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The data consists of annual measurements of standing aboveground plant biomass, annual aboveground net primary productivity and annual soil respiration between 1998 and 2012. Data were collected from seven European shrublands that were subject to the climate manipulations drought and warming. Sites were located in the United Kingdom (UK), the Netherlands (NL), Denmark ( two sites, DK-B and DK-M), Hungary (HU), Spain (SP) and Italy (IT). All field sites consisted of untreated control plots, plots where the plant canopy air is artificially warmed during night time hours, and plots where rainfall is excluded from the plots at least during the plants growing season. Standing aboveground plant biomass (grams biomass per square metre) was measured in two undisturbed areas within the plots using the pin-point method (UK, DK-M, DK-B), or along a transect (IT, SP, HU, NL). Aboveground net primary productivity was calculated from measurements of standing aboveground plant biomass estimates and litterfall measurements. Soil respiration was measured in pre-installed opaque soil collars bi-weekly, monthly, or in measurement campaigns (SP only). The datasets provided are the basis for the data analysis presented in Reinsch et al. (2017) Shrubland primary production and soil respiration diverge along European climate gradient. Scientific Reports 7:43952 https://doi.org/10.1038/srep43952 Standing biomass was measured using the non-destructive pin-point method to assess aboveground biomass. Measurements were conducted at the state of peak biomass specific for each site. Litterfall was measured annually using litterfall traps. Litter collected in the traps was dried and the weight was measured. Aboveground biomass productivity was estimated as the difference between the measured standing biomass in year x minus the standing biomass measured the previous year. Soil respiration was measured bi-weekly or monthly, or in campaigns (Spain only). It was measured on permanently installed soil collars in treatment plots. The Gaussen Index of Aridity (an index that combines information on rainfall and temperature) was calculated using mean annual precipitation, mean annual temperature. The reduction in precipitation and increase in temperature for each site was used to calculate the Gaussen Index for the climate treatments for each site. Data of standing biomass and soil respiration was provided by the site responsible. Data from all sites were collated into one data file for data analysis. A summary data set was combined with information on the Gaussen Index of Aridity Data were then exported from these Excel spreadsheet to .csv files for ingestion into the EIDC.

This dataset contains the data used in the manuscript "Physiological adaptation of the diatom Pseudo-nitzschia delicatissima under copper starvation" accepted for publication in April 2023 in Marine Environmental Research. In the open ocean and particularly in iron (Fe)-limited environment, copper (Cu) deficiency might limit the growth of phytoplankton species. Cu is an essential trace metal used in electron-transfer reactions, such as respiration and photosynthesis, when bound to specific enzymes. Some phytoplankton species, such as the diatom Pseudo-nitzschia spp. can cope with Cu starvation through adaptative strategies. This dataset contains the data collected during the experimental starvation of a strain of the diatom P. delicatissima under laboratory controlled conditions.

# Coral reef state influences resilience to acute climate-mediated disturbances\_Table S1 [https://doi.org/10.5061/dryad.rfj6q57gz](https://doi.org/10.5061/dryad.rfj6q57gz) The dataset provides a summary of all publications included in the analysis for this study and the key statistics obtained from the studies and used in the analyses. The dataset includes details about the publication, spatial identifiers (e.g. realm, province, ecoregion) unique site code, information on the disturbance type and timing, the pre-and post-disturbance coral cover, the 5-year annual recovery rate, the recovery shape and recovery completeness classifications. Please see details Methods in the journal article "Coral reef state influences resilience to acute climate-mediated disturbances" as published in Global Ecology and Biogeography. ## Description of the data and file structure Each column provides the following information: | Column | Detail | | ------ | ------ | | Realm | All studies were assigned to an ‘ecoregion’, ‘province’ and ‘realm’ based on their spatial location in Spalding et al. (2007)’s spatial classification system for coastal and shelf waters. | | Province | All studies were assigned