Biology subjects

Paul, E.

Publications and source records attributed to Paul, E..

15 recordsLinked to original sources

bioRxiv2026

Dynamic Changes in Endometrial Folding and Secretory Activity Across the Menstrual Cycle

Embryo implantation remains a major limitation of assisted reproductive technology, with failure occurring in approximately 30% of euploid embryo transfers. Implantation requires a synchronized dialogue between the blastocyst and receptive endometrium during the window of implantation (WOI), yet minimally invasive approaches to characterize the structural and molecular features of receptivity remain limited. We analyzed paired sonohysterogram images and uterine lavage samples collected during the proliferative and mid-secretory phases from subjects with regular ovulatory cycles and proven fertility. Endometrial folds were quantified, and lavage samples were analyzed by Luminex multiplex immunoassay. Folds were present in both phases but were significantly more abundant during the mid-secretory WOI, independent of imaging view and endometrial thickness. Folding correlated strongly with circulating estradiol level during the proliferative phase but not the mid-secretory phase, and folding patterns between phases were not correlated, suggesting distinct regulatory mechanisms. Consistent with these structural patterns, uterine lavage demonstrated phase-specific differences in expression of factors associated with endometrial receptivity and implantation, with glandular epithelium, and myeloid-lineage cells emerging as major contributors. Together, these findings identify coordinated structural and secretory processes during the WOI and support further evaluation of endometrial folding and uterine lavage as complementary, minimally invasive markers of endometrial receptivity.

developmental biology↗

bioRxiv2026

Aberrations in stromal signaling, muscle contractility and epithelial architecture underlie poor embryo-implantation outcomes after murine ovarian stimulation

Ovarian stimulation is widely used in assisted reproductive technologies, yet its effects on uterine architecture and embryo implantation remain poorly understood. Using a mouse model, we show that ovarian stimulation or superovulation disrupts pre-implantation luminal epithelial folding and induces aberrant smooth muscle structure and contractile function. These structural defects result in embryo trapping within aberrant longitudinal folds, impaired implantation chamber formation, misalignment of the embryo-uterine axis, and subsequent embryo loss. These ovarian stimulation effects were reversible after rest and restoration of normal estrus cycling. Transcriptomic analysis suggests widespread disruption in the stroma and immune compartments of the stimulated uteri. Pathway analysis revealed significant disruption of stromal extracellular matrix and enhanced probability of stroma-immune communication via collagen signaling. Platelet derived growth factor receptor A (PDGFRA) expression was elevated in both the stroma and smooth muscle of the stimulated uteri. Short-term pharmacological inhibition of PDGFRA in the stimulated uteri prior to implantation fully restored epithelial fold transition and implantation chamber formation and partially restored smooth muscle architecture and contractility. Importantly, PDGFRA protein was also elevated in endometrial biopsies from women undergoing ovarian stimulation when compared to natural cycle biopsies. Together, this study establishes muscle contractions and stromal and smooth muscle PDGFRA signaling as novel non-cell autonomous regulators of uterine epithelial architecture critical for embryo implantation.

Developmental Biology↗

bioRxiv2026

Immunodominance Hierarchy of Endogenous BBN963 Bladder Cancer Antigens Remains Stable Under Anti-PD1 and Anti-CTLA4 Immunotherapy

Immune checkpoint inhibition (ICI) is clinically active against multiple cancers, including urothelial cancer at the non-muscle invasive, muscle-invasive, and metastatic stages. Despite this, large numbers of patients experience disease progression and relapse after treatment with ICI-containing regimens. Tumor antigen-specific T cells are critical to ICI response, however few studies have evaluated the breadth and magnitude of tumor antigen-specific T cell responses with ICI therapy. In this study, we mapped the tumor antigen immunodominance hierarchy in the BBN963 model of murine basal-like bladder cancer for endogenous tumor neoantigens expressed physiologically. We used a high-throughput matrixed ELISpot assay to detect CD8+ T cell responses to predicted BBN963 tumor antigens derived from multiple mutational genomic sources. We found CD8+ T cell responses were directed against a subset of tumor antigens forming a stable and reproducible immunodominance hierarchy across individual mice. Treatment with anti-PD-1 or anti-CTLA-4 did not substantially reshape this hierarchy or broadly shift dominant responses to previously defined subdominant epitopes. Predicted peptide MHC binding stability and affinity was associated with antigen immunogenicity. Cancer-testis antigens, endogenous retroviral antigens, and SNV-derived tumor antigens that were immunogenic were found across tumor subclones. By diversifying the immunogenic antigen repertoire beyond SNVs, we achieved nearly 100% tumor subclone coverage, suggesting that broader antigen selection could help immunotherapy target more tumor subclones. In conclusion, this study supports the stability of the immunodominance hierarchy under ICI therapy and a role for broadening antigen discovery to multiple expressional sources in immunotherapy design.

