Biology subjects

Valentin, A.

Publications and source records attributed to Valentin, A..

8 recordsLinked to original sources

bioRxiv2026

SirenScan reveals the conserved presence of siren RNAs and their evolutionary diversification across angiosperms

O_LISiren RNAs are exceptionally abundant reproductive small interfering RNAs (siRNAs), yet whether they represent a conserved feature of angiosperm reproduction has remained unclear because siren loci lack a standardized definition and identification strategy. C_LIO_LIWe developed SirenScan, a computational framework that integrates cumulation-based and density-based analyses to identify siren loci from small RNA sequencing data. Using SirenScan, we systematically compared ovule and vegetative tissues across representative angiosperm lineages, including the early-diverging angiosperm Nymphaea colorata. C_LIO_LIWe demonstrate that the presence of siren RNAs is a conserved feature of angiosperm ovules, although the degree of expression dominance varies substantially among species. Siren loci are consistently enriched within transposable elements (TEs), while the associated TE superfamilies and cis-regulatory motifs exhibit lineage-specific diversification. In Brassicaceae, siren loci share conserved CLASSY3-associated sequence motifs, whereas other angiosperm lineages display distinct motif compositions, suggesting evolutionary diversification of Polymerase IV targeting mechanisms. C_LIO_LIOur findings establish siren RNAs as a conserved component of the angiosperm reproductive small RNA landscape and support a model in which conserved RNA-directed DNA methylation machinery is coupled with lineage-specific regulatory mechanisms and transposon landscapes to shape siren locus evolution. SirenScan provides a robust and standardized framework for comparative studies of siren RNA biology and reproductive epigenomics across plant species. C_LI

plant biology↗

bioRxiv2026

Characterization of Ovine Abortion in Uruguay Reveals Extensive Non-Clonal Diversity and Multiple Evolutionary Origins of Toxoplasma gondii

Toxoplasma gondii is a globally prevalent zoonotic parasite with multiple life stages and transmission routes, including ingestion and transplacental transmission. It is a major cause of abortion in sheep, goats and pigs, among other production animals, worldwide. While Type II strains are common in livestock in North America and Europe, non-archetypal, non-clonal genotypes are highly prevalent in South America. This study aimed to determine the molecular epidemiology of T. gondii strains causing sheep abortion in Uruguay. Phylogenomic analyses confirmed significant divergence among typed strains and revealed similarities with genotypes previously detected in the human population. Two novel strains, were isolated and characterized, uncovering the connection between their genetic makeup and phenotypes. Differences in virulence could be correlated to differences in gene copy number of the pseudo kinase ROP5 - further highlighting this virulence factor as relevant in wild strains. Whole-genome sequencing further confirmed the divergence among Uruguayan isolates, uncovering at least three distinct evolutionary origins. Overall, our findings highlight the circulation of virulent non-clonal lineages with links to human infections and underscore the importance of furthering genomic surveillance in South America to better understand Toxoplasmas transmission dynamics, pathogenic potential, and zoonotic risk.

molecular biology↗

bioRxiv2026

Deep Learning Enabled 3D Multi-Omic Analysis Reveals Molecular Signatures of Heterogeneous Response to Chemotherapy in Pancreatic Cancer

Resistance to systemic therapy is a major unmet challenge in pancreatic cancer. To identify potential mechanisms of resistance, we developed a novel 3D pipeline in clinical samples that uses deep learning to classify sensitive and persistent tumor cell populations based on morphological features, enabling subsequent molecular characterization of intratumoral heterogeneity. We applied this automated 3D pipeline to a cohort of human pancreatic cancer samples treated with neoadjuvant chemotherapy, identifying heterogeneity in response to therapy both between and within tumors. Application of spatial proteomics to these sensitive and persistent regions identified enhanced epithelial-to-mesenchymal transition and non-classical cell states in persistent cells, confirming our morphological classification. Integration of spatial transcriptomics in multiple pancreatic cancer cohorts associated fibroblast-cancer crosstalk via syndecans with resistance to cytotoxic therapy. Our validated 3D multi-omic pipeline is now poised for application to clinical trials, enabling discovery of resistance mechanisms and design of new therapeutic combinations to circumvent resistance. Statement of significanceWe developed a novel 3D multi-omic pipeline to identify mechanisms of resistance to chemotherapy in clinical samples. This approach associated fibroblast-cancer crosstalk via syndecans with resistance to cytotoxic therapy and is poised for broader application in neoadjuvant clinical trials.

