Modeling trophic dependencies and exchanges among insects' bacterial symbionts in a host-simulated environment

Transcriptome data reveal beneficial effects of Rickettsia (Rickettsiales: Rickettsiaceae) on Bemisia tabaci(Hemiptera: Aleyrodidae) through nutritional factors and defense mechanisms

Whitefly Bemisia tabaci (Hemiptera: Aleyrodidae) is a destructive insect pest of many crops. Rickettsia infection in different cryptic species of B. tabaci has been observed worldwide. Understanding the interactions between these 2 organisms is critical to developing Rickettsia-based strategies to control B. tabaci and thereby reduce the transmission of related vector-borne viruses. In this study, we investigated the effects of Rickettsia infection on the biological characteristics of the Middle East Asia Minor 1 (MEAM1) strain of B. tabaci through biological analysis of infected and uninfected individuals. The results of this study suggest that Rickettsia may confer fitness benefits. These benefits include increased fertility, improved survival rates, accelerated development, and resulted in female bias. We also investigated the transcriptomics impact of Rickettsia infection on B. tabaci by performing a comparative RNA-seq analysis of nymphs and adult females, both with and without the infection. Our analysis revealed 218 significant differentially expressed genes (DEGs) in infected nymphs compared to uninfected ones and 748 significant DEGs in infected female adults compared to their uninfected whiteflies. Pathway analysis further revealed that Rickettsia can affect many important metabolic pathways in whiteflies. The results suggest that Rickettsia plays an essential role in energy metabolism, and nutrient synthesis in the B. tabaci MEAM1, and depends on metabolites obtained from the host to ensure its survival. Overall, our findings suggest that Rickettsia has beneficial effects on B. tabaci and offered insights into the potential molecular mechanisms governing the interactions between Rickettsia and B. tabaci MEAM1.

Engineering natural microbiomes toward enhanced bioremediation by microbiome modeling

Engineering natural microbiomes for biotechnological applications remains challenging, as metabolic interactions within microbiomes are largely unknown, and practical principles and tools for microbiome engineering are still lacking. Here, we present a combinatory top-down and bottom-up framework to engineer natural microbiomes for the construction of function-enhanced synthetic microbiomes. We show that application of herbicide and herbicide-degrader inoculation drives a convergent succession of different natural microbiomes toward functional microbiomes (e.g., enhanced bioremediation of herbicide-contaminated soils). We develop a metabolic modeling pipeline, SuperCC, that can be used to document metabolic interactions within microbiomes and to simulate the performances of different microbiomes. Using SuperCC, we construct bioremediation-enhanced synthetic microbiomes based on 18 keystone species identified from natural microbiomes. Our results highlight the importance of metabolic interactions in shaping microbiome functions and provide practical guidance for engineering natural microbiomes.

Whitefly-Plant Interactions: An Integrated Molecular Perspective

The rapid advances in available transcriptomic and genomic data and our understanding of the physiology and biochemistry of whitefly-plant interactions have allowed us to gain new and significant insights into the biology of whiteflies and their successful adaptation to host plants. In this review, we provide a comprehensive overview of the mechanisms that whiteflies have evolved to overcome the challenges of feeding on phloem sap. We also highlight the evolution and functions of gene families involved in host perception, evaluation, and manipulation; primary metabolism; and metabolite detoxification. We discuss the emerging themes in plant immunity to whiteflies, focusing on whitefly effectors and their sites of action in plant defense-signaling pathways. We conclude with a discussion of advances in the genetic manipulation of whiteflies and the potential that they hold for exploring the interactions between whiteflies and their host plants, as well as the development of novel strategies for the genetic control of whiteflies.

Community-scale models of microbiomes: Articulating metabolic modelling and metagenome sequencing

Building models is essential for understanding the functions and dynamics of microbial communities. Metabolic models built on genome-scale metabolic network reconstructions (GENREs) are especially relevant as a means to decipher the complex interactions occurring among species. Model reconstruction increasingly relies on metagenomics, which permits direct characterisation of naturally occurring communities that may contain organisms that cannot be isolated or cultured. In this review, we provide an overview of the field of metabolic modelling and its increasing reliance on and synergy with metagenomics and bioinformatics. We survey the means of assigning functions and reconstructing metabolic networks from (meta-)genomes, and present the variety and mathematical fundamentals of metabolic models that foster the understanding of microbial dynamics. We emphasise the characterisation of interactions and the scaling of model construction to large communities, two important bottlenecks in the applicability of these models. We give an overview of the current state of the art in metagenome sequencing and bioinformatics analysis, focusing on the reconstruction of genomes in microbial communities. Metagenomics benefits tremendously from third-generation sequencing, and we discuss the opportunities of long-read sequencing, strain-level characterisation and eukaryotic metagenomics. We aim at providing algorithmic and mathematical support, together with tool and application resources, that permit bridging the gap between metagenomics and metabolic modelling.

