Biology subjects

Kalavros, N.

Publications and source records attributed to Kalavros, N..

9 recordsLinked to original sources

bioRxiv2026

Cholinergic modulation engages a CHRM1-IL-17 neuroimmune axis to promote diabetic wound repair

Chronic non-healing wounds are a major complication of diabetes, where persistent inflammation fails to transition into effective tissue repair (1-3). Here we identify cholinergic modulation as a mechanism that promotes productive inflammation and diabetic wound healing. Using a healing-associated fibroblast signature derived from single-cell profiling of human diabetic foot ulcers (3), we identified the acetylcholinesterase (AChE) inhibitor metrifonate as a candidate inducer of reparative wound programs. Topical delivery accelerated healing in diabetic mice and increased acetylcholine availability within the wound. Complementary receptor pharmacology identified muscarinic acetylcholine receptor 1 (CHRM1) as the principal receptor mediating the reparative response. Transcriptomic and cellular analyses revealed early induction of IL-17-associated inflammatory programs and enrichment of CHRM1NK1.1 innate lymphocytes. Depletion of NK1.1 cells or neutralization of IL-17A abolished the pro-healing effect, whereas spatial transcriptomics revealed treatment-associated organization of NK cells within vascular niches. Localized treatment further promoted repair in diabetic porcine wounds and human skin models. Together, these findings identify a CHRM1-IL-17 neuroimmune axis linking cholinergic modulation to diabetic wound repair and establish local AChE inhibition as a potential therapeutic strategy for chronic wounds.

immunology↗

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↗

bioRxiv2026

Aging-related Transcriptomic Changes with Spatial Resolution in the Human Prefrontal Cortex

The human prefrontal cortex (PFC), whose laminar organization is essential for cognitive function, is among the first regions to show age-related functional decline1,2. Single-cell sequencing studies revealed cell type-dependent aging effects but lacked spatial specificity3-6. Spatial transcriptomics (ST) advanced our molecular understanding of the human PFC7, yet whether aging-driven changes differ across PFC layers remains unclear. Here, we performed whole-transcriptome ST on postmortem PFC from 37 individuals across the adult lifespan. We mapped cortical layers and revealed aging mechanisms across layers. This represents one of the largest and most comprehensive lifespan ST analysis of the human PFC brain, offering crucial insight into how the brain ages and identifying potential molecular targets to mitigate cognitive aging and extend healthspan.

neuroscience↗

bioRxiv2025

Spatial transcriptomic profiling identifies lacrimal gland epithelial cell-driven mechanisms underlying autoimmunity in Sjogrens disease.

Sjogrens disease (SjD) is a second most prevalent rheumatic disease involving autoimmune pathology of tear-producing lacrimal glands that leads to a common clinical manifestation of chronic ocular surface disease. Despite advances in understanding of SjD, lacrimal gland pathology remains incompletely understood limiting diagnosis and treatment. Here we analyze spatial transcriptomic profile of lacrimal glands from wild-type (C57Bl/6) mice and Thrombospondin (TSP)-1-/- mice, a spontaneous mouse model of SjD. We uncover molecular mechanisms underlying functional loss of major epithelial cell subtypes - acinar, duct and myoepithelial cells. We identify potential early mechanisms and markers of glandular damage. By integrating spatial and cellular profiles, we uncover the presence of antigen presenting cells in the proximity of duct epithelial cells that were not described previously in lacrimal glands. We further identify role of epithelial cells as active participants in promoting or sustaining inflammation. Our findings help reveal potential molecular and cellular cues that drive periductal infiltrates containing B cells and Tfh cells that form germinal centers to facilitate local autoantibody production. Overall, our study can provide a framework for therapeutic targeting of epithelial cell types and multicellular interactions underlying autoimmune pathology. Significance StatementTears produced by lacrimal glands are critical for protecting the eye surface by preventing tissue dryness and maintaining normal vision. Disruption of this function due to autoimmune inflammation in Sjogrens Disease compromises the protection of the eye surface causing dryness, a condition with a potential for sight-threatening complications like infections and ulcers. Understanding cellular and molecular interactions that lead to functional loss and autoimmune inflammation of the lacrimal gland is critical for developing effective therapies. We have analyzed transcriptional profile of glandular cells in a tissue section where their morphology and interactions with surrounding cells is preserved. By comparing glands from normal and mice with Sjogrens disease we identify molecular mechanisms that can form the basis for new therapies.

