Rapid and consistent protein identification across large clinical cohorts is an important goal for clinical proteomics. With the development of data-independent technologies (DIA/SWATH-MS), it is now possible to analyze hundreds of samples with great reproducibility and quantitative accuracy. However, this technology benefits from empirically derived spectral libraries that define the detectable set of peptides and proteins. Here, we apply a simple and accessible tip-based workflow for the generation of spectral libraries to provide a comprehensive overview on the plasma proteome in individuals with and without active tuberculosis (TB). To boost protein coverage, we utilized nonconventional proteases such as GluC and AspN together with the gold standard trypsin, identifying more than 30,000 peptides mapping to 3309 proteins. Application of this library to quantify plasma proteome differences in TB infection recovered more than 400 proteins in 50 min of MS acquisition, including diagnostic Mycobacterium tuberculosis (Mtb) proteins that have previously been detectable primarily by antibody-based assays and intracellular proteins not previously described to be in plasma.

Since the recent outbreak of COVID-19, there have been intense efforts to understand viral pathogenesis and host immune response to combat SARS-CoV-2. It has become evident that different host alterations can be identified in SARS-CoV-2 infection based on whether infected cells, animal models or clinical samples are studied. Although nasopharyngeal swabs are routinely collected for SARS-CoV-2 detection by RT-PCR testing, host alterations in the nasopharynx at the proteomic level have not been systematically investigated. Thus, we sought to characterize the host response through global proteome profiling of nasopharyngeal swab specimens. A mass spectrometer combining trapped ion mobility spectrometry (TIMS) and high-resolution QTOF mass spectrometer with parallel accumulation-serial fragmentation (PASEF) was deployed for unbiased proteome profiling. First, deep proteome profiling of pooled nasopharyngeal swab samples was performed in the PASEF enabled DDA mode, which identified 7723 proteins that were then used to generate a spectral library. This approach provided peptide level evidence of five missing proteins for which MS/MS spectrum and mobilograms were validated with synthetic peptides. Subsequently, quantitative proteomic profiling was carried out for 90 individual nasopharyngeal swab samples (45 positive and 45 negative) in DIA combined with PASEF, termed as diaPASEF mode, which resulted in a total of 5023 protein identifications. Of these, 577 proteins were found to be upregulated in SARS-CoV-2 positive samples. Functional analysis of these upregulated proteins revealed alterations in several biological processes including innate immune response, viral protein assembly, and exocytosis. To the best of our knowledge, this study is the first to deploy diaPASEF for quantitative proteomic profiling of clinical samples and shows the feasibility of adopting such an approach to understand mechanisms and pathways altered in diseases.

For yeast cells, tolerance to high levels of ethanol is vital both in their natural environment and in industrially relevant conditions. We recently genotyped experimentally evolved yeast strains adapted to high levels of ethanol and identified mutations linked to ethanol tolerance. In this study, by integrating genomic sequencing data with quantitative proteomics profiles from six evolved strains (data set identifier PXD006631) and construction of protein interaction networks, we elucidate exactly how the genotype and phenotype are related at the molecular level. Our multi-omics approach points to the rewiring of numerous metabolic pathways affected by genomic and proteomic level changes, from energy-producing and lipid pathways to differential regulation of transposons and proteins involved in cell cycle progression. One of the key differences is found in the energy-producing metabolism, where the ancestral yeast strain responds to ethanol by switching to respiration and employing the mitochondrial electron transport chain. In contrast, the ethanol-adapted strains appear to have returned back to energy production mainly via glycolysis and ethanol fermentation, as supported by genomic and proteomic level changes. This work is relevant for synthetic biology where systems need to function under stressful conditions, as well as for industry and in cancer biology, where it is important to understand how the genotype relates to the phenotype.

Prediction of residue-level structural attributes and protein-level structural classes helps model protein tertiary structures and understand protein functions. Existing methods are either specialized on only one class of proteins or developed to predict only a specific type of residue-level attribute. In this work, we develop a new deep-learning method, named Membrane Association and Secondary Structure Predictor (MASSP), for accurately predicting both residue-level structural attributes (secondary structure, location, orientation, and topology) and protein-level structural classes (bitopic, α-helical, β-barrel, and soluble). MASSP integrates a multilayer two-dimensional convolutional neural network (2D-CNN) with a long short-term memory (LSTM) neural network into a multitasking framework. Our comparison shows that MASSP performs equally well or better than the state-of-the-art methods in predicting residue-level secondary structures, boundaries of transmembrane segments, and topology. Furthermore, it achieves outstanding accuracy in predicting protein-level structural classes. MASSP automatically distinguishes the structural classes of input sequences and identifies transmembrane segments and topologies if present, making it broadly applicable to different classes of proteins. In summary, MASSP's good performance and broad applicability make it well suited for annotating residue-level attributes and protein-level structural classes at the proteome scale.

