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

Benchmarking mass spectrometry based proteomics algorithms using a simulated database

Protein sequencing algorithms process data from a variety of instruments that has been generated under diverse experimental conditions. Currently there is no way to predict the accuracy of an algorithm for a given data set. Most of the published algorithms and associated software has been evaluated on limited number of experimental data sets. However, these performance evaluations do not cover the complete search space the algorithmand the software might encounter in real-world. To this end, we present a database of simulated spectra that can be used to benchmark any spectra to peptide search engine. We demonstrate the usability of this database by bench marking two popular peptide sequencing engines. We show wide variation in the accuracy of peptide deductions and a complete quality profile of a given algorithm can be useful for practitioners and algorithm developers. All benchmarking data is available at https://users.cs.fiu.edu/~fsaeed/Benchmark.html.

Structure of the catalytic core of the Integrator complex

The Integrator is a specialized 3' end-processing complex involved in cleavage and transcription termination of a subset of nascent RNA polymerase II transcripts, including small nuclear RNAs (snRNAs). We provide evidence of the modular nature of the Integrator complex by biochemically characterizing its two subcomplexes, INTS5/8 and INTS10/13/14. Using cryoelectron microscopy (cryo-EM), we determined a 3.5-Å-resolution structure of the INTS4/9/11 ternary complex, which constitutes Integrator's catalytic core. Our structure reveals the spatial organization of the catalytic nuclease INTS11, bound to its catalytically impaired homolog INTS9 via several interdependent interfaces. INTS4, a helical repeat protein, plays a key role in stabilizing nuclease domains and other components. In this assembly, all three proteins form a composite electropositive groove, suggesting a putative RNA binding path within the complex. Comparison with other 3' end-processing machineries points to distinct features and a unique architecture of the Integrator's catalytic module.

Integrative proteomics identifies thousands of distinct, multi-epitope, and high-affinity nanobodies

The antibody immune response is essential for the survival of mammals. However, we still lack a systematic understanding of the antibody repertoire. Here, we developed a proteomic strategy to survey, at an unprecedented scale, the landscape of antigen-engaged, circulating camelid heavy-chain antibodies, whose minimal binding fragments are called VHH antibodies or nanobodies. The sensitivity and robustness of this approach were validated with three antigens spanning orders of magnitude in immune responses; thousands of distinct, high-affinity nanobody families were reliably identified and quantified. Using high-throughput structural modeling, cross-linking mass spectrometry, mutagenesis, and deep learning, we mapped and analyzed the epitopes of >100,000 antigen-nanobody complexes. Our results revealed a surprising diversity of ultrahigh-affinity camelid nanobodies for specific antigen binding on various dominant epitope clusters. Nanobodies utilize both shape and charge complementarity to enable highly selective antigen binding. Interestingly, we found that nanobody-antigen binding can mimic conserved intracellular protein-protein interactions. A record of this paper's Transparent Peer Review process is included in the Supplemental information.

Proteomics analysis as an approach to understand the formation of pale, soft, and exudative (PSE) pork

We investigated ten pale, soft, and exudative (PSE), and ten normal meat samples from pig carcasses. The meat quality at 0, 5, 12, and 24 h post-mortem and the key enzyme activities at 0 and 24 h post-mortem were determined. We selected three PSE and three normal samples for proteomics analysis at 0 h and 24 h post-mortem. No remarkable differences in pyruvate kinase (PK) and lactate dehydrogenase (LDH) activity were observed between samples at 0 h post-mortem; however, creatine kinase (CK) activity was significantly higher in PSE meat. Hexokinase (HK) activity in PSE samples was higher than that in normal samples at 24 h post-mortem. Bioinformatics analysis of the proteome showed that PSE was related to glycolysis, TCA cycle, oxidative phosphorylation, muscle tissue structure, signal transduction, and molecular chaperones. This research found that proteins such as troponin T slow skeletal muscle isoform X, GADPH, L-lactate dehydrogenase A chain, and gamma-enolase isoform X1 might be responsible for PSE.

Boosting MS1-only Proteomics with Machine Learning Allows 2000 Protein Identifications in Single-Shot Human Proteome Analysis Using 5 min HPLC Gradient

Proteome-wide analyses rely on tandem mass spectrometry and the extensive separation of proteolytic mixtures. This imposes considerable instrumental time consumption, which is one of the main obstacles in the broader acceptance of proteomics in biomedical and clinical research. Recently, we presented a fast proteomic method termed DirectMS1 based on ultrashort LC gradients as well as MS1-only mass spectra acquisition and data processing. The method allows significant reduction of the proteome-wide analysis time to a few minutes at the depth of quantitative proteome coverage of 1000 proteins at 1% false discovery rate (FDR). In this work, to further increase the capabilities of the DirectMS1 method, we explored the opportunities presented by the recent progress in the machine-learning area and applied the LightGBM decision tree boosting algorithm to the scoring of peptide feature matches when processing MS1 spectra. Furthermore, we integrated the peptide feature identification algorithm of DirectMS1 with the recently introduced peptide retention time prediction utility, DeepLC. Additional approaches to improve the performance of the DirectMS1 method are discussed and demonstrated, such as using FAIMS for gas-phase ion separation. As a result of all improvements to DirectMS1, we succeeded in identifying more than 2000 proteins at 1% FDR from the HeLa cell line in a 5 min gradient LC-FAIMS/MS1 analysis. The data sets generated and analyzed during the current study have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the data set identifier PXD023977.

Proteogenomic insights into the biology and treatment of HPV-negative head and neck squamous cell carcinoma

We present a proteogenomic study of 108 human papilloma virus (HPV)-negative head and neck squamous cell carcinomas (HNSCCs). Proteomic analysis systematically catalogs HNSCC-associated proteins and phosphosites, prioritizes copy number drivers, and highlights an oncogenic role for RNA processing genes. Proteomic investigation of mutual exclusivity between FAT1 truncating mutations and 11q13.3 amplifications reveals dysregulated actin dynamics as a common functional consequence. Phosphoproteomics characterizes two modes of EGFR activation, suggesting a new strategy to stratify HNSCCs based on EGFR ligand abundance for effective treatment with inhibitory EGFR monoclonal antibodies. Widespread deletion of immune modulatory genes accounts for low immune infiltration in immune-cold tumors, whereas concordant upregulation of multiple immune checkpoint proteins may underlie resistance to anti-programmed cell death protein 1 monotherapy in immune-hot tumors. Multi-omic analysis identifies three molecular subtypes with high potential for treatment with CDK inhibitors, anti-EGFR antibody therapy, and immunotherapy, respectively. Altogether, proteogenomics provides a systematic framework to inform HNSCC biology and treatment.

Integrated RNA-seq and Proteomic Studies Reveal Resource Reallocation towards Energy Metabolism and Defense in Skeletonema marinoi in Response to CO2 Increase

