Paliy Lab
Diggs Laboratory for Life Science Research : Department of Biochemistry and Molecular Biology : Wright State University
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Research Interests

The research in our laboratory is focused on the studies of complex microbial communities associated with human gastrointestinal system. We use a variety of research techniques including ribosomal gene sequencing, metagenomics, phylogenetic microarrays, and fluorescent in situ hybridization to gain knowledge of community composition and function, its changes in disease, and its response to diet perturbations. We employ mathematical modeling to generate hypotheses of possible microbial and host-microbial interactions, that we then test in the in vitro gut simulator system. We also study individual microbial species to gain insight into the specific roles these members play in our lives and how they interact with each other.

Below we describe our current research directions and projects in more detail, and also list experimental and computational approaches that we use.


Introduction to human intestinal microbiota

Typical human body contains about 1012-1013 eukaryotic cells. However, most of the cells in our body are not our own, nor are they even human. They are microbial. From the invisible strands of fungi waiting to sprout between our toes to the kilogram of bacterial matter in our guts, humans house about 1013-1014 microbial cells. Almost every epithelial surface of our body contains a complex microbial community – skin, mouth, vagina, and gastrointestinal tract. Microbes can live even in the highly acidic environment of the stomach (pH=2). All these epithelial surfaces are populated by communities of microorganisms, collectively called microbiota, rather than by individual species, and often include hundreds and even thousands of different microbial members. Majority of these organisms are bacteria, though archaea, fungi, eukaryotic microorganisms, and viruses also take residence in different epithelial niches.

Renewed interest in the human microbiota is associated with the recognition of the important relationships these microbes form with our bodies. For example, microbiota of the gut participate in host energy metabolism by breaking down complex polysaccharides in the diet. Resident microbes also protect the host from pathogen invasion through competition for resources or directly by inhibiting pathogen growth. Moreover, microbiota modulate the proper development and functioning of the human immune system, transform or excrete toxic substances, and help maintain epithelial homeostasis. At the same time, microbiota dysbiosis, defined as the perturbation of the normal microbial profile, has been linked to a number of human diseases including dental plaque, bacterial vaginosis, psoriasis, atopic dermatitis, and cystic fibrosis.

The focus of our laboratory is on theanalysis of microbes colonizing human gastrointestinal system. In each healthy human, this gut community contains several hundreds different species, which display complex network of interactions not only among themselves but also with the human tissues and cells. Many recent studies from our and other groups also showed that alterations in the levels of certain microbial groups in the gut may be linked to several disorders including irritable bowel syndrome, inflammatory bowel disease, obesity, diabetes, and colon cancer.

Distribution and abundance of bacteria in human gastrointestinal tract. [Figure modified from B. Sartor Gastroenterology 2008]

Different approaches can be taken to study these complex microbial populations. We can look at the community membership where interrogation of small ribosomal subunit (SSU) RNA genes is used extensively to obtain the phylogenetic structure of the community. We can profile community functionality by cataloguing the pool of functional genes and proteins present in the population through meta-genomic, meta-transcriptomic, and meta-proteomic approaches. We can also study the metabolic processes in microbial conglomerates by measuring levels of produced and consumed metabolites with metabolomic and metabonomic techniques. We can employ in vitro and animal model systems to carry out controlled studies of possible metabolic and immunological links among microbes and with host cells. And we can use a variety of computational and bioinformatic tools to link these diferent types of data together to derive significant associations and possible cause-effect hypotheses.

The goal of such research is to learn sufficient detail about human associated microbiota to allow basic scientists and medical doctors to design new treatment options to prevent, diagnose, and treat many of the diseases mentioned above. Microbial community profiling with microarrays, for example, can be used in clinical setting to diagnose many different diseases non-invasively and relatively inexpensively. Live microbial species with discovered beneficial properties called probiotics can be prescribed to patients in order to influence microbiota composition and shift this community back into a healthier state. Specific mixtures of probiotic species can be provided to each individual patient based on the particular disbalanced state of their microbiota (personalized medicine). Finally, a complete replacement of patient microbiota can also be carried out to treat severe cases of microbiota disbiosis, as is the case of the use of fecal microbiota transplantation to cure Clostridium difficile bacterial infection of the gut.


Recent projects

For an up to date list of publications, please visit the Publications page.

