crossm Exploring the Impacts of Postharvest Processing on the Microbiota and Metabolite Profiles during Green Coffee Bean Production Florac De Bruyn,a Sophia Jiyuan Zhang,a Vasileios Pothakos,a Julio Torres,b Charles Lambot,b Alice V. Moroni,c Michael Callanan,c Wilbert Sybesma,c Stefan Weckx,a Luc De Vuysta Research Group of Industrial Microbiology and Food Biotechnology, Faculty of Sciences and Bioengineering Sciences, Vrije Universiteit Brussel, Brussels, Belgiuma; Nestlé R&D Centre Tours, Tours, Franceb; Nestlé Research Centre, Lausanne, Switzerlandc

ABSTRACT The postharvest treatment and processing of fresh coffee cherries can

impact the quality of the unroasted green coffee beans. In the present case study, freshly harvested Arabica coffee cherries were processed through two different wet and dry methods to monitor differences in the microbial community structure and in substrate and metabolite profiles. The changes were followed throughout the postharvest processing chain, from harvest to drying, by implementing up-to-date techniques, encompassing multiple-step metagenomic DNA extraction, highthroughput sequencing, and multiphasic metabolite target analysis. During wet processing, a cohort of lactic acid bacteria (i.e., Leuconostoc, Lactococcus, and Lactobacillus) was the most commonly identified microbial group, along with enterobacteria and yeasts (Pichia and Starmerella). Several of the metabolites associated with lactic acid bacterial metabolism (e.g., lactic acid, acetic acid, and mannitol) produced in the mucilage were also found in the endosperm. During dry processing, acetic acid bacteria (i.e., Acetobacter and Gluconobacter) were most abundant, along with Pichia and non-Pichia (Candida, Starmerella, and Saccharomycopsis) yeasts. Accumulation of associated metabolites (e.g., gluconic acid and sugar alcohols) took place in the drying outer layers of the coffee cherries. Consequently, both wet and dry processing methods significantly influenced the microbial community structures and hence the composition of the final green coffee beans. This systematic approach to dissecting the coffee ecosystem contributes to a deeper understanding of coffee processing and might constitute a state-of-the-art framework for the further analysis and subsequent control of this complex biotechnological process.

Received 17 August 2016 Accepted 21 October 2016 Accepted manuscript posted online 28 October 2016 Citation De Bruyn F, Zhang SJ, Pothakos V, Torres J, Lambot C, Moroni AV, Callanan M, Sybesma W, Weckx S, De Vuyst L. 2017. Exploring the impacts of postharvest processing on the microbiota and metabolite profiles during green coffee bean production. Appl Environ Microbiol 83:e02398-16. https:// Editor Johanna Björkroth, University of Helsinki Copyright © 2016 American Society for Microbiology. All Rights Reserved. Address correspondence to Luc De Vuyst, [email protected] F.D.B., S.J.Z., and V.P. contributed equally to this article.

IMPORTANCE Coffee production is a long process, starting with the harvest of cof-

fee cherries and the on-farm drying of their beans. In a later stage, the dried green coffee beans are roasted and ground in order to brew a cup of coffee. The on-farm, postharvest processing method applied can impact the quality of the green coffee beans. In the present case study, freshly harvested Arabica coffee cherries were processed through wet and dry processing in four distinct variations. The microorganisms present and the chemical profiles of the coffee beans were analyzed throughout the postharvest processing chain. The up-to-date techniques implemented facilitated the investigation of differences related to the method applied. For instance, different microbial groups were associated with wet and dry processing methods. Additionally, metabolites associated with the respective microorganisms accumulated on the final green coffee beans. KEYWORDS coffee bean fermentation, wet processing, dry processing, highthroughput sequencing, metabolite target analysis, UPLC-MS/MS, green coffee beans January 2017 Volume 83 Issue 1 e02398-16

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ostharvest processing of coffee cherries yields green coffee beans, which need to be roasted and ground to obtain the desired characteristic coffee aroma and taste (1). These processes are the main drivers of consumers’ perception of coffee beverage quality. The cherries and beans are the fruits and seeds of the coffee plant (Coffea sp., family Rubiaceae), which is cultivated in plantations, mainly in the equatorial zone. The on-farm postharvest coffee processing is essential for ensuring high coffee cup quality (2) and constitutes a chain of interlinked phases mainly aimed to remove the mucilage of the cherries and dry the beans to a low moisture content of 10 to 12% (mass/mass). The final quality of the green coffee beans is thus dependent on the agricultural and farm practices applied, which in turn depend on the coffee plant cultivar, geography, weather conditions, and infrastructure available (3). Even when all of these factors are fixed within one type of postharvest processing, a multitude of variations exist, as a standardized pipeline for the production of green coffee beans is lacking (4). After harvesting of the cherries, the outer layers of the coffee drupe (i.e., hull and pulp) are easily removed, while the mucilage, parchment, and silverskin are firmly attached to the beans. Different methods are employed to eliminate all of these layers, commonly referred to as wet and dry coffee processing. During wet processing, the hull and pulp of the cherries are mechanically stripped from the beans (depulping). The inside of the cherries thus gets exposed to environmental contamination, and the mucilage is subsequently removed by spontaneous microbial fermentation in a water tank for 6 to 24 h (5–15). This is followed by washing of the fermented beans and sun drying. Dry processing involves direct drying of the whole cherries on cement patios or aerated trays for 14 to 30 days, during which spontaneous fermentation occurs (8, 16–18). Both wet- and dry-processed coffees are finally subjected to dehulling to obtain the green coffee beans (4). In all processes, the spontaneous fermentation step is highly variable and hence needs to be further investigated to understand its contribution to the final coffee cup quality. Microorganisms are ubiquitous during the different stages of postharvest coffee processing (2, 19–22). Enterobacteriaceae and other Gram-negative bacteria, including acetic acid bacteria (AAB), bacilli, lactic acid bacteria (LAB), yeasts, and filamentous fungi have been found through culture-dependent and -independent methods during coffee fermentation processes (5-18, 22). The occurrence and activity of specific microbial groups can be associated with diverse functionalities during processing, for instance, the degradation of pulp pectin and the depletion of mucilage carbohydrates (5–7, 12, 23). Their metabolite production capacities can have beneficial and/or detrimental effects on the sensory characteristics of the green coffee beans and final coffee cup quality (3, 24–26). However, it is not yet clear to what extent microorganisms are essential for the production of high-quality coffee (2). Recently, the ability of naturally occurring yeasts to act as selected starter cultures and to influence the in-cup attributes of coffee has been shown during semidry processing (14, 15, 27). Also, the beans (endosperms) remain metabolically active during coffee processing and are impacted by the processing method implemented, thereby affecting the final coffee cup quality (2, 28–33). Despite the complexity of coffee processing and the numerous factors contributing to the quality traits of the green coffee beans, all studies conducted so far only targeted specific steps of the processing (2, 19, 21). Their primary goal has been the identification of the microbiota associated with the fermentation part of the processing and the chemical profiling of the green coffee beans and/or bean germination process. However, no study has been performed to unravel the evolution of the microbial species and concomitant substrate degradation and metabolite production or the chemical profiles of distinct cherry layers throughout the coffee-processing chain. This study aimed to apply a systematic approach for monitoring the evolution of the microorganisms, substrates, and metabolites during an entire chain of both wet and dry coffee processing carried out under various conditions in Ecuador (Fig. 1 shows details of the four experiments). These conditions were chosen to represent a more and a less favorable postharvest practice to gain insight into the potential correlation of specific January 2017 Volume 83 Issue 1 e02398-16 2