Cryptic community structure and metabolic interactions among the heritable facultative symbionts of the pea aphid

Most insects harbour influential, yet non-essential heritable microbes in their hemocoel. Communities of these symbionts exhibit low diversity. But their frequent multi-species nature raises intriguing questions on roles for symbiont-symbiont synergies in host adaptation, and on the stability of the symbiont communities, themselves. In this study, we build on knowledge of species-defined symbiont community structure across US populations of the pea aphid, Acyrthosiphon pisum. Through extensive symbiont genotyping, we show that pea aphids' microbiomes can be more precisely defined at the symbiont strain level, with strain variability shaping five out of nine previously reported co-infection trends. Field data provide a mixture of evidence for synergistic fitness effects and symbiont hitchhiking, revealing causes and consequences of these co-infection trends. To test whether within-host metabolic interactions predict common versus rare strain-defined communities, we leveraged the high relatedness of our dominant, community-defined symbiont strains vs. 12 pea aphid-derived Gammaproteobacteria with sequenced genomes. Genomic inference, using metabolic complementarity indices, revealed high potential for cooperation among one pair of symbionts-Serratia symbiotica and Rickettsiella viridis. Applying the expansion network algorithm, through additional use of pea aphid and obligate Buchnera symbiont genomes, Serratia and Rickettsiella emerged as the only symbiont community requiring both parties to expand holobiont metabolism. Through their joint expansion of the biotin biosynthesis pathway, these symbionts may span missing gaps, creating a multi-party mutualism within their nutrient-limited, phloem-feeding hosts. Recent, complementary gene inactivation, within the biotin pathways of Serratia and Rickettsiella, raises further questions on the origins of mutualisms and host-symbiont interdependencies.

A framework for the targeted recruitment of crop-beneficial soil taxa based on network analysis of metagenomics data

BACKGROUND

The design of ecologically sustainable and plant-beneficial soil systems is a key goal in actively manipulating root-associated microbiomes. Community engineering efforts commonly seek to harness the potential of the indigenous microbiome through substrate-mediated recruitment of beneficial members. In most sustainable practices, microbial recruitment mechanisms rely on the application of complex organic mixtures where the resources/metabolites that act as direct stimulants of beneficial groups are not characterized. Outcomes of such indirect amendments are unpredictable regarding engineering the microbiome and achieving a plant-beneficial environment.

RESULTS

This study applied network analysis of metagenomics data to explore amendment-derived transformations in the soil microbiome, which lead to the suppression of pathogens affecting apple root systems. Shotgun metagenomic analysis was conducted with data from 'sick' vs 'healthy/recovered' rhizosphere soil microbiomes. The data was then converted into community-level metabolic networks. Simulations examined the functional contribution of treatment-associated taxonomic groups and linked them with specific amendment-induced metabolites. This analysis enabled the selection of specific metabolites that were predicted to amplify or diminish the abundance of targeted microbes functional in the healthy soil system. Many of these predictions were corroborated by experimental evidence from the literature. The potential of two of these metabolites (dopamine and vitamin B₁₂) to either stimulate or suppress targeted microbial groups was evaluated in a follow-up set of soil microcosm experiments. The results corroborated the stimulant's potential (but not the suppressor) to act as a modulator of plant beneficial bacteria, paving the way for future development of knowledge-based (rather than trial and error) metabolic-defined amendments. Our pipeline for generating predictions for the selective targeting of microbial groups based on processing assembled and annotated metagenomics data is available at https://github.com/ot483/NetCom2 .

CONCLUSIONS

This research demonstrates how genomic-based algorithms can be used to formulate testable hypotheses for strategically engineering the rhizosphere microbiome by identifying specific compounds, which may act as selective modulators of microbial communities. Applying this framework to reduce unpredictable elements in amendment-based solutions promotes the development of ecologically-sound methods for re-establishing a functional microbiome in agro and other ecosystems. Video Abstract.

