The primary application of the developed microarray was the detection of intestinal pathogens that cause gastroenteritis. Additionally, the chip should provide information on the resident intestinal flora and potentially probiotic bacteria. The Gastroenteritis-Chip was tested with clinical samples of patients and healthy individuals to compare its performance and value as diagnostic tool with current clinical methods. However, this comparison is only possible in terms of pathogenic bacteria, which for the clinical laboratories routinely test, but not the resident and potentially probiotic bacteria. The amount of available samples was not as high as desired, because a direct clinical access was not given in our institute.
Furthermore, the Gastroenteritis-Chip was used to investigate faecal samples from children and a trial with piglets with not array-detectable or unknown pathogen.
Multivariate analysis was applied to investigate, whether the intestinal flora was significantly changed by infection with bacterial or viral pathogens and whether the human and pig flora had established in the inoculated piglets. Three independent methods of multivariate analysis were applied, that is to say principle component analysis (PCA), partial least squares analysis (PLSA), and one-way analysis of variance (ANOVA).
PCA and PLSA are popular methods to analyze complex biological data (Franklin 1999;
Wang 2004b; Zhang 2009b) and have been already applied for the analysis of microarray data. However, according to literature survey this application was restricted to expression microarrays only (Fernandez-Perez 2010; Kim 2007; Jonnalagadda 2008; de Haan 2009).
Community changes upon intestinal infections were also studied using multivariate analysis for DGGE and clone library data (Feng 2010; Zhang 2009b). Here, the first application of PCA and PLSA for the analysis of microarray data regarding the intestinal community composition was shown.
PCA analyzes data without previous knowledge about classification of samples. The calculated principle components are sorted according to their ability to predict group affiliation. This has the advantage of being independent from the investigators intention. If a classification of samples according to biological characteristics can be seen in PCA and the principle components explain considerable amount of variation in the data, the predicted
130 difference is probably significant. However, separation of data by PCA is sometimes not very clear. PLSA is superior towards PCA because it solves both, classification and regression problems (Zhang 2009b; Rudi 2004; Idborg 2005). In PLSA, the data are analyzed under the knowledge of sample classification. The results in this work support this observation, as groups were separated slightly better by PLSA than PCA.
ANOVA in its simplest form is a statistical test to compare whether the means of several data sets are significantly different. It was published that in finding the important variables for group differentiation, PLS outperforms classical ANOVA (Zhang 2009b). This was not supported by this work. Here, PLSA generally picked more variables than necessary for a model with highest accuracy. One-way ANOVA usually found only the necessary variables.
Only in one case, it selected one additional variable, which reduced PLS model accuracy.
This observation suggests combining both methods to make reliable predictions.
4.3.1 Clinical samples from gastroenteritis patients and healthy volunteers The identification of pathogens is the most important task of the developed microarray. The analysis of clinical faecal isolates with the final array allowed the correct identification of 67%
of the samples with a clinically confirmed bacterial pathogen. Unfortunately, one from four (25%) Campylobacter-, three from eight (38%) Clostridium toxin-, one (100%) Yersinia-, and six from eighteen (33%) Salmonella-positive samples were not correctly identified. This was not satisfying and might have several reasons. The present array had a detection limit of 103 genome equivalents after DNA isolation. The infectious dose can be less than 103 cells (Tab.
1.2). When comparing the Ct values from real-time PCR and the detection by microarray, it can be noted, that mainly samples with high Ct values (low pathogen numbers) were not identified by the microarray. Perhaps, the pathogen number was lower than the detection limit of the microarray. In case of Salmonella spp. with one exception infections with Ct<28.1 were identified but samples with higher Ct values were not. The Campylobacter-positive sample, which was not identified, had a Ct value of 29.41 in real-time PCR, whereas the correctly determined specimens had Ct values of 29.44, 24.89 and 19.66. For C. difficile, the correlation was not given, but this might be related to the different targets, that is the toxin genes in real-time PCR and the 16S rRNA gene on the microarray.