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FIG 1 Experimental setup of the case study of four coffee-processing experiments carried out at the Nanegal station (Ecuador). For the wet and dry processing, the orange line depicts the standard wet process (SW) and standard dry process (SD). The green line represents the extended fermentation wet process (EW) (36 h) and heaped dry process (HD). A pool of freshly handpicked coffee cherries (CB sample) served for all wet- and dry-processing experiments. Concerning the wet processing, sample W1 refers to the depulped beans used for both wet-processing variations. Samples SW2 and EW2 correspond to the beans postfermentation and prior to washing, SW3 and EW3 constitute beans postsoaking, and samples SW4 to SW7 and EW4 to EW7 represent beans during sun drying. Regarding the dry processing, samples SD1 to SD10 and HD1 to HD10 were recovered during sun drying.

microorganisms with a certain processing. High-throughput sequencing of metagenomic DNA, targeting both the bacterial and fungal diversity, and robust metabolite target analysis of a broad range of chemical compounds in the coffee pulp, mucilage, and endosperm were undertaken. RESULTS Microbial community structures. The average numbers of raw V4 and internal transcribed spacer 1 (ITS1) sequences per sample reached approximately 200,000 and 94,000 sequences, respectively. The estimated sequencing coverage ranged between 98.2 and 99.7%. (i) Freshly harvested coffee cherries. The initial surface contamination of the freshly harvested coffee cherries (CB sample) encompassed bacterial operational taxonomic units (OTUs) belonging to the Enterobacteriaceae (especially Klebsiella pneumoniae), AAB (Gluconobacter spp.), and soil-associated bacteria such as Dyella kyungheensis (Fig. 2A and 3). Only a small portion of the reads was attributed to the LAB species Leuconostoc mesenteroides/pseudomesenteroides. In regard to the fungal diversity, Pichia kluyveri/fermentans was highly abundant (Fig. 2B and 3). Based on the principal-coordinate analysis (PCoA) performed on all microbial community structures, the CB sample was clearly differentiated from all other coffee-processing samples (Fig. 4). January 2017 Volume 83 Issue 1 e02398-16 3

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FIG 2 Relative abundances (percentage) of bacterial (A) and fungal (B) operational taxonomic units (OTUs) occurring in selected samples throughout the four postharvest coffee-processing experiments. (A) Bacterial OTUs with relative abundance values greater than 0.1% in at least three samples. (B) Distribution of minor fungal OTUs with a scale from 85 to 100% to better show the fungal diversity because of the dominance (87 to 99%) of a large OTU assigned to the genus Pichia.

(ii) Wet coffee processing. Upon depulping (W1), the OTU Leuconostoc increased in relative abundance, and it was the most prevalent bacterial taxon throughout the wet processing, especially during fermentation (SW2 and EW2) (Fig. 2A and 3). The most prevalent fungal taxon was Pichia, whereas the OTUs Starmerella and Candida increased in relative abundances mainly during the fermentation and soaking stage (Fig. 2B and 3). Over the course of sun drying (SW4 and SW5), the arrangement of the OTUs stayed nearly unvaried. Overall, all wet-processed samples grouped closer on the PCoA biplots, depicting similarities in their microbial community structures that mainly encompassed LAB and only limited fungal diversity (Fig. 4). However, certain differences in the evolution of the microbial communities occurred. While Leuconostoc spp., Lactococcus, and Weissella were predominant during fermentation in SW, a higher incidence of lactobacilli was found from fermentation in EW. This indicated a shift toward more acid-tolerant LAB communities, which corresponded to a decrease in the pH of the fermenting mass from 4.5 (after 16 h of fermentation) to 4.0 (after 36 h of fermentation). LAB-associated OTUs decreased during drying. Further, the abundance of enterobacJanuary 2017 Volume 83 Issue 1 e02398-16 4

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FIG 3 Pseudo-heatmap showing the species diversity and relative abundances (percentage) of bacterial and fungal species occurring in selected samples throughout the four postharvest coffee-processing experiments. The color key at the bottom of the heatmap indicates the relative abundances of the species in the sample.

