Faculty Bibliography

The extracellular matrix (ECM) regulates innate immunity, but its role in HSV-1 keratitis is unclear. Here, we identify the ECM proteoglycans lumican (Lum) and biglycan (Bgn) as key regulators of early antiviral defense in the cornea. Lum ^-/- and Bgn

The transcription factor early B-cell factor 1 (EBF1) plays critical developmental roles in numerous organ systems, including B-cells, kidney, bone, and heart. During cardiogenesis, cardiomyocyte expression of EBF1 is reportedly undetectable and its effects on myocyte development and proliferation are thought to reflect a non-cell autonomous mechanism, acting via intercellular communication from EBF1 expressing non-myocyte cells. Here, using single-cell transcriptional profiling, we confirm the absence of Ebf1 transcripts in cardiomyocytes. Furthermore, employing computational receptor-ligand interaction analysis of dissociated cells from wildtype and EBF1-deficient hearts, we show that loss of function of this pioneer transcription factor enhances fibroblast to myocyte signaling via the collagen-integrin pathway. Finally, we generated fibroblast-specific EBF1 knockout mice using a PDGFRα-Cre transgenic driver, and found a nearly identical phenotype to that of the generalized knockout, with runting, premature death, an increase in left ventricular relative wall thickness and cardiomyocyte hyperplasia. These findings provide further mechanistic insight into the non-cell autonomous mechanism of action of EBF1 in cardiac growth and development.

Myelofibrosis (MF) is a chronic, progressive myeloproliferative neoplasm characterized by bone marrow fibrosis, ineffective blood cell production, and neoplastic extramedullary hematopoiesis (EMH) occurring primarily within the spleen. To explore the molecular mechanisms underlying splenic EMH, we performed single-cell transcriptional and chromatin profiling of cells from MF spleens that had been surgically removed. We demonstrate significant expansion of hematopoietic stem and progenitor cells, coupled with aberrant differentiation toward the erythroid and megakaryocytic lineages, associated with a significant enrichment of inflammatory pathways with enhanced NF-κB signaling and IFN responses, as well as dysregulation of the inferred function of differentiation-defining transcription factors. Finally, we report a significant remodeling of the immune microenvironment in MF spleens, characterized by emergence of dysfunctional T cell subsets and inflammatory memory B cells, suggesting the concomitant establishment of a pro-inflammatory and immune-tolerant tumor microenvironment within the spleen that influences hematopoietic cell differentiation and impairs tumor immune surveillance.

Children with Down syndrome (DS) frequently develop transient abnormal myelopoiesis that can evolve to the myeloid leukemia of DS (ML-DS). TAM spontaneously resolves in most cases but progresses to ML-DS with additional mutations, most commonly in the cohesin complex. However, the mechanisms by which these alterations promote leukemia are unknown. We leveraged the RAD21-mutant CMY cell line and RAD21-corrected CMY isogenic clones, as well as patient data, to investigate the effect of cohesin mutations during leukemia progression. RNA-sequencing revealed that HLA-class II genes were significantly down-regulated with cohesin mutations. Furthermore, HLA-DR was found to be lower in ML-DS relative to TAM, and these decreased levels were associated with increased risk of leukemia progression. Multi-omic analyses revealed that haploinsufficiency of RAD21 altered chromatin accessibility and impaired the occupancy of GATA1s and CIITA, the master regulator of HLA-class II gene expression. Chromatin binding of CIITA was increased with RAD21 correction, providing a mechanism by which restoration of cohesin improves HLA-class II expression. Finally, decreased levels of RAD21 or STAG2 expression in other subtypes of AML also exhibited reduced expression of HLA-class II genes. Thus, cohesin may contribute to leukemia by altering HLA-class II gene expression.

