Summary: A new method for studying the mircobiome has allowed researchers to identify a connection between metabolism in gut bacteria and the development of diabetes.
Source: Orebro University.
Researchers at Örebro University have, together with a well-known research team in Denmark, developed a method for studying how metabolism in gut bacteria influences health. Their method will now be published in its entirety in the scientific journal Nature Protocols.
The method, which researchers Tuulia Hyötyläinen and Matej Orešič have worked on and developed for more than ten years, is used for metabolomics studies—where information about thousands of molecules involved in the cells’ metabolism is retrieved by means of chemical analysis. Using this method, researchers have been able to, among other things, study the connection between metabolism in gut bacteria and the development of diabetes, a study which was published in Nature 2016.
“But the method may be used by all researchers wanting to apply metabolomics in their studies,” says Matej Orešič, researcher in medicine at Örebro University.
Scientific methods as important as results
By applying this method used by the Örebro researchers, it is possible to analyse some 2,000 metabolites from one blood sample. Metabolites are microscopic molecules, for example amino acids, lipids and sugar molecules, formed as a result of metabolism.
“Collecting data is an important part of the analysis but doesn’t always require as much time as the actual data analysis. That’s when this large amount of collected data will need to be linked to with biological and medical questions,” says Tuulia Hyötyläinen, Professor of Chemistry at Örebro University.
In the published article, the working method protocol is described in detail. As is often the case, the method section of a scientific article is difficult for other researchers to reproduce, especially in complex studies with extensive data quantities.
“Scientific methods are as important as research results. It is important to use reliable methods to produce data of high quality,” explains Matej Orešič.
Preventing gluten intolerance
The research duo is now working on identifying how metabolism in young children can affect the development of gluten intolerance later in life.
“We have seen changes in the metabolism of lipids in babies before they have been exposed to gluten via their diet. This discovery can perhaps lead to a better understanding of the development of gluten intolerance and perhaps help us to prevent the disease,” adds Matej Orešič.
They are also investigating the relationship between exposure to various environmental pollutants in early life and the development of type 1 diabetes.
“We are trying to find biomarkers which may be used in the diagnosis of various diseases and at an earlier stage than is currently possible. It is also interesting to study which metabolites that are produced by gut bacteria and how these then influence our metabolism,” says Tuulia Hyötyläinen.
“We are working continuously on improving the entire analysis chain, from taking specimens to data analysis. And now other researchers may also use our method,” adds Matej Orešič.
About this neuroscience research article
Source:Orebro University Publisher: Organized by NeuroscienceNews.com. Image Source: NeuroscienceNews.com image is in the public domain. Original Research:Abstract for “A computational framework to integrate high-throughput ‘-omics’ datasets for the identification of potential mechanistic links” by Helle Krogh Pedersen, Sofia K. Forslund, Valborg Gudmundsdottir, Anders Østergaard Petersen, Falk Hildebrand, Tuulia Hyötyläinen, Trine Nielsen, Torben Hansen, Peer Bork, S. Dusko Ehrlich, Søren Brunak, Matej Oresic, Oluf Pedersen & Henrik Bjørn Nielsen in Nature Protocols. Published October 31 2018. doi:10.1038/s41596-018-0064-z
Cite This NeuroscienceNews.com Article
[cbtabs][cbtab title=”MLA”]Orebro University”Connection Between Intestinal Bacteria and Diabetes Development.” NeuroscienceNews. NeuroscienceNews, 8 November 2018. <https://neurosciencenews.com/microbiome-diabetes-10167/>.[/cbtab][cbtab title=”APA”]Orebro University(2018, November 8). Connection Between Intestinal Bacteria and Diabetes Development. NeuroscienceNews. Retrieved November 8, 2018 from https://neurosciencenews.com/microbiome-diabetes-10167/[/cbtab][cbtab title=”Chicago”]Orebro University”Connection Between Intestinal Bacteria and Diabetes Development.” https://neurosciencenews.com/microbiome-diabetes-10167/ (accessed November 8, 2018).[/cbtab][/cbtabs]
A computational framework to integrate high-throughput ‘-omics’ datasets for the identification of potential mechanistic links
We recently presented a three-pronged association study that integrated human intestinal microbiome data derived from shotgun-based sequencing with untargeted serum metabolome data and measures of host physiology. Metabolome and microbiome data are high dimensional, posing a major challenge for data integration. Here, we present a step-by-step computational protocol that details and discusses the dimensionality-reduction techniques used and methods for subsequent integration and interpretation of such heterogeneous types of data. Dimensionality reduction was achieved through a combination of data normalization approaches, binning of co-abundant genes and metabolites, and integration of prior biological knowledge. The use of prior knowledge to overcome functional redundancy across microbiome species is one central advance of our method over available alternative approaches. Applying this framework, other investigators can integrate various ‘-omics’ readouts with variables of host physiology or any other phenotype of interest (e.g., connecting host and microbiome readouts to disease severity or treatment outcome in a clinical cohort) in a three-pronged association analysis to identify potential mechanistic links to be tested in experimental settings. Although we originally developed the framework for a human metabolome–microbiome study, it is generalizable to other organisms and environmental metagenomes, as well as to studies including other -omics domains such as transcriptomics and proteomics. The provided R code runs in ~1 h on a standard PC.