The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases

Applications of genome-scale metabolic models to the study of human diseases: A systematic review

BACKGROUND AND OBJECTIVES

Genome-scale metabolic networks (GEMs) represent a valuable modeling and computational tool in the broad field of systems biology. Their ability to integrate constraints and high-throughput biological data enables the study of intricate metabolic aspects and processes of different cell types and conditions. The past decade has witnessed an increasing number and variety of applications of GEMs for the study of human diseases, along with a huge effort aimed at the reconstruction, integration and analysis of a high number of organisms. This paper presents a systematic review of the scientific literature, to pursue several important questions about the application of constraint-based modeling in the investigation of human diseases. Hopefully, this paper will provide a useful reference for researchers interested in the application of modeling and computational tools for the investigation of metabolic-related human diseases.

METHODS

This systematic review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Elsevier Scopus®, National Library of Medicine PubMed® and Clarivate Web of Science™ databases were enquired, resulting in 566 scientific articles. After applying exclusion and eligibility criteria, a total of 169 papers were selected and individually examined.

RESULTS

The reviewed papers offer a thorough and up-to-date picture of the latest modeling and computational approaches, based on genome-scale metabolic models, that can be leveraged for the investigation of a large variety of human diseases. The numerous studies have been categorized according to the clinical research area involved in the examined disease. Furthermore, the paper discusses the most typical approaches employed to derive clinically-relevant information using the computational models.

CONCLUSIONS

The number of scientific papers, utilizing GEM-based approaches for the investigation of human diseases, suggests an increasing interest in these types of approaches; hopefully, the present review will represent a useful reference for scientists interested in applying computational modeling approaches to investigate the aetiopathology of human diseases; we also hope that this work will foster the development of novel applications and methods for the discovery of clinically-relevant insights on metabolic-related diseases.

Identifying and mathematically modeling the time-course of extracellular metabolic markers associated with resistance to ceftolozane/tazobactam in Pseudomonas aeruginosa

Extracellular bacterial metabolites have potential as markers of bacterial growth and resistance emergence but have not been evaluated in dynamic in vitro studies. We investigated the dynamic metabolomic footprint of a multidrug-resistant hypermutable Pseudomonas aeruginosa isolate exposed to ceftolozane/tazobactam as continuous infusion (4.5 g/day, 9 g/day) in a hollow-fiber infection model over 7-9 days in biological replicates (n = 5). Bacterial samples were collected at 0, 7, 23, 47, 71, 95, 143, 167, 191, and 215 h, the supernatant quenched, and extracellular metabolites extracted. Metabolites were analyzed via untargeted metabolomics, including hierarchical clustering and correlation with quantified total and resistant bacterial populations. The time-courses of five (of 1,921 detected) metabolites from enriched pathways were mathematically modeled. Absorbed L-arginine and secreted L-ornithine were highly correlated with the total bacterial population (r -0.79 and 0.82, respectively, P<0.0001). Ribose-5-phosphate, sedoheptulose-7-phosphate, and trehalose-6-phosphate correlated with the resistant subpopulation (0.64, 0.64, and 0.67, respectively, P<0.0001) and were likely secreted due to resistant growth overcoming oxidative and osmotic stress induced by ceftolozane/tazobactam. Using pharmacokinetic/pharmacodynamic-based transduction models, these metabolites were successfully modeled based on the total or resistant bacterial populations. The models well described the abundance of each metabolite across the differing time-course profiles of biological replicates, based on bacterial killing and, importantly, resistant regrowth. These proof-of-concept studies suggest that further exploration is warranted to determine the generalizability of these findings. The metabolites modeled here are not exclusive to bacteria. Future studies may use this approach to identify bacteria-specific metabolites correlating with resistance, which would ultimately be extremely useful for clinical translation.

Flexible B12 ecophysiology of Phaeocystis antarctica due to a fusion B12-independent methionine synthase with widespread homologues

Coastal Antarctic marine ecosystems are significant in carbon cycling because of their intense seasonal phytoplankton blooms. Southern Ocean algae are primarily limited by light and iron (Fe) and can be co-limited by cobalamin (vitamin B12). Micronutrient limitation controls productivity and shapes the composition of blooms which are typically dominated by either diatoms or the haptophyte Phaeocystis antarctica. However, the vitamin requirements and ecophysiology of the keystone species P. antarctica remain poorly characterized. Using cultures, physiological analysis, and comparative omics, we examined the response of P. antarctica to a matrix of Fe-B12 conditions. We show that P. antarctica is not auxotrophic for B12, as previously suggested, and identify mechanisms underlying its B12 response in cultures of predominantly solitary and colonial cells. A combination of proteomics and proteogenomics reveals a B12-independent methionine synthase fusion protein (MetE-fusion) that is expressed under vitamin limitation and interreplaced with the B12-dependent isoform under replete conditions. Database searches return homologues of the MetE-fusion protein in multiple Phaeocystis species and in a wide range of marine microbes, including other photosynthetic eukaryotes with polymorphic life cycles as well as bacterioplankton. Furthermore, we find MetE-fusion homologues expressed in metaproteomic and metatranscriptomic field samples in polar and more geographically widespread regions. As climate change impacts micronutrient availability in the coastal Southern Ocean, our finding that P. antarctica has a flexible B12 metabolism has implications for its relative fitness compared to B12-auxotrophic diatoms and for the detection of B12-stress in a more diverse set of marine microbes.

Recent advances in applying omic technologies for studying acetic acid bacteria in industrial vinegar production: A comprehensive review

Vinegar and related bioproducts containing acetic acid as the main component are among the most appreciated fermented foodstuffs in numerous European and Asian countries because of their exceptional organoleptic and bio-healthy properties. Regarding the acetification process and obtaining of final products, there is still a lack of knowledge on fundamental aspects, especially those related to the study of biodiversity and metabolism of the present microbiota. In this context, omic technologies currently allow for the massive analysis of macromolecules and metabolites for the identification and characterization of these microorganisms working in their natural media without the need for isolation. This review approaches comprehensive research on the application of omic tools for the identification of vinegar microbiota, mainly acetic acid bacteria, with subsequent emphasis on the study of the microbial diversity, behavior, and key molecular strategies used by the predominant groups throughout acetification. The current omics tools are enabling both the finding of new vinegar microbiota members and exploring underlying strategies during the elaboration process. The species Komagataeibacter europaeus may be a model organism for present and future research in this industry; moreover, the development of integrated meta-omic analysis may facilitate the achievement of numerous of the proposed milestones. This work might provide useful guidance for the vinegar industry establishing the first steps towards the improvement of the acetification conditions and the development of new products with sensory and bio-healthy profiles adapted to the agri-food market.

Reconstruction and metabolic profiling of the genome-scale metabolic network model of Pseudomonas stutzeri A1501

Pseudomonas stutzeri A1501 is a non-fluorescent denitrifying bacteria that belongs to the gram-negative bacterial group. As a prominent strain in the fields of agriculture and bioengineering, there is still a lack of comprehensive understanding regarding its metabolic capabilities, specifically in terms of central metabolism and substrate utilization. Therefore, further exploration and extensive studies are required to gain a detailed insight into these aspects. This study reconstructed a genome-scale metabolic network model for P. stutzeri A1501 and conducted extensive curations, including correcting energy generation cycles, respiratory chains, and biomass composition. The final model, iQY1018, was successfully developed, covering more genes and reactions and having higher prediction accuracy compared with the previously published model iPB890. The substrate utilization ability of 71 carbon sources was investigated by BIOLOG experiment and was utilized to validate the model quality. The model prediction accuracy of substrate utilization for P. stutzeri A1501 reached 90 %. The model analysis revealed its new ability in central metabolism and predicted that the strain is a suitable chassis for the production of Acetyl CoA-derived products. This work provides an updated, high-quality model of P. stutzeri A1501for further research and will further enhance our understanding of the metabolic capabilities.

