Advances in proteomics data analysis and display using an accurate mass and time tag approach

SLB-msSIM: a spectral library-based multiplex segmented SIM platform for single-cell proteomic analysis

Mass spectrometry (MS)-based single-cell proteomics, while highly challenging, offers unique potential for a wide range of applications to interrogate cellular heterogeneity, trajectories, and phenotypes at a functional level. We report here the development of the spectral library-based multiplex segmented selected ion monitoring (SLB-msSIM) method, a conceptually unique approach with significantly enhanced sensitivity and robustness for single-cell analysis. The single-cell MS data is acquired by msSIM technique, which sequentially applies multiple isolation cycles with the quadrupole using a wide isolation window in each cycle to accumulate and store precursor ions in the C-trap for a single scan in the Orbitrap. Proteomic identification is achieved through spectral matching using a well-defined spectral library. We applied the SLB-msSIM method to interrogate cellular heterogeneity among multiple cell lines and to analyze cellular trajectories during epithelial-mesenchymal transition. Our results demonstrate that SLB-msSIM is a highly sensitive and robust platform applicable to a wide range of single-cell proteomic studies.

Comprehensive Overview of Bottom-Up Proteomics Using Mass Spectrometry

Proteomics is the large scale study of protein structure and function from biological systems through protein identification and quantification. "Shotgun proteomics" or "bottom-up proteomics" is the prevailing strategy, in which proteins are hydrolyzed into peptides that are analyzed by mass spectrometry. Proteomics studies can be applied to diverse studies ranging from simple protein identification to studies of proteoforms, protein-protein interactions, protein structural alterations, absolute and relative protein quantification, post-translational modifications, and protein stability. To enable this range of different experiments, there are diverse strategies for proteome analysis. The nuances of how proteomic workflows differ may be challenging to understand for new practitioners. Here, we provide a comprehensive overview of different proteomics methods. We cover from biochemistry basics and protein extraction to biological interpretation and orthogonal validation. We expect this Review will serve as a handbook for researchers who are new to the field of bottom-up proteomics.

Ultrafast Proteomics

Current stage of proteomic research in the field of biology, medicine, development of new drugs, population screening, or personalized approaches to therapy dictates the need to analyze large sets of samples within the reasonable experimental time. Until recently, mass spectrometry measurements in proteomics were characterized as unique in identifying and quantifying cellular protein composition, but low throughput, requiring many hours to analyze a single sample. This was in conflict with the dynamics of changes in biological systems at the whole cellular proteome level upon the influence of external and internal factors. Thus, low speed of the whole proteome analysis has become the main factor limiting developments in functional proteomics, where it is necessary to annotate intracellular processes not only in a wide range of conditions, but also over a long period of time. Enormous level of heterogeneity of tissue cells or tumors, even of the same type, dictates the need to analyze biological systems at the level of individual cells. These studies involve obtaining molecular characteristics for tens, if not hundreds of thousands of individual cells, including their whole proteome profiles. Development of mass spectrometry technologies providing high resolution and mass measurement accuracy, predictive chromatography, new methods for peptide separation by ion mobility and processing of proteomic data based on artificial intelligence algorithms have opened a way for significant, if not radical, increase in the throughput of whole proteome analysis and led to implementation of the novel concept of ultrafast proteomics. Work done just in the last few years has demonstrated the proteome-wide analysis throughput of several hundred samples per day at a depth of several thousand proteins, levels unimaginable three or four years ago. The review examines background of these developments, as well as modern methods and approaches that implement ultrafast analysis of the entire proteome.

A compendium of multi-omics data illuminating host responses to lethal human virus infections

Human infections caused by viral pathogens trigger a complex gamut of host responses that limit disease, resolve infection, generate immunity, and contribute to severe disease or death. Here, we present experimental methods and multi-omics data capture approaches representing the global host response to infection generated from 45 individual experiments involving human viruses from the Orthomyxoviridae, Filoviridae, Flaviviridae, and Coronaviridae families. Analogous experimental designs were implemented across human or mouse host model systems, longitudinal samples were collected over defined time courses, and global multi-omics data (transcriptomics, proteomics, metabolomics, and lipidomics) were acquired by microarray, RNA sequencing, or mass spectrometry analyses. For comparison, we have included transcriptomics datasets from cells treated with type I and type II human interferon. Raw multi-omics data and metadata were deposited in public repositories, and we provide a central location linking the raw data with experimental metadata and ready-to-use, quality-controlled, statistically processed multi-omics datasets not previously available in any public repository. This compendium of infection-induced host response data for reuse will be useful for those endeavouring to understand viral disease pathophysiology and network biology.

Open-tubular trap columns: towards simple and robust liquid chromatography separations for single-cell proteomics

Nanoflow liquid chromatography-mass spectrometry is key to enabling in-depth proteome profiling of trace samples, including single cells, but these separations can lack robustness due to the use of narrow-bore columns that are susceptible to clogging. In the case of single-cell proteomics, offline cleanup steps are generally omitted to avoid losses to additional surfaces, and online solid-phase extraction/trap columns frequently provide the only opportunity to remove salts and insoluble debris before the sample is introduced to the analytical column. Trap columns are traditionally short, packed columns used to load and concentrate analytes at flow rates greater than those employed in analytical columns, and since these first encounter the uncleaned sample mixture, trap columns are also susceptible to clogging. We hypothesized that clogging could be avoided by using large-bore porous layer open tubular trap columns (PLOTrap). The low back pressure ensured that the PLOTraps could also serve as the sample loop, thus allowing sample cleanup and injection with a single 6-port valve. We found that PLOTraps could effectively remove debris to avoid column clogging. We also evaluated multiple stationary phases and PLOTrap diameters to optimize performance in terms of peak widths and sample loading capacities. Optimized PLOTraps were compared to conventional packed trap columns operated in forward and backflush modes, and were found to have similar chromatographic performance of backflushed traps while providing improved debris removal for robust analysis of trace samples.

Recent Advances and Techniques for Identifying Novel Antibacterial Targets

BACKGROUND

With the emergence of drug-resistant bacteria, the development of new antibiotics is urgently required. Target-based drug discovery is the most frequently employed approach for the drug development process. However, traditional drug target identification techniques are costly and time-consuming. As research continues, innovative approaches for antibacterial target identification have been developed which enabled us to discover drug targets more easily and quickly.

METHODS

In this review, methods for finding drug targets from omics databases have been discussed in detail including principles, procedures, advantages, and potential limitations. The role of phage-driven and bacterial cytological profiling approaches is also discussed. Moreover, current article demonstrates the advancements being made in the establishment of computational tools, machine learning algorithms, and databases for antibacterial target identification.

RESULTS

Bacterial drug targets successfully identified by employing these aforementioned techniques are described as well.

CONCLUSION

The goal of this review is to attract the interest of synthetic chemists, biologists, and computational researchers to discuss and improve these methods for easier and quicker development of new drugs.

Comprehensive Overview of Bottom-Up Proteomics using Mass Spectrometry

Proteomics is the large scale study of protein structure and function from biological systems through protein identification and quantification. "Shotgun proteomics" or "bottom-up proteomics" is the prevailing strategy, in which proteins are hydrolyzed into peptides that are analyzed by mass spectrometry. Proteomics studies can be applied to diverse studies ranging from simple protein identification to studies of proteoforms, protein-protein interactions, protein structural alterations, absolute and relative protein quantification, post-translational modifications, and protein stability. To enable this range of different experiments, there are diverse strategies for proteome analysis. The nuances of how proteomic workflows differ may be challenging to understand for new practitioners. Here, we provide a comprehensive overview of different proteomics methods to aid the novice and experienced researcher. We cover from biochemistry basics and protein extraction to biological interpretation and orthogonal validation. We expect this work to serve as a basic resource for new practitioners in the field of shotgun or bottom-up proteomics.

