Biology subjects

Andreoletti, G.

Publications and source records attributed to Andreoletti, G..

10 recordsLinked to original sources

bioRxiv2026

The SEA-AD DREAM Challenge: Community benchmarking human and AI agent solutions for Alzheimer's disease neuropathology prediction from single-nucleus transcriptomics

Single-nucleus transcriptomic atlases offer an unprecedented opportunity to connect cellular molecular states with Alzheimer's disease (AD) neuropathology, but whether these profiles encode reproducible, predictive information about pathological burden remains unclear. We present the SEA-AD DREAM Challenge, an open, international, model-to-data competition built on the Seattle Alzheimer's Disease Brain Cell Atlas to predict Alzheimer's disease neuropathological severity from single-nucleus RNA-sequencing data. Participants developed containerized models to predict categorical neuropathological staging, including overall Alzheimer's disease neuropathologic change, Braak stage, Thal phase, and CERAD score, as well as quantitative amyloid-{beta} and phospho-tau burden measured by 6E10 and AT8 immunohistochemistry. Across 17 eligible teams from 15 countries, the crowdsourcing framework enabled systematic comparison of diverse computational approaches and surfaced a broad landscape of modeling strategies and candidate predictive features. Top-performing methods achieved near-perfect prediction of categorical staging, with the best submission reaching a quadratic weighted kappa of 1.0 for the Overall AD Neuropathological Change score (ADNC), and competitive prediction of quantitative pathological burden in held-out data, with a best concordance correlation coefficient of 0.48. Post hoc perturbation analyses revealed that top categorical-stage predictions relied heavily on donor-level metadata-driven signals rather than transcriptomic features, whereas quantitative pathology prediction was more robust and supported by transcriptomic and cell-type-associated features with potential biological relevance to AD progression. The challenge also introduced the first AI Agent Track in a DREAM Challenge, providing an early benchmark for autonomous and human-guided agentic model development in single-cell neuroscience. This work demonstrates that single-nucleus transcriptomes encode substantial information about Alzheimer's disease pathology, establishes a reproducible benchmark for molecular neuropathology prediction, and highlights critical principles for designing privacy-preserving, leakage-aware community challenges using deeply phenotyped human brain data.

neuroscience↗

bioRxiv2025

High-Fidelity Tuning of Olfactory Mixture Distances in the Perceptual Space of Smell Through a Community Effort

A central goal in sensory science is to establish quantitative mappings between physical stimuli and perceptual responses. While such mappings are well characterized in vision and audition, they remain poorly defined in olfaction, limiting progress toward understanding the representations of smell. Predicting perceptual similarity between odor mixtures offers a promising route to formalize these relationships. To advance this effort, the DREAM (Dialogue for Reverse Engineering Assessment and Methods) Olfactory Mixtures Prediction Challenge assembled a curated, cross-study dataset describing the similarity of 507 mixture pairs and an unpublished test set of 46 mixture pairs. Teams competed to predict the perceptual similarity of mixture pairs, and then collaborated post-challenge to create an ensemble combining top-performing models that notably improves predictions over the existing state-of-the-art models. Moreover, ensemble model maintains high predictive accuracy in novel validation set. Our model provides a reproducible framework for neuroscientists, chemists, and engineers to compare odor mixtures and provides a foundation for future efforts towards better understanding the olfactory properties of mixtures.

neuroscience↗

bioRxiv2024

Critical assessment of missense variant effect predictors on disease-relevant variant data

Regular, systematic, and independent assessment of computational tools used to predict the pathogenicity of missense variants is necessary to evaluate their clinical and research utility and suggest directions for future improvement. Here, as part of the sixth edition of the Critical Assessment of Genome Interpretation (CAGI) challenge, we assess missense variant effect predictors (or variant impact predictors) on an evaluation dataset of rare missense variants from disease-relevant databases. Our assessment evaluates predictors submitted to the CAGI6 Annotate-All-Missense challenge, predictors commonly used by the clinical genetics community, and recently developed deep learning methods for variant effect prediction. To explore a variety of settings that are relevant for different clinical and research applications, we assess performance within different subsets of the evaluation data and within high-specificity and high-sensitivity regimes. We find strong performance of many predictors across multiple settings. Meta-predictors tend to outperform their constituent individual predictors; however, several individual predictors have performance similar to that of commonly used meta-predictors. The relative performance of predictors differs in high-specificity and high-sensitivity regimes, suggesting that different methods may be best suited to different use cases. We also characterize two potential sources of bias. Predictors that incorporate allele frequency as a predictive feature tend to have reduced performance when distinguishing pathogenic variants from very rare benign variants, and predictors supervised on pathogenicity labels from curated variant databases often learn label imbalances within genes. Overall, we find notable advances over the oldest and most cited missense variant effect predictors and continued improvements among the most recently developed tools, and the CAGI Annotate-All-Missense challenge (also termed the Missense Marathon) will continue to assess state-of-the-art methods as the field progresses. Together, our results help illuminate the current clinical and research utility of missense variant effect predictors and identify potential areas for future development.

