Hexoses

Mycelial cell wall fractionation was performed according to the method described by Fontaine et al.[86] (link) with slight modification. Briefly, wt and Δhac1 strains were grown in a 1.2-liter fermenter in liquid Sabouraud medium. After 24 h of cultivation (linear growth phase), the mycelia were collected by filtration, washed extensively with water and disrupted in a Dyno-mill (W. A. Bachofen AG, Basel, Switzerland) cell homogenizer using 0.5-mm glass beads at 4°C. The disrupted mycelial suspension was centrifuged (3,000×g for 10 min), and the cell wall fraction (pellet) obtained was washed three times with water, subsequently boiled in 50 mM Tris-HCl buffer (pH 7.5) containing 50 mM EDTA, 2% SDS and 40 mM β-mercaptoethanol (β-ME) for 15 min, twice. The sediment obtained after centrifugation (3,000×g, 10 min) was washed five times with water and then incubated in 1 M NaOH containing 0.5 M NaBH₄ at 65°C for 1 h, twice. The insoluble pellet obtained upon centrifugation of this alkali treated sample (3,000×g, 10 min, AI-fraction) was washed with water to neutrality, while the supernatant (AS-fraction) was neutralized and dialyzed against water. Both fractions were freeze-dried and stored at −20°C until further use. Hexose composition in the samples were estimated by gas-liquid chromatography using a Perichrom PR2100 Instrument (Perichrom, Saulx-les-Chartreux, France) equipped with flame ionization detector (FID) and fused silica capillary column (30 m×0.32 mm id) filled with BP1, using meso-inositol as the internal standard. Derivatized hexoses (alditol acetates) were obtained after hydrolysis (4N trifluoroacetic acid/8N hydrochloric acid, 100°C, 4 h), reduction and peracetylation. Monosaccharide composition (percent) was calculated from the peak areas with respect to that of the internal standard.

Multifunctionality is a human construct rather than a single measurable process, and involves quantifying the provision of multiple ecosystem processes and services simultaneously63 64 (link). These include, among other, nutrient cycling (for example, nutrient availability, mineralization), primary production (for example, net primary productivity) and organic matter decomposition (for example, lignin degradation). To obtain a quantitative multifunctionality index for each site, we first normalized (log-transform when needed) and standardized each of the six functions measured (ammonium, nitrate, potential net N mineralization, soil phosphorus, DNA content and plant productivity) using the Z-score transformation. These standardized ecosystem functions were then averaged to obtain a multifunctionality index25 (link). This index is widely used in the multifunctionality literature4 (link)8 (link)25 (link)63 64 (link), and provides a straightforward and easy-to-interpret measure of the ability of different communities to sustain multiple functions simultaneously4 (link)8 (link)25 (link). The multifunctionality index was independently obtained for the Drylands and Scotland data sets. While we calculated our multifunctionality index based on the six functions that were available for both Drylands and Scotland data sets to facilitate the comparison and generalization of our results, another 11 and nine functions were available for each of these data sets, respectively. To further test whether the functions used to estimate multifunctionality could be biasing our results, we recalculated our averaging multifunctionality index including all the available functions available per data set (eight and 17 for the Scotland and Drylands data sets, respectively). These extra functions included glucose substrate-induced respiration57 (link) and basal respiration57 (link) (corrected by bulk density) in Scotland, and activity of phosphatase and β-glucosidase, dissolved organic N, proteins, aminoacids, phenols, aromatic compounds, hexoses, pentoses, HCl-P and potential N transformation rate in the Drylands data set25 (link).
Multifunctionality averaging approaches such as the index used in this manuscript do not take into account the number of functions with high performance. This entails some problems because unevenly strong functions may bias this index to high multifunctionality performance when actually only few functions maximize (it might be necessary that different functions maximize at the same time to avoid potential limiting factors in the system). Also it does not allow to see potential trade-offs between functions, which might maximize when others minimize. To solve this problem, a threshold approach was used63 . In this technique, every function is standardized using the maximum of its value within the data set. We transformed every observation of every function into a percentage of the maximum performance of each function. To control for potential artefacts derived from the fact that the maximum value is necessarily one only measure, we used as maximum value the average of the top 5% of all plots value. We aimed to evaluate the relationship between diversity and the number of functions which perform higher than a given threshold. Since the choice of a threshold for multiple functions is arbitrary, Byrnes et al.63 developed a method that basically performs regressions between the number of functions surpassing a threshold and the diversity throughout thresholds from 0 to 99%. Each threshold represents a level of functional performance and the regressions indicate whether diversity is able to increment the number of functions working beyond that level of performance.
We plotted the resulting regressions in Supplementary Fig. 10 (one colour per threshold). To evaluate the significance of these regressions we plotted the effect (slope of regression) of diversity versus number of functions along different thresholds with their confidence interval at 95% in Supplementary Fig. 11. These analyses were conducted using Matlab v.7.0 (The MathWorks, Inc., Natick, Massachusetts, United States).

