On the other hand, increasing evidence indicates that gut microorganisms do play crucial roles in these metabolic diseases. The human gastrointestinal tract consists of different gut microbiota. This human gut microbiota plays important roles in energy harvesting from diet (Flint., 2011; Yassour et al., 2016), digestion, metabolism, synthesis of vitamins, immune system development (Purchiaroni et al., 2013) and fat storage regulation (Graessler et al., 2012). In addition to that, the modulation of gut-derived peptide secretion, regulation of active fatty acid tissue composition and chronic low-grade endotoxemia also links the gut microbiota and metabolic diseases (Musso et al., 2010; Indias et al., 2014). Therefore, the gut microbiota is considered as one of the most fascinating reservoirs. The gut microbial diversity hosts approximately 400 to 1000 bacterial species. Studies had been conducted in order to prove that the differences in gut microbiota lead to obesity and type 2 diabetes. Several studies have indicated that obesity is related to an increase in the phylum Firmicutes with a decrease in phylum Bacteroidetes (Backhed et al., 2004; Ley et al., 2005; Turnbaugh et al., 2009). Therefore, the role of gut microbiota in obesity and type 2 diabetes is a current public health problem.

 

The bacterial phylum that will be analyzed in this study are Actinobacteria, Bacteroidetes, Cyanobacteria, Elusimicrobia, Firmicutes, Proteobacteria, Fusobacteria, Synergistetes and Tenericutes. On the other hand, a total of 16 bacterial families will be analyzed. The families Veillonellaceae, Ruminococcaceae, Lachnospiroceae and Lactobacillaceae belong to the phylum Firmicutes. Besides, families Desulfovibrionaceae, Alcaligenes and Enterobacteriaceae belong to phylum Fusobacteria. The family Coriobacteriaceae, Bifidobacteriaceae belongs to phylum Actinobacteria. Meanwhile, family Bacteroidaceae, Prevotellaceae, Rikenellaceae, Barnesiellaceae, Odoribacteriaceae, Paraprevotelleceae and Porphyromonadaceae belongs to phylum Bacteroidetes.

 

Larsen et al. (2010) showed that the relative abundance of Firmicutes was lower in type 2 diabetes subjects. On the other hand, obese subjects show increase in abundance of Firmicutes with decrease in abundance of Bacteroidetes (Ley et al., 2005). Moreover, when obese human were put on fat-restricted or carbohydrate-restricted low-calorie diet, an increase in Bacteroidetes and a decrease in Firmicutes was reported (Ley et al., 2006). Besides, the metagenome study conducted by Graessler et al. (2013) among obese subjects showed decrease in Firmicutes after the RYGB operation compared to the abundance of the bacteria before the operation. Bacteroidetes was associated with a protein-rich diet (Hartstra et al., 2015). However, Schweirtz et al. (2010) and Zhang et al. (2009) showed opposite findings in obese subjects where increase in Bacteroidetes was associated with obesity rather than increase in Firmicutes as showed by Ley et al. (2005) and Ley et al. (2006). Therefore, obesity is always associated with Firmicutes and Bacteroidetes which can harvest energy from complex polysaccharide (Fernandes et al., 2014).

 

Traditionally, bacteria are studied by culture-based methods, however it is proved difficult to culture majority of the gut microbiota. Therefore, the culture-independent methods have been developed. The culture-independent method applies the advantage of next generation sequencing to study the microbial DNA (Allin et al., 2015). Moreover, the DNA based studies are dominated by targeted 16S rRNA gene sequencing such as quantitative real time PCR (qPCR) and fluorescent in situ hybridisation (FISH), and un-targeted whole-genome shotgun sequencing (Allin et al., 2015). The whole-genome shotgun sequencing is the metagenome analysis (Larsen et al., 2010). Metagenome analysis is one of the examples of advanced high-throughput sequencing methods introduced recently. Metagenomic is the study of genetic material retrieved directly from environmental samples including the gut (Mandal et al., 2015). Besides, metagenomic analysis of complex microbial communities represents a powerful analysis system alternative to widely used rRNA sequencing analysis (Riesenfeld et al., 2004). Besides, Turnbaugh et al. (2009) summarized that the metagenomic studies have already generated some 3 gigabases (Gb) of microbial sequence from 33 individuals from the Japan or United States. Therefore, this study aims to identify the diversity of gut microflora in the gut of healthy, obese and type 2 diabetes adults in a pilot study through metagenome analysis.

