Biology subjects

Rosebrock, D.

Publications and source records attributed to Rosebrock, D..

10 recordsLinked to original sources

bioRxiv2026

A brain immune phenotype independent of Alzheimer's Disease associates with amyloid susceptibility and systemic inflammation

Transcriptomic and proteomic studies have consistently demonstrated substantial molecular heterogeneity across patients with Alzheimer's disease (AD) and other neurodegenerative disorders, leading to the proposal of multiple disease subtypes. However, almost all previous studies have focused on heterogeneity within diseased cohorts, whereas comparatively little attention has been paid to the molecular diversity of neurologically healthy individuals. In the present study, we demonstrate that one of the most prominent transcriptomic programs characterized by a strong immune component previously interpreted as an AD-specific immune subtype is in fact a brain phenotype that exists independently of AD yet interacts with AD susceptibility and is selectively associated with amyloid pathology. The transcriptional effect of the immune phenotype exceeds that of AD pathology-induced changes and is accompanied by alterations of brain cellular compositions and a distinct systemic inflammation. Our findings suggest that part of the molecular heterogeneity observed in neurological diseases might arise from pre-existing brain phenotypes rather than representing disease-specific subtypes. These insights support a precision medicine approach for Alzheimer's disease, whereby individuals with an immune phenotype of the brain could be prospectively identified and monitored, enabling the application of targeted preventive strategies before substantial neuropathology and cognitive decline emerge.

neuroscience↗

bioRxiv2026

Multimodal evidence for impaired glymphatic brain clearance as a mediator of "inflammaging" in schizophrenia - integrating insights from in vivo neuroimaging, omics, and post-mortem single nucleus-RNA-Seq data

Persistent cognitive impairment and reduced life expectancy in schizophrenia may arise from inflammation-driven accelerated aging ("inflammaging"), yet the underlying mechanisms remain unclear. Impaired glymphatic clearance, dependent on polarized aquaporin-4 (AQP4) localization at astrocytic endfeet, offers a plausible mechanistic link between inflammation and brain aging. Here, we integrate in vivo neuroimaging with post-mortem single-nucleus transcriptomics from distinct patient cohorts to demonstrate glymphatic dysfunction in schizophrenia and define its molecular basis. We identify reduced astrocytic expression of components of the dystrophin-associated protein complex (SNTA1, DAG1) required for AQP4 polarization. Both in vivo neuroimaging and post-mortem gene expression signatures of glymphatic dysfunction are restricted to an inflammatory subgroup characterized by peripheral immune activation and pro-inflammatory glial transcriptional profiles. Moreover, imaging signatures of glymphatic impairment are associated with schizophrenia polygenic risk scores enriched for immune - and oxidative stress - related aging pathways as well as cognitive dysfunction. At the cellular level, glymphatic dysfunction coincides with loss of homeostatic programs in microglia and astrocytes, increased inflammatory signaling, and endothelial signatures of blood-brain barrier disruption. Across modalities, glymphatic impairment is linked to accelerated aging, including increased brain and epigenetic age, and aging-related gene expression signatures.

neuroscience↗

bioRxiv2026

Characterization and pharmacological modulation of Alzheimers disease-associated human microglial states

Microglia are central mediators of Alzheimers disease (AD) pathogenesis, yet the mechanisms driving disease-associated microglial states and their therapeutic modulation remain poorly understood. Here, we integrated single-nucleus transcriptomic datasets across the AD spectrum and identified disease- and lipid-associated microglia (DLaM) as a major AD-enriched population linked to genetic risk, neuropathology and cognitive decline. To model this state experimentally, we screened AD-relevant perturbations in human induced pluripotent stem cell (hiPSC)-derived microglia and found that ferric ammonium citrate (FAC) reproducibly induced a DLaM-like state characterized by lipid accumulation, lysosomal dysfunction and impaired A{beta} phagocytosis. Using a transcriptomics-based state-reversion screen, we identified LY2090314 as a potent modulator that restored microglial function and induced a distinct lysosomal-metabolic state. These findings establish a framework for transcriptomic disease-state-guided therapeutic discovery in AD.

