T E S I S
QUE PARA OBTENER EL GRADO DE MAESTRO EN BIOCIENCIAS
I.B.Q. Fernando Astudillo Melgar
Chilpancingo de los Bravo, Gro., junio 2018
Este trabajo fue realizado en el Laboratorio de Investigación en Biotecnología de la Facultad de Ciencias Químico Biológicas de la Universidad Autónoma de Guerrero. Se contó con la colaboración del Laboratorio de Biología de Sistemas y Biología Sintética del Centro de Ciencias Genómicas de la Universidad Nacional Autónoma de México y con el Departamento de Microbiología Molecular del Instituto de Biotecnología de la Universidad Nacional Autónoma de México
Bajo la dirección de:
Dr. Gerardo Huerta Beristaín
La codirección de:
Dr. José Utrilla Carreri
La asesoría externa de:
Dr. Adrián Ochoa Leyva Biol. Filiberto Sánchez López
M.B. José Raunel Tinoco Valencia Dr. Carlos Ortuño Pineda
Al comité tutoral:
Dra. Ma. Elena Moreno Godínez Dra. Jeiry Toribio Jiménez
Dr. Miguel Ángel Mendoza Catalán
Durante los estudios en la Maestría en Biociencias, el C. Fernando Astudillo Melgar, recibió beca CONACYT con No. registro: 597135. Y apoyo del proyecto FOMIX-CONACyT-Edo. De Guerrero 249671.
También se agradece el apoyo proporcionado del proyecto DGAPA-PAPIIT IA201518
Al Dr. Gerardo. No tengo palabras para agradecer todo lo que ha hecho por mi, es una excelente persona, confió en mi sin ni siquiera conocerme y eso es algo que no cualquiera hace. Hizo mi estadía en la maestría tan agradable, aprendí tanto de usted y no nada más como profesionista, sino también como persona. Gracias por las enseñanzas, las clases, los momentos de risa y el bully. Espero que siga teniendo mucho éxito Dr. Lo admiro mucho.
A mis asesores externos. Al Dr. José. Gracias por la oportunidad de trabajar en su laboratorio por brindarme las herramientas para el desarrollo del trabajo y por las observaciones y consejos durante el desarrollo del proyecto. Al Dr. Adrián. Por todo su apoyo, por el material y orientación brindada, por permitirme trabajar en su laboratorio y por su asesoría.
A mi sínodo. Al Dr Migue, Dra. Ma Elena y Dra. Jeiry. Por su apoyo, revisiones y consejos que tuvieron en bien darme durante el desarrollo del proyecto. Además gracias por facilitarme equipos y material cuando los necesité.
Biol. Filiberto y al M.B. Raunel. Por su apoyo y asesoría en la realización de experimentos.
A los profesores de seminario. Al Dr. Carlos. Muchas gracias por todo su apoyo, paciencia, consejos y tanto más, es una gran persona, me ayudo tanto en muchos aspectos, es un gran profesionista, lo admiro mucho. A la Dra. Paty. Gracias por todas las veces que me ayudo, siempre que necesite un favor usted tuvo en bien ayudarme, sus consejos, sus pláticas, la admiro mucho. Gracias a ambos son unos excelentes profesores de seminario y amigos.
A la Maestra Daysi. Gracias por brindarme la oportunidad de convivir con su familia, de abrirme las puertas de su casa, las pláticas, los momentos de risa, los consejos, los juegos y por muchas cosas más muchas gracias!. A Canek y Nikte son unos chicos muy geniales, gracias por todos los juegos y aventuras que tuvimos, estoy seguro que serán unos excelentes profesionistas y personas, ya que tienen muy buenos ejemplos en casa, los aprecio mucho.
A mis amigos de generación. No pongo compañeros porque para mí, todos son mis amigos, gracias por todo! Fue una aventura muy bonita, llena de momentos buenos, algunos malos, mucho aprendizaje, convivios, risas, estrés, bueno mucho de todo. Ana, Carlos, Dave, Mary, Augusto, Karen y Yonas, gracias por la oportunidad de haberlos conocido, de brindarme su amistad, espero siempre sigamos en contacto.
