Christopher J. Marx

Background: A common assumption of microorganisms is that laboratory stocks will remain genetically and phenotypically constant over time, and across laboratories. It is becoming increasingly clear, however, that mutations can ruin strain integrity and drive the divergence or “domestication” of stocks. Since its discovery in 1960, a stock of Methylobacterium extorquens AM1 (“AM1”) has remained in the lab, propagated across numerous growth and storage conditions, researchers, and facilities. To explore the extent to which this lineage has diverged, we compared our own “Modern” stock of AM1 to a sample archived at a culture stock center shortly after the strain’s discovery. Stored as a lyophilized sample, we hypothesized that this Archival strain would better reflect the first-ever isolate of AM1 and reveal ways in which our Modern stock has changed through laboratory domestication or other means. Results: Using whole-genome re-sequencing, we identified some 29 mutations – including single nucleotide polymorphisms, small indels, the insertion of mobile elements, and the loss of roughly 36 kb of DNA - that arose in the laboratory-maintained Modern lineage. Contrary to our expectations, Modern was both slower and less fit than Archival across a variety of growth substrates, and showed no improvement during long-term growth and storage. Modern did, however, outperform Archival during growth on nutrient broth, and in resistance to rifamycin, which was selected for by researchers in the 1980s. Recapitulating selection for rifamycin resistance in replicate Archival populations showed that mutations to RNA polymerase B (rpoB) substantially decrease growth in the absence of antibiotic, offering an explanation for slower growth in Modern stocks. Given the large number of genomic changes arising from domestication (28), it is somewhat surprising that the single other mutation attributed to purposeful laboratory selection accounts for much of the phenotypic divergence between strains. Conclusions: These results highlight the surprising degree to which AM1 has diverged through a combination of unintended laboratory domestication and purposeful selection for rifamycin resistance. Instances of strain divergence are important, not only to ensure consistency of experimental results, but also to explore how microbes in the lab diverge from one another and from their wild counterparts.

Adaptation of one set of traits is often accompanied by attenuation of traits important in other selective environments, leading to fitness trade-offs. The mechanisms that either promote or prevent the emergence of trade-offs remain largely unknown, and are difficult to discern in most systems. Here, we investigate the basis of trade-offs that emerged during experimental evolution of Methylobacterium extorquens AM1 to distinct growth substrates. After 1500 generations of adaptation to a multi-carbon substrate, succinate (S), many lineages had lost the ability to use one-carbon compounds such as methanol (M), generating a mixture of M(+) and M(-) evolved phenotypes. We show that trade-offs in M(-) strains consistently arise via antagonistic pleiotropy through recurrent selection for loss-of-function mutations to ftfL (formate-tetrahydrofolate ligase), which improved growth on S while simultaneously eliminating growth on M. But if loss of FtfL was beneficial, why were M trade-offs not found in all populations? We discovered that eliminating FtfL was not universally beneficial on S, as it was neutral or even deleterious in certain evolved lineages that remained M(+) . This suggests that sign epistasis with earlier arising mutations prevented the emergence of mutations that drove trade-offs through antagonistic pleiotropy, limiting the evolution of metabolic specialists in some populations.

The most powerful genome-scale framework to model metabolism, flux balance analysis (FBA), is an evolutionary optimality model. It hypothesizes selection upon a proposed optimality criterion in order to predict the set of internal fluxes that would maximize fitness. Here we present a direct test of the optimality assumption underlying FBA by comparing the central metabolic fluxes predicted by multiple criteria to changes measurable by a ¹³C-labeling method for experimentally-evolved strains. We considered datasets for three Escherichia coli evolution experiments that varied in their length, consistency of environment, and initial optimality. For ten populations that were evolved for 50,000 generations in glucose minimal medium, we observed modest changes in relative fluxes that led to small, but significant decreases in optimality and increased the distance to the predicted optimal flux distribution. In contrast, seven populations evolved on the poor substrate lactate for 900 generations collectively became more optimal and had flux distributions that moved toward predictions. For three pairs of central metabolic knockouts evolved on glucose for 600–800 generations, there was a balance between cases where optimality and flux patterns moved toward or away from FBA predictions. Despite this variation in predictability of changes in central metabolism, two generalities emerged. First, improved growth largely derived from evolved increases in the rate of substrate use. Second, FBA predictions bore out well for the two experiments initiated with ancestors with relatively sub-optimal yield, whereas those begun already quite optimal tended to move somewhat away from predictions. These findings suggest that the tradeoff between rate and yield is surprisingly modest. The observed positive correlation between rate and yield when adaptation initiated further from the optimum resulted in the ability of FBA to use stoichiometric constraints to predict the evolution of metabolism despite selection for rate. The most common method of modeling genome-scale metabolism, flux balance analysis, involves using known stoichiometry to define feasible metabolic states and then choosing between these states by proposing that evolution has selected a metabolic flux that optimizes fitness. But does evolution optimize metabolism, and if so, what component of metabolism equates to fitness? We directly tested the underlying assumption of stoichiometric optimality by comparing predicted flux distributions with changes in fluxes that occurred following experimental evolution. Across three experiments ranging in length from a few hundred to fifty thousand generations, we found that substrate uptake – an input to the model – always increased, but supposed optimality criteria such as yield only increased sometimes. Despite this, there was a clear trend. Highly optimal ancestors evolved slightly lower yield in the course of increasing the overall rate, whereas more sub-optimal strains were able to increase both. These results suggest that flux balance analysis is capable of predicting either the initial metabolic behavior of strains or how they will evolve, but not both.