Proteomic analyses of venom from a Spider Hawk, Pepsis decorata

Background

The composition of the venom from solitary wasps is poorly known, although these animals are considered sources of bioactive substances. Until the present moment, there is only one proteomic characterization of the venom of wasps of the family Pompilidae and this is the first proteomic characterization for the genus Pepsis.

Methods

To elucidate the components of Pepsis decorata venom, the present work sought to identify proteins using four different experimental conditions, namely: (A) crude venom; (B) reduced and alkylated venom; (C) trypsin-digested reduced and alkylated venom, and; (D) chymotrypsin-digested reduced and alkylated venom. Furthermore, three different mass spectrometers were used (Ion Trap-Time of Flight, Quadrupole-Time of Flight, and Linear Triple Quadruple).

Results

Proteomics analysis revealed the existence of different enzymes related to the insect's physiology in the venom composition. Besides toxins, angiotensin-converting enzyme (ACE), hyaluronidase, and Kunitz-type inhibitors were also identified.

Conclusion

The data showed that the venom of Pepsis decorata is mostly composed of proteins involved in the metabolism of arthropods, as occurs in parasitic wasps, although some classical toxins were recorded, and among them, for the first time, ACE was found in the venom of solitary wasps. This integrative approach expanded the range of compounds identified in protein analyses, proving to be efficient in the proteomic characterization of little-known species. It is our understanding that the current work will provide a solid base for future studies dealing with other Hymenoptera venoms.

Online protein digestion in membranes between capillary electrophoresis and mass spectrometry

This research employs pepsin-containing membranes to digest proteins online after a capillary electrophoresis (CE) separation and prior to tandem mass spectrometry. Proteolysis after the separation allows the peptides from a given protein to enter the mass spectrometer in a single plug. Thus, migration time can serve as an additional criterion for confirming the identification of a peptide. The membrane resides in a sheath-flow electrospray ionization (ESI) source to enable digestion immediately before spray into the mass spectrometer, thus limiting separation of the digested peptides. Using the same membrane, digestion occurred reproducibly during 20 consecutive CE analyses performed over a 10 h period. Additionally, after separating a mixture of six unreduced proteins with CE, online digestion facilitated protein identification with at least 2 identifiable peptides for all the proteins. Sequence coverages were >75% for myoglobin and carbonic anhydrase II but much lower for proteins containing disulfide bonds. Development of methods for efficient separation of reduced proteins or identification of cross-linked peptides should enhance sequence coverages for proteins with disulfide bonds. Migration times for the peptides identified from a specific protein differed by <∼30 s, which allows for rejection of some spurious peptide identifications.

Spectral Library-Based Single-Cell Proteomics Resolves Cellular Heterogeneity

Dissecting the proteome of cell types and states at single-cell resolution, while being highly challenging, has significant implications in basic science and biomedicine. Mass spectrometry (MS)-based single-cell proteomics represents an emerging technology for system-wide, unbiased profiling of proteins in single cells. However, significant challenges remain in analyzing an extremely small amount of proteins collected from a single cell, as a proteome-wide amplification of proteins is not currently feasible. Here, we report an integrated spectral library-based single-cell proteomics (SLB-SCP) platform that is ultrasensitive and well suited for a large-scale analysis. To overcome the low MS/MS signal intensity intrinsically associated with a single-cell analysis, this approach takes an alternative approach by extracting a breadth of information that specifically defines the physicochemical characteristics of a peptide from MS1 spectra, including monoisotopic mass, isotopic distribution, and retention time (hydrophobicity), and uses a spectral library for proteomic identification. This conceptually unique MS platform, coupled with the DIRECT sample preparation method, enabled identification of more than 2000 proteins in a single cell to distinguish different proteome landscapes associated with cellular types and heterogeneity. We characterized individual normal and cancerous pancreatic ductal cells (HPDE and PANC-1, respectively) and demonstrated the substantial difference in the proteomes between HPDE and PANC-1 at the single-cell level. A significant upregulation of multiple protein networks in cancer hallmarks was identified in the PANC-1 cells, functionally discriminating the PANC-1 cells from the HPDE cells. This integrated platform can be built on high-resolution MS and widely accepted proteomic software, making it possible for community-wide applications.

Label-Free Profiling of up to 200 Single-Cell Proteomes per Day Using a Dual-Column Nanoflow Liquid Chromatography Platform

Single-cell proteomics (SCP) has great potential to advance biomedical research and personalized medicine. The sensitivity of such measurements increases with low-flow separations (<100 nL/min) due to improved ionization efficiency, but the time required for sample loading, column washing, and regeneration in these systems can lead to low measurement throughput and inefficient utilization of the mass spectrometer. Herein, we developed a two-column liquid chromatography (LC) system that dramatically increases the throughput of label-free SCP using two parallel subsystems to multiplex sample loading, online desalting, analysis, and column regeneration. The integration of MS1-based feature matching increased proteome coverage when short LC gradients were used. The high-throughput LC system was reproducible between the columns, with a 4% difference in median peptide abundance and a median CV of 18% across 100 replicate analyses of a single-cell-sized peptide standard. An average of 621, 774, 952, and 1622 protein groups were identified with total analysis times of 7, 10, 15, and 30 min, corresponding to a measurement throughput of 206, 144, 96, and 48 samples per day, respectively. When applied to single HeLa cells, we identified nearly 1000 protein groups per cell using 30 min cycles and 660 protein groups per cell for 15 min cycles. We explored the possibility of measuring cancer therapeutic targets with a pilot study comparing the K562 and Jurkat leukemia cell lines. This work demonstrates the feasibility of high-throughput label-free single-cell proteomics.

Domont G, C. S. Nogueira F, Marko-Varga G, Sanchez A. Mapping the Melanoma Plasma Proteome (MPP) Using Single-Shot Proteomics Interfaced with the WiMT Database

Plasma analysis by mass spectrometry-based proteomics remains a challenge due to its large dynamic range of 10 orders in magnitude. We created a methodology for protein identification known as Wise MS Transfer (WiMT). Melanoma plasma samples from biobank archives were directly analyzed using simple sample preparation. WiMT is based on MS1 features between several MS runs together with custom protein databases for ID generation. This entails a multi-level dynamic protein database with different immunodepletion strategies by applying single-shot proteomics. The highest number of melanoma plasma proteins from undepleted and unfractionated plasma was reported, mapping >1200 proteins from >10,000 protein sequences with confirmed significance scoring. Of these, more than 660 proteins were annotated by WiMT from the resulting ~5800 protein sequences. We could verify 4000 proteins by MS1t analysis from HeLA extracts. The WiMT platform provided an output in which 12 previously well-known candidate markers were identified. We also identified low-abundant proteins with functions related to (i) cell signaling, (ii) immune system regulators, and (iii) proteins regulating folding, sorting, and degradation, as well as (iv) vesicular transport proteins. WiMT holds the potential for use in large-scale screening studies with simple sample preparation, and can lead to the discovery of novel proteins with key melanoma disease functions.

Exposure to an Environmental Mixture of Polycyclic Aromatic Hydrocarbons Induces Hepatic Cytochrome P450 Enzymes in Mice

Cytochrome P450 enzymes (CYPs) play an important role in bioactivating or detoxifying polycyclic aromatic hydrocarbons (PAHs), common environmental contaminants. While it is widely accepted that exposure to PAHs induces CYPs, effectively increasing rates of xenobiotic metabolism, dose- and time-response patterns of CYP induction are not well-known. In order to better understand dose- and time-response relationships of individual CYPs following induction, we exposed B6129SF1/J mice to single or repeated doses (2-180 μmol/kg/d) of benzo[a]pyrene (BaP) or Supermix-10, a mixture of the top 10 most abundant PAHs found at the Portland Harbor Superfund Site. In hepatic microsomes from exposed mice, we measured amounts of active CYPs using activity-based protein profiling and total CYP expression using global proteomics. We observed rapid Cyp1a1 induction after 6 h at the lowest PAH exposures and broad induction of many CYPs after 3 daily PAH doses at 72 h following the first dose. Using samples displaying Cyp1a1 induction, we observed significantly higher metabolic affinity for BaP metabolism (K_m reduced 3-fold), 3-fold higher intrinsic clearance, but no changes to the V_max. Mice dosed with the highest PAH exposures exhibited 1.7-5-fold higher intrinsic clearance rates for BaP compared to controls and higher V_max values indicating greater amounts of enzymes capable of metabolizing BaP. This study demonstrates exposure to PAHs found at superfund sites induces enzymes in dose- and time-dependent patterns in mice. Accounting for specific changes in enzyme profiles, relative rates of PAH bioactivation and detoxification, and resulting risk will help translate internal dosimetry of animal models to humans and improve risk assessments of PAHs at superfund sites.

