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1. Simple biochemical features underlie transcriptional activation domain diversity and dynamic, fuzzy binding to Mediator

   https://doi.org/10.7554/ELIFE.68068  

   Sanborn, Adrian L; Yeh, Benjamin T; Feigerle, Jordan T; Hao, Cynthia V; Townshend, Raphael JL; Lieberman Aiden, Erez; Dror, Ron O; Kornberg, Roger D (April 2021, eLife)

   null (Ed.)

   Cells adapt and respond to changes by regulating the activity of their genes. To turn genes on or off, they use a family of proteins called transcription factors. Transcription factors influence specific but overlapping groups of genes, so that each gene is controlled by several transcription factors that act together like a dimmer switch to regulate gene activity. The presence of transcription factors attracts proteins such as the Mediator complex, which activates genes by gathering the protein machines that read the genes. The more transcription factors are found near a specific gene, the more strongly they attract Mediator and the more active the gene is. A specific region on the transcription factor called the activation domain is necessary for this process. The biochemical sequences of these domains vary greatly between species, yet activation domains from, for example, yeast and human proteins are often interchangeable. To understand why this is the case, Sanborn et al. analyzed the genome of baker’s yeast and identified 150 activation domains, each very different in sequence. Three-quarters of them bound to a subunit of the Mediator complex called Med15. Sanborn et al. then developed a machine learning algorithm to predict activation domains in both yeast and humans. This algorithm also showed that negatively charged and greasy regions on the activation domains were essential to be activated by the Mediator complex. Further analyses revealed that activation domains used different poses to bind multiple sites on Med15, a behavior known as ‘fuzzy’ binding. This creates a high overall affinity even though the binding strength at each individual site is low, enabling the protein complexes to remain dynamic. These weak interactions together permit fine control over the activity of several genes, allowing cells to respond quickly and precisely to many changes. The computer algorithm used here provides a new way to identify activation domains across species and could improve our understanding of how living things grow, adapt and evolve. It could also give new insights into mechanisms of disease, particularly cancer, where transcription factors are often faulty. 

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2. Distinguishing different modes of growth using single-cell data

   https://doi.org/10.7554/eLife.72565  

   Kar, Prathitha; Tiruvadi-Krishnan, Sriram; Männik, Jaana; Männik, Jaan; Amir, Ariel (December 2021, eLife)

   All cells – from bacteria to humans – tightly control their size as they grow and divide. Cells can also change the speed at which they grow, and the pattern of how fast a cell grows with time is called ‘mode of growth’. Mode of growth can be ‘linear’, when cells increase their size at a constant rate, or ‘exponential’, when cells increase their size at a rate proportional to their current size. A cell’s mode of growth influences its inner workings, so identifying how a cell grows can reveal information about how a cell will behave. Scientists can measure the size of cells as they age and identify their mode of growth using single cell imaging techniques. Unfortunately, the statistical methods available to analyze the large amounts of data generated in these experiments can lead to incorrect conclusions. Specifically, Kar et al. found that scientists had been using specific types of plots to analyze growth data that were prone to these errors, and may lead to misinterpreting exponential growth as linear and vice versa. This discrepancy can be resolved by ensuring that the plots used to determine the mode of growth are adequate for this analysis. But how can the adequacy of a plot be tested? One way to do this is to generate synthetic data from a known model, which can have a specific and known mode of growth, and using this data to test the different plots. Kar et al. developed such a ‘generative model’ to produce synthetic data similar to the experimental data, and used these data to determine which plots are best suited to determine growth mode. Once they had validated the best statistical methods for studying mode of growth, Kar et al. applied these methods to growth data from the bacterium Escherichia coli . This showed that these cells have a form of growth called ‘super-exponential growth’. These findings identify a strategy to validate statistical methods used to analyze cell growth data. Furthermore, this strategy – the use of generative models to produce synthetic data to test the accuracy of statistical methods – could be used in other areas of biology to validate statistical approaches. 

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3. Epidermal threads reveal the origin of hagfish slime

   https://doi.org/10.7554/eLife.81405  

   Zeng, Yu; Plachetzki, David C; Nieders, Kristen; Campbell, Hannah; Cartee, Marissa; Pankey, M Sabrina; Guillen, Kennedy; Fudge, Douglas (March 2023, eLife)

