Journal of Proteomics

BackgroundMicrobiome research is providing important new insights into the metabolic interactions of complex microbial ecosystems involved in fields as diverse as the pathogenesis of human diseases, agriculture and climate change. Poor correlations typically observed between RNA and protein expression datasets make it hard to accurately infer microbial protein synthesis from metagenomic data. Additionally, mass spectrometry-based metaproteomic analyses typically rely on focussed search libraries based on prior knowledge for protein identification that may not represent all the proteins present in a set of samples. Metagenomic 16S rRNA sequencing will only target the bacterial component, while whole genome sequencing is at best an indirect measure of expressed proteomes. We describe a novel approach, MetaNovo, that combines existing open-source software tools to perform scalable de novo sequence tag matching with a novel algorithm for probabilistic optimization of the entire UniProt knowledgebase to create tailored databases for target-decoy searches directly at the proteome level, enabling analyses without prior expectation of sample composition or metagenomic data generation, and compatible with standard downstream analysis pipelines. ResultsWe compared MetaNovo to published results from the MetaPro-IQ pipeline on 8 human mucosal-luminal interface samples, with comparable numbers of peptide and protein identifications, many shared peptide sequences and a similar bacterial taxonomic distribution compared to that found using a matched metagenome database - but simultaneously identified many more non-bacterial peptides than the previous approaches. MetaNovo was also benchmarked on samples of known microbial composition against matched metagenomic and whole genomic database workflows, yielding many more MS/MS identifications for the expected taxa, with improved taxonomic representation, while also highlighting previously described genome sequencing quality concerns for one of the organisms, and identifying a known sample contaminant without prior expectation. ConclusionsBy estimating taxonomic and peptide level information directly on microbiome samples from tandem mass spectrometry data, MetaNovo enables the simultaneous identification of peptides from all domains of life in metaproteome samples, bypassing the need for curated sequence search databases. We show that the MetaNovo approach to mass spectrometry metaproteomics is more accurate than current gold standard approaches of tailored or matched genomic database searches, can identify sample contaminants without prior expectation and yields insights into previously unidentified metaproteomic signals, building on the potential for complex mass spectrometry metaproteomic data to speak for itself. The pipeline source code is available on GitHub1 and documentation is provided to run the software as a singularity-compatible docker image available from the Docker Hub2.

Global warming poses a grave threat to plant survival, adversely affecting growth and agricultural productivity. To develop thermotolerant crops, a profound comprehension of plant responses to heat stress at the molecular level is imperative. Leveraging a novel fusion of 15N-stable isotope labeling and the ProteinTurnover algorithm, we meticulously investigated proteome dynamics in Arabidopsis thaliana seedlings subjected to moderate heat stress (30{degrees}C). This innovative approach facilitated a comprehensive analysis of proteomic changes across diverse cellular fractions. Our study unveiled significant turnover rate alterations in 571 proteins, with a median increase of 1.4-fold, indicative of accelerated protein dynamics under heat stress. Notably, root soluble proteins exhibited more subdued changes, suggesting tissue-specific adaptations. Moreover, we observed noteworthy turnover variations in proteins associated with redox signaling, stress response, and metabolism, underscoring the complexity of the response network. Conversely, proteins involved in carbohydrate metabolism and mitochondrial ATP synthesis displayed minimal turnover changes, signifying their stability. This exhaustive examination sheds light on the proteomic adjustments of Arabidopsis seedlings to moderate heat stress, elucidating the delicate balance between proteome stability and adaptability. These findings significantly augment our understanding of plant thermal resilience and offer crucial insights for the development of crops endowed with enhanced thermotolerance.

