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Vivax malaria is an infectious disease caused by Plasmodium vivax, a parasitic protozoan transmitted by female Anopheline mosquitoes. Historically, vivax malaria has often been regarded as a benign self-limiting infection due to the observation of low parasitemia in Duffy-positive patients in endemic transmission areas and the virtual absence of infections in Duffy-negative individuals in Sub Saharan Africa. However, the latest estimates show that the burden of the disease is not decreasing in many countries and cases of vivax infections in Duffy-negative individuals are increasingly reported throughout Africa. This raised questions about the accuracy of diagnostics and the evolution of interactions between humans and parasites. For a long time, our knowledge on P. vivax biology has been hampered due to the limited access to biological material and the lack of robust in vitro culture methods. Consequently, little is currently known about P. vivax blood stage invasion mechanisms. The introduction of omics technologies with novel and accessible techniques such as third generation sequencing and RNA sequencing at single cell level, two-dimensional electrophoresis, liquid chromatography, and mass spectrometry, has progressively improved our understanding of P. vivax genetics, transcripts, and proteins. This review aims to provide broad insights into P. vivax invasion mechanisms generated by genomics, transcriptomics, and proteomics and to illustrate the importance of integrated multi-omics studies.

Abstract Background Comparing the composition of microbial communities among groups of interest (e.g., patients vs healthy individuals) is a central aspect in microbiome research. It typically involves sequencing, data processing, statistical analysis and graphical display. Such an analysis is normally obtained by using a set of different applications that require specific expertise for installation, data processing and in some cases, programming skills. Results Here, we present SHAMAN, an interactive web application we developed in order to facilitate the use of (i) a bioinformatic workflow for metataxonomic analysis, (ii) a reliable statistical modelling and (iii) to provide the largest panel of interactive visualizations among the applications that are currently available. SHAMAN is specifically designed for non-expert users. A strong benefit is to use an integrated version of the different analytic steps underlying a proper metagenomic analysis. The application is freely accessible at http://shaman.pasteur.fr/ , and may also work as a standalone application with a Docker container (aghozlane/shaman), conda and R. The source code is written in R and is available at https://github.com/aghozlane/shaman . Using two different datasets (a mock community sequencing and a published 16S rRNA metagenomic data), we illustrate the strengths of SHAMAN in quickly performing a complete metataxonomic analysis. Conclusions With SHAMAN, we aim at providing the scientific community with a platform that simplifies reproducible quantitative analysis of metagenomic data.

Summary: Age-related macular degeneration (AMD) is a leading cause of vision loss. To better understand disease pathogenesis and identify causal genes in GWAS loci for AMD risk, we present a comprehensive database of human retina and retinal pigment epithelium (RPE). Our database comprises macular and non-macular RNA sequencing (RNA-seq) profiles from 129 donors, a genome-wide expression quantitative trait loci (eQTL) dataset that includes macula-specific retina and RPE/choroid, and single-nucleus RNA-seq (NucSeq) from human retina and RPE with subtype resolution from more than 100,000 cells. Using NucSeq, we find enriched expression of AMD candidate genes in RPE cells. We identify 15 putative causal genes for AMD on the basis of co-localization of genetic association signals for AMD risk and eye eQTL, including the genes TSPAN10 and TRPM1. These results demonstrate the value of our human eye database for elucidating genetic pathways and potential therapeutic targets for ocular diseases. : To find genes underlying risk for age-related macular degeneration, Orozco et al. analyze human ocular transcriptomes in conjunction with genotypes and build a single-nucleus atlas. They identify 15 putative causal genes, of which TSPAN10 and TRPM1 were enriched in retinal pigment epithelium, the site of early disease pathology. Keywords: eQTL, NucSeq, single cell, RNA-seq, age-related macular degeneration, AMD, retinal pigment epithelium, retina, GWAS