genetics↗

bioRxiv2023

Functional Screening in human HSPCs identifies optimized protein-based enhancers of Homology Directed Repair

Homology Directed Repair (HDR) enables precise genome editing and holds great promise in the gene therapy field. However, the implementation of HDR-based therapies is hindered by limited efficiency in comparison to methods that exploit alternative DNA repair routes, such as Non-Homologous End Joining (NHEJ). In this study, we demonstrate the development of a functional, pooled screening platform utilizing an HDR-based readout to identify protein-based reagents that improve HDR outcomes in human hematopoietic stem and progenitor cells (HSPCs), a clinically relevant cell type for gene therapy. We leveraged this screening platform to explore sequence diversity at the binding interface of the NHEJ inhibitor i53 and its target, 53BP1, and we identified optimized i53 variants that enable new intermolecular bonds and robustly increase HDR. These variants specifically reduce insertion-deletion outcomes and also synergize with a DNAPK inhibitor to increase HDR rates. When applied at manufacturing scale, the incorporation of improved variants results in a significant increase in cells with at least one repaired allele and improved HDR in long-term HSPCs subpopulations, while not increasing off-target editing or gross chromosomal rearrangements. We anticipate the pooled screening platform will enable discovery of future gene editing reagents that improve HDR outcomes, such as the i53 variants reported here.

molecular biology↗

bioRxiv2020

Ethnicity-specific transcriptomic variation in immune cells and correlation with disease activity in systemic lupus erythematosus

Systemic lupus erythematosus (SLE) is a heterogeneous autoimmune disease in which outcomes vary among different racial groups. The aim of this study is to leverage large-scale transcriptomic data from diverse populations to better sub-classify SLE patients into more clinically actionable groups. We leverage cell sorted RNA-seq data (CD14+ monocytes, B cells, CD4+T cells, and NK cells) from 120 SLE patients (63 Asian and 57 White individuals) and apply a four tier analytical approach to identify SLE subgroups within this multiethnic cohort: unsupervised clustering, differential expression analyses, gene co-expression analyses, and machine learning. K-means clustering on the individual cell type data resulted in three clusters for CD4 and CD14, and two clusters for B cells and NK cells. Correlation analysis revealed significant positive associations between the transcriptomic clusters of each immune cell and clinical parameters including disease activity and ethnicity. We then explored differentially expressed genes between Asian and White groups for each cell-type. The shared differentially expressed genes across the four cell types were involved in SLE or other autoimmune related pathways. Co-expression analysis identified similarly regulated genes across samples and grouped these genes into modules. Samples were grouped into White-high, Asians-high (high disease activity defined by SLEDAI score >=6) and White-low, Asians-low (SLEDAI < 6). Random forest classification of disease activity in the White and Asian cohorts showed the best classification in CD4+ T cells in White. The results from these analyses will help stratify patients based on their gene expression signatures to enable precision medicine for SLE.

bioinformatics↗

bioRxiv2020

Transcriptomics-based drug repositioning pipeline identifies therapeutic candidates for COVID-19

The novel SARS-CoV-2 virus emerged in December 2019 and has few effective treatments. We applied a computational drug repositioning pipeline to SARS-CoV-2 differential gene expression signatures derived from publicly available data. We utilized three independent published studies to acquire or generate lists of differentially expressed genes between control and SARS-CoV-2-infected samples. Using a rank-based pattern matching strategy based on the Kolmogorov-Smirnov Statistic, the signatures were queried against drug profiles from Connectivity Map (CMap). We validated sixteen of our top predicted hits in live SARS-CoV-2 antiviral assays in either Calu-3 or 293T-ACE2 cells. Validation experiments in human cell lines showed that 11 of the 16 compounds tested to date (including clofazimine, haloperidol and others) had measurable antiviral activity against SARS-CoV-2. These initial results are encouraging as we continue to work towards a further analysis of these predicted drugs as potential therapeutics for the treatment of COVID-19.

bioinformatics↗

bioRxiv2020

A Transcriptional Switch Governing Fibroblast Plasticity Underlies Reversibility of Chronic Heart Disease

