Bioinformatics analysis of whole-genome shotgun metagenomic data

Next-generation sequencing technologies, with their low costs and high throughputs, have benefited the field of microbial research to a great degree. The application of whole-genome shotgun sequencing to DNA extracted from an environmental sample enables avoiding the usually complex method of cultivation of pure cultures of microorganisms in the laboratory. This protocol is referred to as whole-genome shotgun metagenomic sequencing. The analysis of sequencing data mainly aims at the taxonomic and functional characterization of the microbial sample. Many algorithms and tools have been developed for the same. The design of the analysis pipeline is usually dictated by the specific project at hand. In this thesis, we describe several aspects of analyzing whole-genome shotgun metage- nomic data. Analysis usually begins with the quality check of raw sequencing data followed by its preprocessing to improve the read quality. When dealing with datasets containing several number of large samples, the preprocessing of the samples can take up considerable time and effort. However, if the binning of reads into different taxonomic and functional categories is the aim, a read with bad quality automatically gets filtered making the initial preprocessing unnecessary. Thus we first look into the effect of preprocessing on the ensuing analysis of the metagenomic samples. Next, we assess the correspondence between the different systems of functional classification typically used for metagenomic analyses. The reference proteins in databases like the NCBI-NR may have none or multiple identifiers belonging to a particular classification system. Consequently, a read aligning to such a reference may be placed into a functional group depending on the mapping of the reference to functional identifiers. We study the correspondence between the different classification systems using a few metagenomic samples. Further, we describe the analysis of a dataset of human gut metagenomic samples obtained from obese patients undergoing a weight-loss diet-intervention. The obese patients were also detected positive for non-alcoholic fatty liver disease (NAFLD) and Metabolic Syndrome. The analysis is carried out using the popular metagenomic analysis tools DIAMOND and MEGAN. This study was carried out in order to track the effect of the diet-intervention on the gut flora composition and to relate the clinical parameters like weight-loss, NAFLD and metabolic syndrome to the microbiome. A metagenomic sample could be subjected to analysis based directly on the reads or on an assembly. Both methods have their pros and cons. We explore the differences seen in the taxonomic and functional compositions between those two strategies and conclude that both provide similar results with minor differences depending on the sample being assembled. At the end, we describe how a gene-centric assembly can be carried out with the tools DIAMOND and MEGAN and demonstrate the usefulness of such a gene-centric assembly in a metagenomic analysis pipeline by carrying out a gene-centric assembly across different gene families and metagenomic samples

Multi-omics assessment of dilated cardiomyopathy using non-negative matrix factorization

Dilated cardiomyopathy (DCM), a myocardial disease, is heterogeneous and often results in heart failure and sudden cardiac death. Unavailability of cardiac tissue has hindered the comprehensive exploration of gene regulatory networks and nodal players in DCM. In this study, we carried out integrated analysis of transcriptome and methylome data using nonnegative matrix factorization from a cohort of DCM patients to uncover underlying latent factors and covarying features between whole-transcriptome and epigenome omics datasets from tissue biopsies of living patients. DNA methylation data from Infinium HM450 and mRNA Illumina sequencing of n = 33 DCM and n = 24 control probands were filtered, analyzed and used as input for matrix factorization using R NMF package. Mann-Whitney U test showed 4 out of 5 latent factors are significantly different between DCM and control probands (P<0.05). Characterization of top 10% features driving each latent factor showed a significant enrichment of biological processes known to be involved in DCM pathogenesis, including immune response (P = 3.97E-21), nucleic acid binding (P = 1.42E-18), extracellular matrix (P = 9.23E-14) and myofibrillar structure (P = 8.46E-12). Correlation network analysis revealed interaction of important sarcomeric genes like Nebulin, Tropomyosin alpha-3 and ERC-protein 2 with CpG methylation of ATPase Phospholipid Transporting 11A0, Solute Carrier Family 12 Member 7 and Leucine Rich Repeat Containing 14B, all with significant P values associated with correlation coefficients >0.7. Using matrix factorization, multiomics data derived from human tissue samples can be integrated and novel interactions can be identified. Hypothesis generating nature of such analysis could help to better understand the pathophysiology of complex traits such as DCM

Epigenetic Regulation of Alternative mRNA Splicing in Dilated Cardiomyopathy

In recent years, the genetic architecture of dilated cardiomyopathy (DCM) has been more thoroughly elucidated. However, there is still insufficient knowledge on the modifiers and regulatory principles that lead to the failure of myocardial function. The current study investigates the association of epigenome-wide DNA methylation and alternative splicing, both of which are important regulatory principles in DCM. We analyzed screening and replication cohorts of cases and controls and identified distinct transcriptomic patterns in the myocardium that differ significantly, and we identified a strong association of intronic DNA methylation and flanking exons usage (p < 2 × 10−16). By combining differential exon usage (DEU) and differential methylation regions (DMR), we found a significant change of regulation in important sarcomeric and other DCM-associated pathways. Interestingly, inverse regulation of Titin antisense non-coding RNA transcript splicing and DNA methylation of a locus reciprocal to TTN substantiate these findings and indicate an additional role for non-protein-coding transcripts. In summary, this study highlights for the first time the close interrelationship between genetic imprinting by DNA methylation and the transport of this epigenetic information towards the dynamic mRNA splicing landscape. This expands our knowledge of the genome–environment interaction in DCM besides simple gene expression regulation

