Modern DIA proteomics increasingly emphasizes throughput and depth for large-cohort studies, but methods are often optimized using proxy metrics that can mask losses in quantifiable signal and statistical power. Here, we evaluate how data points per peak and other chromatographic features jointly contribute to quantification and downstream biological discovery. Using a matrix-matched calibration curve dataset, we checked how the number of data points per peak (DPPP) affects the limits of detection and quantification (LOD/LOQ). Reduced DPPP minimally affected LOD but substantially degraded LOQ. Feature modeling and nonparametric association analyses identified precursor peak area as the strongest feature-level predictor of LOQ, whereas DPPP showed weaker and context-dependent effects. Simulations of chromatographic peak integration recapitulated these trends, showing that increased sampling primarily improves integration precision, while quantitative accuracy is strongly governed by peak height and peak shape. Finally, when comparing 20 cancer vs 20 control plasma samples processed with Seer Proteograph, the decrease in DPPP led to a loss of statistical significance for proteins with low-abundance precursors. These findings argue that DIA optimization should prioritize LOQ and statistical power metrics─not identifications alone─by balancing sampling density with chromatographic peak height and quality to maximize useful biological signal.

Varicella-zoster virus (VZV) infects most humans and causes chickenpox, shingles and central nervous system pathologies. The molecular basis for these phenotypes remains elusive. Here we conducted a multi-proteomic survey on 64 individual VZV proteins and infection-induced perturbations in a neuronal cell line, identifying 900 interactors and 3,618 regulated host proteins. Data integration suggested molecular functions of viral proteins, such as a mechanism for the ORF61-mediated IFI16 degradation via the recruitment of E3 ligase co-factors. Moreover, we identified proviral host factors (MPP8 and ZNF280D) as potential targets to limit infection. Integration of exome sequencing analysis from patients with VZV-associated central nervous system pathologies identified nephrocystin 4 as a viral restriction factor, and its S862N variant, which showed reduced activity and decreased binding to the regulatory proteins 14-3-3. Collectively, our study provides a comprehensive herpesvirus-host interface resource, which aids our understanding of disease-associated molecular perturbations and data-driven identification of antiviral treatment options.

Global campaign against COVID-19 have vaccinated a significant portion of the world population in recent years. Combating the COVID-19 pandemic with mRNA vaccines played a pivotal role in the global immunization effort. However, individual responses to a vaccine are diverse and lead to varying vaccination efficacy. Despite significant progress, a complete understanding of the molecular mechanisms driving the individual immune response to the COVID-19 vaccine remains elusive. To address this gap, we combined a novel nanoparticle-based proteomic workflow with tandem mass tag (TMT) labeling, to quantitatively assess the proteomic changes in a cohort of 12 volunteers following two doses of the Pfizer-BioNTech mRNA COVID-19 vaccine. This optimized protocol seamlessly integrates comprehensive proteome analysis with enhanced throughput by leveraging the enrichment of low-abundant plasma proteins by engineered nanoparticles. Our data demonstrate the ability of this workflow to quantify over 3,000 proteins, providing the deepest view into COVID-19 vaccine-related plasma proteome study. We identified 69 proteins with boosted responses post-second dose and 74 proteins differentially regulated between individuals who contracted COVID-19 despite vaccination and those who did not. These findings offer valuable insights into individual variability in response to vaccination, demonstrating the potential of personalized medicine approaches in vaccine development.