Taxonomic and environmental distribution of bacterial amino acid auxotrophies

Many microorganisms are auxotrophic-unable to synthesize the compounds they require for growth. With this work, we quantify the prevalence of amino acid auxotrophies across a broad diversity of bacteria and habitats. We predicted the amino acid biosynthetic capabilities of 26,277 unique bacterial genomes spanning 12 phyla using a metabolic pathway model validated with empirical data. Amino acid auxotrophy is widespread across bacterial phyla, but we conservatively estimate that the majority of taxa (78.4%) are able to synthesize all amino acids. Our estimates indicate that amino acid auxotrophies are more prevalent among obligate intracellular parasites and in free-living taxa with genomic attributes characteristic of 'streamlined' life history strategies. We predicted the amino acid biosynthetic capabilities of bacterial communities found in 12 unique habitats to investigate environmental associations with auxotrophy, using data compiled from 3813 samples spanning major aquatic, terrestrial, and engineered environments. Auxotrophic taxa were more abundant in host-associated environments (including the human oral cavity and gut) and in fermented food products, with auxotrophic taxa being relatively rare in soil and aquatic systems. Overall, this work contributes to a more complete understanding of amino acid auxotrophy across the bacterial tree of life and the ecological contexts in which auxotrophy can be a successful strategy.

Short-Term Dietary Intervention with Whole Oats Protects from Antibiotic-Induced Dysbiosis

Antibiotic-induced gut microbiome dysbiosis (AID) is known to be influenced by host dietary composition. However, how and when diet modulates gut dysbiosis remains poorly characterized. Thus, here, we utilize a multi-omics approach to characterize how a diet supplemented with oats, a rich source of microbiota-accessible carbohydrates, or dextrose impacts amoxicillin-induced changes to gut microbiome structure and transcriptional activity. We demonstrate that oat administration during amoxicillin challenge provides greater protection from AID than the always oats or recovery oats diet groups. In particular, the group in which oats were provided at the time of antibiotic exposure induced the greatest protection against AID while the other oat diets saw greater effects after amoxicillin challenge. The oat diets likewise reduced amoxicillin-driven elimination of Firmicutes compared to the dextrose diet. Functionally, gut communities fed dextrose were carbohydrate starved and favored respiratory metabolism and consequent metabolic stress management while oat-fed communities shifted their transcriptomic profile and emphasized antibiotic stress management. The metabolic trends were exemplified when assessing transcriptional activity of the following two common gut commensal bacteria: Akkermansia muciniphila and Bacteroides thetaiotaomicron. These findings demonstrate that while host diet is important in shaping how antibiotics effect the gut microbiome composition and function, diet timing may play an even greater role in dietary intervention-based therapeutics. IMPORTANCE We utilize a multi-omics approach to demonstrate that diets supplemented with oats, a rich source of microbiota-accessible carbohydrates, are able to confer protection against antibiotic-induced dysbiosis (AID). Our findings affirm that not only is host diet important in shaping antibiotics effects on gut microbiome composition and function but also that the timing of these diets may play an even greater role in managing AID. This work provides a nuanced perspective on dietary intervention against AID and may be informative on preventing AID during routine antibiotic treatment.

Sphingomonas sediminicola Is an Endosymbiotic Bacterium Able to Induce the Formation of Root Nodules in Pea (Pisum sativum L.) and to Enhance Plant Biomass Production

The application of bacterial bio-inputs is a very attractive alternative to the use of mineral fertilisers. In ploughed soils including a crop rotation pea, we observed an enrichment of bacterial communities with Sphingomonas (S.) sediminicola. Inoculation experiments, cytological studies, and de novo sequencing were used to investigate the beneficial role of S. sediminicola in pea. S. sediminicola is able to colonise pea plants and establish a symbiotic association that promotes plant biomass production. Sequencing of the S. sediminicola genome revealed the existence of genes involved in secretion systems, Nod factor synthesis, and nitrogenase activity. Light and electron microscopic observations allowed us to refine the different steps involved in the establishment of the symbiotic association, including the formation of infection threads, the entry of the bacteria into the root cells, and the development of differentiated bacteroids in root nodules. These results, together with phylogenetic analysis, demonstrated that S. sediminicola is a non-rhizobia that has the potential to develop a beneficial symbiotic association with a legume. Such a symbiotic association could be a promising alternative for the development of more sustainable agricultural practices, especially under reduced N fertilisation conditions.

Filling gaps in bacterial catabolic pathways with computation and high-throughput genetics

To discover novel catabolic enzymes and transporters, we combined high-throughput genetic data from 29 bacteria with an automated tool to find gaps in their catabolic pathways. GapMind for carbon sources automatically annotates the uptake and catabolism of 62 compounds in bacterial and archaeal genomes. For the compounds that are utilized by the 29 bacteria, we systematically examined the gaps in GapMind's predicted pathways, and we used the mutant fitness data to find additional genes that were involved in their utilization. We identified novel pathways or enzymes for the utilization of glucosamine, citrulline, myo-inositol, lactose, and phenylacetate, and we annotated 299 diverged enzymes and transporters. We also curated 125 proteins from published reports. For the 29 bacteria with genetic data, GapMind finds high-confidence paths for 85% of utilized carbon sources. In diverse bacteria and archaea, 38% of utilized carbon sources have high-confidence paths, which was improved from 27% by incorporating the fitness-based annotations and our curation. GapMind for carbon sources is available as a web server (http://papers.genomics.lbl.gov/carbon) and takes just 30 seconds for the typical genome.

Evolutionarily recent dual obligatory symbiosis among adelgids indicates a transition between fungus- and insect-associated lifestyles

Adelgids (Insecta: Hemiptera: Adelgidae) form a small group of insects but harbor a surprisingly diverse set of bacteriocyte-associated endosymbionts, which suggest multiple replacement and acquisition of symbionts over evolutionary time. Specific pairs of symbionts have been associated with adelgid lineages specialized on different secondary host conifers. Using a metagenomic approach, we investigated the symbiosis of the Adelges laricis/Adelges tardus species complex containing betaproteobacterial ("Candidatus Vallotia tarda") and gammaproteobacterial ("Candidatus Profftia tarda") symbionts. Genomic characteristics and metabolic pathway reconstructions revealed that Vallotia and Profftia are evolutionary young endosymbionts, which complement each other's role in essential amino acid production. Phylogenomic analyses and a high level of genomic synteny indicate an origin of the betaproteobacterial symbiont from endosymbionts of Rhizopus fungi. This evolutionary transition was accompanied with substantial loss of functions related to transcription regulation, secondary metabolite production, bacterial defense mechanisms, host infection, and manipulation. The transition from fungus to insect endosymbionts extends our current framework about evolutionary trajectories of host-associated microbes.

Music of metagenomics-a review of its applications, analysis pipeline, and associated tools

This humble effort highlights the intricate details of metagenomics in a simple, poetic, and rhythmic way. The paper enforces the significance of the research area, provides details about major analytical methods, examines the taxonomy and assembly of genomes, emphasizes some tools, and concludes by celebrating the richness of the ecosystem populated by the "metagenome."

Four families of folate-independent methionine synthases

Although most organisms synthesize methionine from homocysteine and methyl folates, some have “core” methionine synthases that lack folate-binding domains and use other methyl donors. In vitro, the characterized core synthases use methylcobalamin as a methyl donor, but in vivo, they probably rely on corrinoid (vitamin B12-binding) proteins. We identified four families of core methionine synthases that are distantly related to each other (under 30% pairwise amino acid identity). From the characterized enzymes, we identified the families MesA, which is found in methanogens, and MesB, which is found in anaerobic bacteria and archaea with the Wood-Ljungdahl pathway. A third uncharacterized family, MesC, is found in anaerobic archaea that have the Wood-Ljungdahl pathway and lack known forms of methionine synthase. We predict that most members of the MesB and MesC families accept methyl groups from the iron-sulfur corrinoid protein of that pathway. The fourth family, MesD, is found only in aerobic bacteria. Using transposon mutants and complementation, we show that MesD does not require 5-methyltetrahydrofolate or cobalamin. Instead, MesD requires an uncharacterized protein family (DUF1852) and oxygen for activity.

