🌍 Environmental Biotechnology Overview

Microorganisms are everywhere — from soil and water to humans. They affect:

• Global warming (through carbon cycling),
• Nutrient cycles (N, P, S),
• Human health (gut microbiome, pathogens),
• Biotechnology (biogas, bioenergy, bioplastics, waste recovery).

💡 Environmental biotechnology applies microbes to improve water, wastewater, and resource recovery systems.

🦠 “A World of Bacteria”

Bacteria impact:

• Food (fermentation, spoilage)
• Disease (e.g., Yersinia pestis = plague; Staphylococcus aureus = MRSA)
• Symbiosis (mutualistic relationships)
• Sustainability (waste recycling, pollution reduction)

🧩 Names link to functions! — knowing what a bacterium does often starts with knowing its name.

🔍 How Do We Identify Bacteria?

1. Isolation and Cultivation

Classic method:

• Grow bacteria on agar.
• Study colony properties and metabolism.

Problems:

• Only a few species grow easily.
• Often not the target species.
• Slow and labor-intensive. ✅ Gives high-resolution info about metabolism.

2. FISH (Fluorescence In Situ Hybridization)

🧬 Uses fluorescent probes targeting specific genes.

• No need to isolate/culture.
• Visualize cells directly under microscope.
• Can count and locate bacteria. ⚠️ Limitation: you only detect what your probe is designed for — “you find what you look for.”

3. DNA Sequencing (focus: 16S rRNA gene)

Revolutionized microbial identification 🚀

• No culture or microscopy needed.
• Detects all bacteria in a sample.
• Fast, relatively cheap.
• Generates massive data. ❗But: you don’t “see” the bacteria — only their DNA signatures.

🧬 What is 16S rRNA Amplicon Sequencing?

Every bacterium has a genome (≈3,000–5,000 genes). Among them, the 16S rRNA gene is special:

• Present in all bacteria.
• Conserved (similar across species) but has variable regions that act like fingerprints 🔍.

👉 This makes it perfect for taxonomic identification.

🎯 Why 16S rRNA?

• Found in ribosomes, which all cells have.
• rRNA = structural RNA, not coding.
• Its mix of conserved and variable regions allows universal detection + differentiation between species.

⚖️ Advantages & Disadvantages of 16S Sequencing

+ Pros

• Universal gene (found in all bacteria)
• Conserved + variable regions = ideal for primers & taxonomy
• Massive reference databases (e.g., SILVA, RDP, MiDAS)

– Cons

• Different bacteria have different copy numbers → biases abundance.
• No truly universal primers → some species missed.
• Doesn’t show activity (only presence).

📊 16S rRNA Results

Output: a list of bacterial taxa and their relative abundances (% of reads) Example:

Thousands of species can appear from a single sample.

🧪 Workflow: Identification by DNA Sequencing

Main steps:

1. Sampling 🧫 – collect environmental material.
2. DNA Extraction 🧬 – purify DNA, remove proteins & RNA.
3. Library Preparation 📦 – amplify 16S gene, attach sequencing adaptors.
4. Sequencing 💡 – use Illumina, PacBio, or Nanopore.
5. Bioinformatics 🖥️ – merge reads, remove noise, cluster, assign taxonomy.
6. Interpretation 🧠 – relate community data to environmental metadata.

This process is iterative — data refinement improves identification.

🧰 Library Preparation Workflow

1. PCR amplification – amplify 16S fragment.
2. Purification – remove reagents & primers.
3. QC (Quality Check) – verify product presence and concentration (“TapeStation”).
4. Pooling – combine libraries at same molarity (4 nM).

🧫 DNA Sequencing Platforms

🧩 Illumina

• Works by bridge amplification and sequencing by synthesis.
• Produces short but accurate reads (≈150–300 bp).
• Ideal for 16S sequencing.

🌀 PacBio

• Long-read sequencing via single-molecule real-time (SMRT) technology.
• Captures full-length 16S genes.

⚡ Nanopore

• DNA passes through a protein pore.
• Electrical current changes create “squiggle” patterns representing DNA sequence.
• Portable and real-time.

💻 Bioinformatics Steps

1. Demultiplexing – sort reads by sample.
2. Quality Control (QC) – trim low-quality regions, remove primers.
3. Chimera removal – discard artificial sequences.
4. Clustering or ASV detection:
   • OTUs (Operational Taxonomic Units): sequences grouped by 97% similarity.
   • ASVs (Amplicon Sequence Variants): unique sequences, higher precision.
5. Assign taxonomy using databases (SILVA, RDP, Greengenes, MiDAS).
6. Generate count table – number of reads per taxon per sample.

🔬 OTU vs ASV

🧮 The number of reads per ASV/OTU reflects relative abundance of bacteria.

📂 Output Files

• '.fa' → FASTA file with ASV sequences.
• '.tsv' → Count table + taxonomy.

📚 Databases used:

• SILVA, RDP, Greengenes, MiDAS (ecosystem-specific for wastewater, etc.)

🌱 Microbial Community Concepts

Core community

Set of bacterial taxa consistently found across similar samples — the “signature” microbiome of that environment.

Alpha Diversity 🧩

Measures diversity within one sample. Includes richness (number of species) and evenness (how balanced their abundances are).

Beta Diversity 🔁

Measures differences in microbial communities between samples. Used to compare sites, treatments, or time points.

Example visualization: PCoA, NMDS, or heatmaps.

🧭 Summary

Microbial community analysis integrates:

• Environmental sampling,
• Molecular biology (DNA extraction, PCR),
• Sequencing technology,
• Bioinformatics, and
• Ecological interpretation.

Together, they reveal who’s there, how many, and how communities change across environments 🌎🧫