**9.3.1 Temporal development of the infant gut microbiota**

Based on signals from the GA-map array we determined the temporal development of the gut microbiota in the infants. These results are pressented in Figure 6.

The prevalence data showed that proteobacteria and lactobacilli reached a maximum between one month and one year, while the bacteroides subgroup containing B. fragilis reached a maximum after one to two years. Surprisingly, we found a high prevalence of bifidobacteria for infants older than one month. This is in contrast to another recent microarray screening of the infant gut microbiota, where they found that bifidobacteria were underrepressented (Palmer et al., 2007). This demsontrates the importance of using an optimal primer pair in the amplification of the 16S rRNA gene.

### **9.3.2 Modelling age as a function of the microbiota composition**

We used the temporal information in the gut microbiota to determine if it is possible to describe age as a function of the composition. This was done using generalized additive models (GAM).

The following function was derived:

age = s(E.coli) + s(Clostridium) + s(Staphylococcus) + s(Bif.breve) + s(Bacteroides.dorei.vulg.theta.frag.)

The functions s to the data are shown in Figure 7.

**9.3 Application of the GA-map array to describe the temporal development of the gut** 

We have evaluated the recently developed GA-map infant microarray as a high throughput assay for screening of the gut microbiota. We analyzed 216 faecal samples collected from a cohort of 47 infants from 1 day until 2 years of age. To test the predictive ability of the assay we asked the question whether we could predict the age of the infants based on the

The Prevention of Allergy Among Children in Trondheim (PACT) study is a large population based intervention study in Norway focused on childhood allergy (Oyen et al., 2006). The samples included here is a subset from the PACT study. Mechanical lysis was used for cell disruption, and an automated magnetic bead-based method was used for DNA

We experienced that the primer pairs commonly used for amplification of the full-length 16S rRNA gene showed poor amplification of bifidobacteria. To circumvent this problem we developed a novel primer pair to obtain a near full-length 16S rRNA universal amplicon. The amplicon was evaluated both theoretically based on sequences in the RDP II database, and experimentally for bacterial species expected in the infant gut. We found that all the currently known infant gut bacteria were amplified with this new, optimized primer pair. A primer pair that is able to representably amplify the 16S rRNA gene from all the bacteria

Based on signals from the GA-map array we determined the temporal development of the

The prevalence data showed that proteobacteria and lactobacilli reached a maximum between one month and one year, while the bacteroides subgroup containing B. fragilis reached a maximum after one to two years. Surprisingly, we found a high prevalence of bifidobacteria for infants older than one month. This is in contrast to another recent microarray screening of the infant gut microbiota, where they found that bifidobacteria were underrepressented (Palmer et al., 2007). This demsontrates the importance of using an

We used the temporal information in the gut microbiota to determine if it is possible to describe age as a function of the composition. This was done using generalized additive

> age = s(E.coli) + s(Clostridium) + s(Staphylococcus) + s(Bif.breve) + s(Bacteroides.dorei.vulg.theta.frag.)

purification. The approach is previously described by Skånseng et al. (2006)

present in the sample is critical for proper analysis of the sample.

gut microbiota in the infants. These results are pressented in Figure 6.

**9.3.1 Temporal development of the infant gut microbiota** 

optimal primer pair in the amplification of the 16S rRNA gene.

**9.3.2 Modelling age as a function of the microbiota composition** 

**microbiota in infants** 

microarray data.

models (GAM).

The following function was derived:

The functions s to the data are shown in Figure 7.

Fig. 6. The prevalence of the G-map bacteria was determined within age groups. The color code indicates the prevalence from absent (white) to present in all samples (black).

Fig. 7. Bacteria which are important for age prediction. For each bacterium the age contribution is a function of probe signal. Adding all the age contributions gives the predicted age (see function above). The final panel shows a regression between the observed and predicted ages for all the samples in our data.

Gut Microbiota in Disease Diagnostics 115

Table 1. Single probe GA-map diagnostics of CD (Frøyland, 2010)

Many medical doctors find that an analysis of a patients gut microbiota is helpful in obtaining a more complete picture of the condition of the patient, and thus determining the disease state and best treatment. Several such tests exists in the marketplace. The GA-map technology is positioned to become a very powerful test for this purpose, with its complex

The general process for the application of the GA-map assay for diagnostic purposes is

After implementation of the pilot diagnostic assays, new approaches and diseases will be implemented under the GA-map umbrella. There will also be a transformation over time

There is currently a major focus on exploring the gut microbiota. This is mainly done through the application of explorative techniques such as next generation sequencing. The next phase will be the validation where the discoveries are validated using targeted techniques such as microarray analyses or quantitative PCR. The final phase will be to identify correlations between microbiota and disease that will give some added value

**9.4.3 Test for assessing health condition of individuals** 

**10. Future directions of gut microbiota diagnostics** 

probe selection and high through-put capabilities.

illustrated in Figure 8.

towards decentralized analyses.

High E. coli and staphylococci abundance predict an early age, while for the bacteroidetes and B. breve a medium abundance predicts a high age (Fig. 7 B and D). However, very high abundance predicts an early age. The Clostridium probe, on the other hand, shows a complex association with age.

Figure 7 shows that the age can be modelled with high accuracy from the microbiota composition.We found that the observed and modelled age gave a squared regression coefficient of 0.6. These results show that the development of the human gut microbiota is very structured with age, and that it can be predicted. The consequence is that it could potentially be possible to determine the normal development of the microbiota in infants, and use deviations from the normal pattern in identifying disorders. For instance, from our results we can conclude that high levels of staphylococcus for older children would clearly indicate some kind of deviation form the normal development, and indicate some kind of diseased state.
