Across their dendritic trees, neurons distribute thousands of protein species that are necessary for maintaining synaptic function and plasticity and that need to be produced continuously and trafficked to their final destination. As each dendritic branchpoint splits the protein flow, increasing branchpoints decreases the total protein number downstream. Consequently, a neuron needs to produce more proteins to maintain a minimal protein number at distal synapses. Combining in vitro experiments and a theoretical framework, we show that proteins that diffuse within the cell plasma membrane are, on average, 35% more effective at reaching downstream locations than proteins that diffuse in the cytoplasm. This advantage emerges from a bias for forward motion at branchpoints when proteins diffuse within the plasma membrane. Using 3D electron microscopy (EM) data, we show that pyramidal branching statistics and the diffusion lengths of common proteins fall into a region that minimizes the overall protein need.

The mechanisms regulating community composition and local dominance of trees in species-rich forests are poorly resolved, but the importance of interactions with soil microbes is increasingly acknowledged. Here, we show that tree seedlings that interact via root-associated fungal hyphae with soils beneath neighbouring adult trees grow faster and have greater survival than seedlings that are isolated from external fungal mycelia, but these effects are observed for species possessing ectomycorrhizas (ECM) and not arbuscular mycorrhizal (AM) fungi. Moreover, survival of naturally-regenerating AM seedlings over ten years is negatively related to the density of surrounding conspecific plants, while survival of ECM tree seedlings displays positive density dependence over this interval, and AM seedling roots contain greater abundance of pathogenic fungi than roots of ECM seedlings. Our findings show that neighbourhood interactions mediated by beneficial and pathogenic soil fungi regulate plant demography and community structure in hyperdiverse forests.

This study aimed to analyse the critical height of a column whose weight varies vertically in order to obtain a simple scaling law for a tree where the weight distribution considered. We modelled trees as cantilevers that were fixed to the ground and formulated a self-buckling problem for various weight distributions. A formula for calculating the critical height was derived in a simple form that did not include special functions. We obtained a theoretical clarification of the effect of the weight distribution of heavy columns on the buckling behaviour. A widely applicable scaling law for trees was obtained. We found that an actual tree manages to distribute the weight of its trunk and branches along its vertical extent in a manner that adequately secures its critical height. The method and findings of this study are applicable to a wide range of fields, such as the simplification of complicated buckling problems and the study of tree shape quantification.

Phylogenetic tree comparison metrics are an important tool in the study of evolution, and hence the definition of such metrics is an interesting problem in phylogenetics. In a paper in Taxon fifty years ago, Sokal and Rohlf proposed to measure quantitatively the difference between a pair of phylogenetic trees by first encoding them by means of their half-matrices of cophenetic values, and then comparing these matrices. This idea has been used several times since then to define dissimilarity measures between phylogenetic trees but, to our knowledge, no proper metric on weighted phylogenetic trees with nested taxa based on this idea has been formally defined and studied yet. Actually, the cophenetic values of pairs of different taxa alone are not enough to single out phylogenetic trees with weighted arcs or nested taxa.

Characterization of the response of the host immune system is important in understanding the bidirectional interactions between the host and microbial pathogens. For research on the host site, flow cytometry has become one of the major tools in immunology. Advances in technology and reagents allow now the simultaneous assessment of multiple markers on a single cell level generating multidimensional data sets that require multivariate statistical analysis. We explored the explanatory power of the supervised machine learning method called "induction of decision trees" in flow cytometric data. In order to examine whether the production of a certain cytokine is depended on other cytokines, datasets from intracellular staining for six cytokines with complex patterns of co-expression were analyzed by induction of decision trees. After weighting the data according to their class probabilities, we created a total of 13,392 different decision trees for each given cytokine with different parameter settings. For a more realistic estimation of the decision trees' quality, we used stratified fivefold cross validation and chose the "best" tree according to a combination of different quality criteria. While some of the decision trees reflected previously known co-expression patterns, we found that the expression of some cytokines was not only dependent on the co-expression of others per se, but was also dependent on the intensity of expression. Thus, for the first time we successfully used induction of decision trees for the analysis of high dimensional flow cytometric data and demonstrated the feasibility of this method to reveal structural patterns in such data sets.

Anthracnose caused by Colletotrichum is one of the most severe diseases of Hevea brasiliensis. However, research on the diversity and geographical distribution of Colletotrichum remains limited in China. In this study, we investigated the phylogenetic diversity of Colletotrichum isolates associated with symptomatic tissues of H.brasiliensis from four provinces of China (Hainan, Guangdong, Guangxi, and Yunnan). Based on multi-locus phylogenetic analyses and phenotypic characteristics, five species were distinguished, including two known species (C. fructicola, C. siamense), one novel species of C. gloeosporioides species complex (C. ledongense), and two novel species of C. acutatum species complex (C. bannanense and C. australisinense). Of these, C. siamense and C. australisinense have been recognized as major causative agents of anthracnose of H. brasiliensis.

