Plasma Proteomics Identify Biomarkers and Pathogenesis of COVID-19.

The coronavirus disease 2019 (COVID-19) pandemic is a global public health crisis. However, little is known about the pathogenesis and biomarkers of COVID-19. Here, we profiled host responses to COVID-19 by performing plasma proteomics of a cohort of COVID-19 patients, including non-survivors and survivors recovered from mild or severe symptoms, and uncovered numerous COVID-19-associated alterations of plasma proteins. We developed a machine-learning-based pipeline to identify 11 proteins as biomarkers and a set of biomarker combinations, which were validated by an independent cohort and accurately distinguished and predicted COVID-19 outcomes. Some of the biomarkers were further validated by enzyme-linked immunosorbent assay (ELISA) using a larger cohort. These markedly altered proteins, including the biomarkers, mediate pathophysiological pathways, such as immune or inflammatory responses, platelet degranulation and coagulation, and metabolism, that likely contribute to the pathogenesis. Our findings provide valuable knowledge about COVID-19 biomarkers and shed light on the pathogenesis and potential therapeutic targets of COVID-19.

Posttranscriptional regulation of de novo lipogenesis by glucose-induced O-GlcNAcylation.

O-linked ß-N-acetyl glucosamine (O-GlcNAc) is attached to proteins under glucose-replete conditions; this posttranslational modification results in molecular and physiological changes that affect cell fate. Here we show that posttranslational modification of serine/arginine-rich protein kinase 2 (SRPK2) by O-GlcNAc regulates de novo lipogenesis by regulating pre-mRNA splicing. We found that O-GlcNAc transferase O-GlcNAcylated SRPK2 at a nuclear localization signal (NLS), which triggers binding of SRPK2 to importin α. Consequently, O-GlcNAcylated SRPK2 was imported into the nucleus, where it phosphorylated serine/arginine-rich proteins and promoted splicing of lipogenic pre-mRNAs. We determined that protein nuclear import by O-GlcNAcylation-dependent binding of cargo protein to importin α might be a general mechanism in cells. This work reveals a role of O-GlcNAc in posttranscriptional regulation of de novo lipogenesis, and our findings indicate that importin α is a "reader" of an O-GlcNAcylated NLS.

GPS-SUMO 2.0: an updated online service for the prediction of SUMOylation sites and SUMO-interacting motifs.

Small ubiquitin-like modifiers (SUMOs) are tiny but important protein regulators involved in orchestrating a broad spectrum of biological processes, either by covalently modifying protein substrates or by noncovalently interacting with other proteins. Here, we report an updated server, GPS-SUMO 2.0, for the prediction of SUMOylation sites and SUMO-interacting motifs (SIMs). For predictor training, we adopted three machine learning algorithms, penalized logistic regression (PLR), a deep neural network (DNN), and a transformer, and used 52 404 nonredundant SUMOylation sites in 8262 proteins and 163 SIMs in 102 proteins. To further increase the accuracy of predicting SUMOylation sites, a pretraining model was first constructed using 145 545 protein lysine modification sites, followed by transfer learning to fine-tune the model. GPS-SUMO 2.0 exhibited greater accuracy in predicting SUMOylation sites than did other existing tools. For users, one or multiple protein sequences or identifiers can be input, and the prediction results are shown in a tabular list. In addition to the basic statistics, we integrated knowledge from 35 public resources to annotate SUMOylation sites or SIMs. The GPS-SUMO 2.0 server is freely available at https://sumo.biocuckoo.cn/. We believe that GPS-SUMO 2.0 can serve as a useful tool for further analysis of SUMOylation and SUMO interactions.

PTMD 2.0: an updated database of disease-associated post-translational modifications.

Various post-translational modifications (PTMs) participate in nearly all aspects of biological processes by regulating protein functions, and aberrant states of PTMs are frequently associated with human diseases. Here, we present a comprehensive database of PTMs associated with diseases (PTMD 2.0), including 342 624 PTM-disease associations (PDAs) in 15 105 proteins for 93 types of PTMs and 2083 diseases. Based on the distinct PTM states in diseases, we classified all PDAs into six categories: upregulation (U) or downregulation (D) of PTM levels, absence (A) or presence (P) of PTMs, and creation (C) or disruption (N) of PTM sites. We provided detailed annotations for each PDA and carefully annotated disease-associated proteins by integrating the knowledge from 101 additional resources that covered 13 aspects, including disease-associated information, variation and mutation, protein-protein interaction, protein functional annotation, DNA and RNA element, protein structure, chemical-target relationship, mRNA expression, protein expression/proteomics, subcellular localization, biological pathway annotation, functional domain annotation and physicochemical property. With a data volume of ∼8 GB, we anticipate that PTMD 2.0 will serve as a fundamental resource for further analysing the relationships between PTMs and diseases. The online service of PTMD 2.0 is freely available at https://ptmd.biocuckoo.cn/.

