Biology subjects
Publications and source records attributed to Lindsay, R. J..
Withdrawal StatementThe authors have withdrawn this manuscript due to a duplicate posting of manuscript number BIORXIV/2023/532793. Therefore, the authors do not wish this work to be cited as reference for the project. If you have any questions, please contact the corresponding author. The correct preprint can be found at doi: 10.1101/2023.03.15.532793
Many organisms live in predictable environments with periodic variation in growth condition which can allow populations to accumulate cryptic genetic variations. Cryptic variations can facilitate adaptation to new environments, as observed in evolution experiments with a ribozyme and a protein. Whether the same holds for cell populations remains unclear. Alternatively, living in a near-constant condition can lead to loss of nonessential cellular functions, which could be maladaptive in new environments. Through laboratory evolution experiments in yeast, we show that populations grown in a predictable nutrient-rich environment for 1000 generations start to lose their ability to respond and adapt to new stressful environments. Growth of yeast populations in the nutrient-rich environment was associated with modest fitness increase in this environment, metabolic remodeling, and increased lipid accumulation. In novel stressful environments, however, these populations generally had reduced fitness, except in salt-stress where lipid accumulation seemed to provide osmotic protection. We further found that adaptation to stressors was primarily driven by de novo mutations, with very little contribution from the mutations accumulated prior to the exposure to stressors. Thus, our work suggests that in the absence of occurrence of new environments, natural populations might not accumulate cryptic variations that could be beneficial for adaptation to these environments. In addition, presence of selection in predictable condition in natural populations may purge away some of the cryptic variations. Taken together, these findings raise questions about persistence of cryptic variations in natural populations and their importance in evolutionary adaptation.
The evening complex (EC) is a tripartite DNA repressor and a core component of the circadian clock that provides a mechanism for temperature-responsive growth and development of many plants. ELF3, a component of the EC, is a disordered scaffolding protein that blocks transcription of growth genes at low temperature. At increased temperature EC DNA binding is disrupted and ELF3 is sequestered in a reversible nuclear condensate, allowing transcription and growth to proceed. The condensation is driven by a low complexity prion-like domain (PrD), and the sensitivity of the temperature response is modulated by the length of a variable polyQ tract, with a longer polyQ tract corresponding to enhanced condensate formation and hypocotyl growth at increased temperature. Here, a series of computational studies provides evidence that polyQ tracts promote formation of temperature-sensitive helices in flanking residues with potential impacts for EC stability under increasing temperature. REST2 simulations uncover a heat-induced population of condensation-prone conformations that results from the exposure of sticky aromatic residues by temperature-responsive breaking of long-range contacts. Coarse-grained Martini simulations reveal both polyQ tract length and sequence context modulate the temperature dependence of cluster formation. Understanding the molecular mechanism underlying the ELF3-PrD temperature response in plants has implications for technologies including modular temperature-response elements for heat-responsive protein design and agricultural advances to enable optimization of crop yields and allow plants to thrive in increasingly inhospitable environments.
Any conformational ensemble of biopolymers, whether they are proteins or chromosomes, can be described using contact matrices derived from experimental or computational studies. One powerful approach to extract meaningful information from these contact matrices is to perform principal component analysis (PCA) on the covariance matrix of the contact data. Indeed, PCA on Hi-C chromosome contact matrices has revealed the spatial segregation of active and inactive chromatin. Separately, PCA on contact matrices from snapshots of protein conformations has characterized correlated fluctuations of protein domains. However, despite the similarities of these data and analyses, there has been little synergy between the PCA approaches and the comparison of resulting biological insights obtained for protein and chromosome structures. We note that, to date, different styles of analyses were applied exclusively to each biomolecule type: explicit contact correlation analysis (E-PCA) for proteins and implicit contact correlation (I-PCA) for chromosomes. In this work, we compare the results of applying both methods to both classes of biopolymers. While I-PCA reveals only average features of 3D chromosome structure, we find that applying E-PCA to an ensemble of chromosome structures from microscopy data reveals the dominant motion (concerted fluctuation) of the chromosome. Applying E-PCA to Hi-C data across the human blood cell lineage isolates the aspects of chromosome structure that most strongly differentiate cell types. Conversely, when we apply I-PCA to simulation snapshots of two protein complexes, the major component reports the consensus features of the structure, while the previously applied E-PCA characterizes correlated deviations from the mean structure.