Probiotic Mixture Ameliorates a Diet-Induced MASLD/MASH Murine Model through the Regulation of Hepatic Lipid Metabolism and the Gut Microbiome.

Metabolic dysfunction-associated steatotic liver disease (MASLD) is a prevalent metabolic disease that has no effective treatment. Our proprietary probiotic mixture, Prohep, has been proven in a previous study to be helpful in reducing hepatocellular carcinoma (HCC) in vivo. However, its prospective benefits on the treatment of other liver diseases such as MASLD, which is one of the major risk factors in the development of HCC, are unclear. To investigate the potential of Prohep in modulating the development and progression of MASLD, we first explored the effect of Prohep supplementation via voluntary intake in a high-fat diet (HFD)-induced MASLD/metabolic dysfunction-associated steatohepatitis (MASH) murine model. Our results indicated that Prohep alleviated HFD-induced liver steatosis and reduced excessive hepatic lipid accumulation and improved the plasma lipid profile when compared with HFD-fed control mice through suppressing hepatic de novo lipogenesis and cholesterol biosynthesis gene expressions. In addition, Prohep was able to modulate the gut microbiome, modify the bile acid (BA) profile, and elevate fecal short-chain fatty acid (SCFA) levels. Next, in a prolonged HFD-feeding MASLD/MASH model, we observed the effectiveness of Prohep in preventing the transition from MASLD to MASH via amelioration in hepatic steatosis, inflammation, and fibrosis. Taken together, Prohep could ameliorate HFD-induced MASLD and control the MASLD-to-MASH progression in mice. Our findings provide distinctive insights into the development of novel microbial therapy for the management of MASLD and MASH.

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.

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.

DPHL v.2: An updated and comprehensive DIA pan-human assay library for quantifying more than 14,000 proteins.

A comprehensive pan-human spectral library is critical for biomarker discovery using mass spectrometry (MS)-based proteomics. DPHL v.1, a previous pan-human library built from 1,096 data-dependent acquisition (DDA) MS data of 16 human tissue types, allows quantifying of 10,943 proteins. Here, we generated DPHL v.2 from 1,608 DDA-MS data. The data included 586 DDA-MS data acquired from 18 tissue types, while 1,022 files were derived from DPHL v.1. DPHL v.2 thus comprises data from 24 sample types, including several cancer types (lung, breast, kidney, and prostate cancer, among others). We generated four variants of DPHL v.2 to include semi-tryptic peptides and protein isoforms. DPHL v.2 was then applied to two colorectal cancer cohorts. The numbers of identified and significantly dysregulated proteins increased by at least 21.7% and 14.2%, respectively, compared with DPHL v.1. Our findings show that the increased human proteome coverage of DPHL v.2 provides larger pools of potential protein biomarkers.

Dear-DIAXMBD: Deep Autoencoder Enables Deconvolution of Data-Independent Acquisition Proteomics.

Data-independent acquisition (DIA) technology for protein identification from mass spectrometry and related algorithms is developing rapidly. The spectrum-centric analysis of DIA data without the use of spectra library from data-dependent acquisition data represents a promising direction. In this paper, we proposed an untargeted analysis method, Dear-DIAXMBD, for direct analysis of DIA data. Dear-DIAXMBD first integrates the deep variational autoencoder and triplet loss to learn the representations of the extracted fragment ion chromatograms, then uses the k-means clustering algorithm to aggregate fragments with similar representations into the same classes, and finally establishes the inverted index tables to determine the precursors of fragment clusters between precursors and peptides and between fragments and peptides. We show that Dear-DIAXMBD performs superiorly with the highly complicated DIA data of different species obtained by different instrument platforms. Dear-DIAXMBD is publicly available at https://github.com/jianweishuai/Dear-DIA-XMBD.

Zearalenone attenuates colitis associated colorectal tumorigenesis through Ras/Raf/ERK pathway suppression and SCFA-producing bacteria promotion.