Rising atmospheric CO2 concentrations are causing ocean acidification (OA) with significant consequences for marine organisms. Because CO2 is essential for photosynthesis, the effect of elevated CO2 on phytoplankton is more complex and the mechanism is poorly understood. Here we applied RNA-seq and iTRAQ proteomics to investigate the impacts of CO2 increase (from ∼400 to 1000 ppm) on the temperate coastal marine diatom Skeletonema marinoi We identified 32,389 differentially expressed genes (DEGs) and 1,826 differentially expressed proteins (DEPs) from elevated CO2 conditions, accounting for 48.5% of total genes and 25.9% of total proteins we detected, respectively. Elevated pCO2 significantly inhibited the growth of S marinoi, and the 'omic' data suggested that this might be due to compromised photosynthesis in the chloroplast and raised mitochondrial energy metabolism. Furthermore, many genes/proteins associated with nitrogen metabolism, transcriptional regulation, and translational regulation were markedly up-regulated, suggesting enhanced protein synthesis. In addition, S marinoi exhibited higher capacity of ROS production and resistance to oxidative stress. Overall, elevated pCO2 seems to repress photosynthesis and growth of S marinoi, and through massive gene expression reconfiguration induce cells to increase investment in protein synthesis, energy metabolism and antioxidative stress defense, likely to maintain pH homeostasis and population survival. This survival strategy may deprive this usually dominant diatom in temperate coastal waters of its competitive advantages in acidified environments.Importance Rising atmospheric CO2 concentrations are causing ocean acidification with significant consequences for marine organisms. Chain-forming centric diatoms of Skeletonema is one of the most successful groups of eukaryotic primary producers with widespread geographic distribution. Among the recognized 28 species, S. marinoi can be a useful model for investigating the ecological, genetic, physiological, and biochemical characteristics of diatoms in temperate coastal regions. In this study, we found that the elevated pCO2 seems to repress photosynthesis and growth of S. marinoi, and through massive gene expression reconfiguration induce cells to increase investment in protein synthesis, energy metabolism and antioxidative stress defense, likely to maintain pH homeostasis and population survival. This survival strategy may deprive this usually dominant diatom in temperate coastal waters of its competitive advantages in acidified environments.

Reflections on the HUPO Human Proteome Project, the Flagship Project of the Human Proteome Organization, at 10 Years

We celebrate the 10th anniversary of the launch of the HUPO Human Proteome Project (HPP) and its major milestone of confident detection of at least one protein from each of 90% of the predicted protein-coding genes, based on the output of the entire proteomics community. The Human Genome Project reached a similar decadal milestone 20 years ago. The HPP has engaged proteomics teams around the world, strongly influenced data-sharing, enhanced quality assurance, and issued stringent guidelines for claims of detecting previously "missing proteins." This invited perspective complements papers on "A High-Stringency Blueprint of the Human Proteome" and "The Human Proteome Reaches a Major Milestone" in special issues of Nature Communications and Journal of Proteome Research, respectively, released in conjunction with the October 2020 virtual HUPO Congress and its celebration of the 10th anniversary of the HUPO HPP.

Expanding the Cross-Link Coverage of a Carboxyl-Group Specific Chemical Cross-Linking Strategy for Structural Proteomics Applications

Carboxyl-group specific chemical cross-linking is gaining an increased interest as a structural mass spectrometry/structural proteomics technique that is complementary to the more commonly used amine-specific chemistry using succinimide esters. One of these protocols uses a combination of dihydrazide linkers and the coupling reagent DMTMM [4-(4,6-dimethoxy-1,3,5-triazin-2-yl)-4-methylmorpholinium] chloride, which allows performing the reaction at neutral pH. The reaction yields two types of products, carboxyl-carboxyl cross-links that incorporate the dihydrazide linker and zero-length carboxyl-amine cross-links induced by DMTMM alone. Until now, it has not been systematically investigated how the balance between the two products is affected by experimental conditions. Here, we studied the role of the ratios of the two reagents (using pimelic dihydrazide and DMTMM) and demonstrate that the concentration of the two reagents can be systematically adjusted to favor one reaction product over the other. Using a set of five model proteins, we observed that the number of identified cross-linked peptides could be more than doubled by a combination of three different reaction conditions. We also applied this strategy to the bovine 20S proteasome and the Escherichia coli 70S ribosome, again demonstrating complementarity and increased cross-link coverage.

Discovery and proteomics analysis of effective compounds in Valeriana jatamansi jones for the treatment of anxiety

ETHNOPHARMACOLOGICAL RELEVANCE

Zhizhu Xiang (ZZX for short) is the root and rhizome of Valeriana jatamansi Jones, which is a Traditional Chinese Medicine (TCM) used to treat various mood disorders for more than 2000 years, especially anxiety. However, there have been few investigations to clarify the compounds in ZZX for the treatment of anxiety.

AIM OF THE STUDY

Our previous study has identified five anti-anxiety components, including hesperidin, isochlorogenic acid A, isochlorogenic acid B and isochlorogenic acid C and chlorogenic acid, from extract of ZZX. In order to find the optimal combination and the underlying mechanism of these five components in the treatment of anxiety disorder, researches were designed based on uniform design method and proteomic technology.

MATERIALS AND METHODS

The samples with different proportion and content of the five active components were arranged by uniform design method. Then a mathematical model was formulated using partial least square method and stepwise regression analysis. Moreover, the empty bottle stress-induced anxiety rat model was established, and the anti-anxiety effect was recorded by the unconditioned reflex elevated maze test and the open field test. In addition, the isobaric tags for relative and absolute quantitation (iTRAQ) technique, along with the multidimensional liquid chromatography and high-resolution mass spectrometry were applied in proteomic study. At last, the result of proteomic analysis was further confirmed by Western blot.

RESULTS

The optimal combination of the components from the extract of ZZX was 1.153 mg/kg hesperidin, 2.197 mg/kg Isochlorogenic acid A, 0.699 mg/kg Isochlorogenic acid B and 1.249 mg/kg Chlorogenic acid. Total 6818 proteins were identified using proteomic analysis and 80 differentially expressed proteins were used for further bioinformatic analysis. These proteins were involved in the neuroactive ligand-receptor interaction, protein digestion and absorption, cholesterol metabolism, Chagas disease, and AGE/RAGE signaling pathway.

CONCLUSIONS

The composition and proportion of anti-anxiety components in extract of ZZX was disclosed, and there was an anti-anxiety effect for the combined components of flavonoids and phenolic acids. Through proteomic analysis and Western blot, it was found that the effective components of extract of ZZX can exert synergistic anti-anxiety effects via the regulation of multi-signaling pathways. These findings could provide a preliminary research basis for the development of new low-toxic, efficient, stable and controllable anti-anxiety drugs.

Single-cell proteomic and transcriptomic analysis of macrophage heterogeneity using SCoPE2

BACKGROUND

Macrophages are innate immune cells with diverse functional and molecular phenotypes. This diversity is largely unexplored at the level of single-cell proteomes because of the limitations of quantitative single-cell protein analysis.

RESULTS

To overcome this limitation, we develop SCoPE2, which substantially increases quantitative accuracy and throughput while lowering cost and hands-on time by introducing automated and miniaturized sample preparation. These advances enable us to analyze the emergence of cellular heterogeneity as homogeneous monocytes differentiate into macrophage-like cells in the absence of polarizing cytokines. SCoPE2 quantifies over 3042 proteins in 1490 single monocytes and macrophages in 10 days of instrument time, and the quantified proteins allow us to discern single cells by cell type. Furthermore, the data uncover a continuous gradient of proteome states for the macrophages, suggesting that macrophage heterogeneity may emerge in the absence of polarizing cytokines. Parallel measurements of transcripts by 10× Genomics suggest that our measurements sample 20-fold more protein copies than RNA copies per gene, and thus, SCoPE2 supports quantification with improved count statistics. This allowed exploring regulatory interactions, such as interactions between the tumor suppressor p53, its transcript, and the transcripts of genes regulated by p53.

CONCLUSIONS

Even in a homogeneous environment, macrophage proteomes are heterogeneous. This heterogeneity correlates to the inflammatory axis of classically and alternatively activated macrophages. Our methodology lays the foundation for automated and quantitative single-cell analysis of proteins by mass spectrometry and demonstrates the potential for inferring transcriptional and post-transcriptional regulation from variability across single cells.

Single-cell proteomic and transcriptomic analysis of macrophage heterogeneity using SCoPE2

BACKGROUND

Macrophages are innate immune cells with diverse functional and molecular phenotypes. This diversity is largely unexplored at the level of single-cell proteomes because of the limitations of quantitative single-cell protein analysis.