RIPK1, RIPK3, ZBP1 and TRIF, the four mammalian proteins harboring RIP homotypic interaction motif (RHIM) domains, are key components of inflammatory signaling and programmed cell death. RHIM-domain protein activation is mediated by their oligomerization; however, mechanisms that promote a return to homeostasis remain unknown. Here we show that autophagy is critical for the turnover of all RHIM-domain proteins. Macrophages lacking the autophagy gene Atg16l1accumulated highly insoluble forms of RIPK1, RIPK3, TRIF and ZBP1. Defective autophagy enhanced necroptosis by Tumor necrosis factor (TNF) and Toll-like receptor (TLR) ligands. TNF-mediated necroptosis was mediated by RIPK1 kinase activity, whereas TLR3- or TLR4-mediated death was dependent on TRIF and RIPK3. Unexpectedly, combined deletion of Atg16l1 and Zbp1 accelerated LPS-mediated necroptosis and sepsis in mice. Thus, ZBP1 drives necroptosis in the absence of the RIPK1-RHIM, but suppresses this process when multiple RHIM-domain containing proteins accumulate. These findings identify autophagy as a central regulator of innate inflammation governed by RHIM-domain proteins.

The phase change microcapsule (mPCM) is one of the primary candidates in the fields of energy storage and thermal regulation. In this study, electro-spraying, as a green, high-efficiency electrohydrodynamic atomization technology, is applied to the microencapsulation of two phase change materials (PCM) (n-hexadecane and n-eicosane) with three loading contents (30%, 50%, and 70% by weight) in a polycaprolactone matrix. Ethyl acetate (EA) and chloroform (Chl) were chosen as solvents to prepare the working solutions. The objective of this study is to clarify the microencapsulation process during electro-spraying and to optimize the structure and properties of the electro-sprayed mPCM. The structures, morphologies, and thermal properties of the mPCM were characterized by optical microscopy (OM), scanning electron microscopy (SEM), differential scanning calorimeter (DSC), thermogravimetric analysis (TGA), and fourier transform infrared spectroscopy (FT-IR). Electro-sprayed spherical and non-porous mPCM have been successfully prepared. The mean diameter and the particle size distribution depend mainly on the choice of the n-alkane, as well as the solvent used to prepare the working solutions. Meanwhile, the structure formation of electro-sprayed mPCM and the loading content of PCM were mainly influenced by the evaporation of the solvent and the phase separation between PCM and poly(caprolactone) (PCL) matrix. During the shell formation or PCL solidification, the control of the PCM leaching out of the matrix allows improving the loading content. Finally, based on a high latent heat and simple formation process, the electro-spraying route of PCM is a green, non-toxic, and high-efficiency direction for energy storage and heat regulation.

Linseed oil which has various biomedical applications was encapsulated by chitosan (Chi)-based microcapsules in the development of a suitable carrier. Oil droplets formed in oil-in-water emulsion using sodium dodecyl sulfate (SDS) as emulsifier was stabilized by Chi, and microcapsules with multilayers were formed by alternate additions of SDS and Chi solutions in an emulsion through electrostatic interaction. No chemical cross-linker was used in the study and the multilayer shell membrane was formed by ionic gelation using Chi and SDS. The rigidification of the shell membrane of microcapsules was achieved by alkali treatment in the presence of a small amount of 1-butanol to reduce aggregation. A trisodium citrate solution was used to stabilize the charge of microcapsules by ionic cross-linking. Effects of butanol during alkali treatment and citrate in post alkali treatment were monitored in terms of morphology and the chemical properties of microcapsules. Various characterization techniques revealed that the aggregation was decreased and surface roughness was increased with layer formation.

