Tag: methods

Total RNA Sequencing reveals microbial communities in human blood and disease specific effects

Posted on by

Together with researchers from UC San Francisco, UC Davis, Oregon State University, and the Netherlands, our group recently published a paper on bioRxiv that presents a new method capable of identifying different microbes present in human blood. Our paper is featured in the Stanford University digest of microbiome papers, as well as an international science and technology news source. Here, we demonstrate the potential use of total RNA to study the relationship of specific diseases with microbes that inhabit the human body.

A growing body of evidence suggests that the human microbiome plays an important role in health and disease. In order to investigate the specific ways that microbes may influence disease development, we developed a novel ‘lost and found’ pipeline. Here, we use whole blood RNA sequencing (RNA-Seq) reads to detect a variety of microbial organisms. Our ‘lost and found’ pipeline utilizes high quality reads that fail to map to the human genome as candidate microbial reads. Since RNA-Seq has become a widely used technology in recent years with many large datasets available, we believe that our pipeline has great potential for application across tissues and disease types.

We applied our ‘lost and found’ pipeline to study the composition of blood microbiome in almost two hundred individuals, including healthy control individuals and patients with schizophrenia, bipolar disorder, and amyotrophic lateral sclerosis (also known as ALS or Lou Gehrig’s disease). Using this pipeline, we detected bacterial and archaeal phyla in blood using RNA sequencing (RNA-Seq) data. Our analyses of these data, including examination of positive and negative control datasets, suggest that detected phyla are in fact representative of the actual microbial communities in the individuals’ blood.

In total, we observed 23 distinct microbial phyla with on average 4.1 ± 2.0 phyla per individual. Phylogenetic classification is performed using Phylosift, which assigns the filtered candidate microbial reads to the microbial genes from 23 distinct taxa on the phylum level. The large majority of taxa that were observed in our sample are not universally present in all individuals, except for Proteobacteria that dominate all samples with 73.4% ± 18.3% relative abundance (dark green color). Here, we can see the genomic abundances of microbial taxa at phylum level of classification for each of the four groups:
figure2_v2
Further, in comparison to individuals in the other three groups, we observed a significantly increased microbial diversity in blood samples from individuals with schizophrenia. We replicated this finding with an independent schizophrenia case-control study. The increased microbial diversity observed in schizophrenia could be part of the disease etiology (i.e., causing schizophrenia) or may be a secondary effect of disease status. In the absence of a direct link with genetic susceptibility and the reported correlation with the immune system, we hypothesize that the observed effect in schizophrenia is secondary to disease. This phenomena may be a consequence of lifestyle differences of schizophrenia patients, including cigarette smoking, drug use, or other environmental exposures. Future targeted and/or longitudinal studies with larger sample sizes, detailed clinical phenotypes, and more in-depth sequencing are needed to corroborate this hypothesis.

We hope that our finding of increased diversity in schizophrenia will ultimately lead to a better understanding of the functional mechanisms underlying the connection between immune system, blood microbiome, and disease etiology. With the increasing availability of large scale RNA-Seq datasets collected from different phenotypes and tissue types, we anticipate that the application of our ‘lost and found’ pipeline will lead to the generation of a range of novel hypotheses, ultimately aiding our understanding of the role of the microbiome in health and disease.

This project was led by Serghei Mangul and Loes Olde Loohuis (Roel Ophoff group). This was a joint project with the Roel Ophoff group at Center for Neurobehavioral Genetics at the Semel Institute for Neuroscience and Human Behavior, University California, Los Angeles, CA, USA.

The article is available at: http://biorxiv.org/content/early/2016/06/07/057570.

The full citation to our paper is:

Sorry, no publications matched your criteria.

Dumpster diving in RNA-seq to find the source of every last read

Posted on by

Our group recently developed the Read Origin Protocol (ROP) method to discover the source of all reads in an RNA-seq experiment. Reads originate from complex RNA molecules, recombinant antibodies and microbial communities. ROP accounts for 98.8% of all reads across poly(A) and ribo-depletion protocols, compared to 83.8% by conventional reference-based protocols. We find that the vast majority of unmapped reads are human in origin and originate from diverse sources, including repetitive elements, non-co-linear elements or recombined B and T cell receptors (BCR/TCR). In addition to human RNA, a large number of reads were microbial in origin, often occurring in sufficient numbers to study the taxonomic composition of microbial communities.

 

Picture1

 

The majority of RNA-Seq analyses begin by mapping each experimentally produced sequence (i.e., read) to a set of annotated reference sequences for the organism of interest. For both biological and technical reasons, a significant fraction of reads remains unmapped. Our study is the first that systematically accounts for almost all reads in RNA-seq studies. We demonstrate the value of analyzing unmapped reads present in the RNA-seq data to better understand the functional mechanisms underlying the connection between immune system, microbiome, human gene expression, and disease etiology.

