Finding novel metabolic genes through plant-prokaryote phylogenomics

Physiological and metabolic traits of Taxol biosynthesis of endophytic fungi inhabiting plants: Plant-microbial crosstalk, and epigenetic regulators

Attenuating the Taxol productivity of fungi with the subculturing and storage under axenic conditions is the challenge that halts the feasibility of fungi to be an industrial platform for Taxol production. This successive weakening of Taxol productivity by fungi could be attributed to the epigenetic down-regulation and molecular silencing of most of the gene clusters encoding Taxol biosynthetic enzymes. Thus, exploring the epigenetic regulating mechanisms controlling the molecular machinery of Taxol biosynthesis could be an alternative prospective technology to conquer the lower accessibility of Taxol by the potent fungi. The current review focuses on discussing the different molecular approaches, epigenetic regulators, transcriptional factors, metabolic manipulators, microbial communications and microbial cross-talking approaches on restoring and enhancing the Taxol biosynthetic potency of fungi to be industrial platform for Taxol production.

Exploiting the Biosynthetic Potency of Taxol from Fungal Endophytes of Conifers Plants; Genome Mining and Metabolic Manipulation

Endophytic fungi have been considered as a repertoire for bioactive secondary metabolites with potential application in medicine, agriculture and food industry. The biosynthetic pathways by fungal endophytes raise the argument of acquisition of these machineries of such complex metabolites from the plant host. Diterpenoids "Taxol" is the most effective anticancer drug with highest annual sale, since its discovery in 1970 from the Pacific yew tree, Taxus brevifolia. However, the lower yield of Taxol from this natural source (bark of T. brevifolia), availability and vulnerability of this plant to unpredicted fluctuation with the ecological and environmental conditions are the challenges. Endophytic fungi from Taxus spp. opened a new avenue for industrial Taxol production due to their fast growth, cost effectiveness, independence on climatic changes, feasibility of genetic manipulation. However, the anticipation of endophytic fungi for industrial Taxol production has been challenged by the loss of its productivity, due to the metabolic reprograming of cells, downregulating the expression of its encoding genes with subculturing and storage. Thus, the objectives of this review were to (1) Nominate the endophytic fungal isolates with the Taxol producing potency from Taxaceae and Podocarpaceae; (2) Emphasize the different approaches such as molecular manipulation, cultural optimization, co-cultivation for enhancing the Taxol productivities; (3) Accentuate the genome mining of the rate-limiting enzymes for rapid screening the Taxol biosynthetic machinery; (4) Triggering the silenced rate-limiting genes and transcriptional factors to activates the biosynthetic gene cluster of Taxol.

Systematic prediction of functionally linked genes in bacterial and archaeal genomes

Functionally linked genes in bacterial and archaeal genomes are often organized into operons. However, the composition and architecture of operons are highly variable and frequently differ even among closely related genomes. Therefore, to efficiently extract reliable functional predictions for uncharacterized genes from comparative analyses of the rapidly growing genomic databases, dedicated computational approaches are required. We developed a protocol to systematically and automatically identify genes that are likely to be functionally associated with a 'bait' gene or locus by using relevance metrics. Given a set of bait loci and a genomic database defined by the user, this protocol compares the genomic neighborhoods of the baits to identify genes that are likely to be functionally linked to the baits by calculating the abundance of a given gene within and outside the bait neighborhoods and the distance to the bait. We exemplify the performance of the protocol with three test cases, namely, genes linked to CRISPR-Cas systems using the 'CRISPRicity' metric, genes associated with archaeal proviruses and genes linked to Argonaute genes in halobacteria. The protocol can be run by users with basic computational skills. The computational cost depends on the sizes of the genomic dataset and the list of reference loci and can vary from one CPU-hour to hundreds of hours on a supercomputer.