 

2 Materials and Methods

2.1 Subject recruitment

This study included a total of 16 human subjects that includes six healthy subjects, five obese subjects and five type 2 diabetes subjects. The healthy and obese subjects were recruited from Faculty of Medicine and Health Sciences, Universiti Putra Malaysia (UPM), while the type 2 diabetes subjects were recruited from Endocrine Clinic, Universiti Kebangsaan Malaysia Medical Centre (UKMMC). The inclusion and exclusion criteria for subject’s recruitment were as listed in Table 1 below. The subjects who met the inclusion and exclusion criteria were given consent form and respondent information sheet to be full filled.

2.2 Stool sample collection

The subjects were explained and briefed on how to collect the stool samples correctly in order to avoid possible cross-contamination. Approximately 5.00 g of stool samples was collected in a sterile disposable stool collecting container (Labsystem) by the subjects themselves. The stool samples were collected within 4 h of defecation and kept in -4 °C before they were brought to Microbiology Laboratory, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia for processing.

 

2.3 DNA extraction

Approximate of 0.22 g of each stool sample was aliquoted into two sterile 2.00 ml microcentrifuge tube (Axygen.USA), while the excess stool sample in the container were kept at -80 C for future use. These aliquots were used for genomic DNA extraction. The DNA extraction was conducted using QIAamp DNA Stool mini kit (Qiagen, Germany) according to the manufacturer's protocol. The extracted DNA was kept at -30 C for further analysis.

 

2.4 DNA purity and concentration determination

The genomic DNA was analyzed on 1.0 % (w/v) agarose gel electrophoresis for the presence of DNA and viewed using the AlphaImager 2200. Following that, the concentration and purity of the genomic DNA was analyzed using Nanodrop ND-1000 v3.7.1 (Thermo Fischer, USA). The DNA was then used for PCR reaction.

 

2.5 Polymerase chain reaction (PCR) amplification

A total of 25 µl of PCR master mix was prepared. PCR was performed in a 25 µl reaction volume consisting of 12.5 µl of 2X KAPA HiFi HotStart ReadyMix (KAPABIOSYSTEMS, South Africa), 0.75 µl of forward primer (10 µM) and 0.75 µl of reverse primer (10 µM), 6 µl of nuclease free water and 5 µl of DNA template. The primer sequences and cycling protocol were as listed in Table 2 below. The PCR products were then analyzed on 2.0 % (w/v) agarose gel electrophoresis for confirmation of the desired band. All the samples produced the desired band of 330 bp and were continued with PCR using the 16S rRNA primers attached with specific adapters targeting the V3 region to prepare the samples for metagenomic sequencing and analysis. The primers and cycling protocol were as listed in Table 3 below with the same mixture volume of master mix as described above. The PCR were conducted and the PCR products were viewed on 2.0 % (w/v) agarose gel electrophoresis.

2.6 Gel-based size selection PCR products

The DNA fragments (~330 bp) were excised using a sterile blade and transferred into a clean weighed 2.00 ml microcentrifuge tube. The purification of the PCR product was conducted using the QIAquick Gel Extraction Kit (Qiagen, Germany). The purified products were analyzed again on the 2.0 % (w/v) agarose gel electrophoresis to assess extraction efficiency. The concentration and purity of the purified DNA were also determined as mentioned previously.

 

2.7 Sequencing, bioinformatics and statistical analysis

Finally, the samples were aliquoted into new sterile 0.2 ml PCR tubes and delivered to the sequencing service provider (Science Vision Sdn. Bhd). Amplicon sequencing was performed on a MiSeq desktop sequencer using the run configuration of 2 x 150 bp. The sequencing data were analyzed using the QIIME 1.8.0 Bioinformatics software. The data were then entered into SPSS 21.0 software where the normality of the data was determined and the skewed data were analyzed using the Kruskall-Wallis statistical analysis. The median(IQR) and p-value of each analysis were tabulated respectively.

