Biology subjects

Giudice, L. C.

Publications and source records attributed to Giudice, L. C..

10 recordsLinked to original sources

bioRxiv2026

Simvastatin and Primaquine Identified as Potential Endometriosis Therapeutics via a Novel Epithelial-Stromal Assembloid Drug Screening Assay

Background: Endometriosis is an estrogen-driven, inflammatory disorder affecting ~10% of menstruators, causing severe pain. Current treatments that reduce estrogen or inflammation have inconsistent efficacy and poor tolerability. Therapies identified via drug-repositioning methods, which target patient-specific pathways, offer a promising alternative. Simvastatin and primaquine were identified as potential treatments, strongly reversing endometriosis-associated gene expression pathways, and behavioral testing in an animal model showed that these drugs diminish endometriosis-associated pain. This paper examines their effects in human assembloids to provide proof-of-concept validation and insights into their mechanisms of action relevant to endometriosis. Methods: We established human endometrial assembloids using immortalized epithelial (12Z) and stromal fibroblast (iEc) cell lines, as well as primary tissue from patients. Assembloids were generated in 96-well agarose molds with both cell types. We tested ibuprofen, fenoprofen, primaquine, and simvastatin. Following a 24-hour exposure, assembloids and monolayer cultures were harvested and stranded mRNA-seq libraries were sequenced on an Illumina NovaSeqX Plus System. Results: Gene set enrichment analysis showed key pathway reversals: Primaquine reversed the chemical carcinogenesis ROS pathway in assembloids. Simvastatin reversed cytokine-cytokine receptor interaction in both epithelial and stromal cell lines, and the cytoskeleton in muscle cells pathway in stromal cell lines. Fenoprofen reversed the calcium signaling pathway in stromal cell lines. Conclusion: Simvastatin induced the most significant gene expression changes, notably reversing cytokine-cytokine receptor interactions in both cell lines, mirroring findings from our rat model. Despite the small sample size limitation, these experiments highlight the promise of assembloid models to test therapeutic candidates for endometriosis.

molecular biology↗

bioRxiv2026

Predicting Endometriosis Status and Menstrual Cycle Phase Using DNA Methylation

Endometriosis is a chronic inflammatory disease associated with pelvic pain, infertility, and delayed diagnosis. Growing evidence suggests that altered DNA methylation contributes to disease development and could serve as a biomarker for disease. We developed a leakage-safe machine learning pipeline to classify endometriosis case-control status and menstrual cycle phase using genome-wide DNA methylation data from eutopic endometrial tissue. The dataset consisted of 984 samples profiled using the Illumina Infinium MethylationEPIC array, with measurements across approximately 759,000 CpG sites. Technical variation was corrected using SmartSVA batch correction. Ridge logistic regression models were trained using stratified 80/20 train-test splits, with regularization strength selected via stratified cross-validation. Feature selection approaches included ridge coefficient ranking, per-CpG t-tests, and univariate logistic regression with FDR correction. Model validity was evaluated using label-shuffling analyses. Menstrual cycle phase classification showed strong performance (mean cross-validation AUROC: 0.971, held-out test AUROC: 0.989), reflecting genome-wide hormonally driven methylation. Ridge regression produced lower but meaningful performance for endometriosis classification (mean cross-validation AUROC: 0.854, held-out test AUROC: 0.875). Ridge coefficient-based feature selection identified compact predictive CpG sets, supporting the hypothesis that endometriosis-associated methylation signal is distributed across many loci rather than a few highly predictive CpGs. Pathway enrichment analyses identified substantial enrichment for menstrual cycle phase but limited enrichment for disease status following FDR correction, consistent with a diffuse endometriosis-associated signal. These findings demonstrate that ridge regression can detect methylation patterns associated with both endometriosis and menstrual cycle phase, highlighting the importance of accounting for cycle-related epigenetic variation in endometrial DNA methylation studies.

bioinformatics↗

bioRxiv2026

Integrating single-cell and bulk transcriptomic perturbation resources reveals complementary therapeutic spaces for drug repurposing

