Insights from a workplace SARS-CoV-2 specimen collection program, with genomes placed into global sequence phylogeny.

In 2020, the Department of Energy established the National Virtual Biotechnology Laboratory (NVBL) to address key challenges associated with COVID-19. As part of that effort, Pacific Northwest National Laboratory (PNNL) established a capability to collect and analyze specimens from employees who self-reported symptoms consistent with the disease. During the spring and fall of 2021, 688 specimens were screened for SARS-CoV-2, with 64 (9.3%) testing positive using reverse-transcriptase quantitative PCR (RT-qPCR). Of these, 36 samples were released for research. All 36 positive samples released for research were sequenced and genotyped. Here, the relationship between patient age and viral load as measured by Ct values was measured and determined to be only weakly significant. Consensus sequences for each sample were placed into a global phylogeny and transmission dynamics were investigated, revealing that the closest relative for many samples was from outside of Washington state, indicating mixing of viral pools within geographic regions.

An analysis pipeline for the inference of protein-protein interaction networks.

We present a platform for the reconstruction of protein-protein interaction networks inferred from Mass Spectrometry (MS) bait-prey data. The Software Environment for Biological Network Inference (SEBINI), an environment for the deployment of network inference algorithms that use high-throughput data, forms the platform core. Among the many algorithms available in SEBINI is the Bayesian Estimator of Probabilities of Protein-Protein Associations (BEPro3) algorithm, which is used to infer interaction networks from such MS affinity isolation data. Also, the pipeline incorporates the Collective Analysis of Biological Interaction Networks (CABIN) software. We have thus created a structured workflow for protein-protein network inference and supplemental analysis.

A general system for studying protein-protein interactions in Gram-negative bacteria.

One of the most promising methods for large-scale studies of protein interactions is isolation of an affinity-tagged protein with its in vivo interaction partners, followed by mass spectrometric identification of the copurified proteins. Previous studies have generated affinity-tagged proteins using genetic tools or cloning systems that are specific to a particular organism. To enable protein-protein interaction studies across a wider range of Gram-negative bacteria, we have developed a methodology based on expression of affinity-tagged "bait" proteins from a medium copy-number plasmid. This construct is based on a broad-host-range vector backbone (pBBR1MCS5). The vector has been modified to incorporate the Gateway DEST vector recombination region, to facilitate cloning and expression of fusion proteins bearing a variety of affinity, fluorescent, or other tags. We demonstrate this methodology by characterizing interactions among subunits of the DNA-dependent RNA polymerase complex in two metabolically versatile Gram-negative microbial species of environmental interest, Rhodopseudomonas palustris CGA010 and Shewanella oneidensis MR-1. Results compared favorably with those for both plasmid and chromosomally encoded affinity-tagged fusion proteins expressed in a model organism, Escherichia coli.

A Bayesian estimator of protein-protein association probabilities.

UNLABELLED: The Bayesian Estimator of Protein-Protein Association Probabilities (BEPro aff3) is a software tool for estimating probabilities of protein-protein association between bait and prey protein pairs using data from multiple-bait, multiple-replicate, protein liquid chromatography tandem mass spectrometry LC-MS/MS affinity isolation experiments. AVAILABILITY: BEPro (3) is public domain software, has been tested on WIndows XP, Linux and Mac OS, and is freely available from http://www.pnl.gov/statistics/BEPro3. SUPPLEMENTARY INFORMATION: A user guide, example dataset with analysis and additional documentation are included with the BEPro (3) download.

Statistically inferring protein-protein associations with affinity isolation LC-MS/MS assays.

