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Merkley, Eric D.

Publications and source records attributed to Merkley, Eric D..

13 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Proteomics Analysis of Human Contaminant Proteins

Complete characterization of unknowns via proteomics remains challenging. There exist regions of mass spectrometry-based proteomics data where empirical measurements are not attributed to peptides, and/or sequenced peptides from mass spectra are not attributed to any source. These uncharacterized regions are known as the “dark” proteome. Many proteomics tools rely on some a priori knowledge of sample composition; few tools allow for investigation of unknowns without relying on composition assumptions. Further, the potential low abundance of minor traces in these uncharacterized regions can make elucidation of the “dark” proteome challenging. Herein, we describe the development and evaluation of approaches to study the “dark” proteome and move towards an untargeted approach for more complete characterization, namely by studying minor human protein traces in non-human samples and combining that approach with non-human source organism identification without relying on assumptions. Human protein markers, in the form of genetically variant peptides, have been extensively examined in a variety of human matrices, including blood, plasma, and hair, but have yet to be investigated in non-human samples, such as cell cultures, as human contaminant traces. Genetically variant peptides are those that are found in proteins carrying single nucleotide polymorphisms. In this work, we aimed to (1) investigate the feasibility of detecting human contaminant genetically variant peptides (GVPs) in a diverse set of non-human organisms using public proteomics data and a computational pipeline, as well as to (2) develop a combined capability for untargeted source organism characterization and GVP detection. To our knowledge, this is the first report of applying these approaches towards a more complete proteomic characterization of unknowns. We successfully demonstrate the feasibility of broad human contaminant GVP detection in proteomics data, develop a better understanding of GVP detectability, characterize the sample-to-sample variability in GVP detection, and identify a core set of GVPs that can potentially be used as markers indicative of the human contaminant traces portion of the “dark” proteome. Further, we developed and evaluated a combined pipeline, MARLOWE-GVP, that enables both untargeted source organism characterization and GVP detection. We show high accuracy of correct source organism characterization and high degree of similarity of human contaminant GVP detection compared to the conventional approach. Success on both these efforts have allowed us to advance our understanding and characterization of the “dark” proteome.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Bayesian Inference for Integrating Yarrowia lipolytica Multiomics Datasets with Metabolic Modeling

Optimizing the metabolism of microbial cell factories for yields and titers is a critical step for economically viable production of bioproducts and biofuels. In this process, tuning the expression of individual enzymes to obtain the desired pathway flux is a challenging step, in which data from separate multiomics techniques must be integrated with existing biological knowledge to determine where changes should be made. Following a design-build-test-learn strategy, building on recent advances in Bayesian metabolic control analysis, we identify in this work key enzymes in the oleaginous yeast Yarrowia lipolytica that correlate with the production of itaconate by integrating a metabolic model with multiomics measurements. To this extent, we quantify the uncertainty for a variety of key parameters, known as flux control coefficients (FCCs), needed to improve the bioproduction of target metabolites and statistically obtain key correlations between the measured enzymes and boundary flux. Based on the top five significant FCCs and five correlated enzymes, our results show phosphoglycerate mutase, acetyl-CoA synthetase (ACSm), carbonic anhydrase (HCO3E), pyrophosphatase (PPAm), and homoserine dehydrogenase (HSDxi) enzymes in rate-limiting reactions that can lead to increased itaconic acid production.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Flying Blind, or Just Flying Under the Radar? The Underappreciated Power of De Novo Methods of Mass Spectrometric Peptide Identification

Mass spectrometry-based proteomics is a popular and powerful method for precise and highly multiplexed protein identification. The most common method of analyzing untargeted proteomics data is called database searching, where the database is simply a collection of protein sequences from the target organism, derived from genome sequencing. Experimental peptide tandem mass spectra are compared to simplified models of theoretical spectra calculated from the translated genomic sequences. However, in several interesting application areas, such as forensics, archaeology, venomics and others, a genome sequence may not be available, or the correct genome sequence to use is not known. In these cases, de novo peptide identification can play an important role. De novo peptide identification infers peptide sequence directly from the tandem mass spectrum without reference to a sequence database, usually using graph-based or machine learning algorithms. In this review, we provide a basic overview of de novo peptide identification methods and applications, briefly covering de novo algorithms and tools, and focusing in more depth on recent applications from venomics, metaproteomics, forensics, and characterization of antibody drugs.