(Phospho)Proteomic dataset of ischemia- and ultrasound- stimulated mouse cardiac endothelial cells in vitro

Cardiac endothelial cells respond to both ischemia and therapeutic ultrasound; the proteomic changes underlying these responses are unknown. This data article provides raw and processed data resulting from our global, unbiased phosphoproteomics investigation conducted on primary mouse cardiac endothelial cells exposed to ischemia (2-hour oxygen glucose deprivation) and ultrasound (250 kHz, 1.2 MPa) in vitro [1]. Proteins were extracted from cell lysates and enriched phosphopeptides were analyzed with a high mass accuracy liquid chromatrography (LC) - tandem mass spectrometry (MS/MS) proteomic platform, yielding multiple alterations in both total protein levels and phosphorylation events in response to ischemic injury and ultrasound. This dataset can be used as a reference for future studies on the cardiac endothelial response to ischemia and the mechanistic underpinnings of the cellular response to ultrasound, with the potential to yield clinically relevant therapeutic targets.

Phosphoproteomic response of cardiac endothelial cells to ischemia and ultrasound

Myocardial infarction and subsequent therapeutic interventions activate numerous intracellular cascades in every constituent cell type of the heart. Endothelial cells produce several protective compounds in response to therapeutic ultrasound, under both normoxic and ischemic conditions. How endothelial cells sense ultrasound and convert it to a beneficial biological response is not known. We adopted a global, unbiased phosphoproteomics approach aimed at understanding how endothelial cells respond to ultrasound. Here, we use primary cardiac endothelial cells to explore the cellular signaling events underlying the response to ischemia-like cellular injury and ultrasound exposure in vitro. Enriched phosphopeptides were analyzed with a high mass accuracy liquid chromatrography (LC) - tandem mass spectrometry (MS/MS) proteomic platform, yielding multiple alterations in both total protein levels and phosphorylation events in response to ischemic injury and ultrasound. Application of pathway algorithms reveals numerous protein networks recruited in response to ultrasound including those regulating RNA splicing, cell-cell interactions and cytoskeletal organization. Our dataset also permits the informatic prediction of potential kinases responsible for the modifications detected. Taken together, our findings begin to reveal the endothelial proteomic response to ultrasound and suggest potential targets for future studies of the protective effects of ultrasound in the ischemic heart.

IonQuant Enables Accurate and Sensitive Label-Free Quantification With FDR-Controlled Match-Between-Runs

Missing values weaken the power of label-free quantitative proteomic experiments to uncover true quantitative differences between biological samples or experimental conditions. Match-between-runs (MBR) has become a common approach to mitigate the missing value problem, where peptides identified by tandem mass spectra in one run are transferred to another by inference based on m/z, charge state, retention time, and ion mobility when applicable. Though tolerances are used to ensure such transferred identifications are reasonably located and meet certain quality thresholds, little work has been done to evaluate the statistical confidence of MBR. Here, we present a mixture model-based approach to estimate the false discovery rate (FDR) of peptide and protein identification transfer, which we implement in the label-free quantification tool IonQuant. Using several benchmarking datasets generated on both Orbitrap and timsTOF mass spectrometers, we demonstrate superior performance of IonQuant with FDR-controlled MBR compared with MaxQuant (19-38 times faster; 6-18% more proteins quantified and with comparable or better accuracy). We further illustrate the performance of IonQuant and highlight the need for FDR-controlled MBR, in two single-cell proteomics experiments, including one acquired with the help of high-field asymmetric ion mobility spectrometry separation. Fully integrated in the FragPipe computational environment, IonQuant with FDR-controlled MBR enables fast and accurate peptide and protein quantification in label-free proteomics experiments.

Boosting MS1-only Proteomics with Machine Learning Allows 2000 Protein Identifications in Single-Shot Human Proteome Analysis Using 5 min HPLC Gradient

Proteome-wide analyses rely on tandem mass spectrometry and the extensive separation of proteolytic mixtures. This imposes considerable instrumental time consumption, which is one of the main obstacles in the broader acceptance of proteomics in biomedical and clinical research. Recently, we presented a fast proteomic method termed DirectMS1 based on ultrashort LC gradients as well as MS1-only mass spectra acquisition and data processing. The method allows significant reduction of the proteome-wide analysis time to a few minutes at the depth of quantitative proteome coverage of 1000 proteins at 1% false discovery rate (FDR). In this work, to further increase the capabilities of the DirectMS1 method, we explored the opportunities presented by the recent progress in the machine-learning area and applied the LightGBM decision tree boosting algorithm to the scoring of peptide feature matches when processing MS1 spectra. Furthermore, we integrated the peptide feature identification algorithm of DirectMS1 with the recently introduced peptide retention time prediction utility, DeepLC. Additional approaches to improve the performance of the DirectMS1 method are discussed and demonstrated, such as using FAIMS for gas-phase ion separation. As a result of all improvements to DirectMS1, we succeeded in identifying more than 2000 proteins at 1% FDR from the HeLa cell line in a 5 min gradient LC-FAIMS/MS1 analysis. The data sets generated and analyzed during the current study have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the data set identifier PXD023977.

Biosaur: An open-source Python software for liquid chromatography-mass spectrometry peptide feature detection with ion mobility support

RATIONALE

One of the important steps in initial data processing of peptide mass spectra is the detection of peptide features in full-range mass spectra. Ion mobility offers advantages over previous methods performing this detection by providing an additional structure-specific separation dimension. However, there is a lack of open-source software that utilizes these advantages and detects peptide features in mass spectra acquired along with ion mobility data using new instruments such as timsTOF and/or FAIMS-Orbitrap.

METHODS

Recently, a utility called Dinosaur was presented, which provides an efficient way for feature detection in peptide ion mass spectra. In this work we extended its functionality by developing Biosaur software to fully employ the additional information provided by ion mobility data. Biosaur was developed using the Python 3.8 programming language.

RESULTS

Biosaur supports the processing of data acquired using mass spectrometers with ion mobility capabilities, specifically timsTOF and FAIMS. In addition, it processes mass spectra obtained in negative ion mode and reports cosine correlation table for peptide features which is useful for differentiation between in-source fragments and semi-tryptic peptides.

CONCLUSIONS

Biosaur is a utility for detecting peptide features in liquid chromatography-mass spectra with ion mobility and negative ion supports. The software is distributed with an open-source APACHE 2.0 license and is freely available on Github: https://github.com/abdrakhimov1/Biosaur.

Proteomic assessment of serum biomarkers of longevity in older men

The biological bases of longevity are not well understood, and there are limited biomarkers for the prediction of long life. We used a high-throughput, discovery-based proteomics approach to identify serum peptides and proteins that were associated with the attainment of longevity in a longitudinal study of community-dwelling men age ≥65 years. Baseline serum in 1196 men were analyzed using liquid chromatography-ion mobility-mass spectrometry, and lifespan was determined during ~12 years of follow-up. Men who achieved longevity (≥90% expected survival) were compared to those who died earlier. Rigorous statistical methods that controlled for false positivity were utilized to identify 25 proteins that were associated with longevity. All these proteins were in lower abundance in long-lived men and included a variety involved in inflammation or complement activation. Lower levels of longevity-associated proteins were also associated with better health status, but as time to death shortened, levels of these proteins increased. Pathway analyses implicated a number of compounds as important upstream regulators of the proteins and implicated shared networks that underlie the observed associations with longevity. Overall, these results suggest that complex pathways, prominently including inflammation, are linked to the likelihood of attaining longevity. This work may serve to identify novel biomarkers for longevity and to understand the biology underlying lifespan.

