Next-generation sequencing for microbiome profiling

Introduction

The human gut harbors trillions of microorganisms collectively termed the gut microbiome, representing one of the most densely populated microbial ecosystems on Earth [1] . This complex community performs essential functions including nutrient metabolism, vitamin synthesis, immune system development, and protection against pathogenic colonization [2] . Advances in next-generation sequencing technologies have revolutionized microbiome research, enabling comprehensive characterization of microbial communities with unprecedented depth and resolution [3] . Global microbiome surveys have revealed substantial variation in gut microbial composition across human populations, driven by factors including diet, geography, lifestyle, and cultural practices [4] . Industrialized populations typically demonstrate reduced microbial diversity compared to traditional societies, a pattern attributed to Western dietary patterns, antibiotic exposure, and reduced environmental microbial contact [5] . The Firmicutes-to-Bacteroidetes ratio, a commonly reported community metric, shifts toward Firmicutes dominance in populations consuming high-fat, low-fiber diets characteristic of industrialized societies [6] .

Indonesia represents the world's fourth most populous nation with extraordinary ethnic, cultural, and dietary diversity across its 17,000-island archipelago [7] . Traditional Indonesian diets vary substantially from rice-based agricultural patterns in Java to sago-dependent systems in Papua, marine-focused diets in coastal regions, and tuberbased consumption in highland areas [8] . This dietary heterogeneity, combined with varying degrees of urbanization and Western dietary influence, creates natural experiments for understanding diet-microbiome relationships within a single national context. Despite representing a significant proportion of global population, Southeast Asian microbiomes remain substantially understudied compared to Western and East Asian populations [9] . The few Indonesian microbiome investigations published to date have employed limited sample sizes, focused on single populations, or utilized lower-resolution profiling methodologies [10] . Comprehensive characterization across diverse Indonesian populations using contemporary next-generation sequencing approaches is lacking, representing a significant gap in understanding global microbiome variation. The 16S ribosomal RNA gene serves as the standard marker for bacterial taxonomic profiling owing to its universal presence, conserved primer sites, and variable regions enabling genus-level identification [11] . Amplicon sequencing of the V3-V4 hypervariable region provides optimal balance between taxonomic resolution and amplicon length compatible with short-read Illumina platforms [12] . Contemporary bioinformatic pipelines including QIIME2 with DADA2 denoising enable amplicon sequence variant resolution, surpassing the operational taxonomic unit approaches previously employed [13] . This research aimed to characterize gut microbiome composition across four Indonesian population groups representing distinct geographic, dietary, and lifestyle contexts using 16S rRNA next-generation sequencing. The specific objectives included comparing microbial community structure at multiple taxonomic levels, evaluating alpha and beta diversity metrics across populations, identifying differentially abundant taxa associated with population groups, and exploring functional implications through predictive metagenomics. We hypothesized that traditional populations maintaining indigenous dietary patterns would demonstrate higher microbial diversity and distinct taxonomic profiles compared to urban populations adopting Western-influenced diets.

Sequencing Technology and Bioinformatics Pipeline

The Illumina MiSeq platform was selected for 16S rRNA amplicon sequencing based on its established performance for microbial community profiling, 2×300 bp read length enabling complete V3-V4 region coverage with paired-end overlap, and cost-effectiveness for population-scale investigations [14] . The V3-V4 hypervariable region (approximately 460 bp) provides superior taxonomic resolution compared to shorter amplicons while maintaining compatibility with Illumina short-read chemistry. QIIME2 (version 2021.11) served as the primary bioinformatics framework given its modular architecture, reproducibility through provenance tracking, and extensive plugin ecosystem [15] . DADA2 was employed for denoising and amplicon sequence variant inference, providing singlenucleotide resolution without arbitrary clustering thresholds. Taxonomic classification utilized the SILVA 138 reference database with naive Bayes classifiers trained on the V3-V4 region. Functional prediction through PICRUSt2 inferred metagenomic content from 16S profiles using marker gene copy number normalization and reference genome databases.

