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Gut bacteria in young diabetic kids show differences


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Gut bacteria in young diabetic kids show differences

  1. 1. Gut Bacteria in Young Diabetic Kids Show Differences and DNA microarray principle analyzes
  2. 2. • New research published in Diabetologia (the journal of the European Association for the Study of Diabetes) shows that children diagnosed with type 1 diabetes have a less balanced composition of gut bacteria compared with children of the same age without diabetes. • The research is by Dr Marcus de Goffau and Dr Hermie Harmsen, University Medical Center Groningen, the Netherlands, and colleagues. • The incidence of type 1 diabetes is increasing worldwide, showing a particularly sharp increase among children under the age of 5 years. • Recent studies indicate that adverse changes in gut microbiota are associated with the development of type 1 diabetes, but little is known about the microbiota in children who have diabetes at an early age. • Thus in this new study the microbiota of children aged 1 years with new-onset type 1 diabetes was compared with the microbiota of age-matched healthy controls.
  3. 3. • A deep global analysis of the gut microbiota composition was done by phylogenetic microarray analysis using a Human Intestinal Tract Chip (HITChip), an analytical device designed specifically for studying gut bacteria ( I will give summary about this technique in the next slide). • Patients were recruited into two research projects – the DIPP (Finnish Type 1 Diabetes Prediction and Prevention) study in Finland and the international VirDiab (Viruses in Diabetes) study, which included cases and control children from seven European countries. • Faecal samples were collected from children newly diagnosed with type 1 diabetes and controls. DNA was successfully isolated from 28 diabetic children: four from France, one from Greece, three from Estonia, two from Lithuania and 18 from Finland.
  4. 4. • The diabetic children were matched with control children according to age; DNA was isolated successfully from 27 control children. • One of the control children was from Lithuania and the rest were from Finland. • The samples were collected from the diabetic children within 4 weeks of the diagnosis of diabetes and were coupled with samples from age- matched controls. • Samples were collected by the parents at home and shipped by mail at ambient temperature to the laboratory, where they were subsequently stored at -75°C.
  5. 5. • The researchers found that in children younger than three years, the combined abundance of the class Bacilli (notably streptococci) and the phylum Bacteroidetes were higher in diabetic children, whereas the combined abundance of the important (usually beneficial) Clostridium clusters IV and XIVa was higher in the healthy controls(Clostridium Clusters IV and XIVa That Can Promote the Induction of Foxp3+ Regulatory Treg Cells in Gut as a Novel Strategy to Prevent and Treat Inflammatory Bowel Diseases).
  6. 6. • Controls aged three years and older were characterized by a higher fraction of butyrate- producing species within Clostridium clusters IV and XIVa than was seen in the corresponding diabetic children or in children from the younger age groups, while the diabetic children older than three years could be differentiated by having an unusually high microbial diversity. • An increased diversity is often associated with unstable or with unusual bacterial networks; in children with celiac disease or adults with colorectal cancer an abnormally high microbial diversity is found.
  7. 7. • The authors discuss that the ideal scenario for the gut is to have the right balance of bacteria to produce the fermentation product butyrate, which is readily absorbed by the human gut and turned into energy. • Production of sufficient butyrate by bacteria in the gut leads to optimal gut function and prevents/minimizes inflammation and other metabolic problems. • The authors explain that, as the gut microbiota of very young children (1-3 years) is still developing very rapidly, the proper kind of balance to produce butyrate is not yet exactly the same as it is when children are older than 3 years. • The authors also recalculated their results without the non-Finnish children to adjust for any geographical differences, and their main findings remained unchanged.
  8. 8. • They say: "The results from both age groups suggest that non-diabetic children have a more balanced microbiota in which butyrate-producing species appear to hold a pivotal position. • Although distinct differences have been found in each age category between the healthy and diabetic children, the main differences with regard to Clostridium clusters IV and XIVa appear to represent two sides of the same coin, as they together emphasize the importance of developing balanced bacterial cross-feeding complexes that have sufficient potential for butyrate formation." • They add: "Dietary interventions aimed at achieving or maintaining optimal butyrate production levels might measurably reduce the risk of developing type 1 diabetes, especially in children with genetic risk for developing type 1 diabetes."
  9. 9. • The authors say more work needs to be done on establishing exactly what foods are best for promoting ideal gut conditions, however they conclude: "We think a diet high in fruits and vegetables is best as these are rich in fibre/complex carbohydrates, which are important because butyrate- producing species are dependent upon them indirectly via cross-feeding relations with fibre degraders. • Simple sugars, on the other hand, cause an overabundance of species which are very proficient in quickly utilizing sugars— Streptococci for example—thus outcompeting or limiting the amount of species which are beneficial for human health. • Excessive protein and animal fat consumption might similarly indirectly negatively affect butyrate production as they stimulate non-butyrate-producing species which are very good in utilizing this type of food source (such as Bacteroides).”