Analysis of gut microbiota in Clostridium difficile infected patients following fecal microbiota transplantation. [Shankar et al Microbiome 2014]

Clostridium difficile is an opportunistic human intestinal pathogenic bacterium, and C. difficile infection (CDI) is one of the main causes of antibiotic-induced diarrhea and colitis that can lead to death. One successful approach to combat CDI, particularly recurrent form of CDI, is through transplantation of fecal microbiota from a healthy donor to the infected patient. In this technique, fecal microbiota obtained from a healthy donor is processed, standardized, and subsequently transplanted into patients suffering from the recurrent C. difficile infection. FMT is highly successful (>90% success rate).

In this study we investigated the distal gut microbial communities of several CDI patients before and after fecal microbiota transplantation, and we compared these communities to the composition of the donor’s fecal microbiota. The tiemline of the procedure and sample collection are shown in the image. We utilized phylogenetic Microbiota Array, high-throughput Illumina sequencing, and fluorescent in situ hybridization to profile microbiota composition down to the genus and species level resolution.

The colon of each CDI patient was host to a severely compromised intestinal microbial community, which was significantly reduced in diversity and richness. This was likely a result of C. difficile proliferation as well as due to the antibiotic treatment used in an attempt to rid the disease. While the healthy gut microbiota is usually dominated by bacterial classes Clostridia and Bacteroidia, fecal samples of the CDI patients were abundant in Gammaproteobacteria and Bacilli. In comparison, the donor community was dominated by Clostridia and had significantly higher diversity.

The patients' distal gut communities were completely transformed within three days following fecal transplantation. The transplanted gut communities were highly similar among recipients post-transplantation and were indistinguishable from that of the donor (see PCoA image). In each case, the gut microbiota restoration led to a complete patient recovery and symptom alleviation. The transplanted communities remained stable in each patient for at least four months.

Thus, C. difficile infection can be successfully treated by fecal microbiota transplantation, which offers simple and highly effective (>90% success rate) treatment option.

Figure. Changes in microbiota diversity and composition following fecal transplantation in CDI patients [use "view image" to expand].

Figure. Separation of all profiled samples in PCoA space.

Analysis of distal gut microbiota in adolescent children and adults. [Agans et al FEMS-ME 2011]

Several projects in our laboratory have looked at the gut microbiota in teenage children. In this work, we used phylogenetic Microbiota Array, developed previously in our laboratory, for high-throughput analysis of distal gut microbiota in healthy adolescent children 11-18 years of age. Samples obtained from healthy adults were used for comparison. We were able to separate child and adult samples based solely on the composition of their fecal microbiota (principal components analysis, see figure). All samples were dominated by class Clostridia. While we have detected many different genera in analyzed samples, the top 12 abundant genera contributed three-forth of total microbial abundance. All the other genera had relatively low representation. Genus Ruminococcus, which contains many prominent polysaccharide degrading members, was by far the most abundant. Genera Bifidobacterium and Clostridium were more abundant among adolescent samples than those from adults.

Overall, this study suggested that the gut microbiome of adolescent children is different from that of adults.

Figure. Separation of adolescent (kHLT, green) and adult (aHLT, purple) samples in PCA space.

Figure. Distribution of genus abundances among profiled adult and adolescent samples.

Quantitative profiling of gut microbiota of children with diarrhea-predominant irritable bowel syndrome. [Rigsbee et al Amer J Gastroenterol 2012 and Shankar et al Gut Microbes 2013]

The goal of this study was to determine the distal gut microbiota in two groups of pre- and adolescent children: healthy volunteers and children diagnosed with diarrhea-predominant irritable bowel syndrome (IBS-D). A focus on adolescent children was justified by the higher prevalence of IBS in this population demographic. Phylogenetic Microbiota Array was used to obtain quantitative measurements of bacterial presence and abundance in subjects’ fecal samples. We utilized high-throughput DNA sequencing, quantitative PCR, and fluorescent in situ hybridization to confirm microarray findings.

Both sample groups were dominated by the phyla Firmicutes, Bacteroidetes, and Actinobacteria, which cumulatively constituted 91% of overall sample composition on average. A core microbiome shared among analyzed samples encompassed 55 bacterial phylotypes dominated by genus Ruminococcus; members of genera Clostridium, Faecalibacterium, Roseburia, Streptococcus, and Bacteroides were also present. The core was smaller for the IBS group, and most analyzed samples contained unique phylotypes only detected in that single sample (see image).