Material and Methods

Material

This cross-sectional research was conducted at the Department of Microbiology and Genomics, Faculty of Medicine, Universitas Indonesia, Jakarta, between August 2020 and June 2022. The research protocol received approval from the Health Research Ethics Committee, Faculty of Medicine, Universitas Indonesia (Protocol KET-894/UN2.F1/ETIK/PPM.00.02/2020, approved July 15, 2020). All participants provided written informed consent following detailed explanation of research procedures including sample collection and genetic analysis. The investigation adhered to Declaration of Helsinki principles. Participants were recruited from four distinct Indonesian populations: urban Jakarta representing modernized lifestyle with Western dietary influence, rural Central Java representing traditional Javanese agricultural communities, coastal South Sulawesi representing marine-dependent fishing communities, and highland Papua representing indigenous populations maintaining traditional dietary practices. Inclusion criteria encompassed healthy adults aged 25-55 years with stable residence in their respective regions for at least five years. Body mass index was frequency-matched across groups to minimize confounding. Exclusion criteria included antibiotic use within three months, probiotic or prebiotic supplementation within one month, gastrointestinal disease history, diabetes mellitus, inflammatory bowel disease, recent gastrointestinal infection, pregnancy or lactation, and chronic medication use affecting gut microbiota. Dietary patterns were characterized using validated food frequency questionnaires adapted for Indonesian populations. Sample size calculations based on anticipated effect sizes from global microbiome surveys indicated requirement for 60-90 participants per group with 80% power.

Methods

Participants provided fresh fecal samples in sterile collection containers with DNA stabilization buffer. Samples were transported on ice within four hours to local laboratories for initial processing, then shipped frozen to the central facility in Jakarta. DNA extraction employed QIAamp PowerFecal Pro DNA Kit following manufacturer protocols with mechanical lysis using bead beating. DNA quality and quantity were assessed by NanoDrop spectrophotometry and Qubit fluorometry. Samples with A260/A280 ratios below 1.8 or concentrations below 10 ng/μL underwent re-extraction. The V3-V4 region of 16S rRNA gene was amplified using primers 341F (5'-CCTACGGGNGGCWGCAG-3') and 806R

(5'-GACTACHVGGGTATCTAATCC-3') with Illumina adapter overhangs. PCR reactions contained 12.5 ng template DNA, 200 nM each primer, and Q5 High-Fidelity DNA Polymerase in 25 μL total volume. Cycling conditions included initial denaturation at 98°C for 30 seconds, 25 cycles of 98°C for 10 seconds, 55°C for 30 seconds, and 72°C for 30 seconds, followed by final extension at 72°C for 2 minutes. Amplicons were purified using AMPure XP beads and indexed using Nextera XT Index Kit. Library quality was verified by Agilent Bioanalyzer and quantified by qPCR. Pooled libraries were sequenced on Illumina MiSeq using 2×300 bp paired-end chemistry. Sequence processing in QIIME2 included primer trimming, quality filtering, denoising with DADA2, and chimera removal. Taxonomic classification employed pre-trained naive Bayes classifiers against SILVA 138 database. Statistical analyses utilized R version 4.1.0 with phyloseq, vegan, and DESeq2 packages. Significance threshold was p<0.05 with Benjamini-Hochberg correction for multiple comparisons. F/B = Firmicutes-to-Bacteroidetes ratio; PD = phylogenetic diversity. All between-group comparisons significant at p<0.001 (Kruskal-Wallis test). Phylum-level composition demonstrated consistent dominance of Firmicutes and Bacteroidetes across all populations, though their relative proportions varied substantially. Urban Jakarta participants exhibited highest Firmicutes abundance (58%) and lowest Bacteroidetes (24%), yielding the highest F/B ratio of 2.42. Highland Papua showed inverse pattern with Bacteroidetes exceeding Firmicutes, producing the only F/B ratio below unity. This gradient correlated strongly with degree of traditional dietary maintenance versus Western dietary adoption. Alpha diversity analysis revealed consistent patterns across multiple metrics with highland Papua demonstrating highest diversity and urban Jakarta showing lowest values. Shannon diversity index ranged from 3.2±0.4 in urban participants to 4.1±0.25 in highland populations, representing 28% higher diversity in traditional communities. Chao1 species richness estimates showed even larger differences, with highland Papua exceeding urban Jakarta by 61%. These diversity gradients correlate with dietary fiber intake patterns documented through food frequency questionnaires.

