Microbes dominate life on Earth. Evolutionary pressure exerted on microbial communities by environmental stressors such as climate change and pollution has global impact. Understanding the environment-specific microbial molecular functions is, therefore, a critical challenge vital for the analysis of microbiome behavior and, potentially, synthetic function optimization.
How does the environment drive microbiome consolidation into a functional unit? The recent emergence of high-throughput genomic sequencing, coupled with our growing analytical capacities, has unlocked new horizons in our understanding of the microbial world. There are currently over 100K sequenced metagenomic samples in the public domain. However, making sense of this deluge of data requires efficient and accurate computational techniques. The identification of microbial clades resident in a particular environmental niche is only an estimate of the microbiome’s functional potential. Instead, our function-based approach to bacterial evaluation, FuSiON (see Zhu, C., Delmont, T.O., Vogel, T.M., Bromberg, Y. (2015) Functional basis of microorganism classification. PLoS Comput Biol. 11(8): e1004472) can, potentially, be applied to microbiome analysis of functional diversity (work in progress).
Annotating metagenome-encoded functions is complicated by the difficulty of assembly of sequenced reads. To address this issue, we are developing an algorithm for mapping raw metagenomic reads to the functions of their corresponding “parent” genes. In testing, the prototype of our method can recapitulate the correct experimentally annotated functionality of parent proteins for the majority of reads. We will further develop tools to estimate the microbiome diversity by mapping, via FuSiON, the functions identified from metagenome reads to functions present in individual bacterial genomes. We expect that our approach will recapitulate the genomes that can be assembled, as well as an additional large set of organisms present in the microbiome, but not identifiable with current techniques.
We hypothesize that a community specific to an environmental niche contains organisms with clearly differentiated functional roles. Thus, its metagenome likely comprises a significant number of niche-specific functional clusters of a few orthologs each. Moreover, the specific combinations of functions, i.e. functional pathways, represented in a given metagenome will provide clues to its microbial diversity and, arguably more importantly, elucidate its functional capacity. Comparison of functional pathways across metagenomes will highlight the emergent functionality of microbiomes; i.e. functions that can only be carried via community-wide interaction. As the interactions between organisms in a microbiome are, to a large extent, carried out by secreted proteins, we expect to observe a higher fraction of these as inter-organism interaction increases. To evaluate this hypothesis we recently developed a method for the prediction, from read data, of one type of secreted proteins (type III effectors, see Goldberg, T., Rost, B., Bromberg, Y. (2016) Sequence-based prediction of bacterial type III effector proteins. Nat Sci Rep In press). Overall, low levels of non-essential gene redundancy and a high number of secreted proteins should indicate symbiosis within a well-established microbiome.
Different ecological niches present different challenges to their inhabitants. The organisms occupying these environments evolve over time to obtain and retain the features necessary for successful habitation. Our new methods will help computationally annotate the organismal and functional diversity present in a microbiome, as well as highlight the interactions that make this type of microbial living possible.
TUM-IAS funded doctoral candidate:
Yannick Mahlich, Bioinformatics