terial taxa was lower in EW than in SW, especially after fermentation and soaking but increased during drying. Also, a rise in the relative abundances of soil-associated OTUs Acinetobacter, Janthinobacterium, and Cellulosimicrobium followed the decrease in moisture content (Fig. 3; see also Fig. S1 in the supplemental material). (iii) Dry coffee processing. During dry processing (both SD and HD), the LAB species L. mesenteroides/pseudomesenteroides and AAB taxa (Acetobacter and Gluconobacter) were present in high relative abundances (Fig. 2A and 3). The Enterobacteriaceae OTUs decreased compared to those for the CB and wet-processed samples. Other bacterial OTUs appeared sporadically and in variable relative abundances over the course of drying, for instance, reflecting environmental contamination (SD7). Further examples are Lactobacillaceae, Enterobacteriaceae (mostly K. pneumoniae), Enterococcaceae, Brucellaceae (especially Ochrobactrum pseudogrignonense), Stenotrophomonas, and Janthinobacterium lividum. The fungal diversity of the dry-processed samples was greater than that of the wet-processed samples. Although Pichia was still the major fungal taxon, the occurrences of Starmerella bacillaris and Candida spp. were higher than in wet processing (Fig. 2). The PCoA underpinned the distinction between wetand dry-processing samples based on their microbial community structures (Fig. 4). January 2017 Volume 83 Issue 1 e02398-16 5

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FIG 4 Principal-coordinate analysis (PCoA) biplots based on Bray-Curtis dissimilarities of the bacterial (A) and fungal (B) community structures for the coffee samples analyzed.

Significant differences between SD and HD were found. During the first 6 days of HD, the heaped and nonstirred cherries remained moist, as liquid exuded from the pulp, and no significant decrease in the moisture content was noted (Fig. S1). Also, visible chalk-dust mycelia were formed on these cherries, and a strong odor developed, indicating high microbial activity. Moreover, dominance of AAB and the presence of the mold-like yeast Saccharomycopsis crataegensis were found during HD (Fig. 2 and 3). The co-occurrence and coexclusion plot, based on a Spearman rank-order correlation matrix (see Fig. S2 in the supplemental material) confirmed the positive relationship among Gluconobacter spp., Acetobacter spp., and S. crataegensis as well as non-Pichia yeasts. Metabolite target analysis. (i) Freshly harvested coffee cherries. The moisture content of the mucilage (85%) and endosperm (51%) of the fresh coffee cherries as well as the concentrations of all targeted metabolites differed substantially (Fig. 5A and B). The mucilage was rich in fructose (27% on dry mass), glucose (21%), sucrose (9%), and organic acids (7.3%), among which malic acid, quinic acid, and gluconic acid were the most abundant. In contrast, the endosperm had high levels of sucrose (8% on dry mass) and low concentrations of monosaccharides, whereas the most prevalent organic acids (2.4%) were citric acid, malic acid, and quinic acid. In addition, the trigonelline (1.0%) and caffeine concentrations (0.9%) were higher in the endosperm than in the mucilage, whereas the acetic acid concentration was lower. (ii) Wet coffee processing. As the mucilage was completely removed from the endosperm after fermentation, metabolite quantification of the mucilage was performed up to the soaking stage. In the mucilage, sucrose was completely consumed by the end of fermentation in both SW and EW. Fructose and glucose concentrations decreased, and this drop was more intense during EW. A substantial accumulation of metabolites associated with microbial activity occurred, including acetic acid, ethanol, glycerol, lactic acid, and mannitol (Fig. 5A). These compounds started to accumulate after depulping, and the concentrations increased proportionally to the time of fermentation. The organic acid profile of the mucilage was also modified during fermentation, as the concentrations of gluconic acid, malic acid, and quinic acid decreased. Propionic acid, butyric acid, isobutyric acid, valeric acid, isovaleric acid, hexanoic acid, and oxalic acid concentrations were below the quantification limits. The caffeine and trigonelline concentrations decreased upon processing. January 2017 Volume 83 Issue 1 e02398-16 6

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FIG 5 Metabolite profiles of mucilage (A) and endosperm (B) from wet-processed coffee samples and of hull and pulp (C) and endosperm (D) from dry-processed coffee samples. In the case of the compositional analysis of the endosperms (B and D), all other layers (i.e., hull and pulp, parchment and silverskin, or dried hull) were manually removed prior to extraction. The metabolite target analysis encompassed carbohydrates, sugar alcohols, organic acids, short-chain fatty acids, ethanol, and coffee-specific compounds. The metabolite concentrations represent the averages of three independent extractions. The standard error in all cases was less than ⫾5% and is thus not shown in the charts.

Compared to the mucilage, less extensive change in metabolite concentrations occurred in the endosperms of the coffee-processing samples. After fermentation, the fructose, glucose, sucrose, and caffeine concentrations in the endosperms decreased significantly (P ⬍ 0.05). The extended fermentation time resulted in a further drop of the sucrose concentration and in increases in the acetic acid, ethanol, glycerol, glucuronic acid, lactic acid, mannitol, and succinic acid concentrations. The accumulation of these compounds was proportional to the duration of fermentation. After 24 h of soaking, the concentrations of the majority of these targeted compounds, especially ethanol, fructose, glucose, glucuronic acid, lactic acid, and mannitol, dropped. In addition, the concentrations of citric acid, quinic acid, caffeine, and trigonelline decreased after the soaking step. During drying, the endosperms of both SW and EW followed a comparable pattern, with a decrease in sucrose, glucose, fructose, ethanol, caffeine, and trigonelline concentrations and a slight increase in caffeic acid and erythritol concentrations (Fig. 5B). (iii) Dry coffee processing. Clear changes in metabolite concentrations occurred in the dried outer layers of SD and HD (Fig. 5C), whereas fewer changes were found in the endosperms (Fig. 5D). No sucrose was found in the outer layers of dry-processed cherries, indicating a fast and complete depletion upon drying. In addition, glucose and January 2017 Volume 83 Issue 1 e02398-16 7

