core segments in protein structures. However the termini of the segments are often ill-defined and it is difficult to decide unambiguously which residues at the edge of the segments have to be included. In addition, there is a "twilight zone" where secondary structure segments depart significantly from the idealized models of Pauling and Corey. For these segments, one has to decide whether the observed structural variations are merely distorsions or whether they constitute a break in the secondary structure. METHODS: To address these problems, we have developed a method for secondary structure assignment, called KAKSI. Assignments made by KAKSI are compared with assignments given by DSSP, STRIDE, XTLSSTR, PSEA and SECSTR, as well as secondary structures found in PDB files, on 4 datasets (X-ray structures with different resolution range, NMR structures). RESULTS: A detailed comparison of KAKSI assignments with those of STRIDE and PSEA reveals that KAKSI assigns slightly longer helices and strands than STRIDE in case of one-to-one correspondence between the segments. However, KAKSI tends also to favor the assignment of several short helices when STRIDE and PSEA assign longer, kinked, helices. Helices assigned by KAKSI have geometrical characteristics close to those described in the PDB. They are more linear than helices assigned by other methods. The same tendency to split long segments is observed for strands, although less systematically. We present a number of cases of secondary structure assignments that illustrate this behavior. CONCLUSION: Our method provides valuable assignments which favor the regularity of secondary structure segments.

T-Coffee: Tree-based consistency objective function for alignment evaluation.

T-Coffee, for Tree-based consistency objective function for alignment evaluation, is a versatile multiple sequence alignment (MSA) method suitable for aligning virtually any type of biological sequences. T-Coffee provides more than a simple sequence aligner; rather it is a framework in which alternative alignment methods and/or extra information (i.e., structural, evolutionary, or experimental information) can be combined to reach more accurate and more meaningful MSAs. T-Coffee can be used either by running input data via the Web server ( http://tcoffee.crg.cat/apps/tcoffee/index.html ) or by downloading the T-Coffee package. Here, we present how the package can be used in its command line mode to carry out the most common tasks and multiply align proteins, DNA, and RNA sequences. This chapter particularly emphasizes on the description of T-Coffee special flavors also called "modes," designed to address particular biological problems.

Efficient and interpretable prediction of protein functional classes by correspondence analysis and compact set relations.

Predicting protein functional classes such as localization sites and modifications plays a crucial role in function annotation. Given a tremendous amount of sequence data yielded from high-throughput sequencing experiments, the need of efficient and interpretable prediction strategies has been rapidly amplified. Our previous approach for subcellular localization prediction, PSLDoc, archives high overall accuracy for Gram-negative bacteria. However, PSLDoc is computational intensive due to incorporation of homology extension in feature extraction and probabilistic latent semantic analysis in feature reduction. Besides, prediction results generated by support vector machines are accurate but generally difficult to interpret. In this work, we incorporate three new techniques to improve efficiency and interpretability. First, homology extension is performed against a compact non-redundant database using a fast search model to reduce running time. Second, correspondence analysis (CA) is incorporated as an efficient feature reduction to generate a clear visual separation of different protein classes. Finally, functional classes are predicted by a combination of accurate compact set (CS) relation and interpretable one-nearest neighbor (1-NN) algorithm. Besides localization data sets, we also apply a human protein kinase set to validate generality of our proposed method. Experiment results demonstrate that our method make accurate prediction in a more efficient and interpretable manner. First, homology extension using a fast search on a compact database can greatly accelerate traditional running time up to twenty-five times faster without sacrificing prediction performance. This suggests that computational costs of many other predictors that also incorporate homology information can be largely reduced. In addition, CA can not only efficiently identify discriminative features but also provide a clear visualization of different functional classes. Moreover, predictions based on CS achieve 100% precision. When combined with 1-NN on unpredicted targets by CS, our method attains slightly better or comparable performance compared with the state-of-the-art systems.

Using the T-Coffee package to build multiple sequence alignments of protein, RNA, DNA sequences and 3D structures.

