Introduction

META_1 The metagenomics revolution: an introduction About this free course This free course is an adapted extract from the Open University course . This version of the content may include video, images and interactive content that may not be optimised for your device. You can experience this free course as it was originally designed on OpenLearn, the home of free learning from The Open University – There you’ll also be able to track your progress via your activity record, which you can use to demonstrate your learning.

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All rights falling outside the terms of the Creative Commons licence are retained or controlled by The Open University. Head of Intellectual Property, The Open University Introduction This course looks at metagenomics and its applications. Metagenomics studies the genetic material collected directly from the environment, allowing scientists to analyse all the living organisms present in a sample without the need for seeing them or growing them in a laboratory. By examining DNA or RNA from places such as soil, water, air, or the human body, metagenomics reveals the wide range of life that exists. While it is widely used in microbiology and health, metagenomics also has important applications in monitoring biodiversity in ecosystems, and supporting advances in biotechnology. This course will introduce the key ideas behind metagenomics and show how these methods are used across many scientific disciplines. This OpenLearn course provides a sample of level 1 study in Science at The Open University. Learning outcomes By the end of this course, you should be able to: understand the basic concepts in metagenomics demonstrate an understanding of how metagenomics data are generated and interpreted explain the differences between metagenomics and targeted sequencing understand potential applications and limitations of metagenomics in microbiology, infectious diseases, and conservation science make sense of information presented in different ways, including written, numerical, and audio-visual. 1 What is metagenomics and how does it work? All living organisms contain genetic material in the form of deoxyribonucleic acid (DNA) and ribonucleic acid (RNA), which store the information needed to build and maintain life. DNA and RNA molecules are not only found inside living cells, but are also released into the surrounding environment when the cells die or can be isolated from shedded hair, faeces, or other biological secretions. Click on the three environments in Figure 1 to discover which organisms live there. Figure 1 (interactive) Living organisms. SoilFungi (e.g. molds, mycorrhizal fungi)Nematodes (e.g. roundworms)EarthwormsInsectsBacteria and virusesPondAlgae (e.g. green algae, diatoms)Zooplankton (e.g. rotifers, copepods)Aquatic plantsFish and amphibiansBacteria and virusesIntestineBacteria (e.g. Bacteroides, Lactobacillus)Viruses (especially bacteriophages)Fungi (e.g. Candida)Protozoa (rare in healthy individuals) The genetic material found in the environment can act like a molecular fingerprint, allowing scientists to study and identify entire biological communities without directly observing the organisms themselves. If a genome is the complete set of genetic material found in a single organism or cell, then the collective genetic material from all organisms in a particular sample is called metagenome. Thus, metagenomics is the study of the collection of all individual genomes in the sample. Besides allowing scientists to identify any living organism in the sample, metagenomics can also determine the genes that individuals have within their genome. Because genes contain instructions for making proteins, identifying the genes in a metagenome allows scientists to predict what functions the organisms can carry out in that environment, such as breaking down nutrients, producing energy, resisting antibiotics, or causing disease. What is meant by the statement that metagenomics reveals ‘who is there and what they do’? Metagenomics is the study of all genetic material found in an environmental sample. It tells us ‘who is there’ by identifying the organisms present based on their DNA or RNA, and ‘what they do’ by analysing the genes they carry. This predicts their biological functions and activities in that environment. The origin of metagenomics can be traced to the late 20th century, when microbiologists sought ways to study microorganisms that could not be easily grown in the laboratory. More specifically, metagenomics as a term was first introduced by Jo Handelsman and colleagues in 1998 to describe the study of the combined genomes of microorganisms directly from environmental samples (Handelsman et al, 1998). Now that you know what metagenomics is, it is time to get a bit more into how it works in practice.

1.1 Preparing the genetic material The process begins with collecting an environmental or biological sample. Different samples are collected in different ways. For example, water from a river is collected by submerging a sterile bottle below the water surface. Regardless of the sample and how it is collected, the important thing is to make sure that the genetic material is not degraded. Scientists usually add chemical preservatives that ‘fix’ DNA and RNA, or keep samples cold and away from heat or direct light. The genetic material cannot be used as it is directly from a sample but must be isolated and cleaned from contaminants that can interfere with the steps required for metagenomics. These steps also help in making the genetic material more concentrated and easier to sequence later. During extraction, the cells are opened up (lysed) to release their contents. The DNA and RNA are then purified using specialised chemicals, more often using commercially available extraction kits which come in a column format.

While in most cases scientists look for DNA, there are many viruses (like flu or the Ebola virus) which have a genome made of RNA. The sample extraction process is similar to DNA. However, RNA cannot be sequenced directly and must first be turned into DNA. This is accomplished using a particular chemical reaction called reverse transcription. Now that the genetic material from the sample is ready, it can proceed for the next step.

1.2 Sequencing the genetic material Following extraction and purification, sequencing is the next step in the metagenomic workflow. Sequencing means reading the genetic instructions stored in the DNA. DNA is like a long string made of four letters (A, T, G, C, the nucleotides). Sequencing tells us the exact order of those letters in a piece of DNA. Which of the following best defines the process of DNA sequencing? A method used to copy DNA during cell division. A technique that determines the exact order of letters (nucleotides) in a piece of DNA A process in which RNA is translated into protein. A technique for separating pieces of DNA using an electric current. Metagenomics uses an approach called shotgun sequencing: the DNA is broken into many small pieces, which are sequenced all at once. Why do you think the sequencing used in metagenomics is called a shotgun approach? Because the DNA is broken into many random pieces, similar to how pellets from a shotgun spread out randomly when fired. Scientists can use different approaches and instruments to sequence DNA, but they are beyond the scope of this course. It is worth pointing out that metagenomics became a reality and then reached its full potential thanks to the evolution of DNA sequencing methods. It is important to note that early sequencing methods were slow and expensive. However, the advent of high‑throughput next‑generation sequencing in the early 2000s drastically increased speed while reducing cost, making it possible to sequence millions of DNA fragments simultaneously from a sample. More recently, new sequencing technologies that are higher in accuracy and can read longer stretches of DNA have further improved genome reconstruction and functional analysis.