Urine was collected from nonobese and obese mice and centrifuged at 1,000g for 10 minutes to remove cellular debris. Lysosome-enriched subcellular fractions were isolated from kidneys using a modified version of a method described previously (66 (link)). Kidneys were homogenized with pestles in 1 mL of subcellular fractionation buffer (HEPES 20 mM, sucrose 250 mM, KCl 10 mM, MgCl₂ 1.5 mM, EDTA 1 mM, EGTA 1 mM, dithiothreitol 8 mM, pH adjusted to 7.5 with NaOH). Debris and nuclei were pelleted at 750g for 12 minutes. The supernatant was centrifuged at 10,000g for 35 minutes to pellet the lysosome-enriched fraction. The pellet was washed once with subcellular fractionation buffer. Lipid extraction from urine and the lysosome-enriched fraction was performed using the Bligh and Dyer method with minor modifications (67 (link)). BMP, phosphatidylcholine, phosphatidylethanolamine, phosphatidylglycerol, phosphatidylinositol, phosphatidylserine, lysophosphatidylcholine, lysophosphatidylethanolamine, monoacylglycerol, diacylglycerol, triacylglycerol, cholesterol, ceramide, hexose ceramide, lactosylceramide, and sphingomyelin were analyzed by supercritical fluid chromatography (SFC) (Nexera UC system, Shimadzu; equipped with an ACQUITY UPC² Torus diethylamine [DEA] column: 3.0 mm inner diameter [i.d.] × 100 mm, 1.7 μm particle size, Waters) and triple quadrupole mass spectrometry (TQMS; LCMS-8060, Shimadzu) (DEA-SFC/MS/MS) in multiple reaction monitoring (MRM) mode (68 (link)). Fatty acids and cholesterylester were analyzed using an SFC (Shimadzu) with an ACQUITY UPC² HSS C18 SB column (3.0 mm i.d. × 100 mm, 1.8 μm particle size, Waters) coupled with a TQMS (Shimadzu) (C18-SFC/MS/MS) in MRM mode (69 (link)). The amount of each lipid species was normalized either to the urine creatinine concentration, measured using a QuantiChrom Creatinine Assay Kit (DICT-500) (BioAssay Systems), or to kidney weight.

The yeast complementation vectors or empty vectors were transformed into the hexose-uptake deficient yeast mutant EYB4000 (Wieczorke et al., 1999 (link)). Then, transformed yeasts were screened in synthetic dropout (SD)-Ura media, supplemented with 2% maltose. For complementation growth assays, yeasts were grown overnight in liquid SD media to an optical density at 600 nm (OD₆₀₀) of ~0.6, then OD₆₀₀ was adjusted to ~0.3 with water. Five-microliter aliquots of serial dilutions were plated on SD media containing 2% maltose (as control) or 2% other hexoses. Growth photographs were taken after incubation at 30 °C for three days.
For subcellular localization of SWEET proteins in yeast cells, transformed yeasts cultured in SD media supplemented with 2% maltose were collected, washed three times with water, and then applied on microscope slide. Fluorescence signals were detected using a Zeiss LSM710NLO confocal laser-scanning microscope. Excitation/emission wavelength were 488 nm for GFP signal.