Additionally, the DNA extraction and sample storage may have influence on the successful identification of intestinal pathogens. Previous attempts to amplify fragments longer than 1,000 bp from the isolated DNA failed due to highly degraded DNA. In this regard, especially low abundant species would be more affected in terms of percentages. The storage of the isolated DNA at -20°C accounted for several weeks a nd transport to the laboratory for 1-2 days. This might also be the reason for the weak or impossible amplification of five faecal DNA isolates. In contrast to the microarray approach, the detection by real-time PCR was based on amplification of shorter DNA fragments (~100 bp).
Missing some pathogens resulted in a relatively low clinical sensitivity. The sensitivity of microarrays for intestinal pathogens in real samples is a general problem. Jin et al. missed 11% of the pathogens in clinical samples with their array, which had a comparable detection limit (Jin 2006). However, another publication with the same detection limit stated 100%
sensitivity but the array had only 95% specificity (Mao 2008). Other authors of microarrays for pathogen detection did not validate their arrays with clinical samples (Kostic 2007; Li 2006).
Few samples displayed positive results for pathogens, which were not detected in the clinical routine. This lowered the clinical specificity to 89%, but as the clinical routine diagnostics was not corrected for potential failure, one has to judge this result carefully. Regarding sample S32, it is very likely that after positive identification of C. difficile, no further investigation for other potential pathogens was performed and a real co-infection with C. jejuni was present in this sample. In S50, where Salmonella ssp. was detected, the real situation remained unclear. One healthy individual, who was a staff member of the hospital, and two hospitalized persons also showed a false positive hybridization result for C. difficile, which is in low
131 amounts a normal inhabitant of the gut flora. In hospitals the occurrence of spores is much higher than elsewhere (Bartlett 2002), which might have resulted in a detectable colonization with C. difficile not secreting any toxin. False positive results, when referring to clinical diagnosis, were also found by other studies (Mao 2008). In principle, false positive results are a less critical problem than false negatives, but for an alone-standing diagnostic tool, they are not acceptable.
The false positive results for the Vibrio genus-specific probe in some faecal DNA isolates were supposed to be a product of undetected cross-reactivity of this probe with other intestinal bacteria. A Vibrio-infection was not detected in the hospital in either of the samples and the array gave no positive signals for the usual Vibrio-species, which cause intestinal infections. In-silico and during verification of the probes, no relevant cross-reactivity was found for the Vibrio-probe, but as there is only one Vibrio-probe on the chip, a positive result for this species is not as reliable as for all other species. A pathogen identification of a Vibrio spp. should therefore always require a positive signal of the respective species-specific probe sets. The positive results for the Mycobacterium genus-specific probe in some specimens were also not accompanied by signals for the M. avium species. In-silico no full matches with other intestinal organisms were found, but the 16S-probe had a 16nt match with Oryza sativa chromosome 5 and the 23S-probe a 16nt match with some E. coli sequences. This could have resulted in positive signals for this genus and illustrates the necessity of a multiple probe-concept as well as the importance to check for cross-reactivity with food components, although it is expected that most food components have been degraded through intestinal tract passage.
Analyzing the age distribution of patients with respect to the clinically confirmed pathogen revealed differences, but the significance had to be validated with higher sample numbers.
C. difficile was more prevalent in the age group >60 years (75%), while Salmonella spp. was found mainly in the younger patients of 4-17 years (71%). Campylobacter spp. was detected mainly in the middle ages of 18-60 years (75%). In case of C. difficile, this could be attributed to the higher hospitalization rate of elderly people, who are then exposed to this nosocomial pathogen.
