A Scalable Approach for Protein False Discovery Rate Estimation in Large Proteomic Data Sets

Comparing plastid proteomes points towards a higher plastidial redox turnover in vascular tissues than in mesophyll cells

Plastids are complex organelles that vary in size and function depending on the cell type. Accordingly, they can be referred to as amyloplasts, chloroplasts, chromoplasts, etioplasts, proplasts to only cite a few denominations. Over the past decades, methods based on density gradients and differential centrifugations have been extensively used for the purification of plastids. However, these methods need large amounts of starting material, and hardly provide a tissue-specific resolution. Here, we applied our IPTACT (Isolation of Plastids TAgged in specific Cell Types) method, which involves the biotinylation of plastids in vivo using one-shot transgenic lines expressing the TOC64 gene coupled with a biotin ligase receptor particle and the BirA biotin ligase, to isolate plastids from mesophyll and companion cells of Arabidopsis thaliana using tissue specific pCAB3 and pSUC2 promoters, respectively. Subsequently, a proteome profiling was performed, and allowed the identification of 1672 proteins, among which 1342 were predicted plastidial, and 705 were fully confirmed according to SUBA5. Interestingly, although 92% of plastidial proteins were equally distributed between the two tissues, we observed an accumulation of proteins associated with jasmonic acid biosynthesis, plastoglobuli (e.g. NDC1, VTE1, PGL34, ABC1K1) and cyclic electron flow in plastids originating from vascular tissues. Besides demonstrating the technical feasibility of isolating plastids in a tissue-specific manner, our work provides strong evidence that plastids from vascular tissue have a higher redox turnover to ensure optimal functioning, notably under high solute strength as encountered in vascular cells.

Redox Proteomic Profile of Tirapazamine-Resistant Murine Hepatoma Cells

3-Amino-1,2,4-benzotriazine-1,4-dioxide (tirapazamine, TPZ) and other heteroaromatic N-oxides (ArN→O) exhibit tumoricidal, antibacterial, and antiprotozoal activities. Their action is attributed to the enzymatic single-electron reduction to free radicals that initiate the prooxidant processes. In order to clarify the mechanisms of aerobic mammalian cytotoxicity of ArN→O, we derived a TPZ-resistant subline of murine hepatoma MH22a cells (resistance index, 5.64). The quantitative proteomic of wild-type and TPZ-resistant cells revealed 5818 proteins, of which 237 were up- and 184 down-regulated. The expression of the antioxidant enzymes aldehyde- and alcohol dehydrogenases, carbonyl reductases, catalase, and glutathione reductase was increased 1.6-5.2 times, whereas the changes in the expression of glutathione peroxidase, superoxide dismutase, thioredoxin reductase, and peroxiredoxins were less pronounced. The expression of xenobiotics conjugating glutathione-S-transferases was increased by 1.6-2.6 times. On the other hand, the expression of NADPH:cytochrome P450 reductase was responsible for the single-electron reduction in TPZ and for the 2.1-fold decrease. These data support the fact that the main mechanism of action of TPZ under aerobic conditions is oxidative stress. The unchanged expression of intranuclear antioxidant proteins peroxiredoxin, glutaredoxin, and glutathione peroxidase, and a modest increase in the expression of DNA damage repair proteins, tend to support non-site-specific but not intranuclear oxidative stress as a main factor of TPZ aerobic cytotoxicity.

Principles of phosphoproteomics and applications in cancer research

Phosphorylation constitutes the most common and best-studied regulatory post-translational modification in biological systems and archetypal signalling pathways driven by protein and lipid kinases are disrupted in essentially all cancer types. Thus, the study of the phosphoproteome stands to provide unique biological information on signalling pathway activity and on kinase network circuitry that is not captured by genetic or transcriptomic technologies. Here, we discuss the methods and tools used in phosphoproteomics and highlight how this technique has been used, and can be used in the future, for cancer research. Challenges still exist in mass spectrometry phosphoproteomics and in the software required to provide biological information from these datasets. Nevertheless, improvements in mass spectrometers with enhanced scan rates, separation capabilities and sensitivity, in biochemical methods for sample preparation and in computational pipelines are enabling an increasingly deep analysis of the phosphoproteome, where previous bottlenecks in data acquisition, processing and interpretation are being relieved. These powerful hardware and algorithmic innovations are not only providing exciting new mechanistic insights into tumour biology, from where new drug targets may be derived, but are also leading to the discovery of phosphoproteins as mediators of drug sensitivity and resistance and as classifiers of disease subtypes. These studies are, therefore, uncovering phosphoproteins as a new generation of disruptive biomarkers to improve personalised anti-cancer therapies.

Deep thermal profiling for detection of functional proteoform groups

The complexity of the functional proteome extends considerably beyond the coding genome, resulting in millions of proteoforms. Investigation of proteoforms and their functional roles is important to understand cellular physiology and its deregulation in diseases but challenging to perform systematically. Here we applied thermal proteome profiling with deep peptide coverage to detect functional proteoform groups in acute lymphoblastic leukemia cell lines with different cytogenetic aberrations. We detected 15,846 proteoforms, capturing differently spliced, cleaved and post-translationally modified proteins expressed from 9,290 genes. We identified differential co-aggregation of proteoform pairs and established links to disease biology. Moreover, we systematically made use of measured biophysical proteoform states to find specific biomarkers of drug sensitivity. Our approach, thus, provides a powerful and unique tool for systematic detection and functional annotation of proteoform groups.

ProInfer: An interpretable protein inference tool leveraging on biological networks

In mass spectrometry (MS)-based proteomics, protein inference from identified peptides (protein fragments) is a critical step. We present ProInfer (Protein Inference), a novel protein assembly method that takes advantage of information in biological networks. ProInfer assists recovery of proteins supported only by ambiguous peptides (a peptide which maps to more than one candidate protein) and enhances the statistical confidence for proteins supported by both unique and ambiguous peptides. Consequently, ProInfer rescues weakly supported proteins thereby improving proteome coverage. Evaluated across THP1 cell line, lung cancer and RAW267.4 datasets, ProInfer always infers the most numbers of true positives, in comparison to mainstream protein inference tools Fido, EPIFANY and PIA. ProInfer is also adept at retrieving differentially expressed proteins, signifying its usefulness for functional analysis and phenotype profiling. Source codes of ProInfer are available at https://github.com/PennHui2016/ProInfer.

Isolation of extracellular fluids reveals novel secreted bioactive proteins from muscle and fat tissues

Proteins are secreted from cells to send information to neighboring cells or distant tissues. Because of the highly integrated nature of energy balance systems, there has been particular interest in myokines and adipokines. These are challenging to study through proteomics because serum or plasma contains highly abundant proteins that limit the detection of proteins with lower abundance. We show here that extracellular fluid (EF) from muscle and fat tissues of mice shows a different protein composition than either serum or tissues. Mass spectrometry analyses of EFs from mice with physiological perturbations, like exercise or cold exposure, allowed the quantification of many potentially novel myokines and adipokines. Using this approach, we identify prosaposin as a secreted product of muscle and fat. Prosaposin expression stimulates thermogenic gene expression and induces mitochondrial respiration in primary fat cells. These studies together illustrate the utility of EF isolation as a discovery tool for adipokines and myokines.

Integrated microRNA and secretome analysis of human endometrial organoids reveal the miR-3194-5p/Aquaporin/S100A9 module in regulating trophoblast functions

Successful placentation requires delicate communication between the endometrium and trophoblasts. The invasion and integration of trophoblasts into the endometrium during early pregnancy is crucial to placentation. Dysregulation of these functions is associated with various pregnancy complications, such as miscarriage and preeclampsia. The endometrial microenvironment has an important influence on trophoblast cell functions. The precise effect of the endometrial gland secretome on trophoblast functions remains uncertain. We hypothesized that the hormonal environment regulates the miRNA profile and secretome of the human endometrial gland, which subsequently modulates trophoblast functions during early pregnancy. Human endometrial tissues were obtained from endometrial biopsies with written consent. Endometrial organoids were established in matrix gel under defined culture conditions. They were treated with hormones mimicking the environment of the proliferative phase (Estrogen, E2), secretory phase (E2+Progesterone, P4), and early pregnancy (E2+P4+Human Chorionic Gonadotropin, hCG). miRNA-seq was performed on the treated organoids. Organoid secretions were also collected for mass spectrometric analysis. The viability and invasion/migration of the trophoblasts after treatment with the organoid secretome were determined by cytotoxicity assay and transwell assay, respectively. Endometrial organoids with the ability to respond to sex steroid hormones were successfully developed from human endometrial glands. By establishing the first secretome profiles and miRNA atlas of these endometrial organoids to the hormonal changes followed by trophoblast functional assays, we demonstrated that sex steroid hormones modulate aquaporin (AQP)1/9 and S100A9 secretions through miR-3194 activation in endometrial epithelial cells, which in turn enhanced trophoblast migration and invasion during early pregnancy. By using a human endometrial organoid model, we demonstrated for the first time that the hormonal regulation of the endometrial gland secretome is crucial to regulating the functions of human trophoblasts during early pregnancy. The study provides the basis for understanding the regulation of early placental development in humans.

