From gene modules to gene markers: an integrated AI-human approach selects CD38 to represent plasma cell-associated transcriptional signatures.

BACKGROUND: Knowledge-driven prioritization of candidate genes derived from large-scale molecular profiling data for targeted transcriptional profiling assays is challenging due to the vast amount of biomedical literature that needs to be harnessed. We present a workflow leveraging Large Language Models (LLMs) to prioritize candidate genes within module M12.15, a plasma cell-associated module from the BloodGen3 repertoire, by integrating knowledge-driven prioritization with data-driven analysis of transcriptome profiles. METHODS: The workflow involves a two-step process: (1) high-throughput screening using LLMs to score and rank the 17 genes of module M12.15 based on six predefined criteria, and (2) prioritization employing high-resolution scoring and fact-checking, with human experts validating and refining AI-generated scores. RESULTS: The first step identified five candidate genes (CD38, TNFRSF17, IGJ, TOP2A, and TYMS). Following human-augmented LLM scoring and fact checking, as part of the second step, CD38 and TNFRSF17 emerged as the top candidates. Next, transcriptome profiling data from three datasets was incorporated in the workflow to assess expression levels and correlations with the module average across various conditions and cell types. It is on this basis that CD38 was prioritized as the top candidate, with TNFRSF17 and IGJ identified as promising alternatives. CONCLUSION: This study introduces a systematic framework that integrates LLMs with human expertise for gene prioritization. Our analysis identified CD38, TNFRSF17, and IGJ as the top candidates within the plasma cell-associated module M12.15 from the BloodGen3 repertoire, with their relative rankings varying systematically based on specific evaluation criteria, from plasma cell biology to therapeutic relevance. This criterion-dependent ranking demonstrates the ability of the framework to perform nuanced, multi-faceted evaluations. By combining knowledge-driven analysis with data-driven metrics, our approach provides a balanced and comprehensive method for biomarker selection. The methodology established here offers a reproducible and scalable approach that can be applied across diverse biological contexts and extended to analyze large module repertoires

Analysis of functional connectivity changes from childhood to old age: A study using HCP-D, HCP-YA, and HCP-A datasets.

We present a new clustering-enabled regression approach to investigate how functional connectivity (FC) of the entire brain changes from childhood to old age. By applying this method to resting-state functional magnetic resonance imaging data aggregated from three Human Connectome Project studies, we cluster brain regions that undergo identical age-related changes in FC and reveal diverse patterns of these changes for different region clusters. While most brain connections between pairs of regions show minimal yet statistically significant FC changes with age, only a tiny proportion of connections exhibit practically significant age-related changes in FC. Among these connections, FC between region clusters from the same functional network tends to decrease over time, whereas FC between region clusters from different networks demonstrates various patterns of age-related changes. Moreover, our research uncovers sex-specific trends in FC changes. Females show much higher FC mainly within the default mode network, whereas males display higher FC across several more brain networks. These findings underscore the complexity and heterogeneity of FC changes in the brain throughout the lifespan

Amyotrophic lateral sclerosis and frontotemporal dementia mutation reduces endothelial TDP-43 and causes blood-brain barrier defects.

Mutations in the TARDBP gene encoding TDP-43 protein are linked to loss of function in neurons and familial frontotemporal dementia (FTD) and amyotrophic lateral sclerosis (ALS). We recently identified reduced nuclear TDP-43 in capillary endothelial cells (ECs) of donors with ALS-FTD. Because blood-brain barrier (BBB) permeability increases in ALS-FTD, we postulated that reduced nuclear TDP-43 in ECs might contribute. Here, we show that nuclear TDP-43 is reduced in ECs of mice with an ALS-FTD–associated mutation in TDP-43 (TardbpG348C) and that this leads to cell-autonomous loss of junctional complexes and BBB integrity. Targeted excision of TDP-43 in brain ECs recapitulates BBB defects and loss of junctional complexes and ultimately leads to fibrin deposition, gliosis, phospho-Tau accumulation, and impaired memory and social interaction. Transcriptional changes in TDP-43–deficient ECs resemble diseased brain ECs. These data show that nuclear loss of TDP-43 in brain ECs disrupts the BBB and causes hallmarks of FTD

Spatiotemporal Profiling Defines Persistence and Resistance Dynamics during Targeted Treatment of Melanoma.

