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bioRxiv2017

A LaeA- and BrlA-dependent cellular network governs tissue-specific secondary metabolism in the human pathogen Aspergillus fumigatus

Biosynthesis of many ecologically important secondary metabolites (SMs) in filamentous fungi is controlled by several global transcriptional regulators, like the chromatin modifier LaeA, and tied to both development and vegetative growth. In Aspergillus molds, asexual development is regulated by the BrlA>AbaA>WetA transcriptional cascade. To elucidate BrlA pathway involvement in SM regulation, we examined the transcriptional and metabolic profiles of {Delta}brlA, {Delta}abaA, {Delta}wetA and wild-type strains of the human pathogen Aspergillus fumigatus. We find that BrlA, in addition to regulating production of developmental SMs, regulates vegetative SMs and the SrbA-regulated hypoxia stress response in a concordant fashion to LaeA. We further show that the transcriptional and metabolic equivalence of {Delta}brlA and {Delta}laeA is mediated by a LaeA requirement preventing heterochromatic marks in the brlA promoter. These results provide a framework for the cellular network regulating not only fungal SMs but diverse cellular processes linked to virulence of this pathogen.

microbiology↗

bioRxiv2017

Compositional clustering in task structure learning

Humans are remarkably adept at generalizing knowledge between experiences in a way that can be difficult for computers. Often, this entails generalizing constituent pieces of experiences that do not fully overlap, but nonetheless share useful similarities with, previously acquired knowledge. However, it is often unclear how knowledge gained in one context should generalize to another. Previous computational models and data suggest that rather than learning about each individual context, humans build latent abstract structures and learn to link these structures to arbitrary contexts, facilitating generalization. In these models, task structures that are more popular across contexts are more likely to be revisited in new contexts. However, these models predict that structures are either re - used as a whole or created from scratch, prohibiting the ability to generalize constituent parts of learned structures. This contrasts with ecological settings, where some aspects of task structure, such as the transition function, will be shared between context separately from other aspects, such as the reward function. Here, we develop a novel non - parametric Bayesian agent that forms independent latent clusters for transition and reward functions that may have different popularity across contexts. We compare this agent to an agent that jointly clusters both across a range of task domains. We show that relative performance of the two agents depends on the statistics of the task domain, including the mutual information between transition and reward functions in the environment, and the stochasticity of the observations. We formalize our analysis through an information theoretic account of the priors, and develop a meta learning agent that can dynamically arbitrate between strategies across task domains. We argue that this provides a first step in allowing for compositional structures in reinforcement learners, which should be provide a better model of human learning and additional flexibility for artificial agents.\n\nAuthor summaryA musician may learn to generalize behaviors across instruments for different purposes, for example, reusing hand motions used when playing classical on the flute to play jazz on the saxophone. Conversely, she may learn to play a single song across many instruments that require completely distinct physical motions, but nonetheless transfer knowledge between them. This degree of compositionality is often absent from computational frameworks of learning, forcing agents either to generalize entire learned policies or to learn new policies from scratch. Here, we propose a solution to this problem that allows an agent to generalize components of a policy independently and compare it to an agent that generalizes components as a whole. We show that the degree to which one form of generalization is favored over the other is dependent on the features of task domain, with independent generalization of task components favored in environments with weak relationships between components or high degrees of noise and joint generalization of task components favored when there is a clear, discoverable relationship between task components. Furthermore, we show that the overall meta structure of the environment can be learned and leveraged by an agent that dynamically arbitrates between these forms of structure learning.

neuroscience↗

bioRxiv2017

Diet predicts milk composition in primates

Mammalian mothers pay heavy energetic costs to fuel the growth of their offspring. These costs are highest during lactation. Energy transmitted to offspring in the form of milk must ultimately come from the maternal diet, but there have been few comparative studies of the relationship between milk properties and mammalian diets. We used interspecific data on primate milk composition and wild diets to establish that concentrations of milk protein and sugar are predicted by diet independent of maternal mass, litter mass, and infant parking behavior such that increasing folivory or faunivory increases protein concentration but decreases sugar concentration. Milk energy density is unrelated to diet, though infant parking species do produce more energy-dense milk. While parking effects have been previously explained as a result of mother-infant separation, the mechanisms causing the relationship between nutrient packaging in milk and maternal diet are currently unclear. However, they likely reflect evolved differences in maternal energetics related to maternal foraging ecology, infant growth patterns, or the relative dietary abundance of nutrients costly to synthesize in milk among primate species.