Methionine is one of the amino acids that make up proteins, and the final step in methionine synthesis is the transfer of a methyl group. In most organisms, the methyl group is obtained from methyl folates, but some anaerobic bacteria and archaea are thought to use corrinoid (vitamin B12-binding) proteins instead. By analyzing the sequences of the potential methionine synthases across the genomes of diverse bacteria and archaea, we identified four families of folate-independent methionine synthases. For three of these families, we can use co-occurrence with corrinoid proteins to predict their likely partners. We show that the fourth family does not require vitamin B12; instead, it obtains methyl groups from an oxygen-dependent partner protein. Our results will help us understand the growth requirements of diverse bacteria and archaea.

multiGSEA: a GSEA-based pathway enrichment analysis for multi-omics data

Background

Gaining biological insights into molecular responses to treatments or diseases from omics data can be accomplished by gene set or pathway enrichment methods. A plethora of different tools and algorithms have been developed so far. Among those, the gene set enrichment analysis (GSEA) proved to control both type I and II errors well. In recent years the call for a combined analysis of multiple omics layers became prominent, giving rise to a few multi-omics enrichment tools. Each of these has its own drawbacks and restrictions regarding its universal application.

Results

Here, we present the multiGSEA package aiding to calculate a combined GSEA-based pathway enrichment on multiple omics layers. The package queries 8 different pathway databases and relies on the robust GSEA algorithm for a single-omics enrichment analysis. In a final step, those scores will be combined to create a robust composite multi-omics pathway enrichment measure. multiGSEA supports 11 different organisms and includes a comprehensive mapping of transcripts, proteins, and metabolite IDs.

Conclusions

With multiGSEA we introduce a highly versatile tool for multi-omics pathway integration that minimizes previous restrictions in terms of omics layer selection, pathway database availability, organism selection and the mapping of omics feature identifiers. multiGSEA is publicly available under the GPL-3 license at https://github.com/yigbt/multiGSEA and at bioconductor: https://bioconductor.org/packages/multiGSEA.

Pharmacoinformatics approaches to identify potential hits against tetraacyldisaccharide 4'-kinase (LpxK) of Pseudomonas aeruginosa

Pseudomonas aeruginosa infection can cause pneumonia and urinary tract infection and the management of Pseudomonas aeruginosa infection is critical in multidrug resistance, hospital-acquired bacteremia and ventilator-associated pneumonia. The key enzymes of lipid A biosynthesis in Pseudomonas aeruginosa are promising drug targets. However, the enzyme tetraacyldisaccharide 4'-kinase (LpxK) has not been explored as a drug target so far. Several pharmacoinformatics tools such as comparative metabolic pathway analysis (Metacyc), data mining from a database of essential genes (DEG), homology modeling, molecular docking, pharmacophore based virtual screening, ADMET prediction and molecular dynamics simulation were used in identifying novel lead compounds against this target. The top virtual hits STOCK6S-33288, 43621, 39892, 37164 and 35740 may serve as the templates for the design and synthesis of potent LpxK inhibitors in the management of serious Pseudomonas aeruginosa infection.

GapMind: Automated Annotation of Amino Acid Biosynthesis

GapMind is a Web-based tool for annotating amino acid biosynthesis in bacteria and archaea (http://papers.genomics.lbl.gov/gaps). GapMind incorporates many variant pathways and 130 different reactions, and it analyzes a genome in just 15 s. To avoid error-prone transitive annotations, GapMind relies primarily on a database of experimentally characterized proteins. GapMind correctly handles fusion proteins and split proteins, which often cause errors for best-hit approaches. To improve GapMind's coverage, we examined genetic data from 35 bacteria that grow in defined media without amino acids, and we filled many gaps in amino acid biosynthesis pathways. For example, we identified additional genes for arginine synthesis with succinylated intermediates in Bacteroides thetaiotaomicron, and we propose that Dyella japonica synthesizes tyrosine from phenylalanine. Nevertheless, for many bacteria and archaea that grow in minimal media, genes for some steps still cannot be identified. To help interpret potential gaps, GapMind checks if they match known gaps in related microbes that can grow in minimal media. GapMind should aid the identification of microbial growth requirements.IMPORTANCE Many microbes can make all of the amino acids (the building blocks of proteins). In principle, we should be able to predict which amino acids a microbe can make, and which it requires as nutrients, by checking its genome sequence for all of the necessary genes. However, in practice, it is difficult to check for all of the alternative pathways. Furthermore, new pathways and enzymes are still being discovered. We built an automated tool, GapMind, to annotate amino acid biosynthesis in bacterial and archaeal genomes. We used GapMind to list gaps: cases where a microbe makes an amino acid but a complete pathway cannot be identified in its genome. We used these gaps, together with data from mutants, to identify new pathways and enzymes. However, for most bacteria and archaea, we still do not know how they can make all of the amino acids.

Gene-Wise Burden of Coding Variants Correlates to Noncoding Pharmacogenetic Risk Variants

Genetic variability can modulate individual drug responses. A significant portion of pharmacogenetic variants reside in the noncoding genome yet it is unclear if the noncoding variants directly influence protein function and expression or are present on a haplotype including a functionally relevant genetic variation (synthetic association). Gene-wise variant burden (GVB) is a gene-level measure of deleteriousness, reflecting the cumulative effects of deleterious coding variants, predicted in silico. To test potential associations between noncoding and coding pharmacogenetic variants, we computed a drug-level GVB for 5099 drugs from DrugBank for 2504 genomes of the 1000 Genomes Project and evaluated the correlation between the long-known noncoding variant-drug associations in PharmGKB, with functionally relevant rare and common coding variants aggregated into GVBs. We obtained the area under the receiver operating characteristics curve (AUC) by comparing the drug-level GVB ranks against the corresponding pharmacogenetic variants-drug associations in PharmGKB. We obtained high overall AUCs (0.710 ± 0.022-0.734 ± 0.018) for six different methods (i.e., SIFT, MutationTaster, Polyphen-2 HVAR, Polyphen-2 HDIV, phyloP, and GERP⁺⁺), and further improved the ethnicity-specific validations (0.759 ± 0.066-0.791 ± 0.078). These results suggest that a significant portion of the long-known noncoding variant-drug associations can be explained as synthetic associations with rare and common coding variants burden of the corresponding pharmacogenes.

Effectiveness of Bacillus pumilus PDSLzg-1, an innovative Hydrocarbon-Degrading Bacterium conferring antifungal and plant growth-promoting function

Genome of the hydrocarbon-degrading bacterium Bacillus pumilus PDSLzg-1 was analyzed. A group of gene clusters and pathways associated with nitrogen fixation, plant-bacterial interactions, plant growth-promoting hormone synthesis, antibiotics, secondary metabolite, and disease resistance were identified. In addition, 0.06 mg/L of 3-indoleacrylic acid (IAA) and 2 mg/L of gibberellin (GA) were, respectively, detected in PDSLzg-1 fermentation broth by high-performance liquid chromatography (HPLC). Up-regulated expression levels of 11 key genes related to GA and IAA biosynthesis pathways were detected after the induction of 0.2% n-hexadecane. Furthermore, bioassays showed that PDSLzg-1 fermentation could significantly promote the length and biomass of the stems and roots of Triticum aestivum L., while inhibited Colletotrichum truncatum colonization. Results indicated that B. pumilus PDSLzg-1 had plant growth-promoting and antifungal functions, besides its potential applications in phyto-microbial bioremediation combinations for oil-contaminated soil.