cancer biology↗

bioRxiv2025

Endothelial NMDA receptor involvement in retinal neurovascular damage following prenatal alcohol exposure in mouse model

Prenatal alcohol exposure (PAE) induces neurodevelopmental damage leading to fetal alcohol spectrum disorders (FASD) by altering both brain and ocular development. Recent data showed that PAE impairs brain cortical and retinal vasculature leading to defective positioning of interneurons. In the retina, PAE disturbs vascular development and the association of calretinin neurons with vessels. The NMDA receptor (NMDAR) is a major target of alcohol in the brain, and both ligand binding to NMDARs and the expression of NMDAR subunits are altered in FASD. Given that NMDAR is also expressed in endothelial cells and that glutamate stimulation of endothelial NMDAR (eNMDAR) regulates cortical interneuron positioning along blood vessels, we hypothesize that eNMDAR is critical for retinal vascular development and mediates PAE-induced defects. Using an in vivo model of FASD and transgenic mice lacking, specifically in endothelial cells, the GluN1 subunit of the NMDAR, this study aimed to characterize the neurovascular phenotype of the developing retina. The results show that eNMDAR knockout delays the formation of the superficial vascular plexus and prevents the alterations in vascular organization and neuronal density induced by PAE, particularly cells positioned closer to the retinal vasculature, namely ganglion, amacrine, and horizontal cells. Moreover, eNMDAR deletion led to an increased number of calretinin-positive interneurons in contact with perforating vessels and prevents the decrease induced by PAE. Together, these findings demonstrate that eNMDARs are essential for normal retinal neurovascular development and mediate, at least in part, the detrimental effects of ethanol exposure in FASD. Significance statementUsing a murine model of Fetal Alcohol Spectrum Disorder (FASD) and transgenic mice lacking the GluN1 subunit of the NMDA receptor specifically in endothelial cells (eNMDAR), this study demonstrates that eNMDAR plays a critical role in mediating prenatal alcohol exposure (PAE)-induced neurovascular abnormalities in the retina. Loss of eNMDAR alters the progression of the superficial vascular plexus and prevents the vascular impairments typically observed following PAE. In addition, eNMDAR deletion protects against PAE-induced neuronal damage, particularly affecting retinal ganglion cells, calbindin-positive, and calretinin-positive interneurons. Notably, this study identifies, for the first time, a role for endothelial NMDAR in regulating neurovascular interactions between retinal vessels and calretinin-positive neurons, highlighting this receptor as a key molecular mediator of ethanol-induced retinal damage.

neuroscience↗

bioRxiv2025

PIVOT: an open-source tool for multi-omic spatial data registration

Advances in spatial profiling have resulted in the generation of multi-omic atlases that span biological scales. In general, multiple workflows are required for image registration, coordinate registration, and spot deconvolution to integrate modalities. To improve the throughput of registration of multi-omic cohorts, we introduce PIVOT, a user-friendly and open-source interface for streamlined nonlinear registration. We demonstrate PIVOTs strengths through registration of three multi-omic datasets, and show comparison of its performance to existing workflows.

bioinformatics↗

bioRxiv2024

GCS-H2 is essential for growth as it acts as the main relay for mitochondrial lipoylation in heterotrophic tissues of Arabidopsis thaliana