1.3 Making sense of the sequences In the previous section you learned that millions of DNA fragments can be sequenced. But how do scientists make sense of all this information? What comes next is a bit like assembling a puzzle. Powerful computer programs compare the DNA fragments and look for regions with a matching sequence of letters: When two sequences have the same letters at their ends, the program assumes they come from the same original piece of DNA and joins them together. By repeating this process many times, these fragments are combined into longer (assembled) sequences.

The computers compare the assembled sequences to known genes in a database that contains known DNA sequences from many different organisms, and identify the organism(s) they come from (taxonomic assignment). The biological function of a gene can also be predicted. For example, scientists can predict which energy source a group of microorganism uses by looking at which type of metabolic genes are found in the sample. Sometimes, not all sequences in a sample can be identified, meaning that they don’t have a matching sequence in the databases. The more scientists sequence genomes and organisms from the environment, the bigger the databases become and so the capacity to identify and study genes with metagenomics expands. You can imagine that metagenomic studies generate a lot of data, making their storage and sharing very important. Data are often stored in online repositories such as the NCBI Sequence Read Archive (SRA).

You will now try to make sense of some metagenomic data in a short activity. Activity 1 Allow 20 minutes for completing this activity In this activity, you will look at the results of a metagenomics study where total DNA was extracted from water samples from two different ponds (A and B) and then sequenced with the shotgun approach. The goal was to determine which of the two ponds had the highest number of different species in it (e.g. which pond was more biodiverse). The table below shows the DNA sequences found in the two ponds (represented as strings of A, T, G, C), but they have not been identified yet. On the right, you can see a list of reference DNA sequences of organisms expected to be found in the ponds. Question 1 Match the DNA sequences in Pond A and B with the reference sequences on the right (coloured box) and fill the identification column with the name of the organism corresponding to the sequence Table 1 DNA sequences found in the two ponds and reference organism sequences

Pond A	Pond B	Reference sequence	Organism
GCGCGC	TATCCC	AATTTA	Water flea
GCGCGC	TATATA	GCGCGC	Mosquito
GGGCCC	AATTTA	GGGCCC	Bacterium
GCGCGC	GGGCCC	TATATA	Fish species A
GCGCGC	CATACA	TATCCC	Fish species B
CATACA	AATTTA	CATACA	Water hyacinth
GCGCGC	AATTTA	ATAGGG	Frog
CATACA	ATAGGG
GGGCCC	CGGGGG
GCGCGC	CCCCCC

Table 1 (completed) DNA sequences found in the two ponds and reference organism sequences

Pond A	Identification in A	Pond B	Identification in B	Reference sequence	Organism
GCGCGC	Mosquito	TATCCC	Fish species B	AATTTA	Water flea
GCGCGC	Mosquito	TATATA	Fish species A	GCGCGC	Mosquito
GGGCCC	Bacterium	AATTTA	Water flea	GGGCCC	Bacterium
GCGCGC	Mosquito	GGGCCC	Bacterium	TATATA	Fish species A
GCGCGC	Mosquito	CATACA	Water hyacinth	TATCCC	Fish species B
CATACA	Alga	AATTTA	Water flea	CATACA	Water hyacinth
GCGCGC	Mosquito	AATTTA	Water flea	ATAGGG	Frog
CATACA	Water hyacinth	ATAGGG	Frog
GGGCCC	Bacterium	CGGGGG	Unknown
GCGCGC	Mosquito	CCCCCC	Unknown

Question 2 Which are the most found sequences (organisms) in the two ponds? Calculate their frequency. As an example, if in a pond C a total of 10 sequences were found and half belong to a fish, then 5/10=50% is the frequency of fish sequences in then pond. In pond A, mosquito sequences were the most common (6/10=60%) while in pond B sequences of the water flea (3/10=30%). Question 3 Which of the two ponds can be considered the more biodiverse, considering the DNA sequences that you could identify? Pond B is more diverse, because sequencing data showed the presence of up to 7 different organisms. By comparison, only 3 were found in pond A. Question 4 Were all the sequences identified? If not, why do you think this happened? In Pond B two sequences did not match any of the reference organisms. This means that they did not have a reference similar enough in the database used for the identification. The most likely explanation is that these sequences belong to organism(s) for which we do not have a sequence yet and they may represent unknown organisms.

