Biology subjects

Annunziata, R.

Publications and source records attributed to Annunziata, R..

4 recordsLinked to original sources

bioRxiv2026

Evolutionary landscapes of zygotic genome activation across animals

During early animal embryogenesis, gene expression control shifts from maternally deposited products to newly transcribed RNA through zygotic genome activation (ZGA). Although essential and universal, ZGA remains poorly understood outside a few model species. Here, we generated a transcriptomic atlas of early embryogenesis across 61 animal species from 13 phyla and used a unified computational framework to infer ZGA timing systematically. We find extensive variation in ZGA onset across animals, but show that the ratio between genome size and egg cytoplasmic volume, a proxy for the nuclear-to-cytoplasmic ratio, robustly predicts when genome activation begins. We also reveal that zygotic genes also differ from maternal transcripts in genomic architecture, function and evolutionary conservation, suggesting that ZGA follows a conserved molecular logic despite flexible transcriptomic outputs.

evolutionary biology↗

bioRxiv2025

Fast segmentation with the NextBrain histological atlas

Structural brain analysis at the subregion level offers critical insights into healthy aging and neurodegenerative diseases. The NextBrain histological atlas was recently introduced to support such fine-grained investigations, but its existing Bayesian segmentation framework remains computationally prohibitive, particularly for large-scale studies. We present a new, open-source tool that dramatically accelerates segmentation using a hybrid approach combining: machine learning, contrast-adaptive segmentation; target-specific image synthesis; and fast diffeomorphic registration (all three with GPU support). Our method enables highly granular segmentation of brain MRI scans of any resolution and contrast (in vivo or ex vivo) at a fraction of the computational cost of the original method (<5 minutes on a GPU). We validate our tool on four different modalities (in vivo MRI, ex vivo MRI, HiP-CT, and photography) across a total of approximately 4,000 brain scans. Our results demonstrate that the accelerated approach achieves comparable accuracy to the original method in terms of Dice scores, while reducing runtime by over an order of magnitude. This work enables high-resolution anatomical analysis at unprecedented scale and flexibility, providing a practical solution for large neuroimaging studies. Our tool is publicly available in FreeSurfer (https://surfer.nmr.mgh.harvard.edu/fswiki/HistoAtlasSegmentation).

neuroscience↗

bioRxiv2024

A next-generation, histological atlas of the human brain and its application to automated brain MRI segmentation

Magnetic resonance imaging (MRI) is the standard tool to image the human brain in vivo. In this domain, digital brain atlases are essential for subject-specific segmentation of anatomical regions of interest (ROIs) and spatial comparison of neuroanatomy from different subjects in a common coordinate frame. High-resolution, digital atlases derived from histology (e.g., Allen atlas [7], BigBrain [13], Julich [15]), are currently the state of the art and provide exquisite 3D cytoarchitectural maps, but lack probabilistic labels throughout the whole brain. Here we present NextBrain, a next - generation probabilistic atlas of human brain anatomy built from serial 3D histology and corresponding highly granular delineations of five whole brain hemispheres. We developed AI techniques to align and reconstruct [~]10,000 histological sections into coherent 3D volumes with joint geometric constraints (no overlap or gaps between sections), as well as to semi-automatically trace the boundaries of 333 distinct anatomical ROIs on all these sections. Comprehensive delineation on multiple cases enabled us to build the first probabilistic histological atlas of the whole human brain. Further, we created a companion Bayesian tool for automated segmentation of the 333 ROIs in any in vivo or ex vivo brain MRI scan using the NextBrain atlas. We showcase two applications of the atlas: automated segmentation of ultra-high-resolution ex vivo MRI and volumetric analysis of Alzheimers disease and healthy brain ageing based on [~]4,000 publicly available in vivo MRI scans. We publicly release: the raw and aligned data (including an online visualisation tool); the probabilistic atlas; the segmentation tool; and ground truth delineations for a 100 m isotropic ex vivo hemisphere (that we use for quantitative evaluation of our segmentation method in this paper). By enabling researchers worldwide to analyse brain MRI scans at a superior level of granularity without manual effort or highly specific neuroanatomical knowledge, NextBrain holds promise to increase the specificity of MRI findings and ultimately accelerate our quest to understand the human brain in health and disease.

neuroscience↗

bioRxiv2018

A bHLH-PAS protein regulates light-dependent rhythmic processes in the marine diatom Phaeodactylum tricornutum

Periodic light-dark cycles govern the timing of basic biological processes in organisms inhabiting land as well as the sea, where life evolved. Although prominent marine phytoplanktonic organisms such as diatoms show robust diurnal rhythms in growth, cell cycle and gene expression, the molecular foundations controlling these processes are still obscure. By exploring the regulatory landscape of diatom diurnal rhythms, we unveil the function of a Phaeodactylum tricornutum bHLH-PAS protein, PtbHLH1a, in the regulation of light-dependent diurnal rhythms. Peak expression of PtbHLH1a mRNA occurs toward the end of the light period and it adjusts to photoperiod changes. Ectopic over-expression of PtbHLH1a results in lines showing a phase shift in diurnal cell fluorescence, compared to the wild-type cells, and with altered cell cycle progression and gene expression. Reduced oscillations in gene expression are also observed in overexpression lines compared to wild-type in continuous darkness, showing that the regulation of rhythmicity by PtbHLH1a is not directly dependent on light inputs and cell division. PtbHLH1a homologs are widespread in diatom genomes which may indicate a common function in many species. This study adds new elements to understand diatom biology and ecology and offers new perspectives to elucidate timekeeping mechanisms in marine organisms belonging to a major, but underinvestigated branch of the tree of life.\n\nSIGNIFICANCE STATEMENTMost organisms experience diurnal light-dark changes and show rhythms of basic biological processes such that they occur at optimal times of the day. The ocean harbours a huge diversity of organisms showing light-dependent rhythms, but their molecular foundations are still largely unknown. In this study, we discover a novel protein, PtbHLH1a that regulates cell division, gene expression and the diurnal timing of these events in the marine diatom Phaedoactylum tricornutum. The identification of PtbHLH1a-like genes in many diatom species suggests a conserved function in diurnal rhythm regulation in the most species-rich group of algae in the ocean. This study unveils critical features of diatom biology and advances the field of marine rhythms and their environmental regulation.

molecular biology↗