Gonadal atrophy is the most typical and dramatic manifestation of intraspecific hybrid dysgenesis syndrome leading to sterility in Drosophila melanogaster dysgenic progeny. The P-M system of hybrid dysgenesis is primarily associated with germ cell degeneration during the early stages of Drosophila embryonic development at elevated temperatures. In the present study, we have defined the phase of germ cell death as beginning at the end of embryogenesis immediately following gonad formation. However, the temperature-dependent screening of germ cell developmental patterns in the dysgenic background showed that early germ cells are susceptible to the hybrid dysgenesis at any Drosophila life-cycle stage, including in the imago. Electron microscopy of germ cells after dysgenesis induction revealed significant changes in subcellular structure, especially mitochondria, prior to cellular breakdown. The mitochondrial pathology can promote the activation of cell death pathways in dysgenic germ cells, which leads to gonadal atrophy.

Intraspecific hybrid dysgenesis (HD) appears after some strains of D. melanogaster are crossed. The predominant idea is that the movement of transposable P elements causes HD. It is believed that P elements appeared in the D. melanogaster genome in the middle of the last century by horizontal transfer, simultaneously with the appearance of HD determinants. A subsequent simultaneous expansion of HD determinants and P elements occurred. We analyzed the current distribution of HD determinants in natural populations of D. melanogaster and found no evidence of their further spread. However, full-sized P elements were identified in the genomes of all analyzed natural D. melanogaster strains independent of their cytotypes. Thus, the expansion of P elements does not correlate with the expansion of HD determinants. We found that the ovaries of dysgenic females did not contain germ cells despite the equal number of primordial germ cells in early stages in dysgenic and non-dysgenic embryos. We propose that HD does not result from DNA damage caused by P element transposition, but it would be the disruption in the regulation of dysgenic ovarian formation that causes the dysgenic phenotypes.

Babesia infection was studied in 21 blood samples of dogs with symptoms of babesiosis and among 72 Dermacentor reticulatus and 70 Ixodes persulcatus ticks from southwestern Siberia, Russia. Babesia DNA was detected by hemi-nested PCR based on the 18S rRNA gene with subsequent direct sequencing. All of the analyzed canine blood samples and three D. reticulatus, but none from I. persulcatus ticks studied were shown to contain Babesia DNA. Nucleotide sequences of the Babesia 18S rRNA gene fragment of 354 bp long for all 24 positive samples appeared to belong to the subspecies Babesia canis canis and differed only at three positions. The Babesia nucleotide sequences from 17 canine blood samples and from one D. reticulatus tick were identical to each other and to previously known B. canis canis from canine blood in Slovenia. Four canine blood samples and the second tick sample contained a mixture of two nucleotide sequences previously found in canine blood. B. canis canis nucleotide sequence from the third tick differed in the unique nucleotide transition and could correspond to a new genetic variant. Thus, the main etiological agent of canine babesiosis in Novosibirsk region is B. canis canis, and D. reticulatus, but not I. persulcatus, ticks could serve as a vector of this infectious agent. To our knowledge, this is the first report of the B. canis canis nucleotide sequences from ticks.

The fruit fly Drosophila melanogaster is a classic research object in genetics and systems biology. In the genetic analysis of flies, a routine task is to determine the offspring size and gender ratio in their populations. Currently, these estimates are made manually, which is a very time-consuming process. The counting and gender determination of flies can be automated by using image analysis with deep learning neural networks on mobile devices. We proposed an algorithm based on the YOLOv4-tiny network to identify Drosophila flies and determine their gender based on the protocol of taking pictures of insects on a white sheet of paper with a cell phone camera. Three strategies with different types of augmentation were used to train the network. The best performance (F1 = 0.838) was achieved using synthetic images with mosaic generation. Females gender determination is worse than that one of males. Among the factors that most strongly influencing the accuracy of fly gender recognition, the fly’s position on the paper was the most important. Increased light intensity and higher quality of the device cameras have a positive effect on the recognition accuracy. We implement our method in the FlyCounter Android app for mobile devices, which performs all the image processing steps using the device processors only. The time that the YOLOv4-tiny algorithm takes to process one image is less than 4 s.