Decon2LS: An open-source software package for automated processing and visualization of high resolution mass spectrometry data

Abstract Background Data generated from liquid chromatography coupled to high-resolution mass spectrometry (LC-MS)-based studies of a biological sample can contain large amounts of biologically significant information in the form of proteins, peptides, and metabolites. Interpreting this data involves inferring the masses and abundances of biomolecules injected into the instrument. Because of the inherent complexity of mass spectral patterns produced by these biomolecules, the analysis is significantly enhanced by using visualization capabilities to inspect and confirm results. In this paper we describe Decon2LS, an open-source software package for automated processing and visualization of high-resolution MS data. Drawing extensively on algorithms developed over the last ten years for ICR2LS, Decon2LS packages the algorithms as a rich set of modular, reusable processing classes for performing diverse functions such as reading raw data, routine peak finding, theoretical isotope distribution modelling, and deisotoping. Because the source code is openly available, these functionalities can now be used to build derivative applications in relatively fast manner. In addition, Decon2LS provides an extensive set of visualization tools, such as high performance chart controls. Results With a variety of options that include peak processing, deisotoping, isotope composition, etc, Decon2LS supports processing of multiple raw data formats. Deisotoping can be performed on an individual scan, an individual dataset, or on multiple datasets using batch processing. Other processing options include creating a two dimensional view of mass and liquid chromatography (LC) elution time features, generating spectrum files for tandem MS data, creating total intensity chromatograms, and visualizing theoretical peptide profiles. Application of Decon2LS to deisotope different datasets obtained across different instruments yielded a high number of features that can be used to identify and quantify peptides in the biological sample. Conclusion Decon2LS is an efficient software package for discovering and visualizing features in proteomics studies that require automated interpretation of mass spectra. Besides being easy to use, fast, and reliable, Decon2LS is also open-source, which allows developers in the proteomics and bioinformatics communities to reuse and refine the algorithms to meet individual needs. Decon2LS source code, installer, and tutorials may be downloaded free of charge at http://http:/ncrr.pnl.gov/software/

Systems analysis of multiple regulator perturbations allows discovery of virulence factors in Salmonella

<p>Abstract</p> <p>Background</p> <p>Systemic bacterial infections are highly regulated and complex processes that are orchestrated by numerous virulence factors. Genes that are coordinately controlled by the set of regulators required for systemic infection are potentially required for pathogenicity.</p> <p>Results</p> <p>In this study we present a systems biology approach in which sample-matched multi-omic measurements of fourteen virulence-essential regulator mutants were coupled with computational network analysis to efficiently identify <it>Salmonella </it>virulence factors. Immunoblot experiments verified network-predicted virulence factors and a subset was determined to be secreted into the host cytoplasm, suggesting that they are virulence factors directly interacting with host cellular components. Two of these, SrfN and PagK2, were required for full mouse virulence and were shown to be translocated independent of either of the type III secretion systems in <it>Salmonella </it>or the type III injectisome-related flagellar mechanism.</p> <p>Conclusions</p> <p>Integrating multi-omic datasets from <it>Salmonella </it>mutants lacking virulence regulators not only identified novel virulence factors but also defined a new class of translocated effectors involved in pathogenesis. The success of this strategy at discovery of known and novel virulence factors suggests that the approach may have applicability for other bacterial pathogens.</p

Technologies and Approaches to Elucidate and Model the Virulence Program of Salmonella

Salmonella is a primary cause of enteric diseases in a variety of animals. During its evolution into a pathogenic bacterium, Salmonella acquired an elaborate regulatory network that responds to multiple environmental stimuli within host animals and integrates them resulting in fine regulation of the virulence program. The coordinated action by this regulatory network involves numerous virulence regulators, necessitating genome-wide profiling analysis to assess and combine efforts from multiple regulons. In this review we discuss recent high-throughput analytic approaches used to understand the regulatory network of Salmonella that controls virulence processes. Application of high-throughput analyses have generated large amounts of data and necessitated the development of computational approaches for data integration. Therefore, we also cover computer-aided network analyses to infer regulatory networks, and demonstrate how genome-scale data can be used to construct regulatory and metabolic systems models of Salmonella pathogenesis. Genes that are coordinately controlled by multiple virulence regulators under infectious conditions are more likely to be important for pathogenesis. Thus, reconstructing the global regulatory network during infection or, at the very least, under conditions that mimic the host cellular environment not only provides a bird's eye view of Salmonella survival strategy in response to hostile host environments but also serves as an efficient means to identify novel virulence factors that are essential for Salmonella to accomplish systemic infection in the host