PASS-DIA: A Data-Independent Acquisition Approach for Discovery Studies

A data-independent acquisition (DIA) approach is being increasingly adopted as a promising strategy for identification and quantitation of proteomes. As most DIA data sets are acquired with wide isolation windows, highly complex MS/MS spectra are generated, which negatively impacts obtaining peptide information through classical protein database searches. Therefore, the analysis of DIA data mainly relies on the evidence of the existence of peptides from prebuilt spectral libraries. Consequently, one major weakness of this method is that it does not account for peptides that are not included in the spectral library, precluding the use of DIA for discovery studies. Here, we present a strategy termed Precursor ion And Small Slice-DIA (PASS-DIA) in which MS/MS spectra are acquired with small isolation windows (slices) and MS/MS spectra are interpreted with accurately determined precursor ion masses. This method enables the direct application of conventional spectrum-centric analysis pipelines for peptide identification and precursor ion-based quantitation. The performance of PASS-DIA was observed to be superior to both data-dependent acquisition (DDA) and conventional DIA experiments with 69 and 48% additional protein identifications, respectively. Application of PASS-DIA for the analysis of post-translationally modified peptides again highlighted its superior performance in characterizing phosphopeptides (77% more), N-terminal acetylated peptides (56% more), and N-glycopeptides (83% more) as compared to DDA alone. Finally, the use of PASS-DIA to characterize a rare proteome of human fallopian tube organoids enabled 34% additional protein identifications than DDA alone and revealed biologically relevant pathways including low abundance proteins. Overall, PASS-DIA is a novel DIA approach for use as a discovery tool that outperforms both conventional DDA and DIA experiments to provide additional protein information. We believe that the PASS-DIA method is an important strategy for discovery-type studies when deeper proteome characterization is required.

Meta-analysis of peptides to detect protein significance

Shotgun assays are widely used in biotechnologies to characterize large molecules, which are hard to be measured as a whole directly. For instance, in Liquid Chromatography - Mass Spectrometry (LC-MS) shotgun experiments, proteins in biological samples are digested into peptides, and then peptides are separated and measured. However, in proteomics study, investigators are usually interested in the performance of the whole proteins instead of those peptide fragments. In light of meta-analysis, we propose an adaptive thresholding method to select informative peptides, and combine peptide-level models to protein-level analysis. The meta-analysis procedure and modeling rationale can be adapted to data analysis of other types of shotgun assays.

Sensitive Top-Down Proteomics Analysis of a Low Number of Mammalian Cells Using a Nanodroplet Sample Processing Platform

Top-down proteomics is a powerful tool for characterizing genetic variations and post-translational modifications at intact protein level. However, one significant technical gap of top-down proteomics is the inability to analyze a low amount of biological samples, which limits its access to isolated rare cells, fine needle aspiration biopsies, and tissue substructures. Herein, we developed an ultrasensitive top-down platform by incorporating a microfluidic sample preparation system, termed nanoPOTS (nanodroplet processing in one pot for trace samples), into a top-down proteomic workflow. A unique combination of a nonionic detergent dodecyl-β-d-maltopyranoside (DDM) with urea as protein extraction buffer significantly improved both protein extraction efficiency and sample recovery. We hypothesize that the DDM detergent improves protein recovery by efficiently reducing nonspecific adsorption of intact proteins on container surfaces, while urea serves as a strong denaturant to disrupt noncovalent complexes and release intact proteins for downstream analysis. The nanoPOTS-based top-down platform reproducibly and quantitatively identified ∼170 to ∼620 proteoforms from ∼70 to ∼770 HeLa cells containing ∼10 to ∼115 ng of total protein. A variety of post-translational modifications including acetylation, myristoylation, and iron binding were identified using only less than 800 cells. We anticipate the nanoPOTS top-down proteomics platform will be broadly applicable in biomedical research, particularly where clinical specimens are not available in amounts amenable to standard workflows.

Potential of proteomics to probe microbes

An organism exposed to a plethora of environmental perturbations undergoes proteomic changes which enable the characterization of total proteins in it. Much of the proteomic information is obtained from genomic data. Additional information on the proteome such as posttranslational modifications, protein-protein interactions, protein localization, metabolic pathways, and so on are deduced using proteomic tools which genomics and transcriptomics fail to offer. The proteomic analysis allows identification of precise changes in proteins, which in turn solve the complexity of microbial population providing insights into the microbial metabolism, cellular pathways, and behavior of microorganisms in new environments. Furthermore, they provide clues for the exploitation of their special features for biotechnological applications. Numerous techniques for the analysis of microbial proteome such as electrophoretic, chromatographic, mass spectrometric-based methods as well as quantitative proteomics are available which facilitate protein separation, expression, identification, and quantification of proteins. An understanding of the potential of each of the proteomic tools has created a significant impact on diverse microbiological aspects and the same has been discussed in this review.

Short-Term Stable Isotope Probing of Proteins Reveals Taxa Incorporating Inorganic Carbon in a Hot Spring Microbial Mat

The upper green layer of the chlorophototrophic microbial mats associated with the alkaline siliceous hot springs of Yellowstone National Park consists of oxygenic cyanobacteria (Synechococcus spp.), anoxygenic Roseiflexus spp., and several other anoxygenic chlorophototrophs. Synechococcus spp. are believed to be the main fixers of inorganic carbon (Ci), but some evidence suggests that Roseiflexus spp. also contribute to inorganic carbon fixation during low-light, anoxic morning periods. Contributions of other phototrophic taxa have not been investigated. In order to follow the pathway of Ci incorporation into different taxa, mat samples were incubated with [13C]bicarbonate for 3 h during the early-morning, low-light anoxic period. Extracted proteins were treated with trypsin and analyzed by mass spectrometry, leading to peptide identifications and peptide isotopic profile signatures containing evidence of 13C label incorporation. A total of 25,483 peptides, corresponding to 7,221 proteins, were identified from spectral features and associated with mat taxa by comparison to metagenomic assembly sequences. A total of 1,417 peptides, derived from 720 proteins, were detectably labeled with 13C. Most 13C-labeled peptides were derived from proteins of Synechococcus spp. and Roseiflexus spp. Chaperones and proteins of carbohydrate metabolism were most abundantly labeled. Proteins involved in photosynthesis, Ci fixation, and N2 fixation were also labeled in Synechococcus spp. Importantly, most proteins of the 3-hydroxypropionate bi-cycle for Ci fixation in Roseiflexus spp. were labeled, establishing that members of this taxocene contribute to Ci fixation. Other taxa showed much lower [13C]bicarbonate incorporation.IMPORTANCE Yellowstone hot spring mats have been studied as natural models for understanding microbial community ecology and as modern analogs of stromatolites, the earliest community fossils on Earth. Stable-isotope probing of proteins (Pro-SIP) permitted short-term interrogation of the taxa that are involved in the important process of light-driven Ci fixation in this highly active community and will be useful in linking other metabolic processes to mat taxa. Here, evidence is presented that Roseiflexus spp., which use the 3-hydroxypropionate bi-cycle, are active in Ci fixation. Because this pathway imparts a lower degree of selection of isotopically heavy Ci than does the Calvin-Benson-Bassham cycle, the results suggest a mechanism to explain why the natural abundance of 13C in mat biomass is greater than expected if only the latter pathway were involved. Understanding how mat community members influence the 13C/12C ratios of mat biomass will help geochemists interpret the 13C/12C ratios of organic carbon in the fossil record.

