FlyVar: a database for genetic variation in Drosophila melanogaster

SNP-SVant: A Computational Workflow to Predict and Annotate Genomic Variants in Organisms Lacking Benchmarked Variants

Whole-genome sequencing is widely used to investigate population genomic variation in organisms of interest. Assorted tools have been independently developed to call variants from short-read sequencing data aligned to a reference genome, including single nucleotide polymorphisms (SNPs) and structural variations (SVs). We developed SNP-SVant, an integrated, flexible, and computationally efficient bioinformatic workflow that predicts high-confidence SNPs and SVs in organisms without benchmarked variants, which are traditionally used for distinguishing sequencing errors from real variants. In the absence of these benchmarked datasets, we leverage multiple rounds of statistical recalibration to increase the precision of variant prediction. The SNP-SVant workflow is flexible, with user options to tradeoff accuracy for sensitivity. The workflow predicts SNPs and small insertions and deletions using the Genome Analysis ToolKit (GATK) and predicts SVs using the Genome Rearrangement IDentification Software Suite (GRIDSS), and it culminates in variant annotation using custom scripts. A key utility of SNP-SVant is its scalability. Variant calling is a computationally expensive procedure, and thus, SNP-SVant uses a workflow management system with intermediary checkpoint steps to ensure efficient use of resources by minimizing redundant computations and omitting steps where dependent files are available. SNP-SVant also provides metrics to assess the quality of called variants and converts between VCF and aligned FASTA format outputs to ensure compatibility with downstream tools to calculate selection statistics, which are commonplace in population genomics studies. By accounting for both small and large structural variants, users of this workflow can obtain a wide-ranging view of genomic alterations in an organism of interest. Overall, this workflow advances our capabilities in assessing the functional consequences of different types of genomic alterations, ultimately improving our ability to associate genotypes with phenotypes. © 2024 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol: Predicting single nucleotide polymorphisms and structural variations Support Protocol 1: Downloading publicly available sequencing data Support Protocol 2: Visualizing variant loci using Integrated Genome Viewer Support Protocol 3: Converting between VCF and aligned FASTA formats.

MED13 and glycolysis are conserved modifiers of α-synuclein-associated neurodegeneration

α-Synuclein (α-syn) is important in synucleinopathies such as Parkinson's disease (PD). While genome-wide association studies (GWASs) of synucleinopathies have identified many risk loci, the underlying genes have not been shown for most loci. Using Drosophila, we screened 3,471 mutant chromosomes for genetic modifiers of α-synuclein and identified 12 genes. Eleven modifiers have human orthologs associated with diseases, including MED13 and CDC27, which lie within PD GWAS loci. Drosophila Skd/Med13 and glycolytic enzymes are co-upregulated by α-syn-associated neurodegeneration. While elevated α-syn compromises mitochondrial function, co-expressing skd/Med13 RNAi and α-syn synergistically increase the ratio of oxidized-to-reduced glutathione. The resulting neurodegeneration can be suppressed by overexpressing a glycolytic enzyme or treatment with deferoxamine, suggesting that compensatory glycolysis is neuroprotective. In addition, the functional relationship between α-synuclein, MED13, and glycolytic enzymes is conserved between flies and mice. We propose that hypoxia-inducible factor and MED13 are part of a druggable pathway for PD.

Combined use of feature engineering and machine-learning to predict essential genes in Drosophila melanogaster

Characterizing genes that are critical for the survival of an organism (i.e. essential) is important to gain a deep understanding of the fundamental cellular and molecular mechanisms that sustain life. Functional genomic investigations of the vinegar fly, Drosophila melanogaster, have unravelled the functions of numerous genes of this model species, but results from phenomic experiments can sometimes be ambiguous. Moreover, the features underlying gene essentiality are poorly understood, posing challenges for computational prediction. Here, we harnessed comprehensive genomic-phenomic datasets publicly available for D. melanogaster and a machine-learning-based workflow to predict essential genes of this fly. We discovered strong predictors of such genes, paving the way for computational predictions of essentiality in less-studied arthropod pests and vectors of infectious diseases.

