Vision and Change Through the Genome Consortium for Active Teaching using Next-Generation Sequencing (GCAT-SEEK)

Development of the Genome Consortium on Active Teaching using Next Generation Sequencing (GCAT-SEEK) is described. Workshops, educational modules, assessment resources, data analysis software and computer hardware available for faculty are described

Overexpression of the chloroplastic 2-oxoglutarate/malate transporter disturbs carbon and nitrogen homeostasis in rice

The chloroplastic 2-oxaloacetate (OAA)/malate transporter (OMT1 or DiT1) takes part in the malate valve that protects chloroplasts from excessive redox poise through export of malate and import of OAA. Together with the glutamate/malate transporter (DCT1 or DiT2), it connects carbon with nitrogen assimilation, by providing 2-oxoglutarate for the GS/GOGAT (glutamine synthetase/glutamate synthase) reaction and exporting glutamate to the cytoplasm. OMT1 further plays a prominent role in C4 photosynthesis: OAA resulting from phosphoenolpyruvate carboxylation is imported into the chloroplast, reduced to malate by plastidic NADP-malate dehydrogenase, and then exported for transport to bundle sheath cells. Both transport steps are catalyzed by OMT1, at the rate of net carbon assimilation. To engineer C4 photosynthesis into C3 crops, OMT1 must be expressed in high amounts on top of core C4 metabolic enzymes. We report here high-level expression of ZmOMT1 from maize in rice (Oryza sativa ssp. indica IR64). Increased activity of the transporter in transgenic rice was confirmed by reconstitution of transporter activity into proteoliposomes. Unexpectedly, overexpression of ZmOMT1 in rice negatively affected growth, CO2 assimilation rate, total free amino acid content, tricarboxylic acid cycle metabolites, as well as sucrose and starch contents. Accumulation of high amounts of aspartate and the impaired growth phenotype of OMT1 rice lines could be suppressed by simultaneous overexpression of ZmDiT2. Implications for engineering C4 rice are discussed

Ecological selection for small microbial genomes along a temperate-to-thermal soil gradient

Small bacterial and archaeal genomes provide insights into the minimal requirements for life(1) and are phylogenetically widespread(2). However, the precise environmental pressures that constrain genome size in free-living microorganisms are unknown. A study including isolates has shown that thermophiles and other bacteria with high optimum growth temperatures often have small genomes(3). It is unclear whether this relationship extends generally to microorganisms in nature(4,5) and more specifically to microorganisms that inhabit complex and highly variable environments, such as soils(3,6,7). To understand the genomic traits of thermally adapted microorganisms, here we investigated metagenomes from a 45 °C gradient of temperate-to-thermal soils that lie over the ongoing Centralia, Pennsylvania (USA) coal-seam fire. We found that hot soils harboured distinct communities with small genomes and small cell sizes relative to those in ambient soils. Hot soils notably lacked genes that encode known two-component regulatory systems, and antimicrobial production and resistance genes. Our results provide field evidence for the inverse relationship between microbial genome size and temperature in a diverse, free-living community over a wide range of temperatures that support microbial life. (1.) Hutchison, C. A. et al. Design and synthesis of a minimal bacterial genome. Science 351, aad6253 (2016). (2.) Hug, L. A. et al. A new view of the tree of life. Nat. Microbiol. 1, 16048 (2016). (3.) Sabath, N., Ferrada, E., Barve, A. & Wagner, A. Growth temperature and genome size in bacteria are negatively correlated, suggesting genomic streamlining during thermal adaptation. Genome Biol. Evol. 5, 966–977 (2013). (4.) Huete-Stauffer, T. M., Arandia-Gorostidi, N., Alonso-Sáez, L. & Morán, X. A. G. Experimental warming decreases the average size and nucleic acid content of marine bacterial communities. Front. Microbiol. 7, 730 (2016). (5.) Swan, B. K. et al. Prevalent genome streamlining and latitudinal divergence of planktonic bacteria in the surface ocean. Proc. Natl Acad. Sci. USA 110, 11463–11468 (2013). (6.) Brewer, T. E., Handley, K. M., Carini, P., Gilbert, J. A. & Fierer, N. Genome reduction in an abundant and ubiquitous soil bacterium ‘Candidatus Udaeobacter copiosus’. Nat. Microbiol. 2, 16198 (2016). (7.) Giovannoni, S. J., Thrash, J. C. & Temperton, B. Implications of streamlining theory for microbial ecology. ISME J. 8, 1553–1565 (2014)

Barriers to Integration of Bioinformatics into Undergraduate Life Sciences Education

Bioinformatics, a discipline that combines aspects of biology, statistics, and computer science, is increasingly important for biological research. However, bioinformatics instruction is rarely integrated into life sciences curricula at the undergraduate level. To understand why, the Network for Integrating Bioinformatics into Life Sciences Education (NIBLSE, “nibbles”) recently undertook an extensive survey of life sciences faculty in the United States. The survey responses to open-ended questions about barriers to integration were subjected to keyword analysis. The barrier most frequently reported by the ~1,260 respondents was lack of faculty training. Faculty at associate’s-granting institutions report the least training in bioinformatics and the least integration of bioinformatics into their teaching. Faculty from underrepresented minority groups (URMs) in STEM reported training barriers at a higher rate than others, although the number of URM respondents was small. Interestingly, the cohort of faculty with the most recently awarded PhD degrees reported the most training but were teaching bioinformatics at a lower rate than faculty who earned their degrees in previous decades. Other barriers reported included lack of student interest in bioinformatics; lack of student preparation in mathematics, statistics, and computer science; already overly full curricula; and limited access to resources, including hardware, software, and vetted teaching materials. The results of the survey, the largest to date on bioinformatics education, will guide efforts to further integrate bioinformatics instruction into undergraduate life sciences education

Demographics of survey respondents.

<p>The number of responses (y-axes) for each of the demographic variables (x-axes) on the survey, as follows: (A) Gender. (B) Race (People of Color and White); four categories in Race—American Indian or Alaska Native, Asian, Black or African American, Native Hawaiian or other Pacific Islander—were combined into People of Color (POC) due to very small sample numbers for each category. (C) Ethnicity (Hispanic-Latino, non-Hispanic/Latino). (D) Minority Serving (whether or not the respondent’s home institution is classified as minority-serving). (E) Highest Degree (highest degree earned: Bachelor’s, Master’s, Professional Degree, PhD). (F) Year Earned (year that the highest degree was earned; responses were grouped in the following bins: Before 1980, 1980 to 1989, 1990 to 1999, 2000 to 2009, and After 2009). (G) Training (level of bioinformatics training: None, Self-taught, Short workshop, Undergraduate/PostBacc training, Graduate class, and Graduate degree); four categories in Training—Undergraduate course, Undergraduate certificate, Undergraduate degree, and Post-baccalaureate certificate—were grouped together into “Undergrad” (undergraduate/post-baccalaureate training) due to small sample numbers in these categories. (H) Carnegie (Carnegie classification of the respondent’s home institution: Associate’s, Baccalaureate, Master’s, Doctoral). (I) Total Students (total number of students at the respondent’s home institution). (J) Total Undergraduates (number of undergraduates at the respondent’s home institution). (K) Undergraduate Majors (number of undergraduate majors in the respondent’s home department). (L) Faculty (number of faculty in the respondent’s home department).</p