Multi-organ proteomic landscape of COVID-19 autopsies.

The molecular pathology of multi-organ injuries in COVID-19 patients remains unclear, preventing effective therapeutics development. Here, we report a proteomic analysis of 144 autopsy samples from seven organs in 19 COVID-19 patients. We quantified 11,394 proteins in these samples, in which 5,336 were perturbed in the COVID-19 patients compared to controls. Our data showed that cathepsin L1, rather than ACE2, was significantly upregulated in the lung from the COVID-19 patients. Systemic hyperinflammation and dysregulation of glucose and fatty acid metabolism were detected in multiple organs. We also observed dysregulation of key factors involved in hypoxia, angiogenesis, blood coagulation, and fibrosis in multiple organs from the COVID-19 patients. Evidence for testicular injuries includes reduced Leydig cells, suppressed cholesterol biosynthesis, and sperm mobility. In summary, this study depicts a multi-organ proteomic landscape of COVID-19 autopsies that furthers our understanding of the biological basis of COVID-19 pathology.

A Comparative Analysis of Data Analysis Tools for Data-Independent Acquisition Mass Spectrometry.

Data-independent acquisition (DIA) mass spectrometry-based proteomics generates reproducible proteome data. The complex processing of the DIA data has led to the development of multiple data analysis tools. In this study, we assessed the performance of five tools (OpenSWATH, EncyclopeDIA, Skyline, DIA-NN, and Spectronaut) using six DIA datasets obtained from TripleTOF, Orbitrap, and TimsTOF Pro instruments. By comparing identification and quantification metrics and examining shared and unique cross-tool identifications, we evaluated both library-based and library-free approaches. Our findings indicate that library-free approaches outperformed library-based methods when the spectral library had limited comprehensiveness. However, our results also suggest that constructing a comprehensive library still offers benefits for most DIA analyses. This study provides comprehensive guidance for DIA data analysis tools, benefiting both experienced and novice users of DIA-mass spectrometry technology.

Potential Use of Serum Proteomics for Monitoring COVID-19 Progression to Complement RT-PCR Detection.

RT-PCR is the primary method to diagnose COVID-19 and is also used to monitor the disease course. This approach, however, suffers from false negatives due to RNA instability and poses a high risk to medical practitioners. Here, we investigated the potential of using serum proteomics to predict viral nucleic acid positivity during COVID-19. We analyzed the proteome of 275 inactivated serum samples from 54 out of 144 COVID-19 patients and shortlisted 42 regulated proteins in the severe group and 12 in the non-severe group. Using these regulated proteins and several key clinical indexes, including days after symptoms onset, platelet counts, and magnesium, we developed two machine learning models to predict nucleic acid positivity, with an AUC of 0.94 in severe cases and 0.89 in non-severe cases, respectively. Our data suggest the potential of using a serum protein-based machine learning model to monitor COVID-19 progression, thus complementing swab RT-PCR tests. More efforts are required to promote this approach into clinical practice since mass spectrometry-based protein measurement is not currently widely accessible in clinic.

ProteomeExpert: a Docker image-based web server for exploring, modeling, visualizing and mining quantitative proteomic datasets.

SUMMARY: The rapid progresses of high-throughput sequencing technology-based omics and mass spectrometry-based proteomics, such as data-independent acquisition and its penetration to clinical studies have generated increasing number of proteomic datasets containing hundreds to thousands of samples. To analyze these quantitative proteomic datasets and other omics (e.g. transcriptomics and metabolomics) datasets more efficiently and conveniently, we present a web server-based software tool ProteomeExpert implemented in Docker, which offers various analysis tools for experimental design, data mining, interpretation and visualization of quantitative proteomic datasets. ProteomeExpert can be deployed on an operating system with Docker installed or with R language environment. AVAILABILITY AND IMPLEMENTATION: The Docker image of ProteomeExpert is freely available from https://hub.docker.com/r/lifeinfo/proteomeexpert. The source code of ProteomeExpert is also openly accessible at http://www.github.com/guomics-lab/ProteomeExpert/. In addition, a demo server is provided at https://proteomic.shinyapps.io/peserver/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Proteomic and metabolomic investigation of serum lactate dehydrogenase elevation in COVID-19 patients.

