A metaproteomic approach to study human-microbial ecosystems at the mucosal luminal interface

Aberrant interactions between the host and the intestinal bacteria are thought to contribute to the pathogenesis of many digestive diseases. However, studying the complex ecosystem at the human mucosal-luminal interface (MLI) is challenging and requires an integrative systems biology approach. Therefore, we developed a novel method integrating lavage sampling of the human mucosal surface, high-throughput proteomics, and a unique suite of bioinformatic and statistical analyses. Shotgun proteomic analysis of secreted proteins recovered from the MLI confirmed the presence of both human and bacterial components. To profile the MLI metaproteome, we collected 205 mucosal lavage samples from 38 healthy subjects, and subjected them to high-throughput proteomics. The spectral data were subjected to a rigorous data processing pipeline to optimize suitability for quantitation and analysis, and then were evaluated using a set of biostatistical tools. Compared to the mucosal transcriptome, the MLI metaproteome was enriched for extracellular proteins involved in response to stimulus and immune system processes. Analysis of the metaproteome revealed significant individual-related as well as anatomic region-related (biogeographic) features. Quantitative shotgun proteomics established the identity and confirmed the biogeographic association of 49 proteins (including 3 functional protein networks) demarcating the proximal and distal colon. This robust and integrated proteomic approach is thus effective for identifying functional features of the human mucosal ecosystem, and a fresh understanding of the basic biology and disease processes at the MLI. © 2011 Li et al

Cross-talk between phosphorylation and lysine acetylation in a genome-reduced bacterium

The effect of kinase, phosphatase and N-acetyltransferase deletions on proteome phosphorylation and acetylation was investigated in Mycoplasma pneumoniae. Bi-directional cross-talk between post-transcriptional modifications suggests an underlying regulatory molecular code in prokaryotes

The Perseus computational platform for comprehensive analysis of (prote)omics data

A main bottleneck in proteomics is the downstream biological analysis of highly multivariate quantitative protein abundance data generated using mass spectrometry-based analysis. We developed the Perseus software platform (http://www.perseus-framework.org) to support biological and biomedical researchers in interpreting protein quantification, interaction and post-translational modification data. Perseus contains a comprehensive portfolio of statistical toots for high-dimensional omics data analysis covering normalization, pattern recognition, time-series analysis, cross-omics comparisons and multiple hypothesis testing. A machine learning module supports the classification and validation of patient groups for diagnosis and prognosis, and it also detects predictive protein signatures. Central to Perseus is a user-friendly, interactive workflow environment that provides complete documentation of computational methods used in a publication. ALL activities in Perseus are realized as plugins, and users can extend the software by programming their own, which can be shared through a plugin store. We anticipate that Perseus's arsenal of algorithms and its intuitive usability will empower interdisciplinary analysis of complex large data sets

Detection of loci associated with water-soluble carbohydrate accumulation and environmental adaptation in white clover (Trifolium repens L.) : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Plant Biology at Massey University, Palmerston North, New Zealand

White clover (Trifolium repens L.) is an economically important forage legume in New Zealand/Aotearoa (NZ). It provides quality forage and a source of bioavailable nitrogen fixed through symbiosis with soil Rhizobium bacteria. This thesis investigated the genetic basis of two traits of significant agronomic interest in white clover. These were foliar water-soluble carbohydrate (WSC) accumulation and soil moisture deficit (SMD) tolerance. Previously generated divergent WSC lines of white clover were characterised for foliar WSC and leaf size. Significant (p < 0.05) divergence in foliar WSC content was observed between five breeding pools. Little correlation was observed between WSC and leaf size, indicating that breeding for increased WSC content could be achieved in large and small leaf size classes of white clover in as few as 2 – 3 generations. Genotyping by sequencing (GBS) data were obtained for 1,113 white clover individuals (approximately 47 individuals from each of 24 populations). Population structure was assessed using discriminant analysis of principal components (DAPC) and individuals were assigned to 11 genetic clusters. Divergent selection created a structure that differentiated high and low WSC populations. Outlier detection methodologies using PCAdapt, BayeScan and KGD-FST applied to the GBS data identified 33 SNPs in diverse gene families that discriminated high and low WSC populations. One SNP associated with the starch biosynthesis gene, glgC was identified in a genome-wide association study (GWAS) of 605 white clover individuals. Transcriptome and proteome analyses also provided evidence to suggest that high WSC levels in different breeding pools were achieved through sorting of allelic variants of carbohydrate metabolism pathway genes. Transcriptome and proteome analyses suggested 14 gene models from seven carbohydrate gene families (glgC, WAXY, glgA, glgB, BAM, AMY and ISA3) had responded to artificial selection. Patterns of SNP variation in the AMY, glgC and WAXY gene families separated low and high WSC individuals. Allelic variants in these gene families represent potential targets for assisted breeding of high WSC levels. Overall, multiple lines of evidence corroborate the importance of glgC for increasing foliar WSC accumulation in white clover. Soil moisture deficit (SMD) tolerance was investigated in naturalised populations of white clover collected from 17 sites representing contrasting SMD across the South Island/Te Waipounamu of NZ. Weak genetic differentiation of populations was detected in analyses of GBS data, with three genetic clusters identified by ADMIXTURE. Outlier detection and environmental association analyses identified 64 SNPs significantly (p < 0.05) associated with environmental variation. Mapping of these SNPs to the white clover reference genome, together with gene ontology analyses, suggested some SNPs were associated with genes involved in carbohydrate metabolism and root morphology. A common set of allelic variants in a subset of the populations from high SMD environments may also identify targets for selective breeding, but this variation needs further investigation

Poor transcript-protein correlation in the brain: negatively correlating gene products reveal neuronal polarity as a potential cause