1.4 Amplicon (targeted) sequencing There is also another DNA sequencing-based method scientists can use, called amplicon sequencing or metabarcoding. While metagenomics sequences literally everything from a sample, sequencing in metabarcoding targets a specific genetic marker sometimes referred as a barcode. A barcode is a region of DNA that is shared by all members of a group of organisms (for example, animals), but its sequence is slightly different between the members of a specific group (for example, species of fish) which the barcode makes possible to distinguish. Different genetic markers are used for different organisms. For example, the 16S ribosomal RNA gene is used for bacteria, while fungi and animals are identified using the Internal Transcribed Spacer region (ITS, typically ITS1 or ITS2) and the cytochrome c oxidase subunit I gene (COI), respectively. Before being sequenced, the barcode region is copied millions of times using a specific chemical reaction called polymerase chain reaction (PCR). Since the barcode is copied up to millions of times, scientists do not need a lot of DNA (or a sample of DNA that is necessarily very high quality) to start with. You can see how PCR works by watching the video below. Video 1 Overview of how PCR works. NARRATOR: Polymerase Chain Reaction, or PCR, uses repeated cycles of heating and cooling to make many copies of a specific region of DNA. First, the temperature is raised to near boiling, causing the double-stranded DNA to separate, or denature, into single strands. When the temperature is decreased, short DNA sequences known as primers bind or anneal to complementary matches on the target DNA sequence. The primers bracket the target sequence to be copied. At a slightly higher temperature, the enzyme Taq polymerase, shown here in blue, binds to the primed sequences and adds nucleotides to extend the second strand. This completes the first cycle. In subsequent cycles, the process of denaturing, annealing, and extending are repeated to make additional DNA copies. After three cycles, the target sequence defined by the primers begins to accumulate. After 30 cycles, as many as a billion copies of the target sequence are produced from a single starting molecule. Metabarcoding is cheaper and faster that metagenomics, but PCR amplification sometimes is more efficient for some species and not others: this issue, called amplification bias, makes the results of metabarcoding less reliable if we want to measure which species is more abundant in the sample. Additionally, no functional information relating to the genes of individual organisms within the sample can be obtained through metabarcoding. Some important differences between metagenomics and metabarcoding are summarised in the table below. Table 2 Comparison of metagenomics and metabarcoding

Feature	Metagenomics (shotgun sequencing)	Metabarcoding (amplicon/targeted sequencing)
Main target	All DNA in the sample	Specific barcode genes
Amplification of DNA?	No	Yes (PCR)
What does it tell us?	Taxonomic and functional profiles	Taxonomic composition only
DNA requirements	Both high quality and quantity	Low to moderate, fragmented DNA acceptable
Quantification	More accurate for relative genome abundance	Less accurate (PCR amplification bias)
Cost	Higher	Lower

A group of scientists wants to study the types of species present in a river to monitor pollution. They have a limited budget and need results quickly. Should they use metagenomics or metabarcoding? Give one reason for your choice. They should use metabarcoding because it is cheaper and more feasible for large numbers of samples. Metabarcoding targets specific genes, reducing sequencing costs and making it suitable for identifying species when detailed functional information is not required.

2 Metagenomics in action As you learned in the previous section, metagenomics originated in the field of microbiology. From discovering new microbes and understanding how they behave in certain environments, to detecting biological threats to people and animal health, metagenomics can be a very powerful tool. However, metagenomics plays an increasingly important role also in conservation science. In the next sections you will see how broadly metagenomics can be applied in our world.

2.1 Uncovering life in extreme environments on Earth and beyond Metagenomics bypasses one of the most severe limitations of classical microbiology: most microorganisms cannot be cultivated in the laboratory. This limitation is particularly relevant for microbes living in extreme environments. An extreme environment is a habitat whose physical or chemical conditions (such as temperature, salinity, pH, pressure, radiation, or nutrient availability) cannot be tolerated by most known organisms but still support specially adapted forms of life. Organisms capable of living in these extreme environments are called extremophiles. Box 1 Extreme environments on Earth Our planet hosts a variety of habitats with extreme physico-chemical conditions. You may not expect to find life there, but in reality, these environments are still home for many microbes. Deep-sea hydrothermal vents are found along mid-ocean ridges at depths often exceeding 2,000 meters, where complete darkness, immense pressure, and steep chemical gradients prevail. Vent fluids can reach temperatures above 350°C and are enriched in reduced chemicals such as hydrogen sulfide, methane, and metals. Here, only organisms that use chemical energy rather than sunlight to produce organic matter can thrive and they form the base of complex ecosystems that include (besides microbes) also tube worms, crustaceans, and molluscs. Hyper-saline environments, such as salt lakes, salt flats, and salterns (such as the Dead Sea), are characterised by salt concentrations far exceeding that of seawater. Such high salinity imposes severe osmotic stress to cells, disrupting cellular processes for most organisms. However, halophilic (salt-loving) microorganisms have evolved specialised adaptations such as highly charged proteins and compatible solutes keeping their cells stable. Polar regions, including Antarctic ice sheets and subglacial lakes, are cold environments with permanently low temperatures, limited nutrients, and long periods of darkness. Microbial life persists within ice, snow, and liquid water trapped beneath glaciers by adopting slow metabolic rates, cold-adapted enzymes, and protective cellular structures. Figure 6 (interactive) Examples of extreme environments found on Earth. The first image is a photograph of a deep-sea hydrothermal vent, with a yeti crab; a deep-sea crustacean that thrives in extreme environments. The second image is a photograph of a salt lake. The third image is a photograph of an Antarctic ice sheet. By enabling the direct analysis of genetic material recovered from environmental samples, metagenomics gives scientists precious information about how life persists in these very particular places. Researchers from China (Xamxidin et al, 2025) looked at the microorganisms living in Lake Barkol, a highly salty lake, by extracting the DNA from water and sediment samples. The research identified more than 300 different bacteria and archaea. Archaea are a group of single‑celled organisms similar to bacteria, and they often live in extreme environments with very salty, hot, acidic, or oxygen‑poor conditions. Around 97% of the genomes could not be identified at the species level, indicating that the lake hosts a large amount of previously uncharacterised microbial life. Metabolic reconstruction revealed the presence of diverse carbon fixation pathways occurring inside living cells, including the Calvin-Benson-Bassham (CBB) cycle and the Arnon-Buchanan reductive tricarboxylic acid (rTCA) cycle, and evidence for both nitrogen fixation and denitrification processes. Using metagenomic data, scientists also found that microbes sampled in water and sediment showed distinct ways of dealing with the high salinity of the environment: some allow salts to enter their cells, while other keep the salt out. Studying life in extreme environments on Earth is also central to astrobiology. Places such as hypersaline lakes, deep‑sea hydrothermal vents, acidic hot springs, polar ice, and radiation‑exposed deserts resemble conditions thought to exist on Mars or planets beyond our solar system. Discovering that life can thrive under extreme cold, heat, salinity, pressure, or lack of sunlight shows that life’s limits are much broader than once assumed, expanding the range of environments considered potentially habitable beyond Earth. Extremophiles reveal the mechanisms that allow life to persist under harsh conditions. By studying their metabolism, stress‑response systems, and energy sources, scientists learn how organisms cope with challenges such as dehydration, DNA damage, or energy scarcity. This helps astrobiologists predict what forms alien life might take – for example, microbes that rely on chemical energy rather than sunlight. Extreme‑environment research also helps identify biosignatures: specific gas combinations, mineral changes, or metabolic pathways (such as carbon or nitrogen cycling) that are difficult to explain without the presence of life forms. Why is metagenomics particularly useful for studying microorganisms in extreme environments like Lake Barkol? Microorganisms living in extreme environments cannot be easily grown in the laboratory, and most of them cannot be grown at all. Metagenomics allow scientists to study their diversity and properties without needing them alive, and it is a fundamental tool to discover new organisms. The study found genes linked to carbon and nitrogen cycling. Why do these findings suggest that Lake Barkol is an active ecosystem rather than a biologically ‘dead’ environment? These cycling processes require living cells, enzymes, and energy, and they occur continuously in response to environmental conditions. In contrast, an environment devoid of life would show little to no evidence of such coordinated, enzyme-driven chemical transformations. How can extremophiles help scientists in their search for life beyond Earth? Extremophiles are important because they show that life can survive and remain active under conditions once thought to be uninhabitable. By studying how extremophiles obtain energy, protect their cells, and carry out metabolism in harsh environments on Earth, scientists can identify the possible conditions under which life might exist on other planets or moons. Extremophiles also help researchers recognise chemical or biological signs of life that space missions can look for beyond Earth. Finally, metagenomics can help scientists discovering new genes and biological properties with applications in medicine and biotechnology. A perfect example is the polymerase chain reaction, which you encountered in one of the previous sections: the Taq polymerase, the enzyme copying DNA during PCR, was originally found in bacteria living in hot springs.