Although free-living and obligate intracellular bacteria are both polarized it is unclear whether the underlying polarization mechanisms and effector proteins are conserved. Here we dissect at the cytological, functional and structural level a conserved polarization module from the free living α-proteobacterium Caulobacter crescentus and an orthologous system from an obligate intracellular (rickettsial) pathogen. The NMR solution structure of the zinc-finger (ZnR) domain from the bifunctional and bipolar ZitP pilus assembly/motility regulator revealed conserved interaction determinants for PopZ, a bipolar matrix protein that anchors the ParB centromere-binding protein and other regulatory factors at the poles. We show that ZitP regulates cytokinesis and the localization of ParB and PopZ, targeting PopZ independently of the previously known binding sites for its client proteins. Through heterologous localization assays with rickettsial ZitP and PopZ orthologs, we document the shared ancestries, activities and structural determinants of a (bi-)polarization system encoded in free-living and obligate intracellular α-proteobacteria.

Electrosprayed polycaprolactone (PCL) microparticles are widely used in medical tissue engineering, drug control release delivery, and food packaging due to their prominent structures and properties. In electrospraying, the selection of a suitable solvent system as the carrier of PCL is fundamental and a prerequisite for the stabilization of electrospraying, and the control of morphology and structure of electrosprayed particles. The latter is not only critical for diversifying the characteristics of electrosprayed particles and achieving improvement in their properties, but also promotes the efficiency of the process and deepens the applications of electrosprayed particles in various fields. In order to make it systematic and more accessible, this review mainly concludes the effects of different solution properties on the operating parameters in electrospraying on the formation of Taylor cone and the final structure as well as the morphology. Meanwhile, correlations between operating parameters and electrospraying stages are summarized as well. Finally, this review provides detailed guidance on the selection of a suitable solvent system regarding the desired morphology, structure, and applications of PCL particles.

Melanocytes are specialized cells that generate unique organelles called melanosomes in which melanin is synthesized and stored. Melanosome biogenesis and melanocyte pigmentation require the transport and delivery of melanin synthesizing enzymes, such as tyrosinase and related proteins (e.g., TYRP1), from endosomes to maturing melanosomes. Among the proteins controlling endosome-melanosome transport, AP-1 together with KIF13A coordinates the endosomal sorting and trafficking of TYRP1 to melanosomes. We identify here β1-adaptin AP-1 subunit-derived peptides of 5 amino acids that block the interaction of KIF13A with AP-1 in cells. Incubating these peptides with human MNT-1 cells or 3D-reconstructed pigmented epidermis decreases pigmentation by impacting the maturation of melanosomes in fully pigmented organelles. This study highlights that peptides targeting the intracellular trafficking of melanocytes are candidate molecules to tune pigmentation in health and disease.

Biological data analysis is frequently performed with command line software. While this practice provides considerable flexibility for computationally savy individuals, such as investigators trained in bioinformatics, this also creates a barrier to the widespread use of data analysis software by investigators trained as biologists and/or clinicians. Workflow systems such as Galaxy and Taverna have been developed to try and provide generic user interfaces that can wrap command line analysis software. These solutions are useful for problems that can be solved with workflows, and that do not require specialized user interfaces. However, some types of analyses can benefit from custom user interfaces. For instance, developing biomarker models from high-throughput data is a type of analysis that can be expressed more succinctly with specialized user interfaces. Here, we show how Language Workbench (LW) technology can be used to model the biomarker development and validation process. We developed a language that models the concepts of Dataset, Endpoint, Feature Selection Method and Classifier. These high-level language concepts map directly to abstractions that analysts who develop biomarker models are familiar with. We found that user interfaces developed in the Meta-Programming System (MPS) LW provide convenient means to configure a biomarker development project, to train models and view the validation statistics. We discuss several advantages of developing user interfaces for data analysis with a LW, including increased interface consistency, portability and extension by language composition. The language developed during this experiment is distributed as an MPS plugin (available at http://campagnelab.org/software/bdval-for-mps/).