Evolutionary conservation has been considered a hallmark of essential basic functions in cells. Therefore, the study of evolutionarily conserved post-translational modifications (PTMs) can provide insight into their role in protein function. In this context, mass spectrometry can identify and quantify thousands of PTM sites. However, a major bottleneck lies in analyzing the large amounts of data collected by the mass spectrometer. Here we address the need for a protein sequence alignment tool for multiple PTMs across several species. We developed a tool named PTMOverlay that takes peptide identification output files and overlays PTM sites onto multiple protein sequence alignments. Examining 31 bacteria isolates, we combined their protein sequences with select PTM types, including acetylation, phosphorylation, monomethylation, dimethylation, and trimethylation. The tool revealed a variety of conserved modification sites on the bacterial central carbon metabolism. Further structural analysis revealed possible interactions between methylated arginine and lysine residues with phosphothreonine/serine sites on the homodimer interface of enolase. Overall, this tool can parse large amounts of mass spectrometry data and allows for more informed and efficient selection of sites for future studies of protein function.

Prerequisite for a successful proteomics experiment is a high-quality lysis of the sample of interest, resulting in a large number of identified proteins as well as a high coverage of protein sequences. Therefore, the choice of suitable lysis conditions is crucial. Many buffers were previously employed in proteomics studies, yet a comprehensive comparison of lysate preparation conditions was so far missing. In this study, we compared the efficiency of four commonly used lysis buffers, containing the agents NP40, SDS, urea or GdnHCl, in four different types of biological samples (suspension and adherent cell lines, primary mouse cells and mouse liver tissue). After liquid chromatography-mass spectrometry (LC-MS) measurement and MaxQuant analysis, we compared chromatograms, intensities, number of identified proteins and the localization of the identified proteins. Overall, SDS emerged as the most reliable reagent, ensuring stable performance and reproducibility across diverse samples. Furthermore, our data advocated for a dual-sample lysis approach, including that the resulting pellet is lysed again after the initial lysis with a urea lysis buffer and subsequently both lysates are combined for a single LC-MS run to maximize the proteome coverage. However, none of the investigated lysis buffers proved to be superior in every category, indicating that the lysis buffer of choice depends on the proteins of interest and on the biological question. Further, we demonstrated with our systematic studies the establishment of conditions that allows to perform global proteomics and affinity purification-based interactome characterization from the same lysate. In sum our results provide guidance for the best-suited lysis buffer for mass spectrometry-based proteomics depending on the question of interest.

We investigated how seed proteolysis was enhanced by germination, by subsequent homogenisation (disrupting sprout compartments), and by co-incubation of homogenates from different species. Mass spectrometry of released peptides tracked proteolytic signatures from chickpea, lentil, mung and broccoli proteins, in soaked seeds, in sprouted seeds, and after sprout homogenisation followed by incubation alone or in mixture with other sprouts. The proteolytic signatures differed markedly among the four species, and in the different treatment conditions. After homogenisation, legumain-like cleavage (after asparagine) increased in lentil, and proline-rich peptides increased in broccoli. For co-incubated homogenised sprouts, each species homogenate significantly contributed 6 to 57% of proteolytic patterns in peptides of other species, with chickpea and broccoli homogenates notably releasing metabolic protein peptides from mung and lentil. Thus, germination, homogenisation and homogenate species mixtures can each contribute to proteolysis of seed peptides, potentially increasing digestibility and reducing allergenicity. HIGHLIGHTSO_LIProteolysis motifs in soaked seeds and sprouts very diverse among species C_LIO_LISeed germination proteolysis altered by homogenisation C_LIO_LISeed germination proteolysis altered by co-incubation of different species C_LIO_LIFoods based on homogenised sprout mixtures may release more digestible peptides C_LI

Cervical cancer, which is the fourth most common gynaecological cancer across the globe, has a poorly understood molecular pathogenesis and etiology. Current methods of diagnosis are based on cytology, histology and presence of Human Papillomavirus (HPV). Shortcomings of these methods lie with poor quality of smears which is very common in Papanicolaou (pap) smears, limited sensitivity in terms of early detection, dependence on presence of HPV, etc. Due to its high sensitivity and non-targeted approach, Matrix Assisted Laser Desorption Ionization based Imaging Mass Spectrometry (MALDI-IMS) might be advantageous to understand the pathogenesis, molecular mechanism, precise identification of surgical margins and identification of novel biomarkers. Since it can also identify proteins in the extracellular matrix, it is especially beneficial for the tissue types with sparse cells and excessive extracellular matrix. Although tissue proteome profiling for cervical cancer were reported, the heterogeneous distribution of proteins across cervical cancer tissues havent been explored. In this study, we employed a non-targeted MALDI-IMS based approach to profile the spatial distribution of proteins within cervical cancer tissues. We observed overexpression of Keratin 5 and Prelamin A/C in the region of cervical cancer tissues which were categorically labelled with cancerous morphology using histopathological examination. Both these proteins have been earlier associated with progression and aggressiveness of other cancers like breast and prostate cancers. However, no such reports are available for cervical cancer. Further studies are required on a large dataset to validate and quantitate these proteins as biomarkers for early diagnosis and prognosis of cervical cancer.

