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.

The rise of the sourdough: Genome-scale metabolic modeling-based approach to design sourdough starter communities with tailored-made properties.

Sourdough starters harbor microbial consortia that benefit the final product's aroma and volume. The complex nature of these spontaneously developed communities raises challenges in predicting the fermentation phenotypes. Herein, we demonstrated for the first time in this field the potential of genome-scale metabolic modeling (GEMs) in the study of sourdough microbial communities. Broad in-silico modeling of microbial growth was applied on communities composed of yeast (Saccharomyces cerevisiae) and different Lactic Acid Bacteria (LAB) species, which mainly predominate in sourdough starters. Simulations of model-represented communities associated specific bacterial compositions with sourdough phenotypes. Based on ranking the phenotypic performances of different combinations, Pediococcus spp. - Lb. sakei group members were predicted to have an optimal effect considering the increase in S. cerevisiae growth abilities and overall CO2 secretion rates. Flux Balance Analysis (FBA) revealed mutual relationships between the Pediococcus spp. - Lb. sakei group members and S. cerevisiae through bidirectional nutrient dependencies, and further underlined that these bacteria compete with the yeast over nutrients to a lesser extent than the rest LAB species. Volatile compounds (VOCs) production was further modeled, identifying species-specific and community-related VOCs production profiles. The in-silico models' predictions were validated by experimentally building synthetic sourdough communities and assessing the fermentation phenotypes. The Pediococcus spp. - Lb. sakei group was indeed associated with increased yeast cell counts and fermentation rates, demonstrating a 25 % increase in the average leavening rates during the first 10 fermentation hours compared to communities with a lower representation of these group members. Overall, these results provide a possible novel strategy towards the de-novo design of sourdough starter communities with tailored-made characterizations, including a shortened leavening period.

Functional analysis of the apple fruit microbiome based on shotgun metagenomic sequencing of conventional and organic orchard samples.

Fruits harbour abundant and diverse microbial communities that protect them from post-harvest pathogens. Identification of functional traits associated with a given microbiota can provide a better understanding of their potential influence. Here, we focused on the epiphytic microbiome of apple fruit. We suggest that shotgun metagenomic data can indicate specific functions carried out by different groups and provide information on their potential impact. Samples were collected from the surface of 'Golden Delicious' apples from four orchards that differ in their geographic location and management practice. Approximately 1 million metagenes were predicted based on a high-quality assembly. Functional profiling of the microbiome of fruits from orchards differing in their management practice revealed a functional shift in the microbiota. The organic orchard microbiome was enriched in pathways involved in plant defence activities; the conventional orchard microbiome was enriched in pathways related to the synthesis of antibiotics. The functional significance of the variations was explored using microbial network modelling algorithms to reveal the metabolic role of specific phylogenetic groups. The analysis identified several associations supported by other published studies. For example, the analysis revealed the nutritional dependencies of the Capnodiales group, including the Alternaria pathogen, on aromatic compounds.

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.

Comparative study of bacterial community dynamics in different soils following application of the herbicide atrazine.

Microbial communities in cultivated soils control the fate of pollutants associated with agricultural practice. The present study was designed to explore the response of bacterial communities to the application of the widely-used herbicide atrazine in three different crop fields that differ significantly in their physicochemical structure and nutritional content: the nutrient-rich (with relatively high carbon and nitrogen content) Newe Yaar (NY) and Ha-Ogen (HO) soils and the nutrient-poor, sandy Sde-Eliyahu (SE) soil. The 16 S rRNA gene amplicon sequencing revealed the nutrient poor HO soil differs in its response to atrazine in comparison to the two nutrient-rich soils both in the shortest persistence of atrazine and its effect on community structure and composition. Potential reported bacterial degraders of atrazine such as Pseudomonas, Clostridium and Bacillus were more abundant in contaminated sandy/poor soils (HO) whereas bacteria known for nitrogen cycling such as Azospirillum, Sinorhizobium, Nitrospira and Azohydromonas were significantly more abundant in the nutrient rich contaminated SE soils. No significant increase of potential indigenous degrader Arthrobacter was detected in SE and NY soils whereas a significant increase was recorded with HO soils. An overall shift in bacterial community composition following atrazine application was observed only in the nutrient poor soil. Understanding atrazine persistence and microbiome response to its application of in dependence with soil types serve the design of precision application strategies.

