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Assessing Strontium and Vulnerability to Strontium in Private Drinking Water Systems in Virginia
Scott, Veronica; Juran, Luke; Ling, Erin; Benham, Brian L.; Spiller, Asa (MDPI, 2020-04-08)
A total of 1.7 million Virginians rely on private drinking water (PDW) systems and 1.3 million of those people do not know their water quality. Because most Virginians who use PDW do not know the quality of that water and since strontium poses a public health risk, this study investigates sources of strontium in PDW in Virginia and identifies the areas and populations most vulnerable. Physical factors such as rock type, rock age, and fertilizer use have been linked to elevated strontium concentrations in drinking water. Social factors such as poverty, poor diet, and adolescence also increase social vulnerability to health impacts of strontium. Using water quality data from the Virginia Household Water Quality Program (VAHWQP) and statistical and spatial analyses, physical vulnerability was found to be highest in the Ridge and Valley province of Virginia where agricultural land use and geologic formations with high strontium concentrations (e.g., limestone, dolomite, sandstone, shale) are the dominant aquifer rocks. In terms of social vulnerability, households with high levels of strontium are more likely than the average VAHWQP participant to live in a food desert. This study provides information to help 1.7 million residents of Virginia, as well as populations in neighboring states, understand their risk of exposure to strontium in PDW.
Assessment and validation of total water storage in the Chesapeake Bay watershed using GRACE
Sridhar, Venkataramana; Ali, Syed Azhar; Lakshmi, Venkataraman (Elsevier, 2019-05-22)
The Chesapeake Bay is the largest estuary in the United States, and its catchment has heterogeneous hydrological and geomorphologic characteristics. It includes seven major river basins: James, Patuxent, Potomac, Rappahannock, Susquehanna, Western Shore, Eastern Shore, and York. Remote sensing data, along with in-situ observations of streamflow and simulated water budget components, can provide significant understanding of variability in water resources availability in this diverse watershed. In this study, we quantify the terrestrial water storage using both remote sensing and in-situ data and hydrologic model outputs in the Chesapeake Bay watershed. Total water storage change (TWSC) was calculated based on the combination of three methods to identify the best approach in estimating TWSC. These methods evaluated different sources of data, including Parameter elevation Regression on Independent Slopes Model (PRISM) precipitation, MODIS ET, U.S. Geological Survey observed streamflow, and the Variable Infiltration Capacity (VIC) model. Estimated TWSC were in close agreement with GRACE-derived TWSC when we employed VIC-simulated streamflow after calibration with observed streamflow. However, the use of VIC-simulated ET or MODIS-derived ET yielded similar results for TWSC. Assessment of TWSC during extreme events (drought) during the summer months revealed that predicting ET is critical for TWSC in June–August and that VIC-simulated TWSC could be a reliable proxy for GRACE data to assess the water availability in the watershed.
A Bayesian Assignment Method for Ambiguous Bisulfite Short Reads
Tran, H.; Wu, X.; Tithi, S.; Sun, M.-A.; Xie, H.; Zhang, L. (PLOS, 2016-03-24)
DNA methylation is an epigenetic modification critical for normal development and diseases. The determination of genome-wide DNA methylation at single-nucleotide resolution is made possible by sequencing bisulfite treated DNA with next generation high-throughput sequencing. However, aligning bisulfite short reads to a reference genome remains challenging as only a limited proportion of them (around 50–70%) can be aligned uniquely; a significant proportion, known as multireads, are mapped to multiple locations and thus discarded from downstream analyses, causing financial waste and biased methylation inference. To address this issue, we develop a Bayesian model that assigns multireads to their most likely locations based on the posterior probability derived from information hidden in uniquely aligned reads. Analyses of both simulated data and real hairpin bisulfite sequencing data show that our method can effectively assign approximately 70% of the multireads to their best locations with up to 90% accuracy, leading to a significant increase in the overall mapping efficiency. Moreover, the assignment model shows robust performance with low coverage depth, making it particularly attractive considering the prohibitive cost of bisulfite sequencing. Additionally, results show that longer reads help improve the performance of the assignment model. The assignment model is also robust to varying degrees of methylation and varying sequencing error rates. Finally, incorporating prior knowledge on mutation rate and context specific methylation level into the assignment model increases inference accuracy. The assignment model is implemented in the BAM-ABS package and freely available at https://github.com/zhanglabvt/BAM_ABS.
