Molecular biology and evolution最新文献

Bacterial evolution through horizontal gene transfer (HGT) reflects their community interactions. In this way, HGT networks do well at mapping community interactions, but offer little toward controlling them-an important step in the translation of synthetic strains into natural contexts. Toxin-antitoxin (TA) systems serve as ubiquitous and diverse agents of selection; however, their utility is limited by their erratic distribution in hosts. Here we examine the heterogeneous distribution of TAs as a consequence of their mobility. By systematically mapping TA systems across a 10,000 plasmid network, we find HGT communities have unique and predictable TA signatures. We propose these TA signatures arise from plasmid competition and have further potential to signal the degree to which plasmids, hosts, and phage interact. To emphasize these relationships, we construct an HGT network based solely on TA similarity, framing specific selection markers in the broader context of bacterial communities. This work both clarifies the evolution of TA systems and unlocks a common framework for manipulating community interactions through TA compatibility.

Plants have evolved mechanisms to anticipate and adjust their growth and development in response to environmental changes. Understanding the key regulators of plant performance is crucial to mitigate the negative influence of global climate change on crop production. EARLY FLOWERING 3 (ELF3) is one such regulator playing a critical role in the circadian clock and thermomorphogenesis. In Arabidopsis thaliana, ELF3 contains a prion-like domain (PrLD) that acts as a thermosensor, facilitating liquid-liquid phase separation at high ambient temperatures. To assess the conservation of this function across the plant kingdom, we traced the evolutionary emergence of ELF3, with a focus on the presence of PrLDs. We found that the PrLD, primarily influenced by the length of polyglutamine (polyQ) repeats, is most prominent in Brassicales. Analyzing 319 natural A. thaliana accessions, we confirmed the previously described wide range of polyQ length variation in AtELF3, but found it to be only weakly associated with geographic origin, climate conditions, and classic temperature-responsive phenotypes. Interestingly, similar polyQ length variation was not observed in several other investigated Bassicaceae species. Based on these findings, available prediction tools and limited experimental evidence, we conclude that the emergence of PrLD, and particularly polyQ length variation, is unlikely to be a key driver of environmental adaptation. Instead, it likely adds an additional layer to ELF3's role in thermomorphogenesis in A. thaliana, with its relevance in other species yet to be confirmed.

Maroons in Suriname and French Guiana descend from enslaved Africans who escaped the plantations during colonial times. Maroon farmers still cultivate a large diversity of rice, their oldest staple crop. The oral history and written records of Maroons by colonial authorities provide contrasting perspectives on the origins of Maroon rice. Here, we analyzed the genomic ancestry of 136 newly sequenced Maroon rice varieties and found seven genomic groups that differ in their geographical associations. We interpreted these findings in light of ethnobotanical and archival investigations to reconstruct the historical contexts associated with the introduction of rice varieties to the Guianas. We found that two rice groups trace to West Africa, which we propose are linked to the transatlantic slave trade (c. 1526 to 1825). We posit that the Maroon rice stock additionally contains varieties that derive from rice introduced by indentured laborers from Java (1890 onwards), USA rice breeders (1932 onwards), and Hmong refugees who fled the Vietnam War (1991). Furthermore, on the Maroon fields, we found rice types never documented before that were derived from crosses. Overall, our results demonstrate that the Maroon farmers prioritize maintenance of a high stock diversity, which we posit reflects the expertise they inherited from their (African) ancestors. Ignored by agricultural modernization initiatives, Maroon farmers today are custodians of a unique cultural heritage. Notably, the genomic findings underline many Maroon stories about their past. We anticipate that a similar study approach can be applied to other heirloom crops of (Indigenous) communities that may have preserved their history on their farms to reconstruct, acknowledge, and honor the past.

