Omics.pnl.gov: A Portal for the Distribution and Sharing of Multi-Disciplinary Pan-Omics Information

Complete Chromosome-Scale Genome Sequence Resource for Sporisorium panici-leucophaei, the Causal Agent of Sourgrass Smut Disease

Here, we present the first complete chromosome-level genome assembly of the smut fungus strain Sporisorium panici-leucophaei SPL10A, the causal agent of the sourgrass (Digitaria insularis) smut disease. Combining Illumina paired-end and Nanopore long reads, we generated a final assembly composed of 23 chromosomes (22 nuclear and one mitochondrial) with 18,915,934 bp. Gene prediction accomplished using extrinsic evidence from the sugarcane smut fungus Sporisorium scitamineum originated a total of 6,402 protein-encoding genes. The secretome (388 proteins) and the effectorome repertoires (68 candidates) were also predicted, given their crucial roles in plant-pathogen interactions. The complete telomere-to-telomere chromosome sequences of this poorly studied fungus will provide a valuable resource for future comparative genomic studies among smuts to unravel their underlying pathogenicity mechanisms.[Formula: see text] Copyright © 2021 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

Do the complementarities of electrokinetic and chromatographic procedures represent the "Swiss knife" in proteomic investigation? An overview of the literature in the past decade

This report reviews the literature of the past decade dealing with the combination of electrokinetic and chromatographic strategies in the proteomic field. Aim of this article is to highlight how the application of complementary techniques may contribute to substantially improve protein identification. Several studies here considered demonstrate that exploring the combination of these approaches can be a strategy to enrich the extent of proteomic information achieved from a sample. The coupling of "top-down" and "bottom-up" proteomics may result in the generation of a hybrid analytical tool, very efficient not only for large-scale profiling of complex proteomes but also for studying specific subproteomes. The range of applications described, while evidencing a continuous boost in the imagination of researchers for developing new combinations of methods for protein separation, also underlines the adaptability of these techniques to a wide variety of samples. This report points out the general usefulness of combining different procedures for proteomic analysis, an approach that allows researchers to go deeper in the proteome of samples under investigation.

Automated data extraction from in situ protein-stable isotope probing studies

Protein-stable isotope probing (protein-SIP) has strong potential for revealing key metabolizing taxa in complex microbial communities. While most protein-SIP work to date has been performed under controlled laboratory conditions to allow extensive isotope labeling of the target organism(s), a key application will be in situ studies of microbial communities for short periods of time under natural conditions that result in small degrees of partial labeling. One hurdle restricting large-scale in situ protein-SIP studies is the lack of algorithms and software for automated data processing of the massive data sets resulting from such studies. In response, we developed Stable Isotope Probing Protein Extraction Resources software (SIPPER) and applied it for large-scale extraction and visualization of data from short-term (3 h) protein-SIP experiments performed in situ on phototrophic bacterial mats isolated from Yellowstone National Park. Several metrics incorporated into the software allow it to support exhaustive analysis of the complex composite isotopic envelope observed as a result of low amounts of partial label incorporation. SIPPER also enables the detection of labeled molecular species without the need for any prior identification.

In silico identification software (ISIS): a machine learning approach to tandem mass spectral identification of lipids

MOTIVATION

Liquid chromatography-mass spectrometry-based metabolomics has gained importance in the life sciences, yet it is not supported by software tools for high throughput identification of metabolites based on their fragmentation spectra. An algorithm (ISIS: in silico identification software) and its implementation are presented and show great promise in generating in silico spectra of lipids for the purpose of structural identification. Instead of using chemical reaction rate equations or rules-based fragmentation libraries, the algorithm uses machine learning to find accurate bond cleavage rates in a mass spectrometer employing collision-induced dissociation tandem mass spectrometry.

RESULTS

A preliminary test of the algorithm with 45 lipids from a subset of lipid classes shows both high sensitivity and specificity.

