Inflammation in Children with CKD Linked to Gut Dysbiosis and Metabolite Imbalance.

BACKGROUND: CKD is characterized by a sustained proinflammatory response of the immune system, promoting hypertension and cardiovascular disease. The underlying mechanisms are incompletely understood but may be linked to gut dysbiosis. Dysbiosis has been described in adults with CKD; however, comorbidities limit CKD-specific conclusions. METHODS: We analyzed the fecal microbiome, metabolites, and immune phenotypes in 48 children (with normal kidney function, CKD stage G3-G4, G5 treated by hemodialysis [HD], or kidney transplantation) with a mean±SD age of 10.6±3.8 years. RESULTS: Serum TNF-α and sCD14 were stage-dependently elevated, indicating inflammation, gut barrier dysfunction, and endotoxemia. We observed compositional and functional alterations of the microbiome, including diminished production of short-chain fatty acids. Plasma metabolite analysis revealed a stage-dependent increase of tryptophan metabolites of bacterial origin. Serum from patients on HD activated the aryl hydrocarbon receptor and stimulated TNF-α production in monocytes, corresponding to a proinflammatory shift from classic to nonclassic and intermediate monocytes. Unsupervised analysis of T cells revealed a loss of mucosa-associated invariant T (MAIT) cells and regulatory T cell subtypes in patients on HD. CONCLUSIONS: Gut barrier dysfunction and microbial metabolite imbalance apparently mediate the proinflammatory immune phenotype, thereby driving the susceptibility to cardiovascular disease. The data highlight the importance of the microbiota-immune axis in CKD, irrespective of confounding comorbidities.

SODAR: managing multiomics study data and metadata.

Scientists employing omics in life science studies face challenges such as the modeling of multiassay studies, recording of all relevant parameters, and managing many samples with their metadata. They must manage many large files that are the results of the assays or subsequent computation. Users with diverse backgrounds, ranging from computational scientists to wet-lab scientists, have dissimilar needs when it comes to data access, with programmatic interfaces being favored by the former and graphical ones by the latter. We introduce SODAR, the system for omics data access and retrieval. SODAR is a software package that addresses these challenges by providing a web-based graphical user interface for managing multiassay studies and describing them using the ISA (Investigation, Study, Assay) data model and the ISA-Tab file format. Data storage is handled using the iRODS data management system, which handles large quantities of files and substantial amounts of data. SODAR also offers programmable APIs and command-line access for metadata and file storage. SODAR supports complex omics integration studies and can be easily installed. The software is written in Python 3 and freely available at https://github.com/bihealth/sodar-server under the MIT license.

Concepts and Software Package for Efficient Quality Control in Targeted Metabolomics Studies: MeTaQuaC.

Targeted quantitative mass spectrometry metabolite profiling is the workhorse of metabolomics research. Robust and reproducible data are essential for confidence in analytical results and are particularly important with large-scale studies. Commercial kits are now available which use carefully calibrated and validated internal and external standards to provide such reliability. However, they are still subject to processing and technical errors in their use and should be subject to a laboratory's routine quality assurance and quality control measures to maintain confidence in the results. We discuss important systematic and random measurement errors when using these kits and suggest measures to detect and quantify them. We demonstrate how wider analysis of the entire data set alongside standard analyses of quality control samples can be used to identify outliers and quantify systematic trends to improve downstream analysis. Finally, we present the MeTaQuaC software which implements the above concepts and methods for Biocrates kits and other target data sets and creates a comprehensive quality control report containing rich visualization and informative scores and summary statistics. Preliminary unsupervised multivariate analysis methods are also included to provide rapid insight into study variables and groups. MeTaQuaC is provided as an open source R package under a permissive MIT license and includes detailed user documentation.

TaxIt: An Iterative Computational Pipeline for Untargeted Strain-Level Identification Using MS/MS Spectra from Pathogenic Single-Organism Samples.

Untargeted accurate strain-level classification of a priori unidentified organisms using tandem mass spectrometry is a challenging task. Reference databases often lack taxonomic depth, limiting peptide assignments to the species level. However, the extension with detailed strain information increases runtime and decreases statistical power. In addition, larger databases contain a higher number of similar proteomes. We present TaxIt, an iterative workflow to address the increasing search space required for MS/MS-based strain-level classification of samples with unknown taxonomic origin. TaxIt first applies reference sequence data for initial identification of species candidates, followed by automated acquisition of relevant strain sequences for low level classification. Furthermore, proteome similarities resulting in ambiguous taxonomic assignments are addressed with an abundance weighting strategy to increase the confidence in candidate taxa. For benchmarking the performance of our method, we apply our iterative workflow on several samples of bacterial and viral origin. In comparison to noniterative approaches using unique peptides or advanced abundance correction, TaxIt identifies microbial strains correctly in all examples presented (with one tie), thereby demonstrating the potential for untargeted and deeper taxonomic classification. TaxIt makes extensive use of public, unrestricted, and continuously growing sequence resources such as the NCBI databases and is available under open-source BSD license at https://gitlab.com/rki_bioinformatics/TaxIt.

