Browsing by Author "Chen, Ye"

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    Multi-Glycomic Characterization of Fiber from AOAC Methods Defines the Carbohydrate Structures
    Couture, Garret; Luthria, Devanand L.; Chen, Ye; Bacalzo Jr, Nikita P.; Tareq, Fakir S.; Harnly, James; Phillips, Katherine M.; Pehrsson, Pamela R.; McKillop, Kyle; Fukagawa, Naomi K.; Lebrilla, Carlito B. (American Chemical Society, 2022-11)
    Dietary fiber has long been known to be an essential component of a healthy diet, and recent investigations into the gut microbiome-health paradigm have identified fiber as a prime determinant in this interaction. Further, fiber is now known to impact the gut microbiome in a structure-specific manner, conferring differential bioactivities to these specific structures. However, current analytical methods for food carbohydrate analysis do not capture this important structural information. To address this need, we utilized rapid-throughput LC-MS methods to develop a novel analytical pipeline to determine the structural composition of soluble and insoluble fiber fractions from two AOAC methods (991.43 and 2017.16) at the total monosaccharide, glycosidic linkage, and free saccharide level. Two foods were chosen for this proof-of-concept study: oats and potato starch. For oats, both AOAC methods gave similar results. Insoluble fiber was found to be comprised of linkages corresponding to beta-glucan, arabinoxylan, xyloglucan, and mannan, while soluble fiber was found to be mostly beta-glucan, with small amounts of arabinogalactan. For raw potato starch, each AOAC method gave markedly different results in the soluble fiber fractions. These observed differences are attributable to the resistant starch content of potato starch and the different starch digestion conditions used in each method. Together, these tools are a means to obtain the complex structures present within dietary fiber while retaining "classical" determinations such as soluble and insoluble fiber. These efforts will provide an analytical framework to connect gravimetric fiber determinations with their constituent structures to better inform gut microbiome and clinical nutrition studies.
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    The power, potential, and pitfalls of open access biodiversity data in range size assessments: Lessons from the fishes
    Smith, Jennifer A.; Benson, Abigail L.; Chen, Ye; Yamada, Steffany A.; Mims, Meryl C. (Elsevier, 2019-11-14)
    Geographic rarity is a driver of a species’ intrinsic risk of extinction. It encompasses multiple key components including range size, which is one of the most commonly measured estimates of geographic rarity. Range size estimates are often used to prioritize conservation efforts when there are multiple candidate species, because data for other components of rarity such as population size are sparse, or do not exist for species of interest. Range size estimates can provide rankings of species vulnerability to changing environments or threats, identifying rare species for future study or conservation initiatives. However, range sizes can be estimated by several different metrics, and the degree of overlap in the identification of the rarest or most common species across methodologies is not well understood. This knowledge gap compromises our ability to prioritize correctly rare species, and presents a particularly difficult challenge for stream-dwelling organisms with distributions constrained to river networks. We evaluated the relationship of multiple range size estimates of a subset of freshwater fishes native to the United States to determine the degree of overlap in rarity rankings using different data sources and grain sizes. We used publicly available, open access data from the Global Biodiversity Information Facility (GBIF) to calculate extent of occurrence (minimum convex polygons) and area of occupancy (total area occupied, measured across various grain sizes). We compared range sizes estimated using GBIF data with the best available estimates of current distributions described by publicly available digital maps (NatureServe) to evaluate the efficacy of GBIF data in assessments of range size. We found strong correlations between range size estimates across analytical approaches and data sources with no detectable bias of taxonomy. We found that variation among rarity rankings was highest for species with intermediate range sizes indicating that the approaches considered here generally converge when used to identify the rarest or the most common species. Importantly, our results show that the rarest, and perhaps the most vulnerable, species are consistently identified across common methodological approaches. More broadly, our results support the use of open access biodiversity data that include opportunistically collated and collected point occurrence records as a complement to coarse-grain (e.g., whole range map) approaches, as we observed no systematic bias or deviation across data sources in our analyses. This indicates databases such as the GBIF may help fill important fundamental and applied knowledge gaps for many poorly understood species, particularly in a broad-scale, multispecies framework.
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    Quantitative Bottom-Up Glycomic Analysis of Polysaccharides in Food Matrices Using Liquid Chromatography-Tandem Mass Spectrometry
    Bacalzo, Nikita P.; Couture, Garret; Chen, Ye; Castillo, Juan J.; Phillips, Katherine M.; Fukagawa, Naomi K.; Lebrilla, Carlito B. (American Chemical Society, 2022-12)
    Carbohydrates are the most abundant biomolecules in nature, and specifically, polysaccharides are present in almost all plants and fungi. Due to their compositional diversity, polysaccharide analysis remains challenging. Compared to other biomolecules, high-throughput analysis for carbohydrates has yet to be developed. To address this gap in analytical science, we have developed a multiplexed, high-throughput, and quantitative approach for polysaccharide analysis in foods. Specifically, polysaccharides were depolymerized using a nonenzymatic chemical digestion process followed by oligosaccharide fingerprinting using high performance liquid chromatography-quadru-pole time-of-flight mass spectrometry (HPLC-QTOF-MS). Both label-free relative quantitation and absolute quantitation were done based on the abundances of oligosaccharides produced. Method validation included evaluating recovery for a range of polysaccharide standards and a breakfast cereal standard reference material. Nine polysaccharides (starch, cellulose, beta-glucan, mannan, galactan, arabinan, xylan, xyloglucan, chitin) were successfully quantitated with sufficient accuracy (5-25% bias) and high reproducibility (2- 15% CV). Additionally, the method was used to identify and quantitate polysaccharides from a diverse sample set of food samples. Absolute concentrations of nine polysaccharides from apples and onions were obtained using an external calibration curve, where varietal differences were observed in some of the samples. The methodology developed in this study will provide complementary polysaccharide-level information to deepen our understanding of the interactions of dietary polysaccharides, gut microbial community, and human health.