Similarly to the previous project, by utilizing ordination and classification computational techniques such as PCA, PCoA, and OPLS-DA, we were able to determine sample origin (healthy or IBS) based on its microbiota composition. Several genera were found to be differentially abundant in the gut of healthy and IBS groups: levels of Veillonella, Prevotella, Lactobacillus, and Parasporobacterium were increased in children diagnosed with IBS, whereas members of Bifidobacterium and Verrucomicrobium were less abundant in those individuals.

By calculating a nonparametric correlation matrix among abundances of different genera in all samples, we also examined potential associations among intestinal microbes. Strong positive correlations were found between abundances of Veillonella and both Haemophilus and Streptococcus, between Anaerovorax and Verrucomicrobium, and between Tannerella and Anaerophaga. Many positive associations were lost in IBS that can potentially indicate a loss of important mutualistic interactions among microbiota members in this disease - this can lead to changes in metabolite levels in intestinal lumen, which in turn can result in host physiological response.

Figure. Core microbiome in IBS (A) and healthy (B) children.

Figure. Associations among abundances of microbial genera.

Biomathematical modeling of multi-template PCR. [Paliy and Foy Bioinf 2011]

Many biological systems are inherently complex and consist of many hundreds of members and relationships. Comprehensive experimental study of such systems is often impossible or prohibitive. In such situations, mathematical modeling of the system based on limited amount of experimental data can provide insights into the structure and function of these complex systems that otherwise would be difficult to obtain.

In this study we have developed a mathematical model aimed to simulate multi-template PCR amplification of complex 16S ribosomal DNA sample (obtained from microbial community) and subsequent detection of these amplified 16S rDNA species by phylogenetic microarray. Many current studies of complex microbial communities rely on the isolation of community genomic DNA, amplification of 16S ribosomal RNA genes, and subsequent examination of community structure through interrogation of the amplified 16S rDNA pool by high-throughput sequencing, phylogenetic microarrays, or quantitative PCR. However, the behavior and optimal experimental conditions of such multi-template PCR amplification were not well studied. To build the model we used parameters estimated from the experimental results obtained with Microbiota Array, and focused our attention on how microbial species detection and the accuracy of species abundance estimates depend on the number of PCR cycles used to amplify 16S rDNA. Figure shows the results of our simulations: both species detection and the accuracy of abundance measurements initially improved with each additional PCR cycle and reached optimum between 15-20 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 (see figure). Overall, the outcomes of the model simulations matched well available experimental data.

Figure. Accuracy of abundance measurements of microbial community members as a function of PCR amplification parameters (lower is better).


Techniques that we utilize

Clinical studies.

We work closely with a number of medical doctors and hospitals with an aim to profile human gut microbial communities and determine potential differences in these communities in patients with different GI disorders. Such studies can be done cross-sectionally where we identify a group of people all diagnosed with a particular disorder, and then obtain either a stool or a biopsy sample from them at one specific time. Alternatively, a longitudinal analysis can be performed, where each patient, which enrolled in a study, provides a number of samples over a period of time. Whereas the first approach allows us to compare microbiota differences between two different gut states (usually healthy vs disease), the second approach lets us follow microbiota dynamic changes in the patients' GI tracts as they receive treatment or go through different procedures.

To date we have profiled gut microbiota in patients diagnosed with irritable bowel syndrome (IBS), inflammatory bowel disease (IBD) andClostridium difficile infection. We have also compared gut microbial communities in different populations of healthy people - children and adults, healthy adults undergoing specific diet changes, and children receiving probiotic and prebiotic supplementations.

In vitro human gut simulator.

To complement our clinical studies we have established and validated an in vitro simulator of human intestinal tract (see image below). The simulator consists of three continuously linked fermentation vessels, each set to mimic environmental conditions of different sections of human GI tract known to house the majority of gut microbes: vessel 1 simulates the ascending colon, vessel 2 simulates the transverse colon, and vessel 3 models the descending colon. The medium, which closely matches food bolus contents that reach colon, is supplied to vessel 1, and vessel contents are periodically transferred from vessel 1 to vessel 2, from vessel 2 to vessel 3, and from vessel 3 to waste collection flask. Additional vessels simulating stomach and small intestinal regions of the GI tract can also be added when needed.

