Microbial community structure and soil pH correspond to methane production in Arctic Alaska soils

While there is no doubt that biogenic methane production in the Arctic is an important aspect of global methane emissions, the relative roles of microbial community characteristics and soil environmental conditions in controlling Arctic methane emissions remains uncertain. Here, relevant methane‐cycling microbial groups were investigated at two remote Arctic sites with respect to soil potential methane production (PMP). Percent abundances of methanogens and iron‐reducing bacteria correlated with increased PMP, while methanotrophs correlated with decreased PMP. Interestingly, α‐diversity of the methanogens was positively correlated with PMP, while β‐diversity was unrelated to PMP. The β‐diversity of the entire microbial community, however, was related to PMP. Shannon diversity was a better correlate of PMP than Simpson diversity across analyses, while rarefied species richness was a weak correlate of PMP. These results demonstrate the following: first, soil pH and microbial community structure both probably control methane production in Arctic soils. Second, there may be high functional redundancy in the methanogens with regard to methane production. Third, iron‐reducing bacteria co‐occur with methanogens in Arctic soils, and iron‐reduction‐mediated effects on methanogenesis may be controlled by α‐ and β‐diversity. And finally, species evenness and rare species abundances may be driving relationships between microbial groups, influencing Arctic methane production

Tundra water budget and implications of precipitation underestimation

Difficulties in obtaining accurate precipitation measurements have limited meaningful hydrologic assessment for over a century due to performance challenges of conventional snowfall and rainfall gauges in windy environments. Here, we compare snowfall observations and bias adjusted snowfall to end-of-winter snow accumulation measurements on the ground for 16 years (1999–2014) and assess the implication of precipitation underestimation on the water balance for a low-gradient tundra wetland near Utqiagvik (formerly Barrow), Alaska (2007–2009). In agreement with other studies, and not accounting for sublimation, conventional snowfall gauges captured 23–56% of end-of-winter snow accumulation. Once snowfall and rainfall are bias adjusted, long-term annual precipitation estimates more than double (from 123 to 274 mm), highlighting the risk of studies using conventional or unadjusted precipitation that dramatically under-represent water balance components. Applying conventional precipitation information to the water balance analysis produced consistent storage deficits (79 to 152 mm) that were all larger than the largest actual deficit (75 mm), which was observed in the unusually low rainfall summer of 2007. Year-to-year variability in adjusted rainfall (±33 mm) was larger than evapotranspiration (±13 mm). Measured interannual variability in partitioning of snow into runoff (29% in 2008 to 68% in 2009) in years with similar end-of-winter snow accumulation (180 and 164 mm, respectively) highlights the importance of the previous summer's rainfall (25 and 60 mm, respectively) on spring runoff production. Incorrect representation of precipitation can therefore have major implications for Arctic water budget descriptions that in turn can alter estimates of carbon and energy fluxes

SUMOylation inhibits FOXM1 activity and delays mitotic transition

The forkhead box transcription factor FOXM1 is an essential effector of G2/M-phase transition, mitosis and the DNA damage response. As such, it is frequently deregulated during tumorigenesis. Here we report that FOXM1 is dynamically modified by SUMO1 but not by SUMO2/3 at multiple sites. We show that FOXM1 SUMOylation is enhanced in MCF-7 breast cancer cells in response to treatment with epirubicin and mitotic inhibitors. Mutation of five consensus conjugation motifs yielded a SUMOylation-deficient mutant FOXM1. Conversely, fusion of the E2 ligase Ubc9 to FOXM1 generated an auto-SUMOylating mutant (FOXM1-Ubc9). Analysis of wild-type FOXM1 and mutants revealed that SUMOylation inhibits FOXM1 activity, promotes translocation to the cytoplasm and enhances APC/Cdh1-mediated ubiquitination and degradation. Further, expression of the SUMOylation-deficient mutant enhanced cell proliferation compared with wild-type FOXM1, whereas the FOXM1-Ubc9 fusion protein resulted in persistent cyclin B1 expression and slowed the time from mitotic entry to exit. In summary, our findings suggest that SUMOylation attenuates FOXM1 activity and causes mitotic delay in cytotoxic drug response

Mapping arctic tundra vegetation communities using field spectroscopy and multispectral satellite data in North Alaska, USA

