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Mosquitoes transmit medically important human pathogens, including viruses like dengue virus and parasites such as Plasmodium spp., the causative agent of malaria. Mosquito microbiomes are critically important for the ability of mosquitoes to transmit disease-causing agents. However, while large collections of bacterial isolates and genomic data exist for vertebrate microbiomes, the vast majority of work in mosquitoes to date is based on 16S rRNA gene amplicon data that provides limited taxonomic resolution and no functional information. To address this gap and facilitate future studies using experimental microbiome manipulations, we generated a bacterial Mosquito-Associated Isolate Collection (MosAIC) consisting of 392 bacterial isolates with extensive metadata and high-quality draft genome assemblies that are publicly available, both isolates and sequence data, for use by the scientific community. MosAIC encompasses 142 species spanning 29 bacterial families, with members of the Enterobacteriaceae comprising 40% of the collection. Phylogenomic analysis of 3 genera, Enterobacter, Serratia, and Elizabethkingia, reveal lineages of mosquito-associated bacteria isolated from different mosquito species in multiple laboratories. Investigation into species' pangenomes further reveals clusters of genes specific to these lineages, which are of interest for future work to test for functions connected to mosquito host association. Altogether, we describe the generation of a physical collection of mosquito-associated bacterial isolates, their genomic data, and analyses of selected groups in context of genome data from closely related isolates, providing a unique, highly valuable resource for research on bacterial colonisation and adaptation within mosquito hosts. Future efforts will expand the collection to include broader geographic and host species representation, especially from individuals collected from field populations, as well as other mosquito-associated microbes, including fungi, archaea, and protozoa. Mosquito microbiomes have an important role in disease transmission. This study presents MosAIC (Mosquito-Associated Isolate Collection), a collection of 392 mosquito-associated bacterial isolates and their genomic data, all publicly available and thus a valuable resource to study bacterial colonization and adaptation in mosquitoes. [ABSTRACT FROM AUTHOR]

Experimental data about gene functions curated from the primary literature have enormous value for research scientists in understanding biology. Using the Gene Ontology (GO), manual curation by experts has provided an important resource for studying gene function, especially within model organisms. Unprecedented expansion of the scientific literature and validation of the predicted proteins have increased both data value and the challenges of keeping pace. Capturing literature-based functional annotations is limited by the ability of biocurators to handle the massive and rapidly growing scientific literature. Within the community-oriented wiki framework for GO annotation called the Gene Ontology Normal Usage Tracking System (GONUTS), we describe an approach to expand biocuration through crowdsourcing with undergraduates. This multiplies the number of high-quality annotations in international databases, enriches our coverage of the literature on normal gene function, and pushes the field in new directions. From an intercollegiate competition judged by experienced biocurators, Community Assessment of Community Annotation with Ontologies (CACAO), we have contributed nearly 5,000 literature-based annotations. Many of those annotations are to organisms not currently well-represented within GO. Over a 10-year history, our community contributors have spurred changes to the ontology not traditionally covered by professional biocurators. The CACAO principle of relying on community members to participate in and shape the future of biocuration in GO is a powerful and scalable model used to promote the scientific enterprise. It also provides undergraduate students with a unique and enriching introduction to critical reading of primary literature and acquisition of marketable skills., Competing Interests: The authors have declared that no competing interests exist.

Misconceptions, or alternative conceptions, are incorrect understandings that students have incorporated into their prior knowledge. The goal of this study was the identification of misconceptions in microbiology held by undergraduate students upon entry into an introductory, general microbiology course. This work was the first step in developing a microbiology concept inventory based on the American Society for Microbiology's Recommended Curriculum Guidelines for Undergraduate Microbiology. Responses to true/false (T/F) questions accompanied by written explanations by undergraduate students at a diverse set of institutions were used to reveal misconceptions for fundamental microbiology concepts. These data were analyzed to identify the most difficult core concepts, misalignment between explanations and answer choices, and the most common misconceptions for each core concept. From across the core concepts, nineteen misconception themes found in at least 5% of the coded answers for a given question were identified. The top five misconceptions, with coded responses ranging from 19% to 43% of the explanations, are described, along with suggested classroom interventions. Identification of student misconceptions in microbiology provides a foundation upon which to understand students' prior knowledge and to design appropriate tools for improving instruction in microbiology.

If we are to teach effectively, tools are needed to measure student learning. A widely used method for quickly measuring student understanding of core concepts in a discipline is the concept inventory (CI). Using the American Society for Microbiology Curriculum Guidelines (ASMCG) for microbiology, faculty from 11 academic institutions created and validated a new microbiology concept inventory (MCI). The MCI was developed in three phases. In phase one, learning outcomes and fundamental statements from the ASMCG were used to create T/F questions coupled with open responses. In phase two, the 743 responses to MCI 1.0 were examined to find the most common misconceptions, which were used to create distractors for multiple-choice questions. MCI 2.0 was then administered to 1,043 students. The responses of these students were used to create MCI 3.0, a 23-question CI that measures students' understanding of all 27 fundamental statements. MCI 3.0 was found to be reliable, with a Cronbach's alpha score of 0.705 and Ferguson's delta of 0.97. Test item analysis demonstrated good validity and discriminatory power as judged by item difficulty, item discrimination, and point-biserial correlation coefficient. Comparison of pre- and posttest scores showed that microbiology students at 10 institutions showed an increase in understanding of concepts after instruction, except for questions probing metabolism (average normalized learning gain was 0.15). The MCI will enable quantitative analysis of student learning gains in understanding microbiology, help to identify misconceptions, and point toward areas where efforts should be made to develop teaching approaches to overcome them.