Dynamic Epistasis under Varying Environmental Perturbations

Epistasis describes the phenomenon that mutations at different loci do not have independent effects with regard to certain phenotypes. Understanding the global epistatic landscape is vital for many genetic and evolutionary theories. Current knowledge for epistatic dynamics under multiple conditions is limited by the technological difficulties in experimentally screening epistatic relations among genes. We explored this issue by applying flux balance analysis to simulate epistatic landscapes under various environmental perturbations. Specifically, we looked at gene-gene epistatic interactions, where the mutations were assumed to occur in different genes. We predicted that epistasis tends to become more positive from glucose-abundant to nutrient-limiting conditions, indicating that selection might be less effective in removing deleterious mutations in the latter. We also observed a stable core of epistatic interactions in all tested conditions, as well as many epistatic interactions unique to each condition. Interestingly, genes in the stable epistatic interaction network are directly linked to most other genes whereas genes with condition-specific epistasis form a scale-free network. Furthermore, genes with stable epistasis tend to have similar evolutionary rates, whereas this co-evolving relationship does not hold for genes with condition-specific epistasis. Our findings provide a novel genome-wide picture about epistatic dynamics under environmental perturbations.Comment: 22 pages, 9 figure

Yeasts as models in evolutionary biology

A report of the workshop 'Evolutionary and Environmental Genomics of Yeasts', Heidelberg, Germany, 1-5 October 2008

Genetic Evidence for Elevated Pathogenicity of Mitochondrial DNA Heteroplasmy in Autism Spectrum Disorder

Increasing clinical and biochemical evidence implicate mitochondrial dysfunction in the pathophysiology of Autism Spectrum Disorder (ASD), but little is known about the biological basis for this connection. A possible cause of ASD is the genetic variation in the mitochondrial DNA (mtDNA) sequence, which has yet to be thoroughly investigated in large genomic studies of ASD. Here we evaluated mtDNA variation, including the mixture of different mtDNA molecules in the same individual (i.e., heteroplasmy), using whole-exome sequencing data from mother-proband-sibling trios from simplex families (n = 903) where only one child is affected by ASD. We found that heteroplasmic mutations in autistic probands were enriched at non-polymorphic mtDNA sites (P = 0.0015), which were more likely to confer deleterious effects than heteroplasmies at polymorphic mtDNA sites. Accordingly, we observed a ~1.5-fold enrichment of nonsynonymous mutations (P = 0.0028) as well as a ~2.2-fold enrichment of predicted pathogenic mutations (P = 0.0016) in autistic probands compared to their non-autistic siblings. Both nonsynonymous and predicted pathogenic mutations private to probands conferred increased risk of ASD (Odds Ratio, OR[95% CI] = 1.87[1.14–3.11] and 2.55[1.26–5.51], respectively), and their influence on ASD was most pronounced in families with probands showing diminished IQ and/or impaired social behavior compared to their non-autistic siblings. We also showed that the genetic transmission pattern of mtDNA heteroplasmies with high pathogenic potential differed between mother-autistic proband pairs and mother-sibling pairs, implicating developmental and possibly in utero contributions. Taken together, our genetic findings substantiate pathogenic mtDNA mutations as a potential cause for ASD and synergize with recent work calling attention to their unique metabolic phenotypes for diagnosis and treatment of children with ASD

Different evolutionary patterns between young duplicate genes in the human genome

BACKGROUND: Following gene duplication, two duplicate genes may experience relaxed functional constraints or acquire different mutations, and may also diverge in function. Whether the two copies will evolve in different patterns remains unclear, however, because previous studies have reached conflicting conclusions. In order to resolve this issue, by providing a general picture, we studied 250 independent pairs of young duplicate genes from the whole human genome. RESULTS: We showed that nearly 60% of the young duplicate gene pairs have evolved at the amino-acid level at significantly different rates from each other. More than 25% of these gene pairs also showed significantly different ratios of nonsynonymous to synonymous rates (K(a)/K(s )ratios). Moreover, duplicate pairs with different rates of amino-acid substitution also tend to differ in the K(a)/K(s )ratio, with the fast-evolving copy tending to have a slightly higher K(s )than the slow-evolving one. Lastly, a substantial portion of fast-evolving copies have accumulated amino-acid substitutions evenly across the protein sequences, whereas most of the slow-evolving copies exhibit uneven substitution patterns. CONCLUSIONS: Our results suggest that duplicate genes tend to evolve in different patterns following the duplication event. One copy evolves faster than the other and accumulates amino-acid substitutions evenly across the sequence, whereas the other copy evolves more slowly and accumulates amino-acid substitutions unevenly across the sequence. Such different evolutionary patterns may be largely due to different functional constraints on the two copies

Differential selection on gene translation efficiency between the filamentous fungus Ashbya gossypii and yeasts