The Proteograph Product Suite, a multiplexed nanoparticle (NP) protein corona-based workflow, substantially improves the depth of detection of proteins by mass spectrometry (MS) by compressing the dynamic range of protein abundances. Here, we evaluate its quantitative performance and suitability for large-scale studies. Using multispecies spike-in experiments, we assessed fold change accuracy, linearity, precision, and the lower limit of quantification (LLOQ) across multiple MS platforms. Combined with the Orbitrap Astral MS, the Proteograph XT assay enabled identification of more than 7,000 plasma proteins. In mixed-species dilution experiments, fold change accuracy was preserved, with Proteograph quantifying 3.5 times more proteins than the Neat plasma workflow at the same fold change error threshold. Similar accuracy was observed with the Orbitrap Exploris 480 MS, and we also demonstrate that different proteome backgrounds do not impact the accuracy. Data produced with NPs from the four distinct NP batches (each supporting >100,000 assays) showed only a 4% increase in protein intensity CV across batches. Together, these results demonstrate that the Proteograph Product Suite provides depth as well as quantitative accuracy and precision to power new biomarker discovery and biological understanding in population-scale plasma proteomics cohorts.

Alzheimer's disease (AD) and related dementias (ADRD) is a complex disease with multiple pathophysiological drivers that determine clinical symptomology and disease progression. These diseases develop insidiously over time, through many pathways and disease mechanisms and continue to have a huge societal impact for affected individuals and their families. While emerging blood-based biomarkers, such as plasma p-tau181 and p-tau217, accurately detect Alzheimer neuropthology and are associated with faster cognitive decline, the full extension of plasma proteomic changes in ADRD remains unknown. Earlier detection and better classification of the different subtypes may provide opportunities for earlier, more targeted interventions, and perhaps a higher likelihood of successful therapeutic development. In this study, we aim to leverage unbiased mass spectrometry proteomics to identify novel, blood-based biomarkers associated with cognitive decline. 1,786 plasma samples from 1,005 patients were collected over 12 years from partcipants in the Massachusetts Alzheimer's Disease Research Center Longitudinal Cohort Study. Patient metadata includes demographics, final diagnoses, and clinical dementia rating (CDR) scores taken concurrently. The Proteograph™ Product Suite (Seer, Inc.) and liquid-chromatography mass-spectrometry (LC-MS) analysis were used to process the plasma samples in this cohort and generate unbiased proteomics data. Data-independent acquisition (DIA) mass spectrometry results yielded 36,259 peptides and 4,007 protein groups. Linear mixed effects models revealed 138 differentially abundant proteins between AD and healthy controls. Machine learning classification models for AD diagnosis identified potential candidate biomarkers including MBP, BGLAP, and APoD. Cox regression models were created to determine the association of proteins with disease progression and suggest CLNS1A, CRISPLD2, and GOLPH3 as targets of further investigation as potential biomarkers. The Proteograph workflow provided deep, unbiased coverage of the plasma proteome at a speed that enabled a cohort study of almost 1,800 samples, which is the largest, deep, unbiased proteomics study of ADRD conducted to date.

The abundance of a protein is defined by its continuous synthesis and degradation, a process known as protein turnover. Here, we systematically profiled the turnover of proteins in influenza A virus (IAV)-infected cells using a pulse-chase stable isotope labeling by amino acids in cell culture (SILAC)-based approach combined with downstream statistical modeling. We identified 1,798 virus-affected proteins with turnover changes (tVAPs) out of 7,739 detected proteins (data available at pulsechase.innatelab.org). In particular, the affected proteins were involved in RNA transcription, splicing and nuclear transport, protein translation and stability, and energy metabolism. Many tVAPs appeared to be known IAV-interacting proteins that regulate virus propagation, such as KPNA6, PPP6C, and POLR2A. Notably, our analysis identified additional IAV host and restriction factors, such as the splicing factor GPKOW, that exhibit significant turnover rate changes while their total abundance is minimally affected. Overall, we show that protein turnover is a critical factor both for virus replication and antiviral defense.

There is a significant unmet need for clinical reflex tests that increase the specificity of prostate-specific antigen blood testing, the longstanding but imperfect tool for prostate cancer diagnosis. Towards this endpoint, we present the results from a discovery study that identifies new prostate-specific antigen reflex markers in a large-scale patient serum cohort using differentiating technologies for deep proteomic interrogation. We detect known prostate cancer blood markers as well as novel candidates. Through bioinformatic pathway enrichment and network analysis, we reveal associations of differentially abundant proteins with cytoskeletal, metabolic, and ribosomal activities, all of which have been previously associated with prostate cancer progression. Additionally, optimized machine learning classifier analysis reveals proteomic signatures capable of detecting the disease prior to biopsy, performing on par with an accepted clinical risk calculator benchmark.

