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Borden, J.

Publications and source records attributed to Borden, J..

3 recordsLinked to original sources

Sciara coprophila larvae upregulate DNA repair pathways and downregulate developmental regulators in response to ionizing radiation

Robust DNA damage prevention and repair strategies are crucial to faithful reproduction and inheritance of the genetic material. Although many molecular pathways that respond to DNA damage are well conserved through evolution, the quality and effectiveness of these systems can vary between species. Studies dating back for nearly a century document that the dark-winged fungus gnat Sciara coprophila (Order: Diptera; sub-order: Nematocera) is relatively resistant to irradiation-induced mutations that cause visible phenotypes when compared to the fruit fly Drosophila melanogaster (Order: Diptera; sub-order: Brachycera). However, the molecular responses to irradiation for S. coprophila have yet to be analyzed. To address this gap, we first characterized the effects of ionizing radiation on S. coprophila throughout its life cycle. Our data show that developing S. coprophila embryos are highly sensitive to even low doses of gamma-irradiation, whereas larvae can tolerate up to 80 Gy and still retain their ability to develop to adulthood with a developmental delay of 5 to 8 extra days in the larval stage. To survey the genes involved in the early transcriptional response to irradiation, we compared RNA-seq profiles of larvae with and without radiation treatment. Our analysis showed that 327 genes are differentially expressed in irradiated larvae, with 232 genes upregulated and 95 genes downregulated relative to controls. The upregulated genes were enriched for DNA damage response genes, including those involved in DNA repair, cell cycle arrest, and apoptosis, whereas the down-regulated genes were enriched for developmental regulators, consistent with the developmental delay observed in irradiated larvae. Thus, our study has laid the groundwork to further dissect how Sciara copes with radiation-induced damage.

genomics↗

Sysrev: A FAIR Platform For Data Curation And Systematic Evidence Review

AbstractWell-curated datasets are essential to evidence based decision making and to the integration of artificial intelligence with human reasoning across disciplines. However, many sources of data remain siloed, unstructured, and/or unavailable for complementary and secondary research. Sysrev was developed to address these issues. First, Sysrev was built to aid in systematic evidence reviews (SER), where digital documents are evaluated according to a well defined process, and where Sysrev provides an easy to access, publicly available and free platform for collaborating in SER projects. Secondly, Sysrev addresses the issue of unstructured, siloed, and inaccessible data in the context of generalized data extraction, where human and machine learning algorithms are combined to extract insights and evidence for better decision making across disciplines. Sysrev uses FAIR - Findability, Accessibility, Interoperability, and Reuse of digital assets - as primary principles in design. Sysrev was developed primarily because of an observed need to reduce redundancy, reduce inefficient use of human time and increase the impact of evidence based decision making. This publication is an introduction to Sysrev as a novel technology, with an overview of the features, motivations and use cases of the tool. MethodsSysrev.com is a FAIR motivated web platform for data curation and SER. Sysrev allows users to create data curation projects called "sysrevs" wherein users upload documents, define review tasks, recruit reviewers, perform review tasks, and automate review tasks. ConclusionSysrev is a web application designed to facilitate data curation and SERs. Thousands of publicly accessible Sysrev projects have been created, accommodating research in a wide variety of disciplines. Described use cases include data curation, managed reviews, and SERs.

scientific communication and education↗

Top-down engineering of complex communities by directed evolution

Directed evolution has been used for decades to engineer biological systems from the top-down. Generally, it has been applied at or below the organismal level, by iteratively sampling the mutational landscape in a guided search for genetic variants of higher function. Above the organismal level, a small number of studies have attempted to artificially select microbial communities and ecosystems, with uneven and generally modest success. Our theoretical understanding of artificial ecosystem selection is still limited, particularly for large assemblages of asexual organisms, and we know little about designing efficient methods to direct their evolution. To address this issue, we have developed a flexible modeling framework that allows us to systematically probe any arbitrary selection strategy on any arbitrary set of communities and selected functions, in a wide range of ecological conditions. By artificially selecting hundreds of in-silico microbial metacommunities under identical conditions, we examine the fundamental limits of the two main breeding methods used so far, and prescribe modifications that significantly increase their power. We identify a range of directed evolution strategies that, particularly when applied in combination, are better suited for the top-down engineering of large, diverse, and stable microbial consortia. Our results emphasize that directed evolution allows an ecological structure-function landscape to be navigated in search for dynamically stable and ecologically and functionally resilient high-functioning communities.

evolutionary biology↗