Resistance of BRAF-mutant melanomas to targeted therapy arises from the ability of cells to enter a persister state, evade treatment with relative dormancy, and repopulate the tumor when reactivated. A better understanding of the temporal dynamics and specific pathways leading into and out of the persister state is needed to identify strategies to prevent treatment failure. Using spatial transcriptomics in patient-derived xenograft models, we captured clonal lineage evolution during treatment. The persister state showed increased oxidative phosphorylation, decreased proliferation, and increased invasive capacity, with central-to-peripheral gradients. Phylogenetic tracing identified intrinsic and acquired resistance mechanisms (e.g., dual-specific phosphatases, reticulon-4, and cyclin-dependent kinase 2) and suggested specific temporal windows of potential therapeutic susceptibility. Deep learning-enabled analysis of histopathologic slides revealed morphologic features correlating with specific cell states, demonstrating that juxtaposition of transcriptomics and histologic data enabled identification of phenotypically distinct populations from using imaging data alone. In summary, this study defined state change and lineage selection during melanoma treatment with spatiotemporal resolution, elucidating how choice and timing of therapeutic agents will impact the ability to eradicate resistant clones. Significance: Tracking clonal progression during treatment uncovers conserved, global transcriptional changes and local clone-clone and spatial patterns underlying the emergence of resistance, providing insights into therapy-induced tumor evolution

Elevated mitochondrial membrane potential is a therapeutic vulnerability in Dnmt3a-mutant clonal hematopoiesis.

The competitive advantage of mutant hematopoietic stem and progenitor cells (HSPCs) underlies clonal hematopoiesis (CH). Drivers of CH include aging and inflammation; however, how CH-mutant cells gain a selective advantage in these contexts is an unresolved question. Using a murine model of CH (Dnmt3aR878H/+), we discover that mutant HSPCs sustain elevated mitochondrial respiration which is associated with their resistance to aging-related changes in the bone marrow microenvironment. Mutant HSPCs have DNA hypomethylation and increased expression of oxidative phosphorylation gene signatures, increased functional oxidative phosphorylation capacity, high mitochondrial membrane potential (Δψm), and greater dependence on mitochondrial respiration compared to wild-type HSPCs. Exploiting the elevated Δψm of mutant HSPCs, long-chain alkyl-TPP molecules (MitoQ, d-TPP) selectively accumulate in the mitochondria and cause reduced mitochondrial respiration, mitochondrial-driven apoptosis and ablate the competitive advantage of HSPCs ex vivo and in vivo in aged recipient mice. Further, MitoQ targets elevated mitochondrial respiration and the selective advantage of human DNMT3A-knockdown HSPCs, supporting species conservation. These data suggest that mitochondrial activity is a targetable mechanism by which CH-mutant HSPCs gain a selective advantage over wild-type HSPCs

Comprehensive single-cell aging atlas of healthy mammary tissues reveals shared epigenomic and transcriptomic signatures of aging and cancer.

Aging is the greatest risk factor for breast cancer; however, how age-related cellular and molecular events impact cancer initiation is unknown. In this study, we investigated how aging rewires transcriptomic and epigenomic programs of mouse mammary glands at single-cell resolution, yielding a comprehensive resource for aging and cancer biology. Aged epithelial cells exhibit epigenetic and transcriptional changes in metabolic, pro-inflammatory and cancer-associated genes. Aged stromal cells downregulate fibroblast marker genes and upregulate markers of senescence and cancer-associated fibroblasts. Among immune cells, distinct T cell subsets (Gzmk+, memory CD4+, γδ) and M2-like macrophages expand with age. Spatial transcriptomics reveals co-localization of aged immune and epithelial cells in situ. Lastly, we found transcriptional signatures of aging mammary cells in human breast tumors, suggesting possible links between aging and cancer. Together, these data uncover that epithelial, immune and stromal cells shift in proportions and cell identity, potentially impacting cell plasticity, aged microenvironment and neoplasia risk

Impact of Age and Sex on Bladder Function and Stum Gene Expression in C57BL/6J Mice

