Gut bacteria in young diabetic kids show differences
Gut Bacteria in Young Diabetic
Kids Show Differences
DNA microarray principle
• 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.
• 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.
• 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-
• 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.
• 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
• 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
• 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.
• 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
• Production of sufficient butyrate by bacteria in the
gut leads to optimal gut function and
prevents/minimizes inflammation and other
• 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
• 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."
• 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
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
• DNA microarray principle
• DNA microarrays allow for rapid
measurement and visualization of differential
expression between genes at the whole genome
• If technique implementation is quite
complicated, it’s principle is very easy.
• Here are described the major steps involved in
• Microarray production process
• Target preparation
• Slide scanning
• Data analysis Expression profile clustering
• Microarray production process:
• DNA fragments amplified by PCR technique are spotted on
a microscopic glass slide coated with polylysine prior to
• 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:
• 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
• On this stage, DNA from the first culture with a green dye,
whereas DNA from the second culture is labeled with a red
• 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.
•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
• Then obtained two images
where grey scales represent
fluorescent intensities read
• Data analysis:
• Obtained now two microarray images from which we have to
calculate the number of DNA molecules in each experimental
• 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
• 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.
• 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).