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Summary Few studies have "quantitatively" probed how adaptive mutations result in increased fitness. Even in simple microbial evolution experiments, with full knowledge of the underlying mutations and specific growth conditions, it is challenging to determine where within a growth-saturation cycle those fitness gains occur. A common implicit assumption is that most benefits derive from an increased exponential growth rate. Here, we instead show that, in batch serial transfer experiments, adaptive mutants' fitness gains can be dominated by benefits that are accrued in one growth cycle, but not realized until the next growth cycle. For thousands of evolved clones (most with only a single mutation), we systematically varied the lengths of fermentation, respiration, and stationary phases to assess how their fitness, as measured by barcode sequencing, depends on these phases of the growth-saturation-dilution cycles. These data revealed that, whereas all adaptive lineages gained similar and modest benefits from fermentation, most of the benefits for the highest fitness mutants came instead from the time spent in respiration. From monoculture and high-resolution pairwise fitness competition experiments for a dozen of these clones, we determined that the benefits "accrued" during respiration are only largely "realized" later as a shorter duration of lag phase in the following growth cycle. These results reveal hidden complexities of the adaptive process even under ostensibly simple evolutionary conditions, in which fitness gains can accrue during time spent in a growth phase with little cell division, and reveal that the memory of those gains can be realized in the subsequent growth cycle.

The Gene Ontology (GO) is a structured controlled vocabulary developed to describe the roles and locations of gene products in a consistent fashion, in a way that can be shared across organisms. The unicellular fungus Candida albicans is similar in many ways to the model organism Saccharomyces cerevisiae, but as both a commensal and a pathogen of humans, differs greatly in its lifestyle. With an expanding at-risk population of immunosuppressed patients, increased use of invasive medical procedures, the increasing prevalence of drug resistance, and the emergence of additional Candida species as serious pathogens, it has never been more critical to improve our understanding of Candida biology to guide the development of better treatments. In this brief review, we examine the importance of GO in the annotation of C. albicans gene products, with a focus on those involved in pathogenesis. We also discuss how sequence information combined with GO facilitates the transfer of knowledge across related species, and the challenges and opportunities that such an approach presents.

Background and Purpose Breast cancer is diagnosed worldwide in approximately one million women annually and radiation therapy is an integral part of treatment. The purpose of this study was to investigate the molecular basis underlying response to radiotherapy in breast cancer tissue. Material and Methods Tumour biopsies were sampled before radiation and after 10 treatments (of 2 Gray (Gy) each) from 19 patients with breast cancer receiving radiation therapy. Gene expression microarray analyses were performed to identify in vivo radiation-responsive genes in tumours from patients diagnosed with breast cancer. The mutation status of the TP53 gene was determined by using direct sequencing. Results and conclusion Several genes involved in cell cycle regulation and DNA repair were found to be significantly induced by radiation treatment. Mutations were found in the TP53 gene in 39% of the tumours and the gene expression profiles observed seemed to be influenced by the TP53 mutation status.

Studies of gene expression during the eukaryotic cell cycle in whole-culture synchronized cultures have been published using many methodologies. These procedures alter the state of the cell cycle for a population of cells, rather than purifying a population of cells that are in the same state. Criticism of these methods (e.g. see Cooper, this issue, pp. 266-269, ) suggests that these studies are flawed, and posits that such methodologies cannot be used to study the cell cycle because they alter the size and age distributions of the cultures. We believe that whole-culture cell cycle studies work even though they alter the size and age distributions: these cells still progress through the cell cycle and although we do not suggest that the methods are perfect, we will explain how these microarray studies have successfully identified cell cycle regulated genes and why these results are biologically meaningful.

Summary Background Soft-tissue tumours are derived from mesenchymal cells such as fibroblasts, muscle cells, or adipocytes, but for many such tumours the histogenesis is controversial. We aimed to start molecular characterisation of these rare neoplasms and to do a genome-wide search for new diagnostic markers. Methods We analysed gene-expression patterns of 41 softtissue tumours with spotted cDNA microarrays. After removal of errors introduced by use of different microarray batches, the expression patterns of 5520 genes that were well defined were used to separate tumours into discrete groups by hierarchical clustering and singular value decomposition. Findings Synovial sarcomas, gastrointestinal stromal tumours, neural tumours, and a subset of the leiomyosarcomas, showed strikingly distinct gene-expression patterns. Other tumour categories—malignant fibrous histiocytoma, liposarcoma, and the remaining leiomyosarcomas—shared molecular profiles that were not predicted by histological features or immunohistochemistry. Strong expression of known genes, such as KIT in gastrointestinal stromal tumours, was noted within gene sets that distinguished the different sarcomas. However, many uncharacterised genes also contributed to the distinction between tumour types. Interpretation These results suggest a new method for classification of soft-tissue tumours, which could improve on the method based on histological findings. Large numbers of uncharacterised genes contributed to distinctions between the tumours, and some of these could be useful markers for diagnosis, have prognostic significance, or prove possible targets for treatment.

DNA microarray technology has resulted in the generation of large complex data sets, such that the bottleneck in biological investigation has shifted from data generation, to data analysis. This review discusses some of the algorithms and tools for the analysis and organisation of microarray expression data, including clustering methods, partitioning methods, and methods for correlating expression data to other biological data.