Recent studies have detected phylogenetic signals in pathogen-host networks for both soil-borne and leaf-infecting fungi, suggesting that pathogenic fungi may track or coevolve with their preferred hosts. However, a phylogenetically concordant relationship between multiple hosts and multiple fungi in has rarely been investigated. Using next-generation high-throughput DNA sequencing techniques, we analyzed fungal taxa associated with diseased leaves, rotten seeds, and infected seedlings of subtropical trees. We compared the topologies of the phylogenetic trees of the soil and foliar fungi based on the internal transcribed spacer (ITS) region with the phylogeny of host tree species based on matK, rbcL, atpB, and 5.8S genes. We identified 37 foliar and 103 soil pathogenic fungi belonging to the Ascomycota and Basidiomycota phyla and detected significantly nonrandom host-fungus combinations, which clustered on both the fungus phylogeny and the host phylogeny. The explicit evidence of congruent phylogenies between tree hosts and their potential fungal pathogens suggests either diffuse coevolution among the plant-fungal interaction networks or that the distribution of fungal species tracked spatially associated hosts with phylogenetically conserved traits and habitat preferences. Phylogenetic conservatism in plant-fungal interactions within a local community promotes host and parasite specificity, which is integral to the important role of fungi in promoting species coexistence and maintaining biodiversity of forest communities.

Chloroplasts are essential organelles in the cells of plants and algae. The functions of these specialized plastids are largely dependent on the ~3000 proteins residing in the organelle. Although chloroplasts are capable of a limited amount of semiautonomous protein synthesis - their genomes encode ~100 proteins - they must import more than 95% of their proteins after synthesis in the cytosol. Imported proteins generally possess an N-terminal extension termed a transit peptide. The importing translocons are made up of two complexes in the outer and inner envelope membranes, the so-called Toc and Tic machineries, respectively. The Toc complex contains two precursor receptors, Toc159 and Toc34, a protein channel, Toc75, and a peripheral component, Toc64/OEP64. The Tic complex consists of as many as eight components, namely Tic22, Tic110, Tic40, Tic20, Tic21 Tic62, Tic55 and Tic32. This general Toc/Tic import pathway, worked out largely in pea chloroplasts, appears to operate in chloroplasts in all green plants, albeit with significant modifications. Sub-complexes of the Toc and Tic machineries are proposed to exist to satisfy different substrate-, tissue-, cell- and developmental requirements. In this review, we summarize our understanding of the functions of Toc and Tic components, comparing these components of the import machinery in green algae through trees. We emphasize recent findings that point to growing complexities of chloroplast protein import process, and use the evolutionary relationships between proteins of different species in an attempt to define the essential core translocon components and those more likely to be responsible for regulation. This article is part of a Special Issue entitled: Protein Import and Quality Control in Mitochondria and Plastids.

As one of the most common post-translational modifications (PTMs), protein phosphorylation plays an important role in various biological processes, such as signaling transduction, cellular metabolism, differentiation, growth, regulation and apoptosis. Protein phosphorylation is of great value not only in illustrating the underlying molecular mechanisms but also in treatment of diseases and design of new drugs. Recently, there is an increasing interest in automatically extracting phosphorylation information from biomedical literatures. However, it still remains a challenging task due to the tremendous volume of literature and circuitous modes of expression for protein phosphorylation. To address this issue, we propose a novel text-mining method for efficiently retrieving and extracting protein phosphorylation information from literature. By employing natural language processing (NLP) technologies, this method transforms each sentence into dependency parse trees that can precisely reflect the intrinsic relationship of phosphorylation-related key words, from which detailed information of substrates, kinases and phosphorylation sites is extracted based on syntactic patterns. Compared with other existing approaches, the proposed method demonstrates significantly improved performance, suggesting it is a powerful bioinformatics approach to retrieving phosphorylation information from a large amount of literature. A web server for the proposed method is freely available at http://bioinformatics.ustc.edu.cn/pptm/.

Branch lengths are an important attribute of phylogenetic trees, providing essential information for many studies in evolutionary biology. Yet, part of the current methodology to reconstruct a phylogeny from genomic information - namely supertree methods - focuses on the topology or structure of the phylogenetic tree, rather than the evolutionary divergences associated to it. Moreover, accurate methods to estimate branch lengths - typically based on probabilistic analysis of a concatenated alignment - are limited by large demands in memory and computing time, and may become impractical when the data sets are too large.