Cell-free protein synthesis of norovirus virus-like particles
Sheng, Jiayuan; Lei, Shaohua; Yuan, Lijuan; Feng, Xueyang (Royal Society of Chemistry, 2017-05-25)
Norovirus vaccine development largely depends on recombinant virus-like-particles (VLPs). Norovirus VLPs have been produced in several cell-based expression systems with long production times. Here we report, for the first time, that norovirus VLPs can be expressed and assembled by using a cell-free protein expression system within four hours.
Characterization of future drought conditions in the Lower Mekong River Basin
Thilakarathne, Madusanka; Sridhar, Venkataramana (Elsevier, 2017-07-29)
This study evaluates future changes to drought characteristics in the Lower Mekong River Basin using climate model projections. The Lower Mekong Basin (LMB), covering Thailand, Cambodia, Laos and Vietnam, is vulnerable to increasing droughts. Univariate analysis was employed in this study to compare drought characteristics associated with different return periods for the historical period 1964–2005 and future scenarios (RCP 4.5 2016–2057, RCP 4.5 2058–2099, RCP 8.5 2016–2057 and RCP 8.5 2058–2099). Because a single drought event is defined by several correlated characteristics, drought risk assessment by a multivariate analysis was deemed appropriate, and a multivariate analysis of droughts was conducted using copula functions to investigate the differences in the trivariate joint occurrence probabilities of the historical period and future scenarios. The Standardized Precipitation Index (SPI) was selected as the drought index because of its ability to detect and compare metrological droughts across time and space scales. Historical precipitation data from 1964 to 2005 and future precipitation projections from 2016 to 2099 for 15 global circulation models (GCMs) obtained from the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) dataset were employed. In all future scenarios, the Lower LMB and 3S subbasins were expected to experience more severe and intense droughts. The multivariate drought risk assessment revealed an increase in drought risks in the LMB. However, the Chi-Mun subbasin may experience an alleviation of future drought characteristics. Because the basin was expected to experience an increase in average monthly precipitation in most months, the variability in magnitude suggested that the LMB region requires adaptation strategies to address future drought occurrences.
Coenzyme Engineering of a Hyperthermophilic 6-Phosphogluconate Dehydrogenase from NADP(+) to NAD(+) with Its Application to Biobatteries
Chen, Hui; Zhu, Zhiguang; Huang, Rui; Zhang, Y. H. Percival (Nature Publishing Group, 2016-11-02)
Engineering the coenzyme specificity of redox enzymes plays an important role in metabolic engineering, synthetic biology, and biocatalysis, but it has rarely been applied to bioelectrochemistry. Here we develop a rational design strategy to change the coenzyme specificity of 6-phosphogluconate dehydrogenase (6PGDH) from a hyperthermophilic bacterium Thermotoga maritima from its natural coenzyme NADP(+) to NAD(+). Through amino acid-sequence alignment of NADP(+)-and NAD(+)-preferred 6PGDH enzymes and computer-aided substrate-coenzyme docking, the key amino acid residues responsible for binding the phosphate group of NADP(+) were identified. Four mutants were obtained via site-directed mutagenesis. The best mutant N32E/R33I/T34I exhibited a x 6.4 x 10(4)-fold reversal of the coenzyme selectivity from NADP(+) to NAD(+). The maximum power density and current density of the biobattery catalyzed by the mutant were 0.135 mW cm(-2) and 0.255 mA cm(-2), similar to 25% higher than those obtained from the wide-type 6PGDH-based biobattery at the room temperature. By using this 6PGDH mutant, the optimal temperature of running the biobattery was as high as 65 degrees C, leading to a high power density of 1.75 mW cm(-2). This study demonstrates coenzyme engineering of a hyperthermophilic 6PGDH and its application to high-temperature biobatteries.
