Smoking accelerates aging of the small airway epithelium

Background

Aging involves multiple biologically complex processes characterized by a decline in cellular homeostasis over time leading to a loss and impairment of physiological integrity and function. Specific cellular hallmarks of aging include abnormal gene expression patterns, shortened telomeres and associated biological dysfunction. Like all organs, the lung demonstrates both physiological and structural changes with age that result in a progressive decrease in lung function in healthy individuals. Cigarette smoking accelerates lung function decline over time, suggesting smoking accelerates aging of the lung. Based on this data, we hypothesized that cigarette smoking accelerates the aging of the small airway epithelium, the cells that take the initial brunt of inhaled toxins from the cigarette smoke and one of the primary sites of pathology associated with cigarette smoking.

Methods

Using the sensitive molecular parameters of aging-related gene expression and telomere length, the aging process of the small airway epithelium was assessed in age matched healthy nonsmokers and healthy smokers with no physical manifestation of lung disease or abnormalities in lung function.

Results

Analysis of a 73 gene aging signature demonstrated that smoking significantly dysregulates 18 aging-related genes in the small airway epithelium. In an independent cohort of male subjects, smoking significantly reduced telomere length in the small airway epithelium of smokers by 14% compared to nonsmokers.

Conclusion

These data provide biologic evidence that smoking accelerates aging of the small airway epithelium.

Electronic supplementary material

The online version of this article (doi:10.1186/s12931-014-0094-1) contains supplementary material, which is available to authorized users.     

De novo ChIP-seq analysis

Methods for the analysis of chromatin immunoprecipitation sequencing (ChIP-seq) data start by aligning the short reads to a reference genome. While often successful, they are not appropriate for cases where a reference genome is not available. Here we develop methods for de novo analysis of ChIP-seq data. Our methods combine de novo assembly with statistical tests enabling motif discovery without the use of a reference genome. We validate the performance of our method using human and mouse data. Analysis of fly data indicates that our method outperforms alignment based methods that utilize closely related species.

Electronic supplementary material

The online version of this article (doi:10.1186/s13059-015-0756-4) contains supplementary material, which is available to authorized users.     

Tools for T-RFLP data analysis using Excel

Background

Terminal restriction fragment length polymorphism (T-RFLP) analysis is a DNA-fingerprinting method that can be used for comparisons of the microbial community composition in a large number of samples. There is no consensus on how T-RFLP data should be treated and analyzed before comparisons between samples are made, and several different approaches have been proposed in the literature. The analysis of T-RFLP data can be cumbersome and time-consuming, and for large datasets manual data analysis is not feasible. The currently available tools for automated T-RFLP analysis, although valuable, offer little flexibility, and few, if any, options regarding what methods to use. To enable comparisons and combinations of different data treatment methods an analysis template and an extensive collection of macros for T-RFLP data analysis using Microsoft Excel were developed.

Results

The Tools for T-RFLP data analysis template provides procedures for the analysis of large T-RFLP datasets including application of a noise baseline threshold and setting of the analysis range, normalization and alignment of replicate profiles, generation of consensus profiles, normalization and alignment of consensus profiles and final analysis of the samples including calculation of association coefficients and diversity index. The procedures are designed so that in all analysis steps, from the initial preparation of the data to the final comparison of the samples, there are various different options available. The parameters regarding analysis range, noise baseline, T-RF alignment and generation of consensus profiles are all given by the user and several different methods are available for normalization of the T-RF profiles. In each step, the user can also choose to base the calculations on either peak height data or peak area data.

Conclusions

The Tools for T-RFLP data analysis template enables an objective and flexible analysis of large T-RFLP datasets in a widely used spreadsheet application.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-014-0361-7) contains supplementary material, which is available to authorized users.     

Cancer progression modeling using static sample data

As molecular profiling data continue to accumulate, the design of integrative computational analyses that can provide insights into the dynamic aspects of cancer progression becomes feasible. Here, we present a novel computational method for the construction of cancer progression models based on the analysis of static tumor samples. We demonstrate the reliability of the method with simulated data, and describe the application to breast cancer data. Our findings support a linear, branching model for breast cancer progression. An interactive model facilitates the identification of key molecular events in the advance of disease to malignancy.

Electronic supplementary material

The online version of this article (doi:10.1186/s13059-014-0440-0) contains supplementary material, which is available to authorized users.     

