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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,531 records · Page 85
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Local Spatial Heterogeneity of Holocene Carbon Accumulation throughout the Peat Profile of an Ombrotrophic Northern Minnesota Bog

Here, we evaluated the spatial heterogeneity of historical carbon accumulation rates in a forested, ombrotrophic bog in Minnesota to aid understanding of responses to an ongoing decade-long warming manipulation. Eighteen peat cores indicated that the bog has been accumulating carbon for over 11,000 years, to yield 176±40 kg C m –2 to 225±58 cm of peat depth. Estimated peat basal ages ranged from 5100 to 11,100 cal BP. The long-term apparent rate of carbon accumulation over the entire peat profile was 22±2 kg C m –2 yr –1 . Plot location within the study area did not affect carbon accumulation rates, but estimated basal ages were younger in profiles from plots closer to the bog lagg and farther from the bog outlet. In addition, carbon accumulation varied considerably over time. Early Holocene net carbon accumulation rates were 30±6 g C m –2 yr –1 . Around 3300 calendar BP, net carbon accumulation rates dropped to 15±8 g C m –2 yr –1 until the last century when net accumulation rates increased again to 74±57 g C m –2 yr –1 . During this period of low accumulation, regional droughts may have lowered the water table, allowing for enhanced aerobic decomposition and making the bog more susceptible to fire. These results suggest that experimental warming treatments, as well as a future warmer climate may reduce net carbon accumulation in peat in this and other southern boreal peatlands. Furthermore, our we caution against historical interpretations extrapolated from one or a few peat cores.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Plasma Protein Turnover Rates in Rats Using Stable Isotope Labeling, Global Proteomics, and Activity-Based Protein Profiling

Protein turnover is important for general health on cellular and organism scales providing a strategy to replace old, damaged, or dysfunctional proteins. Protein turnover also informs of biomarker kinetics, as a better understanding of synthesis and degradation of proteins increases the clinical utility of biomarkers. Here, turnover rates of plasma proteins in rats were measured in vivo using a pulse-chase stable isotope labeling experiment. During the pulse, rats (n=5) were fed 13C6-labeled lysine (“heavy”) feed for 23 days to label proteins. During the chase, feed was changed to an unlabeled equivalent feed (“light”), and blood was repeatedly sampled from rats over 10 time points for 28 days. Plasma samples were digested with trypsin, and analyzed with liquid chromatography-tandem mass spectrometry (LC-MS/MS). MaxQuant was used to identify peptides and proteins, and quantify heavy:light lysine ratios. A system of ordinary differential equations was used to calculate protein turnover rates. Using this approach, 273 proteins were identified, and turnover rates were quantified for 157 plasma proteins with half-lives ranging 0.3-103 days. For the ~70 most abundant proteins, variability in turnover rates among rats was low (median coefficient of variation: 0.09). Activity-based protein profiling was applied to pooled plasma samples to enrich serine hydrolases using a fluorophosphonate (FP2) activity-based probe. This enrichment resulted in turnover rates for an additional 17 proteins. This study is the first to measure global plasma protein turnover rates in rats in vivo, measure variability of protein turnover rates in any animal model, and utilize activity-based protein profiling for enhancing measurements of targeted, low-abundant proteins, such as those commonly used as biomarkers. Measured protein turnover rates will be important for understanding of the role of protein turnover in cellular and organism health as well as increasing the utility of protein biomarkers through better understanding of processes governing biomarker kinetics.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

The Drug-Resistant Variant P167S Expands the Substrate Profile of CTX-M β-Lactamases for Oxyimino-Cephalosporin Antibiotics by Enlarging the Active Site upon Acylation