2.2 Detecting antimicrobial resistance in the environment You may have heard the term One Health before. The One Health approach recognises that the health of humans, animals, plants, and the environment are deeply interconnected, and must be addressed together rather than in isolation. Many of the most pressing global health challenges, including food security and emerging infections, arise at the interfaces between these systems. Another global health challenge is the threat posed by antimicrobial resistance (AMR), when bacteria and other microorganisms evolve the ability to survive treatment with antibiotics and antimicrobial drugs, making infections harder or impossible to treat. While AMR is often discussed as a clinical or hospital‑based problem, it is in fact a system‑wide issue. Antimicrobials are used not only in human medicine but also in livestock production, aquaculture, plant agriculture, and consumer products. Resistant bacteria and their antimicrobial resistance genes (ARGs) can then move between humans, animals, food, water, soil, and wildlife through direct contact, waste streams, and environmental pollution. ARGs can also be exchanged between bacteria.

Why do you think metagenomics can be of great help in monitoring AMR in a One Health context? Metagenomics allows the detection and characterisation of antimicrobial resistance genes across humans, animals, food, and environmental samples without the need for culturing. This makes it possible to capture the full diversity of ARGs and microbial reservoirs. Activity 2 Allow 20 minutes for completing this activity Below you will find the abstract of a peer-reviewed research paper on AMR and metagenomics (Kilonzo-Nthenge et al 2024). Read it through and then answer the questions. Agricultural practices significantly influence microbial diversity and the distribution of virulence and antimicrobial resistance (AMR) genes, with implications for ecosystem health and food safety. This study used metagenomic sequencing to analyse 60 samples (30 per state) including water, soil, and manure (10 each) from Alabama (a mix of cattle and poultry sources) and Tennessee (primarily from cattle). The results highlighted a rich microbial diversity, predominantly comprising Bacteria (67%) and Viruses (33%), with a total of over 1,950 microbial species identified. The dominant bacterial phyla were Proteobacteria, Cyanobacteria, Actinobacteria, Firmicutes, and Bacteroidetes, with the viral communities primarily represented by Phixviricota and Uroviricota. Distinct state-specific microbial profiles were evident, with Alabama demonstrating a higher prevalence of viral populations and unique bacterial phyla compared to Tennessee. The influence of environmental and agricultural practices was reflected in the microbial compositions: soil samples were notably rich in Actinobacteria, water samples were dominated by Proteobacteria and Cyanobacteria, and manure samples from Alabama showed a predominance of Actinobacteria. Further analyses, including diversity assessment and enterotype clustering, revealed complex microbial structures. Tennessee showed higher microbial diversity and phylogenetic complexity across most sample types compared to Alabama, with poultry-related samples displaying distinct diversity trends. Principal Coordinate Analysis (PCoA) highlighted notable state-specific variations, particularly in manure samples. Differential abundance analysis demonstrated elevated levels of Deinococcus and Ligilactobacillus in Alabama, indicating regional effects on microbial distributions. The virulome analysis revealed a significant presence of virulence genes in samples from Alabama. The community resistome was extensive, encompassing 109 AMR genes across 18 antibiotic classes, with manure samples displaying considerable diversity. Ecological analysis of the interactions between AMR gene subtypes and microbial taxa revealed a sophisticated network, often facilitated by bacteriophages. These findings underscore the critical role of agricultural practices in shaping microbial diversity and resistance patterns, highlighting the need for targeted AMR mitigation strategies in agricultural ecosystems to protect both public health and environmental integrity. Question 1 Why did the authors include water, soil, and manure samples in their analysis, rather than focusing on only one sample type? Including water, soil, and manure captures different environmental compartments influenced by agriculture, allowing the study to assess how microbes and AMR genes move across interconnected ecosystems. Question 2 Based on the abstract, calculate the number of soil, manure, and water samples collected in the study across both states. Was the number of samples balanced? Why do you think it is important? The study analysed 60 samples in total, consisting of 10 soil, 10 water, and 10 manure samples per state. Since two states were included, this results in 20 samples per type (soil = 20, water = 20, manure = 20). The number of samples was balanced. This is important because it ensures that differences in microbial diversity or AMR patterns reflect real biological or environmental variation, rather than being driven by unequal sampling across environments or regions. Question 3 Besides bacteria, which other organisms were detected in the samples? Was there any difference in their occurrence between Alabama and Tennessee? Viruses were also detected in the samples. Samples from Alabama showed a higher occurrence of viruses compared to samples from Tennessee. Question 4 The authors use the term resistome in the abstract. Thinking about the metagenome which you encountered in the first section, what do you think resistome means? The resistome is the complete collection of all antimicrobial resistance genes present in a microbial community or sample (as the metagenome is the collection of all genetic material in a sample). Question 5 Why are manure samples particularly important for understanding AMR patterns in this study? Manure samples showed the greatest diversity of AMR genes across multiple antibiotic classes, highlighting manure as a key reservoir and potential dissemination point for resistance genes within agricultural systems and into the wider environment.

