Direct sub-atmospheric pressure ionization mass spectrometry: Evaporation/sublimation-driven ionization is amazing, fundamentally, and practically.

This paper covers direct sub-atmospheric pressure ionization mass spectrometry (MS). The discovery, applications, and mechanistic aspects of novel ionization processes for use in MS that are not based on the high-energy input from voltage, laser, and/or high temperature but on sublimation/evaporation within a region linking a higher to lower pressure and modulated by heat and collisions, are discussed, including how this new reality has guided a series of discoveries, instrument developments, and commercialization. A research focus, inter alia, is on how best to understand, improve, and use these novel ionization processes, which convert volatile and nonvolatile compounds from solids (sublimation) or liquids (evaporation) into gas-phase ions for analysis by MS providing reproducible, accurate, sensitive, and prompt results. Our perception on how these unprecedented versus traditional ionization processes/methods relate to each other, how they can be made to coexist on the same mass spectrometer, and an outlook on new and expanded applications (e.g., clinical, portable, fast, safe, and autonomous) is presented, and is based on ST's Opening lecture presentation at the Nordic Mass spectrometry Conference, Geilo, Norway, January 2023. Focus will be on matrix-assisted ionization (MAI) and solvent-assisted ionization (SAI) MS covering the period from 2010 to 2023; a potential paradigm shift in the making.

SARS-CoV-2 variants evolve convergent strategies to remodel the host response.

SARS-CoV-2 variants of concern (VOCs) emerged during the COVID-19 pandemic. Here, we used unbiased systems approaches to study the host-selective forces driving VOC evolution. We discovered that VOCs evolved convergent strategies to remodel the host by modulating viral RNA and protein levels, altering viral and host protein phosphorylation, and rewiring virus-host protein-protein interactions. Integrative computational analyses revealed that although Alpha, Beta, Gamma, and Delta ultimately converged to suppress interferon-stimulated genes (ISGs), Omicron BA.1 did not. ISG suppression correlated with the expression of viral innate immune antagonist proteins, including Orf6, N, and Orf9b, which we mapped to specific mutations. Later Omicron subvariants BA.4 and BA.5 more potently suppressed innate immunity than early subvariant BA.1, which correlated with Orf6 levels, although muted in BA.4 by a mutation that disrupts the Orf6-nuclear pore interaction. Our findings suggest that SARS-CoV-2 convergent evolution overcame human adaptive and innate immune barriers, laying the groundwork to tackle future pandemics.

The microenvironment dictates glycocalyx construction and immune surveillance.

Efforts to identify anti-cancer therapeutics and understand tumor-immune interactions are built with in vitro models that do not match the microenvironmental characteristics of human tissues. Using in vitro models which mimic the physical properties of healthy or cancerous tissues and a physiologically relevant culture medium, we demonstrate that the chemical and physical properties of the microenvironment regulate the composition and topology of the glycocalyx. Remarkably, we find that cancer and age-related changes in the physical properties of the microenvironment are sufficient to adjust immune surveillance via the topology of the glycocalyx, a previously unknown phenomenon observable only with a physiologically relevant culture medium.

Deep learning from harmonized peptide libraries enables retention time prediction of diverse post translational modifications.

In proteomics experiments, peptide retention time (RT) is an orthogonal property to fragmentation when assessing detection confidence. Advances in deep learning enable accurate RT prediction for any peptide from sequence alone, including those yet to be experimentally observed. Here we present Chronologer, an open-source software tool for rapid and accurate peptide RT prediction. Using new approaches to harmonize and false-discovery correct across independently collected datasets, Chronologer is built on a massive database with >2.2 million peptides including 10 common post-translational modification (PTM) types. By linking knowledge learned across diverse peptide chemistries, Chronologer predicts RTs with less than two-thirds the error of other deep learning tools. We show how RT for rare PTMs, such as OGlcNAc, can be learned with high accuracy using as few as 10-100 example peptides in newly harmonized datasets. This iteratively updatable workflow enables Chronologer to comprehensively predict RTs for PTM-marked peptides across entire proteomes.

Global detection of human variants and isoforms by deep proteome sequencing.