Integrated View of Baseline Protein Expression in Human Tissues

The availability of proteomics datasets in the public domain, and in the PRIDE database, in particular, has increased dramatically in recent years. This unprecedented large-scale availability of data provides an opportunity for combined analyses of datasets to get organism-wide protein abundance data in a consistent manner. We have reanalyzed 24 public proteomics datasets from healthy human individuals to assess baseline protein abundance in 31 organs. We defined tissue as a distinct functional or structural region within an organ. Overall, the aggregated dataset contains 67 healthy tissues, corresponding to 3,119 mass spectrometry runs covering 498 samples from 489 individuals. We compared protein abundances between different organs and studied the distribution of proteins across these organs. We also compared the results with data generated in analogous studies. Additionally, we performed gene ontology and pathway-enrichment analyses to identify organ-specific enriched biological processes and pathways. As a key point, we have integrated the protein abundance results into the resource Expression Atlas, where they can be accessed and visualized either individually or together with gene expression data coming from transcriptomics datasets. We believe this is a good mechanism to make proteomics data more accessible for life scientists.

Transcriptome and Proteome of Methicillin-Resistant Staphylococcus aureus Small-Colony Variants Reveal Changed Metabolism and Increased Immune Evasion

Methicillin-resistant Staphylococcus aureus (MRSA) infection has become a public health crisis. Recently, we isolated small-colony variants (SCVs) of MRSA, which are characterized by slow growth, decreased virulence, increased antibiotic resistance, and immune evasion. In the present study, we provided proteomic and transcriptomic profiles of clinical MRSA sequence type 239 (ST239) normal strains and SCVs and attempted to identify the key genes or pathways closely related to SCV formation and survival. RNAs and proteins were extracted and subjected to RNA sequencing and mass spectrometry, and the transcriptome and proteome were evaluated via bioinformatic analysis. The results were verified by functional assays. In total, 822 differentially expressed genes (DEGs) and 773 differentially expressed proteins (DEPs) were identified; of these, 286 DEGs and DEPs were correlated and subjected to Kyoto Encyclopedia Genes and Genomes analysis. Some pathways were significant, including ABC transporters, ribosome biogenesis, and metabolic pathways such as glycolysis/gluconeogenesis and the citrate cycle (tricarboxylic acid [TCA] cycle). Based on these results, we found that the downregulation of ABC transporters and the TCA cycle pathway resulted in electron transport chain deficiencies and reduced ATP production in SCVs, leading to a dependence on glycolysis and its upregulation. In addition, the upregulation of capsule polysaccharides and the downregulation of surface proteins prevented phagocytosis and reduced the adhesion of host cells, contributing to immune evasion by SCVs. These findings contribute to a better understanding of the mechanisms of SCV formation and survival. IMPORTANCE Small-colony variants (SCVs) of Staphylococcus aureus have drawn increasing research attention. Owing to their slow growth, atypical colony morphology, and unusual metabolic characteristics, SCVs often cause confusion in the laboratory. Furthermore, clinical treatment of SCVs is challenging owing to their antibiotic resistance and immune evasion, leading to persistent and recurrent infections. However, the mechanisms underlying their formation remain unclear. In this study, we isolated SCVs of methicillin-resistant S. aureus and provided transcriptomic and proteomic profiles of normal strains and SCVs. Based on our analysis, glycolysis upregulation and TCA cycle downregulation affected the electron transport chain and energy supply, leading to slower metabolism. Moreover, capsular biosynthesis was increased, while the number of surface proteins decreased, thus promoting immune evasion by SCVs.

Mechanistic insights into RNA surveillance by the canonical poly(A) polymerase Pla1 of the MTREC complex

The S. pombe orthologue of the human PAXT connection, Mtl1-Red1 Core (MTREC), is an eleven-subunit complex that targets cryptic unstable transcripts (CUTs) to the nuclear RNA exosome for degradation. It encompasses the canonical poly(A) polymerase Pla1, responsible for polyadenylation of nascent RNA transcripts as part of the cleavage and polyadenylation factor (CPF/CPSF). In this study we identify and characterise the interaction between Pla1 and the MTREC complex core component Red1 and analyse the functional relevance of this interaction in vivo. Our crystal structure of the Pla1-Red1 complex shows that a 58-residue fragment in Red1 binds to the RNA recognition motif domain of Pla1 and tethers it to the MTREC complex. Structure-based Pla1-Red1 interaction mutations show that Pla1, as part of MTREC complex, hyper-adenylates CUTs for their efficient degradation. Interestingly, the Red1-Pla1 interaction is also required for the efficient assembly of the fission yeast facultative heterochromatic islands. Together, our data suggest a complex interplay between the RNA surveillance and 3'-end processing machineries.

Sample multiplexing-based targeted pathway proteomics with real-time analytics reveals the impact of genetic variation on protein expression

Targeted proteomics enables hypothesis-driven research by measuring the cellular expression of protein cohorts related by function, disease, or class after perturbation. Here, we present a pathway-centric approach and an assay builder resource for targeting entire pathways of up to 200 proteins selected from >10,000 expressed proteins to directly measure their abundances, exploiting sample multiplexing to increase throughput by 16-fold. The strategy, termed GoDig, requires only a single-shot LC-MS analysis, ~1 µg combined peptide material, a list of up to 200 proteins, and real-time analytics to trigger simultaneous quantification of up to 16 samples for hundreds of analytes. We apply GoDig to quantify the impact of genetic variation on protein expression in mice fed a high-fat diet. We create several GoDig assays to quantify the expression of multiple protein families (kinases, lipid metabolism- and lipid droplet-associated proteins) across 480 fully-genotyped Diversity Outbred mice, revealing protein quantitative trait loci and establishing potential linkages between specific proteins and lipid homeostasis.

Comparative Proteomic Analysis Provides New Insights into the Molecular Basis of Thermal-Induced Parthenogenesis in Silkworm (Bombyx mori)

Artificial parthenogenetic induction via thermal stimuli in silkworm is an important technique that has been used in sericultural production. However, the molecular mechanism underlying it remains largely unknown. We have created a fully parthenogenetic line (PL) with more than 85% occurrence and 80% hatching rate via hot water treatment and genetic selection, while the parent amphigenetic line (AL) has less than 30% pigmentation rate and less than 1% hatching rate when undergoing the same treatment. Here, isobaric tags for relative and absolute quantitation (iTRAQ)-based analysis were used to investigate the key proteins and pathways associated with silkworm parthenogenesis. We uncovered the unique proteomic features of unfertilized eggs in PL. In total, 274 increased abundance proteins and 211 decreased abundance proteins were identified relative to AL before thermal induction. Function analysis displayed an increased level of translation and metabolism in PL. After thermal induction, 97 increased abundance proteins and 187 decreased abundance proteins were identified. An increase in stress response-related proteins and decrease in energy metabolism suggested that PL has a more effective response to buffer the thermal stress than AL. Cell cycle-related proteins, including histones, and spindle-related proteins were decreased in PL, indicating an important role of this decrease in the process of ameiotic parthenogenesis.

Effect of MnTBAP on sperm ultra-rapid freezing and its proteomics study

MnTBAP is a new synthetic antioxidant that has been used for the cryopreservation of sperm. However, the exact mechanism of its cryoprotection at the molecular level is largely unknown. Therefore, in this study, normal human semen samples were selected and MnTBAP (0, 5, 10, 20, 40 μM) was added to sperm freezing medium to assess changes in kinetics parameters, apoptosis, reactive oxygen species (ROS), and DNA fragmentation index (DFI) after sperm ultra-rapid freezing. The tandem masstagging (TMT) proteomics technique was used to further investigate the changes in proteins after sperm ultra-rapid freezing. The kinetic parameters of sperm after ultra-rapid freezing and thawing were significantly reduced and apoptosis, ROS production and DFI were significantly increased. The addition of 40 μM MnTBAP improved the kinetic parameters, while it reduced apoptosis, ROS production, and DFI of sperm after ultra-rapid freezing and thawing (P < 0.05). Compared with the fresh semen, 1978 differential proteins were identified in the frozen-thawed sperm without MnTBAP and 1888 differential proteins were identified in the frozen-thawed sperm with MnTBAP (40 μM) added. The proteins affected during ultra-rapid freezing were mainly related to sperm metabolism, flagellar structure motility, apoptosis, intracellular signaling, capacitation and fertilization, while the addition of MnTBAP reduced the alterations of these proteins.

