Elevational Gradients Impose Dispersal Limitation on Streptomyces.

Dispersal governs microbial biogeography, but the rates and mechanisms of dispersal remain poorly characterized for most microbial taxa. Dispersal limitation is driven by limits on dissemination and establishment, respectively. Elevation gradients create striking patterns of biogeography because they produce steep environmental gradients at small spatial scales, and these gradients offer a powerful tool to examine mechanisms of dispersal limitation. We focus on Streptomyces, a bacterial genus common to soil, by using a taxon-specific phylogenetic marker, the RNA polymerase-encoding rpoB gene. By targeting Streptomyces, we assess dispersal limitation at finer phylogenetic resolution than is possible using whole community analyses. We characterized Streptomyces diversity at local spatial scales (100 to 3,000 m) in two temperate forest sites located in the Adirondacks region of New York State: Woods Lake (<100 m elevation change), and Whiteface Mountain (>1,000 m elevation change). Beta diversity varied considerably at both locations, indicative of dispersal limitation acting at local spatial scales, but beta diversity was significantly higher at Whiteface Mountain. Beta diversity varied across elevation at Whiteface Mountain, being lowest at the mountain's base. We show that Streptomyces taxa exhibit elevational preferences, and these preferences are phylogenetically conserved. These results indicate that habitat preferences influence Streptomyces biogeography and suggest that barriers to establishment structure Streptomyces communities at higher elevations. These data illustrate that Streptomyces biogeography is governed by dispersal limitation resulting from a complex mixture of stochastic and deterministic processes.

Streptomyces apricus sp. nov., isolated from soil.

A novel Streptomyces strain, SUN51T, was isolated from soils sampled in Wisconsin, USA, as part of a Streptomyces biogeography survey. Genome sequencing revealed that this strain had less than 90 % average nucleotide identity (ANI) to type species of Streptomyces: SUN51T was most closely related to Streptomyces dioscori A217T (99.5 % 16S rRNA gene identity, 89.4 % ANI). Genome size was estimated at 8.81 Mb, and the genome DNA G+C content was 72 mol%. The strain possessed the cellular fatty acids anteiso-C15 : 0, iso-C16 : 0, 16 : 1 ω7c, anteiso-C17 : 0, iso-C14 : 0 and C16 : 0. The predominant menaquinones were MK-9 H4, MK-9 H6 and MK-9 H8. Strain SUN51T contained the polar lipids phosphatidic acid, phosphatidyl ethanolamine, phosphatidyl glycerol and diphosphatidyl glycerol. The cell wall contained ll-diaminopimelic acid. The strain could grow on a broad range of carbon sources and tolerate temperatures of up to 40 °C. The results of the polyphasic study confirmed that this isolate represents a novel species of the genus Streptomyces, for which the name Streptomyces apricus sp. nov. is proposed. The type strain of this species is SUN51T (=NRRL B-65543T=JCM 33736T).

Classroom Management Strategies to Improve Learning Experiences for Online Courses.

Hybrid classrooms (taught simultaneously to both in-person and online students) have become increasingly common in the course of the coronavirus disease 2019 (COVID-19) pandemic, and they have offered multiple benefits and challenges. We offer several recommendations to improve student engagement and classroom experience in such classrooms, especially for online learners who may face greater barriers to participation. These recommendations were constructed based on survey responses from students in a microbiology classroom who were categorized as in-person or online learners depending on their chosen modalities. Briefly, increasing familiarity, encouraging but not mandating camera use in online small groups, tailoring active learning activities for the online population, and enabling access to lecture recordings and transcripts were identified as strategies that would promote student engagement and improve student outcomes. As online learning will likely play an important role in higher education in the future, educators will need to continue to rethink and adapt some familiar classroom strategies to resonate with a virtual audience. In any case, lessons learned in a hybrid course can be applied to a range of teaching modalities, to provide students with a more engaging and supportive learning environment.

Advancing Equity and Inclusion in Microbiome Research and Training.

This article proposes ways to improve inclusion and training in microbiome science and advocates for resource expansion to improve scientific capacity across institutions and countries. Specifically, we urge mentors, collaborators, and decision-makers to commit to inclusive and accessible research and training that improves the quality of microbiome science and begins to rectify long-standing inequities imposed by wealth disparities and racism that stall scientific progress.

Introducing the Microbes and Social Equity Working Group: Considering the Microbial Components of Social, Environmental, and Health Justice.

Humans are inextricably linked to each other and our natural world, and microorganisms lie at the nexus of those interactions. Microorganisms form genetically flexible, taxonomically diverse, and biochemically rich communities, i.e., microbiomes that are integral to the health and development of macroorganisms, societies, and ecosystems. Yet engagement with beneficial microbiomes is dictated by access to public resources, such as nutritious food, clean water and air, safe shelter, social interactions, and effective medicine. In this way, microbiomes have sociopolitical contexts that must be considered. The Microbes and Social Equity (MSE) Working Group connects microbiology with social equity research, education, policy, and practice to understand the interplay of microorganisms, individuals, societies, and ecosystems. Here, we outline opportunities for integrating microbiology and social equity work through broadening education and training; diversifying research topics, methods, and perspectives; and advocating for evidence-based public policy that supports sustainable, equitable, and microbial wealth for all.

CAFE MOCHA: An Integrated Platform for Discovering Clinically Relevant Molecular Changes in Cancer-An Example of Distant Metastasis- and Recurrence-Linked Classifiers in Head and Neck Squamous Cell Carcinoma.

PURPOSE: With large amounts of multidimensional molecular data on cancers generated and deposited into public repositories such as The Cancer Genome Atlas and International Cancer Genome Consortium, a cancer type agnostic and integrative platform will help to identify signatures with clinical relevance. We devised such a platform and showcase it by identifying a molecular signature for patients with metastatic and recurrent (MR) head and neck squamous cell carcinoma (HNSCC). METHODS: We devised a statistical framework accompanied by a graphical user interface-driven application, Clinical Association of Functionally Established MOlecular CHAnges ( CAFE MOCHA; https://github.com/binaypanda/CAFEMOCHA), to discover molecular signatures linked to a specific clinical attribute in a cancer type. The platform integrates mutations and indels, gene expression, DNA methylation, and copy number variations to discover a classifier first and then to predict an incoming tumor for the same by pulling defined class variables into a single framework that incorporates a coordinate geometry-based algorithm called complete specificity margin-based clustering, which ensures maximum specificity. CAFE MOCHA classifies an incoming tumor sample using either its matched normal or a built-in database of normal tissues. The application is packed and deployed using the install4j multiplatform installer. We tested CAFE MOCHA in HNSCC tumors (n = 513) followed by validation in tumors from an independent cohort (n = 18) for discovering a signature linked to distant MR. RESULTS: CAFE MOCHA identified an integrated signature, MR44, associated with distant MR HNSCC, with 80% sensitivity and 100% specificity in the discovery stage and 100% sensitivity and 100% specificity in the validation stage. CONCLUSION: CAFE MOCHA is a cancer type and clinical attribute agnostic statistical framework to discover integrated molecular signatures.