The Arctic is currently undergoing intense changes in climate; vegetation composition and productivity are expected to respond to such changes. To understand the impacts of climate change on the function of Arctic tundra ecosystems within the global carbon cycle, it is crucial to improve the understanding of vegetation distribution and heterogeneity at multiple scales. Information detailing the fine-scale spatial distribution of tundra communities provided by high resolution vegetation mapping, is needed to understand the relative contributions of and relationships between single vegetation community measurements of greenhouse gas fluxes (e.g., ~1 m chamber flux) and those encompassing multiple vegetation communities (e.g., ~300 m eddy covariance measurements). The objectives of this study were: (1) to determine whether dominant Arctic tundra vegetation communities found in different locations are spectrally distinct and distinguishable using field spectroscopy methods; and (2) to test which combination of raw reflectance and vegetation indices retrieved from field and satellite data resulted in accurate vegetation maps and whether these were transferable across locations to develop a systematic method to map dominant vegetation communities within larger eddy covariance tower footprints distributed along a 300 km transect in northern Alaska. We showed vegetation community separability primarily in the 450-510 nm, 630-690 nm and 705-745 nm regions of the spectrum with the field spectroscopy data. This is line with the different traits of these arctic tundra communities, with the drier, often non-vascular plant dominated communities having much higher reflectance in the 450-510 nm and 630-690 nm regions due to the lack of photosynthetic material, whereas the low reflectance values of the vascular plant dominated communities highlight the strong light absorption found here. High classification accuracies of 92% to 96% were achieved using linear discriminant analysis with raw and rescaled spectroscopy reflectance data and derived vegetation indices. However, lower classification accuracies (~70%) resulted when using the coarser 2.0 m WorldView-2 data inputs. The results from this study suggest that tundra vegetation communities are separable using plot-level spectroscopy with hand-held sensors. These results also show that tundra vegetation mapping can be scaled from the plot level (<1 m) to patch level (<500 m) using spectroscopy data rescaled to match the wavebands of the multispectral satellite remote sensing. We find that developing a consistent method for classification of vegetation communities across the flux tower sites is a challenging process, given thespatial variability in vegetation communities and the need for detailed vegetation survey data for training and validating classification algorithms. This study highlights the benefits of using fine-scale field spectroscopy measurements to obtain tundra vegetation classifications for landscape analyses and use in carbon flux scaling studies. Improved understanding of tundra vegetation distributions will also provide necessary insight into the ecological processes driving plant community assemblages in Arctic environments

Characterizing permafrost active layer dynamics and sensitivity to landscape spatial heterogeneity in Alaska

An important feature of the Arctic is large spatial heterogeneity in active layer conditions, which is generally poorly represented by global models and can lead to large uncertainties in predicting regional ecosystem responses and climate feedbacks. In this study, we developed a spatially integrated modeling and analysis framework combining field observations, local-scale ( ∼ 50 m resolution) active layer thickness (ALT) and soil moisture maps derived from low-frequency (L + P-band) airborne radar measurements, and global satellite environmental observations to investigate the ALT sensitivity to recent climate trends and landscape heterogeneity in Alaska. Modeled ALT results show good correspondence with in situ measurements in higherpermafrost-probability (PP ≥ 70 %) areas (n = 33; R = 0.60; mean bias = 1.58 cm; RMSE = 20.32 cm), but with larger uncertainty in sporadic and discontinuous permafrost areas. The model results also reveal widespread ALT deepening since 2001, with smaller ALT increases in northern Alaska (mean trend = 0.32 ± 1.18 cm yr−1 ) and much larger increases (> 3 cm yr−1 ) across interior and southern Alaska. The positive ALT trend coincides with regional warming and a longer snow-free season (R = 0.60 ± 0.32). A spatially integrated analysis of the radar retrievals and model sensitivity simulations demonstrated that uncertainty in the spatial and vertical distribution of soil organic carbon (SOC) was the largest factor affecting modeled ALT accuracy, while soil moisture played a secondary role. Potential improvements in characterizing SOC heterogeneity, including better spatial sampling of soil conditions and advances in remote sensing of SOC and soil moisture, will enable more accurate predictions of active layer conditions and refinement of the modeling framework across a larger domain

Mechanistic Modeling of Microtopographic Impacts on CO2 and CH4 Fluxes in an Alaskan Tundra Ecosystem Using the CLM-Microbe Model

Spatial heterogeneities in soil hydrology have been confirmed as a key control on CO2 and CH4 fluxes in the Arctic tundra ecosystem. In this study, we applied a mechanistic ecosystem model, CLM-Microbe, to examine the microtopographic impacts on CO2 and CH4 fluxes across seven landscape types in Utqiaġvik, Alaska: trough, low-centered polygon (LCP) center, LCP transition, LCP rim, high-centered polygon (HCP) center, HCP transition, and HCP rim. We first validated the CLM-Microbe model against static-chamber measured CO2 and CH4 fluxes in 2013 for three landscape types: trough, LCP center, and LCP rim. Model application showed that low-elevation and thus wetter landscape types (i.e., trough, transitions, and LCP center) had larger CH4 emissions rates with greater seasonal variations than high-elevation and drier landscape types (rims and HCP center). Sensitivity analysis indicated that substrate availability for methanogenesis (acetate, CO2&nbsp;+&nbsp;H2) is the most important factor determining CH4 emission, and vegetation physiological properties largely affect the net ecosystem carbon exchange and ecosystem respiration in Arctic tundra ecosystems. Modeled CH4 emissions for different microtopographic features were upscaled to the eddy covariance (EC) domain with an area-weighted approach before validation against EC-measured CH4 fluxes. The model underestimated the EC-measured CH4 flux by 20% and 25% at daily and hourly time steps, suggesting the importance of the time step in reporting CH4 flux. The strong microtopographic impacts on CO2 and CH4 fluxes call for a model-data integration framework for better understanding and predicting carbon flux in the highly heterogeneous Arctic landscape