The high prevalence of colorectal cancer (CRC) and its leading death causing rate have placed a considerable burden on patients and healthcare providers. There is a need for a therapy that has fewer adverse effects and greater efficiency. Zearalenone (ZEA), an estrogenic mycotoxin, has been demonstrated to exert apoptotic properties when administrated in higher doses. However, it is unclear whether such apoptotic effect remains valid in an in vivo setting. The current study aimed to investigate the effect of ZEA on CRC and its underlying mechanisms in the azoxymethane/ dextran sodium sulfate (AOM/DSS) model. Our results revealed that ZEA significantly lowered the total number of tumours, colon weight, colonic crypt depth, collagen fibrosis and spleen weight. ZEA suppressed Ras/Raf/ERK/cyclin D1 pathway, increasing the expression of apoptosis parker, cleaved caspase 3, while decreasing the expression of proliferative marker, Ki67 and cyclin D1. The gut microbiota composition in ZEA group showed higher stability and lower vulnerability in the microbial community when compared to AOM/DSS group. ZEA increased the abundance of short chain fatty acids (SCFAs) producing bacteria unidentified Ruminococcaceae, Parabacteroidies and Blautia, as well as the faecal acetate content. Notably, unidentified Ruminococcaceae and Parabacteroidies were substantially correlated with the decrease in tumour count. Overall, ZEA demonstrated a promising inhibitory effect on colorectal tumorigenesis and exhibited the potential for further development as a CRC treatment.

IL-17C neutralization protects the kidney against acute injury and chronic injury.

BACKGROUND: Interleukin-17C (IL-17C), a member of the IL-17 cytokine family, plays a pathogenic role in kidney diseases. Our previous studies have shown that pre-administration of IL-17C neutralizing antibody attenuated acute kidney injury (AKI, a common acute inflammation associated renal disease). In this study, we explored whether post-ischemia reperfusion (IR) of IL-17C blockade has therapeutic effects on AKI and whether IL-17C is involved in the pathogenesis of diabetic nephropathy (DN), a major type of chronic inflammation-associated kidney disease. METHODS: 12-week-old male C57BL/6JGpt mice were treated with IL-17C neutralizing antibody or normal IgG control antibody at 3 h after reperfusion. Renal injury, inflammation, and oxidative stress were assessed. Additionally, we examined renal IL-17C expression in patients with DN and db/db mice and evaluated albuminuria, mesangial matrix accumulation and podocyte loss in db/db mice with IL-17C neutralization. Knockdown of NF-κB p65 using siRNA, and blocking Hypoxia-inducible factor-1α (HIF-1α) using YC-1 in mice and HIF-1α Decoy in HK2 cells were investigated to explore the possible signaling pathway involved in IL-17C regulation. FINDINGS: We found that delayed IL-17C neutralization had similar reno-protective effects on renal ischemia-reperfusion injury (IRI). Additionally, renal IL-17C expression was increased in patients with DN and db/db mice, while IL-17C blockade significantly attenuated DN, accompanied with blunted albuminuria, mesangial matrix accumulation, and podocyte loss. Moreover, IL-17C neutralization significantly repressed the expression of downstream pro-inflammatory cytokines, inflammatory cell infiltration, and Th17/IL-17A activation both in mice with renal IRI and DN. Mechanistical studies demonstrated that hypoxia or high glucose-induced IL-17C up-regulation was predominantly mediated by NF-κB pathway. INTERPRETATION: IL-17C participates in the pathogenesis of AKI and DN and inhibition of IL-17C shows potential as a therapeutic strategy for AKI and DN. FUNDING: The National Natural Science Foundation of China (81770741, 81700601 and 81870504).

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.

COVIDpro: Database for mining protein dysregulation in patients with COVID-19.