A todos los GHB team (Banduqui). Itzel, Muchas gracias por todo, eres una gran amiga y te quiero mucho, compartimos tanto estrés y alegrías, aventuras en donde estuvimos solos trabajando mientras toda la escuela en fiesta, bueno tanto, eres una gran amiga. Sofi, Romy, Angy, Ana, Rous, Alhee, son unas personas muy lindas, gracias por todo su apoyo y cariño, los momentos de locura y risa que me brindaron, la confianza, y la enseñanza de que con esfuerzo se pueden lograr muchas cosas, las quiero mucho. Marino, Leyva gracias por la ayuda y los momentos de risa. Ada, gracias por todas las buenas pláticas, los favores, las aventuras, el bully, pero sobre todo por tu amistad. Itzel, Aydee y Brian, mis “niños” gracias por confiar y seguir este proyecto tan bonito, gracias por permitirme conocerlos y en especial por ser muy buenos amigos. A todos los miembros “nuevos” del equipo, Gema, Ale, Liss, Alberto, Venecia, Genesis y Perla, por la convivencia compartida.
A mi amiga Amparo, por todos los buenos ratos vividos, los consejos, las pláticas, los momentos de estrés, bueno por muchas cosas, eres una gran amiga te quiero mucho.
Al Dr. Javier. Por su apoyo, las pláticas, sus consejos y por el equipo que me facilito durante toda la maestría.
A todos los profesores que conforman esta gran maestría que es Biociencias. Por todo el apoyo y enseñanzas obtenidas durante el transcurso del posgrado. A la Biol. Lupita por el apoyo en el área administrativa.
Fernando Astudillo Melgar
El presente trabajo va dedicado principalmente a dos grandes personas que se han sacrificado y luchado por brindarme una buena educación y valores, y a los que les debo todo. A mi padre, al cual le agradezco todos sus consejos, amor, cuidado, apoyo, orientación, los cuales me han servido a lo largo de mi vida. A mi madre, que siempre ha estado cuidándome con sus consejos apoyo y amor incondicional. Son unos excelentes seres humanos, siempre han sido y serán mi motor para salir adelante, un ejemplo a seguir como profesionistas y como personas; los admiro y amo con todo el corazón.
A mis hermanos, mis sobrinos, mi cuñada. Gracias por todo el apoyo brindado, los consejos, los ánimos, los encargos que hacía, las vueltas que a veces los hice pasar, los abrazos que me llenan de energía para seguir adelante, los momentos que se preocupaban junto conmigo y por muchas cosas más gracias. Con este escrito quiero compartirles uno de mis sueños que se realiza porque es un gran logro que también les pertenece ya que me han brindado todo cuanto he necesitado, en especial el cariño y apoyo durante el transcurso de mi vida.
A mis tíos, mi abuelita y mi prima. Que durante mis estancias en Morelos siempre me estuvieron cuidando y al pendiente de mi. Sus apapachos, comidas, paseos, ánimos, consejos, en fin… toda la energía que a uno le hace falta cuando está lejos del hogar base, y digo base porque sin duda ustedes me hicieron sentir en un hogar. Gracias.
ANÁLISIS DE LA DIVERSIDAD BACTERIANA Y FUNCIONAL DE LA TUBA CON UN ENFOQUE BIOINFORMÁTICO.
Table of contents
Material and Methods............................................................................................................ 3
Supplementary material....................................................................................................... 21
3 Fernando Astudillo-Melgar1,3, Adrián Ochoa-Leyva2, José Utrilla3* Gerardo Huerta-
6 1.- Laboratorio de Investigación en Biotecnología, Universidad Autónoma de Guerrero,
7 Chilpancingo, México.
8 2.- Departamento de Microbiología molecular, Instituto de Biotecnología- Universidad
9 Nacional Autónoma de México, Cuernavaca, México.
10 3.- Programa de Biología de Sistemas y Biología Sintética, Centro de Ciencias Genómicas -
11 Universidad Nacional Autónoma de México, Cuernavaca, México.
12 *Co-corresponding authors
13 e-mail: email@example.com, firstname.lastname@example.org
39 A wide variety of fermented food products such as yogurt, alcoholic beverages, bread and
40 sauces are produced worldwide. During the production process of these fermented foods
41 different microorganisms contribute to the organoleptic and biochemical characteristics
42 (Tang et al. 2017). Recent studies in fermented food have shown that microbial ecology
43 aspects such as diversity, their spatial distribution and ecological interaction, have a strong
44 influence on metabolic production and chemical composition (Escalante et al. 2015).
