Biology subjects

Anvar, S. Y.

Publications and source records attributed to Anvar, S. Y..

5 recordsLinked to original sources

bioRxiv2020

A single QTL with large effect is associated with female functional virginity in an asexual parasitoid wasp

During the transition from sexual to asexual reproduction, a suite of reproduction-related sexual traits become superfluous, and may be selected against if costly. Female functional virginity refers to asexual females resisting to mate or not fertilizing eggs after mating. These traits appear to be among the first that evolve during the gradual transition from sexual to asexual reproduction. The genetic basis of female functional virginity remains elusive. Previously, we reported that female functional virginity segregates as a single recessive locus in the asexual parasitoid wasp Asobara japonica. Here, we investigate the genetic basis of this trait by quantitative trait loci (QTL) mapping and candidate gene analyses. Consistent with the segregation of phenotypes, a single QTL of large effect was found spanning over 4.23 Mb and comprising at least 131 protein-coding genes, of which 15 featured sex-biased expression in the related sexual Asobara tabida. We speculate that two of these 15 genes may be of particular interest: CD151 antigen and nuclear pore complex protein Nup50. Overall, our results are consistent with a single gene or a cluster of linked genes underlying rapid evolution of female functional virginity in the transition to asexuality. Once a mutation for rejection to mate has swept through a population, the region comprising the gene(s) does not get smaller due to lack of recombination in asexuals.

evolutionary biology↗

bioRxiv2020

A unifying model to predict variable drug response for personalised medicine

Pharmacogenomics is a key component of personalized medicine. It promises a safer and more effective drug treatment by individualizing the choice of drug and dose based on an individuals genetic profile1,2. The majority of commonly prescribed drugs are metabolized by a small set of Cytochrome P450 (CYP) enzymes3. In clinical practice, genetic biomarkers are being used to categorize patients into predefined *-alleles to predict CYP450 enzyme activity and adjust drug dosages accordingly. Yet, this approach has important limitations as it leaves a large part of variability in drug response unexplained4,5. Here, we present a novel approach and introduce a continuous scale (instead of categorical) assignments to predict metabolic enzyme activity. The proposed strategy uses full gene sequencing data, a neural network model and CYP2D6 mediated tamoxifen metabolism from a prospective study of 561 breast cancer patients. The model explains 79% of the interindividual variability in CYP2D6 activity compared to 54% with the conventional approach. It is capable of assigning accurate enzyme activity to alleles containing previously uncharacterized combinations of variants and were replicated in an independent cohort of tamoxifen treated patients, a cohort of Venlafaxine users as well as in vitro functional assays using HEK cells. These results demonstrate the advantage of a continuous scale and a completely phased genotype for prediction of CYP450 enzyme activity and thereby enables more accurate prediction of individual drug response.

pharmacology and toxicology↗

bioRxiv2019

What is in a lichen? A metagenomic approach to reconstruct the holo-genome of Umbilicaria pustulata

Lichens are valuable models in symbiosis research and promising sources of biosynthetic genes for biotechnological applications. Most lichenized fungi grow slowly, resist aposymbiotic cultivation, and are generally poor candidates for experimentation. Obtaining contiguous, high quality genomes for such symbiotic communities is technically challenging. Here we present the first assembly of a lichen holo-genome from metagenomic whole genome shotgun data comprising both PacBio long reads and Illumina short reads. The nuclear genomes of the two primary components of the lichen symbiosis - the fungus Umbilicaria pustulata (33 Mbp) and the green alga Trebouxia sp. (53 Mbp) - were assembled at contiguities comparable to single-species assemblies. The analysis of the read coverage pattern revealed a relative cellular abundance of approximately 20:1 (fungus:alga). Gap-free, circular sequences for all organellar genomes were obtained. The community of lichen-associated bacteria is dominated by Acidobacteriaceae, and the two largest bacterial contigs belong to the genus Acidobacterium. Gene set analyses showed no evidence of horizontal gene transfer from algae or bacteria into the fungal genome. Our data suggest a lineage-specific loss of a putative gibberellin-20-oxidase in the fungus, a gene fusion in the fungal mitochondrion, and a relocation of an algal chloroplast gene to the algal nucleus. Major technical obstacles during reconstruction of the holo-genome were coverage differences among individual genomes surpassing three orders of magnitude. Moreover, we show that G/C-rich inverted repeats paired with non-random sequencing error in PacBio data can result in missing gene predictions. This likely poses a general problem for genome assemblies based on long reads.

genomics↗

bioRxiv2019

Reference-free resolution of long-read metagenomic data

BackgroundRead binning is a key step in proper and accurate analysis of metagenomics data. Typically, this is performed by comparing metagenomics reads to known microbial sequences. However, microbial communities usually contain mixtures of hundreds to thousands of unknown bacteria. This restricts the accuracy and completeness of alignment-based approaches. The possibility of reference-free deconvolution of environmental sequencing data could benefit the field of metagenomics, contributing to the estimation of metagenome complexity, improving the metagenome assembly, and enabling the investigation of new bacterial species that are not visible using standard laboratory or alignment-based bioinformatics techniques.\n\nResultsHere, we apply an alignment-free method that leverages on k-mer frequencies to classify reads within a single long read metagenomic dataset. In addition to a series of simulated metagenomic datasets, we generated sequencing data from a bioreactor microbiome using the PacBio RSII single-molecule real-time sequencing platform. We show that distances obtained after the comparison of k-mer profiles can reveal relationships between reads within a single metagenome, leading to a clustering per species.\n\nConclusionsIn this study, we demonstrated the possibility to detect substructures within a single metagenome operating only with the information derived from the sequencing reads. The obtained results are highly important as they establish a principle that might potentially expand the toolkit for the detection and investigation of previously unknow microorganisms.

bioinformatics↗

bioRxiv2017

Full-length mRNA sequencing uncovers a widespread coupling between transcription and mRNA processing

The multifaceted control of gene expression requires tight coordination of regulatory mechanisms at transcriptional and post-transcriptional level. Here, we studied the interdependence of transcription, splicing and polyadenylation events on single mRNA molecules by full-length mRNA sequencing. In MCF-7 breast cancer cells, we found 2,700 genes with interdependent alternative transcription, splicing and polyadenylation events, both in proximal and distant parts of mRNA molecules. The analysis of three human primary tissues revealed similar patterns of interdependency between transcription and mRNA processing events. We predict thousands of novel Open Reading Frames from the sequence of full-length mRNAs and obtained evidence for their translation by shotgun proteomics. The mapping database rescued 358 previously unassigned peptides and improved the assignment of others. By recognizing sample-specific amino-acid changes and novel splicing patterns, full-length mRNA sequencing improved proteogenomics analysis of MCF-7 cells. Our findings demonstrate that our understanding of transcriptome complexity is far from complete and provides a basis to reveal largely unresolved mechanisms that coordinate transcription and mRNA processing.

molecular biology↗