Axonal Lysosomal Assays for Characterizing the Effects of LRRK2 G2019S.

The degeneration of axon terminals before the soma, referred to as "dying back", is a feature of Parkinson's disease (PD). Axonal assays are needed to model early PD pathogenesis as well as identify protective therapeutics. We hypothesized that defects in axon lysosomal trafficking as well as injury repair might be important contributing factors to "dying back" pathology in PD. Since primary human PD neurons are inaccessible, we developed assays to quantify axonal trafficking and injury repair using induced pluripotent stem cell (iPSC)-derived neurons with LRRK2 G2019S, which is one of the most common known PD mutations, and isogenic controls. We observed a subtle axonal trafficking phenotype that was partially rescued by a LRRK2 inhibitor. Mutant LRRK2 neurons showed increased phosphorylated Rab10-positive lysosomes, and lysosomal membrane damage increased LRRK2-dependent Rab10 phosphorylation. Neurons with mutant LRRK2 showed a transient increase in lysosomes at axotomy injury sites. This was a pilot study that used two patient-derived lines to develop its methodology; we observed subtle phenotypes that might correlate with heterogeneity in LRRK2-PD patients. Further analysis using additional iPSC lines is needed. Therefore, our axonal lysosomal assays can potentially be used to characterize early PD pathogenesis and test possible therapeutics.

Machine learning and phylogenetic analysis allow for predicting antibiotic resistance in M. tuberculosis.

BACKGROUND: Antimicrobial resistance (AMR) poses a significant global health threat, and an accurate prediction of bacterial resistance patterns is critical for effective treatment and control strategies. In recent years, machine learning (ML) approaches have emerged as powerful tools for analyzing large-scale bacterial AMR data. However, ML methods often ignore evolutionary relationships among bacterial strains, which can greatly impact performance of the ML methods, especially if resistance-associated features are attempted to be detected. Genome-wide association studies (GWAS) methods like linear mixed models accounts for the evolutionary relationships in bacteria, but they uncover only highly significant variants which have already been reported in literature. RESULTS: In this work, we introduce a novel phylogeny-related parallelism score (PRPS), which measures whether a certain feature is correlated with the population structure of a set of samples. We demonstrate that PRPS can be used, in combination with SVM- and random forest-based models, to reduce the number of features in the analysis, while simultaneously increasing models' performance. We applied our pipeline to publicly available AMR data from PATRIC database for Mycobacterium tuberculosis against six common antibiotics. CONCLUSIONS: Using our pipeline, we re-discovered known resistance-associated mutations as well as new candidate mutations which can be related to resistance and not previously reported in the literature. We demonstrated that taking into account phylogenetic relationships not only improves the model performance, but also yields more biologically relevant predicted most contributing resistance markers.

A genome-wide scan of wastewater E. coli for genes under positive selection: focusing on mechanisms of antibiotic resistance.

Antibiotic resistance is a global health threat and consequently, there is a need to understand the mechanisms driving its emergence. Here, we hypothesize that genes and mutations under positive selection may contribute to antibiotic resistance. We explored wastewater E. coli, whose genomes are highly diverse. We subjected 92 genomes to a statistical analysis for positively selected genes. We obtained 75 genes under positive selection and explored their potential for antibiotic resistance. We found that eight genes have functions relating to antibiotic resistance, such as biofilm formation, membrane permeability, and bacterial persistence. Finally, we correlated the presence/absence of non-synonymous mutations in positively selected sites of the genes with a function in resistance against 20 most prescribed antibiotics. We identified mutations associated with antibiotic resistance in two genes: the porin ompC and the bacterial persistence gene hipA. These mutations are located at the surface of the proteins and may hence have a direct effect on structure and function. For hipA, we hypothesize that the mutations influence its interaction with hipB and that they enhance the capacity for dormancy as a strategy to evade antibiotics. Overall, genomic data and positive selection analyses uncover novel insights into mechanisms driving antibiotic resistance.

The Art of Designing DNA Nanostructures with CAD Software.

Since the arrival of DNA nanotechnology nearly 40 years ago, the field has progressed from its beginnings of envisioning rather simple DNA structures having a branched, multi-strand architecture into creating beautifully complex structures comprising hundreds or even thousands of unique strands, with the possibility to exactly control the positions down to the molecular level. While the earliest construction methodologies, such as simple Holliday junctions or tiles, could reasonably be designed on pen and paper in a short amount of time, the advent of complex techniques, such as DNA origami or DNA bricks, require software to reduce the time required and propensity for human error within the design process. Where available, readily accessible design software catalyzes our ability to bring techniques to researchers in diverse fields and it has helped to speed the penetration of methods, such as DNA origami, into a wide range of applications from biomedicine to photonics. Here, we review the historical and current state of CAD software to enable a variety of methods that are fundamental to using structural DNA technology. Beginning with the first tools for predicting sequence-based secondary structure of nucleotides, we trace the development and significance of different software packages to the current state-of-the-art, with a particular focus on programs that are open source.