Model-driven multi-omic data analysis elucidates metabolic immunomodulators of macrophage activation

Macrophages are central players in immune response, manifesting divergent phenotypes to control inflammation and innate immunity through release of cytokines and other signaling factors. Recently, the focus on metabolism has been reemphasized as critical signaling and regulatory pathways of human pathophysiology, ranging from cancer to aging, often converge on metabolic responses. Here, we used genome-scale modeling and multi-omics (transcriptomics, proteomics, and metabolomics) analysis to assess metabolic features that are critical for macrophage activation. We constructed a genome-scale metabolic network for the RAW 264.7 cell line to determine metabolic modulators of activation. Metabolites well-known to be associated with immunoactivation (glucose and arginine) and immunosuppression (tryptophan and vitamin D3) were among the most critical effectors. Intracellular metabolic mechanisms were assessed, identifying a suppressive role for de-novo nucleotide synthesis. Finally, underlying metabolic mechanisms of macrophage activation are identified by analyzing multi-omic data obtained from LPS-stimulated RAW cells in the context of our flux-based predictions. Our study demonstrates metabolism's role in regulating activation may be greater than previously anticipated and elucidates underlying connections between activation and metabolic effectors.

Advances in the mass spectrometry of membrane proteins: from individual proteins to intact complexes

Rapid advances in structural genomics and in large-scale proteomic projects have yielded vast amounts of data on soluble proteins and their complexes. Despite these advances, progress in studying membrane proteins using mass spectrometry (MS) has been slow. This is due in part to the inherent solubility and dynamic properties of these proteins, but also to their low abundance and the absence of polar side chains in amino acid residues. Considerable progress in overcoming these challenges is, however, now being made for all levels of structural characterization. This progress includes MS studies of the primary structure of membrane proteins, wherein sophisticated enrichment and trapping procedures are allowing multiple posttranslational modifications to be defined through to the secondary structure level in which proteins and peptides have been probed using hydrogen exchange, covalent, or radiolytic labeling methods. Exciting possibilities now exist to go beyond primary and secondary structure to reveal the tertiary and quaternary interactions of soluble and membrane subunits within intact assemblies of more than 700 kDa.

Experimental annotation of post-translational features and translated coding regions in the pathogen Salmonella Typhimurium

Background

Complete and accurate genome annotation is crucial for comprehensive and systematic studies of biological systems. However, determining protein-coding genes for most new genomes is almost completely performed by inference using computational predictions with significant documented error rates (> 15%). Furthermore, gene prediction programs provide no information on biologically important post-translational processing events critical for protein function.

Results

We experimentally annotated the bacterial pathogen Salmonella Typhimurium 14028, using "shotgun" proteomics to accurately uncover the translational landscape and post-translational features. The data provide protein-level experimental validation for approximately half of the predicted protein-coding genes in Salmonella and suggest revisions to several genes that appear to have incorrectly assigned translational start sites, including a potential novel alternate start codon. Additionally, we uncovered 12 non-annotated genes missed by gene prediction programs, as well as evidence suggesting a role for one of these novel ORFs in Salmonella pathogenesis. We also characterized post-translational features in the Salmonella genome, including chemical modifications and proteolytic cleavages. We find that bacteria have a much larger and more complex repertoire of chemical modifications than previously thought including several novel modifications. Our in vivo proteolysis data identified more than 130 signal peptide and N-terminal methionine cleavage events critical for protein function.

Conclusion

This work highlights several ways in which application of proteomics data can improve the quality of genome annotations to facilitate novel biological insights and provides a comprehensive proteome map of Salmonella as a resource for systems analysis.

Hydrophobic Proteome Analysis of Triple Negative and Hormone-Receptor-Positive-Her2-Negative Breast Cancer by Mass Spectrometer

Introduction

It is widely believed that discovery of specific, sensitive, and reliable tumor biomarkers can improve the treatment of cancer. Currently, there are no obvious targets that can be used in treating triple-negative breast cancer (TNBC).

Methods

To better understand TNBC and find potential biomarkers for targeted treatment, we combined a novel hydrophobic fractionation protocol with mass spectrometry LTQ-orbitrap to explore and compare the hydrophobic sub-proteome of TNBC with another subtype of breast cancer, hormone-receptor-positive-Her2-negative breast cancer (non-TNBC).

Results

Hydrophobic sub-proteome of breast cancer is rich in membrane proteins. Hundreds of proteins with various defined key cellular functions were identified from TNBC and non-TNBC tumors. In this study, protein profiles of TNBC and non-TNBC were systematically examined, compared, and validated. We have found that nine keratins are down-regulated and several heat shock proteins are up-regulated in TNBC tissues. Our study may provide insights of molecules that are responsible for the aggressiveness of TNBC.

Conclusion

The initial results obtained using a combination of hydrophobic fractionation and nano-LC mass spectrometry analysis of these proteins appear promising in the discovery of potential cancer biomarkers and bio-signatures. When sufficiently refined, this approach may prove useful in improving breast cancer treatment.