cancer biology↗

bioRxiv2026

Multimodal alignments of in vivo imaging and spatial biology datasets at cellular resolution

Parallel revolutions in intravital microscopy and spatial biology techniques have respectively enabled large-scale recordings of cellular dynamics in live animals and multi-dimensional molecular profiling at single-cell resolution. However, due to the challenges of aligning data from different modalities at cellular resolution, these two transformational approaches have generally been applied on separate biological samples, stymying the ability to link activity patterns and molecular attributes in the same exact cells. To enable routine, multimodal investigations of cells in vivo dynamics and molecular content, we created TRU-FACT (Total Registration Under Functional Activity, Connectivity, and Transcriptomics), a broadly applicable experimental and computational pipeline for registering large populations of individual cells across intravital imaging and spatial biology datasets. The pipeline combines three key innovations: an optomechanical tissue handling and alignment method to parallelize specimen planes, a graph-theoretic method to register individual cells based on their geometric relationships to neighboring cells, and a statistical framework that provides for each cell an a posteriori probability of correct registration. We validated TRU-FACT with several preparations for imaging neural Ca2+ activity in cortical and deep brain areas in head-fixed and freely behaving mice, RNA-barcode-expressing viruses for labeling neural projections, and low- and high-plex spatial transcriptomic methods. In mice performing a skilled reaching task, TRU-FACT alignments revealed the movement-related signaling patterns of intratelencephalic, extratelencephalic, and striatum-, superior colliculus-, and thalamus-projecting motor cortical neurons. Overall, TRU-FACT constitutes a scalable, multimodal discovery platform that is applicable to diverse tissue-types and spatial biology techniques, thereby enabling multiscale analyses of many complex biological systems.

neuroscience↗

bioRxiv2026

Conformalized Multiview Learning

SO_SCPLOWUMMARYC_SCPLOWThere is an overwhelmingly large body of literature and numerous algorithms already available on "multimodal AI", based on different modeling and fusion paradigms. As multimodal AI becomes fully integrated into healthcare applications, the ability to say "Im not sure" or "I dont know" when uncertain is a necessary capability for safe clinical deployment, requiring the augmentation of current multimodal AI techniques. Our goal in this work is not to add yet another method to the growing toolbox of multimodal AI, but rather to (i) clarify how existing supervised multimodal AI methods can be understood through the lens of uncertainty quantification, and (ii) develop a unified framework that provides valid and interpretable uncertainty estimates for multimodal AI practitioners. We introduce Coracle, a conformalized framework for multimodal AI that seamlessly adapts to early, late, and intermediate fusion. Coracle provides theoretical marginal confidence guarantees and achieves valid finite-sample coverage without relying on distributional assumptions. Through extensive simulations and analyses of diverse biomedical multiview datasets, we demonstrate that Coracle consistently outperforms existing non-conformalized methods in both uncertainty estimation and calibration. By equipping published multiview models with valid, interpretable confidence measures, Coracle advances the development of trustworthy, uncertainty-aware statistical tools for biomedical decision support. Coracle is publicly available as an open-source R package at https://github.com/himelmallick/Coracle.

