Proteomic and Metabolomic Characterization of COVID-19 Patient Sera.

Early detection and effective treatment of severe COVID-19 patients remain major challenges. Here, we performed proteomic and metabolomic profiling of sera from 46 COVID-19 and 53 control individuals. We then trained a machine learning model using proteomic and metabolomic measurements from a training cohort of 18 non-severe and 13 severe patients. The model was validated using 10 independent patients, 7 of which were correctly classified. Targeted proteomics and metabolomics assays were employed to further validate this molecular classifier in a second test cohort of 19 COVID-19 patients, leading to 16 correct assignments. We identified molecular changes in the sera of COVID-19 patients compared to other groups implicating dysregulation of macrophage, platelet degranulation, complement system pathways, and massive metabolic suppression. This study revealed characteristic protein and metabolite changes in the sera of severe COVID-19 patients, which might be used in selection of potential blood biomarkers for severity evaluation.

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.

DIA-Based Proteomics Identifies IDH2 as a Targetable Regulator of Acquired Drug Resistance in Chronic Myeloid Leukemia.

Drug resistance is a critical obstacle to effective treatment in patients with chronic myeloid leukemia. To understand the underlying resistance mechanisms in response to imatinib mesylate (IMA) and adriamycin (ADR), the parental K562 cells were treated with low doses of IMA or ADR for 2 months to generate derivative cells with mild, intermediate, and severe resistance to the drugs as defined by their increasing resistance index. PulseDIA-based (DIA [data-independent acquisition]) quantitative proteomics was then employed to reveal the proteome changes in these resistant cells. In total, 7082 proteins from 98,232 peptides were identified and quantified from the dataset using four DIA software tools including OpenSWATH, Spectronaut, DIA-NN, and EncyclopeDIA. Sirtuin signaling pathway was found to be significantly enriched in both ADR-resistant and IMA-resistant K562 cells. In particular, isocitrate dehydrogenase (NADP(+)) 2 was identified as a potential drug target correlated with the drug resistance phenotype, and its inhibition by the antagonist AGI-6780 reversed the acquired resistance in K562 cells to either ADR or IMA. Together, our study has implicated isocitrate dehydrogenase (NADP(+)) 2 as a potential target that can be therapeutically leveraged to alleviate the drug resistance in K562 cells when treated with IMA and ADR.

Modulation of Gut Microbial Biomarkers and Metabolites in Cancer Management by Tea Compounds.

Cancers are causing millions of deaths and leaving a huge clinical and economic burden. High costs of cancer drugs are limiting their access to the growing number of cancer cases. The development of more affordable alternative therapy could reach more patients. As gut microbiota plays a significant role in the development and treatment of cancer, microbiome-targeted therapy has gained more attention in recent years. Dietary and natural compounds can modulate gut microbiota composition while providing broader and more accessible access to medicine. Tea compounds have been shown to have anti-cancer properties as well as modulate the gut microbiota and their related metabolites. However, there is no comprehensive review that focuses on the gut modulatory effects of tea compounds and their impact on reshaping the metabolic profiles, particularly in cancer models. In this review, the effects of different tea compounds on gut microbiota in cancer settings are discussed. Furthermore, the relationship between these modulated bacteria and their related metabolites, along with the mechanisms of how these changes led to cancer intervention are summarized.

COVIDpro: Database for Mining Protein Dysregulation in Patients with COVID-19.

The ongoing pandemic of the coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 still has limited treatment options. Our understanding of the molecular dysregulations that occur in response to infection remains incomplete. We developed a web application COVIDpro (https://www.guomics.com/covidPro/) that includes proteomics data obtained from 41 original studies conducted in 32 hospitals worldwide, involving 3077 patients and covering 19 types of clinical specimens, predominantly plasma and serum. The data set encompasses 53 protein expression matrices, comprising a total of 5434 samples and 14,403 unique proteins. We identified a panel of proteins that exhibit significant dysregulation, enabling the classification of COVID-19 patients into severe and non-severe disease categories. The proteomic signatures achieved promising results in distinguishing severe cases, with a mean area under the curve of 0.87 and accuracy of 0.80 across five independent test sets. COVIDpro serves as a valuable resource for testing hypotheses and exploring potential targets for novel treatments in COVID-19 patients.

