Biology subjects

Petr, M.

Publications and source records attributed to Petr, M..

10 recordsLinked to original sources

bioRxiv2026

Focal neuronal loss and cell replacement in a connectome model of fly steering

Focal neuronal loss after stroke, traumatic brain injury or spinal cord injury can disrupt circuits and result in persistent motor deficits. Which cells and connections must be restored to recover a specific behavior is difficult to establish experimentally. Here we use a connectome-constrained spiking model of the fruit-fly nervous system and decode motor-neuron output to simulate visually guided steering. After loss of 4,000 neurons, path straightness ranged from 0.04 to 0.95 depending on lesion location. At a fixed sensorimotor site, a 2,000-neuron lesion reduced simulated displacement to 6.5% of intact. Across 100 stochastic simulation realizations of the connectome, replacing 100 cells with their original connections increased displacement to 62.5% of intact when motor-control pathways were prioritized, compared with 10.1% after random selection of 100 cells and 48.5% after random selection of 1,000 cells. When connections were instead inferred from cell type, the same 100 intended replacements reached 60.0% of intact with matched identities and 6.7% with non-target identities, indicating that cell identity and type-dependent integration are required for recovery. Our model generates testable predictions for genetic ablation experiments in fruit flies and identifies candidate cellular and circuit requirements for motor repair that can inform cell replacement therapies and future models of mammalian locomotion.

neuroscience↗

bioRxiv2026

Promises and limitations of local ancestry inference in imputed ancient genomes

Recent advances in genome imputation have enabled the application of state-of-the-art statistical methods--originally developed for present-day genomes--to ancient genomes. One class of such methods, known as local ancestry inference (LAI), can model an individuals genome as a mosaic of tracts assigned to different putative ancestral sources, revealing patterns of genetic ancestry across the genome. However, most LAI methods have been designed to study recent admixture events in human history, and they generally assume large panels of present-day genomes. Despite the recent availability of high-quality imputed ancient genomes, it remains unknown to what degree LAI inference is reliable for such datasets. Ancient DNA is often characterized by heterogeneous geographic and temporal sampling, varying degrees of divergence between ancient source proxies and admixing populations, and complex demographic histories. Here, we performed an extensive set of population genetic simulations to evaluate the accuracy of four popular LAI methods-RFMix, FLARE, MOSAIC and simpLAI-under different demographic scenarios, various temporal sampling schemes, sample sizes, and admixture dates. We quantify the accuracy of these methods as a function of different parameters in practically relevant scenarios, and provide general guidelines for future studies utilizing LAI in ancient DNA research.

evolutionary biology↗

bioRxiv2025

demografr: A toolkit for simulation-based inference in population genetics

Simulation-based inference methods such as Approximate Bayesian Computation (ABC) are a popular class of techniques in evolutionary biology and population genetics. These methods are particularly useful for fitting complex models, as they can bypass the need to compute an exact likelihood function, instead relying on comparisons between observed and simulated summary statistics. However, numerous technical and practical hurdles often hinder the application of these methods to new modeling problems. They require the integration of disparate scripts and software for simulation, computing summary statistics, and inference into pipelines which have to be tailor-made for each specific research project. Moreover, computational costs of population genetic simulations can make inference laborious due to costly file format conversions throughout the entire procedure. Here we present a new R package called demografr (github.com/bodkan/demografr), which aims to alleviate most issues with traditional simulation-based inference workflows while facilitating their application to novel and complex modeling problems. First, demografr leverages the R simulation toolkit slendr for interactive, user-friendly encoding of complex demographic models. Second, demografr allows the user to set up parameter distributions and run simulations from them automatically, with built-in support for parallelization. For this purpose, automated inference routines for ABC or grid-based parameter exploration are provided, with individual components available for developing other simulation-based workflows in the future. Third, demografr allows a wide range of summary statistics to be efficiently computed directly in R, without any need for file conversion. In addition to the default slendr simulation engine, demografr also supports user-defined msprime and SLiM simulation scripts as well as entirely customizable summary statistic functions. We illustrate the features of demografr on examples which would traditionally require complex workflows with hundreds of lines of code: estimating parameters of a demographic model via coalescent simulations using ABC, exploring the influence of a set of parameters on summary statistics of interest using parameter grid search, and showcasing the ability to integrate fully customized simulation code written in pure Python or SLiM.

evolutionary biology↗

bioRxiv2023

Exploring the effects of ecological parameters on the spatial structure of genetic tree sequences

