Biology subjects

Knutsdottir, H.

Publications and source records attributed to Knutsdottir, H..

4 recordsLinked to original sources

bioRxiv2025

Random survival forests identify myocardial gene signatures associated with survival in heart failure with preserved ejection fraction

Heart failure with preserved ejection fraction (HFpEF) continues to be poorly understood at the molecular level. While previous studies have identified gene expression signatures unique to HFpEF, genes associated with clinical decompensation have not been determined. Here, we performed exploratory analysis of myocardial RNA-seq data to identify genes associated with event-free survival in HFpEF. We analyzed previously published RNA sequencing data of right ventricular septal endomyocardial biopsies from HFpEF patients (n=41) with paired clinical, echocardiographic, and outcome data (including mortality and heart failure hospitalizations). We constructed random survival forests with forward stepwise regularization (the "variable hunting" method) to determine genes associated with time to first event using a combined end-point of heart failure hospitalization or all-cause death. Selection of candidate forest variables was tested with both random and weighted sampling methods. We identified 33 genes that are predictive of survival in HFpEF. This set includes genes previously implicated in heart failure, including ADAMTSL2, ADRB1, BMP6, and METRNL. Survival forests constructed using these genes outperform those constructed from clinical and hemodynamic parameters alone (out-of-bag C-index 0.894 vs 0.545). Moreover, in survival forests constructed from both gene data and hemodynamic measurements, individual survival genes consistently showed higher variable importance than clinical/hemodynamic parameters. Our study shows that random survival forests identify myocardial gene signatures that may better model HFpEF prognosis than clinical measurements. Further studies are warranted to validate findings in independent cohorts.

genomics↗

bioRxiv2022

A comprehensive single-cell breast tumor atlas defines cancer epithelial and immune cell heterogeneity and interactions predicting anti-PD-1 therapy response

We present an integrated single-cell RNA-seq resource of the breast tumor microenvironment consisting of 236,363 cells from 119 biopsy samples across 8 publicly available datasets. In this computational study, we first leverage this novel resource to define cancer epithelial cell heterogeneity based on two clinically relevant markers and identify six new and distinct subsets of natural killer cells. We then illustrate how cancer epithelial cell heterogeneity impacts immune cell interactions. We develop T cell InteractPrint, which considers how cancer epithelial cell heterogeneity shifts the predicted strength of T cell interactions. We use InteractPrint to predict response to immune checkpoint inhibition (ICI) in two clinical trials testing immunotherapy in patients with breast cancer. T cell InteractPrint was predictive in both trials (AUC = 0.81 and 0.84), versus PD-L1 expression (AUC = 0.54 and 0.72). This result provides an alternative predictive biomarker to PD-L1 to select patients who should receive ICI. STATEMENT OF SIGNIFICANCEWe developed a novel integrated single-cell atlas of the breast tumor microenvironment to interrogate breast tumor cell heterogeneity and define how heterogenous cancer epithelial cell and immune cell interactions predict response to anti-PD-1 therapy.

cancer biology↗

bioRxiv2021

PDE9 Inhibition Activates PPARa to Stimulate Mitochondrial Fat Metabolism and Reduce Cardiometabolic Syndrome

Central obesity with cardiometabolic syndrome (CMS) is a major global contributor to human disease, and effective therapies are needed. Here, we show inhibiting cyclic-GMP selective phosphodiesterase-9A (PDE9-I) suppresses established diet-induced obesity and CMS in ovariectomized female and male mice. PDE9-I reduces abdominal, hepatic, and myocardial fat accumulation, stimulates mitochondrial activity in brown and white fat, and improves CMS, without altering activity or food intake. PDE9 localizes to mitochondria, and its inhibition stimulates lipolysis and mitochondrial respiration coupled to PPAR-dependent gene regulation. PPAR upregulation is required for PDE9-I metabolic efficacy and is absent in non-ovariectomized females that also display no metabolic benefits from PDE9-I. The latter is compatible with estrogen receptor- altering PPAR chromatin binding identified by ChIPSeq. In humans with heart failure and preserved ejection fraction, myocardial expression of PPARA and its regulated genes is reduced versus control. These findings support testing PDE9-I to treat obesity/CMS in men and postmenopausal women. SummaryOral inhibition of phosphodiesterase type 9 stimulates mitochondrial fat metabolism and lipolysis, reducing central obesity without changing appetite

physiology↗

bioRxiv2019

A methodology for morphological feature extraction and unsupervised cell classification

Cell morphology is an important indicator of cell state, function, stage of development, and fate in both normal and pathological conditions. Cell shape is among key indicators used by pathologists to identify abnormalities or malignancies. With rapid advancements in the speed and amount of biological data acquisition, including images and movies of cells, computer-assisted identification and analysis of images becomes essential. Here, we report on techniques for recognition of cells in microscopic images and automated cell shape classification. We illustrate how our unsupervised machine-learning-based approach can be used to classify distinct cell shapes from a large number of microscopic images.\n\nTechnical AbstractWe develop a methodology to segment cells from microscopy images and compute quantitative descriptors that characterize their morphology. Using unsupervised techniques for dimensionality reduction and density-based clustering, we perform label-free cell shape classification. Cells are identified with minimal user input using mathematical morphology and region-growing segmentation methods. Physical quantities describing cell shape and size (including area, perimeter, Feret diameters, etc.) are computed along with other features including shape factors and Hus image moments.\n\nCorrelated features are combined to obtain a low-dimensional (2-D or 3-D) embedding of data points corresponding to individual segmented cell shapes. Finally, a hierarchical density-based clustering algorithm (HDBSCAN) is used to classify cells. We compare cell classification results obtained from different combinations of features to identify a feature set that delivers optimum classification performance for our test data consisting of phase-contrast microscopy images of a pancreatic-cancer cell line, MIA PaCa-2.

bioinformatics↗