Bladder dysfunction has an intense emotional and economic impact on both patients and their families. However, it is alarmingly under researched and misunderstood. Currently, there are no genetic targets for bladder function. This study aims to connect bladder function, Stum expression, aging, and sex in C57BL/6J mice. In a recent study, a gene atlas of the mouse bladder was generated using bulkRNAseq, single-cell RNAseq, single-nucleus RNAseq, and spatial transcriptomics (Visium) (bioRxiv 2021.09.20.461121). Among the novel detrusor smooth muscle (DSM) specific genes identified in this study was Stum. Stum has been found to be key in the function of proprioceptive neurons (Desai et.al., 2014). Similar proprioception cells are present in the bladder and play a role in sensing bladder pressure and therefore urination (Gonzalez et.al., 2014). In previous experiments, our lab has shown that when Stum was knocked-out, male bladder function decreased. This was not reflected in the female population. Further investigation of Stum expression and bladder function, as well as the location of Stum expression in the bladder could uncover novel information about bladder function across age and sex. Through void spot assay (VSA) it was found that older males had lower bladder function (expressed in higher number of voids) than their younger counterparts (p=0.05), as well as females of the same age (p=0.02). Females maintained bladder function (no significant difference between age groups). RNAscope assay of bladder tissue revealed Stum to be more prevalent in the mucosa in females (p=0.0005), and more prevalent in the muscularis tissue in males (p=0.0003). qPCR analysis that old males had lower Stum expression than females of the same age (p=0.02). The findings of this study indicate that Stum may be a sex-linked explanation for declining bladder function, and the first genetic target for bladder dysfunction therapy

Statistical Power to Detect QTL Peaks in Genetic Modifier Crosses

Monogenic diseases often display broad variation in age of onset and severity. This variation may be due to environmental or genetic factors. The genetic causes of this variation can be identified using a “modifier screen”, in which mice carrying a dominant disease-causing gene are crossed with genetically diverse mice. Genetic diversity can be introduced through recombinant inbred lines, such as the BXD or Collaborative Cross (CC), or outcross mice, such as the Diversity Outbred (DO) to create an F1 mouse population in which investigators perform quantitative trait locus (QTL) mapping. We ran QTL mapping simulations with varying heritability, effect size, and sample size in BXD, CC, DO, BXD-F1, CC-F1, and DO-F1 mouse populations to determine the power and precision to detect QTL peaks in these populations. We found that the power in the F1 populations is half that of the power in the corresponding parent populations. We also found that the support interval was larger in the F1 and that the QTL position was more variable in the F1. Modifier mapping studies require more mice than mapping studies with the parent populations, and the findings in this study can assist investigators in determining the necessary sample size

Unsupervised Machine Learning for Visual Frailty Prediction in Mice

Frailty quantifies biological aging and predicts adverse health outcomes, but manual frailty index (FI) assessments are labor-intensive and variable across scorers. We previously developed the visual frailty index (vFI), which uses machine vision to estimate FI from video-based behavioral features. Here, we developed the Unsupervised Visual Frailty Index (uvFI), which uses unsupervised behavioral segmentation of open-field videos from 851 mice (540 C57BL/6J, 311 Diversity Outbred; 1126 trials). We used an unsupervised behavior segmentation model to extracted data-driven features from behavioral syllables, transitions, and poses to predict frailty (uvFI), age (uvFRIGHT), and proportion of life lived (uvPLL). We achieved a mean absolute error (MAE) of 1.37 ± 0.122 for frailty and 14.9 ± 1.18 weeks for age, with accuracy further improved by combining supervised and unsupervised features. These results show that unsupervised behavioral features capture aging signatures, enabling scalable and reproducible frailty assessment

Gridded Visualization of Statistical Trees for High‐Dimensional Multipartite Data in Systems Genetics

In systems genetics and other multi-omics research, exploring high-dimensional relationships among molecular and physiological variables across individuals poses significant challenges. We present the Gridded Trees interface, a novel interactive visualization tool designed to facilitate the exploration of conditional inference trees, which are hierarchical models of relationships in these complex datasets. Traditional static tools struggle to reveal patterns in tree-structured data, but the Gridded Trees interface provides interactive, coordinated views, allowing users to navigate between overview and detail, filter data dynamically, and compare molecular-physiological relationships across subgroups. By combining filtering techniques, strip plots, Sankey diagrams, and small multiples, the Gridded Trees interface enhances exploratory data analysis and supports hypothesis generation. In our systems genetics research use case, this tool has revealed significant associations among microbial populations and addiction-related behavioral traits in genetically diverse mice. The Gridded Trees interface suggests broad potential for visualizing hierarchical and multipartite data across domains. A preprint of this paper as well as Supplemental Materials are available on OSF at https://osf.io/9emn5/