Protocol for identifying and comparing molecular prognosis subtypes of IgAN using R.

By providing a comprehensive view of protein dynamics, quantitative proteomics has emerged as a powerful tool for a better understanding of disease mechanisms. Here, we present a general workflow for identifying and comparing molecular subtypes of disease using proteomics data using R software. We describe steps for data preprocessing, feature selection, determination of subtypes, and functional interpretation of subtypes. These analyses can help us understand the nature of heterogeneous diseases, which is crucial for accurate diagnosis and personalized treatment. For complete details on the use and execution of this protocol, please refer to Chen et al.1.

Large-Scale Proteome Profiling Identifies Biomarkers Associated with Suspected Neurosyphilis Diagnosis.

Neurosyphilis (NS) is a central nervous system (CNS) infection caused by Treponema pallidum (T. pallidum). NS can occur at any stage of syphilis and manifests as a broad spectrum of clinical symptoms. Often referred to as "the great imitator," NS can be easily overlooked or misdiagnosed due to the absence of standard diagnostic tests, potentially leading to severe and irreversible organ dysfunction. In this study, proteomic and machine learning model techniques are used to characterize 223 cerebrospinal fluid (CSF) samples to identify diagnostic markers of NS and provide insights into the underlying mechanisms of the associated inflammatory responses. Three biomarkers (SEMA7A, SERPINA3, and ITIH4) are validated as contributors to NS diagnosis through multicenter verification of an additional 115 CSF samples. We anticipate that the identified biomarkers will become effective tools for assisting in diagnosis of NS. Our insights into NS pathogenesis in brain tissue may inform therapeutic strategies and drug discoveries for NS patients.

Dynamic atlas of immune cells reveals multiple functional features of macrophages associated with progression of pulmonary fibrosis.

Idiopathic pulmonary fibrosis (IPF) is a chronic interstitial lung disease with a high mortality rate and unclarified aetiology. Immune response is elaborately regulated during the progression of IPF, but immune cells subsets are complicated which has not been detailed described during IPF progression. Therefore, in the current study, we sought to investigate the role of immune regulation by elaborately characterize the heterogeneous of immune cells during the progression of IPF. To this end, we performed single-cell profiling of lung immune cells isolated from four stages of bleomycin-induced pulmonary fibrosis-a classical mouse model that mimics human IPF. The results revealed distinct components of immune cells in different phases of pulmonary fibrosis and close communication between macrophages and other immune cells along with pulmonary fibrosis progression. Enriched signals of SPP1, CCL5 and CXCL2 were found between macrophages and other immune cells. The more detailed definition of the subpopulations of macrophages defined alveolar macrophages (AMs) and monocyte-derived macrophages (mo-Macs)-the two major types of primary lung macrophages-exhibited the highest heterogeneity and dynamic changes in expression of profibrotic genes during disease progression. Our analysis suggested that Gpnmb and Trem2 were both upregulated in macrophages and may play important roles in pulmonary fibrosis progression. Additionally, the metabolic status of AMs and mo-Macs varied with disease progression. In line with the published data on human IPF, macrophages in the mouse model shared some features regarding gene expression and metabolic status with that of macrophages in IPF patients. Our study provides new insights into the pathological features of profibrotic macrophages in the lung that will facilitate the identification of new targets for disease intervention and treatment of IPF.

Integrated analysis of circulating and tissue proteomes reveals that fibronectin 1 is a potential biomarker in papillary thyroid cancer.

