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Chicago Team Develops Method for Sequencing Transfer RNAs in Microbiomes


SAN FRANCISCO (GenomeWeb) – Transfer RNAs make up around 20 percent of total RNA and can provide insights on metabolic activity and protein synthesis, yet are rarely studied due to the challenges associated with sequencing them.

Now, a group of researchers from the University of Chicago has developed a method to sequence transfer RNAs in a microbiome, along with bioinformatics tools for analysis.

The researchers described the method for sequencing microbiome samples last month in Nature Communications, which built off previous work in which they developed a tRNA-seq method for human cells. They plan to commercialize the technology through a University of Chicago spinout company, which is tentatively named 4SR Biosciences.

Transfer RNAs are difficult to sequence because they are chemically modified, explained Tao Pan, co-senior author of the study and professor of biochemistry and molecular biology at the University of Chicago. Yet, sequencing tRNAs from an environmental microbial community could shed light on how a microbiome responds to changing environmental conditions. The moleculess can also serve as phylogenetic markers, similar to 16S rRNA. A. Murat Eren, assistant professor of medicine at the University of Chicago and co-senior author, said that the main challenge with sequencing tRNAs is reverse transcribing the molecules into cDNA, a standard step in RNA-seq experiments.

Because tRNA is chemically modified with methyl groups and because it has a more "rigid" structure, and the methyl groups block the operation of the reverse transcriptase, Eren said. "So the ability to characterize them has been limited."

In order to address this problem, the researchers made use of two different enzymes: one that could remove the methyl groups and a thermophilic reverse transcriptase that could read through some of the tRNA modifications. Then, they divided the sample in half, removing the methyl groups from one half and leaving the other half untreated.

Next, the team proceeded with cDNA synthesis on both the treated and untreated half of the sample using the thermophilic reverse transcriptase, which, unlike the version of the enzyme typically used in RNA-seq, can transcribe through most of the chemical modifications, Eren said. However, when it does so, it leaves a mutation, providing another means of tracking the locations of the modifications.

The researchers also developed bioinformatics tools to analyze the data. One challenge in developing bioinformatics tools is that there are very few reference tRNA sequences. Last year, Pan, Eren and other University of Chicago researchers were awarded $1 million from the Keck Foundation to develop better bioinformatics tools for analyzing tRNA-seq data from microbiomes.

In the recently published proof-of-concept study, the researchers tested their method on gut microbiome samples from mice fed high-fat and low-fat diets. The team first compared the tRNA-seq method with 16S rRNA sequencing for its ability to characterize the microbial community. On average, they identified around 2.2 million tRNA sequences per sample that could be assigned to a specific location and 3.8 million other reads that were either too short to be assigned or were ambiguous. Short sequences can occur when a methyl group is not removed or if other structural motifs hinder cDNA synthesis.

Next, they analyzed the tRNA sequences and the 16S rRNA sequences. To analyze the tRNA-seq data, the researchers designed bioinformatics tools to identify sequences that had conserved sequence motifs and secondary structures. For the six most abundant bacterial classes, the two methods matched, but proportions of some of the bacteria classes differed. For instance, tRNA-seq showed higher fractions of Clostridia and Actinobacteria and lower fractions of Bacteriodia and Erysipelotrichia classes. The authors noted there could be a number of reasons for this discrepancy, including the use of PCR in the 16s rRNA workflow.

Finally, the researchers analyzed the modification patterns in the tRNA-seq data, comparing the low-fat fed mice with the ones on a high-fat diet. This ability is important, because rather than simply looking at the makeup of a gut microbiome based on diet, tRNA-seq enables researchers to look at how the "microbes modify their machinery in response to their environmental conditions," said Eren, noting that the study showed that "identical species had different modification patterns between high-fat and low-fat fed mice."

Pan said that the next step is to expand the study to evaluate other microbiome sample types and other environmental conditions — for instance, how the human oral microbiome responds to changes in diet or how microbes in the open ocean respond to changes in conditions like nutrient availability.

The team will also be looking to improve the technology. Right now, one major limitation is that it requires large sample inputs, which is fine for abundant samples like gut microbiomes, but not ideal for rarer sample types, like ocean microbial communities, Pan said.

Ultimately, he said, tRNA-seq could be used as a complementary tool to other analyses like 16S rRNA sequencing and metagenomic sequencing. "That was the ultimate goal, to make this as one of several ways to characterize the microbiome."

The investigators do not have a timeline for commercializing the tools or launching its startup, but Pan said that they plan to offer the method both as a custom service for researchers and also to eventually develop it into a kit form.