Biology subjects

Blau, H.

Publications and source records attributed to Blau, H..

6 recordsLinked to original sources

bioRxiv2026

Restoration of Capacity to Build Muscle Strength in Geriatric Mice by Inhibition of the Gerozyme 15-Prostaglandin Dehydrogenase

Loss of skeletal muscle mass and strength with age drives sarcopenia, a syndrome affecting >100 million people worldwide that leads to loss of mobility, independence, and increased mortality. Mechanical overload induces hypertrophy in young muscle, but this response is markedly attenuated with age--a poorly understood phenomenon termed "anabolic resistance." Here we test whether impaired paracrine communication between myofibers and their niche underlies this loss of plasticity in geriatric mice. In aged muscle, pharmacological inhibition of 15-PGDH restores prostaglandin E2 (PGE2) bioavailability and rescues the anabolic response, increasing muscle growth and contractile strength. Single-nuclei RNA-seq revealed a paracrine circuit: PGE2 drives IGF1 synthesis in type IIb myonuclei, which signals to stromal, myogenic, myonuclear, and immune cells. Blocking IGF1 receptor signaling abolished these gains, placing PGE2 upstream of an IGF1-mediated circuit that coordinates multicellular hypertrophy. Thus, 15-PGDH inhibition is a pharmacological strategy to overcome the anabolic resistance and rebuild muscle in aging.

cell biology↗

bioRxiv2024

Replacing non-biomedical concepts improves embedding of biomedical concepts

ObjectivesConcept embeddings are low-dimensional vector representations of concepts such as MeSH:D009203 (Myocardial Infarction), whose similarity in the embedded vector space reflects their semantic similarity. Here, we test the hypothesis that non-biomedical concept synonym replacement can improve the quality of biomedical concepts embeddings. Materials and methodsWe developed an approach that leverages WordNet to replace sets of synonyms with the most common representative of the synonym set. ResultsWe tested our approach on 1055 concept sets and found that, on average, the mean intracluster distance was reduced by 8% in the vector-space. Assuming that homophily of related concepts in the vector space is desirable, our approach tends to improve the quality of embeddings. Discussion and ConclusionThis pilot study shows that non-biomedical synonym replacement tends to improve the quality of embeddings of biomedical concepts using the Word2Vec algorithm. We have implemented our approach in a freely available Python package available at https://github.com/TheJacksonLaboratory/wn2vec.

bioinformatics↗

bioRxiv2023

Sex biased human thymic architecture guides T cell development through spatially defined niches

Within the thymus, regulation of the cellular cross-talk directing T cell development is dependent on spatial interactions within specialized niches. To create a holistic, spatially defined map of tissue niches guiding postnatal T cell development we employed the multidimensional imaging platform CO-detection by indEXing (CODEX), as well as CITE-seq and ATAC-seq. We generated age-matched 4-5-month-old postnatal thymus datasets for male and female donors, and identify significant sex differences in both T cell and thymus biology. We demonstrate a crucial role for JAG ligands in directing thymic-like dendritic cell development, reveal important functions of a novel population of ECM- fibroblasts, and characterize the medullary niches surrounding Hassalls corpuscles. Together, these data represent a unique age-matched spatial multiomic resource to investigate how sex-based differences in thymus regulation and T cell development arise, and provide an essential resource to understand the mechanisms underlying immune function and dysfunction in males and females.

immunology↗

bioRxiv2022

Machine learning-based classification of binary dynamic fluorescence signals reveals muscle stem cell fate transitions in response to pro-regenerative niche factors

The proper regulation of muscle stem cell (MuSC) fate by cues from the niche is essential for regeneration of skeletal muscle. How pro-regenerative niche factors control the dynamics of MuSC fate decisions remains unknown due to limitations of population-level endpoint assays. To address this knowledge gap, we developed a novel binary dynamic fluorescence time lapse imaging analysis (BDFA) approach that leverages machine learning classification strategies to track single cell fate decisions with high temporal resolution. Using two fluorescent reporters that read out maintenance of stemness and myogenic commitment, we constructed detailed lineage trees for individual MuSCs and their progeny, classifying each division event as symmetric self-renewing, asymmetric, or symmetric committed. Our analysis reveals that treatment with the lipid metabolite, prostaglandin E2 (PGE2), accelerates the rate of MuSC proliferation over time, while biasing division events toward symmetric self-renewal. In contrast, the IL6 family member, Oncostatin M (OSM) decreases the proliferation rate after the first generation, while blocking myogenic commitment. These insights into the dynamics of MuSC regulation by niche cues were uniquely enabled by our BDFA approach. We anticipate that similar binary live cell readouts derived from BDFA will markedly expand our understanding of how niche factors control tissue regeneration in real time.

cell biology↗

bioRxiv2021

Supervised learning with word embeddings derived from PubMed captures latent knowledge about protein kinases and cancer

Inhibiting protein kinases (PKs) that cause cancers has been an important topic in cancer therapy for years. So far, almost 8% of more than 530 PKs have been targeted by FDA-approved medications and around 150 protein kinase inhibitors (PKIs) have been tested in clinical trials. We present an approach based on natural language processing and machine learning to the relations between PKs and cancers, predicting PKs whose inhibition would be efficacious to treat a certain cancer. Our approach represents PKs and cancers as semantically meaningful 100-dimensional vectors based on co-occurrence patterns in PubMed abstracts. We use information about phase I-IV trials in ClinicalTrials.gov to construct a training set for random forest classification. In historical data, associations between PKs and specific cancers could be predicted years in advance with good accuracy. Our model may be a tool to predict the relevance of inhibiting PKs with specific cancers.

bioinformatics↗

bioRxiv2020

KG-COVID-19: a framework to produce customized knowledge graphs for COVID-19 response

Integrated, up-to-date data about SARS-CoV-2 and coronavirus disease 2019 (COVID-19) is crucial for the ongoing response to the COVID-19 pandemic by the biomedical research community. While rich biological knowledge exists for SARS-CoV-2 and related viruses (SARS-CoV, MERS-CoV), integrating this knowledge is difficult and time consuming, since much of it is in siloed databases or in textual format. Furthermore, the data required by the research community varies drastically for different tasks - the optimal data for a machine learning task, for example, is much different from the data used to populate a browsable user interface for clinicians. To address these challenges, we created KG-COVID-19, a flexible framework that ingests and integrates biomedical data to produce knowledge graphs (KGs) for COVID-19 response. This KG framework can also be applied to other problems in which siloed biomedical data must be quickly integrated for different research applications, including future pandemics. BIGGER PICTUREAn effective response to the COVID-19 pandemic relies on integration of many different types of data available about SARS-CoV-2 and related viruses. KG-COVID-19 is a framework for producing knowledge graphs that can be customized for downstream applications including machine learning tasks, hypothesis-based querying, and browsable user interface to enable researchers to explore COVID-19 data and discover relationships.

bioinformatics↗