Electron acceptors determine the BTEX degradation capacity of anaerobic microbiota via regulating the microbial community.

Anaerobic degradation is the major pathway for microbial degradation of benzene, toluene, ethylbenzene, and xylenes (BTEX) under electron acceptor lacking conditions. However, how exogenous electron acceptors modulate BTEX degradation through shaping the microbial community structure remains poorly understood. Here, we investigated the effect of various exogenous electron acceptors on BTEX degradation as well as methane production in anaerobic microbiota, which were enriched from the same contaminated soil. It was found that the BTEX degradation capacities of the anaerobic microbiota gradually increased along with the increasing redox potentials of the exogenous electron acceptors supplemented (WE: Without exogenous electron acceptors < SS: Sulfate supplement < FS: Ferric iron supplement < NS: Nitrate supplement), while the complexity of the co-occurring networks (e.g., avgK and links) of the microbiota gradually decreased, showing that microbiota supplemented with higher redox potential electron acceptors were less dependent on the formation of complex microbial interactions to perform BTEX degradation. Microbiota NS showed the highest degrading capacity and the broadest substrate-spectrum for BTEX, and it could metabolize BTEX through multiple modules which not only contained fewer species but also different key microbial taxa (eg. Petrimonas, Achromobacter and Comamonas). Microbiota WE and FS, with the highest methanogenic capacities, shared common core species such as Sedimentibacter, Acetobacterium, Methanobacterium and Smithella/Syntrophus, which cooperated with Geobacter (microbiota WE) or Desulfoprunum (microbiota FS) to perform BTEX degradation and methane production. This study demonstrates that electron acceptors may alter microbial function by reshaping microbial community structure and regulating microbial interactions and provides guidelines for electron acceptor selection for bioremediation of aromatic pollutant-contaminated anaerobic sites.

Assembly and dynamics of the apple carposphere microbiome during fruit development and storage.

Microbial communities associated with fruit can contribute to quality and pathogen resistance, but little is known about their assembly and dynamics during fruit development and storage. Three apple cultivars growing under the same environmental conditions were utilized to examine the apple carposphere microbiome composition and structure at different developmental stages and storage. There was a significant effect (Adonis, p ≤ 0.001) of fruit genotype and its developmental stages and storage times on the fruit surface microbial assemblage and a strong temporal microbial community succession was detected (Mantel test: R ≤ 0.5, p = 0.001) in both bacterial and fungal communities. A set of 15 bacterial and 35 fungal core successional taxa and members exhibiting differential abundances at different fruit stages were identified. For the first time, we show the existence of underlying universal dynamics in the assembly of fruit-associated microbiomes. We also provide evidence of strong microbial cross-domain associations and uncover potential microbe-microbe correlations in the apple carposphere. Together our findings shed light on how the fruit carposphere assemble and change over time, and provide new insights into fruit microbial ecology.

Modeling-Guided Amendments Lead to Enhanced Biodegradation in Soil.

