Biology subjects

Marroni, F.

Publications and source records attributed to Marroni, F..

7 recordsLinked to original sources

bioRxiv2026

A segmental duplication-mediated deletion leads to neocentromere formation in orangutans

Centromeres ensure faithful chromosome segregation, yet how new centromeres arise and replace canonical ones remains poorly understood. Here, we investigate a polymorphic centromere repositioning event on the orangutan chromosome 10 using near-telomere-to-telomere assemblies, epigenetic profiling, and population-scale data. We identify striking heterogeneity in canonical centromeres, ranging from large, higher-order repeat -satellite arrays to short, monomeric -satellite tracts, alongside the emergence of neocentromeres lacking -satellite DNA. We show a segmental duplication-mediated deletion of 3.6 Mbp that removed the higher-order repeat array, promoting centromere repositioning and neocentromere formation. Phylogenetic analyses reveal complex evolutionary dynamics, including introgression and incomplete lineage sorting in orangutan lineages. These findings demonstrate that centromere identity can evolve through structural variation and epigenetic reprogramming, highlighting its remarkable plasticity in primate genomes.

genomics↗

bioRxiv2025

The promise of long-read RNA-seq: reducing bias in analyses of allele imbalance

Inaccurate allele and gene expression counts due to map bias and genome ambiguity lead to high false positive and false negative rates in studies of allelic imbalance. We demonstrate that long read RNA-seq and straightforward quality control measures can be used to reduce bias in allele counts in case studies from four species: Drosophila melanogaster, a diploid insect; Solanum tuberosum, an autopolyploid plant; Pongo abelii, a highly heterozygous diploid primate, and Homo sapiens. We recommend 1) mapping to a personalized genome to increase the number of allele assignments 2) tracking multimapping reads and tuning mapping parameters to ensure accurate allele and gene expression counts and 3) evaluating apparent extreme allele bias to identify errors in genome assembly and annotation. We show that these steps can be executed in a straightforward manner and recommend tools for each step.

genetics↗

bioRxiv2025

Variations in the root-soil system influence the grapevine holobiont by shaping plant physiology and root microbiome

Soil-dwelling bacteria and fungi play a crucial role in plant health and productivity by engaging in complex interactions that shape and are shaped by soil physico-chemical properties. In this study, we employed a multi-omics approach to investigate how variations in soil composition affect the grapevine holobiont. Grape plantlets were grown in three distinct soil types, namely sand, peat, and peat-manure. To further assess how variation in soil and root conditions affects the holobionts response, we included treatments involving soil autoclaving and root surface sterilisation across all soil types. We found that soil type significantly influences leaf multielement composition and concentration, while also shaping the bacterial and fungal communities associated with the plant rhizosphere. This shift led to changes in taxa involved in nitrogen fixation, biocontrol, and pathogenicity. Autoclaving soils consistently reduced bacterial diversity across all soil types, whereas fungal communities were less affected. In contrast, thermal treatment of roots had only a minor impact on microbial community composition but did induce transcriptional changes in the root and altered leaf macronutrient concentrations. Our findings indicate that differences in soil composition reshape the entire root-soil continuum, ultimately affecting plant physiology at multiple levels--from root function to leaf nutrient status. This highlights that the soil is not a passive growth medium but a key determinant of grape holobiont structure and function. These results reinforce the view that plant health and adaptation arise from integrated, dynamic interactions among the host, its associated microbiome, and the surrounding soil matrix.

plant biology↗

bioRxiv2025

Multiparent Recombinant Inbred lines crossed to a tester provide novel insights into sources of cis and trans regulation of gene expression

We propose crossing multi-parent recombinant-inbred-lines (RILs) to a common tester and measuring allele specific gene expression in the offspring. Testing whether allelic imbalance between two RIL x Tester crosses is equal, is a test of cis or trans depending on the RIL alleles compared. The study design also enables to separate two sources of trans variation, genetic and environmental, detected via interactions with cis effects. Examining these components of regulatory variation, we demonstrate this approach in a long-read RNA-seq experiment in female abdominal tissue at two time points in Drosophila melanogaster. Among the 40% of all loci that show evidence of genetic variation in cis, trans effects due to the environment are detectable in 31% of loci and trans effects due to genetic background are detectable in 19% of loci with little overlap in sources of trans variation. The loci identified in this study are associated with loci previously reported to exhibit genetic variation in gene expression in a range of tissues and large population samples, suggesting that there is consistent variation for genetic regulation of gene expression. We show that eleven loci in a QTL for thermotolerance, previously shown to differ in expression based on temperature, have evidence for regulation of gene expression regardless of the environment, including Cpr67B, a cuticular protein suggesting a potential functional role for standing variation in gene expression. This study provides a blueprint for efficiently identifying regulatory variation in gene expression, as the tester design maximizes cis variation and enables the efficient assessment of all pairs of RIL alleles relative to the tester, a much smaller study compared to the pairwise direct assessment.