The probes for the non-pathogenic, intestinal community on the Gastroenteritis-Chip detect dominant members of the intestinal community. Nonetheless, the observed individual species pattern was highly variable in all specimens and the most abundant bacterial species were generally detected more often than the less abundant ones. The most frequently detected species were Bacteroides spp., B. fragilis, Enterococcus, Veillonella, F. prausnitzii, Roseburia spp., and E. coli. It was published that in healthy individuals F. prausnitzii and related species account for 5.3 ± 3% to 16.5 ± 7% (Suau 2001) and R. intestinalis and related species for around 7% (Aminov 2006) of faecal bacteria. F. prausnitzii (clostridial cluster IV) and R. intestinalis (cluster XIV) immensely contribute to the intestinal butyrate formation (Barcenilla 2000; Duncan 2002). The Bacteroides spp. account for about 20% of the faecal flora (Franks 1998) and Veillonella spp. for about 0.1% (Harmsen 2002). The bifidobacteria proportion was determined to be 3% of faecal bacteria by Franks et al.
According to their investigation this genus is highly variable compared to other bacterial groups (Franks 1998). As most faecal DNA samples in this work were derived from ill individuals, it cannot be expected to detect all members of a healthy intestinal flora. The natural flora is negatively influenced by both the entered pathogen and the antibiotic therapy, if yet started. However, a diseased state due to an infection might have different influence on highly variable or dominant groups of bacteria and on stable or less abundant species.
Furthermore, Lactococcus lactis was identified, which is a normal inhabitant of the gut flora, a dairy starter culture and used as probiotic bacterium, was identified as well. According to literature, Enterobacteriaceae, including E. coli, account for about 0.1-0.2% and the Enterococcus/Lactobacillus group for 0.01% of faecal bacteria (Harmsen 2002). Although Atopobium spp. makes up for around 5% of faecal bacteria (Matsuki 2004), this genera was detected only in two samples of the patients in our study. Eubacterium biforme and L. acidophilus were both found in three faecal DNA isolates.
132 Multivariate analysis revealed no significant difference in the composition of the resident intestinal flora detected by microarray between healthy and hospitalized individuals. It can be regarded as problematic that the group of healthy individuals was very small compared to the group of gastroenteritis patients. Moreover, the group of gastroenteritis patients was inhomogeneous with respect to age and clinical diagnose. Each around 1/3 of the hospitalized persons belonged to one of the following age groups: 4-17 years, 18-60 years, and 61-86 years. Additionally, no pathogen was clinically confirmed in 22 samples (27 including not amplified samples) and four different bacterial pathogens were identified in 30 samples. Although detailed data about the patient’s background were not available, it can be supposed that the gastroenteritis had multiple reasons including also viral infections and perhaps inflammatory bowl diseases or irritable bowel syndrome. The influence on the intestinal background flora might be very different. However, performing PCA and PLSA with the samples of individuals with bacterial gastroenteritis only and healthy subjects did not improve group discrimination.
However, the main reason for unsuccessful discrimination of gastroenteritis patients from healthy individuals might have been the insufficient information depth of the microarray with respect to the intestinal microbiota. It is possible that intestinal infections affect at first the low abundant species, which were not covered by the present microarray. Nevertheless, PLSA identified B. fragilis as a species, which was mainly influenced by the health status of the host. This result was not supported by one-way ANOVA, but B. fragilis was previously detected with higher abundance in infected guts (Myers 1987; Sack 1994) and was also suggested a biomarker species for gut infection (Zhang 2009b).
The hybridization results of real samples were additionally analyzed with respect to the observed standard deviation between replicate spots of probe sets. This analysis proved a robust and reproducible array performance with standard deviations below 20% in 90% of cases and an average standard deviation of 11%. This was a satisfying result for a not fully automated system. A comparison with other arrays is hardly possible, as these data are seldom published. For some arrays correlation coefficients of technical replicates between 0.5 and 0.95 were published (Draghici 2006).
4.3.2 Children with rotavirus infection and healthy individuals
The faecal flora of rotavirus-infected children and healthy individuals was investigated by the Gastroenteritis-Chip using the silver deposition method for detection and a new type of array support. The change in the array support from Eppendorf epoxy-coated 3D slides to Nexterion epoxy-coated slides was a consequence of the stop of production of the former ones. After intensive optimization of the spotting conditions (not described in this work), the same array quality could be reached. Despite the non-3D surface of the new slides, no loss in signal intensity was observed. The switch to the silver deposition detection method was an adaption to the instrumental conditions in the laboratory at Shanghai University. By this, the successful implementation of an alternative detection method was shown, which allows using the Gastroenteritis-Chip without expensive and large instrumentation. Nevertheless, the silver deposition method introduces many additional handling steps into the identification procedure, which is undesirable for clinical applications. The miniaturization and automation of the detection procedure may produce relief in this regard (see chap. 4.5).