<p>Abstract</p> <p>Background</p> <p>The filamentous fungus <it>Ashbya gossypii </it>grows into a multicellular mycelium that is distinct from the unicellular morphology of its closely related yeast species. It has been proposed that genes important for cell cycle regulation play central roles for such phenotypic differences. Because <it>A. gossypii </it>shares an almost identical set of cell cycle genes with the typical yeast <it>Saccharomyces cerevisiae</it>, the differences might occur at the level of orthologous gene regulation. Codon usage patterns were compared to identify orthologous genes with different gene regulation between <it>A. gossypii </it>and nine closely related yeast species.</p> <p>Results</p> <p>Here we identified 3,151 orthologous genes between <it>A. gossypii </it>and nine yeast species. Two groups of genes with significant differences in codon usage (gene translation efficiency) were identified between <it>A. gossypii </it>and yeasts. 333 genes (Group I) and 552 genes (Group II) have significantly higher translation efficiency in <it>A. gossypii </it>and yeasts, respectively. Functional enrichment and pathway analysis show that Group I genes are significantly enriched with cell cycle functions whereas Group II genes are biased toward metabolic functions.</p> <p>Conclusion</p> <p>Because translation efficiency of a gene is closely related to its functional importance, the observed functional distributions of orthologous genes with different translation efficiency might account for phenotypic differentiation between <it>A. gossypii </it>and yeast species. The results shed light on the mechanisms for pseudohyphal growth in pathogenic yeast species.</p

A robust and efficient method for estimating enzyme complex abundance and metabolic flux from expression data

A major theme in constraint-based modeling is unifying experimental data, such as biochemical information about the reactions that can occur in a system or the composition and localization of enzyme complexes, with highthroughput data including expression data, metabolomics, or DNA sequencing. The desired result is to increase predictive capability resulting in improved understanding of metabolism. The approach typically employed when only gene (or protein) intensities are available is the creation of tissue-specific models, which reduces the available reactions in an organism model, and does not provide an objective function for the estimation of fluxes, which is an important limitation in many modeling applications. We develop a method, flux assignment with LAD (least absolute deviation) convex objectives and normalization (FALCON), that employs metabolic network reconstructions along with expression data to estimate fluxes. In order to use such a method, accurate measures of enzyme complex abundance are needed, so we first present a new algorithm that addresses quantification of complex abundance. Our extensions to prior techniques include the capability to work with large models and significantly improved run-time performance even for smaller models, an improved analysis of enzyme complex formation logic, the ability to handle very large enzyme complex rules that may incorporate multiple isoforms, and depending on the model constraints, either maintained or significantly improved correlation with experimentally measured fluxes. FALCON has been implemented in MATLAB and ATS, and can be downloaded from: https://github.com/bbarker/FALCON. ATS is not required to compile the software, as intermediate C source code is available, and binaries are provided for Linux x86-64 systems. FALCON requires use of the COBRA Toolbox, also implemented in MATLAB.Comment: 30 pages, 12 figures, 4 table

Assembly and analysis of the genome sequence of the yeast Brettanomyces naardenensis CBS 7540

Brettanomyces naardenensis is a spoilage yeast with potential for biotechnological applications for production of innovative beverages with low alcohol content and high attenuation degree. Here, we present the first annotated genome of B. naardenensis CBS 7540. The genome of B. naardenensis CBS 7540 was assembled into 76 contigs, totaling 11,283,072 nucleotides. In total, 5168 protein-coding sequences were annotated. The study provides functional genome annotation, phylogenetic analysis, and discusses genetic determinants behind notable stress tolerance and biotechnological potential of B. naardenensis

Shotgun metagenomic sequencing reveals skin microbial variability from different facial sites

Biogeography (body site) is known to be one of the main factors influencing the composition of the skin microbial community. However, site-associated microbial variability at a fine-scale level was not well-characterized since there was a lack of high-resolution recognition of facial microbiota across kingdoms by shotgun metagenomic sequencing. To investigate the explicit microbial variance in the human face, 822 shotgun metagenomic sequencing data from Han Chinese recently published by our group, in combination with 97 North American samples from NIH Human Microbiome Project (HMP), were reassessed. Metagenomic profiling of bacteria, fungi, and bacteriophages, as well as enriched function modules from three facial sites (forehead, cheek, and the back of the nose), was analyzed. The results revealed that skin microbial features were more alike in the forehead and cheek while varied from the back of the nose in terms of taxonomy and functionality. Analysis based on biogeographic theories suggested that neutral drift with niche selection from the host could possibly give rise to the variations. Of note, the abundance of porphyrin-producing species, i.e., Cutibacterium acnes, Cutibacterium avidum, Cutibacterium granulosum, and Cutibacterium namnetense, was all the highest in the back of the nose compared with the forehead/cheek, which was consistent with the highest porphyrin level on the nose in our population. Sequentially, the site-associated microbiome variance was confirmed in American populations; however, it was not entirely consistent. Furthermore, our data revealed correlation patterns between Propionibacterium acnes bacteriophages with genus Cutibacterium at different facial sites in both populations; however, C. acnes exhibited a distinct correlation with P. acnes bacteriophages in Americans/Chinese. Taken together, in this study, we explored the fine-scale facial site-associated changes in the skin microbiome and provided insight into the ecological processes underlying facial microbial variations