Arriving at the University of Virginia in the autumn of 1969, Donald Hunt began his 50+ year career in academics with the study of organometallic chemistry, on which he had done his PhD thesis, and mass spectrometry, to which he was introduced while a postdoc in Klaus Biemann's laboratory at the Massachusetts Institute of Technology. In the 1970s, Hunt's lab pioneered the use of negative chemical ionization (CI) to enhance sensitivity for studying organic molecules, developed a system for simultaneously obtaining positive and negative CI spectra to augment structure elucidation, and built a prototype triple quadrupole instrument so effective at collisional dissociation that its commercial counterpart became the analytical instrument of choice for mixture analysis for the next decade and beyond. Foreseeing that the future lay in the analysis of biological molecules, by the end of the decade Hunt shifted his focus to peptides. The analysis of protein fragments had suddenly become more accessible thanks to the advent of the triple quadrupole and Barber's invention of fast atom bombardment. As the '80s began and Hunt and his team sought to pursue larger and larger pieces of proteins, his attention turned to the development of mass spectrometers with greater mass range. While recounting their memories of these events, several of Hunt's students and colleagues pay tribute to his support for them as individuals, as well as to his infectious enthusiasm for scientific endeavors that he so generously shared.

Hepatocellular carcinoma (HCC) is associated with one of the highest mortality rates among cancers, rendering its early diagnosis clinically invaluable. Serum biomarkers, specifically alpha-fetoprotein (AFP), represent the most promising and widely used diagnostic biomarkers for HCC. However, its detection rate is low in the early stages of HCC progression, and distinguishing specific false positives for other liver-related diseases, such as cirrhosis and acute hepatitis, remains challenging. Therefore, this study was conducted to identify biomarkers for hepatitis B (HBV)-related liver diseases by screening differentially expressed autoantibodies against tumor-associated antigens (TAAbs). We designed a large-scale multistage investigation, encompassing initial screening, HCC-focused, and ELISA validation cohorts to identify potential TAAbs in HBV-related liver diseases, spanning from healthy control (HC) individuals to patients with chronic hepatitis B (CHB), hepatitis B-related cirrhosis (HBC), and HCC, using protein microarray technology. The differential biological characteristics of TAAbs were analyzed using bioinformatics analysis. Validation of tumor-specific biomarkers for HCC was performed using ELISA. In the screening cohort, 547 candidate TAAbs were identified in the HCC group compared to those in the HC group. In the HCC-focused cohort, 64, 61, and 65 candidate TAAbs were identified in the CHB, HBC, and HCC groups, respectively, compared to those in the HC group. Thirty-four proteins exhibited continuously elevated expression from HCs to patients with CHB, HBC, and HCC. Among these, nine were identified as cancer-specific proteins. In the validation cohort, UBE2Z, CNOT3, and EID3 were correlated with liver function indicators in patients with hepatitis B-related HCC. Overall, UBE2Z, CNOT3, and EID3 emerged as cancer-specific biomarkers for HBV-related liver disease, providing a scientific basis for clinical application.

Cirrhosis, advanced liver disease, affects 2-5 million Americans. While most patients have compensated cirrhosis and may be fairly asymptomatic, many decompensate and experience life-threatening complications such as gastrointestinal bleeding, confusion (hepatic encephalopathy), and ascites, reducing life expectancy from 12 to less than 2 years. Among patients with compensated cirrhosis, identifying patients at high risk of decompensation is critical to optimize care and reduce morbidity and mortality. Therefore, it is important to preferentially direct them towards specialty care which cannot be provided to all patients with cirrhosis. We used discovery Top-down Proteomics (TDP) to identify differentially expressed proteoforms (DEPs) in the plasma of patients with progressive stages of liver cirrhosis with the ultimate goal to identify candidate biomarkers of disease progression. In this pilot study, we identified 209 DEPs across three stages of cirrhosis (compensated, compensated with portal hypertension, and decompensated), of which 115 derived from proteins enriched in the liver at a transcriptional level and discriminated the three stages of cirrhosis. Enrichment analyses demonstrated DEPs are involved in several metabolic and immunological processes known to be impacted by cirrhosis progression. We have preliminarily defined the plasma proteoform signatures of cirrhosis patients, setting the stage for ongoing discovery and validation of biomarkers for early diagnosis, risk stratification, and disease monitoring.