RESULTS

To overcome this limitation, we develop SCoPE2, which substantially increases quantitative accuracy and throughput while lowering cost and hands-on time by introducing automated and miniaturized sample preparation. These advances enable us to analyze the emergence of cellular heterogeneity as homogeneous monocytes differentiate into macrophage-like cells in the absence of polarizing cytokines. SCoPE2 quantifies over 3042 proteins in 1490 single monocytes and macrophages in 10 days of instrument time, and the quantified proteins allow us to discern single cells by cell type. Furthermore, the data uncover a continuous gradient of proteome states for the macrophages, suggesting that macrophage heterogeneity may emerge in the absence of polarizing cytokines. Parallel measurements of transcripts by 10× Genomics suggest that our measurements sample 20-fold more protein copies than RNA copies per gene, and thus, SCoPE2 supports quantification with improved count statistics. This allowed exploring regulatory interactions, such as interactions between the tumor suppressor p53, its transcript, and the transcripts of genes regulated by p53.

CONCLUSIONS

Even in a homogeneous environment, macrophage proteomes are heterogeneous. This heterogeneity correlates to the inflammatory axis of classically and alternatively activated macrophages. Our methodology lays the foundation for automated and quantitative single-cell analysis of proteins by mass spectrometry and demonstrates the potential for inferring transcriptional and post-transcriptional regulation from variability across single cells.

Stroma Transcriptomic and Proteomic Profile of Prostate Cancer Metastasis Xenograft Models Reveals Prognostic Value of Stroma Signatures

Resistance acquisition to androgen deprivation treatment and metastasis progression are a major clinical issue associated with prostate cancer (PCa). The role of stroma during disease progression is insufficiently defined. Using transcriptomic and proteomic analyses on differentially aggressive patient-derived xenografts (PDXs), we investigated whether PCa tumors predispose their microenvironment (stroma) to a metastatic gene expression pattern. RNA sequencing was performed on the PCa PDXs BM18 (castration-sensitive) and LAPC9 (castration-resistant), representing different disease stages. Using organism-specific reference databases, the human-specific transcriptome (tumor) was identified and separated from the mouse-specific transcriptome (stroma). To identify proteomic changes in the tumor (human) versus the stroma (mouse), we performed human/mouse cell separation and subjected protein lysates to quantitative Tandem Mass Tag labeling and mass spectrometry. Tenascin C (TNC) was among the most abundant stromal genes, modulated by androgen levels in vivo and highly expressed in castration-resistant LAPC9 PDX. The tissue microarray of primary PCa samples (n = 210) showed that TNC is a negative prognostic marker of the clinical progression to recurrence or metastasis. Stroma markers of osteoblastic PCa bone metastases seven-up signature were induced in the stroma by the host organism in metastatic xenografts, indicating conserved mechanisms of tumor cells to induce a stromal premetastatic signature. A 50-gene list stroma signature was identified based on androgen-dependent responses, which shows a linear association with the Gleason score, metastasis progression and progression-free survival. Our data show that metastatic PCa PDXs, which differ in androgen sensitivity, trigger differential stroma responses, which show the metastasis risk stratification and prognostic biomarker potential.

Bringing New Methods to the Seed Proteomics Platform: Challenges and Perspectives

For centuries, crop plants have represented the basis of the daily human diet. Among them, cereals and legumes, accumulating oils, proteins, and carbohydrates in their seeds, distinctly dominate modern agriculture, thus play an essential role in food industry and fuel production. Therefore, seeds of crop plants are intensively studied by food chemists, biologists, biochemists, and nutritional physiologists. Accordingly, seed development and germination as well as age- and stress-related alterations in seed vigor, longevity, nutritional value, and safety can be addressed by a broad panel of analytical, biochemical, and physiological methods. Currently, functional genomics is one of the most powerful tools, giving direct access to characteristic metabolic changes accompanying plant development, senescence, and response to biotic or abiotic stress. Among individual post-genomic methodological platforms, proteomics represents one of the most effective ones, giving access to cellular metabolism at the level of proteins. During the recent decades, multiple methodological advances were introduced in different branches of life science, although only some of them were established in seed proteomics so far. Therefore, here we discuss main methodological approaches already employed in seed proteomics, as well as those still waiting for implementation in this field of plant research, with a special emphasis on sample preparation, data acquisition, processing, and post-processing. Thereby, the overall goal of this review is to bring new methodologies emerging in different areas of proteomics research (clinical, food, ecological, microbial, and plant proteomics) to the broad society of seed biologists.

A computational method for detection of ligand-binding proteins from dose range thermal proteome profiles

Detecting ligand-protein interactions in living cells is a fundamental challenge in molecular biology and drug research. Proteome-wide profiling of thermal stability as a function of ligand concentration promises to tackle this challenge. However, current data analysis strategies use preset thresholds that can lead to suboptimal sensitivity/specificity tradeoffs and limited comparability across datasets. Here, we present a method based on statistical hypothesis testing on curves, which provides control of the false discovery rate. We apply it to several datasets probing epigenetic drugs and a metabolite. This leads us to detect off-target drug engagement, including the finding that the HDAC8 inhibitor PCI-34051 and its analog BRD-3811 bind to and inhibit leucine aminopeptidase 3. An implementation is available as an R package from Bioconductor (https://bioconductor.org/packages/TPP2D). We hope that our method will facilitate prioritizing targets from thermal profiling experiments.

2D-thermal proteome profiling (2D-TPP) is a powerful assay for probing interactions of proteins with small molecules in their native context. Here the authors provide a statistical method for false discovery rate controlled analysis for 2D-TPP applications.

HLA-A and HLA-DRB1 may play a unique role in ovarian teratoma-associated anti-N-methyl-D-aspartate receptor encephalitis

BACKGROUND

Ovarian teratoma-associated anti-N-methyl-D-aspartate receptor encephalitis (NMDAR-E) is a severe autoimmune neurological disorder, and the influence of teratoma-induced autoantibodies on the pathogenesis remains unclear.

METHODS

Ovarian teratoma tissues were collected from teratoma patients with and without NMDAR-E. Proteins were extracted and then analyzed using iTRAQ-coupled LC-MS/MS, which was followed by bioinformatics analysis. Candidate proteins were verified by Western blotting and immunohistochemistry.

RESULTS

In total, 36 differentially expressed proteins (DEPs) were identified between the control group and NMDAR-E group, and the bioinformatics analysis revealed that the DEPs were mainly involved in immune-related pathways, especially HLA-A and HLA-DRB1. The western blotting results for HLA-A and HLA-DRB1 were consistent with the results of the iTRAQ analysis. Additionally, the immunohistochemical data revealed that the aggregation of HLA-A (+) and HLA-DRB1 (+) cells was more apparent in the teratoma tissues of NMDAR-E patients compared with that in the tissues of controls.

CONCLUSION

Our investigation indicated that HLA-A and HLA-DRB1 might be involved in mediating ovarian teratoma-associated NMDAR-E. These findings provide new insights into the pathophysiological mechanisms and provide information for the functional exploration of proteins in the future.

Beyond Target-Decoy Competition: Stable Validation of Peptide and Protein Identifications in Mass Spectrometry-Based Discovery Proteomics

In bottom-up discovery proteomics, target-decoy competition (TDC) is the most popular method for false discovery rate (FDR) control. Despite unquestionable statistical foundations, this method has drawbacks, including its hitherto unknown intrinsic lack of stability vis-à-vis practical conditions of application. Although some consequences of this instability have already been empirically described, they may have been misinterpreted. This article provides evidence that TDC has become less reliable as the accuracy of modern mass spectrometers improved. We therefore propose to replace TDC by a totally different method to control the FDR at the spectrum, peptide, and protein levels, while benefiting from the theoretical guarantees of the Benjamini-Hochberg framework. As this method is simpler to use, faster to compute, and more stable than TDC, we argue that it is better adapted to the standardization and throughput constraints of current proteomic platforms.

