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"Evolutionary conservation"
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Recent Insights into Plant miRNA Biogenesis: Multiple Layers of miRNA Level Regulation
MicroRNAs are small RNAs, 20–22 nt long, the main role of which is to downregulate gene expression at the level of mRNAs. MiRNAs are fundamental regulators of plant growth and development in response to internal signals as well as in response to abiotic and biotic factors. Therefore, the deficiency or excess of individual miRNAs is detrimental to particular aspects of a plant’s life. In consequence, the miRNA levels must be appropriately adjusted. To obtain proper expression of each miRNA, their biogenesis is controlled at multiple regulatory layers. Here, we addressed processes discovered to influence miRNA steady-state levels, such as MIR transcription, co-transcriptional pri-miRNA processing (including splicing, polyadenylation, microprocessor assembly and activity) and miRNA-encoded peptides synthesis. MiRNA stability, RISC formation and miRNA export out of the nucleus and out of the plant cell also define the levels of miRNAs in various plant tissues. Moreover, we show the evolutionary conservation of miRNA biogenesis core proteins across the plant kingdom.
Journal Article
ENNGene: an Easy Neural Network model building tool for Genomics
Background
The recent big data revolution in Genomics, coupled with the emergence of Deep Learning as a set of powerful machine learning methods, has shifted the standard practices of machine learning for Genomics. Even though Deep Learning methods such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) are becoming widespread in Genomics, developing and training such models is outside the ability of most researchers in the field.
Results
Here we present ENNGene—Easy Neural Network model building tool for Genomics. This tool simplifies training of custom CNN or hybrid CNN-RNN models on genomic data via an easy-to-use Graphical User Interface. ENNGene allows multiple input branches, including sequence, evolutionary conservation, and secondary structure, and performs all the necessary preprocessing steps, allowing simple input such as genomic coordinates. The network architecture is selected and fully customized by the user, from the number and types of the layers to each layer's precise set-up. ENNGene then deals with all steps of training and evaluation of the model, exporting valuable metrics such as multi-class ROC and precision-recall curve plots or TensorBoard log files. To facilitate interpretation of the predicted results, we deploy Integrated Gradients, providing the user with a graphical representation of an attribution level of each input position. To showcase the usage of ENNGene, we train multiple models on the RBP24 dataset, quickly reaching the state of the art while improving the performance on more than half of the proteins by including the evolutionary conservation score and tuning the network per protein.
Conclusions
As the role of DL in big data analysis in the near future is indisputable, it is important to make it available for a broader range of researchers. We believe that an easy-to-use tool such as ENNGene can allow Genomics researchers without a background in Computational Sciences to harness the power of DL to gain better insights into and extract important information from the large amounts of data available in the field.
Journal Article
The evolution of ovarian somatic cells characterized by transcriptome and chromatin accessibility across rodents, monkeys, and humans
Abstract
The ovary plays a crucial role in the reproductive system of female mammals by producing mature oocytes through folliculogenesis. Non-human model organisms are extensively utilized in research on human ovarian biology, thus necessitating the investigation of conservation and divergence in molecular mechanisms across species. In this study, we employed integrative single-cell analysis of transcriptome and chromatin accessibility to identify the evolutionary conservation and divergence patterns of ovaries among humans, monkeys, mice, rats, and rabbits. Our analyses revealed that theca cells exhibited the most significant changes during evolution based on scRNA-seq and scATAC-seq datasets. Furthermore, we discovered common cis-regulatory architectures in theca cells across species by conducting joint analyses of scRNA-seq and scATAC-seq datasets. These findings have potential applications in non-human biomedical and genetic research to validate molecular mechanisms found in human organisms. Additionally, our investigation into non-coding genomic regions identified intergenic highly transcribed regions (igHTRs) that may contribute to the evolution of species-specific phenotypic traits. Overall, our study provides valuable insights into understanding the molecular characteristics of adult ovaries while offering new perspectives for studying human ovarian physiology and diseases.
Journal Article
Using Pseudoenzymes to Probe Evolutionary Design Principles of Enzymes
Enzymes are governed by unique evolutionary design principles as their catalytic sites were shown to induce long-range evolutionary conservation gradients. We have recently used a comparative bioinformatics approach to disentangle structural determinants from other possible determinants of the evolutionary conservation gradients. The approach is based on comparing the evolutionary patterns of enzymes to those of pseudoenzymes with the same tertiary structure where the catalytic functionality is turned off. This approach provides a way to evaluate several hypotheses regarding the origin of the observed evolutionary conservation gradient in enzymes. The conclusions from such comparative analyses are important for a better understanding of the unique evolutionary design principles of enzymes, which can in turn potentially guide the design of new and improved enzymes.