High-breathable, antimicrobial and water-repellent face mask for breath monitoring.

Face masks with multiple functionalities and exceptional durability have attracted increasing interests during the COVID-19 pandemic. How to integrate the antibacterial property, comfortability during long-time wearing, and breath monitoring capability together on a face mask is still challenging. Here we developed a kind of face mask that assembles the particles-free water-repellent fabric, antibacterial fabric, and hidden breath monitoring device together, resulting in the highly breathable, water-repellent, and antibacterial face mask with breath monitoring capability. Based on the rational design of the functional layers, the mask shows exceptional repellency to micro-fogs generated during breathing while maintaining high air permeability and inhibiting the passage of bacteria-containing aerogel. More importantly, the multi-functional mask can also monitor the breath condition in a wireless and real-time fashion, and collect the breath information for epidemiological analysis. The resultant mask paves the way to develop multi-functional breath-monitoring masks that can aid the prevention of the secondary transmission of bacteria and viruses while preventing potential discomfort and face skin allergy during long-period wearing.

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.

Integration of protein context improves protein-based COVID-19 patient stratification.

BACKGROUND: Classification of disease severity is crucial for the management of COVID-19. Several studies have shown that individual proteins can be used to classify the severity of COVID-19. Here, we aimed to investigate whether integrating four types of protein context data, namely, protein complexes, stoichiometric ratios, pathways and network degrees will improve the severity classification of COVID-19. METHODS: We performed machine learning based on three previously published datasets. The first was a SWATH (sequential window acquisition of all theoretical fragment ion spectra) MS (mass spectrometry) based proteomic dataset. The second was a TMTpro 16plex labeled shotgun proteomics dataset. The third was a SWATH dataset of an independent patient cohort. RESULTS: Besides twelve proteins, machine learning also prioritized two complexes, one stoichiometric ratio, five pathways, and five network degrees, resulting a 25-feature panel. As a result, a model based on the 25 features led to effective classification of severe cases with an AUC of 0.965, outperforming the models with proteins only. Complement component C9, transthyretin (TTR) and TTR-RBP (transthyretin-retinol binding protein) complex, the stoichiometric ratio of SAA2 (serum amyloid A proteins 2)/YLPM1 (YLP Motif Containing 1), and the network degree of SIRT7 (Sirtuin 7) and A2M (alpha-2-macroglobulin) were highlighted as potential markers by this classifier. This classifier was further validated with a TMT-based proteomic data set from the same cohort (test dataset 1) and an independent SWATH-based proteomic data set from Germany (test dataset 2), reaching an AUC of 0.900 and 0.908, respectively. Machine learning models integrating protein context information achieved higher AUCs than models with only one feature type. CONCLUSION: Our results show that the integration of protein context including protein complexes, stoichiometric ratios, pathways, network degrees, and proteins improves phenotype prediction.

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.

BatchServer: A Web Server for Batch Effect Evaluation, Visualization, and Correction.

Batch effects are unwanted data variations that may obscure biological signals, leading to bias or errors in subsequent data analyses. Effective evaluation and elimination of batch effects are necessary for omics data analysis. In order to facilitate the evaluation and correction of batch effects, here we present BatchSever, an open-source R/Shiny based user-friendly interactive graphical web platform for batch effects analysis. In BatchServer, we introduced autoComBat, a modified version of ComBat, which is the most widely adopted tool for batch effect correction. BatchServer uses PVCA (Principal Variance Component Analysis) and UMAP (Manifold Approximation and Projection) for evaluation and visualization of batch effects. We demonstrate its applications in multiple proteomics and transcriptomic data sets. BatchServer is provided at https://lifeinfor.shinyapps.io/batchserver/ as a web server. The source codes are freely available at https://github.com/guomics-lab/batch_server.

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.

Transcriptome wide analysis of long non-coding RNA-associated ceRNA regulatory circuits in psoriasis.