proteomics, mass spectrometry, forensics, de novo ↗
DOE OSTI2020

Applications in Forensic Proteomics: Protein Identification and Profiling. ACS Symposium Series

Proteomics is a mature research tool in the life sciences, and it can be a powerful addition to the forensic science toolbox. This work presents several areas in which proteomics was used to answer forensic questions. Illustrating current applications of proteomics methods, this work introduces opportunities for proteomics to answer compelling questions in forensic science and biosecurity. These "case studies" will be valuable to both practicing forensic scientists and researchers developing proteomics methods.

proteomics, forensics, mass spectrometry, toxins, ↗
DOE OSTI2019

Uniformly 15N-Labeled Recombinant Ricin A-chain as an Internal Retention Time Standard for Increased Confidence in Forensic Identification of Ricin by Untargeted Nanoflow Liquid Chromatography-Tandem Mass Spectrometry

Ricin, a toxic protein from the castor plant, is of forensic and biosecurity interest because of its high toxicity and common occurrence in crimes and attempted crimes. Qualitative methods to detect ricin are therefore needed. Untargeted liquid chromatography-tandem mass spectrometry (LC-MS/MS) proteomics methods are well-suited because of their high speci-ficity. Specificity in LC-MS/MS comes from both the LC and MS components. However, modern untargeted proteomics methods often use nanoflow LC, which has less reproducible retention times than standard-flow LC, making it challenging to use retention time as a point of identification in a forensic assay. We address this challenge by using retention times rela-tive to a standard, namely uniformly 15N–labeled ricin A-chain produced recombinantly in a bacterial expression system. This material, added as an internal standard prior to trypsin digestion, produces a stable isotope labeled standard for every ricin tryptic peptide in the sample. We show that the MS signals for 15N and natural isotopic abundance ricin peptides are distinct, with mass shifts that correspond to the numbers of nitrogen atoms in each peptide or fragment. We also show that, as expected, labeled and unlabeled peptides coelute, with relative retention time differences of less than 0.2%.

ricin, mass spectrometry, isotope, nitrogen-15, re↗
DOE OSTI2019

Constructing a Tandem Mass Spectral Library for Forensic Ricin Identification

Ricin, a protein found in castor seeds, is a lethal toxin that is designated as a category 2 select agent. Because castor seeds are easy to obtain and the toxin can be easily extracted, cases of attempted ricin poisoning are relatively common. A shotgun proteomics method for ricin identification has recently been developed (manuscript in preparation), in which ricin peptides are identified by liquid chromatography-tandem mass spectrometry (LC-MS/MS) followed by proteomics database search, and peptide-spectrum matches are verified and compared to standard spectra by a human expert. To make this process more reproducible, objective, and high-throughput, we have created a ricin spectral library for peptide identification to supplement the human review step. To construct these spectral libraries, two pure ricin samples (from a proposed standard reference material) and crude castor seed extracts were digested with trypsin and analyzed using a standard shotgun LC-MS/MS protocol. Spectral libraries were created from the filtered search results from four database search tools. The library was then used in a search using SpectraST on samples from castor seeds. Analysis showed that the spectral library search resulted in more peptides identified from crude castor seed samples compared to MS-GF+ and Sequest plus Percolator. These results suggest that computational comparison of putative ricin peptide spectra to library spectra can be an effective method of confirming the presence of ricin, and that spectral library search may be suitable to augment the more manual and subjective aspects of the currently recommended human expert review.

Spectral Library, Ricin, forensic, proteomic↗
DOE OSTI2019

Probabilistic limit of detection for ricin identification using a shotgun proteomics assay

Robust and highly specific methods for the detection of the protein toxin ricin are of interest to the law enforcement community. In previous studies, methods based on liquid chromatography-tandem mass spectrometry shotgun proteomics have been proposed. The successful implementation of this approach relies on specific data evaluation criteria addressing (1) the quality of the mass spectrometric data, (2) the confidence of peptide identifications (peptide-spectrum matches), and (3) the number and sequence specificity of peptides detected. We present such data evaluation criteria, and use a novel approach to establish the limit of detection for this ricin assay. Specifically, we use logistic regression to determine the probability of detection for individual ricin peptides at different concentrations. We then apply basic rules from probability theory, combining these individual peptide probabilities into an overall assay limit of detection. This procedure yields an assay limit of detection for ricin at 42.5 ng on column or 21.25 ng/µL for a 2-µL injection. We also show that, in spite of the conventional wisdom that detergents are deleterious to mass spectrometric analyses, the presence of Tween-20 did not prevent detection of ricin peptides, and indeed assays performed in buffers that included Tween-20 gave better results than assays performed using other buffer formulations with or without detergent removal.