Colonies of the fungus Aspergillus niger are highly differentiated to adapt to local carbon source variation

Saprobic fungi, such as Aspergillus niger, grow as colonies consisting of a network of branching and fusing hyphae that are often considered to be relatively uniform entities in which nutrients can freely move through the hyphae. In nature, different parts of a colony are often exposed to different nutrients. We have investigated, using a multi-omics approach, adaptation of A. niger colonies to spatially separated and compositionally different plant biomass substrates. This demonstrated a high level of intra-colony differentiation, which closely matched the locally available substrate. The part of the colony exposed to pectin-rich sugar beet pulp and to xylan-rich wheat bran showed high pectinolytic and high xylanolytic transcript and protein levels respectively. This study therefore exemplifies the high ability of fungal colonies to differentiate and adapt to local conditions, ensuring efficient use of the available nutrients, rather than maintaining a uniform physiology throughout the colony.

A Universal Stress Protein That Controls Bacterial Stress Survival in Micrococcus luteus

Bacteria have remarkable mechanisms to survive severe external stresses, and one of the most enigmatic is the nonreplicative persistent (NRP) state. Practically, NRP bacteria are difficult to treat, and so inhibiting the proteins underlying this survival state may render such bacteria more susceptible to external stresses, including antibiotics. Unfortunately, we know little about the proteins and mechanisms conferring survival through the NRP state. Here, we report that a universal stress protein (Usp) is a primary regulator of bacterial survival through the NRP state in Micrococcus luteus NCTC 2665, a biosafety level 1 (BSL1) mycobacterial relative. Usps are widely conserved, and bacteria, including Mycobacterium tuberculosis, Mycobacterium smegmatis, and Escherichia coli, have multiple paralogs with overlapping functions that have obscured their functional roles. A kanamycin resistance cassette inserted into the M. luteus universal stress protein A 616 gene (ΔuspA616::kan M. luteus) ablates the UspA616 protein and drastically impairs M. luteus survival under even short-term starvation (survival, 83% wild type versus 32% ΔuspA616::kan M. luteus) and hypoxia (survival, 96% wild type versus 48% ΔuspA616::kan M. luteus). We observed no detrimental UspA616 knockout phenotype in logarithmic growth. Proteomics demonstrated statistically significant log-phase upregulation of glyoxylate pathway enzymes isocitrate lyase and malate synthase in ΔuspA616::kan M. luteus We note that these enzymes and the M. tuberculosis UspA616 homolog (Rv2623) are important in M. tuberculosis virulence and chronic infection, suggesting that Usps are important stress proteins across diverse bacterial species. We propose that UspA616 is a metabolic switch that controls survival by regulating the glyoxylate shunt.IMPORTANCE Bacteria tolerate severe external stresses, including antibiotics, through a nonreplicative persistent (NRP) survival state, yet the proteins regulating this survival state are largely unknown. We show a specific universal stress protein (UspA616) controls the NRP state in Micrococcus luteus Usps are widely conserved across bacteria, but their biological function(s) has remained elusive. UspA616 inactivation renders M. luteus susceptible to stress: bacteria die instead of adapting through the NRP state. UspA616 regulates malate synthase and isocitrate lyase, glyoxylate pathway enzymes important for chronic Mycobacterium tuberculosis infection. These data show that UspA616 regulates NRP stress survival in M. luteus and suggest a function for homologous proteins in other bacteria. Importantly, inhibitors of UspA616 and homologs may render NRP bacteria more susceptible to stresses, including current antibiotics.

Tandem mass tag-based quantitative proteomic analysis of lycorine treatment in highly pathogenic avian influenza H5N1 virus infection

Highly pathogenic H5N1 influenza viruses (HPAIV) cause rapid systemic illness and death in susceptible animals, leading to a disease with high morbidity and mortality rates. Although vaccines and drugs are the best solution to prevent this threat, a more effective treatment for H5 strains of influenza has yet to be developed. Therefore, the development of therapeutics/drugs that combat H5N1 influenza virus infection is becoming increasingly important. Lycorine, the major component of Amaryllidaceae alkaloids, exhibits better protective effects against A/CK/GD/178/04 (H5N1) (GD178) viruses than the commercial neuraminidase (NA) inhibitor oseltamivir in our prior study. Lycorine demonstrates outstanding antiviral activity because of its inhibitory activity against the export of viral ribonucleoprotein complexes (vRNPs) from the nucleus. However, how lycorine affects the proteome of AIV infected cells is unknown. Therefore, we performed a comparative proteomic analysis to identify changes in protein expression in AIV-infected Madin-Darby Canine Kidney cells treated with lycorine. Three groups were designed: mock infection group (M), virus infection group (V), and virus infection and lycorine-treated after virus infection group (L). The multiplexed tandem mass tag (TMT) approach was employed to analyze protein level in this study. In total, 5,786 proteins were identified from the three groups of cells by using TMT proteomic analysis. In the V/M group, 1,101 proteins were identified, of which 340 differentially expressed proteins (DEPs) were determined during HPAIV infection; among the 1,059 proteins identified from the lycorine-treated group, 258 proteins presented significant change. Here, 71 proteins showed significant upregulation or downregulation of expression in the virus-infected/mock and virus-infected/lycorine-treated comparisons, and the proteins in each fraction were functionally classified further. Interestingly, lycorine treatment decreased the levels of the nuclear pore complex protein 93 (Nup93, E2RSV7), which is associated with nuclear–cytoplasmic transport. In addition, Western blot experiments confirmed that the expression of Nup93 was significantly downregulated in lycorine treatment but induced after viral infection. Our results may provide new insights into how lycorine may trap vRNPs in the nucleus and suggest new potential therapeutic targets for influenza virus.

Opportunities and Challenges in Activity-Based Protein Profiling of Mycobacteria

Mycobacteria, from saprophytic to pathogenic species, encounter diverse environments that demand metabolic versatility and rapid adaptation from these bacteria for their survival. The human pathogen Mycobacterium tuberculosis, for example, can enter a reversible state of dormancy in which it is metabolically active, but does not increase in number, and which is believed to enable its survival in the human host for years, with attendant risk for reactivation to active tuberculosis. Driven by the need to combat mycobacterial diseases like tuberculosis, efforts to understand such adaptations have benefitted in recent years from application of activity-based probes. These studies have been inspired by the potential of these chemical tools to uncover protein function for previously unannotated proteins, track shifts in protein activity as a function of environment, and provide a streamlined method for screening and developing inhibitors. Here we seek to contextualize progress thus far with achieving these goals and highlight the unique challenges and opportunities for activity-based probes to further our understanding of protein function and regulation, bacterial physiology, and antibiotic development.

Accurate mass and retention time library of serum lipids for type 1 diabetes research

Dysregulated lipid species are linked to various disease pathologies and implicated as potential biomarkers for type 1 diabetes (T1D). However, it is challenging to comprehensively profile the blood specimen lipidome with full structural details of every lipid molecule. The commonly used reversed-phase liquid chromatography-tandem mass spectrometry (RPLC-MS/MS)-based lipidomics approach is powerful for the separation of individual lipid species, but lipids belonging to different classes may still co-elute and result in ion suppression and misidentification of lipids. Using offline mixed-mode and RPLC-based two-dimensional separations coupled with MS/MS, a comprehensive lipidomic profiling was performed on human sera pooled from healthy and T1D subjects. The elution order of lipid molecular species on RPLC showed good correlations to the total number of carbons in fatty acyl chains and total number of double bonds. This observation together with fatty acyl methyl ester analysis was used to enhance the confidence of identified lipid species. The final T1D serum lipid library database contains 753 lipid molecular species with accurate mass and RPLC retention time uniquely annotated for each of the species. This comprehensive human serum lipid library can serve as a database for high-throughput RPLC-MS-based lipidomic analysis of blood samples related to T1D and other childhood diseases. Graphical abstract.