2.3 Detecting old and new pathogens In a clinical setting, when a patient shows the symptoms of an infection, the identification of the responsible pathogen relies on targeted assays that only detect organisms we already suspect. However, it is possible that the pathogen may be entirely new to science or is appearing in an unusual host or location. This is a scenario where metagenomics can play a crucial role. As you have learned across the previous sections, metagenomics is an agnostic approach. In medicine and microbiology, an agnostic approach refers to testing or investigation methods that do not target a specific suspected pathogen in advance but instead look broadly for any possible cause. By sequencing all genetic material in a sample, clinicians can move beyond ‘negative test’ results and uncover hidden or unexpected causes of illness. Box 2 New technologies for preventing the next pandemic Sequencing technology is constantly evolving, providing scientists and clinicians of new ways of using metagenomics. One example is the Oxford Nanopore MinION. This is a portable, real‑time DNA and RNA sequencing device. Its small size and relatively low infrastructure requirements make it especially valuable for use directly in the field, including farms, wildlife reserves, and veterinary laboratories, where early pathogen detection is critical. Early identification of such pathogens supports timely public health responses and outbreak control. Agnostic pathogen discovery via metagenomics is a valuable tool in identifying zoonotic viruses or other pathogens that cross from animals to humans, and it can play a crucial role in preventing the next pandemic. Figure 8 (interactive) Testing animal samples to detect zoonotic pathogens. The first image is a photograph of a bat being swabbed to test for viruses.The second image is a photograph of a trap being prepared to collect mosquitoes for pathogen testing.The third image is a photograph of the Oxford Nanopore MinION being prepared for sequencing DNA from a sample. Acute febrile illness (AFI) is one of the most common reasons for healthcare attendance in sub‑Saharan Africa, yet a large proportion of cases remain without an identified cause after routine testing. In a recent study in Uganda (Ashraf et al, 2025) researchers looked at 210 patients where standard diagnostics could not determine the cause of their fever, along with 20 samples from known outbreak situations. Following metagenomic sequencing, viral pathogens were identified in 19% (44 out of 230) of the sequenced samples. The detected viruses included respiratory, gastrointestinal, blood-borne, hepatitis, and mosquito‑ or tick‑borne viruses. Importantly, the study uncovered pathogens that had not been clinically suspected, including Crimean–Congo haemorrhagic fever virus, Rift Valley fever virus, dengue virus, and yellow fever virus, together accounting for 3% of cases. All these are high-consequence pathogens, meaning that they can cause severe disease and have major public health, economic, or societal impacts. The researchers also detected Le Dantec virus, which was not seen in Uganda since 1969. Why was metagenomic sequencing useful in this study? Since metagenomic sequencing is an agnostic method, it can detect unexpected, rare, and emerging viruses that routine tests had missed. What is the public health importance of finding viruses such as yellow fever and Crimean–Congo haemorrhagic fever in undiagnosed patients? These viruses can cause severe illness outbreaks. Detecting them early helps improve surveillance, guide outbreak responses, and reduce the risk of widespread transmission.