Extensive use of agrochemicals is emerging as a serious environmental issue coming at the cost of the pollution of soil and water resources. Bioremediation techniques such as biostimulation are promising strategies used to remove pollutants from agricultural soils by supporting the indigenous microbial degraders. Though considered cost-effective and eco-friendly, the success rate of these strategies typically varies, and consequently, they are rarely integrated into commercial agricultural practices. In the current study, we applied metabolic-based community-modeling approaches for promoting realistic in terra solutions by simulation-based prioritization of alternative supplements as potential biostimulants, considering a collection of indigenous bacteria. Efficacy of biostimulants as enhancers of the indigenous degrader Paenarthrobacter was ranked through simulation and validated in pot experiments. A two-dimensional simulation matrix predicting the effect of different biostimulants on additional potential indigenous degraders (Pseudomonas, Clostridium, and Geobacter) was crossed with experimental observations. The overall ability of the models to predict the compounds that act as taxa-selective stimulants indicates that computational algorithms can guide the manipulation of the soil microbiome in situ and provides an additional step toward the educated design of biostimulation strategies. IMPORTANCE Providing the food requirements of a growing population comes at the cost of intensive use of agrochemicals, including pesticides. Native microbial soil communities are considered key players in the degradation of such exogenous substances. Manipulating microbial activity toward an optimized outcome in efficient biodegradation processes conveys a promise of maintaining intensive yet sustainable agriculture. Efficient strategies for harnessing the native microbiome require the development of approaches for processing big genomic data. Here, we pursued metabolic modeling for promoting realistic in terra solutions by simulation-based prioritization of alternative supplements as potential biostimulants, considering a collection of indigenous bacteria. Our genomic-based predictions point at strategies for optimizing biodegradation by the native community. Developing a systematic, data-guided understanding of metabolite-driven targeted enhancement of selected microorganisms lays the foundation for the design of ecologically sound methods for optimizing microbiome functioning.

QTL mapping and genomic analyses of earliness and fruit ripening traits in a melon Recombinant Inbred Lines population supported by de novo assembly of their parental genomes.

Earliness and ripening behavior are important attributes of fruits on and off the vine, and affect quality and preference of both growers and consumers. Fruit ripening is a complex physiological process that involves metabolic shifts affecting fruit color, firmness, and aroma production. Melon is a promising model crop for the study of fruit ripening, as the full spectrum of climacteric behavior is represented across the natural variation. Using Recombinant Inbred Lines (RILs) population derived from the parental lines "Dulce" (reticulatus, climacteric) and "Tam Dew" (inodorus, non-climacteric) that vary in earliness and ripening traits, we mapped QTLs for ethylene emission, fruit firmness and days to flowering and maturity. To further annotate the main QTL intervals and identify candidate genes, we used Oxford Nanopore long-read sequencing in combination with Illumina short-read resequencing, to assemble the parental genomes de-novo. In addition to 2.5 million genome-wide SNPs and short InDels detected between the parents, we also highlight here the structural variation between these lines and the reference melon genome. Through systematic multi-layered prioritization process, we identified 18 potential polymorphisms in candidate genes within multi-trait QTLs. The associations of selected SNPs with earliness and ripening traits were further validated across a panel of 177 diverse melon accessions and across a diallel population of 190 F1 hybrids derived from a core subset of 20 diverse parents. The combination of advanced genomic tools with diverse germplasm and targeted mapping populations is demonstrated as a way to leverage forward genetics strategies to dissect complex horticulturally important traits.

A Synergistic Consortium Involved in rac-Dichlorprop Degradation as Revealed by DNA Stable Isotope Probing and Metagenomic Analysis.