Tundra photosynthesis captured by satellite-observed solar-induced chlorophyll fluorescence

Accurately quantifying the timing and magnitude of respiration and photosynthesis by high‐latitude ecosystems is important for understanding how a warming climate influences global carbon cycling. Data‐driven estimates of photosynthesis across Arctic regions often rely on satellite‐derived enhanced vegetation index (EVI); we find that satellite observations of solar‐induced chlorophyll fluorescence (SIF) provide a more direct proxy for photosynthesis. We model Alaskan tundra CO2 cycling (2012–2014) according to temperature and shortwave radiation and alternately input EVI or SIF to prescribe the annual seasonal cycle of photosynthesis. We find that EVI‐based seasonality indicates spring “green‐up” to occur 9 days prior to SIF‐based estimates, and that SIF‐based estimates agree with aircraft and tower measurements of CO2. Adopting SIF, instead of EVI, for modeling the seasonal cycle of tundra photosynthesis can result in more accurate estimates of growing season duration and net carbon uptake by arctic vegetation

Hepatitis C virus cell-cell transmission and resistance to direct-acting antiviral agents

Hepatitis C virus (HCV) is transmitted between hepatocytes via classical cell entry but also uses direct cell-cell transfer to infect neighboring hepatocytes. Viral cell-cell transmission has been shown to play an important role in viral persistence allowing evasion from neutralizing antibodies. In contrast, the role of HCV cell-cell transmission for antiviral resistance is unknown. Aiming to address this question we investigated the phenotype of HCV strains exhibiting resistance to direct-acting antivirals (DAAs) in state-of-the-art model systems for cell-cell transmission and spread. Using HCV genotype 2 as a model virus, we show that cell-cell transmission is the main route of viral spread of DAA-resistant HCV. Cell-cell transmission of DAA-resistant viruses results in viral persistence and thus hampers viral eradication. We also show that blocking cell-cell transmission using host-targeting entry inhibitors (HTEIs) was highly effective in inhibiting viral dissemination of resistant genotype 2 viruses. Combining HTEIs with DAAs prevented antiviral resistance and led to rapid elimination of the virus in cell culture model. In conclusion, our work provides evidence that cell-cell transmission plays an important role in dissemination and maintenance of resistant variants in cell culture models. Blocking virus cell-cell transmission prevents emergence of drug resistance in persistent viral infection including resistance to HCV DAAs

Late presentation increases risk and costs of non-infectious comorbidities in people with HIV: An Italian cost impact study

Background: Late presentation (LP) at the time of HIV diagnosis is defined as presentation with AIDS whatever the CD4 cell count or with CD4 <350 cells/mm. The objective of our study was to assess the prevalence of non-infectious comorbidities (NICM) and multimorbidity among HIV-positive individuals with and without a history of LP (HIV + LP and HIV + EP, respectively), and compare them to matched HIV-negative control participants from a community-based cohort. The secondary objective was to provide estimates and determinants of direct cost of medical care in HIV patients. Methods: We performed a matched cohort study including HIV + LP and HIV + EP among people attending the Modena HIV Metabolic Clinic (MHMC) in 2014. HIV-positive participants were matched in a 1:3 ratio with HIV-negative participants from the CINECA ARNO database. Multimorbidity was defined as the concurrent presence of 652 NICM. Logistic regression models were constructed to evaluate associated predictors of NICM and multimorbidity. Results: We analyzed 452 HIV + LP and 73 HIV + EP participants in comparison to 1575 HIV-negative controls. The mean age was 46 \ub1 9 years, 27.5% were women. Prevalence of NICM and multimorbidity were fourfold higher in the HIV + LP compared to the general population (p < 0.001), while HIV + EP present an intermediate risk. LP was associated with increased total costs in all age strata, but appear particularly relevant in patients above 50 years of age, after adjusting for age, multimorbidity, and antiretroviral costs. Conclusions: LP with HIV infection is still very frequent in Italy, is associated with higher prevalence of NICM and multimorbidity, and contributes to higher total care costs. Encouraging early testing and access to care is still urgently needed