Metabolome-Informed Microbiome Analysis Refines Metadata Classifications and Reveals Unexpected Medication Transfer in Captive Cheetahs.

Even high-quality collection and reporting of study metadata in microbiome studies can lead to various forms of inadvertently missing or mischaracterized information that can alter the interpretation or outcome of the studies, especially with nonmodel organisms. Metabolomic profiling of fecal microbiome samples can provide empirical insight into unanticipated confounding factors that are not possible to obtain even from detailed care records. We illustrate this point using data from cheetahs from the San Diego Zoo Safari Park. The metabolomic characterization indicated that one cheetah had to be moved from the non-antibiotic-exposed group to the antibiotic-exposed group. The detection of the antibiotic in this second cheetah was likely due to grooming interactions with the cheetah that was administered antibiotics. Similarly, because transit time for stool is variable, fecal samples within the first few days of antibiotic prescription do not all contain detected antibiotics, and the microbiome is not yet affected. These insights significantly altered the way the samples were grouped for analysis (antibiotic versus no antibiotic) and the subsequent understanding of the effect of the antibiotics on the cheetah microbiome. Metabolomics also revealed information about numerous other medications and provided unexpected dietary insights that in turn improved our understanding of the molecular patterns on the impact on the community microbial structure. These results suggest that untargeted metabolomic data provide empirical evidence to correct records and aid in the monitoring of the health of nonmodel organisms in captivity, although we also expect that these methods may be appropriate for other social animals, such as cats.IMPORTANCE Metabolome-informed analyses can enhance omics studies by enabling the correct partitioning of samples by identifying hidden confounders inadvertently misrepresented or omitted from carefully curated metadata. We demonstrate here the utility of metabolomics in a study characterizing the microbiome associated with liver disease in cheetahs. Metabolome-informed reinterpretation of metagenome and metabolome profiles factored in an unexpected transfer of antibiotics, preventing misinterpretation of the data. Our work suggests that untargeted metabolomics can be used to verify, augment, and correct sample metadata to support improved grouping of sample data for microbiome analyses, here for nonmodel organisms in captivity. However, the techniques also suggest a path forward for correcting clinical information in microbiome studies more broadly to enable higher-precision analyses

Intraseasonal response of mixed layer temperature and salinity in the Bay of Bengal to heat and freshwater flux

Buoy and satellite data show pronounced subseasonal oscillations of sea surface temperature (SST) in the summertime Bay of Bengal. The SST oscillations are forced mainly by surface heat flux associated with the active break cycle of the south Asian summer monsoon. The input of freshwater (FW) from summer rain and rivers to the bay is large, but not much is known about subseasonal salinity variability. We use 2002-2007 observations from three Argo floats with 5 day repeat cycle to study the subseasonal response of temperature and salinity to surface heat and freshwater flux in the central Bay of Bengal. About 95 of Argo profiles show a shallow halocline, with substantial variability of mixed layer salinity. Estimates of surface heat and freshwater flux are based on daily satellite data sampled along the float trajectory. We find that intraseasonal variability of mixed layer temperature is mainly a response to net surface heat flux minus penetrative radiation during the summer monsoon season. In winter and spring, however, temperature variability appears to be mainly due to lateral advection rather than local heat flux. Variability of mixed layer freshwater content is generally independent of local surface flux (precipitation minus evaporation) in all seasons. There are occasions when intense monsoon rainfall leads to local freshening, but these are rare. Large fluctuations in FW appear to be due to advection, suggesting that freshwater from rivers and rain moves in eddies or filaments

Observed subseasonal variability of heat flux and the SST response of the tropical Indian Ocean

We develop an experimental daily surface heat flux data set based on satellite observations to study subseasonal variability (periods shorter than 90 days) in the tropical Indian Ocean. We use incoming shortwave and longwave radiation from the International Satellite Cloud Climatology Project, and sea surface temperature (SST) from microwave sensors, to estimate net radiative flux. Latent and sensible heat fluxes are estimated from scatterometer winds and near-surface air temperature and specific humidity from Atmospheric Infrared Sounder (AIRS) observations calibrated to buoy data. Seasonal biases in net heat flux are generally within 10 W m(-2) of estimates from moorings, and the phases and amplitudes of subseasonal variability of heat fluxes are realistic. We find that the contribution of subseasonal changes in air-sea humidity gradients to latent heat flux equals or exceeds the contribution of subseasonal changes in wind speed in all seasons. SST responds coherently to subseasonal oscillations of net heat flux associated with active and suppressed phases of atmospheric convection in the summer hemisphere. Thus, subseasonal SST changes are mainly forced by heat flux in the northeast Indian Ocean in northern summer, and in the 15 degrees S-5 degrees N latitude belt in southern summer. In the winter hemisphere, subseasonal SST changes are not a one-dimensional response to heat flux, implying that they are mainly due to oceanic advection, entrainment, or vertical mixing. The coherent evolution of subseasonal SST variability and surface heat flux suggests active coupling between SST and large-scale, organized tropical convection in the summer season