Heller, Natalie C.↗
DOE OSTI2019

Proteomics for Microbial Forensics

Mass spectrometry-based proteomics is a powerful tool for the detection and characterization of microbes of forensic and national security concern. Both targeted and untargeted proteomics methods have been developed for the taxonomic classification of unknown microbial samples. Targeted proteomics assays are can be designed for specific microbes of security concern. Untargeted, library-based matrix-assisted laser desorption-ionization time of flight (MALDI-TOF) mass spectrometry is now extensively used in the medical field for microbial identification. Several research groups have developed data analysis pipelines for organism identification/taxonomic classification from untargeted liquid chromatography-tandem mass spectrometry (LC-MS/MS) data, an application which has some overlap with the field of metaproteomics. This chapter reviews these organism identification techniques. However, organism identification/classification is also commonly done by whole genome sequencing. Using examples from published literature, this chapter also highlights several examples of how proteomics approaches can provide information that cannot be acquired from DNA sequencing alone, such as distinguishing laboratory-adapted bacteria from closely-related wild isolates, and characterizing the growth medium of bacteria and the host cells of virus particles.

proteomics, microbial forensics, mass spectrometry↗
DOE OSTI2019

The statistical defensibility of forensic proteomics

The U.S. Federal court system maintains very high standards for admissibility of scientific evidence. In particular, to allow such evidence into court, a judge must be satisfied it is both reliable, in other words, based on a robust, reproducible, and accurate method, and relevant, meaning it adds weight to the prosecution or defense claims in a case. In 1993, the U.S. Supreme Court issued landmark guidelines for judges considering whether or not to admit scientific testimony. The resulting Daubert criteria changed the forensic sciences in a profound way, leading investigators, researchers, and legal experts to question the scientific validity of established forensic methods that had been widely accepted for years. The Daubert criteria also caused a paradigm shift in the development of new forensic methods, forcing researchers to better prepare new techniques for use in an increasingly adversarial environment. In this chapter, we discuss the current state of forensic proteomics in the context of modern forensics. We present the Daubert criteria, discuss seven elements of a defensible method, and provide guidelines for building statistical defensibility of this emerging discipline.

Jarman, Kristin H.↗
DOE OSTI2018

Perspective on Improving Environmental Monitoring of Biothreats

For more than a decade, the United States has performed environmental monitoring by collecting and analyzing air samples for a handful of biological threat agents (BTAs) in order to detect a possible biological attack. This effort has faced numerous technical challenges including timeliness, sampling efficiency, sensitivity, specificity, and robustness. The cost of city-wide environmental monitoring using conventional technology has also been a challenge. A large group of scientists with expertise in bioterrorism defense met to assess the objectives and current efficacy of environmental monitoring and to identify operational and technological changes that could enhance its efficacy and cost-effectiveness, thus enhancing its value. The highest priority operational change that was identified was to abandon the current concept of city-wide environmental monitoring because the operational costs were too high and its value was compromised by low detection sensitivity and other environmental factors. Instead, it was suggested that the focus should primarily be on indoor monitoring and secondarily on special-event monitoring because objectives are tractable and these operational settings are aligned with likelihood and risk assessments. The highest priority technological change identified was the development of a reagent-less, real-time sensor that can identify a potential airborne release and trigger secondary tests of greater sensitivity and specificity for occasional samples of interest. This technological change could be transformative with the potential to greatly reduce operational costs and thereby create the opportunity to expand the scope and effectiveness of environmental monitoring.

Dunbar, John↗
DOE OSTI2018

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 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 samples where conditions vary and large databases may be needed. We propose statistical criteria for organism identification that applies regardless of sample quality or data analysis pipeline. These criteria are straightforward, producing a p-value on the result. We test the criteria on LC-MS/MS datasets representing 919 different bacterial and toxin datasets acquired with multiple data collection platforms. Results reveal a >95% correct species-level identification rate, demonstrating the effectiveness and robustness of proteomics-based organism/toxin identification.

Jarman, Kristin H.↗