BilR is a gut microbial enzyme that reduces bilirubin to urobilinogen.

Metabolism of haem by-products such as bilirubin by humans and their gut microbiota is essential to human health, as excess serum bilirubin can cause jaundice and even neurological damage. The bacterial enzymes that reduce bilirubin to urobilinogen, a key step in this pathway, have remained unidentified. Here we used biochemical analyses and comparative genomics to identify BilR as a gut-microbiota-derived bilirubin reductase that reduces bilirubin to urobilinogen. We delineated the BilR sequences from similar reductases through the identification of key residues critical for bilirubin reduction and found that BilR is predominantly encoded by Firmicutes species. Analysis of human gut metagenomes revealed that BilR is nearly ubiquitous in healthy adults, but prevalence is decreased in neonates and individuals with inflammatory bowel disease. This discovery sheds light on the role of the gut microbiome in bilirubin metabolism and highlights the significance of the gut-liver axis in maintaining bilirubin homeostasis.

Simple Tool for Rapidly Assessing the Quality of Multiplexed Single Cell Proteomics Data.

Recent advances in the sensitivity and speed of mass spectrometers coupled with improved sample preparation methods have enabled the field of single cell proteomics to proliferate. While heavy development is occurring in the label free space, dramatic improvements in throughput are provided by multiplexing with tandem mass tags. Hundreds or thousands of single cells can be analyzed with this method, yielding large data sets which may contain poor data arising from loss of material during cell sorting or poor digestion, labeling, and lysis. To date, no tools have been described that can assess data quality prior to data processing. We present herein a lightweight python script and accompanying graphic user interface that can rapidly quantify reporter ion peaks within each MS/MS spectrum in a file. With simple summary reports, we can identify single cell samples that fail to pass a set quality threshold, thus reducing analysis time waste. In addition, this tool, Diagnostic Ion Data Analysis Reduction (DIDAR), will create reduced MGF files containing only spectra possessing a user-specified number of single cell reporter ions. By reducing the number of spectra that have excessive zero values, we can speed up sample processing with little loss in data completeness as these spectra are removed in later stages in data processing workflows. DIDAR and the DIDAR GUI are compatible with all modern operating systems and are available at: https://github.com/orsburn/DIDARSCPQC. All files described in this study are available at www.massive.ucsd.edu as accession MSV000088887.

Discovery of the gut microbial enzyme responsible for bilirubin reduction to urobilinogen.

The degradation of heme and the interplay of its catabolic derivative, bilirubin, between humans and their gut microbiota is an essential facet of human health. However, the hypothesized bacterial enzyme that reduces bilirubin to urobilinogen, a key step that produces the excretable waste products of this pathway, has remained unidentified. In this study, we used a combination of biochemical analyses and comparative genomics to identify a novel enzyme, BilR, that can reduce bilirubin to urobilinogen. We delineated the BilR sequences from other members of the Old Yellow Enzyme family through the identification of key residues in the active site that are critical for bilirubin reduction and found that BilR is predominantly encoded by Firmicutes in the gut microbiome. Our analysis of human gut metagenomes showed that BilR is a common feature of a healthy adult human microbiome but has a decreased prevalence in neonates and IBD patients. This discovery sheds new light on the role of the gut microbiome in bilirubin metabolism and highlights the significance of the gut-liver axis in maintaining bilirubin homeostasis.

A novel approach to interrogating the effects of chemical warfare agent exposure using organ-on-a-chip technology and multiomic analysis.