Biomarkers are molecular markers found in clinical samples which may aid disease diagnosis or prognosis. High-throughput techniques allow prospecting for such signature molecules by comparing gene expression between normal and sick cells. Cancer-testis antigens (CTAs) are promising candidates for cancer biomarkers due to their limited expression to the testis in normal conditions versus their aberrant expression in various tumors. CTAs are routinely identified by transcriptomics, but a comprehensive characterization of their protein levels in different tissues is still necessary. Mass spectrometry-based proteomics allows the characterization of many cellular types and the production of large amounts of data while computational tools allow the comparison of multiple datasets, and together those may corroborate insights obtained at the transcriptomic level. Here a computational meta-analysis explores the CTAs protein abundance in the proteomic layer of healthy and tumor tissues. The combined datasets present the expression patterns of 17,200 unique proteins, including 241 known CTAs previously described at the transcriptomic level. Those were further ranked as significantly enriched in tumor tissues (22 proteins), exclusive to tumor tissues (42 proteins) or abundant in healthy tissues (32 proteins). This analysis illustrates the possibilities for tumor proteome characterization and the consequent identification of biomarker candidates and/or therapeutic targets.

Filamentous faungi play essential roles in biotechnology as producers of valuable bioproducts and, conversely, as opportunistic pathogens. Aspergillus nidulans is a widely used model organism for fungal genetics and cell biology; however, comprehensive proteomic references for this species remain limited. In this study, we applied a data-independent acquisition-parallel accumulation serial fragmentation (DIA-PASEF) approach to enable efficient and in-depth proteome profiling of A. nidulans. Leveraging ion mobility-based ion cloud information from DDA-PASEF experiments, we developed DIA-PASEF methods that identified 3,904 proteins across biological triplicates grown in rich medium. Compared to prior studies, this represents an increase in protein identifications of more than 140% and was achieved with more than five-fold reduction in analysis time. We employed this newly developed DIA-PASEF methodology to conduct both proteomic and phosphoproteomic analyses under iron-depleted conditions in an MpkA protein-kinase deficient mutant ({Delta}mpkA). The {Delta}mpkA strain exhibited expression of approximately 500 additional proteins and occupancy of over 1,800 additional phosphosites relative to a control. Differentially expressed and phosphorylated proteins increased by more than an order of magnitude in the {Delta}mpkA mutant across both iron-replete and iron-deplete conditions. Gene Ontology (GO) enrichment analysis revealed broader and distinct biological processes under iron-depleted conditions, highlighting adaptive responses specific to iron limitation and MAPK pathway disruption. This work establishes a high-coverage proteomic resource for A. nidulans and provides novel insights into fungal stress responses and signaling network perturbation. Importantly, high-throughput proteomic profiling reveals that limited iron availability and MAPK pathway disruption increases siderophore biosynthesis.