An average shotgun proteomics experiment detects approximately 10,000 human proteins from a single sample. However, individual proteins are typically identified by peptide sequences representing a small fraction of their total amino acids. Hence, an average shotgun experiment fails to distinguish different protein variants and isoforms. Deeper proteome sequencing is therefore required for the global discovery of protein isoforms. Using six different human cell lines, six proteases, deep fractionation and three tandem mass spectrometry fragmentation methods, we identify a million unique peptides from 17,717 protein groups, with a median sequence coverage of approximately 80%. Direct comparison with RNA expression data provides evidence for the translation of most nonsynonymous variants. We have also hypothesized that undetected variants likely arise from mutation-induced protein instability. We further observe comparable detection rates for exon-exon junction peptides representing constitutive and alternative splicing events. Our dataset represents a resource for proteoform discovery and provides direct evidence that most frame-preserving alternatively spliced isoforms are translated.

Reduced endosomal microautophagy activity in aging associates with enhanced exocyst-mediated protein secretion.

Autophagy is essential for protein quality control and regulation of the functional proteome. Failure of autophagy pathways with age contributes to loss of proteostasis in aged organisms and accelerates the progression of age-related diseases. In this work, we show that activity of endosomal microautophagy (eMI), a selective type of autophagy occurring in late endosomes, declines with age and identify the sub-proteome affected by this loss of function. Proteomics of late endosomes from old mice revealed an aberrant glycation signature for Hsc70, the chaperone responsible for substrate targeting to eMI. Age-related Hsc70 glycation reduces its stability in late endosomes by favoring its organization into high molecular weight protein complexes and promoting its internalization/degradation inside late endosomes. Reduction of eMI with age associates with an increase in protein secretion, as late endosomes can release protein-loaded exosomes upon plasma membrane fusion. Our search for molecular mediators of the eMI/secretion switch identified the exocyst-RalA complex, known for its role in exocytosis, as a novel physiological eMI inhibitor that interacts with Hsc70 and acts directly at the late endosome membrane. This inhibitory function along with the higher exocyst-RalA complex levels detected in late endosomes from old mice could explain, at least in part, reduced eMI activity with age. Interaction of Hsc70 with components of the exocyst-RalA complex places this chaperone in the switch from eMI to secretion. Reduced intracellular degradation in favor of extracellular release of undegraded material with age may be relevant to the spreading of proteotoxicity associated with aging and progression of proteinopathies.

Data-Independent Acquisition Protease-Multiplexing Enables Increased Proteome Sequence Coverage Across Multiple Fragmentation Modes.

The use of multiple proteases has been shown to increase protein sequence coverage in proteomics experiments; however, due to the additional analysis time required, it has not been widely adopted in routine data-dependent acquisition (DDA) proteomic workflows. Alternatively, data-independent acquisition (DIA) has the potential to analyze multiplexed samples from different protease digests, but has been primarily optimized for fragmenting tryptic peptides. Here we evaluate a DIA multiplexing approach that combines three proteolytic digests (Trypsin, AspN, and GluC) into a single sample. We first optimize data acquisition conditions for each protease individually with both the canonical DIA fragmentation mode (beam type CID), as well as resonance excitation CID, to determine optimal consensus conditions across proteases. Next, we demonstrate that application of these conditions to a protease-multiplexed sample of human peptides results in similar protein identifications and quantitative performance as compared to trypsin alone, but enables up to a 63% increase in peptide detections, and a 45% increase in nonredundant amino acid detections. Nontryptic peptides enabled noncanonical protein isoform determination and resulted in 100% sequence coverage for numerous proteins, suggesting the utility of this approach in applications where sequence coverage is critical, such as protein isoform analysis.

Toward Comprehensive Plasma Proteomics by Orthogonal Protease Digestion.

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.

Adhesion-mediated mechanosignaling forces mitohormesis.

Mitochondria control eukaryotic cell fate by producing the energy needed to support life and the signals required to execute programed cell death. The biochemical milieu is known to affect mitochondrial function and contribute to the dysfunctional mitochondrial phenotypes implicated in cancer and the morbidities of aging. However, the physical characteristics of the extracellular matrix are also altered in cancerous and aging tissues. Here, we demonstrate that cells sense the physical properties of the extracellular matrix and activate a mitochondrial stress response that adaptively tunes mitochondrial function via solute carrier family 9 member A1-dependent ion exchange and heat shock factor 1-dependent transcription. Overall, our data indicate that adhesion-mediated mechanosignaling may play an unappreciated role in the altered mitochondrial functions observed in aging and cancer.