1Geographic space is a fundamental dimension of evolutionary change, determining how individuals disperse and interact with each other. Consequently, space has an important influence on the structure of genealogies and the distribution of genetic variants over time. Recently, the development of highly flexible simulation tools and computational methods for genealogical inference has greatly increased the potential for incorporating space into models of population genetic variation. It is now possible to explore how spatial ecological parameters can influence the distribution of genetic variation among individuals in unprecedented detail. In this study, we explore the effects of three specific parameters (the dispersal distance, competition distance and mate choice distance) on the spatial structure of genealogies. We carry out a series of in silico experiments using forwards-in-time simulations to determine how these parameters influence the distance between closely- and distantly-related individuals. We also assess the accuracy of the maximum likelihood estimation of the dispersal distance in a Gaussian model of dispersal from tree-sequence data, and highlight how it is affected by realistic factors such as finite habitat size and limited data. We find overall that the scale of mate choice in particular has marked patterns on short and long terms patterns of dispersal, as well as on the positions of individuals within a habitat. Our results showcase the potential for linking phylogeography, population genetics and ecology, in order to answer fundamental questions about the nature of spatial interactions across a landscape.

evolutionary biology↗

bioRxiv2022

slendr: a framework for spatio-temporal population genomic simulations on geographic landscapes

One of the goals of population genetics is to understand how evolutionary forces shape patterns of genetic variation over time. However, because populations evolve across both time and space, most evolutionary processes also have an important spatial component, acting through phenomena such as isolation by distance, local mate choice, or uneven distribution of resources. This spatial dimension is often neglected, partly due to the lack of tools specifically designed for building and evaluating complex spatio-temporal population genetic models. To address this methodological gap, we present a new framework for simulating spatially-explicit genomic data, implemented in a new R package called slendr (www.slendr.net), which leverages a SLiM simulation back-end script bundled with the package. With this framework, the users can programmatically and visually encode spatial population ranges and their temporal dynamics (i.e., population displacements, expansions, and contractions) either on real Earth landscapes or on abstract custom maps, and schedule splits and gene-flow events between populations using a straightforward declarative language. Additionally, slendr can simulate data from traditional, non-spatial models, either with SLiM or using an alternative built-in coalescent msprime back end. Together with its R-idiomatic interface to the tskit library for tree-sequence processing and analysis, slendr opens up the possibility of performing efficient, reproducible simulations of spatio-temporal genomic data entirely within the R environment, leveraging its wealth of libraries for geospatial data analysis, statistics, and visualization. Here, we present the design of the slendr R package and demonstrate its features on several practical example workflows.

genomics↗

bioRxiv2022

Emergence of Heartbeat Frailty in Advanced Age: Perspectives from Life-Long EKG Recordings in Mice

SAN failure, aka sick-sinus syndrome, which features sinus bradycardia, SAN impulse pauses, and irregularity of RR interval rhythms are manifestations of SAN cell dysfunction that increases exponentially with advanced age, i.e., SAN frailty. Abnormalities in intrinsic RR interval variability may be the earliest signatures of SAN cell dysfunction leading to SAN frailty in late life. We measured RR interval variability within EKG timeseries prior to and during double autonomic blockade in long-lived C57/BL6 mice at 3 month intervals from 6 months of age until the end of life. Long-lived mice (those that achieved the median cohort lifespan of 24 months and beyond) displayed relatively minor changes in intrinsic RR interval variability prior to 21 months of age. Between 21 and 30 months of age, marked changes in intrinsic RR interval variability signatures in time, frequency, non-linear, and fragmentation domains result in a marked increase in the mean intrinsic RR interval. The effects of autonomic input partially compensated for the prolongation of the mean RR interval by impacting the age-associated deterioration in the RR interval variability signatures toward a youthful pattern. Cross-sectional analyses of other subsets of mice at ages at or beyond the median life span of our longitudinal cohort demonstrated increased non-cardiac, constitutional, whole body frailty, a decrease in energetic efficiency, and an increase in respiratory exchange ratio. In this context, we interpret the progressive increase in intrinsic RR interval variability beyond 21 months of age to be an indication of heartbeat frailty.