Influence of coastal upwelling on the air–sea gas exchange of CO2 in a Baltic Sea Basin

During coastal upwelling cold water from the ocean interior with high CO2 concentration is brought up to the surface, allowing this water to interact with the atmosphere. This sets the stage for events with potentially altered sea–air CO2 fluxes. Four upwelling events off the east coast of Gotland in the Baltic Sea were analyzed to assess the impact of upwelling on the air–sea exchange of CO2. For each event, the observed pCO2 were found to be a function of sea-surface temperature (SST) in the upwelling area, which allowed satellite observations of SST to form a proxy for surface water pCO2. A bulk formula was then used to estimate the air–sea CO2 flux during the upwelling events. The results show that the CO2 fluxes in the study area are highly influenced by the upwelling. Comparing with idealized cases without upwelling yields relatively large differences, ranging between 19 and 250% in reduced uptake/increased emission of CO2. Upwelling may also influence the CO2 fluxes on larger scales. A rough estimate indicates that it may also be of significant importance for the average annual CO2 flux from the Baltic Sea. Including upwelling possibly decreases the Baltic Sea annual average uptake by up to 25%

Intraseasonal response of mixed layer temperature and salinity in the Bay of Bengal to heat and freshwater flux

Buoy and satellite data show pronounced subseasonal oscillations of sea surface temperature (SST) in the summertime Bay of Bengal. The SST oscillations are forced mainly by surface heat flux associated with the active break cycle of the south Asian summer monsoon. The input of freshwater (FW) from summer rain and rivers to the bay is large, but not much is known about subseasonal salinity variability. We use 2002-2007 observations from three Argo floats with 5 day repeat cycle to study the subseasonal response of temperature and salinity to surface heat and freshwater flux in the central Bay of Bengal. About 95% of Argo profiles show a shallow halocline, with substantial variability of mixed layer salinity. Estimates of surface heat and freshwater flux are based on daily satellite data sampled along the float trajectory. We find that intraseasonal variability of mixed layer temperature is mainly a response to net surface heat flux minus penetrative radiation during the summer monsoon season. In winter and spring, however, temperature variability appears to be mainly due to lateral advection rather than local heat flux. Variability of mixed layer freshwater content is generally independent of local surface flux (precipitation minus evaporation) in all seasons. There are occasions when intense monsoon rainfall leads to local freshening, but these are rare. Large fluctuations in FW appear to be due to advection, suggesting that freshwater from rivers and rain moves in eddies or filaments

The potential of using remote sensing data to estimate air-sea CO2 exchange in the Baltic Sea

In this article, we present the first climatological map of air-sea CO2 flux over the Baltic Sea based on remote sensing data: estimates of pCO(2) derived from satellite imaging using self-organizing map classifications along with class-specific linear regressions (SOMLO methodology) and remotely sensed wind estimates. The estimates have a spatial resolution of 4 km both in latitude and longitude and a monthly temporal resolution from 1998 to 2011. The CO2 fluxes are estimated using two types of wind products, i.e. reanalysis winds and satellite wind products, the higher-resolution wind product generally leading to higher-amplitude flux estimations. Furthermore, the CO2 fluxes were also estimated using two methods: the method of Wanninkhof et al. (2013) and the method of Rutgersson and Smedman (2009). The seasonal variation in fluxes reflects the seasonal variation in pCO(2) unvaryingly over the whole Baltic Sea, with high winter CO2 emissions and high pCO(2) uptakes. All basins act as a source for the atmosphere, with a higher degree of emission in the southern regions (mean source of 1.6 mmol m(-2) d(-1) for the South Basin and 0.9 for the Central Basin) than in the northern regions (mean source of 0.1 mmol m(-2) d(-1)) and the coastal areas act as a larger sink (annual uptake of -4.2 mmol m(-2) d(-1)) than does the open sea (4 mmol m(-2) d(-1)). In its entirety, the Baltic Sea acts as a small source of 1.2 mmol m(-2) d(-1) on average and this annual uptake has increased from 1998 to 2012

