Pitaya contains various types of polyphenols, flavonoid and vitamins which are beneficial for health and it is among the most important commercial tropical fruits worldwide. Endophytic bacteria might be beneficial for plant growth and yield. However, bacterial diversity in pitaya is poorly characterized. In this study, fruits of white and red pitayas from three different origins (Thailand, Vietnam and China) were chosen for endophytic bacteria diversity investigation by using Illumina HiSeq second-generation high-throughput sequencing technology. Large number of endophytic bacteria were detected and 22 phyla, 56 classes, 81 orders, 122 families and 159 genera were identified. Endophytic bacteria diversity was uneven among pitaya fruits from different origins and bacteria structure was different between white pitaya group and red pitaya group. Phylum Bacteroidetes, classes Bacteroidia and Coriobacteriia, orders Bacteroidales and Coriobacteriales, families Prevotellaceae, Bacteroidaceae, Ruminococcaceae, Paraprevotellaceae, Rikenellaceae, Alcaligenaceae and Coriobacteriaceae, genera Prevotella, Bacteroides, Roseburia, Faecalibacterium and Sutterella were statistically significant different species (P < 0.05) between white and red pitayas. These findings might be useful for growth improvement, fruit preservation and processing of different pitaya species from different origins.
Pitayas (dragon fruits) are originally native to Latin America and the West Indies and are cultivated in tropical and subtropical regions all over the world. Pitaya belong to the genus Hylocereus, three major species of pitaya are Hylocereus undatus (white pitaya), Hylocereus polyrhizus (red pitaya) and Hylocereus megalanthus (yellow pitaya). The white and red pitayas are the most widely cultivated because of their economic values and health benefits (Ortiz and Takahashi, 2015; Suh et al., 2014). Both white and red pitayas contain various types of polyphenols, flavonoid and vitamins which are beneficial for health (Esquivel et al., 2007). The antidiabetic effect of white and red pitayas has been recently demonstrated. It is reported that the consumption of white pitaya attenuates insulin resistance and hepatic steatosis in diet-induced obese mice. Consumption of red pitaya could decrease total cholesterol, triglyceride and low-density lipoprotein cholesterol levels and increase the high-density lipoprotein cholesterol levels in type 2 diabetic subjects (Song et al., 2016). In addition, red pitaya may serve as a therapy for attenuating some signs of high-carbohydrate and high-fat diet-induced metabolic syndrome (Ramli et al., 2014). However, the active components in the flesh of white and red pitayas are different. Most of the betalain-related metabolites, the main contributors to antioxidant activity, were significantly higher in the flesh of red pitaya than in white pitaya (Ortiz and Takahashi, 2015; Suh et al., 2014).
Endophytic bacteria live in almost all studied plant species. Plants provide nutrients for the growth of endophytes, meantime, many endophytes give beneficial feedback for their host by different ways, including growth promotion, pathogens suppression, contaminants remove, phosphate solubilization, nitrogen fixation, etc. It has been reported that endophytic bacteria and host plants are in an obligate mutualism through an evolutionary process (Assmus et al., 1995; Hardoim et al., 2008; Long et al., 2008; Sessitsch et al., 2002). Studies of endophytic bacteria diversity could improve knowledge about bacteria-plant interactions and this knowledge could be used for improving production and quality of the host plants (Berg, 2009; Compant et al., 2005; Compant et al., 2010). In the case of pitaya, the endophytic bacterial diversity is still poorly characterized.