Papillary thyroid cancer (PTC) is the most frequent subtype of thyroid cancer, but 20% of cases are indeterminate (i.e., cannot be accurately diagnosed) based on preoperative cytology, which might lead to surgical removal of a normal thyroid gland. To address this concern, we performed an in-depth analysis of the serum proteomes of 26 PTC patients and 23 healthy controls using antibody microarrays and data-independent acquisition mass spectrometry (DIA-MS). We identified a total of 1091 serum proteins spanning 10-12 orders of magnitude. 166 differentially expressed proteins were identified that participate in complement activation, coagulation cascades, and platelet degranulation pathways. Furthermore, the analysis of serum proteomes before and after surgery indicated that the expression of proteins such as lactate dehydrogenase A and olfactory receptor family 52 subfamily B member 4, which participate in fibrin clot formation and extracellular matrix-receptor interaction pathways, were changed. Further analysis of the proteomes of PTC and neighboring tissues revealed integrin-mediated pathways with possible crosstalk between the tissue and circulating compartments. Among these cross-talk proteins, circulating fibronectin 1 (FN1), gelsolin (GSN) and UDP-glucose 4-epimerase (GALE) were indicated as promising biomarkers for PTC identification and validated in an independent cohort. In differentiating between patients with benign nodules or PTC, FN1 produced the best ELISA result (sensitivity = 96.89%, specificity = 91.67%). Overall, our results present proteomic landscapes of PTC before and after surgery as well as the crosstalk between tissue and the circulatory system, which is valuable to understand PTC pathology and improve PTC diagnostics in the future.

Proteomic profiling of IgA nephropathy reveals distinct molecular prognostic subtypes.

IgA nephropathy (IgAN) is a heterogeneous disease, which poses a series of challenges to accurate diagnosis and personalized therapy. Herein, we constructed a systematic quantitative proteome atlas from 59 IgAN and 19 normal control donors. Consensus sub-clustering of proteomic profiles divided IgAN into three subtypes (IgAN-C1, C2, and C3). IgAN-C2 had similar proteome expression patterns with normal control, while IgAN-C1/C3 exhibited higher level of complement activation, more severe mitochondrial injury, and significant extracellular matrix accumulation. Interestingly, the complement mitochondrial extracellular matrix (CME) pathway enrichment score achieved a high diagnostic power to distinguish IgAN-C2 from IgAN-C1/C3 (AUC>0.9). In addition, the proteins related to mesangial cells, endothelial cells, and tubular interstitial fibrosis were highly expressed in IgAN-C1/C3. Most critically, IgAN-C1/C3 had a worse prognosis compared to IgAN-C2 (30% eGFR decline, p = 0.02). Altogether, we proposed a molecular subtyping and prognostic system which could help to understand IgAN heterogeneity and improve the treatment in the clinic.

Spatial region-resolved proteome map reveals mechanism of COVID-19-associated heart injury.

Direct myocardial and vascular injuries due to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection-driven inflammation is the leading cause of acute cardiac injury associated with coronavirus disease 2019 (COVID-19). However, in-depth knowledge of the injury characteristics of the heart affected by inflammation is lacking. In this study, using a quantitative spatial proteomics strategy that combines comparative anatomy, laser-capture microdissection, and histological examination, we establish a region-resolved proteome map of the myocardia and microvessels with obvious inflammatory cells from hearts of patients with COVID-19. A series of molecular dysfunctions of myocardia and microvessels is observed in different cardiac regions. The myocardia and microvessels of the left atrial are the most susceptible to virus infection and inflammatory storm, suggesting more attention should be paid to the lesion and treatment of these two parts. These results can guide in improving clinical treatments for cardiovascular diseases associated with COVID-19.

Application of an iPSC-Derived Organoid Model for Localized Scleroderma Therapy.

Localized scleroderma (LoS) is a rare chronic disease with extensive tissue fibrosis, inflammatory infiltration, microvascular alterations, and epidermal appendage lesions. However, a deeper understanding of the pathogenesis and treatment strategies of LoS is currently limited. In the present work, a proteome map of LoS skin is established, and the pathological features of LoS skin are characterized. Most importantly, a human-induced pluripotent stem cell-derived epithelial and mesenchymal (EM) organoids model in a 3D culture system for LoS therapy is established. According to the findings, the application of EM organoids on scleroderma skin can significantly reduce the degree of skin fibrosis. In particular, EM organoids enhance the activity of epidermal stem cells in the LoS skin and promotes the regeneration of sweat glands and blood vessels. These results highlight the potential application of organoids for promoting the recovery of scleroderma associated phenotypes and skin-associated functions. Furthermore, it can provide a new therapeutic alternative for patients suffering from disfigurement and skin function defects caused by LoS.

