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Environmental genomics - why microbes matter

Microbial genome size is used as a tool for understanding the ecological and evolutionary forces acting on microorganisms within an environment.

But why does size matter when looking at bacteria? From an ecological perspective, microbial genome size (the total amount of DNA contained within one copy of a single genome) may reflect environmental complexity, metabolic lifestyle and/or community niche. And that may make a good environmental predictor.

Microbial genome size has been linked to environmental versatility, with some studies suggesting that having a larger genome size would allow the microorganisms to be adapted to more environmental conditions (generalist microorganisms). On the other hand, from an evolutionary perspective, reduced genome size may either reflect genetic drift in small populations or genome streamlining in large populations.  

Carmen and her sampling group

Carmen Astudillo-Garcia and her sampling group in the field

Genomics Aotearoa postdoctoral fellow Carmen Astudillo-Garcia joined the Environmental Metagenomics team to investigate whether the genetic complexity of microbial life varies along environmental gradients, using microbial genome size as a proxy for metabolic versatility. Her aims include:

  • To confirm if average genome size varies among species across an environmental gradient (a gradual change in the physical or chemical properties of an ecosystem through space or time that affects living organisms and the functioning of the ecosystem)
  • To identify if differences in average microbial genome size are caused solely by differences in community composition, or if they are mediated by gene gain or loss that occurs within species across environmental gradients.

To do this, she is investigating the effects of the salinity gradient generated across a river-estuary system on the microbial communities present in both the water column and sediment.

Analysing genomic data from an ecological point of view can provide a more comprehensive understanding of the structure and functioning of microbial communities. It can help to answer a fundamental question in evolutionary biology - how organisms adapt to their environment. So understanding the variation in genome size across the selected environmental gradient will increase our understanding on how microorganisms adapt to fluctuating environments by changing their genes and genomes. This knowledge will be crucial to predict how future environmental change will affect microbial species diversity and, therefore, ecosystem productivity. 

Carmen Astudillo-Garcia

Carmen Astudillo-Garcia

Carmen’s research interests include investigating ecological and biogeographical patterns affecting microbial communities and, more specifically, the exploration of latitudinal and environmental gradients in microbial genome size. As a FRDF postdoctoral research fellow at the University of Auckland in the lab of Associate Professor Gavin Lear, she is also investigating global latitudinal gradients of microbial richness and average microbial genome size.

Carmen's previous career

Carmen completed her BSc in Environmental Sciences and MSc in Environmental Science and Technology in Madrid, Spain. She then moved to New Zealand to join the School of Biological Sciences at the University of Auckland to do a PhD in the microbiology of marine sponges under the supervision of Associate Professor Mike Taylor. During her PhD, Carmen investigated the diversity, structure and function of microbial communities associated with different marine sponge species, utilising a multidisciplinary approach involving high-throughput DNA sequencing technologies, molecular biology techniques and ecological network analyses. After finishing her PhD, she worked for Watercare Laboratory Services Ltd, developing protocols to identify and quantify ammonia-oxidizing archaea and ammonia-oxidizing bacteria involved in nitrogen removal in wastewater treatment plants.

Carmen’s areas of expertise:

  • Microbial ecology
  • Ecological network analysis
  • (Meta)genomics and (meta)transcriptomics analysis
  • Metabolic pathways reconstruction