Cytotype Affects the Capability of the Whitefly Bemisia tabaci MED Species To Feed and Oviposit on an Unfavorable Host Plant

The acquisition of nutritional obligate primary endosymbionts (P-symbionts) allowed phloemo-phageous insects to feed on plant sap and thus colonize novel ecological niches. P-symbionts often coexist with facultative secondary endosymbionts (S-symbionts), which may also influence their hosts' niche utilization ability. The whitefly Bemisia tabaci is a highly diversified species complex harboring, in addition to the P-symbiont "Candidatus Portiera aleyrodidarum," seven S-symbionts whose roles remain poorly understood. Here, we compare the phenotypic and metabolic responses of three B. tabaci lines differing in their S-symbiont community, reared on three different host plants, hibiscus, tobacco, or lantana, and address whether and how S-symbionts influence insect capacity to feed and produce offspring on those plants. We first show that hibiscus, tobacco, and lantana differ in their free amino acid composition. Insects' performance, as well as free amino acid profile and symbiotic load, were shown to be plant dependent, suggesting a critical role for the plant nutritional properties. Insect fecundity was significantly lower on lantana, indicating that it is the least favorable plant. Remarkably, insects reared on this plant show a specific amino acid profile and a higher symbiont density compared to the two other plants. In addition, this plant was the only one for which fecundity differences were observed between lines. Using genetically homogeneous hybrids, we demonstrate that cytotype (mitochondria and symbionts), and not genotype, is a major determinant of females' fecundity and amino acid profile on lantana. As cytotypes differ in their S-symbiont community, we propose that these symbionts may mediate their hosts' suitable plant range. IMPORTANCE Microbial symbionts are universal in eukaryotes, and it is now recognized that symbiotic associations represent major evolutionary driving forces. However, the extent to which symbionts contribute to their hosts' ecological adaptation and subsequent diversification is far from being fully elucidated. The whitefly Bemisia tabaci is a sap feeder associated with multiple coinfecting intracellular facultative symbionts. Here, we show that plant species simultaneously affect whiteflies' performance, amino acid profile, and symbiotic density, which could be partially explained by differences in plant nutritional properties. We also demonstrate that, on lantana, the least favorable plant used in our study, whiteflies' performance is determined by their cytotype. We propose that the host plant utilization in B. tabaci is influenced by its facultative symbiont community composition, possibly through its impact on the host dietary requirements. Altogether, our data provide new insights into the impact of intracellular microorganisms on their animal hosts' ecological niche range and diversification.

Whitefly endosymbionts: IPM opportunity or tilting at windmills?

Whiteflies are sap-sucking insects responsible for high economic losses. They colonize hundreds of plant species and cause direct feeding damage and indirect damage through transmission of devastating viruses. Modern agriculture has seen a history of invasive whitefly species and populations that expand to novel regions, bringing along fierce viruses. Control efforts are hindered by fast virus transmission, insecticide-resistant populations, and a wide host range which permits large natural reservoirs for whiteflies. Augmentative biocontrol by parasitoids while effective in suppressing high population densities in greenhouses falls short when it comes to preventing virus transmission and is ineffective in the open field. A potential source of much needed novel control strategies lays within a diverse community of whitefly endosymbionts. The idea to exploit endosymbionts for whitefly control is as old as identification of these bacteria, yet it still has not come to fruition. We review where our knowledge stands on the aspects of whitefly endosymbiont evolution, biology, metabolism, multitrophic interactions, and population dynamics. We show how these insights are bringing us closer to the goal of better integrated pest management strategies. Combining most up to date understanding of whitefly-endosymbiont interactions and recent technological advances, we discuss possibilities of disrupting and manipulating whitefly endosymbionts, as well as using them for pest control.

NetCom: A Network-Based Tool for Predicting Metabolic Activities of Microbial Communities Based on Interpretation of Metagenomics Data

The study of microbial activity can be viewed as a triangle with three sides: environment (dominant resources in a specific habitat), community (species dictating a repertoire of metabolic conversions) and function (production and/or utilization of resources and compounds). Advances in metagenomics enable a high-resolution description of complex microbial communities in their natural environments and support a systematic study of environment-community-function associations. NetCom is a web-tool for predicting metabolic activities of microbial communities based on network-based interpretation of assembled and annotated metagenomics data. The algorithm takes as an input, lists of differentially abundant enzymatic reactions and generates the following outputs: (i) pathway associations of differently abundant enzymes; (ii) prediction of environmental resources that are unique to each treatment, and their pathway associations; (iii) prediction of compounds that are produced by the microbial community, and pathway association of compounds that are treatment-specific; (iv) network visualization of enzymes, environmental resources and produced compounds, that are treatment specific (2 and 3D). The tool is demonstrated on metagenomic data from rhizosphere and bulk soil samples. By predicting root-specific activities, we illustrate the relevance of our framework for forecasting the impact of soil amendments on the corresponding microbial communities. NetCom is available online.