zoology↗

bioRxiv2017

Hox-logic of preadaptations for social insect symbiosis in rove beetles

How symbiotic lifestyles evolve from free-living ecologies is poorly understood. In Metazoas largest family, Staphylinidae (rove beetles), numerous lineages have evolved obligate behavioral symbioses with ants or termites. Widespread convergence of this lifestyle is thought to stem from a novel, chemically defended body plan that enables free-living species to infiltrate colonies and undergo extreme evolutionary specialization. Here we show how this innovative body plan evolved, via new Hox functions in staphylinids that remodeled the coleopteran groundplan. Using a model staphylinid, Dalotia coriaria, we reveal the Hox basis for changes in thoracic appendage morphology that shortened the beetle elytron and left the abdomen physically unprotected, selecting for an abdominal defense gland that was likely key to unlocking ant and termite societies. We present evidence that the gland evolved through a novel, combinatorial role of the abdominal Hox proteins AbdA and AbdB. These proteins function together to specify distinct gland cell types in neighboring segmental compartments, each cell type synthesizing a different class of compound--irritant, ester and solvent. Only when secreted together do these compounds constitute a bioactive secretion, providing an example of emergent chemical functionality that arises from synergy between individual gland cell types. Hox-controlled specification of glandular diversity implies a modularity in compound biosynthesis that likely catalyzed the evolvability of rove beetle chemistry, including the capacity of symbiotic taxa to produce potent compounds for host deception. This study reveals how Hox-controlled body axis modifications predispose a major animal to convergently evolve into symbionts.

evolutionary biology↗

bioRxiv2017

Motility in multicellular Chlamydomonas reinhardtii

The advent of multicellularity was a watershed event in the history of life, yet the transition from unicellularity to multicellularity is not well understood. Multicellularity opens up opportunities for innovations in intercellular communication, cooperation, and specialization, which can provide selective advantages under certain ecological conditions. The unicellular alga Chlamydomonas reinhardtii has never had a multicellular ancestor yet it is closely related to the volvocine algae, a clade containing taxa that range from simple unicells to large, specialized multicellular colonies. Simple multicellular structures have been observed to evolve in C. reinhardtii in response to predation or to settling rate-based selection. Structures formed in response to predation consist of individual cells grouped within a shared transparent extracellular matrix. Evolved isolates form such structures obligately under culture conditions in which their wild type ancestors do not, indicating that newly-evolved multicellularity is heritable. C. reinhardtii is capable of photosynthesis, and possesses an eyespot and two flagella with which it moves towards or away from light in order to optimize input of radiant energy. Motility contributes to C. reinhardtii fitness because it allows cells or colonies to achieve this optimum. Utilizing phototaxis to assay motility, we determined that newly evolved multicellular strains do not exhibit significant directional movement, even though the flagellae of their constituent unicells are present and active. In C. reinhardtii the first steps towards multicellularity in response to predation appear to result in a trade-off between motility and differential survivorship, a trade-off that must overcome by further genetic change to ensure the long-term success of the new multicellular organism.

evolutionary biology↗

bioRxiv2017

Influenza Interaction with Cocirculating Pathogens, and Its Impact on Surveillance, Pathogenesis and Epidemic Profile: A Key Role for Mathematical Modeling