Background: Due to the ever increasing use of diverse microbial taxa in basic research and industrial settings, there is a growing need for genetic tools to alter the physiology of these organisms. In particular, there is a dearth of inducible expression systems available for bacteria outside commonly used γ-proteobacteria, such as Escherichia coli or Pseudomonas species. To this end, we have sought to develop a pair of inducible expression vectors for use in the α-proteobacterium Methylobacterium extorquens, a model methylotroph. Findings: We found that the P _R promoter from rhizobial phage 16-3 was active in M. extorquens and engineered the promoter to be inducible by either p-isopropyl benzoate (cumate) or anhydrotetracycline. These hybrid promoters, P _R/cmtO and P _R/tetO , were found to have high levels of expression in M. extorquens with a regulatory range of 10-fold and 30-fold, respectively. Compared to an existing cumate-inducible (10-fold range), high-level expression system for M. extorquens, P _R/cmtO and P _R/tetO have 33% of the maximal activity but were able to repress gene expression 3 and 8-fold greater, respectively. Both promoters were observed to exhibit homogeneous, titratable activation dynamics rather than on-off, switch-like behavior. The utility of these promoters was further demonstrated by complementing loss of function of ftfL - essential for growth on methanol - where we show P _R/tetO is capable of not only fully complementing function but also producing a conditional null phenotype. These promoters have been incorporated into a broad-host-range backbone allowing for potential use in a variety of bacterial hosts. Conclusions: We have developed two novel expression systems for use in M. extorquens. The expression range of these vectors should allow for increased ability to explore cellular physiology in M. extorquens. Further, the P _R/tetO promoter is capable of producing conditional null phenotypes, previously unattainable in M. extorquens. As both expression systems rely on the use of membrane permeable inducers, we suspect these expression vectors will be useful for ectopic gene expression in numerous proteobacteria.

Methylobacterium extorquens strains are the best-studied methylotrophic model system, and their metabolism of single carbon compounds has been studied for over 50 years. Here we develop a new system for high-throughput batch culture of M. extorquens in microtiter plates by jointly optimizing the properties of the organism, the growth media and the culturing system. After removing cellulose synthase genes in M. extorquens strains AM1 and PA1 to prevent biofilm formation, we found that currently available lab automation equipment, integrated and managed by open source software, makes possible reliable estimates of the exponential growth rate. Using this system, we developed an optimized growth medium for M. extorquens using response surface methodologies. We found that media that used EDTA as a metal chelator inhibited growth and led to inconsistent culture conditions. In contrast, the new medium we developed with a PIPES buffer and metals chelated by citrate allowed for fast and more consistent growth rates. This new M ethylobacterium P IPES (‘MP’) medium was also robust to large deviations in its component ingredients which avoided batch effects from experiments that used media prepared at different times. MP medium allows for faster and more consistent growth than other media used for M. extorquens .

Organisms cope with physiological stressors through acclimatizing mechanisms in the short-term and adaptive mechanisms over evolutionary timescales. During adaptation to an environmental or genetic perturbation, beneficial mutations can generate numerous physiological changes: some will be novel with respect to prior physiological states, while others might either restore acclimatizing responses to a wild-type state, reinforce them further, or leave them unchanged. We examined the interplay of acclimatizing and adaptive responses at the level of global gene expression in Methylobacterium extorquens AM1 engineered with a novel central metabolism. Replacing central metabolism with a distinct, foreign pathway resulted in much slower growth than wild-type. After 600 generations of adaptation, however, eight replicate populations founded from this engineered ancestor had improved up to 2.5-fold. A comparison of global gene expression in wild-type, engineered, and all eight evolved strains revealed that the vast majority of changes during physiological adaptation effectively restored acclimatizing processes to wild-type expression states. On average, 93% of expression perturbations from the engineered strain were restored, with 70% of these occurring in perfect parallel across all eight replicate populations. Novel changes were common but typically restricted to one or a few lineages, and reinforcing changes were quite rare. Despite this, cases in which expression was novel or reinforced in parallel were enriched for loci harboring beneficial mutations. One case of parallel, reinforced changes was the pntAB transhydrogenase that uses NADH to reduce NADP⁺ to NADPH. We show that PntAB activity was highly correlated with the restoration of NAD(H) and NADP(H) pools perturbed in the engineered strain to wild-type levels, and with improved growth. These results suggest that much of the evolved response to genetic perturbation was a consequence rather than a cause of adaptation and that physiology avoided “reinventing the wheel” by restoring acclimatizing processes to the pre-stressed state. Acclimatizing and adaptive (evolutionary) processes allow organisms to thrive despite cellular and environmental perturbations. Our work examined whether adaptation restores stress responses towards wild-type (pre-stressed) versus novel physiological states during adaptation by studying a bacterium (Methylobacterium extorquens AM1) that was experimentally engineered and evolved with a novel central metabolism. The engineered strain was much slower and less fit than wild-type, but eight replicate populations evolved for six hundred generations showed substantial improvements. We found that changes in gene expression during adaptation consistently restored acclimatizing processes to the wild-type state, often in 8/8 evolved lines. Novel changes were common and largely restricted to one lineage; however, highly parallel novel changes revealed loci harboring beneficial mutations. Even rarer were reinforced changes, such as pntAB transhydrogenase, which increased beyond immediate acclimation during evolution to restore NAD(P)(H) metabolism and improve growth. Overall, a few novel or reinforcing changes drove the mass-restoration of physiology back to wild-type.

