Towards the human intestinal microbiota phylogenetic core

The paradox of a host specificity of the human faecal microbiota otherwise acknowledged as characterized by global functionalities conserved between humans led us to explore the existence of a phylogenetic core. We investigated the presence of a set of bacterial molecular species that would be altogether dominant and prevalent within the faecal microbiota of healthy humans. A total of 10 456 non-chimeric bacterial 16S rRNA sequences were obtained after cloning of PCR-amplified rDNA from 17 human faecal DNA samples. Using alignment or tetranucleotide frequency-based methods, 3180 operational taxonomic units (OTUs) were detected. The 16S rRNA sequences mainly belonged to the phyla Firmicutes (79.4%), Bacteroidetes (16.9%), Actinobacteria (2.5%), Proteobacteria (1%) and Verrumicrobia (0.1%). Interestingly, while most of OTUs appeared individual-specific, 2.1% were present in more than 50% of the samples and accounted for 35.8% of the total sequences. These 66 dominant and prevalent OTUs included members of the genera Faecalibacterium , Ruminococcus , Eubacterium , Dorea , Bacteroides , Alistipes and Bifidobacterium . Furthermore, 24 OTUs had cultured type strains representatives which should be subjected to genome sequence with a high degree of priority. Strikingly, 52 of these 66 OTUs were detected in at least three out of four recently published human faecal microbiota data sets, obtained with very different experimental procedures. A statistical model confirmed these OTUs prevalence. Despite the species richness and a high individual specificity, a limited number of OTUs is shared among individuals and might represent the phylogenetic core of the human intestinal microbiota. Its role in human health deserves further study.     

Application of DNA microarrays to study human alcoholism

An emerging idea is that long-term alcohol abuse results in changes in gene expression in the brain and that these changes are responsible at least partly for alcohol tolerance, dependence and neurotoxicity. The overall goal of our research is to identify genes which are differentially expressed in the brains of well-characterized human alcoholics as compared with non-alcoholics. This should identify as-yet-unknown alcohol-responsive genes, and may well confirm changes in the expression of genes which have been delineated in animal models of alcohol abuse. Cases were carefully selected and samples pooled on the basis of relevant criteria; differential expression was monitored by microarray hybridization. The inherent diversity of human alcoholics can be exploited to identify genes associated with specific pathological processes, as well as to assess the effects of concomitant disease, severity of brain damage, drinking behavior, and factors such as gender and smoking history. Initial results show selective changes in gene expression in alcoholics; of particular importance is a coordinated reduction in genes coding for myelin components.     

Optimizing the analysis of human intestinal microbiota with phylogenetic microarray

Phylogenetic microarrays present an attractive strategy to high-throughput interrogation of complex microbial communities. In this work, we present several approaches to optimize the analysis of intestinal microbiota with the recently developed Microbiota Array. First, we determined how 16S rDNA-specific PCR amplification influenced bacterial detection and the consistency of measured abundance values. Bacterial detection improved with an increase in the number of PCR amplification cycles, but 25 cycles were sufficient to achieve the maximum possible detection. A PCR-caused deviation in the measured abundance values was also observed. We also developed two mathematical algorithms that aimed to account for a predicted cross-hybridization of 16S rDNA fragments among different species, and to adjust the measured hybridization signal based on the number of 16S rRNA gene copies per species genome. The 16S rRNA gene copy adjustment indicated that the presence of members of the class Clostridia might be overestimated in some 16S rDNA-based studies. Finally, we show that the examination of total community RNA with phylogenetic microarray can provide estimates of the relative metabolic activity of individual community members. Complementary profiling of genomic DNA and total RNA isolated from the same sample presents an opportunity to assess population structure and activity in the same microbial community.     