Recent advance of bioinformatics and analytical apparatuses such as next generation DNA sequencer (NGS) and mass spectrometer (MS) has brought a big wave of comprehensive study to biology. Comprehensive study targeting all genes, transcripts (RNAs), proteins, metabolites, hormones, ions or phenotypes is called genomics, transcriptomics, proteomics, metabolomics, hormonomics, ionomics or phenomics, respectively. These omics are powerful approaches to identify key genes for important traits, to clarify events of physiological mechanisms and to reveal unknown metabolic pathways in crops. Recently, the use of omics approach has increased dramatically in fruit tree research. Although the most reported omics studies on fruit trees are transcriptomics, proteomics and metabolomics, and a few is reported on hormonomics and ionomics. In this article, we reviewed recent omics studies of major fruit trees, i.e. citrus, grapevine and rosaceae fruit trees. The effectiveness and prospects of omics in fruit tree research will as well be highlighted.

We recently described FastTree, a tool for inferring phylogenies for alignments with up to hundreds of thousands of sequences. Here, we describe improvements to FastTree that improve its accuracy without sacrificing scalability.

Phylogenetic inference is an attractive means to reconstruct transmission histories and epidemics. However, there is not a perfect correspondence between transmission history and virus phylogeny. Both node height and topological differences may occur, depending on the interaction between within-host evolutionary dynamics and between-host transmission patterns. To investigate these interactions, we added a within-host evolutionary model in epidemiological simulations and examined if the resulting phylogeny could recover different types of contact networks. To further improve realism, we also introduced patient-specific differences in infectivity across disease stages, and on the epidemic level we considered incomplete sampling and the age of the epidemic. Second, we implemented an inference method based on approximate Bayesian computation (ABC) to discriminate among three well-studied network models and jointly estimate both network parameters and key epidemiological quantities such as the infection rate. Our ABC framework used both topological and distance-based tree statistics for comparison between simulated and observed trees. Overall, our simulations showed that a virus time-scaled phylogeny (genealogy) may be substantially different from the between-host transmission tree. This has important implications for the interpretation of what a phylogeny reveals about the underlying epidemic contact network. In particular, we found that while the within-host evolutionary process obscures the transmission tree, the diversification process and infectivity dynamics also add discriminatory power to differentiate between different types of contact networks. We also found that the possibility to differentiate contact networks depends on how far an epidemic has progressed, where distance-based tree statistics have more power early in an epidemic. Finally, we applied our ABC inference on two different outbreaks from the Swedish HIV-1 epidemic.

The expansion of machine learning to high-stakes application domains such as medicine, finance, and criminal justice, where making informed decisions requires clear understanding of the model, has increased the interest in interpretable machine learning. The widely used Classification and Regression Trees (CART) have played a major role in health sciences, due to their simple and intuitive explanation of predictions. Ensemble methods like gradient boosting can improve the accuracy of decision trees, but at the expense of the interpretability of the generated model. Additive models, such as those produced by gradient boosting, and full interaction models, such as CART, have been investigated largely in isolation. We show that these models exist along a spectrum, revealing previously unseen connections between these approaches. This paper introduces a rigorous formalization for the additive tree, an empirically validated learning technique for creating a single decision tree, and shows that this method can produce models equivalent to CART or gradient boosted stumps at the extremes by varying a single parameter. Although the additive tree is designed primarily to provide both the model interpretability and predictive performance needed for high-stakes applications like medicine, it also can produce decision trees represented by hybrid models between CART and boosted stumps that can outperform either of these approaches.

Coffee is one of the most important commodities in the global market. Of the 130 species of Coffea, only Coffea arabica and Coffea canephora are actually cultivated on a large scale. Despite the economic and social importance of coffee, little research has been done on the coffee tree microbiome. To assess the structure and function of the rhizosphere microbiome, we performed a deep shotgun metagenomic sequencing of the rhizospheres of five different species, C. arabica, C. canephora, Coffea stenophylla, Coffea racemosa, and Coffea liberica. Our findings indicated that C. arabica and C. stenophylla have different microbiomes, while no differences were detected between the other Coffea species. The core rhizosphere microbiome comprises genera such as Streptomyces, Mycobacterium, Bradyrhizobium, Burkholderia, Sphingomonas, Penicillium, Trichoderma, and Rhizophagus, several of which are potential plant-beneficial microbes. Streptomyces and mycorrhizal fungi dominate the microbial communities. The concentration of sucrose in the rhizosphere seems to influence fungal communities, and the concentration of caffeine/theobromine has little effect on the microbiome. We also detected a possible relationship between drought tolerance in Coffea and known growth-promoting microorganisms. The results provide important information to guide future studies of the coffee tree microbiome to improve plant production and health. IMPORTANCE The microbiome has been identified as a fundamental factor for the maintenance of plant health, helping plants to fight diseases and the deleterious effects of abiotic stresses. Despite this, in-depth studies of the microbiome have been limited to a few species, generally with a short life cycle, and perennial species have mostly been neglected. The coffee tree microbiome, on the other hand, has gained interest in recent years as Coffea trees are perennial tropical species of enormous importance, especially for developing countries. A better understanding of the microorganisms associated with coffee trees can help to mitigate the deleterious effects of climate change on the crop, improving plant health and making the system more sustainable.