MoGCN: A Multi-Omics Integration Method Based on Graph Convolutional Network for Cancer Subtype Analysis.

In light of the rapid accumulation of large-scale omics datasets, numerous studies have attempted to characterize the molecular and clinical features of cancers from a multi-omics perspective. However, there are great challenges in integrating multi-omics using machine learning methods for cancer subtype classification. In this study, MoGCN, a multi-omics integration model based on graph convolutional network (GCN) was developed for cancer subtype classification and analysis. Genomics, transcriptomics and proteomics datasets for 511 breast invasive carcinoma (BRCA) samples were downloaded from the Cancer Genome Atlas (TCGA). The autoencoder (AE) and the similarity network fusion (SNF) methods were used to reduce dimensionality and construct the patient similarity network (PSN), respectively. Then the vector features and the PSN were input into the GCN for training and testing. Feature extraction and network visualization were used for further biological knowledge discovery and subtype classification. In the analysis of multi-dimensional omics data of the BRCA samples in TCGA, MoGCN achieved the highest accuracy in cancer subtype classification compared with several popular algorithms. Moreover, MoGCN can extract the most significant features of each omics layer and provide candidate functional molecules for further analysis of their biological effects. And network visualization showed that MoGCN could make clinically intuitive diagnosis. The generality of MoGCN was proven on the TCGA pan-kidney cancer datasets. MoGCN and datasets are public available at https://github.com/Lifoof/MoGCN. Our study shows that MoGCN performs well for heterogeneous data integration and the interpretability of classification results, which confers great potential for applications in biomarker identification and clinical diagnosis.

iProX in 2021: connecting proteomics data sharing with big data.

The rapid development of proteomics studies has resulted in large volumes of experimental data. The emergence of big data platform provides the opportunity to handle these large amounts of data. The integrated proteome resource, iProX (https://www.iprox.cn), which was initiated in 2017, has been greatly improved with an up-to-date big data platform implemented in 2021. Here, we describe the main iProX developments since its first publication in Nucleic Acids Research in 2019. First, a hyper-converged architecture with high scalability supports the submission process. A hadoop cluster can store large amounts of proteomics datasets, and a distributed, RESTful-styled Elastic Search engine can query millions of records within one second. Also, several new features, including the Universal Spectrum Identifier (USI) mechanism proposed by ProteomeXchange, RESTful Web Service API, and a high-efficiency reanalysis pipeline, have been added to iProX for better open data sharing. By the end of August 2021, 1526 datasets had been submitted to iProX, reaching a total data volume of 92.42TB. With the implementation of the big data platform, iProX can support PB-level data storage, hundreds of billions of spectra records, and second-level latency service capabilities that meet the requirements of the fast growing field of proteomics.

Time-Resolved Extracellular Matrix Atlas of the Developing Human Skin Dermis.

Skin aging is a physiological issue that is still relatively poorly understood. Studies have demonstrated that the dermal extracellular matrix (ECM) plays important roles in skin aging. However, the roles of the changes in ECM characteristics and the molecules that are secreted to the extracellular space and are involved in the formation of the dermal matrix from birth to old age remain unclear. To explore the way in which the ECM microenvironment supports the functions of skin development across different age groups is also poorly understood, we used a decellularization method and matrisome analysis to compare the composition, expression, and function of the dermal ECM in toddler, teenager, adult, and elderly skin. We found that the collagens, glycoproteins, proteoglycans, and regulatory factors that support skin development and interact with these core ECM proteins were differentially expressed at different ages. ECM expression markers occurring during the process of skin development were identified. In addition, our results elucidated the characteristics of ECM synthesis, response to skin development, and the features of the ECM that support epidermal stem cell growth via the basement membrane during skin aging.