T-Coffee (Tree-based consistency objective function for alignment evaluation) is a versatile multiple sequence alignment (MSA) method suitable for aligning most types of biological sequences. The main strength of T-Coffee is its ability to combine third party aligners and to integrate structural (or homology) information when building MSAs. The series of protocols presented here show how the package can be used to multiply align proteins, RNA and DNA sequences. The protein section shows how users can select the most suitable T-Coffee mode for their data set. Detailed protocols include T-Coffee, the default mode, M-Coffee, a meta version able to combine several third party aligners into one, PSI (position-specific iterated)-Coffee, the homology extended mode suitable for remote homologs and Expresso, the structure-based multiple aligner. We then also show how the T-RMSD (tree based on root mean square deviation) option can be used to produce a functionally informative structure-based clustering. RNA alignment procedures are described for using R-Coffee, a mode able to use predicted RNA secondary structures when aligning RNA sequences. DNA alignments are illustrated with Pro-Coffee, a multiple aligner specific of promoter regions. We also present some of the many reformatting utilities bundled with T-Coffee. The package is an open-source freeware available from http://www.tcoffee.org/.

Large-scale screening of a targeted Enterococcus faecalis mutant library identifies envelope fitness factors.

Spread of antibiotic resistance among bacteria responsible for nosocomial and community-acquired infections urges for novel therapeutic or prophylactic targets and for innovative pathogen-specific antibacterial compounds. Major challenges are posed by opportunistic pathogens belonging to the low GC% gram-positive bacteria. Among those, Enterococcus faecalis is a leading cause of hospital-acquired infections associated with life-threatening issues and increased hospital costs. To better understand the molecular properties of enterococci that may be required for virulence, and that may explain the emergence of these bacteria in nosocomial infections, we performed the first large-scale functional analysis of E. faecalis V583, the first vancomycin-resistant isolate from a human bloodstream infection. E. faecalis V583 is within the high-risk clonal complex 2 group, which comprises mostly isolates derived from hospital infections worldwide. We conducted broad-range screenings of candidate genes likely involved in host adaptation (e.g., colonization and/or virulence). For this purpose, a library was constructed of targeted insertion mutations in 177 genes encoding putative surface or stress-response factors. Individual mutants were subsequently tested for their i) resistance to oxidative stress, ii) antibiotic resistance, iii) resistance to opsonophagocytosis, iv) adherence to the human colon carcinoma Caco-2 epithelial cells and v) virulence in a surrogate insect model. Our results identified a number of factors that are involved in the interaction between enterococci and their host environments. Their predicted functions highlight the importance of cell envelope glycopolymers in E. faecalis host adaptation. This study provides a valuable genetic database for understanding the steps leading E. faecalis to opportunistic virulence.

Cyclosporin A treatment of Leishmania donovani reveals stage-specific functions of cyclophilins in parasite proliferation and viability.

BACKGROUND: Cyclosporin A (CsA) has important anti-microbial activity against parasites of the genus Leishmania, suggesting CsA-binding cyclophilins (CyPs) as potential drug targets. However, no information is available on the genetic diversity of this important protein family, and the mechanisms underlying the cytotoxic effects of CsA on intracellular amastigotes are only poorly understood. Here, we performed a first genome-wide analysis of Leishmania CyPs and investigated the effects of CsA on host-free L. donovani amastigotes in order to elucidate the relevance of these parasite proteins for drug development. METHODOLOGY/PRINCIPAL FINDINGS: Multiple sequence alignment and cluster analysis identified 17 Leishmania CyPs with significant sequence differences to human CyPs, but with highly conserved functional residues implicated in PPIase function and CsA binding. CsA treatment of promastigotes resulted in a dose-dependent inhibition of cell growth with an IC50 between 15 and 20 microM as demonstrated by proliferation assay and cell cycle analysis. Scanning electron microscopy revealed striking morphological changes in CsA treated promastigotes reminiscent to developing amastigotes, suggesting a role for parasite CyPs in Leishmania differentiation. In contrast to promastigotes, CsA was highly toxic to amastigotes with an IC50 between 5 and 10 microM, revealing for the first time a direct lethal effect of CsA on the pathogenic mammalian stage linked to parasite thermotolerance, independent from host CyPs. Structural modeling, enrichment of CsA-binding proteins from parasite extracts by FPLC, and PPIase activity assays revealed direct interaction of the inhibitor with LmaCyP40, a bifunctional cyclophilin with potential co-chaperone function. CONCLUSIONS/SIGNIFICANCE: The evolutionary expansion of the Leishmania CyP protein family and the toxicity of CsA on host-free amastigotes suggest important roles of PPIases in parasite biology and implicate Leishmania CyPs in key processes relevant for parasite proliferation and viability. The requirement of Leishmania CyP functions for intracellular parasite survival and their substantial divergence form host CyPs defines these proteins as prime drug targets.