Energy Metabolites as Biomarkers in Ischemic and Dilated Cardiomyopathy

With more than 25 million people affected, heart failure (HF) is a global threat. As energy production pathways are known to play a pivotal role in HF, we sought here to identify key metabolic changes in ischemic- and non-ischemic HF by using a multi-OMICS approach. Serum metabolites and mRNAseq and epigenetic DNA methylation profiles were analyzed from blood and left ventricular heart biopsy specimens of the same individuals. In total we collected serum from n = 82 patients with Dilated Cardiomyopathy (DCM) and n = 51 controls in the screening stage. We identified several metabolites involved in glycolysis and citric acid cycle to be elevated up to 5.7-fold in DCM (p = 1.7 × 10−6 ). Interestingly, cardiac mRNA and epigenetic changes of genes encoding rate-limiting enzymes of these pathways could also be found and validated in our second stage of metabolite assessment in n = 52 DCM, n = 39 ischemic HF and n = 57 controls. In conclusion, we identified a new set of metabolomic biomarkers for HF. We were able to identify underlying biological cascades that potentially represent suitable intervention targets

Clinical parameters of the study population.

<p>A. Relative weight loss during the observation period of two years consisting of one year intervention program with very low calorie diet (VLCD) during the first 3 months, reintroduction of normal food (reintrod.) during month 3–6, and weight maintenance therapy under normal diet during month 7–12, followed by a one-year-observation without intervention. Each line represents a patient (n = 16). Patients were grouped into those with persistent success (PS group, >10% RWL at T24, black lines and symbols) or no persistent success (NS group, <10% RWL at T24, grey lines and symbols). B. Change of insulin resistance during time. Insulin resistance was assessed using the HOMA-IR as described in Subjects and Methods. C. Change of liver steatosis assessed by sonography (circles) and fatty liver index (squares). Data in B and C are indicated as means +/- 95% confidence intervals (n = 16), **P<0.01 and ***P<0.001 (as compared to baseline, Wilcoxon’s test).</p

Bacterial species changes during weight loss intervention.

<p>Cladogram (based on 16S sequences) displaying the most abundant species from genera influenced by the intervention. Significant changes between T0 and T3 (left column of squares), between T3 and T6 (middle column), and between T6 and T24 (right column) are indicated by a star(p < 0.05). Blue squares indicate a decrease, red an increase in abundance. Species are colored according to the phyla they belong to (blue: Spirochaetae, pink: Bacteroidetes, green: Firmicutes, light pink: Proteobacteria, orange: Actinobacteria, brown: Verrucomicrobia, red: Synergistetes). This tree was created using the free software EvolView [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0149564#pone.0149564.ref027" target="_blank">27</a>].</p

Characterization of the Gut Microbial Community of Obese Patients Following a Weight-Loss Intervention Using Whole Metagenome Shotgun Sequencing

<div><p>Background/Objectives</p><p>Cross-sectional studies suggested that obesity is promoted by the gut microbiota. However, longitudinal data on taxonomic and functional changes in the gut microbiota of obese patients are scarce. The aim of this work is to study microbiota changes in the course of weight loss therapy and the following year in obese individuals with or without co-morbidities, and to asses a possible predictive value of the gut microbiota with regard to weight loss maintenance.</p><p>Subjects/Methods</p><p>Sixteen adult patients, who followed a 52-week weight-loss program comprising low calorie diet, exercise and behavioral therapy, were selected according to their weight-loss course. Over two years, anthropometric and metabolic parameters were assessed and microbiota from stool samples was functionally and taxonomically analyzed using DNA shotgun sequencing.</p><p>Results</p><p>Overall the microbiota responded to the dietetic and lifestyle intervention but tended to return to the initial situation both at the taxonomical and functional level at the end of the intervention after one year, except for an increase in <i>Akkermansia</i> abundance which remained stable over two years (12.7x10³ counts, 95%CI: 322–25100 at month 0; 141x10³ counts, 95%CI: 49-233x10³ at month 24; p = 0.005). The <i>Firmicutes/Bacteroidetes</i> ratio was higher in obese subjects with metabolic syndrome (0.64, 95%CI: 0.34–0.95) than in the “healthy obese” (0.27, 95%CI: 0.08–0.45, p = 0.04). Participants, who succeeded in losing their weight consistently over the two years, had at baseline a microbiota enriched in <i>Alistipes</i>, <i>Pseudoflavonifractor</i> and enzymes of the oxidative phosphorylation pathway compared to patients who were less successful in weight reduction.</p><p>Conclusions</p><p>Successful weight reduction in the obese is accompanied with increased <i>Akkermansia</i> numbers in feces. Metabolic co-morbidities are associated with a higher <i>Firmicutes/Bacteroidetes</i> ratio. Most interestingly, microbiota differences might allow discrimination between successful and unsuccessful weight loss prior to intervention.</p></div

Abundance change of genera and metabolic pathways during the study.

<p>A. Relative abundance of five genera, which were the most abundant among those that changed during time. B. Akkermansia abundance. C. Relative abundance of all KEGG pathways that changed during whole study. D. Metabolic pathways that changed from baseline to T3. Abbreviations: bios, biosynthesis; met, metabolism. Statistics: Relative abundances are expressed in percent (abundance at T0 is 100%). Each dot is the mean at a given time point. Relative abundances at different time points were compared using the Friedman test (A, C: over the six time points), or the Wilcoxon test (B, D, *p < 0.05: between baseline and T3 or T24).</p

Correlations between bacterial genera or pathways and parameters related to the metabolic syndrome.

<p>Correlations between bacterial genera or pathways and parameters related to the metabolic syndrome.</p

Clinical parameters along the study period.

<p>Clinical parameters along the study period.</p