An efficient algorithm to explore liquid association on a genome-wide scale

Background

The growing wealth of public available gene expression data has made the systemic studies of how genes interact in a cell become more feasible. Liquid association (LA) describes the extent to which coexpression of two genes may vary based on the expression level of a third gene (the controller gene). However, genome-wide application has been difficult and resource-intensive. We propose a new screening algorithm for more efficient processing of LA estimation on a genome-wide scale and apply its use to a Saccharomyces cerevisiae data set.

Results

On a test subset of the data, the fast screening algorithm achieved >99.8% agreement with the exhaustive search of LA values, while reduced run time by 81–93 %. Using a well-known yeast cell-cycle data set with 6,178 genes, we identified triplet combinations with significantly large LA values. In an exploratory gene set enrichment analysis, the top terms for the controller genes in these triplets with large LA values are involved in some of the most fundamental processes in yeast such as energy regulation, transportation, and sporulation.

Conclusion

In summary, in this paper we propose a novel, efficient algorithm to explore LA on a genome-wide scale and identified triplets of interest in cell cycle pathways using the proposed method in a yeast data set. A software package named fastLiquidAssociation for implementing the algorithm is available through http://www.bioconductor.org.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-014-0371-5) contains supplementary material, which is available to authorized users.     

An evidence-based approach to identify aging-related genes in Caenorhabditis elegans

Background

Extensive studies have been carried out on Caenorhabditis elegans as a model organism to elucidate mechanisms of aging and the effects of perturbing known aging-related genes on lifespan and behavior. This research has generated large amounts of experimental data that is increasingly difficult to integrate and analyze with existing databases and domain knowledge. To address this challenge, we demonstrate a scalable and effective approach for automatic evidence gathering and evaluation that leverages existing experimental data and literature-curated facts to identify genes involved in aging and lifespan regulation in C. elegans.

Results

We developed a semantic knowledge base for aging by integrating data about C. elegans genes from WormBase with data about 2005 human and model organism genes from GenAge and 149 genes from GenDR, and with the Bio2RDF network of linked data for the life sciences. Using HyQue (a Semantic Web tool for hypothesis-based querying and evaluation) to interrogate this knowledge base, we examined 48,231 C. elegans genes for their role in modulating lifespan and aging. HyQue identified 24 novel but well-supported candidate aging-related genes for further experimental validation.

Conclusions

We use semantic technologies to discover candidate aging genes whose effects on lifespan are not yet well understood. Our customized HyQue system, the aging research knowledge base it operates over, and HyQue evaluations of all C. elegans genes are freely available at http://hyque.semanticscience.org.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0469-4) contains supplementary material, which is available to authorized users.     

Genome-wide DNA methylation changes in skeletal muscle between young and middle-aged pigs

Background

Age-related physiological, biochemical and functional changes in mammalian skeletal muscle have been shown to begin at the mid-point of the lifespan. However, the underlying changes in DNA methylation that occur during this turning point of the muscle aging process have not been clarified. To explore age-related genomic methylation changes in skeletal muscle, we employed young (0.5 years old) and middle-aged (7 years old) pigs as models to survey genome-wide DNA methylation in the longissimus dorsi muscle using a methylated DNA immunoprecipitation sequencing approach.

Results

We observed a tendency toward a global loss of DNA methylation in the gene-body region of the skeletal muscle of the middle-aged pigs compared with the young group. We determined the genome-wide gene expression pattern in the longissimus dorsi muscle using microarray analysis and performed a correlation analysis using DMR (differentially methylated region)-mRNA pairs, and we found a significant negative correlation between the changes in methylation levels within gene bodies and gene expression. Furthermore, we identified numerous genes that show age-related methylation changes that are potentially involved in the aging process. The methylation status of these genes was confirmed using bisulfite sequencing PCR. The genes that exhibited a hypomethylated gene body in middle-aged pigs were over-represented in various proteolysis and protein catabolic processes, suggesting an important role for these genes in age-related muscle atrophy. In addition, genes associated with tumorigenesis exhibited aged-related differences in methylation and expression levels, suggesting an increased risk of disease associated with increased age.

Conclusions

This study provides a comprehensive analysis of genome-wide DNA methylation patterns in aging pig skeletal muscle. Our findings will serve as a valuable resource in aging studies, promoting the pig as a model organism for human aging research and accelerating the development of comparative animal models in aging research.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-653) contains supplementary material, which is available to authorized users.     