Organ-on-a-chip platforms are utilized in global bioanalytical and toxicological studies as a way to reduce materials and increase throughput as compared to in vivo based experiments. These platforms bridge the infrastructure and regulatory gaps between in vivo animal work and human systems, with models that exemplify active biological pathways. In conjunction with the advent of increased capabilities associated with next generation sequencing and mass spectrometry based '-omic' technologies, organ-on-a-chip platforms provide an excellent opportunity to investigate the global changes at multiple biological levels, including the transcriptome, proteome and metabolome. When investigated concurrently, a complete profile of cellular and regulatory perturbations can be characterized following treatment with specific agonists. In this study, global effects were observed and analyzed following liver chip exposure to the chemical warfare agent, VX. Even though the primary mechanism of action of VX (i.e. acetylcholinesterase inhibition) is well characterized, recent in vivo studies suggest additional protein binding partners that are implicated in metabolism and cellular energetic pathways. In addition, secondary toxicity associated with peripheral organ systems, especially in human tissues, is not well defined. Our results demonstrate the potential of utilizing an organ-on-a-chip platform as a surrogate system to traditional in vivo studies. This is realized by specifically indicating significant dysregulation of several cellular processes in response to VX exposure including but not limited to amino acid synthesis, drug metabolism, and energetics pathways.

Normalization of organ-on-a-Chip samples for mass spectrometry based proteomics and metabolomics via Dansylation-based assay.

Mass spectrometry based 'omics pairs well with organ-on-a-chip-based investigations, which often have limited cellular material for sampling. However, a common issue with these chip-based platforms is well-to-well or chip-to-chip variability in the proteome and metabolome due to factors such as plate edge effects, cellular asynchronization, effluent flow, and limited cell count. This causes high variability in the quantitative multi-omics analysis of samples, potentially masking true biological changes within the system. Solutions to this have been approached via data processing tools and post-acquisition normalization strategies such as constant median, constant sum, and overall signal normalization. Unfortunately, these methods do not adequately correct for the large variations, resulting in a need for increased biological replicates. The methods in this work utilize a dansylation based assay with a subset of labeled metabolites that allow for pre-acquisition normalization to better correlate the biological perturbations that truly occur in chip-based platforms. BCA protein assays were performed in tandem with a proteomics pipeline to achieve pre-acquisition normalization. The CN Bio PhysioMimix was seeded with primary hepatocytes and challenged with VX after six days of culture, and the metabolome and proteome were analyzed using the described normalization methods. A decreased coefficient of variation percentage is achieved, significant changes are observed through the proteome and metabolome, and better classification of biological replicates acquired because of these strategies.

NrnA is a 5'-3' exonuclease that processes short RNA substrates in vivo and in vitro.

Bacterial RNases process RNAs until only short oligomers (2-5 nucleotides) remain, which are then processed by one or more specialized enzymes until only nucleoside monophosphates remain. Oligoribonuclease (Orn) is an essential enzyme that acts in this capacity. However, many bacteria do not encode for Orn and instead encode for NanoRNase A (NrnA). Yet, the catalytic mechanism, cellular roles and physiologically relevant substrates have not been fully resolved for NrnA proteins. We herein utilized a common set of reaction assays to directly compare substrate preferences exhibited by NrnA-like proteins from Bacillus subtilis, Enterococcus faecalis, Streptococcus pyogenes and Mycobacterium tuberculosis. While the M. tuberculosis protein specifically cleaved cyclic di-adenosine monophosphate, the B. subtilis, E. faecalis and S. pyogenes NrnA-like proteins uniformly exhibited striking preference for short RNAs between 2-4 nucleotides in length, all of which were processed from their 5' terminus. Correspondingly, deletion of B. subtilis nrnA led to accumulation of RNAs between 2 and 4 nucleotides in length in cellular extracts. Together, these data suggest that many Firmicutes NrnA-like proteins are likely to resemble B. subtilis NrnA to act as a housekeeping enzyme for processing of RNAs between 2 and 4 nucleotides in length.

Proteomic, Metabolomic, and Lipidomic Analyses of Lung Tissue Exposed to Mustard Gas.