rac-Dichlorprop, a commonly used phenoxyalkanoic acid herbicide, is frequently detected in environments and poses threats to environmental safety and human health. Microbial consortia are thought to play key roles in rac-dichlorprop degradation. However, the compositions of the microbial consortia involved in rac-dichlorprop degradation remain largely unknown. In this study, DNA stable isotope probing (SIP) and metagenomic analysis were integrated to reveal the key microbial consortium responsible for rac-dichlorprop degradation in a rac-dichlorprop-degrading enrichment. OTU340 (Sphingobium sp.) and OTU348 (Sphingopyxis sp.) were significantly enriched in the rac-[13C]dichlorprop-labeled heavy DNA fractions. A rac-dichlorprop degrader, Sphingobium sp. strain L3, was isolated from the enrichment by a traditional enrichment method but with additional supplementation of the antibiotic ciprofloxacin, which was instructed by metagenomic analysis of the associations between rac-dichlorprop degraders and antibiotic resistance genes. As revealed by functional profiling of the metagenomes of the heavy DNA, the genes rdpA and sdpA, involved in the initial degradation of the (R)- and (S)-enantiomers of dichlorprop, respectively, were mostly taxonomically assigned to Sphingobium species, indicating that Sphingopyxis species might harbor novel dichlorprop-degrading genes. In addition, taxonomically diverse bacterial genera such as Dyella, Sphingomonas, Pseudomonas, and Achromobacter were presumed to synergistically cooperate with the key degraders Sphingobium/Sphingopyxis for enhanced degradation of rac-dichlorprop. IMPORTANCE Understanding of the key microbial consortium involved in the degradation of the phenoxyalkanoic acid herbicide rac-dichlorprop is pivotal for design of synergistic consortia used for enhanced bioremediation of herbicide-contaminated sites. However, the composition of the microbial consortium and the interactions between community members during the biodegradation of rac-dichlorprop are unclear. In this study, DNA-SIP and metagenomic analysis were integrated to reveal that the metabolite 2,4-dichlorophenol degraders Dyella, Sphingomonas, Pseudomonas, and Achromobacter synergistically cooperated with the key degraders Sphingobium/Sphingopyxis for enhanced degradation of rac-dichlorprop. Our study provides new insights into the synergistic degradation of rac-dichlorprop at the community level and implies the existence of novel degrading genes for rac-dichlorprop in nature.

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.

Strategies for Enhancing in vitro Degradation of Linuron by Variovorax sp. Strain SRS 16 Under the Guidance of Metabolic Modeling.

Phenyl urea herbicides are being extensively used for weed control in both agricultural and non-agricultural applications. Linuron is one of the key herbicides in this family and is in wide use. Like other phenyl urea herbicides, it is known to have toxic effects as a result of its persistence in the environment. The natural removal of linuron from the environment is mainly carried through microbial biodegradation. Some microorganisms have been reported to mineralize linuron completely and utilize it as a carbon and nitrogen source. Variovorax sp. strain SRS 16 is one of the known efficient degraders with a recently sequenced genome. The genomic data provide an opportunity to use a genome-scale model for improving biodegradation. The aim of our study is the construction of a genome-scale metabolic model following automatic and manual protocols and its application for improving its metabolic potential through iterative simulations. Applying flux balance analysis (FBA), growth and degradation performances of SRS 16 in different media considering the influence of selected supplements (potential carbon and nitrogen sources) were simulated. Outcomes are predictions for the suitable media modification, allowing faster degradation of linuron by SRS 16. Seven metabolites were selected for in vitro validation of the predictions through laboratory experiments confirming the degradation-promoting effect of specific amino acids (glutamine and asparagine) on linuron degradation and SRS 16 growth. Overall, simulations are shown to be efficient in predicting the degradation potential of SRS 16 in the presence of specific supplements. The generated information contributes to the understanding of the biochemistry of linuron degradation and can be further utilized for the development of new cleanup solutions without any genetic manipulation.

Global analysis of the apple fruit microbiome: are all apples the same?

We present the first worldwide study on the apple (Malus × domestica) fruit microbiome that examines questions regarding the composition and the assembly of microbial communities on and in apple fruit. Results revealed that the composition and structure of the fungal and bacterial communities associated with apple fruit vary and are highly dependent on geographical location. The study also confirmed that the spatial variation in the fungal and bacterial composition of different fruit tissues exists at a global level. Fungal diversity varied significantly in fruit harvested in different geographical locations and suggests a potential link between location and the type and rate of postharvest diseases that develop in each country. The global core microbiome of apple fruit was represented by several beneficial microbial taxa and accounted for a large fraction of the fruit microbial community. The study provides foundational information about the apple fruit microbiome that can be utilized for the development of novel approaches for the management of fruit quality and safety, as well as for reducing losses due to the establishment and proliferation of postharvest pathogens. It also lays the groundwork for studying the complex microbial interactions that occur on apple fruit surfaces.

Genome-scale reconstruction of Paenarthrobacter aurescens TC1 metabolic model towards the study of atrazine bioremediation.