The protein-protein interaction ontology: for better representing and capturing the biological context of protein interaction.

BACKGROUND: With the rapid increase in the amount of Protein-Protein Interaction (PPI) data, the establishment of an event-centered PPI ontology that contains temporal and spatial vocabularies is urgently needed to clarify PPI biological annotations. In this paper, we propose a precisely designed schema - PPIO (PPI Ontology) for representing the biological context of PPIs. RESULTS: Inspired by the event model and the distinct characteristics of PPI events, PPIO consists of six core aspects of the information required for reporting a PPI event, including the interactor (who), the biological process (when), the subcellular location (where), the interaction type (how), the biological function (what) and the detection method (which). PPIO is implemented through the integration of appropriate terms from the corresponding vocabularies/ontologies, e.g., Gene Ontology, Protein Ontology, PSI-MI/MOD, etc. To assess PPIO, an approach based on PPIO in developed to extract PPI biological annotations from an open standard corpus "BioCreAtIvE-PPI". The experiment results demonstrate PPIO's high performance, a precision of 0.69, a recall of 0.72 and an F-score of 0.70. CONCLUSIONS: PPIO is a well-constructed essential ontology in the interpretation of PPI biological context. The results of the experiments conducted on the BioCreAtIvE corpus demonstrate that PPIO is able to facilitate PPI annotation extraction from biomedical literature effectively and enrich essential annotation for PPIs.

A System-Wide Spatiotemporal Characterization of ErbB Receptor Complexes by Subcellular Fractionation Integrated Quantitative Mass Spectrometry.

Precise spatiotemporal regulation of protein complex assembly is essential for cells to achieve a meaningful rely of information flow via intracellular signaling networks in response to extracellular cues, whose disruption would lead to disease. Although various attempts have been made for spatial and/or temporal analysis of protein complexes, it is still a challenge to track cell-wide dynamics of a particular protein complex under physiological conditions. Here we describe a workflow that combines endogenous expression of tagged proteins, organelle marker distribution-directed subcellular fractionation, scaffold protein-mediated receptor complex purification, and targeted proteomics for spatiotemporal quantification of protein complexes in whole cell scale. We applied our method to investigate the assembly kinetics of EGF-dependent ErbB receptor complexes. After fractionation using the density gradient centrifugation and organelle assignment based on organelle markers, endogenous ErbB complex in different subcellular fractionation was efficiently enriched. By using targeted mass spectrometry, ErbB complex components that expressed medium to low level was precisely quantified with in-depth coverage, simultaneously in time and subcellular spaces. Our results revealed a sophisticated scheme of complex behaviors characterized by multiple subcomplexes with distinct molecular composition formed across subcellular fractions enriched with cytosol, plasma membrane, endosome, or mitochondria, implying organelle-specific ErbB functions. Remarkably, our results demonstrated for the first time that activated ErbB receptors might increase their signaling range through promoting a cytosolic, receptor-free subcomplex, consisting of Shc1, Grb2, Arhgef5, Garem1, and Lrrk1. These findings emphasize the potential of our strategy as a powerful tool to study spatiotemporal dynamics of protein complexes.

The "Matrisome" reveals the characterization of skin keloid microenvironment.

Keloids are fibroproliferative dermal tumors of unknown origin that are characterized by the overabundant accumulation of extracellular matrix (ECM) components. The mechanism of keloid formation has remained unclear because of a poor understanding of its molecular basis. In this study, the dermal ECM components of keloids were identified and the pathological features of keloid formation were characterized using large-scale quantitative proteomic analyses of decellularized keloid biomatrix scaffolds. We identified a total of 267 dermal core ECM and ECM-associated proteins that were differentially expressed between patients with keloids and healthy controls. Skin mechanical properties and biological processes including protease activity, wound healing, and adhesion were disordered in keloids. The integrated network analysis of the upregulated ECM proteins revealed multiple signaling pathways involved in these processes that may lead to keloid formation. Our findings may improve the scientific basis of keloid treatment and provide new ideas for the establishment of keloid models.