Adaptation of STIM1 structure-function relationships for optogenetic control of calcium signaling.

In cellular contexts, the oscillation of calcium ions (Ca2+) is intricately linked to various physiological processes, such as cell proliferation, metabolism, and survival. Stromal interaction molecule 1 (STIM1) proteins form a crucial regulatory component in the store-operated calcium entry process. The structural attributes of STIM1 are vital for its functionality, encompassing distinct domains situated in the endoplasmic reticulum lumen and the cytoplasm. The intraluminal domain enables the timely detection of diminishing Ca2+ concentrations, prompting structural modifications that activate the cytoplasmic domain. This activated cytoplasmic domain undergoes conformational alterations and engages with membrane components, opening a channel that facilitates the influx of Ca2+ from the extracellular environment. Given its multiple domains and interaction mechanisms, STIM1 plays a foundational role in cellular biology. This review focuses on the design of optogenetic tools inspired by the structure and function of STIM1. These tools offer a groundbreaking approach for studying and manipulating intracellular Ca2+ signaling with precise spatiotemporal control. We further explore the practical applications of these tools, spanning fundamental scientific research, clinical studies, and their potential for translational research.

AnimalTFDB 4.0: a comprehensive animal transcription factor database updated with variation and expression annotations.

Transcription factors (TFs) are proteins that interact with specific DNA sequences to regulate gene expression and play crucial roles in all kinds of biological processes. To keep up with new data and provide a more comprehensive resource for TF research, we updated the Animal Transcription Factor Database (AnimalTFDB) to version 4.0 (http://bioinfo.life.hust.edu.cn/AnimalTFDB4/) with up-to-date data and functions. We refined the TF family rules and prediction pipeline to predict TFs in genome-wide protein sequences from Ensembl. As a result, we predicted 274 633 TF genes and 150 726 transcription cofactor genes in AnimalTFDB 4.0 in 183 animal genomes, which are 86 more species than AnimalTFDB 3.0. Besides double data volume, we also added the following new annotations and functions to the database: (i) variations (including mutations) on TF genes in various human cancers and other diseases; (ii) predicted post-translational modification sites (including phosphorylation, acetylation, methylation and ubiquitination sites) on TFs in 8 species; (iii) TF regulation in autophagy; (iv) comprehensive TF expression annotation for 38 species; (v) exact and batch search functions allow users to search AnimalTFDB flexibly. AnimalTFDB 4.0 is a useful resource for studying TF and transcription regulation, which contains comprehensive annotation and classification of TFs and transcription cofactors.

GPS 6.0: an updated server for prediction of kinase-specific phosphorylation sites in proteins.

Protein phosphorylation, catalyzed by protein kinases (PKs), is one of the most important post-translational modifications (PTMs), and involved in regulating almost all of biological processes. Here, we report an updated server, Group-based Prediction System (GPS) 6.0, for prediction of PK-specific phosphorylation sites (p-sites) in eukaryotes. First, we pre-trained a general model using penalized logistic regression (PLR), deep neural network (DNN), and Light Gradient Boosting Machine (LightGMB) on 490 762 non-redundant p-sites in 71 407 proteins. Then, transfer learning was conducted to obtain 577 PK-specific predictors at the group, family and single PK levels, using a well-curated data set of 30 043 known site-specific kinase-substrate relations in 7041 proteins. Together with the evolutionary information, GPS 6.0 could hierarchically predict PK-specific p-sites for 44046 PKs in 185 species. Besides the basic statistics, we also offered the knowledge from 22 public resources to annotate the prediction results, including the experimental evidence, physical interactions, sequence logos, and p-sites in sequences and 3D structures. The GPS 6.0 server is freely available at https://gps.biocuckoo.cn. We believe that GPS 6.0 could be a highly useful service for further analysis of phosphorylation.

Supported Ionic Liquid Membrane with Highly-permeable Polyamide Armor by In Situ Interfacial Polymerization for Durable CO2 Separation.