Meta-omics analysis of elite athletes identifies a performance-enhancing microbe that functions via lactate metabolism

The human gut microbiome is linked to many states of human health and disease.¹ The metabolic repertoire of the gut microbiome is vast, but the health implications of these bacterial pathways are poorly understood. In this study, we identify a link between members of the genus Veillonella and exercise performance. We observed an increase Veillonella relative abundance in marathon runners post-marathon and isolated a strain of Veillonella atypica from stool samples. Inoculation of this strain into mice significantly increased exhaustive treadmill runtime. Veillonella utilize lactate as their sole carbon source, which prompted us to perform shotgun metagenomic analysis in a cohort of elite athletes, finding that every gene in a major pathway metabolizing lactate to propionate is at higher relative abundance post-exercise. Using ¹³C₃-labeled lactate in mice we demonstrate that serum lactate crosses the epithelial barrier into the lumen of the gut. We also show that intrarectal instillation of propionate is sufficient to reproduce the increased treadmill runtime performance observed with V. atypica gavage. Taken together, these studies reveal that V. atypica improves runtime via its metabolic conversion of exercise-induced lactate into propionate, thereby identifying a natural, microbiome-encoded enzymatic process that enhances athletic performance.

Systems Biology and Multi-Omics Integration: Viewpoints from the Metabolomics Research Community

The use of multiple omics techniques (i.e., genomics, transcriptomics, proteomics, and metabolomics) is becoming increasingly popular in all facets of life science. Omics techniques provide a more holistic molecular perspective of studied biological systems compared to traditional approaches. However, due to their inherent data differences, integrating multiple omics platforms remains an ongoing challenge for many researchers. As metabolites represent the downstream products of multiple interactions between genes, transcripts, and proteins, metabolomics, the tools and approaches routinely used in this field could assist with the integration of these complex multi-omics data sets. The question is, how? Here we provide some answers (in terms of methods, software tools and databases) along with a variety of recommendations and a list of continuing challenges as identified during a peer session on multi-omics integration that was held at the recent 'Australian and New Zealand Metabolomics Conference' (ANZMET 2018) in Auckland, New Zealand (Sept. 2018). We envisage that this document will serve as a guide to metabolomics researchers and other members of the community wishing to perform multi-omics studies. We also believe that these ideas may allow the full promise of integrated multi-omics research and, ultimately, of systems biology to be realized.

Curated BLAST for Genomes

Given a microbe’s genome sequence, we often want to predict what capabilities the organism has, such as which nutrients it requires or which energy sources it can use. Or, we know the organism has a capability and we want to find the genes involved. Scientists often use automated gene annotations to find relevant genes, but automated annotations are often vague or incorrect. Curated BLAST finds candidate genes for a capability without relying on automated annotations. First, Curated BLAST finds proteins (usually from other organisms) whose functions have been studied experimentally and whose curated descriptions match a query. Then, it searches the genome of interest for similar proteins and returns a list of candidates. Curated BLAST is fast and often finds relevant genes that are missed by automated annotation.

Curated BLAST for Genomes finds candidate genes for a process or an enzymatic activity within a genome of interest. In contrast to annotation tools, which usually predict a single activity for each protein, Curated BLAST asks if any of the proteins in the genome are similar to characterized proteins that are relevant. Given a query such as an enzyme’s name or an EC number, Curated BLAST searches the curated descriptions of over 100,000 characterized proteins, and it compares the relevant characterized proteins to the predicted proteins in the genome of interest. In case of errors in the gene models, Curated BLAST also searches the six-frame translation of the genome. Curated BLAST is available at http://papers.genomics.lbl.gov/curated.

IMPORTANCE Given a microbe’s genome sequence, we often want to predict what capabilities the organism has, such as which nutrients it requires or which energy sources it can use. Or, we know the organism has a capability and we want to find the genes involved. Scientists often use automated gene annotations to find relevant genes, but automated annotations are often vague or incorrect. Curated BLAST finds candidate genes for a capability without relying on automated annotations. First, Curated BLAST finds proteins (usually from other organisms) whose functions have been studied experimentally and whose curated descriptions match a query. Then, it searches the genome of interest for similar proteins and returns a list of candidates. Curated BLAST is fast and often finds relevant genes that are missed by automated annotation.

The Patchy Distribution of Restriction⁻Modification System Genes and the Conservation of Orphan Methyltransferases in Halobacteria

Restriction⁻modification (RM) systems in bacteria are implicated in multiple biological roles ranging from defense against parasitic genetic elements, to selfish addiction cassettes, and barriers to gene transfer and lineage homogenization. In bacteria, DNA-methylation without cognate restriction also plays important roles in DNA replication, mismatch repair, protein expression, and in biasing DNA uptake. Little is known about archaeal RM systems and DNA methylation. To elucidate further understanding for the role of RM systems and DNA methylation in Archaea, we undertook a survey of the presence of RM system genes and related genes, including orphan DNA methylases, in the halophilic archaeal class Halobacteria. Our results reveal that some orphan DNA methyltransferase genes were highly conserved among lineages indicating an important functional constraint, whereas RM systems demonstrated patchy patterns of presence and absence. This irregular distribution is due to frequent horizontal gene transfer and gene loss, a finding suggesting that the evolution and life cycle of RM systems may be best described as that of a selfish genetic element. A putative target motif (CTAG) of one of the orphan methylases was underrepresented in all of the analyzed genomes, whereas another motif (GATC) was overrepresented in most of the haloarchaeal genomes, particularly in those that encoded the cognate orphan methylase.

Transcriptional and Chemical Changes in Soybean Leaves in Response to Long-Term Aphid Colonization

Soybean aphids (Aphis glycines Matsumura) are specialized insects that feed on soybean (Glycine max) phloem sap. Transcriptome analyses have shown that resistant soybean plants mount a fast response that limits aphid feeding and population growth. Conversely, defense responses in susceptible plants are slower and it is hypothesized that aphids block effective defenses in the compatible interaction. Unlike other pests, aphids can colonize plants for long periods of time; yet the effect on the plant transcriptome after long-term aphid feeding has not been analyzed for any plant-aphid interaction. We analyzed the susceptible and resistant (Rag1) transcriptome response to aphid feeding in soybean plants colonized by aphids (biotype 1) for 21 days. We found a reduced resistant response and a low level of aphid growth on Rag1 plants, while susceptible plants showed a strong response consistent with pattern-triggered immunity. GO-term analyses identified chitin regulation as one of the most overrepresented classes of genes, suggesting that chitin could be one of the hemipteran-associated molecular pattern that triggers this defense response. Transcriptome analyses also indicated the phenylpropanoid pathway, specifically isoflavonoid biosynthesis, was induced in susceptible plants in response to long-term aphid feeding. Metabolite analyses corroborated this finding. Aphid-treated susceptible plants accumulated daidzein, formononetin, and genistein, although glyceollins were present at low levels in these plants. Choice experiments indicated that daidzein may have a deterrent effect on aphid feeding. Mass spectrometry imaging showed these isoflavones accumulate likely in the mesophyll cells or epidermis and are absent from the vasculature, suggesting that isoflavones are part of a non-phloem defense response that can reduce aphid feeding. While it is likely that aphid can initially block defense responses in compatible interactions, it appears that susceptible soybean plants can eventually mount an effective defense in response to long-term soybean aphid colonization.

