Bacterial colonization of the airways during the first year of life

The latest paper from the group is out in Cell Host & Microbe:

The Infant Nasopharyngeal Microbiome Impacts Severity of Lower Respiratory Infection and Risk of Asthma Development
Shu Mei Teo, Danny Mok, Kym Pham, Merci Kusel, Michael Serralha, Niamh Troy, Barbara J. Holt, Belinda J. Hales, Michael L. Walker, Elysia Hollams, Yury A. Bochkov, Kristine Grindle, Sebastian L. Johnston, James E. Gern, Peter D. Sly, Patrick G. Holt, Kathryn E. Holt*, Michael Inouye*


Also see preprint in BioRxiv

Graphical abstract

Basically, what we did is to perform 16S rRNA sequencing (V4 region, via MiSeq) on post-nasal aspirates (basically snot washed out of babies’ noses) collected from children as part of a cohort study called the Childhood Asthma Study run by the Telethon Kids institute in Perth, Western Australia.

Samples were collected at around 2 months, 6 months and 12 months of age, during periods of respiratory health (i.e. no symptoms of respiratory illnesss for at least 1 month). Also, every time a baby developed symptoms of respiratory illness, a study nurse would come and examine the child and take another sample. The snots were divided into 4 aliquots each and cryofrozen until last year, when one aliquot each was thawed for DNA extraction.

The 16S data showed very simple communities of bacteria in each sample… basically each sample was dominated by a single genus, either Staphylococcus, Corynebacterium, Alloiococcus, Moraxella, Streptococcus, or Haemophilus. Temporal colonization patterns indicated that most children were initially colonized with skin-associated organisms (Staphylococcus or Corynebacterium), which was replaced during the first year of life by stable colonization with Alloiococcus or Moraxella.


Amongst the samples taken during episodes of respiratory illness, we found that Moraxella, Streptococcus, or Haemophilus were very common. We also found the presence of these bacterial genera was associated with increased severity of symptoms of respiratory illness – samples with these bacteria were more likely to be from infections that spread to the lower respiratory tract (i.e. where the child had wheeze or rattly chest) and experienced fever (signalling an inflammatory response).

Importantly, we found that the children who experienced lower respiratory illness with fever were more likely to be chronic wheezers (i.e. asthmatic) at the age of 5 or 10 years, especially if they became sensitised to aeroallergens during their second year of life.

This suggests that Moraxella, Streptococcus, or Haemophilus colonization in infancy may be risk factors for severe respiratory illness and later asthma development. This could potentially explain earlier studies showing that colonization with M. catarrhalis, S. pneumoniae or H. influenzae (detected by culture) at the age of 1 month is associated with increased risk of asthma in childhood.

Many more details are available in the paper! http://www.cell.com/cell-host-microbe/abstract/S1931-3128(15)00125-0

Clarifier: Bacterial populations and communities

[This was originally posted by Kat on her BacPathGenomics blog, April 2011]

Two areas where next-gen sequencing is making a big impact in the bacterial world are the analysis of ‘bacterial populations’ and ‘bacterial communities’. While these might sound similar, they are actually very different.

In common parlance we sometimes use ‘population’ and ‘community’ somewhat interchangeably in talking about groups of humans. We might say that in Melbourne, coffee drinking is common in the local population, or that it is common in the local community. What we mean is that it’s common among people living in Melbourne.

In biology, the term ‘population’ has a specific meaning – a group of individuals of the same species (i.e. able to interbreed; but note this concept is complex in bacteria), defined by time and space. So we can talk about the currrent human population of the Earth, or the human population of Melbourne 20 years ago. Note that this is intimately tied up with the concept of species, as separation into two distinct populations is a key step towards diverging into different species. On the other hand, ‘community’ refers more generally to the group of organisms inhabiting a particular ecological niche, which could include any number of species. So for example we could talk about the population of karri trees (a species of eucalyptus found in the south west of Western Australia), or the community of plants inhabiting the karri forrest.

Bacterial populations

When we talk about bacterial populations, what we mean is investigating the population structure of a particular bacterial species/subtype… in theory this aims to understand the population in its entirety, but in practice usually involves studying lots of individual members of the population and making inferences about the population as a whole. We can attempt to understand populations at different levels of localisation…. e.g. we can study a highly localised population, like the population of Salmonella Typhi inhabiting the gall bladder of a typhoid carrier; or more expansive populations of Salmonella Typhi circulating in a city, a country or around the globe.