BackgroundCurrent proteomics techniques allow rapid identification and quantification of proteins within any given biological source. In particular, nanoUHPLC/High-Resolution nanoESI-MS/MS enables the characterization of proteins in complex biological samples due to its high sensitivity, accuracy, and scalability. However, LC-MS/MS proteomics might still be susceptible to laboratory and sample-associated contaminants, which can significantly compromise the quality and reliability of data. Therefore, an accurate identification and annotation of such contaminants is crucial for the development of robust proteomics databases and spectral-libraries related search engines. This approach is of special interest in the field of secretome and extracellular vesicles (EVs), membrane-enclosed nanostructures that contain a variety of proteins crucial for cell-to-cell communication and translational applications. ResultsWhen working in ex vivo/in vitro settings, proteins from fetal bovine serum (FBS), commonly employed in standard cell culture media, may interfere with the proteome analysis. To address this issue, we conceived and designed SPROUTS_DB, Serum Protein Repository Of Unwanted Target(ed) Sequences DataBase, a dedicated resource to catalog serum-derived contaminants. Starting from media supplemented with EV-depleted FBS, we simulated cell growth conditions - in the absence of cells - followed by ultracentrifugation. LC-MS/MS analysis of these samples resulted in the identification of a novel set of 1,288 contaminant proteins, which has been deposited in the ProteomeXchange repository (identifier PXD044137). SPROUTS_DB contains primarily soluble proteins, mainly related to the Gene Ontology categories Extracellular Region and Extracellular Space, in line with the nature of the starting sample. In contrast, only a small fraction of the contaminants is classified as membrane-associated proteins, supporting the limited vesicle contamination in the complete medium, due to the use of EV-depleted FBS. Of note, we demonstrated that SPROUTS_DB outperforms existing contaminants databases, ensuring that only peptide spectra relevant to the examined sample are retained and identified as true positive data. ConclusionsConsidering that even proteins from phylogenetically distant organisms share extensive stretches of sequences, SPROUTS_DB is designed to discern contaminants from real sample proteins of interest, minimizing false positive identifications. To the best of our knowledge, SPROUTS_DB is the most updated database of contaminants useful for proteomics investigations of cellular secretomes and EV-containing samples.

Phosphorylation is the most studied post-translational modification, and has multiple biological functions. In this study, we have re-analysed publicly available mass spectrometry proteomics datasets enriched for phosphopeptides from Asian rice (Oryza sativa). In total we identified 15,522 phosphosites on serine, threonine and tyrosine residues on rice proteins. We identified sequence motifs for phosphosites, and link motifs to enrichment of different biological processes, indicating different downstream regulation likely caused by different kinase groups. We cross-referenced phosphosites against the rice 3,000 genomes, to identify single amino acid variations (SAAVs) within or proximal to phosphosites that could cause loss of a site in a given rice variety. The data was clustered to identify groups of sites with similar patterns across rice family groups, for example those highly conserved in Japonica, but mostly absent in Aus type rice varieties - known to have different responses to drought. These resources can assist rice researchers to discover alleles with significantly different functional effects across rice varieties. The data has been loaded into UniProt Knowledge-Base - enabling researchers to visualise sites alongside other data on rice proteins e.g. structural models from AlphaFold2, PeptideAtlas and the PRIDE database - enabling visualisation of source evidence, including scores and supporting mass spectra.

Gliomas account for most brain malignancies, with astrocytomas being the most common subtype. Among these, glioblastoma (GBM) stands out as the most aggressive form, exhibiting a median survival time of just 15 months despite intensive therapy. Current diagnostic practices rely on magnetic resonance imaging (MRI) and histopathological analysis, which often necessitate invasive surgical sampling. This underscores the need for minimally invasive diagnostic tools capable of characterizing glioma progression and guiding treatment strategies. Advances in glioma classification have integrated histological and molecular markers, notably IDH1 mutations, which are prognostically significant, particularly in low-grade gliomas and in the previously defined "secondary GBM" (IDH-mutant astrocytoma grade 4). This study aimed to explore the potential of serum proteomics as a non-invasive diagnostic tool using MALDI-TOF mass spectrometry (MS) combined with machine learning techniques. We analyzed serum samples from 269 patients, employing machine learning models to differentiate between healthy individuals and astrocytoma patients. The MALDI-TOF MS approach achieved a balanced accuracy of 94.5% in distinguishing GBM patients from healthy controls. However, it showed limited efficacy in classifying tumor grades or determining IDH1 mutational status. Further investigation using bottom-up proteomics by GeLC-MS/MS identified potential biomarkers, such as transthyretin, previously associated with high-grade gliomas. These findings highlight the promise of MALDI-TOF MS in identifying serum-based biomarkers for astrocytoma diagnosis. While the results are promising, further validation in independent cohorts is essential to assess the clinical utility of these biomarkers for non-invasive glioma diagnostics and patient monitoring.