Mathematical modeling of 16S ribosomal DNA amplification reveals optimal conditions for the interrogation of complex microbial communities with phylogenetic microarrays

MOTIVATION: Many current studies of complex microbial communities rely on the isolation of community genomic DNA, amplification of 16S ribosomal RNA genes (rDNA) and subsequent examination of community structure through interrogation of the amplified 16S rDNA pool by high-throughput sequencing, phylogenetic microarrays or quantitative PCR. RESULTS: Here we describe the development of a mathematical model aimed to simulate multitemplate amplification of 16S ribosomal DNA sample and subsequent detection of these amplified 16S rDNA species by phylogenetic microarray. Using parameters estimated from the experimental results obtained in the analysis of intestinal microbial communities with Microbiota Array, we show that both species detection and the accuracy of species abundance estimates depended heavily on the number of PCR cycles used to amplify 16S rDNA. Both parameters initially improved with each additional PCR cycle and reached optimum between 15 and 20 cycles of amplification. The use of more than 20 cycles of PCR amplification and/or more than 50 ng of starting genomic DNA template was, however, detrimental to both the fraction of detected community members and the accuracy of abundance estimates. Overall, the outcomes of the model simulations matched well available experimental data. Our simulations also showed that species detection and the accuracy of abundance measurements correlated positively with the higher sample-wide PCR amplification rate, lower template-to-template PCR bias and lower number of species in the interrogated community. The developed model can be easily modified to simulate other multitemplate DNA mixtures as well as other microarray designs and PCR amplification protocols.     

Proteomic interrogation of human chromatin

Chromatin proteins provide a scaffold for DNA packaging and a basis for epigenetic regulation and genomic maintenance. Despite understanding its functional roles, mapping the chromatin proteome (i.e. the "Chromatome") is still a continuing process. Here, we assess the biological specificity and proteomic extent of three distinct chromatin preparations by identifying proteins in selected chromatin-enriched fractions using mass spectrometry-based proteomics. These experiments allowed us to produce a chromatin catalog, including several proteins ranging from highly abundant histone proteins to less abundant members of different chromatin machinery complexes. Using a Normalized Spectral Abundance Factor approach, we quantified relative abundances of the proteins across the chromatin enriched fractions giving a glimpse into their chromosomal abundance. The large-scale data sets also allowed for the discovery of a variety of novel post-translational modifications on the identified chromatin proteins. With these comparisons, we find one of the probed methods to be qualitatively superior in specificity for chromatin proteins, but inferior in proteomic extent, evidencing a compromise that must be made between biological specificity and broadness of characterization. Additionally, we attempt to identify proteins in eu- and heterochromatin, verifying the enrichments by characterizing the post-translational modifications detected on histone proteins from these chromatin regions. In summary, our results provide insights into the value of different methods to extract chromatin-associated proteins and provide starting points to study the factors that may be involved in directing gene expression and other chromatin-related processes.     

Novel phylogenetic assignment database for terminal-restriction fragment length polymorphism analysis of human colonic microbiota

Various molecular-biological approaches using the 16S rRNA gene sequence have been used for the analysis of human colonic microbiota. Terminal- restriction fragment length polymorphism (T-RFLP) analysis is suitable for a rapid comparison of complex bacterial communities. Terminal-restriction fragment (T-RF) length can be calculated from a known sequence, thus one can predict bacterial species on the basis of their T-RF length by this analysis. The aim of this study was to build a phylogenetic assignment database for T-RFLP analysis of human colonic microbiota (PAD-HCM), and to demonstrate the effectiveness of PAD-HCM compared with the results of 16S rRNA gene clone library analysis. PAD-HCM was completed to include 342 sequence data obtained using four restriction enzymes. Approximately 80% of the total clones detected by 16S rRNA gene clone library analysis were the same bacterial species or phylotypes as those assigned from T-RF using PAD-HCM. Moreover, large T-RFs consisted of common species or phylotypes detected by both analytical methods. All pseudo-T-RFs identified by mung bean nuclease digestion could not be assigned to a bacterial species or phylotype, and this finding shows that pseudo-T-RFs can also be predicted using PAD-HCM. We conclude that PAD-HCM built in this study enables the prediction of T-RFs at the species level including difficult-to-culture bacteria, and that it is very useful for the T-RFLP analysis of human colonic microbiota.     

Application of independent component analysis to microarrays

We apply linear and nonlinear independent component analysis (ICA) to project microarray data into statistically independent components that correspond to putative biological processes, and to cluster genes according to over- or under-expression in each component. We test the statistical significance of enrichment of gene annotations within clusters. ICA outperforms other leading methods, such as principal component analysis, k-means clustering and the Plaid model, in constructing functionally coherent clusters on microarray datasets from Saccharomyces cerevisiae, Caenorhabditis elegans and human.     

