Researchers from the US and the UK used a molecular clock modeling method to take a look at the H1N1 influenza A virus behind the 1918 pandemic, using full length sequence data for influenza A viruses from a range of subtypes and host preferences. The team did molecular clock analyses on each influenza A gene using a few hundred sequences apiece, generating data that puts the emergence of the 1918 pandemic flu virus sometime prior to 1907 or so. The culprit appears to have been a human H1 virus that nabbed genes from an avian strain, study authors suggest, taking on the ability to infect swine instead of humans in the years after the pandemic. They note that individuals in their 20s to 40s may have been especially susceptible to the 1918 strain, since it contained antigens distinct from those found in the H3N8 flu virus that's believed to have been circulating when that group of individuals were children.
Microbes' ability to swap metabolic products such as amino acids appears to stem from evolutionary events that optimize each bug's interactions and diminish excess biosynthetic responsibilities, according to a study by Harvard University's George Church, Columbia University's Harris Wang, and others. The team used synthetic Escherichia coli communities and comparative genomics on thousands of other microbes from various environmental contexts to assess this so-called syntrophic exchange of amino acids. Results of the experiments indicated that microbes form more cooperative relationships when required to share amino acids that take a lot of biosynthetic wherewithal to produce. Based on their findings, the study's authors argue that "amino acid biosynthesis has been broadly optimized to reduce individual metabolic burden in favor of enhanced cross-feeding to support synergistic growth across the biosphere."
A Peking University team presents a microfluidics-based method for sequencing the transcriptomes of individual cells. By capturing and cracking open single cells directly on the microfluidic device prior to reverse transcription and sequencing, the researchers found that they could ratchet up the precision and sensitivity of existing single cell RNA sequencing approaches. In proof-of-principle experiments on 94 individual mouse embryonic stem cells, they demonstrated the feasibility of using the approach to track authentic variation between the transcriptomes of various single cells.