Designing host-associated microbiomes using the consumer/resource model.

A key step towards rational microbiome engineering is the in silico sampling of realistic microbial communities that correspond to desired host phenotypes, and vice versa. This remains challenging due to a lack of generative models that simultaneously model compositions of host-associated microbiomes and host phenotypes. To that end, we present a machine learning model based on the consumer/resource (C/R) framework. In the model, variation in microbial ecosystem composition arises due to differences in the availability of effective resources (latent variables) while species' resource preferences remain conserved. Variation in the same latent variables is used to model phenotypic variation across hosts. In silico microbiomes generated by our model accurately reproduce universal and dataset-specific statistics of bacterial communities. The model allows us to address two salient questions in microbiome design: (1) which host phenotypes maximally constrain the composition of the host-associated microbiome? and (2) what are plausible microbiome compositions corresponding to user-specified host phenotypes? Thus, our model aids the design and analysis of microbial communities associated with host phenotypes of interest.

In silico, in vitro and in vivo characterization of host-associated Latilactobacillus curvatus strains for potential probiotic applications in farmed Atlantic salmon (Salmo salar).

Salmon aquaculture is the fastest growing animal protein production system in the world; however, intensive farming leads to poor weight gain, stress, and disease outbreaks. Probiotics offer the potential to enhance growth performance and feed efficiency in Atlantic salmon, as well as immunostimulate fish against common pathogens, benefitting farmers and consumers with more efficient production. Here, we isolated and identified 900 native microbial isolates including 18 Lactobacilli from the farmed salmon intestines. Based on whole-genome sequencing and phylogenetic analysis, the Lactobacillus candidates belonged to Latilactobacillus curvatus (L. curvatus) species and formed two distinct phylogenetic groups. Using bioinformatics and in vitro analyses, we selected two candidates L. curvatus ATCC PTA-127116 and L. curvatus ATCC PTA-127117, which showed desirable safety and probiotic properties. The two L. curvatus candidates were evaluated for safety and efficacy (higher final weight) in Atlantic salmon alongside spore-forming Bacilli isolated from salmon, poultry, and swine. All the tested candidates were safe to salmon with no adverse effects. While we did not see efficacy in any Bacillus supplemented groups, compared to untreated group, the group administered with the two L. curvatus strains consortium in feed for seven weeks in freshwater showed indicators of improvement in final body weight by 4.2%. Similarly, the two L. curvatus candidates were also evaluated for safety and efficacy in Atlantic salmon in saltwater; the group administered with the two L. curvatus strains consortium in feed for 11 weeks showed indicators of improvement in final body weight by 4.7%. Comprehensive metabolomics analyses in the presence of different prebiotics and/or additives identified galactooligosaccharide as a potential prebiotic to enhance the efficacy of two L. curvatus candidates. All together, these data provide comprehensive genomic, phenotypic and metabolomic evidence of safety and desirable probiotic properties as well as indicators of in vivo efficacy of two novel endogenous L. curvatus candidates for potential probiotic applications in Atlantic salmon. The in vivo findings need to be confirmed in larger performance studies, including field trials.

Growth promotion and antibiotic induced metabolic shifts in the chicken gut microbiome.

Antimicrobial growth promoters (AGP) have played a decisive role in animal agriculture for over half a century. Despite mounting concerns about antimicrobial resistance and demand for antibiotic alternatives, a thorough understanding of how these compounds drive performance is missing. Here we investigate the functional footprint of microbial communities in the cecum of chickens fed four distinct AGP. We find relatively few taxa, metabolic or antimicrobial resistance genes similarly altered across treatments, with those changes often driven by the abundances of core microbiome members. Constraints-based modeling of 25 core bacterial genera associated increased performance with fewer metabolite demands for microbial growth, pointing to altered nitrogen utilization as a potential mechanism of narasin, the AGP with the largest performance increase in our study. Untargeted metabolomics of narasin treated birds aligned with model predictions, suggesting that the core cecum microbiome might be targeted for enhanced performance via its contribution to host-microbiota metabolic crosstalk.

Multi-Omics Characterization of Host-Derived Bacillus spp. Probiotics for Improved Growth Performance in Poultry.