 

3 Results

Based on the Table 5, the analysis of bacterial phylum showed that six phylum were commonly encountered in the human intestine that includes Actinobacteria, Bacteroidetes, Cyanobacteria, Firmicutes, Fusobacteria and Proteobacteria (Andersson et al., 2008). The Elusimicrobia, Synergistetes and Tenericutes phyla were as well detected in the analysis. The two highly detected phyla were Bacteroidetes and Firmicutes. Bacteroidetes was detected the highest in the type 2 diabetes group with median(IQR)% of 66.50(39.00)% compared to 63.50(21.55)% in obese and the lowest in healthy with 54.45(39.48)%. Meanwhile for Firmicutes, healthy group showed the highest detection with median(IQR)% of 31.95(33.28)% with 27.00(13.55)% in obese and in type 2 diabetes subjects with 27.70(19.35)%. The p-value obtained for Bacteroidetes and Firmicutes phyla were respectively 0.892 and 0.616. This finding for type 2 diabetes subjects in current study goes in line with the previous study conducted by Brown et al. (2000). However, the finding for obese subjects in this study is opposite to the previously reported study by Ley et al. (2006) and Turnbaugh et al. (2009). Turnbaugh et al. (2009) reported that the mice on diet rich in fat and sugar and low in plant polysaccharides experienced increase in Firmicutes and decrease in Bacteroidetes. Besides, the gut microbiota of the mice changed in just 1 day. Meanwhile, Figure 1 shows the relative abundance of gut microbiota at phylum level for each subject. These differences suggest that relationship between the metabolic diseases and gut microbiota composition.

Based on the Table 6, the analysis of bacterial families showed distribution between 16 families. The bacterial families that were highly detected were Veillonellaceae, Prevotellaceae, Ruminococcaceae, Bacteroidaceae and Lachnospiroceae. Veillonellaceae was detected highest in obese subjects with median(IOR)% of 7.80(7.55)% compared to 3.55(3.30)% in healthy and 6.60(15.95)% in type 2 diabetes subjects (P=0.158). As for Prevotellaceae, the obese subjects showed highest detection of 27.50(48.20)% compared to 0.20(21.68)% in healthy and 10.90(57.05)% in type 2 diabetes subjects (P=0.325). The detection of Ruminococcaceae was also different among the subjects where healthy subjects showed highest detection with 17.00(12.15)% and 11.20(14.20)% in obese and 7.60(7.00)% in type 2 diabetes subjects (P=0.194). The Bacteroidaceae showed highest detection in healthy with 35.30(48.50)% compared to 27.80(36.10)% in obese and 14.50(55.05)% in type 2 diabetes (P=0.685). Finally, the Lachnospiroceae was highly detected in healthy subjects with 6.15(11.05)% with 4.20(2.65)% and 2.70(7.80)% respectively in obese and type 2 diabetes subjects (P=0.192). Meanwhile, Figure 2 shows the relative abundance of gut microbiota at family level for each subject. Although there are differences noticed especially in the family Bacteroidaceae, Prevotellaceae and Ruminococcaceae, there were no comparisons done with the previously reported data. This is due to the lack of published data on gut microbiota at family level. Besides, many of the studies published reported on species-, phylum-specific or genus-specific composition of the gut microbiota in contrast to bacterial families (Angelakis et al., 2012).

4 Discussions

The human intestine contains over 5000 bacterial species (Dethlefsen et al., 2008) that are beneficial to the human host. These organisms help in immune competence, nutrition as well as cell development in the human host (Turnbaugh et al., 2006). Obesity and type 2 diabetes were heterogenous and multifactorial diseases that are influenced by a number of different environmental and genetic factors (Burcelin et al., 2011). The inclusion and exclusion criteria used for the subject’s recruitment proved that the subjects were not diagnosed with other critical diseases that may influence the final findings of this study. However, previously reported studies were biased towards sampling people from Western, Caucasian and Japan countries. Besides, little is known about the Malaysian gut microbiota composition and to our knowledge, there were no related metagenomic studies targeting the gut microbiota composition of Malaysian obese and type 2 diabetes subjects in comparison to healthy subjects have been published so far.

 

Therefore, to address this lack of information, a pilot study was conducted where the gut microbiota of five obese and five type 2 diabetes subjects in comparison to six healthy subjects were analyzed with the metagenomic sequencing approach. The bacterial DNA for all the 16 samples for the analysis was successfully extracted and the 16S rRNA V3 region was successfully amplified. Table 5 shows the median(IQR)% and p-value of bacterial phylum in healthy, obese and type 2 diabetes adult subjects. Table 6 shows the median(IQR)% and p-value of bacterial families in healthy, obese and type 2 diabetes adult subjects. The metagenomic analysis revealed that most of the identified microorganisms were members of phylum Bacteroidetes and Firmicutes and that the latter constituted approximately 70% of the total community (Eckburg et al., 2005). Therefore, it can be concluded that at phylum level, the community composition of Malaysian gut microbiota seems to be similar to that previously reported study on other human populations (Eckburg et al., 2005). Turnbaugh et al. (2009) summarized that this situation is due to the members of Bacteroidetes and Firmicutes phylum possess the same functional genes, the relative abundance of the broad functional categories of genes closely related to amino acid as well as carbohydrate metabolism that are generally consistent regardless of the sample surveyed. The overall microbial gene pool also remains constant throughout life, while the microorganisms are continuously replaced due to the environmental barriers.