Serum proteome profiling provides a deep understanding of the 'gut-liver axis' in relation to liver injury and regeneration.

The gut-liver axis is one of the major contributors to the transport of products from the intestine or intestinal microbes with the progression of liver regeneration. However, the influence of proteins from the hepatic portal vein (HPV), the bridge of enterohepatic circulation, on liver regeneration is unclear. For first time, we applied a quantitative proteomics approach to characterize the molecular pathology of the HPV sera of mice with antibiotic-induced intestinal flora disorder during acute liver injury. The biological processes of lipid metabolism and wound healing were enriched in the HPV of mice with intestinal flora disorder, whereas energy metabolism, liver regeneration, and cytoskeletal processes were downregulated. Moreover, 95 and 35 proteins potentially promoting or inhibiting liver regeneration, respectively, were identified in HPV serum. Our findings will be beneficial to liver donors during liver transplantation.

PANDA: A comprehensive and flexible tool for quantitative proteomics data analysis.

SUMMARY: As the experiment techniques and strategies in quantitative proteomics are improving rapidly, the corresponding algorithms and tools for protein quantification with high accuracy and precision are continuously required to be proposed. Here, we present a comprehensive and flexible tool named PANDA for proteomics data quantification. PANDA, which supports both label-free and labeled quantifications, is compatible with existing peptide identification tools and pipelines with considerable flexibility. Compared with MaxQuant on several complex datasets, PANDA was proved to be more accurate and precise with less computation time. Additionally, PANDA is an easy-to-use desktop application tool with user-friendly interfaces. AVAILABILITY AND IMPLEMENTATION: PANDA is freely available for download at https://sourceforge.net/projects/panda-tools/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PPICurator: A Tool for Extracting Comprehensive Protein-Protein Interaction Information.

Protein-protein interaction extraction through biological literature curation is widely employed for proteome analysis. There is a strong need for a tool that can assist researchers in extracting comprehensive PPI information through literature curation, which is critical in research on protein, for example, construction of protein interaction network, identification of protein signaling pathway, and discovery of meaningful protein interaction. However, most of current tools can only extract PPI relations. None of them are capable of extracting other important PPI information, such as interaction directions, effects, and functional annotations. To address these issues, this paper proposes PPICurator, a novel tool for extracting comprehensive PPI information with a variety of logic and syntax features based on a new support vector machine classifier. PPICurator provides a friendly web-based user interface. It is a platform that automates the extraction of comprehensive PPI information through literature, including PPI relations, as well as their confidential scores, interaction directions, effects, and functional annotations. Thus, PPICurator is more comprehensive than state-of-the-art tools. Moreover, it outperforms state-of-the-art tools in the accuracy of PPI relation extraction measured by F-score and recall on the widely used open datasets. PPICurator is available at https://ppicurator.hupo.org.cn.

LiverWiki: a wiki-based database for human liver.

BACKGROUND: Recent advances in omics technology have produced a large amount of liver-related data. A comprehensive and up-to-date source of liver-related data is needed to allow biologists to access the latest data. However, current liver-related data sources each cover only a specific part of the liver. It is difficult for them to keep pace with the rapid increase of liver-related data available at those data resources. Integrating diverse liver-related data is a critical yet formidable challenge, as it requires sustained human effort. RESULTS: We present LiverWiki, a first wiki-based database that integrates liver-related genes, homolog genes, gene expressions in microarray datasets and RNA-Seq datasets, proteins, protein interactions, post-translational modifications, associated pathways, diseases, metabolites identified in the metabolomics datasets, and literatures into an easily accessible and searchable resource for community-driven sharing. LiverWiki houses information in a total of 141,897 content pages, including 19,787 liver-related gene pages, 17,077 homolog gene pages, 50,251 liver-related protein pages, 36,122 gene expression pages, 2067 metabolites identified in the metabolomics datasets, 16,366 disease-related molecules, and 227 liver disease pages. Other than assisting users in searching, browsing, reviewing, refining the contents on LiverWiki, the most important contribution of LiverWiki is to allow the community to create and update biological data of liver in visible and editable tables. This integrates newly produced data with existing knowledge. Implemented in mediawiki, LiverWiki provides powerful extensions to support community contributions. CONCLUSIONS: The main goal of LiverWiki is to provide the research community with comprehensive liver-related data, as well as to allow the research community to share their liver-related data flexibly and efficiently. It also enables rapid sharing new discoveries by allowing the discoveries to be integrated and shared immediately, rather than relying on expert curators. The database is available online at http://liverwiki.hupo.org.cn /.

