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Genetic amplification, mutation, and translocation are known to play a causal role in the upregulation of an oncogene in cancer cells. Here, we report an emerging role of microRNA, the epigenetic deregulation of which may also lead to this oncogenic activation. SOX4, an oncogene belonging to the SRY-related high mobility group box family, was found to be overexpressed (P < 0.005) in endometrial tumors (n = 74) compared with uninvolved controls (n = 20). This gene is computationally predicted to be the target of a microRNA, miR-129-2. When compared with the matched endometria, the expression of miR-129-2 was lost in 27 of 31 primary endometrial tumors that also showed a concomitant gain of SOX4 expression (P < 0.001). This inverse relationship is associated with hypermethylation of the miR-129-2 CpG island, which was observed in endometrial cancer cell lines (n = 6) and 68% of 117 endometrioid endometrial tumors analyzed. Reactivation of miR-129-2 in cancer cells by pharmacologic induction of histone acetylation and DNA demethylation resulted in decreased SOX4 expression. In addition, restoration of miR-129-2 by cell transfection led to decreased SOX4 expression and reduced proliferation of cancer cells. Further analysis found a significant correlation of hypermethylated miR-129-2 with microsatellite instability and MLH1 methylation status (P < 0.001) and poor overall survival (P < 0.039) in patients. Therefore, these results imply that the aberrant expression of SOX4 is, in part, caused by epigenetic repression of miR-129-2 in endometrial cancer. Unlike the notion that promoter hypomethylation may upregulate an oncogene, we present a new paradigm in which hypermethylation-mediated silencing of a microRNA derepresses its oncogenic target in cancer cells. [Cancer Res 2009;69(23):9038–46]

Supplementary Table 2 from Xenoestrogen-Induced Epigenetic Repression of microRNA-9-3 in Breast Epithelial Cells

Supplementary Figures 1-6, Tables 1-15, Methods from Epigenetic Silencing Mediated through Activated PI3K/AKT Signaling in Breast Cancer

Supplementary Figures 1-6 from Epigenetic Repression of microRNA-129-2 Leads to Overexpression of SOX4 Oncogene in Endometrial Cancer

Supplementary Table 1 from Xenoestrogen-Induced Epigenetic Repression of microRNA-9-3 in Breast Epithelial Cells

The interplay between histone modifications and promoter hypermethylation provides a causative explanation for epigenetic gene silencing in cancer. Less is known about the upstream initiators that direct this process. Here, we report that the Cystatin M (CST6) tumor suppressor gene is concurrently down-regulated with other loci in breast epithelial cells cocultured with cancer-associated fibroblasts (CAF). Promoter hypermethylation of CST6 is associated with aberrant AKT1 activation in epithelial cells, as well as the disabled INNP4B regulator resulting from the suppression by CAFs. Repressive chromatin, marked by trimethyl-H3K27 and dimethyl-H3K9, and de novo DNA methylation is established at the promoter. The findings suggest that microenvironmental stimuli are triggers in this epigenetic cascade, leading to the long-term silencing of CST6 in breast tumors. Our present findings implicate a causal mechanism defining how tumor stromal fibroblasts support neoplastic progression by manipulating the epigenome of mammary epithelial cells. The result also highlights the importance of direct cell-cell contact between epithelial cells and the surrounding fibroblasts that confer this epigenetic perturbation. Because this two-way interaction is anticipated, the described coculture system can be used to determine the effect of epithelial factors on fibroblasts in future studies. [Cancer Res 2008;68(24):10257–66]

Supplementary Methods, Figures 1-5, Tables 1-5 from Breast Cancer–Associated Fibroblasts Confer AKT1-Mediated Epigenetic Silencing of Cystatin M in Epithelial Cells

Transmissible plasmids spread genes encoding antibiotic resistance and other traits to new bacterial species. Here we report that laboratory populations of Escherichia coli with a newly acquired IncQ plasmid often evolve ‘satellite plasmids’ with deletions of accessory genes and genes required for plasmid replication. Satellite plasmids are molecular parasites: their presence reduces the copy number of the full-length plasmid on which they rely for their continued replication. Cells with satellite plasmids gain an immediate fitness advantage from reducing burdensome expression of accessory genes. Yet, they maintain copies of these genes and the complete plasmid, which potentially enables them to benefit from and transmit the traits they encode in the future. Evolution of satellite plasmids is transient. Cells that entirely lose accessory gene function or plasmid mobility dominate in the long run. Satellite plasmids also evolve in Snodgrassella alvi colonizing the honey bee gut, suggesting that this mechanism may broadly contribute to the importance of IncQ plasmids as agents of bacterial gene transfer in nature., Newly acquired plasmids are frequently lost due to fitness costs. Here, Zhang et al. show that the evolution of satellite plasmids with gene deletions can reduce fitness costs by driving down the copy number of full plasmids and thus favor maintenance of the full plasmid and its novel accessory genes.

