Abstract: Introduction: The fungal priority pathogen Cryptococcus neoformans causes cryptococcal meningoencephalitis in immunocompromised individuals and leads to hundreds of thousands of deaths per year. The undesirable side effects of existing treatments, the need for long application times to prevent the disease from recurring, the lack of resources for these treatment methods to spread over all continents necessitate the search for new treatment methods. Methods: Genome-scale models have been shown to be valuable in studying the metabolism of many organisms. Here we present the first genome-scale metabolic model for C. neoformans, iCryptococcus. This comprehensive model consists of 1,270 reactions, 1,143 metabolites, 649 genes, and eight compartments. The model was validated, proving accurate when predicting the capability of utilizing different carbon and nitrogen sources and growth rate in comparison to experimental data. Results and Discussion: The compatibility of the in silico Cryptococcus metabolism under infection conditions was assessed. The steroid and amino acid metabolisms found in the essentiality analyses have the potential to be drug targets for the therapeutic strategies to be developed against Cryptococcus species. iCryptococcus model can be applied to explore new targets for antifungal drugs along with essential gene, metabolite and reaction analyses and provides a promising platform for elucidation of pathogen metabolism.

Abstract: In the severe acute respiratory coronavirus disease 2019 (COVID-19) pandemic, there is an urgent need to develop effective treatments. Through a network-based drug repurposing approach, several effective drug candidates are identified for treating COVID-19 patients in different clinical stages. The proposed approach takes advantage of computational prediction methods by integrating publicly available clinical transcriptome and experimental data. We identify 51 drugs that regulate proteins interacted with SARS-CoV-2 protein through biological pathways against COVID-19, some of which have been experimented in clinical trials. Among the repurposed drug candidates, lovastatin leads to differential gene expression in clinical transcriptome for mild COVID-19 patients, and estradiol cypionate mainly regulates hormone-related biological functions to treat severe COVID-19 patients. Multi-target mechanisms of drug candidates are also explored. Erlotinib targets the viral protein interacted with cytokine and cytokine receptors to affect SARS-CoV-2 attachment and invasion. Lovastatin and testosterone block the angiotensin system to suppress the SARS-CoV-2 infection. In summary, our study has identified effective drug candidates against COVID-19 for patients in different clinical stages and provides comprehensive understanding of potential drug mechanisms.

Abstract: Alternative polyadenylation (APA) is a widespread posttranscriptional regulation process. APA generates diverse mRNA isoforms with different 3' UTR lengths, affecting mRNA expression, miRNA binding regulation and alternative splicing events. Previous studies have demonstrated the important roles of APA in tumorigenesis and cancer progression through diverse aspects. Thus, a comprehensive functional landscape of diverse APA events would aid in a better understanding of the underlying mechanisms related to APA in human cancers. Here, we built CAFuncAPA (https://relab.xidian.edu.cn/CAFuncAPA/) to systematically annotate the functions of 15478 APA events in human pan-cancers. Specifically, we first identified APA events associated with cancer survival and tumor progression. We annotated the potential downstream effects of APA on genes/isoforms expression, regulation of miRNAs, RNA binding proteins (RBPs) and alternative splicing events. Moreover, we also identified up-regulators of APA events, including the effects of genetic variants on poly(A) sites and RBPs, as well as the effect of methylation phenotypes on APA events. These findings suggested that CAFuncAPA can be a helpful resource for a better understanding of APA regulators and potential functions in cancer biology.

Abstract: Breast cancer is a persistent threat to women worldwide. A large proportion of breast cancers are dependent on the estrogen receptor alpha (ERalpha) for tumor progression. Therefore, targeting ERalpha with antagonists, such as tamoxifen, or estrogen deprivation by aromatase inhibitors remain standard therapies for ERalpha + breast cancer. The clinical benefits of monotherapy are often counterbalanced by off-target toxicity and development of resistance. Combinations of more than two drugs might be of great therapeutic value to prevent resistance, and to reduce doses, and hence, decrease toxicity. We mined data from the literature and public repositories to construct a network of potential drug targets for synergistic multidrug combinations. With 9 drugs, we performed a phenotypic combinatorial screen with ERalpha + breast cancer cell lines. We identified two optimized low-dose combinations of 3 and 4 drugs of high therapeutic relevance to the frequent ERalpha + /HER2-/PI3Kalpha-mutant subtype of breast cancer. The 3-drug combination targets ERalpha in combination with PI3Kalpha and cyclin-dependent kinase inhibitor 1 (p21). In addition, the 4-drug combination contains an inhibitor for poly (ADP-ribose) polymerase 1 (PARP1), which showed benefits in long-term treatments. Moreover, we validated the efficacy of the combinations in tamoxifen-resistant cell lines, patient-derived organoids, and xenograft experiments. Thus, we propose multidrug combinations that have the potential to overcome the standard issues of current monotherapies.

Abstract: N-glycosylation plays a key role in modulating the bioactivity of monoclonal antibodies (mAbs), as well as the light chain (LC) isotype can influence their physicochemical properties. However, investigating the impact of such features on mAbs conformational behavior is a big challenge, due to the very high flexibility of these biomolecules. In this work we investigate, by accelerated molecular dynamics (aMD), the conformational behavior of two commercial immunoglobulins G1 (IgG1), representative of kappa and lambda LCs antibodies, in both their fucosylated and afucosylated forms. Our results show, through the identification of a stable conformation, how the combination of fucosylation and LC isotype modulates the hinge behavior, the Fc conformation and the position of the glycan chains, all factors potentially affecting the binding to the FcgammaRs. This work also represents a technological enhancement in the conformational exploration of mAbs, making aMD a suitable approach to clarify experimental results.