to an ‘ecoregion’, ‘province’ and ‘realm’ based on their spatial location in Spalding et al. (2007)’s spatial classification system for coastal and shelf waters. | | Ecoregion | All studies were assigned to an ‘ecoregion’, ‘province’ and ‘realm’ based on their spatial location in Spalding et al. (2007)’s spatial classification system for coastal and shelf waters. | | Unique study identifier | Unique identifiers for the lowest sampling unit in the dataset. In cases where there were data for different regions, reefs, islands/atolls, sites, reef zones, depths, and/or multiple disturbances within a publication or time-series, data from these publications were divided into separate ‘studies’. | | Publication/Dataset | Unique identifiers for the publication or dataset (generally the surname of the first author followed by the year of publication). | | Publication title | Title of the publication or dataset from which the data were sourced. | | Publication year | Year the publication from the which the data were sourced was published. | | Country/Territory | Name of the country or location from which the data came. | | Site latitude | Latitude of the study site from where the data came. | | Site longitude | Longitude of the study site from where the data came. | | Disturbance type | Classification of disturbance: Temperature stress, Cyclone/ severe storm, Runoff or Multiple. | | Disturbance.year | Year of the disturbance. | | Mean coral cover pre-disturbance | Pre-disturbance coral cover as extracted from the publication or dataset as the closest data point prior to disturbance. If there is an NA value in this column then there was no pre-disturbance data available and a measure of impact was not calculated. | | Mean coral cover post-disturbance | Post-disturbance coral cover as extracted from the publication or dataset as the closest data point prior to disturbance. If there is an NA value in this column then there was no pre-disturbance data available and a measure of impact was not calculated. | | Impact (lnRR) | Impact measure: the log response ratio of pre- to post-disturbance percentage coral cover. If there is an NA value in this column then there was no pre-disturbance data available and a measure of impact was not calculated. | | Time-averaged recovery rate | Recovery rate as percentage coral cover per year in the approximate 5-year time window following disturbance. See main Methods text in manuscript for more detail. If there is an NA value in this column then the available time-series following disturbance did not satisfy the criteria for inclusion in the calculation of recovery rate. | | Recovery shape | Recovery shape category: linear, accelerating, decelerating, logistic, flatline or null. If there is an NA value in this column then the available time-series following disturbance did not satisfy the criteria for inclusion in classification of recovery shape. | | Recovery completeness | Recovery completeness category: complete recovery – coral is observed to reach its pre-disturbance coral cover, signs of recovery – a positive trajectory but not reaching pre-disturbance cover in the time period examined, undetermined – no clear pattern in recovery, the null model was the top model, no recovery – the null model was the top model but the linear model had slope and standard error in slope near zero and further decline – the top model had a negative trend. If there is an NA value in this column then the available time-series following disturbance did not satisfy the criteria for inclusion in classification of recovery shape. | | Reference | Source for the data. | ## Sharing/Access information Data was derived from the following sources: **Appendix 1. Full list of references providing the data used in impact and recovery analyses supporting Table S1** Arceo, H. O., Quibilan, M. C., Aliño, P. M., Lim, G., & Licuanan, W. Y. (2001). Coral bleaching in Philippine reefs: Coincident evidences with mesoscale thermal anomalies. Bulletin of Marine Science, 69(2), 579-593. Aronson, R. B., Precht, W. F., Toscano, M. A., & Koltes, K. H. (2002). The 1998 bleaching event and its aftermath on a coral reef in Belize. Marine Biology, 141(3), 435-447. Aronson, R. B., Sebens, K. P., & Ebersole, J. P. (1994). Hurricane Hugo's impact on Salt River submarine canyon, St. Croix, US Virgin Islands. 