Zika virus (ZIKV) has emerged as a global health issue, yet neither antiviral therapy nor a vaccine are available. ZIKV is an enveloped RNA virus, replicating in the cytoplasm in close association with ER membranes. Here, we isolate ER membranes from ZIKV-infected cells and determine their proteome. Forty-six host cell factors are enriched in ZIKV remodeled membranes, several of these having a role in redox and methylation pathways. Four proteins are characterized in detail: thioredoxin reductase 1 (TXNRD1) contributing to folding of disulfide bond containing proteins and modulating ZIKV secretion; aldo-keto reductase family 1 member C3 (AKR1C3), regulating capsid protein abundance and thus, ZIKV assembly; biliverdin reductase B (BLVRB) involved in ZIKV induced lipid peroxidation and increasing stability of viral transmembrane proteins; adenosylhomocysteinase (AHCY) indirectly promoting m 6 A methylation of ZIKV RNA by decreasing the level of S- adenosyl homocysteine and thus, immune evasion. These results highlight the involvement of redox and methylation enzymes in the ZIKV life cycle and their accumulation at virally remodeled ER membranes.

ABSTRACT Cap0-mRNA is characterized by a 5’-5’triphosphate-linked N7-methylated guanosine(m7G). In higher eukaryotes, the methyltransferase CMTR1 additionally methylates the 2’O-position of the penultimate mRNA nucleotide(N1) ribose (cap1-mRNA). While the m7G cap is essential for mRNA export and translation initiation by the eIF4F complex, the N1-2’O-methylation prevents recognition of cap1-mRNA by the antiviral RNA receptors RIG-I and IFIT1, but a function beyond immunotolerance remained elusive. Here, we generated CMTR1 -knockout( CMTR1 -/- ) cells and found that type-I-interferon(IFN-I) treatment resulted in IFIT1-mediated reduction of cell viability and broad mRNA translation. Consequently, stimulation of the antiviral receptor RIG-I in CMTR1 -/- cells revealed an IFIT1 dependent dramatic reduction of IFN-I and chemokine protein induction, demonstrating the importance of N1-2’O-methylation for antiviral responses. Additionally, IFN-I- and IFIT1-independent effects were observed: CMTR1 -/- cells were smaller, divided slower, and exhibited a reduced transcription of mRNAs coding ribosomal proteins (RP), 5’TOP-RNA and snoRNA host genes(SNHG). Additionally, proteome and transcriptome analysis revealed that expression of NVL2, an essential factor in ribosome biogenesis, is strongly suppressed by an alternative-splicing event of NVL2 mRNA in CMTR1 -/- cells. This reduction could only be rescued by catalytically active CMTR1. Altogether, besides antiviral immunity N1-2’O-methylation by CMTR1 has broad effects on cellular physiology and controls splicing of NVL2.

Background:The wide dynamic range of circulating proteins coupled with the diversity of proteoforms present in plasma has historically impeded comprehensive and quantitative characterization of the plasma proteome at scale. Automated nanoparticle (NP) protein corona-based proteomics workflows can efficiently compress the dynamic range of protein abundances into a mass spectrometry (MS)-accessible detection range. This enhances the depth and scalability of quantitative MS-based methods, which can elucidate the molecular mechanisms of biological processes, discover new protein biomarkers, and improve comprehensiveness of MS-based diagnostics.Methods:Investigating multi-species spike-in experiments and a cohort, we investigated fold-change accuracy, linearity, precision, and statistical power for the using the Proteograph™ Product Suite, a deep plasma proteomics workflow, in conjunction with multiple MS instruments.Results:We show that NP-based workflows enable accurate identification (false discovery rate of 1%) of more than 6,000 proteins from plasma (Orbitrap Astral) and, compared to a gold standard neat plasma workflow that is limited to the detection of hundreds of plasma proteins, facilitate quantification of more proteins with accurate fold-changes, high linearity, and precision. Furthermore, we demonstrate high statistical power for the discovery of biomarkers in small- and large-scale cohorts.Conclusions:The automated NP workflow enables high-throughput, deep, and quantitative plasma proteomics investigation with sufficient power to discover new biomarker signatures with a peptide level resolution.