It has increasingly been recognized that adapting populations of microbes contain not one, but many lineages continually arising and competing at once. This process, termed “clonal interference,” alters the rate and dynamics of adaptation and biases winning mutations toward those with the largest selective effect. Here we uncovered a dramatic example of clonal interference between multiple similar mutations occurring at the same locus within replicate populations of Methylobacterium extorquens AM1. Because these mutational events involved the transposition of an insertion sequence into a narrow window of a single gene, they were both readily detectable at low frequencies and could be distinguished due to differences in insertion sites. This allowed us to detect up to 17 beneficial alleles of this type coexisting in a single population. Despite conferring a large selective benefit, the majority of these alleles rose and then fell in frequency due to other lineages emerging that were more fit. By comparing allele-frequency dynamics to the trajectories of fitness gains by these populations, we estimated the fitness values of the genotypes that contained these mutations. Collectively across all populations, these alleles arose upon backgrounds with a wide range of fitness values. Within any single population, however, multiple alleles tended to rise and fall synchronously during a single wave of multiple genotypes with nearly identical fitness values. These results suggest that alleles of large benefit arose repeatedly in failed “soft sweeps” during narrow windows of adaptation due to the combined effects of epistasis and clonal interference.

Few areas of science have benefited more from the expansion in sequencing capability than the study of microbial communities. Can sequence data, besides providing hypotheses of the functions the members possess, detect the evolutionary and ecological processes that are occurring? For example, can we determine if a species is adapting to one niche, or if it is diversifying into multiple specialists that inhabit distinct niches? Fortunately, adaptation of populations in the laboratory can serve as a model to test our ability to make such inferences about evolution and ecology from sequencing. Even adaptation to a single niche can give rise to complex temporal dynamics due to the transient presence of multiple competing lineages. If there are multiple niches, this complexity is augmented by segmentation of the population into multiple specialists that can each continue to evolve within their own niche. For a known example of parallel diversification that occurred in the laboratory, sequencing data gave surprisingly few obvious, unambiguous signs of the ecological complexity present. Whereas experimental systems are open to direct experimentation to test hypotheses of selection or ecological interaction, the difficulty in “seeing ecology” from sequencing for even such a simple system suggests translation to communities like the human microbiome will be quite challenging. This will require both improved empirical methods to enhance the depth and time resolution for the relevant polymorphisms and novel statistical approaches to rigorously examine time-series data for signs of various evolutionary and ecological phenomena within and between species.

Biased codon usage in protein-coding genes is pervasive, whereby amino acids are largely encoded by a specific subset of possible codons. Within individual genes, codon bias is stronger at evolutionarily conserved residues, favoring codons recognized by abundant tRNAs. Although this observation suggests an overall pattern of selection for translation speed and/or accuracy, other work indicates that transcript structure or binding motifs drive codon usage. However, our understanding of codon bias evolution is constrained by limited experimental data on the fitness effects of altering codons in functional genes. To bridge this gap, we generated synonymous variants of a key enzyme-coding gene in Methylobacterium extorquens. We found that mutant gene expression, enzyme production, enzyme activity, and fitness were all significantly lower than wild-type. Surprisingly, encoding the gene using only rare codons decreased fitness by 40%, whereas an allele coded entirely by frequent codons decreased fitness by more than 90%. Increasing gene expression restored mutant fitness to varying degrees, demonstrating that the fitness disadvantage of synonymous mutants arose from a lack of beneficial protein rather than costs of protein production. Protein production was negatively correlated with the frequency of motifs with high affinity for the anti-Shine-Dalgarno sequence, suggesting ribosome pausing as the dominant cause of low mutant fitness. Together, our data support the idea that, although a particular set of codons are favored on average across a genome, in an individual gene selection can either act for or against codons depending on their local context.