International audienceTranscription, translation, and turnover of transcripts and proteins are essential for cellular function. The contribution of those factors to protein levels is under debate, as transcript levels and cognate protein levels do not necessarily correlate due to regulation of translation and protein turnover. Here we propose neuronal polarity as a third factor that is particularly evident in the CNS, leading to considerable distances between somata and axon terminals. Consequently, transcript levels may negatively correlate with cognate protein levels in CNS regions, i.e., transcript and protein levels behave reciprocally. To test this hypothesis, we performed an integrative inter‐omics study and analyzed three interconnected rat auditory brainstem regions (cochlear nuclear complex, CN; superior olivary complex, SOC; inferior colliculus, IC) and the rest of the brain as a reference. We obtained transcript and protein sets in these regions of interest (ROIs) by DNA microarrays and label‐free mass spectrometry, and performed principal component and correlation analyses. We found 508 transcript|protein pairs and detected poor to moderate transcript|protein correlation in all ROIs, as evidenced by coefficients of determination from 0.34 to 0.54. We identified 57‐80 negatively correlating gene products in the ROIs and intensively analyzed four of them for which the correlation was poorest. Three cognate proteins (Slc6a11, Syngr1, Tppp) were synaptic and hence candidates for a negative correlation because of protein transport into axon terminals. Thus, we systematically analyzed the negatively correlating gene products. Gene ontology analyses revealed overrepresented transport/synapse‐related proteins, supporting our hypothesis. We present 30 synapse/transport‐related proteins with poor transcript|protein correlation. In conclusion, our analyses support that protein transport in polar cells is a third factor that influences the protein level and, thereby, the transcript|protein correlation

Stage-specific proteomes from onchocerca ochengi, sister species of the human river blindness parasite, uncover adaptations to a nodular lifestyle

Despite 40 years of control efforts, onchocerciasis (river blindness) remains one of the most important neglected tropical diseases, with 17 million people affected. The etiological agent, Onchocerca volvulus, is a filarial nematode with a complex lifecycle involving several distinct stages in the definitive host and blackfly vector. The challenges of obtaining sufficient material have prevented high-throughput studies and the development of novel strategies for disease control and diagnosis. Here, we utilize the closest relative of O. volvulus, the bovine parasite Onchocerca ochengi, to compare stage-specific proteomes and host-parasite interactions within the secretome. We identified a total of 4260 unique O. ochengi proteins from adult males and females, infective larvae, intrauterine microfilariae, and fluid from intradermal nodules. In addition, 135 proteins were detected from the obligate Wolbachia symbiont. Observed protein families that were enriched in all whole body extracts relative to the complete search database included immunoglobulin-domain proteins, whereas redox and detoxification enzymes and proteins involved in intracellular transport displayed stage-specific overrepresentation. Unexpectedly, the larval stages exhibited enrichment for several mitochondrial-related protein families, including members of peptidase family M16 and proteins which mediate mitochondrial fission and fusion. Quantification of proteins across the lifecycle using the Hi-3 approach supported these qualitative analyses. In nodule fluid, we identified 94 O. ochengi secreted proteins, including homologs of transforming growth factor-β and a second member of a novel 6-ShK toxin domain family, which was originally described from a model filarial nematode (Litomosoides sigmodontis). Strikingly, the 498 bovine proteins identified in nodule fluid were strongly dominated by antimicrobial proteins, especially cathelicidins. This first high-throughput analysis of an Onchocerca spp. proteome across the lifecycle highlights its profound complexity and emphasizes the extremely close relationship between O. ochengi and O. volvulus The insights presented here provide new candidates for vaccine development, drug targeting and diagnostic biomarkers

Co-regulation map of the human proteome enables identification of protein functions

This is the author accepted manuscript. The final version is available from Nature Research via the DOI in this recordData availability: All mass spectrometry raw files generated in-house have been deposited in the ProteomeXchange Consortium (http://proteomecentral.proteomexchange.org) via the PRIDE partner repository36 with the dataset identifier PXD008888. The co-regulation map is hosted on our website at www.proteomeHD.net, and pair-wise co-regulation scores are available through STRING (https://string-db.org). A network of the top 0.5% co-regulated protein pairs can be explored interactively on NDEx (https://doi.org/10.18119/N9N30Q).Code availability: Data analysis was performed in R 3.5.1. R scripts and input files required to reproduce the results of this manuscript are available in the following GitHub repository: https://github.com/Rappsilber-Laboratory/ProteomeHD. R scripts related specifically to the benchmarking of the treeClust algorithm using synthetic data are available in the following GitHub repository: https://github.com/Rappsilber-Laboratory/treeClust-benchmarking. The R package data.table was used for fast data processing. Figures were prepared using ggplot2, gridExtra, cowplot and viridis.Note that the title of the AAM is different from the published versionThe annotation of protein function is a longstanding challenge of cell biology that suffers from the sheer magnitude of the task. Here we present ProteomeHD, which documents the response of 10,323 human proteins to 294 biological perturbations, measured by isotope-labelling mass spectrometry. We reveal functional associations between human proteins using the treeClust machine learning algorithm, which we show to improve protein co-regulation analysis due to robust selectivity for close linear relationships. Our co-regulation map identifies a functional context for many uncharacterized proteins, including microproteins that are difficult to study with traditional methods. Co-regulation also captures relationships between proteins which do not physically interact or co-localize. For example, co-regulation of the peroxisomal membrane protein PEX11β with mitochondrial respiration factors led us to discover a novel organelle interface between peroxisomes and mitochondria in mammalian cells. The co-regulation map can be explored at www.proteomeHD.net .Biotechnology & Biological Sciences Research Council (BBSRC)European Commissio