An eco-systems biology approach for modeling tritrophic networks reveals the influence of dietary amino acids on symbiont dynamics of Bemisia tabaci

Metabolic conversions allow organisms to produce essential metabolites from the available nutrients in an environment, frequently requiring metabolic exchanges among co-inhabiting organisms. Here, we applied genomic-based simulations for exploring tri-trophic interactions among the sap-feeding insect whitefly (Bemisia tabaci), its host-plants, and symbiotic bacteria. The simplicity of this ecosystem allows capturing the interacting organisms (based on genomic data) and the environmental content (based on metabolomics data). Simulations explored the metabolic capacities of insect-symbiont combinations under environments representing natural phloem. Predictions were correlated with experimental data on the dynamics of symbionts under different diets. Simulation outcomes depict a puzzle of three-layer origins (plant-insect-symbionts) for the source of essential metabolites across habitats and stratify interactions enabling the whitefly to feed on diverse hosts. In parallel to simulations, natural and artificial feeding experiments provide supporting evidence for an environment-based effect on symbiont dynamics. Based on simulations, a decrease in the relative abundance of a symbiont can be associated with a loss of fitness advantage due to an environmental excess in amino-acids whose production in a deprived environment used to depend on the symbiont. The study demonstrates that genomic-based predictions can bridge environment and community dynamics and guide the design of symbiont manipulation strategies.

Deep learning approaches for natural product discovery from plant endophytic microbiomes

Plant microbiomes are not only diverse, but also appear to host a vast pool of secondary metabolites holding great promise for bioactive natural products and drug discovery. Yet, most microbes within plants appear to be uncultivable, and for those that can be cultivated, their metabolic potential lies largely hidden through regulatory silencing of biosynthetic genes. The recent explosion of powerful interdisciplinary approaches, including multi-omics methods to address multi-trophic interactions and artificial intelligence-based computational approaches to infer distribution of function, together present a paradigm shift in high-throughput approaches to natural product discovery from plant-associated microbes. Arguably, the key to characterizing and harnessing this biochemical capacity depends on a novel, systematic approach to characterize the triggers that turn on secondary metabolite biosynthesis through molecular or genetic signals from the host plant, members of the rich 'in planta' community, or from the environment. This review explores breakthrough approaches for natural product discovery from plant microbiomes, emphasizing the promise of deep learning as a tool for endophyte bioprospecting, endophyte biochemical novelty prediction, and endophyte regulatory control. It concludes with a proposed pipeline to harness global databases (genomic, metabolomic, regulomic, and chemical) to uncover and unsilence desirable natural products.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1186/s40793-021-00375-0.

Metage2Metabo, microbiota-scale metabolic complementarity for the identification of key species

To capture the functional diversity of microbiota, one must identify metabolic functions and species of interest within hundreds or thousands of microorganisms. We present Metage2Metabo (M2M) a resource that meets the need for de novo functional screening of genome-scale metabolic networks (GSMNs) at the scale of a metagenome, and the identification of critical species with respect to metabolic cooperation. M2M comprises a flexible pipeline for the characterisation of individual metabolisms and collective metabolic complementarity. In addition, M2M identifies key species, that are meaningful members of the community for functions of interest. We demonstrate that M2M is applicable to collections of genomes as well as metagenome-assembled genomes, permits an efficient GSMN reconstruction with Pathway Tools, and assesses the cooperation potential between species. M2M identifies key organisms by reducing the complexity of a large-scale microbiota into minimal communities with equivalent properties, suitable for further analyses.

All the microbes that live in a specific environment, for example an organ, are collectively called the microbiota. In humans, the microbiota of the gut has been extensively studied by sequencing the DNA of the different microbes to identify them and determine the roles they play in health and disease. The DNA sequences of all the members of the microbiota is called the metagenome.

The chemical reactions that the gut microbiota perform to produce energy and make the biomolecules they need to survive are collectively referred to as the metabolism of these microbes. Studying the metabolism of the gut microbiota can help researchers understand the roles of the different microbes. However, the large variety of species in the gut microbiota and gaps in the information about them render these studies difficult, despite technology improving quickly.

To tackle this issue, Belcour, Frioux et al developed a new piece of software called Metage2Metabo (M2M) that simulates the metabolism of the gut microbiota and describes the metabolic relationships between the different microbes. Metage2Metabo analyses the roles of the metabolic genes of a large number of microbe species to establish how they complement each other metabolically. Then, it can calculate the minimum number of species needed to perform a metabolic role of interest within that microbiota, and which key species are associated with that role.

To test the new software, Belcour, Frioux et al. used Metage2Metabo to analyse genomes from the human gut microbiota and from the cow rumen (one of the cow’s stomachs). They showed that even if the metagenome was incomplete, the software was able to make stable predictions of key species involved in metabolic complementarity. Additionally, they also illustrated how the method can be used to study the gut microbiota of individuals.