SNP-CRISPR: A Web Tool for SNP-Specific Genome Editing

CRISPR-Cas9 is a powerful genome editing technology in which a single guide RNA (sgRNA) confers target site specificity to achieve Cas9-mediated genome editing. Numerous sgRNA design tools have been developed based on reference genomes for humans and model organisms. However, existing resources are not optimal as genetic mutations or single nucleotide polymorphisms (SNPs) within the targeting region affect the efficiency of CRISPR-based approaches by interfering with guide-target complementarity. To facilitate identification of sgRNAs (1) in non-reference genomes, (2) across varying genetic backgrounds, or (3) for specific targeting of SNP-containing alleles, for example, disease relevant mutations, we developed a web tool, SNP-CRISPR (https://www.flyrnai.org/tools/snp_crispr/). SNP-CRISPR can be used to design sgRNAs based on public variant data sets or user-identified variants. In addition, the tool computes efficiency and specificity scores for sgRNA designs targeting both the variant and the reference. Moreover, SNP-CRISPR provides the option to upload multiple SNPs and target single or multiple nearby base changes simultaneously with a single sgRNA design. Given these capabilities, SNP-CRISPR has a wide range of potential research applications in model systems and for design of sgRNAs for disease-associated variant correction.

Genetic analyses uncover pleiotropic compensatory roles for Drosophila Nucleobindin-1 in inositol trisphosphate-mediated intracellular calcium homeostasis

Nucleobindin-1 is an EF-hand calcium-binding protein with a distinctive profile, predominantly localized to the Golgi in insect and wide-ranging vertebrate cell types, alike. Its putative involvements in intracellular calcium (Ca²⁺) homeostasis have never been phenotypically characterized in any model organism. We have analyzed an adult-viable mutant that completely disrupts the G protein α-subunit binding and activating (GBA) motif of Drosophila Nucleobindin-1 (dmNUCB1). Such disruption does not manifest any obvious fitness-related, morphological/developmental, or behavioral abnormalities. A single copy of this mutation or the knockdown of dmnucb1 in restricted sets of cells variously rescues pleiotropic mutant phenotypes arising from impaired inositol 1,4,5-trisphosphate receptor (IP3R) activity (in turn depleting cytoplasmic Ca²⁺ levels across diverse tissue types). Additionally, altered dmNUCB1 expression or function considerably reverses lifespan and mobility improvements effected by IP3R mutants, in a Drosophila model of amyotrophic lateral sclerosis. Homology modeling-based analyses further predict a high degree of conformational conservation in Drosophila, of biochemically validated structural determinants in the GBA motif that specify in vertebrates, the unconventional Ca²⁺-regulated interaction of NUCB1 with Gαi subunits. The broad implications of our findings are hypothetically discussed, regarding potential roles for NUCB1 in GBA-mediated, Golgi-associated Ca²⁺ signaling, in health and disease.

Phylogeny of the Genus Drosophila

Understanding phylogenetic relationships among taxa is key to designing and implementing comparative analyses. The genus Drosophila, which contains over 1600 species, is one of the most important model systems in the biological sciences. For over a century, one species in this group, Drosophila melanogaster, has been key to studies of animal development and genetics, genome organization and evolution, and human disease. As whole-genome sequencing becomes more cost-effective, there is increasing interest in other members of this morphologically, ecologically, and behaviorally diverse genus. Phylogenetic relationships within Drosophila are complicated, and the goal of this paper is to provide a review of the recent taxonomic changes and phylogenetic relationships in this genus to aid in further comparative studies.

Engineering species-like barriers to sexual reproduction

Controlling the exchange of genetic information between sexually reproducing populations has applications in agriculture, eradication of disease vectors, control of invasive species, and the safe study of emerging biotechnology applications. Here we introduce an approach to engineer a genetic barrier to sexual reproduction between otherwise compatible populations. Programmable transcription factors drive lethal gene expression in hybrid offspring following undesired mating events. As a proof of concept, we target the ACT1 promoter of the model organism Saccharomyces cerevisiae using a dCas9-based transcriptional activator. Lethal overexpression of actin results from mating this engineered strain with a strain containing the wild-type ACT1 promoter.

Genetic isolation of a genetically modified organism represents a useful strategy for biocontainment. Here the authors use dCas9-VP64-driven gene expression to construct a ‘species-like’ barrier to reproduction between two otherwise compatible populations.

The Power of Natural Variation for Model Organism Biology

Genetic background effects have long been recognized and, in some cases studied, but they are often viewed as a nuisance by molecular biologists. We suggest that genetic variation currently represents a critical frontier for molecular studies. Human genetics has seen a surge of interest in genetic variation and its contributions to disease, but insights into disease mechanisms are difficult since information about gene function is lacking. By contrast, model organism genetics has excelled at revealing molecular mechanisms of cellular processes, but often de-emphasizes genetic variation and its functional consequences. We argue that model organism biology would benefit from incorporating natural variation, both to capture how well laboratory lines exemplify the species they represent and to inform on molecular processes and their variability. Such a synthesis would also greatly expand the relevance of model systems for studies of complex trait variation, including disease.