Lyme disease, caused by an infection with the spirochete Borrelia burgdorferi, is the most common vector-borne disease in North America. B. burgdorferi strains harbor extensive genomic and proteomic variability and further comparison is key to understanding the spirochetes infectivity and biological impacts of identified sequence variants. To achieve this goal, both transcript and mass spectrometry (MS)-based proteomics was applied to assemble peptide datasets of laboratory strains B31, MM1, B31-ML23, infective isolates B31-5A4, B31-A3, and 297, and other public datasets, to provide a publicly available Borrelia PeptideAtlas (http://www.peptideatlas.org/builds/borrelia/). Included is information on total proteome, secretome, and membrane proteome of these B. burgdorferi strains. Proteomic data collected from 35 different experiment datasets, with a total of 855 mass spectrometry runs, identified 76,936 distinct peptides at a 0.1% peptide false-discovery-rate, which map to 1,221 canonical proteins (924 core canonical and 297 noncore canonical) and covers 86% of the total base B31 proteome. The diverse proteomic information from multiple isolates with credible data presented by the Borrelia PeptideAtlas can be useful to pinpoint potential protein targets which are common to infective isolates and may be key in the infection process.

Early non-invasive detection of tumor is an urgent clinical need for managing urothelial bladder cancer. Cystoscopy and cytology are the current standards for diagnosis of recurrence, but are limited by low sensitivity. Quantitative proteomics tool was employed to identify important deregulated molecules in bladder cancer tissues and validated using Western blot and immunohistochemistry analysis. A set of 1137 proteins were identified in four paired bladder cancer patients. Among these, 64 proteins were deregulated in all cases among which 9 were commonly up-regulated. The Ingenuity Pathway Analysis (IPA) generated top 11 Networks in which three commonly upregulated (SERPING1, SOD2 and HSPB6) proteins were involved and selected for further validation. Tissue expression of SOD2, SERPING1 and HSPB6 monitored in an independent sample set (n=18) by immuno-histochemical analysis showed similar profile. Western blot analysis of these proteins in urine of bladder cancer (n=26) and healthy subjects (n=10) showed a specificity and sensitivity of >80% for SOD2 and so was selected for further validation in a separate set (n=150) by ELISA. Significant elevation in urinary SOD2 level was found in urothelial bladder cancer patients compared to healthy controls and in recurrent cases compared to primary (p-value<0.001). Kaplan Meier survival analysis showed urinary SOD2 concentration >2,100 pg/ml was significantly associated with poorer survival.Cumulative survival of patient with low SOD2 concentration was 34.4% compared to 18.9% in patient with high SOD2 at 24 months (p=0.025). The study identifies SOD2 as a non-invasive biomarker which may help to extend the period between cystoscopies during follow-up. SignificanceCystoscopy is an invasive and painful method commonly used for diagnosis of urothelial bladder cancer. Non-invasive methods having high specificity and sensitivity to monitor the patients for recurrence are unavailable. Our study reveals significantly higher SOD2 level in drug naive and reoccurring bladder cancer tissues, and similar profile was observed in the parallel urine samples. Hence, SOD2 seems to be a useful biomarker of recurrent urothelial bladder cancer and predict the survival of patients.

Protein glycosylation, and in particular N-linked glycans, is a hall mark of Eukaryotic cells and has been well studied in mammalian cells and parasites. However, little research has been conducted to investigate the conservation and variation of protein glycosylation pathways in other eukaryotic organisms. Euglena gracilis is an industrially important microalga, used in the production of biofuels and nutritional supplements. It is evolutionarily highly divergent from green algae and more related to Kinetoplastid pathogens. It was recently shown that E. gracilis possesses the machinery for producing a range of protein glycosylations and make simple N-glycans, but the modified proteins were not identified. This study identifies the glycosylated proteins, including transporters, extra cellular proteases and those involved in cell surface signalling. Notably, many of the most highly expressed and glycosylated proteins are not related to any known sequences and are therefore likely to be involved in important novel functions in Euglena.