Histopathologic and proteogenomic heterogeneity reveals features of clear cell renal cell carcinoma aggressiveness

Clear cell renal cell carcinomas (ccRCCs) represent ∼75% of RCC cases and account for most RCC-associated deaths. Inter- and intratumoral heterogeneity (ITH) results in varying prognosis and treatment outcomes. To obtain the most comprehensive profile of ccRCC, we perform integrative histopathologic, proteogenomic, and metabolomic analyses on 305 ccRCC tumor segments and 166 paired adjacent normal tissues from 213 cases. Combining histologic and molecular profiles reveals ITH in 90% of ccRCCs, with 50% demonstrating immune signature heterogeneity. High tumor grade, along with BAP1 mutation, genome instability, increased hypermethylation, and a specific protein glycosylation signature define a high-risk disease subset, where UCHL1 expression displays prognostic value. Single-nuclei RNA sequencing of the adverse sarcomatoid and rhabdoid phenotypes uncover gene signatures and potential insights into tumor evolution. In vitro cell line studies confirm the potential of inhibiting identified phosphoproteome targets. This study molecularly stratifies aggressive histopathologic subtypes that may inform more effective treatment strategies.

RBM43 links adipose inflammation and energy expenditure through translational regulation of PGC1α

Adipose thermogenesis involves specialized mitochondrial function that counteracts metabolic disease through dissipation of chemical energy as heat. However, inflammation present in obese adipose tissue can impair oxidative metabolism. Here, we show that PGC1α, a key governor of mitochondrial biogenesis and thermogenesis, is negatively regulated at the level of mRNA translation by the little-known RNA-binding protein RBM43. Rbm43 is expressed selectively in white adipose depots that have low thermogenic potential, and is induced by inflammatory cytokines. RBM43 suppresses mitochondrial and thermogenic gene expression in a PGC1α-dependent manner and its loss protects cells from cytokine-induced mitochondrial impairment. In mice, adipocyte-selective Rbm43 disruption increases PGC1α translation, resulting in mitochondrial biogenesis and adipose thermogenesis. These changes are accompanied by improvements in glucose homeostasis during diet-induced obesity that are independent of body weight. The action of RBM43 suggests a translational mechanism by which inflammatory signals associated with metabolic disease dampen mitochondrial function and thermogenesis.

Integrating Identification and Quantification Uncertainty for Differential Protein Abundance Analysis with Triqler

Protein quantification for shotgun proteomics is a complicated process where errors can be introduced in each of the steps. Triqler is a Python package that estimates and integrates errors of the different parts of the label-free protein quantification pipeline into a single Bayesian model. Specifically, it weighs the quantitative values by the confidence we have in the correctness of the corresponding PSM. Furthermore, it treats missing values in a way that reflects their uncertainty relative to observed values. Finally, it combines these error estimates in a single differential abundance FDR that not only reflects the errors and uncertainties in quantification but also in identification. In this tutorial, we show how to (1) generate input data for Triqler from quantification packages such as MaxQuant and Quandenser, (2) run Triqler and what the different options are, (3) interpret the results, (4) investigate the posterior distributions of a protein of interest in detail, and (5) verify that the hyperparameter estimations are sensible.

In silico model of atherosclerosis with individual patient calibration to enable precision medicine for cardiovascular disease

OBJECTIVE

Guidance for preventing myocardial infarction and ischemic stroke by tailoring treatment for individual patients with atherosclerosis is an unmet need. Such development may be possible with computational modeling. Given the multifactorial biology of atherosclerosis, modeling must be based on complete biological networks that capture protein-protein interactions estimated to drive disease progression. Here, we aimed to develop a clinically relevant scale model of atherosclerosis, calibrate it with individual patient data, and use it to simulate optimized pharmacotherapy for individual patients.

APPROACH AND RESULTS

The study used a uniquely constituted plaque proteomic dataset to create a comprehensive systems biology disease model for simulating individualized responses to pharmacotherapy. Plaque tissue was collected from 18 patients with 6735 proteins at two locations per patient. 113 pathways were identified and included in the systems biology model of endothelial cells, vascular smooth muscle cells, macrophages, lymphocytes, and the integrated intima, altogether spanning 4411 proteins, demonstrating a range of 39-96% plaque instability. After calibrating the systems biology models for individual patients, we simulated intensive lipid-lowering, anti-inflammatory, and anti-diabetic drugs. We also simulated a combination therapy. Drug response was evaluated as the degree of change in plaque stability, where an improvement was defined as a reduction of plaque instability. In patients with initially unstable lesions, simulated responses varied from high (20%, on combination therapy) to marginal improvement, whereas patients with initially stable plaques showed generally less improvement.

CONCLUSION

In this pilot study, proteomics-based system biology modeling was shown to simulate drug response based on atherosclerotic plaque instability with a power of 90%, providing a potential strategy for improved personalized management of patients with cardiovascular disease.

Quantitative proteome and phosphoproteome datasets of DNA replication and mitosis in Saccharomyces cerevisiae

Cell division is a highly regulated process that secures the generation of healthy progeny in all organisms, from yeast to human. Dysregulation of this process can lead to uncontrolled cell proliferation and genomic instability, both which are hallmarks of cancer. Cell cycle progression is dictated by a complex network of kinases and phosphatases. These enzymes act on their substrates in a highly specific temporal manner ensuring that the process of cell division is unidirectional and irreversible. Key events of the cell cycle, such as duplication of genetic material and its redistribution to daughter cells, occur in S-phase and mitosis, respectively. Deciphering the dynamics of phosphorylation/dephosphorylation events during these cell cycle phases is important. Here, we showcase a quantitative proteomic and phosphoproteomic mass spectrometry dataset that profiles both early and late phosphorylation events and associated proteome alterations that occur during S-phase and mitotic arrest in the model organism S. cerevisiae. This dataset is of broad interest as the molecular mechanisms governing cell cycle progression are conserved throughout evolution. The data has been deposited in ProteomeXchange with the dataset identifier PXD037291.

Quantitative proteome dataset profiling of UBC4 and UBC5 deletion strains in Saccharomyces cerevisiae

The Ubiquitin-Proteasome System (UPS) regulates many cellular processes in eukaryotic cells. Ubiquitylation by the UPS mainly directs proteins to proteasomal degradation, but it can also have non-degradative functions, such as regulating protein activity or localization. The small protein ubiquitin is conjugated to its substrates via a cascade of E1-E2-E3 enzymes. Dysregulation of the UPS has been implicated in the genesis and progression of many diseases, such as neurodegenerative diseases and cancer; thus, the UPS components are attractive targets for developing pharmaceutical drugs. E2s, or ubiquitin conjugating enzymes, are central players of the UPS. E2s function in tandem with specific ubiquitin ligases (E3s) to transfer ubiquitin to substrates. Here, we present the first proteome stability analysis of two closely related ubiquitin conjugating enzymes, Ubc4 and Ubc5, in S. cerevisiae. These two E2s are nearly identical, having 92% sequence identity and differing by only 11 amino acid residues. This dataset is of broad interest because higher eukaryotes express ubiquitin conjugating enzymes that are analogous to the yeast Ubc4/5. The data have been deposited in ProteomeXchange with the dataset identifier PXD037315.

Architecture of the outbred brown fat proteome defines regulators of metabolic physiology

Brown adipose tissue (BAT) regulates metabolic physiology. However, nearly all mechanistic studies of BAT protein function occur in a single inbred mouse strain, which has limited the understanding of generalizable mechanisms of BAT regulation over physiology. Here, we perform deep quantitative proteomics of BAT across a cohort of 163 genetically defined diversity outbred mice, a model that parallels the genetic and phenotypic variation found in humans. We leverage this diversity to define the functional architecture of the outbred BAT proteome, comprising 10,479 proteins. We assign co-operative functions to 2,578 proteins, enabling systematic discovery of regulators of BAT. We also identify 638 proteins that correlate with protection from, or sensitivity to, at least one parameter of metabolic disease. We use these findings to uncover SFXN5, LETMD1, and ATP1A2 as modulators of BAT thermogenesis or adiposity, and provide OPABAT as a resource for understanding the conserved mechanisms of BAT regulation over metabolic physiology.

Reanalysis of ProteomicsDB Using an Accurate, Sensitive, and Scalable False Discovery Rate Estimation Approach for Protein Groups

Estimating false discovery rates (FDRs) of protein identification continues to be an important topic in mass spectrometry-based proteomics, particularly when analyzing very large datasets. One performant method for this purpose is the Picked Protein FDR approach which is based on a target-decoy competition strategy on the protein level that ensures that FDRs scale to large datasets. Here, we present an extension to this method that can also deal with protein groups, that is, proteins that share common peptides such as protein isoforms of the same gene. To obtain well-calibrated FDR estimates that preserve protein identification sensitivity, we introduce two novel ideas. First, the picked group target-decoy and second, the rescued subset grouping strategies. Using entrapment searches and simulated data for validation, we demonstrate that the new Picked Protein Group FDR method produces accurate protein group-level FDR estimates regardless of the size of the data set. The validation analysis also uncovered that applying the commonly used Occam's razor principle leads to anticonservative FDR estimates for large datasets. This is not the case for the Picked Protein Group FDR method. Reanalysis of deep proteomes of 29 human tissues showed that the new method identified up to 4% more protein groups than MaxQuant. Applying the method to the reanalysis of the entire human section of ProteomicsDB led to the identification of 18,000 protein groups at 1% protein group-level FDR. The analysis also showed that about 1250 genes were represented by ≥2 identified protein groups. To make the method accessible to the proteomics community, we provide a software tool including a graphical user interface that enables merging results from multiple MaxQuant searches into a single list of identified and quantified protein groups.

