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Recent high-throughput omics techniques have produced a large amount of biological data. Visualization of big omics data is essential to answer a wide range of biological problems. As a concise but comprehensive strategy, a heatmap can analyze and visualize high-dimensional and heterogeneous biomolecular expression data in an attractive artwork. In 2014, we developed a stand-alone software package, Heat map Illustrator (HemI 1.0), which implemented three clustering methods and seven distance metrics for heatmap illustration. Here, we significantly improved 1.0 and released the online service of HemI 2.0, in which 7 clustering methods and 22 types of distance metrics were implemented. In HemI 2.0, the clustering results and publication-quality heatmaps can be exported directly. For an in-depth analysis of the data, we further added an option of enrichment analysis for 12 model organisms, with 15 types of functional annotations. The enrichment results can be visualized in five idioms, including bubble chart, bar graph, coxcomb chart, pie chart and word cloud. We anticipate that HemI 2.0 can be a helpful web server for visualization of biomolecular expression data, as well as the additional enrichment analysis. HemI 2.0 is freely available for all users at: https://hemi.biocuckoo.org/.

Squamous cervical cancer (SCC) is a major cause of death in women, yet its molecular characteristics are poorly understood. Here, we profiled histopathological and molecular alterations in SCC, and used large language models (LLMs) for interpretation, reasoning, and understanding of multi-modal data. We implemented an immersive-knowledge prompting (iKLP) strategy to trigger LLMs, which interpreted 17.8%-20.3% of omic alterations known to be associated with cancer. Also, the emergence of cross-disciplinary reasoning in LLMs helped for interpreting phenotypic effects of SCC molecular alterations, exemplified by a prognostic biomarker HRG, and targetable kinases, CDK18 and CDK9. With experimental validations, LLM-reasoning showed >2-fold increased confidence for 68.5% of analyzed molecules. Strikingly, LLMs understood the information flow of cell-cell communications, and uncovered a CDK18-mediated immune escaping axis in orchestrating the crosstalk of malignant, immune, and niche cells. We anticipate that LLMs can be used for completely distinguishing between knowns and unknowns in any scientific problems.

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.

Here, we reported the compendium of protein lysine modifications (CPLM 4.0, http://cplm.biocuckoo.cn/), a data resource for various post-translational modifications (PTMs) specifically occurred at the side-chain amino group of lysine residues in proteins. From the literature and public databases, we collected 450 378 protein lysine modification (PLM) events, and combined them with the existing data of our previously developed protein lysine modification database (PLMD 3.0). In total, CPLM 4.0 contained 592 606 experimentally identified modification events on 463 156 unique lysine residues of 105 673 proteins for up to 29 types of PLMs across 219 species. Furthermore, we carefully annotated the data using the knowledge from 102 additional resources that covered 13 aspects, including variation and mutation, disease-associated information, protein-protein interaction, protein functional annotation, DNA & RNA element, protein structure, chemical-target relation, mRNA expression, protein expression/proteomics, subcellular localization, biological pathway annotation, functional domain annotation, and physicochemical property. Compared to PLMD 3.0 and other existing resources, CPLM 4.0 achieved a >2-fold increase in collection of PLM events, with a data volume of ∼45GB. We anticipate that CPLM 4.0 can serve as a more useful database for further study of PLMs.

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/.

Rationale: Children usually develop less severe symptoms responding to Coronavirus Disease 2019 (COVID-19) than adults. However, little is known about the molecular alterations and pathogenesis of COVID-19 in children. Methods: We conducted plasma proteomic and metabolomic profilings of the blood samples of a cohort containing 18 COVID-19-children with mild symptoms and 12 healthy children, which were enrolled from hospital admissions and outpatients, respectively. Statistical analyses were performed to identify molecules specifically altered in COVID-19-children. We also developed a machine learning-based pipeline named inference of biomolecular combinations with minimal bias (iBM) to prioritize proteins and metabolites strongly altered in COVID-19-children, and experimentally validated the predictions. Results: By comparing to the multi-omic data in adults, we identified 44 proteins and 249 metabolites differentially altered in COVID-19-children against healthy children or COVID-19-adults. Further analyses demonstrated that both deteriorative immune response/inflammation processes and protective antioxidant or anti-inflammatory processes were markedly induced in COVID-19-children. Using iBM, we prioritized two combinations that contained 5 proteins and 5 metabolites, respectively, each exhibiting a total area under curve (AUC) value of 100% to accurately distinguish COVID-19-children from healthy children or COVID-19-adults. Further experiments validated that all the 5 proteins were up-regulated upon coronavirus infection. Interestingly, we found that the prioritized metabolites inhibited the expression of pro-inflammatory factors, and two of them, methylmalonic acid (MMA) and mannitol, also suppressed coronaviral replication, implying a protective role of these metabolites in COVID-19-children. Conclusion: The finding of a strong antagonism of deteriorative and protective effects provided new insights on the mechanism and pathogenesis of COVID-19 in children that mostly underwent mild symptoms. The identified metabolites strongly altered in COVID-19-children could serve as potential therapeutic agents of COVID-19.

Background: Children usually develop less severe disease responding to COVID-19 than adults. However, little is known about the detailed mechanism and pathogenesis of children with COVID-19 (CC), and its difference to adults with COVID-19 (AC). Methods: We conducted a plasma proteomic and metabolomic profiling, using the blood samples of 30 children including 18 CC cases and 12 healthy children (HC). By comparing the multi-omic data of AC, standard statistical tests were used to identify differentially expressed proteins (DEPs) and metabolites (DEMs) exclusively altered in CC. Enrichment analyses were conducted to identify biological processes/pathways specifically enriched in CC. To identify potential CC-specific biomarkers, we developed a new machine learning-based method named inference of biomarker combinations with minimal bias (iBM). Further experiments were conducted to validate the predicted metabolic markers. Findings: By quantifying 757 proteins and 1174 metabolites, we identified 44 DEPs and 249 DEMs exclusively altered in CC. Enrichment analyses demonstrated that in CC both deleterious immune response/inflammation processes and protective anti-inflammatory processes were strongly induced in the proteomic level, whereas protective anabolism-related processes were enriched in the metabolomic level. Using iBM, we prioritized two CC-specific biomarker combinations that contained 5 proteins and 5 metabolites, respectively, each exhibiting a total area under curve (AUC) value of 100% to accurately distinguish CC from HC or AC. Further experiments showed that the identified metabolites not only inhibited the expression of pro-inflammatory factors, but also suppressed coronaviral replication, implying that these factors played key roles in protecting pediatric patients from both viral infection and infection-induced inflammation. Interpretation: The finding of a strong antagonism of deleterious and protective effects provided new insights on the mechanism and pathogenesis of CC cases that mostly undergo mild symptoms. The identified CC-specific biomarkers could serve as candidate drug targets or therapeutic agents of COVID-19. Funding Statement: This work was supported by the Strategic Priority Research Program of CAS (XDB29010300 to X.Z.), the National Science and Technology Major Project (2018ZX10101004 to X.Z.), National Natural Science Foundation of China (81873964 to Y.Q., 31930021, 31970633 and 34671360 to Y.X., and 31670161 to X.Z.), Grant from the CAS Youth Innovation Promotion Association (2020332 to Y.Q.), the program for HUST Academic Frontier Youth Team (Y.X.). Declaration of Interests: The authors declare no conflicts of interest. Ethics Approval Statement: All work performed in this study was approved by the Guangzhou Women and Children's Medical Center Ethics Committee and Written informed consent was waived by the Ethics Commission of the designated hospital for emerging infectious diseases.