 

In Table 5, the obese subjects had higher Bacteroidetes with lower Firmicutes, meanwhile, the type 2 diabetes subjects also had higher Bacteroidetes with lower Firmicutes. The findings for type type 2 diabetes subjects goes in line with previously reported study by Brown et al. (2000) that showed similar changes in Bacteroidetes and Firmicutes. However, the findings for obese subjects did not go in line with previous study (Ley et al., 2006; Turnbaugh et al., 2009) as these studies reported high level of Firmicutes in obese subjects compared to Bacteroidetes. As another example, the murine studies showed that carbohydrate-reduced diets result in enriched populations of bacteria from Bacteroidetes (Walker et al., 2011). Besides, Hildebrandt et al. (2009) also found decrease in abundance of Bacteroidetes and increase in Firmicutes and Proteoacteria in mice after changing from standard chow to a high-fat diet. However, in contrast to these previous findings, another study by Schwiertz et al. (2010) showed reduction of Firmicutes in obese subjects and supported the findings in current study. Besides that, there are also studies that found no differences between the Firmicutes and Bacteroidetes at the phylum level among obese subjects (Duncan et al., 2008; Jumpertz et al., 2011). Although the p-value obtained were not significant (P<0.05), it shows the observable differences in the gut microbiota composition. The analysis showed that there were differences among the bacterial community respective to the group of study either healthy, obese or type 2 diabetes. These differences do play important roles in the development of metabolic diseases namely obesity and type 2 diabetes (Turnbaugh et al., 2009).

 

The study by Zhang et al. (2013) proved that the phylum Firmicutes were more abundant in the diabetic group. Moreover, the study by Larsen et al. (2010) suggested the association between type 2 diabetes and obesity. The metabolic diseases such as obesity and type 2 diabetes have always been associated with chronic, low-grade inflammation (Hotamisligil and Erbay, 2008). The reduced levels of glucose tolerance and type 2 diabetes should be considered when microorganisms are associated with obesity as well as to develop strategies to overcome obesity (Larsen et al., 2010). Besides, the macrophages in obese subjects expresses pro-inflammatory cytokines TNFa, IL6 and INOS (Weisberg et al., 2003) in adipose tissues and the gut microbiota then initiates the inflammation and insulin resistancy that is associated with obesity (Ley, 2010). Moreover, (Napolitano et al., 2014) also suggest the causal relationships between metabolic disorders and gut microbiota composition. Furthermore, animal studies also support the association between dysbiosis of the gut microbiota and low grade inflammation, obesity and type 2 diabetes through the alteration in gut permeability, insulin resistance and endotoxin-mediated inflammation (Cani and Delzenne, 2011; Cani et al., 2012).

 

However, a more depth study needs to be conducted in order to address the exact association. Besides, the number of subjects recruited in this study is inadequate to represent the total population in Malaysia. Therefore, more number of subjects needs to be recruited in the future study in order to represent the Malaysian population significantly. Besides, the application of metagenome analysis provides a faster and easier way of analysis on human gut microbiota communities.

 

5 Conclusions

The metagenome analysis offers the clear understanding on the differences in the bacterial community in healthy, obese and type 2 diabetes adults in Malaysia. This study also indicates that there are differences in the bacterial community that influences the health of human as well as development of obesity and type 2 diabetes. The differences in the detection of Bacteroidetes and Firmicutes in obese subjects from this current study suggest that the gut microbiota community undergo changes due to differences in the lifestyle and diet. Although there are no significant findings from this study, the differences in the percentage of the bacterial detection in each group suggest the influence of gut microbiota in obesity and type 2 diabetes. Besides, this study is also important as currently there are very few studies of gut microbiota association to metabolic diseases published in Malaysia. Therefore, a more in depth study with more subjects is required to deduce the association of diet, lifestyle and nutrition with the gut microbiota community in the development of metabolic diseases especially obesity and type 2 diabetes as well as to represent the Malaysian communities.

 

Notes

The sequencing data were submitted to NCBI and the SRA accession number SRP079939 were obtained. This research was funded by Fundamental Research Grant Scheme (FRGS) of Ministry of Higher Education Malaysia (04-01-12-1132FR).

 

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