A multi-purpose, regenerable, proteome-scale, human phosphoserine resource for phosphoproteomics

Mass-spectrometry-based phosphoproteomics has become indispensable for understanding cellular signaling in complex biological systems. Despite the central role of protein phosphorylation, the field still lacks inexpensive, regenerable, and diverse phosphopeptides with ground-truth phosphorylation positions. Here, we present Iterative Synthetically Phosphorylated Isomers (iSPI), a proteome-scale library of human-derived phosphoserine-containing phosphopeptides that is inexpensive, regenerable, and diverse, with precisely known positions of phosphorylation. We demonstrate possible uses of iSPI, including use as a phosphopeptide standard, a tool to evaluate and optimize phosphorylation-site localization algorithms, and a benchmark to compare performance across data analysis pipelines. We also present AScorePro, an updated version of the AScore algorithm specifically optimized for phosphorylation-site localization in higher energy fragmentation spectra, and the FLR viewer, a web tool for phosphorylation-site localization, to enable community use of the iSPI resource. iSPI and its associated data constitute a useful, multi-purpose resource for the phosphoproteomics community.

Improving Peptide-Level Mass Spectrometry Analysis via Double Competition

The analysis of shotgun proteomics data often involves generating lists of inferred peptide-spectrum matches (PSMs) and/or of peptides. The canonical approach for generating these discovery lists is by controlling the false discovery rate (FDR), most commonly through target-decoy competition (TDC). At the PSM level, TDC is implemented by competing each spectrum's best-scoring target (real) peptide match with its best match against a decoy database. This PSM-level procedure can be adapted to the peptide level by selecting the top-scoring PSM per peptide prior to FDR estimation. Here, we first highlight and empirically augment a little known previous work by He et al., which showed that TDC-based PSM-level FDR estimates can be liberally biased. We thus propose that researchers instead focus on peptide-level analysis. We then investigate three ways to carry out peptide-level TDC and show that the most common method ("PSM-only") offers the lowest statistical power in practice. An alternative approach that carries out a double competition, first at the PSM and then at the peptide level ("PSM-and-peptide"), is the most powerful method, yielding an average increase of 17% more discovered peptides at 1% FDR threshold relative to the PSM-only method.

MetaLP: An integrative linear programming method for protein inference in metaproteomics

Metaproteomics based on high-throughput tandem mass spectrometry (MS/MS) plays a crucial role in characterizing microbiome functions. The acquired MS/MS data is searched against a protein sequence database to identify peptides, which are then used to infer a list of proteins present in a metaproteome sample. While the problem of protein inference has been well-studied for proteomics of single organisms, it remains a major challenge for metaproteomics of complex microbial communities because of the large number of degenerate peptides shared among homologous proteins in different organisms. This challenge calls for improved discrimination of true protein identifications from false protein identifications given a set of unique and degenerate peptides identified in metaproteomics. MetaLP was developed here for protein inference in metaproteomics using an integrative linear programming method. Taxonomic abundance information extracted from metagenomics shotgun sequencing or 16s rRNA gene amplicon sequencing, was incorporated as prior information in MetaLP. Benchmarking with mock, human gut, soil, and marine microbial communities demonstrated significantly higher numbers of protein identifications by MetaLP than ProteinLP, PeptideProphet, DeepPep, PIPQ, and Sipros Ensemble. In conclusion, MetaLP could substantially improve protein inference for complex metaproteomes by incorporating taxonomic abundance information in a linear programming model.

Global, in situ analysis of the structural proteome in individuals with Parkinson's disease to identify a new class of biomarker

Parkinson's disease (PD) is a prevalent neurodegenerative disease for which robust biomarkers are needed. Because protein structure reflects function, we tested whether global, in situ analysis of protein structural changes provides insight into PD pathophysiology and could inform a new concept of structural disease biomarkers. Using limited proteolysis-mass spectrometry (LiP-MS), we identified 76 structurally altered proteins in cerebrospinal fluid (CSF) of individuals with PD relative to healthy donors. These proteins were enriched in processes misregulated in PD, and some proteins also showed structural changes in PD brain samples. CSF protein structural information outperformed abundance information in discriminating between healthy participants and those with PD and improved the discriminatory performance of CSF measures of the hallmark PD protein α-synuclein. We also present the first analysis of inter-individual variability of a structural proteome in healthy individuals, identifying biophysical features of variable protein regions. Although independent validation is needed, our data suggest that global analyses of the human structural proteome will guide the development of novel structural biomarkers of disease and enable hypothesis generation about underlying disease processes.

Molecular Characteristics and Quantitative Proteomic Analysis of Klebsiella pneumoniae Strains with Carbapenem and Colistin Resistance

Carbapenem-resistant Klebsiella pneumoniae (CRKP) are usually multidrug resistant (MDR) and cause serious therapeutic problems. Colistin is a critical last-resort therapeutic option for MDR bacterial infections. However, increasing colistin use has led to the emergence of extensively drug-resistant (XDR) strains, raising a significant challenge for healthcare. In order to gain insight into the antibiotic resistance mechanisms of CRKP and identify potential drug targets, we compared the molecular characteristics and the proteomes among drug-sensitive (DS), MDR, and XDR K. pneumoniae strains. All drug-resistant isolates belonged to ST11, harboring bla_KPC and hypervirulent genes. None of the plasmid-encoded mcr genes were detected in the colistin-resistant XDR strains. Through a tandem mass tag (TMT)-labeled proteomic technique, a total of 3531 proteins were identified in the current study. Compared to the DS strains, there were 247 differentially expressed proteins (DEPs) in the MDR strains and 346 DEPs in the XDR strains, respectively. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis revealed that a majority of the DEPs were involved in various metabolic pathways, which were beneficial to the evolution of drug resistance in K. pneumoniae. In addition, a total of 67 DEPs were identified between the MDR and XDR strains. KEGG enrichment and protein-protein interaction network analysis showed their participation in cationic antimicrobial peptide resistance and two-component systems. In conclusion, our results highlight the emergence of colistin-resistant and hypervirulent CRKP, which is a noticeable superbug. The DEPs identified in our study are of great significance for the exploration of effective control strategies against infections of CRKP.

Extended N-Terminal Acetyltransferase Naa50 in Filamentous Fungi Adds to Naa50 Diversity

Most eukaryotic proteins are N-terminally acetylated by a set of Nα acetyltransferases (NATs). This ancient and ubiquitous modification plays a fundamental role in protein homeostasis, while mutations are linked to human diseases and phenotypic defects. In particular, Naa50 features species-specific differences, as it is inactive in yeast but active in higher eukaryotes. Together with NatA, it engages in NatE complex formation for cotranslational acetylation. Here, we report Naa50 homologs from the filamentous fungi Chaetomium thermophilum and Neurospora crassa with significant N- and C-terminal extensions to the conserved GNAT domain. Structural and biochemical analyses show that CtNaa50 shares the GNAT structure and substrate specificity with other homologs. However, in contrast to previously analyzed Naa50 proteins, it does not form NatE. The elongated N-terminus increases Naa50 thermostability and binds to dynein light chain protein 1, while our data suggest that conserved positive patches in the C-terminus allow for ribosome binding independent of NatA. Our study provides new insights into the many facets of Naa50 and highlights the diversification of NATs during evolution.

Mechanobiological responses of astrocytes in optic nerve head due to biaxial stretch

Background

Elevated intraocular pressure (IOP) is the main risk factor for glaucoma, which might cause the activation of astrocytes in optic nerve head. To determine the effect of mechanical stretch on the astrocytes, we investigated the changes in cell phenotype, proteins of interest and signaling pathways under biaxial stretch.

Method

The cultured astrocytes in rat optic nerve head were stretched biaxially by 10 and 17% for 24 h, respectively. Then, we detected the morphology, proliferation and apoptosis of the stretched cells, and performed proteomics analysis. Protein expression was analyzed by Isobaric tags for relative and absolute quantification (iTRAQ) mass spectrometry. Proteins of interest and signaling pathways were screened using Gene Ontology enrichment analysis and pathway enrichment analysis, and the results were verified by western blot and the gene-chip data from Gene Expression Omnibus (GEO) database.

