MetaPath: identifying differentially abundant metabolic pathways in metagenomic datasets

Enabled by rapid advances in sequencing technology, metagenomic studies aim to characterize entire communities of microbes bypassing the need for culturing individual bacterial members. One major goal of metagenomic studies is to identify specific functional adaptations of microbial communities to their habitats. The functional profile and the abundances for a sample can be estimated by mapping metagenomic sequences to the global metabolic network consisting of thousands of molecular reactions. Here we describe a powerful analytical method (MetaPath) that can identify differentially abundant pathways in metagenomic datasets, relying on a combination of metagenomic sequence data and prior metabolic pathway knowledge. First, we introduce a scoring function for an arbitrary subnetwork and find the max-weight subnetwork in the global network by a greedy search algorithm. Then we compute two p values (p abund and p struct ) using nonparametric approaches to answer two different statistical questions: (1) is this subnetwork differentically abundant? (2) What is the probability of finding such good subnetworks by chance given the data and network structure? Finally, significant metabolic subnetworks are discovered based on these two p values. In order to validate our methods, we have designed a simulated metabolic pathways dataset and show that MetaPath outperforms other commonly used approaches. We also demonstrate the power of our methods in analyzing two publicly available metagenomic datasets, and show that the subnetworks identified by MetaPath provide valuable insights into the biological activities of the microbiome. We have introduced a statistical method for finding significant metabolic subnetworks from metagenomic datasets. Compared with previous methods, results from MetaPath are more robust against noise in the data, and have significantly higher sensitivity and specificity (when tested on simulated datasets). When applied to two publicly available metagenomic datasets, the output of MetaPath is consistent with previous observations and also provides several new insights into the metabolic activity of the gut microbiome. The software is freely available at http://metapath.cbcb.umd.edu .https://doi.org/10.1186/1753-6561-5-S2-S

Analytical Tools and Databases for Metagenomics in the Next-Generation Sequencing Era

Metagenomics has become one of the indispensable tools in microbial ecology for the last few decades, and a new revolution in metagenomic studies is now about to begin, with the help of recent advances of sequencing techniques. The massive data production and substantial cost reduction in next-generation sequencing have led to the rapid growth of metagenomic research both quantitatively and qualitatively. It is evident that metagenomics will be a standard tool for studying the diversity and function of microbes in the near future, as fingerprinting methods did previously. As the speed of data accumulation is accelerating, bioinformatic tools and associated databases for handling those datasets have become more urgent and necessary. To facilitate the bioinformatics analysis of metagenomic data, we review some recent tools and databases that are used widely in this field and give insights into the current challenges and future of metagenomics from a bioinformatics perspective.

Deep Sequencing of the Oral Microbiome Reveals Signatures of Periodontal Disease

The oral microbiome, the complex ecosystem of microbes inhabiting the human mouth, harbors several thousands of bacterial types. The proliferation of pathogenic bacteria within the mouth gives rise to periodontitis, an inflammatory disease known to also constitute a risk factor for cardiovascular disease. While much is known about individual species associated with pathogenesis, the system-level mechanisms underlying the transition from health to disease are still poorly understood. Through the sequencing of the 16S rRNA gene and of whole community DNA we provide a glimpse at the global genetic, metabolic, and ecological changes associated with periodontitis in 15 subgingival plaque samples, four from each of two periodontitis patients, and the remaining samples from three healthy individuals. We also demonstrate the power of whole-metagenome sequencing approaches in characterizing the genomes of key players in the oral microbiome, including an unculturable TM7 organism. We reveal the disease microbiome to be enriched in virulence factors, and adapted to a parasitic lifestyle that takes advantage of the disrupted host homeostasis. Furthermore, diseased samples share a common structure that was not found in completely healthy samples, suggesting that the disease state may occupy a narrow region within the space of possible configurations of the oral microbiome. Our pilot study demonstrates the power of high-throughput sequencing as a tool for understanding the role of the oral microbiome in periodontal disease. Despite a modest level of sequencing (∼2 lanes Illumina 76 bp PE) and high human DNA contamination (up to ∼90%) we were able to partially reconstruct several oral microbes and to preliminarily characterize some systems-level differences between the healthy and diseased oral microbiomes

Exploring the Human Microbiome: The Potential Future Role of Next-Generation Sequencing in Disease Diagnosis and Treatment