Benzo[ a]pyrene Induction of Glutathione S-Transferases: An Activity-Based Protein Profiling Investigation

Polycyclic aromatic hydrocarbons (PAHs) are ubiquitous environmental contaminants generated from combustion of carbon-based matter. Upon ingestion, these molecules can be bioactivated by cytochrome P450 monooxygenases to oxidized toxic metabolites. Some of these metabolites are potent carcinogens that can form irreversible adducts with DNA and other biological macromolecules. Conjugative enzymes, such as glutathione S-transferases or UDP-glucuronosyltransferases, are responsible for the detoxification and/or facilitate the elimination of these carcinogens. While responses to PAH exposures have been extensively studied for the bioactivating cytochrome P450 enzymes, much less is known regarding the response of glutathione S-transferases in mammalian systems. In this study, we investigated the expression and activity responses of murine hepatic glutathione S-transferases to benzo[ a]pyrene exposure using global proteomics and activity-based protein profiling for chemoproteomics, respectively. Using this approach, we identified several enzymes exhibiting increased activity including GSTA2, M1, M2, M4, M6, and P1. The activity of one GST enzyme, GSTA4, was found to be downregulated with increasing B[ a]P dose. Activity responses of several of these enzymes were identified as being expression-independent when comparing global and activity-based data sets, possibly alluding to as of yet unknown regulatory post-translational mechanisms.

Collision cross section compendium to annotate and predict multi-omic compound identities

Ion mobility mass spectrometry (IM-MS) expands the analyte coverage of existing multi-omic workflows by providing an additional separation dimension as well as a parameter for characterization and identification of molecules - the collision cross section (CCS). This work presents a large, Unified CCS compendium of >3800 experimentally acquired CCS values obtained from traceable molecular standards and measured with drift tube ion mobility-mass spectrometers. An interactive visualization of this compendium along with data analytic tools have been made openly accessible. Represented in the compendium are 14 structurally-based chemical super classes, consisting of a total of 80 classes and 157 subclasses. Using this large data set, regression fitting and predictive statistics have been performed to describe mass-CCS correlations specific to each chemical ontology. These structural trends provide a rapid and effective filtering method in the traditional untargeted workflow for identification of unknown biochemical species. The utility of the approach is illustrated by an application to metabolites in human serum, quantified trends of which were used to assess the probability of an unknown compound belonging to a given class. CCS-based filtering narrowed the chemical search space by 60% while increasing the confidence in the remaining isomeric identifications from a single class, thus demonstrating the value of integrating predictive analyses into untargeted experiments to assist in identification workflows. The predictive abilities of this compendium will improve in specificity and expand to more chemical classes as additional data from the IM-MS community is contributed. Instructions for data submission to the compendium and criteria for inclusion are provided.

Proteogenomic Characterization of Human Early-Onset Gastric Cancer

We report proteogenomic analysis of diffuse gastric cancers (GCs) in young populations. Phosphoproteome data elucidated signaling pathways associated with somatic mutations based on mutation-phosphorylation correlations. Moreover, correlations between mRNA and protein abundances provided potential oncogenes and tumor suppressors associated with patient survival. Furthermore, integrated clustering of mRNA, protein, phosphorylation, and N-glycosylation data identified four subtypes of diffuse GCs. Distinguishing these subtypes was possible by proteomic data. Four subtypes were associated with proliferation, immune response, metabolism, and invasion, respectively; and associations of the subtypes with immune- and invasion-related pathways were identified mainly by phosphorylation and N-glycosylation data. Therefore, our proteogenomic analysis provides additional information beyond genomic analyses, which can improve understanding of cancer biology and patient stratification in diffuse GCs.

The human brainome: network analysis identifies HSPA2 as a novel Alzheimer&rsquo;s disease target

Our hypothesis is that changes in gene and protein expression are crucial to the development of late-onset Alzheimer’s disease. Previously we examined how DNA alleles control downstream expression of RNA transcripts and how those relationships are changed in late-onset Alzheimer’s disease. We have now examined how proteins are incorporated into networks in two separate series and evaluated our outputs in two different cell lines. Our pipeline included the following steps: (i) predicting expression quantitative trait loci; (ii) determining differential expression; (iii) analysing networks of transcript and peptide relationships; and (iv) validating effects in two separate cell lines. We performed all our analysis in two separate brain series to validate effects. Our two series included 345 samples in the first set (177 controls, 168 cases; age range 65–105; 58% female; KRONOSII cohort) and 409 samples in the replicate set (153 controls, 141 cases, 115 mild cognitive impairment; age range 66–107; 63% female; RUSH cohort). Our top target is heat shock protein family A member 2 (HSPA2), which was identified as a key driver in our two datasets. HSPA2 was validated in two cell lines, with overexpression driving further elevation of amyloid-β40 and amyloid-β42 levels in APP mutant cells, as well as significant elevation of microtubule associated protein tau and phosphorylated-tau in a modified neuroglioma line. This work further demonstrates that studying changes in gene and protein expression is crucial to understanding late onset disease and further nominates HSPA2 as a specific key regulator of late-onset Alzheimer’s disease processes.10.1093/brain/awy215_video1awy215media15824729224001.

Multifunctional Activity-Based Protein Profiling of the Developing Lung

Lung diseases and disorders are a leading cause of death among infants. Many of these diseases and disorders are caused by premature birth and underdeveloped lungs. In addition to developmentally related disorders, the lungs are exposed to a variety of environmental contaminants and xenobiotics upon birth that can cause breathing issues and are progenitors of cancer. In order to gain a deeper understanding of the developing lung, we applied an activity-based chemoproteomics approach for the functional characterization of the xenometabolizing cytochrome P450 enzymes, active ATP and nucleotide binding enzymes, and serine hydrolases using a suite of activity-based probes (ABPs). We detected P450 activity primarily in the postnatal lung; using our ATP-ABP, we characterized a wide range of ATPases and other active nucleotide- and nucleic acid-binding enzymes involved in multiple facets of cellular metabolism throughout development. ATP-ABP targets include kinases, phosphatases, NAD- and FAD-dependent enzymes, RNA/DNA helicases, and others. The serine hydrolase-targeting probe detected changes in the activities of several proteases during the course of lung development, yielding insights into protein turnover at different stages of development. Select activity-based probe targets were then correlated with RNA in situ hybridization analyses of lung tissue sections.

Application of multiplexed ion mobility spectrometry towards the identification of host protein signatures of treatment effect in pulmonary tuberculosis

Rationale:

The monitoring of TB treatments in clinical practice and clinical trials relies on traditional sputum-based culture status indicators at specific time points. Accurate, predictive, blood-based protein markers would provide a simpler and more informative view of patient health and response to treatment.

Objective:

We utilized sensitive, high throughput multiplexed ion mobility-mass spectrometry (IM-MS) to characterize the serum proteome of TB patients at the start of and at 8 weeks of rifamycin-based treatment. We sought to identify treatment specific signatures within patients as well as correlate the proteome signatures to various clinical markers of treatment efficacy.

Methods:

Serum samples were collected from 289 subjects enrolled in CDC TB Trials Consortium Study 29 at time of enrollment and at the end of the intensive phase (after 40 doses of TB treatment). Serum proteins were immunoaffinity-depleted of high abundant components, digested to peptides and analyzed for data acquisition utilizing a unique liquid chromatography IM-MS platform (LC-IM-MS). Linear mixed models were utilized to identify serum protein changes in the host response to antibiotic treatment as well as correlations with culture status end points.

Results:

A total of 10,137 peptides corresponding to 872 proteins were identified, quantified, and used for statistical analysis across the longitudinal patient cohort. In response to TB treatment, 244 proteins were significantly altered. Pathway/network comparisons helped visualize the interconnected proteins, identifying up regulated (lipid transport, coagulation cascade, endopeptidase activity) and down regulated (acute phase) processes and pathways in addition to other cross regulated networks (inflammation, cell adhesion, extracellular matrix). Detection of possible lung injury serum proteins such as HPSE, significantly downregulated upon treatment. Analyses of microbiologic data over time identified a core set of serum proteins (TTHY, AFAM, CRP, RET4, SAA1, PGRP2) which change in response to treatment and also strongly correlate with culture status. A similar set of proteins at baseline were found to be predictive of week 6 and 8 culture status.

