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Kemp, J.

Publications and source records attributed to Kemp, J..

2 recordsLinked to original sources
bioRxiv2018

Reciprocal carbon subsidies between autotrophs and bacteria in stream food webs under stoichiometric constraints

O_LISoils are currently leaching out their organic matter at an increasing pace and darkening aquatic ecosystems due to climate and land use change, or recovery from acidification. The implications for stream biogeochemistry and food webs remain largely unknown, notably the metabolic balance (biotic CO2 emissions), reciprocal subsidies between autotrophs and bacteria, and trophic transfer efficiencies.\nC_LIO_LIWe use a flow food web approach to test how a small addition of labile dissolved organic matter affects the strength and dynamics of the autotrophs-bacteria interaction in streams. Our paired streams whole-ecosystem experimental approach combined with continuous whole-stream metabolism and stable isotope probing allowed to unravel carbon fluxes in the control and treatment streams.\nC_LIO_LIWe increased the natural supply of dissolved organic matter for three weeks by only 12% by continuously adding 0.5 mg L-1 of sucrose with a {delta}13C signature different from the natural organic matter. Both photosynthesis and heterotrophic respiration increased rapidly following C addition, but this was short lived due to N and P stoichiometric constraints. The resulting peak in heterotrophic respiration was of similar magnitude to natural peaks in the control observed when soils were hydrologically connected to the streams and received soil derived carbon.\nC_LIO_LICarbon reciprocal subsidies between autotrophs and bacteria in the control stream accounted for about 50% of net primary production and 75% of bacterial production, under low flow conditions when stream water was hydrologically disconnected from soil water. The reciprocal subsidies were weaker by 33% (autotrophs to bacteria) and 55% (bacteria to autotrophs) in the treatment relative to the control. Net primary production relied partly (11% in the control) on natural allochthonous dissolved organic carbon via the CO2 produced by bacterial respiration.\nC_LIO_LIMany large changes in ecosystem processes were observed in response to the sucrose addition. The light use efficiency of the autotrophs increased by 37%. Ecosystem respiration intensified by 70%, and the metabolic balance became relatively more negative, i.e. biotic CO2 emissions increased by 125%. Heterotrophic respiration and production increased by 89%, and this was reflected by a shorter (-40%) uptake length (SwOC) and faster (+92%) mineralisation velocity of organic carbon. The proportion of DOC flux respired and organic carbon use efficiency by bacteria increased by 112%.\nC_LIO_LIMacroinvertebrate consumer density increased by 72% due to sucrose addition and consumer production was 1.8 times higher in the treatment than in the control at the end of the experiment. The trophic transfer efficiencies from resources to consumers were similar between the control and the treatment (2-5%).\nC_LIO_LISynthesis. Part of the carbon derived from natural allochthonous organic matter can feed the autotrophs via the CO2 produced by stream bacterial respiration, intermingling the green and brown webs. The interaction between autotrophs and bacteria shifted from mutualism to competition with carbon addition under nutrient limitation (N, P) increasing biotic CO2 emissions. Without nutrient limitation, mutualism could be reinforced by a positive feedback loop, maintaining the same biotic CO2 emissions. A small increase in dissolved organic carbon supply from climate and land use change could have large effects on stream food web and biogeochemistry with implications for the global C cycle under stoichiometric constraints.\nC_LI

ecology↗
bioRxiv2018

Machine Learning to Predict Osteoporotic Fracture Risk from Genotypes

BackgroundGenomics-based prediction could be useful since genome-wide genotyping costs less than many clinical tests. We tested whether machine learning methods could provide a clinically-relevant genomic prediction of quantitative ultrasound speed of sound (SOS)--a risk factor for osteoporotic fracture.\n\nMethodsWe used 341,449 individuals from UK Biobank with SOS measures to develop genomically-predicted SOS (gSOS) using machine learning algorithms. We selected the optimal algorithm in 5,335 independent individuals and then validated it and its ability to predict incident fracture in an independent test dataset (N = 80,027). Finally, we explored whether genomic pre-screening could complement a UK-based osteoporosis screening strategy, based on the validated tool FRAX.\n\nResultsgSOS explained 4.8-fold more variance in SOS than FRAX clinical risk factors (CRF) alone (r2 = 23% vs. 4.8%). A standard deviation decrease in gSOS, adjusting for the CRF-FRAX score was associated with a higher increased odds of incident major osteoporotic fracture (1,491 cases / 78,536 controls, OR = 1.91 [1.70-2.14], P = 10-28) than that for measured SOS (OR = 1.60 [1.50-1.69], P = 10-52) and femoral neck bone mineral density (147 cases / 4,594 controls, OR = 1.53 [1.27-1.83], P = 10-6). Individuals in the bottom decile of the gSOS distribution had a 3.25-fold increased risk of major osteoporotic fracture (P = 10-18) compared to the top decile. A gSOS-based FRAX score, identified individuals at high risk for incident major osteoporotic fractures better than the CRF-FRAX score (P = 10-14). Introducing a genomic pre-screening step into osteoporosis screening in 4,741 individuals reduced the number of required clinical visits from 2,455 to 1,273 and the number of BMD tests from 1,013 to 473, while only reducing the sensitivity to identify individuals eligible for therapy from 99% to 95%.\n\nInterpretationThe use of genotypes in a machine learning algorithm resulted in a clinically-relevant prediction of SOS and fracture, with potential to impact healthcare resource utilization.\n\nResearch in ContextO_ST_ABSEvidence Before this StudyC_ST_ABSGenome-wide association studies have identified many loci associated with risk of clinically-relevant fracture risk factors, such as SOS. Yet, it is unclear if such information can be leveraged to identify those at risk for disease outcomes, such as osteoporotic fractures. Most previous attempts to predict disease risk from genotypes have used polygenic risk scores, which may not be optimal for genomic-prediction. Despite these obstacles, genomic-prediction could enable screening programs to be more efficient since most people screened in a population are not determined to have a level of risk that would prompt a change in clinical care. Genomic pre-screening could help identify individuals whose risk of disease is low enough that they are unlikely to benefit from screening.\n\nAdded Value of this StudyUsing a large dataset of 426,811 individuals we trained and tested a machine learning algorithm to genomically-predict SOS. This metric, gSOS, had performance characteristics for predicting fracture risk that were similar to measured SOS and femoral neck BMD. Implementing a gSOS-based pre-screening step into the UK-based osteoporosis treatment guidelines reduced the number of individuals who would require screening clinical visits and skeletal testing by approximately 50%, while having little impact on the sensitivity to identify individuals at high risk for osteoporotic fracture.\n\nImplications of all of the Available EvidenceClinically-relevant genomic prediction of heritable traits is feasible using the machine learning algorithm presented here in large sample sizes. Genome-wide genotyping is now less expensive than many clinical tests, needs to be performed once over a lifetime and could risk stratify for multiple heritable traits and diseases years prior to disease onset, providing an opportunity for prevention. The implementation of such algorithms could improve screening efficiency, yet their cost-effectiveness will need to be ascertained in subsequent analyses.

genomics↗