Identification of cancer biomarkers of prognostic value using specific gene regulatory networks (GRN): a novel role of RAD51AP1 for ovarian and lung cancers

To date, microarray analyses have led to the discovery of numerous individual ‘molecular signatures’ associated with specific cancers. However, there are serious limitations for the adoption of these multi-gene signatures in the clinical environment for diagnostic or prognostic testing as studies with more power need to be carried out. This may involve larger richer cohorts and more advanced analyses. In this study, we conduct analyses—based on gene regulatory network—to reveal distinct and common biomarkers across cancer types. Using microarray data of triple-negative and medullary breast, ovarian and lung cancers applied to a combination of glasso and Bayesian networks (BNs), we derived a unique network-containing genes that are uniquely involved: small proline-rich protein 1A (SPRR1A), follistatin like 1 (FSTL1), collagen type XII alpha 1 (COL12A1) and RAD51 associated protein 1 (RAD51AP1). RAD51AP1 and FSTL1 are significantly overexpressed in ovarian cancer patients but only RAD51AP1 is upregulated in lung cancer patients compared with healthy controls. The upregulation of RAD51AP1 was mirrored in the bloods of both ovarian and lung cancer patients, and Kaplan–Meier (KM) plots predicted poorer overall survival (OS) in patients with high expression of RAD51AP1. Suppression of RAD51AP1 by RNA interference reduced cell proliferation in vitro in ovarian (SKOV3) and lung (A549) cancer cells. This effect appears to be modulated by a decrease in the expression of mTOR-related genes and pro-metastatic candidate genes. Our data describe how an initial in silico approach can generate novel biomarkers that could potentially support current clinical practice and improve long-term outcomes.

Multiscale analysis of autotroph-heterotroph interactions in a high-temperature microbial community

Interactions among microbial community members can lead to emergent properties, such as enhanced productivity, stability, and robustness. Iron-oxide mats in acidic (pH 2-4), high-temperature (> 65 °C) springs of Yellowstone National Park contain relatively simple microbial communities and are well-characterized geochemically. Consequently, these communities are excellent model systems for studying the metabolic activity of individual populations and key microbial interactions. The primary goals of the current study were to integrate data collected in situ with in silico calculations across process-scales encompassing enzymatic activity, cellular metabolism, community interactions, and ecosystem biogeochemistry, as well as to predict and quantify the functional limits of autotroph-heterotroph interactions. Metagenomic and transcriptomic data were used to reconstruct carbon and energy metabolisms of an important autotroph (Metallosphaera yellowstonensis) and heterotroph (Geoarchaeum sp. OSPB) from the studied Fe(III)-oxide mat communities. Standard and hybrid elementary flux mode and flux balance analyses of metabolic models predicted cellular- and community-level metabolic acclimations to simulated environmental stresses, respectively. In situ geochemical analyses, including oxygen depth-profiles, Fe(III)-oxide deposition rates, stable carbon isotopes and mat biomass concentrations, were combined with cellular models to explore autotroph-heterotroph interactions important to community structure-function. Integration of metabolic modeling with in situ measurements, including the relative population abundance of autotrophs to heterotrophs, demonstrated that Fe(III)-oxide mat communities operate at their maximum total community growth rate (i.e. sum of autotroph and heterotroph growth rates), as opposed to net community growth rate (i.e. total community growth rate subtracting autotroph consumed by heterotroph), as predicted from the maximum power principle. Integration of multiscale data with ecological theory provides a basis for predicting autotroph-heterotroph interactions and community-level cellular organization.

Acetylenotrophy: a hidden but ubiquitous microbial metabolism?

Acetylene (IUPAC name: ethyne) is a colorless, gaseous hydrocarbon, composed of two triple bonded carbon atoms attached to hydrogens (C2H2). When microbiologists and biogeochemists think of acetylene, they immediately think of its use as an inhibitory compound of certain microbial processes and a tracer for nitrogen fixation. However, what is less widely known is that anaerobic and aerobic microorganisms can degrade acetylene, using it as a sole carbon and energy source and providing the basis of a microbial food web. Here, we review what is known about acetylene degrading organisms and introduce the term 'acetylenotrophs' to refer to the microorganisms that carry out this metabolic pathway. In addition, we review the known environmental sources of acetylene and postulate the presence of an hidden acetylene cycle. The abundance of bacteria capable of using acetylene and other alkynes as an energy and carbon source suggests that there are energy cycles present in the environment that are driven by acetylene and alkyne production and consumption that are isolated from atmospheric exchange. Acetylenotrophs may have developed to leverage the relatively high concentrations of acetylene in the pre-Cambrian atmosphere, evolving later to survive in specialized niches where acetylene and other alkynes were produced.

From Network Analysis to Functional Metabolic Modeling of the Human Gut Microbiota

An important hallmark of the human gut microbiota is its species diversity and complexity. Various diseases have been associated with a decreased diversity leading to reduced metabolic functionalities. Common approaches to investigate the human microbiota include high-throughput sequencing with subsequent correlative analyses. However, to understand the ecology of the human gut microbiota and consequently design novel treatments for diseases, it is important to represent the different interactions between microbes with their associated metabolites. Computational systems biology approaches can give further mechanistic insights by constructing data- or knowledge-driven networks that represent microbe interactions. In this minireview, we will discuss current approaches in systems biology to analyze the human gut microbiota, with a particular focus on constraint-based modeling. We will discuss various community modeling techniques with their advantages and differences, as well as their application to predict the metabolic mechanisms of intestinal microbial communities. Finally, we will discuss future perspectives and current challenges of simulating realistic and comprehensive models of the human gut microbiota.

Reconstruction and Analysis of Central Metabolism in Microbes

Genome-scale metabolic models (GEMs) generated from automated reconstruction pipelines often lack accuracy due to the need for extensive gapfilling and the inference of periphery metabolic pathways based on lower-confidence annotations. The central carbon pathways and electron transport chains are among the most well-understood regions of microbial metabolism, and these pathways contribute significantly toward defining cellular behavior and growth conditions. Thus, it is often useful to construct a simplified core metabolic model (CMM) that is comprised of only the high-confidence central pathways. In this chapter, we discuss methods for producing core metabolic models (CMM) based on genome annotations. With its reduced scope compared to GEMs, CMM reconstruction focuses on accurate representation of the central metabolic pathways related to energy biosynthesis and accurate energy yield predictions. We demonstrate the reconstruction and analysis of CMMs using the DOE Systems Biology Knowledgebase (KBase). The complete workflow is available at http://kbase.us/core-models/.

PaperBLAST: Text Mining Papers for Information about Homologs

With the recent explosion of genome sequencing data, there are now millions of uncharacterized proteins. If a scientist becomes interested in one of these proteins, it can be very difficult to find information as to its likely function. Often a protein whose sequence is similar, and which is likely to have a similar function, has been studied already, but this information is not available in any database. To help find articles about similar proteins, PaperBLAST searches the full text of scientific articles for protein identifiers or gene identifiers, and it links these articles to protein sequences. Then, given a protein of interest, it can quickly find similar proteins in its database by using standard software (BLAST), and it can show snippets of text from relevant papers. We hope that PaperBLAST will make it easier for biologists to predict proteins’ functions.

Large-scale genome sequencing has identified millions of protein-coding genes whose function is unknown. Many of these proteins are similar to characterized proteins from other organisms, but much of this information is missing from annotation databases and is hidden in the scientific literature. To make this information accessible, PaperBLAST uses EuropePMC to search the full text of scientific articles for references to genes. PaperBLAST also takes advantage of curated resources (Swiss-Prot, GeneRIF, and EcoCyc) that link protein sequences to scientific articles. PaperBLAST’s database includes over 700,000 scientific articles that mention over 400,000 different proteins. Given a protein of interest, PaperBLAST quickly finds similar proteins that are discussed in the literature and presents snippets of text from relevant articles or from the curators. PaperBLAST is available at http://papers.genomics.lbl.gov/.

IMPORTANCE With the recent explosion of genome sequencing data, there are now millions of uncharacterized proteins. If a scientist becomes interested in one of these proteins, it can be very difficult to find information as to its likely function. Often a protein whose sequence is similar, and which is likely to have a similar function, has been studied already, but this information is not available in any database. To help find articles about similar proteins, PaperBLAST searches the full text of scientific articles for protein identifiers or gene identifiers, and it links these articles to protein sequences. Then, given a protein of interest, it can quickly find similar proteins in its database by using standard software (BLAST), and it can show snippets of text from relevant papers. We hope that PaperBLAST will make it easier for biologists to predict proteins’ functions.