Conclusion:

A comprehensive host serum protein dataset reflective of TB treatment effect is defined. A repeating set of serum proteins (TTHY, AFAM, CRP, RET4, SAA1, PGRP2, among others) were found to change significantly in response to treatment, to strongly correlate with culture status, and at baseline to be predictive of future culture conversion. If validated in cohorts with long term follow-up to capture failure and relapse of TB, these protein markers could be developed for monitoring of treatment in clinical trials and in patient care.

Spatially Resolved Proteome Mapping of Laser Capture Microdissected Tissue with Automated Sample Transfer to Nanodroplets

Current mass spectrometry (MS)-based proteomics approaches are ineffective for mapping protein expression in tissue sections with high spatial resolution because of the limited overall sensitivity of conventional workflows. Here we report an integrated and automated method to advance spatially resolved proteomics by seamlessly coupling laser capture microdissection (LCM) with a recently developed nanoliter-scale sample preparation system termed nanoPOTS (Nanodroplet Processing in One pot for Trace Samples). The workflow is enabled by prepopulating nanowells with DMSO, which serves as a sacrificial capture liquid for microdissected tissues. The DMSO droplets efficiently collect laser-pressure catapulted LCM tissues as small as 20 μm in diameter with success rates >87%. We also demonstrate that tissue treatment with DMSO can significantly improve proteome coverage, likely due to its ability to dissolve lipids from tissue and enhance protein extraction efficiency. The LCM-nanoPOTS platform was able to identify 180, 695, and 1827 protein groups on average from 12-μm-thick rat brain cortex tissue sections having diameters of 50, 100, and 200 μm, respectively. We also analyzed 100-μm-diameter sections corresponding to 10-18 cells from three different regions of rat brain and comparatively quantified ∼1000 proteins, demonstrating the potential utility for high-resolution spatially resolved mapping of protein expression in tissues.

High-Fat Diets Alter the Modulatory Effects of Xenobiotics on Cytochrome P450 Activities

Cytochrome P450 monooxygenase (P450) enzymes metabolize critical endogenous chemicals and oxidize nearly all xenobiotics. Dysregulated P450 activities lead to altered capacity for drug metabolism and cellular stress. The effects of mixed exposures on P450 expression and activity are variable and elusive. A high-fat diet (HFD) is a common exposure that results in obesity and associated pathologies including hepatotoxicity. Herein, we report the effects of cigarette smoke on P450 activities of normal weight and HFD induced obese mice. Activity-based protein profiling results indicate that HFD mice had significantly decreased P450 activity, likely instigated by proinflammatory chemicals, and that P450 enzymes involved in detoxification, xenobiotic metabolism, and bile acid synthesis were effected by HFD and smoke interaction. Smoking increased activity of all lung P450 and coexposure to diet effected P450 2s1. We need to expand our understanding of common exposures coupled to altered P450 metabolism to enhance the safety and efficacy of therapeutic drug dosing.

Subnanogram proteomics: impact of LC column selection, MS instrumentation and data analysis strategy on proteome coverage for trace samples

One of the greatest challenges for mass spectrometry (MS)-based proteomics is the limited ability to analyze small samples. Here we investigate the relative contributions of liquid chromatography (LC), MS instrumentation and data analysis methods with the aim of improving proteome coverage for sample sizes ranging from 0.5 ng to 50 ng. We show that the LC separations utilizing 30-μm-i.d. columns increase signal intensity by >3-fold relative to those using 75-μm-i.d. columns, leading to 32% increase in peptide identifications. The Orbitrap Fusion Lumos MS significantly boosted both sensitivity and sequencing speed relative to earlier generation Orbitraps (e.g., LTQ-Orbitrap), leading to a ~3-fold increase in peptide identifications and 1.7-fold increase in identified protein groups for 2 ng tryptic digests of the bacterium S. oneidensis. The Match Between Runs algorithm of open-source MaxQuant software further increased proteome coverage by ~ 95% for 0.5 ng samples and by ~42% for 2 ng samples. Using the best combination of the above variables, we were able to identify >3,000 proteins from 10 ng tryptic digests from both HeLa and THP-1 mammalian cell lines. We also identified >950 proteins from subnanogram archaeal/bacterial cocultures. The present ultrasensitive LC-MS platform achieves a level of proteome coverage not previously realized for ultra-small sample loadings, and is expected to facilitate the analysis of subnanogram samples, including single mammalian cells.

Characterization of the Ovarian Tumor Peptidome

Aberrant degradation of proteins is associated with many pathological states, including cancers. Mass spectrometric analysis of the tumor peptidome has the potential to provide biological insights on proteolytic processing in cancer. However, attempts to use the tumors peptidome information in cancer research have been fairly limited to date, largely due to the lack of effective approaches for robust peptidomics identification and quantification, and the prevalence of confounding factors and biases associated with sample handling and processing. To address this need, we have recently developed an effective and robust analytical platform as well as a novel informatics approach for comprehensive analyses of tissue peptidomes. The ability of this new peptidomics pipeline for high-throughput, comprehensive, and quantitative peptidomics analysis, as well as the suitability of clinical ovarian tumor samples with postexcision delay limited to less than 60min before freezing for peptidomics analysis, has been demonstrated. These initial analyses set a stage for further determination of molecular details and functional significance of the peptidomic activities in ovarian cancer.

Mucosal and systemic responses of immunogenic vaccines candidates against enteric Escherichia coli infections in ruminants: A review

Innumerable Escherichia coli of animal origin are identified, which are of economic significance, likewise, cattle, sheep and goats are the carrier of enterohaemorrhagic E. coli, which are less pathogenic, and can spread to people by way of direct contact and through the contamination of foodstuff or portable drinking water, causing serious illness. The immunization of ruminants has been carried out for ages and is largely acknowledged as the most economical and maintainable process of monitoring E. coli infection in ruminants. Yet, only a limited number of E. coli vaccines are obtainable. Mucosal surfaces are the most important ingress for E. coli and thus mucosal immune responses function as the primary means of fortification. Largely contemporary vaccination processes are done by parenteral administration and merely limited number of E. coli vaccines are inoculated via mucosal itinerary, due to its decreased efficacy. Nevertheless, aiming at maximal mucosal partitions to stimulate defensive immunity at both mucosal compartments and systemic site epitomises a prodigious task. Enormous determinations are involved in order to improve on novel mucosal E. coli vaccines candidate by choosing apposite antigens with potent immunogenicity, manipulating novel mucosal itineraries of inoculation and choosing immune-inducing adjuvants. The target of E. coli mucosal vaccines is to stimulate a comprehensive, effective and defensive immunity by specifically counteracting the antibodies at mucosal linings and by the stimulation of cellular immunity. Furthermore, effective E. coli mucosal vaccine would make vaccination measures stress-free and appropriate for large number of inoculation. On account of contemporary advancement in proteomics, metagenomics, metabolomics and transcriptomics research, a comprehensive appraisal of the immeasurable genes and proteins that were divulged by a bacterium is now in easy reach. Moreover, there exist marvellous prospects in this bourgeoning technologies in comprehending the host bacteria affiliation. Accordingly, the flourishing knowledge could massively guarantee to the progression of immunogenic vaccines against E. coli infections in both humans and animals. This review highlight and expounds on the current prominence of mucosal and systemic immunogenic vaccines for the prevention of E. coli infections in ruminants.

Temporal expression profiling of plasma proteins reveals oxidative stress in early stages of Type 1 Diabetes progression

Blood markers other than islet autoantibodies are greatly needed to indicate the pancreatic beta cell destruction process as early as possible, and more accurately reflect the progression of Type 1 Diabetes Mellitus (T1D). To this end, a longitudinal proteomic profiling of human plasma using TMT-10plex-based LC-MS/MS analysis was performed to track temporal proteomic changes of T1D patients (n=11) across 9 serial time points, spanning the period of T1D natural progression, in comparison with those of the matching healthy controls (n=10). To our knowledge, the current study represents the largest (>2000 proteins measured) longitudinal expression profiles of human plasma proteome in T1D research. By applying statistical trend analysis on the temporal expression patterns between T1D and controls, and Benjamini-Hochberg procedure for multiple-testing correction, 13 protein groups were regarded as having statistically significant differences during the entire follow-up period. Moreover, 16 protein groups, which play pivotal roles in response to oxidative stress, have consistently abnormal expression trend before seroconversion to islet autoimmunity. Importantly, the expression trends of two key reactive oxygen species-decomposing enzymes, Catalase and Superoxide dismutase were verified independently by ELISA.