Supported ionic liquid membranes (SILMs), owing to their capacities in harnessing physicochemical properties of ionic liquid for exceptional CO2 solubility, have emerged as a promising platform for CO2 extraction. Despite great achievements, existing SILMs suffer from poor structural and performance stability under high-pressure or long-term operations, significantly limiting their applications. Herein, a one-step and in situ interfacial polymerization strategy is proposed to elaborate a thin, mechanically-robust, and highly-permeable polyamide armor on the SILMs to effectively protect ionic liquid within porous supports, allowing for intensifying the overall stability of SILMs without compromising CO2 separation performance. The armored SILMs have a profound increase of breakthrough pressure by 105% compared to conventional counterparts without armor, and display high and stable operating pressure exceeding that of most SILMs previously reported. It is further demonstrated that the armored SILMs exhibit ultrahigh ideal CO2/N2 selectivity of about 200 and excellent CO2 permeation of 78 barrers upon over 150 h operation, as opposed to the full failure of CO2 separation performance within 36 h using conventional SILMs. The design concept of armor provides a flexible and additional dimension in developing high-performance and durable SILMs, pushing the practical application of ionic liquids in separation processes.

GPS-Uber: a hybrid-learning framework for prediction of general and E3-specific lysine ubiquitination sites.

As an important post-translational modification, lysine ubiquitination participates in numerous biological processes and is involved in human diseases, whereas the site specificity of ubiquitination is mainly decided by ubiquitin-protein ligases (E3s). Although numerous ubiquitination predictors have been developed, computational prediction of E3-specific ubiquitination sites is still a great challenge. Here, we carefully reviewed the existing tools for the prediction of general ubiquitination sites. Also, we developed a tool named GPS-Uber for the prediction of general and E3-specific ubiquitination sites. From the literature, we manually collected 1311 experimentally identified site-specific E3-substrate relations, which were classified into different clusters based on corresponding E3s at different levels. To predict general ubiquitination sites, we integrated 10 types of sequence and structure features, as well as three types of algorithms including penalized logistic regression, deep neural network and convolutional neural network. Compared with other existing tools, the general model in GPS-Uber exhibited a highly competitive accuracy, with an area under curve values of 0.7649. Then, transfer learning was adopted for each E3 cluster to construct E3-specific models, and in total 112 individual E3-specific predictors were implemented. Using GPS-Uber, we conducted a systematic prediction of human cancer-associated ubiquitination events, which could be helpful for further experimental consideration. GPS-Uber will be regularly updated, and its online service is free for academic research at http://gpsuber.biocuckoo.cn/.

Identification and validation of anti-protein arginine methyltransferase 5 (PRMT5) antibody as a novel biomarker for systemic sclerosis (SSc).

OBJECTIVES: In the complex panorama of autoimmune diseases, the characterisation of pivotal contributing autoantibodies that are involved in disease progression remains challenging. This study aimed to employ a global antibody profiling strategy to identify novel antibodies and investigate their association with systemic sclerosis (SSc). METHODS: We implemented this strategy by conducting immunoprecipitation (IP) following on-bead digestion with the sera of patients with SSc or healthy donors, using antigen pools derived from cell lysates. The enriched antigen-antibody complex was proceeded with mass spectrometry (MS)-based quantitative proteomics and over-represented by bioinformatics analysis. The candidate antibodies were then orthogonally validated in two independent groups of patients with SSc. Mice were immunised with the target antigen, which was subsequently evaluated by histological examination and RNA sequencing. RESULTS: The IP-MS analysis, followed by validation in patients with SSc, revealed a significant elevation in anti-PRMT5 antibodies among patients with SSc. These antibodies exhibited robust diagnostic accuracy in distinguishing SSc from healthy controls and other autoimmune conditions, including systemic lupus erythematosus and Sjögren's syndrome, with an area under the curve ranging from 0.900 to 0.988. The elevation of anti-PRMT5 antibodies was verified in a subsequent independent group with SSc using an additional method, microarray. Notably, 31.11% of patients with SSc exhibited seropositivity for anti-PRMT5 antibodies. Furthermore, the titres of anti-PRMT5 antibodies demonstrated a correlation with the progression or regression trajectory in SSc. PRMT5 immunisation displayed significant inflammation and fibrosis in both the skin and lungs of mice. This was concomitant with the upregulation of multiple proinflammatory and profibrotic pathways, thereby underscoring a potentially pivotal role of anti-PRMT5 antibodies in SSc. CONCLUSIONS: This study has identified anti-PRMT5 antibodies as a novel biomarker for SSc.