Evaluating the role of natural selection in the evolution of gene regulation

Surveys of gene expression reveal extensive variability both within and between a wide range of species. Compelling cases have been made for adaptive changes in gene regulation, but the proportion of expression divergence attributable to natural selection remains unclear. Distinguishing adaptive changes driven by positive selection from neutral divergence resulting from mutation and genetic drift is critical for understanding the evolution of gene expression. Here, we review the various methods that have been used to test for signs of selection in genomic expression data. We also discuss properties of regulatory systems relevant to neutral models of gene expression. Despite some potential caveats, published studies provide considerable evidence for adaptive changes in gene expression. Future challenges for studies of regulatory evolution will be to quantify the frequency of adaptive changes, identify the genetic basis of expression divergence and associate changes in gene expression with specific organismal phenotypes.     

A new look towards BAC-based array CGH through a comprehensive comparison with oligo-based array CGH

Background

Natural populations of the teleost fish Fundulus heteroclitus tolerate a broad range of environmental conditions including temperature, salinity, hypoxia and chemical pollutants. Strikingly, populations of Fundulus inhabit and have adapted to highly polluted Superfund sites that are contaminated with persistent toxic chemicals. These natural populations provide a foundation to discover critical gene pathways that have evolved in a complex natural environment in response to environmental stressors.

Results

We used Fundulus cDNA arrays to compare metabolic gene expression patterns in the brains of individuals among nine populations: three independent, polluted Superfund populations and two genetically similar, reference populations for each Superfund population. We found that up to 17% of metabolic genes have evolved adaptive changes in gene expression in these Superfund populations. Among these genes, two (1.2%) show a conserved response among three polluted populations, suggesting common, independently evolved mechanisms for adaptation to environmental pollution in these natural populations.

Conclusion

Significant differences among individuals between polluted and reference populations, statistical analyses indicating shared adaptive changes among the Superfund populations, and lack of reduction in gene expression variation suggest that common mechanisms of adaptive resistance to anthropogenic pollutants have evolved independently in multiple Fundulus populations. Among three independent, Superfund populations, two genes have a common response indicating that high selective pressures may favor specific responses.     

Pathway Correlation Profile of Gene-Gene Co-Expression for Identifying Pathway Perturbation

Identifying perturbed or dysregulated pathways is critical to understanding the biological processes that change within an experiment. Previous methods identified important pathways that are significantly enriched among differentially expressed genes; however, these methods cannot account for small, coordinated changes in gene expression that amass across a whole pathway. In order to overcome this limitation, we use microarray gene expression data to identify pathway perturbation based on pathway correlation profiles. By identifying the distribution of gene-gene pair correlations within a pathway, we can rank the pathways based on the level of perturbation and dysregulation. We have shown this successfully for differences between two experimental conditions in Escherichia coli and changes within time series data in Saccharomyces cerevisiae, as well as two estrogen receptor response classes of breast cancer. Overall, our method made significant predictions as to the pathway perturbations that are involved in the experimental conditions.     

Identification of fitness determinants in Enterococcus faecalis by differential proteomics

Enterococcus (E.) faecalis is found as commensal in healthy humans, in a variety of fermented foods. It can serve as probiotic but also as pathogen causing endocarditis, bacteremia and urinary tract infections. We have employed a proteomic study with E. faecalis strain OG1RF under different growth conditions and in contact to mouse intestinal cells to identify novel latent and adaptive fitness determinants. These relate to changes in catabolic pathways (BudA), protein biosynthesis (AsnS), cellular surface biosynthesis (RmlA) and regulatory mechanisms (OmpR). This knowledge can be used to derive novel evidence-based targets, which can be used to further elucidate gene expression changes enhancing pathogenicity or fitness in a commensal strain and possibly delineate this species into groups of higher and lower risk for applications in a food or a medical context versus improved treatment strategies of the so far hard to cure diseases.     

A Scalable Open-Source Pipeline for Large-Scale Root Phenotyping of Arabidopsis

Large-scale phenotyping of multicellular organisms is one of the current challenges in biology. We present a comprehensive and scalable pipeline that allows for the efficient phenotyping of root growth traits on a large scale. This includes a high-resolution, low-cost acquisition setup as well as the automated image processing software BRAT. We assess the performance of this pipeline in Arabidopsis thaliana under multiple growth conditions and show its utility by performing genome-wide association studies on 16 root growth traits quantified by BRAT each day during a 5-d time-course experiment. The most significantly associated genome region for root growth rate is a locus encoding a calcium sensing receptor. We find that loss of function and overexpression of this gene can significantly alter root growth in a growth condition dependent manner and that the minor natural allele of the Calcium Sensor Receptor locus is highly significantly enriched in populations in coastal areas, demonstrating the power of our approach to identify regulators of root growth that might have adaptive relevance.     