β-Lactamases are enzymes produced by bacterial cells that provide resistance to β-lactam antibiotics. The CTX-M class of β-lactamases provides resistance against the antibiotic, cefotaxime, but not a related oxyimino-cephalosporin antibiotic, ceftazidime. β-lactamases that carry the P167S substitution, however, have been reported to provide ceftazidime resistance. The mechanism by which the P167S substitution expands the substrate profile of CTX-M enzymes is not known. In this study, CTX-M-14 was used as the model enzyme to study the structural changes caused by the P167S mutation that may accelerate ceftazidime turnover. X-ray crystallography was used to determine the structures of the CTX-M-14 P167S apo-enzyme along with the structures of the S70G/P167S, E166A/P167S and E166A mutant enzymes complexed with ceftazidime as well as the E166A/P167S apo-enzyme. The S70G and E166A mutations allow the capture of the enzyme-substrate complex and acylated forms of the ceftazidime molecule, respectively. The results showed a large conformational change in the Ω-loop of the CTX-M-14 ceftazidime acyl-enzyme complex of the P167S mutant but not in the enzyme-substrate complex suggesting the conformational change occurs upon acylation. The conformational change results in a larger active site cavity that prevents steric clash between the aminothiazole ring of ceftazidime and the Asn170 residue in the Ω-loop, allowing for accommodation of ceftazidime for hydrolysis. In addition, the conformational change in the Ω-loop was not observed in the E166A/P167S apoenzyme, suggesting the presence of acylated ceftazidime influences the conformational change. Finally, the E166A acyl-enzyme structure with ceftazidime did not exhibit the altered Ω-loop conformation, indicating the P167S substitution is required for the change. Taken together, the results reveal that the P167S substitution and the presence of acylated ceftazidime both drive the structure towards a conformational change of the Ω-loop and that in CTX-M P167S enzymes found in drug-resistant bacteria this will lead to increased ceftazidime hydrolysis. Lastly, this study demonstrates how a naturally occurring substitution can dramatically alter the active site to expand the substrate profile of an enzyme due to antibiotic selective pressure.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Molecular and Dual-Isotopic Profiling of the Microbial Controls on Nitrogen Leaching in Agricultural Soils under Managed Aquifer Recharge

Nitrate (NO 3 – ) leaching is a serious health and ecological concern in global agroecosystems, particularly those under the application of agricultural-managed aquifer recharge (Ag-MAR); however, there is an absence of information on microbial controls affecting NO 3 – leaching outcomes. We combine natural dual isotopes of NO 3 – ( 15 N/ 14 N and 18 O/ 16 O) with metagenomics, quantitative polymerase chain reaction (PCR), and a threshold indicator taxa analysis (TITAN) to investigate the activities, taxon profiles, and environmental controls of soil microbiome associated with NO 3 – leaching at different depths from Californian vineyards under Ag-MAR application. The isotopic signatures demonstrated a significant priming effect (P < 0.01) of Ag-MAR on denitrification activities in the topsoil (0–10 cm), with a 12–25-fold increase of 15 N–NO 3 – and 18 O–NO 3 – after the first 24 h of flooding, followed by a sharp decrease in the enrichment of both isotopes with ~80% decline in denitrification activities thereafter. In contrast, deeper soils (60–100 cm) showed minimal or no denitrification activities over the course of Ag-MAR application, thus resulting in 10–20-fold of residual NO 3 – being leached. Metagenomic profiling and laboratory microcosm demonstrated that both nitrifying and denitrifying groups, responsible for controlling NO 3 – leaching, decreased in abundance and potential activity rates with soil depth. TITAN suggested that Nitrosocosmicus and Bradyrhizobium, as the major nitrifier and denitrifier, had the highest and lowest tipping points with regard to the NO 3 – changes (P < 0.05), respectively. Overall, our study provides new insight into specific depth limitations of microbial controls on soil NO 3 – leaching in agroecosystems.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Bioactivity Profiling of Chemical Mixtures for Hazard Characterization

Abstract The assessment and regulation of chemical toxicity to protect human health and the environment are done one chemical at a time and seldom at environmentally relevant concentrations. However, chemicals are found in the environment as mixtures, and their toxicity is largely unknown. Understanding the hazard posed by chemicals within the mixture is critical to enforce protective measures. Here, we demonstrate the application of bioactivity profiling of environmental water samples using the sentinel and ecotoxicology model species Daphnia to reveal the biomolecular response induced by exposure to real-world mixtures. We exposed a Daphnia strain to 30 sampled waters of the Chaobai River and measured the gene expression response profiles. Using a multiblock correlation analysis, we establish correlations between chemical mixtures identified in 30 water samples with gene expression patterns induced by these chemical mixtures. We identified 80 metabolic pathways putatively activated by mixtures of inorganic ions, heavy metals, polycyclic aromatic hydrocarbons, industrial chemicals, and a set of biocides, pesticides, and pharmacologically active substances. Our data-driven approach discovered both known bioactivity signatures with previously described modes of action and new pathways linked to undiscovered potential hazards. This study demonstrates the feasibility of reducing the complexity of real-world mixture toxicity to characterize the biomolecular effects of a defined number of chemical components based on gene expression monitoring of the sentinel species Daphnia.

