Biology subjects

Hartman, M.

Publications and source records attributed to Hartman, M..

9 recordsLinked to original sources

bioRxiv2026

Cell line resources for the study of neurofibromin: functions, phenotypes, and drug discovery/development

Our labs have been studying neurofibromin function and phenotype for over a decade with the intent of generating targeted therapeutics for Neurofibromatosis type 1 (NF1). In the process, we have generated numerous human cell lines containing variants within the NF1 gene. Herein, we present data characterizing these cell lines and make them publicly available for use by researchers both within and outside the NF1 community. We describe lines that contain both well-characterized patient-specific variants either at their endogenous locus or as exogenous cDNAs, as well as variants of uncertain significance (VUS), engineered as heterozygous, homozygous, and compound heterozygous variants. Methods to generate each line and subsequent validation steps are detailed including targeted sequencing, Western blot analysis for neurofibromin expression and ERK activation. The utility of each line is dependent on the variant of interest, the parental cell line, and the mechanism of action relevant to possible therapeutic targeting.

genetics↗

bioRxiv2026

Landscape of reversion alterations in homologous recombination genes reveals evolutionary constraints

Reversion mutations (REVs) restore homologous recombination repair (HRR) and confer resistance to PARP inhibitors (PARPi) in HR-deficient cancers. Yet, their prevalence, mechanisms, and biological constraints remain undefined. We analyzed genomic profiling of 609,464 tissue and liquid biopsy samples across multiple cancer types to delineate the pan-cancer landscape of REVs. REVs were identified in eight HRR genes, most frequently BRCA2 and BRCA1 and notably never in ATM or CHEK2. REVs exclusively impacted truncating pathogenic variants, predominantly through large in-frame and exon-level deletions, associated with repetitive sequences. Conserved functional domains are relatively depleted of REVs. Structural modeling and functional studies support that exon-level deletions preserve critical domain architecture and confer PARPi resistance. The study establishes HRR reversion as a structurally permissive, yet evolutionarily constrained, resistance mechanism with implications on response, monitoring, and therapeutic strategy. One Sentence SummaryHomologous recombination reversion evolves through structurally tolerated, microhomology-driven alterations that restore DNA repair under therapeutic selection across cancers.

cancer biology↗

bioRxiv2025

Metabolic immunity to infection is driven by mitochondrial one-carbon metabolism

As large consumers of cellular metabolites, mitochondria are positioned to compete with invading microbes for the nutrients they require to grow. Yet, little is known of whether cells weaponize mitochondrial metabolism during infection. We found that the transcription factor ATF4 activated a mitochondrial metabolic defense based on the essential B vitamin folate. During infection with the human parasite Toxoplasma gondii, ATF4 increased mitochondrial DNA (mtDNA) levels by driving the one-carbon (1C) metabolism processes that occur in mitochondria and use folate. The activation of ATF4 depended on host detection of parasite effector proteins, and restricted parasite growth by limiting its access to folate(s) required for dTMP synthesis. Impairing host mitochondrial 1C metabolism downstream of ATF4 promoted parasite growth, while forcing host dependence on mitochondrial 1C metabolism had the opposite effect. ATF4 activation also promoted a host-protective response in a mouse model of Toxoplasma infection. Thus, ATF4 rewires mitochondrial metabolism to activate a folate-based metabolic immunity against Toxoplasma. Our work paves the way for future studies exploring noncanonical defense strategies mediated by mitochondria and the role of folate metabolism during infectious disease. One-Sentence SummaryATF4 rewires mitochondrial metabolism during infection to drive a host-protective response based on folate competition.

cell biology↗

bioRxiv2025

Polygenic Risk Scores Across Genomic Platforms for Reliable Breast Cancer Risk Stratification

PurposeWe evaluated differences in a 313-variant breast cancer polygenic risk score (PRS313) across genomic platforms and their impact on risk stratification. MethodsWe compared PRS313 derived from genotyping arrays (Global Screening Array [GSA], OncoArray-500K [OncoArray], Global Diversity Array [GDA], custom Axiom_PrecipV1 array [ThermoFisher]) and low-coverage genome sequencing (lc-WGS) in 2 cell lines and 92 individuals. Probes were designed for all variants on ThermoFisher (success rate: 259/313). Sanger sequencing was performed to profile indels. Concordance of high-risk classification (PRSscore>0.6) across platforms was assessed using Kappa statistics. ResultsPRS313-lc-WGS was identical in the 4 cell line repeats. In saliva samples, indel concordance with Sanger sequencing varied widely (Kappa: 0.007-1.000). PRS313-ThermoFisher was predictable from other platforms using linear models, despite systematic differences. Greater agreement was observed between arrays with high imputation overlap (e.g., GDA[~]GSA slope=0.986). Pre-calibration agreement in high-risk classification was moderate (Fleiss Kappa=0.552) and improved post-calibration (Kappa=0.650). Arrays with similar designs showed higher pre-calibration agreement (Kappa=0.745). Calibration narrowed high-risk proportions from 4-45% to 15-21% -28% were high-risk by any platform, while 8% were high-risk across all five. ConclusionPlatform-specific biases affect PRS interpretation. Calibration enhances consistency in identifying high-risk individuals. STATEMENT OF SIGNIFICANCEThis study compares the performance of a validated 313-variant breast cancer polygenic risk score across platforms, revealing systematic biases in risk stratification and raising concerns about including inconsistent indels in the model.