Combined statistical and spatially distributed hydrological model for evaluating future drought indices in Virginia
Kang, Hyunwoo; Sridhar, Venkataramana (Elsevier, 2017-06-06)
Study region: Virginia, United States. Study focus: Climate change is expected to impact the intensity and severity of droughts; therefore, it is necessary to simulate future drought conditions using temperature and precipitation projections and hydrological models to derive reliable hydrological variables and drought indices. The objective of this study was to evaluate climate change influences on future drought potential and water resources in five major river basins in Virginia. In this study, the Soil and Water Assessment Tool (SWAT) and Coupled Model Intercomparison Project Phase 5 (CMIP5) climate models were used to compute a Standardized Soil Moisture Index (SSI), a Multivariate Standardized Drought Index (MSDI), and a Modified Palmer Drought Severity Index (MPDSI) for both historic and future periods. The drought conditions were evaluated, and their occurrences were determined at river basin scales. New hydrological insights for the region: The results of the ensemble mean of SSI indicated that there was an overall increase in agricultural drought occurrences projected in the New (> 1.3 times) and Rappahannock (> 1.13 times) river basins due to increases in evapotranspiration and surface and groundwater flow. However, MSDI and MPDSI exhibited a decrease in projected future drought, despite increases in precipitation, which suggests that it is essential to use hybridmodeling approaches and to interpret application-specific drought indices that consider both precipitation and temperature changes.
Data on floating treatment wetland aided nutrient removal from agricultural runoff using two wetland species
Spangler, Jonathan T.; Sample, David J.; Fox, Laurie J.; Owen, James S. Jr.; White, Sarah A. (Elsevier, 2018-12-15)
The data presented in this article are related to the research article entitled “Floating treatment wetland aided nutrient removal from agricultural runoff using two wetland species” (Spangler et al., 2018). This Data in Brief article provides data on concentrations of common ions, macro- and micro-nutrients and metals every other week during a floating treatment wetland (FTW) mesocosm experiment, and macro- and micro-nutrient contents in cumulative plant tissues, data on continuously monitored water temperature, and nitrogen and phosphorus removal curves assessed every other week. The full data set is made available to enable critical or extended analysis of the research.
Deriving the Reservoir Conditions for Better Water Resource Management Using Satellite-Based Earth Observations in the Lower Mekong River Basin
Ali, Syed Azhar; Sridhar, Venkataramana (MDPI, 2019-12-03)
The Mekong River basin supported a large population and ecosystem with abundant water and nutrient supply. However, the impoundments in the river can substantially alter the flow downstream and its timing. Using limited observations, this study demonstrated an approach to derive dam characteristics, including storage and flow rate, from remote-sensing-based data. Global Reservoir and Lake Monitor (GRLM), River-Lake Hydrology (RLH), and ICESat-GLAS, which generated altimetry from Jason series and inundation areas from Landsat 8, were used to estimate the reservoir surface area and change in storage over time. The inflow simulated by the variable infiltration capacity (VIC) model from 2008 to 2016 and the reservoir storage change were used in the mass balance equation to calculate outflows for three dams in the basin. Estimated reservoir total storage closely resembled the observed data, with a Nash-Sutcliffe efficiency and coefficient of determination more than 0.90 and 0.95, respectively. An average decrease of 55% in outflows was estimated during the wet season and an increase of up to 94% in the dry season for the Lam Pao. The estimated decrease in outflows during the wet season was 70% and 60% for Sirindhorn and Ubol Ratana, respectively, along with a 36% increase in the dry season for Sirindhorn. Basin-wide demand for evapotranspiration, about 935 mm, implicitly matched with the annual water diversion from 1000 to 2300 million m³. From the storage–discharge rating curves, minimum storage was also evident in the monsoon season (June–July), and it reached the highest in November. This study demonstrated the utility of remote sensing products to assess the impacts of dams on flows in the Mekong River basin.