Long non-coding RNAs (lncRNAs) play critical roles in regulating immune-associated diseases and chronic inflammatory disorders. Here, we found that lncRNAs involve in the pathogenesis of psoriasis through integrative analysis of RNA-seq data sets from a psoriasis cohort. Then, lncRNA-protein-coding genes (PCGs) co-expression network analysis demonstrated that lncRNAs extensively interact with IFN-γ signalling pathway-associated genes. Further, we validated 3 lncRNAs associate with IFN-γ signalling pathway activation upon IFN-γ stimulated in HaCaT cells, and loss of function experiments indicate their functional roles in the activation of inflammatory cytokine genes. Additionally, microRNA target screening analysis showed that lncRNAs may regulate JAK/STAT pathway activity through complete endogenous RNA (ceRNA) mechanism. Further experimental validation of PRKCQ-AS1/STAT1/miR-545-5p regulatory circuitry showed that lncRNAs regulate the expression of JAK/STAT signalling pathway genes through competing for miR-545-5p. In summary, our results demonstrated that dysregulation of lncRNA-JAK/STAT pathway axis promotes the inflammation level in psoriasis and thus provide potential therapeutic targets for psoriasis treatments.

Evaluation of short-term (16-week) effectiveness and safety of guselkumab in patients with psoriasis: A prospective real-life study on the Chinese population.

Real-life data on guselkumab in psoriasis are limited and not available in China hitherto. This study aimed to evaluate the short-term effectiveness and safety of guselkumab in patients with psoriasis under Chinese real-life conditions and to explore the effect of guselkumab on CD4+ CD25+ Foxp3+ regulatory T cells (Tregs). A Chinese prospective and real-life study involving patients with psoriasis in Dermatology Hospital of Southern Medical University, Guangzhou, China from April to September 2020 was conducted. A total of 45 patients with psoriasis were finally enrolled in the study. Psoriasis Area Severity Index (PASI) 90 and 100 responses at week 16 were achieved by 88.6% and 45.5% of patients, respectively. The analysis of PASI response in different subgroups showed no statistically significant difference. Univariate logistic regression analysis revealed that at week 16, none of the variables were associated with decreasing PASI 90 response, whereas age at onset of disease was a predictor of PASI 100 response. Dynamic detection of CD4+ CD25+ Foxp3+ Tregs frequency from peripheral blood suggested a stable maintained trend in terms of guselkumab treatment duration. No severe adverse events occurred during the follow-up period. This study confirmed the short-term effectiveness and safety of guselkumab, as well as its good tolerance against psoriasis, in the Chinese population. Guselkumab treatment maintains levels of Tregs in patients with psoriasis.

Data-Independent Acquisition Mass Spectrometry-Based Proteomics and Software Tools: A Glimpse in 2020.

This review provides a brief overview of the development of data-independent acquisition (DIA) mass spectrometry-based proteomics and selected DIA data analysis tools. Various DIA acquisition schemes for proteomics are summarized first including Shotgun-CID, DIA, MSE , PAcIFIC, AIF, SWATH, MSX, SONAR, WiSIM, BoxCar, Scanning SWATH, diaPASEF, and PulseDIA, as well as the mass spectrometers enabling these methods. Next, the software tools for DIA data analysis are classified into three groups: library-based tools, library-free tools, and statistical validation tools. The approaches are reviewed for generating spectral libraries for six selected library-based DIA data analysis software tools which are tested by the authors, including OpenSWATH, Spectronaut, Skyline, PeakView, DIA-NN, and EncyclopeDIA. An increasing number of library-free DIA data analysis tools are developed including DIA-Umpire, Group-DIA, PECAN, PEAKS, which facilitate identification of novel proteoforms. The authors share their user experience of when to use DIA-MS, and several selected DIA data analysis software tools. Finally, the state of the art DIA mass spectrometry and software tools, and the authors' views of future directions are summarized.

Generating Proteomic Big Data for Precision Medicine.

Here, the authors reason that the complexity of medical problems and proteome science might be tackled effectively with deep learning (DL) technology. However, deployment of DL for proteomics data requires the acquisition of data sets from a large number of samples. Based on the success of DL in medical imaging classification, proteome data from thousands of samples are arguably the minimal input for DL. Contemporary proteomics is turning high-throughput thanks to the rapid progresses of sample preparation and liquid chromatography mass spectrometry methods. In particular, data-independent acquisition now enables the generation of hundreds to thousands of quantitative proteome maps from clinical specimens in clinical cohorts with only limited sample amounts in clinical cohorts. Upheavals in the design of large-scale clinical proteomics studies might be required to generate proteomic big data and deploy DL to tackle complex medical problems.