Different maturities drive proteomic and metabolomic changes in Chinese black truffle

Chinese black truffle (Tuber indicum) is rich in nutrition. However, commercial interests lead to the aroma components and nutrients of T. indicum being greatly affected by overexploitation without consideration of their maturity. This study investigated the proteomic and metabolomic profiles of truffle fruiting bodies at different maturities using a meta-proteomic approach. Among the 3007 identified proteins, the most up-expressed protein in the mature ascocarps was involved in the peptidyl-diphthamide biosynthetic process, while thiamine metabolism was the most differentially expressed pathway. Furthermore, a total of 54 metabolites identified upon LC-MS differed significantly, with 30 being up-expressed in the mature ascocarps, including organic acids, carnitine substances and polysaccharides. Additionally, the ash, protein, fat, crude fiber and total sugar contents were all higher in the mature ascocarps. Overall, our findings reveal that mature truffles have a higher nutritional value, providing a basis for further exploring protein functionality of T. indicum at different maturities.

Investigation of Proteomic and Phosphoproteomic Responses to Signaling Network Perturbations Reveals Functional Pathway Organizations in Yeast

Governance of protein phosphorylation by kinases and phosphatases constitutes an essential regulatory network in eukaryotic cells. Network dysregulation leads to severe consequences and is often a key factor in disease pathogenesis. Previous studies revealed multiple roles for protein phosphorylation and pathway structures in cellular functions from different perspectives. We seek to understand the roles of kinases and phosphatases from a protein homeostasis point of view. Using a streamlined tandem mass tag (SL-TMT) strategy, we systematically measure proteomic and phosphoproteomic responses to perturbations of phosphorylation signaling networks in yeast deletion strains. Our results emphasize the requirement for protein normalization for more complete interpretation of phosphorylation data. Functional relationships between kinases and phosphatases were characterized at both proteome and phosphoproteome levels in three ways: (1) Gene Ontology enrichment analysis, (2) Δgene-Δgene correlation networks, and (3) molecule covariance networks. This resource illuminates kinase and phosphatase functions and pathway organizations.

Foodborne Titanium Dioxide Nanoparticles Induce Stronger Adverse Effects in Obese Mice than Non-Obese Mice: Gut Microbiota Dysbiosis, Colonic Inflammation, and Proteome Alterations

The recent ban of titanium dioxide (TiO₂ ) as a food additive (E171) in France intensified the controversy on safety of foodborne-TiO₂ nanoparticles (NPs). This study determines the biological effects of TiO₂ NPs and TiO₂ (E171) in obese and non-obese mice. Oral consumption (0.1 wt% in diet for 8 weeks) of TiO₂ (E171, 112 nm) and TiO₂ NPs (33 nm) does not cause severe toxicity in mice, but significantly alters composition of gut microbiota, for example, increased abundance of Firmicutes phylum and decreased abundance of Bacteroidetes phylum and Bifidobacterium and Lactobacillus genera, which are accompanied by decreased cecal levels of short-chain fatty acids. Both TiO₂ (E171) and TiO₂ NPs increase abundance of pro-inflammatory immune cells and cytokines in the colonic mucosa, indicating an inflammatory state. Importantly, TiO₂ NPs cause stronger colonic inflammation than TiO₂ (E171), and obese mice are more susceptible to the effects. A microbiota transplant study demonstrates that altered fecal microbiota by TiO₂ NPs directly mediate inflammatory responses in the mouse colon. Furthermore, proteomic analysis shows that TiO₂ NPs cause more alterations in multiple pathways in the liver and colon of obese mice than non-obese mice. This study provides important information on the health effects of foodborne inorganic nanoparticles.

The Insulin Receptor Adaptor IRS2 is an APC/C Substrate That Promotes Cell Cycle Protein Expression and a Robust Spindle Assembly Checkpoint

Insulin receptor substrate 2 (IRS2) is an essential adaptor that mediates signaling downstream of the insulin receptor and other receptor tyrosine kinases. Transduction through IRS2-dependent pathways is important for coordinating metabolic homeostasis, and dysregulation of IRS2 causes systemic insulin signaling defects. Despite the importance of maintaining proper IRS2 abundance, little is known about what factors mediate its protein stability. We conducted an unbiased proteomic screen to uncover novel substrates of the Anaphase Promoting Complex/Cyclosome (APC/C), a ubiquitin ligase that controls the abundance of key cell cycle regulators. We found that IRS2 levels are regulated by APC/C activity and that IRS2 is a direct APC/C target in G1 Consistent with the APC/C's role in degrading cell cycle regulators, quantitative proteomic analysis of IRS2-null cells revealed a deficiency in proteins involved in cell cycle progression. We further show that cells lacking IRS2 display a weakened spindle assembly checkpoint in cells treated with microtubule inhibitors. Together, these findings reveal a new pathway for IRS2 turnover and indicate that IRS2 is a component of the cell cycle control system in addition to acting as an essential metabolic regulator.

EDF1 coordinates cellular responses to ribosome collisions

Translation of aberrant mRNAs induces ribosomal collisions, thereby triggering pathways for mRNA and nascent peptide degradation and ribosomal rescue. Here we use sucrose gradient fractionation combined with quantitative proteomics to systematically identify proteins associated with collided ribosomes. This approach identified Endothelial differentiation-related factor 1 (EDF1) as a novel protein recruited to collided ribosomes during translational distress. Cryo-electron microscopic analyses of EDF1 and its yeast homolog Mbf1 revealed a conserved 40S ribosomal subunit binding site at the mRNA entry channel near the collision interface. EDF1 recruits the translational repressors GIGYF2 and EIF4E2 to collided ribosomes to initiate a negative-feedback loop that prevents new ribosomes from translating defective mRNAs. Further, EDF1 regulates an immediate-early transcriptional response to ribosomal collisions. Our results uncover mechanisms through which EDF1 coordinates multiple responses of the ribosome-mediated quality control pathway and provide novel insights into the intersection of ribosome-mediated quality control with global transcriptional regulation.

Pseudo-RNA-Binding Domains Mediate RNA Structure Specificity in Upstream of N-Ras

RNA-binding proteins (RBPs) commonly feature multiple RNA-binding domains (RBDs), which provide these proteins with a modular architecture. Accumulating evidence supports that RBP architectural modularity and adaptability define the specificity of their interactions with RNA. However, how multiple RBDs recognize their cognate single-stranded RNA (ssRNA) sequences in concert remains poorly understood. Here, we use Upstream of N-Ras (Unr) as a model system to address this question. Although reported to contain five ssRNA-binding cold-shock domains (CSDs), we demonstrate that Unr includes an additional four CSDs that do not bind RNA (pseudo-RBDs) but are involved in mediating RNA tertiary structure specificity by reducing the conformational heterogeneity of Unr. Disrupting the interactions between canonical and non-canonical CSDs impacts RNA binding, Unr-mediated translation regulation, and the Unr-dependent RNA interactome. Taken together, our studies reveal a new paradigm in protein-RNA recognition, where interactions between RBDs and pseudo-RBDs select RNA tertiary structures, influence RNP assembly, and define target specificity.

The gain-of-function allele bamA E470K bypasses the essential requirement for BamD in β-barrel outer membrane protein assembly

The outer membrane (OM) of gram-negative bacteria confers innate resistance to toxins and antibiotics. Integral β-barrel outer membrane proteins (OMPs) function to establish and maintain the selective permeability of the OM. OMPs are assembled into the OM by the β-barrel assembly machine (BAM), which is composed of one OMP-BamA-and four lipoproteins-BamB, C, D, and E. BamB, C, and E can be removed individually with only minor effects on barrier function; however, depletion of either BamA or BamD causes a global defect in OMP assembly and results in cell death. We have identified a gain-of-function mutation, bamA _E470K , that bypasses the requirement for BamD. Although bamD::kan bamA _E470K cells exhibit growth and OM barrier defects, they assemble OMPs with surprising robustness. Our results demonstrate that BamD does not play a catalytic role in OMP assembly, but rather functions to regulate the activity of BamA.