Dynamic probabilistic threshold networks to infer signaling pathways from time-course perturbation data

Background

Network inference deals with the reconstruction of molecular networks from experimental data. Given N molecular species, the challenge is to find the underlying network. Due to data limitations, this typically is an ill-posed problem, and requires the integration of prior biological knowledge or strong regularization. We here focus on the situation when time-resolved measurements of a system’s response after systematic perturbations are available.

Results

We present a novel method to infer signaling networks from time-course perturbation data. We utilize dynamic Bayesian networks with probabilistic Boolean threshold functions to describe protein activation. The model posterior distribution is analyzed using evolutionary MCMC sampling and subsequent clustering, resulting in probability distributions over alternative networks. We evaluate our method on simulated data, and study its performance with respect to data set size and levels of noise. We then use our method to study EGF-mediated signaling in the ERBB pathway.

Conclusions

Dynamic Probabilistic Threshold Networks is a new method to infer signaling networks from time-series perturbation data. It exploits the dynamic response of a system after external perturbation for network reconstruction. On simulated data, we show that the approach outperforms current state of the art methods. On the ERBB data, our approach recovers a significant fraction of the known interactions, and predicts novel mechanisms in the ERBB pathway.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2105-15-250) contains supplementary material, which is available to authorized users.     

Gene expression associated with white syndromes in a reef building coral,Acropora hyacinthus

Background

Corals are capable of launching diverse immune defenses at the site of direct contact with pathogens, but the molecular mechanisms of this activity and the colony-wide effects of such stressors remain poorly understood. Here we compared gene expression profiles in eight healthy Acropora hyacinthus colonies against eight colonies exhibiting tissue loss commonly associated with white syndromes, all collected from a natural reef environment near Palau. Two types of tissues were sampled from diseased corals: visibly affected and apparently healthy.

Results

Tag-based RNA-Seq followed by weighted gene co-expression network analysis identified groups of co-regulated differentially expressed genes between all health states (disease lesion, apparently healthy tissues of diseased colonies, and fully healthy). Differences between healthy and diseased tissues indicate activation of several innate immunity and tissue repair pathways accompanied by reduced calcification and the switch towards metabolic reliance on stored lipids. Unaffected parts of diseased colonies, although displaying a trend towards these changes, were not significantly different from fully healthy samples. Still, network analysis identified a group of genes, suggestive of altered immunity state, that were specifically up-regulated in unaffected parts of diseased colonies.

Conclusions

Similarity of fully healthy samples to apparently healthy parts of diseased colonies indicates that systemic effects of white syndromes on A. hyacinthus are weak, which implies that the coral colony is largely able to sustain its physiological performance despite disease. The genes specifically up-regulated in unaffected parts of diseased colonies, instead of being the consequence of disease, might be related to the originally higher susceptibility of these colonies to naturally occurring white syndromes.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1540-2) contains supplementary material, which is available to authorized users.     

Phenotypic flexibility as a measure of health: the optimal nutritional stress response test

Nutrition research is struggling to demonstrate beneficial health effects, since nutritional effects are often subtle and long term. Health has been redefined as the ability of our body to cope with daily-life challenges. Physiology acts as a well-orchestrated machinery to adapt to the continuously changing environment. We term this adaptive capacity “phenotypic flexibility.” The phenotypic flexibility concept implies that health can be measured by the ability to adapt to conditions of temporary stress, such as physical exercise, infections or mental stress, in a healthy manner. This may offer a more sensitive way to assess changes in health status of healthy subjects. Here, we performed a systematic review of 61 studies applying different nutritional stress tests to quantify health and nutritional health effects, with the objective to define an optimal nutritional stress test that has the potential to be adopted as the golden standard in nutrition research. To acknowledge the multi-target role of nutrition, a relevant subset of 50 processes that govern optimal health, with high relevance to diet, was used to define phenotypic flexibility. Subsequently, we assessed the response of biomarkers related to this subset of processes to the different challenge tests. Based on the obtained insights, we propose a nutritional stress test composed of a high-fat, high-caloric drink, containing 60 g palm olein, 75 g glucose and 20 g dairy protein in a total volume of 400 ml. The use of such a standardized nutritional challenge test in intervention studies is expected to demonstrate subtle improvements of phenotypic flexibility, thereby enabling substantiation of nutritional health effects.