Molecular Time-Course and the Metabolic Basis of Entry into Dauer in Caenorhabditis elegans

When Caenorhabditis elegans senses dauer pheromone (daumone), signaling inadequate growth conditions, it enters the dauer state, which is capable of long-term survival. However, the molecular pathway of dauer entry in C. elegans has remained elusive. To systematically monitor changes in gene expression in dauer paths, we used a DNA microarray containing 22,625 gene probes corresponding to 22,150 unique genes from C. elegans. We employed two different paths: direct exposure to daumone (Path 1) and normal growth media plus liquid culture (Path 2). Our data reveal that entry into dauer is accomplished through the multi-step process, which appears to be compartmentalized in time and according to metabolic flux. That is, a time-course of dauer entry in Path 1 shows that dauer larvae formation begins at post-embryonic stage S4 (48 h) and is complete at S6 (72 h). Our results also suggest the presence of a unique adaptive metabolic control mechanism that requires both stage-specific expression of specific genes and tight regulation of different modes of fuel metabolite utilization to sustain the energy balance in the context of prolonged survival under adverse growth conditions. It is apparent that worms entering dauer stage may rely heavily on carbohydrate-based energy reserves, whereas dauer larvae utilize fat or glyoxylate cycle-based energy sources. We created a comprehensive web-based dauer metabolic database for C. elegans (www.DauerDB.org) that makes it possible to search any gene and compare its relative expression at a specific stage, or evaluate overall patterns of gene expression in both paths. This database can be accessed by the research community and could be widely applicable to other related nematodes as a molecular atlas.     

Immortality,but not oncogenic transformation,of primary human cells leads to epigenetic reprogramming of DNA methylation and gene expression

Tumourigenic transformation of normal cells into cancer typically involves several steps resulting in acquisition of unlimited growth potential, evasion of apoptosis and non-responsiveness to growth inhibitory signals. Both genetic and epigenetic changes can contribute to cancer development and progression. Given the vast genetic heterogeneity of human cancers and difficulty to monitor cancer-initiating events in vivo, the precise relationship between acquisition of genetic mutations and the temporal progression of epigenetic alterations in transformed cells is largely unclear. Here, we use an in vitro model system to investigate the contribution of cellular immortality and oncogenic transformation of primary human cells to epigenetic reprogramming of DNA methylation and gene expression. Our data demonstrate that extension of replicative life span of the cells is sufficient to induce accumulation of DNA methylation at gene promoters and large-scale changes in gene expression in a time-dependent manner. In contrast, continuous expression of cooperating oncogenes in immortalized cells, although essential for anchorage-independent growth and evasion of apoptosis, does not affect de novo DNA methylation at promoters and induces subtle expression changes. Taken together, these observations imply that cellular immortality promotes epigenetic adaptation to highly proliferative state, whereas transforming oncogenes confer additional properties to transformed human cells.     

Novel Aspects of the Acid Response Network of E. coli K-12 Are Revealed by a Study of Transcriptional Dynamics

Understanding gene regulation and its adaptive significance requires not only a detailed knowledge of individual molecular interactions that give rise to changes in gene expression but also an overview of complete genetic networks and the ways in which components within them interact. Increasingly, such studies are being done using luminescent or fluorescent reporter proteins that enable monitoring of gene expression dynamics in real time, particularly during changes in expression. We show here that such an approach is valid for dissecting the responses of the AR2 or GAD network of Escherichia coli K-12 to changes in pH, which is one of the most complex networks known in E. coli. In addition to confirming several regulatory interactions that have been revealed by previous studies, this approach has identified new components in this system that lead to complex dynamics of gene expression following a drop in pH, including an auto-regulatory loop involving the YdeO activator protein and novel roles for the PhoP protein.     

Measuring Growth and Gene Expression Dynamics of Tumor-Targeted S. Typhimurium Bacteria

The goal of these experiments is to generate quantitative time-course data on the growth and gene expression dynamics of attenuated S. typhimurium bacterial colonies growing inside tumors. We generated model xenograft tumors in mice by subcutaneous injection of a human ovarian cancer cell line, OVCAR-8 (NCI DCTD Tumor Repository, Frederick, MD). We transformed attenuated strains of S. typhimurium bacteria (ELH430:SL1344 phoPQ- ¹) with a constitutively expressed luciferase (luxCDABE) plasmid for visualization². These strains specifically colonize tumors while remaining essentially non-virulent to the mouse¹. Once measurable tumors were established, bacteria were injected intravenously via the tail vein with varying dosage. Tumor-localized, bacterial gene expression was monitored in real time over the course of 60 hours using an in vivo imaging system (IVIS). At each time point, tumors were excised, homogenized, and plated to quantitate bacterial colonies for correlation with gene expression data. Together, this data yields a quantitative measure of the in vivo growth and gene expression dynamics of bacteria growing inside tumors.     

Monitoring Stress-Related Genes during the Process of Biomass Propagation of Saccharomyces cerevisiae Strains Used for Wine Making

Physiological capabilities and fermentation performance of Saccharomyces cerevisiae strains to be employed during industrial wine fermentations are critical for the quality of the final product. During the process of biomass propagation, yeast cells are dynamically exposed to a mixed and interrelated group of known stresses such as osmotic, oxidative, thermic, and/or starvation. These stressing conditions can dramatically affect the parameters of the fermentation process and the technological abilities of the yeast, e.g., the biomass yield and its fermentative capacity. Although a good knowledge exists of the behavior of S. cerevisiae under laboratory conditions, insufficient knowledge is available about yeast stress responses under the specific media and growth conditions during industrial processes. We performed growth experiments using bench-top fermentors and employed a molecular marker approach (changes in expression levels of five stress-related genes) to investigate how the cells respond to environmental changes during the process of yeast biomass production. The data show that in addition to the general stress response pathway, using the HSP12 gene as a marker, other specific stress response pathways were induced, as indicated by the changes detected in the mRNA levels of two stress-related genes, GPD1 and TRX2. These results suggest that the cells were affected by osmotic and oxidative stresses, demonstrating that these are the major causes of the stress response throughout the process of wine yeast biomass production.