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fructose concentrations decreased; however, this drop was more gradual in SD than in HD. The glycerol and mannitol concentrations increased intensively and peaked in the SD3 and HD5 samples. Arabitol, sorbitol, and xylitol concentrations also increased in the outer layers during drying. Organic acid concentrations, encompassing lactic acid (1.0% in SD5, 2.1% in HD3), gluconic acid (8.7% in SD5, 14.3% in HD3), and glucuronic acid (1.7% in SD3, 3.1% in HD1) also increased during drying, with higher concentrations generated in the HD samples than in the SD samples (Fig. 5C). A sharp increase in acetic acid concentrations took place in SD1 and HD1, whereas they decreased afterwards. Regarding the endosperm, the monosaccharide and sucrose concentrations decreased gradually during drying, whereas the glucose and fructose concentrations had a secondary peak in the SD6 and HD3 samples (Fig. 4D). Small concentrations of glycerol were found, reaching their highest values in the SD3 and HD3 samples. Acetic acid, ethanol, and lactic acid reached their highest concentrations in the SD2 and HD3 samples, and they decreased afterwards (slower during HD than during SD). Accumulation of glucuronic acid, gluconic acid, and succinic acid concentrations took place at the beginning of drying and followed a gradual decrease upon processing as was found for the aforementioned compounds. Overall, the principal-component analysis (PCA) performed on the endosperm metabolite data showed not only a clear distinction between wet- and dry-processed samples but also clear grouping between samples during the beginning of processing and those close to the end (Fig. 6). These clusters were corroborated by the drop in lactic acid, mannitol, and sucrose concentrations (Fig. 6). (iv) Final green coffee beans. The metabolite profiles of the green coffee bean samples (SW7, EW7, SD10, and HD10) differed. Significantly higher concentrations of monosaccharides, myo-inositol, ethanol, fumaric acid, lactic acid, succinic acid, and 5-caffeoylquinic acid (5-CQA) were found in EW7 than in SW7, whereas lower sucrose concentrations were found in EW7 than in SW7 (see Fig. S3 in the supplemental material). In HD10, significantly higher concentrations of glucose, fumaric acid, gluconic acid, succinic acid, and caffeic acid occurred than in SD10, while the concentrations of glycerol, mannitol, myo-inositol, acetic acid, and 5-CQA were lower. Overall, wetprocessed green coffee beans contained higher citric acid and erythritol concentrations and lower concentrations of fructose, glucose, caffeic acid, caffeine, trigonelline, and certain CQA isomers (i.e., 3-CQA, 4-CQA, 3,4-diCQA, and 4,5-diCQA) than dry-processed green coffee beans. DISCUSSION The present case study on coffee processing represents a systematic approach for the monitoring of both microbial community shifts and metabolite profiles associated with coffee cherry substrates (i.e., pulp and mucilage) and coffee beans throughout the postharvest processing chain. The molecular analysis conducted was based on an enzymatic total DNA extraction method, targeting bacteria, yeasts, and filamentous fungi, suitable for metagenomic purposes. The high-throughput sequencing (HTS) of short-length amplicons of bacteria and fungi under the same barcode was an economical way to evaluate the total microbial diversity of this complex ecosystem. Challenges were the equimolar pooling of the two DNA template libraries that resulted in a 2:1 ratio of generated reads (V4/ITS1) and adapter read-throughs in the ITS1 sequences because of the various length of the ITS regions of Ascomycota and Basidiomycota (34). The metabolite target analysis employed rapid sample preparation steps combined with three different extraction methods and various chromatographic separation and detection techniques, allowing high discriminatory power among structurally related compounds in complex matrices. The microbial community structure on the surface of the freshly harvested coffee cherries was composed of Enterobacteriaceae, fungi, and soil microorganisms that naturally occur in the phyllosphere (35, 36). As soon as the coffee cherries started exuding, these prototrophic microorganisms were succeeded by fermentative LAB species such as L. mesenteroides/pseudomesenteroides and the yeast species P. kluyveri/ January 2017 Volume 83 Issue 1 e02398-16 8

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FIG 6 Three-dimensional (3-D) plot of the PCA of the chemical composition of the coffee samples analyzed. The plot is based on the quantification data of the metabolite target analyses. The ellipses indicate the approximate grouping of the two sample clusters corresponding to the coffee-processing methods. The coordinates of the centroid of each cluster (i.e., wet- and dry-processing samples) are given. Principal components PC1, PC2, and PC3 account for 71% of the variance in the data matrix, and their correlation with the different variables is graphically shown next to the axes. PC1 was positively correlated with the presence of free glucose and fructose, whereas it was negatively correlated with that of erythritol. PC2 was positively correlated with the caffeic acid concentration and reversely correlated with that of sucrose. Last, PC3 correlated with high lactic acid and mannitol concentrations.

fermentans that dominated during processing. In general, L. mesenteroides is associated with cereal, vegetable, and fruit fermentations (37–40), and P. kluyveri has often been found in coffee and cocoa bean fermentations (19, 41). During wet processing, acidification was ascribed to the accumulation of lactic acid and acetic acid by LAB species belonging to the taxa Lactococcus, Leuconostoc, and Weissella in the mucilage. Extended fermentation selected for more acid-tolerant lactobacilli. The concomitant increase in the mannitol concentration in the mucilage corroborated the activity of heterofermentative leuconostocs. These are common patterns and activities during plant fermentation processes (37, 39, 40, 42–45). The yeast diversity of coffee processing often encompasses the taxa Candida, Hanseniaspora, Pichia, and Saccharomyces (11, 14, 16–19). In the current study, the yeast diversity was more restricted, as shown by the high relative abundance of P. kluyveri/fermentans. Changes in the fermenting mucilage were also reflected in the endosperms, where high concentrations of microbial end metabolites (e.g., acetic acid, ethanol, glycerol, lactic acid, and mannitol) occurred. In addition, the anoxia of underwater submersion triggered the germination of the endosperms, resulting in an anaerobic carbohydrate consumption response, which was even more intense during the extended fermentation of depulped and thus injured coffee beans (28, 32, 33, 46, 47). The coffee beans under anoxia consumed the carbohydrate resources continuously through glycolysis, as the sucrose concentration decreased in the endosperms. Alternatively, during soaking, January 2017 Volume 83 Issue 1 e02398-16 9