Culture dependent methods were used to detect the composition of food associated microbiotas for decades, but these methods are difficult to reveal the real composition of microbiota due to some limitations, such as selective isolation, uncultivable state and outcompeted of low numbers of some microorganisms (Amann et al., 1995; Ercolini et al., 2001). Culture independent methods were developed then to overcome these problems partly. These methods could determine the composition and diversity of complex microbial communities with high speed and sensitivity (Giraffa and Neviani, 2001; Jany and Barbier, 2008). One of the most powerful methods is next generation sequencing (NGS) technology, and it has become more and more important in food quality and safety studies nowadays. NGS could produce millions of sequences in a single run and partly avoid some inherent biases in culture dependent methodology. Different microbial taxa, including uncultivable groups and some small groups which are difficult to detect by cultivated methods, could be identified based on sequence information (Ercolini, 2013; Mayo et al., 2014). In addition, some cryptobiotic, dormant, moribund or latent bacteria, which might affect food quality and safety, could be identified using NGS (Davey, 2011; Mayo et al., 2014). Studies of the microbiota of milk, fermented dairy products, plant, meat and fermented foods using NGS technologies had been reported more and more recently (Mayo et al., 2014). Microaerophilic and anaerobic specific spoilage organisms in vacuum-packed ham had been characterized using culture-plating techniques and MiSeq NGS technologies also some microorganisms which facilitated changes in the pH value and organoleptic characteristics of the product were found (Piotrowska-Cyplik et al., 2017). Comparative analysis of the metagenome mined from four diverse naturally fermented foods (bamboo shoot, milk, fish, soybean) were carried out to study the biases caused by different DNA extraction methods, Illumina MiSeq NGS showed the recovery of different bacteria varied by different DNA extraction methods (Keisam et al., 2016). Assisted with traditional culture dependent and independent methods, NGS technologies have played key roles in studies on food quality and safety (Piotrowska-Cyplik et al., 2017).
In this study, fruits of white and red pitayas from three different origins (Sing Buri, Thailand, Ho Chi Minh City, Vietnam and Jinghong, China) were chosen for endophytic bacteria diversity investigation by using Illumina HiSeq next-generation high-throughput sequencing technology. The aim of this work was to explore endophytic bacteria diversity in white and red pitayas from different origins. Identification of endophytic bacterial composition and diversity in pitayas might be useful for improving their production and quality.
Materials and Methods
Sample collection. Fresh fruits of white and red pitayas were collected from tropical and subtropical regions of Thailand, Vietnam and China. Ten white pitaya and ten red pitaya fresh mature fruits were randomly chosen from each site, 60 fruits were chosen in total. All the samples were stored at 4°C and processed within 12 h.
Surface decontamination of the fruits. Sixty fruits were washed by tap water and distilled water several times, then immersed three times in 70% ethanol (v/v) for 1 min. The flesh of surface decontaminated fruits was sampled in a laminar flow cabinet at room temperature.
Genomic DNA extraction. All ten flesh samples from the same site were pooled as one sample and mixed thoroughly. Finally, six samples (white pitayas from Thailand, Vietnam and China and red pitayas from Thailand, Vietnam and China) were generated for genomic DNA extraction. Genomic DNA was extracted by DNA quick plant system kit (tiangen, China) after maceration in liquid nitrogen following the manufacturer’s instructions. DNA concentration and purity were monitored on 1% agarose gels. According to the concentration, DNA was diluted to 1 ng/μl using sterile water.
PCR amplification of 16S rDNA-V4 region. PCR experiments were performed with Phusion® High-Fidelity PCR master mix with GC buffer (New England Biolabs) to ensure amplification efficiency and accuracy and run in an Eppendorf Gradient Thermocycler (Brinkman Instruments, Westbury, NY). With diluted genomic DNA as template, 16S rDNA-V4 region was amplified with specific primers 515F (5’-GTT TCG GTG CCA GCM GCC GCG GTA A-3’) and 806R (5’-GCC AAT GGA CTA CHV GGG TWT CTA AT-3’) with the barcode (Berry et al., 2011; Klindworth et al., 2013).
Libraries construction and sequencing. The PCR products were mixed with the same volume of 1 × loading buffer (contained SYB green) and checked by electrophoresis on 2% agarose gel. Samples with bright main strip between 400–450 bp were chosen for further experiments. PCR products were mixed in equidensity ratios. Then, the mixture of PCR products was purified with Qiagen Gel Extraction Kit (Qiagen, Germany). Sequencing libraries were generated using TruSeq® DNA PCR-Free Sample Preparation Kit (Illumina, USA) following manufacturer’s recommendations and index codes were added. The library quality was assessed on the Qubit@ 2.0 Fluorometer (Thermo Scientific) and Agilent Bioanalyzer 2100 system. At last, the library was sequenced on an Illumina HiSeq 2500 platform and 250 bp paired-end reads were generated.