Summary Understanding how cells are likely to evolve can guide medical interventions and bioengineering efforts that must contend with unwanted mutations. The adaptome of a cell—the neighborhood of genetic changes that are most likely to drive adaptation in a given environment—can be mapped by tracking rare beneficial variants during the early stages of clonal evolution. We used multiplex adaptome capture sequencing (mAdCap-seq), a procedure that combines unique molecular identifiers and hybridization-based enrichment, to characterize mutations in eight Escherichia coli genes known to be under selection in a laboratory environment. We tracked 301 mutations at frequencies as low as 0.01% and inferred the fitness effects of 240 of these mutations. There were distinct molecular signatures of selection on protein structure and function for the three genes with the most beneficial mutations. Our results demonstrate how mAdCap-seq can be used to deeply profile a targeted portion of a cell’s adaptome.

Clonal populations of cells continuously evolve new genetic diversity, but it takes a significant amount of time for the progeny of a single cell with a new beneficial mutation to outstrip both its ancestor and competitors to fully dominate a population. If these driver mutations can be discovered earlier—while they are still extremely rare—and profiled in large numbers, it may be possible to anticipate the future evolution of similar cell populations. For example, one could diagnose the likely course of incipient diseases, such as cancer and bacterial infections, and better judge which treatments will be effective, by tracking rare drug-resistant variants. To test this approach, we replayed the first 500 generations of a >70,000-generationEscherichia coliexperiment and examined the trajectories of new mutations in eight genes known to be under positive selection in this environment in six populations. By employing a deep sequencing procedure using unique molecular identifiers and target enrichment we were able to track 301 beneficial mutations at frequencies as low as 0.01% and infer the fitness effects of 240 of these. Distinct molecular signatures of selection on protein structure and function were evident for the three genes in which beneficial mutations were most common (nadR, pykF,andtopA). We detected mutations hundreds of generations before they became dominant and tracked beneficial alleles in genes that were not mutated in the long-term experiment until thousands of generations had passed. This type of targeted adaptome sequencing approach could function as an early warning system to inform interventions that aim to prevent undesirable evolution.

Experimental studies of evolution using microbes have a long tradition, and these studies have increased greatly in number and scope in recent decades. Most such experiments have been short in duration, typically running for weeks or months. A venerable exception, the long-term evolution experiment (LTEE) with Escherichia coli has continued for 30 years and 70,000 bacterial generations. The LTEE has become one of the cornerstones of the field of experimental evolution, in general, and the BEACON Center for the Study of Evolution in Action, in particular. Science laboratories and experiments usually have finite lifespans, but we hope that the LTEE can continue far into the future. There are practical issues associated with maintaining such a long-term experiment. One issue, which we address here, is whether key measurements made at one time and place are reproducible, within reasonable limits, at other times and places. This issue comes to the forefront when one considers moving an experiment like the LTEE from one lab to another. To that end, the Barrick lab at The University of Texas at Austin, measured the fitness values of samples from the 12 LTEE populations at 2,000, 10,000, and 50,000 generations and compared the new data to data previously obtained at Michigan State University. On balance, the datasets agree very well. More generally, this finding shows the value of simplicity in experimental design, such as using a chemically defined growth medium and appropriately storing samples from microbiological experiments. Even so, one must be vigilant in checking assumptions and procedures given the potential for uncontrolled factors (e.g., water quality) to affect outcomes. This vigilance is perhaps especially important for a trait like fitness, which integrates all aspects of organismal performance and may therefore be sensitive to any number of subtle environmental influences.

Lineage tracking delivers essential quantitative insight into dynamic, probabilistic cellular processes, such as somatic tumor evolution and differentiation. Methods for high diversity lineage quantitation rely on sequencing a population of DNA barcodes. However, manipulation of specific individual lineages is not possible with this approach. To address this challenge, we developed a functionalized lineage tracing tool, Control of Lineages by Barcode Enabled Recombinant Transcription (COLBERT), that enables high diversity lineage tracing and lineage-specific manipulation of gene expression. This modular platform utilizes expressed barcode gRNAs to both track cell lineages and direct lineage-specific gene expression.

Unwanted evolution of designed DNA sequences limits metabolic and genome engineering efforts. Engineered functions that are burdensome to host cells and slow their replication are rapidly inactivated by mutations, and unplanned mutations with unpredictable effects often accumulate alongside designed changes in large-scale genome editing projects. We developed a directed evolution strategy, Periodic Reselection for Evolutionarily Reliable Variants (PResERV), to discover mutations that prolong the function of a burdensome DNA sequence in an engineered organism. Here, we used PResERV to isolate E. coli cells that replicate ColE1 plasmids with higher fidelity. We found mutations in DNA polymerases I and IV and in RNase E that reduce plasmid mutation rates by 6-to 30-fold. The PResERV method implicitly selects to maintain the growth rate of host cells, and high plasmid copy numbers and gene expression levels are maintained in some of the evolved E. coli strains, indicating that it is possible to improve the genetic stability of cellular chassis without encountering trade-offs in other desirable performance characteristics. Utilizing these new antimutator E. coli and applying PResERV to other organisms in the future promises to prevent evolutionary failures and unpredictability to provide a more stable genetic foundation for synthetic biology.