systems biology↗

bioRxiv2025

Bayesian Cooperative Learning for Multimodal Integration

Bayesian multimodal models that integrate data from multiple sources, studies, or modalities have garnered considerable attention in recent years. However, existing methods either rely on computationally expensive Markov chain Monte Carlo (MCMC) schemes or adopt variational approaches that forgo principled uncertainty quantification. To address this limitation and cater to practical needs, we abandon the MCMC framework and turn to resampling-based posterior inference for multimodal integration. Our method, Bayesian Cooperative Learning (BayesCOOP), embeds fast maximum a posteriori (MAP) estimation within a Bayesian bootstrap, combining a novel jittered group spike-and-slab prior with an efficient expectation-maximization (E M) coordinate descent algorithm under randomly weighted data perturbations. Averaging posterior summaries (MAP estimates) across bootstrap replicates yields approximate posterior samples that retain Bayesian interpretability while avoiding the computational burden of traditional sampling-based inference. We establish theoretical connections between BayesCOOPs pseudo-posterior averaging and posterior contraction principles, demonstrating near-optimal posterior consistency under sparsity. Extensive simulation studies and analyses of pregnancy multi-omics datasets demonstrate that BayesCOOP substantially outperforms state-of-the-art early, intermediate, and late fusion approaches in estimation, prediction, and uncertainty assessment. The open-source implementation of BayesCOOP is available at https://github.com/himelmallick/BayesCOOP.

bioinformatics↗

bioRxiv2025

Single cell mapping of the human endometrium and first trimester decidua identifies distinct epithelial and stromal cell contributions to fertility

The human endometrium undergoes dynamic changes across the menstrual cycle to establish a receptive state for embryo implantation. Using bulk and single-cell RNA sequencing, we characterized gene expression dynamics in the cycling endometrium and the decidua from early pregnancy. We are the first to demonstrate that during the mid-secretory phase--the period encompassing the window of implantation--secretory glandular epithelial cells undergo notable transcriptional changes in cell-cell communication. Through comprehensive analyses, we identified the Glandular Epithelium Receptivity Module (GERM) signature, comprising 556 genes associated with endometrial receptivity. This GERM signature was consistently perturbed across multiple datasets of endometrial samples from women with impaired fertility, validating its relevance as a marker of receptivity. In addition to epithelial changes, we observed shifts in stromal cell populations, notably involving decidual and senescent subsets, which also play key roles in modulating implantation. Together, these findings provide a high-resolution transcriptomic atlas of the receptive and early pregnant endometrium and shed light on key molecular pathways underlying successful implantation.

molecular biology↗

bioRxiv2025

Repurposing without Reinventing the Wheel - Ensemble Models for Differential Analysis

Inspired by ensemble models in machine learning, we propose a general framework for aggregating multiple distinct base models to enhance the power of published differential association analysis (DAA) methods. We demonstrate this approach by augmenting popular DAA models with one or more biologically motivated alternatives. This creates an ensemble that bypasses the challenge of selecting an optimal model and instead combines the strengths of complementary statistical models to achieve superior performance. Our proposed ensemble learning approach is platform-agnostic and can augment any existing DAA method, providing a general and flexible framework for various downstream modeling tasks across domains and data types. We performed extensive benchmarking across both simulated and experimental datasets spanning single-cell gene expression, bulk transcriptomics, and microbiome metagenomics, where the ensemble strategy vastly outperformed non-ensemble methods, identified more differential patterns than the competing methods, and displayed good control of false positive and false discovery rates across diversified scenarios. In addition to highlighting a substantial performance boost for state-of-the-art DAA methods, this work has practical implications for mitigating the so-called reproducibility crisis in omics data science. An open-source R package implementing the ensemble strategy is publicly available at https://github.com/himelmallick/DAssemble.

bioinformatics↗

bioRxiv2023

Human organoid modeling of congenital malformations caused by RFX6 mutations reveal an essential role for this transcription factor in establishing and maintaining duodenal identity upstream of PDX1