Silent Witness: Dual-Species Transcriptomics Reveals Epithelial Immunological Quiescence to Helminth Larval Encounter and Fostered Larval Development.

Gastrointestinal nematodes are among the most prevalent parasites infecting humans and livestock worldwide. Infective larvae of the soil-transmitted nematode Ascaris spp. enter the host and start tissue migration by crossing the intestinal epithelial barrier. The initial interaction of the intestinal epithelium with the parasite, however, has received little attention. In a time-resolved interaction model of porcine intestinal epithelial cells (IPEC-J2) and infective Ascaris suum larvae, we addressed the early transcriptional changes occurring simultaneously in both organisms using dual-species RNA-Seq. Functional analysis of the host response revealed an overall induction of metabolic activity, without induction of immune responsive genes or immune signaling pathways and showing suppression of chemotactic genes like CXCL8/IL-8 or CHI3L1. Ascaris larvae, when getting in contact with the epithelium, showed induction of genes that orchestrate motor activity and larval development, such as myosin, troponin, myoglobin, and protein disulfide isomerase 2 (PDI-2). In addition, excretory-secretory products that likely facilitate parasite invasion were increased, among them, aspartic protease 6 or hyaluronidase. Integration of host and pathogen data in an interspecies gene co-expression network indicated links between nematode fatty acid biosynthesis and host ribosome assembly/protein synthesis. In summary, our study provides new molecular insights into the early factors of parasite invasion, while at the same time revealing host immunological unresponsiveness. Reproducible software for dual RNA-Seq analysis of non-model organisms is available at https://gitlab.com/mkuhring/project_asuum and can be applied to similar studies.

Estimating the computational limits of detection of microbial non-model organisms.

Mass spectrometry has become a key instrument for proteomic studies of single bacteria as well as microbial communities. However, the identification of spectra from MS/MS experiments is still challenging, in particular for non-model organisms. Due to the limited amount of related protein reference sequences, underexplored organisms often remain completely unidentified or their spectra match to peptides of uncertain degree of relation. Alternative strategies such as error-tolerant spectra searches or proteogenomic approaches may reduce the amount of unidentified spectra and lead to peptide matches on more related taxonomic levels. However, to what extent these strategies may be successful is difficult to judge prior to an MS/MS experiment. In this contribution, we introduce a method to estimate the suitability of databases of interest. Further, it allows estimating the possible influence of error-tolerant searches and proteogenomic approaches on databases of interest with respect to the number of unidentified spectra and the taxonomic distances of identified spectra. Furthermore, we provide an implementation of our approach that supports experimental design by evaluating the benefit and need of different search strategies with respect to present databases and organisms under study. We provide several examples which highlight the different effects of additional search strategies on databases and organisms with varying amount of known relative species available.

SuRankCo: supervised ranking of contigs in de novo assemblies.

BACKGROUND: Evaluating the quality and reliability of a de novo assembly and of single contigs in particular is challenging since commonly a ground truth is not readily available and numerous factors may influence results. Currently available procedures provide assembly scores but lack a comparative quality ranking of contigs within an assembly. RESULTS: We present SuRankCo, which relies on a machine learning approach to predict quality scores for contigs and to enable the ranking of contigs within an assembly. The result is a sorted contig set which allows selective contig usage in downstream analysis. Benchmarking on datasets with known ground truth shows promising sensitivity and specificity and favorable comparison to existing methodology. CONCLUSIONS: SuRankCo analyzes the reliability of de novo assemblies on the contig level and thereby allows quality control and ranking prior to further downstream and validation experiments.

iPiG: integrating peptide spectrum matches into genome browser visualizations.

Proteogenomic approaches have gained increasing popularity, however it is still difficult to integrate mass spectrometry identifications with genomic data due to differing data formats. To address this difficulty, we introduce iPiG as a tool for the integration of peptide identifications from mass spectrometry experiments into existing genome browser visualizations. Thereby, the concurrent analysis of proteomic and genomic data is simplified and proteomic results can directly be compared to genomic data. iPiG is freely available from https://sourceforge.net/projects/ipig/. It is implemented in Java and can be run as a stand-alone tool with a graphical user-interface or integrated into existing workflows. Supplementary data are available at PLOS ONE online.