Statistical analysis. Paired-end reads obtained by sequencing were divided into six groups according to the unique barcode and truncated by cutting off the barcode and primer sequence. Paired-end reads were merged to generate raw tags (Magoc and Salzberg, 2011); quality filtering on the raw tags was performed to obtain the high-quality clean tags (Bokulich et al., 2013; Caporaso et al., 2010). Clean tags were compared with the reference database to detect and remove chimera sequences to generate effective Tags (Edgar et al., 2011; Haas et al., 2011). Sequences were analyzed by Uparse software and were assigned to the same OTUs with ≥ 97% similarity (Edgar, 2013). Representative sequence for each OTU was annotated by GreenGene database based on RDP classifier and multiple sequence alignment was performed by MUSCLE software (DeSantis et al., 2006; Edgar, 2004; Wang et al., 2007). Alpha diversity and beta diversity analysis, including chao1, shannon, simpson, ACE, good-coverage, rarefaction analysis, rank abundance analysis, principal component analysis (PCA), principal coordinate analysis (PCoA), unweighted pair-group method with arithmetic means (UPGMA), non-metric multi-dimensional scaling (NMDS) analysis and T-test analysis were performed by QIIME and displayed with R software (Caporaso et al., 2010).
Nucleotide sequence accession number. All the raw sequences after assembling and filtering were deposited in the NCBI SRA database under accession number SRP079944.
OTU annotation and analysis. After quality filtering and chimera sequences removal, 417,519 effective sequences of six groups were obtained in total (Table I). White pitayas from China (HLGW.3) containing more effective tags than from Thailand (HLGW.1) and Vietnam (HLGW.2), while red pitayas from three different origins (HLGR.1, Thailand; HLGR.2, Vietnam; HLGR.3, China) containing a similar amount of effective tags. Red pitayas contained 21.95% more effective tags on average than white pitayas. Nonetheless, white pitayas contained 43.8% more OTUs than red pitayas. OTUs from different origins varied from 313 to 603.
Sequence result from six samples.
Top three microorganism populations from six samples were enumerated at phylum, class, order, family and genus level. respectively. The relative abundances of bacterial populations were different in each pitaya sample (Table II). Cyanobacteria, Proteobacteria and Bacteroidetes were found to be the most dominant phyla; Alphaproteobacteria, Bacteroidia and Clostridia were found to be the most dominant classes; Rickettsiales, Bacteroidales and Clostridiales were found to be the most dominant orders; Mitochondria, Prevotellaceae and Bacteroidaceae were found to be the most dominant families; Prevotella, Bacteroides and Acutodesmus were found to be the most dominant genera.
Relative abundance of the top three bacteria at different levels of taxonomy.
Alpha diversity analysis. Alpha diversity indices were calculated to analyze bacterial richness and diversity within communities (Table III). All of the alpha indices of white pitaya group (HLGW) were higher than in red pitaya group (HLGR). Rarefaction analysis was carried out to determine the sequence coverage of six samples (Fig. 1). All of the rarefaction curves are almost approaching their asymptotes indicated the sequence amount is adequate. Rank abundance curves indicated the species richness and species evenness of each sample were different (Fig. 2). Venn graphs were constructed to show the number of shared and unique OTUs in different groups (Fig. 3). Each sample contained a certain number of unique OTUs and white pitaya group contained much more unique OTUs than the red pitaya group.
Alpha indices table.
Beta diversity analysis. A heatmap of Beta diversity index was constructed (Fig. 4). Principal component analysis (PCA), principal coordinate analysis (PCoA), non-Metric multi-dimensional scaling (NMDS) and unweighted pair-group method with arithmetic mean (UPGMA) were carried out to evaluate differences of samples in species complexity (Fig. 5–8). As a result, all analysis showed that the microbial communities were different between white and red pitaya groups. Samples from white pitaya and red pitaya were clustered together respectively. The bacterial community structure of each sample in red pitaya group showed higher similarity.
