Bibliographic record
Abstract
This past year, we witnessed the synthetic construction of a minimal genome capable of supporting a viable, free-living, cell (Hutchison et al., 2016). Although the genome of this synthetic microbe contains only 473 genes, 149 of them have unknown function. The mystery of this result is humbling as it illuminates that we do not yet understand the molecular underpinnings of core biological functions and identifies future research directions towards defining those functions. Another major hallmark of this work was its definition of the ‘essential’ core genome, which confers viability, and the ‘quasi-essential’ genome that was not sufficient for viability, but was necessary to achieve reasonable growth rates of the cell. The finding of both an essential and a quasi-essential genome opens an important philosophical question: What is the definition of a core genome? Are the traits encoded by the core genome confined to those that confer viability or are there equally important functions that determine whether an organism achieves our definition of ecological success? My gaze into the Crystal Ball elevates and frames this question of defining core versus accessory traits as we gain ground in large-scale ‘omics analyses and observe extremely rapid adaptations of microbial communities to ecological pressures that often rely heavily on lateral gene transfer. I predict that accessory traits, defined for simplicity as non-essential for viability, will provide increasingly important information regarding the success of microbial communities. Furthermore, I predict that the lines between defining core and accessory functions will become fuzzier as we recognize that the same enzyme can play core or accessory roles, alone or in combination with other enzymes, depending on physiological – and environmental – context. The current explosion of genome-sequence information has resulted in robust, publicly accessible databases that have revealed remarkable insights into microbial genome structure and content. I have been fortunate to work within two large coordinated research networks focused on the sequencing and annotation of methane-, ammonia- and nitrite-oxidizing microorganisms from culture collections. One of the main goals of both research networks was to define core functions that allow for aerobic growth on methane, ammonia or nitrite as sole energy-yielding substrates. As anticipated, our gaze into the genomes largely confirmed well-characterized sets of core genes discovered through decades of bench work prior to the genome-sequencing revolution (Trotsenko and Murrell, 2008; Klotz and Stein, 2011). Unexpectedly, the genomes encoded a large diversity and abundance of accessory modules, a finding that radically altered our perception of these cohorts as metabolic specialists. For instance, we found that aerobic methanotrophic bacteria encoding functional modules for denitrification can thrive under extreme hypoxia by respiring nitrate and nitrite, which explains their success in oxygen-devoid ecosystems (Kits et al., 2015; Oswald et al., 2016). Although it was already known that bacterial methanotrophs in the NC10 phylum grow in anoxic environments by generating O2 internally (Ettwig et al., 2010) and that some gammaproteobacterial methanotrophs grow under extreme hypoxia by fermentation (Kalyuzhnaya et al., 2013), our results were unexpected because none of the previously characterized methanotroph genomes encoded inventory capable of supporting electrogenic anaerobic respiration. In the ammonia-oxidizing bacteria, accessory denitrification modules can support nitrifier denitrification activity, resulting in the emission of nitrous oxide, but only if the module includes a cytochrome c-dependent nitric oxide reductase (Kozlowski et al., 2016a). Also in the ammonia-oxidizing bacteria, nitrite reductase (nirK) is a common accessory gene that can be deleted or absent from their genomes without significant loss of core function (Kozlowski et al., 2014). The picture is quite different in the ammonia-oxidizing Thaumarchaeota where nirK appears to be a requisite member of the core genome, and nitric oxide reductase genes are missing altogether (Kozlowski et al., 2016b). This difference in core versus accessory function of the same enzyme, NirK, in ammonia-oxidizing microbes was initially confounding. However, evidence is continuing to mount that the same enzyme, NirK, is as equally important to (thaumarchaeotal) nitrification as it is to (bacterial) denitrification, and its metabolic context is multifaceted. If we now examine the Nitrospirae bacterial phylum, a wealth of unexpected inventory and activity has been recently characterized, suggesting that nitrite oxidation is but one of their many possible functions. For instance, members of the Nitrospira can use hydrogen, formate or nitrite as an energy source, they can decompose urea or cyanate to enable cross-feeding with their ammonia-oxidizing partners, they can use a variety of electron acceptors, and some are capable of oxidizing ammonia all the way to nitrate in the ‘comammox’ process (Daims et al., 2016). This immense catabolic versatility within the Nitrospirae presents an important caveat to the question of core versus accessory genome as some genes and modules are present in all available Nitrospira genomes, and many functions overlap. As microbiologists, we define these multiple functions as ‘facultative’, whereby the diminishment or loss of one will be compensated for by an entirely different function, or multiple functions can mix-and-match to adapt the microbe to a particular availability of nutrients. What remains undefined is which facultative function of a microbial cohort makes up their ‘original’ core genome. Perhaps all facultative functions should be considered as accessories, in which case our correlations of phylogeny to function should be reconsidered and expanded for many cohorts. The expansion of function within any phylogenetic cluster muddies the waters when it comes to describing the diversity and roles of microbial communities based on a collection of individual gene markers. As a consequence, the notion of catabolic flexibility within single cells as a driver of their ecological success begs for new ways to identify and describe their persistence and functionality in relation to both their genomes and environment. Given these few examples from methanotrophs and nitrifiers, we can glean some important lessons: (1) a given enzyme can participate in more than one pathway or process; (2) we cannot determine a priori whether a gene is a member of the core or accessory genome without examining its functional context in the physiology of cultivated representatives and (3) a single cell can perform multiple, facultative, catabolic functions that are equally important to its survival and success. These lessons are critical in the analysis of meta'omic data as genes/transcripts/peptides are binned based on our biased preconceptions of what units ‘should’ belong to a particular organism, pathway or process because our algorithms map the collected sequences to our preexisting databases. Continuing with the naïve binning of genes into prescribed categories will preclude a clear understanding of how microbial communities actually work, function and evolve in nature. The challenge before us, then, is to expand our vision (and algorithms) of microorganisms from discrete taxa performing specific functions of core genomes to units of relative genetic flexibility that evolve as rapidly as environmental change. We should also recognize that accessory functions might be insufficient for cellular viability in laboratory experiments, but these accessories could make all the difference for whether a microbe is ultimately successful. The author would like to express gratitude to Martin G. Klotz and K. Dimitri Kits for critical comments, editing and helpful discussions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".