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Status of Caribbean coral reefs after bleaching and hurricanes in 2005. Wismer, S., Tebbett, S. B., Streit, R. P., & Bellwood, D. R. (2019). Spatial mismatch in fish and coral loss following 2016 mass coral bleaching. Science of the Total Environment, 650, 1487-1498. Woolsey, E., Bainbridge, S. J., Kingsford, M. J., & Byrne, M. (2012). Impacts of cyclone Hamish at One Tree Reef: Integrating environmental and benthic habitat data. Marine Biology, 159(4), 793-803. Aim: Understand the interplay between resistance and recovery on coral reefs, and investigate dependence on pre- and post-disturbance states, to inform generalisable reef resilience theory across large spatial and temporal scales. Location: Tropical coral reefs globally. Time period: 1966 to 2017. Major taxa studied: Scleratinian hard corals. Methods: We conducted a literature search to compile a global dataset of total coral cover before and after acute storms, temperature stress, and coastal runoff from flooding events. We used meta-regression to identify variables that explained significant variation in disturbance impact, including disturbance type, year, depth, and pre-disturbance coral cover. We further investigated the influence of these same variables, as well as post-disturbance coral cover and disturbance impact, on recovery rate. We examined the shape of recovery, assigning qualitatively distinct, ecologically relevant, population growth trajectories: linear, logistic, logarithmic (decelerating), and a second-order quadratic (accelerating). Results: We analysed 427 disturbance impacts and 117 recovery trajectories. Accelerating and logistic were the most common recovery shapes, underscoring non-linearities and recovery lags. A complex but meaningful relationship between the state of a reef pre- and post-disturbance, disturbance impact magnitude, and recovery rate was identified. Fastest recovery rates were predicted for intermediate to large disturbance impacts, but a decline in this rate was predicted when more than ~75% of pre-disturbance cover was lost. We identified a shifting baseline, with declines in both pre-and post-disturbance coral cover over the 50 year study period. Main conclusions: We breakdown the complexities of coral resilience, showing interplay between resistance and recovery, as well as dependence on both pre- and post-disturbance states, alongside documenting a chronic decline in these states. This has implications for predicting coral reef futures and implementing actions to enhance resilience. The dataset provides a summary of all studies included in the analysis and the key statistics obtained from the studies and used in the analyses for the manuscript entitled "Coral reef state influences resilience to acute climate-mediated disturbances" as published in Global Ecology and Biogeography. The dataset includes details about the publication, spatial identifiers (e.g. realm, province, ecoregion) unique site code, information on the disturbance type and timing, the pre-and post-disturbance coral cover, the 5-year annual recovery rate, the recovery shape and recovery completeness classifications. Please see details Methods in the journal article "Coral reef state influences resilience to acute climate-mediated disturbances" as published in Global Ecology and Biogeography.

Large-scale reforestation can potentially bring both benefits and risks to the water cycle, which needs to be better quantified under future climates to inform reforestation decisions. We identified 477 water-insecure basins worldwide accounting for 44.6% (380.2 Mha) of the global reforestation potential. As many of these basins are in the Asia-Pacific, we used regional coupled land-climate modelling for the period 2041–2070 to reveal that reforestation increases evapotranspiration and precipitation for most water-insecure regions over the Asia-Pacific. This resulted in a statistically significant increase in water yield (p < 0.05) for the Loess Plateau-North China Plain, Yangtze Plain, Southeast China and Irrawaddy regions. Precipitation feedback was influenced by the degree of initial moisture limitation affecting soil moisture response and thus evapotranspiration, as well as precipitation advection from other reforested regions and moisture transport away from the local region. Reforestation also reduces the probability of extremely dry months in most of the water-insecure regions. However, some regions experience non-significant declines in net water yield due to heightened evapotranspiration outstripping increases in precipitation, or declines in soil moisture and advected precipitation. This dataset contains raw data outputs for Teo et al. (2022), Global Change Biology. Please see the published paper for further details on methods. For enquiries, please contact the corresponding authors: hcteo [at] u.nus.edu or lianpinkoh [at] nus.edu.sg.  Shapefiles can be opened with any GIS program such as ArcMap or QGIS. CSV files can be opened with any spreadsheet program such as Microsoft Excel or OpenOffice.

