Introduction

The human gut microbiome comprises trillions of microorganisms that profoundly influence host physiology, immunity, and metabolism. Dysbiosis—disruption of the normal microbial community—has been associated with inflammatory bowel disease, obesity, type 2 diabetes, colorectal cancer, and neurological disorders. 16S rRNA amplicon sequencing enables culture-independent profiling of microbial communities, while shotgun metagenomics provides functional information. Computational analysis requires robust statistical methods for diversity analysis, differential abundance testing, and functional inference.

Methods

OTU Table Generation

Synthetic 16S amplicon data was generated for 60 samples (30 healthy, 30 disease) with 300 OTUs across 7 phyla (Firmicutes 45%, Bacteroidetes 30%, Proteobacteria 10%, Actinobacteria 8%, Verrucomicrobia 4%, Fusobacteria 2%, Tenericutes 1%). Disease samples were simulated with increased Firmicutes (25%) and decreased Bacteroidetes (6%) abundance using Dirichlet-multinomial sampling.

Alpha Diversity

Four alpha diversity metrics were computed: Shannon entropy (H = -Σp·log(p)), Simpson's diversity (D = 1 - Σp²), Chao1 richness estimator (S_obs + n1²/2n2), and Faith's phylogenetic diversity (sum of branch lengths for present OTUs).

Beta Diversity and PERMANOVA

Bray-Curtis dissimilarity was computed between all sample pairs. Principal Coordinates Analysis (PCoA) was performed on the distance matrix using classical MDS. PERMANOVA was performed with 499 permutations to test for significant community differences between groups.

Differential Abundance

DESeq2-style differential abundance testing was implemented using size-factor normalization (geometric mean method) and Wald test on log-normalized counts. Benjamini-Hochberg FDR correction was applied with threshold q<0.05 and |log2FC|>1.

Functional Pathway Inference

PICRUSt-style functional inference was performed by mapping OTU abundances to 50 functional pathways using a simulated OTU-pathway association matrix. Differential pathway analysis was performed using the same NB testing framework.

Dysbiosis Index

A composite dysbiosis index was computed combining the Firmicutes/Bacteroidetes ratio deviation from healthy reference and Shannon diversity z-score.

Results

Alpha diversity was significantly reduced in disease samples across all metrics (Shannon: healthy=4.21 vs disease=3.89). PERMANOVA revealed significant community-level differences (F=1.544, p<0.0001). Differential abundance analysis identified 16 OTUs enriched and 19 depleted in disease. The Firmicutes/Bacteroidetes ratio increased 3.6-fold in disease (2.00 vs 7.26, p=1.8×10⁻⁴). Functional pathway analysis identified significant changes in butyrate synthesis, LPS biosynthesis, and bile acid metabolism pathways.

Discussion

MicrobiomeEngine provides a comprehensive framework for microbiome analysis. The significant PERMANOVA result confirms community-level dysbiosis in disease samples. The F/B ratio increase is consistent with observations in inflammatory bowel disease and obesity. Future extensions include longitudinal microbiome tracking, network analysis of co-occurrence patterns, and integration with host transcriptomics.

Code Availability

Full source code: https://github.com/BioTender-max/MicrobiomeEngine

# pip install numpy scipy matplotlib
python microbiome_engine.py

Key Results

• Samples: 60 (30 healthy, 30 disease)
• OTUs: 300, Pathways: 50
• PERMANOVA: F=1.544, p<0.0001
• Differential OTUs: 16 up, 19 down
• F/B ratio: 2.00 (healthy) vs 7.26 (disease)
• Dysbiosis p=1.8×10⁻⁴