T-test analysis. T-test was carried out to find statistically significant different species (P < 0.05) between white and red pitaya groups at different taxonomy levels. As a result, the differences in the relative abundance of phylum Bacteroidetes (p = 0.017), classes Bacteroidia (p = 0.017) and Coriobacteria (p = 0.023), orders Bacteroidales (p = 0.017) and Coriobacteriales (0.023), families Prevotellaceae (p = 0.001), Bacteroidaceae (p = 0.006), Ruminococcaceae (p = 0.026), Paraprevotellaceae (p = 0.015), Rikenellaceae (p = 0.043), Alcaligenaceae (p = 0.023) and Coriobacteriaceae (p = 0.023), genera Prevotella (p = 0.001), Bacteroides (p = 0.003), Roseburia (p = 0.041), Faecalibacterium (p = 0.034) and Sutterella (p = 0.023) showed statistically significant between white and red pitaya groups. All of these species in white pitaya group are much more than in red pitaya group.
In this study, we demonstrated the endophytic bacteria diversity in fruits of pitaya by using Illumina HiSeq second-generation high-throughput sequencing technology which can provide larger amount information than ever before. Large number of endophytic bacteria was found to colonize in pitaya flesh. A total of 417,519 effective sequences and 2822 OTUs with 97% similarity were obtained from six samples. Among them, 22 phyla, 56 classes, 81 orders, 122 families and 159 genera were identified.
Endophytic bacteria diversity was uneven among pitaya fruits from different origins. Rank abundance and venn graphs reflected the structure of each sample. In white pitaya group, samples from Vietnam and China shared more OTUs than other combinations. Samples from Vietnam contained most OTUs and samples from Thailand possessed most unique OTUs. Genera Ruminococcus, RFN20, 02d06, BF311 in samples from Thailand, genera Escherichia, Bulleidia and Anaerostipes in samples from Vietnam and genera Oscillospira, Chlamydia and Succinivibrio in samples from China showed higher relative frequencies respectively. In red pitaya group, samples from Vietnam and China shared more OTUs than other combinations. Samples from China contained most OTUs and samples from Vietnam possessed most unique OTUs. Genus Clostridium in samples from Thailand, genera Bradyrhizobium and Mesorhizobium in samples from Vietnam and genera Halomonas and Lactobacillus in samples from China showed higher relative frequencies, respectively.
Endophytic bacteria structure was different between the white pitaya group and red pitaya group. Although the white pitaya group contained a lower number of effective tags, it contained 43.8% OTUs more than the red pitaya group, which might be caused by different environmental factors in different pitaya fruits. All the alpha indices of the white pitaya group were higher than for the red pitaya group which indicates the bacterial richness and diversity were higher in the white pitaya group. Venn graph of the two groups also supported this conclusion. White pitaya group contains over three times more unique OTUs than the red pitaya group. To further investigate diversity differences between white and red pitaya, beta diversity analysis was carried out. The heatmap of beta diversity index clearly showed the difference between the two groups. PCA, PCoA, NMDS and UPGMA analysis evidently demonstrated the bacterial diversity between white and pitaya groups was different. To explore the significant different species which might be used as biomarkers for white and red groups respectively, T-test was carried out. As a result, phylum Bacteroidetes, classes Bacteroidia and Coriobacteria, orders Bacteroidales and Coriobacteriales, families Prevotellaceae, Bacteroidaceae, Ruminococcaceae, Paraprevotellaceae, Rikenellaceae, Alcaligenaceae and Coriobacteriaceae, genera Prevotella, Bacteroides, Roseburia, Faecalibacterium and Sutterella were significantly much more in white pitaya group. Since the study showed the active components in flesh of white and red pitayas were different and the main contributors to antioxidant activity were significantly higher in red pitaya than in white pitaya (Suh et al., 2014), the relationship between endophytic bacterial diversity and their living environment needs further study.