Identification and analysis of lipid droplet-related proteome in the adipose tissue of grass carp (Ctenopharyngodon idella) under fed and starved conditions

Fat accumulation in the mesenteric adipose tissue is a serious problem in grass carp (Ctenopharyngodon idella) culture. Lipid droplet-related proteins (LDRPs) are involved in the formation, degradation, and biological functions of lipid droplets. In this study, we aimed to provide reference proteomics data to study lipid droplet regulation in fish. We isolated LDRPs from the mesenteric adipose tissue of grass carp (1-year-old) after normal feeding and 7 days of starvation, and identified and analysed them using isobaric tags for relative and absolute quantitation (iTRAQ) technology. Short-term starvation had no significant effect on the body weight, condition factor, visceral index, hepatopancreas index, intraperitoneal fat index, adipose tissue triglyceride content, and adipocyte size of grass carp. Nine hundred and fifty proteins were identified and annotated using the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases; they are involved in a variety of metabolic and signalling pathways, including amino acid, lipid, and carbohydrate metabolism, and the PI3K-Akt signalling pathway. There were 296 differentially expressed proteins (DEPs), with 143 up-regulated and 153 down-regulated proteins. Three proteins involved in triglyceride and fatty acid syntheses and two proteins involved in autophagy were up-regulated, and six proteins involved in lipid catabolism were down-regulated. These results indicate that under short-term starvation, lipid droplets in the adipose tissue of grass carp may maintain their shape by promoting fat production and inhibiting lipolysis, and autophagy may be one of the main strategies for coping with short-term energy deprivation.

Systematic quantitative analysis of ribosome inventory during nutrient stress

Mammalian cells reorganize their proteomes in response to nutrient stress through translational suppression and degradative mechanisms using the proteasome and autophagy systems1,2. Ribosomes are central targets of this response, as they are responsible for translation and subject to lysosomal turnover during nutrient stress3-5. The abundance of ribosomal (r)-proteins (around 6% of the proteome; 107 copies per cell)6,7 and their high arginine and lysine content has led to the hypothesis that they are selectively used as a source of basic amino acids during nutrient stress through autophagy4,7. However, the relative contributions of translational and degradative mechanisms to the control of r-protein abundance during acute stress responses is poorly understood, as is the extent to which r-proteins are used to generate amino acids when specific building blocks are limited7. Here, we integrate quantitative global translatome and degradome proteomics8 with genetically encoded Ribo-Keima5 and Ribo-Halo reporters to interrogate r-protein homeostasis with and without active autophagy. In conditions of acute nutrient stress, cells strongly suppress the translation of r-proteins, but, notably, r-protein degradation occurs largely through non-autophagic pathways. Simultaneously, the decrease in r-protein abundance is compensated for by a reduced dilution of pre-existing ribosomes and a reduction in cell volume, thereby maintaining the density of ribosomes within single cells. Withdrawal of basic or hydrophobic amino acids induces translational repression without differential induction of ribophagy, indicating that ribophagy is not used to selectively produce basic amino acids during acute nutrient stress. We present a quantitative framework that describes the contributions of biosynthetic and degradative mechanisms to r-protein abundance and proteome remodelling in conditions of nutrient stress.

Quantitative Proteomics of the Cancer Cell Line Encyclopedia

Proteins are essential agents of biological processes. To date, large-scale profiling of cell line collections including the Cancer Cell Line Encyclopedia (CCLE) has focused primarily on genetic information whereas deep interrogation of the proteome has remained out of reach. Here, we expand the CCLE through quantitative profiling of thousands of proteins by mass spectrometry across 375 cell lines from diverse lineages to reveal information undiscovered by DNA and RNA methods. We observe unexpected correlations within and between pathways that are largely absent from RNA. An analysis of microsatellite instable (MSI) cell lines reveals the dysregulation of specific protein complexes associated with surveillance of mutation and translation. These and other protein complexes were associated with sensitivity to knockdown of several different genes. These data in conjunction with the wider CCLE are a broad resource to explore cellular behavior and facilitate cancer research.

The Archaeal Proteome Project advances knowledge about archaeal cell biology through comprehensive proteomics

While many aspects of archaeal cell biology remain relatively unexplored, systems biology approaches like mass spectrometry (MS) based proteomics offer an opportunity for rapid advances. Unfortunately, the enormous amount of MS data generated often remains incompletely analyzed due to a lack of sophisticated bioinformatic tools and field-specific biological expertise for data interpretation. Here we present the initiation of the Archaeal Proteome Project (ArcPP), a community-based effort to comprehensively analyze archaeal proteomes. Starting with the model archaeon Haloferax volcanii, we reanalyze MS datasets from various strains and culture conditions. Optimized peptide spectrum matching, with strict control of false discovery rates, facilitates identifying > 72% of the reference proteome, with a median protein sequence coverage of 51%. These analyses, together with expert knowledge in diverse aspects of cell biology, provide meaningful insights into processes such as N-terminal protein maturation, N-glycosylation, and metabolism. Altogether, ArcPP serves as an invaluable blueprint for comprehensive prokaryotic proteomics.

iTRAQ-based quantitative proteomics analysis of cantaloupe (Cucumis melo var. saccharinus) after cold storage

Background

Cantaloupe is susceptible to cold stress when it is stored at low temperatures, resulting in the loss of edible and commercial quality. To ascertain the molecular mechanisms of low temperatures resistance in cantaloupe, a cold-sensitive cultivar, Golden Empress-308 (GE) and a cold-tolerant cultivar, Jia Shi-310 (JS), were selected in parallel for iTRAQ quantitative proteomic analysis.

Results

The two kinds of commercial cultivars were exposed to a temperature of 0.5 °C for 0, 12 and 24 days. We found that the cold-sensitive cultivar (GE) suffered more severe damage as the length of the cold treatment increased. Proteomic analysis of both cultivars indicated that the number of differentially expressed proteins (DEPs) changed remarkably during the chilly treatment. JS expressed cold-responsive proteins more rapidly and mobilized more groups of proteins than GE. Furthermore, metabolic analysis revealed that more amino acids were up-regulated in JS during the early phases of low temperatures stress. The DEPs we found were mainly related to carbohydrate and energy metabolism, structural proteins, reactive oxygen species scavenging, amino acids metabolism and signal transduction. The consequences of phenotype assays, metabolic analysis and q-PCR validation confirm the findings of the iTRAQ analysis.

Conclusion

We found that the prompt response and mobilization of proteins in JS allowed it to maintain a higher level of cold tolerance than GE, and that the slower cold responses in GE may be a vital reason for the severe chilling injury commonly found in this cultivar. The candidate proteins we identified will form the basis of future studies and may improve our understanding of the mechanisms of cold tolerance in cantaloupe.

Immediate Adaptation Analysis Implicates BCL6 as an EGFR-TKI Combination Therapy Target in NSCLC

Drug resistance is a major obstacle to curative cancer therapies, and increased understanding of the molecular events contributing to resistance would enable better prediction of therapy response, as well as contribute to new targets for combination therapy. Here we have analyzed the early molecular response to epidermal growth factor receptor (EGFR) inhibition using RNA sequencing data covering 13,486 genes and mass spectrometry data covering 10,138 proteins. This analysis revealed a massive response to EGFR inhibition already within the first 24 h, including significant regulation of hundreds of genes known to control downstream signaling, such as transcription factors, kinases, phosphatases and ubiquitin E3-ligases. Importantly, this response included upregulation of key genes in multiple oncogenic signaling pathways that promote proliferation and survival, such as ERBB3, FGFR2, JAK3, and BCL6, indicating an early adaptive response to EGFR inhibition. Using a library of more than 500 approved and experimental compounds in a combination therapy screen, we could show that several kinase inhibitors with targets including JAK3 and FGFR2 increased the response to EGFR inhibitors. Further, we investigated the functional impact of BCL6 upregulation in response to EGFR inhibition using siRNA-based silencing of BCL6. Proteomics profiling revealed that BCL6 inhibited transcription of multiple target genes including p53, resulting in reduced apoptosis which implicates BCL6 upregulation as a new EGFR inhibitor treatment escape mechanism. Finally, we demonstrate that combined treatment targeting both EGFR and BCL6 act synergistically in killing lung cancer cells. In conclusion, or data indicates that multiple different adaptive mechanisms may act in concert to blunt the cellular impact of EGFR inhibition, and we suggest BCL6 as a potential target for EGFR inhibitor-based combination therapy.