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the osmotic pressure facilitated the loss of monosaccharides and microbial metabolites accumulated upon fermentation. Hence, the soaking step carried out on the fermented coffee beans facilitated a significant washout of these compounds, which may impact the quality of the brewed coffee because of a lower degree of acidity and will lead to a milder flavor. It is well known that the loss of dry matter is associated with fermentation and soaking due to endogenous metabolism and exosmosis, thereby influencing coffee cup quality (4, 48). During drying of the coffee beans, the moisture content decreased and shifted their microbial contamination to environmental taxa related to soil (mainly Gram-negative bacteria) (49). Also, the drying step induced a drought stress response and aerobic respiration in the endosperms (31), which slowed down the glucose and fructose turnover rate (31). All of these processing steps contributed to differences in the concentrations of coffee-specific compounds too. Consequently, technological aspects (especially the duration of fermentation, soaking, and drying) can be decisive for the composition of the endosperms, as the accumulation of microbial metabolites and endogenous mobilization of resource macromolecules might alter the overall coffee bean composition. In the case of dry processing, mainly AAB occurred next to yeast communities composed of P. kluyveri/fermentans, Candida spp., and S. bacillaris. Hence, a clear distinction between the microbial community structures of wet and dry processing of coffee was made, which was confirmed by the evolution patterns of the targeted chemical compounds. Especially in the case of the heaped dry process, the prevalence of Acetobacter, Gluconobacter, and the appearance of S. crataegensis was facilitated. The latter species was able to produce an extensive mycelium, which might explain the mold-like appearance of the heaped beans upon processing. Also, this yeast species has a negative or weak capacity to ferment glucose and can assimilate gluconic acid, which is produced by AAB (50, 51). These metabolic characteristics might give it a competitive advantage over other microorganisms in the heaped dry coffee-processing malpractice. The incidence of yeasts along with the Acetobacter and Gluconobacter species increased the concentrations of acetic acid, ethanol, glycerol, and gluconic acid in the dried outer layers, especially during heaped dry processing. Also, a minor accumulation of microbial metabolites, such as gluconic acid, glycerol, and mannitol took place in the endosperm. These findings support the effect of microorganisms on the chemical profile of dry-processed coffee beans and might imply a slow but observable migration of microbial metabolites to the endosperm. This is, for instance, the case when beans are spoiled by fungal contamination, resulting in poor-quality coffee that tastes moldy and earthy (24). In addition, stress during drying can result in a bimodal pattern response, which corresponded to the relapsing peak in glucose and fructose concentrations (31). The absence of depulping injury and anoxia contributed to higher concentrations of free monosaccharides in the final dry-processed green coffee beans than in wet-processed ones. Moreover, the slow aeration and subsequent decrease in moisture in the heaped dry process might account for the chlorogenic acid (CGA) degradation and caffeic acid generation by endogenous enzymes as well as the higher concentration of volatiles in the endosperms compared to wet-processed coffee beans (52). Higher concentrations of caffeic acid, caffeine, fructose, glucose, trigonelline, and certain CGA isomers in dry- rather than wet-processed green coffee beans have also been shown for beans from different origins (9, 30, 53). In general, the resulting chemical profiles of green coffee beans are strongly associated with the final coffee cup quality (54–58). For instance, caffeine, CGAs, and trigonelline are responsible for the bitterness and astringency of the final coffee beverage. As the dry-processed green coffee beans contain more of such compounds, higher bitterness and astringency levels would be expected in the corresponding coffee beverages than in the wet-processed ones (9, 58–60). Finally, most compounds mentioned above undergo extensive changes during roasting, mainly Maillard reactions, and hence contribute to differences in coffee flavors (24, 30, 60, 61). In conclusion, the present case study monitored the evolution of the bacterial and fungal diversity along with substrates consumed and metabolites produced during the January 2017 Volume 83 Issue 1 e02398-16 10