Metabolomics-assisted biotechnological interventions for developing plant-based functional foods and nutraceuticals

Today, the dramatic changes in types of food consumed have led to an increased burden of chronic diseases. Therefore, the emphasis of food research is not only to ensure quality food that can supply adequate nutrients to prevent nutrition related diseases, but also to ensure overall physical and mental-health. This has led to the concept of functional foods and nutraceuticals (FFNs), which can be ideally produced and delivered through plants. Metabolomics can help in getting the most relevant functional information, and thus has been considered the greatest -OMICS technology to date. However, metabolomics has not been exploited to the best potential in plant sciences. The technology can be leveraged to identify the health promoting compounds and metabolites that can be used for the development of FFNs. This article reviews (i) plant-based FFNs-related metabolites and their health benefits; (ii) use of different analytic platforms for targeted and non-targeted metabolite profiling along with experimental considerations; (iii) exploitation of metabolomics to develop FFNs in plants using various biotechnological tools; and (iv) potential use of metabolomics in plant breeding. We have also provided some insights into integration of metabolomics with latest genome editing tools for metabolic pathway regulation in plants.

Insights into Butyrate Production in a Controlled Fermentation System via Gene Predictions

Butyrate is a common fatty acid produced in important fermentative systems, such as the human/animal gut and other H₂ production systems. Despite its importance, there is little information on the partnerships between butyrate producers and other bacteria. The objective of this work was to uncover butyrate-producing microbial communities and possible metabolic routes in a controlled fermentation system aimed at butyrate production. The butyrogenic reactor was operated at 37°C and pH 5.5 with a hydraulic retention time of 31 h and a low hydrogen partial pressure (PH₂). High-throughput sequencing and metagenome functional prediction from 16S rRNA data showed that butyrate production pathways and microbial communities were different during batch (closed) and continuous-mode operation. Lactobacillaceae, Lachnospiraceae, and Enterococcaceae were the most abundant phylotypes in the closed system without PH₂ control, whereas Prevotellaceae, Ruminococcaceae, and Actinomycetaceae were the most abundant phylotypes under continuous operation at low PH₂. Putative butyrate producers identified in our system were from Prevotellaceae, Clostridiaceae, Ruminococcaceae, and Lactobacillaceae. Metagenome prediction analysis suggests that nonbutyrogenic microorganisms influenced butyrate production by generating butyrate precursors such as acetate, lactate, and succinate. 16S rRNA gene analysis suggested that, in the reactor, a partnership between identified butyrogenic microorganisms and succinate (i.e., Actinomycetaceae), acetate (i.e., Ruminococcaceae and Actinomycetaceae), and lactate producers (i.e., Ruminococcaceae and Lactobacillaceae) took place under continuous-flow operation at low PH₂. IMPORTANCE This study demonstrates how bioinformatics tools, such as metagenome functional prediction from 16S rRNA genes, can help understand biological systems and reveal microbial interactions in controlled systems (e.g., bioreactors). Results obtained from controlled systems are easier to interpret than those from human/animal studies because observed changes may be specifically attributed to the design conditions imposed on the system. Bioinformatics analysis allowed us to identify potential butyrogenic phylotypes and associated butyrate metabolism pathways when we systematically varied the PH₂ in a carefully controlled fermentation system. Our insights may be adapted to butyrate production studies in biohydrogen systems and gut models, since butyrate is a main product and a crucial fatty acid in human/animal colon health.

Succession in the petroleum reservoir microbiome through an oil field production lifecycle

Subsurface petroleum reservoirs are an important component of the deep biosphere where indigenous microorganisms live under extreme conditions and in isolation from the Earth’s surface for millions of years. However, unlike the bulk of the deep biosphere, the petroleum reservoir deep biosphere is subject to extreme anthropogenic perturbation, with the introduction of new electron acceptors, donors and exogenous microbes during oil exploration and production. Despite the fundamental and practical significance of this perturbation, there has never been a systematic evaluation of the ecological changes that occur over the production lifetime of an active offshore petroleum production system. Analysis of the entire Halfdan oil field in the North Sea (32 producing wells in production for 1–15 years) using quantitative PCR, multigenic sequencing, comparative metagenomic and genomic bins reconstruction revealed systematic shifts in microbial community composition and metabolic potential, as well as changing ecological strategies in response to anthropogenic perturbation of the oil field ecosystem, related to length of time in production. The microbial communities were initially dominated by slow growing anaerobes such as members of the Thermotogales and Clostridiales adapted to living on hydrocarbons and complex refractory organic matter. However, as seawater and nitrate injection (used for secondary oil production) delivered oxidants, the microbial community composition progressively changed to fast growing opportunists such as members of the Deferribacteres, Delta-, Epsilon- and Gammaproteobacteria, with energetically more favorable metabolism (for example, nitrate reduction, H₂S, sulfide and sulfur oxidation). This perturbation has profound consequences for understanding the microbial ecology of the system and is of considerable practical importance as it promotes detrimental processes such as reservoir souring and metal corrosion. These findings provide a new conceptual framework for understanding the petroleum reservoir biosphere and have consequences for developing strategies to manage microbiological problems in the oil industry.

Analyses of the probiotic property and stress resistance-related genes of Lactococcus lactis subsp. lactis NCDO 2118 through comparative genomics and in vitro assays

Lactococcus lactis subsp. lactis NCDO 2118 was recently reported to alleviate colitis symptoms via its anti-inflammatory and immunomodulatory activities, which are exerted by exported proteins that are not produced by L. lactis subsp. lactis IL1403. Here, we used in vitro and in silico approaches to characterize the genomic structure, the safety aspects, and the immunomodulatory activity of this strain. Through comparative genomics, we identified genomic islands, phage regions, bile salt and acid stress resistance genes, bacteriocins, adhesion-related and antibiotic resistance genes, and genes encoding proteins that are putatively secreted, expressed in vitro and absent from IL1403. The high degree of similarity between all Lactococcus suggests that the Symbiotic Islands commonly shared by both NCDO 2118 and KF147 may be responsible for their close relationship and their adaptation to plants. The predicted bacteriocins may play an important role against the invasion of competing strains. The genes related to the acid and bile salt stresses may play important roles in gastrointestinal tract survival, whereas the adhesion proteins are important for persistence in the gut, culminating in the competitive exclusion of other bacteria. Finally, the five secreted and expressed proteins may be important targets for studies of new anti-inflammatory and immunomodulatory proteins. Altogether, the analyses performed here highlight the potential use of this strain as a target for the future development of probiotic foods.

Managing uncertainty in metabolic network structure and improving predictions using EnsembleFBA

Genome-scale metabolic network reconstructions (GENREs) are repositories of knowledge about the metabolic processes that occur in an organism. GENREs have been used to discover and interpret metabolic functions, and to engineer novel network structures. A major barrier preventing more widespread use of GENREs, particularly to study non-model organisms, is the extensive time required to produce a high-quality GENRE. Many automated approaches have been developed which reduce this time requirement, but automatically-reconstructed draft GENREs still require curation before useful predictions can be made. We present a novel approach to the analysis of GENREs which improves the predictive capabilities of draft GENREs by representing many alternative network structures, all equally consistent with available data, and generating predictions from this ensemble. This ensemble approach is compatible with many reconstruction methods. We refer to this new approach as Ensemble Flux Balance Analysis (EnsembleFBA). We validate EnsembleFBA by predicting growth and gene essentiality in the model organism Pseudomonas aeruginosa UCBPP-PA14. We demonstrate how EnsembleFBA can be included in a systems biology workflow by predicting essential genes in six Streptococcus species and mapping the essential genes to small molecule ligands from DrugBank. We found that some metabolic subsystems contributed disproportionately to the set of predicted essential reactions in a way that was unique to each Streptococcus species, leading to species-specific outcomes from small molecule interactions. Through our analyses of P. aeruginosa and six Streptococci, we show that ensembles increase the quality of predictions without drastically increasing reconstruction time, thus making GENRE approaches more practical for applications which require predictions for many non-model organisms. All of our functions and accompanying example code are available in an open online repository.