Accelerated Lysis and Proteolytic Digestion of Biopsy-Level Fresh-Frozen and FFPE Tissue Samples Using Pressure Cycling Technology.

Pressure cycling technology (PCT)-assisted tissue lysis and digestion have facilitated reproducible and high-throughput proteomic studies of both fresh-frozen (FF) and formalin-fixed paraffin-embedded (FFPE) tissue of biopsy scale for biomarker discovery. Here, we present an improved PCT method accelerating the conventional procedures by about two-fold without sacrificing peptide yield, digestion efficiency, peptide, and protein identification. The time required for processing 16 tissue samples from tissues to peptides is reduced from about 6 to about 3 h. We analyzed peptides prepared from FFPE hepatocellular carcinoma (HCC) tissue samples by the accelerated PCT method using multiple MS acquisition methods, including short-gradient SWATH-MS, PulseDIA-MS, and 10-plex TMT-based shotgun MS. The data showed that up to 8541 protein groups could be reliably quantified from the thus prepared peptide samples. We applied the accelerated sample preparation method to 25 pairs (tumorous and matched benign) of HCC samples followed by a single-shot, 15 min gradient SWATH-MS analysis. An average of 18 453 peptides from 2822 proteins were quantified in at least 20% samples in this cohort, while 1817 proteins were quantified in at least 50% samples. The data not only identified the previously known dysregulated proteins such as MCM7, MAPRE1, and SSRP1 but also discovered promising novel protein markers, including DRAP1 and PRMT5. In summary, we present an accelerated PCT protocol that effectively doubles the throughput of PCT-assisted sample preparation of biopsy-level FF and FFPE samples without compromising protein digestion efficiency, peptide yield, and protein identification.

IL-17C has a pathogenic role in kidney ischemia/reperfusion injury.

Cytokines are necessary to trigger the inflammatory response in kidney ischemia/reperfusion injury. Interleukin-17C (IL-17C), a unique member of the IL-17 family, is a cytokine produced by epithelial cells implicated in host defense and autoimmune diseases. However, little is known about the role of IL-17C in acute kidney injury. We investigated this and found that IL-17C was significantly increased in kidney biopsies of patients and mice with acute kidney injury. Exposure to hypoxia induced upregulation of IL-17C in kidney tubular epithelial cells. To further investigate the role of IL-17C, kidney ischemia/reperfusion injury was induced in mice. Inhibition of IL-17C action with a neutralizing antibody or IL-17 receptor E (IL-17RE) knockout attenuated tubular injury, kidney oxidative stress, and kidney inflammation. Mechanistically, both IL-17C neutralization and IL-17RE knockout attenuated TH17 activation and IL-17A expression in kidneys of mice with acute kidney injury. TNF-α and IL-1ß, downstream cytokines of IL-17C, were also reduced in IL-17C antibody pretreated and IL-17RE knockout mice. Additionally, IL-17C knockdown with siRNA decreased hypoxia-induced inflammation in kidney tubular cells and silencing IL-17RE abrogated the effects of IL-17C in kidney tubular cells. Thus, IL-17C may participate in the inflammatory response of acute kidney injury and inhibition of IL-17C or blockade of IL-17 RE may be a novel therapeutic strategy for the treatment of acute kidney injury.

A protective role of renalase in diabetic nephropathy.