This work presents a new method for determining the metabolic relationships between species within a microbiota. The software is highly flexible and could be adapted to identify key members within different communities. In the context of the gut microbiota, the predictions of Metage2Metabo could shed lights on the interactions between the host and the microbes and contribute to a better understanding of microbe environments.

Whitefly Endosymbionts: Biology, Evolution, and Plant Virus Interactions

Whiteflies (Hemiptera: Aleyrodidae) are sap-feeding global agricultural pests. These piercing-sucking insects have coevolved with intracellular endosymbiotic bacteria that help to supplement their nutrient-poor plant sap diets with essential amino acids and carotenoids. These obligate, primary endosymbionts have been incorporated into specialized organs called bacteriomes where they sometimes coexist with facultative, secondary endosymbionts. All whitefly species harbor the primary endosymbiont Candidatus Portiera aleyrodidarum and have a variable number of secondary endosymbionts. The secondary endosymbiont complement harbored by the cryptic whitefly species Bemisia tabaci is particularly complex with various assemblages of seven different genera identified to date. In this review, we discuss whitefly associated primary and secondary endosymbionts. We focus on those associated with the notorious B. tabaci species complex with emphasis on their biological characteristics and diversity. We also discuss their interactions with phytopathogenic begomoviruses (family Geminiviridae), which are transmitted exclusively by B. tabaci in a persistent-circulative manner. Unraveling the complex interactions of these endosymbionts with their insect hosts and plant viruses could lead to advancements in whitefly and whitefly transmitted virus management.

Using automated reasoning to explore the metabolism of unconventional organisms: a first step to explore host-microbial interactions

Systems modelled in the context of molecular and cellular biology are difficult to represent with a single calibrated numerical model. Flux optimisation hypotheses have shown tremendous promise to accurately predict bacterial metabolism but they require a precise understanding of metabolic reactions occurring in the considered species. Unfortunately, this information may not be available for more complex organisms or non-cultured microorganisms such as those evidenced in microbiomes with metagenomic techniques. In both cases, flux optimisation techniques may not be applicable to elucidate systems functioning. In this context, we describe how automatic reasoning allows relevant features of an unconventional biological system to be identified despite a lack of data. A particular focus is put on the use of Answer Set Programming, a logic programming paradigm with combinatorial optimisation functionalities. We describe its usage to over-approximate metabolic responses of biological systems and solve gap-filling problems. In this review, we compare steady-states and Boolean abstractions of metabolic models and illustrate their complementarity via applications to the metabolic analysis of macro-algae. Ongoing applications of this formalism explore the emerging field of systems ecology, notably elucidating interactions between a consortium of microbes and a host organism. As the first step in this field, we will illustrate how the reduction in microbiotas according to expected metabolic phenotypes can be addressed with gap-filling problems.

From bag-of-genes to bag-of-genomes: metabolic modelling of communities in the era of metagenome-assembled genomes

Metagenomic sequencing of complete microbial communities has greatly enhanced our understanding of the taxonomic composition of microbiotas. This has led to breakthrough developments in bioinformatic disciplines such as assembly, gene clustering, metagenomic binning of species genomes and the discovery of an incredible, so far undiscovered, taxonomic diversity. However, functional annotations and estimating metabolic processes from single species - or communities - is still challenging. Earlier approaches relied mostly on inferring the presence of key enzymes for metabolic pathways in the whole metagenome, ignoring the genomic context of such enzymes, resulting in the 'bag-of-genes' approach to estimate functional capacities of microbiotas. Here, we review recent developments in metagenomic bioinformatics, with a special focus on emerging technologies to simulate and estimate metabolic information, that can be derived from metagenomic assembled genomes. Genome-scale metabolic models can be used to model the emergent properties of microbial consortia and whole communities, and the progress in this area is reviewed. While this subfield of metagenomics is still in its infancy, it is becoming evident that there is a dire need for further bioinformatic tools to address the complex combinatorial problems in modelling the metabolism of large communities as a 'bag-of-genomes'.

NetMet: A Network-Based Tool for Predicting Metabolic Capacities of Microbial Species and their Interactions

Metabolic conversions allow organisms to produce a set of essential metabolites from the available nutrients in an environment, frequently requiring metabolic exchanges among co-inhabiting organisms. Genomic-based metabolic simulations are being increasingly applied for exploring metabolic capacities, considering different environments and different combinations of microorganisms. NetMet is a web-based tool and a software package for predicting the metabolic performances of microorganisms and their corresponding combinations in user-defined environments. The algorithm takes, as input, lists of (i) species-specific enzymatic reactions (EC numbers), and (ii) relevant metabolic environments. The algorithm generates, as output, lists of (i) compounds that individual species can produce in each given environment, and (ii) compounds that are predicted to be produced through complementary interactions. The tool is demonstrated in two case studies. First, we compared the metabolic capacities of different haplotypes of the obligatory fruit and vegetable pathogen Candidatus Liberibacter solanacearum to those of their culturable taxonomic relative Liberibacter crescens. Second, we demonstrated the potential production of complementary metabolites by pairwise combinations of co-occurring endosymbionts of the plant phloem-feeding whitefly Bemisia tabaci.