Evidence is mounting that influenza virus, a major contributor to the global disease burden, interacts with other pathogens infecting the human respiratory tract. Taking into account interactions with other pathogens may be critical to determining the real influenza burden and the full impact of public health policies targeting influenza. That necessity is particularly true for mathematical modeling studies, which have become critical in public health decision-making, despite their usually focusing on lone influenza virus acquisition and infection, thereby making broad oversimplifications regarding pathogen ecology. Herein, we review evidence of influenza virus interaction with bacteria and viruses, and the modeling studies that incorporated some of these. Despite the many studies examining possible associations between influenza and Streptococcus pneumoniae, Staphylococcus aureus, Haemophilus influenzae, Neisseria meningitides, respiratory syncytial virus, human rhinoviruses, human parainfluenza viruses, etc., very few mathematical models have integrated other pathogens alongside influenza. A notable exception is the recent modeling of the pneumococcus-influenza interaction, which highlighted potential influenza-related increased pneumococcal transmission and pathogenicity. That example demonstrates the power of dynamic modeling as an approach to test biological hypotheses concerning interaction mechanisms and estimate the strength of those interactions. We explore how different interference mechanisms may lead to unexpected incidence trends and misinterpretations. Using simple transmission models, we illustrate how existing interactions might impact public health surveillance systems and demonstrate that the development of multipathogen models is essential to assess the true public health burden of influenza, and help improve planning and evaluation of control measures. Finally, we identify the public health needs, surveillance, modeling and biological challenges, and propose avenues of research for the coming years.\n\nAuthor SummaryInfluenza is a major pathogen responsible for important morbidity and mortality burdens worldwide. Mathematical models of influenza virus acquisition have been critical to understanding its epidemiology and planning public health strategies of infection control. It is increasingly clear that microbes do not act in isolation but potentially interact within the host. Hence, studying influenza alone may lead to masking effects or misunderstanding information on its transmission and severity. Herein, we review the literature on bacterial and viral species that interact with the influenza virus, interaction mechanisms, and mathematical modeling studies integrating interactions. We report evidence that, beyond the classic secondary bacterial infections, many pathogenic bacteria and viruses probably interact with influenza. Public health relevance of pathogen interactions is detailed, showing how potential misreading or a narrow outlook might lead to mistaken public health decisionmaking. We describe the role of mechanistic transmission models in investigating this complex system and obtaining insight into interactions between influenza and other pathogens. Finally, we highlight benefits and challenges in modeling, and speculate on new opportunities made possible by taking a broader view: including basic science, clinical relevance and public health.

epidemiology↗

bioRxiv2017

Demographic fluctuation of community-acquired antibiotic-resistant Staphylococcus aureus lineages: potential role of flimsy antibiotic exposure

Community-acquired (CA) -as opposed to hospital acquired- methicillin-resistant Staphylococcus aureus (MRSA) lineages arose worldwide during the 1990s. To determine which factors, including selective antibiotic pressure, govern the expansion of two major lineages of CA-MRSA, namely \"USA300\" in Northern America and the \"European ST80\" in North Africa, Europe and the Middle East, we explored virulence factor expression, and fitness levels with or without antibiotics. The sampled strains were collected in a temporal window representing various steps of the epidemics, reflecting predicted effective population size as inferred from whole genome analysis. In addition to slight variations in virulence factor expression and biofilm production that might influence the ecological niches of theses lineages, competitive fitness experiments revealed that the biological cost of resistance to methicillin, fusidic-acid and fluoroquinolone is totally reversed in the presence of trace amount of antibiotics. Our results suggest that low-level antibiotics exposure in human and animal environments contributed to the expansion of both European-ST80 and USA300 lineages in community setting. This surge was likely driven by antibiotic (ab)use promoting the accumulation of antibiotics as environmental pollutants. The current results provide a novel link between effective population size increase of a pathogen and a selective advantage conferred by antibiotic resistance.

microbiology↗

bioRxiv2017

Game theoretic consideration of transgenic bacteria in the human gut microbiota as a pro-biotic prophylactic for metabolic syndrome

A game theoretic treatment is introduced to explore the role of nutrition of the gut bacterial microflora as a potential pro-biotic therapy. Rational design of functional foods and nutraceuticals has far reaching public health and therapeutic benefits. Understanding ecological dynamics and how phenotypic manipulations of microbe-microbe interactions of the gut microbiota can provide direct health benefit is currently a fundamental question in bioengineering and more widely in the food, diet and health industries. This work considers a hypothetical adjustment of the microbiome by introducing a transgenic bacterial species that contributes to increased exposure of omega 3 fats in the gut by converting them from the omega 6 fats, dominant in the Western diet. The ratio of the two fats circulating in blood are risk markers, indicators of metabolic syndrome and related conditions. In this work, we consider nutritional exposure to a pro-biotic, a live culture of transgenic bacteria contributing omega 3 fats from omega 6 in the diet. Maintaining a long-term co-existence between native (indigenous) and transgenic bacteria is a challenge. Game theory is the appropriate tool for handling this conflict. The long-term co-existence is guaranteed if the two strains engage in the Snowdrift game. Our game theoretic treatment provides the basis of a model mechanism for prophylactic nutritional therapy for metabolic syndrome by the transgenic bacteria, providing support of indigenous gut microbiota and additional supplementation of a pro-biotic.

bioengineering↗

bioRxiv2017

Novel prosthecate bacteria from the candidate phylum Acetothermia revealed by culture-independent genomics and advanced microscopy

