The Drosophila melanogaster Y chromosome is currently known to contain 13 single-copy protein-coding genes, six of which are essential for male fertility, as well as several non-coding genes and abundant repetitive DNA. Localization of Y-linked sequences has traditionally relied on labor-intensive crosses using Kennison's translocation strains, which map Y-linked loci by generating flies deficient for each of the six Y-chromosome fertility regions (ks-1, ks-2, kl-1, kl-2, kl-3, and kl-5). Here we present Digital Kennison, a computational pipeline that recasts this classical mapping strategy as a sequence-based analysis. The pipeline queries eight genomic databases derived from Kennison's strains using BLAST and read coverage, assigning sequences to fertility regions or the centromeric region with a calibrated confidence score. We benchmarked the method on 60 Y-linked sequences spanning all seven regions, including single-copy protein-coding genes, Mst77Y family members, non-coding RNAs, and the centromere. Digital Kennison achieved 97% precision while resolving challenging cases, including boundary-spanning genes (PRY and Ppr-Y), fragmented Mst77Y copies, and FDY, which has a closely related autosomal paralog. Beyond validating known localizations, the pipeline localized the unmapped gene CG41561 to the kl-1region and reassigned the transcript CR40629-RC from the kl-2 region to kl-5. It also localized 7 of 16 recently transferred Y-linked sequences, including 4 with high confidence. Applied to 904 R6 scaffolds, Digital Kennison assigned 75% to fertility regions, including five currently annotated as autosomal-pericentromeric. Digital Kennison reduces sequence localization from weeks of genetic crosses to minutes of computation while preserving the power of classical translocation mapping.
Drosophila nepalensis is a cold-adapted drosophilid endemic to the Himalayan region. Its ability to survive in harsh, cold conditions makes it a valuable Drosophila model for investigating how adaptation to thermal extremes may influence species persistence under future climate change. Here, we report the first de novo genome assembly of D. nepalensis, based on a hybrid sequencing strategy that combines Illumina short reads and Oxford Nanopore long reads. Illumina sequencing generated 49.88 million 150 bp paired-end reads (∼14.96 Gbp), while Nanopore sequencing produced 1.35 million long reads totaling ∼0.76 Gbp. The assembled genome spanned ∼178 Mb with an N50 of 83.6 kb and 98% BUSCO completeness, comparable to other well-annotated Drosophila genomes. Annotation identified 10,560 protein-coding genes, including transcription factor-rich and stress-related domains such as zinc fingers, WD40 repeats, and ankyrin motifs. Comparative orthology analysis across 6 Drosophila species identified 14,168 orthologous clusters, of which 9,173 were shared among all 6 species, indicating a conserved core genomic set across the sampled taxa. D. nepalensis showed 83 unique orthogroups and 50 singletons, suggesting some lineage-specific gene expansions associated with cold adaptation and endemicity, including families encoding caspase-family apoptotic regulators, chromatin remodeling proteins (HMGB/protamine-like), and SNARE-domain vesicle trafficking factors. Gene family evolution analysis revealed the highest expansions in the cold-tolerant Himalayan drosophilid, D. nepalensis, including significant expansions in serine protease, chaperone, and neurotransmitter transporter families, alongside dramatic contractions of core histone gene families, suggesting lineage-specific chromatin remodeling and ecological specialization.
Genetic perturbations are one of the great strengths of the model organism Drosophila melanogaster, with approaches such as classical mutagenesis and RNA interference enabling a wealth of biological discoveries. A more recent approach for altering gene expression is CRISPR/Cas9-based mutagenesis. As with any new tool, however, its use must be optimized. High expression of Cas9 has been shown to cause cytotoxicity in some cell types, including class IV dendritic arborization (da) neurons. In this study, we provide evidence that Cas9 expression causes cytotoxicity in class I da neurons, in addition to class IV da neurons, both of which are widely used to study neuronal development and regeneration. We then systematically evaluated available Cas9 transgenes designed to titrate Cas9 expression, called uCas9 transgenes. We show that the expression of these uCas9 transgenes results in little to no cytotoxicity in various classes of da neurons. Immunostaining revealed drastic reductions in Cas9 protein levels for da neurons expressing the uCas9(L) transgene. Lastly, we demonstrate that the uCas9(L) transgene effectively and specifically gene edits in both class I and class IV da neurons, lowering the expression of GFP-tagged proteins and producing loss-of-function morphological phenotypes when targeting endogenous loci. Thus, we refine the use of CRISPR mutagenesis in Drosophila da neurons through titration of Gal4/UAS-mediated Cas9 expression using existing uCas9 transgenes, a feasible and flexible approach that may be useful for other labs encountering Cas9 cytotoxicity in their own model systems.
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) continues to pose a threat to humans as well as domestic and wild animals. The variability in severity of clinical signs, the zoonotic potential, and the host-specific response to infection contribute to the persistence of circulation of disease. In wildlife species, white-tailed deer have been shown to be more permissive to infection than bovids. However, among bovids, American bison have shown a greater susceptibility than cattle. In this study, we investigate the transcriptomic response to experimental SARS-CoV-2 infection in bison over time. Substantial numbers of differentially expressed genes were identified between pre- and 2, 5, 7, 14, and 21 days post-inoculation. Kyoto Encyclopedia of Genes and Genomes and Gene Ontology term analysis identified associations with immune response, inflammatory response, and viral infection including COVID-19. Ingenuity Pathway Analysis of the coronavirus pathway highlighted differences in signaling at days 2 versus 21 post-inoculation. We additionally examined changes in the nasal microbiome of bison over the course of experimental infection, which suggested an increase in opportunity for secondary infection causing pathogens such as Mannheimia. Collectively, this study presents a profile of bison transcriptomic response to SARS-CoV-2 infection and continues to expand our understanding of variation in host response.
Metabolomics provides direct insight into physiological state, but for small organisms such as Drosophila melanogaster, it typically requires pooling individuals to obtain sufficient material. Pool sizes vary widely across studies with little justification, and the impact pooling and biological replication have on metabolomic characterization and signal detection remains poorly understood. We evaluated the effects of pool size and biological replication on metabolomic profiles and signal detection using two complementary designs in D. melanogaster. First, we tested how pooling (5, 50, or 100 individuals) affects metabolomic structure and reproducibility in inbred and outbred populations. Second, we tested how pool size interacts with replicate number to affect detection of diet-associated metabolite changes under a high-sugar perturbation. Pool size shaped metabolomic profiles: pools of five individuals consistently differed from larger pools, which improved reproducibility in a dataset-dependent manner. In the dietary experiment, smaller pools showed reduced sensitivity, detecting fewer true diet-associated metabolites without increasing false discoveries, and replicate downsampling showed that pool size and replication independently shape signal retention. Detection depended on effect size and variability: metabolites with larger, more stable effects were consistently retained, while smaller more variable effects were rapidly lost under reduced sampling. Beta-binomial modeling confirmed that detection probability reflects a balance between signal strength and measurement variability, with pool size and replicate number independently shaping this relationship. Together, these results show that metabolomic inference depends on the interplay of signal, noise, and sampling design, with pool size and replication jointly shaping the detectability, stability, and interpretation of biological signals.