Figure 1data_Exp 2Figure 1 data: Condition of experimental seedlings in hummocks with contrasting shrub density and tree canopy in Experiment 2: No Trees - Low Shrub biomass (NTLS), No Trees - High Shrub biomass (NTHS), Present Trees - Low Shrub biomass (PTLS) and Present Trees - High shrub biomass (PTHS) during the warmest growing season (2011) and at the end of the experiment (2013). Seedling condition was defined as: healthy (< 50% of the needles turned yellow or brown) or unhealthy (> 50% of the needles turned yellow or brown). Seedlings were 1 month old at plantation time in the July 2010.Table 1_environmental conditions_Exp 1Table 1 data: Environmental conditions and vegetation characteristics in hummocks (circular and bands) and lawns for Experiment 1. Water table depth below surface is an average for the four growing seasons (2010-2013)Table 2_ photosynthesis data_Exp 1Table 2 photosynthesis data: Photosynthesis rates for experimental pine seedlings in hummocks (circular and bands) versus adjacent lawns for Experiment 1.Table 2_seedling responses_Exp 1Table 2 data: Responses of experimental pine seedlings in hummocks (circular and bands) versus adjacent lawns for Experiment 1 after 4 growing seasons. ST: Seeds inserted on top of moss; SB: Seeds inserted below moss; Small seedling (1 month old at plantation time); Large seedling (2 months old at plantation time). Emergence = % of planted seeds emerged after 1 year. Condition = % healthy seedlings. Stem growth corresponds to vertical stem growth for germinating (ST and SB) seedlings and new stem growth for older (small and large) seedlings.Table 3_regression seedling-environment_Exp 1Table 3 data for generalized linear models assessing the responses of experimental pine seedlings in hummocks (circular and bands) and adjacent lawns for Experiment 1 during the whole experimental period (2010-2013). ST: Seedlings from seeds inserted on top of moss; SB: Seedlings from seeds inserted below moss; Small seedling (1 month old at plantation time); Large seedling (2 months old at plantation time). Condition = % healthy seedlings. Growth = stem growth.Table 4_Environmental data_Exp 2Table 4: Environmental conditions in hummocks with contrasting shrub density and tree canopy in Experiment 2: No Trees - Low Shrub biomass (NTLS), No Trees - High Shrub biomass (NTHS), Present Trees - Low Shrub biomass (PTLS) and Present Trees - High shrub biomass (PTHS).Table 4 and Table S5a_seedling performance_Exp 2Table 4: Seedling performance in hummocks with contrasting shrub density and tree canopy in Experiment 2: No Trees - Low Shrub biomass (NTLS), No Trees - High Shrub biomass (NTHS), Present Trees - Low Shrub biomass (PTLS) and Present Trees - High shrub biomass (PTHS). Seedling emergence, condition and survival from seeds inserted below the moss (SB), and from small planted seedlings.Table S3_cox regression (survival analysis)_Exp 1Table S3: Data for Cox survival analysis for experimental pine seedlings in hummocks (circular and bands) versus adjacent lawns during 2010-2013. ST: Seedlings from seeds inserted on top of moss; SB: Seedlings from seeds inserted below moss; Small seedling (1 month old, 10 cm tall at plantation time); Large seedling (2 months old, 30 cm tall at plantation time).Table S4_ regression seedling-environment 2011_Exp 1Table S4: Data for generalized linear models assessing the responses of experimental pine seedlings in hummocks (circular and bands) and adjacent lawns for Experiment 1 in 2011. Small seedling (1 month old, 10 cm tall at plantation time); Large seedling (2 months old, 30 cm tall at plantation time). Condition = % healthy seedlings. Growth = stem growth. Boreal ecosystems are warming roughly twice as fast as the global average, resulting in woody expansion that could further speed up the climate warming. Boreal peatbogs are waterlogged systems that store more than 30% of the global soil carbon. Facilitative effects of shrubs and trees on the establishment of new individuals could increase tree cover with profound consequences for the structure and functioning of boreal peatbogs, carbon sequestration and climate. We conducted two field experiments in boreal peatbogs to assess the mechanisms that explain tree seedling recruitment and to estimate the strength of positive feedbacks between shrubs and trees. We planted seeds and seedlings of Pinus sylvestris in microsites with contrasting water-tables and woody cover and manipulated both shrub canopy and root competition. We monitored seedling emergence, growth and survival for up to four growing seasons and assessed how seedling responses related to abiotic and biotic conditions. We found that tree recruitment is more successful in drier topographical microsites with deeper water-tables. On these hummocks, shrubs have both positive and negative effects on tree seedling establishment. Shrub cover improved tree seedling condition, growth and survival during the warmest growing season. In turn, higher tree basal area correlates positively with soil nutrient availability, shrub biomass and abundance of tree juveniles. Synthesis. Our results suggest that shrubs facilitate tree colonization of peatbogs which further increases shrub growth. These facilitative effects seem to be stronger under warmer conditions suggesting that a higher frequency of warmer and dry summers may lead to stronger positive interactions between shrubs and trees that could eventually facilitate a shift from moss to tree-dominated systems.