   Hagfishes are deep-sea animals, and they represent one of the oldest living relatives of animals with backbones. To defend themselves against predators, they produce a remarkable slime that is reinforced with fibers and can clog a predator’s gills, thwarting the attack. The slime deploys in less than half a second, exuding from specialized glands on the hagfish’s body and expanding up to 10,000 times its ejected volume. The defensive slime is highly dilute, consisting mostly of sea water, with low concentrations of mucus and strong, silk-like threads that are approximately 20 centimeters long. Where and how hagfish slime evolved remains a mystery. Zeng et al. set out to answer where on the hagfish’s body the slime glands originated, and how they may have evolved. First, Zeng et al. examined hagfishes and found that cells in the surface layer of their skin (the epidermis) produce threads roughly two millimeters in length that are released when the hagfish’s skin is damaged. These threads mix with the mucus that is produced by ruptured skin cells to form a slime that likely adheres to predators’ mouths. This slime could be a precursor of the slime produced by the specialized glands. To test this hypothesis, Zeng et al. analyzed which genes are turned on and off both in the hagfishes’ skin and in their slime glands. The patterns they found are consistent with the slime glands originating from the epidermis. Based on these results, Zeng et al. propose that ancient hagfishes first evolved the ability to produce slime with anti-predator effects when their skin was damaged in attacks. Over time, hagfishes that could produce and store more slime and eject it actively into a predator’s mouth likely had a better chance of surviving. This advantage may have led to the appearance of increasingly specialized glands that could carry out these functions. The findings of Zeng et al. will be of interest to evolutionary biologists, marine biologists, and those studying the ecology of predator-prey interactions. Because of its unique material properties, hagfish slime is also of interest to biophysicists, bioengineers and those engaged in biomimetic research. The origin of hagfish slime glands is an interesting example of how a new trait evolved, and may provide insight into the evolution of other adaptive traits. 

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4. The skin microbiome facilitates adaptive tetrodotoxin production in poisonous newts

   https://doi.org/10.7554/eLife.53898  

   Vaelli, Patric M; Theis, Kevin R; Williams, Janet E; O'Connell, Lauren A; Foster, James A; Eisthen, Heather L (April 2020, eLife)

   Rough-skinned newts produce tetrodotoxin or TTX, a deadly neurotoxin that is also present in some pufferfish, octopuses, crabs, starfish, flatworms, frogs, and toads. It remains a mystery why so many different creatures produce this toxin. One possibility is that TTX did not evolve in animals at all, but rather it is made by bacteria living on or in these creatures. In fact, scientists have already shown that TTX-producing bacteria supply pufferfish, octopus, and other animals with the toxin. However, it was not known where TTX in newts and other amphibians comes from. TTX kills animals by blocking specialized ion channels and shutting down the signaling between neurons, but rough-skinned newts appear insensitive to this blockage, making it likely that they have evolved defenses against the toxin. Some garter snakes that feed on these newts have also evolved to become immune to the effects of TTX. If bacteria are the source of TTX in the newts, the emergence of newt-eating snakes resistant to TTX must be putting evolutionary pressure on both the newts and the bacteria to boost their anti-snake defenses. Learning more about these complex relationships will help scientists better understand both evolution and the role of beneficial bacteria. Vaelli et al. have now shown that bacteria living on rough-skinned newts produce TTX. In the experiments, bacteria samples were collected from the skin of the newts and grown in the laboratory. Four different types of bacteria from the samples collected produced TTX. Next, Vaelli et al. looked at five genes that encode the channels normally affected by TTX in newts and found that all them have mutations that prevent them from being blocked by this deadly neurotoxin. This suggests that bacteria living on newts shape the evolution of genes critical to the animals’ own survival. Helpful bacteria living on and in animals have important effects on animals’ physiology, health, and disease. But understanding these complex interactions is challenging. Rough-skinned newts provide an excellent model system for studying the effects of helpful bacteria living on animals. Vaelli et al. show that a single chemical produced by bacteria can impact diverse aspects of animal biology including physiology, the evolution of their genes, and their interactions with other creatures in their environment. 

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5. Gene regulatory network reconstruction using single-cell RNA sequencing of barcoded genotypes in diverse environments

   https://doi.org/10.7554/eLife.51254  

   Jackson, Christopher A; Castro, Dayanne M; Saldi, Giuseppe-Antonio; Bonneau, Richard; Gresham, David (January 2020, eLife)

   Organisms switch their genes on and off to adapt to changing environments. This takes place thanks to complex networks of regulators that control which genes are actively ‘read’ by the cell to create the RNA molecules that are needed at the time. Piecing together these networks is key to fully understand the inner workings of living organisms, and how to potentially modify or artificially create them. Single-cell RNA sequencing is a powerful new tool that can measure which genes are turned on (or ‘expressed’) in an individual cell. Datasets with millions of gene expression profiles for individual cells now exist for organisms such as mice or humans. Yet, it is difficult to use these data to reconstruct networks of regulators; this is partly because scientists are not sure if the computational methods normally used to build these networks also work for single-cell RNA sequencing data. One way to check if this is the case is to use the methods on single-cell datasets from organisms where the networks of regulators are already known, and check whether the computational tools help to reach the same conclusion. Unfortunately, the regulatory networks in the organisms for which scientists have a lot of single-cell RNA sequencing data are still poorly known. There are living beings in which the networks are well characterised – such as yeast – but it has been difficult to do single-cell sequencing in them at the scale seen in other organisms. Jackson, Castro et al. first adapted a system for single-cell sequencing so that it would work in yeast. This generated a gene expression dataset of over 40,000 yeast cells. They then used a computational method (called the Inferelator) on these data to construct networks of regulators, and the results showed that the method performed well. This allowed Jackson, Castro et al. to start mapping how different networks connect, for example those that control the response to the environment and cell division. This is one of the benefits of single-cell RNA methods: cell division for example is not a process that can be examined at the level of a population, since the cells may all be at different life stages. In the future, the dataset will also be useful to scientists to benchmark a variety of single cell computational tools. 

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