Omic data from evolved E. coli are consistent with computed optimal growth from genome-scale models

Proteomic and transcriptomic data from wild-type and laboratory-evolved strains of Escherichia coli are consistent with predicted pathway usage from optimal growth rate solutions.In laboratory-evolved strains, there is an upregulation of the pathways in the computed optimal growth states, and downregulation of non-functional pathways.Known regulatory mechanisms are only partially responsible for altered metabolic pathway activity

Redeploying β-lactam antibiotics as a novel antivirulence strategy for the treatment of methicillin-resistant <i>Staphylococcus aureus</i> infections

Innovative approaches to the use of existing antibiotics is an important strategy in efforts to address the escalating antimicrobial resistance crisis. We report a new approach to the treatment of methicillin-resistant Staphylococcus aureus (MRSA) infections by demonstrating that oxacillin can be used to significantly attenuate the virulence of MRSA despite the pathogen being resistant to this drug. Using mechanistic in vitro assays and in vivo models of invasive pneumonia and sepsis, we show that oxacillin-treated MRSA strains are significantly attenuated in virulence. This effect is based primarily on the oxacillin-dependent repression of the accessory gene regulator quorum-sensing system and altered cell wall architecture, which in turn lead to increased susceptibility to host killing of MRSA. Our data indicate that beta-lactam antibiotics should be included in the treatment regimen as an adjunct antivirulence therapy for patients with MRSA infections. This would represent an important change to current clinical practice for treatment of MRSA infection, with the potential to significantly improve patient outcomes in a safe, cost-effective manner

A Systems Biology Approach to Infectious Disease Research: Innovating the Pathogen-Host Research Paradigm

The twentieth century was marked by extraordinary advances in our understanding of microbes and infectious disease, but pandemics remain, food and waterborne illnesses are frequent, multidrug-resistant microbes are on the rise, and the needed drugs and vaccines have not been developed. The scientific approaches of the past—including the intense focus on individual genes and proteins typical of molecular biology—have not been sufficient to address these challenges. The first decade of the twenty-first century has seen remarkable innovations in technology and computational methods. These new tools provide nearly comprehensive views of complex biological systems and can provide a correspondingly deeper understanding of pathogen-host interactions. To take full advantage of these innovations, the National Institute of Allergy and Infectious Diseases recently initiated the Systems Biology Program for Infectious Disease Research. As participants of the Systems Biology Program, we think that the time is at hand to redefine the pathogen-host research paradigm

VESPA: software to facilitate genomic annotation of prokaryotic organisms through integration of proteomic and transcriptomic data

<p>Abstract</p> <p>Background</p> <p>The procedural aspects of genome sequencing and assembly have become relatively inexpensive, yet the full, accurate structural annotation of these genomes remains a challenge. Next-generation sequencing transcriptomics (RNA-Seq), global microarrays, and tandem mass spectrometry (MS/MS)-based proteomics have demonstrated immense value to genome curators as individual sources of information, however, integrating these data types to validate and improve structural annotation remains a major challenge. Current visual and statistical analytic tools are focused on a single data type, or existing software tools are retrofitted to analyze new data forms. We present Visual Exploration and Statistics to Promote Annotation (VESPA) is a new interactive visual analysis software tool focused on assisting scientists with the annotation of prokaryotic genomes though the integration of proteomics and transcriptomics data with current genome location coordinates.</p> <p>Results</p> <p>VESPA is a desktop Java™ application that integrates high-throughput proteomics data (peptide-centric) and transcriptomics (probe or RNA-Seq) data into a genomic context, all of which can be visualized at three levels of genomic resolution. Data is interrogated via searches linked to the genome visualizations to find regions with high likelihood of mis-annotation. Search results are linked to exports for further validation outside of VESPA or potential coding-regions can be analyzed concurrently with the software through interaction with BLAST. VESPA is demonstrated on two use cases (<it>Yersinia pestis </it>Pestoides F and <it>Synechococcus </it>sp. PCC 7002) to demonstrate the rapid manner in which mis-annotations can be found and explored in VESPA using either proteomics data alone, or in combination with transcriptomic data.</p> <p>Conclusions</p> <p>VESPA is an interactive visual analytics tool that integrates high-throughput data into a genomic context to facilitate the discovery of structural mis-annotations in prokaryotic genomes. Data is evaluated via visual analysis across multiple levels of genomic resolution, linked searches and interaction with existing bioinformatics tools. We highlight the novel functionality of VESPA and core programming requirements for visualization of these large heterogeneous datasets for a client-side application. The software is freely available at <url>https://www.biopilot.org/docs/Software/Vespa.php</url>.</p