Affinity isolation of protein complexes followed by protein identification by LC-MS/MS is an increasingly popular approach for mapping protein interactions. However, systematic and random assay errors from multiple sources must be considered to confidently infer authentic protein-protein interactions. To address this issue, we developed a general, robust statistical method for inferring authentic interactions from protein prey-by-bait frequency tables using a binomial-based likelihood ratio test (LRT) coupled with Bayes' Odds estimation. We then applied our LRT-Bayes' algorithm experimentally using data from protein complexes isolated from Rhodopseudomonas palustris. Our algorithm, in conjunction with the experimental protocol, inferred with high confidence authentic interacting proteins from abundant, stable complexes, but few or no authentic interactions for lower-abundance complexes. The algorithm can discriminate against a background of prey proteins that are detected in association with a large number of baits as an artifact of the measurement. We conclude that the experimental protocol including the LRT-Bayes' algorithm produces results with high confidence but moderate sensitivity. We also found that Monte Carlo simulation is a feasible tool for checking modeling assumptions, estimating parameters, and evaluating the significance of results in protein association studies.

Automated purification of recombinant proteins: combining high-throughput with high yield.

Protein crystallography, mapping protein interactions, and other functional genomic approaches require purifying many different proteins, each of sufficient yield and homogeneity, for subsequent high-throughput applications. To fill this requirement efficiently, there is a need to develop robust, automated, high-throughput protein expression, and purification processes. We developed and compared two alternative workflows for automated purification of recombinant proteins based on expression of bacterial genes in Escherichia coli (E. coli). The first is a filtration separation protocol in which proteins of interest are expressed in a large volume, 800 ml of E. coli cultures, then isolated by filtration purification using Ni-NTA-Agarose (Qiagen). The second is a smaller scale magnetic separation method in which proteins of interest are expressed in a small volume, 25 ml, of E. coli cultures then isolated using a 96-well purification system with MagneHis Ni2+ Agarose (Promega). Both workflows provided comparable average yields of proteins, about 8 microg of purified protein per optical density unit of bacterial culture measured at 600 nm. We discuss advantages and limitations of these automated workflows, which can provide proteins with more than 90% purity and yields in the range of 100 microg to 45 mg per purification run, as well as strategies for optimizing these protocols.

Simple protein complex purification and identification method for high-throughput mapping of protein interaction networks.

Most current methods for purification and identification of protein complexes use endogenous expression of affinity-tagged bait, tandem affinity tag purification of protein complexes followed by specific elution of complexes from beads, and gel separation and in-gel digestion prior to mass spectrometric analysis of protein interactors. We propose a single affinity tag in vitro pull-down assay with denaturing elution, trypsin digestion in organic solvent, and LC-ESI MS/MS protein identification using SEQUEST analysis. Our method is simple and easy to scale-up and automate, making it suitable for high-throughput mapping of protein interaction networks and functional proteomics.

A proteomic approach to characterize protein shedding.

Shedding (i.e. proteolysis of ectodomains of membrane proteins) plays an important pathophysiological role. In order to study the feasibility of identifying shed proteins, we analyzed serum-free media of human mammary epithelial cells by mass spectrometry following induction of shedding by the phorbol ester, 4 beta-phorbol 12-myristate 13-acetate (PMA). Different means of sample preparation, including biotinylation of cell surface proteins, isolation of glycosylated proteins, and preparation of crude protein fractions, were carried out to develop the optimal method of sample processing. The collected proteins were digested with trypsin and analyzed by reversed-phase capillary liquid chromatography interfaced to an ion-trap mass spectrometer. The resulting peptide spectra were interpreted using the program SEQUEST. Analyzing the sample containing the crude protein mixture without chemical modification or separation resulted in the greatest number of identifications, including putatively shed proteins. Overall, 45 membrane-associated proteins were identified including 22 that contain at least one transmembrane domain and 23 that indirectly associate with the extracellular surface of the plasma membrane. Of the 22 transmembrane proteins, 18 were identified by extracellular peptides providing strong evidence they originate from regulated proteolysis or shedding processes. We combined results from the different experiments and used a peptide count method to estimate changes in protein abundance. Using this approach, we identified two proteins, syndecan-4 and hepatoma-derived growth factor, whose abundances increased in media of cells treated with PMA. We also detected proteins whose abundances decreased after PMA treatment such as 78 kDa glucose-regulated protein and lactate dehydrogenase A. Further analysis using immunoblotting validated the abundance changes for syndecan-4 and 78 kDa glucose-regulated protein as a result of PMA treatment. These results demonstrate that tandem mass spectrometry can be used to identify shed proteins and to estimate changes in protein abundance.