Journal Article
Inferring the mammal tree: Species-level sets of phylogenies for questions in ecology, evolution, and conservation
Big, time-scaled phylogenies are fundamental to connecting evolutionary processes to modern biodiversity patterns. Yet inferring reliable phylogenetic trees for thousands of species involves numerous trade-offs that have limited their utility to comparative biologists. To establish a robust evolutionary timescale for all approximately 6,000 living species of mammals, we developed credible sets of trees that capture root-to-tip uncertainty in topology and divergence times. Our \"backbone-and-patch\" approach to tree building applies a newly assembled 31-gene supermatrix to two levels of Bayesian inference: (1) backbone relationships and ages among major lineages, using fossil node or tip dating, and (2) species-level \"patch\" phylogenies with nonoverlapping in-groups that each correspond to one representative lineage in the backbone. Species unsampled for DNA are either excluded (\"DNA-only\" trees) or imputed within taxonomic constraints using branch lengths drawn from local birth-death models (\"completed\" trees). Joining time-scaled patches to backbones results in species-level trees of extant Mammalia with all branches estimated under the same modeling framework, thereby facilitating rate comparisons among lineages as disparate as marsupials and placentals. We compare our phylogenetic trees to previous estimates of mammal-wide phylogeny and divergence times, finding that (1) node ages are broadly concordant among studies, and (2) recent (tip-level) rates of speciation are estimated more accurately in our study than in previous \"supertree\" approaches, in which unresolved nodes led to branch-length artifacts. Credible sets of mammalian phylogenetic history are now available for download at http://vertlife.org/phylosubsets, enabling investigations of long-standing questions in comparative biology.
Journal Article
MitoFish and MiFish Pipeline: A Mitochondrial Genome Database of Fish with an Analysis Pipeline for Environmental DNA Metabarcoding
Fish mitochondrial genome (mitogenome) data form a fundamental basis for revealing vertebrate evolution and hydrosphere ecology. Here, we report recent functional updates of MitoFish, which is a database of fish mitogenomes with a precise annotation pipeline MitoAnnotator. Most importantly, we describe implementation of MiFish pipeline for metabarcoding analysis of fish mitochondrial environmental DNA, which is a fast-emerging and powerful technology in fish studies. MitoFish, MitoAnnotator, and MiFish pipeline constitute a key platform for studies of fish evolution, ecology, and conservation, and are freely available at http://mitofish.aori.u-tokyo.ac.jp/ (last accessed April 7th, 2018).
Journal Article
Short-chain fatty acids: linking diet, the microbiome and immunity
The short-chain fatty acids (SCFAs) butyrate, propionate and acetate are microbial metabolites and their availability in the gut and other organs is determined by environmental factors, such as diet and use of antibiotics, that shape the diversity and metabolism of the microbiota. SCFAs regulate epithelial barrier function as well as mucosal and systemic immunity via evolutionary conserved processes that involve G protein-coupled receptor signalling or histone deacetylase activity. Indicatively, the anti-inflammatory role of butyrate is mediated through direct effects on the differentiation of intestinal epithelial cells, phagocytes, B cells and plasma cells, and regulatory and effector T cells. Intestinally derived SCFAs also directly and indirectly affect immunity at extra-intestinal sites, such as the liver, the lungs, the reproductive tract and the brain, and have been implicated in a range of disorders, including infections, intestinal inflammation, autoimmunity, food allergies, asthma and responses to cancer therapies. An ecological understanding of microbial communities and their interrelated metabolic states, as well as the engineering of butyrogenic bacteria may support SCFA-focused interventions for the prevention and treatment of immune-mediated diseases.Short-chain fatty acids (SCFAs) are microbial metabolites that regulate mucosal barrier integrity and immune cell functions. This Review summarizes latest insights into how SCFA levels might determine inflammatory and allergic disease outcomes by controlling the crosstalk between diet, the microbiome and immunity.
Journal Article
The worldwide importance of honey bees as pollinators in natural habitats
The western honey bee (Apis mellifera) is the most frequent floral visitor of crops worldwide, but quantitative knowledge of its role as a pollinator outside of managed habitats is largely lacking. Here we use a global dataset of 80 published plant–pollinator interaction networks as well as pollinator effectiveness measures from 34 plant species to assess the importance of A. mellifera in natural habitats. Apis mellifera is the most frequent floral visitor in natural habitats worldwide, averaging 13% of floral visits across all networks (range 0–85%), with 5% of plant species recorded as being exclusively visited by A. mellifera. For 33% of the networks and 49% of plant species, however, A. mellifera visitation was never observed, illustrating that many flowering plant taxa and assemblages remain dependent on non-A. mellifera visitors for pollination. Apis mellifera visitation was higher in warmer, less variable climates and on mainland rather than island sites, but did not differ between its native and introduced ranges. With respect to single-visit pollination effectiveness, A. mellifera did not differ from the average non-A. mellifera floral visitor, though it was generally less effective than the most effective non-A. mellifera visitor. Our results argue for a deeper understanding of how A. mellifera, and potential future changes in its range and abundance, shape the ecology, evolution, and conservation of plants, pollinators, and their interactions in natural habitats.
Journal Article