Polyploids arise from whole-genome duplication (WGD) events, which have played important roles in genome evolution across eukaryotes. WGD can increase genome complexity, yield phenotypic novelty, and influence adaptation. Neo-polyploids have been reported to often show seemingly stochastic epigenetic and transcriptional changes, but this leaves open the question whether these changes persist in evolved polyploids. A powerful approach to address this is to compare diploids, neo-polyploids, and evolved polyploids of the same species. Arabidopsis arenosa is a species that allows us to do this-natural diploid and autotetraploid populations exist, while neo-tetraploids can be artificially generated. Here, we use ATAC-seq to assay local chromatin accessibility, and RNA-seq to study gene expression on matched leaf and petal samples from diploid, neo-tetraploid and evolved tetraploid A. arenosa. We found over 8,000 differentially accessible chromatin regions across all samples. These are largely tissue specific and show distinct trends across cytotypes, with roughly 70% arising upon WGD. Interestingly, only a small proportion is associated with expression changes in nearby genes. However, accessibility variation across cytotypes associates strongly with the number of nearby transposable elements. Relatively few genes were differentially expressed upon genome duplication, and ∼60% of these reverted to near-diploid levels in the evolved tetraploid, suggesting that most initial perturbations do not last. Our results provide new insights into how epigenomic and transcriptional mechanisms jointly respond to genome duplication and subsequent evolution of autopolyploids, and importantly, show that one cannot be directly predicted from the other.

Estimating the statistical robustness of the inferred tree(s) constitutes an integral part of most phylogenetic analyses. Commonly, one computes and assigns a branch support value to each inner branch of the inferred phylogeny. The still most widely used method for calculating branch support on trees inferred under maximum likelihood (ML) is the Standard, nonparametric Felsenstein bootstrap support (SBS). Due to the high computational cost of the SBS, a plethora of methods has been developed to approximate it, for instance, via the rapid bootstrap (RB) algorithm. There have also been attempts to devise faster, alternative support measures, such as the SH-aLRT (Shimodaira-Hasegawa-like approximate likelihood ratio test) or the UltraFast bootstrap 2 (UFBoot2) method. Those faster alternatives exhibit some limitations, such as the need to assess model violations (UFBoot2) or unstable behavior in the low support interval range (SH-aLRT). Here, we present the educated bootstrap guesser (EBG), a machine learning-based tool that predicts SBS branch support values for a given input phylogeny. EBG is on average 9.4 (σ=5.5) times faster than UFBoot2. EBG-based SBS estimates exhibit a median absolute error of 5 when predicting SBS values between 0 and 100. Furthermore, EBG also provides uncertainty measures for all per-branch SBS predictions and thereby allows for a more rigorous and careful interpretation. EBG can, for instance, predict SBS support values on a phylogeny comprising 1,654 SARS-CoV2 genome sequences within 3 h on a mid-class laptop. EBG is available under GNU GPL3.

RNA viruses exhibit vast phylogenetic diversity and can significantly impact public health and agriculture. However, current bioinformatics tools for viral discovery from metagenomic data frequently generate false positive virus results, overestimate viral diversity, and misclassify virus sequences. Additionally, current tools often fail to determine virus-host associations, which hampers investigation of the potential threat posed by a newly detected virus. To address these issues we developed VirID, a software tool specifically designed for the discovery and characterization of RNA viruses from metagenomic data. The basis of VirID is a comprehensive RNA-dependent RNA polymerase database to enhance a workflow that includes RNA virus discovery, phylogenetic analysis, and phylogeny-based virus characterization. Benchmark tests on a simulated data set demonstrated that VirID had high accuracy in profiling viruses and estimating viral richness. In evaluations with real-world samples, VirID was able to identify RNA viruses of all types, but also provided accurate estimations of viral genetic diversity and virus classification, as well as comprehensive insights into virus associations with humans, animals, and plants. VirID therefore offers a robust tool for virus discovery and serves as a valuable resource in basic virological studies, pathogen surveillance, and early warning systems for infectious disease outbreaks.

Understanding the main determinants of protein evolution is a fundamental challenge in biology. Despite many decades of active research, the molecular and cellular mechanisms underlying the substantial variability of evolutionary rates across cellular proteins are not currently well understood. It also remains unclear how protein molecular function is optimized in the context of multicellular species and why many proteins, such as enzymes, are only moderately efficient on average. Our analysis of genomics and functional datasets reveals in multiple organisms a strong inverse relationship between the optimality of protein molecular function and the rate of protein evolution. Furthermore, we find that highly expressed proteins tend to be substantially more functionally optimized. These results suggest that cellular expression costs lead to more pronounced functional optimization of abundant proteins and that the purifying selection to maintain high levels of functional optimality significantly slows protein evolution. We observe that in multicellular species both the rate of protein evolution and the degree of protein functional efficiency are primarily affected by expression in several distinct cell types and tissues, specifically, in developed neurons with upregulated synaptic processes in animals and in young and fast-growing tissues in plants. Overall, our analysis reveals how various constraints from the molecular, cellular, and species' levels of biological organization jointly affect the rate of protein evolution and the level of protein functional adaptation.