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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.

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.

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.