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entire chain of both wet and dry processing under favorable and less favorable processing conditions. At the same time, specific endosperm metabolite changes were followed. This was made possible by simultaneous HTS of metagenomic DNA and metabolite target analysis of a broad range of chemical compounds. The results showed that the different processing conditions influenced the composition and activity of the microbial communities as well as metabolite accumulation in the endosperms. In addition, the current findings corroborate the effect of microorganisms on the chemical profiles of coffee beans and support the idea of using a starter culture during coffee processing for improved process control and prevention of spoilage and eventually steering the sensory differentiation of roasted coffee, as has been performed for cocoa bean fermentation (62, 63). Further studies should ultimately allow strengthening of the understanding of the impact of the microbiota on coffee cup quality and provide robust data for the development of commercial starter cultures. MATERIALS AND METHODS Coffee cultivar and coffee-processing experiments. The coffee cultivar used throughout this study was Coffea arabica L. var. typica. Four coffee-processing experiments were performed at a coffee plantation near Nanegal (Nestlé Ecuador; latitude and longitude coordinates, 0°11=25.8⬙N 78°40=41.4⬙W; altitude, 1,329 m;⫹78%C2%B040=41.4 %22W/@0.190509,-78.678155,13z/data⫽!4m2!3m1!1s0x0:0x0) in May to July 2014 (Fig. 1). Approximately 160 kg of healthy mature cherries were selectively handpicked and served as a pool of common starting material for all experiments. Two parallel wet-processing experiments were performed, which differed in fermentation time. Initially, 100 kg of cherries were depulped mechanically (UCBE 500; Penagos, Bucaramanga, Colombia). The depulped beans were then allowed to ferment spontaneously in clean plastic containers (50 cm by 30 cm by 20 cm). A first part of the depulped beans was left to ferment for 16 h and a second part for 36 h. In this way, half of the depulped beans underwent a standard wet process (SW), while the other half was subjected to an extended fermentation wet process (EW). The fermented beans were manually washed with clean water and soaked for 24 h. Finally, the soaked beans were dispersed onto cement patios for drying until they reached a moisture content of approximately 12% (mass/mass). Simultaneously with the SW and EW experiments, two dry processing experiments were performed. Approximately 30 kg of fresh cherries (originating from the same aforementioned pool of harvested cherries) were equally divided between a standard dry process (SD), in which the cherries were spread in a monolayer on a covered aerated drying tray and stirred daily, and a heaped dry process (HD) wherein the cherries were heaped on the drying tray (4 to 6 cherries deep) without stirring during the first 6 days. Afterwards, the HD cherries were stirred daily as well. The cherries underwent sun drying until a moisture content of approximately 12% (mass/mass) was reached. The moisture content was evaluated by means of a mini GAC plus moisture tester (Dickey-john, Auburn, IL). Sampling during coffee processing. Coffee-processing samples (i.e., freshly harvested cherries, depulped beans, fermented beans, soaked beans, drying beans, and dry-processed cherries) were collected at specific time points and immediately stored at ⫺20°C until further analysis. A uniform code was assigned to each processing sample, containing information on the type of variation (standard [S], extended [E], and heaped [H]), processing method (wet [W] and dry [D]), and sampling point (1–10) (Fig. 1). The pH was measured by means of pH-Fix strips 0 to 14 (Macherey-Nagel; Düren, Germany) at the end of the fermentation steps of SW and EW. The moisture contents of all cherry and bean samples were determined by mass difference through drying in an oven at 100°C for 24 h. Microbial community analysis. (i) Total DNA extraction. An innovative approach of metagenomic DNA extraction was performed. Total DNA was extracted from thawed coffee-processing samples by first detaching microbial cells present on the cherries or beans via manual inversion (2 treatments of 2 min with a 15-min pause) in 25 ml of saline solution (8.5 g/liter NaCl; Merck, Darmstadt, Germany). Depending on the type of sample, 6 cherries or 20 to 50 beans were used for total microbial DNA extraction. After manual inversion, the resulting suspensions were filtered through a 20-␮m average pore-size 50-ml Steriflip filter (Merck) to eliminate coarse impurities. The filtrates were pelletized by centrifugation (14,000 ⫻ g, 10 min). The pellets were washed with 1 ml of TES buffer (50 mM Tris base, 1 mM EDTA, 6.7% [mass/vol] sucrose [pH 8.0]). Subsequently, several consecutive enzymatic steps were applied to cover all microbial communities potentially present. To obtain fungal cell lysis, the pellets were resuspended in 300 ␮l of 50 mM phosphate buffer (pH 6.0) and incubated with chitinase (500 mU/ml; Sigma-Aldrich, St. Louis, MO) at 37°C for 2 h, followed by centrifugation at 8,000 ⫻ g for 10 min. The pellets were then resuspended in 600 ␮l of sorbitol buffer (21% [mass/vol] sorbitol [VWR International, Darmstadt, Germany], 50 mM Tris base [pH 7.5]) containing a cocktail of 0.2 U of lyticase (Sigma-Aldrich), 200 U of Zymolyase (G-Biosciences, St. Louis, MO), and 1.23 ␮l of 2-mercaptoethanol (Merck), and the mixtures were incubated at 30°C for 1 h. Following this initial fungal cell lysis treatment, the suspensions were washed with sorbitol buffer. Then, bacterial cells were lysed by adding 300 ␮l of STET buffer (8% [mass/vol] sucrose, 50 mM Tris base, 50 mM EDTA, 5% [mass/vol] Triton X-100 [pH 8.0]) and incubating the suspensions with a cocktail of 0.1 U of mutanolysin (Sigma-Aldrich) and 8 mg of lysozyme (Merck) at 37°C for 1 h. Subsequently, 40 ␮l of a 20% (mass/mass) sodium dodecyl sulfate solution and 0.1 g of sterile glass beads were added before the suspensions were vortexed intensively for 1 min. Protein January 2017 Volume 83 Issue 1 e02398-16 11