Microbial feed ingredients or probiotics have been used widely in the poultry industry to improve production efficiency. Spore-forming Bacillus spp. offer advantages over traditional probiotic strains as Bacillus spores are resilient to high temperature, acidic pH, and desiccation. This results in increased strain viability during manufacturing and feed-pelleting processes, extended product shelf-life, and increased stability within the animal's gastrointestinal tract. Despite numerous reports on the use of Bacillus spores as feed additives, detailed characterizations of Bacillus probiotic strains are typically not published. Insufficient characterizations can lead to misidentification of probiotic strains in product labels, and the potential application of strains carrying virulence factors, toxins, antibiotic resistance, or toxic metabolites. Hence, it is critical to characterize in detail the genomic and phenotypic properties of these strains to screen out undesirable properties and to tie individual traits to clinical outcomes and possible mechanisms. Here, we report a screening workflow and comprehensive multi-omics characterization of Bacillus spp. for use in broiler chickens. Host-derived Bacillus strains were isolated and screened for desirable probiotic properties. The phenotypic, genomic and metabolomic analyses of three probiotic candidates, two Bacillus amyloliquefaciens (Ba ATCC PTA126784 and ATCC PTA126785), and a Bacillus subtilis (Bs ATCC PTA126786), showed that all three strains had promising probiotic traits and safety profiles. Inclusion of Ba ATCC PTA12684 (Ba-PTA84) in the feed of broiler chickens resulted in improved growth performance, as shown by a significantly improved feed conversion ratio (3.3%), increased of European Broiler Index (6.2%), and increased average daily gain (ADG) (3.5%). Comparison of the cecal microbiomes from Ba PTA84-treated and control animals suggested minimal differences in microbiome structure, indicating that the observed growth promotion presumably was not mediated by modulation of cecal microbiome.

SiGMoiD: A super-statistical generative model for binary data.

In modern computational biology, there is great interest in building probabilistic models to describe collections of a large number of co-varying binary variables. However, current approaches to build generative models rely on modelers' identification of constraints and are computationally expensive to infer when the number of variables is large (N~100). Here, we address both these issues with Super-statistical Generative Model for binary Data (SiGMoiD). SiGMoiD is a maximum entropy-based framework where we imagine the data as arising from super-statistical system; individual binary variables in a given sample are coupled to the same 'bath' whose intensive variables vary from sample to sample. Importantly, unlike standard maximum entropy approaches where modeler specifies the constraints, the SiGMoiD algorithm infers them directly from the data. Due to this optimal choice of constraints, SiGMoiD allows us to model collections of a very large number (N>1000) of binary variables. Finally, SiGMoiD offers a reduced dimensional description of the data, allowing us to identify clusters of similar data points as well as binary variables. We illustrate the versatility of SiGMoiD using multiple datasets spanning several time- and length-scales.

The Relationship between the Misfolding Avoidance Hypothesis and Protein Evolutionary Rates in the Light of Empirical Evidence.

For more than a decade, the misfolding avoidance hypothesis (MAH) and related theories have dominated evolutionary discussions aimed at explaining the variance of the molecular clock across cellular proteins. In this study, we use various experimental data to further investigate the consistency of the MAH predictions with empirical evidence. We also critically discuss experimental results that motivated the MAH development and that are often viewed as evidence of its major contribution to the variability of protein evolutionary rates. We demonstrate, in Escherichia coli and Homo sapiens, the lack of a substantial negative correlation between protein evolutionary rates and Gibbs free energies of unfolding, a direct measure of protein stability. We then analyze multiple new genome-scale data sets characterizing protein aggregation and interaction propensities, the properties that are likely optimized in evolution to alleviate deleterious effects associated with toxic protein misfolding and misinteractions. Our results demonstrate that the propensity of proteins to aggregate, the fraction of charged amino acids, and protein stickiness do correlate with protein abundances. Nevertheless, across multiple organisms and various data sets we do not observe substantial correlations between proteins' aggregation- and stability-related properties and evolutionary rates. Therefore, diverse empirical data support the conclusion that the MAH and similar hypotheses do not play a major role in mediating a strong negative correlation between protein expression and the molecular clock, and thus in explaining the variability of evolutionary rates across cellular proteins.

