Study on genotype and phenotype of novel CYP2D6 variants using pharmacokinetic and pharmacodynamic models with metoprolol as a substrate drug
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High-coverage whole-genome sequencing of a Jakun individual from the “Orang Asli” Proto-Malay subtribe from Peninsular Malaysia
Jakun, a Proto-Malay subtribe from Peninsular Malaysia, is believed to have inhabited the Malay Archipelago during the period of agricultural expansion approximately 4 thousand years ago (kya). However, their genetic structure and population history remain inconclusive. In this study, we report the genome structure of a Jakun female, based on whole-genome sequencing, which yielded an average coverage of 35.97-fold. We identified approximately 3.6 million single-nucleotide variations (SNVs) and 517,784 small insertions/deletions (indels). Of these, 39,916 SNVs were novel (referencing dbSNP151), and 10,167 were nonsynonymous (nsSNVs), spanning 5674 genes. Principal Component Analysis (PCA) revealed that the Jakun genome sequence closely clustered with the genomes of the Cambodians (CAM) and the Metropolitan Malays from Singapore (SG_MAS). The ADMIXTURE analysis further revealed potential admixture from the EA and North Borneo populations, as corroborated by the results from the F3, F4, and TreeMix analyses. Mitochondrial DNA analysis revealed that the Jakun genome carried the N21a haplogroup (estimated to have occurred ~19 kya), which is commonly found among Malays from Malaysia and Indonesia. From the whole-genome sequence data, we identified 825 damaging and deleterious nonsynonymous single-nucleotide polymorphisms (nsSNVs) affecting 720 genes. Some of these variants are associated with age-related macular degeneration, atrial fibrillation, and HDL cholesterol level. Additionally, we located a total of 3310 variants on 32 core adsorption, distribution, metabolism, and elimination (ADME) genes. Of these, 193 variants are listed in PharmGKB, and 21 are nsSNVs. In summary, the genetic structure identified in the Jakun individual could enhance the mapping of genetic variants for disease-based population studies and further our understanding of the human migration history in Southeast Asia.
Dominant HPAIV H5N1 genotypes of Germany 2021/2022 are linked to high virulence in Pekin ducklings
Highly pathogenic avian influenza viruses (HPAIV) of H5 clade 2.3.4.4b pose an ongoing threat worldwide. It remains unclear whether this panzootic situation would favor low virulent phenotypes expected by the ‘avirulence hypothesis’ of viral evolution. Assessing virulence in Pekin ducklings in an intramuscular infection model revealed that the two genotypes that dominated the epidemiological situation in Germany during the period 2021 and 2022 (EU-RL:CH and EU-RL:AB) were of high virulence. In contrast, rare genotypes were of intermediate virulence. The genetic constellation of these reassortants pointed to an important role of the viral polymerase complex (RdRP), particularly the PB1 genome segment, in shaping virulence in ducklings. Occulo-nasal infection of ducklings confirmed the phenotypes for two representative viruses and indicated a more efficient replication for the high virulence strain. These observations would be in line with the ‘virulence-transmission trade-off’ model for describing HPAIV epidemiology in wild birds in Germany.
Modeling critical dosing strategies for stromal-induced resistance to cancer therapy
Complex interactions between stromal cells, tumor cells and therapies can influence environmental factors that in turn impact anticancer treatment efficacy. Disentangling these phenomena is critical for understanding treatment response and designing effective dosing strategies. We propose a mathematical model for a common tumor-stromal interaction motif where stromal cells secrete factors that promote drug resistance. We demonstrate that the presence of this interaction modulates the therapeutic dose window of efficacy and can lead to nonmonotonic treatment response. We consider combination strategies that target stromal cells and their secretome, and identify strategies that constrain drug concentrations within the efficacious window for long-term response. We explore an experimental dataset from colorectal cancer cells treated with anti-EGFR targeting therapy, cetuximab, where cancer-associated fibroblasts increase epidermal growth factor secretion under treatment. We apply our general approach to identify a critical drug concentration threshold and study effective dosing regimens for single-drug and combination therapies.
Mechanisms of NLRP3 activation and inhibition elucidated by functional analysis of disease-associated variants
The NLRP3 inflammasome is a multiprotein complex that mediates caspase-1 activation and the release of proinflammatory cytokines, including interleukin (IL)-1β and IL-18. Gain-of-function variants in the gene encoding NLRP3 (also called cryopyrin) lead to constitutive inflammasome activation and excessive IL-1β production in cryopyrin-associated periodic syndromes (CAPS). Here we present functional screening and automated analysis of 534 NLRP3 variants from the international INFEVERS registry and the ClinVar database. This resource captures the effect of NLRP3 variants on ASC speck formation spontaneously, at low temperature, after inflammasome stimulation and with the specific NLRP3 inhibitor MCC950. Most notably, our analysis facilitated the updated classification of NLRP3 variants in INFEVERS. Structural analysis suggested multiple mechanisms by which CAPS variants activate NLRP3, including enhanced ATP binding, stabilizing the active NLRP3 conformation, destabilizing the inactive NLRP3 complex and promoting oligomerization of the pyrin domain. Furthermore, we identified pathogenic variants that can hypersensitize the activation of NLRP3 in response to nigericin and cold temperature exposure. We also found that most CAPS-related NLRP3 variants can be inhibited by MCC950; however, NLRP3 variants with changes to proline affecting helices near the inhibitor binding site are resistant to MCC950, as are variants in the pyrin domain, which likely trigger activation directly with the pyrin domain of ASC. Our findings could help stratify the CAPS population for NLRP3 inhibitor clinical trials and our automated methodologies can be implemented for molecules with a different mechanism of activation and in laboratories worldwide that are interested in adding new functionally validated NLRP3 variants to the resource. Overall, our study provides improved diagnosis for patients with CAPS, mechanistic insight into the activation of NLRP3 and stratification of patients for the future application of targeted therapeutics.
Personalized prediction of anticancer potential of non-oncology drugs through learning from genome derived molecular pathways
Advances in cancer genomics have significantly expanded our understanding of cancer biology. However, the high cost of drug development limits our ability to translate this knowledge into precise treatments. Approved non-oncology drugs, comprising a large repository of chemical entities, offer a promising avenue for repurposing in cancer therapy. Herein we present CHANCE, a supervised machine learning model designed to predict the anticancer activities of non-oncology drugs for specific patients by simultaneously considering personalized coding and non-coding mutations. Utilizing protein–protein interaction networks, CHANCE harmonizes multilevel mutation annotations and integrates pharmacological information across different drugs into a single model. We systematically benchmarked the performance of CHANCE and show its predictions are better than previous model and highly interpretable. Applying CHANCE to approximately 5000 cancer samples indicated that >30% might respond to at least one non-oncology drug, with 11% non-oncology drugs predicted to have anticancer activities. Moreover, CHANCE predictions suggested an association between SMAD7 mutations and aspirin treatment response. Experimental validation using tumor cells derived from seven patients with pancreatic or esophageal cancer confirmed the potential anticancer activity of at least one non-oncology drug for five of these patients. To summarize, CHANCE offers a personalized and interpretable approach, serving as a valuable tool for mining non-oncology drugs in the precision oncology era.
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