Result

The results showed that rearrangement of the actin cytoskeleton in response to stimulation by mechanical stress and proliferation rate of astrocytes decreased under 10 and 17% stretch condition, while there was no significant difference on the apoptosis rate of astrocytes in both groups. In the iTRAQ quantitative experiment, there were 141 differential proteins in the 10% stretch group and 140 differential proteins in the 17% stretch group. These proteins include low-density lipoprotein receptor-related protein (LRP6), caspase recruitment domain family, member 10 (CARD10), thrombospondin 1 (THBS1) and tetraspanin (CD81). The western blot results of LRP6, THBS1 and CD81 were consistent with that of iTRAQ experiment. ANTXR2 and CARD10 were both differentially expressed in the mass spectrometry results and GEO database. We also screened out the signaling pathways associated with astrocyte activation, including Wnt/β–catenin pathway, NF-κB signaling pathway, PI3K-Akt signaling pathway, MAPK signaling pathway, Jak-STAT signaling pathway, ECM-receptor interaction, and transforming growth factor-β (TGF-β) signaling pathway.

Conclusion

Mechanical stimulation can induce changes in cell phenotype, some proteins and signaling pathways, which might be associated with astrocyte activation. These proteins and signaling pathways may help us have a better understanding on the activation of astrocytes and the role astrocyte activation played in glaucomatous optic neuropathy.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12886-022-02592-8.

Amelioration of pathologic α-synuclein-induced Parkinson's disease by irisin

Physical activity provides clinical benefit in Parkinson's disease (PD). Irisin is an exercise-induced polypeptide secreted by skeletal muscle that crosses the blood-brain barrier and mediates certain effects of exercise. Here, we show that irisin prevents pathologic α-synuclein (α-syn)-induced neurodegeneration in the α-syn preformed fibril (PFF) mouse model of sporadic PD. Intravenous delivery of irisin via viral vectors following the stereotaxic intrastriatal injection of α-syn PFF cause a reduction in the formation of pathologic α-syn and prevented the loss of dopamine neurons and lowering of striatal dopamine. Irisin also substantially reduced the α-syn PFF-induced motor deficits as assessed behaviorally by the pole and grip strength test. Recombinant sustained irisin treatment of primary cortical neurons attenuated α-syn PFF toxicity by reducing the formation of phosphorylated serine 129 of α-syn and neuronal cell death. Tandem mass spectrometry and biochemical analysis revealed that irisin reduced pathologic α-syn by enhancing endolysosomal degradation of pathologic α-syn. Our findings highlight the potential for therapeutic disease modification of irisin in PD.

Multi-omics analysis of an in vitro photoaging model and protective effect of umbilical cord mesenchymal stem cell-conditioned medium

Background

Skin ageing caused by long-term ultraviolet (UV) irradiation is a complex biological process that involves multiple signalling pathways. Stem cell-conditioned media is believed to have anti-ageing effects on the skin. The purpose of this study was to explore the biological effects of UVB irradiation and anti-photoaging effects of human umbilical cord mesenchymal stem cell-conditioned medium (hUC-MSC-CM) on HaCaT cells using multi-omics analysis with a novel cellular photoaging model.

Methods

A cellular model of photoaging was constructed by irradiating serum-starved HaCaT cells with 20 mJ/cm² UVB. Transcriptomics and proteomics analyses were used to explore the biological effects of UVB irradiation on photoaged HaCaT cells. Changes in cell proliferation, apoptosis, and migration, the cell cycle, and expression of senescence genes and proteins were measured to assess the protective effects of hUC-MSC-CM in the cellular photoaging model.

Results

The results of the multi-omics analysis revealed that UVB irradiation affected various biological functions of cells, including cell proliferation and the cell cycle, and induced a senescence-associated secretory phenotype. hUC-MSC-CM treatment reduced cell apoptosis, inhibited G1 phase arrest in the cell cycle, reduced the production of reactive oxygen species, and promoted cell motility. The qRT-PCR results indicated that MYC, IL-8, FGF-1, and EREG were key genes involved in the anti-photoaging effects of hUC-MSC-CM. The western blotting results demonstrated that C-FOS, C-JUN, TGFβ, p53, FGF-1, and cyclin A2 were key proteins involved in the anti-photoaging effects of hUC-MSC-CM.

Conclusion

Serum-starved HaCaT cells irradiated with 20 mJ/cm² UVB were used to generate an innovative cellular photoaging model, and hUC-MSC-CM demonstrates potential as an anti-photoaging treatment for skin.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13287-022-03137-y.

Biological effect of dysregulated LBX1 on adolescent idiopathic scoliosis through modulating muscle carbohydrate metabolism

BACKGROUND CONTEXT

Abnormal energy metabolism such as lower body weight and body mass index (BMI) and less fat mass is widely reported in patients with adolescent idiopathic scoliosis (AIS) and has been implicated in deformity development. However, the underlying mechanism is largely unclear. LBX1 is one of the promising AIS predisposing genes validated by multicenter studies.

PURPOSE

This study aimed to identify differentially expressed proteins (DEPs) relating to energy metabolism in AIS by using proteomic and metabolic analysis and to explore if the expression of these DEPs is associated with clinical parameters and modulated by LBX1.

STUDY DESIGN

This is a cross-sectional study using clinical data and biological samples followed by basic study using a cellular model.

PATIENT SAMPLE

Plasma samples were collected from Chinese girls with nonprogressive and progressive AIS (N=7 and 8, respectively) and age-matched healthy girls (N=50). Paraspinal muscle tissues were collected intraoperatively from concave and convex side of the apex of the major spinal curve in AIS (N=24) and either side from nonscoliosis patients (N=14).

OUTCOME MEASURES

Radiological Cobb angle and basic anthropometric data of recruited subjects were measured. The DEPs and metabolites were compared in plasma using proteomics and metabolomics technique. The relative expression of selected genes was measured in muscles.

METHODS

Plasma samples from AIS were collected at first clinical visit and were further divided into nonprogressive or progressive groups according to Cobb angle changes in 6-year follow-up. Age-matched healthy girls were recruited as control. High-performance liquid chromatography-mass spectrometry based proteomic analysis was carried out in three groups to identify DEPs and their annotated metabolic pathways. An independent cohort was used for validation by gas chromatography-mass spectrometry based metabolomic analysis. Paraspinal muscles were subjected to quantitative polymerase chain reaction (qPCR) followed by correlation analysis. Human skeletal muscle myoblast (HSMM) was used as the cellular model.

RESULTS

The likelihood of aberrant galactose metabolism and glycolysis was found to be associated with AIS curve progression as evidenced by the thirteen DEPs and seven related metabolites according to proteomic and metabolomic analysis. Some of the DEPs showed significantly altered expression in AIS concave and convex sides paraspinal muscles compared with those in nonscoliosis control. Four DEPs were found significantly and negatively correlated with LBX1 in AIS convex side paraspinal muscles. Overexpressing LBX1 in HSMM cells led to increased expression of three DEPs and decreased expression of three DEPs, respectively.

CONCLUSIONS

This is the first integrated proteomic and metabolomic analysis on AIS. Our findings show dysregulated galactose metabolism and glycolysis pathways in progressive group of AIS, suggesting the presence of abnormal energy metabolism at early stage of this disease, and their association with higher risk of progressing into more severe curvature. Evidence from ex vivo study with human muscle biopsies and in vitro study with human myoblast cells propose the possible effect of LBX1 on these two pathways in skeletal muscles. The present study provides new evidence of LBX1 function in AIS via modulating effect on the expression of energy metabolism related genes. This study might provide new insights into etiopathogenesis and development of novel treatment strategy targeting on abnormal body weight and BMI in patients with AIS. Additionally, the plasma proteomic and metabolomic studies suggested new candidates as biomarkers for establishing predictive model for AIS onset/progression.

Gut bacterial nutrient preferences quantified in vivo

Great progress has been made in understanding gut microbiomes' products and their effects on health and disease. Less attention, however, has been given to the inputs that gut bacteria consume. Here, we quantitatively examine inputs and outputs of the mouse gut microbiome, using isotope tracing. The main input to microbial carbohydrate fermentation is dietary fiber and to branched-chain fatty acids and aromatic metabolites is dietary protein. In addition, circulating host lactate, 3-hydroxybutyrate, and urea (but not glucose or amino acids) feed the gut microbiome. To determine the nutrient preferences across bacteria, we traced into genus-specific bacterial protein sequences. We found systematic differences in nutrient use: most genera in the phylum Firmicutes prefer dietary protein, Bacteroides dietary fiber, and Akkermansia circulating host lactate. Such preferences correlate with microbiome composition changes in response to dietary modifications. Thus, diet shapes the microbiome by promoting the growth of bacteria that preferentially use the ingested nutrients.