The environmental conditions (temperature, pH, relative volume, peristaltic movement, dilution with lumenal contents, etc) in each vessel closely match those experimentally measured in the corresponding sections of the human gut. This bioreactor is able to maintain high microbial density over weeks of continuous incubation in conditions that can be precisely controlled by an investigator. Because we can change environmental conditions in this system at will, can draw samples at any point, and can precisely perturb the community, this system is well suited to carry out human gut microbiota studies that so far have not been done in the field. We can obtain samples from each simulated gut section (rather than being limited to stool samples), and we can also perform types of perturbation experiments that would be unethical to carry out on human volunteers (addition of toxin or community destruction).

Figure. Schematic representation of in vitro simulator of human intestinal tract.

Phylogenetic microarrays.

In a recently completed project we have designed, developed, and validated a custom high-throughput phylogenetic Microbiota Array containing probes to 16S rRNA genes of 775 different microbial species (phylotypes) of human gut microbiota. The Microbiota Array is based on the Affymetrix, Inc. platform and contains sets of phylogenetic 16S rRNA gene probes (25-mer probes, 5-11 probe pairs in each set, each probe pair consists of perfect match and mismatch probes, each set interrogates a separate phylotype) allowing detection of 775 bacterial phylotypes of human intestinal microbiota. This design allows us to detect the presence and directly measure abundance level of all these species in each interrogated sample. Compared to the alternative technologies advantages of phylogenetic microarrays include (i) ability to measure each phylotype presence and abundance in all samples, (ii) fully quantitative nature of the acquired data allowing direct comparisons of levels of each phylotype among samples; and (iii) short processing and data acquisition times (only two days from sample to data using Microbiota Array).

The designed microarray has been rigorously validated and used successfully in previous studies. We have shown that the microarray can be used not only to examine population structure of intestinal microbiota by profiling total genomic DNA isolated from fecal samples and biopsies, but it can also provide quantitative assessment of relative metabolic activities of individual community members through the interrogation of total community RNA.

Figure. Microbiota Array.

This Microbiota Array is now used to study the composition of human gut microbiota in a variety of recently completed and ongoing projects. The goal of many of these studies is to test whether gut microbial communities are changed in individuals diagnosed with different gastrointestinal diseases. If such differences are found, this would allow us to make putative associations between disease state and specific microbiota changes which can eventually lead to the development of novel drugs such as pro- and prebiotics.

Next generation sequencing.

Next generation sequencing (NGS) is a novel approach that provides massive amounts of nucleotide sequence information in a single run. The main difference between a standard Sanger sequencing (which uses chain-termination method) and these new high-throughput methods is the parallelization of the sequencing reaction in NGS. Typically, millions of different nucleotide sequences can be read by NGS machines simultaneously. This allows researchers to obtain 100- and 1000-fold higher amount of data within the same amount of time as compared to Sanger method. The parallelization also lowers per-nucleotide sequence acquisition costs by several hundred folds.

This technology has already been employed to study genome sequence variation, ancient DNA, DNA methylation, protein-DNA interactions, alternative splicing, small RNA populations, and mRNA transcription and regulation. NGS is especially well suited to the analysis of microbial communities. Since many such communities are composed of hundreds of different members, the overall community contains millions of different genes, which can only be profiled to reasonable depth by NGS.

Figure. Ion Torrent Personal Genome Machine.

Recent research in our laboratory utilized three different NGS methods. 454-based pyrosequencing was used to confirm microarray results in the analysis of fecal microbiota in healthy and IBS children. Illumina HiSeq platform was used to assess microbial community changes in patients undergoing fecal transplantation treatment for recurrent Clostridium difficile infection. And we employed Ion Torrent PGM machine to profile differences in gut communities of children consuming different diets.

Supported by a recent NSF Major Research Instrumentation Grant, we have now established a high-throughput sequencing capability at WSU based on the Ion Torrent next-generation sequencer. More information about the NGS facility can be found here. We will continue to use the Ion Torrent based high-throughput sequencing in our ongoing projects, and we also work to develop new and improved analytical and visualization tools to assess the obtained data.

Fluorescent in situ hybridization.

Fluorescent in situ hybridization (FISH) is a cell and DNA detection method that allows visualization and enumeration of DNA molecules based on the selective hybridization of specific fluorescently labeled oligonucleotide probes. In microbiological research, FISH can be utilized to selectively visualize specific species and cells in an environmental sample if we design probes uniquely targeting DNA in those cells. In a typical protocol, cell mixture is first fixed, the oligo probes are allowed to hybridize to DNA in cells, and after sample washing the probe signal is observed and measured under a fluorescent microscope.