Members of the candidate phylum Acetothermia are globally distributed and detected in various habitats. However, little is known about their physiology and ecological importance. In this study, an OTU belonging to Acetothermia was detected at high abundance in two full-scale anaerobic digesters. The first closed genome from this phylum was obtained by differential coverage binning of metagenomes and scaffolding with nanopore data. Genome annotation and metabolic reconstruction suggested an anaerobic chemoheterotrophic lifestyle in which the bacterium obtain energy and carbon via fermentation of peptides, amino acids, and simple sugars to acetate, formate, and hydrogen. The morphology was unusual and composed of a central rod-shaped cell with bipolar prosthecae as revealed by fluorescence in situ hybridization combined with confocal laser scanning microscopy, Raman microspectroscopy and atomic force microscopy. We hypothesize that these prosthecae allow for increased nutrient uptake by greatly expanding the cell surface area, providing a competitive advantage under nutrient-limited conditions.

microbiology↗

bioRxiv2017

How Cognitive and Reactive Fear Circuits Optimize Escape Decisions in Humans

Flight initiation distance (FID), the distance at which an organism flees from an approaching threat, is an ecological metric of cost-benefit functions of escape decisions. We adapted the FID paradigm to investigate how fast or slow attacking virtual predators constrain escape decisions. We show that rapid escape decisions rely on reactive fear circuits in the periaqueductal gray and midcingulate cortex (MCC), while protracted escape decisions, defined by larger buffer zones, were associated with cognitive fear circuits which include posterior cingulate cortex, hippocampus and the ventromedial prefrontal cortex, circuits implicated in strategic avoidance and behavioral flexibility. Using a Bayesian Decision Model, we further show that optimization of escape decisions under rapid flight were localized to the MCC, a region involved in adaptive motor control, while the hippocampus is implicated in optimizing decisions that update and control slower escape initiation. These results demonstrate an unexplored link between defensive survival circuits and their role in adaptive escape decisions.\n\nSignificanceHumans, like other animals, have evolved a set of circuits whose primary function is survival. In the case of predation, these circuits include reactive fear circuits involved in fast and immediate escape decisions and cognitive fear circuits that are involved in the conscious feeling of threat as well as slow strategic escape. Using neuroimaging combined with computational modeling, we support this differentiation of fear circuits by showing that fast escape decisions are elicited by the periaqueductal gray and MCC, regions involved in reactive flight. Conversely, slower escape decisions rely on the hippocampus, posterior cingulate cortex and prefrontal cortex, a circuit implicated in behavioral flexibility. These results support the role of the defensive survival circuitry in escape decisions and a separation of fear into reactive and cognitive circuits.

neuroscience↗

bioRxiv2017

Retinal specializations to a micro-predatory and crypto-benthic life-style in the Mediterranean triplefin blenny Tripterygion delaisi

The environment and lifestyle of a species are known to exert selective pressure on the visual system, often demonstrating a tight link between visual morphology and ecology. Many studies have predicted the visual requirements of a species by examining the anatomical features of the eye. However, among the vast number of studies on visual specializations in aquatic animals, only a few have focused on small benthic fishes that occupy a heterogeneous and spatially complex visual environment. This study investigates the general retinal anatomy including the topography of both the photoreceptor and ganglion cell populations and estimates the spatial resolving power of the eye of the Mediterranean triplefin Tripterygion delaisi. Retinal wholemounts were prepared to systematically and quantitatively analyze photoreceptor and retinal ganglion cell densities using design-based stereology. To further examine the retinal structure, we also used magnetic resonance imaging and histological examination of retinal cross sections. Observations of the triplefin's eyes revealed them to be highly mobile, allowing them to view the surroundings without body movements. A rostral aphakic gap and the elliptical shape of the eye extend its visual field rostrally and allow for a rostro-caudal accommodatory axis, enabling this species to focus on prey at close range. Single and twin cones dominate the retina and are consistently arranged in one of two regular patterns, which may enhance motion detection and color vision. The retina features a prominent, dorso-temporal, convexiclivate fovea with an average density of 104,400 double and 30,800 single cones per mm2, and 81,000 retinal ganglion cells per mm2. Based on photoreceptor spacing, spatial resolving power was calculated to be between 6.7 and 9.0 cycles per degree. Location and resolving power of the fovea would benefit the detection and identification of small prey in the lower frontal region of the visual field.