A proteomics approach reveals digestive and nutritional responses to food intake in anadromous Coilia nasus

The estuarine tapertail anchovy, Coilia nasus, is an anadromous fish that undertakes over a 600-km spawning migration along the Yangtze River of China. They generally cease feeding during this process, but we recently documented that a small proportion of them appear to feed. Research on proteomic responses is essential for understanding the phenomenon of C. nasus feeding. In this study, we used an iTRAQ-based proteomics approach to study the changes in protein expression in response to food intake in C. nasus following voluntary fasting. Coilia nasus in the feeding group (CSI) were fed shrimp or small fish, whereas those in the control group (CSN) were starved. We identified 3279 proteins in the gastric tissue/stomach, of which 279 were significantly differentially expressed. In all, 133 differentially expressed proteins (DEPs) were upregulated and 146 proteins were downregulated in CSI compared with those in CSN C. nasus. In addition to gastric acid secretion caused by gastric distention, a functional analysis suggested that a series of DEPs were involved mainly in the regulation of protein digestion (e.g., carboxypeptidase A1 and chymotrypsin A-like), immune response (e.g., lysozyme and alpha 2-macroglobulin), and nutrition metabolism (e.g., glyceraldehyde 3-phosphate dehydrogenase, glycogenin, long-chain acyl-CoA synthetase, and creatine kinase). Real-time PCR confirmed that the mRNA levels of the DEPs were similar those obtained using iTRAQ. These results indicate that the nutrients obtained through food were effectively utilized by C. nasus, thereby providing energy for swimming, gonadal maturation, primary metabolism, and an enhanced immune function to better resist pathogen interference. This research contributes to the elucidation of nutritional regulation mechanisms of C. nasus to better protect the wild population.

Transcriptomic and proteomic retinal pigment epithelium signatures of age-related macular degeneration

There are currently no treatments for geographic atrophy, the advanced form of age-related macular degeneration. Hence, innovative studies are needed to model this condition and prevent or delay its progression. Induced pluripotent stem cells generated from patients with geographic atrophy and healthy individuals were differentiated to retinal pigment epithelium. Integrating transcriptional profiles of 127,659 retinal pigment epithelium cells generated from 43 individuals with geographic atrophy and 36 controls with genotype data, we identify 445 expression quantitative trait loci in cis that are asssociated with disease status and specific to retinal pigment epithelium subpopulations. Transcriptomics and proteomics approaches identify molecular pathways significantly upregulated in geographic atrophy, including in mitochondrial functions, metabolic pathways and extracellular cellular matrix reorganization. Five significant protein quantitative trait loci that regulate protein expression in the retinal pigment epithelium and in geographic atrophy are identified - two of which share variants with cis- expression quantitative trait loci, including proteins involved in mitochondrial biology and neurodegeneration. Investigation of mitochondrial metabolism confirms mitochondrial dysfunction as a core constitutive difference of the retinal pigment epithelium from patients with geographic atrophy. This study uncovers important differences in retinal pigment epithelium homeostasis associated with geographic atrophy.

Quantitative proteome remodeling characterization of two human reference pluripotent stem cell lines during neurogenesis and cardiomyogenesis

Human pluripotent stem cells (PSCs) have become popular tools within the research community to study developmental and model diseases. While many induced-PSCs (iPSCs) from various genetic background sources are currently available, scientific advancement has been hampered by the considerable phenotypic variations observed between different iPSC lines. A recent collaborative effort selected a novel iPSC line to address this and encourage the adoption of a standardized iPSC line termed KOLF2.1J. Here, leveraging the multiplexing power of isobaric labeling, we systematically investigate, at the 10k proteome level, the relative protein abundance profiles of the KOLF2.1J reference iPSC line upon two distinct cell state differentiation trajectories. In addition, we side-by-side systematically compare this line with the H9 line, an established embryonically derived PSC line that we previously characterized. We noticed differences in the basal proteome of the two cell lines and highlighted the differentially expressed proteins. While the difference between the cell line's proteome subsisted upon differentiation, the global proteome remodeling trajectory was highly similar during the tested differentiation routes. We thus conclude that the KOLF2.1J line performs well at the proteome level upon the neuro and cardiomyogenesis differentiation protocol used. We believe this dataset will serve as a resource of value for the research community.

Quantitative proteomics analysis to assess protein expression levels in the ovaries of pubescent goats

Background

Changes in the abundance of ovarian proteins play a key role in the regulation of reproduction. However, to date, no studies have investigated such changes in pubescent goats. Herein we applied isobaric tags for relative and absolute quantitation (iTRAQ) and liquid chromatography–tandem mass spectrometry to analyze the expression levels of ovarian proteins in pre-pubertal (n = 3) and pubertal (n = 3) goats.

Results

Overall, 7,550 proteins were recognized; 301 (176 up- and 125 downregulated) were identified as differentially abundant proteins (DAPs). Five DAPs were randomly selected for expression level validation by Western blotting; the results of Western blotting and iTRAQ analysis were consistent. Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis indicated that DAPs were enriched in olfactory transduction, glutathione metabolism, and calcium signaling pathways. Besides, gene ontology functional enrichment analysis revealed that several DAPs enriched in biological processes were associated with cellular process, biological regulation, metabolic process, and response to stimulus. Protein–protein interaction network showed that proteins interacting with CDK1, HSPA1A, and UCK2 were the most abundant.

Conclusions

We identified 301 DAPs, which were enriched in olfactory transduction, glutathione metabolism, and calcium signaling pathways, suggesting the involvement of these processes in the onset of puberty. Further studies are warranted to more comprehensively explore the function of the identified DAPs and aforementioned signaling pathways to gain novel, deeper insights into the mechanisms underlying the onset of puberty.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-022-08699-y.

dia-PASEF data analysis using FragPipe and DIA-NN for deep proteomics of low sample amounts

The dia-PASEF technology uses ion mobility separation to reduce signal interferences and increase sensitivity in proteomic experiments. Here we present a two-dimensional peak-picking algorithm and generation of optimized spectral libraries, as well as take advantage of neural network-based processing of dia-PASEF data. Our computational platform boosts proteomic depth by up to 83% compared to previous work, and is specifically beneficial for fast proteomic experiments and those with low sample amounts. It quantifies over 5300 proteins in single injections recorded at 200 samples per day throughput using Evosep One chromatography system on a timsTOF Pro mass spectrometer and almost 9000 proteins in single injections recorded with a 93-min nanoflow gradient on timsTOF Pro 2, from 200 ng of HeLa peptides. A user-friendly implementation is provided through the incorporation of the algorithms in the DIA-NN software and by the FragPipe workflow for spectral library generation.

Proteomic repository data submission, dissemination, and reuse: key messages

INTRODUCTION

The creation of ProteomeXchange data workflows in 2012 transformed the field of proteomics, consisting of the standardization of data submission and dissemination and enabling the widespread reanalysis of public MS proteomics data worldwide. ProteomeXchange has triggered a growing trend toward public dissemination of proteomics data, facilitating the assessment, reuse, comparative analyses, and extraction of new findings from public datasets. By 2022, the consortium is integrated by PRIDE, PeptideAtlas, MassIVE, jPOST, iProX, and Panorama Public.

AREAS COVERED

Here, we review and discuss the current ecosystem of resources, guidelines, and file formats for proteomics data dissemination and reanalysis. Special attention is drawn to new exciting quantitative and post-translational modification-oriented resources. The challenges and future directions on data depositions including the lack of metadata and cloud-based and high-performance software solutions for fast and reproducible reanalysis of the available data are discussed.

EXPERT OPINION

The success of ProteomeXchange and the amount of proteomics data available in the public domain have triggered the creation and/or growth of other protein knowledgebase resources. Data reuse is a leading, active, and evolving field; supporting the creation of new formats, tools, and workflows to rediscover and reshape the public proteomics data.

SubCellBarCode: integrated workflow for robust spatial proteomics by mass spectrometry

The molecular functions of a protein are defined by its inherent properties in relation to its environment and interaction network. Within a cell, this environment and network are defined by the subcellular location of the protein. Consequently, it is crucial to know the localization of a protein to fully understand its functions. Recently, we have developed a mass spectrometry- (MS) and bioinformatics-based pipeline to generate a proteome-wide resource for protein subcellular localization across multiple human cancer cell lines ( www.subcellbarcode.org ). Here, we present a detailed wet-lab protocol spanning from subcellular fractionation to MS-sample preparation and analysis. A key feature of this protocol is that it includes all generated cell fractions without discarding any material during the fractionation process. We also describe the subsequent quantitative MS-data analysis, machine learning-based classification, differential localization analysis and visualization of the output. For broad applicability, we evaluated the pipeline by using MS data generated by two different peptide pre-fractionation approaches, namely high-resolution isoelectric focusing and high-pH reverse-phase fractionation, as well as direct analysis without pre-fractionation by using long-gradient liquid chromatography-MS. Moreover, an R package covering the dry-lab part of the method was developed and made available through Bioconductor. The method is straightforward and robust, and the entire protocol, from cell harvest to classification output, can be performed within 1-2 weeks. The protocol enables accurate classification of proteins to 15 compartments and 4 neighborhoods, visualization of the output data and differential localization analysis including treatment-induced protein relocalization, condition-dependent localization or cell type-specific localization. The SubCellBarCode package is freely available at https://bioconductor.org/packages/devel/bioc/html/SubCellBarCode.html .