Sulfur mustard (HD) poses a serious threat due to its relatively simple production process. Exposure to HD in the short-term causes an inflammatory response, while long-term exposure results in DNA and RNA damage. Respiratory tract tissue models were exposed to relatively low concentrations of HD and collected at 3 and 24 h post exposure. Histology, cytokine ELISAs, and mass spectrometric-based analyses were performed. Histology and ELISA data confirmed previously seen lung damage and inflammatory markers from HD exposure. The multi-omic mass spectrometry data showed variation in proteins and metabolites associated with increased inflammation, as well as DNA and RNA damage. HD exposure causes DNA and RNA damage that results in variation of proteins and metabolites that are associated with transcription, translation and cellular energy.

Chemical probing provides insight into the native assembly state of a bacterial microcompartment.

Bacterial microcompartments (BMCs) are widespread in bacteria and are used for a variety of metabolic purposes, including catabolism of host metabolites. A suite of proteins self-assembles into the shell and cargo layers of BMCs. However, the native assembly state of these large complexes remains to be elucidated. Herein, chemical probes were used to observe structural features of a native BMC. While the exterior could be demarcated with fluorophores, the interior was unexpectedly permeable, suggesting that the shell layer may be more dynamic than previously thought. This allowed access to cross-linking chemical probes, which were analyzed to uncover the protein interactome. These cross-links revealed a complex multivalent network among cargo proteins that contained encapsulation peptides and demonstrated that the shell layer follows discrete rules in its assembly. These results are consistent overall with a model in which biomolecular condensation drives interactions of cargo proteins before envelopment by shell layer proteins.

Standard Flow Multiplexed Proteomics (SFloMPro)-An Accessible Alternative to NanoFlow Based Shotgun Proteomics.

Multiplexed proteomics using isobaric tagging allows for simultaneously comparing the proteomes of multiple samples. In this technique, digested peptides from each sample are labeled with a chemical tag prior to pooling sample for LC-MS/MS with nanoflow chromatography (NanoLC). The isobaric nature of the tag prevents deconvolution of samples until fragmentation liberates the isotopically labeled reporter ions. To ensure efficient peptide labeling, large concentrations of labeling reagents are included in the reagent kits to allow scientists to use high ratios of chemical label per peptide. The increasing speed and sensitivity of mass spectrometers has reduced the peptide concentration required for analysis, leading to most of the label or labeled sample to be discarded. In conjunction, improvements in the speed of sample loading, reliable pump pressure, and stable gradient construction of analytical flow HPLCs has continued to improve the sample delivery process to the mass spectrometer. In this study we describe a method for performing multiplexed proteomics without the use of NanoLC by using offline fractionation of labeled peptides followed by rapid "standard flow" HPLC gradient LC-MS/MS. Standard Flow Multiplexed Proteomics (SFloMPro) enables high coverage quantitative proteomics of up to 16 mammalian samples in about 24 h. In this study, we compare NanoLC and SFloMPro analysis of fractionated samples. Our results demonstrate that comparable data is obtained by injecting 20 µg of labeled peptides per fraction with SFloMPro, compared to 1 µg per fraction with NanoLC. We conclude that, for experiments where protein concentration is not strictly limited, SFloMPro is a competitive approach to traditional NanoLC workflows with improved up-time, reliability and at a lower relative cost per sample.

Proteomic and metabolomic profiling of acute and chronic stress events associated with military exercises.

By characterizing physiological changes that occur in warfighters during simulated combat, we can start to unravel the key biomolecular components that are linked to physical and cognitive performance. Viable field-based sensors for the warfighter must be rapid and noninvasive. In an effort to facilitate this, we applied a multiomics pipeline to characterize the stress response in the saliva of warfighters to correlate biomolecular changes with overall performance and health. In this study, two different stress models were observed - one of chronic stress and one of acute stress. In both models, significant perturbations in the immune, metabolic, and protein manufacturing/processing systems were observed. However, when differentiating between stress models, specific metabolites associated with the "fight or flight" response and protein folding were seen to be discriminate of the acute stress model.

Utilizing machine learning with knockoff filtering to extract significant metabolites in Crohn's disease with a publicly available untargeted metabolomics dataset.

