Pratik Jagtap

Complete genome sequence and analysis of Wolinella succinogenes

To understand the origin and emergence of pathogenic bacteria, knowledge of the genetic inventory from their nonpathogenic relatives is a prerequisite. Therefore, the 2.11-megabase genome sequence of Wolinella succinogenes, which is closely related to the pathogenic bacteria Helicobacter pylori and Campylobacter jejuni, was determined. Despite being considered nonpathogenic to its bovine host, W. succinogenes holds an extensive repertoire of genes homologous to known bacterial virulence factors. Many of these genes have been acquired by lateral gene transfer, because part of the virulence plasmid pVir and an N-linked glycosylation gene cluster were found to be syntenic between C. jejuni and genomic islands of W. succinogenes. In contrast to other host-adapted bacteria, W. succinogenes does harbor the highest density of bacterial sensor kinases found in any bacterial genome to date, together with an elaborate signaling circuitry of the GGDEF family of proteins. Because the analysis of the W. succinogenes genome also revealed genes related to soil- and plant-associated bacteria such as the nif genes, W. succinogenes may represent a member of the epsilon proteobacteria with a life cycle outside its host.

A two-step database search method improves sensitivity in peptide sequence matches for metaproteomics and proteogenomics studies

Large databases (>106 sequences) used in metaproteomic and proteogenomic studies present challenges in matching peptide sequences to MS/MS data using database‐search programs. Most notably, strict filtering to avoid false‐positive matches leads to more false negatives, thus constraining the number of peptide matches. To address this challenge, we developed a two‐step method wherein matches derived from a primary search against a large database were used to create a smaller subset database. The second search was performed against a target‐decoy version of this subset database merged with a host database. High confidence peptide sequence matches were then used to infer protein identities. Applying our two‐step method for both metaproteomic and proteogenomic analysis resulted in twice the number of high confidence peptide sequence matches in each case, as compared to the conventional one‐step method. The two‐step method captured almost all of the same peptides matched by the one‐step method, with a majority of the additional matches being false negatives from the one‐step method. Furthermore, the two‐step method improved results regardless of the database search program used. Our results show that our two‐step method maximizes the peptide matching sensitivity for applications requiring large databases, especially valuable for proteogenomics and metaproteomics studies.

Temporal quantitative proteomics by iTRAQ 2D-LC-MS/MS and corresponding mRNA expression analysis identify post-transcriptional modulation of actin-cytoskeleton regulators during TGF-β-Lnduced epithelial-mesenchymal transition

To gain insights into how TGF-beta regulates epithelial-mesenchymal transition (EMT), we assessed the time course of proteins and mRNAs during EMT by multiplex iTRAQ labeling and 2D-LC-MS/MS, and by hybridization, respectively. Temporal iTRAQ analysis identified 66 proteins as differentially expressed during EMT, including newly associated proteins calpain, fascin and macrophage-migration inhibitory factor (MIF). Comparing protein and mRNA expression overtime showed that all the 14 up-regulated proteins involved in the actin-cytoskeleton remodeling were accompanied by increases in corresponding mRNA expression. Interestingly, siRNA mediated knockdown of cofilin1 potentiated TGF-beta-induced EMT. Further analysis of cofilin1 and beta-actin revealed an increase in their mRNA stability in response to TGF-beta, contributing to the observed increase in mRNA and protein expression. These results are the first demonstration of post-transcriptional regulation of cytoskeletal remodelling and a key role for cofilin1 during TGF-beta-induced EMT.