genetics↗

bioRxiv2023

A spatial-temporal map of glutamatergic neurogenesis in embryonic cerebellar nuclei uncovers a high degree of cellular heterogeneity

The nuclei are the main output structures of the cerebellum. Each and every cerebellar cortical computation reaches several areas of the brain by means of CN processing and integration. Nevertheless our knowledge of these structures is still limited compared to the cerebellar cortex. Here, we present a genetic inducible fate mapping study characterizing rhombic lip-derived glutamatergic neurons of the nuclei, the most conspicuous family of long-range cerebellar efferent neurons. Glutamatergic neurons mainly occupy dorsal and lateral territories of the lateral and interposed nuclei, as well as the entire medial nucleus. They are born starting from about embryonic day 9.5, with a peak between 10.5 and 12.5, and invade the nuclei with a lateral to medial progression. While some markers label a heterogeneous population of neurons sharing a common location (Brn2), others appear to be lineage specific (Tbr1, Lmx1a, Meis2). A comparative analysis of Tbr1 and Lmx1a distributions reveals an incomplete overlap in their expression domains, in keeping with the existence of separate efferent subpopulations. Finally, some tagged glutamatergic progenitors are not labeled by any of the markers used in this study, disclosing further complexity. Taken together, our results obtained in late embryonic nuclei shed light on the heterogeneity of the excitatory neuron pool, underlying the diversity in connectivity and functions of this largely unexplored cerebellar territory. Our findings lay the groundwork for focused functional analyses of individual subpopulations of nuclear neurons.

developmental biology↗

bioRxiv2021

Power Calculator for Detecting Allelic Imbalance Using Hierarchical Bayesian Model

Allelic imbalance (AI) is the differential expression of the two alleles in a diploid. AI can vary between tissues, treatments, and environments. Statistical methods for testing in this area exist, with impacts of explosive type I error in the presence of bias well understood. However, for study design, the more important and understudied problem is the type II error and power. As the biological questions for this type of study explode, and the costs of the technology plummet, what is more important: reads or replicates? How small of an interaction can be detected while keeping the type I error at bay? Here we present a simulation study that demonstrates that the proper model can control type I error below 5% for most scenarios. We find that a minimum of 2400, 480, and 240 allele specific reads divided equally among 12, 5, and 3 replicates is needed to detect a 10%, 20%, and 30%, respectively, deviation from allelic balance in a condition with power >80%. A minimum of 960 and 240 allele specific reads is needed to detect a 20% or 30% difference in AI between conditions with comparable power but these reads need to be divided amongst 8 replicates. Higher numbers of replicates increase power more than adding coverage without affecting type I error. We provide a Python package that enables simulation of AI scenarios and enables individuals to estimate type I error and power in detecting AI and differences in AI between conditions tailored to their own specific study needs.

bioinformatics↗

bioRxiv2020

Testcrosses are an efficient strategy for identifying cis regulatory variation: Bayesian analysis of allele specific expression (BASE)

Allelic imbalance (AI) occurs when alleles in a diploid individual are differentially expressed and indicates cis acting regulatory variation. What is the distribution of allelic effects in a natural population? Are all alleles the same? Are all alleles distinct? Tests of allelic effect are performed by crossing individuals and comparing expression between alleles directly in the F1. However, a crossing scheme that compares alleles pairwise is a prohibitive cost for more than a handful of alleles as the number of crosses is at least (n2-n)/2 where n is the number of alleles. We show here that a testcross design followed by a hypothesis test of AI between testcrosses can be used to infer differences between non-tester alleles, allowing n alleles to be compared with n crosses. Using a mouse dataset where both testcrosses and direct comparisons have been performed, we show that [~]75% of the predicted differences between non-tester alleles are validated in a background of [~]10% differences in AI. The testing for AI involves several complex bioinformatics steps. BASE is a complete bioinformatics pipeline that incorporates state-of-the-art error reduction techniques and a flexible Bayesian approach to estimating AI and formally comparing levels of AI between conditions. The modular structure of BASE has been packaged in Galaxy, made available in Nextflow and sbatch. (https://github.com/McIntyre-Lab/BASE_2020). In the mouse data, the direct test identifies more cis effects than the testcross. Cis-by-trans interactions with trans-acting factors on the X contributing to observed cis effects in autosomal genes in the direct cross remains a possible explanation for the discrepancy.

bioinformatics↗