Donald Hunt has made seminal contributions to the fields of proteomics, immunology, epigenetics, and glycobiology. The foundation of every important work to come out of the Hunt Laboratory is de novo peptide sequencing. For decades, he taught hundreds of students, postdocs, engineers, and scientists to directly interpret mass spectral data. To honor his legacy and ensure that the art of de novo sequencing is not lost, we have adapted his teaching materials into "The Hunt Lab Guide to De Novo Peptide Sequence Analysis by Tandem Mass Spectrometry". In addition to the de novo sequencing tutorials, we present two freely available software tools that facilitate manual interpretation of mass spectra and validation of search results. The first, "Hunt Lab Peptide Fragment Calculator", calculates precursor and fragment mass-to-charge ratios for any peptide. The second program, "Predator Protein Fragment Calculator", was inspired in part by the fragment calculator developed in the Hunt Lab. Its capabilities are enhanced to facilitate interpretation of mass spectral data derived from intact proteins. We hope that the combination of these educational tools will continue to benefit students and researchers by empowering them to interpret data on their own.

The 68th Benzon Foundation Symposium brought together leading experts to explore the integration of mass spectrometry (MS)-based proteomics and artificial intelligence in revolutionizing personalized medicine. This report highlights key discussions on recent technological advances in MS-based proteomics, including improvements in sensitivity, throughput, and data analysis. Particular emphasis was placed on plasma proteomics and its potential for biomarker discovery across various diseases. The symposium addressed critical challenges in translating proteomic discoveries to clinical practice, including standardization, regulatory considerations and the need for robust 'business cases' to motivate adoption. Promising applications were presented in areas such as cancer diagnostics, neurodegenerative diseases, and cardiovascular health. The integration of proteomics with other omics technologies and imaging methods was explored, showcasing the power of multi-modal approaches in understanding complex biological systems. Artificial intelligence emerged as a crucial tool for the acquisition of large-scale proteomic datasets, extracting meaningful insights, and enhancing clinical decision-making. By fostering dialogue between academic researchers, industry leaders in proteomics technology, and clinicians, the symposium illuminated potential pathways for proteomics to transform personalized medicine, advancing the cause of more precise diagnostics and targeted therapies.

Phosphorylation is an indispensable regulatory mechanism in cells, with specific sites on kinases that can significantly enhance their activity. Although several such critical phosphorylation sites (phos-sites) have been experimentally identified, many more remain to be explored. To date, no computational method exists to systematically identify these critical phos-sites on kinases. In this study, we introduce PhoSiteformer, a transformer-inspired foundational model designed to generate embeddings of phos-sites using phosphorylation mass spectrometry (phos-MS) data. Recognizing the complementary insights offered by protein sequence data and phos-MS data, we developed a classification model, CSPred, which employs a bimodal fusion strategy. CSPred combines embeddings from PhoSiteformer with those from the protein language model ProtT5. Our approach successfully identified 77 critical phos-sites on 58 human kinases. Two of these sites, T517 on PRKG1 and T735 on PRKD3, have been experimentally verified. This study presents the first systematic and computational approach to identify critical phos-sites that enhance kinase activity.

Post-transcriptional regulation of gene expression by miRNAs likely makes significant contributions to mRNA abundance at the embryo-maternal interface. In this study, we investigated how miR-26a-5p and miR-125b-5p contribute to molecular changes occurring in the uterine luminal epithelium, which serves as the first site of signal exchange between the mother and developing embryo. To measure de novo protein synthesis after miRNA delivery to primary uterine luminal epithelial cells, we employed pulsed stable isotope labeling by amino acids (pSILAC). We found that both miRNAs alter the proteome of luminal epithelial cells, impacting numerous cellular functions, immune responses, as well as intracellular and second messenger signaling pathways. Additionally, we identified several features of miRNA-mRNA interactions that may influence the targeting efficiency of miR-26a-5p and miR-125b-5p. Overall, our study suggests a complex interaction of miR-26a-5p and miR-125b-5p with their respective targets. However, both appear to cooperatively function in modulating the cellular environment of the luminal epithelium, facilitating the morphological and molecular changes that occur during the intensive communication between the embryo and uterus at pregnancy.