Engineering↗
DOE OSTI2015

Measuring Compositions in Organic Depth Profiling: Results from a VAMAS Interlaboratory Study

We report the results of a VAMAS (Versailles Project on Advanced Materials and Standards) interlaboratory study on the measurement of composition in organic depth profiling. Layered samples with known binary compositions of Irganox 1010 and either Irganox 1098 or Fmoc-pentafluoro-L-phenylalanine in each layer were manufactured in a single batch and distributed to more than 20 participating laboratories. The samples were analyzed using argon cluster ion sputtering and either X-ray Photoelectron Spectroscopy (XPS) or Time-of-Flight Secondary Ion Mass Spectrometry (ToF-SIMS) to generate depth profiles. Participants were asked to estimate the volume fractions in two of the layers and were provided with the compositions of all other layers. Participants using XPS provided volume fractions within 0.03 of the nominal values. Participants using ToF-SIMS either made no attempt, or used various methods that gave results ranging in error from 0.02 to over 0.10 in volume fraction, the latter representing a 50% relative error for a nominal volume fraction of 0.2. Error was predominantly caused by inadequacy in the ability to compensate for primary ion intensity variations and the matrix effect in SIMS. Matrix effects in these materials appear to be more pronounced as the number of atoms in both the primary analytical ion and the secondary ion increase. Using the participants’ data we show that organic SIMS matrix effects can be measured and are remarkably consistent between instruments. We provide recommendations for identifying and compensating for matrix effects. Finally we demonstrate, using a simple normalization method, that virtually all ToF-SIMS participants could have obtained estimates of volume fraction that were at least as accurate and consistent as XPS.

Environmental Molecular Sciences Laboratory↗
DOE OSTI2020

Block Design with Common Reference Samples Enables Robust Large-Scale Label-Free Quantitative Proteome Profiling

Label-free quantitative proteomics has become an increasingly popular tool for profiling global protein abundances. However, one major weakness is the potential performance drift of the LC-MS platform over time, which in turn limits its utility for analyzing large-scale sample sets. To address this, in this work we introduce an experimental and data analysis scheme based on a block-design with common controls within each block for enabling LC-MS-based large-scale label-free quantification. In this scheme, a large number of samples (e.g., >100 samples) are analyzed in smaller, and more manageable blocks, minimizing instrument drift and variability within a block. Furthermore, each designated block also contains common controls (or reference) samples for normalization within and across blocks. We demonstrated the effectiveness of this method by profiling the proteome response of human macrophage THP-1 cells to 11 engineered nanomaterials (ENMs) at two different doses. A total of 116 samples were analyzed in six blocks, yielding an average coverage of 4500 proteins per sample. The data revealed consistent quantification of proteins across all six blocks, as shown by highly stable quantification of house-keeping proteins in all samples and high levels of quantification correlation among samples from different blocks. The data also demonstrated that label-free quantification is robust and accurate enough to quantify even very subtle abundance changes as well as large fold-changes without potential ratio compression as often encountered with isobaric labeling. Our streamlined workflow is easy to implement and can be readily adapted to other large cohort studies for reproducible label-free proteome quantification.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Enhance Low Level Temperature and Moisture Profiles Through Combining NUCAPS, ABI Observations, and RTMA Analysis

Thermodynamic information from low levels in the atmosphere is crucial for operational weather forecasts and meteorological researchers. The NOAA Unique Combined Atmospheric Processing System (NUCAPS) sounding products have been proven beneficial to fill the data gap between synoptic radiosonde observations (RAOBs). However, compared with the upper troposphere, the accuracy of NUCAPS soundings in the low levels still needs improvement. In this study, a deep neural network (DNN) is applied to fuse multiple data sources to enhance the NUCAPS temperature and moisture profiles in the lower atmosphere. The network is developed by combining satellite observations, including NUCAPS sounding retrievals and high resolution geostationary satellite observations from the Advanced Baseline Imager, and surface analysis from the Real-Time Mesoscale Analysis (RTMA) as inputs, while collocated soundings from ECMWF re-analysis version 5 are used as the benchmark for the training. The performance of the model is evaluated by using the independent testing data set, data from a different year, as well as collocated RAOBs, showing improvement to the temperature and moisture profiles by reducing the root-mean-squared-error (RMSE) by more than 30% in the lower atmosphere (from 700 hPa to surface) in both clear sky and partially cloudy conditions. A convective event from June 18, 2017 is presented to illustrate the application of the enhanced low level soundings on high impact weather events. The enhanced soundings from fused data capture the large surface-based convective available potential energy structures in the preconvection environment, which is very useful for severe storm nowcasting and forecasting applications.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Modeling Profiles of Micrometeorological Variables in a Tropical Premontane Rainforest Using Multi-Layered CLM (CLM-ML)