physiology↗

bioRxiv2021

Modelling the spatiotemporal spread of beneficial alleles using ancient genomes

Ancient genome sequencing technologies now provide the opportunity to study natural selection in unprecedented detail. Rather than making inferences from indirect footprints left by selection in present-day genomes, we can directly observe whether a given allele was present or absent in a particular region of the world at almost any period of human history within the last 10,000 years. Methods for studying selection using ancient genomes often rely on partitioning individuals into discrete time periods or regions of the world. However, a complete understanding of natural selection requires more nuanced statistical methods which can explicitly model allele frequency changes in a continuum across space and time. Here we introduce a method for inferring the spread of a beneficial allele across a landscape using two-dimensional partial differential equations. Unlike previous approaches, our framework can handle time-stamped ancient samples, as well as genotype likelihoods and pseudohaploid sequences from low-coverage genomes. We apply the method to a panel of published ancient West Eurasian genomes to produce dynamic maps showcasing the inferred spread of candidate beneficial alleles over time and space. We also provide estimates for the strength of selection and diffusion rate for each of these alleles. Finally, we highlight possible avenues of improvement for accurately tracing the spread of beneficial alleles in more complex scenarios.

evolutionary biology↗

bioRxiv2020

Inhibition of the neuromuscular acetylcholine receptor with atracurium activates FOXO/DAF-16-induced longevity

Transcriptome-based drug screening is emerging as a powerful tool to identify geroprotective compounds to intervene in age-related disease. We hypothesized that, by mimicking the transcriptional signature of the highly conserved longevity intervention of FOXO3 (daf-16 in worms) overexpression, we could identify and repurpose compounds with similar downstream effects to increase longevity. Our in silico screen, utilizing the LINCS transcriptome database of genetic and compound interventions, identified several FDA-approved compounds that activate FOXO downstream targets in mammalian cells. These included the neuromuscular blocker atracurium, which also robustly extends both lifespan and healthspan in C. elegans. This longevity is dependent on both daf-16 signaling and inhibition of the neuromuscular acetylcholine receptor. Other neuromuscular blockers tubocurarine and pancuronium caused similar healthspan benefits. We demonstrate nuclear localization of DAF-16 upon atracurium treatment, and, using RNAseq transcriptomics, identify activation of DAF-16 downstream effectors. Together, these data demonstrate the capacity to mimic genetic lifespan interventions with drugs, and in doing so, reveal that the neuromuscular acetylcholine receptor regulates the highly conserved FOXO/DAF-16 longevity pathway.

molecular biology↗

bioRxiv2020

The evolutionary history of Neandertal and Denisovan Y chromosomes

Ancient DNA has allowed the study of various aspects of human history in unprecedented detail. However, because the majority of archaic human specimens preserved well enough for genome sequencing have been female, comprehensive studies of Y chromosomes of Denisovans and Neandertals have not yet been possible. Here we present sequences of the first Denisovan Y chromosomes (Denisova 4 and Denisova 8), as well as the Y chromosomes of three late Neandertals (Spy 94a, Mezmaiskaya 2 and El Sidron 1253). We find that the Denisovan Y chromosomes split around 700 thousand years ago (kya) from a lineage shared by Neandertal and modern human Y chromosomes, which diverged from each other around 370 kya. The phylogenetic relationships of archaic and modern human Y chromosomes therefore differ from population relationships inferred from their autosomal genomes, and mirror the relationships observed on the level of mitochondrial DNA. This provides strong evidence that gene flow from an early lineage related to modern humans resulted in the replacement of both the mitochondrial and Y chromosomal gene pools in late Neandertals. Although unlikely under neutrality, we show that this replacement is plausible if the low effective population size of Neandertals resulted in an increased genetic load in their Y chromosomes and mitochondrial DNA relative to modern humans.

evolutionary biology↗

bioRxiv2018

The limits of long-term selection against Neandertal introgression

Several studies have suggested that introgressed Neandertal DNA was subjected to negative selection in modern humans due to deleterious alleles that had accumulated in the Neandertals after they split from the modern human lineage. A striking observation in support of this is an apparent monotonic decline in Neandertal ancestry observed in modern humans in Europe over the past 45 thousand years. Here we show that this apparent decline is an artifact caused by gene flow between West Eurasians and Africans, which is not taken into account by statistics previously used to estimate Neandertal ancestry. When applying a more robust statistic that takes advantage of two high-coverage Neandertal genomes, we find no evidence for a change in Neandertal ancestry in Western Europe over the past 45 thousand years. We use whole-genome simulations of selection and introgression to investigate a wide range of model parameters, and find that negative selection is not expected to cause a significant long-term decline in genome-wide Neandertal ancestry. Nevertheless, these models recapitulate previously observed signals of selection against Neandertal alleles, in particular a depletion of Neandertal ancestry in conserved genomic regions that are likely to be of functional importance. Thus, we find that negative selection against Neandertal ancestry has not played as strong a role in recent human evolution as had previously been assumed.

evolutionary biology↗