Application of phenotypic microarrays to environmental microbiology

Environmental organisms are extremely diverse and only a small fraction has been successfully cultured in the laboratory. Culture in micro wells provides a method for rapid screening of a wide variety of growth conditions and commercially available plates contain a large number of substrates, nutrient sources, and inhibitors, which can provide an assessment of the phenotype of an organism. This review describes applications of phenotype arrays to anaerobic and thermophilic microorganisms, use of the plates in stress response studies, in development of culture media for newly discovered strains, and for assessment of phenotype of environmental communities. Also discussed are considerations and challenges in data interpretation and visualization, including data normalization, statistics, and curve fitting.     

Application of sector protein microarrays to clinical samples

Many protein functions are conferred by posttranslational modifications, which allow proteins to perform specific cellular tasks. Protein microarrays enable specific detection of posttranslational modifications not attainable by gene arrays. Reverse-phase protein microarrays have been widely adopted for use with clinical biopsy specimens because they have many advantages including highly reproducible printing of cellular lysates onto array surfaces, buit-in dilution curves, and direct detection using one antibody per analyte. This results in high-sensitivity, broad dynamic range, and favorable precision. Reverse-phase arrays have been restricted to a one slide/one antibody format. Although this is suitable for analyzing treatment effects over populations of samples, it is not well suited to individual patient assessments. One means of reaching this goal is the sector array format. Through the sector array, multiple antibody probes can be multiplexed on a single slide containing replicate immobilized aliquots from one patient. Thus, on one slide, a complete set of analytes can be characterized and used to support a therapy decision. This article describes a method for constructing sector arrays and demonstrates feasibility and adequate sensitivity applied to apoptosis related pathways.     

Cross-species hybridisation of pig RNA to human nylon microarrays

Background

The objective of this research was to investigate the reproducibility of cross-species microarray hybridisation. Comparisons between same- and cross-species hybridisations were also made. Nine hybridisations between a single pig skeletal muscle RNA sample and three human cDNA nylon microarrays were completed. Three replicate hybridisations of two different amounts of pig RNA, and of human skeletal muscle RNA were completed on three additional microarrays.

Results

Reproducibility of microarray hybridisations of pig cDNA to human microarrays was high, as determined by Spearman and Pearson correlation coefficients and a Kappa statistic. Variability among replicate hybridisations was similar for human and pig data, indicating the reproducibility of results were not compromised in cross-species hybridisations. The concordance between data generated from hybridisations using pig and human skeletal muscle RNA was high, further supporting the use of human microarrays for the analysis of gene expression in the pig. No systematic effect of stripping and re-using nylon microarrays was found, and variability across microarrays was minimal.

Conclusion

The majority of genes generated highly reproducible data in cross-species microarray hybridisations, although approximately 6% were identified as highly variable. Experimental designs that include at least three replicate hybridisations for each experimental treatment will enable the variability of individual genes to be considered appropriately. The use of cross-species microarray analysis looks promising. However, additional validation is needed to determine the specifiCity of cross-species hybridisations, and the validity of results.     

Supervised classification of human microbiota

Recent advances in DNA sequencing technology have allowed the collection of high-dimensional data from human-associated microbial communities on an unprecedented scale. A major goal of these studies is the identification of important groups of microorganisms that vary according to physiological or disease states in the host, but the incidence of rare taxa and the large numbers of taxa observed make that goal difficult to obtain using traditional approaches. Fortunately, similar problems have been addressed by the machine learning community in other fields of study such as microarray analysis and text classification. In this review, we demonstrate that several existing supervised classifiers can be applied effectively to microbiota classification, both for selecting subsets of taxa that are highly discriminative of the type of community, and for building models that can accurately classify unlabeled data. To encourage the development of new approaches to supervised classification of microbiota, we discuss several structures inherent in microbial community data that may be available for exploitation in novel approaches, and we include as supplemental information several benchmark classification tasks for use by the community.     