Metabolomic data processing pipelines have been improving in recent years, allowing for greater feature extraction and identification. Lately, machine learning and robust statistical techniques to control false discoveries are being incorporated into metabolomic data analysis. In this paper, we introduce one such recently developed technique called aggregate knockoff filtering to untargeted metabolomic analysis. When applied to a publicly available dataset, aggregate knockoff filtering combined with typical p-value filtering improves the number of significantly changing metabolites by 25% when compared to conventional untargeted metabolomic data processing. By using this method, features that would normally not be extracted under standard processing would be brought to researchers' attention for further analysis.

The Burkholderia pseudomallei hmqA-G Locus Mediates Competitive Fitness against Environmental Gram-Positive Bacteria.

Burkholderia pseudomallei is an opportunistic pathogen that is responsible for the disease melioidosis in humans and animals. The microbe is a tier 1 select agent because it is highly infectious by the aerosol route, it is inherently resistant to multiple antibiotics, and no licensed vaccine currently exists. Naturally acquired infections result from contact with contaminated soil or water sources in regions of endemicity. There have been few reports investigating the molecular mechanism(s) utilized by B. pseudomallei to survive and persist in ecological niches harboring microbial competitors. Here, we report the isolation of Gram-positive bacteria from multiple environmental sources and show that ∼45% of these isolates are inhibited by B. pseudomallei in head-to-head competition assays. Two competition-deficient B. pseudomallei transposon mutants were identified that contained insertion mutations in the hmqA-G operon. This large biosynthetic gene cluster encodes the enzymes that produce a family of secondary metabolites called 4-hydroxy-3-methyl-2-alkylquinolines (HMAQs). Liquid chromatography and mass spectrometry conducted on filter-sterilized culture supernatants revealed five HMAQs and N-oxide derivatives that were produced by the parental strain but were absent in an isogenic hmqD deletion mutant. The results demonstrate that B. pseudomallei inhibits the growth of environmental Gram-positive bacteria in a contact-independent manner via the production of HMAQs by the hmqA-G operon. IMPORTANCE Burkholderia pseudomallei naturally resides in water, soil, and the rhizosphere and its success as an opportunistic pathogen is dependent on the ability to persist in these harsh habitats long enough to come into contact with a susceptible host. In addition to adapting to limiting nutrients and diverse chemical and physical challenges, B. pseudomallei also has to interact with a variety of microbial competitors. Our research shows that one of the ways in which B. pseudomallei competes with Gram-positive environmental bacteria is by exporting a diverse array of closely related antimicrobial secondary metabolites.

Structure of E3 ligase E6AP with a proteasome-binding site provided by substrate receptor hRpn10.

Regulated proteolysis by proteasomes involves ~800 enzymes for substrate modification with ubiquitin, including ~600 E3 ligases. We report here that E6AP/UBE3A is distinguished from other E3 ligases by having a 12 nM binding site at the proteasome contributed by substrate receptor hRpn10/PSMD4/S5a. Intrinsically disordered by itself, and previously uncharacterized, the E6AP-binding domain in hRpn10 locks into a well-defined helical structure to form an intermolecular 4-helix bundle with the E6AP AZUL, which is unique to this E3. We thus name the hRpn10 AZUL-binding domain RAZUL. We further find in human cells that loss of RAZUL by CRISPR-based gene editing leads to loss of E6AP at proteasomes. Moreover, proteasome-associated ubiquitin is reduced following E6AP knockdown or displacement from proteasomes, suggesting that E6AP ubiquitinates substrates at or for the proteasome. Altogether, our findings indicate E6AP to be a privileged E3 for the proteasome, with a dedicated, high affinity binding site contributed by hRpn10.

The Cannabis Proteome Draft Map Project.