The gastrointestinal (GI) tract consists of highly specialized organs from the proximal esophagus to the distal colon, each with unique functions. Rare congenital malformations of the GI tract, including organ atresia, agenesis or mis-patterning are linked to gene mutations although the molecular basis of these malformations has been poorly studied due to lack of model systems to study human development. We identified a patient with compound heterozygous mutations in the transcription factor RFX6 with pancreatic agenesis as previously described. In addition, the patient had duodenal mal-rotation and atresia suggesting that establishment of the proximal small intestine was impaired in these patients. To identify the molecular basis of the intestinal malformation we generated induced pluripotent stem cell lines from this patient, and derived human intestinal organoid (HIOs) to identify how mutations in RFX6 impact intestinal patterning and function. We identified that the duodenal identity of HIOs and patient tissues had adopted a more distal small intestinal signature, including expression of SATB2, normally expressed in the ileum and colon. CRISPR-mediated correction of RFX6 restored duodenal identity, including expression of PDX1, which is required for duodenal development. Using transcriptomic approaches in HIOs and Xenopus embryos we identified that PDX1 is a downstream transcriptional target of RFX6 and that PDX1 expression in a RFX6 mutant background was sufficient to rescue duodenal identity. However, RFX6 had a PDX1-independent role in regulating expression of components of WNT, HH, and BMP signaling pathways that are critical for establishing early regional identity in the GI tract. In summary, we have identified that RFX6 is one of the most upstream regulators early intestinal patterning in vertebrates and that it acts by regulating key transcriptional and signaling pathways.

developmental biology↗

bioRxiv2022

An integrated Bayesian framework for multi-omics prediction and classification

With the growing commonality of multi-omics datasets, there is now increasing evidence that integrated omics profiles lead to the more efficient discovery of clinically actionable biomarkers that enable better disease outcome prediction and patient stratification. Several methods exist to perform host phenotype prediction from crosssectional, single-omics data modalities but decentralized frameworks that jointly analyze multiple time-dependent omics data to highlight the integrative and dynamic impact of repeatedly measured biomarkers are currently limited. In this article, we propose a novel Bayesian ensemble method to consolidate prediction by combining information across several longitudinal and cross-sectional omics data layers. Unlike existing frequentist paradigms, our approach enables uncertainty quantification in prediction as well as interval estimation for a variety of quantities of interest based on posterior summaries. We apply our method to four published multi-omics datasets and demonstrate that it recapitulates known biology in addition to providing novel insights while also outperforming existing methods in estimation, prediction, and uncertainty quantification. Our open-source software is publicly available at https://github.com/himelmallick/IntegratedLearner.

bioinformatics↗

bioRxiv2022

Uptake of aminoglycosides through outer membrane porins in Escherichia coli

Aminoglycosides are important clinical antibiotics but their molecular uptake mechanism is still not completely understood. Here we quantify and compare the passive transport of three aminoglycosides (kanamycin, gentamicin, and amikacin) across general or sugar specific porins of Escherichia coli (OmpF, OmpC, LamB and ChiP). Our analysis revealed that permeation of aminoglycosides (Kanamycin/Gentamycin/Amikacin) is about the same through ChiP ({approx}5/3/2 molecules/s), OmpF ({approx}10/15/<1 molecules/s) and OmpC ({approx}11/8/<1 molecules/s). In contrast, LamB of smaller pore diameter has no significant permeation ([≤]1/1/1 molecules/s, all values recalculated for a gradient of 10 {micro}M). Biological assays confirmed the relevance of these translocations for antibiotic potency.

biophysics↗

bioRxiv2022

Single TolC-AcrA complex formation monitoredby time dependent single-channel electrophysiology

Characterizing protein-protein interaction on a single molecular level is a challenge, experimentally as well as the interpretation of the data. For example, Gram-negative bacteria contain protein complexes spanning the outer and inner cell wall being able to efflux effectively cell toxic substances. Within the search for antibiotics with novel modes of action, inhibition of the efflux pump assembly is an interesting possible target. Recent seminal work revealed the high-resolution structure of the tripartic composition TolC-AcrA-AcrB. Here, we reconstitute a single TolC homotrimer into a planar lipid membrane and follow the assembly of AcrA using the modulation of the ion current through TolC during binding. In particular, the presence of AcrA increases the average ionic current through TolC and, moreover, reduces the ion-current fluctuations caused by flickering of TolC. Here, we demonstrate that statistical properties of ion-current fluctuations (the power spectral density) provide a complementary measure of the interaction of the TolC-AcrA complex with potential inhibitors. Both characteristics, the average current and the current noise, taken into consideration together, provide more robust information.

