Gene duplication provides raw material for functional innovation. Recent advances have shed light on two fundamental questions regarding gene duplication: which genes tend to undergo duplication? And how does natural selection subsequently act on them? Genomic data suggest that different gene classes tend to be retained after single-gene and whole-genome duplications. We also know that functional differences between duplicate genes can originate in several different ways, including mutations that directly impart new functions, subdivision of ancestral functions and selection for changes in gene dosage. Interestingly, in many cases the 'new' function of one copy is a secondary property that was always present, but that has been co-opted to a primary role after the duplication.

Turning a hobby into a job: how duplicated genes find new functions.

An understanding of the basic defect in the inherited disorder cystic fibrosis requires cloning of the cystic fibrosis gene and definition of its protein product. In the absence of direct functional information, chromosomal map position is a guide for locating the gene. Chromosome walking and jumping and complementary DNA hybridization were used to isolate DNA sequences, encompassing more than 500,000 base pairs, from the cystic fibrosis region on the long arm of human chromosome 7. Several transcribed sequences and conserved segments were identified in this cloned region. One of these corresponds to the cystic fibrosis gene and spans approximately 250,000 base pairs of genomic DNA.

Identification of the cystic fibrosis gene: Chromosome walking and jumping

Adaptive laboratory evolution is a frequent method in biological studies to gain insights into the basic mechanisms of molecular evolution and adaptive changes that accumulate in microbial populations during long term selection under specified growth conditions. Although regularly performed for more than 25 years, the advent of transcript and cheap next-generation sequencing technologies has resulted in many recent studies, which successfully applied this technique in order to engineer microbial cells for biotechnological applications. Adaptive laboratory evolution has some major benefits as compared with classical genetic engineering but also some inherent limitations. However, recent studies show how some of the limitations may be overcome in order to successfully incorporate adaptive laboratory evolution in microbial cell factory design. Over the last two decades important insights into nutrient and stress metabolism of relevant model species were acquired, whereas some other aspects such as niche-specific differences of non-conventional cell factories are not completely understood. Altogether the current status and its future perspectives highlight the importance and potential of adaptive laboratory evolution as approach in biotechnological engineering.

/pdf/adaptive-laboratory-evolution-principles-and-applications-3ty5z73rgw.pdf

Adaptive laboratory evolution – principles and applications for biotechnology

Availability of key nutrients, such as sugars, amino acids, and nitrogen compounds, dictates the developmental programs and the growth rates of yeast cells. A number of overlapping signaling networks—those centered on Ras/protein kinase A, AMP-activated kinase, and target of rapamycin complex I, for instance—inform cells on nutrient availability and influence the cells’ transcriptional, translational, posttranslational, and metabolic profiles as well as their developmental decisions. Here I review our current understanding of the structures of the networks responsible for assessing the quantity and quality of carbon and nitrogen sources. I review how these signaling pathways impinge on transcriptional, metabolic, and developmental programs to optimize survival of cells under different environmental conditions. I highlight the profound knowledge we have gained on the structure of these signaling networks but also emphasize the limits of our current understanding of the dynamics of these signaling networks. Moreover, the conservation of these pathways has allowed us to extrapolate our finding with yeast to address issues of lifespan, cancer metabolism, and growth control in more complex organisms.

https://www.genetics.org/content/genetics/192/1/73.full.pdf

Nutritional Control of Growth and Development in Yeast

https://www.cell.com/trends/genetics/pdf/S0168-9525(06)00033-3.pdf

How did Saccharomyces evolve to become a good brewer

Modern yeast living in fleshy fruits rapidly convert sugars into bulk ethanol through pyruvate. Pyruvate loses carbon dioxide to produce acetaldehyde, which is reduced by alcohol dehydrogenase 1 (Adh1) to ethanol, which accumulates. Yeast later consumes the accumulated ethanol, exploiting Adh2, an Adh1 homolog differing by 24 (of 348) amino acids. As many microorganisms cannot grow in ethanol, accumulated ethanol may help yeast defend resources in the fruit1. We report here the resurrection of the last common ancestor2 of Adh1 and Adh2, called AdhA. The kinetic behavior of AdhA suggests that the ancestor was optimized to make (not consume) ethanol. This is consistent with the hypothesis that before the Adh1-Adh2 duplication, yeast did not accumulate ethanol for later consumption but rather used AdhA to recycle NADH generated in the glycolytic pathway. Silent nucleotide dating suggests that the Adh1-Adh2 duplication occurred near the time of duplication of several other proteins involved in the accumulation of ethanol, possibly in the Cretaceous age when fleshy fruits arose. These results help to connect the chemical behavior of these enzymes through systems analysis to a time of global ecosystem change, a small but useful step towards a planetary systems biology.

/pdf/resurrecting-ancestral-alcohol-dehydrogenases-from-yeast-1p8vslsugn.pdf

Resurrecting ancestral alcohol dehydrogenases from yeast.

The medical community requires computational tools that distinguish missense genetic differences having phenotypic impact within the vast number of sense mutations that do not. Tools that do this will become increasingly important for those seeking to use human genome sequence data to predict disease, make prognoses, and customize therapy to individual patients. An approach, termed DETECTER, is proposed to identify sites in a protein sequence where amino acid replacements are likely to have a significant effect on phenotype, including causing genetic disease. This approach uses a model-dependent tool to estimate the normalized replacement rate at individual sites in a protein sequence, based on a history of those sites extracted from an evolutionary analysis of the corresponding protein family. This tool identifies sites that have higher-than-average, average, or lower-than-average rates of change in the lineage leading to the sequence in the population of interest. The rates are then combined with sequence data to determine the likelihoods that particular amino acids were present at individual sites in the evolutionary history of the gene family. These likelihoods are used to predict whether any specific amino acid replacements, if introduced at the site in a modern human population, would have a significant impact on fitness. The DETECTER tool is used to analyze the cystic fibrosis transmembrane conductance regulator (CFTR) gene family. In this system, DETECTER retrodicts amino acid replacements associated with the cystic fibrosis disease with greater accuracy than alternative approaches. While this result validates this approach for this particular family of proteins only, the approach may be applicable to the analysis of polymorphisms generally, including SNPs in a human population.

/pdf/application-of-detecter-an-evolutionary-genomic-tool-to-349b03l0yo.pdf

Danny W De Kee

Papers

Resurrecting ancestral alcohol dehydrogenases from yeast.

Application of DETECTER, an evolutionary genomic tool to analyze genetic variation, to the cystic fibrosis gene family.