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.

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.

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.