The interaction between the human microbiome and immune system has an effect on several human metabolic functions and impacts our well-being. Additionally, the interaction between humans and microbes can also play a key role in determining the wellness or disease status of the human body. Dysbiosis is related to a plethora of diseases, including skin, inflammatory, metabolic, and neurological disorders. A better understanding of the host-microbe interaction is essential for determining the diagnosis and appropriate treatment of these ailments. The significance of the microbiome on host health has led to the emergence of new therapeutic approaches focused on the prescribed manipulation of the host microbiome, either by removing harmful taxa or reinstating missing beneficial taxa and the functional roles they perform. Culturing large numbers of microbial taxa in the laboratory is problematic at best, if not impossible. Consequently, this makes it very difficult to comprehensively catalog the individual members comprising a specific microbiome, as well as understanding how microbial communities function and influence host-pathogen interactions. Recent advances in sequencing technologies and computational tools have allowed an increasing number of metagenomic studies to be performed. These studies have provided key insights into the human microbiome and a host of other microbial communities in other environments. In the present review, the role of the microbiome as a therapeutic agent and its significance in human health and disease is discussed. Advances in high-throughput sequencing technologies for surveying host-microbe interactions are also discussed. Additionally, the correlation between the composition of the microbiome and infectious diseases as described in previously reported studies is covered as well. Lastly, recent advances in state-of-the-art bioinformatics software, workflows, and applications for analysing metagenomic data are summarized

From Hydra to Humans: Insights into molecular mechanisms of aging and longevity

Human aging is characterized by progressive functional decline that coincides with both increased morbidity and mortality. Aging affects every human being and only few individuals achieve longevity, a very special phenotype marked by extraordinary healthy aging. This thesis consists of three chapters; each one is devoted to a separate project that contributes to the growing body of knowledge about aging and longevity. The work required the compilation, management and analysis of diverse big data sets and the application of cutting-edge statistical and computational methods. Chapter 1 - A functional genomics study was conducted in the potentially immortal freshwater polyp Hydra using body part-specific microarray and RNA sequencing data. The results revealed gene expression patterns that allow boundary maintenance during Hydra’s continuous cell proliferation and tissue self-renewal. Furthermore, this study provided evidence for de-acetylation as a key mechanism underlying compartmentalization. Surprisingly, FoxO, which is known to substantially drive developmental processes and stem cell renewal in Hydra, did not seem to be affected by the acetylation status. Chapter 2 - Long-lived individuals (LLI, >95 years of age) epitomize the healthy aging phenotype and are thought to carry beneficial genetic variants that predispose to human longevity. Despite extensive research efforts, only few of these genetic factors in LLI have been identified so far. In contrast to previous investigations which mainly focused on intronic variants, a genome-wide exome-based case-control study was performed. DNA samples of more than 1,200 German LLI, including 599 centenarians (≥100 years), and about 6,900 younger controls were used for single-variant and gene-based association analyses that yielded two new candidate longevity genes, fructosamine 3 kinase related protein (FN3KRP) and phosphoglycolate phosphatase (PGP). FN3KRP functions in the deglycation of proteins to restore their function, while PGP via controlling glycerol-3-phosphate levels affects both glucose and fat metabolism. Given the biological functions of the genes, their longevity-associations appear very plausible. Chapter 3 - In recent years, the intestinal microbiome (GM) has increasingly gained attention in aging and longevity research. A 16S rRNA microbiome study was conducted using 1301 stool samples of healthy individuals (age range: 19 - 104 years) that were drawn from three cohorts. The aim was to investigate potential associations among GM composition, host genetics and environmental factors during aging. The GM composition changed with age, showing an increase of opportunistic pathogens that may generate an inflammatory environment in the gut. Age explained only ~1% of the inter-individual variation, whereas anthropometric measures, genetic background and dietary patterns together explained 20%. Strikingly, clear GM population stratification in terms of four enterotype-like clusters was observed, which were predominantly associated with dietary patterns. The correction for these clusters was shown to increase the comparability of findings from the different cohorts. In addition, the LLI showed a specific gut microbial pattern, which is in line with previously published reports. The present work shows that a thorough bioinformatics expertise helps to address the complexity of the two phenotypes aging and longevity. One highlight of the thesis is the discovery of two new candidate longevity loci that, in view of the limited output of previous study approaches, enlarge the existing database