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1. Marine Microeukaryote Metatranscriptomics: Sample Processing and Bioinformatic Workflow Recommendations for Ecological Applications

   https://doi.org/10.3389/fmars.2022.867007  

   Cohen, Natalie R.; Alexander, Harriet; Krinos, Arianna I.; Hu, Sarah K.; Lampe, Robert H. (June 2022, Frontiers in Marine Science)

   Microeukaryotes (protists) serve fundamental roles in the marine environment as contributors to biogeochemical nutrient cycling and ecosystem function. Their activities can be inferred through metatranscriptomic investigations, which provide a detailed view into cellular processes, chemical-biological interactions in the environment, and ecological relationships among taxonomic groups. Established workflows have been individually put forth describing biomass collection at sea, laboratory RNA extraction protocols, and bioinformatic processing and computational approaches. Here, we present a compilation of current practices and lessons learned in carrying out metatranscriptomics of marine pelagic protistan communities, highlighting effective strategies and tools used by practitioners over the past decade. We anticipate that these guidelines will serve as a roadmap for new marine scientists beginning in the realms of molecular biology and/or bioinformatics, and will equip readers with foundational principles needed to delve into protistan metatranscriptomics. 

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2. Early prediction of level-of-care requirements in patients with COVID-19

   https://doi.org/10.7554/eLife.60519  

   Hao, Boran; Sotudian, Shahabeddin; Wang, Taiyao; Xu, Tingting; Hu, Yang; Gaitanidis, Apostolos; Breen, Kerry; Velmahos, George C; Paschalidis, Ioannis Ch (October 2020, eLife)

   null (Ed.)

   The new coronavirus (now named SARS-CoV-2) causing the disease pandemic in 2019 (COVID-19), has so far infected over 35 million people worldwide and killed more than 1 million. Most people with COVID-19 have no symptoms or only mild symptoms. But some become seriously ill and need hospitalization. The sickest are admitted to an Intensive Care Unit (ICU) and may need mechanical ventilation to help them breath. Being able to predict which patients with COVID-19 will become severely ill could help hospitals around the world manage the huge influx of patients caused by the pandemic and save lives. Now, Hao, Sotudian, Wang, Xu et al. show that computer models using artificial intelligence technology can help predict which COVID-19 patients will be hospitalized, admitted to the ICU, or need mechanical ventilation. Using data of 2,566 COVID-19 patients from five Massachusetts hospitals, Hao et al. created three separate models that can predict hospitalization, ICU admission, and the need for mechanical ventilation with more than 86% accuracy, based on patient characteristics, clinical symptoms, laboratory results and chest x-rays. Hao et al. found that the patients’ vital signs, age, obesity, difficulty breathing, and underlying diseases like diabetes, were the strongest predictors of the need for hospitalization. Being male, having diabetes, cloudy chest x-rays, and certain laboratory results were the most important risk factors for intensive care treatment and mechanical ventilation. Laboratory results suggesting tissue damage, severe inflammation or oxygen deprivation in the body's tissues were important warning signs of severe disease. The results provide a more detailed picture of the patients who are likely to suffer from severe forms of COVID-19. Using the predictive models may help physicians identify patients who appear okay but need closer monitoring and more aggressive treatment. The models may also help policy makers decide who needs workplace accommodations such as being allowed to work from home, which individuals may benefit from more frequent testing, and who should be prioritized for vaccination when a vaccine becomes available. 

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3. Linking Biodiversity Data Using Evolutionary History

   https://doi.org/10.3897/biss.3.36207  

   McTavish, Emily Jane (June 2019, Biodiversity Information Science and Standards)

   All life on earth is linked by a shared evolutionary history. Even before Darwin developed the theory of evolution, Linnaeus categorized types of organisms based on their shared traits. We now know these traits derived from these species’ shared ancestry. This evolutionary history provides a natural framework to harness the enormous quantities of biological data being generated today. The Open Tree of Life project is a collaboration developing tools to curate and share evolutionary estimates (phylogenies) covering the entire tree of life (Hinchliff et al. 2015, McTavish et al. 2017). The tree is viewable at https://tree.opentreeoflife.org, and the data is all freely available online. The taxon identifiers used in the Open Tree unified taxonomy (Rees and Cranston 2017) are mapped to identifiers across biological informatics databases, including the Global Biodiversity Information Facility (GBIF), NCBI, and others. Linking these identifiers allows researchers to easily unify data from across these different resources (Fig. 1). Leveraging a unified evolutionary framework across the diversity of life provides new avenues for integrative wide scale research. Downstream tools, such as R packages developed by the R OpenSci foundation (rotl, rgbif) (Michonneau et al. 2016, Chamberlain 2017) and others tools (Revell 2012), make accessing and combining this information straightforward for students as well as researchers (e.g. https://mctavishlab.github.io/BIO144/labs/rotl-rgbif.html). Figure 1. Example linking phylogenetic relationships accessed from the Open Tree of Life with specimen location data from Global Biodiversity Information Facility. For example, a recent publication by Santorelli et al. 2018 linked evolutionary information from Open Tree with species locality data gathered from a local field study as well as GBIF species location records to test a river-barrier hypothesis in the Amazon. By combining these data, the authors were able test a widely held biogeographic hypothesis across 1952 species in 14 taxonomic groups, and found that a river that had been postulated to drive endemism, was in fact not a barrier to gene flow. However, data provenance and taxonomic name reconciliation remain key hurdles to applying data from these large digital biodiversity and evolution community resources to answering biological questions. In the Amazonian river analysis, while they leveraged use of GBIF records as a secondary check on their species records, they relied on their an intensive local field study for their major conclusions, and preferred taxon specific phylogenetic resources over Open Tree where they were available (Santorelli et al. 2018). When Li et al. 2018 assessed large scale phylogenetic approaches, including Open Tree, for measuring community diversity, they found that synthesis phylogenies were less resolved than purpose-built phylogenies, but also found that these synthetic phylogenies were sufficient for community level phylogenetic diversity analyses. Nonetheless, data quality concerns have limited adoption of analyses data from centralized resources (McTavish et al. 2017). Taxonomic name recognition and reconciliation across databases also remains a hurdle for large scale analyses, despite several ongoing efforts to improve taxonomic interoperability and unify taxonomies, such at Catalogue of Life + (Bánki et al. 2018). In order to support innovative science, large scale digital data resources need to facilitate data linkage between resources, and address researchers' data quality and provenance concerns. I will present the model that the Open Tree of Life is using to provide evolutionary data at the scale of the entire tree of life, while maintaining traceable provenance to the publications and taxonomies these evolutionary relationships are inferred from. I will discuss the hurdles to adoption of these large scale resources by researchers, as well as the opportunities for new research avenues provided by the connections between evolutionary inferences and biodiversity digital databases. 