In a pandemic, such as the one caused by the coronavirus in 2020, fast action is required to provide insights into the disease mechanisms and to find potential drug targets to slow the progress of the disease and lower mortality. We developed CoVex, an interactive online platform for the exploration of the SARS-CoV-2 host interactome and the identification of drug candidates. CoVex integrates the virus-human protein interactions, human protein-protein interactions, and drug-target interactions, and allows for visual exploration of the data. The exploration is guided by network-based systems medicine algorithms that enable the assembly of ranked lists of candidates for already approved drugs. Unlike novel drugs, approved drugs have the advantage that most of their side effects and contraindications are known. Additionally, they are already available and can be distributed quickly, which makes them promising candidates to stop the uncontrolled spread of the disease early. CoVex is an example of how this could be achieved in future pandemics and provides valuable lessons on what is required in such a situation to maximize the potential of computational approaches that can significantly speed up drug discovery.

Constant exposure of the airways to inhaled pathogens requires efficient early immune responses protecting against infections. How bacteria on the epithelial surface are detected and first-line protective mechanisms are initiated are not well understood. We have recently shown that tracheal brush cells (BCs) express functional taste receptors. Here we report that bitter taste signaling in murine BCs induces neurogenic inflammation. We demonstrate that BC signaling stimulates adjacent sensory nerve endings in the trachea to release the neuropeptides CGRP and substance P that mediate plasma extravasation, neutrophil recruitment, and diapedesis. Moreover, we show that bitter tasting quorum-sensing molecules from Pseudomonas aeruginosa activate tracheal BCs. BC signaling depends on the key taste transduction gene Trpm5, triggers secretion of immune mediators, among them the most abundant member of the complement system, and is needed to combat P. aeruginosa infections. Our data provide functional insight into first -line defense mechanisms against bacterial infections of the lung.

Introducing engineered nanoparticles (NPs) into a biofluid such as blood plasma leads to the formation of a selective and reproducible protein corona at the particle-protein interface, driven by the relationship between protein-NP affinity and protein abundance. This enables scalable systems that leverage protein-nano interactions to overcome current limitations of deep plasma proteomics in large cohorts. Here the importance of the protein to NP-surface ratio (P/NP) is demonstrated and protein corona formation dynamics are modeled, which determine the competition between proteins for binding. Tuning the P/NP ratio significantly modulates the protein corona composition, enhancing depth and precision of a fully automated NP-based deep proteomic workflow (Proteograph). By increasing the binding competition on engineered NPs, 1.2-1.7× more proteins with 1% false discovery rate are identified on the surface of each NP, and up to 3× more proteins compared to a standard plasma proteomics workflow. Moreover, the data suggest P/NP plays a significant role in determining the in vivo fate of nanomaterials in biomedical applications. Together, the study showcases the importance of P/NP as a key design element for biomaterials and nanomedicine in vivo and as a powerful tuning strategy for accurate, large-scale NP-based deep proteomic studies.