Shotgun metagenomic sequencing provides the capacity to understand microbial community structure and function at unprecedented resolution; however, the current analytical methods are constrained by a focus on taxonomic classifications that may obfuscate functional relationships. Here, we present expam, a tree-based, taxonomy agnostic tool for the identification of biologically relevant clades from shotgun metagenomic sequencing.

Hybridization can drive speciation. We examine the hypothesis that Castanea henryi var. omeiensis is an evolutionary lineage that originated from hybridization between two near-sympatric diploid taxa, C. henryi var. henryi and C. mollissima. We produce a high-quality genome assembly for mollissima and characterize evolutionary relationships among related chestnut taxa. Our results show that C. henryi var. omeiensis has a mosaic genome but has accumulated divergence in all 12 chromosomes. We observe positive correlation between admixture proportions and recombination rates across the genome. Candidate barrier genomic regions, which isolate var. henryi and mollissima, are re-assorted in the hybrid lineage. We further find that the putative barrier segments concentrate in genomic regions with less recombination, suggesting that interaction between natural selection and recombination shapes the evolution of hybrid genomes during hybrid speciation. This study highlights that reassortment of parental barriers is an important mechanism in generating biodiversity.

The recent introduction of next generation sequencing (NGS) technologies represents a major revolution in providing new tools for identifying the genes and/or genomic intervals controlling important traits for selection in breeding programs. In perennial fruit trees with long generation times and large sizes of adult plants, the impact of these techniques is even more important. High-throughput DNA sequencing technologies have provided complete annotated sequences in many important tree species. Most of the high-throughput genotyping platforms described are being used for studies of genetic diversity and population structure. Dissection of complex traits became possible through the availability of genome sequences along with phenotypic variation data, which allow to elucidate the causative genetic differences that give rise to observed phenotypic variation. Association mapping facilitates the association between genetic markers and phenotype in unstructured and complex populations, identifying molecular markers for assisted selection and breeding. Also, genomic data provide in silico identification and characterization of genes and gene families related to important traits, enabling new tools for molecular marker assisted selection in tree breeding. Deep sequencing of transcriptomes is also a powerful tool for the analysis of precise expression levels of each gene in a sample. It consists in quantifying short cDNA reads, obtained by NGS technologies, in order to compare the entire transcriptomes between genotypes and environmental conditions. The miRNAs are non-coding short RNAs involved in the regulation of different physiological processes, which can be identified by high-throughput sequencing of RNA libraries obtained by reverse transcription of purified short RNAs, and by in silico comparison with known miRNAs from other species. All together, NGS techniques and their applications have increased the resources for plant breeding in tree species, closing the former gap of genetic tools between trees and annual species.

Pinus herrerae and P. luzmariae are endemic to western Mexico, where they cover an area of more than 1 million hectares. Pinus herrerae is also cultivated in field trials in South Africa and South America, because of its considerable economic importance as a source of timber and resin. Seed quality, afforestation success and desirable traits may all be influenced by the presence of hybrid trees in seed stands.

The inference of phylogenetic trees is foundational to biology. However, state-of-the-art phylogenomics requires running complex pipelines, at significant computational and labour costs, with additional constraints in sequencing coverage, assembly and annotation quality. To overcome these challenges, we present Read2Tree, which directly processes raw sequencing reads into groups of corresponding genes. In a benchmark encompassing a broad variety of datasets, our assembly-free approach was 10-100x faster than conventional approaches, and in most cases more accurate-the exception being when sequencing coverage was high and reference species very distant. To illustrate the broad applicability of the tool, we reconstructed a yeast tree of life of 435 species spanning 590 million years of evolution. Applied to Coronaviridae samples, Read2Tree accurately classified highly diverse animal samples and near-identical SARS-CoV-2 sequences on a single tree-thereby exhibiting remarkable breadth and depth. The speed, accuracy, and versatility of Read2Tree enables comparative genomics at scale.