In conclusion, a large number of endophytic bacteria is found in fruits of white and red pitaya from Thailand, Vietnam and China. The bacterial diversity was different among pitayas from different origins and between white and red species. Further studies of the roles of these endophytic bacteria are needed. This study might be useful for growth improvement, fruits preservation and processing of different pitaya species from different origins.
Amann, R.I., W. Ludwig and K.H. Schleifer. 1995. Phylogenetic identification and in situ detection of individual microbial cells without cultivation. Microbiol. Rev. 59: 143–169.AmannR.I.W.Ludwig and K.H.Schleifer1995Phylogenetic identification and in situ detection of individual microbial cells without cultivation5914316910.1128/mr.59.1.143-169.19952393587535888Search in Google Scholar
Assmus B., P. Hutzler, G. Kirchhof, R. Amann, J.R. Lawrence and A. Hartmann. 1995. In situ localization of Azospirillum brasilense in the rhizosphere of wheat with fluorescently labeled, rRNA-targeted oligonucleotide probes and scanning confocal laser microscopy. Appl. Environ. Microbiol. 61: 1013–1019.AssmusB.P.HutzlerG.KirchhofR.AmannJ.R.Lawrence and A.Hartmann1995In situ localization of Azospirillum brasilense in the rhizosphere of wheat with fluorescently labeled, rRNA-targeted oligonucleotide probes and scanning confocal laser microscopy611013101910.1128/aem.61.3.1013-1019.1995138838316534951Search in Google Scholar
Berg G. 2009. Plant-microbe interactions promoting plant growth and health: perspectives for controlled use of microorganisms in agriculture. Appl. Microbiol. Biotechnol. 84: 11–18.BergG.2009Plant-microbe interactions promoting plant growth and health: perspectives for controlled use of microorganisms in agriculture84111810.1007/s00253-009-2092-719568745Search in Google Scholar
Berry D., K.B. Mahfoudh, M. Wagner and A. Loy. 2011. Barcoded primers used in multiplex amplicon pyrosequencing bias amplification. Appl. Environ. Microbiol. 77: 7846–7849.BerryD.K.B.MahfoudhM.Wagner and A.Loy2011Barcoded primers used in multiplex amplicon pyrosequencing bias amplification777846784910.1128/AEM.05220-11320918021890669Search in Google Scholar
Bokulich N.A., S. Subramanian, J.J. Faith, D. Gevers, J.I. Gordon, R. Knight, D.A. Mills, and J.G. Caporaso. 2013. Quality-filtering vastly improves diversity estimates from Illumina amplicon sequencing. Nat. Methods. 10: 57–59.BokulichN.A.S.SubramanianJ.J.FaithD.GeversJ.I.GordonR.KnightD.A.Mills and J.G.Caporaso2013Quality-filtering vastly improves diversity estimates from Illumina amplicon sequencing10575910.1038/nmeth.2276353157223202435Search in Google Scholar
Caporaso J.G., J. Kuczynski, J. Stombaugh, K. Bittinger, F.D. Bushman, E.K. Costello, N. Fierer, A.G. Peña, J.K. Goodrich, J.I. Gordon and others. 2010. QIIME allows analysis of high-throughput community sequencing data. Nat. Methods. 7: 335–336.CaporasoJ.G.J.KuczynskiJ.StombaughK.BittingerF.D.BushmanE.K.CostelloN.FiererA.G.PeñaJ.K.GoodrichJ.I.Gordonand others2010QIIME allows analysis of high-throughput community sequencing data733533610.1038/nmeth.f.303315657320383131Search in Google Scholar
Compant S., B. Duffy, J. Nowak, C. Clement and E.A. Barka. 2005. Use of plant growth-promoting bacteria for biocontrol of plant diseases: principles, mechanisms of action, and future prospects. Appl. Environ. Microbiol. 71: 4951–4959.CompantS.B.DuffyJ.NowakC.Clement and E.A.Barka2005Use of plant growth-promoting bacteria for biocontrol of plant diseases: principles, mechanisms of action, and future prospects714951495910.1128/AEM.71.9.4951-4959.2005121460216151072Search in Google Scholar