Agricultural adaptation to climate change is critical for ensuring future food security. Social capital is important for climate change adaptation, but institutions and social networks at multiple scales (e.g., household, community, and institution) have been overlooked in studying agricultural climate change adaptation. We combine data from 13 sites in 11 low-income countries in East Africa, West Africa, and South Asia to explore how multiple scales of social capital relate to household food security outcomes among smallholder farmers. Using social network theory, we define three community organizational social network types (fragmented defined by lack of coordination, brokered defined as having a strong central actor, or shared defined by high coordination) and examine household social capital through group memberships. We find community and household social capital are positively related, with higher household group membership more likely in brokered and shared networks. Household group membership is associated with more than a 10% reduction in average months of food insecurity, an effect moderated by community social network type. In communities with fragmented and shared organizational networks, additional household group memberships is associated with consistent decreases in food insecurity, in some cases up to two months; whereas in brokered networks, reductions in food insecurity are only associated with membership in credit groups. These effects are confirmed by hierarchical random effects models, which control for demographic factors. This suggests that multiple scales of social capital—both within and outside the household—are correlated with household food security. This social capital may both be bridging (across groups) and bonding (within groups) with different implications for how social capital structure affects food security. Efforts to improve food security could recognize the potential for both household and community level social networks and collaboration, which further research can capture by analyzing multiple scales of social capital data.

Pour obtenir un aperçu spécifique des rôles que les micro-organismes pourraient jouer dans la stéatose hépatique non alcoolique (NAFLD), certaines bactéries intestinales et lactiques et une levure (Anaerostipes caccae, Bacteroides thetaiotaomicron, Bifidobacterium longum, Enterococcus fecalis, Escherichia coli, Lactobacillus acidophilus, Lactobacillus fermentum, Lactobacillus plantarum, Weissella confusa, Saccharomyces cerevisiae) ont été caractérisées par une chromatographie liquide haute performance pour la production d'éthanol lorsqu'elles sont cultivées sur différents glucides : hexoses (glucose et fructose), pentoses (arabinose et ribose), disaccharides (lactose et lactulose) et inuline. Les quantités les plus élevées d'éthanol ont été produites par S. cerevisiae, L. fermentum et W. confusa sur le glucose et par S. cerevisiae et W. confusa sur le fructose. En raison de la mannitol-déshydrogénase exprimée dans L. fermentum, la production d'éthanol sur le fructose a été significativement réduite (P < 0,05). Le pyruvate et le citrate, deux accepteurs d'électrons potentiels pour la régénération du NAD+/NADP+, ont considérablement réduit la production d'éthanol avec de l'acétate produit à la place dans L. fermentum cultivé sur glucose et W. confusa cultivé sur glucose et fructose, respectivement. Dans les boues fécales préparées à partir des matières fécales de quatre volontaires en surpoids, on a constaté que l'éthanol était produit lors de l'ajout de fructose. L'ajout d'A. caccae, L. acidophilus, L. fermentum, ainsi que de citrate et de pyruvate, respectivement, a aboli la production d'éthanol. Cependant, l'ajout de W. confusa a entraîné une augmentation significative (P < 0,05) de la production d'éthanol. Ces résultats indiquent que des micro-organismes comme W. confusa, une bactérie lactique hétéro-fermentaire, négative à la mannitol-déshydrogénase, peuvent favoriser la NAFLD par