Biology subjects

Parvizi, P.

Publications and source records attributed to Parvizi, P..

4 recordsLinked to original sources

bioRxiv2022

Somatic copy number variant load in neurons of healthy controls and Alzheimer's disease patients

BackgroundThe possible role of somatic copy number variations (CNVs) in Alzheimers disease (AD) aetiology has been controversial. Although cytogenetic studies suggested increased CNV loads in AD brains, a recent single-cell whole-genome sequencing (scWGS) experiment, studying frontal cortex brain samples, found no such evidence. Here we readdressed this issue using lowcoverage scWGS on pyramidal neurons dissected using laser capture microdissection (LCM) across five brain regions: entorhinal cortex, temporal cortex, hippocampal CA1, hippocampal CA3, and the cerebellum. ResultsAmong reliably detected somatic CNVs identified in 1301 cells obtained from the brains of 13 AD patients and 7 healthy controls, deletions were more frequent compared to duplications. Interestingly, we observed slightly higher frequencies of CNV events in cells from AD compared to similar numbers of cells from controls (4.1% vs. 1.4%, or 0.9% vs. 0.7%, using different filtering approaches), although the differences were not statistically significant. We also observed that LCM-isolated cells show higher within-cell read depth variation compared to cells isolated with fluorescence activated cell sorting (FACS), which we argue may have both biological and technical causes. Furthermore, we found that LCM-isolated neurons in AD harbour slightly more read depth variability than neurons of controls, which might be related to the reported hyperploid profiles of some AD-affected neurons. We also propose a principal component analysis-based denoising approach that significantly reduces within-cell read depth variation in scWGS data. ConclusionsWe find slightly higher somatic CNV frequencies in the brains of AD patients, and higher sequencing coverage variability, although the effects measured do not reach statistical significance. The results call for improved experimental protocols to determine the possible role of CNVs in AD pathogenesis.

bioinformatics↗

bioRxiv2022

BioNE: Integration of network embeddings for supervised learning

A network embedding approach reduces the analysis complexity of large biological networks by converting them to lowdimensional vector representations (features/embeddings). These lower-dimensional vectors can then be used in machine learning prediction tasks with a wide range of applications in computational biology and bioinformatics. Several network embedding approaches have been proposed with different methods of generating vector representations. These network embedding approaches can be quite diverse in terms of data representation and implementation. Moreover, most were not originally developed for biological networks. Therefore comparing and assessing the performance of these diverse models in practice, in biological contexts, can be challenging. To facilitate such comparisons, we have developed the BioNE framework for integration of different embedding methods in prediction tasks. Using this framework one can easily assess, for instance, whether combined vector representations from multiple embedding methods offer complementary information with regards to the network features and thus better performance on prediction tasks. In this paper, we present the BioNE software suite for embedding integration, which applies network embedding methods following standardised network preparation steps, and integrates the vector representations achieved by these methods using three different techniques. BioNE enables selection of prediction models, oversampling methods, feature selection methods, cross-validation type and cross-validation parameters. Availability and implementationBioNE pipeline and detailed explanation of implementation is freely available on GitHub, at https://github.com/pooryaparvizi/BioNE

bioinformatics↗

bioRxiv2018

Mutation accumulation differentially impacts ageing in mammalian tissues

Medawars mutation accumulation (MA) hypothesis explains ageing by the declining force of natural selection with age: slightly deleterious germline mutations that are functional in old age are not effectively eliminated by selection and therefore lead to ageing-related phenotypes. Although widely cited, empirical support for the MA hypothesis, particularly molecular evidence, has remained limited. Here we test one of its predictions, that genes relatively highly expressed in old adults vs. young adults should be under weaker purifying selection than those relatively highly expressed in young adults. To do so, we combine 23 RNA-sequencing and 35 microarray gene expression datasets (including 9 tissues from 5 mammalian species) with protein and regulatory sequence conservation estimates across mammals. We identify age-related decrease in transcriptome conservation (ADICT) in four tissues, brain, liver, lung, and artery, but not in other tissues, most notably muscle and heart. ADICT is driven both by decreased expression of highly conserved genes and up-regulation of poorly conserved genes during ageing, in line with the MA hypothesis. Lowly conserved and up-regulated genes in ADICT-associated tissues have overlapping functional properties, particularly involving apoptosis and inflammation, with no evidence for a history of positive selection. Our results suggest that tissues vary in how evolution has shaped their ageing patterns. We find that in some tissues, genes up-regulated during ageing, possibly in response to accumulating cellular and histological damage, are under weaker purifying selection than other genes. We propose that accumulation of slightly deleterious substitutions in these genes may underlie their suboptimal regulation and activity during ageing, shaping senescent phenotypes such as inflammaging.

evolutionary biology↗

bioRxiv2017

Archaeogenetics of Late Iron Age Cemialo Sırtı, Batman: Investigating maternal genetic continuity in North Mesopotamia since the Neolithic

North Mesopotamia has witnessed dramatic political and social change since the Bronze Age, but the impact of these events on its demographic history is little understood. Here we study this question by analysing the recently excavated Late Iron Age settlement of Cemialo Srt in Batman, southeast Turkey. Archaeological and/or radiocarbon evidence indicate that the site was inhabited during two main periods: the first half of the 2nd millennium BCE and the first millennium BCE. Cemialo Srt reveals nomadic items of the Early Iron Age, as well as items associated with the Late Achaemenid and subsequent Hellenistic Periods. Mitochondrial DNA (mtDNA) haplotypes from 12 Cemialo Srt individuals reveal high genetic diversity in this population, conspicuously higher than early Holocene west Eurasian populations, which supports the notion of increasing population admixture in west Eurasia through the Holocene. Still, in its mtDNA composition, Cemialo Srt shows highest affinity to Neolithic north Syria and Neolithic Anatolia among ancient populations studied, and to modern-day southwest Asian populations. Population genetic simulations do not reject continuity between Neolithic and Iron Age, nor between Iron Age and present-day populations of the region. Despite the regions complex political history and indication for increased genetic diversity over time, we find no evidence for sharp shifts in north Mesopotamian maternal genetic composition within the last 10,000 years.