Because fluorescent in situ hybridization relies on direct visualization of cells, it serves as an excellent validation method for DNA quantification-based microarray and sequencing techniques. We have employed FISH to visualize Clostridia, Bacteroidia, Proteobacteria, and Bifidobacterium cells in fecal microbiota communities by employing FITC-labeled short oligonucleotide probes complementary to regions of 16S rRNA gene unique to each selected bacterial group. DAPI stain (binds to any DNA) was used to visualize all cells (see image).

Figure. Visualization of Proteobacteria cells with FITC-labeled DNA probe.

Anaerobic culturing.

Majority of human gut microbiota members are obligate anaerobes, i.e., they use anaerobic fermentation pathways to generate energy from nutrients. These organisms cannot survive oxygen in their environment because they lack reactive oxygen species detoxifying enzymes such as catalase. In order to grow these organisms in the laboratory, special conditions are required in which all oxygen is removed from the incubator / environment. Several different methods can be utilized to cultivate anaerobic bacteria such as anaerobic chambers, boxes, and sacks, use of nitrogen and anaerobic atmosphere sparging, and addition of reducing compounds to the medium to bind oxygen.

We use a combination of these approaches to cultivate individual members of human gut microbiota, which enables us to study each species independently and assess their metabolism, physiology, and interactions with other members of the community.

Metabolite profiling with proton NMR.

In collaboration with Dr. Nicholas Reo (Department of Biochemistry and Molecular Biology, WSU) we have initiated an analysis of metabolic activity of gut microbial communities by using proton NMR measurements of fecal and lumenal extracts. Proton NMR enables us to detect and quantitatively measure levels of many different small organic compounds based on the magnetic properties of the hydrogen atoms bound to each carbon in the molecule.

Similar to our microarray findings, stool samples from patients with a disease can be separated from healthy ones based on the metabolite levels in their stool samples. Some important metabolites such as short-chain fatty acids are often reduced in the lumen of people with GI disorders, and these metabolic changes can be associated with particular disease states. Since many of these metabolites are produced by resident microbiota, combining microarray and NMR analyses of the same samples will allow us to putatively link metabolite production / consumption and the presence and abundance of different bacterial members.

Figure. Aliphatic region of typical H1-NMR spectrum for two fecal extract samples.

Metabolic modeling.

Typical microbial communities encompass hundreds of different species and phylotypes, each filling a particular environmental niche. Many metabolic interactions are known and presumed to exist among these species, where end-products of metabolism of one species are released into the environment and are used as a source of nutrients and energy by another microbe (see image). However, due to the complexity of these communities, finding all such interactions experimentally is challenging.

One alternative powerful approach to assess possible cross-species interactions is through mathematical modeling of each species metabolism and subsequent alignment of metabolic maps among different microbes. In this approach, we utilize known genome sequences of different community members to build metabolic maps for each of them based on the genome annotation information. We then assess all possible metabolic interactions among community members. Such predictions can then be tested experimentally in a two or three-member community.

Figure. Schematic representation of metabolic interactions among microbiota.

Computational biology and biostatistics.

Many of the experimental approaches that we use in our research generate large amounts of data for many tens and hundreds of samples. An analysis of such large datasets requires special computational approaches that often have to be modified and tailored to each individual project. We therefore develop new and adapt existing biocomputational techniques to simplify the analysis of such data and produce revealing visualizations of the major findings. Among the techniques that we utilize are ordination and classification algorithms such as principal components analysis (PCA), phylogenetically based principal coordinates analysis (PCoA), and orthogonal projection to latent structures discriminant analysis (OPLS-DA), correlation matrix calculations, ecological diversity analyses, data clustering, and compositional analysis.

Figure. Sample of Matlab code that runs PCA analysis shown in the adjacent figure [use "view image" to expand].

Figure. Relative abundance of Oxobacter genus among human fecal samples [Shankar et al. GM 2013].

We also employ a variety of biostatistical approaches to test the statistical significance of the observed differences among samples and between sample groups. In addition to classical ANOVA and regression analyses, these methods include generalized linear models, permutation analyses, rarefaction calculations, and multiple hypothesis testing adjustments.


 
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