Biology subjects

Melnyk, A.

Publications and source records attributed to Melnyk, A..

4 recordsLinked to original sources

bioRxiv2023

On the role of melanistic coloration on thermoregulation in the crepuscular gecko Eublepharis macularius

Body coloration in ectotherms serves multiple biological functions, including avoiding predators, communicating with conspecific individuals, and involvement in thermoregulation. As ectotherms rely on environmental sources of heat to regulate their internal body temperature, stable melanistic body coloration or color change can be used to increase or decrease heat absorption and heat exchange with the environment. While the function of melanistic coloration for thermoregulation has been found to increase solar radiation absorption for heating in many diurnal ectotherms, research on crepuscular and nocturnal ectotherms is lacking. Since crepuscular and nocturnal ectotherms generally absorb heat from the substrate, coloration is likely under different selective pressures than in diurnal ectotherms. We tested if the proportion of dorsal melanistic body coloration is related to differences in body temperature heating and cooling rates in the crepuscular gecko Eublepharis macularius and whether changes in environmental temperature trigger color changes in this species. Temperature measurements of the geckos and of the environment were taken using infrared thermography and temperature loggers. Color data were obtained using objective photography and a newly developed custom software package. We found that body temperature reflected substrate temperatures, and that the proportion of melanistic coloration has no influence on heating or cooling rates or on color changes. These findings suggest that, in E. macularius, melanistic coloration may not be used for thermoregulation. Future research should further test the function of melanistic coloration in other crepuscular and nocturnal vertebrates to understand the evolution of melanistic pattern in animals active in low light conditions.

zoology↗

bioRxiv2021

From Alpha to Zeta: Identifying variants and subtypes of SARS-CoV-2 via clustering

The availability of millions of SARS-CoV-2 sequences in public databases such as GISAID and EMBL-EBI (UK) allows a detailed study of the evolution, genomic diversity and dynamics of a virus like never before. Here we identify novel variants and sub-types of SARS-CoV-2 by clustering sequences in adapting methods originally designed for haplotyping intra-host viral populations. We asses our results using clustering entropy -- the first time it has been used in this context. Our clustering approach reaches lower entropies compared to other methods, and we are able to boost this even further through gap filling and Monte Carlo based entropy minimization. Moreover, our method clearly identifies the well-known Alpha variant in the UK and GISAID datasets, but is also able to detect the much less represented (< 1% of the sequences) Beta (South Africa), Epsilon (California), Gamma and Zeta (Brazil) variants in the GISAID dataset. Finally, we show that each variant identified has high selective fitness, based on the growth rate of its cluster over time. This demonstrates that our clustering approach is a viable alternative for detecting even rare subtypes in very large datasets.

bioinformatics↗

bioRxiv2019

Using Earth Mover’s Distance for Viral Outbreak Investigations

RNA viruses mutate at extremely high rates forming an intra-host viral population of closely related variants (or quasi-species) [4]. High variability of Human Immunodeficiency Virus (HIV) and Hepatitis C virus (HCV) making them particularly dangerous by allowing them to evade the hosts immune system. HIV and HCV outbreaks pose a significant problem for public health for solving which it is critical to infer transmission clusters, i.e., to decide whether two viral samples belong to the same outbreak. Initial approach [10] was based on estimating relatedness between two samples as the distance between consensuses of the corresponding viral populations. The distance between closest pair of representatives from two populations, MinDist, has been shown to be significantly more accurate [2]. Unfortunately, MinDist computation requires a cumbersome RNA-seq data assembly and identification of all viral sequences from a given project. We present a novel approach that allows to bypass read assembly and estimate the distance between viral samples based on k-mer (i.e. a substring of length k) distribution in RNA-seq reads. The experimental validation using sequencing data from HCV outbreaks shows that the proposed algorithms can successfully identify genetic relatedness between viral populations, infer transmission clusters and outbreak sources, as well decide whether the primary spreader is present in the sequenced outbreak sample.

bioinformatics↗

bioRxiv2018

CliqueSNV: Scalable Reconstruction of Intra-Host Viral Populations from NGS Reads

Rapidly evolving RNA viruses continuously produce minority haplotypes that can become dominant if they are drug-resistant or can better evade the immune system. Therefore, early detection and identification of minority viral haplotypes may help to promptly adjust the patient's treatment plan preventing potential disease complications. Minority haplotypes can be identified using next-generation sequencing (NGS), but sequencing noise hinders accurate identification. The elimination of sequencing noise is a non-trivial task that still remains open. Here we propose CliqueSNV based on extracting pairs of statistically linked mutations from noisy reads. This effectively reduces sequencing noise and enables identifying minority haplotypes with the frequency below the sequencing error rate. We comparatively assess the performance of CliqueSNV using an in vitro mixture of nine haplotypes that were derived from the mutation profile of an existing HIV patient. We show that CliqueSNV can accurately assemble viral haplotypes with frequencies as low as 0.1% and maintains consistent performance across short and long bases sequencing platforms.

bioinformatics↗