Transcriptome-based drug repurposing can accelerate therapeutic discovery, but is limited by fragmented resources, inconsistent quality control, and reliance on single perturbation databases. We developed CDRPipe (Computational Drug Repurposing Pipeline), a unified framework that interrogates disease signatures against drug perturbation signatures generated by distinct experimental technologies. Specifically, CDRPipe harmonizes microarray perturbation profiles from the Connectivity Map (CMap; 1,968 quality-filtered experiments) with pseudo-bulk profiles derived from large-scale single-cell RNA sequencing experiments in the Tahoe-100M database (56,827 experiments). CDRPipe standardizes preprocessing, computes rank-based connectivity scores, and evaluates significance using empirical null models. We applied CDRPipe to 233 curated disease signatures from GEO and CREEDS and evaluated performance using known drug-disease associations from Open Targets. Single-cell-derived pseudo-bulk profiles recovered more annotated therapeutics than microarray profiles (median recall 50.0% vs. 6.2%; Wilcoxon p < 10-{superscript 1}{superscript 1}), thought these differences partly reflect differences in drug library composition and clinical annotation coverage. Importantly, the two resources were highly complementary, with only 3.5% overlap in recovered drugs, indicating that integrating predictions across independent perturbation resources expands therapeutic coverage and enables identification of high-confidence consensus candidates. Case studies in autoimmune disease and endometriosis further demonstrate that CDRPipe recovers clinically relevant therapies while revealing technology-dependent patterns of discovery. These results show that integrating heterogeneous transcriptomic perturbation resources improves the robustness and interpretability of transcriptional drug repurposing. One Sentence SummaryIntegrating drug perturbation resources from distinct transcriptomic platforms improves the robustness and accuracy of drug repurposing predictions.

bioinformatics↗

bioRxiv2025

Single-cell-level digital twins for preterm birth prevention strategies

Digital twin models can accelerate therapeutic development by enabling low-risk testing of candidate interventions. In preterm labor (PTL), a major pregnancy complication where clinical trials face unique ethical and financial barriers, digital twins are especially valuable for evaluating new therapies targeting immune dysfunctions driving PTL. Yet, current models lack single-cell resolution, limiting detection of cell-type-specific mechanisms, off-target effects, and the design of personalized interventions. We present Simulated Immunome Modeling of Clinical Outcomes (SIMCO), a single-cell-level digital twin framework that models immunomodulatory treatment effects on the timing of labor using immunome-wide, single-cell simulations. SIMCOs digital twins are trained and validated on a newly generated mass cytometry atlas of the pregnant immunome exposed to nine candidate drugs preselected for PTL prevention. Applying SIMCO to an independent cohort of pregnant individuals, we simulate treatment effects on gestational length, screening for candidate drugs that delay labor timing and providing system-level mechanistic insight for each drug candidate. Tetrahydrofolate, maprotiline, and the combination of aspirin and lansoprazole emerged as top candidates for PTL prevention, delaying labor onset primarily through enhanced mTOR signaling in innate immune cells and attenuated JAK/STAT signaling in naive CD4 T cells. The codebase is available at https://github.com/ofondeur/SIMCO/.

immunology↗

bioRxiv2025

A Transcriptomics-Based Computational Drug Repositioning Pipeline Identifies Simvastatin And Primaquine As Novel Therapeutics For Endometriosis Pain

Endometriosis has limited treatment options, prompting the search for novel therapeutics. We previously used a transcriptomics-based computational drug repositioning pipeline to analyze public bulk transcriptomic data of eutopic endometrium from cases and controls and identified several drug candidates. Fenoprofen, our top in silico candidate, was validated in a rat model of endometriosis-associated pain. Building on this, we evaluated herein two additional candidates, simvastatin (a cholesterol-lowering drug) and primaquine (an antimalarial), based on strong endometrial gene expression reversal scores and favorable safety profiles. Using the rat model, we conducted behavioral testing, bulk RNA sequencing, and differential expression analysis to assess their therapeutic potential. We also assessed endometriosis diagnosis among patients prescribed simvastatin in electronic medical records (EMR) across six University of California (UC) healthcare institutions. In vivo validation using a rat model of endometriosis demonstrated that both simvastatin and primaquine significantly reduced vaginal hyperalgesia, a surrogate marker of endometriosis-related pain. RNA-seq of uteri and lesions confirmed reversal of disease-associated gene expression signatures following treatment. Analysis of UC-wide EMR data found lower relative risk of endometriosis among those prescribed simvastatin compared to a matched control group. Overall, simvastatin and primaquine attenuated pain-associated behaviors and reversed endometriosis-related gene expression changes in an animal model. Moreover, simvastatin prescription was associated with a lower relative risk of endometriosis in our retrospective multi-center cohort study. These findings highlight their potential as repurposed therapeutics for endometriosis and support the effectiveness of computational drug repositioning in identifying new treatment strategies. One Sentence SummarySimvastatin and primaquine reduced endometriosis pain and reversed gene signatures, with simvastatin also linked to lower disease risk.