The samples from infected and healthy children supplied by Shanghai University were not supposed to contain intestinal bacterial pathogens related to gastroenteritis. The infected children suffered from rotavirus gastroenteritis. Therefore, it was aimed to investigate the influence of the infection on the resident intestinal flora, which was possible due to the availability of faecal samples from healthy children.
Unexpectedly, bacterial pathogens were found in three samples of the infected children. The detected C. difficile in three samples might have been a bacterial overgrowth because of the virus attack. Clostridium difficile is a normal inhabitant of the human gut flora but is usually suppressed by other bacteria. When the bacterial equilibrium in the gut is disturbed,
133 C. difficile can quickly proliferate. In contrast, the C. jejuni infection in one sample was perhaps nutritionally acquired. In this case, the virus infection might have been the secondary one. This result suggests that implementation of probes for intestinal viruses in the Gastroenteritis-Chip may be useful, because bacterial and viral infections can occur together and the clinical application of separate tests may lead to undiagnosed infections.
Co-infections with multiple pathogens have a relative high prevalence, as it was shown by Chen et al. in a study with hospitalized children suffering from acute non-bloody, non-mucoid diarrhea. In 303 children, they found 22.8% of polymicrobial infections, including 17.2%
multiple viral and 5.6% viral and bacterial co-infections (Chen 2009). Implementation of further targets, which cannot be detected on basis of the ribosomal genes, would require a modified amplification strategy.
The comparison of the residential flora of the healthy and rotavirus-infected children revealed major differences in the diversity. Dominant groups and species, like Bacteroides, F. prausnitzii, and R. intestinalis, partly of fully disappeared upon infection, which might have been a reaction on the infection but not the antibiotic therapy, which had not yet started. The principle component analysis of the microarray data revealed, that by the composition of the resident flora the children’s faecal samples could be assigned to either the healthy or the infected group. Partial least squares analysis supported this result and the two-component model had a very high accuracy (95%). This was in contrast to the PCA and PLSA of the resident flora of the gastroenteritis patients and healthy individuals from the hospital of Giessen, where a significant separation of both groups was not observed. In this investigation 2/3 of samples were derived from adults. This could explain the different results.
Perhaps the infantile intestinal flora is more prone to changes evoked by intestinal infections, because it is still under development and less stable. PLS analysis identified four microarray targets that were most important in discriminating healthy from infected children: Roseburia spp., F. prausnitzii, Atopobium spp., and E. coli. For Roseburia spp. and Fusobacterium prausnitzii this was partly confirmed by one-way ANOVA, and R. intestinalis was also identified by this method as significantly changed depending on the health status.
Remodelling the PLS model with Roseburia spp. and Fusobacterium prausnitzii only, showed that these two species were sufficient to establish a model with highest accuracy. These two genera belong to the Firmicutes and are dominant members of the intestinal flora as described in chapter 4.3.1. In contrast to the adult intestinal microbiota, the infantile flora is not formed in-depth, which is why the dominant members might be more affected by intestinal infections.
The results from this trial supplement a published work by Zhang et al., where nearly the same samples were analyzed for their faecal composition of Bacteroides spp. using a clone library approach (Zhang 2009b). Samples R1, R2, and R4-R9 corresponded to samples R2-R25 in this work. R26 and R27 were not part of the investigation by Zhang et al. but one additional R-sample. Samples H1-H4 and H7-H12 corresponded to H6-H28 in this work.