Genome-scale metabolic reconstruction of the symbiosis between a leguminous plant and a nitrogen-fixing bacterium

The mutualistic association between leguminous plants and endosymbiotic rhizobial bacteria is a paradigmatic example of a symbiosis driven by metabolic exchanges. Here, we report the reconstruction and modelling of a genome-scale metabolic network of Medicago truncatula (plant) nodulated by Sinorhizobium meliloti (bacterium). The reconstructed nodule tissue contains five spatially distinct developmental zones and encompasses the metabolism of both the plant and the bacterium. Flux balance analysis (FBA) suggests that the metabolic costs associated with symbiotic nitrogen fixation are primarily related to supporting nitrogenase activity, and increasing N2-fixation efficiency is associated with diminishing returns in terms of plant growth. Our analyses support that differentiating bacteroids have access to sugars as major carbon sources, ammonium is the main nitrogen export product of N2-fixing bacteria, and N2 fixation depends on proton transfer from the plant cytoplasm to the bacteria through acidification of the peribacteroid space. We expect that our model, called 'Virtual Nodule Environment' (ViNE), will contribute to a better understanding of the functioning of legume nodules, and may guide experimental studies and engineering of symbiotic nitrogen fixation.

Drosophila-associated bacteria differentially shape the nutritional requirements of their host during juvenile growth

The interplay between nutrition and the microbial communities colonizing the gastrointestinal tract (i.e., gut microbiota) determines juvenile growth trajectory. Nutritional deficiencies trigger developmental delays, and an immature gut microbiota is a hallmark of pathologies related to childhood undernutrition. However, how host-associated bacteria modulate the impact of nutrition on juvenile growth remains elusive. Here, using gnotobiotic Drosophila melanogaster larvae independently associated with Acetobacter pomorum^WJL (Ap^WJL) and Lactobacillus plantarum^NC8 (Lp^NC8), 2 model Drosophila-associated bacteria, we performed a large-scale, systematic nutritional screen based on larval growth in 40 different and precisely controlled nutritional environments. We combined these results with genome-based metabolic network reconstruction to define the biosynthetic capacities of Drosophila germ-free (GF) larvae and its 2 bacterial partners. We first established that Ap^WJL and Lp^NC8 differentially fulfill the nutritional requirements of the ex-GF larvae and parsed such difference down to individual amino acids, vitamins, other micronutrients, and trace metals. We found that Drosophila-associated bacteria not only fortify the host’s diet with essential nutrients but, in specific instances, functionally compensate for host auxotrophies by either providing a metabolic intermediate or nutrient derivative to the host or by uptaking, concentrating, and delivering contaminant traces of micronutrients. Our systematic work reveals that beyond the molecular dialogue engaged between the host and its bacterial partners, Drosophila and its associated bacteria establish an integrated nutritional network relying on nutrient provision and utilization.

A study of gnotobiotic fruit flies shows that the animal is involved in an integrated nutritional network with its facultative commensal bacteria, centered around the utilization and sharing of nutrients.

Dynamics of bacterial composition in the locust reproductive tract are affected by the density-dependent phase

The important role that locust gut bacteria play in their host biology is well accepted. Among other roles, gut bacteria are suggested to be involved in the locust swarming phenomenon. In addition, in many insect orders, the reproductive system is reported to serve as a vector for trans-generation bacterial inoculation. Knowledge of the bacterial composition of the locust reproductive tract is, however, practically absent. Here we characterized the reproductive system bacterial composition of gregarious and solitary females. We investigated its temporal dynamics and how it interacts with the locust phase, by comparative sampling and 16S rRNA amplicon sequencing. We revealed that the bacterial composition of the locust female reproductive tract is mostly constructed of three core genera: Micrococcus, Acinetobacter and Staphylococcus. While solitary females maintained a consistent bacterial composition, in the gregarious phase this consortium demonstrated large temporal shifts, mostly manifested by Brevibacterium blooms. These data are in accord with our previous report on the dynamics of locust hindgut bacterial microbiota, further indicating that locust endosymbionts are affected by their host population density. These newly understood dynamics may have implications beyond their contribution to our knowledge of locust ecology, as aggregation and mass migration are prevalent phenomena across many migrating animals.