Glucocorticoids are the first-line treatment for sensorineural hearing loss, but little is known about the mechanism of their protective effect or the impact of route of administration. The recent development of hollow microneedles enables safe and reliable sampling of perilymph for proteomic analysis. Using these microneedles, we investigate the effect of intratympanic (IT) versus intraperitoneal (IP) dexamethasone administration on guinea pig perilymph proteome. Guinea pigs were treated with IT dexamethasone (n = 6), IP dexamethasone (n = 8), or untreated for control (n = 8) 6 h prior to aspiration. The round window membrane (RWM) was accessed via a postauricular approach, and hollow microneedles were used to perforate the RWM and aspirate 1 μL of perilymph. Perilymph samples were analyzed by liquid chromatography-mass spectrometry-based label-free quantitative proteomics. Mass spectrometry raw data files have been deposited in an international public repository (MassIVE proteomics repository at https://massive.ucsd.edu/) under data set # MSV000086887. In the 22 samples of perilymph analyzed, 632 proteins were detected, including the inner ear protein cochlin, a perilymph marker. Of these, 14 proteins were modulated by IP, and three proteins were modulated by IT dexamethasone. In both IP and IT dexamethasone groups, VGF nerve growth factor inducible was significantly upregulated compared to control. The remaining adjusted proteins modulate neurons, inflammation, or protein synthesis. Proteome analysis facilitated by the use of hollow microneedles shows that route of dexamethasone administration impacts changes seen in perilymph proteome. Compared to IT administration, the IP route was associated with greater changes in protein expression, including proteins involved in neuroprotection, inflammatory pathway, and protein synthesis. Our findings show that microneedles can mediate safe and effective intracochlear sampling and hold promise for inner ear diagnostics.

Global bottom-up mass spectrometry (MS)-based proteomics is widely used for protein identification and quantification to achieve a comprehensive understanding of the composition, structure, and function of the proteome. However, traditional sample preparation methods are time-consuming, typically including overnight tryptic digestion, extensive sample cleanup to remove MS-incompatible surfactants, and offline sample fractionation to reduce proteome complexity prior to online liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis. Thus, there is a need for a fast, robust, and reproducible method for protein identification and quantification from complex proteomes. Herein, we developed an ultrafast bottom-up proteomics method enabled by Azo, a photocleavable, MS-compatible surfactant that effectively solubilizes proteins and promotes rapid tryptic digestion, combined with the Bruker timsTOF Pro, which enables deeper proteome coverage through trapped ion mobility spectrometry (TIMS) and parallel accumulation-serial fragmentation (PASEF) of peptides. We applied this method to analyze the complex human cardiac proteome and identified nearly 4000 protein groups from as little as 1 mg of human heart tissue in a single one-dimensional LC-TIMS-MS/MS run with high reproducibility. Overall, we anticipate this ultrafast, robust, and reproducible bottom-up method empowered by both Azo and the timsTOF Pro will be generally applicable and greatly accelerate the throughput of large-scale quantitative proteomic studies. Raw data are available via the MassIVE repository with identifier MSV000087476.

Chitin is an abundant natural polysaccharide that is hard to degrade because of its crystalline nature and because it is embedded in robust co-polymeric materials containing other polysaccharides, proteins, and minerals. Thus, it is of interest to study the enzymatic machineries of specialized microbes found in chitin-rich environments. We describe a genomic and proteomic analysis of Andreprevotia ripae, a chitinolytic Gram-negative bacterium isolated from an anthill. The genome of A. ripae encodes four secreted family GH19 chitinases of which two were detected and upregulated during growth on chitin. In addition, the genome encodes as many as 25 secreted GH18 chitinases, of which 17 were detected and 12 were upregulated during growth on chitin. Finally, the single lytic polysaccharide monooxygenase (LPMO) was strongly upregulated during growth on chitin. Whereas 66% of the 29 secreted chitinases contained two carbohydrate-binding modules (CBMs), this fraction was 93% (13 out of 14) for the upregulated chitinases, suggesting an important role for these CBMs. Next to an unprecedented multiplicity of upregulated chitinases, this study reveals several chitin-induced proteins that contain chitin-binding CBMs but lack a known catalytic function. These proteins are interesting targets for discovery of enzymes used by nature to convert chitin-rich biomass. The MS proteomic data have been deposited in the PRIDE database with accession number PXD025087.

Genes, sex, age, diet, lifestyle, gut microbiome, and multiple other factors affect human metabolomic profiles. Understanding metabolomic variation is critical in human nutrition research as metabolites that are sensitive to change versus those that are more stable might be more informative for a particular study design. This study aims to identify stable metabolomic regions and determine the genetic and environmental contributions to stability. Using a classic twin design, 1H nuclear magnetic resonance (NMR) urinary metabolomic profiles were measured in 128 twins at baseline, 1 month, and 2 months. Multivariate mixed models identified stable urinary metabolites with intraclass correlation coefficients ≥0.51. Longitudinal twin modeling measured the contribution of genetic and environmental influences to variation in the stable urinary NMR metabolome, comprising stable metabolites. The conservation of an individual's stable urinary NMR metabolome over time was assessed by calculating conservation indices. In this study, 20% of the urinary NMR metabolome is stable over 2 months (intraclass correlation (ICC) 0.51-0.65). Common genetic and shared environmental factors contributed to variance in the stable urinary NMR metabolome over time. Using the stable metabolome, 91% of individuals had good metabolomic conservation indices ≥0.70. To conclude, this research identifies 20% of the urinary NMR metabolome as stable, improves our knowledge of the sources of metabolomic variation over time, and demonstrates the conservation of an individual's urinary NMR metabolome.

