Random phenotypic variation of yeast (Saccharomyces cerevisiae) single-gene knockouts fits a double pareto-lognormal distribution

Nature, Nurture, and Noise: Developmental Instability, Fluctuating Asymmetry, and the Causes of Phenotypic Variation

Phenotypic variation arises from genetic and environmental variation, as well as random aspects of development. The genetic (nature) and environmental (nurture) components of this variation have been appreciated since at least 1900. The random developmental component (noise) has taken longer for quantitative geneticists to appreciate. Here, I sketch the historical development of the concepts of random developmental noise and developmental instability, and its quantification via fluctuating asymmetry. The unsung pioneers in this story are Hugo DeVries (fluctuating variation, 1909), C. H. Danforth (random variation between monozygotic twins, 1919), and Sewall Wright (random developmental variation in piebald guinea pigs, 1920). The first pioneering study of fluctuating asymmetry, by Sumner and Huestis in 1921, is seldom mentioned, possibly because it failed to connect the observed random asymmetry with random developmental variation. This early work was then synthesized by Boris Astaurov in 1930 and Wilhelm Ludwig in 1932, and then popularized by Drosophila geneticists beginning with Kenneth Mather in 1953. Population phenogeneticists are still trying to understand the origins and behavior of random developmental variation. Some of the developmental noise represents true stochastic behavior of molecules and cells, while some represents deterministic chaos, nonlinear feedback, and symmetry breaking.

Cellular economy in fission yeast cells continuously cultured with limited nitrogen resources

In ribosome biogenesis, a large fraction of ribosomes is used for producing ribosomal proteins themselves. Here, we applied simulation and experimentation to determine what fraction of ribosomes should be allocated for the synthesis of ribosomal proteins to optimize cellular economy for growth. We define the “r-fraction” as the fraction of mRNA of the ribosomal protein genes out of the total mRNA, and we simulated the effect of the r-fraction on the number of ribosomes. We then empirically measured the amount of protein and RNA in fission yeast cells cultured with high and low nitrogen sources. In the cells cultured with a low nitrogen source, the r-fraction decreased from 0.46 to 0.42 with a 40% reduction of rRNA, but the reduction of the total protein was smaller at 30%. These results indicate that the r-fraction is internally controlled to optimize the efficiency of protein synthesis at a limited cellular cost.

Essential gene disruptions reveal complex relationships between phenotypic robustness, pleiotropy, and fitness

The concept of robustness in biology has gained much attention recently, but a mechanistic understanding of how genetic networks regulate phenotypic variation has remained elusive. One approach to understand the genetic architecture of variability has been to analyze dispensable gene deletions in model organisms; however, the most important genes cannot be deleted. Here, we have utilized two systems in yeast whereby essential genes have been altered to reduce expression. Using high-throughput microscopy and image analysis, we have characterized a large number of morphological phenotypes, and their associated variation, for the majority of essential genes in yeast. Our results indicate that phenotypic robustness is more highly dependent upon the expression of essential genes than on the presence of dispensable genes. Morphological robustness appears to be a general property of a genotype that is closely related to pleiotropy. While the fitness profile across a range of expression levels is idiosyncratic to each gene, the global pattern indicates that there is a window in which phenotypic variation can be released before fitness effects are observable.

Growth and fluctuating asymmetry of human newborns: influence of inbreeding and parental education

Historically, medical concerns about the deleterious effects of closely inbred marriages have focused on the risk posed by recessive Mendelian disease, with much less attention to developmental instability. We studied the effects of inbreeding (first-cousin marriage) on growth and fluctuating asymmetry of 200 full-term infants (101 inbred and 99 outbred) whose parents were of similar socioeconomic status in Sivas Province, Turkey. In addition to differences in their mean inbreeding coefficients (f = 1/16 for first cousins and f < 1/1,024 for unrelated parents), the consanguineous parents were less well educated (3 years, on average for both husbands and wives). We measured weight, height, head circumference, and chest circumference of the newborns, as well as four bilateral traits (ear width, ear length, and second and fourth digit lengths). After taking education into account, none of the measures of size (weight, height, head circumference, and chest circumference) and fluctuating asymmetry differed between the inbred and outbred groups. Male children of well-educated parents, however, were larger and had less fluctuating asymmetry. Female children of well-educated parents weighed more than those of less well-educated parents, but were otherwise indistinguishable for height, head circumference, chest circumference, and fluctuating asymmetry. We conclude that inbreeding depression causes neither an increase in fluctuating asymmetry of full-term newborns, nor a decrease in body size. Unmeasured variables correlated with education appear to have an effect on fluctuating asymmetry and size of male children and only a weak effect on size (weight) of female children.

PASTA: splice junction identification from RNA-sequencing data

Background

Next generation transcriptome sequencing (RNA-Seq) is emerging as a powerful experimental tool for the study of alternative splicing and its regulation, but requires ad-hoc analysis methods and tools. PASTA (Patterned Alignments for Splicing and Transcriptome Analysis) is a splice junction detection algorithm specifically designed for RNA-Seq data, relying on a highly accurate alignment strategy and on a combination of heuristic and statistical methods to identify exon-intron junctions with high accuracy.

Results

Comparisons against TopHat and other splice junction prediction software on real and simulated datasets show that PASTA exhibits high specificity and sensitivity, especially at lower coverage levels. Moreover, PASTA is highly configurable and flexible, and can therefore be applied in a wide range of analysis scenarios: it is able to handle both single-end and paired-end reads, it does not rely on the presence of canonical splicing signals, and it uses organism-specific regression models to accurately identify junctions.

Conclusions

PASTA is a highly efficient and sensitive tool to identify splicing junctions from RNA-Seq data. Compared to similar programs, it has the ability to identify a higher number of real splicing junctions, and provides highly annotated output files containing detailed information about their location and characteristics. Accurate junction data in turn facilitates the reconstruction of the splicing isoforms and the analysis of their expression levels, which will be performed by the remaining modules of the PASTA pipeline, still under development. Use of PASTA can therefore enable the large-scale investigation of transcription and alternative splicing.