Mass spectrometry-based proteome profiling of trace analytes including single cells benefits from liquid chromatography separations operated at low flow rates (e.g., <50 nL/min). However, high-pressure binary pumps needed to achieve such flow rates are not commercially available, and instead require splitting of the gradient flow to achieve low-nanoliter-per-minute flow rates. Gradient flow splitting can waste solvent and lead to flow inconsistencies. To address this, we have developed a method for creating gradients by combining plugs of mobile phase of increasing solvent strength together in a column, and then relying on Taylor dispersion to form the desired smooth gradient profile. Additionally, our method dramatically reduces costs, as only a single isocratic high-pressure pump is required. Following development of gradient profiles for both 10- and 20-min active gradients, we measured 200 pg injections of HeLa digest using a timsTOF mass spectrometer. Finally, we investigated differences in protein expression between single cells originating from two different colonies of ATG-knockout HeLa cells. Thousands of proteins were quantified, and a potential mechanism explaining differential immune responses of these two colonies upon exposure to viral DNA treatment was determined.

Once ovulated, the oocyte has to be fertilized in a short time window, or it will undergo post-ovulation aging (POA), whose underlying mechanisms are still not elucidated. Here, we optimized single-cell proteomics methods and performed single-cell transcriptomic, proteomic and phosphoproteomic analysis of fresh, POA, and melatonin-treated POA oocytes. POA oocytes showed down-regulation of most differentially expressed proteins, with little correlation with mRNA expression, and the protein changes can be rescued by melatonin treatment. MG132 treatment rescued the decreased fertilization and polyspermy rates, and up-regulated fragmentation and parthenogenesis rates of POA oocytes. MG132-treated oocytes displayed health status at proteome, phosphoproteome and fertilization ability similar to fresh oocytes, suggesting that protein stabilization might be the underlying mechanism for melatonin to rescue POA. The important roles of proteasome-mediated protein degradation during oocyte POA revealed by single-cell multi-omics analyses offer new perspectives for increasing oocyte quality during POA, and improving assisted reproduction technologies.

Studying regulation of protein function at a systems level necessitates an understanding of the interplay among diverse post-translational modifications (PTMs). A variety of proteomics sample processing workflows are currently used to study specific PTMs but rarely characterize multiple types of PTMs from the same sample inputs. Method incompatibilities and laborious sample preparation steps complicate large-scale physiological investigations and can lead to variations in results. The single-pot, solid-phase-enhanced sample preparation (SP3) method for sample cleanup is compatible with different lysis buffers and amenable to automation, making it attractive for high-throughput multi-PTM profiling. Herein, we describe an integrative SP3 workflow for multiplexed quantification of protein abundance, cysteine thiol oxidation, phosphorylation, and acetylation. The broad applicability of this approach is demonstrated using cell and tissue samples, and its utility for studying interacting regulatory networks is highlighted in a time-course experiment of cytokine-treated β-cells. We observed a swift response in global regulation of protein abundances consistent with rapid activation of JAK-STAT and NF-κB signaling pathways. Regulators of these pathways as well as proteins involved in their target processes displayed multi-PTM dynamics indicative of a complex cellular response stages: acute, adaptation, and chronic (prolonged stress). PARP14, a negative regulator of JAK-STAT, had multiple co-localized PTMs that may be involved in intraprotein regulatory crosstalk. Our workflow provides a high-throughput platform that can profile multi-PTMomes from the same sample set, which is valuable in unraveling the functional roles of PTMs and their co-regulation.