Emergent advancements on the intestinal microbiome for human health and disease treatment necessitates well-defined intestinal cellular models to study and rapidly assess host, microbiome, and drug interactions. This study characterized molecular alterations during Caco-2 cell differentiation, an epithelial intestinal model, using quantitative multi-omic approaches. We demonstrated that both spontaneous and medium-induced cellular differentiations displayed similar protein and pathway changes, including the down-regulation of proteins related to translation and proliferation, and up-regulation of proteins related to cell adhesion, molecule binding and metabolic pathways. Acetyl-proteomics revealed decreased histone acetylation and increased acetylation in proteins associated with mitochondria functions in differentiated cells. Butyrate-containing differentiation medium accelerates differentiation, with earlier up-regulation of proteins related to differentiation and host-microbiome interactions. These results emphasize the controlled progression of Caco-2 differentiation toward a specialized intestinal epithelial-like cell. This further enhances their characterization, establishing their suitability for facilitating the effective evaluation of risk and quality in microbiome-directed therapeutics.

1.1Hair has applications in biomarker discovery and forensics, yet the influence of proteomics software tools on hair proteome characterisation remains underexplored. This study compares four bottom-up proteomics workflows (MaxQuant, FragPipe, MetaMorpheus, and SearchGUI/PeptideShaker). Publicly available hair proteomes were analysed following extraction with 1-dodecyl-3-methylimidazolium chloride (DMC), sodium dodecanoate (SDD), sodium dodecyl sulfate (SDS), and urea. Data were acquired on Orbitrap-based DDA platforms. Peptide identification, protein inference, functional annotation, physicochemical properties, and label-free quantification (LFQ) were evaluated. Peptide-level performance differed across tools. MS-GF+ and FragPipe identified the most unique peptides, while X!Tandem reported the fewest. Protein inference showed a dissociation from peptide-level results. MetaMorpheus reported the highest number of protein groups despite only the third highest peptide counts. FragPipe and MaxQuant followed, while PeptideShaker consistently inferred the fewest proteins. Protein-level concordance was low, with only 30.3% overlap across tools and extraction methods. These differences extended to downstream analyses. Functional enrichment showed moderate concordance (38.25% overlap). Physicochemical profiles varied, with MetaMorpheus identifying more hydrophobic proteomes and PeptideShaker more hydrophilic profiles. At the quantitative level, reproducibility depended on extraction buffer. SDS and urea showed lower variability (CV =< 0.025), while DMC and SDD showed higher variability (up to 0.10). Absolute LFQ intensities and differential expression outputs varied across tools despite moderate to strong correlation (r = 0.77 to 0.93). Overall, software choice influences proteome coverage, physicochemical profiles, and quantitative outcomes. Relative trends were partially conserved, but magnitude and significance varied. These findings support careful method selection and multi-tool validation in hair proteomics

Emerging and reemerging infectious diseases represent major public health concerns. The urgent need for infection control measures requires deep understanding of molecular pathogenesis. Global approaches to study biological systems such as mass-spectrometry based proteomics benefited from groundbreaking physical and bioinformatical technological developments over recent years. However, dual proteomic study of highly pathogenic microorganisms and their hosts in complex matrices encountered during infection remains challenging due to high protein dynamic range of samples and requirements imposed in biosafety level 3 or 4 laboratories. Here, we constructed a dual proteomic pipeline of Yersinia pestis in human blood and plasma, mirroring bacteremic phase of plague. We provide the most complete Y. pestis proteome revealing a major reshaping of important bacterial path-ways such as methionine biosynthesis and iron acquisition in human plasma. Remarkably, proteomic profiling in human blood highlights a greater Yersinia outer proteins intoxication of monocytes than neutrophils. Our study unravels global expression changes and points to a specific pathogenic signature during infection, paving the way for future exploration of proteomes in the complex context of host-pathogen interactions. Subject CategoriesMicrobiology, Virology and Host Pathogen Interaction, Proteomics