45 Bacterial consortia interactions in fermented foods promote process of polymer degradation
46 and production of metabolites of interest such as alcohol, aromatics, acetate, lactate among
47 others that contribute to functional and organoleptic properties (Tamang et al. 2016).
48 Palm wine is a traditional beverage made using the sap collected from palm trees. It is
49 consumed in different parts of the world, in Africa it is known as "legmi", in South India as
50 "kallu", while in Borneo it has the names of "bahar" and "goribon" (Velázquez-Monreal et
51 al., 2011). The differences among these beverages are the production process, the coconut
52 tree species and the plant part where the sap is collected (Santiago-Urbina & Ruíz-Terán
53 2014). In Mexico, several traditional fermented beverages are produced such as pulque
54 (Escalante et al. 2016), pozol (Díaz-Ruíz et al. 2003) and Tuba (De la Fuente-Salcido et al.
55 2015). Tuba was brought to Mexico by Philippine influence during the Spanish colonial
56 period. This beverage is produced in the southern pacific coast of Mexico (Guerrero,
57 Colima, Michoacan states). It is obtained from the sap of the inflorescences of Cocos
58 nucifera L and it is consumed as a traditional beverage empirically used as an aid in
59 gastrointestinal problems and as a rehydration drink (Velázquez-Monreal et al. 2011; de la
60 Fuente-Salcido et al. 2015).
61 The importance of bacteria in fermented foods has promoted the application of different
62 strategies to analyze the bacterial diversity and role during elaboration of fermented
63 products. The use of massive sequencing technologies together with recent bioinformatics
64 methods, such as QIIME for diversity analysis (Navas-Molina et al. 2015; Caporaso et al.
65 2011) and PICRUST for functional inference (Langille et al. 2013), have increased the
66 taxonomic and functional information of uncultured bacterial communities in different
67 ecosystems (Filippis et al. 2017). However, those methods have been used mainly in
68 projects such as Human Microbiome and Earth Microbiome (Creer et al. 2016).
69 Nevertheless, in the food area, the applications of them are limited. Some studies in
70 traditional Asian liquors and sauces have established a correlation between microbial
71 diversity and organoleptic properties, increasing the information about bacterial
72 communities in Asian products such as Yucha (Tang et al. 2017; Zhang et al. 2016) and
73 some Mexican traditional beverage as Pulque (Escalante et al. 2008).
74 Here, we study the fermentation profile, population dynamics and bacterial diversity of
75 Tuba produced in the Guerrero coast of Mexico. We sampled sap that was fermented under
76 controlled conditions and sampled commercial Tuba. Using 16S amplicon sequencing and
77 metabolic characteristics, we were able to analyze the diversity and infer functionality of
78 bacterial communities present in all tuba samples. This work provides a basis for the further
79 functional characterization of Tuba in its production process, probiotic potential and other
80 functions as antibiotic and antioxidant biosynthesis.
84 Sap samples were collected from three visibly healthy palm trees in a rural area in
85 Acapulco, Guerrero, Mexico. Commercial Tuba samples were obtained from four different
86 artesian producers in diamante zone from Acapulco, Guerrero Mexico (Figure 1). The
87 climatological conditions of the samples collection site at the sampling day are described in
88 table 1. Samples were transported in sanitized coolers to the laboratory for fermentation and
89 analysis. The sap from palm trees was tagged with the following code a “P” followed by
90 the number of the palm tree and “T” which means the fermentation time (i.e. P1T0).
91 Commercial samples were tagged using the letter L followed by a consecutive number that
92 symbolize the number of the establishment where each sample was obtained.
95 Each sap sample (100 mL as working volume) was fermented in four 250 mL Erlenmeyer
96 flasks corresponding to 0, 12, 24 and 35 hours of fermentation. They were incubated at
97 30°C and 100 rpm of shaking speed in an orbital incubator. Samples were centrifuged
98 (4000 rpm x 15 min) and the pellets were used for DNA extraction, while the supernatants
99 were stored at -20°C for further analysis.
102 Sugars, organic acids and ethanol from laboratory fermented and commercial samples were
103 quantified using two HPLC methods following column manufacturer conditions. Glucose,
104 fructose, sucrose and xylose were quantified using an Aminex HPX-87P (Biorad) column
105 with an IR detector. Acetate and ethanol concentrations were measured using Aminex
106 HPX-87H (Biorad) column and a UV 210 nm detector. Water-soluble proteins were
107 measured by Bradford method modified by Fernández & Galván, 2015. The pH was
108 measured using a potentiometer with 1 mL of the sample.