Molecular function limits divergent protein evolution on planetary timescales.

Functional conservation is known to constrain protein evolution. Nevertheless, the long-term divergence patterns of proteins maintaining the same molecular function and the possible limits of this divergence have not been explored in detail. We investigate these fundamental questions by characterizing the divergence between ancient protein orthologs with conserved molecular function. Our results demonstrate that the decline of sequence and structural similarities between such orthologs significantly slows down after ~1-2 billion years of independent evolution. As a result, the sequence and structural similarities between ancient orthologs have not substantially decreased for the past billion years. The effective divergence limit (>25% sequence identity) is not primarily due to protein sites universally conserved in all linages. Instead, less than four amino acid types are accepted, on average, per site across orthologous protein sequences. Our analysis also reveals different divergence patterns for protein sites with experimentally determined small and large fitness effects of mutations. Editorial note: This article has been through an editorial process in which the authors decide how to respond to the issues raised during peer review. The Reviewing Editor's assessment is that all the issues have been addressed (see decision letter).

Protein Stability and Avoidance of Toxic Misfolding Do Not Explain the Sequence Constraints of Highly Expressed Proteins.

The avoidance of cytotoxic effects associated with protein misfolding has been proposed as a dominant constraint on the sequence evolution and molecular clock of highly expressed proteins. Recently, Leuenberger et al. developed an elegant experimental approach to measure protein thermal stability at the proteome scale. The collected data allow us to rigorously test the predictions of the misfolding avoidance hypothesis that highly expressed proteins have evolved to be more stable, and that maintaining thermodynamic stability significantly constrains their evolution. Notably, reanalysis of the Leuenberger et al. data across four different organisms reveals no substantial correlation between protein stability and protein abundance. Therefore, the key predictions of the misfolding toxicity and related hypotheses are not supported by available empirical data. The data also suggest that, regardless of protein expression, protein stability does not substantially affect the protein molecular clock across organisms.

Two Novel DNAs That Enhance Symptoms and Overcome CMD2 Resistance to Cassava Mosaic Disease.

UNLABELLED: Cassava mosaic begomoviruses (CMBs) cause cassava mosaic disease (CMD) across Africa and the Indian subcontinent. Like all members of the geminivirus family, CMBs have small, circular single-stranded DNA genomes. We report here the discovery of two novel DNA sequences, designated SEGS-1 and SEGS-2 (forsequencesenhancinggeminivirussymptoms), that enhance symptoms and break resistance to CMD. The SEGS are characterized by GC-rich regions and the absence of long open reading frames. Both SEGS enhanced CMD symptoms in cassava (Manihot esculentaCrantz) when coinoculated withAfrican cassava mosaic virus(ACMV),East African cassava mosaic Cameroon virus(EACMCV), orEast African cassava mosaic virus-Uganda(EACMV-UG). SEGS-1 also overcame resistance of a cassava landrace carrying the CMD2 resistance locus when coinoculated with EACMV-UG. Episomal forms of both SEGS were detected in CMB-infected cassava but not in healthy cassava. SEGS-2 episomes were also found in virions and whiteflies. SEGS-1 has no homology to geminiviruses or their associated satellites, but the cassava genome contains a sequence that is 99% identical to full-length SEGS-1. The cassava genome also includes three sequences with 84 to 89% identity to SEGS-2 that together encompass all of SEGS-2 except for a 52-bp region, which includes the episomal junction and a 26-bp sequence related to alphasatellite replication origins. These results suggest that SEGS-1 is derived from the cassava genome and facilitates CMB infection as an integrated copy and/or an episome, while SEGS-2 was originally from the cassava genome but now is encapsidated into virions and transmitted as an episome by whiteflies. IMPORTANCE: Cassava is a major crop in the developing world, with its production in Africa being second only to maize. CMD is one of the most important diseases of cassava and a serious constraint to production across Africa. CMD2 is a major CMD resistance locus that has been deployed in many cassava cultivars through large-scale breeding programs. In recent years, severe, atypical CMD symptoms have been observed occasionally on resistant cultivars, some of which carry the CMD2 locus, in African fields. In this report, we identified and characterized two DNA sequences, SEGS-1 and SEGS-2, which produce similar symptoms when coinoculated with cassava mosaic begomoviruses onto a susceptible cultivar or a CMD2-resistant landrace. The ability of SEGS-1 to overcome CMD2 resistance and the transmission of SEGS-2 by whiteflies has major implications for the long-term durability of CMD2 resistance and underscore the need for alternative sources of resistance in cassava.

