Biology subjects

Harper, J.

Publications and source records attributed to Harper, J..

9 recordsLinked to original sources

bioRxiv2026

Tabular foundation model predicts alternative lengthening of telomeres (ALT) and identifies SMARCAL1 as a target in ALT-driven cancers

Alternative lengthening of telomeres (ALT) is a telomerase-independent pathway used by aggressive cancers to maintain their replicative immortality. Because ALT is absent from normal human cells, it is an appealing target for cancer therapy, but the lack of ability to determine ALT status at scale has hindered discovery. Here, we developed ALTitude, a machine learning method from a tabular foundation model that infers ALT from cell line whole genome sequencing data, without need for paired germline analysis. We deployed ALTitude across the DepMap, doubling the number of known ALT+ cancer models. Systematic integration of ALTitude with CRISPR-Cas9 screens yielded the selective dependency on SMARCAL1 in ALT+ cell lines, where we show it stabilizes the ALT phenotype. Acute depletion of SMARCAL1 leads to G2/M arrest, mitotic catastrophe, and cell death. These data provide a valuable resource for studying ALT-related genomic features and present SMARCAL1 as a therapeutic target for ALT+ malignancies.

cancer biology↗

bioRxiv2025

In vivo reprogramming of cytotoxic effector CD8+ T cells via fractalkine-conjugated mRNA-LNP

Selective in vivo reprogramming of cytotoxic effector CD8+ T (Teff) cells holds tremendous promise as a therapeutic tool but has not yet been accomplished. Here, we demonstrate that fractalkine-conjugated mRNA lipid nanoparticles (mRNA-LNP) can specifically target and deliver mRNA to CX3CR1+ Teff cells in vitro and in vivo. In mice, fractalkine-conjugated LNP target up to 90% of blood and splenic Teff cells, and delivery of IL-2-encoding mRNA to Teff cells enables robust exogenous IL-2 secretion. In rhesus macaques, fractalkine-conjugated mRNA-LNP target up to [~]100% of peripheral blood Teff cells and delivery of CD62L-mRNA enables transient CD62L expression. Collectively, these data demonstrate the potential of natural receptor ligand-based targeting of mRNA-LNP for effective and efficient transient in vivo modification of Teff cells.

immunology↗

bioRxiv2025

Bimodality in pan-cancer proteomics reveals new opportunities for biomarker discovery

Bimodal protein expression, characterized by the distribution of protein expression with two modes, is linked to phenotypic variation across various biological systems. Whereas previous studies focused on RNA expression data, we developed a bimodality model tailored for proteomics to enhance the identification of cancer-associated biomarkers and targets, facilitating precision oncology. We analyzed proteomics data from various cancer types and identified 2401 tumor-associated bimodal proteins. These proteins were evaluated for pathway enrichment, revealing significant associations with critical cancer pathways, such as metabolism of non-essential amino acids, interaction between the extracellular matrix and its receptors on the cell surface, and central carbon metabolism in cancer. Utilizing an AI-enhanced knowledge graph, we further delineated common patterns among pan-cancer tumor-associated bimodal proteins. A case study on the bimodal expression of TROP2 in colon adenocarcinoma highlighted upregulation of MYC and WNT/{beta}-catenin signaling pathways and down-regulation of inflammatory and interferon-related pathways in the TROP2-high group. The biological difference between TROP2-high and TROP2-low groups underscored its significance in determining cancer heterogeneity and differences in cancer vulnerability, which can inform treatment decisions. Our findings show the value of proteomics in uncovering novel biomarkers and advancing precision medicine, setting a precedent for further multi-omics integration and clinical validation.