Description of future drought indices in Virginia
Kang, Hyunwoo; Sridhar, Venkataramana (Elsevier, 2017-07-20)
This article presents projected future drought occurrences in five river basins in Virginia. The Soil and Water Assessment Tool (SWAT) and the Coupled Model Intercomparison Project Phase 5 (CMIP5) climate models were used to derive input variables of multiple drought indices, such as the Standardized Soil Moisture index (SSI), the Multivariate Standardized Drought Index (MSDI), and the Modified Palmer Drought Severity Index (MPDSI) for both historic and future periods. The results of SSI indicate that there was an overall increase in agricultural drought occurrences and that these were caused by increases in evapotranspiration and runoff. However, the results of the MSDI and MPDSI projected a decrease in drought occurrences in future periods due to a greater increase in precipitation in the future. Furthermore, GCM-downscaled products (precipitation and temperature) were verified using comparisons with historic observations, and the results of uncertainty analyses suggest that the lower and upper bounds of future drought projections agree with historic conditions.
Doubling Power Output of Starch Biobattery Treated by the Most Thermostable Isoamylase from an Archaeon Sulfolobus tokodaii
Cheng, Kun; Zhang, Fei; Sun, Fangfang; Chen, Hongge; Zhang, Y. H. Percival (Nature, 2015-08-20)
Biobattery, a kind of enzymatic fuel cells, can convert organic compounds (e.g., glucose, starch) to electricity in a closed system without moving parts. Inspired by natural starch metabolism catalyzed by starch phosphorylase, isoamylase is essential to debranch alpha-1,6-glycosidic bonds of starch, yielding linear amylodextrin – the best fuel for sugar-powered biobattery. However, there is no thermostable isoamylase stable enough for simultaneous starch gelatinization and enzymatic hydrolysis, different from the case of thermostable alpha-amylase. A putative isoamylase gene was mined from megagenomic database. The open reading frame ST0928 from a hyperthermophilic archaeron Sulfolobus tokodaii was cloned and expressed in E. coli. The recombinant protein was easily purified by heat precipitation at 80 °C for 30 min. This enzyme was characterized and required Mg²⁺ as an activator. This enzyme was the most stable isoamylase reported with a half lifetime of 200 min at 90 °C in the presence of 0.5 mM MgCl₂, suitable for simultaneous starch gelatinization and isoamylase hydrolysis. The cuvett-based air-breathing biobattery powered by isoamylase-treated starch exhibited nearly doubled power outputs than that powered by the same concentration starch solution, suggesting more glucose 1-phosphate generated.