Compant S., M.G. van der Heijden and A. Sessitsch. 2010. Climate change effects on beneficial plant-microorganism interactions. FEMS Microbiol. Ecol. 73: 197–214.CompantS.M.G.van der Heijden and A.Sessitsch2010Climate change effects on beneficial plant-microorganism interactions7319721410.1111/j.1574-6941.2010.00900.x20528987Search in Google Scholar
Davey H.M. 2011. Life, death, and in-between: meanings and methods in microbiology. Appl. Environ. Microbiol. 77: 5571–5576.DaveyH.M.2011Life, death, and in-between: meanings and methods in microbiology775571557610.1128/AEM.00744-11316524921705550Search in Google Scholar
DeSantis T.Z., P. Hugenholtz, N. Larsen, M. Rojas, E.L. Brodie, K. Keller, T. Huber, D. Dalevi, P. Hu and G.L. Andersen. 2006. Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Appl. Environ. Microbiol. 72: 5069–5072.DeSantisT.Z.P.HugenholtzN.LarsenM.RojasE.L.BrodieK.KellerT.HuberD.DaleviP.Hu and G.L.Andersen2006Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB725069507210.1128/AEM.03006-05148931116820507Search in Google Scholar
Edgar R.C. 2004. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 32: 1792–1797.EdgarR.C.2004MUSCLE: multiple sequence alignment with high accuracy and high throughput321792179710.1093/nar/gkh340Search in Google Scholar
Edgar R.C. 2013. UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nat. Methods. 10: 996–998.EdgarR.C.2013UPARSE: highly accurate OTU sequences from microbial amplicon reads1099699810.1038/nmeth.2604Search in Google Scholar
Edgar R.C., B.J. Haas, J.C. Clemente, C. Quince and R. Knight. 2011. UCHIME improves sensitivity and speed of chimera detection. Bioinformatics. 27: 2194–2200.EdgarR.C.B.J.HaasJ.C.ClementeC.Quince and R.Knight2011UCHIME improves sensitivity and speed of chimera detection272194220010.1093/bioinformatics/btr381Search in Google Scholar
Ercolini D. 2013. High-throughput sequencing and metagenomics: moving forward in the culture-independent analysis of food microbial ecology. Appl. Environ. Microbiol. 79: 3148–3155.ErcoliniD.2013High-throughput sequencing and metagenomics: moving forward in the culture-independent analysis of food microbial ecology793148315510.1128/AEM.00256-13Search in Google Scholar
Ercolini D., G. Moschetti, G. Blaiotta and S. Coppola. 2001. The potential of a polyphasic PCR-dGGE approach in evaluating microbial diversity of natural whey cultures for water-buffalo Mozzarella cheese production: bias of culture-dependent and culture-independent analyses. Syst. Appl. Microbiol. 24: 610–617.ErcoliniD.G.MoschettiG.Blaiotta and S.Coppola2001The potential of a polyphasic PCR-dGGE approach in evaluating microbial diversity of natural whey cultures for water-buffalo Mozzarella cheese production: bias of culture-dependent and culture-independent analyses2461061710.1078/0723-2020-00076Search in Google Scholar
Esquivel P., F.C. Stintzing and R. Carle. 2007. Phenolic compound profiles and their corresponding antioxidant capacity of purple pitaya (Hylocereus sp.) genotypes. Z Naturforsch C. 62: 636–644.EsquivelP.F.C.Stintzing and R.Carle2007Phenolic compound profiles and their corresponding antioxidant capacity of purple pitaya (Hylocereus sp.) genotypes6263664410.1515/znc-2007-9-1003Search in Google Scholar