Serum lactate dehydrogenase (LDH) has been established as a prognostic indicator given its differential expression in COVID-19 patients. However, the molecular mechanisms underneath remain poorly understood. In this study, 144 COVID-19 patients were enrolled to monitor the clinical and laboratory parameters over 3 weeks. Serum LDH was shown elevated in the COVID-19 patients on admission and declined throughout disease course, and its ability to classify patient severity outperformed other biochemical indicators. A threshold of 247 U/L serum LDH on admission was determined for severity prognosis. Next, we classified a subset of 14 patients into high- and low-risk groups based on serum LDH expression and compared their quantitative serum proteomic and metabolomic differences. The results showed that COVID-19 patients with high serum LDH exhibited differentially expressed blood coagulation and immune responses including acute inflammatory responses, platelet degranulation, complement cascade, as well as multiple different metabolic responses including lipid metabolism, protein ubiquitination and pyruvate fermentation. Specifically, activation of hypoxia responses was highlighted in patients with high LDH expressions. Taken together, our data showed that serum LDH levels are associated with COVID-19 severity, and that elevated serum LDH might be consequences of hypoxia and tissue injuries induced by inflammation.

Computational Optimization of Spectral Library Size Improves DIA-MS Proteome Coverage and Applications to 15 Tumors.

Efficient peptide and protein identifications from data-independent acquisition mass spectrometric (DIA-MS) data typically rely on a project-specific spectral library with a suitable size. Here, we describe subLib, a computational strategy for optimizing the spectral library for a specific DIA data set based on a comprehensive spectral library, requiring the preliminary analysis of the DIA data set. Compared with the pan-human library strategy, subLib achieved a 41.2% increase in peptide precursor identifications and a 35.6% increase in protein group identifications in a test data set of six colorectal tumor samples. We also applied this strategy to 389 carcinoma samples from 15 tumor data sets: up to a 39.2% increase in peptide precursor identifications and a 19.0% increase in protein group identifications were observed. Our strategy for spectral library size optimization thus successfully proved to deepen the proteome coverages of DIA-MS data.

Quantifying the RNA cap epitranscriptome reveals novel caps in cellular and viral RNA.

Chemical modification of transcripts with 5' caps occurs in all organisms. Here, we report a systems-level mass spectrometry-based technique, CapQuant, for quantitative analysis of an organism's cap epitranscriptome. The method was piloted with 21 canonical caps-m7GpppN, m7GpppNm, GpppN, GpppNm, and m2,2,7GpppG-and 5 'metabolite' caps-NAD, FAD, UDP-Glc, UDP-GlcNAc, and dpCoA. Applying CapQuant to RNA from purified dengue virus, Escherichia coli, yeast, mouse tissues, and human cells, we discovered new cap structures in humans and mice (FAD, UDP-Glc, UDP-GlcNAc, and m7Gpppm6A), cell- and tissue-specific variations in cap methylation, and high proportions of caps lacking 2'-O-methylation (m7Gpppm6A in mammals, m7GpppA in dengue virus). While substantial Dimroth-induced loss of m1A and m1Am arose with specific RNA processing conditions, human lymphoblast cells showed no detectable m1A or m1Am in caps. CapQuant accurately captured the preference for purine nucleotides at eukaryotic transcription start sites and the correlation between metabolite levels and metabolite caps.

Three distinct 3-methylcytidine (m3C) methyltransferases modify tRNA and mRNA in mice and humans.

Chemical RNA modifications are central features of epitranscriptomics, highlighted by the discovery of modified ribonucleosides in mRNA and exemplified by the critical roles of RNA modifications in normal physiology and disease. Despite a resurgent interest in these modifications, the biochemistry of 3-methylcytidine (m3C) formation in mammalian RNAs is still poorly understood. However, the recent discovery of trm141 as the second gene responsible for m3C presence in RNA in fission yeast raises the possibility that multiple enzymes are involved in m3C formation in mammals as well. Here, we report the discovery and characterization of three distinct m3C-contributing enzymes in mice and humans. We found that methyltransferase-like (METTL) 2 and 6 contribute m3C in specific tRNAs and that METTL8 only contributes m3C to mRNA. MS analysis revealed that there is an ∼30-40% and ∼10-15% reduction, respectively, in METTL2 and -6 null-mutant cells, of m3C in total tRNA, and primer extension analysis located METTL2-modified m3C at position 32 of tRNAThr isoacceptors and tRNAArg(CCU) We also noted that METTL6 interacts with seryl-tRNA synthetase in an RNA-dependent manner, suggesting a role for METTL6 in modifying serine tRNA isoacceptors. METTL8, however, modified only mRNA, as determined by biochemical and genetic analyses in Mettl8 null-mutant mice and two human METTL8 mutant cell lines. Our findings provide the first evidence of the existence of m3C modification in mRNA, and the discovery of METTL8 as an mRNA m3C writer enzyme opens the door to future studies of other m3C epitranscriptomic reader and eraser functions.

