Biology subjects

Newburger, J. W.

Publications and source records attributed to Newburger, J. W..

3 recordsLinked to original sources

bioRxiv2026

A Large-Scale Deep Normative Modeling of Primary Sulcal Patterns Reveals Deviations in a Spectrum of Disorders

Primary sulcal patterns, the spatial arrangement of the earliest cortical folds, emerge prenatally and remain essentially stable after birth. Alterations are associated with cognitive outcomes and hold promise as clinical biomarkers. However, detecting abnormalities is challenging due to the high topological variability and complexity of normal individual sulcal patterns. Here, we introduce an unsupervised generative model that quantifies individual sulcal pattern deviations against a normative baseline learned from 10,349 typically developing subjects spanning early childhood through adulthood. We combine a generative reconstruction error with a discriminator trained on diffusion-generated pseudo-atypical graphs to leverage their complementary strengths in sensitivity and robustness. The discriminator captures subtle anomalies and is robust to demographic and data quality variations, whereas the generative model enables severity stratification and demonstrates cross-site generalizability. Across two clinical cohorts spanning subtle to overt sulcal abnormalities (congenital heart disease and polymicrogyria), our model captured global and localized condition-related deviations and linked them to neurodevelopmental outcomes. By enabling population-scale, individualized quantification of sulcal deviations, this framework provides a foundation for future clinical risk stratification.

neuroscience↗

bioRxiv2025

Foundation Model Attributions Reveal Shared Inflammatory Program Across Diseases

Determining a genes functional significance within a cellular context has long been a challenge, as absolute expression level is an unreliable indicator. We introduce SIGnature, a framework for scoring gene importance by leveraging attributions derived from single-cell RNA-sequencing (scRNA-seq) foundation models. Attribution scores reduce technical noise, emphasize regulatory genes, and facilitate cross-dataset comparison - a core challenge for scRNA-seq analyses. We developed the SIGnature package as a tool for generating and querying attributions, enabling rapid gene set searches across massive scRNA-seq atlases. We demonstrated its utility using the MS1 monocyte signature, a poorly understood gene program activated in severe COVID-19 and sepsis. Searching 400 studies revealed novel associations between the MS1 signature and multiple hyperinflammatory conditions, including Kawasaki disease. Experimental validation confirmed Kawasaki disease patient serum induces the MS1 phenotype. These findings highlight that SIGnature can uncover shared mechanisms across conditions, demonstrating its power for large-scale signature scoring and cross-disease analysis.

bioinformatics↗

bioRxiv2019

Early post-zygotic mutations contribute to congenital heart disease

BackgroundThe contribution of somatic mosaicism, or genetic mutations arising after oocyte fertilization, to congenital heart disease (CHD) is not well understood. Further, the relationship between mosaicism in blood and cardiovascular tissue has not been determined.\n\nResultsWe developed a computational method, Expectation-Maximization-based detection of Mosaicism (EM-mosaic), to analyze mosaicism in exome sequences of 2530 CHD proband-parent trios. EM-mosaic detected 326 mosaic mutations in blood and/or cardiac tissue DNA. Of the 309 detected in blood DNA, 85/97 (88%) tested were independently confirmed, while 7/17 (41%) candidates of 17 detected in cardiac tissue were confirmed. MosaicHunter detected an additional 64 mosaics, of which 23/46 (50%) among 58 candidates from blood and 4/6 (67%) of 6 candidates from cardiac tissue confirmed. Twenty-five mosaic variants altered CHD-risk genes, affecting 1% of our cohort. Of these 25, 22/22 candidates tested were confirmed. Variants predicted as damaging had higher variant allele fraction than benign variants, suggesting a role in CHD. The frequency of mosaic variants above 10% mosaicism was 0.13/person in blood and 0.14/person in cardiac tissue. Analysis of 66 individuals with matched cardiac tissue available revealed both tissue-specific and shared mosaicism, with shared mosaics generally having higher allele fraction.\n\nConclusionsWe estimate that ~1% of CHD probands have a mosaic variant detectable in blood that could contribute to cardiac malformations, particularly those damaging variants expressed at higher allele fraction compared to benign variants. Although blood is a readily-available DNA source, cardiac tissues analyzed contributed ~5% of somatic mosaic variants identified, indicating the value of tissue mosaicism analyses.

bioinformatics↗