Organisms evolve and adapt via changes in their genomes that improve survival and reproduction in the context of their environment. Few experiments have examined how these genomic signatures of adaptation, which may favor mutations in certain genes or molecular pathways, vary across a set of similar environments that have both shared and distinctive characteristics. We sequenced complete genomes from 30 Escherichia coli lineages that evolved for 2,000 generations in one of five environments that differed only in the temperatures they experienced. Particular “signature” genes acquired mutations in these bacteria in response to selection imposed by specific temperature treatments. Thus, it is sometimes possible to predict aspects of the environment recently experienced by microbial populations from changes in their genome sequences.

Isolated populations derived from a common ancestor are expected to diverge genetically and phenotypically as they adapt to different local environments. To examine this process, 30 populations of Escherichia coli were evolved for 2,000 generations, with six in each of five different thermal regimes: constant 20 °C, 32 °C, 37 °C, 42 °C, and daily alternations between 32 °C and 42 °C. Here, we sequenced the genomes of one endpoint clone from each population to test whether the history of adaptation in different thermal regimes was evident at the genomic level. The evolved strains had accumulated ∼5.3 mutations, on average, and exhibited distinct signatures of adaptation to the different environments. On average, two strains that evolved under the same regime exhibited ∼17% overlap in which genes were mutated, whereas pairs that evolved under different conditions shared only ∼4%. For example, all six strains evolved at 32 °C had mutations in nadR, whereas none of the other 24 strains did. However, a population evolved at 37 °C for an additional 18,000 generations eventually accumulated mutations in the signature genes strongly associated with adaptation to the other temperature regimes. Two mutations that arose in one temperature treatment tended to be beneficial when tested in the others, although less so than in the regime in which they evolved. These findings demonstrate that genomic signatures of adaptation can be highly specific, even with respect to subtle environmental differences, but that this imprint may become obscured over longer timescales as populations continue to change and adapt to the shared features of their environments.

Adaptation by natural selection depends on the rates, effects and interactions of many mutations, making it difficult to determine what proportion of mutations in an evolving lineage are beneficial. Here we analysed 264 complete genomes from 12 Escherichia coli populations to characterize their dynamics over 50,000 generations. The populations that retained the ancestral mutation rate support a model in which most fixed mutations are beneficial, the fraction of beneficial mutations declines as fitness rises, and neutral mutations accumulate at a constant rate. We also compared these populations to mutation-accumulation lines evolved under a bottlenecking regime that minimizes selection. Nonsynonymous mutations, intergenic mutations, insertions and deletions are overrepresented in the long-term populations, further supporting the inference that most mutations that reached high frequency were favoured by selection. These results illuminate the shifting balance of forces that govern genome evolution in populations adapting to a new environment.

New mutations leading to structural variation (SV) in genomes — in the form of mobile element insertions, large deletions, gene duplications, and other chromosomal rearrangements — can play a key role in microbial evolution. Yet, SV is considerably more difficult to predict from short-read genome resequencing data than single-nucleotide substitutions and indels (SN), so it is not yet routinely identified in studies that profile population-level genetic diversity over time in evolution experiments. We implemented an algorithm for detecting polymorphic SV as part of the breseq computational pipeline. This procedure examines split-read alignments, in which the two ends of a single sequencing read match disjoint locations in the reference genome, in order to detect structural variants and estimate their frequencies within a sample. We tested our algorithm using simulated Escherichia coli data and then applied it to 500- and 1000-generation population samples from the Lenski E. coli long-term evolution experiment (LTEE). Knowledge of genes that are targets of selection in the LTEE and mutations present in previously analyzed clonal isolates allowed us to evaluate the accuracy of our procedure. Overall, SV accounted for ~25% of the genetic diversity found in these samples. By profiling rare SV, we were able to identify many cases where alternative mutations in key genes transiently competed within a single population. We also found, unexpectedly, that mutations in two genes that rose to prominence at these early time points always went extinct in the long term. Because it is not limited by the base-calling error rate of the sequencing technology, our approach for identifying rare SV in whole-population samples may have a lower detection limit than similar predictions of SNs in these data sets. We anticipate that this functionality of breseq will be useful for providing a more complete picture of genome dynamics during evolution experiments with haploid microorganisms.