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

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:
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:

The full citation to our paper is:

Mangul, Serghei; Loohuis, Loes Olde M; Ori, Anil; Jospin, Guillaume; Koslicki, David; Yang, Harry Taegyun; Wu, Timothy; Boks, Marco P; Lomen-Hoerth, Catherine; Wiedau-Pazos, Martina; Cantor, Rita; de Vos, Willem M; Kahn, Rene S; Eskin, Eleazar; Ophoff, Roel A

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

In: BioRxiv, (057570), 2016.

Abstract | Links | BibTeX

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

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.




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 ( at University of California, San Francisco.

ROP is available at

The article is available at:

The full citation to our paper is:

Mangul, Serghei; Yang, Harry Taegyun; Strauli, Nicolas; Gruhl, Franziska; Daley, Timothy; Christenson, Stephanie; Andersen, Agata Wesolowska; Spreafico, Roberto; Rios, Cydney; Eng, Celeste; Smith, Andrew D; Hernandez, Ryan D; Ophoff, Roel A; Santana, Jose Rodriguez; Woodruff, Prescott G; Burchard, Esteban; Seibold, Max A; Shifman, Sagiv; Eskin, Eleazar; Zaitlen, Noah

Dumpster diving in RNA-sequencing to find the source of every last read. Journal Article

In: BioRxiv, 2016.

Links | BibTeX

RNA Editing detection using High-Through Sequencing

The central dogma of biology indicates DNA sequence gets transcribed to RNA sequence and then RNA sequence gets translated to protein. Thus, for long time it was known fact that each base of RNA sequence corresponds to an exact base in DNA sequence. However, Mahendran et al. (10.1038/349434a0) discovered for the first time this one to one relation is not necessary true. The phenomena where RNA sequences and DNA sequences are different is known as RNA editing(RNA DNA Difference). Although the underlying cause for RNA editing is still unknown, it is known A to I editing is the most common. A-I editing occurs when adenine (A) DNA base converts to guanine (G) base. On the other hand other sorts of RNA editing in mammalian genomes was known to be rare until recently where Li et al. (10.1126/science.1207018) reported 10,000 cites of RNA editing in human cancer cell lines where a significant number of them are not A-I editing. This study was the first that use the high-through sequencing (HTS) technologies to detect the RNA editing in whole genome scale. Following this study series of works supported the Li et al. (10.1126/science.1207018) results as the RNA editing is more common as was known before HTS era. On the orthogonal direction series of works (10.1371/journal.pone.0025842), (10.1126/science.1209658), (10.1126/science.1210484), and (10.1126/science.1210624) indicate vast majority of RNA editing observed in the HTS data is due to systematic error in sequencing process.

We use mouse as a model organism to study the RNA editing in mammalian genomes. We use the F1 cross of C57BL/6 and DBA. Leveraging the power of F1 mice and the fact both strains where deeply sequenced by Sanger institute (10.1038/nature10413) provide us with an ease framework to study RNA editing in mammalian genomes. Furthermore, to remove any technical artifacts we use biological replicate of the same F1 cross and we consider the mRNA of both liver and adipose tissues. In our paper (10.1534/genetics.112.149054) we used a set of stringent conditions to make sure our results contain no possible sequencing artifacts. Although, our stringent conditions may remove some true positive, our goal is to illustrate the existing of sequencing artifacts and further indicates the RNA editing beside the A-I exists but not as common as A-I editing. We found 63 sites in liver and 216 sites in adipose which are RNA editing.