Mass spectrometry-based draft of the mouse proteome

The laboratory mouse ranks among the most important experimental systems for biomedical research and molecular reference maps of such models are essential informational tools. Here, we present a quantitative draft of the mouse proteome and phosphoproteome constructed from 41 healthy tissues and several lines of analyses exemplify which insights can be gleaned from the data. For instance, tissue- and cell-type resolved profiles provide protein evidence for the expression of 17,000 genes, thousands of isoforms and 50,000 phosphorylation sites in vivo. Proteogenomic comparison of mouse, human and Arabidopsis reveal common and distinct mechanisms of gene expression regulation and, despite many similarities, numerous differentially abundant orthologs that likely serve species-specific functions. We leverage the mouse proteome by integrating phenotypic drug (n > 400) and radiation response data with the proteomes of 66 pancreatic ductal adenocarcinoma (PDAC) cell lines to reveal molecular markers for sensitivity and resistance. This unique atlas complements other molecular resources for the mouse and can be explored online via ProteomicsDB and PACiFIC.

DeepSCP: utilizing deep learning to boost single-cell proteome coverage

Multiplexed single-cell proteomes (SCPs) quantification by mass spectrometry greatly improves the SCP coverage. However, it still suffers from a low number of protein identifications and there is much room to boost proteins identification by computational methods. In this study, we present a novel framework DeepSCP, utilizing deep learning to boost SCP coverage. DeepSCP constructs a series of features of peptide-spectrum matches (PSMs) by predicting the retention time based on the multiple SCP sample sets and fragment ion intensities based on deep learning, and predicts PSM labels with an optimized-ensemble learning model. Evaluation of DeepSCP on public and in-house SCP datasets showed superior performances compared with other state-of-the-art methods. DeepSCP identified more confident peptides and proteins by controlling q-value at 0.01 using target–decoy competition method. As a convenient and low-cost computing framework, DeepSCP will help boost single-cell proteome identification and facilitate the future development and application of single-cell proteomics.

Multi-omics analysis reveals gut microbiota-induced intramuscular fat deposition via regulating expression of lipogenesis-associated genes

The gut microbiome has great effects on the digestion, absorption, and metabolism of lipids. However, the microbiota composition that can alter the fat deposition and the meat quality of pigs remains unclear. Here, we used Laiwu (LW) pigs (a native Chinese breed with higher intramuscular fat) compared with commercial crossbreed Duroc × (Landrace × Yorkshire) (DLY) pigs to investigate the effects of microbiota on meat quality, especially in intramuscular fat content. A total of 32 DLY piglets were randomly allotted to 4 groups and transplanted with fecal microbiota from healthy LW pigs. The results indicated that the high dose of fecal microbiota transplantation (HFMT) selectively enhanced fat deposition in longissimus dorsi (P < 0.05) but decreased backfat thickness (P < 0.05) compared with control group. HFMT significantly altered meat color and increased feed conversation ratio (P < 0.05). Furthermore, the multi-omics analysis revealed that Bacteroides uniformis, Sphaerochaeta globosa, Hydrogenoanaerobacterium saccharovorans, and Pyramidobacter piscolens are the core species which can regulate lipid deposition. A total of 140 male SPF C57BL/6j mice were randomly allotted into 7 groups and administrated with these 4 microbes alone or consortium to validate the relationships between microbiota and lipid deposition. Inoculating the bacterial consortium into mice increased intramuscular fat content (P < 0.05) compared with control mice. Increased expressions of lipogenesis-associated genes including cluster of differentiation 36 (Cd36), diacylglycerol O-acyltransferase 2 (Dgat2), and fatty acid synthase (FASN) were observed in skeletal muscle in the mice with mixed bacteria compared with control mice. Together, our results suggest that the gut microbiota may play an important role in regulating the lipid deposition in the muscle of pigs and mice.

Proteome Analysis of Pancreatic Tumors Implicates Extracellular Matrix in Patient Outcome

Pancreatic cancer remains a disease with unmet clinical needs and inadequate diagnostic, prognostic, and predictive biomarkers. In-depth characterization of the disease proteome is limited. This study thus aims to define and describe protein networks underlying pancreatic cancer and identify protein centric subtypes with clinical relevance. Mass spectrometry-based proteomics was used to identify and quantify the proteome in tumor tissue, tumor-adjacent tissue, and patient-derived xenografts (PDX)-derived cell lines from patients with pancreatic cancer, and tissues from patients with chronic pancreatitis. We identified, quantified, and characterized 11,634 proteins from 72 pancreatic tissue samples. Network focused analysis of the proteomics data led to identification of a tumor epithelium-specific module and an extracellular matrix (ECM)-associated module that discriminated pancreatic tumor tissue from both tumor adjacent tissue and pancreatitis tissue. On the basis of the ECM module, we defined an ECM-high and an ECM-low subgroup, where the ECM-high subgroup was associated with poor prognosis (median survival months: 15.3 vs. 22.9 months; log-rank test, P = 0.02). The ECM-high tumors were characterized by elevated epithelial-mesenchymal transition and glycolytic activities, and low oxidative phosphorylation, E2F, and DNA repair pathway activities. This study offers novel insights into the protein network underlying pancreatic cancer opening up for proteome precision medicine development.

Significance

Pancreatic cancer lacks reliable biomarkers for prognostication and treatment of patients. We analyzed the proteome of pancreatic tumors, nonmalignant tissues of the pancreas and PDX-derived cell lines, and identified proteins that discriminate between patients with good and poor survival. The proteomics data also unraveled potential novel drug targets.

Protective Signature of IFNγ-Stimulated Microglia Relies on miR-124-3p Regulation From the Secretome Released by Mutant APP Swedish Neuronal Cells

Microglia-associated inflammation and miRNA dysregulation are key players in Alzheimer’s disease (AD) pathophysiology. Previously, we showed miR-124 upregulation in APP Swedish SH-SY5Y (SWE) and PSEN1 iPSC-derived neurons and its propagation by the secretome (soluble and exosomal fractions). After modulation with miR-124 mimic/inhibitor, we identified common responsive mechanisms between such models. We also reported miR-124 colocalization with microglia in AD patient hippocampi. Herein, we determined how miR-124 modulation in SWE cells influences microglia polarized subtypes in the context of inflammation. We used a coculture system without cell-to-cell contact formed by miR-124 modulated SWE cells and human CHME3 microglia stimulated with interferon-gamma (IFNγ-MG), in which we assessed their adopted gene/miRNA profile and proteomic signature. The increase of miR-124 in SWE cells/secretome (soluble and exosomal) was mimicked in IFNγ-MG. Treatment of SWE cells with the miR-124 inhibitor led to RAGE overexpression and loss of neuronal viability, while the mimic caused RAGE/HMGB1 downregulation and prevented mitochondria membrane potential loss. When accessing the paracrine effects on microglia, SWE miR-124 inhibitor favored their IFNγ-induced inflammatory signature (upregulated RAGE/HMGB1/iNOS/IL-1β; downregulated IL-10/ARG-1), while the mimic reduced microglia activation (downregulated TNF-α/iNOS) and deactivated extracellular MMP-2/MMP-9 levels. Microglia proteomics identified 113 responsive proteins to SWE miR-124 levels, including a subgroup of 17 proteins involved in immune function/inflammation and/or miR-124 targets. A total of 72 proteins were downregulated (e.g., MAP2K6) and 21 upregulated (e.g., PAWR) by the mimic, while the inhibitor also upregulated 21 proteins and downregulated 17 (e.g., TGFB1, PAWR, and EFEMP1). Other targets were associated with neurodevelopmental mechanisms, synaptic function, and vesicular trafficking. To examine the source of miR-124 variations in microglia, we silenced the RNase III endonuclease Dicer1 to block miRNA canonical biogenesis. Despite this suppression, the coculture with SWE cells/exosomes still raised microglial miR-124 levels, evidencing miR-124 transfer from neurons to microglia. This study is pioneer in elucidating that neuronal miR-124 reshapes microglia plasticity and in revealing the relevance of neuronal survival in mechanisms underlying inflammation in AD-associated neurodegeneration. These novel insights pave the way for the application of miRNA-based neuropharmacological strategies in AD whenever miRNA dysregulated levels are identified during patient stratification.