109 16S amplicon library preparation and sequencing.
110 The DNA extraction from all the samples was performed using the ZR Soil Microbe DNA
111 MiniPrep™ kit (Zymo Research) according to the manufacturer protocol. The DNA was
112 quantified using Qubit Fluorometric Quantitation (Thermo Fisher Scientific). 12.5 ng of
113 total DNA was used for PCR of amplicons of the V3-V4 regions of the 16S rRNA
114 ribosomal gene (Table 2) as described by the Illumina Protocol. All the PCR products were
115 purified (AMPure XP beads - Illumina products) and quantified (Qubit). Finally, all the
116 libraries were sequenced by Illumina MiSeq.
117 Bioinformatics and Statistical analysis
118 The sequences were analyzed using QIIME version -1.9.1software (Caporaso et al. 2011) in
119 Python 2.7. The total sequences were clustered using UCLUST into OTUs tables
120 (operational taxonomic units) using the Greengene database (GG 13_8_otus) as reference
121 with a range of 97% of similarity and using the closed system with the command
122 pick_closed_reference_otus.py. Taxonomy summaries including relative abundance data
123 were generated using summarize_taxa.py, plot_taxa_summary.py and
124 plot_taxa_through_plots.py commands. In all the cases, we used the data filtering option of
125 0.01% in abundances because it is reported that filtering data base decreases the estimation
126 error (Kuczynski et al. 2012; Navas-Molina et al. 2015).
127 Alpha diversity was evaluated using the function of alpha_rarefaction.py from QIIME, that
128 calculate alpha diversity on each sample in an OTUs table, using a variety of alpha
129 diversity metrics as Shannon-Wiener index, Simpson index, Otus_observed and Chao1
130 value. Each metrics result were analyzed by ANOVA applying the Tukey-Kramer test (0.95
131 confidence interval) to estimate significance difference between the samples. Beta diversity
132 was calculated by beta_diversity_through_plots.py, a workflow script for computing beta
133 diversity distance matrices (UniFrac unweighted method) and generating Principal
134 coordinates analysis (PCoA) plots from QIIME.
135 The normalized OTUs table (0.01% abundance filter) was used to estimate functional
136 features present in the samples, using PICRUSt version 1.1.0 (Langille et al. 2013) and the
137 Greengenes databases 16S_13_5 and KO_13_5. The OTUs table was normalized to obtain
138 the metagenomic functional predictions at different hierarchical KEGG levels using
139 normalize_metagenomes.py, predict_metagenomes.py and categorize_by_function.py
140 scripts of the same software.
141 For the statistical studies of the functions, we used STAMP (Statistical analysis of
142 taxonomic and functional profiles version 2.1.3), through ANOVA analysis applying the
143 Tukey-Kramer test (0.95 confidence interval) to evaluated gene abundance of each
144 function. R statistical program (version 3.3.3) was used to make plots using “ggplot2” and
145 “dplyr” libraries.
149 To determine the microenvironmental conditions that affect the microbial communities and
150 metabolic characteristics of the Tuba samples, we evaluated the sugars (sucrose, glucose
151 and fructose), water-soluble proteins, ethanol and acetic acid concentrations as well as the
152 pH value (Supplementary Table 1S). Tuba P1 was the sample with the highest
153 concentration in glucose and fructose with 61.4 and 47.3 g/L respectively at 12 hours, 4.7%
154 (v/v) in ethanol and 6.0 g/L in acetate at 35 hours (Figure 2A). Tuba P2 was the sample
155 with lowest concentration of monosaccharides at the beginning of the fermentation and
156 high sucrose concentration (121.7 g/L), however, at the last fermentation time the ethanol
157 and acetate concentrations were low with 3.5 g/L and 0.6% (v/v) respectively (Figure 2B).