Renalase, a recently discovered secreted flavoprotein, exerts anti-apoptotic and anti-inflammatory effects against renal injury in acute and chronic animal models. However, whether Renalase elicits similar effects in the development of diabetic nephropathy (DN) remains unclear. The studies presented here tested the hypothesis that Renalase may play a key role in the development of DN and may have therapeutic potential for DN. Renalase expression was measured in human kidney biopsies with DN and in kidneys of db/db mice. The role of Renalase in the development of DN was examined using a genetically engineered mouse model: Renalase knockout mice with db/db background. The renoprotective effects of Renalase in DN was evaluated in db/db mice with Renalase overexpression. In addition, the effects of Renalase on high glucose-induced mesangial cells were investigated. Renalase was down-regulated in human diabetic kidneys and in kidneys of db/db mice compared with healthy controls or db/m mice. Renalase homozygous knockout increased arterial blood pressure significantly in db/db mice while heterozygous knockout did not. Renalase heterozygous knockout resulted in elevated albuminuria and increased renal mesangial expansion in db/db mice. Mesangial hypertrophy, renal inflammation, and pathological injury in diabetic Renalase heterozygous knockout mice were significantly exacerbated compared with wild-type littermates. Moreover, Renalase overexpression significantly ameliorated renal injury in db/db mice. Mechanistically, Renalase attenuated high glucose-induced profibrotic gene expression and p21 expression through inhibiting extracellular regulated protein kinases (ERK1/2). The present study suggested that Renalase protected against the progression of DN and might be a novel therapeutic target for the treatment of DN.

Characterization of the molecular mechanism of the autophagy-related Atg8-Atg3 protein interaction in Toxoplasma gondii.

Toxoplasmosis is caused by an obligate intracellular parasite, the protozoan Toxoplasma gondii Discovery of novel drugs against T. gondii infection could circumvent the toxicity of existing drugs and T. gondii resistance to current treatments. The autophagy-related protein 8 (Atg8)-Atg3 interaction in T. gondii is a promising drug target because of its importance for regulating Atg8 lipidation. We reported previously that TgAtg8 and TgAtg3 interact directly. Here we validated that substitutions of conserved residues of TgAtg8 interacting with the Atg8 family-interacting motif (AIM) in Atg3 disrupt the TgAtg8-TgAtg3 interaction and reduce TgAtg8 lipidation and autophagosome formation. These findings were consistent with results reported previously for Plasmodium Atg8, suggesting functional conservation of Atg8 in Toxoplasma and Plasmodium. Moreover, using peptide and AlphaScreen assays, we identified the AIM sequence in TgAtg3 that binds TgAtg8. We determined that the core TgAtg3 AIM contains a Phe239-Ala240-Asp241-Ile242 (239FADI242) signature distinct from the 105WLLP108 signature in the AIM of Plasmodium Atg3. Furthermore, an alanine-scanning assay revealed that the TgAtg8-TgAtg3 interaction in T. gondii also depends strongly on several residues surrounding the core TgAtg3 AIM, such as Asn238, Asp243, and Cys244 These results indicate that distinct AIMs in Atg3 contribute to differences between Toxoplasma and Plasmodium Atg8-Atg3 interactions. By elucidating critical residues involved in the TgAtg8-TgAtg3 interaction, our work paves the way for the discovery of potential anti-toxoplasmosis drugs. The quantitative and straightforward AlphaScreen assay developed here may enable high-throughput screening for small molecules disrupting the TgAtg8-TgAtg3 interaction.

Isolation and Complete Genome Sequence of a Novel Pseudoalteromonas Phage PH357 from the Yangtze River Estuary.

Phage PH357, a novel lytic Pseudoalteromonas lipolytica phage belonging to the Myoviridae family was isolated from the Yangtze River estuary. The microbiological characterization demonstrated that phage PH357 is stable from -20 to 60 °C and the optimal pH 7. The one-step growth curve showed a latent period of 20 min, a rise period of 20 min, and the average burst size was about 85 virions per cell. Complete genome of phage PH357 was determined. Genome of phage PH357 consisted of a linear, double-stranded 136,203 bp DNA molecule with 34.58% G + C content, and 242 putative open reading frames (ORFs) without tRNA. All the predicted ORFs were classified into eight functional groups, including DNA replication, regulation and nucleotide metabolism, transcription, translation, phage packaging, phage structure, lysis, host or phage interactions, and hypothetical protein. A phylogenetic analysis showed that phage PH357 had similarity to the previously published Pseudoalteromonas phage PH101 and Vibrio phages. Furthermore, the study of phage PH357 genome will provide useful information for further research on the interaction between phages and their hosts.