genetics↗

bioRxiv2024

Clarifying misconceptions of biomolecular condensate formation

Biomolecular condensates are liquid-like phase separations of disordered proteins and other molecules within cells. These membrane-less organelles are essential in critical cellular processes and are aberrant in myriad disease. Yet, the thermodynamics of condensate formation are not entirely understood, thus the mechanisms of condensate function remain elusive. Two assumptions about biomolecular condensates are that they form through liquid-liquid phase separation (LLPS) and require multivalent interactions. Here, we show results and propose thermodynamic frameworks that suggest these assumptions are not always correct. We demonstrate that condensation does not follow canonical LLPS and that liquid-like phase separation can arise from monovalent interactions. These results clarify the mechanism of condensate thermodynamics to reframe our understanding of condensates in cellular function and disease.

biophysics↗

bioRxiv2024

Informing grassland ecosystem modeling with in-situ and remote sensing observations

Historical grassland aboveground plant productivity (ANPP) was simulated by the DayCent-UV ecosystem model across the midwestern and western conterminous United States. For this study we developed a novel method for informing the DayCent-UV model and validating its plant productivity estimates for grasslands of the midwestern and western conterminous USA by utilizing a wide range of data sources at multiple scales, from field observations to remotely sensed satellite data. The model phenology was informed by the MODIS MCD12Q2 product, which showed good agreement with in-situ observations of growing season commencement and duration across different grassland ecosystems, and with observed historical trends. Model results from each simulated grid cell were compared to a remote-sensing ANPP modified version offered by the Analysis Rangeland Platform (RAP). This modified RAP ANPP calculation incorporated total annual precipitation, instead of mean annual temperature, as the control factor for the fraction of carbon allocated to roots. Strong temporal correlations were obtained between RAP and DayCent-UV, especially across the Great Plains. Good agreement was also found when the model results were compared with ANPP observations at the site and county level. The data produced by this study will serve as a valuable resource for validation or calibration of various models that aim to capture accurate productivity dynamics across diverse grassland ecosystems. Plain Language SummaryThis research used a computer model called DayCent-UV to simulate daily grassland growth across the central and western regions of the contiguous United States. To improve the agreement between the simulations and real-world conditions, we incorporated data from local field measurements and satellite imagery. This data helped determine the start and end dates of the growing season at each location. The simulated annual growth showed good agreement with satellite estimates from the Rangeland Analysis Platform (RAP), another computer application that monitors rangeland vegetation, and with local observations based on harvesting and weighing vegetation, particularly across the Great Plains. These results are valuable for validating and refining other computer models that aim to accurately simulate plant growth in grassland ecosystems; the predictions of these models are crucial for understanding the balance of carbon between plants, soils, and the atmosphere as the climate changes. Key PointsO_LIThe DayCent-UV model was used to simulate historical aboveground net primary productivity (ANPP) for different grassland ecosystems across the midwestern and western United States. C_LIO_LIMODIS MCD12Q2 was used to provide the phenology for the model. C_LIO_LIThe Rangeland Analysis Platform (RAP) fraction of biomass production allocated to roots calculation was modified, resulting in a stronger agreement between its ANPP estimates and those from the DayCent-UV model. C_LIO_LISite- and county-level ANPP observations were used to validate the model. C_LI

ecology↗

bioRxiv2022

16S rRNA sequencing for profiling microbial communities in idiopathic granulomatous mastitis