Deep metaproteomic analysis of human salivary supernatant

The human salivary proteome is extremely complex, including proteins from salivary glands, serum, and oral microbes. Much has been learned about the host component, but little is known about the microbial component. Here we report a metaproteomic analysis of salivary supernatant pooled from six healthy subjects. For deep interrogation of the salivary proteome, we combined protein dynamic range compression (DRC), multidimensional peptide fractionation, and high‐mass accuracy MS/MS with a novel two‐step peptide identification method using a database of human proteins plus those translated from oral microbe genomes. Peptides were identified from 124 microbial species as well as uncultured phylotypes such as TM7. Streptococcus, Rothia, Actinomyces, Prevotella, Neisseria, Veilonella, Lactobacillus, Selenomonas, Pseudomonas, Staphylococcus, and Campylobacter were abundant among the 65 genera from 12 phyla represented. Taxonomic diversity in our study was broadly consistent with metagenomic studies of saliva. Proteins mapped to 20 KEGG pathways, with carbohydrate metabolism, amino acid metabolism, energy metabolism, translation, membrane transport, and signal transduction most represented. The communities sampled appear to be actively engaged in glycolysis and protein synthesis. This first deep metaproteomic catalog from human salivary supernatant provides a baseline for future studies of shifts in microbial diversity and protein activities potentially associated with oral disease.

Relative Quantification: Characterization of bias, variability and fold changes in mass spectrometry data from iTRAQ labeled peptides

Shotgun proteomics via mass spectrometry (MS) is a powerful technology for biomarker discovery that has the potential to lead to noninvasive disease screening mechanisms. Successful application of MS-based proteomics technologies for biomarker discovery requires accurate expectations of bias, reproducibility, variance, and the true detectable differences in platforms chosen for analyses. Characterization of the variability inherent in MS assays is vital and should affect interpretation of measurements of observed differences in biological samples. Here we describe observed biases, variance structure, and the ability to detect known differences in spike-in data sets for which true relative abundance among defined samples were known and were subsequently measured with the iTRAQ technology on two MS platforms. Global biases were observed within these data sets. Measured variability was a function of mean abundance. Fold changes were biased toward the null and variance of a fold change was a function of protein mass and abundance. The information presented herein will be valuable for experimental design and analysis of the resulting data.

Flexible and accessible workflows for improved proteogenomic analysis using the Galaxy framework.

Proteogenomics combines large-scale genomic and transcriptomic data with mass-spectrometry-based proteomic data to discover novel protein sequence variants and improve genome annotation. In contrast with conventional proteomic applications, proteogenomic analysis requires a number of additional data processing steps. Ideally, these required steps would be integrated and automated via a single software platform offering accessibility for wet-bench researchers as well as flexibility for user-specific customization and integration of new software tools as they emerge. Toward this end, we have extended the Galaxy bioinformatics framework to facilitate proteogenomic analysis. Using analysis of whole human saliva as an example, we demonstrate Galaxy’s flexibility through the creation of a modular workflow incorporating both established and customized software tools that improve depth and quality of proteogenomic results. Our customized Galaxy-based software includes automated, batch-mode BLASTP searching and a Peptide Sequence Match Evaluator tool, both useful for evaluating the veracity of putative novel peptide identifications. Our complex workflow (approximately 140 steps) can be easily shared using built-in Galaxy functions, enabling their use and customization by others. Our results provide a blueprint for the establishment of the Galaxy framework as an ideal solution for the emerging field of proteogenomics.

Adaptation to low temperature and regulation of gene expression in antarctic psychrotrophic bacteria

Exposure to extremes of temperatures cause stresses which are sometimes lethal to living cells. Microorganisms in nature, however, are extremely diverse and some of them can live happily in the freezing cold of Antarctica. Among the cold adapted psychrotrophs and psychrophiles, the psychrotrophic bacteria are the predominant forms in the continental Antarctica. In spite of living in permanently cold area, the antarctic bacteria exhibit, similar to mesophiles, ‘cold-shock’ response albeit at a much lower temperatures, e.g., at 0–5°C. However, because of permanently cold condition and the long isolation of the continent, the microorganisms have acquired new adaptive features in the membranes, enzymes and macromolecular synthesis. Only recently these adaptive modifications are coming into light due to the efforts of various laboratories around the world. However, a lot more is known about adaptive response to low temperature in mesophilic bacteria than in antarctic bacteria. Combined knowledge from the two systems is providing useful clues to the understanding of basic biology of low temperature growing organisms. This article will provide an overview of this area of research with a special reference to sensing of temperature and regulation of gene expression at lower temperature.