The gut microbiota links disease to human genome evolution

A large number of studies have established a causal relationship between the gut microbiota and human disease. In addition, the composition of the microbiota is substantially influenced by the human genome. Modern medical research has confirmed that the pathogenesis of various diseases is closely related to evolutionary events in the human genome. Specific regions of the human genome known as human accelerated regions (HARs) have evolved rapidly over several million years since humans diverged from a common ancestor with chimpanzees, and HARs have been found to be involved in some human-specific diseases. Furthermore, the HAR-regulated gut microbiota has undergone rapid changes during human evolution. We propose that the gut microbiota may serve as an important mediator linking diseases to human genome evolution.     

Modelling cross-hybridization on phylogenetic DNA microarrays increases the detection power of closely related species

DNA microarrays are a popular technique for the detection of microorganisms. Several approaches using specific oligomers targeting one or a few marker genes for each species have been proposed. Data analysis is usually limited to call a species present when its oligomer exceeds a certain intensity threshold. While this strategy works reasonably well for distantly related species, it does not work well for very closely related species: Cross-hybridization of nontarget DNA prevents a simple identification based on signal intensity. The majority of species of the same genus has a sequence similarity of over 90%. For biodiversity studies down to the species level, it is therefore important to increase the detection power of closely related species. We propose a simple, cost-effective and robust approach for biodiversity studies using DNA microarray technology and demonstrate it on scenedesmacean green algae. The internal transcribed spacer 2 (ITS2) rDNA sequence was chosen as marker because it is suitable to distinguish all eukaryotic species even though parts of it are virtually identical in closely related species. We show that by modelling hybridization behaviour with a matrix algebra approach, we are able to identify closely related species that cannot be distinguished with a threshold on signal intensity. Thus this proof-of-concept study shows that by adding a simple and robust data analysis step to the evaluation of DNA microarrays, species detection can be significantly improved for closely related species with a high sequence similarity.     

Application of DNA microarrays in the study of human obesity and type 2 diabetes

DNA microarrays have provided medical researchers with a powerful tool to study the mechanisms of complex diseases, including obesity and type 2 diabetes (T2D). The technology has been used to dissect virtually every aspect of the genetic and molecular basis of these two diseases. Gene expression profiling is the major application of DNA microarrays so far. Subcutaneous fat, visceral fat, adipocyte and preadipocyte, muscle, liver, pancreas and specific nuclei in the hypothalamus under normal and disease conditions are used in addressing the profile of gene expression in obesity and T2D. Comparisons of fat depots in humans and animal models - including ob/ob and db/db mice, diet-induced obese mice, fa/fa Zucker rats, gene knockout (plin (-/-), GLUT4 (-/-)) and transgenic mice (GLUT4-Tg) - have been employed in microarray experiments. The effects of various interventions, such as hormonal and drug treatments, exercise, and surgery, have been studied to determine the expression profile of different developmental stages in cells and the effect of treatment on the two diseases. In this review, the application of microarrays in elucidating the role of retinol binding protein 4 as a link between obesity and T2D is discussed. The possible role in obesity of a common genetic variant near the INSIG2 gene and the discovery of the BBS9 gene are also discussed. The problems and challenges are summarized under eight categories and suggestions for the future direction of research in this area are proposed.     

Composition of human skin microbiota affects attractiveness to malaria mosquitoes

The African malaria mosquito Anopheles gambiae sensu stricto continues to play an important role in malaria transmission, which is aggravated by its high degree of anthropophily, making it among the foremost vectors of this disease. In the current study we set out to unravel the strong association between this mosquito species and human beings, as it is determined by odorant cues derived from the human skin. Microbial communities on the skin play key roles in the production of human body odour. We demonstrate that the composition of the skin microbiota affects the degree of attractiveness of human beings to this mosquito species. Bacterial plate counts and 16S rRNA sequencing revealed that individuals that are highly attractive to An. gambiae s.s. have a significantly higher abundance, but lower diversity of bacteria on their skin than individuals that are poorly attractive. Bacterial genera that are correlated with the relative degree of attractiveness to mosquitoes were identified. The discovery of the connection between skin microbial populations and attractiveness to mosquitoes may lead to the development of new mosquito attractants and personalized methods for protection against vectors of malaria and other infectious diseases.