As a ubiquitous and essential posttranslational modification occurring in both plants and animals, protein N-linked glycosylation regulates various important biological processes. Unlike the well-studied animal N-glycoproteomes, the landscape of rice N-glycoproteome remains largely unexplored. Here, by developing a chemical glycoproteomic strategy based on metabolic glycan labeling (MGL), we report a comprehensive profiling of the N-glycoproteome in rice seedlings. The rice seedlings are incubated with N-azidoacetylgalactosamine (GalNAz) - a monosaccharide analog containing a bioorthogonal functional group - to metabolically label N-glycans, followed by conjugation with an affinity probe via click chemistry for enrichment of the N-glycoproteins. Subsequent mass spectrometry analyses identify a total of 403 N-glycosylation sites and 673 N-glycosylated proteins, which are involved in various important biological processes. In particular, the core components of the endoplasmic reticulum (ER)-associated protein degradation (ERAD) machinery are N-glycosylated, and the N-glycosylation is important for the ERAD-L function. This work not only provides an invaluable resource for studying rice N-glycosylation, but also demonstrates the applicability of MGL in glycoproteomic profiling for crop species.

The foundation of all biological processes is the network of diverse and dynamic protein interactions with other molecules in cells known as the interactome. Understanding the interactome is crucial for elucidating molecular mechanisms but has been a longstanding challenge. Recent developments in mass spectrometry (MS)-based techniques, including affinity purification, proximity labeling, cross-linking, and co-fractionation mass spectrometry (MS), have significantly enhanced our abilities to study the interactome. They do so by identifying and quantifying protein interactions, yielding profound insights into protein organizations and functions. This review summarizes recent advances in MS-based interactomics, focusing on the development of techniques that capture protein-protein, protein-metabolite, and protein-nucleic acid interactions. Additionally, we discuss how integrated MS-based approaches have been applied to diverse biological samples, focusing on significant discoveries that have leveraged our understanding of cellular functions. Finally, we highlight state-of-the-art bioinformatic approaches for predictions of interactome and complex modeling, as well as strategies for combining experimental interactome data with computation methods, thereby enhancing the ability of MS-based techniques to identify protein interactomes. Indeed, advances in MS technologies and their integrations with computational biology provide new directions and avenues for interactome research, leveraging new insights into mechanisms that govern the molecular architecture of living cells and, thereby, our comprehension of biological processes.

Cells have many protective mechanisms against background levels of ionizing radiation (IR) orchestrated by molecular changes in expression, post-translational modifications and subcellular localization. Radiotherapeutic treatment in oncology attempts to overwhelm such mechanisms, but radio-resistance is an ongoing challenge. Here, global subcellular proteomics combined with Bayesian modeling identified 544 differentially localized proteins in A549 cells upon 6 Gy x-ray exposure, revealing subcellular-specific changes of proteins involved in ferroptosis, an iron-dependent cell death, suggestive of potential radio-resistance mechanisms. These observations were independent of expression changes, emphasizing the utility of global subcellular proteomics and the promising prospect of ferroptosis-inducing therapies for combatting radioresistance.

Our genome is not made of naked DNA, but a fiber (chromatin) composed of DNA and proteins packaged into our chromosomes. The basic building block of chromatin is the nucleosome, which has two copies of each of the proteins called histones (H2A, H2B, H3, and H4) wrapped by 146 base pairs of DNA. Regions of our genetic material are found between the more open (euchromatin) and more compact (heterochromatin) regions of the genome that can be variably accessible to the underlying genes. Furthermore, post-translational modifications (PTMs) on histones, such as on H3, are critical for regulating chromatin accessibility and gene expression. While site specific antibodies were the tool of choice for histone PTM analysis in the early days (pre-2000s), enter Don Hunt changing the histone PTM field forever. Don's clever thinking brought new innovative mass spectrometry-based approaches to the epigenetics field. His lab's effort led to the discovery of many new histone modifications and methods to facilitate the detection and quantification of histone PTMs, which are still considered state of the art in the proteomics field today. Due to Don's pioneering work in this area, many labs have been able to jump into the epigenetics field and "Hunt" down their own histone targets. A walkthrough of those early histone years in the Hunt Lab are described by three of us who were fortunate enough to be at the right place, at the right time.