Recently we have seen a relaxation of the historic restrictions on the use and subsequent research on the Cannabis plants, generally classified as Cannabis sativa and Cannabis indica. What research has been performed to date has centered on chemical analysis of plant flower products, namely cannabinoids and various terpenes that directly contribute to phenotypic characteristics of the female flowers. In addition, we have seen many groups recently completing genetic profiles of various plants of commercial value. To date, no comprehensive attempt has been made to profile the proteomes of these plants. We report herein our progress on constructing a comprehensive draft map of the Cannabis proteome. To date we have identified over 17,000 potential protein sequences. Unfortunately, no annotated genome of Cannabis plants currently exists. We present a method by which "next generation" DNA sequencing output and shotgun proteomics data can be combined to produce annotated FASTA files, bypassing the need for annotated genetic information altogether in traditional proteomics workflows. The resulting material represents the first comprehensive annotated protein FASTA for any Cannabis plant. Using this annotated database as reference we can refine our protein identifications, resulting in the confident identification of 13,000 proteins with putative function. Furthermore, we demonstrate that post-translational modifications play an important role in the proteomes of Cannabis flower, particularly lysine acetylation and protein glycosylation. To facilitate the evolution of analytical investigations into these plant materials, we have created a portal to host resources developed from our proteomic and metabolomic analysis of Cannabis plant material as well as our results integrating these resources.

Bolt: a New Age Peptide Search Engine for Comprehensive MS/MS Sequencing Through Vast Protein Databases in Minutes.

Recent increases in mass spectrometry speed, sensitivity, and resolution now permit comprehensive proteomics coverage. However, the results are often hindered by sub-optimal data processing pipelines. In almost all MS/MS peptide search engines, users must limit their search space to a canonical database due to time constraints and q value considerations, but this typically does not reflect the individual genetic variations of the organism being studied. In addition, engines will nearly always assume the presence of only fully tryptic peptides and limit PTMs to a handful. Even on high-performance servers, these search engines are computationally expensive, and most users decide to dial back their search parameters. We present Bolt, a new cloud-based search engine that can search more than 900,000 protein sequences (canonical, isoform, mutations, and contaminants) with 41 post-translation modifications and N-terminal and C-terminal partial tryptic search in minutes on a standard configuration laptop. Along with increases in speed, Bolt provides an additional benefit of improvement in high-confidence identifications. Sixty-one percent of peptides uniquely identified by Bolt may be validated by strong fragmentation patterns, compared with 13% of peptides uniquely identified by SEQUEST and 6% of peptides uniquely identified by Mascot. Furthermore, 30% of unique Bolt identifications were verified by all three software on the longer gradient analysis, compared with only 20% and 27% for SEQUEST and Mascot identifications respectively. Bolt represents, to the best of our knowledge, the first fully scalable, cloud-based quantitative proteomic solution that can be operated within a user-friendly GUI interface. Data are available via ProteomeXchange with identifier PXD012700.

Detection and verification of 2.3 million cancer mutations in NCI60 cancer cell lines with a cloud search engine.

Today we have unprecedented access to human genomic and proteomic data that appear to be rapidly approaching our current understanding of comprehensive coverage. Combining genomic information with shotgun proteomics remains challenging due to the large increase in proteomics search space. However, making this connection between genomic and proteomic information is critical for cancer studies to vaccine development. Furthermore, as we progress towards personalized medicine, it will be essential for proteomics analysis to identify individual mutations and variants in order to fully understand protein networks and to develop personalized therapies. While these advantages are well-established, only a few studies have demonstrated the successful integration of proteomic data with large genomic input. We present and examine the abilities of Bolt, a new cloud-based proteomics search engine to search for the presence of over 2.3 million known cancer mutations in a matter of minutes while still performing a standard proteomics search that includes 31 post translational modifications. We use previously published proteomics data sets and identify mutations that are verified using genomic studies as well as previous proteomics efforts. Our results also emphasize the need to search for mutations in a comprehensive manner while still searching for both common and rare PTMs. SIGNIFICANCE: We present and examine the abilities of Bolt, a new cloud-based proteomics search engine to search for the presence of over 2.3 million known cancer mutations in a matter of minutes while still performing a standard proteomics search that includes 31 post translational modifications. No other proteomics search software can do so.