Giraffa G. and F. Neviani. 2001. DNA-based, culture-independent strategies for evaluating microbial communities in food-associated ecosystems. Int. J. Food Microbiol. 67: 19–34.GiraffaG. and F.Neviani2001DNA-based, culture-independent strategies for evaluating microbial communities in food-associated ecosystems67193410.1016/S0168-1605(01)00445-7Search in Google Scholar
Haas B.J., D. Gevers, A.M. Ear, M. Feldgarden, D.V. Ward, G. Giannoukos, D. Ciulla, D. Tabbaa, S.K. Highlander, E. Sodergren and others. 2011. Chimeric 16S rRNA sequence formation and detection in Sanger and 454-pyrosequenced PCR amplicons. Genome Res. 21: 494–504.HaasB.J.D.GeversA.M.EarM.FeldgardenD.V.WardG.GiannoukosD.CiullaD.TabbaaS.K.HighlanderE.Sodergrenand others2011Chimeric 16S rRNA sequence formation and detection in Sanger and 454-pyrosequenced PCR amplicons2149450410.1101/gr.112730.110304486321212162Search in Google Scholar
Hardoim P.R., L.S. van Overbeek and J.D. Elsas. 2008. Properties of bacterial endophytes and their proposed role in plant growth. Trends Microbiol. 16: 463–471.HardoimP.R.L.S.van Overbeek and J.D.Elsas2008Properties of bacterial endophytes and their proposed role in plant growth1646347110.1016/j.tim.2008.07.00818789693Search in Google Scholar
Jany J.L. and G. Barbier. 2008. Culture-independent methods for identifying microbial communities in cheese. Food Microbiol. 25: 839–848JanyJ.L. and G.Barbier2008Culture-independent methods for identifying microbial communities in cheese2583984810.1016/j.fm.2008.06.00318721671Search in Google Scholar
Keisam S., W. Romi, G. Ahmed, and K. Jeyaram. 2016. Quantifying the biases in metagenome mining for realistic assessment of microbial ecology of naturally fermented foods. Sci. Rep. 6: 34155.KeisamS.W.RomiG.Ahmed, and K.Jeyaram2016Quantifying the biases in metagenome mining for realistic assessment of microbial ecology of naturally fermented foods63415510.1038/srep34155503744727669673Search in Google Scholar
Klindworth A., E. Pruesse, T. Schweer, J. Peplies, C. Quast, M. Horn and F.O. Glockner. 2013. Evaluation of general 16S ribosomal RNA gene PCR primers for classical and next-generation sequencing-based diversity studies. Nucleic Acids Res. 4: 1e1.KlindworthA.E.PruesseT.SchweerJ.PepliesC.QuastM.Horn and F.O.Glockner2013Evaluation of general 16S ribosomal RNA gene PCR primers for classical and next-generation sequencing-based diversity studies41e110.1093/nar/gks808359246422933715Search in Google Scholar
Long H.H., D.D. Schmidt and I.T. Baldwin. 2008. Native bacterial endophytes promote host growth in a species-specific manner; phytohormone manipulations do not result in common growth responses. PLOS ONE. 3: e2702.LongH.H.D.D.Schmidt and I.T.Baldwin2008Native bacterial endophytes promote host growth in a species-specific manner; phytohormone manipulations do not result in common growth responses3e270210.1371/journal.pone.0002702244403618628963Search in Google Scholar
Magoc T. and S.L. Salzberg. 2011. FLASH: fast length adjustment of short reads to improve genome assemblies. Bioinformatics. 27: 2957–2963.MagocT. and S.L.Salzberg2011FLASH: fast length adjustment of short reads to improve genome assemblies272957296310.1093/bioinformatics/btr507319857321903629Search in Google Scholar
Mayo B., C.T Rachid, A. Alegria, A.M. Leite, R.S. Peixoto and S. Delgado. 2014. Impact of next generation sequencing techniques in food microbiology. Curr. Genomics. 15: 293–309MayoB.C.TRachidA.AlegriaA.M.LeiteR.S.Peixoto and S.Delgado2014Impact of next generation sequencing techniques in food microbiology1529330910.2174/1389202915666140616233211413395225132799Search in Google Scholar