Ubiquitin-specific protease 7 (USP7) is implicated in many cancers including colorectal cancer in which it regulates cellular pathways such as Wnt signalling and the P53-MDM2 pathway. With the discovery of small-molecule inhibitors, USP7 has also become a promising target for cancer therapy, and therefore systematically identifying USP7 deubiquitinase interaction partners and substrates has become an important goal. In this study, we selected a colorectal cancer cell model that is highly dependent on USP7 and in which USP7 knockdown significantly inhibited colorectal cancer cell viability, colony formation, and cell-cell adhesion. We then used inducible knockdown of USP7 followed by LC-MS/MS to quantify USP7 dependent proteins. We identified the Ajuba LIM domain protein as an interacting partner of USP7 through co-IP, its substantially reduced protein levels in response to USP7 knockdown, and its sensitivity to the specific USP7 inhibitor FT671. The Ajuba protein has been shown to have oncogenic functions in colorectal and other tumours, including regulation of cell-cell adhesion. We show that both knockdown of USP7 or Ajuba results in a substantial reduction of cell-cell adhesion, with concomitant effects on other proteins associated with adherens junctions. Our findings underlie the role of USP7 in colorectal cancer through its protein interaction networks and show that the Ajuba protein is a component of USP7 protein networks present in colorectal cancer.

The Nuclear Factor I (NFI) family of transcription factors (TFs) plays key roles in cellular differentiation, proliferation, and homeostasis. As such, NFI family members engage in large number of interactions with other proteins and the chromatin. However, despite their well-established significance, the NFIs interactomes, their dynamics, and their functions have not been comprehensively examined. Here, we employed complementary omics-level techniques, i.e. interactomics (affinity purification mass spectrometry (AP-MS) and proximity-dependent biotinylation (BioID)), and chromatin immunoprecipitation sequencing (ChIP-Seq), to obtain a comprehensive view of the NFI proteins and their interactions in different cell lines. Our analyses included all four NFI family members, and a less studied short isoform of NFIB (NFIB4), which lacks the DNA binding domain. We observed that, despite exhibiting redundancy, each family member had unique high-confidence interactors and target genes, suggesting distinct roles within the transcriptional regulatory networks. The study revealed that NFIs interact with other TFs to co-regulate a broad range of regulatory networks and cellular processes. Notably, time-dependent proximity-labeling unveiled a highly dynamic nature of NFI protein-protein interaction networks and hinted at temporal modulation of NFI interactions. Furthermore, gene ontology (GO) enrichment analysis of NFI interactome and targetome revealed the involvement of NFIs in transcriptional regulation, chromatin organization, and cellular signaling pathways, and pathways related with cancer. Additionally, we observed that NFIB4 engages with proteins associated with mRNA regulation, which suggests that NFIs have roles beyond traditional DNA binding and transcriptional modulation. We propose that NFIs may function as potential pioneering TFs, given their role in regulating the DNA binding ability of other TFs and their interactions with key chromatin remodeling complexes, thereby influencing a wide range of cellular processes. These insights into NFI protein-protein interactions and their dynamic, context-dependent nature provide a deeper understanding of gene regulation mechanisms and hint at the role of NFIs as master regulators.

Targeted proteomics methods have been greatly improved and refined over the last decade and are becoming increasingly the method of choice in protein and peptide quantitative assays. Despite the tremendous progress, targeted proteomics assays still suffer from inadequate sensitivity for lower abundant proteins and throughput, especially in complex biological samples. These attributes are essential for establishing targeted proteomics methods at the forefront of clinical use. Here, we report an assay utilizing the SureQuant internal standard-triggered targeted method on a latest generation mass spectrometer coupled with an EvoSep One liquid chromatography platform, which displays high sensitivity and a high throughput of 100 samples per day. We demonstrate the robustness of this method by quantifying proteins spanning six orders of magnitude in human wound fluid exudates, a biological fluid that exhibits sample complexity and composition similar to plasma. Among the targets quantified were low-abundance proteins such at tumor necrosis factor A and interleukin 1-β, highlighting the value of this method in the quantification of trace amounts of invaluable biomarkers that were until recently hardly accessible by targeted proteomics methods. Taken together, this method extends the toolkit of targeted proteomics assays and will help to drive forward mass spectrometry-based proteomics biomarker quantification.