BIOLOGICAL SIGNIFICANCE

The temporal changes of >2000 plasma proteins (at least quantified in two subjects), spanning the entire period of T1D natural progression were provided to the research community. Oxidative stress related proteins have consistently different dysregulated patterns in T1D group than in age-sex matched healthy controls, even prior to appearance of islet autoantibodies - the earliest sign of islet autoimmunity and pancreatic beta cell stress.

Multi-platform 'Omics Analysis of Human Ebola Virus Disease Pathogenesis

The pathogenesis of human Ebola virus disease (EVD) is complex. EVD is characterized by high levels of virus replication and dissemination, dysregulated immune responses, extensive virus- and host-mediated tissue damage, and disordered coagulation. To clarify how host responses contribute to EVD pathophysiology, we performed multi-platform 'omics analysis of peripheral blood mononuclear cells and plasma from EVD patients. Our results indicate that EVD molecular signatures overlap with those of sepsis, imply that pancreatic enzymes contribute to tissue damage in fatal EVD, and suggest that Ebola virus infection may induce aberrant neutrophils whose activity could explain hallmarks of fatal EVD. Moreover, integrated biomarker prediction identified putative biomarkers from different data platforms that differentiated survivors and fatalities early after infection. This work reveals insight into EVD pathogenesis, suggests an effective approach for biomarker identification, and provides an important community resource for further analysis of human EVD severity.

A Global Survey of ATPase Activity in Plasmodium falciparum Asexual Blood Stages and Gametocytes

Effective malaria control and elimination in hyperendemic areas of the world will require treatment of the Plasmodium falciparum (Pf) blood stage that causes disease as well as the gametocyte stage that is required for transmission from humans to the mosquito vector. Most currently used therapies do not kill gametocytes, a highly specialized, non-replicating sexual parasite stage. Further confounding next generation drug development against Pf is the unknown metabolic state of the gametocyte and the lack of known biochemical activity for most parasite gene products in general. Here, we take a systematic activity-based proteomics approach to survey the activity of the large and druggable ATPase family in replicating blood stage asexual parasites and transmissible, non-replicating sexual gametocytes. ATPase activity broadly changes during the transition from asexual schizonts to sexual gametocytes, indicating altered metabolism and regulatory roles of ATPases specific for each lifecycle stage. We further experimentally confirm existing annotation and predict ATPase function for 38 uncharacterized proteins. By mapping the activity of ATPases associated with gametocytogenesis, we assign biochemical activity to a large number of uncharacterized proteins and identify new candidate transmission blocking targets.

High resolution visualization and exo-proteomics reveal the physiological role of XlnR and AraR in plant biomass colonization and degradation by Aspergillus niger

In A. niger, two transcription factors, AraR and XlnR, regulate the production of enzymes involved in degradation of arabinoxylan and catabolism of the released l-arabinose and d-xylose. Deletion of both araR and xlnR in leads to reduced production of (hemi)cellulolytic enzymes and reduced growth on arabinan, arabinogalactan and xylan. In this study, we investigated the colonization and degradation of wheat bran by the A. niger reference strain CBS 137562 and araR/xlnR regulatory mutants using high-resolution microscopy and exo-proteomics. We discovered that wheat bran flakes have a 'rough' and 'smooth' surface with substantially different affinity towards fungal hyphae. While colonization of the rough side was possible for all strains, the xlnR mutants struggled to survive on the smooth side of the wheat bran particles after 20 and 40 h post inoculation. Impaired colonization ability of the smooth surface of wheat bran was linked to reduced potential of ΔxlnR to secrete arabinoxylan and cellulose-degrading enzymes and indicates that XlnR is the major regulator that drives colonization of wheat bran in A. niger.

Identification of Hip BMD Loss and Fracture Risk Markers Through Population-Based Serum Proteomics

Serum proteomics analysis may lead to the discovery of novel osteoporosis biomarkers. The Osteoporotic Fractures in Men (MrOS) study comprises men ≥65 years old in the US who have had repeated BMD measures and have been followed for incident fracture. High-throughput quantitative proteomic analysis was performed on baseline fasting serum samples from non-Hispanic white men using a multidimensional approach coupling liquid chromatography, ion-mobility separation, and mass spectrometry (LC-IMS-MS). We followed the participants for a mean of 4.6 years for changes in femoral neck bone mineral density (BMD) and for incident hip fracture. Change in BMD was determined from mixed effects regression models taking age and weight into account. Participants were categorized into three groups: BMD maintenance (no decline; estimated change ≥0 g/cm2 , n = 453); expected loss (estimated change 0 to 1 SD below the estimated mean change, -0.034 g/cm2 for femoral neck, n = 1184); and accelerated loss (estimated change ≥1 SD below mean change, n = 237). Differential abundance values of 3946 peptides were summarized by meta-analysis to determine differential abundance of each of 339 corresponding proteins for accelerated BMD loss versus maintenance. Using this meta-analytic standardized fold change at cutoffs of ≥1.1 or ≤0.9 (p < 0.10), 20 proteins were associated with accelerated BMD loss. Associations of those 20 proteins with incident hip fracture were tested using Cox proportional hazards models with age and BMI adjustment in 2473 men. Five proteins were associated with incident hip fracture (HR between 1.29 and 1.41 per SD increase in estimated protein abundance). Some proteins have been previously associated with fracture risk (eg, CD14 and SHBG), whereas others have roles in cellular senescence and aging (B2MG and TIMP1) and complement activation and innate immunity (CO7, CO9, CFAD). These findings may inform development of biomarkers for future research in bone biology and fracture prediction. © 2017 American Society for Bone and Mineral Research.

Leucine Biosynthesis Is Involved in Regulating High Lipid Accumulation in Yarrowia lipolytica

The yeast Yarrowia lipolytica is a potent accumulator of lipids, and lipogenesis in this organism can be influenced by a variety of factors, such as genetics and environmental conditions. Using a multifactorial study, we elucidated the effects of both genetic and environmental factors on regulation of lipogenesis in Y. lipolytica and identified how two opposite regulatory states both result in lipid accumulation. This study involved comparison of a strain overexpressing diacylglycerol acyltransferase (DGA1) with a control strain grown under either nitrogen or carbon limitation conditions. A strong correlation was observed between the responses on the transcript and protein levels. Combination of DGA1 overexpression with nitrogen limitation resulted in a high level of lipid accumulation accompanied by downregulation of several amino acid biosynthetic pathways, including that of leucine in particular, and these changes were further correlated with a decrease in metabolic fluxes. This downregulation was supported by the measured decrease in the level of 2-isopropylmalate, an intermediate of leucine biosynthesis. Combining the multi-omics data with putative transcription factor binding motifs uncovered a contradictory role for TORC1 in controlling lipid accumulation, likely mediated through 2-isopropylmalate and a Leu3-like transcription factor.IMPORTANCE The ubiquitous metabolism of lipids involves refined regulation, and an enriched understanding of this regulation would have wide implications. Various factors can influence lipid metabolism, including the environment and genetics. We demonstrated, using a multi-omics and multifactorial experimental setup, that multiple factors affect lipid accumulation in the yeast Yarrowia lipolytica Using integrative analysis, we identified novel interactions between nutrient restriction and genetic factors involving regulators that are highly conserved among eukaryotes. Given that lipid metabolism is involved in many diseases but is also vital to the development of microbial cell factories that can provide us with sustainable fuels and oleochemicals, we envision that our report introduces foundational work to further unravel the regulation of lipid accumulation in eukaryal cells.