BackgroundThe assessment and characterization of the gut microbiome has become a focus of research in the area of human autoimmune diseases. Ankylosing spondylitis is an inflammatory autoimmune disease and evidence showed that ankylosing spondylitis may be a microbiome-driven disease.ResultsTo investigate the relationship between the gut microbiome and ankylosing spondylitis, a quantitative metagenomics study based on deep shotgun sequencing was performed, using gut microbial DNA from 211 Chinese individuals. A total of 23,709 genes and 12 metagenomic species were shown to be differentially abundant between ankylosing spondylitis patients and healthy controls. Patients were characterized by a form of gut microbial dysbiosis that is more prominent than previously reported cases with inflammatory bowel disease. Specifically, the ankylosing spondylitis patients demonstrated increases in the abundance of Prevotella melaninogenica, Prevotella copri, and Prevotella sp. C561 and decreases in Bacteroides spp. It is noteworthy that the Bifidobacterium genus, which is commonly used in probiotics, accumulated in the ankylosing spondylitis patients. Diagnostic algorithms were established using a subset of these gut microbial biomarkers.ConclusionsAlterations of the gut microbiome are associated with development of ankylosing spondylitis. Our data suggest biomarkers identified in this study might participate in the pathogenesis or development process of ankylosing spondylitis, providing new leads for the development of new diagnostic tools and potential treatments.Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-017-1271-6) contains supplementary material, which is available to authorized users.

Abstract. Reduced-complexity climate models (RCMs) are critical in the policy and decision making space, and are directly used within multiple Intergovernmental Panel on Climate Change (IPCC) reports to complement the results of more comprehensive Earth system models. To date, evaluation of RCMs has been limited to a few independent studies. Here we introduce a systematic evaluation of RCMs in the form of the Reduced Complexity Model Intercomparison Project (RCMIP). We expect RCMIP will extend over multiple phases, with Phase 1 being the first. In Phase 1, we focus on the RCMs' global-mean temperature responses, comparing them to observations, exploring the extent to which they emulate more complex models and considering how the relationship between temperature and cumulative emissions of CO2 varies across the RCMs. Our work uses experiments which mirror those found in the Coupled Model Intercomparison Project (CMIP), which focuses on complex Earth system and atmosphere–ocean general circulation models. Using both scenario-based and idealised experiments, we examine RCMs' global-mean temperature response under a range of forcings. We find that the RCMs can all reproduce the approximately 1 ∘C of warming since pre-industrial times, with varying representations of natural variability, volcanic eruptions and aerosols. We also find that RCMs can emulate the global-mean temperature response of CMIP models to within a root-mean-square error of 0.2 ∘C over a range of experiments. Furthermore, we find that, for the Representative Concentration Pathway (RCP) and Shared Socioeconomic Pathway (SSP)-based scenario pairs that share the same IPCC Fifth Assessment Report (AR5)-consistent stratospheric-adjusted radiative forcing, the RCMs indicate higher effective radiative forcings for the SSP-based scenarios and correspondingly higher temperatures when run with the same climate settings. In our idealised setup of RCMs with a climate sensitivity of 3 ∘C, the difference for the ssp585–rcp85 pair by 2100 is around 0.23∘C(±0.12 ∘C) due to a difference in effective radiative forcings between the two scenarios. Phase 1 demonstrates the utility of RCMIP's open-source infrastructure, paving the way for further phases of RCMIP to build on the research presented here and deepen our understanding of RCMs.

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By using multisourced data and two sets of sensitivity runs from the coupled general circulation model CESM1.2.0, we investigated the effects of the spring [March, April, and May (MAM)] surface sensible heating over the Tibetan Plateau (SHTP) on the interannual variability of the North Pacific Ocean sea surface temperature (SST) and mixed layer. The results indicated that an above-normal MAM SHTP can generate a Rossby wave downstream and form an anomalous equivalent barotropic anticyclone over the North Pacific, inducing anticyclonic wind stress anomalies. As a result of Ekman transport and Ekman pumping, sea currents converge near 40°N, accompanied by weak downwelling motion. The mixed layer heat budget diagnosis indicates that the net heat fluxes, together with meridional advection anomalies, contributed significantly to changes in the mixed layer temperature (MLT). As a result, the SST anomalies (SSTAs) and MLT anomalies both present a horseshoelike pattern. In addition, the significant warm SSTAs show a maximum in the late spring, but the significant warm MLT anomalies centered under the sea surface (25-m depth) could be sustained until summer, acting like a signal storage for the anomalous spring SHTP. Moreover, the midlatitude ocean–atmosphere interaction provides a positive feedback on the development of the anomalous anticyclone over the North Pacific, since the SSTA pattern could strengthen the oceanic front and induce more active transient eddy activities. The eddy vorticity forcing that is dominant among the total atmospheric forcings tends to produce an equivalent barotropic atmospheric high pressure, which in turn intensifies the initial anomalous anticyclone.