Adaptive evolution plays a large role in generating the phenotypic diversity observed in nature, yet current methods are impractical for characterizing the molecular basis and fitness effects of large numbers of individual adaptive mutations. Here we used a DNA barcoding approach to generate the genotype-to-fitness map for adaptation-driving mutations from a Saccharomyces cerevisiae population experimentally evolved by serial transfer under limiting glucose. We isolated and measured the fitness of thousands of independent adaptive clones, and sequenced the genomes of hundreds of clones. We found only two major classes of adaptive mutations: self-diploidization, and mutations in the nutrient-responsive Ras/PKA and TOR/Sch9 pathways. Our large sample size and precision of measurement allowed us to determine that there are significant differences in fitness between mutations in different genes, between different paralogs, and even between different classes of mutations within the same gene.

This issue of Genomics is devoted to the discipline of Experimental Evolution, with 8 diverse and complementary papers from prominent labs working in the field. Five of these papers are review articles, which survey the history of the field, its current state of knowledge, its applications, the state of the art, and provide insights into where the field is heading. The three remaining articles are original research articles, each using different organisms to study the evolutionary process. Adams and Rosenzweig ([1]) begin the issue with a historical perspective, starting out not with Novick and Szilard where most such perspectives begin, but instead with Monod, who described the construction of the first continuous culture device wherein growth could be controlled by a single limiting nutrient. This device later became known as the chemostat, and has been a mainstay of experimental evolution studies for several decades. Only in the last ten years or so that it has become feasible to determine the population dynamics within evolving populations, and the molecular changes that occur during experimental evolution, which were previously inferred either from neutral markers, or assaying fitness as it increased. Adams and Rosenzweig coin the term “post-Mullerian” to refer to the complexity that such studies have so far revealed, though it is far from clear how much more complexity awaits, or what “post-post-Mullerian studies will reveal. Dunham and Gresham ([2]) review the advantages that chemostats can offer in the field of Experimental Evolution, specifically how the environment can be kept constant even as the population within undergoes evolutionary change. They contrast chemostats’ constant resource limitation with serial batch culture, in which cells undergo boom and bust cycles with respect to available nutrients, as well as periodic population bottlenecks, then contrast these in turn with yet another continuous culture system, the turbidostat, in which cells are never resource limited. They suggest that the practical challenges of chemostat culture are outweighed by its advantages, though to some extent, this may depend on one’s goals. An environment that is predictably constant frequently selects for loss-of-function mutations ([3]) as cells dispense with unnecessary pathways that presumably carry a cost, because, even though they don’t know it, their next meal is guaranteed. Indeed, systems that might be essential for maintaining homeostasis in a fluctuating environment can often be dispensed with in a constant one, but such mutations may carry fitness costs in other environments. If, for example, the goal is to generate robust strains for industrial applications, selective regimens that best capture the complexity of the intended environment may avoid fixing alleles that demonstrate antagonistic pleiotropy. Winkler and Kao ([4]) describe advances in experimental evolution that have been made specifically with an eye on the industrial environment, in particular the use of adaptive evolution to create improved biocatalysts for a variety of industrial processes. These range from increasing diversity within populations by tuning mutation rates, to promoting recombination between lineages so that multiple beneficial alleles can accumulate in the same genetic background, speeding up the adaptive process. They also describe strategies by which researchers can aim to couple fitness to the production of a desired product (such as a biofuel). While it is straightforward to select for faster growth in just about any environment, the biological system being evolved often achieves increased fitness in unexpected ways that result in lower rather than higher product yield. This often results in a game of evolutionary “Whac-a-Mole”, trying to re-engineer a strain to prevent that particular adaptive mode of failure, just to discover the next one. Experimentally coupling fitness to product output is one mechanism to avoid this time-consuming game. Lang and Desai ([5]) review what has been learned from experimental evolutionary studies about the spectrum of beneficial mutations. The use of tiling microarrays allowed the first genome-wide determination of mutations in evolved strains ([6]), but this was rapidly supplanted by the use of whole genome sequencing. While sequencing is not a panacea, (there are regions of even the yeast genome that are not uniquely mappable with short reads, and it still remains challenging to find indels and structural variants with sufficiently low false positive rates to allow all candidates to be readily tested) it has resulted in the identification of thousands of mutations that have occurred in evolved clones and populations of microbial genomes, with E. coli and S. cerevisiae having the most available data. The challenge now is not to identify the mutations, but instead to distinguish the passengers from the drivers. We will likely never have enough mutations to use an approach such as that used in ([7]), but by exploiting parallelism, coupled with low mutation rates, such that the drivers are not greatly outnumbered by the passengers, we are likely to gain great insight into what types of mutation might be beneficial in which environments, which itself will shed light on how the cell is wired. In the last of the review articles, Blundell and Levy ([8]) discuss the use of lineage tracking. This idea is a satisfying echo of the pioneering efforts in the field, where a poor man’s lineage tracking was achieved by assaying a neutral marker, providing a resolution of a single subpopulation within the overall population. While this idea has been improved upon by the use of fluorescently marked subpopulations (e.g. [9]), the lineage tracking idea discussed by Blundell and Levy is a quantum leap beyond these previous efforts, and may allow us to answer some of the outstanding questions in the field.

Cooper has a simple belief: that the cell cycle is connected to age and size. Furthermore, as a result of this connection in his mind he believes that there are no possible manipulations that can operate on a batch culture to synchronize cells within the cell cycle, such that those cells can undergo a semblance of a normal cell cycle. His formulation of this argument is as a 'fundamental law', the law of conservation of cell-age order (LCCAO). The first part of this law - 'there is no batch treatment of the culture that can lead to an alteration of the cell-age order' - can probably be proved true, in the mathematical sense, and certainly makes intuitive sense. Unfortunately the corollaries of this law are rather suspect, drawing inferences from cell age to cell size to the cell cycle.