immunology↗

bioRxiv2025

Tumor-myeloid crosstalk drives therapy resistance in localized bladder cancer

Neoadjuvant cisplatin-based chemotherapy results in pathologic complete response for only a minority of patients with muscle-invasive bladder cancer (MIBC), and mechanisms of resistance and the effects of chemotherapy on the MIBC microenvironment remain incompletely understood. Here, we defined the single-cell and spatial transcriptomes of cancer and immune cells from MIBC patients with resistance to cisplatin-based chemotherapy. Tumors with persistent MIBC after chemotherapy harbored cancer cells expressing epithelial-to-mesenchymal programs that were associated with worse overall survival in independent cisplatin-treated bladder cancer cohorts. These cisplatin-resistant tumor cells were infiltrated by macrophages that upregulated tumor permissive programs defined by increased PARP14 expression in spatially resolved multicellular niches. Macrophage reprogramming through PARP14 inhibition sensitized tumors to cisplatin via downregulation of tumor cell pathways implicated in resistance. Our results demonstrate that cancer cells and macrophages cooperate to promote cisplatin resistance and identify macrophage-directed PARP14 inhibition as a novel therapeutic strategy to sensitize MIBC to cisplatin.

cancer biology↗

bioRxiv2025

Benchmarking Large Language Models for Predictive Modeling in Biomedical Research With a Focus on Reproductive Health

Generative AI, particularly large language models (LLMs), is increasingly being used in computational biology to support code generation for data analysis. In this study, we evaluated the ability of LLMs to generate functional R and Python code for predictive modeling tasks, leveraging standardized molecular datasets from several recent DREAM (Dialogue for Reverse Engineering Assessments and Methods) Challenges focused on reproductive health. We assessed LLM performance across four predictive tasks derived from three DREAM challenges: gestational age regression from gene expression, gestational age regression from DNA methylation profiles, and classification of preterm birth and early preterm birth from microbiome data. LLMs were prompted with task descriptions, data locations, and target outcomes. LLM-generated code was then run to fit and apply prediction models and generate graphics, and they were ranked based on their success in completing the tasks and achieving strong test set performance. Among the eight LLMs tested, o3-mini-high, 4o, DeepseekR1 and Gemini 2.0 completed at least one task without error. Overall, R code generation was more successful (14/16 tasks) than Python (7/16), attributed to the utility of Bioconductor packages for querying Gene Expression Omnibus data. OpenAIs o3-mini-high outperformed others, completing 7/8 tasks. Test set performance of the top LLM matched or exceeded top-performing teams from the original DREAM challenges. These findings underscore the potential of LLMs to enhance exploratory analysis and democratize access to predictive modeling in omics by automating key components of analysis pipelines, and highlight the potential to increase research output when conducting analyses of standardized datasets from public repositories.

bioinformatics↗

bioRxiv2025

Molecular hallmarks of excitatory and inhibitory neuronal resilience and resistance to Alzheimer's disease

