Biology subjects

Heinrich, J.

Publications and source records attributed to Heinrich, J..

3 recordsLinked to original sources

bioRxiv2022

Oral mTOR Inhibition Limits And Reduces Actinic Keratosis And Cutaneous Squamous Cell Carcinoma In A UVB-Induced Mouse Model

Actinic keratosis (AK) is a skin disease that is characterized by clinical and subclinical cutaneous lesions in sun-exposed areas. It is a considerable burden due to its high occurrence in middle-aged and older populations, as well as its propensity to progress to invasive cutaneous squamous cell carcinoma. The mammalian target of rapamycin (mTOR) pathway is critical in carcinogenesis and tumor development, and it has been shown to be over-activated during skin tumorigenesis, particularly upon ultraviolet (UV) radiation exposure, the key risk factor for AK. However, the ability of mTOR inhibitors to treat AK is not well documented. Herein, we evaluated the effect of oral mTOR inhibitors in vitro and in vivo and found that mTOR inhibitors lower keratinocyte cell proliferation in vitro and both clear and prevent AK and cutaneous squamous cell carcinoma (cSCC) in a UV-B induced SKH1 hairless mouse model of disease. mTOR inhibition reduced the number and size of skin lesions and the frequency of cSCC, resulting in a considerable reduction in disease severity. mTOR inhibition prevented lesion occurrence in areas of field cancerization, without affecting epidermal thickness, keratinocyte proliferation in vivo, or the presence of p53+ cells. Our findings indicate that, when appropriately dosed, oral mTOR inhibitors provide a safe home-based systemic treatment alternative with significant benefits to patients over current topical treatment options.

pharmacology and toxicology↗

bioRxiv2020

A Multi-Phenotype System to Discover Therapies for Age-Related Dysregulation of the Immune Response to Viral Infections

Age-related immune dysregulation contributes to increased susceptibility to infection and disease in older adults. We combined high-throughput laboratory automation with machine learning to build a multi-phenotype aging profile that models the dysfunctional immune response to viral infection in older adults. From a single well, our multi-phenotype aging profile can capture changes in cell composition, physical cell-to-cell interaction, organelle structure, cytokines, and other hidden complexities contributing to age-related dysfunction. This system allows for rapid identification of new potential compounds to rejuvenate older adults immune response. We used our technology to screen thousands of compounds for their ability to make old immune cells respond to viral infection like young immune cells. We observed beneficial effects of multiple compounds, of which two of the most promising were disulfiram and triptonide. Our findings indicate that disulfiram could be considered as a treatment for severe coronavirus disease 2019 and other inflammatory infections.

immunology↗

bioRxiv2018

Visualizing metabolic network dynamics through time-series metabolomics data

New technologies have given rise to an abundance of -omics data, particularly metabolomics data. The scale of these data introduces new challenges for the interpretation and extraction of knowledge, requiring the development of new computational visualization methodologies. Here, we present a new method for the visualization of time-course metabolomics data within the context of metabolic network maps. We demonstrate the utility of this method by examining previously published data for two cellular systems--the human platelet and erythrocyte under cold storage for use in transfusion medicine.\n\nThe results comprise two animated videos that allow for new insights into the metabolic state of both cell types. In the case study of the platelet metabolome during storage, the new visualization technique elucidates a nicotinamide accumulation which mirrors that of hypoxanthine and might, therefore, reflect similar pathway usage. This visual analysis provides a possible explanation for why the salvage reactions in purine metabolism exhibit lower activity during the first few days of the storage period. The second case study displays drastic changes in specific erythrocyte metabolite pools at different times during storage at different temperatures.\n\nIn conclusion, this new visualization technique introduced in this article constitutes a well-suitable approach for large-scale network exploration and advances hypothesis generation. This method can be applied to any system with data and a metabolic map to promote visualization and understand physiology at the network level. More broadly, we hope that our approach will provide the blueprints for new visualizations of other longitudinal -omics data types.\n\nAUTHOR SUMMARYProfiling the dynamic state of a metabolic network through the use of time-course metabolomics technologies allows insights into cellular biochemistry. Interpreting these data together at the systems level provides challenges that can be addressed through the development of new visualization approaches. Here, we present a new method for the visualization of time-course metabolomics data that integrates data into an existing metabolic network map. In brief, the metabolomics data are visualized directly on a network map with dynamic elements (nodes that either change size, fill level, or color corresponding with the concentration) while the user controls the time series (i.e., which time point is being displayed) through a graphical interface. We provide short videos that illustrate the utility of this method through its application to existing data sets for the human platelet and erythrocyte. The results presented here give blueprints for the development of visualization methods for other time-course -omics data types that attempt to understand systems-level physiology.

systems biology↗