Biology subjects

Ley, L.

Publications and source records attributed to Ley, L..

3 recordsLinked to original sources

bioRxiv2025

Uncovering the genetic basis of agronomic traits in over 1,000 grapevine genotypes derived from a disease resistance breeding program

Breeding disease-resistant grapevines that retain agronomic performance under variable climates requires loci and predictions that transfer across related hybrid families. We analyzed 1,081 genotypes from 95 crosses within the French INRAE-ResDur breeding program for 13 phenology, yield and berry-composition traits evaluated at five sites from 2006 to 2024. We integrated within-family QTL mapping, kinship- and population-adjusted multiple-population QTL mapping in 772 progeny, and structure-aware GWAS in 899-968 individuals, depending on the trait, together with cross-environment, cross-trait and local genomic estimated breeding-value analyses. Genetic and phenotypic differentiation among families strongly affected locus detection. Of 76 family-QTL intervals, seven, representing six trait-region hypotheses, were locally concordant across all three mapping frameworks. The strongest recurrent evidence involved a chromosome-16 region for veraison and harvest date, where a localGEBV block at 14.69 Mb ranked first for veraison and second for harvest; chromosome-14 cluster traits and chromosome-1 compactness emerged as additional validation priorities. Cross-environment meta-analysis detected no common fixed-effect association at 5% FDR but revealed extensive heterogeneous evidence. Cross-trait analysis grouped 270 significant marker tests into 54 candidate multi-trait regions, without establishing biological pleiotropy. Population-adjusted localGEBV yielded a mean leave-one-population-out correlation of 0.470 between phenotypic BLUPs and genomic scores across traits. These results distinguish compact haplotype-validation targets from background- and environment-dependent signals, supporting a staged strategy that combines marker- assisted selection for validated recurrent regions with externally validated multi-trait, multi- environment genomic prediction for polygenic traits.

genetics↗

bioRxiv2025

Pan-cancer prediction of tumor immune activation and response to immune checkpoint blockade from tumor transcriptomics and histopathology

Accurately predicting which patients will respond to immune checkpoint blockade (ICB) remains a major challenge. Here, we present TIME_ACT, an unsupervised 66-gene transcriptomic signature of tumor immune activation derived from TCGA (The Cancer Genome Atlas) melanoma data. First, we demonstrate that TIME_ACT scores accurately identify tumors with activated immune microenvironments across different cancer types. Further, analysis of spatial features reveals that tumor microenvironment regions with dense lymphocyte infiltration near tumor cells have high TIME_ACT scores, successfully marking localized immune activation. Second, across 25 transcriptomic ICB cohorts encompassing nine cancer types, TIME_ACT achieves a mean AUC of 0.76 and a mean odds ratio of 5.77, significantly outperforming 30 established transcriptomic signatures and prediction methods for ICB response, including a recently developed foundation model for immunotherapy response prediction. Third, we show that TIME_ACT scores can be accurately inferred from routine tumor histopathology slides and that slide-inferred TIME_ACT scores predict ICB response across nine new independent patient cohorts spanning eight cancer types, achieving a mean AUC of 0.72 and a mean odds ratio of 4.99. These findings establish TIME_ACT as a robust, pan-cancer biomarker that enables accurate, low-cost, and clinically scalable prediction of ICB response from routine histopathology.

cancer biology↗

bioRxiv2022

The effect of lysergic acid diethylamide (LSD) on whole-brain functional and effective connectivity

Psychedelics have emerged as promising candidate treatments for various psychiatric conditions, and given their clinical potential, there is a need to identify biomarkers that underlie their effects. Here, we investigate the neural mechanisms of lysergic acid diethylamide (LSD) using regression dynamic causal modelling (rDCM), a novel technique that assesses whole-brain effective connectivity (EC) during resting-state functional magnetic resonance imaging (fMRI). We modelled data from two randomized, placebo-controlled, double-blind, cross-over trials, in which 45 participants were administered 100g LSD and placebo in two resting-state fMRI sessions. We compared EC against whole-brain functional connectivity (FC) using classical statistics and machine learning methods. Multivariate analyses of EC parameters revealed widespread increases in interregional connectivity and reduced self-inhibition under LSD compared to placebo, with the notable exception of primarily decreased interregional connectivity and increased self-inhibition in occipital brain regions. This finding suggests that LSD perturbs the Excitation/Inhibition balance of the brain. Moreover, random forests classified LSD vs. placebo states based on FC and EC with comparably high accuracy (FC: 85.56%, EC: 91.11%) suggesting that both EC and FC are promising candidates for clinically-relevant biomarkers of LSD effects.

neuroscience↗