Estimating Floodplain Vegetative Roughness Using Drone-Based Laser Scanning and Structure from Motion Photogrammetry
Prior, Elizabeth M.; Aquilina, Charles A.; Czuba, Jonathan A.; Pingel, Thomas J.; Hession, W. Cully (MDPI, 2021-07-03)
Vegetation heights derived from drone laser scanning (DLS), and structure from motion (SfM) photogrammetry at the Virginia Tech StREAM Lab were utilized to determine hydraulic roughness (Manning’s roughness coefficients). We determined hydraulic roughness at three spatial scales: reach, patch, and pixel. For the reach scale, one roughness value was set for the channel, and one value for the entire floodplain. For the patch scale, vegetation heights were used to classify the floodplain into grass, scrub, and small and large trees, with a single roughness value for each. The roughness values for the reach and patch methods were calibrated using a two-dimensional (2D) hydrodynamic model (HEC-RAS) and data from in situ velocity sensors. For the pixel method, we applied empirical equations that directly estimated roughness from vegetation height for each pixel of the raster (no calibration necessary). Model simulations incorporating these roughness datasets in 2D HEC-RAS were validated against water surface elevations (WSE) from seventeen groundwater wells for seven high-flow events during the Fall of 2018 and 2019, and compared to marked flood extents. The reach method tended to overestimate while the pixel method tended to underestimate the flood extent. There were no visual differences between DLS and SfM within the pixel and patch methods when comparing flood extents. All model simulations were not significantly different with respect to the well WSEs (p > 0.05). The pixel methods had the lowest WSE RMSEs (SfM: 0.136 m, DLS: 0.124 m). The other methods had RMSE values 0.01–0.02 m larger than the DLS pixel method. Models with DLS data also had lower WSE RMSEs by 0.01 m when compared to models utilizing SfM. This difference might not justify the increased cost of a DLS setup over SfM (~150,000 vs. ~2000 USD for this study), though our use of the DLS DEM to determine SfM vegetation heights might explain this minimal difference. We expect a poorer performance of the SfM-derived vegetation heights/roughness values if we were using a SfM DEM, although further work is needed. These results will help improve hydrodynamic modeling efforts, which are becoming increasingly important for management and planning in response to climate change, specifically in regions were high flow events are increasing.
Finding What Is Inaccessible: Antimicrobial Resistance Language Use among the One Health Domains
Wind, Lauren L.; Briganti, Jonathan; Brown, Anne M.; Neher, Timothy P.; Davis, Meghan F.; Durso, Lisa M.; Spicer, Tanner; Lansing, Stephanie (MDPI, 2021-04-03)
The success of a One Health approach to combating antimicrobial resistance (AMR) requires effective data sharing across the three One Health domains (human, animal, and environment). To investigate if there are differences in language use across the One Health domains, we examined the peer-reviewed literature using a combination of text data mining and natural language processing techniques on 20,000 open-access articles related to AMR and One Health. Evaluating AMR key term frequency from the European PubMed Collection published between 1990 and 2019 showed distinct AMR language usage within each domain and incongruent language usage across domains, with significant differences in key term usage frequencies when articles were grouped by the One Health sub-specialties (2-way ANOVA; p < 0.001). Over the 29-year period, “antibiotic resistance” and “AR” were used 18 times more than “antimicrobial resistance” and “AMR”. The discord of language use across One Health potentially weakens the effectiveness of interdisciplinary research by creating accessibility issues for researchers using search engines. This research was the first to quantify this disparate language use within One Health, which inhibits collaboration and crosstalk between domains. We suggest the following for authors publishing AMR-related research within the One Health context: (1) increase title/abstract searchability by including both antimicrobial and antibiotic resistance related search terms; (2) include “One Health” in the title/abstract; and (3) prioritize open-access publication.
Future rice farming threatened by drought in the Lower Mekong Basin
Kang, Hyunwoo; Sridhar, Venkataramana; Mainuddin, Mohammed; Le, Duc Trung (Nature Research, 2021-04-30)
The Lower Mekong River basin (LMB) has experienced droughts in recent decades, causing detrimental economic losses and food security conundrums. This study quantified the impact of climate change on drought, and rainfed rice production in the LMB. The Soil and Water Assessment Tool (SWAT) and AquaCrop models were used to evaluate long-term drought indices and rainfed rice yields under historical and future climate conditions (1954–2099) with four climate models and two emission scenarios (RCP 4.5 and RCP8.5) from the Coupled Model Intercomparison Project Phase 5 (CMIP5). We found that rice yield might increase (24–43%) due to the elevated levels of atmospheric CO2 concentration (+ 34.3 to + 121.9%) and increases in precipitation. Contrastingly, considerable decreases in rice yield up to 1.5 ton/ha in the Vietnam Central High Plain (VCHP) region could be expected resulting from reduced precipitation by about 34% during drought years. To avert any major food crisis, an expansion of irrigation areas could be required to compensate for the expected reduction in rice yields. We conclude that a framework combining hydrology and crop models to assess climate change impacts on food production is key to develop adaptation strategies in the future.