Multi-omic data analysis using Galaxy.

[Extract] Comprehensive multi-omic data acquisition has become a reality, largely driven by the availability of high-throughput sequencing technologies for genomes and transcriptomes1, and high-resolution mass spectrometry (MS)2,3 for the in-depth characterization of proteomes and metabolomes. Integrating genomic and proteomic data enables proteogenomic 4 and metaproteomic approaches 4, whereas integrating metabolomic and transcriptomic or proteomic data links biochemical activity profiles to expressed genes and proteins 6. Despite the potential for new discoveries, integrated analysis of raw multi-omic data is an often overlooked challenge 7, demanding the use of disparate software programs and requiring computational resources beyond the capacity of most biological research laboratories. For these reasons, multi-omic approaches remain out of reach for many. Here, we describe how Galaxy 8 can be used as one solution to this problem.

Using Galaxy-P to leverage RNA-Seq for the discovery of novel protein variations

BackgroundCurrent practice in mass spectrometry (MS)-based proteomics is to identify peptides by comparison of experimental mass spectra with theoretical mass spectra derived from a reference protein database; however, this strategy necessarily fails to detect peptide and protein sequences that are absent from the database. We and others have recently shown that customized proteomic databases derived from RNA-Seq data can be employed for MS-searching to both improve MS analysis and identify novel peptides. While this general strategy constitutes a significant advance for the discovery of novel protein variations, it has not been readily transferable to other laboratories due to the need for many specialized software tools. To address this problem, we have implemented readily accessible, modifiable, and extensible workflows within Galaxy-P, short for Galaxy for Proteomics, a web-based bioinformatic extension of the Galaxy framework for the analysis of multi-omics (e.g. genomics, transcriptomics, proteomics) data.ResultsWe present three bioinformatic workflows that allow the user to upload raw RNA sequencing reads and convert the data into high-quality customized proteomic databases suitable for MS searching. We show the utility of these workflows on human and mouse samples, identifying 544 peptides containing single amino acid polymorphisms (SAPs) and 187 peptides corresponding to unannotated splice junction peptides, correlating protein and transcript expression levels, and providing the option to incorporate transcript abundance measures within the MS database search process (reduced databases, incorporation of transcript abundance for protein identification score calculations, etc.).ConclusionsUsing RNA-Seq data to enhance MS analysis is a promising strategy to discover novel peptides specific to a sample and, more generally, to improve proteomics results. The main bottleneck for widespread adoption of this strategy has been the lack of easily used and modifiable computational tools. We provide a solution to this problem by introducing a set of workflows within the Galaxy-P framework that converts raw RNA-Seq data into customized proteomic databases.

Metaproteomic analysis using the Galaxy framework

Metaproteomics characterizes proteins expressed by microorganism communities (microbiome) present in environmental samples or a host organism (e.g. human), revealing insights into the molecular functions conferred by these communities. Compared to conventional proteomics, metaproteomics presents unique data analysis challenges, including the use of large protein databases derived from hundreds or thousands of organisms, as well as numerous processing steps to ensure high data quality. These challenges limit the use of metaproteomics for many researchers. In response, we have developed an accessible and flexible metaproteomics workflow within the Galaxy bioinformatics framework. Via analysis of human oral tissue exudate samples, we have established a modular Galaxy‐based workflow that automates a reduction method for searching large sequence databases, enabling comprehensive identification of host proteins (human) as well as “meta‐proteins” from the nonhost organisms. Downstream, automated processing steps enable basic local alignment search tool analysis and evaluation/visualization of peptide sequence match quality, maximizing confidence in results. Outputted results are compatible with tools for taxonomic and functional characterization (e.g. Unipept, MEGAN5). Galaxy also allows for the sharing of complete workflows with others, promoting reproducibility and also providing a template for further modification and enhancement. Our results provide a blueprint for establishing Galaxy as a solution for metaproteomic data analysis. All MS data have been deposited in the ProteomeXchange with identifier PXD001655 (http://proteomecentral.proteomexchange.org/dataset/PXD001655).