Biology subjects

Nazarenko, T.

Publications and source records attributed to Nazarenko, T..

4 recordsLinked to original sources

bioRxiv2024

PRECISE EXOME ANALYSIS OF BLASTOCYST BIOPSY SCALE SAMPLES USING PRIMARY TEMPLATE-DIRECTED AMPLIFICATION

This study evaluates primary template-directed amplification (PTA) for whole exome sequencing (WES) on small fibroblast cell groups, mimicking the limited cell quantities typical of trophectoderm embryo biopsies. PTAs consistent amplification reduces allelic dropout (ADO) and impoves uniform coverage, overcoming challenges associated with conventional methods such as multiple displacement amplification (MDA). Using fibroblast samples alongside well-characterised genomic references (E701, NA12878), we benchmarked PTA-WES, achieving 97.5% target region coverage at 10x, meeting American College of Medical Genetics and Genomics (ACMG) standards. Preliminary results from embryo biopsies sequenced with PTA-WES showed a median coverage of 102x, significantly improving upon the variability and coverage gaps observed in MDA-WES. The findings support PTAs potential to enhance the clinical applicability of WES for preimplantation genetic testing for monogenic disorders (PGT-M), expanding capabilities to detect inherited and de novo mutations in embryos. Further optimisation and variant detection analyses are planned to evaluate PTAs robustness for routine clinical use.

genetics↗

bioRxiv2023

Oligodendrocytes and neurons contribute to amyloid-β deposition in Alzheimer's disease

In Alzheimers disease (AD), amyloid-{beta} (A{beta}) is thought to be of neuronal origin. However, in single-cell RNAseq datasets from mouse and human, we found transcripts of amyloid precursor protein (APP) and the amyloidogenic-processing machinery equally abundant in oligodendrocytes (OLs). By cell-type-specific deletion of Bace1 in a humanized knock-in AD model, APPNLGF, we demonstrate that almost a third of cortical A{beta} deposited in plaques is derived from OLs. However, excitatory projection neurons must provide a threshold level of A{beta} production for plaque deposition to occur and for oligodendroglial A{beta} to co-aggregate. Indeed, very few plaques are deposited in the absence of neuronally-derived A{beta}, although soluble A{beta} species are readily detected, especially in subcortical white matter. Our data identify OLs as a source of A{beta} in vivo and further underscore a non-linear relationship between cellular A{beta} production and resulting plaque formation. Ultimately, our observations are relevant for therapeutic strategies aimed at disease prevention in AD.

neuroscience↗

bioRxiv2023

Technical and biological sources of unreliability of Infinium type II probes of the Illumina MethylationEPIC BeadChip microarray

The Illumina Methylation array platform has facilitated countless epigenetic studies on DNA methylation (DNAme) in health and disease, yet relatively few studies have so studied its reliability, i.e., the consistency of repeated measures. Here we focus on the reliability of both type I and type II Infinium probes. We propose a method for excluding unreliable probes based on dynamic thresholds for mean intensity (MI) and unreliability, estimated by probe-level simulation of the influence of technical noise on methylation {beta}-values using the background intensities of negative control probes. We validate our method in several datasets, including Illumina MethylationEPIC BeadChip v1.0 data from paired whole blood samples taken six weeks apart. Our analysis revealed that specifically probes with low MI exhibit higher {beta}-value variability between repeated samples. MI was associated with the number of C-bases in the respective probe sequence and correlated negatively with unreliability scores. The unreliability scores were substantiated through validation in a new EPIC v1.0 (blood and cervix) and a publicly available 450k (blood) dataset, as they effectively captured the variability observed in {beta}-values between technical replicates. Finally, despite promising higher robustness, the newer version v2.0 of the MethylationEPIC BeadChip retained a substantial number of probes with poor unreliability scores. To enhance current pre-processing pipelines, we developed an R package to calculate MI and unreliability scores and provide guidance on establishing optimal dynamic score thresholds for a given data set.

genomics↗

bioRxiv2020

Age-dependent DNA methylation Parenclitic Networks in family-based cohort patients with Down Syndrome

Despite the fact that the cause of Down Syndrome (DS) is well established, the underlying molecular mechanisms that contribute to the syndrome and the phenotype of accelerated aging remain largely unknown. DNA methylation profiles are largely altered in DS, but it remains unclear how different methylation regions and probes are structured into a network of interactions. We develop and generalize the Parenclitic Networks approach that enables finding correlations between distant CpG probes (which are not pronounced as stand-alone biomarkers) and quantifies hidden network changes in DNA methylation. DS and a familybased cohort (including healthy siblings and mothers of persons with DS) are used as a case study. Following this approach, we constructed parenclitic networks and obtained different signatures that indicate (i) differences between individuals with DS and healthy individuals; (ii) differences between young and old healthy individuals; (iii) differences between DS individuals and their age-matched siblings, and (iv) difference between DS and the adult population (their mothers). The Gene Ontology analysis showed that the CpG network approach is more powerful than the single CpG approach in identifying biological processes related to DS phenotype. This includes the processes occurring in the central nervous system, skeletal muscles, disorders in carbohydrate metabolism, cardiopathology, and oncogenes. Our open-source software implementation is accessible to all researchers. The software includes a complete workflow, which can be used to construct Parenclitic Networks with any machine learning algorithm as a kernel to build edges. We anticipate a broad applicability of the approach to other diseases.

bioinformatics↗