A systems-level mass spectrometry-based technique for accurate and sensitive quantification of the RNA cap epitranscriptome.

Chemical modifications of transcripts with a 5' cap occur in all organisms and function in many aspects of RNA metabolism. To facilitate analysis of RNA caps, we developed a systems-level mass spectrometry-based technique, CapQuant, for accurate and sensitive quantification of the cap epitranscriptome. The protocol includes the addition of stable isotope-labeled cap nucleotides (CNs) to RNA, enzymatic hydrolysis of endogenous RNA to release CNs, and off-line enrichment of CNs by ion-pairing high-pressure liquid chromatography, followed by a 17 min chromatography-coupled tandem quadrupole mass spectrometry run for the identification and quantification of individual CNs. The total time required for the protocol can be up to 7 d. In this approach, 26 CNs can be quantified in eukaryotic poly(A)-tailed RNA, bacterial total RNA and viral RNA. This protocol can be modified to analyze other types of RNA and RNA from in vitro sources. CapQuant stands out from other methods in terms of superior specificity, sensitivity and accuracy, and it is not limited to individual caps nor does it require radiolabeling. Thanks to its unique capability of accurately and sensitively quantifying RNA caps on a systems level, CapQuant can reveal both the RNA cap landscape and the transcription start site distribution of capped RNA in a broad range of settings.

Base editing-mediated one-step inactivation of the Dnmt gene family reveals critical roles of DNA methylation during mouse gastrulation.

During embryo development, DNA methylation is established by DNMT3A/3B and subsequently maintained by DNMT1. While much research has been done in this field, the functional significance of DNA methylation in embryogenesis remains unknown. Here, we establish a system of simultaneous inactivation of multiple endogenous genes in zygotes through screening for base editors that can efficiently introduce a stop codon. Embryos with mutations in Dnmts and/or Tets can be generated in one step with IMGZ. Dnmt-null embryos display gastrulation failure at E7.5. Interestingly, although DNA methylation is absent, gastrulation-related pathways are down-regulated in Dnmt-null embryos. Moreover, DNMT1, DNMT3A, and DNMT3B are critical for gastrulation, and their functions are independent of TET proteins. Hypermethylation can be sustained by either DNMT1 or DNMT3A/3B at some promoters, which are related to the suppression of miRNAs. The introduction of a single mutant allele of six miRNAs and paternal IG-DMR partially restores primitive streak elongation in Dnmt-null embryos. Thus, our results unveil an epigenetic correlation between promoter methylation and suppression of miRNA expression for gastrulation and demonstrate that IMGZ can accelerate deciphering the functions of multiple genes in vivo.

High-throughput proteomic sample preparation using pressure cycling technology.

High-throughput lysis and proteolytic digestion of biopsy-level tissue specimens is a major bottleneck for clinical proteomics. Here we describe a detailed protocol of pressure cycling technology (PCT)-assisted sample preparation for proteomic analysis of biopsy tissues. A piece of fresh frozen or formalin-fixed paraffin-embedded tissue weighing ~0.1-2 mg is placed in a 150 µL pressure-resistant tube called a PCT-MicroTube with proper lysis buffer. After closing with a PCT-MicroPestle, a batch of 16 PCT-MicroTubes are placed in a Barocycler, which imposes oscillating pressure to the samples from one atmosphere to up to ~3,000 times atmospheric pressure. The pressure cycling schemes are optimized for tissue lysis and protein digestion, and can be programmed in the Barocycler to allow reproducible, robust and efficient protein extraction and proteolysis digestion for mass spectrometry-based proteomics. This method allows effective preparation of not only fresh frozen and formalin-fixed paraffin-embedded tissue, but also cells, feces and tear strips. It takes ~3 h to process 16 samples in one batch. The resulting peptides can be analyzed by various mass spectrometry-based proteomics methods. We demonstrate the applications of this protocol with mouse kidney tissue and eight types of human tumors.