PIK3CA Mutations Contribute to Acquired Cetuximab Resistance in Patients with Metastatic Colorectal Cancer.

Purpose: Mutations in KRAS are considered to be the main drivers of acquired resistance to epidermal growth factor receptor (EGFR) blockade in patients with metastatic colorectal cancer (mCRC). However, the potential role of other genes downstream of the EGFR signaling pathway in conferring acquired resistance has not been extensively investigated.Experimental Design: Using circulating tumor DNA (ctDNA) from patients with mCRC and with acquired cetuximab resistance, we developed a targeted amplicon ultra-deep sequencing method to screen for low-abundance somatic mutations in a panel of genes that encode components of the EGFR signaling pathway. Mutations with significantly increased variant frequencies upon disease progression were selected by using quartile analysis. The functional consequences of the identified mutations were validated in cultured cells.Results: We analyzed 32 patients with acquired cetuximab resistance in a development cohort. Of them, seven (22%) carried five novel PIK3CA mutations, whereas eight (25%) carried previously reported KRAS mutations. Functional studies showed that novel PIK3CA mutations (all in exon 19; p.K944N, p.F930S, p.V955G, p.V955I, and p.K966E) promote cell viability in the presence of cetuximab. Only one novel PIK3CA mutation (p.K944N) was verified in one of the 27 patients with acquired resistance in a validation cohort, simultaneous KRAS and PIK3CA hotspot mutations were detected in two patients. Among the above 59 acquired resistance patients, those with PIK3CA or RAS mutations detected in ctDNA showed a pronounced decrease in progression-free survival than patients with no mutation.Conclusions: The PIK3CA mutations may potentially contribute to acquired cetuximab resistance in patients with mCRC. Clin Cancer Res; 23(16); 4602-16. ©2017 AACR.

Concurrent methylation and demethylation of arsenic by fungi and their differential expression in the protoplasm proteome.

Microbial methylation and demethylation are central to arsenic's (As) biogeochemical cycling. Here, the transformations of monomethylarsonic acid (MMA(V)) (50 mg L-1) for 15 days in cells of As-methylating fungi, Fusarium oxysporum CZ-8F1, Penicillium janthinellum SM-12F4, and Trichoderma asperellum SM-12F1, were evaluated, and trace concentrations of As(III) and As(V) were observed in fungal cell extracts. Trace amounts of DMA(V) were also detected in MMA(V) and P. janthinellum SM-12F4 incubations. In situ X-ray absorption near edge structure (XANES) indicated that after exposure to MMA(V) (500 mg L-1) for 15 days, 28.6-48.6% of accumulated As in fungal cells was DMA(V), followed by 18.4-30.3% from As(V), 0-28.1% from As(III), and 4.8-28.9% from MMA(V). The concurrent methylation and demethylation of As occurs in fungal cells. Furthermore, a majority of proteins involved in metabolism, transport, ATP activity, biosynthesis, signal transduction, DNA activity, translation, and oxidative stress were upregulated in T. asperellum SM-12F1 cells after MMA(V) exposure compared to As(III), As(V), and DMA(V). The detoxification process of T. asperellum SM-12F1 was As species-specific. Methylenetetrahydrofolate reductase (R7YMH0) donation of a methyl group for S-adenosylmethionine (SAM) generation significantly increased following MMA(V) exposure.