Background: The ongoing pandemic of the coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) still has limited treatment options partially due to our incomplete understanding of the molecular dysregulations of the COVID-19 patients. We aimed to generate a repository and data analysis tools to examine the modulated proteins underlying COVID-19 patients for the discovery of potential therapeutic targets and diagnostic biomarkers. Methods: We built a web server containing proteomic expression data from COVID-19 patients with a toolset for user-friendly data analysis and visualization. The web resource covers expert-curated proteomic data from COVID-19 patients published before May 2022. The data were collected from ProteomeXchange and from select publications via PubMed searches and aggregated into a comprehensive dataset. Protein expression by disease subgroups across projects was compared by examining differentially expressed proteins. We also visualize differentially expressed pathways and proteins. Moreover, circulating proteins that differentiated severe cases were nominated as predictive biomarkers. Findings: We built and maintain a web server COVIDpro ( https://www.guomics.com/covidPro/ ) containing proteomics data generated by 41 original studies from 32 hospitals worldwide, with data from 3077 patients covering 19 types of clinical specimens, the majority from plasma and sera. 53 protein expression matrices were collected, for a total of 5434 samples and 14,403 unique proteins. Our analyses showed that the lipopolysaccharide-binding protein, as identified in the majority of the studies, was highly expressed in the blood samples of patients with severe disease. A panel of significantly dysregulated proteins was identified to separate patients with severe disease from non-severe disease. Classification of severe disease based on these proteomic signatures on five test sets reached a mean AUC of 0.87 and ACC of 0.80. Interpretation: COVIDpro is an online database with an integrated analysis toolkit. It is a unique and valuable resource for testing hypotheses and identifying proteins or pathways that could be targeted by new treatments of COVID-19 patients. Funding: National Key R&D Program of China: Key PDPM technologies (2021YFA1301602, 2021YFA1301601, 2021YFA1301603), Zhejiang Provincial Natural Science Foundation for Distinguished Young Scholars (LR19C050001), Hangzhou Agriculture and Society Advancement Program (20190101A04), National Natural Science Foundation of China (81972492) and National Science Fund for Young Scholars (21904107), National Resource for Network Biology (NRNB) from the National Institute of General Medical Sciences (NIGMS-P41 GM103504). Research in context: Evidence before this study: Although an increasing number of therapies against COVID-19 are being developed, they are still insufficient, especially with the rise of new variants of concern. This is partially due to our incomplete understanding of the disease’s mechanisms. As data have been collected worldwide, several questions are now worth addressing via meta-analyses. Most COVID-19 drugs function by targeting or affecting proteins. Effectiveness and resistance to therapeutics can be effectively assessed via protein measurements. Empowered by mass spectrometry-based proteomics, protein expression has been characterized in a variety of patient specimens, including body fluids (e.g., serum, plasma, urea) and tissue (i.e., formalin-fixed and paraffin-embedded (FFPE)). We expert-curated proteomic expression data from COVID-19 patients published before May 2022, from the largest proteomic data repository ProteomeXhange as well as from literature search engines. Using this resource, a COVID-19 proteome meta-analysis could provide useful insights into the mechanisms of the disease and identify new potential drug targets.Added value of this study: We integrated many published datasets from patients with COVID-19 from 11 nations, with over 3000 patients and more than 5434 proteome measurements. We collected these datasets in an online database, and generated a toolbox to easily explore, analyze, and visualize the data. Next, we used the database and its associated toolbox to identify new proteins of diagnostic and therapeutic value for COVID-19 treatment. In particular, we identified a set of significantly dysregulated proteins for distinguishing severe from non-severe patients using serum samples.Implications of all the available evidence: COVIDpro will support the navigation and analysis of patterns of dysregulated proteins in various COVID-19 clinical specimens for identification and verification of protein biomarkers and potential therapeutic targets.

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.

Enzymatic nucleosome acetylation selectively affects activity of histone methyltransferases in vitro.

