Description: Eukaryotic phytoplankton are responsible for at least 20% of annual global carbonfixation.Their diversity and activity are shaped by interactions with prokaryotes as part of complexmicrobiomes. Although differences in their local species diversity have been estimated, westill have a limited understanding of environmental conditions responsible for compositionaldifferences between local species communities on a large scale from pole to pole. Here, weshow, based on pole-to-pole phytoplankton metatranscriptomes and microbial rDNAsequencing, that environmental differences between polar and non-polar upper oceans moststrongly impact the large-scale spatial pattern of biodiversity and gene activity in algalmicrobiomes. The geographic differentiation of co-occurring microbes in algal microbiomescan be well explained by the latitudinal temperature gradient and associated break points intheir beta diversity, with an average breakpoint at 14 °C ± 4.3, separating cold and warmupper oceans. As global warming impacts upper ocean temperatures, we project that breakpoints of beta diversity move markedly pole-wards. Hence, abrupt regime shifts in algalmicrobiomes could be caused by anthropogenic climate change.

Description: The Journal of Physical Chemistry B DOI: 10.1021/jp407562t

Description: Background: Two component systems (TCS) are signalling complexes manifested by a histidine kinase (receptor) and a response regulator (effector). They are the most abundant signalling pathways in prokaryotes and control a wide range of biological processes. The pairing of these two components is highly specific, often requiring costly and time-consuming experimental characterisation. Therefore, there is considerable interest in developing accurate prediction tools to lessen the burden of experimental work and cope with the ever-increasing amount of genomic information. Results: We present a novel meta-predictor, MetaPred2CS, which is based on a support vector machine. MetaPred2CS integrates six sequence-based prediction methods: in-silico two-hybrid, mirror-tree, gene fusion, phylogenetic profiling, gene neighbourhood, and gene operon. To benchmark MetaPred2CS, we also compiled a novel high-quality training dataset of experimentally deduced TCS protein pairs for k-fold cross validation, to act as a gold standard for TCS partnership predictions. Combining individual predictions using MetaPred2CS improved performance when compared to the individual methods and in comparison with a current state-of-the-art meta-predictor. Conclusion: We have developed MetaPred2CS, a support vector machine-based metapredictor for prokaryotic TCS protein pairings. Central to the success of MetaPred2CS is a strategy of integrating individual predictors that improves the overall prediction accuracy, with the in-silico two-hybrid method contributing most to performance. MetaPred2CS outperformed other available systems in our benchmark tests, and is available online at http://metapred2cs.ibers.aber.ac.uk, along with our gold standard dataset of TCS interaction pairs.