CIDer: A Statistical Framework for Interpreting Differences in CID and HCD Fragmentation.

Library searching is a powerful technique for detecting peptides using either data independent or data dependent acquisition. While both large-scale spectrum library curators and deep learning prediction approaches have focused on beam-type CID fragmentation (HCD), resonance CID fragmentation remains a popular technique. Here we demonstrate an approach to model the differences between HCD and CID spectra, and present a software tool, CIDer, for converting libraries between the two fragmentation methods. We demonstrate that just using a combination of simple linear models and basic principles of peptide fragmentation, we can explain up to 43% of the variation between ions fragmented by HCD and CID across an array of collision energy settings. We further show that in some circumstances, searching converted CID libraries can detect more peptides than searching existing CID libraries or libraries of machine learning predictions from FASTA databases. These results suggest that leveraging information in existing libraries by converting between HCD and CID libraries may be an effective interim solution while large-scale CID libraries are being developed.

Mass spectrometry-based protein-protein interaction networks for the study of human diseases.

A better understanding of the molecular mechanisms underlying disease is key for expediting the development of novel therapeutic interventions. Disease mechanisms are often mediated by interactions between proteins. Insights into the physical rewiring of protein-protein interactions in response to mutations, pathological conditions, or pathogen infection can advance our understanding of disease etiology, progression, and pathogenesis and can lead to the identification of potential druggable targets. Advances in quantitative mass spectrometry (MS)-based approaches have allowed unbiased mapping of these disease-mediated changes in protein-protein interactions on a global scale. Here, we review MS techniques that have been instrumental for the identification of protein-protein interactions at a system-level, and we discuss the challenges associated with these methodologies as well as novel MS advancements that aim to address these challenges. An overview of examples from diverse disease contexts illustrates the potential of MS-based protein-protein interaction mapping approaches for revealing disease mechanisms, pinpointing new therapeutic targets, and eventually moving toward personalized applications.

An overview of biological applications and fundamentals of new inlet and vacuum ionization technologies.

RATIONALE: The developments of new ionization technologies based on processes previously unknown to mass spectrometry (MS) have gained significant momentum. Herein we address the importance of understanding these unique ionization processes, demonstrate the new capabilities currently unmet by other methods, and outline their considerable analytical potential. METHODS: The inlet and vacuum ionization methods of solvent-assisted ionization (SAI), matrix-assisted ionization (MAI), and laserspray ionization can be used with commercial and dedicated ion sources producing ions from atmospheric or vacuum conditions for analyses of a variety of materials including drugs, lipids, and proteins introduced from well plates, pipet tips and plate surfaces with and without a laser using solid or solvent matrices. Mass spectrometers from various vendors are employed. RESULTS: Results are presented highlighting strengths relative to ionization methods of electrospray ionization (ESI) and matrix-assisted laser desorption/ionization. We demonstrate the utility of multi-ionization platforms encompassing MAI, SAI, and ESI and enabling detection of what otherwise is missed, especially when directly analyzing mixtures. Unmatched robustness is achieved with dedicated vacuum MAI sources with mechanical introduction of the sample to the sub-atmospheric pressure (vacuum MAI). Simplicity and use of a wide array of matrices are attained using a conduit (inlet ionization), preferably heated, with sample introduction from atmospheric pressure. Tissue, whole blood, urine (including mouse, chicken, and human origin), bacteria strains and chemical on-probe reactions are analyzed directly and, especially in the case of vacuum ionization, without concern of carryover or instrument contamination. CONCLUSIONS: Examples are provided highlighting the exceptional analytical capabilities associated with the novel ionization processes in MS that reduce operational complexity while increasing speed and robustness, achieving mass spectra with low background for improved sensitivity, suggesting the potential of this simple ionization technology to drive MS into areas currently underserved, such as clinical and medical applications.

The Global Phosphorylation Landscape of SARS-CoV-2 Infection.