bioinformatics↗

bioRxiv2024

Rapid dynamics of electrophysiological connectome states are heritable

Time-varying changes in whole-brain connectivity patterns, or connectome state dynamics, are a prominent feature of brain activity with broad functional implications. While infra-slow (<0.1Hz) connectome dynamics have been extensively studied with fMRI, rapid dynamics highly relevant for cognition are poorly understood. Here, we asked whether rapid electrophysiological connectome dynamics constitute subject-specific brain traits and to what extent they are under genetic influence. Using source-localized EEG connectomes during resting-state (N=928, 473 females), we quantified heritability of multivariate (multi-state) features describing temporal or spatial characteristics of connectome dynamics. States switched rapidly every [~]60-500ms. Temporal features were heritable, particularly, Fractional Occupancy (in theta, alpha, beta, and gamma bands) and Transition Probability (in theta, alpha, and gamma bands), representing the duration spent in each state and the frequency of state switches, respectively. Genetic effects explained a substantial proportion of phenotypic variance of these features: Fractional Occupancy in beta (44.3%) and gamma (39.8%) bands and Transition Probability in theta (38.4%), alpha (63.3%), beta (22.6%), and gamma (40%) bands. However, we found no evidence for heritability of spatial features, specifically states Modularity and connectivity pattern. We conclude that genetic effects strongly shape individuals connectome dynamics at rapid timescales, specifically states overall occurrence and sequencing.

neuroscience↗

bioRxiv2024

Cognitive abilities are associated with rapid dynamics of electrophysiological connectome states

Time-varying changes in whole-brain connectivity patterns, or connectome state dynamics, hold significant implications for cognition. However, connectome dynamics at fast (> 1Hz) timescales highly relevant to cognition are poorly understood due to the dominance of inherently slow fMRI in connectome studies. Here, we investigated the behavioral significance of rapid electrophysiological connectome dynamics using source-localized EEG connectomes during resting-state (N=926, 473 females). We focused on dynamic connectome features pertinent to individual differences, specifically those with established heritability: Fractional Occupancy (i.e., the overall duration spent in each recurrent connectome state) in beta and gamma bands, and Transition Probability (i.e., the frequency of state switches) in theta, alpha, beta, and gamma bands. Canonical correlation analysis found a significant relationship between the heritable phenotypes of sub-second connectome dynamics and cognition. Specifically, principal components of Transition Probabilities in alpha (followed by theta and gamma bands) and a cognitive factor representing visuospatial processing (followed by verbal and auditory working memory) most notably contributed to the relationship. We conclude that the specific order in which rapid connectome states are sequenced shapes individuals cognitive abilities and traits. Such sub-second connectome dynamics may inform about behavioral function and dysfunction and serve as endophenotypes for cognitive abilities.

neuroscience↗

bioRxiv2023

CRISPR/Cas9 gene editing to generate Drosophila LexA lines in secondary school classes

Genome editing in vivo with CRISPR/Cas9 generates powerful tools to study gene regulation and function. We developed CRISPR-based methods that permitted secondary school student scientists to convert Drosophila GAL4 lines to LexA lines. Our novel curricula implement a new donor strain optimizing Homology-assisted CRISPR knock-in (HACK) that simplifies screening using light microscopy. Successful curricula adoption by a consortium of schools led to the generation and characterization of 16 novel LexA lines. This includes extensive comparative tissue expression analysis between the parental Gal4 and derived LexA lines. From this collaboration, we established a workflow to systematically generate LexA lines from frequently-used GAL4 lines. Modular courses developed from this effort can be tailored to specific secondary school scheduling needs, and serve as a template for science educators to innovate courses and instructional goals. Our unique collaborations highlight that resources and expertise harnessed by university-based research laboratories can transform experiential science instruction in secondary schools while addressing research needs for the community of science.

genetics↗

bioRxiv2023

An unsupervised learning approach uncovers divergent mesenchymal-like gene expression programs across human neuroblastoma tumors, preclinical models, and chemotherapy-exposed tumors.