158 Tuba P3 showed the highest concentration of ethanol (5% v/v) at the end of the
159 fermentation, nevertheless, the glucose and fructose concentration were 39.8 and 29.1 g/L
160 respectively at 12 hours (Figure 2C). The pH values in Tuba P1, P2 and P3 decreased from
161 3.7 to 2.8 during the fermentation process. The water-soluble protein concentration of the
162 Tuba samples showed low values from 0.006 to 0.01 g/L. In the case of the commercial
163 samples, all of them presented different composition values, nevertheless they had an
164 average values of 40.5 g/L of sucrose, 40.0 g/L of glucose, 42.53 g/L of fructose, 1.6 g/L of
165 acetic acid, 0.1% (v/v) of ethanol and a pH of 4 (Figure 2D).
167 A total of 302,398 sequences were obtained from the Tuba amplicon libraries, with an
168 average of 75,594 sequences per Tuba fermented under controlled conditions (distributed as
169 follows, for the Tuba P1 74,860 reads were obtained; for the Tuba P2, 75,623; for the Tuba
170 P3 76,298) and the four commercial samples had an average of 75,617 sequences. A total
171 of 123 OTUs were detected in all Tuba samples. However, filtering data base with 0.01%
172 relative abundance filter, the OTUs were reduced to 28 as the more abundance. The
173 taxonomic identification was elaborated using the last filter mentioned, which demonstrates
174 the 10 most representative genera of the 16 Tuba samples (Figure 3). The genera that
175 predominate in all the samples were Fructobacillus, Leuconostoc, Gluconacetobacter,
176 Sphingomonas , Vibrio and some genera of the Enterobacteriaceae family. Additionally,
177 analyzing the Enterobacteriaceae populations with the lower abundance we found genera as
178 Erwinia, Klebsiella, Serratia and Cronobacter (Supplementary Figure 1S). The population
179 dynamic had a similar trend in Tuba fermented in controlled conditions but with different
180 percentage in the abundances; we observed an increase of lactic acid bacteria (LABs) until
181 24 h, acetic acid bacteria (AABs) and some proteobacteria as Sphingomonas through the
182 fermentation time and a decrease of Vibrio genus (Figure 3).
184 Alpha diversity tests were performed using the OUTs table obtained with the 0.01 % filter
185 and grouped according to the origin of the sample. We observed a similar behavior in all
186 the four analysis, that means, no matter what base-priority was in the analysis as richness
187 (observed_otus), dominance (Simpson), equity (Shannon index) or singletones (Chao1
188 value) it did not affect diversity results (Supplementary Figure 2S). Tuba P1 was the most
189 diverse with the highest values in the four diversity index, then Tuba P3 and commercial
190 Tuba samples had similar index values, and finally Tuba P2 was the least diverse with the
191 lowest values. After of ANOVA statistical analysis, we found that in Chao1 and
192 Observed_otus tests Tuba P2 was the only showing significant difference. Nevertheless, in
193 Shannon and Simpson index the four groups showed significant difference among each
194 other (Table 3).
195 Beta diversity with Unweighted UniFrac distance was determined using the 0.01% filter.
196 We did not observe groupings by fermentation time (Figure 4A) however, a grouping was
197 observed by origin of the samples (Figure 4B). In the graphic of origin of the sample we
198 also observe a grouping by quadrant of the all the Tuba samples, however Tuba P2 showed
199 the greatest dispersion in the data, which indicated a big difference between the
200 fermentation times in Tuba P2. Similar effect is observed in Tuba P1 where two
201 fermentation times (0 h and 35 h) show similar beta diversity values compared to
202 commercial samples and Tuba P3. Otherwise, the samples, which were in the same
203 quadrant as Tuba P3 and the commercial Tuba, were considered strongly related (Figure 4).
205 After diversity distribution analysis we sought to understand the functionality of the
206 bacterial community in Tuba fermentation, therefore we used PICRUSt algorithm to predict
207 the metagenomic profiles of the samples. Initially, we obtained functional characteristics of
208 the 3 KEGG levels (Level 1: general cellular functions, Level 2: Specific functions i.e.
209 different metabolism, and Level 3: Specific pathway associate with specific function)
210 (http://www. genome.ad.jp/kegg/). We limited our analysis to the level 3 and we discarded
211 elementary cellular functions such as replication, translation, and functions associated with
212 human diseases (cancer) or poorly characterized functions, to analyze specific genes related
213 with functions of biotechnological relevance. Considering the 328 registered functions on
214 KEGG, we found the 19 most abundant functions associated with carbohydrates metabolic
215 process, vitamins, amino acid, antibiotics and antioxidant molecules biosynthesis (Figure
216 5), this suggested that the production of those compounds may be taking place during Tuba
217 fermentation. After an ANOVA test, we found functions without significant difference as
218 the carotenoid biosynthesis (Figure 6A), this means that no matter what is the sample
219 origin, this function may have present at the same gene abundance in the four groups.