Proteomic and metabolomic profiling of urine uncovers immune responses in patients with COVID-19.

The utility of the urinary proteome in infectious diseases remains unclear. Here, we analyzed the proteome and metabolome of urine and serum samples from patients with COVID-19 and healthy controls. Our data show that urinary proteins effectively classify COVID-19 by severity. We detect 197 cytokines and their receptors in urine, but only 124 in serum using TMT-based proteomics. The decrease in urinary ESCRT complex proteins correlates with active SARS-CoV-2 replication. The downregulation of urinary CXCL14 in severe COVID-19 cases positively correlates with blood lymphocyte counts. Integrative multiomics analysis suggests that innate immune activation and inflammation triggered renal injuries in patients with COVID-19. COVID-19-associated modulation of the urinary proteome offers unique insights into the pathogenesis of this disease. This study demonstrates the added value of including the urinary proteome in a suite of multiomics analytes in evaluating the immune pathobiology and clinical course of COVID-19 and, potentially, other infectious diseases.

High-throughput proteomics and AI for cancer biomarker discovery.

Biomarkers are assayed to assess biological and pathological status. Recent advances in high-throughput proteomic technology provide opportunities for developing next generation biomarkers for clinical practice aided by artificial intelligence (AI) based techniques. We summarize the advances and limitations of cancer biomarkers based on genomic and transcriptomic analysis, as well as classical antibody-based methodologies. Then we review recent progresses in mass spectrometry (MS)-based proteomics in terms of sample preparation, peptide fractionation by liquid chromatography (LC) and mass spectrometric data acquisition. We highlight applications of AI techniques in high-throughput clinical studies as compared with clinical decisions based on singular features. This review sets out our approach for discovering clinical biomarkers in studies using proteomic big data technology conjoined with computational and statistical methods.

Embryonic liver developmental trajectory revealed by single-cell RNA sequencing in the Foxa2eGFP mouse.

The liver and gallbladder are among the most important internal organs derived from the endoderm, yet the development of the liver and gallbladder in the early embryonic stages is not fully understood. Using a transgenic Foxa2eGFP reporter mouse line, we performed single-cell full-length mRNA sequencing on endodermal and hepatic cells isolated from ten embryonic stages, ranging from E7.5 to E15.5. We identified the embryonic liver developmental trajectory from gut endoderm to hepatoblasts and characterized the transcriptome of the hepatic lineage. More importantly, we identified liver primordium as the nascent hepatic progenitors with both gut and liver features and documented dynamic gene expression during the epithelial-hepatic transition (EHT) at the stage of liver specification during E9.5-11.5. We found six groups of genes switched on or off in the EHT process, including diverse transcripitional regulators that had not been previously known to be expressed during EHT. Moreover, we identified and revealed transcriptional profiling of gallbladder primordium at E9.5. The present data provides a high-resolution resource and critical insights for understanding the liver and gallbladder development.

The STAT3 Target Mettl8 Regulates Mouse ESC Differentiation via Inhibiting the JNK Pathway.

The capacity of embryonic stem cells (ESCs) to differentiate into all lineages of mature organism is precisely regulated by cellular signaling factors. STAT3 is a crucial transcription factor that plays a central role in maintaining ESC identity. However, the underlying mechanism by which STAT3 directs differentiation is still not completely understood. Here, we show that STAT3 positively regulates gene expression of methyltransferase-like protein 8 (Mettl8) in mouse ESCs. We found that METTL8 is dispensable for pluripotency but affects ESC differentiation. Subsequently, we discovered that METTL8 interacts with Mapkbp1's mRNA, which is an intermediate factor in c-Jun N-terminal kinase (JNK) signaling, and inhibits the translation of the mRNA. Thereby, METTL8 prohibits the activation of JNK signaling and enhances the differentiation of mouse ESCs. Collectively, our study uncovers a STAT3 target, Mettl8, which regulates mouse ESC differentiation via JNK signaling.