Electron micrograph of budding yeast cells
Photo: Ira Herskowitz and E. Schabtach
As I've remarked before, trying to debate with creationists is like boxing with a brain-dead opponent who lacks the cognitive ability to know when they're down and out. Their lumbering body keeps flailing around and like a defeated Donald Trump, insists that they’ve won, and are triumphing over all-comers in the ring.
Creationist frauds have been telling their willfully ignorant cult that they are winning the debate against science and science is about to convert to creationist superstition and abandon the materialist explanations. The science that has produced all of modern technology, including the computers and Internet that they use to inform the world that science is all wrong, and magic done by an unproven supernatural being is a better explanation for the observable facts, endearingly oblivious of the irony.
And yet not a single science paper has ever concluded any such thing and the small handful of pseudo-scientists that the creation cult employs to misinform the world never publish in peer-reviewed science journals. It's almost exactly like science just gazes bemusedly at the hopeless flailings of creationism, which has been on the canvas now for 50 years but still hasn't noticed. The trick has been to remain completely oblivious of real science by never reading anything that might make them wonder if being horizontal on the canvas is the best way of winning a boxing match.
Meanwhile real scientists do real science and science moves on.
An example of this was a paper published recently in Science which is the result on an AI assisted detailed analysis of the genomes of 1,154 strains of the ancient, single-cell yeast, Saccaromycotina, to discover how the different yeasts had evolved. Nowhere in the paper is there the slightest hint that the process that produced the different strains might be magic, not natural evolution. Instead, the research provides more understanding of how the evolutionary processes work.
The research by a team co-led by Professor of Bioinformatics Abigail Leavitt LaBella of the University of North Carolina at Charlotte, is explained in a University of North Carolina news release:
University of North Carolina at Charlotte Assistant Professor of Bioinformatics Abigail Leavitt LaBella has co-led an ambitious research study — published this week in the widely influential journal Science — that reports intriguing findings made through innovative artificial intelligence analysis about yeasts, the small fungi that are key contributors to biotechnology, food production and human health. The findings challenge accepted frameworks within which yeast evolution is studied and provide access to an incredibly rich yeast analysis dataset that could have major implications for future evolutionary biology and bioinformatics research.Although the main body of the paper in Science is behind an expensive paywall, the structured abstract and editor's summary provide mor technical details:
LaBella, who joined UNC Charlotte’s Department of Bioinformatics in the College of Computing and Informatics as an assistant professor and researcher at the North Carolina Research Campus in 2022, conducted the study with co-lead author Dana A. Opulente of Villanova University. They collaborated with fellow researchers from Vanderbilt University and the University of Wisconsin at Madison, along with colleagues from research institutions across the globe.
This is the flagship study of the Y1000+ Project, a massive inter-institutional yeast genome sequencing and phenotyping endeavor that LaBella joined as a postdoctoral researcher at Vanderbilt University.
This study contributes to basic understanding of how the microbes change over time while generating more than 900 new genome sequences for yeasts — many of which could be leveraged in biofungal fields such as agricultural pest control, drug development and biofuels production.Yeasts are single-celled fungi that play critical roles in our everyday lives. They make bread and beer, are used in the production of medicine, can cause infection, and as close relatives to animals have helped us learn about how cancer works. We wanted to know how these small fungi have evolved to have such an incredible range of functions and features. With the characterization of over one thousand yeasts, we found that yeasts do not fit the adage ‘jack of all trades, master of none.’
Assistant Professor Abigail Leavitt LaBella, co-first author
North Carolina Research Center (NCRC)
Department of Bioinformatics and Genomics
The University of North Carolina at Charlotte, Kannapolis, NC, USA.
LaBella and her co-authors — through an artificial intelligence-assisted, machine-learning analysis of the Y1000+ Project's dataset comprising 1,154 strains of the ancient, single-cell yeast Saccaromycotina — attempted to answer an important question. That is: Why do some yeasts eat (or metabolize) only a few types of carbon for energy while others can eat more than a dozen?
The total number of carbon sources used by a yeast for energy is known in ecological terms as its carbon niche-breadth. Humans also vary in their carbon niche breadth — for example, some people can metabolize lactose while others cannot.
Evolutionary biology research has supported two key overarching paradigms about niche breadth, the phenomenon explaining why some yeast organisms (“specialists”) evolve to be able to metabolize only a small number of carbon forms as fuel while others (“generalists”) evolve to be able to consume and grow on a broad variety of carbon forms. One of these paradigms illustrates that being a generalist comes with certain trade-offs compared to being a specialist. Notably, in the latter case, the ability to process a wide range of carbon forms comes at the expense of the yeast’s capacity to process and grow on each carbon form efficiently. The second is that these yeast specialists and generalists evolve to fit either profile due to the combined effects of different intrinsic traits of their respective genomes and different extrinsic influences based on the varying environments in which yeast organisms exist.
LaBella and her colleagues found ample evidence supporting the idea that there are identifiable, intrinsic genetic differences in yeast specialists versus generalists, specifically that generalists tend to have a larger total number of genes than specialists. For example, they found that generalists are more likely to be able to synthesize carnitine, a molecule that is involved in energy production and often sold as an exercise supplement.