animal behavior and cognition↗

bioRxiv2017

Fish Diversity as a Function of Depth and Body Size

We analyze the number of marine fish species as a function of fish body size and occurrence depth. For this purpose, we analyze the FishBase database. We compare these data to predictions of fish species numbers derived from the neutral theory of biodiversity combined with well-established ecological scaling laws, and measured oceanic biomass data. We consider several variants of these scaling laws, and we find that more large fish species exist compared to the prediction, which is especially true for elasmobranchs, possibly due to their overwhelmingly predatory niches. We find species numbers decreasing with occurrence depth somewhat quicker than our predictions based on the decrease of the number of individuals with depth indicates. This is especially true for the elasmobranchs. This is unsurprising, since the individuals versus depth data did not specifically determine elasmobranch biomass, and since sharks are known to be limited to depths < 3,000 m.\n\nFinally, we discuss how a reduced rate of speciation in larger animals could explain why large species are rare, in spite of the advantages of large body sizes outlined in Copes rule.

evolutionary biology↗

bioRxiv2017

Image-based methods for phenotyping growth dynamics and fitness in large plant populations

BackgroundThe model species Arabidopsis thaliana has extensive resources to investigate intraspecific trait variability and the genetic bases of ecologically relevant traits. However, the cost of equipment and software required for high-throughput phenotyping is often a bottleneck for large-scale studies, such as mutant screening or quantitative genetics analyses. Simple tools are needed for the measurement of fitness-related traits, like relative growth rate and fruit production, without investment in expensive infrastructures. Here, we describe methods that enable the estimation of biomass accumulation and fruit number from the analysis of rosette and inflorescence images taken with a regular camera.\n\nResultsWe developed two models to predict plant dry mass and fruit number from the parameters extracted with the analysis of rosette and inflorescence images. Predictive models were trained by sacrificing growing individuals for dry mass estimation, and manually measuring a fraction of individuals for fruit number at maturity. Using a cross-validation approach, we showed that quantitative parameters extracted from image analysis predicts more 90% of both plant dry mass and fruit number. When used on 451 natural accessions, the method allowed modelling growth dynamics, including relative growth rate, throughout the life cycle of various ecotypes. Estimated growth-related traits had high heritability (0.65 < H2 < 0.93), as well as estimated fruit number (H2 = 0.68). In addition, we validated the method for estimating fruit number with rev5, a mutant with increased flower abortion.\n\nConclusionsThe method we propose here is based on the automated computerization of plant images with ImageJ, and subsequent statistical modelling in R. It allows plant biologists to measure growth dynamics and fruit number in hundreds of individuals with simple computing steps that can be repeated and adjusted to a wide range of laboratory conditions. It is thus a flexible toolkit for the measurement of fitness-related traits in large plant populations.

bioinformatics↗

bioRxiv2017

The Cultural Brain Hypothesis: How culture drives brain expansion, underlies sociality, and alters life history

In the last few million years, the hominin brain more than tripled in size. Comparisons across evolutionary lineages suggest that this expansion may be part of a broader trend toward larger, more complex brains in many taxa. Efforts to understand the evolutionary forces driving brain expansion have focused on climatic, ecological, and social factors. Here, building on existing research on learning, we analytically and computationally model the predictions of two closely related hypotheses: The Cultural Brain Hypothesis and the Cumulative Cultural Brain Hypothesis. The Cultural Brain Hypothesis posits that brains have been selected for their ability to store and manage information, acquired through asocial or social learning. The model of the Cultural Brain Hypothesis reveals relationships between brain size, group size, innovation, social learning, mating structures, and the length of the juvenile period that are supported by the existing empirical literature. From this model, we derive a set of predictions--the Cumulative Cultural Brain Hypothesis--for the conditions that favor an autocatalytic take-off characteristic of human evolution. This narrow evolutionary pathway, created by cumulative cultural evolution, may help explain the rapid expansion of human brains and other aspects of our species life history and psychology.

evolutionary biology↗

bioRxiv2017

Lineage specific histories of Mycobacterium tuberculosis dispersal in Africa and Eurasia

Mycobacterium tuberculosis (M.tb) is a globally distributed, obligate pathogen of humans that can be divided into seven clearly defined lineages. Identifying how the ancestral clone of M.tb spread and differentiated is important for identifying the ecological drivers of the current pandemic. We reconstructed M.tb migration in Africa and Eurasia, and investigated lineage specific patterns of spread. Applying evolutionary rates inferred with ancient M.tb genome calibration, we link M.tb dispersal to historical phenomena that altered patterns of connectivity throughout Africa and Eurasia: trans-Indian Ocean trade in spices and other goods, the Silk Road and its predecessors, the expansion of the Roman Empire and, the European Age of Exploration. We find that Eastern Africa and Southeast Asia have been critical in the dispersal of M.tb. Our results reveal complex relationships between spatial dispersal and expansion of M.tb populations, and delineate the independent evolutionary trajectories of bacterial sub-populations underlying the current pandemic.