Warm pool thermodynamics from the Arabian Sea Monsoon Experiment (ARMEX)

Before the onset of the south Asian summer monsoon, sea surface temperature (SST) of the north Indian Ocean warms to 30–32°C. Climatological mean mixed layer depth in spring (March–May) is 10–20 m, and net surface heat flux (Q net ) is 80–100 W m−2 into the ocean. Previous work suggests that observed spring SST warming is small mainly because of (1) penetrative flux of solar radiation through the base of the mixed layer (Q pen ) and (2) advective cooling by upper ocean currents. We estimate the role of these two processes in SST evolution from a two-week Arabian Sea Monsoon Experiment process experiment in April–May 2005 in the southeastern Arabian Sea. The upper ocean is stratified by salinity and temperature, and mixed layer depth is shallow (6 to 12 m). Current speed at 2 m depth is high even under light winds. Currents within the mixed layer are quite distinct from those at 25 m. On subseasonal scales, SST warming is followed by rapid cooling, although the ocean gains heat at the surface: Q net is about 105 W m−2 in the warming phase and 25 W m−2 in the cooling phase; penetrative loss Q pen is 80 W m−2 and 70 W m−2. In the warming phase, SST rises mainly because of heat absorbed within the mixed layer, i.e., Q net minus Q pen ; Q pen reduces the rate of SST warming by a factor of 3. In the second phase, SST cools rapidly because (1) Q pen is larger than Q net and (2) advective cooling is ∼85 W m−2. A calculation using time-averaged heat fluxes and mixed layer depth suggests that diurnal variability of fluxes and upper ocean stratification tends to warm SST on subseasonal timescale. Buoy and satellite data suggest that a typical premonsoon intraseasonal cooling event occurs under clear skies when the ocean is gaining heat through the surface. In this respect, premonsoon SST cooling in the north Indian Ocean is different from that due to the Madden-Julian oscillation or monsoon intraseasonal oscillation

Warm pool thermodynamics from the Arabian Sea Monsoon Experiment (ARMEX)

Before the onset of the south Asian summer monsoon, sea surface temperature (SST) of the north Indian Ocean warms to 30-32&#176; C. Climatological mean mixed layer depth in spring (March-May) is 10-20 m, and net surface heat flux (Q <SUB>net</SUB> ) is 80-100 W m<SUP>-2</SUP> into the ocean. Previous work suggests that observed spring SST warming is small mainly because of (1) penetrative flux of solar radiation through the base of the mixed layer (Q <SUB>pen</SUB> ) and (2) advective cooling by upper ocean currents. We estimate the role of these two processes in SST evolution from a two-week Arabian Sea Monsoon Experiment process experiment in April-May 2005 in the southeastern Arabian Sea. The upper ocean is stratified by salinity and temperature, and mixed layer depth is shallow (6 to 12 m). Current speed at 2 m depth is high even under light winds. Currents within the mixed layer are quite distinct from those at 25 m. On subseasonal scales, SST warming is followed by rapid cooling, although the ocean gains heat at the surface: Q<SUB> net</SUB> is about 105 W m<SUP>-2</SUP> in the warming phase and 25 W m<SUP>-2</SUP> in the cooling phase; penetrative loss Q <SUB>pen</SUB> is 80 W m<SUP>-2</SUP> and 70 W m<SUP>-2</SUP>. In the warming phase, SST rises mainly because of heat absorbed within the mixed layer, i.e., Q <SUB>net</SUB> minus Q <SUB>pen</SUB> ; Q<SUB> pen</SUB> reduces the rate of SST warming by a factor of 3. In the second phase, SST cools rapidly because (1) Q <SUB>pen</SUB> is larger than Q<SUB> net</SUB> and (2) advective cooling is ~85 W m<SUP>-2</SUP>. A calculation using time-averaged heat fluxes and mixed layer depth suggests that diurnal variability of fluxes and upper ocean stratification tends to warm SST on subseasonal timescale. Buoy and satellite data suggest that a typical premonsoon intraseasonal cooling event occurs under clear skies when the ocean is gaining heat through the surface. In this respect, premonsoon SST cooling in the north Indian Ocean is different from that due to the Madden-Julian oscillation or monsoon intraseasonal oscillation