This study updates the multi-layered Community Land Model (CLM-ml) for hillslopes and compares predictions from against observations collected in tropical montane rainforest, Costa Rica. Modifications are made in order to capture a wider array of vertical leaf area distributions, predict CO 2 profiles, account for soil respiration, and adjust wind forcings for difficult topographic settings. Test results indicate that the modified multi-layer CLM model can successfully replicate the shape of various micrometeorological profiles (humidity, CO 2 , temperature, and wind speed) under the canopy. In the single-layer models (CLM4.5 and CLM5), excessive day-to-night differences in leaf temperature and leaf wetness were originally noted, but CLM-ml significantly improved these issues, decreasing the amplitudes of diurnal cycles by 67% and 47%. Sub-canopy considerations, such as canopy shapes and turbulent transfer parameters, also played a significant role in model performance. More importantly, unlike single layer models, the results that CLM-ml produces can be compared to variables measured within the canopy to provide far more detailed diagnostic information. Further observations and model developments, aimed at reflecting surface heterogeneity, will be necessary to adequately capture the complexity and the features of the tropical montane rainforest.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Maritime Aerosol and CCN Profiles Derived From Ship₋Based Measurements Over Eastern North Pacific During MAGIC

Atmospheric aerosols are widely recognized to give rise to a substantial radiative forcing to the climate by scattering and absorbing radiation and by modifying the microphysical, lifetime, and radiative properties of clouds. During the Marine ARM GPCI Investigation of Clouds (MAGIC) over the Eastern North Pacific (ENP), the ship-based measured cloud condensation nuclei (CCN) concentration at 0.2% supersaturation (N CCN,0.2 ) and condensation nuclei concentration (N CN ) had mean values of 116.7 and 219.4 cm ⁻3 , with the highest concentrations found closest to LA due to an increase in aerosol sources. Moving westward, both N CCN,0.2 and N CN gradually decreased until stabilizing near 100 cm ⁻3 and 200 cm ⁻3 , respectively. Using the methods proposed by Ghan and Collins (2004), https://doi.org/10.1175/1520-0426(2004)021<0387:uoisdt>2.0.co;2 and Ghan et al. (2006), https://doi.org/10.1029/2004jd005752, N CCN,0.2 profiles are retrieved using the surface measured N CCN,0.2 as a constraint. For coupled conditions, correlations between the retrieved N CCN,0.2 profiles and cloud-droplet number concentration (N C ) increase from 0.26 at the surface to 0.38 near cloud base, particularly true for non-drizzling clouds. Although the correlations are lower than expected, the percentage increase (46.2%) is encouraging. Finally, the relationships between cloud breakup (CB) and the stratocumulus to cumulus transition (SCT) with environmental conditions and associated aerosols are also studied. The decreased N CN trend east of CB is mainly caused by precipitation scavenging, while the increased N CN trend west of CB is strongly associated with the increased surface wind speed and fewer drizzle events. A further study is needed using high-resolution models to simulate these events.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Turbulence Structures in the Very Stable Boundary Layer Under the Influence of Wind Profile Distortion

Abstract In very stable boundary layers (VSBL), a “cocktail” of submeso motions routinely result in elevated mean wind speed maxima above the ground, acting as a new source of turbulence generation. This new source of turbulent kinetic energy enhances turbulent mixing and causes mean wind profile distortion (WPD). As a results, this transient distortion in the wind profile adjusts the classical log‐law. Addressing how WPD‐induced turbulence regulates flow structures, turbulent fluxes, and transitions in stability regimes across layers remains a challenge. Eddy covariance data measured at four levels on a 62‐m tower are employed to address these questions. It is shown that the WPD initiates large turbulent eddies that penetrate downward, leading to enhanced vertical mixing and comparable turbulent transport efficiencies across layers. As a consequence, turbulence intensity and fluxes are increased. As the WPD is intensified, turbulent fluxes and turbulent flux transport caused by large eddies are also enhanced, leading to a transition from very stable to weakly stable regimes. Due to the influence of WPD‐induced large eddies, the large‐eddy turbulent Prandtl number does not deviate appreciably from unity and the partitioning between turbulent kinetic and potential energies is linearly related to the gradient Richardson number.