Posttranslational modification of histones plays a critical role in regulation of gene expression. These modifications include methylation and acetylation that work in combination to establish transcriptionally active or repressive chromatin states. Histone methyltransferases (HMTs) often have variable levels of activity in vitro depending on the form of substrate used. For example, certain HMTs prefer nucleosomes extracted from human or chicken cells as substrate compared to recombinant nucleosomes reconstituted from bacterially produced histones. We considered that pre-existing histone modifications in the extracted nucleosomes can affect the efficiency of catalysis by HMTs, suggesting functional cross-talk between histone-modifying enzymes within a complex network of interdependent activities. Here we systematically investigated the effect of nucleosome acetylation by EP300, GCN5L2 (KAT2A) and MYST1 (MOF) on histone 3 lysine 4 (H3K4), H3K9 and H4K20 methylation of nucleosomes by nine HMTs (MLL1, MLL3, SET1B, G9a, SETDB1, SUV39H1, SUV39H2, SUV420H1 and SUV420H2) in vitro. Our full kinetic characterization data indicate that site-specific acetylation of nucleosomal histones by specific acetyltransferases can create nucleosomes that are better substrates for specific HMTs. This includes significant increases in catalytic efficiencies of SETDB1, G9a and SUV420H2 after nucleosome acetylation in vitro.

Stratification of follicular thyroid tumours using data-independent acquisition proteomics and a comprehensive thyroid tissue spectral library.

Thyroid nodules occur in about 60% of the population. A major challenge in thyroid nodule diagnosis is to distinguish between follicular adenoma (FA) and carcinoma (FTC). Here, we present a comprehensive thyroid spectral library covering five types of thyroid tissues. This library includes 121 960 peptides and 9941 protein groups. This spectral library can be used to quantify up to 7863 proteins from thyroid tissues, and can also be used to develop parallel reaction monitoring (PRM) assays for targeted protein quantification. Next, to stratify follicular thyroid tumours, we compared the proteomes of 24 FA and 22 FTC samples, and identified 204 differentially expressed proteins (DEPs). Our data suggest altered ferroptosis pathways in malignant follicular carcinoma. In all, 31 selected proteins effectively distinguished follicular tumours. Of those DEPs, nine proteins were further verified by PRM in an independent cohort of 18 FA and 19 FTC. Together, we present a comprehensive spectral library for DIA and targeted proteomics analysis of thyroid tissue specimens, and identified nine proteins that could potentially distinguish FA and FTC.

MicroRNA-17-3p is upregulated in psoriasis and regulates keratinocyte hyperproliferation and pro-inflammatory cytokine secretion by targeting CTR9.

Psoriasis is a chronic inflammatory skin disease. Although miRNAs are reported to be associated with the pathogenesis of psoriasis, the contribution of individual microRNAs toward psoriasis remains unclear. The miR-17-92 cluster regulates cell growth and immune functions that are associated with psoriasis. miR-17-3p is a member of miR-17-92 cluster; however, its role in dermatological diseases remains unclear. Our study aims at investigating the effects of miR-17-3p and its potential target gene on keratinocytes proliferation and secretion of pro-inflammatory cytokine and their involvement in psoriasis. Initially, we found that miR-17-3p was upregulated in psoriatic skin lesions, and bioinformatic analyses suggested that CTR9 is likely to be a target gene of miR-17-3p. Quantitative reverse-transcriptase PCR and immunohistochemical analysis revealed that CTR9 expression was downregulated in psoriatic lesions. Using dual-luciferase reporter assays, we identified CTR9 as a direct target of miR-17-3p. Further functional experiments demonstrated that miR-17-3p promoted the proliferation and pro-inflammatory cytokine secretion of keratinocytes, whereas CTR9 exerted the opposite effects. Gain-of-function studies confirmed that CTR9 suppression partially accounted for the effects of miR-17-3p in keratinocytes. Furthermore, Western blot revealed that miR-17-3p activates the downstream STAT3 signaling pathway while CTR9 inactivates the STAT3 signaling pathway. Together, these findings indicate that miR-17-3p regulates keratinocyte proliferation and pro-inflammatory cytokine secretion partially by targeting the CTR9, which inactivates the downstream STAT3 protein, implying that miR-17-3p might be a novel therapeutic target for psoriasis.

