Drosophila melanogaster Stress Odorant (dSO) Displays the Characteristics of an Interspecific Alarm Cue.

Organisms depend on visual, auditory, and olfactory cues to signal the presence of danger that could impact survival and reproduction. Drosophila melanogaster emits an olfactory alarm signal, termed the Drosophila stress odorant (dSO), in response to mechanical agitation or electric shock. While it has been shown that conspecifics avoid areas previously occupied by stressed individuals, the contextual underpinnings of the emission of, and response to dSO, have received little attention. Using a binary choice assay, we determined that neither age and sex of emitters, nor the time of the day, affected the emission or avoidance of dSO. However, both sex and mating status affected the response to dSO. We also demonstrated that while D. melanogaster, D. simulans, and D. suzukii, have different dSO profiles, its avoidance was not species-specific. Thus, dSO should not be considered a pheromone but a general alarm signal for Drosophila. However, the response levels to both intra- and inter-specific cues differed between Drosophila species and possible reasons for these differences are discussed.

Type IV Collagen Variants in CKD: Performance of Computational Predictions for Identifying Pathogenic Variants.

RATIONALE & OBJECTIVE: Pathogenic variants in type IV collagen have been reported to account for a significant proportion of chronic kidney disease. Accordingly, genetic testing is increasingly used to diagnose kidney diseases, but testing also may reveal rare missense variants that are of uncertain clinical significance. To aid in interpretation, computational prediction (called in silico) programs may be used to predict whether a variant is clinically important. We evaluate the performance of in silico programs for COL4A3/A4/A5 variants. STUDY DESIGN SETTING & PARTICIPANTS: Rare missense variants in COL4A3/A4/A5 were identified in disease cohorts, including a local focal segmental glomerulosclerosis (FSGS) cohort and publicly available disease databases, in which they are categorized as pathogenic or benign based on clinical criteria. TESTS COMPARED & OUTCOMES: All rare missense variants identified in the 4 disease cohorts were subjected to in silico predictions using 12 different programs. Comparisons between the predictions were compared with: (1) variant classification (pathogenic or benign) in the cohorts and (2) functional characterization in a randomly selected smaller number (17) of pathogenic or uncertain significance variants obtained from the local FSGS cohort. RESULTS: In silico predictions correctly classified 75% to 97% of pathogenic and 57% to 100% of benign COL4A3/A4/A5 variants in public disease databases. The congruency of in silico predictions was similar for variants categorized as pathogenic and benign, with the exception of benign COL4A5 variants, in which disease effects were overestimated. By contrast, in silico predictions and functional characterization classified all 9 pathogenic COL4A3/A4/A5 variants correctly that were obtained from a local FSGS cohort. However, these programs also overestimated the effects of genomic variants of uncertain significance when compared with functional characterization. Each of the 12 in silico programs used yielded similar results. LIMITATIONS: Overestimation of in silico program sensitivity given that they may have been used in the categorization of variants labeled as pathogenic in disease repositories. CONCLUSIONS: Our results suggest that in silico predictions are sensitive but not specific to assign COL4A3/A4/A5 variant pathogenicity, with misclassification of benign variants and variants of uncertain significance. Thus, we do not recommend in silico programs but instead recommend pursuing more objective levels of evidence suggested by medical genetics guidelines.