First genotype-phenotype study in TBX4 syndrome : gain-of-function mutations causative for lung disease

Rationale: Despite the increased recognition of TBX4-associated pulmonary arterial hypertension (PAH), genotype-phenotype associations are lacking and may provide important insights. Methods: We assembled a multi-center cohort of 137 patients harboring monoallelic TBX4 variants and assessed the pathogenicity of missense variation (n = 42) using a novel luciferase reporter assay containing T-BOX binding motifs. We sought genotype-phenotype correlations and undertook a comparative analysis with PAH patients with BMPR2 causal variants (n = 162) or no identified variants in PAH-associated genes (n = 741) genotyped via the NIHR BioResource - Rare Diseases (NBR). Results: Functional assessment of TBX4 missense variants led to the novel finding of gain-of-function effects associated with older age at diagnosis of lung disease compared to loss-of-function (p = 0.038). Variants located in the T-BOX and nuclear localization domains were associated with earlier presentation (p = 0.005) and increased incidence of interstitial lung disease (p = 0.003). Event-free survival (death or transplantation) was shorter in the T-BOX group (p = 0.022) although age had a significant effect in the hazard model (p = 0.0461). Carriers of TBX4 variants were diagnosed at a younger age (p < 0.001) and had worse baseline lung function (FEV1, FVC) (p = 0.009) compared to the BMPR2 and no identified causal variant groups. Conclusions: We demonstrated that TBX4 syndrome is not strictly the result of haploinsufficiency but can also be caused by gain-of-function. The pleiotropic effects of TBX4 in lung disease may be in part explained by the differential effect of pathogenic mutations located in critical protein domains

Rainfall interception and the coupled surface water and energy balance

Evaporation from wet canopies (E) can return up to half of incident rainfall back into the atmosphere andis a major cause of the difference in water use between forests and short vegetation. Canopy water budgetmeasurements often suggest values of E during rainfall that are several times greater than those predictedfrom Penman–Monteith theory. Our literature review identified potential issues with both estimationapproaches, producing several hypotheses that were tested using micrometeorological observations from128 FLUXNET sites world-wide. The analysis shows that FLUXNET eddy-covariance measurements tend toprovide unreliable measurements of E during rainfall. However, the other micrometeorological FLUXNETobservations do provide clues as to why conventional Penman–Monteith applications underestimateE. Aerodynamic exchange rather than radiation often drives E during rainfall, and hence errors in airhumidity measurement and aerodynamic conductance calculation have considerable impact. Further-more, evaporative cooling promotes a downwards heat flux from the air aloft as well as from the biomassand soil; energy sources that are not always considered. Accounting for these factors leads to E estimatesand modelled interception losses that are considerably higher. On the other hand, canopy water budgetmeasurements can lead to overestimates of E due to spatial sampling errors in throughfall and stem-flow, underestimation of canopy rainfall storage capacity, and incorrect calculation of rainfall duration.There are remaining questions relating to horizontal advection from nearby dry areas, infrequent large-scale turbulence under stable atmospheric conditions, and the possible mechanical removal of splashdroplets by such eddies. These questions have implications for catchment hydrology, rainfall recycling,land surface modelling, and the interpretation of eddy-covariance measurements.JRC.E.1-Disaster Risk Managemen

Are vegetation-specific model parameters required for estimating gross primary production?

Models of gross primary production (GPP) are currently parameterized with vegetation-specific parameter sets and therefore require accurate information on the distribution of vegetation to drive them. Can this parameterization scheme be replaced with a vegetation-invariant parameter set that can maintain or increase model applicability while reducing errors introduced from the uncertainty of land cover classification? Based on the measurements of ecosystem carbon fluxes from 150 globally distributed sites, we examined the predictive capacity of seven light use efficiency (LUE) models using vegetation invariant parameters across all vegetation types. Two model experiments were conducted using constant parameters for various vegetation types and vegetation-specific parameters. The results showed no significant differences in model performance between the GPP simulations from the two experiments among the seven LUE models. These results indicate that the application of LUE models can potentially be simplified with a constant parameterization scheme that is independent of land cover types and associated vegetation characteristics.JRC.H.7-Climate Risk Managemen

Joint control of terrestrial gross primary productivity by plant phenology and physiology

Terrestrial gross primary productivity (GPP) varies greatly over time and space. A better understanding of this variability is necessary for more accurate predictions of the future climate–carbon cycle feedback. Recent studies have suggested that variability in GPP is driven by a broad range of biotic and abiotic factors operating mainly through changes in vegetation phenology and physiological processes. However, it is still unclear how plant phenology and physiology can be integrated to explain the spatiotemporal variability of terrestrial GPP. Based on analyses of eddy–covariance and satellite-derived data, we decomposed annual terrestrial GPP into the length of the CO2 uptake period (CUP) and the seasonal maximal capacity of CO2 uptake (GPPmax). The product of CUP and GPPmax explained >90% of the temporal GPP variability in most areas of North America during 2000–2010 and the spatial GPP variation among globally distributed eddy flux tower sites. It also explained GPP response to the European heatwave in 2003 (r2 = 0.90) and GPP recovery after a fire disturbance in South Dakota (r2 = 0.88). Additional analysis of the eddy–covariance flux data shows that the interbiome variation in annual GPP is better explained by that in GPPmax than CUP. These findings indicate that terrestrial GPP is jointly controlled by ecosystem-level plant phenology and photosynthetic capacity, and greater understanding of GPPmax and CUP responses to environmental and biological variations will, thus, improve predictions of GPP over time and spac

Joint Control of Terrestrial Gross Primary Productivity by Plant Phenology and Physiology

Terrestrial gross primary productivity (GPP) varies greatly over time and space. A better understanding of this variability is necessary for more accurate predictions of the future climate-carbon cycle feedback. Recent studies have suggested that variability in GPP is driven by a broad range of biotic and abiotic factors operating mainly through changes in vegetation phenology and physiological processes. However, it is still unclear how plant phenology and physiology can be integrated to explain the spatiotemporal variability of terrestrial GPP. Based on analyses of eddy-covariance and satellite-derived data, we decomposed annual terrestrial GPP into the length of CO2 uptake period (CUP) and the seasonal maximal capacity of CO2 uptake (GPPmax). The product of CUP and GPPmax explained >90% of the temporal GPP variability in most areas of North America during 2000‒2010 and the spatial GPP variation among FLUXNET sites. It also explained GPP response to the European heatwave in 2003 (r2 = 0.90) and GPP recovery after a fire disturbance in South Dakota, USA (r2 = 0.88). Further analysis of the eddy-covariance flux data shows that the GPP variability among and within biomes is more explained by GPPmax than CUP. These findings indicate that terrestrial GPP is jointly controlled by ecosystem-level plant phenology and photosynthetic capacity, and it is essential to understand GPPmax and CUP responses to environmental and biological variations so as to predict GPP changes over time and space.JRC.H.7-Climate Risk Managemen