neuroscience↗

bioRxiv2022

CAbiNet: Joint visualization of cells and genes based on a gene-cell graph

In routine single-cell RNA-sequencing (scRNA-seq) analysis workflows, cells are commonly visualized in 2D to show the patterns in the data. However, these visualization approaches do not give any information about the genes that define the cell groups or clusters. It is therefore desirable to display cells and genes simultaneously such that by their relative position to each other information about the genes expression in a cluster can be obtained. Here we propose "Correspondence Analysis based Biclustering on Networks" (CAbiNet) as a novel approach to jointly visualize cells and genes by a non-linear embedding approach, called biMAP. The biMAP allows for easy and interactive exploration of cells and their corresponding marker genes in a single plot. CabiNet additionally offers an intuitive way to perform biclustering jointly on cells and genes, providing a simplified workflow to annotate cell types on the biMAP. CAbiNet is accessible through GitHub as an R package.

bioinformatics↗

bioRxiv2021

Combined tumor and immune signals from genomes or transcriptomes predict outcomes of checkpoint inhibition in melanoma

Cancer immunotherapy with checkpoint blockade (CPB) leads to improved outcomes in melanoma and other tumor types, but a majority of patients do not respond. High tumor mutation burden (TMB) and high levels of tumor-infiltrating T cells have been associated with response to immunotherapy, but integrative models to predict clinical benefit using DNA or RNA alone have not been comprehensively explored. We sequenced DNA and RNA from melanoma patients receiving CPB, and aggregated previously published data, yielding whole exome sequencing data for 189 patients and bulk RNA sequencing data for 178 patients. Using these datasets, we derived genomic and transcriptomic factors that predict overall survival (OS) and response to immunotherapy. Using whole-exome DNA data alone, we calculated T cell burden (TCB) and B cell burden (BCB) based on rearranged TCR/Ig DNA sequences and found that patients whose melanomas have high TMB together with either high TCB or high BCB survived longer and had higher response rates as compared to patients with either low TMB or TCB/BCB. Next, using bulk RNA-Seq data, differential expression analysis identified 83 genes associated with high or low OS. By combining pairs of immune-expressed genes with tumor-expressed genes, we identified three gene pairs associated with response and survival (Bonferroni P<0.05). All three gene pair models were validated in an independent cohort (n=180) (Bonferroni P<0.05). The best performing gene pair model included the lymphocyte-expressed MAP4K1 (Mitogen- Activated Protein Kinase Kinase Kinase Kinase 1) combined with the transcription factor TBX3 (T-Box Transcription Factor 3) which is overexpressed in poorly differentiated melanomas. We conclude that RNA-based (MAP4K1&TBX3) or DNA-based (TCB&TMB) models combining immune and tumor measures improve predictions of outcome after checkpoint blockade in melanoma.

genomics↗

bioRxiv2018

Comprehensive analysis of tumour initiation, spatial and temporal progression under multiple lines of treatment

Driver mutations alter cells from normal to cancer through several evolutionary epochs: premalignancy, early malignancy, subclonal diversification, metastasis and resistance to therapy. Later stages of disease can be explored through analyzing multiple samples collected longitudinally, on or between successive treatments, and finally at time of autopsy. It is also possible to study earlier stages of cancer development through probabilistic reconstruction of developmental trajectories based on mutational information preserved in the genome. Here we present a suite of tools, called Phylogic N-Dimensional with Timing (PhylogicNDT), that statistically model phylogenetic and evolutionary trajectories based on mutation and copy-number data representing samples taken at single or multiple time points. PhylogicNDT can be used to infer: (i) the order of clonal driver events (including in pre-cancerous stages); (ii) subclonal populations of cells and their phylogenetic relationships; and (iii) cell population dynamics. We demonstrate the use of PhylogicNDT by applying it to whole-exome and whole-genome data of 498 lung adenocarcinoma samples (434 previously available and 64 of newly generated data). We identify significantly different progression trajectories across subtypes of lung adenocarcinoma (EGFR mutant, KRAS mutant, fusion-driven and EGFR/KRAS wild type cancers). In addition, we study the progression of fusion-driven lung cancer in 21 patients by analyzing samples from multiple timepoints during treatment with 1st and next generation tyrosine kinase inhibitors. We characterize their subclonal diversification, dynamics, selection, and changes in mutational signatures and neoantigen load. This methodology will enable a systematic study of tumour initiation, progression and resistance across cancer types and therapies.