Neuropilin-1 (Nrp1) guides the development of the nervous and vascular systems, but its role in the mature brain remains to be explored. Here we report that the expression of the 65 kDa isoform of Sema3A, the ligand of Nrp1, by adult vascular endothelial cells, is regulated during the ovarian cycle and promotes axonal sprouting in hypothalamic neurons secreting gonadotropin-releasing hormone (GnRH), the neuropeptide controlling reproduction. Both the inhibition of Sema3A/Nrp1 signaling and the conditional deletion of Nrp1 in GnRH neurons counteract Sema3A-induced axonal sprouting. Furthermore, the localized intracerebral infusion of Nrp1- or Sema3A-neutralizing antibodies in vivo disrupts the ovarian cycle. Finally, the selective neutralization of endothelial-cell Sema3A signaling in adult Sema3aloxP/loxP mice by the intravenous injection of the recombinant TAT-Cre protein alters the amplitude of the preovulatory luteinizing hormone surge, likely by perturbing GnRH release into the hypothalamo-hypophyseal portal system. Our results identify a previously unknown function for 65 kDa Sema3A-Nrp1 signaling in the induction of axonal growth, and raise the possibility that endothelial cells actively participate in synaptic plasticity in specific functional domains of the adult central nervous system, thus controlling key physiological functions such as reproduction.

Language WorkBenches (LWBs) are software engineering tools that help domain experts develop solutions to various classes of problems. Some of these tools focus on non-technical users and provide languages to help organize knowledge while other workbenches provide means to create new programming languages. A key advantage of language workbenches is that they support the seamless composition of independently developed languages. This capability is useful when developing programs that can benefit from different levels of abstraction. We reasoned that language workbenches could be useful to develop bioinformatics software solutions. In order to evaluate the potential of language workbenches in bioinformatics, we tested a prominent workbench by developing an alternative to shell scripting. To illustrate what LWBs and Language Composition can bring to bioinformatics, we report on our design and development of NYoSh (Not Your ordinary Shell). NYoSh was implemented as a collection of languages that can be composed to write programs as expressive and concise as shell scripts. This manuscript offers a concrete illustration of the advantages and current minor drawbacks of using the MPS LWB. For instance, we found that we could implement an environment-aware editor for NYoSh that can assist the programmers when developing scripts for specific execution environments. This editor further provides semantic error detection and can be compiled interactively with an automatic build and deployment system. In contrast to shell scripts, NYoSh scripts can be written in a modern development environment, supporting context dependent intentions and can be extended seamlessly by end-users with new abstractions and language constructs. We further illustrate language extension and composition with LWBs by presenting a tight integration of NYoSh scripts with the GobyWeb system. The NYoSh Workbench prototype, which implements a fully featured integrated development environment for NYoSh is distributed at http://nyosh.campagnelab.org.

Abstract Background The evaluation of information retrieval techniques has traditionally relied on human judges to determine which documents are relevant to a query and which are not. This protocol is used in the Text Retrieval Evaluation Conference (TREC), organized annually for the past 15 years, to support the unbiased evaluation of novel information retrieval approaches. The TREC Genomics Track has recently been introduced to measure the performance of information retrieval for biomedical applications. Results We describe two protocols for evaluating biomedical information retrieval techniques without human relevance judgments. We call these protocols No Title Evaluation (NT Evaluation). The first protocol measures performance for focused searches, where only one relevant document exists for each query. The second protocol measures performance for queries expected to have potentially many relevant documents per query (high-recall searches). Both protocols take advantage of the clear separation of titles and abstracts found in Medline. We compare the performance obtained with these evaluation protocols to results obtained by reusing the relevance judgments produced in the 2004 and 2005 TREC Genomics Track and observe significant correlations between performance rankings generated by our approach and TREC. Spearman's correlation coefficients in the range of 0.79–0.92 are observed comparing bpref measured with NT Evaluation or with TREC evaluations. For comparison, coefficients in the range 0.86–0.94 can be observed when evaluating the same set of methods with data from two independent TREC Genomics Track evaluations. We discuss the advantages of NT Evaluation over the TRels and the data fusion evaluation protocols introduced recently. Conclusion Our results suggest that the NT Evaluation protocols described here could be used to optimize some search engine parameters before human evaluation. Further research is needed to determine if NT Evaluation or variants of these protocols can fully substitute for human evaluations.

