Schrödinger's yeast: the challenge of using transformation to compare fitness among Saccharomyces cerevisiae that differ in ploidy or zygosity.

How the number of genome copies modifies the effect of random mutations remains poorly known. In yeast, researchers have investigated these effects for knock-out or other large-effect mutations, but have not accounted for differences at the mating-type locus. We set out to compare fitness differences among strains that differ in ploidy and/or zygosity using a panel of spontaneously arising mutations acquired in haploid yeast from a previous study. To ensure no genetic differences, even at the mating-type locus, we embarked on a series of transformations, which first sterilized and then temporarily introduced plasmid-borne mating types. Despite these attempts to equalize the haplotypes, fitness variation introduced during transformation swamped the differences among the original mutation-accumulation lines. While colony size looked normal, we observed a bi-modality in the maximum growth rate of our transformed yeast and determined that many of the slow growing lines were respiratory deficient ("petite"). Not previously reported, we found that yeast that were TID1/RDH54 knockouts were less likely to become petite. Even for lines with the same petite status, however, we found no correlation in fitness between the two replicate transformations performed. These results pose a challenge for any study using transformation to measure the fitness effect of genetic differences among strains. By attempting to hold haplotypes constant, we introduced more mutations that overwhelmed our ability to measure fitness differences between the genetic states. In this study, we transformed over one hundred different lines of yeast, using two independent transformations, and found that this common laboratory procedure can cause large changes to the microbe studied. Our study provides a cautionary tale of the need to use multiple transformants in fitness assays.

Development of a data science CURE in microbiology using publicly available microbiome datasets.

Scientific and technological advances within the life sciences have enabled the generation of very large datasets that must be processed, stored, and managed computationally. Researchers increasingly require data science skills to work with these datasets at scale in order to convert information into actionable insights, and undergraduate educators have started to adapt pedagogies to fulfill this need. Course-based undergraduate research experiences (CUREs) have emerged as a leading model for providing large numbers of students with authentic research experiences including data science. Originally designed around wet-lab research experiences, CURE models have proliferated and diversified globally to accommodate a broad range of academic disciplines. Within microbiology, diversity metrics derived from microbiome sequence information have become standard data products in research. In some cases, researchers have deposited data in publicly accessible repositories, providing opportunities for reproducibility and comparative analysis. In 2020, with the onset of the COVID-19 pandemic and concomitant shift to remote learning, the University of British Columbia set out to develop an online data science CURE in microbiology. A team of faculty with collective domain expertise in microbiome research and CUREs developed and implemented a data science CURE in which teams of students learn to work with large publicly available datasets, develop and execute a novel scientific research project, and disseminate their findings in the online Undergraduate Journal of Experimental Microbiology and Immunology. Analysis of the resulting student-authored research articles, including comments from peer reviews conducted by subject matter experts, demonstrate high levels of learning effectiveness. Here, we describe core insights from course development and implementation based on a reverse course design model. Our approach to course design may be applicable to the development of other data science CUREs.

A personal cost of cheating can stabilize reproductive altruism during the early evolution of clonal multicellularity.

Understanding how cooperation evolved and is maintained remains an important and often controversial topic because cheaters that reap the benefits of cooperation without paying the costs can threaten the evolutionary stability of cooperative traits. Cooperation-and especially reproductive altruism-is particularly relevant to the evolution of multicellularity, as somatic cells give up their reproductive potential in order to contribute to the fitness of the newly emerged multicellular individual. Here, we investigated cheating in a simple multicellular species-the green alga Volvox carteri, in the context of the mechanisms that can stabilize reproductive altruism during the early evolution of clonal multicellularity. We found that the benefits cheater mutants can gain in terms of their own reproduction are pre-empted by a cost in survival due to increased sensitivity to stress. This personal cost of cheating reflects the antagonistic pleiotropic effects that the gene coding for reproductive altruism-regA-has at the cell level. Specifically, the expression of regA in somatic cells results in the suppression of their reproduction potential but also confers them with increased resistance to stress. Since regA evolved from a life-history trade-off gene, we suggest that co-opting trade-off genes into cooperative traits can provide a built-in safety system against cheaters in other clonal multicellular lineages.

An integrated, modular approach to data science education in microbiology.

We live in an increasingly data-driven world, where high-throughput sequencing and mass spectrometry platforms are transforming biology into an information science. This has shifted major challenges in biological research from data generation and processing to interpretation and knowledge translation. However, postsecondary training in bioinformatics, or more generally data science for life scientists, lags behind current demand. In particular, development of accessible, undergraduate data science curricula has the potential to improve research and learning outcomes as well as better prepare students in the life sciences to thrive in public and private sector careers. Here, we describe the Experiential Data science for Undergraduate Cross-Disciplinary Education (EDUCE) initiative, which aims to progressively build data science competency across several years of integrated practice. Through EDUCE, students complete data science modules integrated into required and elective courses augmented with coordinated cocurricular activities. The EDUCE initiative draws on a community of practice consisting of teaching assistants (TAs), postdocs, instructors, and research faculty from multiple disciplines to overcome several reported barriers to data science for life scientists, including instructor capacity, student prior knowledge, and relevance to discipline-specific problems. Preliminary survey results indicate that even a single module improves student self-reported interest and/or experience in bioinformatics and computer science. Thus, EDUCE provides a flexible and extensible active learning framework for integration of data science curriculum into undergraduate courses and programs across the life sciences.

The genetic basis for the evolution of soma: mechanistic evidence for the co-option of a stress-induced gene into a developmental master regulator.

In multicellular organisms with specialized cells, the most significant distinction among cell types is between reproductive (germ) cells and non-reproductive/somatic cells (soma). Although soma contributed to the marked increase in complexity of many multicellular lineages, little is known about its evolutionary origins. We have previously suggested that the evolution of genes responsible for the differentiation of somatic cells involved the co-option of life history trade-off genes that in unicellular organisms enhanced survival at a cost to immediate reproduction. In the multicellular green alga, Volvox carteri, cell fate is established early in development by the differential expression of a master regulatory gene known as regA. A closely related RegA-Like Sequence (RLS1) is present in its single-celled relative, Chlamydomonas reinhardtii. RLS1 is expressed in response to stress, and we proposed that an environmentally induced RLS1-like gene was co-opted into a developmental pathway in the lineage leading to V. carteri. However, the exact evolutionary scenario responsible for the postulated co-option event remains to be determined. Here, we show that in addition to being developmentally regulated, regA can also be induced by environmental cues, indicating that regA has maintained its ancestral regulation. We also found that the absence of a functional RegA protein confers increased sensitivity to stress, consistent with RegA having a direct or indirect role in stress responses. Overall, this study (i) provides mechanistic evidence for the co-option of an environmentally induced gene into a major developmental regulator, (ii) supports the view that major morphological innovations can evolve via regulatory changes and (iii) argues for the role of stress in the evolution of multicellular complexity.