The late embryogenesis abundant proteins (LEAPs) are a class of noncatalytic, intrinsically disordered proteins with a malleable structure. Some LEAPs exhibit a protein and/or membrane binding capacity and LEAP binding to various targets has been positively correlated with abiotic stress tolerance. Regarding the LEAPs' presumptive role in protein protection, identifying client proteins (CtPs) to which LEAPs bind is one practicable means of revealing the mechanism by which they exert their function. To this end, we used phage display affinity selection to screen libraries derived from Arabidopsis thaliana seed mRNA with recombinant orthologous LEAPs from Arabidopsis and soybean (Glycine max). Subsequent high-throughput sequencing of DNA from affinity-purified phage was performed to characterize the entire subpopulation of phage retained by each LEAP ortholog. This entailed cataloging in-frame fusions, elimination of false positives, and aligning the hits on the CtP scaffold to reveal domains of respective CtPs that bound to orthologous LEAPs. This approach (paired-end phage sequencing) revealed a subpopulation of the proteome constituting the CtP repertoire in common between the two dehydrin orthologs (LEA14 and GmPm12) compared to bovine serum albumin (unrelated binding control). The veracity of LEAP:CtP binding for one of the CtPs (LEA14 and GmPM12 self-association) was independently assessed using temperature-related intensity change analysis. Moreover, LEAP:CtP interactions for four other CtPs were confirmed in planta using bimolecular fluorescence complementation assays. The results provide insights into the involvement of the dehydrin Y-segments and K-domains in protein binding.

Despite of massive emergence of molecular targeting drugs, the mainstay of advanced gastric cancer (GC) therapy is DNA-damaging drugs. Using a reverse-phase protein array-based proteogenomic analysis of a panel of 8 GC cell lines, we identified genetic alterations and signaling pathways, potentially associated with resistance to DNA-damaging drugs, including 5-fluorouracil (5FU), cisplatin, and etoposide. Resistance to cisplatin and etoposide, but not 5FU, was negatively associated with global copy number loss, vimentin expression, and caspase activity, which are considered hallmarks of previously established EMT subtype. The segregation of 19,392 protein expression time courses by sensitive and resistant cell lines for the drugs tested revealed that 5FU-resistant cell lines had lower changes in global protein dynamics, suggesting their robust protein level regulation, than their sensitive counterparts, whereas the cell lines that are resistant to other drugs showed increased protein dynamics in response to each drug. Despite faint global protein dynamics, 5FU-resistant cell lines showed increased signal transducer and activator of transcription 1 phosphorylation and PD-L1 expression in response to 5FU. In publicly available cohort data, expression of signal transducer and activator of transcription 1 and NFκB target genes induced by proinflammatory cytokines was associated with prolonged survival in GC. In our validation cohort, total lymphocyte count, rather than PD-L1 positivity, predicted a better relapse-free survival rate in GC patients with 5FU-based adjuvant chemotherapy than those with surgery alone. Moreover, total lymphocyte count patients who had no survival benefit from adjuvant chemotherapy were discriminated by expression of IκBα, a potent negative regulator of NFκB. Collectively, our results suggest that 5FU resistance observed in cell lines may be overcome by host immunity or by combination therapy with immune checkpoint blockade.

A personal narrative of my time in the Hunt laboratory and beyond is provided. The impact of the Hunt laboratory on the analysis of peptides and proteins by tandem mass spectrometry is described in the context of the time.

Mass-spectrometry-based proteomics allows the quantification of thousands of proteins, protein variants, and their modifications, in many biological samples. These are derived from the measurement of peptide relative quantities, and it is not always possible to distinguish proteins with similar sequences due to the absence of protein-specific peptides. In such cases, peptide signals are reported in protein groups that can correspond to several genes. Here, we show that multi-gene protein groups have a limited impact on GO-term enrichment, but selecting only one gene per group affects network analysis. We thus present the Cytoscape app Proteo Visualizer (https://apps.cytoscape.org/apps/ProteoVisualizer) that is designed for retrieving protein interaction networks from STRING using protein groups as input and thus allows visualisation and network analysis of bottom-up MS-based proteomics data sets.