Meteorology & Atmospheric Sciences↗
DOE OSTI2025

Evaluation of Monin‐Obukhov Similarity Theory Wind Profiles in Convective Storm Environments and Cold Pools at the ARM Southern Great Plains Atmospheric Observatory

Monin-Obukhov similarity theory (MOST) is widely used in numerical weather prediction to model surface fluxes of momentum, heat, and water vapor. However, MOST is based on assumptions of steady state and horizontally homogeneous turbulence that can lead to prediction errors in and around convective storms. To understand the nature of these errors, we used wind and eddy covariance flux measurements from the Atmospheric Radiation Measurement Southern Great Plains Atmospheric Observatory to evaluate MOST wind profiles in fair-weather and convective storm environments, specifically those of mesoscale convective systems and ordinary thunderstorms. MOST wind profiles agreed well with observations in fair-weather cases, while in convective storm cases the theory systematically overestimated shear in cold pools after gust front passage. Surface layer stability was found to be important in assessing MOST within convective storm environments. The overestimation of wind shear in cold pools suggests the role of non-local fluxes in transferring momentum downward. We discuss reasons for differences and agreement with past studies, and conclude with recommendations to improve prediction of surface winds and fluxes in convective storm simulations.

58 GEOSCIENCES↗
DOE OSTI2016

High-resolution phylogenetic microbial community profiling

Over the past decade, high-throughput short-read 16S rRNA gene amplicon sequencing has eclipsed clone-dependent long-read Sanger sequencing for microbial community profiling. The transition to new technologies has provided more quantitative information at the expense of taxonomic resolution with implications for inferring metabolic traits in various ecosystems. We applied single-molecule real-time sequencing for microbial community profiling, generating full-length 16S rRNA gene sequences at high throughput, which we propose to name PhyloTags. We benchmarked and validated this approach using a defined microbial community. When further applied to samples from the water column of meromictic Sakinaw Lake, we show that while community structures at the phylum level are comparable between PhyloTags and Illumina V4 16S rRNA gene sequences (iTags), variance increases with community complexity at greater water depths. PhyloTags moreover allowed less ambiguous classification. Last, a platform-independent comparison of PhyloTags and in silico generated partial 16S rRNA gene sequences demonstrated significant differences in community structure and phylogenetic resolution across multiple taxonomic levels, including a severe underestimation in the abundance of specific microbial genera involved in nitrogen and methane cycling across the Lake's water column. Thus, PhyloTags provide a reliable adjunct or alternative to cost-effective iTags, enabling more accurate phylogenetic resolution of microbial communities and predictions on their metabolic potential.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

All-optical structuring of laser-driven proton beam profiles

Here, extreme field gradients intrinsic to relativistic laser-interactions with thin solid targets enable compact MeV proton accelerators with unique bunch characteristics. Yet, direct control of the proton beam profile is usually not possible. Here we present a readily applicable all-optical approach to imprint detailed spatial information from the driving laser pulse onto the proton bunch. In a series of experiments, counter-intuitively, the spatial profile of the energetic proton bunch was found to exhibit identical structures as the fraction of the laser pulse passing around a target of limited size. Such information transfer between the laser pulse and the naturally delayed proton bunch is attributed to the formation of quasi-static electric fields in the beam path by ionization of residual gas. Essentially acting as a programmable memory, these fields provide access to a higher level of proton beam manipulation.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Metabolic capabilities mute positive response to direct and indirect impacts of warming throughout the soil profile

Increasing global temperatures are predicted to stimulate soil microbial respiration. The direct and indirect impacts of warming on soil microbes, nevertheless, remain unclear. This is particularly true for understudied subsoil microbes. Here, we show that 4.5 years of whole-profile soil warming in a temperate mixed forest results in altered microbial community composition and metabolism in surface soils, partly due to carbon limitation. However, microbial communities in the subsoil responded differently to warming than in the surface. Throughout the soil profile—but to a greater extent in the subsoil—physiologic and genomic measurements show that phylogenetically different microbes could utilize complex organic compounds, dampening the effect of altered resource availability induced by warming. We find subsoil microbes had 20% lower carbon use efficiencies and 47% lower growth rates compared to surface soils, which constrain microbial communities. Collectively, our results show that unlike in surface soils, elevated microbial respiration in subsoils may continue without microbial community change in the near-term.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