Human-Induced Alterations to Land Use and Climate and Their Responses for Hydrology and Water Management in the Mekong River Basin
Sridhar, Venkataramana; Kang, Hyunwoo; Ali, Syed Azhar (MDPI, 2019-06-25)
The Mekong River Basin (MRB) is one of the significant river basins in the world. For political and economic reasons, it has remained mostly in its natural condition. However, with population increases and rapid industrial growth in the Mekong region, the river has recently become a hotbed of hydropower development projects. This study evaluated these changing hydrological conditions, primarily driven by climate as well as land use and land cover change between 1992 and 2015 and into the future. A 3% increase in croplands and a 1–2% decrease in grasslands, shrublands, and forests was evident in the basin. Similarly, an increase in temperature of 1–6 °C and in precipitation of 15% was projected for 2015–2099. These natural and climate-induced changes were incorporated into two hydrological models to evaluate impacts on water budget components, particularly streamflow. Wet season flows increased by up to 10%; no significant change in dry season flows under natural conditions was evident. Anomaly in streamflows due to climate change was present in the Chiang Saen and Luang Prabang, and the remaining flow stations showed up to a 5% increase. A coefficient of variation <1 suggested no major difference in flows between the pre- and post-development of hydropower projects. The results suggested an increasing trend in streamflow without the effect of dams, while the inclusion of a few major dams resulted in decreased river streamflow of 6% to 15% possibly due to irrigation diversions and climate change. However, these estimates fall within the range of uncertainties in natural climate variability and hydrological parameter estimations. This study offers insights into the relationship between biophysical and anthropogenic factors and highlights that management of the Mekong River is critical to optimally manage increased wet season flows and decreased dry season flows and handle irrigation diversions to meet the demand for food and energy production.
Identification of soil bacteria capable of utilizing a corn ethanol fermentation byproduct
Packard, Holly; Taylor, Zachary W.; Williams, Stephanie L.; Guimarães, Pedro Ivo; Toth, Jackson; Jensen, Roderick V.; Senger, Ryan S.; Kuhn, David D.; Stevens, Ann M. (PLoS, 2019-03-08)
A commercial corn ethanol production byproduct (syrup) was used as a bacterial growth medium with the long-term aim to repurpose the resulting microbial biomass as a protein supplement in aquaculture feeds. Anaerobic batch reactors were used to enrich for soil bacteria metabolizing the syrup as the sole nutrient source over an eight-day period with the goal of obtaining pure cultures of facultative organisms from the reactors. Amplification of the V4 variable region of the 16S rRNA gene was performed using barcoded primers to track the succession of microbes enriched for during growth on the syrup. The resulting PCR products were sequenced using Illumina MiSeq protocols, analyzed via the program QIIME, and the alpha-diversity was calculated. Seven bacterial families were the most prevalent in the bioreactor community after eight days of enrichment: Clostridiaceae, Alicyclobacillaceae, Ruminococcaceae, Burkholderiaceae, Bacillaceae, Veillonellaceae, and Enterobacteriaceae. Pure culture isolates obtained from the reactors, and additional laboratory stock strains, capable of facultative growth, were grown aerobically in microtiter plates with the syrup substrate to monitor growth yield. Reactor isolates of interest were identified at a species level using the full 16S rRNA gene and other biomarkers. Bacillus species, commonly used as probiotics in aquaculture, showed the highest biomass yield of the monocultures examined. Binary combinations of monocultures yielded no apparent synergism between organisms, suggesting competition for nutrients instead of cooperative metabolite conversion.
