Doyle, E. | bioRxiv Science

bioRxiv2023

Community structure of known and previously unknown endobacteria associated with spores of arbuscular mycorrhizal fungi.

Arbuscular mycorrhizal fungi (AMF) are ubiquitous plant root symbionts which can house two endobacteria: Ca. Moeniiplasma glomeromycotorum (CaMg) and Ca. Glomeribacter gigasporarum (CaGg). However, little is known about their distribution and population structure in natural AMF populations and whether AMF can harbour other endobacteria. We isolated AMF from two environments and surveyed the surface-sterilized spores for endobacteria. We found that CaMg and CaGg differed significantly in distribution whereby CaMg were extremely abundant (80%) and CaGg were extremely rare (2%) in both environments. Unexpectedly, we discovered an additional and previously unknown level of bacterial diversity within AMF spores which extended beyond the known endosymbionts, with as many as 277 other bacterial taxa detected in individual spores. Detailed analysis of endobacterial communities inside AMF spores revealed that: (i)CaGg were not limited in distribution to the Gigasporacea family of AMF, as previously thought, (ii) CaMg community structure was driven by AMF host genotype, (iii) a significant inverse correlation existed between the diversity of CaMg and diversity of all other endobacteria. The latter suggests the existence of competition dynamics between different bacterial populations inside AMF spores and provides a basis for generation of testable hypotheses regarding the function of CaMg in AMF biology.

microbiology↗

bioRxiv2020

Digital Imaging and Vision Analysis in Science Project improves the self-efficacy and skill of undergraduate students in computational work

In many areas of science, the ability to use computers to process, analyze, and visualize large data sets has become essential. The mismatch between the ability to generate large data sets and the computing skill to analyze them is arguably the most striking within the life sciences. The Digital Image and Vision Applications in Science (DIVAS) project describes a scaffolded series of interventions implemented over the span of a year to build the coding and computing skill of undergraduate students majoring primarily in the natural sciences. The program is designed as a community of practice, providing support within a network of learners. The program focus, images as data, provides a compelling hook for participating scholars. Scholars begin the program with a one-credit spring semester seminar where they are exposed to image analysis. The program continues in the summer with a one-week, intensive Python and image processing workshop. From there, scholars tackle image analysis problems using a pair programming approach and finish the summer with independent research. Finally, scholars participate in a follow-up seminar the following spring and help onramp the next cohort of incoming scholars. We observed promising growth in participant self-efficacy in computing that was maintained throughout the project as well as significant growth in key computational skills. DIVAS program funding was able to support seventeen DIVAS over three years, with 76% of DIVAS scholars identifying as women and 14% of scholars being members of an underrepresented minority group. Most scholars (82%) entered the program as freshmen, with 89% of DIVAS scholars retained for the duration of the program and 100% of scholars remaining a STEM major one year after completing the program. The outcomes of the DIVAS project support the efficacy of building computational skill through repeated exposure of scholars to relevant applications over an extended period within a community of practice.

scientific communication and education↗