Abstract Background G Protein-Coupled Receptors (GPCRs) are a large and diverse family of membrane proteins whose members participate in the regulation of most cellular and physiological processes and therefore represent key pharmacological targets. Although several bioinformatics resources support research on GPCRs, most of these have been designed based on the traditional assumption that monomeric GPCRs constitute the functional receptor unit. The increase in the frequency and number of reports about GPCR dimerization/oligomerization and the implication of oligomerization in receptor function makes necessary the ability to store and access information about GPCR dimers/oligomers electronically. Results We present here the requirements and ontology (the information scheme to describe oligomers and associated concepts and their relationships) for an information system that can manage the elements of information needed to describe comprehensively the phenomena of both homo- and hetero-oligomerization of GPCRs. The comprehensive information management scheme that we plan to use for the development of an intuitive and user-friendly GPCR-Oligomerization Knowledge Base (GPCR-OKB) is the result of a community dialog involving experimental and computational colleagues working on GPCRs. Conclusion Our long term goal is to disseminate to the scientific community organized, curated, and detailed information about GPCR dimerization/oligomerization and its related structural context. This information will be reported as close to the data as possible so the user can make his own judgment on the conclusions drawn for a particular study. The requirements and ontology described here will facilitate the development of future information systems for GPCR oligomers that contain both computational and experimental information about GPCR oligomerization. This information is freely accessible at http://www.gpcr-okb.org.

Abstract Background Gene expression data are a rich source of information about the transcriptional dis-regulation of genes in cancer. Genes that display differential regulation in cancer are a subtype of cancer biomarkers. Results We present an approach to mine expressed sequence tags to discover cancer biomarkers. A false discovery rate analysis suggests that the approach generates less than 22% false discoveries when applied to combined human and mouse whole genome screens. With this approach, we identify the 200 genes most consistently differentially expressed in cancer (called HM200) and proceed to characterize these genes. When used for prediction in a variety of cancer classification tasks (in 24 independent cancer microarray datasets, 59 classifications total), we show that HM200 and the shorter gene list HM100 are very competitive cancer biomarker sets. Indeed, when compared to 13 published cancer marker gene lists, HM200 achieves the best or second best classification performance in 79% of the classifications considered. Conclusion These results indicate the existence of at least one general cancer marker set whose predictive value spans several tumor types and classification types. Our comparison with other marker gene lists shows that HM200 markers are mostly novel cancer markers. We also identify the previously published Pomeroy-400 list as another general cancer marker set. Strikingly, Pomeroy-400 has 27 genes in common with HM200. Our data suggest that a core set of genes are responsive to the deregulation of pathways involved in tumorigenesis in a variety of tumor types and that these genes could serve as transcriptional cancer markers in applications of clinical interest. Finally, our study suggests new strategies to select and evaluate cancer biomarkers in microarray studies.

Abstract Background The majority of information in the biological literature resides in full text articles, instead of abstracts. Yet, abstracts remain the focus of many publicly available literature data mining tools. Most literature mining tools rely on pre-existing lexicons of biological names, often extracted from curated gene or protein databases. This is a limitation, because such databases have low coverage of the many name variants which are used to refer to biological entities in the literature. Results We present an approach to recognize named entities in full text. The approach collects high frequency terms in an article, and uses support vector machines (SVM) to identify biological entity names. It is also computationally efficient and robust to noise commonly found in full text material. We use the method to create a protein name dictionary from a set of 80,528 full text articles. Only 8.3% of the names in this dictionary match SwissProt description lines. We assess the quality of the dictionary by studying its protein name recognition performance in full text. Conclusion This dictionary term lookup method compares favourably to other published methods, supporting the significance of our direct extraction approach. The method is strong in recognizing name variants not found in SwissProt.