Phaeodactylum tricornutum is a model oleaginous pennate diatom, widely investigated for the accumulation of triacylglycerols (TAG) in lipid droplets during nitrogen (N) starvation. However, lipid droplet breakdown, TAG catabolism, and remobilization upon N replenishment during growth restoration are less studied. Serine hydrolases (SH) constitute a diverse family encompassing proteases, amidases, esterases, and lipases. In this report, we adopted a chemoproteomic approach called Activity-Based Protein Profiling (ABPP) to explore the repertoire of active serine hydrolases to elucidate the mechanisms of lipid metabolism in P. tricornutum (strain Pt4). A superfamily-wide profile of serine hydrolases revealed a differentially active proteome (activome) during N starvation and after nutrient replenishment. We report 30 active serine hydrolases, which were broadly categorized into metabolic serine hydrolases and serine proteases. Lipases appeared to be the major metabolic linchpins prevalent during lipid remobilization. Global transcriptomics analysis provided a complementary insight into the gene expression level of the detected serine hydrolases. It revealed putative phospholipases as central players in membrane lipid turnover and remodeling involved in cellular lipid homeostasis and TAG accumulation. TAG remobilization and lipid droplet breakdown were impaired in the presence of phenyl mercuric acetate (PMA), whose activity as an SH inhibitor was validated by competitive ABPP. Lipid species profiling corroborated the impairment in TAG degradation and the buildup of structural lipids in the presence of PMA after nutrient replenishment. Collectively, our functional proteome approach, coupled with the transcriptome and lipidome data, provides a comprehensive landscape of bona fide active serine hydrolases, including lipases in this model diatom.

Alcohol consumption and high-fat diets often coincide in Western society, exerting negative synergistic effects on the liver. While many studies have demonstrated the impact of ALD and NAFLD on organ protein expression, none have offered a comprehensive view of the dysregulation at the level of the membrane proteome. In this study, we utilize peptidisc and solvent precipitation (SP4) methods to isolate and compare the membrane protein content of the liver with its unique biological functions. Using mice treated with a high-fat diet and ethanol in drinking water, we identified 1,563 liver proteins, with 46% predicted to have a transmembrane segment. Among these, 106 integral membrane proteins are dysregulated compared to the untreated sample. Gene ontology analysis reveals several dysregulated membrane processes associated with lipid metabolism, cell adhesion, xenobiotic processing, and mitochondrial membrane formation. Pathways related to cholesterol and bile acid transport are also mutually affected, suggesting an adaptive mechanism to counter the steatosis of the liver model. Our peptidisc-based membrane proteome profiling thus emerges as an effective way to gain insights into the role of the transmembrane proteome in disease development, warranting further in-depth analysis of the individual effect of the identified dysregulated membrane proteins.

Acquisition of the citrus greening bacterial pathogen, Candidatus Liberibacter asiaticus (CLas) by Asian citrus psyllid (Diaphorina citri) nymphs is required efficient tree-to-tree transmission during the adult stage. Quantitative isotope-labeled protein interaction reporter (PIR) cross-linkers were used in parallel with protein quantification using spectral counting to quantify protein interactions within microbe-enriched cellular fractions of nymph and adult D. citri. Over 100 unique crosslinks were found between five insect histone proteins, and over 30% of these were more abundant in nymph compared to adult insects. Strikingly, some cross-links detected in D. citri proteins are conserved in cross-linking studies on human cells, suggesting these protein interaction topologies were present in the common ancestor ([~]750MYA) or are subject to convergent evolution. Analysis of posttranslational modifications of crosslinked histones revealed the presence of acetylated and methylated lysine residues, which may impact psyllid chromatin structure and gene expression. Histone H3 peptides acetylated in the N terminal tail region were found to be more abundant in nymph compared to adult insects in two orthogonal proteomics methods. The insect life stage-specific histone posttranslational modifications and protein interactions represent physical evidence that metamorphosis is associated with changes in chromatin structure that regulate genome-wide transcriptional reprogramming.