2.4 Improving biodiversity surveys Environmental DNA (eDNA) refers to genetic material that organisms shed into their environment through skin cells, mucus, scales, faeces, urine, gametes, or decomposing tissue. This DNA can be collected from environmental samples such as water, soil, sediment, snow, or air, without needing to capture or even directly observe the organisms themselves. In practice, scientists collect an environmental sample (e.g. a litre of river water), filter it to trap DNA fragments, extract the DNA, and then sequence it, as you learned in the first section. This greatly reduces the cost and effort of sampling compared with traditional methods that rely on trapping, visual surveys, or species‑specific assays. Because organisms do not need to be seen or captured, eDNA is particularly effective for detecting elusive, rare, or endangered species that are difficult to observe directly. Metagenomics also enables simultaneous detection of entire communities, providing a comprehensive picture of biodiversity with minimal disturbance to ecosystems. Box 3 Invasive species and their importance Invasive species are organisms introduced – intentionally or accidentally – outside their native range that establish, spread, and cause harm to ecosystems, economies, or human health. They are now recognised as one of the five major direct drivers of global biodiversity loss, alongside habitat destruction, climate change, pollution, and overexploitation. Invasive species thrive because they often escape their natural predators, parasites, and competitors, allowing them to outcompete native species, alter food webs, change ecosystem processes, and in some cases drive native species to extinction. Their impacts are particularly severe on islands, freshwater systems, and coastal ecosystems, where native species may have evolved in isolation and lack effective defences against novel competitors or predators. Beyond ecological damage, invasive species impose enormous economic costs, affecting agriculture, fisheries, water infrastructure, forestry, and public health. Many invasions go undetected for years, meaning management often begins only after populations are widespread and expensive – or impossible – to eradicate. As a result, early detection, including using environmental DNA and metagenomics, can be a very cost‑effective strategy for invasive species management. Figure 9 (interactive) Invasive species of major global importance. The first image is a photograph of a water hyacinth (Eichhornia crassipes), widely considered the world’s most widespread invasive plant. It forms dense mats that block waterways and reduce oxygen levels in the water. The second image is a photograph of a zebra mussel (Dreissena polymorpha). It has invaded large parts of Europe and North America, displacing native mussels and causing hundreds of millions of dollars in annual damages to water and power infrastructure.The third image is a photograph of a brown tree snake (Boiga irregularis). It was accidentally introduced to Guam, caused the extinction of most of the island’s native forest birds and serves as a classic example of how a single invasive species can collapse entire ecosystems, particularly on islands. The advantage of using eDNA for studying biodiversity is well exemplified by a study on fish diversity in the Danjiang River in the Shaanxi Province, China (Deng et al, 2024). Fish diversity is a key indicator of freshwater ecosystem health, but traditional survey methods such as netting or cage trapping are time‑consuming, costly, and can miss rare or elusive species. By using eDNA metabarcoding of water samples, scientists identified 59 fish species belonging to 8 orders, 19 families, and 40 genera. Several dominant species were identified, alongside eight rare species and two non‑native (exotic) species, highlighting both conservation value and potential ecological threats. Interestingly, when compared with historical data and conventional ground‑cage sampling, eDNA revealed significantly higher species richness, showing it to be more sensitive and comprehensive. What advantage did eDNA metabarcoding have over traditional fish sampling methods in this study? eDNA metabarcoding detected more fish species than ground‑cage sampling because it can identify DNA from species that are rare, elusive, or difficult to catch.

2.5 DNA is in the air Environmental DNA can also be captured from the air by drawing air through filters or liquid collectors that trap tiny biological particles such as skin cells, hair fragments, pollen, fungal spores, and microscopic droplets carrying free DNA. Active air samplers typically use fans or pumps to pull air through fine filters, while passive samplers rely on natural air movement to deposit particles over time. After sampling, DNA is extracted from the filters or liquid and analysed using metabarcoding or metagenomic sequencing to identify the organisms present. Air eDNA can also sample terrestrial biodiversity across a wide area and multiple organisms simultaneously, reducing field effort and cost. One area of particular interest is crop protection. By capturing and sequencing air DNA, farmers and researchers can detect crop pathogens (such as fungal blights or rusts) and insect pests before symptoms are visible. Early warning allows targeted interventions, reducing yield losses and avoiding unnecessary pesticide use.

Activity 3 Allow 20 minutes for completing this activity Below you will find quotes from the abstract of a peer-reviewed research paper on airborne eDNA metagenomics (Nousias et al 2025). Read it through and then answer the questions. ‘Here we report the rapid application of shotgun long-read environmental DNA (eDNA) analysis for non-invasive biodiversity, genetic diversity and pathogen assessments from air. We also compared air eDNA with water and soil eDNA. Coupling long-read sequencing with established cloud-based biodiversity pipelines enabled a 2-day turnaround from airborne sample collection to completed analysis by a single investigator.’ ‘From outdoor air eDNA alone, comprehensive genetic analysis was performed, including population genetics (phylogenetic placement) of a charismatic mammal (bobcat, Lynx rufus) and a venomous spider (golden silk orb weaver, Trichonephila clavipes), and haplotyping humans (Homo sapiens) from natural complex community settings, such as subtropical forests and temperate locations. The rich datasets also enabled deeper analysis of specific species and genomic regions of interest, including viral variant calling, human variant analysis and antimicrobial resistance gene surveillance from airborne DNA.’ ‘Together these approaches can enable rapid simultaneous detection of all life and its genetic diversity from air, water and sediment samples for unbiased non-targeted information-rich genomics-empowered (1) biodiversity monitoring, (2) population genetics, (3) pathogen and disease-vector genomic surveillance, (4) allergen and narcotic surveillance, (5) antimicrobial resistance surveillance and (6) bioprospecting.’ Question 1 What is airborne environmental DNA (eDNA), and how was it used in this study? Airborne eDNA consists of genetic material released into the air from organisms, such as cells, spores, pollen, or fragments of tissue. In this study, air samples were collected non‑invasively and analysed using sequencing to identify DNA from many different organisms at once, without targeting specific species. Question 2 What types of biological information were obtained from air eDNA in this study? The researchers obtained broad biodiversity data as well as detailed genetic information. From air alone, they carried out population genetics, identifying species such as bobcats and spiders, analysed human genetic variation, detected viral variants, and screened for antimicrobial resistance genes. Question 3 What do you think could be an ethical implication of using airborne DNA sequencing, particularly in public spaces? Privacy would be a key issue. Air eDNA can contain human DNA, and as shown in studies like the one above, it may be possible to detect human genetic variants unintentionally. This raises concerns about collecting genetic information from people without their knowledge or consent in public spaces. Question 4 Among potential uses of airborne DNA sequencing, the authors also mention allergen surveillance. What do you think this implies, and how can it be used? Allergen surveillance means monitoring airborne biological sources that can trigger allergic reactions. Pollen from plants, fungal spores (e.g. moulds), dust mites, or animal dander contain DNA or are associated with organisms whose DNA is present in the air. Sequencing air eDNA could improve allergy forecasts, help link symptoms of allergy to specific biological sources, and support public health responses to asthma and hay fever.