Metabolism is the driving force behind all biological activity. Genome-scale metabolic network reconstructions (GENREs) are representations of metabolic systems that can be analyzed mathematically to make predictions about how a system will behave, as well as to design systems with new properties. GENREs have traditionally been reconstructed manually, which can require extensive time and effort. Recent software solutions automate the process (drastically reducing the required effort) but the resulting GENREs are of lower quality and produce less reliable predictions than the manually-curated versions. We present a novel method (“EnsembleFBA”) which accounts for uncertainties involved in automated reconstruction by pooling many different draft GENREs together into an ensemble. We tested EnsembleFBA by predicting the growth and essential genes of the common pathogen Pseudomonas aeruginosa. We found that when predicting growth or essential genes, ensembles of GENREs achieved much better precision or captured many more essential genes than any of the individual GENREs within the ensemble. By improving the predictions that can be made with automatically-generated GENREs, this approach enables the modeling of biochemical systems which would otherwise be infeasible.

Bioactive Nutrients and Nutrigenomics in Age-Related Diseases

The increased life expectancy and the expansion of the elderly population are stimulating research into aging. Aging may be viewed as a multifactorial process that results from the interaction of genetic and environmental factors, which include lifestyle. Human molecular processes are influenced by physiological pathways as well as exogenous factors, which include the diet. Dietary components have substantive effects on metabolic health; for instance, bioactive molecules capable of selectively modulating specific metabolic pathways affect the development/progression of cardiovascular and neoplastic disease. As bioactive nutrients are increasingly identified, their clinical and molecular chemopreventive effects are being characterized and systematic analyses encompassing the "omics" technologies (transcriptomics, proteomics and metabolomics) are being conducted to explore their action. The evolving field of molecular pathological epidemiology has unique strength to investigate the effects of dietary and lifestyle exposure on clinical outcomes. The mounting body of knowledge regarding diet-related health status and disease risk is expected to lead in the near future to the development of improved diagnostic procedures and therapeutic strategies targeting processes relevant to nutrition. The state of the art of aging and nutrigenomics research and the molecular mechanisms underlying the beneficial effects of bioactive nutrients on the main aging-related disorders are reviewed herein.

An Agile Functional Analysis of Metagenomic Data Using SUPER-FOCUS

One of the main goals in metagenomics is to identify the functional profile of a microbial community from unannotated shotgun sequencing reads. Functional annotation is important in biological research because it enables researchers to identify the abundance of functional genes of the organisms present in the sample, answering the question, "What can the organisms in the sample do?" Most currently available approaches do not scale with increasing data volumes, which is important because both the number and lengths of the reads provided by sequencing platforms keep increasing. Here, we present SUPER-FOCUS, SUbsystems Profile by databasE Reduction using FOCUS, an agile homology-based approach using a reduced reference database to report the subsystems present in metagenomic datasets and profile their abundances. SUPER-FOCUS was tested with real metagenomes, and the results show that it accurately predicts the subsystems present in the profiled microbial communities, is computationally efficient, and up to 1000 times faster than other tools. SUPER-FOCUS is freely available at http://edwards.sdsu.edu/SUPERFOCUS .

Convergent patterns in the evolution of mealybug symbioses involving different intrabacterial symbionts

Mealybugs (Insecta: Hemiptera: Pseudococcidae) maintain obligatory relationships with bacterial symbionts, which provide essential nutrients to their insect hosts. Most pseudococcinae mealybugs harbor a unique symbiosis setup with enlarged betaproteobacterial symbionts (‘Candidatus Tremblaya princeps'), which themselves contain gammaproteobacterial symbionts. Here we investigated the symbiosis of the manna mealybug, Trabutina mannipara, using a metagenomic approach. Phylogenetic analyses revealed that the intrabacterial symbiont of T. mannipara represents a novel lineage within the Gammaproteobacteria, for which we propose the tentative name ‘Candidatus Trabutinella endobia'. Combining our results with previous data available for the nested symbiosis of the citrus mealybug Planococcus citri, we show that synthesis of essential amino acids and vitamins and translation-related functions partition between the symbiotic partners in a highly similar manner in the two systems, despite the distinct evolutionary origin of the intrabacterial symbionts. Bacterial genes found in both mealybug genomes and complementing missing functions in both symbioses were likely integrated in ancestral mealybugs before T. mannipara and P. citri diversified. The high level of correspondence between the two mealybug systems and their highly intertwined metabolic pathways are unprecedented. Our work contributes to a better understanding of the only known intracellular symbiosis between two bacteria and suggests that the evolution of this unique symbiosis included the replacement of intrabacterial symbionts in ancestral mealybugs.

Genome-Wide Association Study and QTL Mapping Reveal Genomic Loci Associated with Fusarium Ear Rot Resistance in Tropical Maize Germplasm

Fusarium ear rot (FER) incited by Fusarium verticillioides is a major disease of maize that reduces grain quality globally. Host resistance is the most suitable strategy for managing the disease. We report the results of genome-wide association study (GWAS) to detect alleles associated with increased resistance to FER in a set of 818 tropical maize inbred lines evaluated in three environments. Association tests performed using 43,424 single-nucleotide polymorphic (SNPs) markers identified 45 SNPs and 15 haplotypes that were significantly associated with FER resistance. Each associated SNP locus had relatively small additive effects on disease resistance and accounted for 1-4% of trait variation. These SNPs and haplotypes were located within or adjacent to 38 candidate genes, 21 of which were candidate genes associated with plant tolerance to stresses, including disease resistance. Linkage mapping in four biparental populations to validate GWAS results identified 15 quantitative trait loci (QTL) associated with F. verticillioides resistance. Integration of GWAS and QTL to the maize physical map showed eight colocated loci on chromosomes 2, 3, 4, 5, 9, and 10. QTL on chromosomes 2 and 9 are new. These results reveal that FER resistance is a complex trait that is conditioned by multiple genes with minor effects. The value of selection on identified markers for improving FER resistance is limited; rather, selection to combine small effect resistance alleles combined with genomic selection for polygenic background for both the target and general adaptation traits might be fruitful for increasing FER resistance in maize.

Metabolic Pathway Mining

Understanding metabolic pathways is one of the most important fields in bioscience in the post-genomic era, but curating metabolic pathways requires considerable man-power. As such there is a lack of reliable, experimentally verified metabolic pathways in databases and databases are forced to predict all but the most immediately useful pathways.Text-mining has the potential to solve this problem, but while sophisticated text-mining methods have been developed to assist the curation of many types of biomedical networks, such as protein-protein interaction networks, the mining of metabolic pathways from the literature has been largely neglected by the text-mining community. In this chapter we describe a pipeline for the extraction of metabolic pathways built on freely available open-source components and a heuristic metabolic reaction extraction algorithm.