  10. 10. DNA microarray principle analyzes • Introduction to the transcriptome platform • The Transcriptome Platform form the Biology Department Genomic Service (SGDB) at the E. coli Normal Superior has been created in 1998 by laboratories from ENS, Curie Institute and ESPCI, during the Ile de France genopole development. • The transcriptome platform is currently proposing DNA microarrays from "yeast and guts bacteria " (pan-genomic) and "mouse" (pan- genmoci and dedicated to nervous system development). • Platform produced slides distribution is done according to a scientific collaboration which terms are described in an agreement and a protocol • Although the transcriptome platform does not offer systematically to produce DNA microarrays to order, we are open to every project aiming at producing new types of microarrays (from other organisms or for a restricted collection of genes). • At this prospect, access conditions to the production facilities have to be discussed case by case with the platform head. • DNA microarrays principle • This slides shortly explain the various steps involved in a typical DNA microarray experiment.
  11. 11. • DNA microarray principle • DNA microarrays allow for rapid measurement and visualization of differential expression between genes at the whole genome scale. • If technique implementation is quite complicated, it’s principle is very easy. • Here are described the major steps involved in this process • Microarray production process • Target preparation • Hybridization • Slide scanning • Data analysis Expression profile clustering
  12. 12. • Microarray production process: • DNA fragments amplified by PCR technique are spotted on a microscopic glass slide coated with polylysine prior to spotting process. • The polylysine coating goal is to ensure DNA fixation through electrostatic interactions. • PCR fragments are in this case the expressed part (ORF) of the 6200 Saccharomyces cerevisae genes (baker yeast). • Slide preparation is achieved by blocking the polylysine not fixed to DNA in order to avoid target binding. • Prior to hybridization, DNA is denatured to obtained a single strand DNA on the microarray, this will allow the probe to bind to the complementary strand from the target. • Apart from glass slide microarray other types of chips exist:
  13. 13. • Target preparation: • RNA are extracted from two yeast cultures from which we want to compare expression level. • Messengers RNA are then transformed in cDNA by reverse transcription. • On this stage, DNA from the first culture with a green dye, whereas DNA from the second culture is labeled with a red dye. • Hybridization: • Green labeled cDNA and red labeled ones are mixed together (call the target) and put on the matrix of spotted single strand DNA (call the probe). • The chip is then incubated one night at 60 degrees. • At this temperature, a DNA strand that encounter the complementary strand and match together to create a double strand DNA. • The fluorescent DNA will then hybridize on the spotted ones.
  14. 14. •If replace grey scales by green scales for the first image and red scales for the second one, It obtained by superimposing the two images one image composed of spots going from green ones (where only DNA from the first condition is fixed) to red (where only DNA from the second condition is fixed) passing through the yellow color (where DNA from the two conditions are fixed on equal amount). •Slide scanning: A laser excites each spot and the fluorescent emission gather through a photo- multiplicator (PMT) coupled to a confocal microscope. • Then obtained two images where grey scales represent fluorescent intensities read
  15. 15. • Data analysis: • Obtained now two microarray images from which we have to calculate the number of DNA molecules in each experimental condition. • To do so, we measure the signal amount in the green dye emission wavelength and the signal amount in the red dye emission wavelength. • Then normalize these amount according to various parameters (yeast amount in each culture condition, emission power of each dye, …). • It suppose that the amount of fluorescent DNA fixed is proportional to the mRNA amount present in each cell at the beginning and we calculate the red/green fluorescence ratio. • If this ratio is greater than 1 (red on the image), the gene expression is greater in the second experimental condition, if this ration is smaller than 1 (green on the image), the gene expression is greater in the first condition. • It can visualize these differences in expression using software as the one developed in the laboratory call ArrayPlot (cf below image). • This software allows from the intensities list of spot to display the red intensities of each spot as a function of the green intensities.
  16. 16. • Expression profile clustering: • Then we can try to gather genes that share the same expression profile on several experiments. • This clustering can be done gradually as for phylogenetic analysis, which consist in calculating similarity criteria between expression profiles and gather the most similar ones. • It can also use more complex techniques as principal component analysis or neuronal networks. • At the end hierarchical clustering is usually displayed as a matrix where each column represent one experiment and each row a gene. • Ratios are displayed thanks to a color scale going from green (repressed genes) to red (induced genes).