Biology subjects

English, B.

Publications and source records attributed to English, B..

3 recordsLinked to original sources

bioRxiv2025

Macrophage phagocytosis of Coccidioides promotes its differentiation into the parasitic form

Coccidioides is an endemic fungus that is increasing in prevalence and can cause life threatening disease in otherwise immunocompetent people. In the environment the spores (arthroconidia) develop into hyphae, yet when they are inhaled by a mammalian host, they develop into a unique form called the spherule. The transition to spherule can be triggered in vitro with elevated temperatures and high CO2 levels, but the signals and host cells that might trigger Coccidioides spherulation in vivo are not known. We used live imaging to investigate how macrophages affect the fate of Coccidioides arthroconidia. Under tissue culture conditions, arthroconidia quickly developed into hyphae. The addition of macrophages promoted spherule development and delayed hyphal formation, indicating that innate immune cells can influence Coccidioides development into the pathogenic form. Exposure of arthroconidia to macrophage supernatants was not sufficient to stimulate spherulation, which was dependent on phagocytosis of arthroconidia by macrophages. Transcriptomics analysis of Coccidioides co-cultured with macrophages revealed a signature concordant with spherules grown in vitro and allowed the identification of a core set of spherule-specific transcripts. In addition, we identified Coccidioides transcripts with significantly higher abundance in the presence of macrophages compared to in vitro spherules, suggesting these factors may be needed to survive and thrive in the presence of innate immune cells. This work lays a foundation for uncovering host-pathogen signaling as well as Coccidioides factors that are critical for pathogenesis.

microbiology↗

bioRxiv2022

Novel feature selection methods for construction of accurate epigenetic clocks

Epigenetic clocks allow the accurate prediction of age based on the methylation status of specific CpG sites in a variety of tissues. These predictive models can be used to distinguish the biological age of an organism from its chronological age, and are a powerful tool to measure the effectiveness of aging interventions. There is a growing need for methods to efficiently construct epigenetic clocks. The most common approach is to create clocks using elastic net regression modelling of all measured CpG sites, without first identifying specific features or CpGs of interest. The addition of feature selection approaches provides the opportunity to reduce the cost and time of clock development by decreasing the number of CpG sites included in clocks. Here, we apply both classic feature selection methods and novel combinatorial methods to the development of epigenetic clocks. We perform feature selection on the human whole blood methylation dataset of [~]470,000 CpG features published by Hannum and colleagues (2015). We develop clocks to predict age, using a variety of feature selection approaches, and all clocks have R2 correlation scores of greater than 0.73. The most predictive clock uses 35 CpG sites for a R2 correlation score of 0.87. The five most frequent sites across all clocks are also modelled to build a clock with a R2 correlation score of 0.83. These two clocks are validated on two external datasets where they maintain excellent predictive accuracy and outperform Hannum et als model in accuracy of age prediction despite using significantly less CpGs. We also identify the associated gene regulatory regions of these CpG sites, which may be possible targets for future aging studies. These novel feature selection algorithms will lower the number of sites needed to be sequenced to build clocks and allow conventionally expensive aging epigenetic studies to cost a fraction of what it would normally.

bioinformatics↗

bioRxiv2019

Integration of Structured Biological Data Sources using Biological Expression Language

BackgroundThe integration of heterogeneous, multiscale, and multimodal knowledge and data has become a common prerequisite for joint analysis to unravel the mechanisms and aetiologies of complex diseases. Because of its unique ability to capture this variety, Biological Expression Language (BEL) is well suited to be further used as a platform for semantic integration and harmonization in networks and systems biology.\n\nResultsWe have developed numerous independent packages capable of downloading, structuring, and serializing various biological data sources to BEL. Each Bio2BEL package is implemented in the Python programming language and distributed through GitHub (https://github.com/bio2bel) and PyPI.\n\nConclusionsThe philosophy of Bio2BEL encourages reproducibility, accessibility, and democratization of biological databases. We present several applications of Bio2BEL packages including their ability to support the curation of pathway mappings, integration of pathway databases, and machine learning applications.\n\nTweetA suite of independent Python packages for downloading, parsing, warehousing, and converting multi-modal and multi-scale biological databases to Biological Expression Language

bioinformatics↗