PeakDecoder enables machine learning-based metabolite annotation and accurate profiling in multidimensional mass spectrometry measurements

Multidimensional measurements using state-of-the-art separations and mass spectrometry provide advantages in untargeted metabolomics analyses for studying biological and environmental bio-chemical processes. However, the lack of rapid analytical methods and robust algorithms for these heterogeneous data has limited its application. Here, we develop and evaluate a sensitive and high-throughput analytical and computational workflow to enable accurate metabolite profiling. Our workflow combines liquid chromatography, ion mobility spectrometry and data-independent acquisition mass spectrometry with PeakDecoder, a machine learning-based algorithm that learns to distinguish true co-elution and co-mobility from raw data and calculates metabolite identification error rates. We apply PeakDecoder for metabolite profiling of various engineered strains of Aspergillus pseudoterreus, Aspergillus niger, Pseudomonas putida and Rhodosporidium toruloides. Results, validated manually and against selected reaction monitoring and gas-chromatography platforms, show that 2683 features could be confidently annotated and quantified across 116 microbial sample runs using a library built from 64 standards.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Extracting band edge profiles at semiconductor heterostructures from hard-x-ray core-level photoelectron spectra

Hard-x-ray photoelectron spectroscopy (HAXPES) is a valuable source of information on band-edge profiles deep inside semiconductor thin films and heterojunctions. However, extracting this information requires robust and physically meaningful decomposition of spectra into contributions from individual atomic planes. We present an approach that utilizes the physical requirements of a monotonic dependence of the built-in electrostatic potential on depth and continuity of the potential function and its derivatives. These constraints enable efficient extraction of band-edge profiles and allow one to capture details of the electronic structure, including determination of the signs and magnitudes of the band bending as well as the valence band offsets. The utility of this approach to generate quantitative insight into the electronic structure of complex materials is illustrated for epitaxial SrTiO 3 on intrinsic Si(001).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Understanding the structure and composition of recalcitrant oligosaccharides in hydrolysate using high-throughput biotin-based glycome profiling and mass spectrometry

Novel Immunological and Mass Spectrometry Methods for Comprehensive Analysis of Recalcitrant Oligosaccharides in AFEX Pretreated Corn Stover. Lignocellulosic biomass is a sustainable alternative to fossil fuel and is extensively used for developing bio-based technologies to produce products such as food, feed, fuel, and chemicals. The key to these technologies is to develop cost competitive processes to convert complex carbohydrates present in plant cell wall to simple sugars such as glucose, xylose, and arabinose. Since lignocellulosic biomass is highly recalcitrant, it must undergo a combination of thermochemical treatment such as Ammonia Fiber Expansion (AFEX), dilute acid (DA), Ionic Liquid (IL) and biological treatment such as enzyme hydrolysis and microbial fermentation to produce desired products. However, when using commercial fungal enzymes during hydrolysis, only 75–85% of the soluble sugars generated are monomeric sugars, while the remaining 15–25% are soluble recalcitrant oligosaccharides that cannot be easily utilized by microorganisms. Previously, we successfully separated and purified the soluble recalcitrant oligosaccharides using a combination of charcoal and celite-based separation followed by size exclusion chromatography and studies their inhibitory properties on enzymes. We discovered that the oligosaccharides with higher degree of polymerization (DP) containing methylated uronic acid substitutions were more recalcitrant towards commercial enzyme mixtures than lower DP and neutral oligosaccharides. Here, we report the use of several complementary techniques that include glycome profiling using plant biomass glycan specific monoclonal antibodies (mAbs) to characterize sugar linkages in plant cell walls and enzymatic hydrolysate, matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF-MS) using structurally-informative diagnostic peaks offered by negative ion post-secondary decay spectra, gas chromatography followed by mass spectrometry (GC–MS) to characterize oligosaccharide sugar linkages with and without derivatization. Since oligosaccharides (DP 4–20) are small, it is challenging to mobilize these molecules for mAbs binding and characterization. To overcome this problem, we have applied a new biotin-coupling based oligosaccharide immobilization method that successfully tagged most of the low DP soluble oligosaccharides on to a micro-plate surface followed by specific linkage analysis using mAbs in a high-throughput system. This new approach will help develop more advanced versions of future high throughput glycome profiling methods that can be used to separate and characterize oligosaccharides present in biomarkers for diagnostic applications.

59 BASIC BIOLOGICAL SCIENCES↗