Samples H5 and H6 of the publication by Zhang et al. were not part of this work. Zhang et al.
could also distinguish both groups based on the Bacteroides spp. composition. Three species, B. vulgates, B. fragilis, and B. stercoris, were identified to be significantly changed depending on the health status. One sample, R4 that was equivalent to R15 in this thesis, could not be clearly assigned to the infected group by PLSA. In contrast, PLSA allowed a clear separation of all samples by the microarray-detected resident microbiota.
The results of this application indicate that the present array could also serve as a tool to detect unusual changes in the infantile intestine, which can be an indicator for undetected diseases. In this regard however, the array would require much more validation with clinical samples from children and perhaps a broader spectrum of intestinal residents should be detectable on the species level as well.
134 4.3.3 HFA and PFA piglets
The microarray was also applied to investigate two groups of piglets, which suffered from an unknown intestinal pathogen after inoculation of human or pig microbiota. The main aim was to identify the cause of the disease, but it was also expected that the array and DGGE data would provide information on the establishment of the intestinal flora in the gastro-intestinal tract. This was most interesting with respect to the human intestinal flora, which had to establish in a foreign organism.
Regarding the identification of the pathogen, which was responsible for the diarrhea and which mainly the PFA group suffered from, the information derived from the microarray investigation was contradictory to the observed course of disease. The only pathogen, which was unambiguously identified, was enteropathogenic E. coli in the human flora-associated pigs H2, H4, and H1, H6 after fourteen and twenty one days, respectively. However, in the pig flora associated pigs no pathogen was identified, although these pigs showed the more severe course of disease. Pigs H1 and H2 displayed symptoms before day fourteen, while H6 suffered from diarrhea after day twenty-one. Piglet H4 did not show any symptoms. Two explanations are conceivable; the pathogen was not detected in the other samples due to lacking sensitivity or a second pathogen was causing the disease, which could not be detected by the present microarray. EHEC has a low infective dose of only 10 cells (FDA 2010).
Regarding the bacterial diversity, the results indicated that the inner-species and inter-species transplantation of gut microbiota allows establishment of a donor-like intestinal flora.
This was already shown by ERIC-PCR and PCR-TGGE fingerprinting (Pang 2007) and provides a system for research on gut ecology in human metabolism, nutrition and drug discovery. The pig animal model is in aspects of anatomy and physiology more similar to the human than rodent animal models. In this trial, the inoculated human flora had established much faster in the piglets than the inoculated pig flora did. This originated most likely from the infection, which affected the PFA group much more than the HFA group.
This was supported by the DGGE pattern. Fourteen days after birth, the PFA piglets had still very divers DGGE patterns, which, moreover, differed from the donor profile. In contrast, the HFA profiles were relatively homogenous and similar to the donor.
PCA and PLSA also revealed that the intestinal human flora significantly differs from the pig flora. Using PCA, the full separation occurred not yet 14 days after birth but it was clearly visible at day 21. At day 21, the conventionally raised piglets clustered together with the PFA piglets and the pig flora donor, indicating that the pig flora had established in the recipients and was similar to the conventional intestinal pig flora. By PLSA, the group separation was already observed at day 14 after birth, while 21 days after birth it was more obvious. Several species and genera were identified, which contributed to differentiation between the human and pig flora after inoculation in the piglets. Fourteen days after birth, those were F. prausnitzii and L. delbrueckii, which were typically detected in the pig flora but not in the human flora. Seven days later, F. prausnitzii had also established in most human flora-associated piglets and L. delbrueckii had partly disappeared in the pig microbiota. Now, the species that distinguished the both microbiota were B. fragilis, E. faecalis, E. coli, and B. bifidum. B. fragilis and B. bifidum were unique for the human flora and the two other species were mainly found there, as well. However, these results do not represent marker species to tell between human and pig flora, because this trial was affected by a gastroenteritic infection. Additionally, E. faecalis was detected in nearly all cases without positive genus-probes and can actually not be regarded as present.
From this application of the microarray the conclusion can be drawn, that this diagnostic tool can also be a valuable tool in research to follow the establishment of microbiota in recipients of foreign intestinal flora and, moreover, in infantile GI tracts. In terms of information depth, however, this array would require supplementation because it was not planned and designed for this purpose.