Abstract Background Complex biological database systems have become key computational tools used daily by scientists and researchers. Many of these systems must be capable of executing on multiple different hardware and software configurations and are also often made available to users via the Internet. We have used the Java Data Object (JDO) persistence technology to develop the database layer of such a system known as the SigPath information management system. SigPath is an example of a complex biological database that needs to store various types of information connected by many relationships. Results Using this system as an example, we perform a critical evaluation of current JDO technology; discuss the suitability of the JDO standard to achieve portability, scalability and performance. We show that JDO supports portability of the SigPath system from a relational database backend to an object database backend and achieves acceptable scalability. To answer the performance question, we have created the SigPath JDO application benchmark that we distribute under the Gnu General Public License. This benchmark can be used as an example of using JDO technology to create a complex biological database and makes it possible for vendors and users of the technology to evaluate the performance of other JDO implementations for similar applications. Conclusions The SigPath JDO benchmark and our discussion of JDO technology in the context of biological databases will be useful to bioinformaticians who design new complex biological databases and aim to create systems that can be ported easily to a variety of database backends.

Diffuse low grade gliomas are invasive and incurable brain tumors that inevitably transform into higher grade ones. A classical treatment to delay this transition is radiotherapy (RT). Following RT, the tumor gradually shrinks during a period of typically 6 months to 4 years before regrowing. To improve the patient's health-related quality of life and help clinicians build personalized follow-ups, one would benefit from predictions of the time during which the tumor is expected to decrease. The challenge is to provide a reliable estimate of this regrowth time shortly after RT (i.e. with few data), although patients react differently to the treatment. To this end, we analyze the tumor size dynamics from a batch of 20 high-quality longitudinal data, and propose a simple and robust analytical model, with just 4 parameters. From the study of their correlations, we build a statistical constraint that helps determine the regrowth time even for patients for which we have only a few measurements of the tumor size. We validate the procedure on the data and predict the regrowth time at the moment of the first MRI after RT, with precision of, typically, 6 months. Using virtual patients, we study whether some forecast is still possible just three months after RT. We obtain some reliable estimates of the regrowth time in 75% of the cases, in particular for all "fast-responders". The remaining 25% represent cases where the actual regrowth time is large and can be safely estimated with another measurement a year later. These results show the feasibility of making personalized predictions of the tumor regrowth time shortly after RT.

Current strategies to improve graft outcome following kidney transplantation consider information at the human leukocyte antigen (HLA) loci. Cell surface antigens, in addition to HLA, may serve as the stimuli as well as the targets for the anti-allograft immune response and influence long-term graft outcomes. We therefore performed exome sequencing of DNA from kidney graft recipients and their living donors and estimated all possible cell surface antigens mismatches for a given donor/recipient pair by computing the number of amino acid mismatches in trans-membrane proteins. We designated this tally as the allogenomics mismatch score (AMS). We examined the association between the AMS and post-transplant estimated glomerular filtration rate (eGFR) using mixed models, considering transplants from three independent cohorts (a total of 53 donor-recipient pairs, 106 exomes, and 239 eGFR measurements). We found that the AMS has a significant effect on eGFR (mixed model, effect size across the entire range of the score: -19.4 [-37.7, -1.1], P = 0.0042, χ2 = 8.1919, d.f. = 1) that is independent of the HLA-A, B, DR matching, donor age, and time post-transplantation. The AMS effect is consistent across the three independent cohorts studied and similar to the strong effect size of donor age. Taken together, these results show that the AMS, a novel tool to quantify amino acid mismatches in trans-membrane proteins in individual donor/recipient pair, is a strong, robust predictor of long-term graft function in kidney transplant recipients.