l'éthanol produit à partir de la fermentation du sucre, tandis que d'autres bactéries intestinales et des bactéries lactiques homo- et hétéro-fermentaires mais positives à la mannitol-déshydrogénase peuvent ne pas favoriser la NAFLD. En outre, nos études indiquent que les facteurs alimentaires interférant avec le microbiote gastro-intestinal et le métabolisme microbien peuvent être importants dans la prévention ou la promotion de la NAFLD. Para obtener información específica sobre los roles que podrían desempeñar los microorganismos en la enfermedad del hígado graso no alcohólico (NAFLD, por sus siglas en inglés), algunas bacterias intestinales y del ácido láctico y una levadura (Anaerostipes caccae, Bacteroides thetaiotaomicron, Bifidobacterium longum, Enterococcus fecalis, Escherichia coli, Lactobacillus acidophilus, Lactobacillus fermentum, Lactobacillus plantarum, Weissella confusa, Saccharomyces cerevisiae) se caracterizaron por cromatografía líquida de alto rendimiento para la producción de etanol cuando se cultivaron en diferentes carbohidratos: hexosas (glucosa y fructosa), pentosas (arabinosa y ribosa), disacáridos (lactosa y lactulosa) e inulina. Las cantidades más altas de etanol fueron producidas por S. cerevisiae, L. fermentum y W. confusa en glucosa y por S. cerevisiae y W. confusa en fructosa. Debido a la manitol-deshidrogenasa expresada en L. fermentum, la producción de etanol en fructosa se redujo significativamente (P < 0.05). El piruvato y el citrato, dos aceptores de electrones potenciales para la regeneración de NAD+/NADP+, redujeron drásticamente la producción de etanol con acetato producido en su lugar en L. fermentum cultivado en glucosa y W. confusa cultivado en glucosa y fructosa, respectivamente. En suspensiones fecales preparadas a partir de heces de cuatro voluntarios con sobrepeso, se encontró que el etanol se producía tras la adición de fructosa. La adición de A. caccae, L. acidophilus, L. fermentum, así como citrato y piruvato, respectivamente, abolió la producción de etanol. Sin embargo, la adición de W. confusa resultó en un aumento significativo (P < 0.05) de la producción de etanol. Estos resultados indican que microorganismos como W. confusa, una bacteria de ácido láctico hetero-fermentativa, negativa para manitol-deshidrogenasa, pueden promover NAFLD a través del etanol producido a partir de la fermentación de azúcar, mientras que otras bacterias intestinales y bacterias de ácido láctico homo- y hetero-fermentativas pero positivas para manitol-deshidrogenasa pueden no promover NAFLD. Además, nuestros estudios indican que los factores dietéticos que interfieren con la microbiota gastrointestinal y el metabolismo microbiano pueden ser importantes para prevenir o promover la EHGNA. To gain some specific insight into the roles microorganisms might play in non-alcoholic fatty liver disease (NAFLD), some intestinal and lactic acid bacteria and one yeast (Anaerostipes caccae, Bacteroides thetaiotaomicron, Bifidobacterium longum, Enterococcus fecalis, Escherichia coli, Lactobacillus acidophilus, Lactobacillus fermentum, Lactobacillus plantarum, Weissella confusa, Saccharomyces cerevisiae) were characterized by high performance liquid chromatography for production of ethanol when grown on different carbohydrates: hexoses (glucose and fructose), pentoses (arabinose and ribose), disaccharides (lactose and lactulose), and inulin. Highest amounts of ethanol were produced by S. cerevisiae, L. fermentum and W. confusa on glucose and by S. cerevisiae and W. confusa on fructose. Due to mannitol-dehydrogenase expressed in L. fermentum, ethanol production on fructose was significantly (P < 0.05) reduced. Pyruvate and citrate, two potential electron acceptors for regeneration of NAD+/NADP+, drastically reduced ethanol production with acetate produced instead in L. fermentum grown on glucose and W. confusa grown on glucose and fructose, respectively. In fecal slurries prepared from feces of four overweight volunteers, ethanol was found