Electronic supplementary material

The online version of this article (doi:10.1007/s12263-015-0459-1) contains supplementary material, which is available to authorized users.     

Assessing structural variation in a personal genome—towards a human reference diploid genome

Background

Characterizing large genomic variants is essential to expanding the research and clinical applications of genome sequencing. While multiple data types and methods are available to detect these structural variants (SVs), they remain less characterized than smaller variants because of SV diversity, complexity, and size. These challenges are exacerbated by the experimental and computational demands of SV analysis. Here, we characterize the SV content of a personal genome with Parliament, a publicly available consensus SV-calling infrastructure that merges multiple data types and SV detection methods.

Results

We demonstrate Parliament’s efficacy via integrated analyses of data from whole-genome array comparative genomic hybridization, short-read next-generation sequencing, long-read (Pacific BioSciences RSII), long-insert (Illumina Nextera), and whole-genome architecture (BioNano Irys) data from the personal genome of a single subject (HS1011). From this genome, Parliament identified 31,007 genomic loci between 100 bp and 1 Mbp that are inconsistent with the hg19 reference assembly. Of these loci, 9,777 are supported as putative SVs by hybrid local assembly, long-read PacBio data, or multi-source heuristics. These SVs span 59 Mbp of the reference genome (1.8%) and include 3,801 events identified only with long-read data. The HS1011 data and complete Parliament infrastructure, including a BAM-to-SV workflow, are available on the cloud-based service DNAnexus.

Conclusions

HS1011 SV analysis reveals the limits and advantages of multiple sequencing technologies, specifically the impact of long-read SV discovery. With the full Parliament infrastructure, the HS1011 data constitute a public resource for novel SV discovery, software calibration, and personal genome structural variation analysis.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1479-3) contains supplementary material, which is available to authorized users.     

ANGSD: Analysis of Next Generation Sequencing Data

Background

High-throughput DNA sequencing technologies are generating vast amounts of data. Fast, flexible and memory efficient implementations are needed in order to facilitate analyses of thousands of samples simultaneously.

Results

We present a multithreaded program suite called ANGSD. This program can calculate various summary statistics, and perform association mapping and population genetic analyses utilizing the full information in next generation sequencing data by working directly on the raw sequencing data or by using genotype likelihoods.

Conclusions

The open source c/c++ program ANGSD is available at http://www.popgen.dk/angsd. The program is tested and validated on GNU/Linux systems. The program facilitates multiple input formats including BAM and imputed beagle genotype probability files. The program allow the user to choose between combinations of existing methods and can perform analysis that is not implemented elsewhere.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-014-0356-4) contains supplementary material, which is available to authorized users.     

A computational study of dsDNA pairs and vibrational resonance in separating water

This article investigates the relationship between molecular sequence and dependent interacting behavior of molecular segment pairs and secondly, sequence dependent, vibrational resonance in surrounding normal saline, protein-free water. The development of a molecular model to explore these systems phenomena, the results of several nanoscale molecular dynamics simulations, and analysis of behavior of interacting ΦX174 double-stranded DNA segment pair models in various configurations are presented. Fourier analysis revealed intriguing vibration frequencies within the solvent plane between the segments, while subsequent frequency domain transformation of the time domain waveforms revealed statistically significant resonating harmonic signals in the THz range.

Electronic supplementary material

The online version of this article (doi:10.1007/s11693-014-9157-3) contains supplementary material, which is available to authorized users.     

MSEA: detection and quantification of mutation hotspots through mutation set enrichment analysis

Many cancer genes form mutation hotspots that disrupt their functional domains or active sites, leading to gain- or loss-of-function. We propose a mutation set enrichment analysis (MSEA) implemented by two novel methods, MSEA-clust and MSEA-domain, to predict cancer genes based on mutation hotspot patterns. MSEA methods are evaluated by both simulated and real cancer data. We find approximately 51% of the eligible known cancer genes form detectable mutation hotspots. Application of MSEA in eight cancers reveals a total of 82 genes with mutation hotspots, including well-studied cancer genes, known cancer genes re-found in new cancer types, and novel cancer genes.

Electronic supplementary material

The online version of this article (doi:10.1186/s13059-014-0489-9) contains supplementary material, which is available to authorized users.