Pancreatic ductal adenocarcinoma (PDAC) is a complex disease characterized by high levels of cellular heterogeneity and pronounced microenvironmental remodelling. Dynamic changes during its initiation and progression contribute to resistance to conventional therapies. Building upon key molecular catalogues established by bulk and single-cell profiling studies that have advanced our understanding of PDAC biology, recent advances in spatial biology have provided much-needed insights by elucidating regionally compartmentalized transcriptomic and proteomic programmes within the PDAC microenvironment. In parallel, emerging computational frameworks in digital pathology and artificial intelligence have advanced the field into a high-dimensional, quantitative discipline, particularly for classifying molecular and clinical features from histopathology images. Despite these advancements, integration of these two modalities remains a major challenge. Here, we summarize the convergence of molecular features identified through spatially resolved profiling in PDAC and its precursor lesions, as well as current developments in AI-powered pathology in cancer research. We further propose a multi-modal integration framework that maps molecular states onto morphological and architectural phenotypes, offering a roadmap for spatially informed patient stratification beyond descriptive tissue characterization. We posit that the path forward relies on disciplined cross-scale integration of spatial, histological, and clinical data to ensure meaningful translation into clinical practice.

The diagnosis of myelodysplastic neoplasms (MDS) requires examination of the bone marrow for morphologic evidence of dysplasia. We sought to determine whether a self-supervised learning (SSL) artificial intelligence-based image analysis approach can be used to reliably distinguish MDS from its clinically relevant mimics using bone marrow biopsies (BMBx). The whole-slide images (WSIs) of hematoxylin and eosin (H&E)- and reticulin-stained BMBx sections from 243 unique patients (89 MDS, 55 non-MDS cytopenic controls [NMCCs], and 99 negative control [NC] cases) were partitioned into image tiles for analysis. These image tiles were then processed using the Barlow Twins SSL model to identify histomorphologic phenotype clusters (HPCs). Review of the HPCs revealed the clusters enriched in MDS cases captured known histopathologic features of the disease, including hypercellularity (characterized by enrichment in hypercellular image tiles), dysplastic and loosely clustered megakaryocytes, increased immature hematopoietic cells, increased vascularity, fibrosis, and cell streaming patterns. For external validation, 95 MDS BMBx WSIs from an independent institution were analyzed using the trained model. The model demonstrated consistent HPC enrichment patterns, supporting its robustness and generalizability. The trained ensemble model using H&E- and reticulin-stained slides distinguished MDS from NCs with an area under the curve (AUC) of 0.82, and from age-matched NMCCs with an AUC of 0.80. These findings demonstrate the potential of SSL approaches to capture diagnostically relevant morphologic patterns and to improve the reproducibility of MDS diagnosis.

OBJECTIVE:The relationship between physical activity and sleep with inflammatory arthritis (IA) is understudied, and existing research has relied largely on self-report or short-term assessments. The NIH All of Us database provides long-term accelerometry data, enabling more precise estimation of the association between lifestyle behaviors and IA. METHODS:Participants from the All of Us database who shared electronic health record and Fitbit data were included. Daily activity and sleep metrics were compared between individuals with and without IA using multiple linear regression. Cox proportional hazards regression was used to examine the association of activity and sleep patterns with incident IA in a 10-year follow-up period. RESULTS:A total of 23,855 participants were included, 200 of whom had IA. Participants with IA took fewer daily steps (P < 0.001) and had greater sleep variability (P < 0.001) compared to those without IA. 122 individuals had incident IA. Every 1,000 extra daily steps were associated with a 7% lower risk of IA (hazard ratio [HR] 0.93 [95% confidence interval (CI) 0.87-0.99], P = 0.02). Compared to those who walked <5,000 steps daily, those who walked 5,000 to 10,000 steps and 10,000+ steps had a 41% (HR 0.59 [95% CI 0.39-0.91], P = 0.02) and 50% (HR 0.50 [95% CI 0.29-0.86], P = 0.01) reduction in risk of IA. CONCLUSION/CONCLUSIONS:Individuals with IA had reduced step counts and more sleep variability compared to those without IA, highlighting how physical activity and sleep contribute to IA. Additionally, increased daily step count was associated with decreased risk of incident IA, suggesting a possible research intervention for those at high risk of IA.