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digestion was achieved by incubation of these suspensions with 0.5 mg of proteinase K (Merck) at 56°C for 1 h. Thereupon, 100 ␮l of a 5 M NaCl solution was added to the suspensions and incubated at 65°C for 2 min, after which 80 ␮l of a 10% (mass/mass) cetyltrimethyl ammonium bromide solution was added, and the mixtures were incubated at 65°C for 10 min. Following this, 600 ␮l of chloroform-phenol-isoamyl alcohol solution (49.5:49.5:1.0) was added, and the lysates were shaken vigorously for 5 min. Finally, the solutions were centrifuged at 13,000 ⫻ g for 5 min in 2-ml vials (Phase Lock Gel Heavy; 5 Prime, Hilden, Germany). The DNA contained in the supernatants was purified by binding on and elution from a cellulose acetate membrane, using the DNeasy blood and tissue kit (Qiagen, Venlo, The Netherlands) according to the manufacturer’s instructions. DNA concentrations were measured with a NanoDrop ND-2000 (Thermo Scientific, Wilmington, DE). (ii) Amplification of group-specific loci for HTS. Group-specific loci of both bacterial and fungal DNA were amplified through PCR. The V4 hypervariable region of the bacterial 16S rRNA gene was amplified using the primers F515 (5=-TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGGTGTGCCAGCMGCC GCGGTAA-3=) and R806 (5=-GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGGGACTACHVGGGTWTCTAA T-3=) with an Illumina platform-specific 5= tag (underlined) (64). The PCR assay conditions consisted of an initial step at 94°C for 3 min, followed by 35 cycles at 94°C for 45 s, 50°C for 60 s, and 72°C for 90 s. A final extension was performed at 72°C for 10 min. The fungal ITS1 region was amplified using the primers ITS1 (5=TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGTCCGTAGGTGAACCTTGCGG3=) and ITS2 (5=GTCTC GTGGGCTCGGAGATGTGTATAAGAGACAGGCTGCGTTCTTCATCGATGC3=) with an Illumina platformspecific 5= tag (underlined) (34). The PCR assay conditions consisted of an initial step at 95°C for 2 min, followed by 40 cycles of denaturation at 95°C for 30 s, annealing at 50°C for 30 s, and extension at 72°C for 60 s. A final extension was performed at 72°C for 5 min. Each PCR assay mixture contained 6 ␮l of 10⫻ PCR buffer (Sigma-Aldrich), 2.5 ␮l of 0.1 mg/ml bovine serum albumin (Sigma-Aldrich), 0.2 mM deoxynucleotide triphosphate mixture (Sigma-Aldrich), 1.25 U of Taq DNA polymerase (Roche), 10 to 100 ng of DNA template, and 5 ␮M each primer (Integrated DNA Technologies, Leuven, Belgium). The PCR amplicons were purified using the Wizard SV gel and PCR clean-up system (Promega, Madison, WI) and size selected using Agencourt AMPure XP PCR purification magnetic beads (Beckman Coulter, Brea, CA), following the manufacturers’ instructions. The amplicon size distribution was checked qualitatively by means of a 2100 Bioanalyzer instrument (Agilent Technologies, Santa Clara, CA). Finally, double-stranded DNA concentrations were quantified using the fluorometric Qubit 2.0 quantitation assay (Thermo Fisher, Waltham, MA). (iii) HTS of V4 and ITS1 amplicons. A novel approach was followed for HTS. The bacterial and fungal DNA template libraries of each sample were combined and sequenced under the same index. Briefly, bacterial V4 and fungal ITS1 amplicons originating from the same sample were pooled equimolarly, barcoded with the same index, and diluted if necessary before sequencing. Every pooled sample had a total volume of 30 ␮l. All samples were sequenced using the Illumina MiSeq platform (Illumina, San Diego, CA) in a commercial facility (BRIGHTcore, Jette, Belgium). Two Fastq files were obtained for each sample, encompassing all forward and reverse reads, both deriving from the bacterial and fungal amplicons. (iv) Bioinformatics analysis. The forward and reverse Fastq files of each sample, containing the sequences of the bacterial V4 and fungal ITS1 fragments, were first split by means of an in-house Perl script into two files. Based on the first six nucleotides of the reads corresponding to the respective primers, a first Fastq file contained all the bacterial V4 sequences and a second Fastq file comprised the fungal ITS1 sequences. Both the bacterial and fungal diversities were processed through mothur software v1.36.1, following a workflow described before (65), with some modifications as outlined below. For the V4 sequences (4,440,225 paired reads), removal of primers, generation of contigs, and subsequent quality screening were performed. The unique sequences were aligned against the bacterial 16S rRNA SILVA database and then clustered into groups, allowing a difference of maximum two mismatches. A chimera check was carried out by means of the Uchime algorithm (66). After the removal of chimeric sequences, the taxonomic allocation and generation of operational taxonomic units (OTUs) were performed at a level of 97% identity. For the fungal ITS1 sequences (2,073,445 paired reads), the forward and reverse reads were first trimmed using Cutadapt software (release 1.9.dev6) (67), due to the short length of the targeted ITS1 region in some cases, which resulted in adapter read-throughs (68). Briefly, the sequenced adapters and the overhangs at the 3= end of the forward and reverse reads were trimmed off. The trimmed Fastq files were then processed through mothur for the generation of contigs and quality screening of the paired reads. The unique sequences were classified taxonomically through comparison with the fungal UNITE_ITS1 database (v6_sh_99), as described before (69), and merged into OTUs when the taxonomic allocation was identical. Finally, the representative unique sequences corresponding to the respective bacterial or fungal OTUs were aligned with reference 16S rRNA gene and ITS1 sequences using the BLASTN algorithm (70). When the sequence identity was higher than 99% compared to that for well-described and curated sequences of type and reference strains, an assignment to the species level was performed too. In all cases, the ranking of the identified taxa presented above is based on a decreasing order of relative abundance. Metabolite target analysis. (i) Preparation of extracts from mucilage, pulp, and endosperm. Due to the complexity of wet and dry processing, all coffee cherry layers and endosperms were collected and analyzed separately to monitor the shifts of the metabolite profile along the postharvest processing chain. Therefore, each coffee-processing sample was subjected to a standard preparation protocol prior to metabolite extraction. 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was first separated from the beans, then the mucilage was scraped off, and finally the parchment was detached manually. In the case of dry-processed samples, all dried outer layers were removed manually. In all cases, representative samples (20 g) of cherry layers or endosperm were first frozen in liquid nitrogen (Air Liquide, Louvain-la-Neuve, Belgium) and ground into fine powders with a malt miller (Corona Mill, Bogotá, Colombia). Three types of extraction were used to analyze the metabolites targeted in the final samples. Water extracts were prepared by submerging 0.1 to 0.5 g of sample in 5 g of ultrapure water (Milli-Q; Merck Millipore, Billerica, MA) at room temperature for 30 min. Methanol extracts were prepared by treating 0.1 to 0.5 g of sample in 5 g of 40% (vol/vol) methanol (Sigma-Aldrich) at 40°C for 20 min. Acidic extracts were prepared by adding 0.1 to 0.5 g of sample to 5 g of 0.01 N HCl and ultrasonicating (Ultrason 2510; Branson, Danbury, CT) at room temperature for 20 min. All samples were microcentrifuged (14,000 rpm, 10 min), deproteinized with acetonitrile (Sigma-Aldrich) at a 1:3 ratio, and filtered through 0.2-␮m pore-size filters (Whatman filters; GE Healthcare Life Sciences, Little Chalfont, Buckinghamshire, UK) prior to injection. All samples were both prepared and analyzed in triplicate. (ii) Determination of free carbohydrates and sugar alcohols. The concentrations of free carbohydrates (fructose, galactose, glucose, and sucrose) were determined by high-performance anionexchange chromatography with pulsed amperometric detection (HPAEC-PAD), using an ICS 3000 chromatograph equipped with an AS autosampler, a CarboPac PA-100 column, and an ED-40 PAD detector (Dionex, Sunnyvale, CA). The mobile phases consisted of ultrapure water (eluent A), 100 mM NaOH (eluent B) (J. T. Baker, Deventer, The Netherlands), and 900 mM NaOH (eluent C) (J. T. Baker), with a constant flow rate of 1.0 ml/min and the following gradient: 0.0 to 15.0 min, 95% A, 5% B, and 0% C; 15.5 to 22.0 min, 0% A, 0% B, and 100% C; and 22.5 to 30.0 min, 95% A, 5% B, and 0% C. The identification of the targeted compounds was achieved by injecting pure standards (Sigma-Aldrich). The concentrations of sugar alcohols (arabitol, erythritol, galactitol, glycerol, mannitol, myo-inositol, sorbitol, and xylitol) were determined by HPAEC-PAD, using the same chromatograph as mentioned above, but equipped with a CarboPac MA-1 column (Dionex). The mobile phases consisted of ultrapure water (eluent A) and 730 mM NaOH (eluent B) (J. T. Baker), with a constant flow rate of 1.0 ml/min and the following gradient: 0.0 to 15.0 min, 90% A and 10% B; 40.0 to 55.0 min, 0% A and 100% B; and 55.5 to 65.0 min, 90% A and 10% B. Targeted compounds were identified by injecting pure standards (Sigma-Aldrich). All above-mentioned compounds in the water extracts were quantified by external calibration, including rhamnose as an internal standard (IS). (iii) Determination of short-chain fatty acids and ethanol. Short-chain fatty acids (SCFAs) (namely, acetic acid, propionic acid, butyric acid, isobutyric acid, valeric acid, isovaleric acid, and hexanoic acid) and ethanol were quantified through gas chromatography with flame ionization detection (GC-FID), using a Focus GC apparatus equipped with an AS 3000 autosampler and a flame ionization detector (Interscience, Breda, The Netherlands) and a Stabilwax-DA column (Restek, Bellefonte, PA). Samples (1 ␮l) were injected into the column directly, applying a split ratio of 1:20. The injector temperature was set at 270°C. The oven temperature was programmed as follows: first 40°C for 5 min, followed by a temperature increase to 225°C at a rate of 10°C/min, and then held at 225°C for 5 min. The detector temperature was set at 250°C. Helium (Air Liquide) was used as the carrier gas at a constant flow rate of 1.0 ml/min, and nitrogen gas (Air Liquide) was used as the make-up gas. The volatile compounds were identified by injecting pure standards (Merck). The compounds in the water extracts were quantified by external calibration, with the inclusion of 1-butanol (Merck) as an IS. (iv) Determination of organic acids and coffee bean-specific compounds. Organic acids (citric acid, fumaric acid, gluconic acid, glucuronic acid, isocitric acid, lactic acid, malic acid, oxalic acid, quinic acid, and succinic acid) and coffee bean-specific compounds, namely, caffeine, caffeic acid, and six chlorogenic acid [CGA] isomers (3-, 4-, and 5-caffeoylquinic acids [CQAs], and 3,4-, 3,5-, and 4,5-diCQAs), and trigonelline, were determined by ultraperformance liquid chromatography coupled to mass spectrometry (UPLC-MS), using an Acquity system equipped with an HSS T3 column (150 mm by 2.1 mm; internal diameter, 1.8 ␮m) and a TQ tandem mass spectrometer with a ZSpray electrospray ionization source operating both in the negative (organic acids and CGAs) and positive (other coffee bean-specific compounds) ion modes (Waters, Milford, MA). The following eluents were used: ultrapure water with 0.2% (vol/vol) formic acid (eluent A) (Fluka, St. Louis, MO) and a mixture of methanol (Sigma-Aldrich) and water (950:50) with 0.2% (vol/vol) formic acid (eluent B) (Fluka). In the case of organic acids and coffee-specific compounds (except for CGAs), the gradient elution was as follows: 0.0 to 1.5 min, isocratic 10% B; 1.5 to 3.0 min, linear from 10 to 90% B; 3.0 to 4.0 min, isocratic 90% B; 4.0 to 4.1 min, linear from 90 to 10% B; and 4.1 to 6.0 min, isocratic 10% B. In the case of CGAs, this was 0.0 to 1.5 min, isocratic 10% B; 1.5 to 12.0 min, linear from 10 to 20% B; 12.0 to 20.0 min, linear from 20 to 68% B; 20.0 to 20.2 min, linear from 68 to 100% B; 20.2 to 22.0 min, isocratic 100% B; 22.0 to 22.2 min, linear from 100 to 10% B; and 22.2 to 25.0 min, isocratic 10% B. The flow rate was kept constant at 0.3 ml/min. The targeted compounds were identified by injecting pure standards (CGAs were from Biopurify, Chengdu, China; other compounds used as standards were from Sigma-Aldrich). CGAs in the methanol extracts, and organic acids and other coffee bean-specific compounds in acidic extracts, were quantified by external calibration. The reaction monitoring method that was selected was optimized by IntelliStart (see Table S1 in the supplemental material). Statistical analysis. The microbial community structure data obtained through HTS were exported in BIOM format files and imported in the R environment for statistical analysis ( The OTU tables of bacteria and fungi were preprocessed (i.e., removal of global singletons, alpha-diversity, January 2017 Volume 83 Issue 1 e02398-16 13

