Biology subjects

Kliesmete, Z.

Publications and source records attributed to Kliesmete, Z..

5 recordsLinked to original sources

bioRxiv2025

CroCoNet: a novel framework for cross-species network analysis reveals POU5F1 (OCT4) rewiring

BACKGROUNDGene regulatory changes play a central role in shaping cellular phenotypes across species. To understand how these phenotypes evolve, it is essential to investigate the underlying gene regulatory networks (GRNs). However, most comparative analyses of GRNs remain qualitative and are therefore sensitive to false positives and false negatives. RESULTSTo address this limitation, we introduce CroCoNet (Cross-species Comparison of Networks), an R package for the quantitative comparison of GRNs across species. CroCoNet constructs comparable network modules centered on known transcriptional regulators and quantifies the variability in module topology within and between species. By contrasting these levels of variability, CroCoNet can distinguish true evolutionary divergence from technical and biological confounders. Applying CroCoNet to scRNA-seq data from the early neural differentiation of human, gorilla, and cynomolgus macaque, we identified 20 conserved and 24 diverged modules. Despite the conserved expression pattern of the pluripotency factor POU5F1 (OCT4), its associated module was among the most diverged. This result was independently confirmed through cross-species CRISPRi perturbations coupled with single-cell RNA-seq as a readout. Moreover, we found that great ape- and human-specific LTR7 elements are enriched near POU5F1 module genes, potentially contributing to the cross-species differences in network topology. CONCLUSIONSThese findings demonstrate that CroCoNet can resolve regulatory rewiring and provides a robust framework for studying GRN evolution across closely related species. CroCoNet is available as an open-source R package at https://hellmann-lab.github.io/CroCoNet/.

evolutionary biology↗

bioRxiv2024

Evidence for compensatory evolution within pleiotropic regulatory elements

Pleiotropy, measured as expression breadth across tissues, is one of the best predictors for protein sequence and expression conservation. In this study, we investigated its effect on the evolution of cis-regulatory elements (CREs). To this end, we carefully reanalyzed the Epigenomics Roadmap data for nine fetal tissues, assigning a measure of pleiotropic degree to nearly half a million CREs. To assess the functional conservation of CREs, we generated ATAC-seq and RNA-seq data from humans and macaques. We found that more pleiotropic CREs exhibit greater conservation in accessibility, and the mRNA expression levels of the associated genes are more conserved. This trend of higher conservation for higher degrees of pleiotropy persists when analyzing the transcription factor binding repertoire. In contrast, simple DNA sequence conservation of orthologous sites between species tends to be even lower for pleiotropic CREs than for species-specific CREs. Combining various lines of evidence, we suggest that the lack of sequence conservation for functionally conserved pleiotropic elements is due to compensatory evolution within these large pleiotropic CREs. Furthermore, for less pleiotropic CREs, we find an indication of compensation across CREs. This suggests that pleiotropy is also a good predictor for the functional conservation of CREs, but this is not reflected in the sequence conservation for pleiotropic CREs.

evolutionary biology↗

bioRxiv2023

Expression profiling of the learning striatum

How learning reshapes the brain across temporal and spatial scales, from molecular to cellular levels, remains a fundamental question in neuroscience. In the striatum, ventromedial, dorsomedial and dorsolateral subregions are differentially recruited during early to late learning on a neurophysiological level. Here, we profile the transcriptome of these subregions across 396 biopsies from 66 mice to map molecular dynamics at three learning stages of a visual discrimination task. We find a strong transcriptional change during the early phase that diminishes towards later learning and is indistinguishable among subregions. The 818 genes altered during learning are enriched for neuronal processes, but also include circadian, vascular, and oligodendrocyte-related pathways. We provide a web application for this large learning-related expression dataset, enabling interactive exploration of genes, gene sets, and transcription factor activity, and suggest that this framework will be broadly useful for diverse neuroscience questions.

neuroscience↗

bioRxiv2022

The effect of background noise and its removal on the analysis of single-cell expression data

BACKGROUNDIn droplet-based single-cell and single-nucleus RNA-seq experiments, not all reads associated with one cell barcode originate from the encapsulated cell. Such background noise is attributed to spillage from cell-free ambient RNA or barcode swapping events. Here, we characterize this background noise exemplified by three single-cell RNA-seq (scRNA-seq) and two single-nucleus RNA-seq (snRNA-seq) replicates of mouse kidney cells. For each experiment, kidney cells from two mouse subspecies were pooled, allowing to identify cross-genotype contaminating molecules and estimate the levels of background noise. RESULTSWe find that background noise is highly variable across replicates and individual cells, making up on average 3-35% of the total counts (UMIs) per cell and show that this has a considerable impact on the specificity and detectability of marker genes. In search of the source of background noise, we find that expression profiles of cell-free droplets are very similar to expression profiles of cross-genotype contamination and hence that the majority of background molecules originates from ambient RNA. Finally, we use our genotype-based estimates to evaluate the performance of three methods (CellBender, DecontX, SoupX) that are designed to quantify and remove background noise. We find that CellBender provides the most precise estimates of background noise levels and also yields the highest improvement for marker gene detection. By contrast, clustering and classification of cells are fairly robust towards background noise and only small improvements can be achieved by background removal that may come at the cost of distortions in fine structure. CONCLUSIONOur findings help to better understand the extent, sources and impact of background noise in single-cell experiments and provide guidance on how to deal with it.

bioinformatics↗

bioRxiv2021

TRNP1 sequence, function and regulation co-evolve with cortical folding in mammals

Brain size and cortical folding have increased and decreased recurrently during mammalian evolution. Identifying genetic elements whose sequence or functional properties co-evolve with these traits can provide unique information on evolutionary and developmental mechanisms. A good candidate for such a comparative approach is TRNP1, as it can control proliferation of neural progenitors in mice and ferrets. Here, we investigate the contribution of both regulatory and coding sequences of TRNP1 to brain size and cortical folding in over 30 mammals. We find that the rate of TRNP1 protein evolution ({omega}) significantly correlates with brain size, slightly less with cortical folding and much less with body size. This brain correlation is stronger than for >95% of random control proteins. This co-evolution is likely affecting TRNP1 activity, as we find that TRNP1 from species with larger brains and more cortical folding induce higher proliferation rates in neural stem cells. Furthermore, we compare the activity of putative cis-regulatory elements (CREs) of TRNP1 in a massively parallel reporter assay (MPRA) and identify one CRE that co-evolves with cortical folding in Old World Monkeys and Apes. Our analyses indicate that coding and regulatory changes that increased TRNP1 activity were positively selected either as a cause or a consequence of increases in brain size and cortical folding. They also provide an example how phylogenetic approaches can inform biological mechanisms, especially when combined with molecular phenotypes across several species. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=105 SRC="FIGDIR/small/429919v3_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@1b63549org.highwire.dtl.DTLVardef@16484eborg.highwire.dtl.DTLVardef@527274org.highwire.dtl.DTLVardef@d82c32_HPS_FORMAT_FIGEXP M_FIG C_FIG

evolutionary biology↗