Exploring Hydrogenotrophic Methanogenesis: a Genome Scale Metabolic Reconstruction of Methanococcus maripaludis

Hydrogenotrophic methanogenesis occurs in multiple environments, ranging from the intestinal tracts of animals to anaerobic sediments and hot springs. Energy conservation in hydrogenotrophic methanogens was long a mystery; only within the last decade was it reported that net energy conservation for growth depends on electron bifurcation. In this work, we focus on Methanococcus maripaludis, a well-studied hydrogenotrophic marine methanogen. To better understand hydrogenotrophic methanogenesis and compare it with methylotrophic methanogenesis that utilizes oxidative phosphorylation rather than electron bifurcation, we have built iMR539, a genome scale metabolic reconstruction that accounts for 539 of the 1,722 protein-coding genes of M. maripaludis strain S2. Our reconstructed metabolic network uses recent literature to not only represent the central electron bifurcation reaction but also incorporate vital biosynthesis and assimilation pathways, including unique cofactor and coenzyme syntheses. We show that our model accurately predicts experimental growth and gene knockout data, with 93% accuracy and a Matthews correlation coefficient of 0.78. Furthermore, we use our metabolic network reconstruction to probe the implications of electron bifurcation by showing its essentiality, as well as investigating the infeasibility of aceticlastic methanogenesis in the network. Additionally, we demonstrate a method of applying thermodynamic constraints to a metabolic model to quickly estimate overall free-energy changes between what comes in and out of the cell. Finally, we describe a novel reconstruction-specific computational toolbox we created to improve usability. Together, our results provide a computational network for exploring hydrogenotrophic methanogenesis and confirm the importance of electron bifurcation in this process.

IMPORTANCE

Understanding and applying hydrogenotrophic methanogenesis is a promising avenue for developing new bioenergy technologies around methane gas. Although a significant portion of biological methane is generated through this environmentally ubiquitous pathway, existing methanogen models portray the more traditional energy conservation mechanisms that are found in other methanogens. We have constructed a genome scale metabolic network of Methanococcus maripaludis that explicitly accounts for all major reactions involved in hydrogenotrophic methanogenesis. Our reconstruction demonstrates the importance of electron bifurcation in central metabolism, providing both a window into hydrogenotrophic methanogenesis and a hypothesis-generating platform to fuel metabolic engineering efforts.

Involvement of the Rcs regulon in the persistence of Salmonella Typhimurium in tomatoes

It is becoming clear that human enteric pathogens, like Salmonella, can efficiently colonize vegetative and reproductive organs of plants. Even though the bacterium's ability to proliferate within plant tissues has been linked to outbreaks of salmonellosis, little is known about regulatory and physiological adaptations of Salmonella, or other human pathogens, to their persistence in plants. A screen of Salmonella deletion mutants in tomatoes identified rcsA and rcsB genes as those under positive selection. In tomato fruits, populations of Salmonella rcsB mutants were as much as 100-fold lower than those of the wild type. In the follow-up experiments, competitive fitness of rcsA and rcsB mutants was strongly reduced in tomatoes. Bioinformatics predictions identified a putative Salmonella RcsAB binding box (TTMGGAWWAABCTYA) and revealed an extensive putative RcsAB regulon, of which many members were differentially fit within tomatoes.

Drug Target Identification and Prioritization for Treatment of Ovine Foot Rot: An In Silico Approach

Ovine foot rot is an infection of the feet of sheep, mainly caused by Dichelobacter nodosus. In its virulent form, it is highly contagious and debilitating, causing significant losses in the form of decline in wool growth and quality and poor fertility. Current methods of treatment are ineffective in complete eradication. Effective antibiotic treatment of foot rot is hence necessary to ensure better outcomes during control phases by reduction in culling count and the possibility of carriers of the infection. Using computational approaches, we have identified a set of 297 proteins that are essential to the D. nodosus and nonhomologous with sheep proteins. These proteins may be considered as potential vaccine candidates or drug targets for designing antibiotics against the bacterium. This core set of drug targets have been analyzed for pathway annotation to identify 67 proteins involved in unique bacterial pathways. Choke-point analysis on the drug targets identified 138 choke-point proteins, 29 involved in unique bacterial pathways. Subcellular localization was also predicted for each target to identify the ones that are membrane associated or secreted extracellularly. In addition, a total of 13 targets were identified that are common in at least 10 pathogenic bacterial species.

Metagenomic analysis of sediments under seaports influence in the Equatorial Atlantic Ocean

Maritime ports are anthropogenic interventions capable of causing serious alterations in coastal ecosystems. In this study, we examined the benthic microbial diversity and community structure under the influence of two maritime ports, Mucuripe (MUC) and Pecém (PEC), at Equatorial Atlantic Ocean in Northeast Brazil. Those seaports differ in architecture, time of functioning, cargo handling and contamination. The microbiomes from MUC and PEC were also compared in silico to 11 other globally distributed marine microbiomes. The comparative analysis of operational taxonomic units (OTUs) retrieved by PCR-DGGE showed that MUC presents greater richness and β diversity of Bacteria and Archaea than PEC. In line with these results, metagenomic analysis showed that MUC and PEC benthic microbial communities share the main common bacterial phyla found in coastal environments, although can be distinguish by greater abundance of Cyanobacteria in MUC and Deltaproteobacteria in PEC. Both ports differed in Archaea composition, being PEC port sediments dominated by Thaumarchaeota. The microbiomes showed little divergence in their potential metabolic pathways, although shifts on the microbial taxonomic signatures involved in nitrogen and sulphur metabolic pathways were observed. The comparative analysis of different benthic marine metagenomes from Brazil, Australia and Mexico grouped them by the geographic location rather than by the type of ecosystem, although at phylum level seaport sediments share a core microbiome constituted by Proteobacteria, Cyanobacteria, Actinobacteria, Tenericuteres, Firmicutes, Bacteriodetes and Euryarchaeota. Our results suggest that multiple physical and chemical factors acting on sediments as a result of at least 60years of port operation play a role in shaping the benthic microbial communities at taxonomic level, but not at functional level.

Genome Sequencing and Transposon Mutagenesis of Burkholderia seminalis TC3.4.2R3 Identify Genes Contributing to Suppression of Orchid Necrosis Caused by B. gladioli

From a screen of 36 plant-associated strains of Burkholderia spp., we identified 24 strains that suppressed leaf and pseudobulb necrosis of orchid caused by B. gladioli. To gain insights into the mechanisms of disease suppression, we generated a draft genome sequence from one suppressive strain, TC3.4.2R3. The genome is an estimated 7.67 megabases in size, with three replicons, two chromosomes, and the plasmid pC3. Using a combination of multilocus sequence analysis and phylogenomics, we identified TC3.4.2R3 as B. seminalis, a species within the Burkholderia cepacia complex that includes opportunistic human pathogens and environmental strains. We generated and screened a library of 3,840 transposon mutants of strain TC3.4.2R3 on orchid leaves to identify genes contributing to plant disease suppression. Twelve mutants deficient in suppression of leaf necrosis were selected and the transposon insertions were mapped to eight loci. One gene is in a wcb cluster that is related to synthesis of extracellular polysaccharide, a key determinant in bacterial-host interactions in other systems, and the other seven are highly conserved among Burkholderia spp. The fundamental information developed in this study will serve as a resource for future research aiming to identify mechanisms contributing to biological control.

Efecto de la cisteína en un proceso de biodepiritización de carbones en lecho empacado

<p>Se llevaron a cabo procesos de biodesulfurización de dos carbones colombianos ricos en azufre (“Mina Vieja” y “Vampiro”), en reactores de lecho empacado a nivel de erlenmeyer, utilizando un consorcio de Acidithiobacillus ferrooxidans (ATCC 23270) y Acidithiobacillus thiooxidans (ATCC 15494), evaluando la adición de cisteína a la solución lixiviante. Los ensayos fueron monitoreados por medidas de hierro en solución, pH y potencial redox. Adicionalmente, se hicieron análisis mineralógicos por difracción de rayos X (DRX) antes y después de los experimentos. Los ensayos sin adición de cisteína alcanzaron una oxidación de pirita de 45.3% y 57.9% para “Mina Vieja” y “Vampiro” respectivamente. Cuando se adicionó cisteína, la oxidación aumentó en 14.9% para “Mina Vieja” y 6.4% para “Vampiro”. Por otra parte, todos los ensayos evidenciaron remoción de caolinita, debido a su interacción con el ácido sulfúrico del medio. Con base en los resultados obtenidos, los componentes del carbón influenciaron tanto crecimiento bacteriano como la eficiencia de la cisteína sobre el grado de pirita oxidada.</p>