In diseased organs, stress-activated signaling cascades alter chromatin, triggering broad shifts in transcription and cell state that exacerbate pathology. Fibroblast activation is a common stress response that worsens lung, liver, kidney and heart disease, yet its mechanistic basis remains poorly understood1,2. Pharmacologic inhibition of the BET family of transcriptional coactivators alleviates cardiac dysfunction and associated fibrosis, providing a tool to mechanistically interrogate maladaptive fibroblast states and modulate their plasticity as a potential therapeutic approach3-8. Here, we leverage dynamic single cell transcriptomic and epigenomic interrogation of heart tissue with and without BET inhibition to reveal a reversible transcriptional switch underlying stress-induced fibroblast activation. Transcriptomes of resident cardiac fibroblasts demonstrated robust and rapid toggling between the quiescent fibroblast and activated myofibroblast state in a manner that directly correlated with BET inhibitor exposure and cardiac function. Correlation of single cell chromatin accessibility with cardiac function revealed a novel set of reversibly accessible DNA elements that correlated with disease severity. Among the most dynamic elements was an enhancer regulating the transcription factor MEOX1, which was specifically expressed in activated myofibroblasts, occupied putative regulatory elements of a broad fibrotic gene program, and was required for TGF{beta}-induced myofibroblast activation. CRISPR interference of the most dynamic cis-element within the enhancer, marked by nascent transcription, prevented TGF{beta}-induced activation of Meox1. These findings identify MEOX1 as a central regulator of stress-induced myofibroblast activation associated with cardiac dysfunction. The plasticity and specificity of the BET-dependent regulation of MEOX1 in endogenous tissue fibroblasts provides new trans- and cis- targets for treating fibrotic disease.

molecular biology↗

bioRxiv2020

Crowdsourcing assessment of maternal blood multi-omics for predicting gestational age and preterm birth

Identification of pregnancies at risk of preterm birth (PTB), the leading cause of newborn deaths, remains challenging given the syndromic nature of the disease. We report a longitudinal multi-omics study coupled with a DREAM challenge to develop predictive models of PTB. We found that whole blood gene expression predicts ultrasound-based gestational ages in normal and complicated pregnancies (r=0.83), as well as the delivery date in normal pregnancies (r=0.86), with an accuracy comparable to ultrasound. However, unlike the latter, transcriptomic data collected at <37 weeks of gestation predicted the delivery date of one third of spontaneous (sPTB) cases within 2 weeks of the actual date. Based on samples collected before 33 weeks in asymptomatic women we found expression changes preceding preterm prelabor rupture of the membranes that were consistent across time points and cohorts, involving, among others, leukocyte-mediated immunity. Plasma proteomic random forests predicted sPTB with higher accuracy and earlier in pregnancy than whole blood transcriptomic models (e.g. AUROC=0.76 vs. AUROC=0.6 at 27-33 weeks of gestation).

bioinformatics↗

bioRxiv2020

BRD4 Interacts with GATA4 to Govern Mitochondrial Homeostasis in Adult Cardiomyocytes

Gene regulatory networks control tissue plasticity during basal homeostasis and disease in a cell-type specific manner. Ubiquitously expressed chromatin regulators modulate these networks, yet the mechanisms governing how tissue-specificity of their function is achieved are poorly understood. BRD4, a member of the BET (Bromo- and Extra-Terminal domain) family of ubiquitously expressed acetyl-lysine reader proteins, plays a pivotal role as a coactivator of enhancer signaling across diverse tissue types in both health and disease, and has been implicated as a pharmacologic target in heart failure. However, the cell-specific role of BRD4 in adult cardiomyocytes remains unknown. Here, we show that cardiomyocyte-specific deletion of BRD4 in adult mice leads to acute deterioration of cardiac contractile function with mutant animals demonstrating a transcriptomic signature enriched for decreased expression of genes critical for mitochondrial energy production. Genome-wide occupancy data show that BRD4 enriches at many downregulated genes and preferentially co-localizes with GATA4, a lineage determining cardiac transcription factor not previously implicated in regulation of adult cardiac metabolism. Co-immunoprecipitation assays demonstrate that BRD4 and GATA4 form a complex in a bromodomain-independent manner, revealing a new interaction partner for BRD4 that has functional consequences for target transactivation and may allow for locus and tissue specificity. These results highlight a novel role for a BRD4-GATA4 module in cooperative regulation of a cardiomyocyte specific gene program governing bioenergetic homeostasis in the adult heart.

molecular biology↗

bioRxiv2019

Comparative analysis of diverse cell states establishes an epigenetic basis for inferring regulatory genes governing cell identity

Determining genes orchestrating cell differentiation in development and disease remains a fundamental goal of cell biology. This study establishes a genome-wide metric based on the gene-repressive tri-methylation of histone 3 lysine 27 (H3K27me3) across hundreds of diverse cell types to identify genetic regulators of cell differentiation. We introduce a computational method, TRIAGE, that uses discordance between gene-repressive tendency and expression to identify genetic drivers of cell identity. We apply TRIAGE to millions of genome-wide single-cell transcriptomes, diverse omics platforms, and eukaryotic cells and tissue types. Using a wide range of data, we validate TRIAGEs performance for identifying cell-type specific regulatory factors across diverse species including human, mouse, boar, bird, fish, and tunicate. Using CRISPR gene editing, we use TRIAGE to experimentally validate RNF220 as a regulator of Ciona cardiopharyngeal development and SIX3 as required for differentiation of endoderm in human pluripotent stem cells. A record of this papers Transparent Peer Review process is included in the Supplemental Information.

cell biology↗