Neuroblastoma is a common pediatric cancer, where preclinical studies suggest that a mesenchymal-like gene expression program contributes to chemotherapy resistance. However, clinical outcomes remain poor, implying we need a better understanding of the relationship between patient tumor heterogeneity and preclinical models. Here, we generated single-cell RNA-seq maps of neuroblastoma cell lines, patient-derived xenograft models (PDX), and a genetically engineered mouse model (GEMM). We developed an unsupervised machine learning approach ( automatic consensus nonnegative matrix factorization (acNMF)) to compare the gene expression programs found in preclinical models to a large cohort of patient tumors. We confirmed a weakly expressed, mesenchymal-like program in otherwise adrenergic cancer cells in some pre-treated high-risk patient tumors, but this appears distinct from the presumptive drug-resistance mesenchymal programs evident in cell lines. Surprisingly however, this weak-mesenchymal-like program was maintained in PDX and could be chemotherapy-induced in our GEMM after only 24 hours, suggesting an uncharacterized therapy-escape mechanism. Collectively, our findings improve the understanding of how neuroblastoma patient tumor heterogeneity is reflected in preclinical models, provides a comprehensive integrated resource, and a generalizable set of computational methodologies for the joint analysis of clinical and pre-clinical single-cell RNA-seq datasets.

cancer biology↗

bioRxiv2022

Chronic activation of Toll-like receptor 2 induces an ichthyotic skin phenotype

Ichthyosis defines a group of chronic conditions that manifest phenotypically as a thick layer of fish-like scales in response to disorders of cornification and often affects the entire skin. While the gene mutations that lead to ichthyosis are well documented, the actual signalling mechanisms that lead to scaling are poorly characterised, however recent publications suggest that there are common mechanisms active in ichthyotic tissue, and in analogous models of ichthyosis. Combining gene expression analysis of gene-specific shRNA knockdowns of more severe autosomal recessive congenital ichthyoses (ARCI) and proteomic analysis of skin scale from ARCI patients, we identified a common activation of the Toll-like receptor (TLR) 2 pathway. Exogenous activation of TLR2 led to increased expression of important cornified envelope genes and in organotypic culture caused hyperkeratosis. Conversely blockade of TLR2 signalling in ichthyosis patient keratinocytes and our shRNA models reduced the expression of keratin 1, a structural protein overexpressed in ichthyosis scale. A time-course of Tlr2 activation in rat epidermal keratinocytes revealed that although there was rapid initial activation of innate immune pathways, this was rapidly superseded by widespread up-regulation of epidermal differentiation related proteins. Both NF{kappa}{beta} phosphorylation and the Gata3 up-regulation was associated with this switch and Gata3 overexpression was sufficient to increase Keratin 1 expression. Taken together, these data define a dual role for Tlr2 during epidermal barrier repair, that may be a useful therapeutic modality in treating diseases of epidermal barrier dysfunction.

cell biology↗

bioRxiv2019

Treatment utilization and medical problems in a community sample of adult women with anorexia nervosa

Anorexia nervosa has a prolonged course of illness, making both defining recovery and determining optimal outpatient treatments difficult. Here, we report the types of treatments utilized in a naturalistic sample of adult women with anorexia nervosa in Texas. Participants were recruited from earlier studies of women with anorexia nervosa (AN-C, n = 28) and in weight recovery following anorexia nervosa (AN-WR, n = 18). Participants provided information about both their illness and treatments during their most severe period (severe period) as well as during the two to six years following original assessments (follow-up period). New follow-up groups were defined based on current clinical status (continued eating disorder, AN-CC; newly in recovery, AN-CR; sustained weight-recovery, AN-WS), and clinical utilization was compared across groups. There were no differences in groups related to symptoms or treatments utilized during the severe-period. During the follow-up period, intensive outpatient programs were utilized significantly more by the AN-CC group than the other groups, and dietitians were seen significantly less by the AN-WS group. Medical complications related to the ED were significantly more common in the AN-CC group. All groups maintained similar levels of contact with outpatient psychiatrists, therapists, and primary care physicians.

epidemiology↗