Ortiz T.A. and L.S. Takahashi. 2015. Physical and chemical characteristics of pitaya fruits at physiological maturity. Genet. Mol. Res. 14: 14422–14439.OrtizT.A. and L.S.Takahashi2015Physical and chemical characteristics of pitaya fruits at physiological maturity14144221443910.4238/2015.November.18.526600501Search in Google Scholar
Piotrowska-Cyplik A., K. Myszka, J. Czarny, K. Ratajczak, R. Kowalski, R. Bieganska-Marecik, J. Staninska-Pieta, J. Nowak, and P. Cyplik. 2017. Characterization of specific spoilage organisms (SSOs) in vacuum-packed ham by culture-plating techniques and MiSeq next-generation sequencing technologies. J. Sci. Food Agric. 97: 659–668.Piotrowska-CyplikA.K.MyszkaJ.CzarnyK.RatajczakR.KowalskiR.Bieganska-MarecikJ.Staninska-PietaJ.Nowak and P.Cyplik2017Characterization of specific spoilage organisms (SSOs) in vacuum-packed ham by culture-plating techniques and MiSeq next-generation sequencing technologies9765966810.1002/jsfa.778527144959Search in Google Scholar
Ramli N.S., L. Brown, P. Ismail and A. Rahmat. 2014. Effects of red pitaya juice supplementation on cardiovascular and hepatic changes in high-carbohydrate, high-fat diet-induced metabolic syndrome rats. BMC Complement. Altern. Med. 14: 189.RamliN.S.L.BrownP.Ismail and A.Rahmat2014Effects of red pitaya juice supplementation on cardiovascular and hepatic changes in high-carbohydrate, high-fat diet-induced metabolic syndrome rats1418910.1186/1472-6882-14-189406481424919841Search in Google Scholar
Sessitsch A., B. Reiter, U. Pfeifer and E. Wilhelm. 2002. Cultivation-independent population analysis of bacterial endophytes in three potato varieties based on eubacterial and Actinomycetes-specific PCR of 16S rRNA genes. FEMS Microbiol. Ecol. 39: 23–32.SessitschA.B.ReiterU.Pfeifer and E.Wilhelm2002Cultivation-independent population analysis of bacterial endophytes in three potato varieties based on eubacterial and Actinomycetes-specific PCR of 16S rRNA genes39233210.1111/j.1574-6941.2002.tb00903.x19709181Search in Google Scholar
Song H., Z. Zheng, J. Wu, J. Lai, Q. Chu and X. Zheng. 2016. White Pitaya (Hylocereus undatus) Juice attenuates insulin resistance and hepatic steatosis in diet-induced obese mice. PLOS ONE. 11: e0149670.SongH.Z.ZhengJ.WuJ.LaiQ.Chu and X.Zheng2016White Pitaya (Hylocereus undatus) Juice attenuates insulin resistance and hepatic steatosis in diet-induced obese mice11e014967010.1371/journal.pone.0149670476736826914024Search in Google Scholar
Suh D.H., S. Lee, D.Y. Heo, Y.S. Kim, S.K. Cho and C.H. Lee. 2014. Metabolite profiling of red and white pitayas (Hylocereus polyrhizus and Hylocereus undatus) for comparing betalain biosynthesis and antioxidant activity. J. Agric. Food Chem. 62: 8764–8771.SuhD.H.S.LeeD.Y.HeoY.S.KimS.K.Cho and C.H.Lee2014Metabolite profiling of red and white pitayas (Hylocereus polyrhizus and Hylocereus undatus) for comparing betalain biosynthesis and antioxidant activity628764877110.1021/jf502070425101804Search in Google Scholar
Wang Q., G.M. Garrity, J.M. Tiedje and J.R. Cole. 2007. Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl. Environ. Microbiol. 73: 5261–5267.WangQ.G.M.GarrityJ.M.Tiedje and J.R.Cole2007Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy735261526710.1128/AEM.00062-07195098217586664Search in Google Scholar