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4. Grand Challenges in Coevolution

   https://doi.org/10.3389/fevo.2021.618251  

   Medina, Mónica; Baker, David M.; Baltrus, David A.; Bennett, Gordon M.; Cardini, Ulisse; Correa, Adrienne M.; Degnan, Sandie M.; Christa, Gregor; Kim, Eunsoo; Li, Jingchun; et al (January 2022, Frontiers in ecology and evolution)

   Elgar, Mark A. (Ed.)

   Coevolution—reciprocal evolutionary change between interacting lineages (Thompson, 1994; see Glossary)—is thought to have played a profound role in the evolution of Life on Earth. From similar patterns across the wings of unrelated lineages of butterflies (Hoyal Cuthill and Charleston, 2015), egg mimicry of “cheating” brood parasites (Davies, 2010), to the role of animal pollinators in driving the diversification of flowering plants (Kay and Sargent, 2009), to the ubiquity of sexual reproduction and sexual conflicts (Hamilton, 2002; Arnqvist and Rowe, 2005; King et al., 2009), the formation of the eukaryotic cell (Martin et al., 2015; Imachi et al., 2020), and even the origin of living organisms themselves (Mizuuchi and Ichihashi, 2018), evolutionary changes among interacting lineages have played profound and important roles in the history of Life. This Grand Challenges inaugural contribution encompasses eclectic opinions of the editorial board as to what are the next frontiers of coevolution research in the 21st century. Coevolutionary biology is a field that has garnered a lot of attention in recent years, in part as a result of technical advances in nucleotide sequencing and bioinformatics in the burgeoning field of host–microbial interactions. Many seminal studies of coevolution examined reciprocal evolutionary change between two or a few interacting macroscopic species that imposed selective pressures on one another (e.g., insect or bird pollinators and their flowering host plants). Understanding the contexts under which coevolution occurs, as opposed to scenarios in which each partner adapts independently to a particular environment (Darwin, 1862; Stiles, 1978) is important to elucidate coevolutionary processes. A whole spectrum of organismal interactions has been examined under the lens of coevolution, providing additional context, and nuance to ecological strategies traditionally categorized as ranging from beneficial to detrimental for participating species (Figure 1). In particular, a coevolutionary perspective has revealed that even “mutualisms” are not always fully beneficial or cooperative for the partners involved. Instead, the tendency to “cheat” permeates across symbiotic partnerships (Perez-Lamarque et al., 2020). Conversely, recent evidence suggests that non-lethal predation by co-evolved predators, which has traditionally been assumed to be entirely antagonistic, may provide sessile prey with some indirect benefit through enhanced opportunities to acquire beneficial symbiotic microorganisms (Grupstra et al., 2021). Herein, we discuss some of the recent areas of active research in coevolution, restricting our focus to coevolution between interacting species. 

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5. Advantages of laboratory natural selection in the applied sciences

   https://doi.org/10.1111/jeb.13964  

   Lewis, Jordan A.; Morran, Levi T. (December 2021, Journal of Evolutionary Biology)

   Abstract In the past three decades, laboratory natural selection has become a widely used technique in biological research. Most studies which have utilized this technique are in the realm of basic science, often testing hypotheses related to mechanisms of evolutionary change or ecological dynamics. While laboratory natural selection is currently utilized heavily in this setting, there is a significant gap with its usage in applied studies, especially when compared to the other selection experiment methodologies like artificial selection and directed evolution. This is despite avenues of research in the applied sciences which seem well suited to laboratory natural selection. In this review, we place laboratory natural selection in context with other selection experiments, identify the characteristics which make it well suited for particular kinds of applied research and briefly cover key examples of the usefulness of selection experiments within applied science. Finally, we identify three promising areas of inquiry for laboratory natural selection in the applied sciences: bioremediation technology, identifying mechanisms of drug resistance and optimizing biofuel production. Although laboratory natural selection is currently less utilized in applied science when compared to basic research, the method has immense promise in the field moving forward. 

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