bioinformatics↗

bioRxiv2025

Transcriptomic comparison of early onset preeclampsia and placenta accreta identifies inverse trophoblast and decidua functions at the maternal-fetal interface

Early onset preeclampsia is a placental disorder characterized by shallow implantation, whereas placenta accreta spectrum is a placental disorder of deep placental attachment. This study compares the transcriptome of these two obstetric syndromes. By integrating available microarray and single-cell placenta/decidua transcriptomic datasets, we demonstrated that early onset preeclampsia genes are inversely expressed in placenta accreta, with the most marked differences noted in cell types of decidua, endothelial, and extravillous trophoblasts. Our findings highlight the key functions of trophoblast cell migration and invasion, decidua cell signaling, hypoxia pathways, and global growth factor and collagen contributions to these pregnancy disorders. This research provides new insights into the mechanisms of placentation and unifies these clinical siloes of disease by focusing on the fundamental biology of placental development at the maternal-fetal interface.

molecular biology↗

bioRxiv2025

Anticlustering for Sample Allocation To Minimize Batch Effects

High throughput sequencing is a powerful tool for processing large amounts of DNA and RNA samples in batches. Proper experimental design and statistical methods are required to mitigate systematic technical factors due to differences in batches ("batch effects"), as data variation due to these non-biological factors can mask actual biological differences. We propose using anticlustering as an automated method to assign samples to balanced batches. Anticlustering effectively negates differences in (numeric and/or categorical) covariates among batches, and implements user-defined restrictions on the number of batches, the number of samples per batch, and whether to assign certain samples to the same batch ("must-link constraints"). A simulation study shows that anticlustering is better at achieving balance among batches than previous approaches. An application from the UCSF-Stanford Endometriosis Center for Discovery, Innovation, Training and Community Engagement ("ENACT", https://enactcenter.org/) is presented as a real-life example. In the application, multiple samples provided by an individual had to be processed on the same batch, so that comparisons among different samples of the same patient were not diluted by batch effects. The novel Two Phase Must Link (2PML) anticlustering algorithm realized the must-link restrictions while simultaneously obtaining balance among batches regarding disease stage, menstrual cycle phase, case versus control sample, and clinical site. All methods presented here are accessible via the free and open source R package anticlust (https://cran.r-project.org/package=anticlust). An interactive visualization and web-based batch assignment tool are made available in the Rshiny app "anticlust" (https://anticlust.org/).

bioinformatics↗

bioRxiv2024

A Multiomics, Spatiotemporal, and Single Cell Atlas for Mapping Cell-Type-Specific Dysregulation at the Maternal-Fetal Interface

The placenta, the first organ to functionally mature, undergoes disordered development in many pregnancy complications. Molecular investigations have been hampered by the extreme cellular heterogeneity of the placenta, and this complexity is further exaggerated at the maternal-fetal interface where maternal and fetal cells co-mingle. We generated the paired single nucleus epigenomes and transcriptome for each of [~]200,000 cells at the human maternal-fetal interface from early pregnancy to term. These data identified cell-type-specific transcriptional regulatory programs and uncovered key transcription factors driving the lineage differentiation of placental cytotrophoblasts. Integrating spatial single cell proteomics profiling, we localized the observed cell types in situ, and characterized the dynamic stages and distinct features of endothelial cells of maternal spiral arteries remodeled by extravillous cytotrophoblasts. Integrative analyses of the single cell data across gestation enabled fine-mapping of the developmental trajectories of cytotrophoblasts and decidual stromal cells, and defining the signature molecular profiles of known and novel cell (sub)types. To demonstrate clinical value, we integrated the reference single cell data with large-scale population genomes from pregnancy complications and identified the most vulnerable maternal and fetal cell types in preeclampsia, preterm birth, and miscarriage. This study presents the most comprehensive placental and decidual single cell resource across gestation to date, reveals new insights into the drivers of normal human placentation, and uncovers the cellular basis of dysfunction associated with common pregnancy complications.