ProteomicsDB: a multi-omics and multi-organism resource for life science research

ProteomicsDB (https://www.ProteomicsDB.org) started as a protein-centric in-memory database for the exploration of large collections of quantitative mass spectrometry-based proteomics data. The data types and contents grew over time to include RNA-Seq expression data, drug-target interactions and cell line viability data. In this manuscript, we summarize new developments since the previous update that was published in Nucleic Acids Research in 2017. Over the past two years, we have enriched the data content by additional datasets and extended the platform to support protein turnover data. Another important new addition is that ProteomicsDB now supports the storage and visualization of data collected from other organisms, exemplified by Arabidopsis thaliana. Due to the generic design of ProteomicsDB, all analytical features available for the original human resource seamlessly transfer to other organisms. Furthermore, we introduce a new service in ProteomicsDB which allows users to upload their own expression datasets and analyze them alongside with data stored in ProteomicsDB. Initially, users will be able to make use of this feature in the interactive heat map functionality as well as the drug sensitivity prediction, but ultimately will be able to use all analytical features of ProteomicsDB in this way.

Benchmarking the Orbitrap Tribrid Eclipse for Next Generation Multiplexed Proteomics

The rise of sample multiplexing in quantitative proteomics for the dissection of complex phenotypic comparisons has been advanced by the development of ever more sensitive and robust instrumentation. Here, we evaluated the utility of the Orbitrap Eclipse Tribrid mass spectrometer (advanced quadrupole filter, optimized FTMS scan overhead) and new instrument control software features (Precursor Fit filtering, TurboTMT and Real-time Peptide Search filtering). Multidimensional comparisons of these novel features increased total peptide identifications by 20% for SPS-MS³ methods and 14% for HRMS² methods. Importantly Real-time Peptide Search filtering enabled a ∼2× throughput improvement for quantification. Across the board, these sensitivity increases were attained without sacrificing quantitative accuracy. New hardware and software features enable more efficient characterization in pursuit of comparative whole proteome insights.

Selection of Heating Temperatures Improves the Sensitivity of the Proteome Integral Solubility Alteration Assay

The thermal shift assay is a robust method of discovering protein-ligand interactions by measuring the alterations in protein thermal stability under various conditions. Several thermal shift assays have been developed and their throughput has been advanced greatly by the rapid progress in tandem mass tag-based quantitative proteomics. A recent paper by Gaetani et al. ( J. Proteome Res. 2019, 18 (11), 4027-4037) introduced the proteome integral solubility alteration (PISA) assay, further increasing throughput and simplifying the data analysis. Both ΔSm (a proxy of the difference between areas under the melting curves) and fold changes (ratios between integral samples) are readouts of the PISA assay and positively related to ΔTm (shift in melting temperatures). Here, we show that the magnitudes of these readouts are inherently small in PISA assay, which is a challenge for quantitation. Both simulation and experimental results show that the selection of a subset of heating temperatures ameliorates the small difference problem and improves the sensitivity of the PISA assay.

Quantitative proteomic landscape of metaplastic breast carcinoma pathological subtypes and their relationship to triple-negative tumors

Metaplastic breast carcinoma (MBC) is a highly aggressive form of triple-negative cancer (TNBC), defined by the presence of metaplastic components of spindle, squamous, or sarcomatoid histology. The protein profiles underpinning the pathological subtypes and metastatic behavior of MBC are unknown. Using multiplex quantitative tandem mass tag-based proteomics we quantify 5798 proteins in MBC, TNBC, and normal breast from 27 patients. Comparing MBC and TNBC protein profiles we show MBC-specific increases related to epithelial-to-mesenchymal transition and extracellular matrix, and reduced metabolic pathways. MBC subtypes exhibit distinct upregulated profiles, including translation and ribosomal events in spindle, inflammation- and apical junction-related proteins in squamous, and extracellular matrix proteins in sarcomatoid subtypes. Comparison of the proteomes of human spindle MBC with mouse spindle (CCN6 knockout) MBC tumors reveals a shared spindle-specific signature of 17 upregulated proteins involved in translation and 19 downregulated proteins with roles in cell metabolism. These data identify potential subtype specific MBC biomarkers and therapeutic targets.

Metaplastic breast carcinoma (MBC) is among the most aggressive subtypes of triple-negative breast cancer (TNBC) but the underlying proteome profiles are unknown. Here, the authors characterize the protein signatures of human MBC tissue samples and their relationship to TNBC and normal breast tissue.

Comparative Proteomic Analysis by iTRAQ Reveals that Plastid Pigment Metabolism Contributes to Leaf Color Changes in Tobacco (Nicotiana tabacum) during Curing

Tobacco (Nicotiana tabacum), is a world's major non-food agricultural crop widely cultivated for its economic value. Among several color change associated biological processes, plastid pigment metabolism is of trivial importance in postharvest plant organs during curing and storage. However, the molecular mechanisms involved in carotenoid and chlorophyll metabolism, as well as color change in tobacco leaves during curing, need further elaboration. Here, proteomic analysis at different curing stages (0 h, 48 h, 72 h) was performed in tobacco cv. Bi'na1 with an aim to investigate the molecular mechanisms of pigment metabolism in tobacco leaves as revealed by the iTRAQ proteomic approach. Our results displayed significant differences in leaf color parameters and ultrastructural fingerprints that indicate an acceleration of chloroplast disintegration and promotion of pigment degradation in tobacco leaves due to curing. In total, 5931 proteins were identified, of which 923 (450 up-regulated, 452 down-regulated, and 21 common) differentially expressed proteins (DEPs) were obtained from tobacco leaves. To elucidate the molecular mechanisms of pigment metabolism and color change, 19 DEPs involved in carotenoid metabolism and 12 DEPs related to chlorophyll metabolism were screened. The results exhibited the complex regulation of DEPs in carotenoid metabolism, a negative regulation in chlorophyll biosynthesis, and a positive regulation in chlorophyll breakdown, which delayed the degradation of xanthophylls and accelerated the breakdown of chlorophylls, promoting the formation of yellow color during curing. Particularly, the up-regulation of the chlorophyllase-1-like isoform X2 was the key protein regulatory mechanism responsible for chlorophyll metabolism and color change. The expression pattern of 8 genes was consistent with the iTRAQ data. These results not only provide new insights into pigment metabolism and color change underlying the postharvest physiological regulatory networks in plants, but also a broader perspective, which prompts us to pay attention to further screen key proteins in tobacco leaves during curing.