Inside out: microbiota dynamics during host-plant adaptation of whiteflies

While most insect herbivores are selective feeders, a small proportion of them feed on a wide range of plants. This polyphagous habit requires overcoming a remarkable array of defenses, which often necessitates an adaptation period. Efforts for understanding the mechanisms involved mostly focus on the insect’s phenotypic plasticity. Here, we hypothesized that the adaptation process might partially rely on transient associations with bacteria. To test this, we followed in a field-like experiment, the adaptation process of Bemisia tabaci, a generalist sap feeder, to pepper (a less-suitable host), after switching from watermelon (a suitable host). Amplicon sequencing of 16S rRNA transcripts from hundreds of dissected guts revealed the presence of active “core” and “transient” bacterial communities, dominated by the phyla Proteobacteria, Actinobacteria, and Firmicutes, and increasing differences between populations grown on watermelon and pepper. Insects grown on pepper for over two generations presented a significant increase in specific genera, mainly Mycobacterium, with a predicted enrichment in degradative pathways of xenobiotics and secondary metabolites. This result correlated with a significant increase in the insect’s survival on pepper. Taken together, our findings suggest that gut-associated bacteria can provide an additional flexible metabolic “tool-box” to generalist sap feeders for facilitating a quick host switching process.

Symbionts of the ciliate Euplotes: diversity, patterns and potential as models for bacteria-eukaryote endosymbioses

Endosymbioses between bacteria and eukaryotes are enormously important in ecology and evolution, and as such are intensely studied. Despite this, the range of investigated hosts is narrow in the context of the whole eukaryotic tree of life: most of the information pertains to animal hosts, while most of the diversity is found in unicellular protists. A prominent case study is the ciliate Euplotes, which has repeatedly taken up the bacterium Polynucleobacter from the environment, triggering its transformation into obligate endosymbiont. This multiple origin makes the relationship an excellent model to understand recent symbioses, but Euplotes may host bacteria other than Polynucleobacter, and a more detailed knowledge of these additional interactions is needed in order to correctly interpret the system. Here, we present the first systematic survey of Euplotes endosymbionts, adopting a classical as well as a metagenomic approach, and review the state of knowledge. The emerging picture is indeed quite complex, with some Euplotes harbouring rich, stable prokaryotic communities not unlike those of multicellular animals. We provide insights into the distribution, evolution and diversity of these symbionts (including the establishment of six novel bacterial taxa), and outline differences and similarities with the most well-understood group of eukaryotic hosts: insects.

Metabolic network percolation quantifies biosynthetic capabilities across the human oral microbiome

The biosynthetic capabilities of microbes underlie their growth and interactions, playing a prominent role in microbial community structure. For large, diverse microbial communities, prediction of these capabilities is limited by uncertainty about metabolic functions and environmental conditions. To address this challenge, we propose a probabilistic method, inspired by percolation theory, to computationally quantify how robustly a genome-derived metabolic network produces a given set of metabolites under an ensemble of variable environments. We used this method to compile an atlas of predicted biosynthetic capabilities for 97 metabolites across 456 human oral microbes. This atlas captures taxonomically-related trends in biomass composition, and makes it possible to estimate inter-microbial metabolic distances that correlate with microbial co-occurrences. We also found a distinct cluster of fastidious/uncultivated taxa, including several Saccharibacteria (TM7) species, characterized by their abundant metabolic deficiencies. By embracing uncertainty, our approach can be broadly applied to understanding metabolic interactions in complex microbial ecosystems.

Genome Analysis of Haplotype D of Candidatus Liberibacter Solanacearum

Candidatus Liberibacter solanacearum (Lso) haplotype D (LsoD) is a suspected bacterial pathogen, spread by the phloem-feeding psyllid Bactericera trigonica Hodkinson and found to infect carrot plants throughout the Mediterranean. Haplotype D is one of six haplotypes of Lso that each have specific and overlapping host preferences, disease symptoms, and psyllid vectors. Genotyping of rRNA genes has allowed for tracking the haplotype diversity of Lso and genome sequencing of several haplotypes has been performed to advance a comprehensive understanding of Lso diseases and of the phylogenetic relationships among the haplotypes. To further pursue that aim we have sequenced the genome of LsoD from its psyllid vector and report here its draft genome. Genome-based single nucleotide polymorphism analysis indicates LsoD is most closely related to the A haplotype. Genomic features and the metabolic potential of LsoD are assessed in relation to Lso haplotypes A, B, and C, as well as the facultative strain Liberibacter crescens. We identify genes unique to haplotype D as well as putative secreted effectors that may play a role in disease characteristics specific to this haplotype of Lso.