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and rarefaction) by implementing the vegan package (⫽vegan). The phyloseq package was used to construct principal-coordinate analysis (PCoA) plots based on Bray-Curtis dissimilarities (71). Additionally, a Spearman rank-order correlation matrix was employed to evaluate the dependence among genera. The co-occurrence and coexclusion relationships between all microbial genera were only considered when the correlation was significant at a confidence level of 99%. The quantitative data from the metabolite target analyses were subjected to principal-component analysis (PCA) to identify patterns associated with the coffee processing method applied. One-way analysis of variance (ANOVA) was conducted for the determination of differences in metabolite concentrations between samples, and Duncan’s test was employed. A probability level of 0.05 was considered to be significant for all statistical procedures, and only those data are reported here. All statistical analyses and tests performed were executed through the SPSS v.20 package (IBM, Chicago, IL). Accession number(s). All sequences generated were submitted to the European Nucleotide Archive of the European Bioinformatics Institute (ENA/EBI) under accession number PRJEB14106 and are available at

SUPPLEMENTAL MATERIAL Supplemental material for this article may be found at AEM.02398-16. TEXT S1, PDF file, 0.6 MB. ACKNOWLEDGMENTS We acknowledge financial support from the Research Council of the Vrije Universiteit Brussel (SRP7 and IOF342 projects), the Hercules Foundation (grant UABR09004), and Nestec S.A., a subsidiary of Nestlé S.A. Cyril Moccand and Jay Siddharth are acknowledged for critical reading of the manuscript and Arne Glabasnia and Frédéric Mestdagh for their advice on the analytical methods.

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Exploring the Impacts of Postharvest Processing on the Microbiota and Metabolite Profiles during Green Coffee Bean Production.

The postharvest treatment and processing of fresh coffee cherries can impact the quality of the unroasted green coffee beans. In the present case stud...
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