The causative agent of the coronavirus disease 2019 (COVID-19) pandemic, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has infected millions and killed hundreds of thousands of people worldwide, highlighting an urgent need to develop antiviral therapies. Here we present a quantitative mass spectrometry-based phosphoproteomics survey of SARS-CoV-2 infection in Vero E6 cells, revealing dramatic rewiring of phosphorylation on host and viral proteins. SARS-CoV-2 infection promoted casein kinase II (CK2) and p38 MAPK activation, production of diverse cytokines, and shutdown of mitotic kinases, resulting in cell cycle arrest. Infection also stimulated a marked induction of CK2-containing filopodial protrusions possessing budding viral particles. Eighty-seven drugs and compounds were identified by mapping global phosphorylation profiles to dysregulated kinases and pathways. We found pharmacologic inhibition of the p38, CK2, CDK, AXL, and PIKFYVE kinases to possess antiviral efficacy, representing potential COVID-19 therapies.

A SARS-CoV-2-Human Protein-Protein Interaction Map Reveals Drug Targets and Potential Drug-Repurposing.

An outbreak of the novel coronavirus SARS-CoV-2, the causative agent of COVID-19 respiratory disease, has infected over 290,000 people since the end of 2019, killed over 12,000, and caused worldwide social and economic disruption 1,2 . There are currently no antiviral drugs with proven efficacy nor are there vaccines for its prevention. Unfortunately, the scientific community has little knowledge of the molecular details of SARS-CoV-2 infection. To illuminate this, we cloned, tagged and expressed 26 of the 29 viral proteins in human cells and identified the human proteins physically associated with each using affinity-purification mass spectrometry (AP-MS), which identified 332 high confidence SARS-CoV-2-human protein-protein interactions (PPIs). Among these, we identify 66 druggable human proteins or host factors targeted by 69 existing FDA-approved drugs, drugs in clinical trials and/or preclinical compounds, that we are currently evaluating for efficacy in live SARS-CoV-2 infection assays. The identification of host dependency factors mediating virus infection may provide key insights into effective molecular targets for developing broadly acting antiviral therapeutics against SARS-CoV-2 and other deadly coronavirus strains.

A SARS-CoV-2 protein interaction map reveals targets for drug repurposing.

A newly described coronavirus named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which is the causative agent of coronavirus disease 2019 (COVID-19), has infected over 2.3 million people, led to the death of more than 160,000 individuals and caused worldwide social and economic disruption1,2. There are no antiviral drugs with proven clinical efficacy for the treatment of COVID-19, nor are there any vaccines that prevent infection with SARS-CoV-2, and efforts to develop drugs and vaccines are hampered by the limited knowledge of the molecular details of how SARS-CoV-2 infects cells. Here we cloned, tagged and expressed 26 of the 29 SARS-CoV-2 proteins in human cells and identified the human proteins that physically associated with each of the SARS-CoV-2 proteins using affinity-purification mass spectrometry, identifying 332 high-confidence protein-protein interactions between SARS-CoV-2 and human proteins. Among these, we identify 66 druggable human proteins or host factors targeted by 69 compounds (of which, 29 drugs are approved by the US Food and Drug Administration, 12 are in clinical trials and 28 are preclinical compounds). We screened a subset of these in multiple viral assays and found two sets of pharmacological agents that displayed antiviral activity: inhibitors of mRNA translation and predicted regulators of the sigma-1 and sigma-2 receptors. Further studies of these host-factor-targeting agents, including their combination with drugs that directly target viral enzymes, could lead to a therapeutic regimen to treat COVID-19.

ATM directs DNA damage responses and proteostasis via genetically separable pathways.

The protein kinase ATM is a master regulator of the DNA damage response but also responds directly to oxidative stress. Loss of ATM causes ataxia telangiectasia, a neurodegenerative disorder with pleiotropic symptoms that include cerebellar dysfunction, cancer, diabetes, and premature aging. We genetically separated the activation of ATM by DNA damage from that by oxidative stress using separation-of-function mutations. We found that deficient activation of ATM by the Mre11-Rad50-Nbs1 complex and DNA double-strand breaks resulted in loss of cell viability, checkpoint activation, and DNA end resection in response to DNA damage. In contrast, loss of oxidative activation of ATM had minimal effects on DNA damage-related outcomes but blocked ATM-mediated initiation of checkpoint responses after oxidative stress and resulted in deficiencies in mitochondrial function and autophagy. In addition, expression of a variant ATM incapable of activation by oxidative stress resulted in widespread protein aggregation. These results indicate a direct relationship between the mechanism of ATM activation and its effects on cellular metabolism and DNA damage responses in human cells and implicate ATM in the control of protein homeostasis.