H-protein is part of the multi-protein glycine cleavage system found in both prokaryotes and eukaryotes. The Arabidopsis thaliana genome contains three loci for genes encoding H-protein. We show that in land plants, there are two clades of mitochondrial H-proteins; one that clusters with H1 and H3, and another that clusters with H2, an isoform mostly expressed in heterotrophic tissue and for which a null mutation results in a >99% impeded growth. After showing that all three isoforms fulfill the same metabolic function, we evidenced that the impaired root growth in h2 results from an altered cell cycle progression concomitant to a lower Target Of Rapamycin kinase activity. Subsequent metabolomic approaches revealed an accumulation of storage sugars and a significant decrease of the TCA intermediates and several vitamins in the h2 root cells as compared to the wild-type ones. Additional investigations evidenced that H2 acts as the main relay for lipoylation in root mitochondria, which diverges from the current lipoylation model proposed for photosynthetic tissues. Together, this work provides new insight on the control of the cell cycle by mitochondrial metabolism, and also challenges the current dogma for lipoylation in mitochondria of plant cells. Once sentence summaryThe absence of GCS-H2, an isoform of H-protein found in heterotrophic tissue, impedes the cell cycle, and drastically affects plant growth as it acts as the main relay for mitochondrial lipoylation.

plant biology↗

bioRxiv2023

ProteinFlow: a Python Library to Pre-Process Protein Structure Data for Deep Learning Applications

Over the past few years, deep learning tools for protein design have made significant advances in the field of bioengineering, opening up new opportunities for drug discovery, disease prevention or industrial biotechnology. However, despite the growing interest and excitement surrounding these tools, progress in the field is hindered by a lack of standardized datasets for benchmarking. Most models are trained on data from the Protein Data Bank (PDB), the largest repository of experimentally determined biological macromolecular structures. But filtering and processing this data involves many hyperparameter choices that are often not harmonized across the research community. Moreover, the task of splitting protein data into training and validation subsets with minimal data leakage is not trivial and often overlooked. Here we present ProteinFlow, a computational pipeline to pre-process protein sequence and structural data for deep learning applications. The pipeline is fully configurable and allows the extraction of all levels of protein organization (primary to quaternary), allowing end-users to cater the dataset for a multitude of downstream tasks, such as protein sequence design, protein folding modeling or protein-protein interaction prediction. In addition, we curate a feature-rich benchmarking dataset based on the latest annual release of the PDB and a selection of preprocessing parameters that are widely used across the research community. We showcase its utility by benchmarking a state-of-the-art (SOTA) deep learning model for protein sequence design. The open source code is packaged as a python library and can be accessed on https://github.com/adaptyvbio/ProteinFlow.

bioinformatics↗

bioRxiv2018

Intracranial interictal functional networks predict the effect of subacute cortical stimulation in focal epilepsy

BackgroundElectric neurostimulation is being developed as an alternative treatment for drug-resistant epilepsy patients. A major challenge is to identify, among all possible combinations of stimulation parameters, the most effective stimulation protocol to achieve seizure control for each patient.\n\nObjectiveTo estimate the value of interictal intracranial EEG recordings in identifying the most effective stimulation protocol in order to decrease number of seizures during intracranial subacute cortical stimulation (SCS).\n\nMethodsFive patients undergoing SCS were included in this retrospective study. Bivariate correlation measures were applied to baseline interictal intracranial EEG recordings to infer functional correlation networks. The node strength of these networks was calculated for each recording electrode (indicating the overall correlation between activity from one electrode with the activity from all other electrodes). The relationship between node strength and the change in seizure rate associated with SCS was studied for the electrodes used for stimulation.\n\nResultsAcross all five patients, node strength was negatively correlated with seizure rate ratio (ratio of seizure rate during SCS relative to during baseline) for all frequency bands tested (delta, theta, alpha, beta, gamma, and high gamma), and both the common reference and the common average montage.\n\nConclusionInterictal functional correlation networks contain information that correlates with the effect of SCS on the rate of epileptic seizures, suggesting that stimulation of those regions with higher node strength (derived from correlation networks) is more effective in reducing number of seizures.\n\nHIGHLIGHTSO_LIIn focal epilepsy, effective brain stimulation targets are lacking\nC_LIO_LIWe use baseline interictal functional networks to outline stimulation targets\nC_LIO_LIStimulation of regions with higher node strength is most effective in our study\nC_LI

neuroscience↗