Lung developmental arrest caused by PDGF-A deletion: consequences for the adult mouse lung

PDGF-A is a key contributor to lung development in mice. Its expression is needed for secondary septation of the alveoli and deletion of the gene leads to abnormally enlarged alveolar air spaces in mice. In humans, the same phenotype is the hallmark of bronchopulmonary dysplasia (BPD), a disease that affects premature babies and may have long lasting consequences in adulthood. So far, the knowledge regarding adult effects of developmental arrest in the lung is limited. This is attributable to few follow-up studies of BPD survivors and lack of good experimental models that could help predict the outcomes of this early age disease for the adult individual. In this study, we used the constitutive lung-specific Pdgfa deletion mouse model to analyze the consequences of developmental lung defects in adult mice. We assessed lung morphology, physiology, cellular content, ECM composition and proteomics data in mature mice, that perinatally exhibited lungs with a BPD-like morphology. Histological and physiological analyses both revealed that enlarged alveolar air spaces remained until adulthood, resulting in higher lung compliance and higher respiratory volume in knockout mice. Still, no or only small differences were seen in cellular, ECM and protein content when comparing knockout and control mice. Taken together, our results indicate that Pdgfa deletion-induced lung developmental arrest has consequences for the adult lung at the morphological and functional level. In addition, these mice can reach adulthood with a BPD-like phenotype, which makes them a robust model to further investigate the pathophysiological progression of the disease and test putative regenerative therapies.

TMTpro reagents: a set of isobaric labeling mass tags enables simultaneous proteome-wide measurements across 16 samples

Isobaric labeling empowers proteome-wide expression measurements simultaneously across multiple samples. Here an expanded set of 16 isobaric reagents based on an isobutyl-proline immonium ion reporter structure (TMTpro) is presented. These reagents have similar characteristics to existing tandem mass tag reagents but with increased fragmentation efficiency and signal. In a proteome-scale example dataset, we compared eight common cell lines with and without Torin1 treatment with three replicates, quantifying more than 8,800 proteins (mean of 7.5 peptides per protein) per replicate with an analysis time of only 1.1 h per proteome. Finally, we modified the thermal stability assay to examine proteome-wide melting shifts after treatment with DMSO, 1 or 20 µM staurosporine with five replicates. This assay identified and dose-stratified staurosporine binding to 228 cellular kinases in just one, 18-h experiment. TMTpro reagents allow complex experimental designs-all with essentially no missing values across the 16 samples and no loss in quantitative integrity.

Inference of Multisite Phosphorylation Rate Constants and Their Modulation by Pathogenic Mutations

Multisite protein phosphorylation plays a critical role in cell regulation [1-3]. It is widely appreciated that the functional capabilities of multisite phosphorylation depend on the order and kinetics of phosphorylation steps, but kinetic aspects of multisite phosphorylation remain poorly understood [4-6]. Here, we focus on what appears to be the simplest scenario, when a protein is phosphorylated on only two sites in a strict, well-defined order. This scenario describes the activation of ERK, a highly conserved cell-signaling enzyme. We use Bayesian parameter inference in a structurally identifiable kinetic model to dissect dual phosphorylation of ERK by MEK, a kinase that is mutated in a large number of human diseases [7-12]. Our results reveal how enzyme processivity and efficiencies of individual phosphorylation steps are altered by pathogenic mutations. The presented approach, which connects specific mutations to kinetic parameters of multisite phosphorylation mechanisms, provides a systematic framework for closing the gap between studies with purified enzymes and their effects in the living organism.

Protein Probability Model for High-Throughput Protein Identification by Mass Spectrometry-Based Proteomics

Shotgun proteomics is the method of choice for high-throughput protein identification; however, robust statistical methods are essential to automatize this task while minimizing the number of false identifications. The standard method for estimating the false discovery rate (FDR) of individual identifications and keeping it below a threshold (typically 1%) is the target-decoy approach. However, numerous works have shown that FDR at the protein level may become much larger than FDR at the peptide level. The development of an appropriate scoring model to identify proteins from their peptides using high-throughput shotgun proteomics is highly needed. In this study, we present a novel protein-level scoring algorithm that uses the scores of the identified peptides and maintains all of the properties expected for a true protein probability. We also present a refinement of the picked method to calculate FDR at the protein level. These algorithms can be used together as a robust identification workflow suitable for large-scale proteomics, and we show that the identification performance of this workflow is superior to that of other widely used methods in several samples and using different search engines. Our protein probability model offers the scientific community an algorithm that is easy to integrate into protein identification workflows for the automated analysis of shotgun proteomics data.

EPIFANY: A Method for Efficient High-Confidence Protein Inference

Accurate protein inference in the presence of shared peptides is still one of the key problems in bottom-up proteomics. Most protein inference tools employing simple heuristic inference strategies are efficient but exhibit reduced accuracy. More advanced probabilistic methods often exhibit better inference quality but tend to be too slow for large data sets. Here, we present a novel protein inference method, EPIFANY, combining a loopy belief propagation algorithm with convolution trees for efficient processing of Bayesian networks. We demonstrate that EPIFANY combines the reliable protein inference of Bayesian methods with significantly shorter runtimes. On the 2016 iPRG protein inference benchmark data, EPIFANY is the only tested method that finds all true-positive proteins at a 5% protein false discovery rate (FDR) without strict prefiltering on the peptide-spectrum match (PSM) level, yielding an increase in identification performance (+10% in the number of true positives and +14% in partial AUC) compared to previous approaches. Even very large data sets with hundreds of thousands of spectra (which are intractable with other Bayesian and some non-Bayesian tools) can be processed with EPIFANY within minutes. The increased inference quality including shared peptides results in better protein inference results and thus increased robustness of the biological hypotheses generated. EPIFANY is available as open-source software for all major platforms at https://OpenMS.de/epifany.

Exosomes Transmit Viral Genetic Information and Immune Signals may cause Immunosuppression and Immune Tolerance in ALV-J Infected HD11 cells

Avian leukosis virus (ALV) is oncogenic retrovirus that not only causes immunosuppression but also enhances the host's susceptibility to secondary infection. Exosomes play vital role in the signal transduction cascades that occur in response to viral infection. We want to explore the function of exosomes in the spread of ALV and the body's subsequent immunological response. RNA-sequencing and the isobaric tags for relative and absolute quantitation (iTRAQ) method were used to detect differentially expressed genes (DEGs) and differentially expressed proteins (DEPs) in exosomes secreted by macrophage cells in response to injection with ALV subgroup J (ALV-J). RNA-sequencing identified 513 DEGs in infected cells, with specific differential regulation in mRNA involved in tight junction signaling, TNF signaling, salmonella infection response, and immune response, among other important cellular processes. Differential regulation was observed in 843 lncRNAs, with particular enrichment in those lncRNA targets involved in Rap1 signaling, HTLV-I infection, tight junction signaling, and other signaling pathways. A total of 50 DEPs were identified in the infected cells by iTRAQ. The proteins enriched are involved in immune response, antigen processing, the formation of both MHC protein and myosin complexes, and transport. Combined analysis of the transcriptome and proteome revealed that there were 337 correlations between RNA and protein enrichment, five of which were significant. Pathways that were enriched on both the RNA and protein levels were involved in pathways in cancer, PI3K-Akt signaling pathway, Endocytosis, Epstein-Barr virus infection. These data show that exosomes are transmitters of intercellular signaling in response to viral infection. Exosomes can carry both viral nucleic acids and proteins, making it possible for exosomes to be involved in the viral infection of other cells and the transmission of immune signals between cells. Our sequencing results confirme previous studies on exosomes and further find exosomes may cause immunosuppression and immune tolerance.

A group of serum proteins as potential diagnostic biomarkers for Yin-deficiency-heat syndrome

Yin-deficiency-heat (YDH) syndrome is a very common subhealth status in Traditional Chinese Medicine. However, currently, there is no unified standard for diagnosing YDH syndrome. We applied the iTRAQ-2D LC-MS/MS method to explore the potential of serum protein profiles as biomarker for YDH syndrome. A total of 120 differentially expressed proteins (79 downregulated and 41 upregulated) were identified by the proteomic profiling. The results of KEGG pathway analysis showed that the functions of the differentially expressed proteins were mainly involved in complement and coagulation cascades. The clinical data showed that YDH syndrome was closely related to inflammation and coagulation, compared with the healthy controls. The ELISA validation results indicated that the expression levels of ALB, CFI, and KLKB1 were downregulated in the YDH syndrome group (p < .05). Moreover, we established a decision tree model based on the combination of these three proteins and achieved a sensitivity of 87.5%, a specificity of 84.4%, and AUC of 0.891. The results indicated that the combination of ALB, CFI, and KLKB1 may serve as potential biomarkers for diagnosing YDH syndrome. Our study can provide a new method for YDH syndrome diagnosis, and may also provide an experimental basis to understand the molecular mechanism of YDH syndrome.