Proteoform identification is required to fully understand the biological diversity present in a sample. However, these identifications are often ambiguous because of the challenges in analyzing full length proteins by mass spectrometry. A five-level proteoform classification system was recently developed to delineate the ambiguity of proteoform identifications and to allow for comparisons across software platforms and acquisition methods. Widespread adoption of this system requires software tools to provide classification of the proteoform identifications. We describe here an implementation of the five-level classification system in the software program MetaMorpheus, which provides both bottom-up and top-down identifications. Additionally, we developed a stand-alone program called ProteoformClassifier that allows users to classify proteoform results from any search program, provided that the program writes output that includes the information necessary to evaluate proteoform ambiguity. This stand-alone program includes a small test file and database to evaluate if a given program provides sufficient information to evaluate ambiguity. If the program does not, then ProteoformClassifier provides meaningful feedback to assist developers with implementing the classification system. We tested currently available top-down software programs and found that none of them (other than MetaMorpheus) provided sufficient information regarding identification ambiguity to permit classification.

Direct analysis of amyloid proteins in human plasma will promote rapid screening of brain amyloidosis, the earliest pathological signature of Alzheimer's disease. We developed a microflow liquid chromatography-targeted mass spectrometry assay for quantitation of four intact β-amyloid proteins starting from 1 mL of human plasma samples. This method showed 90% accuracy for predicting brain amyloid using plasma Aβ42/Aβ40 values from 36 cognitively normal individuals in a prospective clinical study (raw data deposited in MassIVE, Data set ID MSV000087451). Our method may contribute to early diagnosis of Alzheimer's disease.

Dried blood spot (DBS) metabolite analysis is a central tool for the clinic, e.g., newborn screening. Instead of applying multiple analytical methods, a single liquid chromatography-mass spectrometry (LC-MS) method was developed for metabolites spanning from highly polar glucose to hydrophobic long-chain acylcarnitines. For liquid chromatography, a diphenyl column and a multi-linear solvent gradient operated at elevated flow rates allowed for an even-spread resolution of diverse metabolites. Injecting moderate volumes of DBS organic extracts directly, in contrast to evaporation and reconstitution, provided substantial increases in analyte recovery. Q Exactive MS settings were also tailored for sensitivity increases, and the method allowed for analyte retention time and peak area repeatabilities of 0.1-0.4 and 2-10%, respectively, for a wide polarity range of metabolites (log P -4.4 to 8.8). The method's performance was suited for both untargeted analysis and targeted approaches evaluated in clinically relevant experiments.

O-Glycosylation changes in misfolded proteins are of particular interest in understanding neurodegenerative conditions such as Parkinson's disease (PD) and incidental Lewy body disease (ILBD). This work outlines optimizations of a microwave-assisted nonreductive release to limit glycan degradation and employs this methodology to analyze O-glycosylation on the human striatum and substantia nigra tissue in PD, ILBD, and healthy controls, working alongside well-established reductive release approaches. A total of 70 O-glycans were identified, with ILBD presenting significantly decreased levels of mannose-core (p = 0.017) and glucuronylated structures (p = 0.039) in the striatum and PD presenting an increase in sialylation (p < 0.001) and a decrease in sulfation (p = 0.001). Significant increases in sialylation (p = 0.038) in PD were also observed in the substantia nigra. This is the first study to profile the whole nigrostriatal O-glycome in healthy, PD, and ILBD tissues, outlining disease biomarkers alongside benefits of employing orthogonal techniques for O-glycan analysis.

In the absence of effective treatment, COVID-19 is likely to remain a global disease burden. Compounding this threat is the near certainty that novel coronaviruses with pandemic potential will emerge in years to come. Pan-coronavirus drugs-agents active against both SARS-CoV-2 and other coronaviruses-would address both threats. A strategy to develop such broad-spectrum inhibitors is to pharmacologically target binding sites on SARS-CoV-2 proteins that are highly conserved in other known coronaviruses, the assumption being that any selective pressure to keep a site conserved across past viruses will apply to future ones. Here we systematically mapped druggable binding pockets on the experimental structure of 15 SARS-CoV-2 proteins and analyzed their variation across 27 α- and β-coronaviruses and across thousands of SARS-CoV-2 samples from COVID-19 patients. We find that the two most conserved druggable sites are a pocket overlapping the RNA binding site of the helicase nsp13 and the catalytic site of the RNA-dependent RNA polymerase nsp12, both components of the viral replication-transcription complex. We present the data on a public web portal (https://www.thesgc.org/SARSCoV2_pocketome/), where users can interactively navigate individual protein structures and view the genetic variability of drug-binding pockets in 3D.