evolutionary biology↗

bioRxiv2017

Genomic characterisation and conservation genetics of the indigenous Irish Kerry cattle breed

Kerry cattle are an endangered landrace heritage breed of cultural importance to Ireland. In the present study we have used genome-wide SNP data (Illumina(R) BovineSNP50 array) to evaluate genomic diversity within the Kerry cattle population and between Kerry cattle and other European cattle breeds. Visualisation of patterns of genetic differentiation and gene flow among cattle breeds using phylogenetic trees with ancestry graphs highlighted, in particular, historical gene flow from the British Shorthorn breed into the ancestral population of modern Kerry cattle. Principal component analysis (PCA) and genetic clustering emphasised the genetic distinctiveness of Kerry cattle relative to comparator British and European cattle breeds. Modelling of genetic effective population size (Ne) revealed a demographic trend of diminishing Ne over time and that recent estimated Ne values for the Kerry breed may be less than the threshold for sustainable genetic conservation. In addition, analysis of genome-wide autozygosity (FROH) showed that genomic inbreeding has increased significantly during the 20 years between 1992 and 2012. Finally, signatures of selection revealed genomic regions subject to natural and artificial selection as Kerry cattle adapted to the climate, physical geography and agro-ecology of southwest Ireland.\n\nNote 1: This is an Associate Editor (D.E.M) Inaugural Article submission to Frontiers in Genetics: Livestock Genomics\n\nNote 2: British English language style preferred for publication of this article.

genomics↗

bioRxiv2017

Pathogen Population Structure Can Explain Hospital Outbreaks

ObjectiveTo analyze Hospital Acquired Infection (HAI) outbreaks using microbial population biology dynamics in order to understand outbreaks as a biological system.\n\nDesignComputational modeling study.\n\nMethodsThe majority of HAI transmission models describe dynamics on the level of the host rather than on the level of the pathogens themselves. Accordingly, epidemiologists often cannot complete transmission chains without direct evidence of either host-host contact or large reservoir populations. Here, we propose an ecology-based model to explain the transmission of pathogens in hospitals. The model is based upon metapopulation biology, which describes a group of interacting localized populations and island biogeography, which provides a basis for how pathogens may be moving between locales. Computational simulation trials are used to assess the applicability of the model.\n\nResultsResults indicate that pathogens survive for extended periods without the need for large reservoirs by living in localized ephemeral populations while continuously transmitting pathogens to new seed populations. Computational simulations show small populations spending significant portions of time at sizes too small to be detected by most surveillance protocols. The number and type of these ephemeral populations enable the overall pathogen population to be sustained.\n\nConclusionsBy modeling hospital pathogens as a metapopulation, observations characteristic of hospital acquired infection outbreaks for which there has previously been no sufficient biological explanation, including how and why empirically successful interventions work, can now be accounted for using population dynamic hypotheses. Epidemiological links between temporally isolated outbreaks are explained via pathogen population dynamics and potential outbreak intervention targets are identified.

epidemiology↗

bioRxiv2017

Rolling a mycobiome down a hill: endophytes in the Taiwanese Cloud Forest

Fungal endophytes of plants are ubiquitous and important to host plant health. Despite their ecological importance, landscape-level patterns of microbial communities in plant hosts are not well-characterized. Fungal wood-inhabiting and foliar endophyte communities from multiple tree hosts were sampled at multiple spatial scales across a 25 ha subtropical research plot in northern Taiwan, using culture-free, community DNA amplicon sequencing methods. Fungal endophyte communities were distinct between leaves and wood, but the mycobiomes were highly variable across and within tree species. Of the variance that could be explained, host tree species was the most important driver of mycobiome community-composition. Within a single tree species, \"core\" mycobiomes were characterized using cooccurrence analysis. These core groups of endophytes in leaves and wood show divergent spatial patterns. For wood endophytes, a more consistent, \"minimal\" core mycobiome coexisted with the host across the extent of the study. For leaf endophytes, the core fungi resembled a more dynamic, \"gradient\" model of the core microbiome, changing across the topography and distance of the study.

microbiology↗