3 Reflecting on some limitations of metagenomics In the previous sections you witnessed how scientist harness the power of metagenomics in a variety of research questions and practical applications. However, it is also important to mention some limitations of this approach, to also reinforce the fact that metagenomics is constantly improving. Metagenomics sequences all DNA in a sample and is very sensitive, meaning that contaminating DNA from the laboratory equipment, chemicals, or the scientists themselves can accidentally be added to a sample. As a result, it can be difficult to know whether all DNA sequences truly come from the environment being studied, leading to incorrect conclusions about which organisms are actually there rather than a fluke. Another challenge is that the amount of DNA found does not always match the number of organisms in the environment. Some species have larger genomes or more copies of certain genes, while other organisms break down more slowly in the environment releasing less DNA and over a longer period of time. Scientists are studying the problem in great detail to improve the interpretation of metagenomic data. As you learned in the section on airborne DNA, metagenomics can also raise ethical concerns, especially when used on human-related samples such as gut microbiomes or wastewater. Environmental samples (including water contaminated with human faeces) may contain human DNA, which could reveal sensitive information about health or identity. Protecting privacy and obtaining informed consent when using metagenomics are important areas that need improvement. Metagenomics from a soil sample reveals two species of worm with very different DNA levels. However, field surveys suggest their populations have similar size. Which limitation is being demonstrated? This is an example of the difficulty of linking DNA abundance to actual organism numbers, because DNA quantity does not directly reflect population size. Scientists analysing sewage samples to study pathogenic microbes and antimicrobial resistance also detected human genetic information. Which metagenomics issue does this raise? This raises ethical concerns about privacy and the handling of human genetic information in metagenomics studies. In this case, human DNA is shed in the faeces and can be sequenced by metagenomics in sewage. A scientist finds unexpected human DNA in a water sample from a subglacial Antarctic lake. Which limitation does this illustrate? This illustrates the risk of contamination, where DNA not belonging to the original sample is accidentally introduced (probably during sample collection or manipulation in the laboratory). 4 Conclusion In conclusion, metagenomics is a powerful approach that transformed how scientists study life by allowing them to analyse genetic material from any sample. Metagenomics makes it possible to explore complex communities of organisms that cannot easily be grown in the laboratory, greatly expanding our understanding of ecosystems and the limits of life. Additionally, metagenomics is a powerful tool at the service of human, animal, and environmental health, with applications ranging from tracking new pathogens, monitoring biodiversity, and discovering useful enzymes and medicines. At the same time, recognising the limitations of metagenomics is essential for appreciating what it can and cannot do. As sequencing technologies improve and analytical methods become more accurate and accessible, metagenomics will continue to grow as a key tool across many scientific fields. If you enjoyed this course, you may want to explore the following OpenLearn resources: What is the genome made of? Microbes – friend or foe? Understanding antibiotic resistance The gut microbiome: balancing the body Bioinformatics: a new asset in the Disease Detectives toolkit References Ashraf, S., Jerome, H., Bugembe, D.L., Ssemwanga, D., Byaruhanga, T., Kayiwa, J. T., Downing, R., Salazar-Gonzalez, J. F., Salazar, M. G., Shepherd, J. G., Wilkie, C., Davis, C., Logan, N., Vattipally, S. B., Wilkie, G. S., da Silva Filipe, A., Ssekagiri, A., Namuwulya, P., Bukenya, H., Kigozi, B. K., McConnell, W. W., Willett, B. J., Balinandi, S., Lutwama, J., Kaleebu, P., Bwogi, J. and Thomson, E. C. (2025) ‘Uncovering the viral aetiology of undiagnosed acute febrile illness in Uganda using metagenomic sequencing’, Nature Communications, 16(1), p. 2844. doi: 10.1038/s41467-025-57696-8. Deng, J., Zhang, X., Yao, X., Rao, J., Dai, F., Wang, H., Wang, Y. and Jiang, W. (2024) ‘eDNA metabarcoding reveals differences in fish diversity and community structure in Danjiang River’, Scientific Reports, 14(1), p. 29460. doi: 10.1038/s41598-024-80907-z. Handelsman, J., Rondon, M.R., Brady, S.F., Clardy, J., Goodman, R.M. (1998) ‘Molecular biological access to the chemistry of unknown soil microbes: a new frontier for natural products’, Chemistry & Biology, 5(10), R245-9. doi: 10.1016/s1074-5521(98)90108-9. Kilonzo-Nthenge, A., Rafiqullah, I., Netherland, M. Jr., Nzomo, M., Mafiz, A., Nahashon, S. and Hasan, N. A. (2024) ‘Comparative metagenomics of microbial communities and resistome in southern farming systems: implications for antimicrobial stewardship and public health’, Frontiers in Microbiology, 15, p. 1443292. doi: 10.3389/fmicb.2024.1443292. Mikko, A., Villegas, J. A., Svensson, D., Karlsson, E., Esseen, P. A., Albrectsen, B. R., Lundin, O., Forsman, M., Berlin, A. and