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.

Proteomics Goes to Court: A Statistical Foundation for Forensic Toxin/Organism Identification Using Bottom-Up Proteomics.

Bottom-up proteomics is increasingly being used to characterize unknown environmental, clinical, and forensic samples. Proteomics-based bacterial identification typically proceeds by tabulating peptide "hits" (i.e., confidently identified peptides) associated with the organisms in a database; those organisms with enough hits are declared present in the sample. This approach has proven to be successful in laboratory studies; however, important research gaps remain. First, the common-practice reliance on unique peptides for identification is susceptible to a phenomenon known as signal erosion. Second, no general guidelines are available for determining how many hits are needed to make a confident identification. These gaps inhibit the transition of this approach to real-world forensic samples where conditions vary and large databases may be needed. In this work, we propose statistical criteria that overcome the problem of signal erosion and can be applied regardless of the sample quality or data analysis pipeline. These criteria are straightforward, producing a p-value on the result of an organism or toxin identification. We test the proposed criteria on 919 LC-MS/MS data sets originating from 2 toxins and 32 bacterial strains acquired using multiple data collection platforms. Results reveal a > 95% correct species-level identification rate, demonstrating the effectiveness and robustness of proteomics-based organism/toxin identification.

Phosphate-containing polyethylene glycol polymers prevent lethal sepsis by multidrug-resistant pathogens.

Antibiotic resistance among highly pathogenic strains of bacteria and fungi is a growing concern in the face of the ability to sustain life during critical illness with advancing medical interventions. The longer patients remain critically ill, the more likely they are to become colonized by multidrug-resistant (MDR) pathogens. The human gastrointestinal tract is the primary site of colonization of many MDR pathogens and is a major source of life-threatening infections due to these microorganisms. Eradication measures to sterilize the gut are difficult if not impossible and carry the risk of further antibiotic resistance. Here, we present a strategy to contain rather than eliminate MDR pathogens by using an agent that interferes with the ability of colonizing pathogens to express virulence in response to host-derived and local environmental factors. The antivirulence agent is a phosphorylated triblock high-molecular-weight polymer (here termed Pi-PEG 15-20) that exploits the known properties of phosphate (Pi) and polyethylene glycol 15-20 (PEG 15-20) to suppress microbial virulence and protect the integrity of the intestinal epithelium. The compound is nonmicrobiocidal and appears to be highly effective when tested both in vitro and in vivo. Structure functional analyses suggest that the hydrophobic bis-aromatic moiety at the polymer center is of particular importance to the biological function of Pi-PEG 15-20, beyond its phosphate content. Animal studies demonstrate that Pi-PEG prevents mortality in mice inoculated with multiple highly virulent pathogenic organisms from hospitalized patients in association with preservation of the core microbiome.

Method development for comprehensive extraction and analysis of marine toxins: Liquid-liquid extraction and tandem liquid chromatography separations coupled to electrospray tandem mass spectrometry.

A variety of toxins are produced by marine and freshwater microorganisms that present a threat to human health. These toxins have diverse chemical properties and specifically, a range of hydrophobicity. Methods for extraction and identification of these toxins are often geared toward specific classes of toxin depending on the sample type. There is a need for a general method of toxin extraction and identification for screening samples where the likely toxin content is not known a priori. We have applied a general method for metabolite extraction to toxin containing samples. This method was coupled with a simple dual liquid chromatography approach for separating a broad range of toxins. This liquid chromatography approach was coupled to triple quadrupole and quadrupole time-of-flight MS/MS platforms. The method was testing on a fish matrix for recovery of palytoxin as well as marine corals for detection of natural mixtures of palytoxin analogues. The recovery of palytoxin was found to produce a linear response (R2 of 0.95) when spiked into the fish matrix with a limit of quantitation of 2.5 ng/µL and recovery efficiency of 73% + /- 9%. The screening of corals revealed varying amount of palytoxin, and in one case, different palytoxin structural analogues. This demonstration illustrates the potential utility of this method for toxin extraction and detection.

Protein abundances can distinguish between naturally-occurring and laboratory strains of Yersinia pestis, the causative agent of plague.

The rapid pace of bacterial evolution enables organisms to adapt to the laboratory environment with repeated passage and thus diverge from naturally-occurring environmental ("wild") strains. Distinguishing wild and laboratory strains is clearly important for biodefense and bioforensics; however, DNA sequence data alone has thus far not provided a clear signature, perhaps due to lack of understanding of how diverse genome changes lead to convergent phenotypes, difficulty in detecting certain types of mutations, or perhaps because some adaptive modifications are epigenetic. Monitoring protein abundance, a molecular measure of phenotype, can overcome some of these difficulties. We have assembled a collection of Yersinia pestis proteomics datasets from our own published and unpublished work, and from a proteomics data archive, and demonstrated that protein abundance data can clearly distinguish laboratory-adapted from wild. We developed a lasso logistic regression classifier that uses binary (presence/absence) or quantitative protein abundance measures to predict whether a sample is laboratory-adapted or wild that proved to be ~98% accurate, as judged by replicated 10-fold cross-validation. Protein features selected by the classifier accord well with our previous study of laboratory adaptation in Y. pestis. The input data was derived from a variety of unrelated experiments and contained significant confounding variables. We show that the classifier is robust with respect to these variables. The methodology is able to discover signatures for laboratory facility and culture medium that are largely independent of the signature of laboratory adaptation. Going beyond our previous laboratory evolution study, this work suggests that proteomic differences between laboratory-adapted and wild Y. pestis are general, potentially pointing to a process that could apply to other species as well. Additionally, we show that proteomics datasets (even archived data collected for different purposes) contain the information necessary to distinguish wild and laboratory samples. This work has clear applications in biomarker detection as well as biodefense.