But unexpectedly, their research found very limited evidence for the anticipated evolutionary trade-off of a yeast’s ability to process many forms of carbon coming at the expense of its ability to do so efficiently and grow accordingly, and vice versa.
While the findings of this specific experiment and the innovative machine-learning mechanisms used in its analysis could have major implications for bioinformatics, ecology, metabolics and evolutionary biology, the publishing of this study means that the Y1000+ Project’s massive compendium of yeast data is now available for scholars worldwide to use as a starting point to amplify their own yeast research.We saw that the yeasts that could grow on lots of carbon substrates are actually very good growers. That was a very surprising finding to us.
This dataset will be a huge resource going forward.
Assistant Professor Abigail Leavitt LaBella
Editor’s summaryAnd still, despite evidence such as this, that scientists are paying not the slightest heed to the flailing brain-dead creationist movement, creationist cult leaders will continue to try to fool their dupes into thinking they're about to deliver the knock-out blow and emerge triumphant from the ring.
Some species are highly specialized in using particular resources or live in a narrow range of environmental conditions. By contrast, generalists have broad ecological niches. The factors underlying the wide variation in niche breadth across species are largely unknown. Opulente et al. investigated whether specialists have more efficient resource use than generalists or if environmental or genomic factors shape the evolution of niche breadth. Using genomes from over 1000 species of Saccharomycotina yeasts and data on their performance growing in 24 different environments, the authors found that genes related to metabolic pathways had the clearest relationship to niche breadth. There was little support for the “ jack of all trades, master of none ” tradeoff across the subphylum. — Bianca Lopez
Structured Abstract
INTRODUCTION
It is often said that the jack-of-all-trades is the master of none. Niche breadth varies widely across the tree of life, from narrow in specialists to broad in generalists. One ecological paradigm explains this variation by invoking trade-offs between niche breadth and performance efficiency. Generalists perform moderately well in many niches, whereas each specialist has an advantage in its own niche. A second paradigm explains niche breadth variation through extrinsic and intrinsic factors. Extrinsic factors are ecological variables that include nutrient availability, temperature, organism interactions, and heterogeneity. Intrinsic factors are encoded by organisms’ genomes and affect how they access and process nutrients and tolerate stresses.
RATIONALE
To study niche breadth macroevolution, we deployed an ancient model subphylum uniquely poised for studies at genomic, metabolic, and ecological scales. The yeast subphylum Saccharomycotina of kingdom Fungi is best known for the model baker’s yeast Saccharomyces cerevisiae and the major human pathogen Candida albicans, but more than 1000 species have radiated during more than 400 million years into diverse ecological niches. Yeasts harbor gene sequence divergence comparable to that of animals and plants and are found in environments ranging from bats to cadaver tanks and from cheese caves to biofuel factories.
RESULTS
We generated a vast dataset of genome sequences of 1154 yeasts from nearly every known species, quantitative metabolic growth data in 24 conditions, and a hierarchical ecological ontology of isolation environments. Using evolutionary, machine learning, and network analyses, we found that yeast metabolic niche breadth is largely shaped by intrinsic factors. Generalist genomes encoded more genes and metabolic reactions, and our machine learning algorithm distinguished generalists from specialists using genome content with high accuracy. The most predictive features in our dataset pointed to specific genes in four pathways or complexes that are directly involved in carbon and energy metabolism, often by enhancing metabolic flexibility and robustness. Through ancestral trait reconstruction and coevolution analyses, we further demonstrated that generalists were more likely to have retained or gained traits, whereas specialists repeatedly arose through pervasive gene and trait loss. We did not find evidence for trade-offs between carbon niche breadth and growth rates; compared with specialists, carbon generalists grew faster in laboratory conditions and on more nitrogen sources. These results suggest that intrinsic genetic factors are a major driver of microbial diversity and niche breadth variation.
CONCLUSION
We generated a genomic, metabolic, and ecological dataset to show how metabolic diversity and niche breadth are encoded in yeast genomes and how these traits have evolved over deep time. Coupling a comprehensive dataset with a robust analytical framework paints a rich portrait of a diverse eukaryotic subphylum with immense impacts on human health, agriculture, and biotechnology that provides a roadmap connecting DNA to diversity.
A comprehensive initiative capturing genomic, metabolic, and ecological diversity among 1154 yeasts of the fungal subphylum Saccharomycotina.
We built a robust phylogeny and generated extensive genomic, phenotypic, and ecological data. We identified carbon niche breadth variation and used machine leaning to identify several intrinsic factors that contribute to carbon generalism.
Abstract
Organisms exhibit extensive variation in ecological niche breadth, from very narrow (specialists) to very broad (generalists). Two general paradigms have been proposed to explain this variation: (i) trade-offs between performance efficiency and breadth and (ii) the joint influence of extrinsic (environmental) and intrinsic (genomic) factors. We assembled genomic, metabolic, and ecological data from nearly all known species of the ancient fungal subphylum Saccharomycotina (1154 yeast strains from 1051 species), grown in 24 different environmental conditions, to examine niche breadth evolution. We found that large differences in the breadth of carbon utilization traits between yeasts stem from intrinsic differences in genes encoding specific metabolic pathways, but we found limited evidence for trade-offs. These comprehensive data argue that intrinsic factors shape niche breadth variation in microbes.
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