BackgroundA significant proportion of individuals maintain healthy cognitive function despite having extensive Alzheimers disease (AD) pathology, known as cognitive resilience. Understanding the molecular mechanisms that protect these individuals can identify therapeutic targets for AD dementia. This study aims to define molecular and cellular signatures of cognitive resilience, protection and resistance, by integrating genetics, bulk RNA, and single-nucleus RNA sequencing data across multiple brain regions from AD, resilient, and control individuals. MethodsWe analyzed data from the Religious Order Study and the Rush Memory and Aging Project (ROSMAP), including bulk (n=631) and multi-regional single nucleus (n=48) RNA sequencing. Subjects were categorized into AD, resilient, and control based on {beta}-amyloid and tau pathology, and cognitive status. We identified and prioritized protected cell populations using whole genome sequencing-derived genetic variants, transcriptomic profiling, and cellular composition distribution. ResultsTranscriptomic results, supported by GWAS-derived polygenic risk scores, place cognitive resilience as an intermediate state in the AD continuum. Tissue-level analysis revealed 43 genes enriched in nucleic acid metabolism and signaling that were differentially expressed between AD and resilience. Only GFAP (upregulated) and KLF4 (downregulated) showed differential expression in resilience compared to controls. Cellular resilience involved reorganization of protein folding and degradation pathways, with downregulation of Hsp90 and selective upregulation of Hsp40, Hsp70, and Hsp110 families in excitatory neurons. Excitatory neuronal subpopulations in the entorhinal cortex (ATP8B1+ and MEF2Chigh) exhibited unique resilience signaling through neurotrophin (modulated by LINGO1) and angiopoietin (ANGPT2/TEK) pathways. We identified MEF2C, ATP8B1, and RELN as key markers of resilient excitatory neuronal populations, characterized by selective vulnerability in AD. Protective rare variant enrichment highlighted vulnerable populations, including somatostatin (SST) inhibitory interneurons, validated through immunofluorescence showing co-expression of rare variant associated RBFOX1 and KIF26B in SST+ neurons in the dorsolateral prefrontal cortex. The maintenance of excitatory-inhibitory balance emerges as a key characteristic of resilience. ConclusionsWe identified molecular and cellular hallmarks of cognitive resilience, an intermediate state in the AD continuum. Resilience mechanisms include preservation of neuronal function, maintenance of excitatory/inhibitory balance, and activation of protective signaling pathways. Specific excitatory neuronal populations appear to play a central role in mediating cognitive resilience, while a subset of vulnerable SST interneurons likely provide compensation against AD-associated dysregulation. This study offers a framework to leverage natural protective mechanisms to mitigate neurodegeneration and preserve cognition in AD.

neuroscience↗

bioRxiv2023

snPATHO-seq: unlocking the pathology archives

Formalin-fixed paraffin-embedded (FFPE) samples are valuable but underutilized in single-cell omics research due to their low DNA and RNA quality. In this study, leveraging recent single-cell genomic technology advances, we introduce a versatile method to derive high-quality single-nucleus transcriptomic data from FFPE samples.

molecular biology↗

bioRxiv2022

A single-nucleus and spatial transcriptomic atlas of the COVID-19 liver reveals topological, functional, and regenerative organ disruption in patients

The molecular underpinnings of organ dysfunction in acute COVID-19 and its potential long-term sequelae are under intense investigation. To shed light on these in the context of liver function, we performed single-nucleus RNA-seq and spatial transcriptomic profiling of livers from 17 COVID-19 decedents. We identified hepatocytes positive for SARS-CoV-2 RNA with an expression phenotype resembling infected lung epithelial cells. Integrated analysis and comparisons with healthy controls revealed extensive changes in the cellular composition and expression states in COVID-19 liver, reflecting hepatocellular injury, ductular reaction, pathologic vascular expansion, and fibrogenesis. We also observed Kupffer cell proliferation and erythrocyte progenitors for the first time in a human liver single-cell atlas, resembling similar responses in liver injury in mice and in sepsis, respectively. Despite the absence of a clinical acute liver injury phenotype, endothelial cell composition was dramatically impacted in COVID-19, concomitantly with extensive alterations and profibrogenic activation of reactive cholangiocytes and mesenchymal cells. Our atlas provides novel insights into liver physiology and pathology in COVID-19 and forms a foundational resource for its investigation and understanding.

genomics↗