Mutations in leucine-rich repeat kinase 2 (LRRK2) are associated with increased risk for developing Parkinson's disease (PD). Previously, we found that LRRK2 G2019S mutation carriers have increased mitochondrial DNA (mtDNA) damage and after zinc finger nuclease-mediated gene mutation correction, mtDNA damage was no longer detectable. While the mtDNA damage phenotype can be unambiguously attributed to the LRRK2 G2019S mutation, the underlying mechanism(s) is unknown. Here, we examine the role of LRRK2 kinase function in LRRK2 G2019S-mediated mtDNA damage, using both genetic and pharmacological approaches in cultured neurons and PD patient-derived cells. Expression of LRRK2 G2019S induced mtDNA damage in primary rat midbrain neurons, but not in cortical neuronal cultures. In contrast, the expression of LRRK2 wild type or LRRK2 D1994A mutant (kinase dead) had no effect on mtDNA damage in either midbrain or cortical neuronal cultures. In addition, human LRRK2 G2019S patient-derived lymphoblastoid cell lines (LCL) demonstrated increased mtDNA damage relative to age-matched controls. Importantly, treatment of LRRK2 G2019S expressing midbrain neurons or patient-derived LRRK2 G2019S LCLs with the LRRK2 kinase inhibitor GNE-7915, either prevented or restored mtDNA damage to control levels. These findings support the hypothesis that LRRK2 G2019S-induced mtDNA damage is LRRK2 kinase activity dependent, uncovering a novel pathological role for this kinase. Blocking or reversing mtDNA damage via LRRK2 kinase inhibition or other therapeutic approaches may be useful to slow PD-associated pathology.

Alzheimer's disease (AD) is mainly a late-onset neurodegenerative disorder. Substantial efforts have been made to solve the complex genetic architecture of AD as a means to identify therapeutic targets. Unfortunately, to date, no disease-altering therapeutics have been developed. As therapeutics are likely to be most effective in the early stages of disease (ie, before the onset of symptoms), a recent focus of AD research has been the identification of protective factors that prevent disease. One example is the discovery of a rare variant in the 3′-UTR of RAB10 that is protective for AD. Here, we review the possible genetic, molecular, and functional role of RAB10 in AD and potential therapeutic approaches to target RAB10.

Gene duplication plays a central role in adaptation to novel environments by providing new genetic material for functional divergence and evolution of biological complexity. Several evolutionary models have been proposed for gene duplication to explain how new gene copies are preserved by natural selection, but these models have rarely been tested using empirical data. Opsin proteins, when combined with a chromophore, form a photopigment that is responsible for the absorption of light, the first step in the phototransduction cascade. Adaptive gene duplications have occurred many times within the animal opsins' gene family, leading to novel wavelength sensitivities. Consequently, opsins are an attractive choice for the study of gene duplication evolutionary models. Odonata (dragonflies and damselflies) have the largest opsin repertoire of any insect currently known. Additionally, there is tremendous variation in opsin copy number between species, particularly in the long-wavelength-sensitive (LWS) class. Using comprehensive phylotranscriptomic and statistical approaches, we tested various evolutionary models of gene duplication. Our results suggest that both the blue-sensitive (BS) and LWS opsin classes were subjected to strong positive selection that greatly weakens after multiple duplication events, a pattern that is consistent with the permanent heterozygote model. Due to the immense interspecific variation and duplicability potential of opsin genes among odonates, they represent a unique model system to test hypotheses regarding opsin gene duplication and diversification at the molecular level.

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BackgroundAccurate detection of homologous relationships of biological sequences (DNA or amino acid) amongst organisms is an important and often difficult task that is essential to various evolutionary studies, ranging from building phylogenies to predicting functional gene annotations. There are many existing heuristic tools, most commonly based on bidirectional BLAST searches that are used to identify homologous genes and combine them into two fundamentally distinct classes: orthologs and paralogs. Due to only using heuristic filtering based on significance score cutoffs and having no cluster post-processing tools available, these methods can often produce multiple clusters constituting unrelated (non-homologous) sequences. Therefore sequencing data extracted from incomplete genome/transcriptome assemblies originated from low coverage sequencing or produced by de novo processes without a reference genome are susceptible to high false positive rates of homology detection.ResultsIn this paper we develop biologically informative features that can be extracted from multiple sequence alignments of putative homologous genes (orthologs and paralogs) and further utilized in context of guided experimentation to verify false positive outcomes. We demonstrate that our machine learning method trained on both known homology clusters obtained from OrthoDB and randomly generated sequence alignments (non-homologs), successfully determines apparent false positives inferred by heuristic algorithms especially among proteomes recovered from low-coverage RNA-seq data. Almost ~42 % and ~25 % of predicted putative homologies by InParanoid and HaMStR respectively were classified as false positives on experimental data set.ConclusionsOur process increases the quality of output from other clustering algorithms by providing a novel post-processing method that is both fast and efficient at removing low quality clusters of putative homologous genes recovered by heuristic-based approaches.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-0955-3) contains supplementary material, which is available to authorized users.