Sequencing has been a great tool for understanding bacterial populations, by allowing lots of individual members of a population (i.e. individual bacterial isolates or colonies) to be compared at the sequence level. Sequence data is ideal for this, as the differences between individuals are often tiny  (i.e. there is very little variation) since they belong to a single population, and DNA sequence data allows us to detect single nucleotide changes (ie provides high resolution). Also, since we have well-developed models of sequence evolution (ie how nucleotide changes accumulate), sequence data can be interpreted using phylogenetic analysis. This really kicked off a decade ago with multi-locus sequence typing (MLST; see wikipedia entry(!) or Maiden et al, 1998 for more info) and is now expanding rapidly with the advent of sequencing platforms that allow whole genomes of hundreds of isolates to be sequenced (e.g. 96 bacterial isolates can be readily sequenced in a single run of the Illumina HiSeq, using multiplexing).

This kind of analysis can be used in public health microbiology and infectious disease epidemiology to trace outbreaks or transmission (sometimes called molecular epidemiology or genomic epidemiology). It can also be used to study the evolution of drug resistance or pathogenesis/virulence in bacterial populations (microevolution, since it is occurring within populations), or the impact of a novel vaccine or drug on a given bacterial population, all of which can be useful for designing and monitoring public health interventions or making treatment recommendations.

A great recent example is the study by Nick Croucher (an immensely talented PhD student) from the Sanger Institute, and numerous collaborators, who compared the genomes of 240 Streptococcus pneumoniae isolates of the PMEN1 subtype, collected from all over the world since 1984. By comparing the genomes of these isolates, they found evidence of frequent homologous recombination with other S. pneumoniae, including exchange of genes encoding the capsule targeted by vaccination and acquisition of drug resistance genes. Assuming the sequenced isolates are reasonably representative of the global population of S. pneumoniae PMEN1, this indicates that the PMEN1 population is not isolated from the rest of the S. pneumoniae population but that there is constant gene exchange within and between S. pneumoniae groups, allowing the bacteria to escape the effects of human interventions including vaccine-induced immunity and exposure to antimicrobial drugs. We already ‘knew’ this could happen in bacterial pathogen populations, but this study provides direct evidence of it occurring in response to a specific vaccine and specific drugs used for treatment. See pubmed entry, unfortunately you need access to Science magazine to read the article.

Bacterial communities

On the other hand, when we talk about bacterial communities, what we mean is investigating the communities of bacteria present in a given sample. This is akin to walking through the forest and taking note of each plant you see, and the analysis methods borrow heavily from ecology. Studies of bacterial communities are being done in just about every kind of sample in which you would expect to find bacteria – from environmental samples (e.g. underwater caves; windscreen splatter) to human body sites (faeces or the gut; skin; nasal passages; read more at the Human Microbiome Project site).

The analysis usually focuses on determining which bacterial taxa (e.g. a genus, species or subgroup) were present in each sample, and their relative abundance. These can be compared across samples to identify taxa that are only present in certain kinds of environments, or whose presence is associated with another property of the sample (e.g. presence in the nose may be associated with development of otitis media). Communities can be examined more holistically to identify broad differences in the bacterial community structures associated with different samples.

Sequencing has dramatically improved the ease with which bacterial communities can be studied, via sequencing of DNA extracted from a given sample (e.g. a soil sample; a fecal sample). Two approaches are possible – sequence the raw DNA extract or amplify a conserved bacterial gene (using PCR) and sequence that. The first is true ‘metagenomics’, as you are sequencing all of the genomes present in the original sample, but this takes a lot sequencing effort and you may not need or want to know every single gene present in the sample. At the moment, Illumina platforms are most appropriate for this application as they have the highest throughput, however their short read lengths make assembly and analysis difficult. The second way, which usually targets the conserved 16S ribosomal RNA gene (‘16S sequencing’), is a more tractable way of determining what species/subgroups of bacteria are present in the sample and estimating their relative abundance. Multiplexing can be achieved by incorporating sample-specific barcodes into the amplicons during PCR, allowing hundreds of samples to be analysed in a single sequencing run.