Identification of Widespread Adenosine Nucleotide Binding in Mycobacterium tuberculosis

SummaryComputational prediction of protein function is frequently error-prone and incomplete. In Mycobacterium tuberculosis (Mtb), ∼25% of all genes have no predicted function and are annotated as hypothetical proteins, severely limiting our understanding of Mtb pathogenicity. Here, we utilize a high-throughput quantitative activity-based protein profiling (ABPP) platform to probe, annotate, and validate ATP-binding proteins in Mtb. We experimentally validate prior in silico predictions of >240 proteins and identify 72 hypothetical proteins as ATP binders. ATP interacts with proteins with diverse and unrelated sequences, providing an expanded view of adenosine nucleotide binding in Mtb. Several hypothetical ATP binders are essential or taxonomically limited, suggesting specialized functions in mycobacterial physiology and pathogenicity

Campylobacter jejuni Demonstrates Conserved Proteomic and Transcriptomic Responses When Co-cultured With Human INT 407 and Caco-2 Epithelial Cells

Major foodborne bacterial pathogens, such as Campylobacter jejuni, have devised complex strategies to establish and foster intestinal infections. For more than two decades, researchers have used immortalized cell lines derived from human intestinal tissue to dissect C. jejuni-host cell interactions. Known from these studies is that C. jejuni virulence is multifactorial, requiring a coordinated response to produce virulence factors that facilitate host cell interactions. This study was initiated to identify C. jejuni proteins that contribute to adaptation to the host cell environment and cellular invasion. We demonstrated that C. jejuni responds to INT 407 and Caco-2 cells in a similar fashion at the cellular and molecular levels. Active protein synthesis was found to be required for C. jejuni to maximally invade these host cells. Proteomic and transcriptomic approaches were then used to define the protein and gene expression profiles of C. jejuni co-cultured with cells. By focusing on those genes showing increased expression by C. jejuni when co-cultured with epithelial cells, we discovered that C. jejuni quickly adapts to co-culture with epithelial cells by synthesizing gene products that enable it to acquire specific amino acids for growth, scavenge for inorganic molecules including iron, resist reactive oxygen/nitrogen species, and promote host cell interactions. Based on these findings, we selected a subset of the genes involved in chemotaxis and the regulation of flagellar assembly and generated C. jejuni deletion mutants for phenotypic analysis. Binding and internalization assays revealed significant differences in the interaction of C. jejuni chemotaxis and flagellar regulatory mutants. The identification of genes involved in C. jejuni adaptation to culture with host cells provides new insights into the infection process

Unified feature association networks through integration of transcriptomic and proteomic data

High-throughput multi-omics studies and corresponding network analyses of multi-omic data have rapidly expanded their impact over the last 10 years. As biological features of different types (e.g. transcripts, proteins, metabolites) interact within cellular systems, the greatest amount of knowledge can be gained from networks that incorporate multiple types of -omic data. However, biological and technical sources of variation diminish the ability to detect cross-type associations, yielding networks dominated by communities comprised of nodes of the same type. We describe here network building methods that can maximize edges between nodes of different data types leading to integrated networks, networks that have a large number of edges that link nodes of different–omic types (transcripts, proteins, lipids etc). We systematically rank several network inference methods and demonstrate that, in many cases, using a random forest method, GENIE3, produces the most integrated networks. This increase in integration does not come at the cost of accuracy as GENIE3 produces networks of approximately the same quality as the other network inference methods tested here. Using GENIE3, we also infer networks representing antibody-mediated Dengue virus cell invasion and receptor-mediated Dengue virus invasion. A number of functional pathways showed centrality differences between the two networks including genes responding to both GM-CSF and IL-4, which had a higher centrality value in an antibody-mediated vs. receptor-mediated Dengue network. Because a biological system involves the interplay of many different types of molecules, incorporating multiple data types into networks will improve their use as models of biological systems. The methods explored here are some of the first to specifically highlight and address the challenges associated with how such multi-omic networks can be assembled and how the greatest number of interactions can be inferred from different data types. The resulting networks can lead to the discovery of new host response patterns and interactions during viral infection, generate new hypotheses of pathogenic mechanisms and confirm mechanisms of disease