Long-term phenotypic evolution of bacteria.

For many decades comparative analyses of protein sequences and structures have been used to investigate fundamental principles of molecular evolution. In contrast, relatively little is known about the long-term evolution of species' phenotypic and genetic properties. This represents an important gap in our understanding of evolution, as exactly these proprieties play key roles in natural selection and adaptation to diverse environments. Here we perform a comparative analysis of bacterial growth and gene deletion phenotypes using hundreds of genome-scale metabolic models. Overall, bacterial phenotypic evolution can be described by a two-stage process with a rapid initial phenotypic diversification followed by a slow long-term exponential divergence. The observed average divergence trend, with approximately similar fractions of phenotypic properties changing per unit time, continues for billions of years. We experimentally confirm the predicted divergence trend using the phenotypic profiles of 40 diverse bacterial species across more than 60 growth conditions. Our analysis suggests that, at long evolutionary distances, gene essentiality is significantly more conserved than the ability to utilize different nutrients, while synthetic lethality is significantly less conserved. We also find that although a rapid phenotypic evolution is sometimes observed within the same species, a transition from high to low phenotypic similarity occurs primarily at the genus level.

Genetic robustness and functional evolution of gene duplicates.

Gene duplications are a major source of evolutionary innovations. Understanding the functional divergence of duplicates and their role in genetic robustness is an important challenge in biology. Previously, analyses of genetic robustness were primarily focused on duplicates essentiality and epistasis in several laboratory conditions. In this study, we use several quantitative data sets to understand compensatory interactions between Saccharomyces cerevisiae duplicates that are likely to be relevant in natural biological populations. We find that, owing to their high functional load, close duplicates are unlikely to provide substantial backup in the context of large natural populations. Interestingly, as duplicates diverge from each other, their overall functional load is reduced. At intermediate divergence distances the quantitative decrease in fitness due to removal of one duplicate becomes smaller. At these distances, yeast duplicates display more balanced functional loads and their transcriptional control becomes significantly more complex. As yeast duplicates diverge beyond 70% sequence identity, their ability to compensate for each other becomes similar to that of random pairs of singletons.

Identification of Cassava MicroRNAs under Abiotic Stress.

The study of microRNAs (miRNAs) in plants has gained significant attention in recent years due to their regulatory role during development and in response to biotic and abiotic stresses. Although cassava (Manihot esculenta Crantz) is tolerant to drought and other adverse conditions, most cassava miRNAs have been predicted using bioinformatics alone or through sequencing of plants challenged by biotic stress. Here, we use high-throughput sequencing and different bioinformatics methods to identify potential cassava miRNAs expressed in different tissues subject to heat and drought conditions. We identified 60 miRNAs conserved in other plant species and 821 potential cassava-specific miRNAs. We also predicted 134 and 1002 potential target genes for these two sets of sequences. Using real time PCR, we verified the condition-specific expression of 5 cassava small RNAs relative to a non-stress control. We also found, using publicly available expression data, a significantly lower expression of the predicted target genes of conserved and nonconserved miRNAs under drought stress compared to other cassava genes. Gene Ontology enrichment analysis along with condition specific expression of predicted miRNA targets, allowed us to identify several interesting miRNAs which may play a role in stress-induced posttranscriptional regulation in cassava and other plants.

Global probabilistic annotation of metabolic networks enables enzyme discovery.

Annotation of organism-specific metabolic networks is one of the main challenges of systems biology. Importantly, owing to inherent uncertainty of computational annotations, predictions of biochemical function need to be treated probabilistically. We present a global probabilistic approach to annotate genome-scale metabolic networks that integrates sequence homology and context-based correlations under a single principled framework. The developed method for global biochemical reconstruction using sampling (GLOBUS) not only provides annotation probabilities for each functional assignment but also suggests likely alternative functions. GLOBUS is based on statistical Gibbs sampling of probable metabolic annotations and is able to make accurate functional assignments even in cases of remote sequence identity to known enzymes. We apply GLOBUS to genomes of Bacillus subtilis and Staphylococcus aureus and validate the method predictions by experimentally demonstrating the 6-phosphogluconolactonase activity of YkgB and the role of the Sps pathway for rhamnose biosynthesis in B. subtilis.