Atrazine is an herbicide and a pollutant of great environmental concern that is naturally biodegraded by microbial communities. Paenarthrobacter aurescens TC1 is one of the most studied degraders of this herbicide. Here, we developed a genome scale metabolic model for P. aurescens TC1, iRZ1179, to study the atrazine degradation process at organism level. Constraint based flux balance analysis and time dependent simulations were used to explore the organism's phenotypic landscape. Simulations aimed at designing media optimized for supporting growth and enhancing degradation, by passing the need in strain design via genetic modifications. Growth and degradation simulations were carried with more than 100 compounds consumed by P. aurescens TC1. In vitro validation confirmed the predicted classification of different compounds as efficient, moderate or poor stimulators of growth. Simulations successfully captured previous reports on the use of glucose and phosphate as bio-stimulators of atrazine degradation, supported by in vitro validation. Model predictions can go beyond supplementing the medium with a single compound and can predict the growth outcomes for higher complexity combinations. Hence, the analysis demonstrates that the exhaustive power of the genome scale metabolic reconstruction allows capturing complexities that are beyond common biochemical expertise and knowledge and further support the importance of computational platforms for the educated design of complex media. The model presented here can potentially serve as a predictive tool towards achieving optimal biodegradation efficiencies and for the development of ecologically friendly solutions for pollutant degradation.

Effect of Washing, Waxing and Low-Temperature Storage on the Postharvest Microbiome of Apple.

There is growing recognition of the role that the microbiome plays in the health and physiology of many plant species. However, considerably less research has been conducted on the postharvest microbiome of produce and the impact that postharvest processing may have on its composition. Here, amplicon sequencing was used to study the effect of washing, waxing, and low-temperature storage at 2 °C for six months on the bacterial and fungal communities of apple calyx-end, stem-end, and peel tissues. The results of the present work reveal that tissue-type is the main factor defining fungal and bacterial diversity and community composition on apple fruit. Both postharvest treatments and low temperature storage had a strong impact on the fungal and bacterial diversity and community composition of these tissue types. Distinct spatial and temporal changes in the composition and diversity of the microbiota were observed in response to various postharvest management practices. The greatest impact was attributed to sanitation practices with major differences among unwashed, washed and washed-waxed apples. The magnitude of the differences, however, was tissue-specific, with the greatest impact occurring on peel tissues. Temporally, the largest shift occurred during the first two months of low-temperature storage, although fungi were more affected by storage time than bacteria. In general, fungi and bacteria were impacted equally by sanitation practices, especially the epiphytic microflora of peel tissues. This research provides a foundation for understanding the impact of postharvest management practices on the microbiome of apple and its potential subsequent effects on postharvest disease management and food safety.

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. NetMet, a new web-based tool, is available at https://freilich-lab-tools.com/.

High-density NGS-based map construction and genetic dissection of fruit shape and rind netting in Cucumis melo.

Melon is an important crop that exhibits broad variation for fruit morphology traits that are the substrate for genetic mapping efforts. In the post-genomic era, the link between genetic maps and physical genome assemblies is key for leveraging QTL mapping results for gene cloning and breeding purposes. Here, using a population of 164 melon recombinant inbred lines (RILs) that were subjected to genotyping-by-sequencing, we constructed and compared high-density sequence- and linkage-based recombination maps that were aligned to the reference melon genome. These analyses reveal the genome-wide variation in recombination frequency and highlight regions of disrupted collinearity between our population and the reference genome. The population was phenotyped over 3 years for fruit size and shape as well as rind netting. Four QTLs were detected for fruit size, and they act in an additive manner, while significant epistatic interaction was found between two neutral loci for this trait. Fruit shape displayed transgressive segregation that was explained by the action of four QTLs, contributed by alleles from both parents. The complexity of rind netting was demonstrated on a collection of 177 diverse accessions. Further dissection of netting in our RILs population, which is derived from a cross of smooth and densely netted parents, confirmed the intricacy of this trait and the involvement of major locus and several other interacting QTLs. A major netting QTL on chromosome 2 co-localized with results from two additional populations, paving the way for future study toward identification of a causative gene for this trait.

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.

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.