ASU Scholarship Showcase

Human protein diversity arises as a result of alternative splicing, single nucleotide polymorphisms (SNPs) and posttranslational modifications. Because of these processes, each protein can exists as multiple variants in vivo. Tailored strategies are needed to study these protein variants and understand their role in health and disease. In this work we utilized quantitative mass spectrometric immunoassays to determine the protein variants concentration of beta-2-microglobulin, cystatin C, retinol binding protein, and transthyretin, in a population of 500 healthy individuals. Additionally, we determined the longitudinal concentration changes for the protein variants from four individuals over a 6 month period. Along with the native forms of the four proteins, 13 posttranslationally modified variants and 7 SNP-derived variants were detected and their concentration determined. Correlations of the variants concentration with geographical origin, gender, and age of the individuals were also examined. This work represents an important step toward building a catalog of protein variants concentrations and examining their longitudinal changes.

A globally integrated carbon observation and analysis system is needed to improve the fundamental understanding of the global carbon cycle, to improve our ability to project future changes, and to verify the effectiveness of policies aiming to reduce greenhouse gas emissions and increase carbon sequestration. Building an integrated carbon observation system requires transformational advances from the existing sparse, exploratory framework towards a dense, robust, and sustained system in all components: anthropogenic emissions, the atmosphere, the ocean, and the terrestrial biosphere. The paper is addressed to scientists, policymakers, and funding agencies who need to have a global picture of the current state of the (diverse) carbon observations.

We identify the current state of carbon observations, and the needs and notional requirements for a global integrated carbon observation system that can be built in the next decade. A key conclusion is the substantial expansion of the ground-based observation networks required to reach the high spatial resolution for CO₂ and CH₄ fluxes, and for carbon stocks for addressing policy-relevant objectives, and attributing flux changes to underlying processes in each region. In order to establish flux and stock diagnostics over areas such as the southern oceans, tropical forests, and the Arctic, in situ observations will have to be complemented with remote-sensing measurements. Remote sensing offers the advantage of dense spatial coverage and frequent revisit. A key challenge is to bring remote-sensing measurements to a level of long-term consistency and accuracy so that they can be efficiently combined in models to reduce uncertainties, in synergy with ground-based data.

Bringing tight observational constraints on fossil fuel and land use change emissions will be the biggest challenge for deployment of a policy-relevant integrated carbon observation system. This will require in situ and remotely sensed data at much higher resolution and density than currently achieved for natural fluxes, although over a small land area (cities, industrial sites, power plants), as well as the inclusion of fossil fuel CO₂ proxy measurements such as radiocarbon in CO₂ and carbon-fuel combustion tracers. Additionally, a policy-relevant carbon monitoring system should also provide mechanisms for reconciling regional top-down (atmosphere-based) and bottom-up (surface-based) flux estimates across the range of spatial and temporal scales relevant to mitigation policies. In addition, uncertainties for each observation data-stream should be assessed. The success of the system will rely on long-term commitments to monitoring, on improved international collaboration to fill gaps in the current observations, on sustained efforts to improve access to the different data streams and make databases interoperable, and on the calibration of each component of the system to agreed-upon international scales.

Errors in the specification or utilization of fossil fuel CO₂ emissions within carbon budget or atmospheric CO₂ inverse studies can alias the estimation of biospheric and oceanic carbon exchange. A key component in the simulation of CO₂ concentrations arising from fossil fuel emissions is the spatial distribution of the emission near coastlines. Regridding of fossil fuel CO₂ emissions (FFCO₂) from fine to coarse grids to enable atmospheric transport simulations can give rise to mismatches between the emissions and simulated atmospheric dynamics which differ over land or water. For example, emissions originally emanating from the land are emitted from a grid cell for which the vertical mixing reflects the roughness and/or surface energy exchange of an ocean surface. We test this potential "dynamical inconsistency" by examining simulated global atmospheric CO₂ concentration driven by two different approaches to regridding fossil fuel CO₂ emissions. The two approaches are as follows: (1) a commonly used method that allocates emissions to grid cells with no attempt to ensure dynamical consistency with atmospheric transport and (2) an improved method that reallocates emissions to grid cells to ensure dynamically consistent results. Results show large spatial and temporal differences in the simulated CO₂ concentration when comparing these two approaches. The emissions difference ranges from −30.3 TgC grid cell^-1 yr^-1 (−3.39 kgC m^-2 yr^-1) to +30.0 TgC grid cell^-1 yr^-1 (+2.6 kgC m^-2 yr^-1) along coastal margins. Maximum simulated annual mean CO₂ concentration differences at the surface exceed ±6 ppm at various locations and times. Examination of the current CO₂ monitoring locations during the local afternoon, consistent with inversion modeling system sampling and measurement protocols, finds maximum hourly differences at 38 stations exceed ±0.10 ppm with individual station differences exceeding −32 ppm. The differences implied by not accounting for this dynamical consistency problem are largest at monitoring sites proximal to large coastal urban areas and point sources. These results suggest that studies comparing simulated to observed atmospheric CO₂ concentration, such as atmospheric CO₂ inversions, must take measures to correct for this potential problem and ensure flux and dynamical consistency.

