RESUMO
Background: Synthetic microbial communities offer an opportunity to conduct reductionist research in tractable model systems. However, deriving abundances of highly related strains within these communities is currently unreliable. 16S rRNA gene sequencing does not resolve abundance at the strain level, standard methods for analysis of shotgun metagenomic sequencing do not account for ambiguous mapping between closely related strains, and other methods such as quantitative PCR (qPCR) scale poorly and are resource prohibitive for complex communities. We present StrainR2, which utilizes shotgun metagenomic sequencing paired with a k-mer-based normalization strategy to provide high accuracy strain-level abundances for all members of a synthetic community, provided their genomes. Results: Both in silico, and using sequencing data derived from gnotobiotic mice colonized with a synthetic fecal microbiota, StrainR2 resolves strain abundances with greater accuracy than other tools utilizing shotgun metagenomic sequencing reads and can resolve complex mixtures of highly related strains. Through experimental validation and benchmarking, we demonstrate that StrainR2's accuracy is comparable to that of qPCR on a subset of strains resolved using absolute quantification. Further, it is capable of scaling to communities of hundreds of strains and efficiently utilizes memory being capable of running both on personal computers and high-performance computing nodes. Conclusions: Using shotgun metagenomic sequencing reads is a viable method for determining accurate strain-level abundances in synthetic communities using StrainR2.
RESUMO
Parkinson's disease (PD) is a complex neurodegenerative disorder with an unclear etiology. Despite significant research efforts, developing disease-modifying treatments for PD remains a major unmet medical need. Notably, drug repositioning is becoming an increasingly attractive direction in drug discovery, and computational approaches offer a relatively quick and resource-saving method for identifying testable hypotheses that promote drug repositioning. We used an artificial intelligence (AI)-based drug repositioning strategy to screen an extensive compound library and identify potential therapeutic agents for PD. Our AI-driven analysis revealed that efavirenz and nevirapine, approved for treating human immunodeficiency virus infection, had distinct profiles, suggesting their potential effects on PD pathophysiology. Among these, efavirenz attenuated α-synuclein (α-syn) propagation and associated neuroinflammation in the brain of preformed α-syn fibrils-injected A53T α-syn Tg mice and α-syn propagation and associated behavioral changes in the C. elegans BiFC model. Through in-depth molecular investigations, we found that efavirenz can modulate cholesterol metabolism and mitigate α-syn propagation, a key pathological feature implicated in PD progression by regulating CYP46A1. This study opens new avenues for further investigation into the mechanisms underlying PD pathology and the exploration of additional drug candidates using advanced computational methodologies.