Abstract: Fusion of the E-26 transformation-specific (ETS)-related gene (ERG) with transmembrane serine protease 2 (TMPRSS2) is a crucial step in the occurrence and progression of approximately 50% of prostate cancers. Despite significant progress in drug discovery, ERG inhibitors have yet to be approved for the clinical treatment of prostate cancer. In this study, we used computer-aided drug design (CADD)-based virtual screening to screen for potential inhibitors of ERG. In vivo and in vitro methods revealed that nifuroxazide (NFZ) inhibited the proliferation of a TMPRSS2:ERG fusion-positive prostate cancer cell line (VCaP) with an IC50 lower than that of ERG-negative prostate cancer cell lines (LNCaP, DU145, and WPMY cells). Poly [ADP-ribose] polymerase 1, the critical mediator of parthanatos, is known to bind ERG and is required for ERG-mediated transcription. NFZ blocked this interaction and overly activated PARP1, leading to cell death that was reduced by olaparib, a PARP1 inhibitor. These results show that NFZ inhibits ERG, leading to parthanatic cell death.

Abstract: The development of efficient computational methods for drug target protein identification can compensate for the high cost of experiments and is therefore of great significance for drug development. However, existing structure-based drug target protein-identification algorithms are limited by the insufficient number of proteins with experimentally resolved structures. Moreover, sequence-based algorithms cannot effectively extract information from protein sequences and thus display insufficient accuracy. Here, we combined the sequence-based self-supervised pretraining protein language model ESM1b with a graph convolutional neural network classifier to develop an improved, sequence-based drug target protein identification method. This complete model, named QuoteTarget, efficiently encodes proteins based on sequence information alone and achieves an accuracy of 95% with the nonredundant drug target and nondrug target datasets constructed for this study. When applied to all proteins from Homo sapiens, QuoteTarget identified 1213 potential undeveloped drug target proteins. We further inferred residue-binding weights from the well-trained network using the gradient-weighted class activation mapping (Grad-Cam) algorithm. Notably, we found that without any binding site information input, significant residues inferred by the model closely match the experimentally confirmed drug molecule-binding sites. Thus, our work provides a highly effective sequence-based identifier for drug target proteins, as well to yield new insights into recognizing drug molecule-binding sites. The entire model is available at https://github.com/Chenjxjx/drug-target-prediction.

Abstract: Primary sclerosing cholangitis (PSC) is a rare autoimmune bile duct disease that is strongly associated with immune-mediated disorders. In this study, we implemented multitrait joint analyses to genome-wide association summary statistics of PSC and numerous clinical and epidemiological traits to estimate the genetic contribution of each trait and genetic correlations between traits and to identify new lead PSC risk-associated loci. We identified seven new loci that have not been previously reported and one new independent lead variant in the previously reported locus. Functional annotation and fine-mapping nominated several potential susceptibility genes such as MANBA and IRF5. Network-based in silico drug efficacy screening provided candidate agents for further study of pharmacological effect in PSC.

Abstract: Gardnerella is the primary pathogenic bacterial genus present in the polymicrobial condition known as bacterial vaginosis (BV). Despite BV's high prevalence and associated chronic and acute women's health impacts, the Gardnerella pangenome is largely uncharacterized at both the genetic and functional metabolic levels. Here, we used genome-scale metabolic models to characterize in silico the Gardnerella pangenome metabolic content. We also assessed the metabolic functional capacity in a BV-positive cervicovaginal fluid context. The metabolic capacity varied widely across the pangenome, with 38.15% of all reactions being core to the genus, compared to 49.60% of reactions identified as being unique to a smaller subset of species. We identified 57 essential genes across the pangenome via in silico gene essentiality screens within two simulated vaginal metabolic environments. Four genes, gpsA, fas, suhB, and psd, were identified as core essential genes critical for the metabolic function of all analyzed bacterial species of the Gardnerella genus. Further understanding these core essential metabolic functions could inform novel therapeutic strategies to treat BV. Machine learning applied to simulated metabolic network flux distributions showed limited clustering based on the sample isolation source, which further supports the presence of extensive core metabolic functionality across this genus. These data represent the first metabolic modeling of the Gardnerella pangenome and illustrate strain-specific interactions with the vaginal metabolic environment across the pangenome. IMPORTANCE Bacterial vaginosis (BV) is the most common vaginal infection among reproductive-age women. Despite its prevalence and associated chronic and acute women's health impacts, the diverse bacteria involved in BV infection remain poorly characterized. Gardnerella is the genus of bacteria most commonly and most abundantly represented during BV. In this paper, we use metabolic models, which are a computational representation of the possible functional metabolism of an organism, to investigate metabolic conservation, gene essentiality, and pathway utilization across 110 Gardnerella strains. These models allow us to investigate in silico how strains may differ with respect to their metabolic interactions with the vaginal-host environment.

Abstract: Chinese medicine (CM) is an important resource for human life understanding and discovery of drugs. However, due to the unclear pharmacological mechanism caused by unclear target, research and international promotion of many active components have made little progress in the past decades of years. CM is mainly composed of multi-ingredients with multi-targets. The identification of targets of multiple active components and the weight analysis of multiple targets in a specific pathological environment, that is, the determination of the most important target is the main obstacle to the mechanism clarification and thus hinders its internationalization. In this review, the main approach to target identification and network pharmacology were summarized. And BIBm (Bayesian inference modeling), a powerful method for drug target identification and key pathway determination was introduced. We aim to provide a new scientific basis and ideas for the development and international promotion of new drugs based on CM.