Modeling microbial communities from atrazine contaminated soils promotes the development of biostimulation solutions

Microbial communities play a vital role in biogeochemical cycles, allowing the biodegradation of a wide range of pollutants. The composition of the community and the interactions between its members affect degradation rate and determine the identity of the final products. Here, we demonstrate the application of sequencing technologies and metabolic modeling approaches towards enhancing biodegradation of atrazine-a herbicide causing environmental pollution. Treatment of agriculture soil with atrazine is shown to induce significant changes in community structure and functional performances. Genome-scale metabolic models were constructed for Arthrobacter, the atrazine degrader, and four other non-atrazine degrading species whose relative abundance in soil was changed following exposure to the herbicide. By modeling community function we show that consortia including the direct degrader and non-degrader differentially abundant species perform better than Arthrobacter alone. Simulations predict that growth/degradation enhancement is derived by metabolic exchanges between community members. Based on simulations we designed endogenous consortia optimized for enhanced degradation whose performances were validated in vitro and biostimulation strategies that were tested in pot experiments. Overall, our analysis demonstrates that understanding community function in its wider context, beyond the single direct degrader perspective, promotes the design of biostimulation strategies.

Metabolic model-based analysis of the emergence of bacterial cross-feeding via extensive gene loss

Background

Metabolic dependencies between microbial species have a significant impact on the assembly and activity of microbial communities. However, the evolutionary origins of such dependencies and the impact of metabolic and genomic architecture on their emergence are not clear.

Results

To address these questions, we developed a novel framework, coupling a reductive evolution model with a multi-species genome-scale metabolic model to simulate the evolution of two-species microbial communities. Simulating thousands of independent evolutionary trajectories, we surprisingly found that under certain environmental and evolutionary settings metabolic dependencies emerged frequently even though our model does not include explicit selection for cooperation. Evolved dependencies involved cross-feeding of a diverse set of metabolites, reflecting constraints imposed by metabolic network architecture. We additionally found metabolic ‘missed opportunities’, wherein species failed to capitalize on metabolites made available by their partners. Examining the genes deleted in each evolutionary trajectory and the deletion timing further revealed both genome-wide properties and specific metabolic mechanisms associated with species interaction.

Conclusion

Our findings provide insight into the evolution of cooperative interaction among microbial species and a unique view into the way such relationships emerge.

Electronic supplementary material

The online version of this article (10.1186/s12918-018-0588-4) contains supplementary material, which is available to authorized users.

To B or Not to B: Comparative Genomics Suggests Arsenophonus as a Source of B Vitamins in Whiteflies

Insect lineages feeding on nutritionally restricted diets such as phloem sap, xylem sap, or blood, were able to diversify by acquiring bacterial species that complement lacking nutrients. These bacteria, considered obligate/primary endosymbionts, share a long evolutionary history with their hosts. In some cases, however, these endosymbionts are not able to fulfill all of their host's nutritional requirements, driving the acquisition of additional symbiotic species. Phloem-feeding members of the insect family Aleyrodidae (whiteflies) established an obligate relationship with Candidatus Portiera aleyrodidarum, which provides its hots with essential amino acids and carotenoids. In addition, many whitefly species harbor additional endosymbionts which may potentially further supplement their host's diet. To test this hypothesis, genomes of several endosymbionts of the whiteflies Aleurodicus dispersus, Aleurodicus floccissimus and Trialeurodes vaporariorum were analyzed. In addition to Portiera, all three species were found to harbor one Arsenophonus and one Wolbachia endosymbiont. A comparative analysis of Arsenophonus genomes revealed that although all three are capable of synthesizing B vitamins and cofactors, such as pyridoxal, riboflavin, or folate, their genomes and phylogenetic relationship vary greatly. Arsenophonus of A. floccissimus and T. vaporariorum belong to the same clade, and display characteristics of facultative endosymbionts, such as large genomes (3 Mb) with thousands of genes and pseudogenes, intermediate GC content, and mobile genetic elements. In contrast, Arsenophonus of A. dispersus belongs to a different lineage and displays the characteristics of a primary endosymbiont-a reduced genome (670 kb) with ~400 genes, 32% GC content, and no mobile genetic elements. However, the presence of 274 pseudogenes suggests that this symbiotic association is more recent than other reported primary endosymbionts of hemipterans. The gene repertoire of Arsenophonus of A. dispersus is completely integrated in the symbiotic consortia, and the biosynthesis of most vitamins occurs in shared pathways with its host. In addition, Wolbachia endosymbionts have also retained the ability to produce riboflavin, flavin adenine dinucleotide, and folate, and may make a nutritional contribution. Taken together, our results show that Arsenophonus hold a pivotal place in whitefly nutrition by their ability to produce B vitamins.