220 Otherwise, there were functions with significant difference, such as peptidases biosynthesis
221 that had more gene abundance in Tuba P2 samples (Figure 6B). Each sample had more
222 abundance in genes associated with a specific function, for example, antioxidant, antibiotic
223 compounds, and folate biosynthesis in Tuba P3, lipopolysaccharide biosynthesis and
224 Lysine genes in Tuba P1, finally the 4 commercial Tuba samples may have bacteria with
225 genes associated mainly with folate biosynthesis and peptidases. Our study allowed to
226 analyze if some of bacterial genera found in Tuba may had gene associated with enzymes
227 of carotenoid biosynthesis, we observed that Sphingomonas and Gluconacetobacter had
228 more abundance percentage in the enzyme 15-cis-phytoene synthase (Figure 7).
231 In the present study, we carried out for the first time the identification of bacterial diversity,
232 the fermentation dynamics in terms of bacterial populations and metabolic changes during
233 Tuba fermentations comparing between laboratory controlled conditions and commercial
234 samples. This comparison was realized by a combination of metabolic analysis and 16S
235 amplicon sequencing during the Tuba fermentation, as well as to infer functions of
236 biotechnological interest that the Tuba may present during the fermentative process.
237 We found that the average of total sugar concentration in sap of the palm trees in the Tuba
238 samples was 130 g/L. Where it contained 77.06% of sucrose, 12.81% of glucose and
239 10.15% of fructose, without presence of xylose. In a study with sap of Phoenix dactylifera
240 was reported that it contained 95.27 % of sucrose, 2.51% glucose and 1.61% of fructose
241 with a neutral pH of 7-7.4 (Santiago-Urbina & Ruíz-Terán 2014). These results suggested
242 that the sap composition is dependent of the palm type. The concentration of sucrose from
243 the sap samples at the start of the laboratory controlled fermentation was high, from 85 g/L
244 to 121 g/L (Supplementary Table 1S). Then, after 12 hours of fermentation for Tuba P1 and
245 P3, and after 24 hours for Tuba P2 the concentration of sucrose was reduced, increasing the
246 concentration of glucose and fructose, presumably by invertase-mediated hydrolysis. The
247 different behavior of Tuba P2 sample may be related with low sucrose hydrolysis, delaying
248 the fermentation process. This may be caused by a lower yeast abundance than other
249 samples, the yeast abundance was not measured in this study, but we did not identify any
250 ethanologenic bacteria, then we may attribute all the ethanol production to yeasts present in
251 the samples. Also, the high concentration of sucrose may also cause retro-inhibition of the
252 invertase enzyme, and the pH values may reduce its catalytic activity (Hsieh et al. 2006;
253 Goosen et al. 2007). Hence, we can only propose that the hydrolysis of sucrose is
254 associated with the presence of Fructobacillus and Leuconostoc genera, because those
255 microorganism present the genes that codes for the invertase β -fructofuranosidase
256 (Supplementary Figure 3S), a further study is needed to show yeast abundance in Tuba
257 samples and its implication in sucrose hydrolysis.
258 In the laboratory controlled fermentations we observed near complete sucrose hydrolysis
259 and lower ethanol production compared to other fermented beverages such as pulque.
260 Pulque shown an absence of sucrose hydrolysis, high ethanol concentration and a high
261 abundance of ethanolic bacteria such as Zymomonas mobilis (Escalante et al. 2008), that
262 genus was not found in our work. Thus, these results suggest that the composition of the
263 bacterial community in Tuba play an important role in the hydrolysis of sucrose at the start
264 of the fermentation. These characteristics are related with the bacterial diversity, because
265 several bacterial genera present in the sap has different metabolism and regulation types
266 that in consequence may inhibit or delay the fermentative process (Tamang et al., 2016).