Improved Drought Prediction Using Near Real-Time Climate Forecasts and Simulated Hydrologic Conditions
Kang, Hyunwoo; Sridhar, Venkataramana (MDPI, 2018-05-30)
Short-term drought forecasting is helpful for establishing drought mitigation plans and for managing risks that often ensue in water resource systems. Additionally, hydrologic modeling using high-resolution spatial and temporal data is used to simulate the land surface water and energy fluxes, including runoff, baseflow, and soil moisture, which are useful for drought forecasting. In this study, the Soil and Water Assessment Tool (SWAT) and Variable Infiltration Capacity (VIC) models are used for short-term drought forecasting in the contiguous United States (CONUS), as many areas in this region are frequently affected by varying drought intensities. Weekly-to-seasonal meteorological inputs are provided by the Climate Prediction Center (CPC) for the retrospective period (January 2012 to July 2017) and Climate Forecasting System version 2 (CFS v2) for the forecasting period (August 2017 to April 2018), and these inputs are used to estimate agricultural and groundwater drought conditions. For drought assessment, three drought indices, namely, the Standardized Soil Moisture index (SSI), the Multivariate Standardized Drought Index (MSDI), and the Standardized Baseflow index (SBI), were analyzed. The accuracy of the forecasting results was verified using several a performance measure (Drought area agreement (%); DA). Generally, eight weeks of lead time forecasting showed good drought predictability from both the SWAT and VIC models for the MSDI simulations (62% for SWAT and 64% for VIC for all drought categories). However, the DA values for eight weeks lead time forecasting for SSI were 23% (SWAT) and 10% (VIC) and 7% (SWAT) and 7% (VIC) for the SBI, respectively. In addition, the accuracies of drought predictions remarkably decreased after eight weeks, and the average DA values were 36% for SWAT and 38% for VIC due to an increase in the uncertainties associated with meteorological variables in CFS v2 products. For example, there are increases in the total number of grids where the absolute values of monthly differences between CFSv2 and CPC observations exceed 20 mm and 1 °C during the forecasting period. Additionally, drought forecasting using only one variable (i.e., SSI and SBI) showed low prediction performances even for the first eight weeks. The results of this study provide insights into drought forecasting methods and provide a better understanding to plan for timely water resource management decisions.
Investigate the Metabolic Reprogramming of Saccharomyces cerevisiae for Enhanced Resistance to Mixed Fermentation Inhibitors via ¹³C Metabolic Flux Analysis
Guo, Weihua; Chen, Yingying; Wei, Na; Feng, Xueyang (PLOS, 2016-08-17)
The fermentation inhibitors from the pretreatment of lignocellulosic materials, e.g., acetic acid and furfural, are notorious due to their negative effects on the cell growth and chemical production. However, the metabolic reprogramming of the cells under these stress conditions, especially metabolic response for resistance to mixed inhibitors, has not been systematically investigated and remains mysterious. Therefore, in this study, ¹³C metabolic flux analysis (¹³C-MFA), a powerful tool to elucidate the intracellular carbon flux distributions, has been applied to two Saccharomyces cerevisiae strains with different tolerances to the inhibitors under acetic acid, furfural, and mixed (i.e., acetic acid and furfural) stress conditions to unravel the key metabolic responses. By analyzing the intracellular carbon fluxes as well as the energy and cofactor utilization under different conditions, we uncovered varied metabolic responses to different inhibitors. Under acetate stress, ATP and NADH production was slightly impaired, while NADPH tended towards overproduction. Under furfural stress, ATP and cofactors (including both NADH and NADPH) tended to be overproduced. However, under dual-stress condition, production of ATP and cofactors was severely impaired due to synergistic stress caused by the simultaneous addition of two fermentation inhibitors. Such phenomenon indicated the pivotal role of the energy and cofactor utilization in resisting the mixed inhibitors of acetic acid and furfural. Based on the discoveries, valuable insights are provided to improve the tolerance of S. cerevisiae strain and further enhance lignocellulosic fermentation.