Stenberg, P. (2025) ‘Sequencing airborne DNA to monitor crop pathogens and pests’, iScience, 28(7), p. 112912. doi: 10.1016/j.isci.2025.112912. Nousias, O., McCauley, M., Stammnitz, M. R., Farrell, J. A., Koda, S. A., Summers, V., Eastman, C. B., Duffy, F. G., Duffy, I. J., Whilde, J. and Duffy, D. J. (2025) ‘Shotgun sequencing of airborne eDNA achieves rapid assessment of whole biomes, population genetics and genomic variation’, Nature Ecology & Evolution, 9(6), pp. 1043–1060. doi: 10.1038/s41559-025-02711-w. Pattis, I., Weaver, L., Burgess, S., Ussher, J. E. and Dyet, K. (2022) ‘Antimicrobial Resistance in New Zealand-A One Health Perspective’, Antibiotics, 11(6), p. 778. doi: 10.3390/antibiotics11060778. Pavlopoulos, G.A., Oulas, A., Iacucci, E., Sifrim, A., Moreau, Y., Schneider, R., Aerts, J., Iliopoulos I (2013) ‘Unraveling genomic variation from next generation sequencing data’, BioData Mining, 6(1), p. 13. doi: 10.1186/1756-0381-6-13. Xamxidin, M., Zhang, X., Zheng, G., Chen, C. and Wu, M. (2025) ‘Metagenomics-assembled genomes reveal microbial metabolic adaptation to athalassohaline environment, the case Lake Barkol, China’, Frontiers in Microbiology, 16, p. 1550346. doi: 10.3389/fmicb.2025.1550346. Acknowledgements This free course was written by Dr Corrado Minetti. Except for third party materials and otherwise stated (see terms and conditions), this content is made available under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 Licence. The material acknowledged below is Proprietary and used under licence (not subject to Creative Commons Licence). Grateful acknowledgement is made to the following sources for permission to reproduce material in this free course: Text Activity 2: Extract from: Kilonzo-Nthenge, A., Rafiqullah, I., Netherland, M. Jr., Nzomo, M., Mafiz, A., Nahashon, S. and Hasan, N. A. (2024) ‘Comparative metagenomics of microbial communities and resistome in southern farming systems: implications for antimicrobial stewardship and public health’, Frontiers in Microbiology, 15, p. 1443292. doi: 10.3389/fmicb.2024.1443292. an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0) Activity 3: Extract quotes from: open access article Nousias, O., McCauley, M., Stammnitz, M. R., Farrell, J. A., Koda, S. A., Summers, V., Eastman, C. B., Duffy, F. G., Duffy, I. J., Whilde, J. and Duffy, D. J. (2025) ‘Shotgun sequencing of airborne eDNA achieves rapid assessment of whole biomes, population genetics and genomic variation’, Nature Ecology & Evolution, 9(6), pp. 1043–1060. doi: 10.1038/s41559-025-02711-w. Figures Course image: Pixabay Figure 1: (interactive) Pixabay Figure 2: adapted courtesy: AAT Bioquest Home | AAT Bioquest Figure 3: The History of DNA Sequencing in Microbe Notes Metagenomics: Principle, Types, Steps, Uses, Examples, Diagram July 6, 2024 by Sanju Tamang https://microbenotes.com/metagenomics/ Figure 4: in Pavlopoulos, G.A., Oulas, A., Iacucci, E. et al. Unraveling genomic variation from next generation sequencing data. BioData Mining 6, 13 (2013). https://doi.org/10.1186/1756-0381-6-13 Figure 5: compiled using public domain illustration and Clipart Figure 6: (interactive) Pixabay Figure 7: Schematic of how AMR bacteria can transmit between humans, animals, and the environment Figure 1. in Antimicrobial Resistance in New Zealand—A One Health Perspective by Isabelle Pattis, Louise Weaver, Sara Burgess, James E. Ussher, Kristin Dyet https://www.mdpi.com/2079-6382/11/6/778 Figure 8: (interactive) left to right: U.S. Air Force photo by Jill Pickett https://commons.wikimedia.org/wiki/File:221205-F-KN521-0064.jpg U.S. Navy photo https://commons.wikimedia.org/wiki/File:US_Navy_050910-N-2653P-132_Forward_Deployable_Preventive_Medicine_Unit_(FDPMU)_East,_removes_a_Light_Trap_provided_by_the_Centers_for_Disease_Control_(CDC)_from_a_tent_city_area_on_board_NAS_JRB_New_Orleans.jpg Mongan, A. E., Tuda, J. S. B. & Runtuwene, L. R. (2020) ‘Portable sequencer in the fight against infectious disease’, Journal of Human Genetics 65, pp. 35–40 . https://doi.org/10.1038/s10038-019-0675-4 https://creativecommons.org/licenses/by/4.0/deed.en Figure 9: (interactive) left to right: Wouter Hagens https://commons.wikimedia.org/wiki/File:Eichhornia_crassipes_C.jpg Bj.schoenmakers https://commons.wikimedia.org/wiki/File:Dreissena_polymorpha_(Zebra_mussel),_Arnhem,_the_Netherlands.jpg Gordon H. Rodda https://commons.wikimedia.org/wiki/File:Brown_tree_snake-Boiga_irregularis.jpg Figure 10: In Amanda Mikko1 · Jose Antonio Villegas2 · Daniel Svensson2 · … · Mats Forsman3 · Anna Berlin5 · Per Stenberg2,3,6 https://www.cell.com/iscience/fulltext/S2589-0042(25)01173-3?uuid=uuid:ab4be347-7063-487c-9438-131757c7e079 Volume 28, Issue 7112912 July 18, 2025 Copyright: © 2025 The Author(s). Published by Elsevier Inc. Deed - Attribution 4.0 International - Creative Commons Every effort has been made to contact copyright owners. If any have been inadvertently overlooked, the publishers will be pleased to make the necessary arrangements at the first opportunity. Don’t miss out If reading this text has inspired you to learn more, you may be interested in joining the millions of people who discover our free learning resources and qualifications by visiting The Open University – www.open.edu/openlearn/free-courses.

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