We applied our method to to RNA-seq data from 53 asthmatic cases and 33 controls collected from three tissues, using both poly(A) selection and ribo-depletion libraries. Using the ROP pipeline we show that immune profiles of asthmatic individuals are significantly different from the controls with decreased T-cell/B-cell receptor diversity and that immune diversity is inversely correlated with microbial load. This case study highlights the potential for novel discoveries without additional TCR/BCR or microbiome sequencing when the information in RNA-seq data is fully leveraged by incorporating the analysis of unmapped reads.

The ROP can not only help researchers make the best use of sequencing data, but will also enable additional scientific questions to be answered with no additional cost. For example, one can now interrogate additional features of the immune system without additional expensive TCR/BCR sequencing. The ‘dumpster diving’ profile of unmapped reads output by our method is not limited to RNA-Seq technology and may be applied to whole-exome and whole-genome sequencing. We anticipate that ‘dumpster diving’ profiling will find broad future applications in studies involving different tissue and disease types.

This project was led by Serghei Mangul and involved Harry Yang (Taegyun), both of whom developed the protocol as open source software. This was a joint project with the Noah Zaitlen group (http://zaitlenlab.ucsf.edu/) at University of California, San Francisco.

ROP is available at https://sergheimangul.wordpress.com/rop/.

The article is available at: http://biorxiv.org/content/early/2016/05/13/053041.

The full citation to our paper is:

Sorry, no publications matched your criteria.

Multiple testing correction in linear mixed models

Posted on by
Our group recently published a new paper on multiple testing applied to genetic studies with population structure.  This project was led by Jong Wha (Joanne) Joo and also involved Farhad Hormozdiari.  The project was joint with Buhm Han’s group.  The approach built upon Buhm Han’s previous work SLIDE (Han et al. 2009; Han and Eskin 2012).
 
Genome-wide association studies (GWAS) have discovered many variants that are associated with complex traits in the human genome. In GWAS, researchers collect both phenotypic information and genetic information on variants spread through the genome from a population. In order to identify the set of variants associated with a trait of interest, we assess correlations between the phenotype and the genetic information at each variant, which we call the genotype. GWAS are now routinely performed on tens of thousands of individuals—and millions of genetic variants.
 
GWAS methodology must address specific problems that are tied to this exceptionally large scale of analysis. One major challenge in GWAS is multiple hypothesis testing. In routine analyses, the significance of hypothesis testing is assessed using the p value as a per-marker threshold. However, GWAS involves computing up to millions of statistical tests in a single study. When using traditional association study techniques, multiple hypothesis testing can generate false positives or spurious associations, and p value threshold for significance must be adjusted to control the overall false positive rate.
Several approaches are useful in correcting these potential pitfalls, including Bonferroni correction and permutation test.
 
Recently, researchers have accepted the linear mixed model (LMM) as standard practice for performing GWAS. The LMM can address two important challenges in GWAS: population structure and insufficient power. Population structure refers to the complex relatedness structure among individuals, which can drive errors in data reporting such as false positives. In many cases, LMM approaches can increase the statistical power and avoid generating false positives by explicitly modeling the population structure’s genetic relationships. Nonetheless, multiple hypothesis testing with LMM approaches may generate some errors of association. Unfortunately, the current approaches for multiple hypothesis testing correction cannot be applied to LMM.  This is because population structure actually affects the correlation structure of the statistics as we show in the paper.
 
To address this issue, we developed the first gold standard approach for multiple hypothesis testing correction in LMM. This method, called multiple testing in transformed space (MultiTrans), can efficiently correct for multiple testing in LMM approaches. MultiTrans is a parametric bootstrapping resampling approach that is the equivalent of the permutation test. Specifically, our approach samples randomized null phenotypes from the distribution fitted by LMM.
 
Straightforward parametric bootstrapping where phenotypes are sampled is prohibitively computationally expensive.  MultiTrans instead utilizes   a Multivariate Normal Distribution to directly samples the association statistics.  The figure shows an overview of our methodology.
figure-overview
The full citation to our paper is:

Sorry, no publications matched your criteria.

 
 
Multiple hypothesis testing is an essential step in GWAS analysis. The correct per-marker threshold differs as a function of species, marker densities, genetic relatedness, and trait heritability—and no previous multiple testing correction methods can comprehensively account for these factors. The method we developed to address this issue, MultiTrans, is an efficient and accurate multiple testing correction approach for LMM. Our method (a) performs a unique transformation of genotype data to account for actual genetic relatedness and heritability under LMM approaches, and (b) efficiently utilizes the multivariate normal distribution. Using MultiTrans, we accurately estimated per-marker thresholds in mouse, yeast, and human datasets—while reducing computation time from months to hours.