to be produced upon addition of fructose. Addition of A. caccae, L. acidophilus, L. fermentum, as well as citrate and pyruvate, respectively, abolished ethanol production. However, addition of W. confusa resulted in significantly (P < 0.05) increased production of ethanol. These results indicate that microorganisms like W. confusa, a hetero-fermentative, mannitol-dehydrogenase negative lactic acid bacterium, may promote NAFLD through ethanol produced from sugar fermentation, while other intestinal bacteria and homo- and hetero-fermentative but mannitol-dehydrogenase positive lactic acid bacteria may not promote NAFLD. Also, our studies indicate that dietary factors interfering with gastrointestinal microbiota and microbial metabolism may be important in preventing or promoting NAFLD. لاكتساب بعض الأفكار المحددة حول الأدوار التي قد تلعبها الكائنات الحية الدقيقة في مرض الكبد الدهني غير الكحولي (NAFLD)، تميزت بعض بكتيريا حمض الأمعاء واللاكتيك وخميرة واحدة (Anaerostipes caccae، Bacteroides thetaiotaomicron، Bifidobacterium longum، Enterococcus fecalis، Escherichia coli، Lactobacillus acidophilus، Lactobacillus fermentum، Lactobacillus plantarum، Weissella confusa، Saccharomyces cerevisiae) بتصوير سائل عالي الأداء لإنتاج الإيثانول عند زراعته على كربوهيدرات مختلفة: hexoses (الجلوكوز والفركتوز)، pentoses (الأرابينوز والريبوز)، disaccharides (اللاكتوز واللاكتولوز)، و inulin. تم إنتاج أعلى كميات من الإيثانول بواسطة S. cerevisiae و L. fermentum و W. confusa على الجلوكوز و S. cerevisiae و W. confusa على الفركتوز. بسبب نازعة هيدروجين المانيتول المعبر عنها في L. fermentum، انخفض إنتاج الإيثانول على الفركتوز بشكل كبير (P < 0.05). قلل البيروفات والسيترات، وهما مستقبلان محتملان للإلكترون لتجديد NAD +/NADP+، بشكل كبير من إنتاج الإيثانول مع الأسيتات المنتجة بدلاً من ذلك في L. fermentum المزروع على الجلوكوز و W. confusa المزروع على الجلوكوز والفركتوز، على التوالي. في الملاط البرازي الذي تم تحضيره من براز أربعة متطوعين يعانون من زيادة الوزن، وجد أن الإيثانول يتم إنتاجه عند إضافة الفركتوز. إضافة A. caccae، L. acidophilus، L. fermentum، وكذلك السترات والبيروفات، على التوالي، ألغت إنتاج الإيثانول. ومع ذلك، أدت إضافة W. confusa إلى زيادة كبيرة في إنتاج الإيثانول (P < 0.05). تشير هذه النتائج إلى أن الكائنات الحية الدقيقة مثل W. confusa، وهي بكتيريا حمض اللاكتيك السلبية غير المتجانسة، قد تعزز NAFLD من خلال الإيثانول المنتج من تخمير السكر، في حين أن البكتيريا المعوية الأخرى وبكتيريا حمض اللاكتيك الإيجابية غير المتجانسة ولكن غير المتجانسة قد لا تعزز NAFLD. أيضًا، تشير دراساتنا إلى أن العوامل الغذائية التي تتداخل مع الكائنات الحية الدقيقة في الجهاز الهضمي والتمثيل الغذائي الميكروبي قد تكون مهمة في منع أو تعزيز NAFLD.

AbstractObjective: The marked increase in the prevalence of obesity in the United States has recently been attributed to the increased fructose consumption. To determine if and how fructose might promote obesity in an animal model, we measured body composition, energy intake, energy expenditure, substrate oxidation, and several endocrine parameters related to energy homeostasis in mice consuming fructose.Research Methods and Procedures: We compared the effects of ad libitum access to fructose (15% solution in water), sucrose (10%, popular soft drink), and artificial sweetener (0% calories, popular diet soft drink) on adipogenesis and energy metabolism in mice.Results: Exposure to fructose water increased adiposity, whereas increased fat mass after consumption of soft drinks or diet soft drinks did not reach statistical significance (n = 9 each group). Total intake of energy was unaltered, because mice proportionally reduced their caloric intake from chow. There was a trend toward reduced energy expenditure and increased respiratory quotient, albeit not significant, in the fructose group. Furthermore, fructose produced a hepatic lipid accumulation with a characteristic pericentral pattern.Discussion: These data are compatible with the conclusion that a high intake of fructose selectively enhances adipogenesis, possibly through a shift of substrate use to lipogenesis.