The SARS‐CoV‐2 infection cycle is a multistage process that relies on functional interactions between the host and the pathogen. Here, we repurposed antiviral drugs against both viral and host enzymes to pharmaceutically block methylation of the viral RNA 2'‐O‐ribose cap needed for viral immune escape. We find that the host cap 2'‐O‐ribose methyltransferase MTr1 can compensate for loss of viral NSP16 methyltransferase in facilitating virus replication. Concomitant inhibition of MTr1 and NSP16 efficiently suppresses SARS‐CoV‐2 replication. Using in silico target‐based drug screening, we identify a bispecific MTr1/NSP16 inhibitor with anti‐SARS‐CoV‐2 activity in vitro and in vivo but with unfavorable side effects. We further show antiviral activity of inhibitors that target independent stages of the host SAM cycle providing the methyltransferase co‐substrate. In particular, the adenosylhomocysteinase (AHCY) inhibitor DZNep is antiviral in in vitro, in ex vivo, and in a mouse infection model and synergizes with existing COVID‐19 treatments. Moreover, DZNep exhibits a strong immunomodulatory effect curbing infection‐induced hyperinflammation and reduces lung fibrosis markers ex vivo. Thus, multispecific and metabolic MTase inhibitors constitute yet unexplored treatment options against COVID‐19. A target‐based drug repurposing approach to inhibit SAM‐dependent methyltransferases (Mtases) shows that the host MTase MTr1 can compensate for loss of the viral encoded NSP16 MTase. Targeting both MTr1 and NSP16 is needed to suppress SARS‐CoV‐2 replication. A target‐based drug repurposing approach shows that inhibition of both host and viral SAM‐dependent methyltransferases is needed to block SARS‐CoV‐2 cap 2'‐O‐ribose methylation and to suppress SARS‐CoV‐2 replication.

The emergence and global spread of SARS-CoV-2 has resulted in the urgent need for an in-depth understanding of molecular functions of viral proteins and their interactions with the host proteome. Several individual omics studies have extended our knowledge of COVID-19 pathophysiology1–10. Integration of such datasets to obtain a holistic view of virus–host interactions and to define the pathogenic properties of SARS-CoV-2 is limited by the heterogeneity of the experimental systems. Here we report a concurrent multi-omics study of SARS-CoV-2 and SARS-CoV. Using state-of-the-art proteomics, we profiled the interactomes of both viruses, as well as their influence on the transcriptome, proteome, ubiquitinome and phosphoproteome of a lung-derived human cell line. Projecting these data onto the global network of cellular interactions revealed crosstalk between the perturbations taking place upon infection with SARS-CoV-2 and SARS-CoV at different levels and enabled identification of distinct and common molecular mechanisms of these closely related coronaviruses. The TGF-β pathway, known for its involvement in tissue fibrosis, was specifically dysregulated by SARS-CoV-2 ORF8 and autophagy was specifically dysregulated by SARS-CoV-2 ORF3. The extensive dataset (available at https://covinet.innatelab.org ) highlights many hotspots that could be targeted by existing drugs and may be used to guide rational design of virus- and host-directed therapies, which we exemplify by identifying inhibitors of kinases and matrix metalloproteases with potent antiviral effects against SARS-CoV-2. Multi-omics profiling of effects of SARS-CoV-2 and SARS-CoV on A549, a lung-derived human cell line, produces a dataset enabling identification of common and virus-specific mechanisms of infection.

The cell intrinsic antiviral response of multicellular organisms developed over millions of years and critically relies on the ability to sense and eliminate viral nucleic acids. Here we use an affinity proteomics approach in evolutionary distant species (human, mouse and fly) to identify proteins that are conserved in their ability to associate with diverse viral nucleic acids. This approach shows a core of orthologous proteins targeting viral genetic material and species-specific interactions. Functional characterization of the influence of 181 candidates on replication of 6 distinct viruses in human cells and flies identifies 128 nucleic acid binding proteins with an impact on virus growth. We identify the family of TAO kinases (TAOK1, -2 and -3) as dsRNA-interacting antiviral proteins and show their requirement for type-I interferon induction. Depletion of TAO kinases in mammals or flies leads to an impaired response to virus infection characterized by a reduced induction of interferon stimulated genes in mammals and impaired expression of srg1 and diedel in flies. Overall, our study shows a larger set of proteins able to mediate the interaction between viral genetic material and host factors than anticipated so far, attesting to the ancestral roots of innate immunity and to the lineage-specific pressures exerted by viruses.