Results

Discussion

This research provides the first comprehensive 16S rRNA next-generation sequencing characterization of gut microbiome variation across diverse Indonesian populations.

The findings reveal substantial differences in microbial community structure associated with geographic location and dietary patterns, with traditional populations maintaining higher diversity and distinct taxonomic profiles compared to urban populations adopting Western-influenced diets [16] . These patterns align with global observations of industrialization effects on gut microbiome composition.

The striking diversity gradient from urban to traditional populations has important health implications. Reduced gut microbial diversity has been associated with numerous chronic diseases including obesity, inflammatory bowel disease, and metabolic syndrome in Western populations [17] . Indonesia is experiencing rapid epidemiological transition with rising prevalence of these conditions particularly in urban areas, suggesting potential microbiome contributions to disease emergence. The 28% higher Shannon diversity observed in highland Papua compared to urban Jakarta represents a substantial difference with likely functional consequences.

The Firmicutes-to-Bacteroidetes ratio gradient across populations reflects well-established diet-microbiome relationships. High F/B ratios in urban populations consuming diets enriched in fat and simple carbohydrates contrast with lower ratios in traditional populations maintaining high-fiber dietary patterns [18] . The observation that highland Papua demonstrated F/B ratio below unity, with Bacteroidetes predominance, is notable and consistent with very high fiber consumption patterns documented in this population through dietary assessment.

The enrichment of Prevotella copri in traditional populations aligns with findings from other agrarian societies globally and reflects this organism's specialization for fiber degradation [19] . P. copri possesses extensive polysaccharide utilization loci enabling metabolism of complex plant carbohydrates characteristic of traditional diets. Conversely, Bacteroides species predominating in urban participants demonstrate adaptation to the protein-rich, low-fiber diets typical of industrialized societies. This taxonomic shift likely influences host metabolism through altered shortchain fatty acid production and energy harvest efficiency. The functional predictions revealing enhanced carbohydrate metabolism in traditional populations versus amino acid metabolism in urban groups provide mechanistic context for observed taxonomic differences [20] . These metabolic pathway shifts reflect microbial community adaptation to available dietary substrates and may influence host physiology through altered metabolite production. The relevance of these findings extends beyond Indonesia to understanding global microbiome variation patterns and their health implications in populations undergoing dietary transition.

Methodological Considerations and Limitations

Several limitations warrant consideration when interpreting these findings. Differential abundance analysis identified Prevotella copri enrichment in traditional populations reflecting fiberadapted metabolism, while Bacteroides species predominated in urban participants consuming protein-rich, lower-fiber diets. These taxonomic shifts carry functional implications through altered metabolite production including short-chain fatty acids that influence host metabolism and immune function. Functional prediction confirmed enhanced carbohydrate metabolism pathways in traditional populations versus amino acid and xenobiotic metabolism in urban groups. The 16S rRNA next-generation sequencing approach enabled high-resolution microbiome profiling with amplicon sequence variant resolution surpassing previous operational taxonomic unit methodologies. Quality metrics indicated robust sequencing performance with consistent depth across populations enabling valid between-group comparisons. The bioinformatic pipeline utilizing QIIME2 with DADA2 denoising and SILVA database classification represents current best practices for microbiome profiling. These findings provide essential reference data for Indonesian microbiome research and establish context for investigating diet-microbiome-health relationships in this understudied population. The demonstrated population-level variation has implications for precision nutrition approaches and microbiome-targeted interventions requiring populationspecific considerations. Future research should incorporate longitudinal designs, shotgun metagenomics for direct functional assessment, and integration with host metabolomics to advance understanding of microbiome contributions to health and disease in Indonesian populations undergoing rapid dietary transition.

Acknowledgement

Acknowledgements

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