The rate of the molecular clock and the cost of gratuitous protein synthesis.

BACKGROUND: The nature of the protein molecular clock, the protein-specific rate of amino acid substitutions, is among the central questions of molecular evolution. Protein expression level is the dominant determinant of the clock rate in a number of organisms. It has been suggested that highly expressed proteins evolve slowly in all species mainly to maintain robustness to translation errors that generate toxic misfolded proteins. Here we investigate this hypothesis experimentally by comparing the growth rate of Escherichia coli expressing wild type and misfolding-prone variants of the LacZ protein. RESULTS: We show that the cost of toxic protein misfolding is small compared to other costs associated with protein synthesis. Complementary computational analyses demonstrate that there is also a relatively weaker, but statistically significant, selection for increasing solubility and polarity in highly expressed E. coli proteins. CONCLUSIONS: Although we cannot rule out the possibility that selection against misfolding toxicity significantly affects the protein clock in species other than E. coli, our results suggest that it is unlikely to be the dominant and universal factor determining the clock rate in all organisms. We find that in this bacterium other costs associated with protein synthesis are likely to play an important role. Interestingly, our experiments also suggest significant costs associated with volume effects, such as jamming of the cellular environment with unnecessary proteins.

Reconstruction and flux-balance analysis of the Plasmodium falciparum metabolic network.

Genome-scale metabolic reconstructions can serve as important tools for hypothesis generation and high-throughput data integration. Here, we present a metabolic network reconstruction and flux-balance analysis (FBA) of Plasmodium falciparum, the primary agent of malaria. The compartmentalized metabolic network accounts for 1001 reactions and 616 metabolites. Enzyme-gene associations were established for 366 genes and 75% of all enzymatic reactions. Compared with other microbes, the P. falciparum metabolic network contains a relatively high number of essential genes, suggesting little redundancy of the parasite metabolism. The model was able to reproduce phenotypes of experimental gene knockout and drug inhibition assays with up to 90% accuracy. Moreover, using constraints based on gene-expression data, the model was able to predict the direction of concentration changes for external metabolites with 70% accuracy. Using FBA of the reconstructed network, we identified 40 enzymatic drug targets (i.e. in silico essential genes), with no or very low sequence identity to human proteins. To demonstrate that the model can be used to make clinically relevant predictions, we experimentally tested one of the identified drug targets, nicotinate mononucleotide adenylyltransferase, using a recently discovered small-molecule inhibitor.

Sequencing analysis of 20,000 full-length cDNA clones from cassava reveals lineage specific expansions in gene families related to stress response.

BACKGROUND: Cassava, an allotetraploid known for its remarkable tolerance to abiotic stresses is an important source of energy for humans and animals and a raw material for many industrial processes. A full-length cDNA library of cassava plants under normal, heat, drought, aluminum and post harvest physiological deterioration conditions was built; 19968 clones were sequence-characterized using expressed sequence tags (ESTs). RESULTS: The ESTs were assembled into 6355 contigs and 9026 singletons that were further grouped into 10577 scaffolds; we found 4621 new cassava sequences and 1521 sequences with no significant similarity to plant protein databases. Transcripts of 7796 distinct genes were captured and we were able to assign a functional classification to 78% of them while finding more than half of the enzymes annotated in metabolic pathways in Arabidopsis. The annotation of sequences that were not paired to transcripts of other species included many stress-related functional categories showing that our library is enriched with stress-induced genes. Finally, we detected 230 putative gene duplications that include key enzymes in reactive oxygen species signaling pathways and could play a role in cassava stress response features. CONCLUSION: The cassava full-length cDNA library here presented contains transcripts of genes involved in stress response as well as genes important for different areas of cassava research. This library will be an important resource for gene discovery, characterization and cloning; in the near future it will aid the annotation of the cassava genome.