Long-Term Homogeneity, Trend, and Change-Point Analysis of Rainfall in the Arid District of Ananthapuramu, Andhra Pradesh State, India
Patakamuri, Sandeep Kumar; Muthiah, Krishnaveni; Sridhar, Venkataramana (MDPI, 2020-01-11)
The objective of this study is to evaluate the homogeneity, trend, and trend change points in the rainfall data. Daily rainfall data was collected for the arid district of Ananthapuramu, Andhra Pradesh state, India from 1981 to 2016 at the subdistrict level and aggregated to monthly, annual, seasonal rainfall totals, and the number of rainy days. After quality checks and homogeneity analysis, a total of 27 rain gauge locations were considered for trend analysis. A serial correlation test was applied to all the time series to identify serially independent series. NonParametric Mann–Kendall test and Spearman’s rank correlation tests were applied to serially independent series. The magnitude of the trend was calculated using Sen’s slope method. For the data influenced by serial correlation, various modified versions of Mann–Kendall tests (pre-whitening, trend-free pre-whitening, bias-corrected pre-whitening, and two variants of variance correction approaches) were applied. A significant increasing summer rainfall trend is observed in six out of 27 stations. Significant decreasing trends are observed at two stations during the southwest monsoon season and at two stations during the northeast monsoon season. To identify the trend change points in the time series, distribution−free cumulative sum test, and sequential Mann–Kendall tests were applied. Two open−source library packages were developed in R language namely, ”modifiedmk” and ”trendchange” to implement the statistical tests mentioned in this paper. The study results benefit water resource management, drought mitigation, socio−economic development, and sustainable agricultural planning in the region.
Metabolic engineering of Saccharomyces cerevisiae to produce 1-hexadecanol from xylose
Guo, Weihua; Sheng, Jiayuan; Zhao, Huimin; Feng, Xueyang (2016-02-01)
Background An advantageous but challenging approach to overcome the limited supply of petroleum and relieve the greenhouse effect is to produce bulk chemicals from renewable materials. Fatty alcohols, with a billion-dollar global market, are important raw chemicals for detergents, emulsifiers, lubricants, and cosmetics production. Microbial production of fatty alcohols has been successfully achieved in several industrial microorganisms. However, most of the achievements were using glucose, an edible sugar, as the carbon source. To produce fatty alcohols in a renewable manner, non-edible sugars such as xylose will be a more appropriate feedstock. Results In this study, we aim to engineer a Saccharomyces cerevisiae strain that can efficiently convert xylose to fatty alcohols. To this end, we first introduced the fungal xylose utilization pathway consisting of xylose reductase (XR), xylitol dehydrogenase (XDH), and xylulose kinase (XKS) into a fatty alcohol-producing S. cerevisiae strain (XF3) that was developed in our previous studies to achieve 1-hexadecanol production from xylose at 0.4 g/L. We next applied promoter engineering on the xylose utilization pathway to optimize the expression levels of XR, XDH, and XKS, and increased the 1-hexadecanol titer by 171 %. To further improve the xylose-based fatty alcohol production, two optimized S. cerevisiae strains from promoter engineering were evolved with the xylose as the sole carbon source. We found that the cell growth rate was improved at the expense of decreased fatty alcohol production, which indicated 1-hexadecanol was mainly produced as a non-growth associated product. Finally, through fed-batch fermentation, we successfully achieved 1-hexadecanol production at over 1.2 g/L using xylose as the sole carbon source, which represents the highest titer of xylose-based 1-hexadecanol reported in microbes to date. Conclusions A fatty alcohol-producing S. cerevisiae strain was engineered in this study to produce 1-hexadecanol from xylose. Although the xylose pathway we developed in this study could be further improved, this proof-of-concept study, for the first time to our best knowledge, demonstrated that the xylose-based fatty alcohol could be produced in S. cerevisiae with potential applications in developing consolidated bioprocessing for producing other fatty acid-derived chemicals.

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