267 We found the 10 more abundant bacterial genera that belong to three main groups, lactic
268 acid bacteria (LABs), acetic acid bacteria (AABs) and proteobacteria (Figure 3). It has been
269 reported that LABs are the main antibiotic and folate producers in fermented products (De
270 la Fuente-Salcido et al. 2015; Rossi et al. 2011) both functions have an important impact on
271 human health. Moreover, some LABs reported in here such as Fructobacillus and
272 Leuconostoc genera are similar phylogenetic and metabolically, nevertheless,
273 Fructobacillus is unable to produce ethanol, redirecting the carbon flow to the production
274 of lactate, (Endo et al. 2015). Other genera found was Lactococcus that produce more
275 lactate than ethanol (Makarova et al. 2006).
276 The acetate production is related with the abundance of AABs such as Gluconacetobacter
277 and Acetobacter genera that was found in all samples. Interestingly, sample P1 showed the
278 higher abundances of Acetobacter, which contributed with the acetate and ethanol
279 production in comparison with the Tuba P2 and P3. Nevertheless, we observed a smaller
280 abundance of the AABs in the commercial samples; contributing with a lower acetate and
281 ethanol concentrations with respect to the laboratory fermented samples. This result is in
282 agreement with other studies, where the authors propose that the growth of the AABs of the
283 Gluconacetobacter and Acetobacter genera is dependent on the presence of acetate and
284 ethanol in the environment (Lisdiyanti et al. 2003). Other researches have stablished these
285 genera as the main acetate producers in products from fruit fermentation (Dellaglio et al.
287 Both Vibrio genera and Enterobacteriaceae family (both proteobacterias) were detected in
288 all the analyzed Tuba samples. Vibrio have been reported as a "natural" pollutant of
289 fermented products (Lee et al. 2015). The abundances of these bacterial groups was
290 reduced through the Tuba fermentation process, this abundance in commercial Tuba
291 samples was similar with the abundance to the initial fermentation points. Finally, we
292 observed a relation between the increase of the abundance of LABs and the decrease of
293 Enterobacteriaceae family. It has been shown that the secretion of peptidases by LAB and
294 AAB limits the cell growth of pathogen such as Vibrio (De la Fuente-Salcido et al. 2015;
295 Lee et al. 2015). Hence, in this case the limitation of the growth of some proteobacteria in
296 the Tuba, may be caused by compounds produced by the bacterial community (such as the
298 The alpha diversity tests, showed that in Chao1 and Observed_otus the Tuba P2 had
299 significant difference but with Shannon and Simpsons index all Tuba samples (P1, P2, P3
300 and commercial) showed significant difference. That difference was due to the focus of
301 each test, Observed_otus and Chao1 had low values for Tuba P2 that means low number of
302 bacterial genus and high dominance. Although, Shannon and Simpson index analyzed the
303 abundance and equity of the population, which means that the four Tuba groups have the
304 same 10 genera but in different abundance (Figure 3). The low values of Tuba P2 in alpha
305 tests may have related to high concentrations of sucrose and low acetate and ethanol. In a
306 study of the bacterial diversity in pulque was established that the diversity is strongly
307 correlated with ethanolic fermentation conditions and aguamiel and pulque composition
308 (Escalante et al. 2008). The microbial beta diversity data showed no significant differences
309 between the samples of each palm. Hence, the 10 most abundant genera of the 16 analyzed
310 samples were associated with the origin of the samples. In some studies, the biotic and
311 abiotic conditions (seasonality, plant physiology, age, soil conditions, and other abiotic
312 variables such as water irrigation and other environmental factors) affected the bacterial
313 diversity at different times of the fermentation (Staley et al. 2014; Fonseca-García et al.
314 2016; Coleman-Derr et al. 2016). Hence, we propose that the sugar concentration and the
315 pH of the Tuba, has an effect on the bacterial diversity of this beverage, contributing to
316 define the metabolic composition and the dominant bacterial genera. Additionally, the sap
317 samples were collected after it was harvested by the producer and we took all the
318 precautions to conserve the initial bacterial community and took it to the laboratory for
319 fermentation (all handling was done in aseptic conditions). Therefore the observed
320 differences in bacterial diversity in the samples is a combination of the palm related abiotic
321 variables and the harvesting procedure itself.