biophysics↗

bioRxiv2020

Altered Mediator dynamics during heat shock in budding yeast

The Mediator complex is central to transcription by RNA polymerase II (Pol II) in eukaryotes. In yeast, Mediator is recruited by activators via its tail module and then facilitates assembly of the pre-initiation complex (PIC), including Pol II, setting the stage for productive transcription. Mediator occupies proximal promoter regions only transiently prior to Pol II escape; interruption of the transcription cycle by inactivation or depletion of Kin28 inhibits Pol II escape and stabilizes Mediator occupancy at promoters. However, whether Mediator occupancy and dynamics differ for gene cohorts induced by stress or alternative growth conditions has not been examined on a genome-wide scale. Here we investigate Mediator occupancy following heat shock or CdCl2 induction, with or without depletion of Kin28. We find that Pol II occupancy exhibits similar dependence on Mediator under normal and heat shock conditions; however, Mediator occupancy does not increase upon Kin28 depletion at most genes active during heat shock, indicating altered dynamics. Furthermore, Mediator occupancy persists at genes repressed by heat shock or CdCl2 induction and exhibits peaks upstream of the proximal promoter whether or not Kin28 is depleted, suggesting that Mediator is recruited by activators but is unable to engage PIC components at these repressed targets. Finally, we show a reduced dependence on PIC components for Mediator occupancy at promoters after heat shock, further supporting an altered dynamics or stronger engagement with activators under these conditions.

molecular biology↗

bioRxiv2020

A common molecular mechanism for cognitive deficits and craving in alcoholism

Alcohol-dependent patients commonly show impairments in executive functions that facilitate craving and can lead to relapse. The medial prefrontal cortex, a key brain region for executive control, is prone to alcohol-induced neuroadaptations. However, the molecular mechanisms leading to executive dysfunction in alcoholism are poorly understood. Here using a bi-directional neuromodulation approach we demonstrate a causal link for reduced prefrontal mGluR2 function and both impaired executive control and alcohol craving. By neuron-specific prefrontal knockdown of mGluR2 in rats, we generated a phenotype of reduced cognitive flexibility and excessive alcohol-seeking. Conversely, restoring prefrontal mGluR2 levels in alcohol-dependent rats rescued these pathological behaviors. Also targeting mGluR2 pharmacologically reduced relapse behavior. Finally, we developed a FDG-PET biomarker to identify those individuals that respond to mGluR2-based interventions. In conclusion, we identified a common molecular pathological mechanism for both executive dysfunction and alcohol craving, and provide a personalized mGluR2-mechanism-based intervention strategy for medication development of alcoholism.

neuroscience↗

bioRxiv2018

nNOS-expressing neurons in the ventral tegmental area and substantia nigra pars compacta

GABA neurons in the ventral tegmental area (VTA) and substantia nigra pars compact (SNc) play key roles in reward and aversion through their local inhibitory control of dopamine neuron activity and through long-range projections to several target regions including the nucleus accumbens. It is not clear if some of these GABA neurons are dedicated local interneurons or if they all collateralize and send projections externally as well as making local synaptic connections. Testing between these possibilities has been challenging in the absence of interneuron-specific molecular markers. We hypothesised that one potential candidate might be neuronal nitric oxide synthase (nNOS), a common interneuronal marker in other brain regions. To test this, we used a combination of immunolabelling (including antibodies for nNOS that we validated in tissue from nNOS-deficient mice) and cell-type-specific virus-based anterograde tracing in mice. We show that nNOS-expressing neurons in the parabrachial pigmented (PBP) part of the VTA and the SNc are GABAergic local interneurons, whereas nNOS-expressing neurons in the Rostral Linear Nucleus (RLi) are mostly glutamatergic and project to a number of regions, including the lateral hypothalamus, the ventral pallidum, and the median raphe nucleus. Taken together, these findings indicate that nNOS is expressed by neurochemically- and anatomically-distinct neuronal sub-groups in a sub-region-specific manner in the VTA and SNc.

neuroscience↗