genomics↗

bioRxiv2022

Deep Immunophenotyping Reveals Endometriosis is Marked by Dysregulation of the Mononuclear Phagocytic System in Endometrium and Peripheral Blood

BackgroundEndometriosis is a chronic, estrogen-dependent disorder where inflammation contributes to disease-associated symptoms of pelvic pain and infertility. Immune dysfunction includes insufficient immune lesion clearance, a pro-inflammatory endometrial environment, and systemic inflammation. Comprehensive understanding of endometriosis immune pathophysiology in different hormonal milieu and disease severity has been hampered by limited direct characterization of immune populations in endometrium, blood, and lesions. Simultaneous deep phenotyping at single cell resolution of complex tissues has transformed our understanding of the immune system and its role in many diseases. Herein, we report mass cytometry and high dimensional analyses to study immune cell phenotypes, abundance, activation states, and functions in endometrium and blood of women with and without endometriosis in different cycle phases and disease stages. MethodsA case-control study was designed. Endometrial biopsies and blood (n=60 total) were obtained from women with (n=20, n=17, respectively) and without (n=14, n=9) endometriosis in the proliferative and secretory cycle phases of the menstrual cycle. Two mass cytometry panels were designed; one broad panel and one specific for mononuclear phagocytic cells (MPC), and all samples were multiplexed to characterize both endometrium and blood immune composition at unprecedented resolution. We combined supervised and unsupervised analyses to finely define the immune cell subsets with an emphasis on MPC. Then, association between cell types, protein expression, disease status, and cycle phase were performed. ResultsThe broad panel highlighted a significant modification of MPC in endometriosis; thus, they were studied in detail with an MPC-focused panel. Endometrial CD91+ macrophages overexpressed SIRP (phagocytosis inhibitor) and CD64 (associated with inflammation) in endometriosis, and they were more abundant in mild versus severe disease. In blood, classical and intermediate monocytes were less abundant in endometriosis, whereas plasmacytoid dendritic cells and non-classical monocytes were more abundant. Non-classical monocytes were higher in severe versus mild disease. ConclusionsA greater inflammatory phenotype and decreased phagocytic capacity of endometrial macrophages in endometriosis are consistent with defective clearance of endometrial cells shed during menses and in tissue homeostasis, with implications in endometriosis pathogenesis and pathophysiology. Different proportions of monocytes and plasmacytoid dendritic cells in blood from endometriosis suggest systemically aberrant functionality of the myeloid system opening new venues for the study of biomarkers and therapies for endometriosis.

immunology↗

bioRxiv2021

Whole-tissue deconvolution and scRNAseq analysis identify altered endometrial cellular compositions and functionality associated with endometriosis

The uterine lining (endometrium) exhibits a pro-inflammatory phenotype in women with endometriosis, resulting in pain, infertility, and poor pregnancy outcomes. The full complement of cell types contributing to this phenotype has yet to be identified, as most studies have focused on bulk tissue or select cell populations. Herein, through integrating whole-tissue deconvolution and single cell RNAseq, we comprehensively characterized immune and nonimmune cell types in endometrium of women with or without disease and their dynamic changes across the menstrual cycle. We designed metrics to evaluate specificity of deconvolution signatures that resulted in single cell identification of 13 novel signatures for immune cell subtypes in healthy endometrium. Guided by statistical metrics, we identified contributions of endometrial epithelial, endothelial, plasmacytoid dendritic cells, classical dendritic cells, monocytes, macrophages, and granulocytes to the endometrial pro-inflammatory phenotype, underscoring roles for nonimmune as well as immune cells to the dysfunctionality of this tissue. Teaser SentenceCell type deconvolution and single cell RNAseq analysis identify altered endometrial cellular compositions in women with endometriosis

bioinformatics↗