Plasma proteomic analysis of autoimmune hepatitis in an improved AIH mouse model

Background

The prevalence of autoimmune hepatitis (AIH) is increasing, and its early clinical diagnosis is difficult. The pathogenesis of AIH remains unclear, and AIH-related studies are largely limited because of lack of suitable mouse models.

Methods

To obtain a good tool for research on AIH, we first established an improved immune-mediated mouse model that can mimic the pathological process of AIH as in the human body, through repeated injections of human cytochrome P450 2D6 (CYP2D6) plasmid. Next, a proteomic analysis based on isobaric tag (IBT) technology was performed to detect the differentially expressed proteins (DEPs), and related biological functions and pathways in the plasma of AIH and normal mice. Finally, we performed enzyme-linked immunosorbent assay (ELISA) to further confirm the most abundant DEP in the plasma of patients with AIH.

Results

Autoantibodies and the characteristic pathology of AIH were observed in our mouse model. Inflammatory infiltration also increased in the livers of AIH mice over time and plateaued by day 42 post the first injection. Chronic hepatitis was most severe on day 35 with the development of fibrosis as well, and the plasma of AIH mice were collected for proteomic analysis. A total of 176 DEPs were found in this experiment, of which 148 DEPs were up-regulated and 28 DEPs were down-regulated. Thirty significant Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways (P < 0.05) were detected. Arginine biosynthesis was found to be the most significant pathway involved in the AIH process. During the Gene Ontology (GO) analysis, most DEPs were found to be involved in the binding, cellular, and metabolic processes. Using ELISA, the most overexpressed DEP, serum amyloid A 1 (SAA1), was confirmed to be increased specifically in the plasma of patients with AIH compared to other chronic hepatitis. Different plasma levels of SAA1 were also found related to different grades of inflammation and stages of fibrosis in the liver of patients with AIH.

Conclusions

Our study is the first to describe the proteomics analysis of a true sense of AIH mouse model, which is beneficial for a better understanding of AIH pathogenesis and identifying potential biomarkers for its clinical diagnosis.

Calculation of False Discovery Rate for Peptide and Protein Identification

Shotgun proteomics is the method of choice for large-scale protein identification. However, the use of a robust statistical workflow to validate such identification is mandatory to minimize false matches, ambiguities, and amplification of error rates from spectra to proteins. In this chapter we emphasize the key concepts to take into account when processing the output of a search engine to obtain reliable peptide or protein identifications. We assume that the reader is already familiar with tandem mass spectrometry so we can focus on the use of statistical confidence methods. After introducing the key concepts we present different software tools and how to use them with an example dataset.

The Proteome of the Dentate Terminal Zone of the Perforant Path Indicates Presynaptic Impairment in Alzheimer Disease

Synaptic dysfunction is an early pathogenic event in Alzheimer disease (AD) that contributes to network disturbances and cognitive decline. Some synapses are more vulnerable than others, including the synapses of the perforant path, which provides the main excitatory input to the hippocampus. To elucidate the molecular mechanisms underlying the dysfunction of these synapses, we performed an explorative proteomic study of the dentate terminal zone of the perforant path. The outer two-thirds of the molecular layer of the dentate gyrus, where the perforant path synapses are located, was microdissected from five subjects with AD and five controls. The microdissected tissues were dissolved and digested by trypsin. Peptides from each sample were labeled with different isobaric tags, pooled together and pre-fractionated into 72 fractions by high-resolution isoelectric focusing. Each fraction was then analyzed by liquid chromatography-mass spectrometry. We quantified the relative expression levels of 7322 proteins, whereof 724 showed significantly altered levels in AD. Our comprehensive data analysis using enrichment and pathway analyses strongly indicated that presynaptic signaling, such as exocytosis and synaptic vesicle cycle processes, is severely disturbed in this area in AD, whereas postsynaptic proteins remained unchanged. Among the significantly altered proteins, we selected three of the most downregulated synaptic proteins; complexin-1, complexin-2 and synaptogyrin-1, for further validation, using a new cohort consisting of six AD and eight control cases. Semi-quantitative analysis of immunohistochemical staining confirmed decreased levels of complexin-1, complexin-2 and synaptogyrin-1 in the outer two-thirds of the molecular layer of the dentate gyrus in AD. Our in-depth proteomic analysis provides extensive knowledge on the potential molecular mechanism underlying synaptic dysfunction related to AD and supports that presynaptic alterations are more important than postsynaptic changes in early stages of the disease. The specific synaptic proteins identified could potentially be targeted to halt synaptic dysfunction in AD.

High-Throughput Identification of Putative Antimicrobial Peptides from Multi-Omics Data of the Lined Seahorse (Hippocampus erectus)

Lined seahorse (Hippocampus erectus), the most widely cultivated seahorse in China, has been in short supply because of its important medicinal value; meanwhile, unnatural deaths caused by various diseases (especially enteritis) have limited their practical large-scale aquaculture. Antimicrobial peptides (AMPs), as the best alternative to antibiotics, have been extensively applied in agricultural practices. In this study, we identified 290 putative AMP sequences from our previously published genome and transcriptome data of the lined seahorse. Among them, 267 are novel, and 118 were validated by our proteome data generated in the present study. It seems that there is a tissue preference in the distribution of AMP/AMP precursor transcripts, such as lectins in the male pouch. In addition, their transcription levels usually varied during development. Interestingly, the representative lectins kept extremely high levels at the pre-pregnancy stage while at relatively lower levels at other stages. Especially Lectin25, with the highest transcription levels and significant developmental changes, has been reported to be involved in seahorse and human pregnancy. The comparison of transcriptome data between one-day and three-month juveniles indicated that Hemoglobin2 (Hemo2) was significantly upregulated in the body, haslet, and brain. Our proteome data of female and male individuals revealed three putative AMP precursors with sexual specificity, including two male-biased cyclin-dependent kinases (CDK-like16 and CDK-like23) and one female-biased bovine pancreatic trypsin inhibitor 2 (BPTI2). In conclusion, our present high-throughput identification of putative AMP sequences from multi-omics (including genomics, transcriptomics, and proteomics) data provides an overview of AMPs in the popular lined seahorse, which lays a solid foundation for further development of AMP-based fish food additives and human drugs.

A Mitochondrial LYR Protein Is Required for Complex I Assembly

Complex I biogenesis requires the expression of both nuclear and mitochondrial genes, the import of proteins, cofactor biosynthesis, and the assembly of at least 49 individual subunits. Assembly factors interact with subunits of Complex I but are not part of the final holocomplex. We show that in Arabidopsis (Arabidopsis thaliana), a mitochondrial matrix protein (EMB1793, At1g76060), which we term COMPLEX I ASSEMBLY FACTOR 1 (CIAF1), contains a LYR domain and is required for Complex I assembly. T-DNA insertion mutants of CIAF1 lack Complex I and the Supercomplex I+III. Biochemical characterization shows that the assembly of Complex I is stalled at 650 and 800 kD intermediates in mitochondria isolated from ciaf1 mutant lines.I. Yeast-two-hybrid interaction and complementation assays indicate that CIAF1 specifically interacts with the 23-kD TYKY-1 matrix domain subunit of Complex I and likely plays a role in Fe-S insertion into this subunit. These data show that CIAF1 plays an essential role in assembling the peripheral matrix arm Complex I subunits into the Complex I holoenzyme.