322 Palm wine is consumed in many places in the world, the Tuba type that is the subject of this
323 study has its own characteristics. It is produced near coconut palm production sites in the
324 Mexican south pacific coast and is consumed as refreshing, hydration drink and empirically
325 used as traditional aid for gastrointestinal discomfort, here we are showing its low alcohol
326 content, however it can reach higher concentrations if fermented for longer time
327 (Velázquez-Monreal et al. 2011). In this study, the functional analysis of the Tuba P1, P2,
328 P3 and commercial samples using PICRUSt showed 18 functions of biotechnological
329 interest (Figure 6), some of them showed significant differences as folate biosynthesis,
330 antibiotic production and peptidases. Other functions were present on all Tuba samples
331 without a significant difference among them such as terpene and carotenoid biosynthesis.
332 These functions have been described in other fermented beverages such as pulque, where
333 they proposed it as a functional product because it has mainly prebiotic and probiotic action
334 with antimicrobial activity and production of nutrients (Escalante et al. 2016). In other
335 study Cocos nucifera L. (Palmacea) water (CW), variety Chandrasankara, was tested for its
336 ability to scavenge free radicals, and they found a good antioxidant activity percentage
337 (Mantena et al. 2003). Beverages made from plants, seeds or fruits have high contents of
338 phenolic compounds that have the capacity to stabilize reactive oxygen and nitrogen
339 species (Richelle et al. 2001), especially red, pink and white color fermented beverages
340 (Martins de Sá et al. 2014). In addition, as we found in this work (Figure 7), microbial
341 communities may be able to produce antioxidant compounds, there are evidence that
342 described LABs genera as antioxidant compound producers, mainly glutathione, folate and
343 butyrate (Wang et al. 2017). Other studies reported bacteria that produce antioxidant
344 compound but it was not been identified yet (Tabbene et al. 2010), or it is a pigment
345 produced in specific conditions by the bacteria (Radhakrishnan et al. 2016).
346 In this work we reported for the first time the bacterial diversity and potential functional
347 analysis through the fermentation process of the Tuba. With the knowledge of microbiota
348 diversity and metabolic functional inference, the Tuba production can be controlled
349 adjusting the presence and abundance of beneficial genera that contributes with the
350 functional characteristics of the Tuba. It also contributed to the stablishing of
351 microbiological basis of its empirical uses. Additionally, the bacterial isolation from these
352 samples may provide us with new species with probiotic potential.
454 the four commercial establishments (commercial samples) and the blue star show the area
455 where sap samples for the laboratory controlled fermentation were obtained. B) Cocos
456 nucifera L (palm tree). Yellow square signaling sap collection zone. C) Sap collection zone.
457 Red arrow indicate the palm structure (inflorescence) where the sap is collected.
459 Figure 2. Metabolic composition of the laboratory fermented Tuba and commercial
461 number correspond to one sample. Right axis represented pH value.
465 0.01% abundance filter OTUs table.
469 with respect to the origin of the sample. Analysis performed by the Unifrac unweighted
470 technique with 0.01% abundance filter and plotted with the Principal Coordinates Analysis
471 (PCoA). The color boxes show a grouping data.
474 with Tukey-Kramer (0.95), the percentage of genes associated with functions, discarding
475 elementary cellular functions. Asterisk show functions with significant difference (p<0.05).
479 significant difference. An ANOVA was performed with Tukey-Kramer (0.95) and plotted
480 with STAMP.
483 Figure 7. Main bacteria with 15-cis-phytoene synthase gene (K02291 KEGG code).
484 Analysis performed with the function “metagenome_contributions.py” obtained by
485 PICRUSt analysis and plotted with R studio.
489 Table 1. Climatological conditions of the study sites.
|PARAMETER||PALM SAP COLLECT||COMMERCIAL COLLECT|
| ||North 16 ° 46'54.53 '' West 99 ° 47'02.73 ''|
| ||Max. 32°C y Min. 24°C||Max. 30°C y Min. 24°C|
| ||0.996 atm||0.996 atm|
| ||Light rain||Light rain|
490 Data provided by Comisión Nacional del Agua (CONAGUA).
492 Table 2. PCR primers targeting 16S rRNA V3-V4 region of bacteria
| ||550 bp|
| ||5'- TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGCCTACGGGN GGCWGCAG-3’|
| ||5' GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGGACTACHV GGGTATCTAATCC-3’.|
497 Supplementary material.
501 filter OTUs table.
505 C) Shannon and D) Simpson. Each population is represented for a specific color in all the
510 “metagenome_contributions.py” obtained by PICRUSt analysis and plotted with R studio.
512 Table 1S. Chemical composition of the Tuba.
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