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.

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.

Cardiac-specific renalase overexpression alleviates CKD-induced pathological cardiac remodeling in mice.

Introduction: CKD-induced pathological cardiac remodeling is characterized by myocardial hypertrophy and cardiac fibrosis. The available therapeutic options are limited, it is thus urgently needed to identify novel therapeutic targets. Renalase (RNLS) is a newly discovered protein secreted by the kidney and was found beneficial in many renal diseases. But whether it exerts protective effects on cardiac remodeling in CKD remains unclear. Methods: RNLS knockout (KO) and wild-type (WT) mice were both used to build CKD models and the adeno-associated virus (AAV9) system was used to overexpress RNLS cardiac specifically. Echocardiography was performed to detect cardiac structural changes every 6 weeks until 18 weeks post-surgery. High throughput sequencing was performed to understand the underlying mechanisms and the effects of RNLS on cardiac fibroblasts were validated in vitro. Results: Knockout of RNLS aggravated cardiac remodeling in CKD, while RNLS cardiac-specific overexpression significantly reduced left ventricular hypertrophy and cardiac fibrosis induced by CKD. The following RNA-sequencing analysis revealed that RNLS significantly downregulated the extracellular matrix (ECM) receptor interaction pathway, ECM organization, and several ECM-related proteins. GSEA results showed RNLS significantly downregulated several profibrotic biological processes of cardiac fibroblasts which were upregulated by CKD, including fibroblast proliferation, leukocyte migration, antigen presentation, cytokine production, and epithelial-mesenchymal transition (EMT). In vitro, we validated that RNLS reduced the primary cardiac fibroblast proliferation and α-SMA expression stimulated by TGF-ß. Conclusion: In this study, we examined the cardioprotective role of RNLS in CKD-induced cardiac remodeling. RNLS may be a potential therapeutic factor that exerts an anti-fibrotic effect in pathological cardiac remodeling.

Data-driven design and controllable synthesis of Pt/carbon electrocatalysts for H2 evolution.

To achieve net-zero emissions, a particular interest has been raised in the electrochemical evolution of H2 by using catalysts. Considering the complexity of designing catalyst, we demonstrate a data-driven strategy to develop optimized catalysts for H2 evolution. This work starts by collecting data of Pt/carbon catalysts, and applying machine learning to reveal the importance of ranking various features. The algorithms reveal that the Pt content and Pt size have the greatest impact on the catalyst overpotentials. Following the data-driven analysis, a space-confined method is used to fabricate the size-controllable Pt nanoclusters that anchor on nitrogen-doped (N-doped) mesoporous carbon nanosheet network. The obtained catalysts use less platinum and exhibit better catalytic activity than current commercial catalysts in alkaline electrolytes. Moreover, the data formed in this work can be used as feedback to further improve the data-driven model, thereby accelerating the development of high-performance catalysts.

Interplay Between Skin Microbiota Dysbiosis and the Host Immune System in Psoriasis: Potential Pathogenesis.

Psoriasis is a multifactorial immune-mediated disease. The highly effective and eligible treatment for psoriasis is limited, for its specific pathogenesis is incompletely elucidated. Skin microbiota is a research hotspot in the pathogenesis of immune-mediated inflammatory skin diseases nowadays, and it may have significant involvement in the provocation or exacerbation of psoriasis with broadly applicable prospects. It is postulated that skin microbiota alternation may interplay with innate immunity such as antimicrobial peptides and Toll-like receptors to stimulate T-cell populations, resulting in immune cascade responses and ultimately psoriasis. Achieving a thorough understanding of its underlying pathogenesis is crucial. Herein, we discuss the potential immunopathogenesis of psoriasis from the aspect of skin microbiota in an attempt to yield insights for novel therapeutic and preventive modalities for psoriasis.

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.

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.