BackgroundIdiopathic granulomatous mastitis (IGM) is a rare, chronic, and benign inflammatory breast disease with ambiguous aetiology. Contrastingly, lactational mastitis (LM) is commonly diagnosed in breastfeeding women. To investigate IGM aetiology, we profiled the microbial flora of pus and skin in IGM and LM patients. MethodsIGM patients were recruited from 2018 to 2020; LM patients were recruited as controls. 16S rRNA sequencing libraries were constructed from 16S rRNA gene amplified from total DNA extracted from pus and skin swabs. Metagenomic analysis was conducted using modified microbiome abundance analysis-suite customised R-resource for paired pus and skin samples. Microbiome multivariable association analyses were performed using linear models. Results21 IGM and 3 LM paired pus and skin samples underwent metagenomic analysis. Bray-Curtis ecological dissimilarity distance showed dissimilarity across four sample types (IGM-pus, IGM-skin, LM-pus, LM-skin; PERMANOVA, p<0.001). No characteristic dominant genus was observed across IGM samples. IGM pus samples were more diverse than corresponding IGM skin samples (Shannon and Simpson index; Wilcoxon paired signed-rank tests, p=0.022 and p=0.07). Corynebacterium kroppenstedtii, reportedly associated with IGM in literature, was higher in IGM pus samples than paired skin sample (Wilcoxon, p=0.022). Three other species and nineteen genera were statistically significant in paired IGM pus-skin comparison after antibiotic treatment adjustment, and multiple comparisons correction. ConclusionsMicrobial profiles are unique between IGM and LM patients. Inter-patient variability and polymicrobial IGM pus samples cannot implicate specific genus or species as an infectious cause for IGM.

microbiology↗

bioRxiv2020

Pleiotropy-guided transcriptome imputation from normal and tumor tissues identifies new candidate susceptibility genes for breast and ovarian cancer

Familial, genome-wide association (GWAS), and sequencing studies and genetic correlation analyses have progressively unraveled the shared or pleiotropic germline genetics of breast and ovarian cancer. In this study, we aimed to leverage this shared germline genetics to improve the power of transcriptome-wide association studies (TWAS) to identify candidate breast cancer and ovarian cancer susceptibility genes. We built gene expression prediction models using the PrediXcan method in 681 breast and 295 ovarian tumors from The Cancer Genome Atlas and 211 breast and 99 ovarian normal tissue samples from the Genotype-Tissue Expression project and integrated these with GWAS meta-analysis data from the Breast Cancer Association Consortium (122,977 cases/105,974 controls) and the Ovarian Cancer Association Consortium (22,406 cases/40,941 controls). The integration was achieved through novel application of a pleiotropy-guided conditional/conjunction false discovery rate approach for the first time in the setting of a TWAS. This identified 14 new candidate breast cancer susceptibility genes spanning 11 genomic regions and 8 new candidate ovarian cancer susceptibility genes spanning 5 genomic regions at conjunction FDR < 0.05 that were > 1 Mb away from known breast and/or ovarian cancer susceptibility loci. We also identified 38 candidate breast cancer susceptibility genes and 17 candidate ovarian cancer susceptibility genes at conjunction FDR < 0.05 at known breast and/or ovarian susceptibility loci. Overlaying candidate causal risk variants identified by GWAS fine mapping onto expression prediction models for genes at known loci suggested that the association for 55% of these genes was driven by the underlying GWAS signal. SignificanceThe 22 new genes identified by our cross-cancer analysis represent promising candidates that further elucidate the role of the transcriptome in mediating germline breast and ovarian cancer risk.

genetics↗

bioRxiv2018

The association between weight at birth and breast cancer risk revisited using Mendelian randomisation

Observational studies suggest that higher birth weight (BW) is associated with increased risk of breast cancer in adult life. We conducted a two-sample Mendelian randomisation (MR) study to assess whether this association is causal. Sixty independent single nucleotide polymorphisms (SNPs) known to be associated at P < 5 x 10-8 with BW were used to construct (1) a 41-SNP instrumental variable (IV) for univariable MR after removing SNPs with pleiotropic associations with other breast cancer risk factors and (2) a 49-SNP IV for multivariable MR after filtering SNPs for data availability. BW predicted by the 41-SNP IV was not associated with overall breast cancer risk in inverse-variance weighted (IVW) univariable MR analysis of genetic association data from 122,977 breast cancer cases and 105,974 controls (odds ratio = 0.86 per 500 g higher BW; 95% confidence interval: 0.73--1.01). Sensitivity analyses using four alternative methods and three alternative IVs, including an IV with 59 of the 60 BW-associated SNPs, yielded similar results. Multivariable MR adjusting for the effects of the 49-SNP IV on birth length, adult height, adult body mass index, age at menarche, and age at menopause using IVW and MR-Egger methods provided estimates consistent with univariable analyses. Results were also similar when all analyses were repeated after restricting to estrogen receptor-positive or -negative breast cancer cases. Point estimates of the odds ratios from most analyses performed indicated an inverse relationship between genetically-predicted BW and breast cancer. Thus, there is little evidence from MR to suggest that the previously observed association between higher BW and increased risk of breast cancer in adult life is causal.

epidemiology↗