The City of Phoenix (Arizona, USA) developed a Tree and Shade Master Plan and a Cool Roofs initiative to ameliorate extreme heat during the summer months in their arid city. This study investigates the impact of the City's heat mitigation strategies on daytime microclimate for a pre-monsoon summer day under current climate conditions and two climate change scenarios. We assessed the cooling effect of trees and cool roofs in a Phoenix residential neighborhood using the microclimate model ENVI-met. First, using xeric landscaping as a base, we created eight tree planting scenarios (from 0% canopy cover to 30% canopy cover) for the neighborhood to characterize the relationship between canopy cover and daytime cooling benefit of trees. In a second set of simulations, we ran ENVI-met for nine combined tree planting and landscaping scenarios (mesic, oasis, and xeric) with regular roofs and cool roofs under current climate conditions and two climate change projections. For each of the 54 scenarios, we compared average neighborhood mid-afternoon air temperatures and assessed the benefits of each heat mitigation measure under current and projected climate conditions. Findings suggest that the relationship between percent canopy cover and air temperature reduction is linear, with 0.14 °C cooling per percent increase in tree cover for the neighborhood under investigation. An increase in tree canopy cover from the current 10% to a targeted 25% resulted in an average daytime cooling benefit of up to 2.0 °C in residential neighborhoods at the local scale. Cool roofs reduced neighborhood air temperatures by 0.3 °C when implemented on residential homes. The results from this city-specific mitigation project will inform messaging campaigns aimed at engaging the city decision makers, industry, and the public in the green building and urban forestry initiatives.

High-resolution, global quantification of fossil fuel CO[subscript 2] emissions is emerging as a critical need in carbon cycle science and climate policy. We build upon a previously developed fossil fuel data assimilation system (FFDAS) for estimating global high-resolution fossil fuel CO[subscript 2] emissions. We have improved the underlying observationally based data sources, expanded the approach through treatment of separate emitting sectors including a new pointwise database of global power plants, and extended the results to cover a 1997 to 2010 time series at a spatial resolution of 0.1°. Long-term trend analysis of the resulting global emissions shows subnational spatial structure in large active economies such as the United States, China, and India. These three countries, in particular, show different long-term trends and exploration of the trends in nighttime lights, and population reveal a decoupling of population and emissions at the subnational level. Analysis of shorter-term variations reveals the impact of the 2008–2009 global financial crisis with widespread negative emission anomalies across the U.S. and Europe. We have used a center of mass (CM) calculation as a compact metric to express the time evolution of spatial patterns in fossil fuel CO[subscript 2] emissions. The global emission CM has moved toward the east and somewhat south between 1997 and 2010, driven by the increase in emissions in China and South Asia over this time period. Analysis at the level of individual countries reveals per capita CO[subscript 2] emission migration in both Russia and India. The per capita emission CM holds potential as a way to succinctly analyze subnational shifts in carbon intensity over time. Uncertainties are generally lower than the previous version of FFDAS due mainly to an improved nightlight data set.

Objective: To estimate the absolute wealth of households using data from demographic and health surveys.

Methods: We developed a new metric, the absolute wealth estimate, based on the rank of each surveyed household according to its material assets and the assumed shape of the distribution of wealth among surveyed households. Using data from 156 demographic and health surveys in 66 countries, we calculated absolute wealth estimates for households. We validated the method by comparing the proportion of households defined as poor using our estimates with published World Bank poverty headcounts. We also compared the accuracy of absolute versus relative wealth estimates for the prediction of anthropometric measures.

Findings: The median absolute wealth estimates of 1 403 186 households were 2056 international dollars per capita (interquartile range: 723-6103). The proportion of poor households based on absolute wealth estimates were strongly correlated with World Bank estimates of populations living on less than 2.00 United States dollars per capita per day (R-2=0.84). Absolute wealth estimates were better predictors of anthropometric measures than relative wealth indexes.