Rgs16 promotes antitumor CD8+ T cell exhaustion

T cells become functionally exhausted in tumors, limiting T cell-based immunotherapies. Although several transcription factors regulating the exhausted T (T_ex) cell differentiation are known, comparatively little is known about the regulators of T_ex cell survival. Here, we reported that the regulator of G protein signaling 16 (Rgs-16) suppressed T_ex cell survival in tumors. By performing lineage tracing using reporter mice in which mCherry marked Rgs16-expressing cells, we identified that Rgs16⁺CD8⁺ tumor-infiltrating lymphocytes (TILs) were terminally differentiated, expressed low levels of T cell factor 1 (Tcf1), and underwent apoptosis as early as 6 days after the onset of Rgs16 expression. Rgs16 deficiency inhibited CD8⁺ T cell apoptosis and promoted antitumor effector functions of CD8⁺ T cells. Furthermore, Rgs16 deficiency synergized with programmed cell death protein 1 (PD-1) blockade to enhance antitumor CD8⁺ T cell responses. Proteomics revealed that Rgs16 interacted with the scaffold protein IQGAP1, suppressed the recruitment of Ras and B-Raf, and inhibited Erk1 activation. Rgs16 deficiency enhanced antitumor CD8⁺ TIL survival in an Erk1-dependent manner. Loss of function of Erk1 decreased antitumor functions of Rgs16-deficient CD8⁺ T cells. RGS16 mRNA expression levels in CD8⁺ TILs of patients with melanoma negatively correlated with genes associated with T cell stemness, such as SELL, TCF7, and IL7R, and predicted low responses to PD-1 blockade. This study uncovers Rgs16 as an inhibitor of T_ex cell survival in tumors and has implications for improving T cell-based immunotherapies.

Activation of MT1/MT2 to Protect Testes and Leydig Cells against Cisplatin-Induced Oxidative Stress through the SIRT1/Nrf2 Signaling Pathway

There is growing concern that chemotherapy drugs can damage Leydig cells and inhibit the production of testosterone. Increasing evidence shows that melatonin benefits the reproductive process. This study mainly explores the protective effect and possible molecular mechanism of melatonin regarding cisplatin-induced oxidative stress in testicular tissue and Leydig cells. We found that there were only Leydig and Sertoli cells in the testes of gastrointestinal tumor patients with azoospermia caused by platinum chemotherapeutic drugs. Melatonin (Mel) receptor 1/melatonin receptor 2 (MT1/MT2) was mainly expressed in human and mouse Leydig cells of the testes. We also observed that the melatonin level in the peripheral blood decreased and oxidative stress occurred in mice treated with cisplatin or gastrointestinal tumor patients treated with platinum-based chemotherapeutic drugs. iTRAQ proteomics showed that SIRT1/Nrf2 signaling and MT1 proteins were downregulated in cisplatin-treated mouse testes. The STRING database predicted that MT1 might be able to regulate the SIRT1/Nrf2 signaling pathway. Melatonin reduced oxidative stress and upregulated SIRT1/Nrf2 signaling in cisplatin-treated mouse testes and Leydig cells. Most importantly, after inhibiting MT1/MT2, melatonin could not upregulate SIRT1/Nrf2 signaling in cisplatin-treated Leydig cells. The MT1/MT2 inhibitor aggravated the cisplatin-induced downregulation of SIRT1/Nrf2 signaling and increased the apoptosis of Leydig cells. We believe that melatonin stimulates SIRT1/Nrf2 signaling by activating MT1/MT2 to prevent the cisplatin-induced apoptosis of Leydig cells.

Predicting fragment intensities and retention time of iTRAQ- and TMTPro-labeled peptides with Prosit-TMT

Isobaric labeling increases the throughput of proteomics by enabling the parallel identification and quantification of peptides and proteins. Over the past decades, a variety of isobaric tags have been developed allowing the multiplexed analysis of up to 18 samples. However, experiments utilizing such tags often exhibit reduced identification rates and thus show decreased analytical depth. Re-scoring has been shown to rescue otherwise missed identifications but was not yet systematically applied on isobarically labeled data. Because iTRAQ 4/8-plex and the recently released TMTpro 16/18-plex share similar characteristics with TMT 6/10/11-plex, we hypothesized that Prosit-TMT, trained exclusively on 6/10/11-plex labeled peptides, may be applicable to these isobaric labeling strategies as well. To investigate this, we re-analyzed nine publicly available datasets covering iTRAQ and TMTpro labeling for samples with human and mouse origin. We highlight that Prosit-TMT shows remarkably good performance when comparing experimentally acquired and predicted fragmentation spectra (R of 0.84 - 0.9) and retention times (ΔRT95% of 3 - 10% gradient time) of peptides. Furthermore, re-scoring substantially increases the number of confidently identified spectra, peptides, and proteins. This article is protected by copyright. All rights reserved.

Prosit-TMT: Deep Learning Boosts Identification of TMT-Labeled Peptides

The prediction of fragment ion intensities and retention time of peptides has gained significant attention over the past few years. However, the progress shown in the accurate prediction of such properties focused primarily on unlabeled peptides. Tandem mass tags (TMT) are chemical peptide labels that are coupled to free amine groups usually after protein digestion to enable the multiplexed analysis of multiple samples in bottom-up mass spectrometry. It is a standard workflow in proteomics ranging from single-cell to high-throughput proteomics. Particularly for TMT, increasing the number of confidently identified spectra is highly desirable as it provides identification and quantification information with every spectrum. Here, we report on the generation of an extensive resource of synthetic TMT-labeled peptides as part of the ProteomeTools project and present the extension of the deep learning model Prosit to accurately predict the retention time and fragment ion intensities of TMT-labeled peptides with high accuracy. Prosit-TMT supports CID and HCD fragmentation and ion trap and Orbitrap mass analyzers in a single model. Reanalysis of published TMT data sets show that this single model extracts substantial additional information. Applying Prosit-TMT, we discovered that the expression of many proteins in human breast milk follows a distinct daily cycle which may prime the newborn for nutritional or environmental cues.

Comparative proteomic analysis between mature and germinating seeds in Paris polyphylla var. yunnanensis

The long dormancy period of Paris polyphylla var. yunnanensis seeds affects the supply of this scarce plant, which is used as an important traditional Chinese medicine. Mature seeds with a globular embryo and germinating seeds with developed embryo were used to explore the mechanisms of seed germination in this species. The protein profiles between the mature and germinating seeds were compared using the isobaric tags for relative and absolute quantification (iTRAQ) approach. Of the 4,488 proteins identified, a total of 1,305 differentially expressed proteins (DEPs) were detected. A Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis of these DEPs indicated that metabolic pathways and the biosynthesis of secondary metabolites were the two top pathways. Additionally, phytohormone quantification shows that the abscisic acid (ABA) level significantly decreased, whereas the GA₃ level dramatically increased among nine endogenous gibberellins (GAs), resulting in a significant increase of the GA₃/ABA ratio in germinating seeds. The biosynthesis pathways of carotenoid as a precursor for ABA production and GA were further analyzed, and showed that proteinic expressions of the candidate genes in the two pathways did not correlate with the transcriptional abundances. However, 9-cis-epoxycarotenoid dioxygenase (NCED), a rate limited enzyme for ABA biosynthesis, was significantly decreased in mRNA levels in germinating seeds. By contrast, gibberellin 20-oxidase (GA20ox), a key enzyme GA biosynthesis, exhibited the major increase in one copy and a slight decrease in three others at the protentional level in germinating seeds. Gibberellin 2-oxidase (GA2ox), an inactivate enzyme in bioactive GAs, has the tendency to down-regulate in mRNA or at the proteinic level in germinating seeds. Altogether, these results suggested that the analyses of ABA and GA levels, the GA₃/ABA ratio, and the expressional patterns of their regulatory genes may provide a novel mechanistic understanding of how phytohormones regulate seed germination in P. polyphylla var. yunnanensis.

The Transcription Regulator YgeK Affects Biofilm Formation and Environmental Stress Resistance in Avian Pathogenic Escherichia coli

Simple Summary

Avian pathogenic Escherichia coli (APEC) is the pathogen responsible for colibacillosis in poultry. Transcriptional regulator ygeK has been shown to decrease APEC’s flagellar formation ability, bacterial motility ability, serum sensitivity, and adhesion ability. However, we did not study the effects of ygeK on biofilm formation and environmental stress resistance in APEC. In this study, we investigated ygeK in APEC biofilm formation and bacterial resistance to different environmental stresses. We also analyzed the multi-level regulation of ygeK in APEC and investigated associations between differentially expressed proteins and key ygeK targets. This work provides a basis for further analysis of APEC pathogenesis mechanisms.

Abstract

Avian pathogenic Escherichia coli (APEC) is one of the most common pathogens in poultry and a potential gene source of human extraintestinal pathogenic E. coli (ExPEC), leading to serious economic losses in the poultry industry and public health concerns. Exploring the pathogenic mechanisms underpinning APEC and the identification of new targets for disease prevention and treatment are warranted. YgeK is a transcriptional regulator in APEC and is localized to the type III secretion system 2 of E. coli. In our previous work, the transcription factor ygeK significantly affected APEC flagella formation, bacterial motility, serum sensitivity, adhesion, and virulence. To further explore ygeK functions, we evaluated its influence on APEC biofilm formation and resistance to environmental stress. Our results showed that ygeK inactivation decreased biofilm formation and reduced bacterial resistance to environmental stresses, including acid and oxidative stress. In addition, the multi-level regulation of ygeK in APEC was analyzed using proteomics, and associations between differentially expressed proteins and the key targets of ygeK were investigated. Overall, we identified ygeK’s new function in APEC. These have led us to better understand the transcriptional regulatory ygeK and provide new clues about the pathogenicity of APEC.