High-efficiency on-line solid-phase extraction coupling to 15-150-microm-i.d. column liquid chromatography for proteomic analysis.

The ability to manipulate and effectively utilize small proteomic samples is important for analyses using liquid chromatography (LC) in combination with mass spectrometry (MS) and becomes more challenging for very low flow rates due to extra column volume effects on separation quality. Here we report on the use of commercial switching valves (150-microm channels) for implementing the on-line coupling of capillary LC columns operated at 10,000 psi with relatively large solid-phase extraction (SPE) columns. With the use of optimized column connections, switching modes, and SPE column dimensions, high-efficiency on-line SPE-capillary and nanoscale LC separations were obtained demonstrating peak capacities of approximately 1000 for capillaries having inner diameters between 15 and 150 microm. The on-line coupled SPE columns increased the sample processing capacity by approximately 400-fold for sample solution volume and approximately 10-fold for sample mass. The proteomic applications of this on-line SPE-capillary LC system were evaluated for analysis of both soluble and membrane protein tryptic digests. Using an ion trap tandem MS it was typically feasible to identify 1100-1500 unique peptides in a 5-h analysis. Peptides extracted from the SPE column and then eluted from the LC column covered a hydrophilicity/hydrophobicity range that included an estimated approximately 98% of all tryptic peptides. The SPE-capillary LC implementation also facilitates automation and enables use of both disposable SPE columns and electrospray emitters, providing a robust basis for automated proteomic analyses.

Characterization of plasma membrane proteins from ovarian cancer cells using mass spectrometry.

To determine how the repertoire of plasma membrane proteins change with disease state, specifically related to cancer, several methods for preparation of plasma membrane proteins were evaluated. Cultured cells derived from stage IV ovarian tumors were grown to 90% confluence and harvested in buffer containing CHAPS detergent. This preparation was centrifuged at low speed to remove insoluble cellular debris resulting in a crude homogenate. Glycosylated proteins in the crude homogenate were selectively enriched using lectin affinity chromatography. The crude homogenate and the lectin purified sample were prepared for mass spectrometric evaluation. The general procedure for protein identification began with trypsin digestion of protein fractions followed by separation by reversed phase liquid chromatography that was coupled directly to a conventional tandem mass spectrometer (i.e. LCQ ion trap). Mass and fragmentation data for the peptides were searched against a human proteome data base using the informatics program SEQUEST. Using this procedure 398 proteins were identified with high confidence, including receptors, membrane-associated ligands, proteases, phosphatases, as well as structural and adhesion proteins. Results indicate that lectin chromatography provides a select subset of proteins and that the number and quality of the identifications improve as does the confidence of the protein identifications for this subset. These results represent the first step in development of methods to separate and successfully identify plasma membrane proteins from advanced ovarian cancer cells. Further characterization of plasma membrane proteins will contribute to our understanding of the mechanisms underlying progression of this deadly disease and may lead to new targeted interventions as well as new biomarkers for diagnosis.

Global analysis of the Deinococcus radiodurans proteome by using accurate mass tags.

Understanding biological systems and the roles of their constituents is facilitated by the ability to make quantitative, sensitive, and comprehensive measurements of how their proteome changes, e.g., in response to environmental perturbations. To this end, we have developed a high-throughput methodology to characterize an organism's dynamic proteome based on the combination of global enzymatic digestion, high-resolution liquid chromatographic separations, and analysis by Fourier transform ion cyclotron resonance mass spectrometry. The peptides produced serve as accurate mass tags for the proteins and have been used to identify with high confidence >61% of the predicted proteome for the ionizing radiation-resistant bacterium Deinococcus radiodurans. This fraction represents the broadest proteome coverage for any organism to date and includes 715 proteins previously annotated as either hypothetical or conserved hypothetical.