Identification of the phosphorylation targets of symbiotic receptor-like kinases using a high-throughput multiplexed assay for kinase specificity.

Detecting the phosphorylation substrates of multiple kinases in a single experiment is a challenge, and new techniques are being developed to overcome this challenge. Here, we used a multiplexed assay for kinase specificity (MAKS) to identify the substrates directly and to map the phosphorylation site(s) of plant symbiotic receptor-like kinases. The symbiotic receptor-like kinases nodulation receptor-like kinase (NORK) and lysin motif domain-containing receptor-like kinase 3 (LYK3) are indispensable for the establishment of root nodule symbiosis. Although some interacting proteins have been identified for these symbiotic receptor-like kinases, very little is known about their phosphorylation substrates. Using this high-throughput approach, we identified several other potential phosphorylation targets for both these symbiotic receptor-like kinases. In particular, we also discovered the phosphorylation of LYK3 by NORK itself, which was also confirmed by pairwise kinase assays. Motif analysis of potential targets for these kinases revealed that the acidic motif xxxsDxxx was common to both of them. In summary, this high-throughput technique catalogs the potential phosphorylation substrates of multiple kinases in a single efficient experiment, the biological characterization of which should provide a better understanding of phosphorylation signaling cascade in symbiosis.

Mitochondrial protein functions elucidated by multi-omic mass spectrometry profiling.

Mitochondrial dysfunction is associated with many human diseases, including cancer and neurodegeneration, that are often linked to proteins and pathways that are not well-characterized. To begin defining the functions of such poorly characterized proteins, we used mass spectrometry to map the proteomes, lipidomes, and metabolomes of 174 yeast strains, each lacking a single gene related to mitochondrial biology. 144 of these genes have human homologs, 60 of which are associated with disease and 39 of which are uncharacterized. We present a multi-omic data analysis and visualization tool that we use to find covariance networks that can predict molecular functions, correlations between profiles of related gene deletions, gene-specific perturbations that reflect protein functions, and a global respiration deficiency response. Using this multi-omic approach, we link seven proteins including Hfd1p and its human homolog ALDH3A1 to mitochondrial coenzyme Q (CoQ) biosynthesis, an essential pathway disrupted in many human diseases. This Resource should provide molecular insights into mitochondrial protein functions.

A proteomic atlas of the legume Medicago truncatula and its nitrogen-fixing endosymbiont Sinorhizobium meliloti.

Legumes are essential components of agricultural systems because they enrich the soil in nitrogen and require little environmentally deleterious fertilizers. A complex symbiotic association between legumes and nitrogen-fixing soil bacteria called rhizobia culminates in the development of root nodules, where rhizobia fix atmospheric nitrogen and transfer it to their plant host. Here we describe a quantitative proteomic atlas of the model legume Medicago truncatula and its rhizobial symbiont Sinorhizobium meliloti, which includes more than 23,000 proteins, 20,000 phosphorylation sites, and 700 lysine acetylation sites. Our analysis provides insight into mechanisms regulating symbiosis. We identify a calmodulin-binding protein as a key regulator in the host and assign putative roles and targets to host factors (bioactive peptides) that control gene expression in the symbiont. Further mining of this proteomic resource may enable engineering of crops and their microbial partners to increase agricultural productivity and sustainability.

Mitochondrial protein hyperacetylation in the failing heart.

Myocardial fuel and energy metabolic derangements contribute to the pathogenesis of heart failure. Recent evidence implicates posttranslational mechanisms in the energy metabolic disturbances that contribute to the pathogenesis of heart failure. We hypothesized that accumulation of metabolite intermediates of fuel oxidation pathways drives posttranslational modifications of mitochondrial proteins during the development of heart failure. Myocardial acetylproteomics demonstrated extensive mitochondrial protein lysine hyperacetylation in the early stages of heart failure in well-defined mouse models and the in end-stage failing human heart. To determine the functional impact of increased mitochondrial protein acetylation, we focused on succinate dehydrogenase A (SDHA), a critical component of both the tricarboxylic acid (TCA) cycle and respiratory complex II. An acetyl-mimetic mutation targeting an SDHA lysine residue shown to be hyperacetylated in the failing human heart reduced catalytic function and reduced complex II-driven respiration. These results identify alterations in mitochondrial acetyl-CoA homeostasis as a potential driver of the development of energy metabolic derangements that contribute to heart failure.