Conclusion: Absolute wealth estimates provide new opportunities for comparative research to assess the effects of economic resources on health and human capital, as well as the long-term health consequences of economic change and inequality.

Proteins can exist as multiple proteoforms in vivo, as a result of alternative splicing and single-nucleotide polymorphisms (SNPs), as well as posttranslational processing. To address their clinical significance in a context of diagnostic information, proteoforms require a more in-depth analysis. Mass spectrometric immunoassays (MSIA) have been devised for studying structural diversity in human proteins. MSIA enables protein profiling in a simple and high-throughput manner, by combining the selectivity of targeted immunoassays, with the specificity of mass spectrometric detection. MSIA has been used for qualitative and quantitative analysis of single and multiple proteoforms, distinguishing between normal fluctuations and changes related to clinical conditions. This mini review offers an overview of the development and application of mass spectrometric immunoassays for clinical and population proteomics studies. Provided are examples of some recent developments, and also discussed are the trends and challenges in mass spectrometry-based immunoassays for the next-phase of clinical applications.

Background: Prior studies have shown that using uterotonics to augment or induce labor before arrival at comprehensive Emergency Obstetric and Neonatal Care (CEmONC) settings (henceforth, “outside uterotonics”) may contribute to perinatal mortality in low- and middle-income countries. We estimate its effect on perinatal mortality in rural Bangladesh.

Methods: Using hospital records (23986 singleton term births, Jan 1, 2009-Dec 31, 2015) from rural Bangladesh, we use a logistic regression model to estimate the increased risk of perinatal death from uterotonics administered outside a CEmONC facility.

Results: Among term births (≥37 weeks gestation), the risk of perinatal death adjusted for key confounders is significantly increased among women reporting uterotonic use outside of CEmONC (OR = 3 · 0, 95 % CI = 2 · 4,3 · 7). This increased risk is particularly high for fresh stillbirths (OR = 4 · 0, 95 % CI = 3 · 0,5 · 3) and intrapartum-related causes of early neonatal deaths (birth asphyxia) (OR = 3 · 1, 95 % CI = 2 · 2,4 · 5).

Conclusions: In this sample, outside uterotonic use was associated with substantially increased risk of fresh stillbirths, deaths due to birth asphyxia, and all perinatal deaths. In settings of high uterotonic use outside of controlled settings, substantial improvement in both stillbirth and early neonatal mortality may be made by reducing such use.

Introduction: Apolipoprotein C-III (apoC-III) regulates triglyceride (TG) metabolism. In plasma, apoC-III exists in non-sialylated (apoC-III_0a without glycosylation and apoC-III[subscript 0b] with glycosylation), monosialylated (apoC-III₁) or disialylated (apoC-III₂) proteoforms. Our aim was to clarify the relationship between apoC-III sialylation proteoforms with fasting plasma TG concentrations.

Methods: In 204 non-diabetic adolescent participants, the relative abundance of apoC-III plasma proteoforms was measured using mass spectrometric immunoassay.

Results: Compared with the healthy weight subgroup (n = 16), the ratios of apoC-III_0a, apoC-III_0b, and apoC-III₁ to apoC-III₂ were significantly greater in overweight (n = 33) and obese participants (n = 155). These ratios were positively correlated with BMI z-scores and negatively correlated with measures of insulin sensitivity (S[subscript i]). The relationship of apoC-III₁ / apoC-III₂ with S_i persisted after adjusting for BMI (p = 0.02). Fasting TG was correlated with the ratio of apoC-III_0a / apoC-III₂ (r = 0.47, p<0.001), apoC-III_0b / apoC-III₂ (r = 0.41, p<0.001), apoC-III₁ / apoC-III₂ (r = 0.43, p<0.001). By examining apoC-III concentrations, the association of apoC-III proteoforms with TG was driven by apoC-III_0a (r = 0.57, p<0.001), apoC-III_0b (r = 0.56. p<0.001) and apoC-III₁ (r = 0.67, p<0.001), but not apoC-III₂ (r = 0.006, p = 0.9) concentrations, indicating that apoC-III relationship with plasma TG differed in apoC-III₂ compared with the other proteoforms.

Conclusion: We conclude that apoC-III_0a, apoC-III_0b, and apoC-III₁, but not apoC-III₂ appear to be under metabolic control and associate with fasting plasma TG. Measurement of apoC-III proteoforms can offer insights into the biology of TG metabolism in obesity.