Aligner optimization increases accuracy and decreases compute times in multi-species sequence data.

As sequencing technologies have evolved, the tools to analyze these sequences have made similar advances. However, for multi-species samples, we observed important and adverse differences in alignment specificity and computation time for bwa- mem (Burrows-Wheeler aligner-maximum exact matches) relative to bwa-aln. Therefore, we sought to optimize bwa-mem for alignment of data from multi-species samples in order to reduce alignment time and increase the specificity of alignments. In the multi-species cases examined, there was one majority member (i.e. Plasmodium falciparum or Brugia malayi) and one minority member (i.e. human or the Wolbachia endosymbiont wBm) of the sequence data. Increasing bwa-mem seed length from the default value reduced the number of read pairs from the majority sequence member that incorrectly aligned to the reference genome of the minority sequence member. Combining both source genomes into a single reference genome increased the specificity of mapping, while also reducing the central processing unit (CPU) time. In Plasmodium, at a seed length of 18 nt, 24.1 % of reads mapped to the human genome using 1.7±0.1 CPU hours, while 83.6 % of reads mapped to the Plasmodium genome using 0.2±0.0 CPU hours (total: 107.7 % reads mapping; in 1.9±0.1 CPU hours). In contrast, 97.1 % of the reads mapped to a combined Plasmodium-human reference in only 0.7±0.0 CPU hours. Overall, the results suggest that combining all references into a single reference database and using a 23 nt seed length reduces the computational time, while maximizing specificity. Similar results were found for simulated sequence reads from a mock metagenomic data set. We found similar improvements to computation time in a publicly available human-only data set.

Distinguishing potential bacteria-tumor associations from contamination in a secondary data analysis of public cancer genome sequence data.

BACKGROUND: A variety of bacteria are known to influence carcinogenesis. Therefore, we sought to investigate if publicly available whole genome and whole transcriptome sequencing data generated by large public cancer genome efforts, like The Cancer Genome Atlas (TCGA), could be used to identify bacteria associated with cancer. The Burrows-Wheeler aligner (BWA) was used to align a subset of Illumina paired-end sequencing data from TCGA to the human reference genome and all complete bacterial genomes in the RefSeq database in an effort to identify bacterial read pairs from the microbiome. RESULTS: Through careful consideration of all of the bacterial taxa present in the cancer types investigated, their relative abundance, and batch effects, we were able to identify some read pairs from certain taxa as likely resulting from contamination. In particular, the presence of Mycobacterium tuberculosis complex in the ovarian serous cystadenocarcinoma (OV) and glioblastoma multiforme (GBM) samples was correlated with the sequencing center of the samples. Additionally, there was a correlation between the presence of Ralstonia spp. and two specific plates of acute myeloid leukemia (AML) samples. At the end, associations remained between Pseudomonas-like and Acinetobacter-like read pairs in AML, and Pseudomonas-like read pairs in stomach adenocarcinoma (STAD) that could not be explained through batch effects or systematic contamination as seen in other samples. CONCLUSIONS: This approach suggests that it is possible to identify bacteria that may be present in human tumor samples from public genome sequencing data that can be examined further experimentally. More weight should be given to this approach in the future when bacterial associations with diseases are suspected.

Mobile elements and viral integrations prompt considerations for bacterial DNA integration as a novel carcinogen.

Insertional mutagenesis has been repeatedly demonstrated in cancer genomes and has a role in oncogenesis. Mobile genetic elements can induce cancer development by random insertion into cancer related genes or by inducing translocations. L1s are typically implicated in cancers of an epithelial cell origin, while Alu elements have been implicated in leukemia as well as epithelial cell cancers. Likewise, viral infections have a significant role in cancer development predominantly through integration into the human genome and mutating or deregulating cancer related genes. Human papilloma virus is the best-known example of viral integrations contributing to carcinogenesis. However, hepatitis B virus, Epstein-Barr virus, and Merkel cell polyomavirus also integrate into the human genome and disrupt cancer related genes. Thus far, the role of microbes in cancer has primarily been attributed to mutations induced through chronic inflammation or toxins, as is the case with Helicobacter pylori and enterotoxigenic Bacteroides fragilis. We hypothesize that like mobile elements and viral DNA, bacterial and parasitic DNA may also integrate into the human somatic genome and be oncogenic. Until recently it was believed that bacterial DNA could not integrate into the human genome, but new evidence demonstrates that bacterial insertional mutagenesis may occur in cancer cells. Although this work does not show causation between bacterial insertions and cancer, it prompts more research in this area. Promising new sequencing technologies may reduce the risk of artifactual chimeric sequences, thus diminishing some of the challenges of identifying novel insertions in the somatic human genome.

A review of bacteria-animal lateral gene transfer may inform our understanding of diseases like cancer.

Lateral gene transfer (LGT) from bacteria to animals occurs more frequently than was appreciated prior to the advent of genome sequencing. In 2007, LGT from bacterial Wolbachia endosymbionts was detected in ~33% of the sequenced arthropod genomes using a bioinformatic approach. Today, Wolbachia/host LGT is thought to be widespread and many other cases of bacteria-animal LGT have been described. In insects, LGT may be more frequently associated with endosymbionts that colonize germ cells and germ stem cells, like Wolbachia endosymbionts. We speculate that LGT may occur from bacteria to a wide variety of eukaryotes, but only becomes vertically inherited when it occurs in germ cells. As such, LGT may happen routinely in somatic cells but never become inherited or fixed in the population. Lack of inheritance of such mutations greatly decreases our ability to detect them. In this review, we propose that such noninherited bacterial DNA integration into chromosomes in human somatic cells could induce mutations leading to cancer or autoimmune diseases in a manner analogous to mobile elements and viral integrations.

Bacteria-human somatic cell lateral gene transfer is enriched in cancer samples.

There are 10× more bacterial cells in our bodies from the microbiome than human cells. Viral DNA is known to integrate in the human genome, but the integration of bacterial DNA has not been described. Using publicly available sequence data from the human genome project, the 1000 Genomes Project, and The Cancer Genome Atlas (TCGA), we examined bacterial DNA integration into the human somatic genome. Here we present evidence that bacterial DNA integrates into the human somatic genome through an RNA intermediate, and that such integrations are detected more frequently in (a) tumors than normal samples, (b) RNA than DNA samples, and (c) the mitochondrial genome than the nuclear genome. Hundreds of thousands of paired reads support random integration of Acinetobacter-like DNA in the human mitochondrial genome in acute myeloid leukemia samples. Numerous read pairs across multiple stomach adenocarcinoma samples support specific integration of Pseudomonas-like DNA in the 5'-UTR and 3'-UTR of four proto-oncogenes that are up-regulated in their transcription, consistent with conversion to an oncogene. These data support our hypothesis that bacterial integrations occur in the human somatic genome and may play a role in carcinogenesis. We anticipate that the application of our approach to additional cancer genome projects will lead to the more frequent detection of bacterial DNA integrations in tumors that are in close proximity to the human microbiome.