bioinformatics↗

bioRxiv2018

A comprehensive analysis of RNA sequences reveals macroscopic somatic clonal expansion across normal tissues

Cancer genome studies have significantly advanced our knowledge of somatic mutations. However, how these mutations accumulate in normal cells and whether they promote pre-cancerous lesions remains poorly understood. Here we perform a comprehensive analysis of normal tissues by utilizing RNA sequencing data from [~]6,700 samples across 29 normal tissues collected as part of the Genotype-Tissue Expression (GTEx) project. We identify somatic mutations using a newly developed pipeline, RNA-MuTect, for calling somatic mutations directly from RNA-seq samples and their matched-normal DNA. When applied to the GTEx dataset, we detect multiple variants across different tissues and find that mutation burden is associated with both the age of the individual and tissue proliferation rate. We also detect hotspot cancer mutations that share tissue specificity with their matched cancer type. This study is the first to analyze a large number of samples across multiple normal tissues, identifying clones with genomic aberrations observed in cancer.

genomics↗

bioRxiv2018

Portraits of genetic intra-tumour heterogeneity and subclonal selection across cancer types

Intra-tumor heterogeneity (ITH) is a mechanism of therapeutic resistance and therefore an important clinical challenge. However, the extent, origin and drivers of ITH across cancer types are poorly understood. To address this question, we extensively characterize ITH across whole-genome sequences of 2,658 cancer samples, spanning 38 cancer types. Nearly all informative samples (95.1%) contain evidence of distinct subclonal expansions, with frequent branching relationships between subclones. We observe positive selection of subclonal driver mutations across most cancer types, and identify cancer type specific subclonal patterns of driver gene mutations, fusions, structural variants and copy-number alterations, as well as dynamic changes in mutational processes between subclonal expansions. Our results underline the importance of ITH and its drivers in tumor evolution, and provide an unprecedented pan-cancer resource of comprehensively annotated subclonal events from whole-genome sequencing data.

cancer biology↗

bioRxiv2017

FireCloud, a scalable cloud-based platform for collaborative genome analysis: Strategies for reducing and controlling costs

FireCloud, one of three NCI Cloud Pilots, is a collaborative genome analysis platform built on a cloud computing infrastructure. FireCloud aims to solve the many challenges presented by the increasingly large data sets and computing requirements employed in cancer research. However, cost uncertainty associated with cloud computings pay-as-you-go model is proving to be a barrier to adoption of cloud computing. In this paper we present guidelines for optimizing workflows to minimize cost and reduce latency. Our guidelines include: (i) dynamic disk sizing to efficiently utilize virtual disks; (ii) tuned provisioning of virtual machines (VMs) using a performance monitoring tool; (iii) taking advantage of steep price discounts of preemptible VMs; and (iv) utilizing the optimal parallelization of a tasks workload.

bioinformatics↗

bioRxiv2017

The evolutionary history of 2,658 cancers

Cancer develops through a process of somatic evolution. Here, we use whole-genome sequencing of 2,778 tumour samples from 2,658 donors to reconstruct the life history, evolution of mutational processes, and driver mutation sequences of 39 cancer types. The early phases of oncogenesis are driven by point mutations in a small set of driver genes, often including biallelic inactivation of tumour suppressors. Early oncogenesis is also characterised by specific copy number gains, such as trisomy 7 in glioblastoma or isochromosome 17q in medulloblastoma. By contrast, increased genomic instability, a nearly four-fold diversification of driver genes, and an acceleration of point mutation processes are features of later stages. Copy-number alterations often occur in mitotic crises leading to simultaneous gains of multiple chromosomal segments. Timing analysis suggests that driver mutations often precede diagnosis by many years, and in some cases decades, providing a window of opportunity for early cancer detection.

cancer biology↗