Biochemical control systems for small molecule damage in plants

As a system, plant metabolism is far from perfect: small molecules (metabolites, cofactors, coenzymes, and inorganic molecules) are frequently damaged by unwanted enzymatic or spontaneous reactions. Here, we discuss the emerging principles in small molecule damage biology. We propose that plants evolved at least three distinct systems to control small molecule damage: (i) repair, which returns a damaged molecule to its original state; (ii) scavenging, which converts reactive molecules to harmless products; and (iii) steering, in which the possible formation of a damaged molecule is suppressed. We illustrate the concept of small molecule damage control in plants by describing specific examples for each of these three categories. We highlight interesting insights that we expect future research will provide on those systems, and we discuss promising strategies to discover new small molecule damage-control systems in plants.

A familiar ring to it: biosynthesis of plant benzoic acids

Plant benzoic acids (BAs) are building blocks or important structural elements for numerous primary and specialized metabolites, including plant hormones, cofactors, defense compounds, and attractants for pollinators and seed dispersers. Many natural products derived from plant BAs or containing benzoyl/benzyl moieties are also of medicinal or nutritional value to humans. Biosynthesis of BAs in plants is a network involving parallel and intersecting pathways spread across multiple subcellular compartments. In this review, a current overview on the metabolism of plant BAs is presented with a focus on the recent progress made on isolation and functional characterization of genes encoding biosynthetic enzymes and intracellular transporters. In addition, approaches for deciphering the complex interactions between pathways of the BAs network are discussed.

Integrative data-mining tools to link gene and function

Information derived from genomic and post-genomic data can be efficiently used to link gene and function. Several web-based platforms have been developed to mine these types of data by integrating different tools. This method paper is designed to allow the user to navigate these platforms in order to make functional predictions. The main focus is on phylogenetic distribution and physical clustering tools, but other tools such as pathway reconstruction, gene fusions, and analysis of high-throughput experimental data are also surveyed.

Dual targeting and retrograde translocation: regulators of plant nuclear gene expression can be sequestered by plastids

Changes in the developmental or metabolic state of plastids can trigger profound changes in the transcript profiles of nuclear genes. Many nuclear transcription factors were shown to be controlled by signals generated in the organelles. In addition to the many different compounds for which an involvement in retrograde signaling is discussed, accumulating evidence suggests a role for proteins in plastid-to-nucleus communication. These proteins might be sequestered in the plastids before they act as transcriptional regulators in the nucleus. Indeed, several proteins exhibiting a dual localization in the plastids and the nucleus are promising candidates for such a direct signal transduction involving regulatory protein storage in the plastids. Among such proteins, the nuclear transcription factor WHIRLY1 stands out as being the only protein for which an export from plastids and translocation to the nucleus has been experimentally demonstrated. Other proteins, however, strongly support the notion that this pathway might be more common than currently believed.

Frontiers in metabolic reconstruction and modeling of plant genomes

A major goal of post-genomic biology is to reconstruct and model in silico the metabolic networks of entire organisms. Work on bacteria is well advanced, and is now under way for plants and other eukaryotes. Genome-scale modelling in plants is much more challenging than in bacteria. The challenges come from features characteristic of higher organisms (subcellular compartmentation, tissue differentiation) and also from the particular severity in plants of a general problem: genome content whose functions remain undiscovered. This problem results in thousands of genes for which no function is known ('undiscovered genome content') and hundreds of enzymatic and transport functions for which no gene is yet identified. The severity of the undiscovered genome content problem in plants reflects their genome size and complexity. To bring the challenges of plant genome-scale modelling into focus, we first summarize the current status of plant genome-scale models. We then highlight the challenges - and ways to address them - in three areas: identifying genes for missing processes, modelling tissues as opposed to single cells, and finding metabolic functions encoded by undiscovered genome content. We also discuss the emerging view that a significant fraction of undiscovered genome content encodes functions that counter damage to metabolites inflicted by spontaneous chemical reactions or enzymatic mistakes.

Mining high-throughput experimental data to link gene and function

Nearly 2200 genomes that encode around 6 million proteins have now been sequenced. Around 40% of these proteins are of unknown function, even when function is loosely and minimally defined as 'belonging to a superfamily'. In addition to in silico methods, the swelling stream of high-throughput experimental data can give valuable clues for linking these unknowns with precise biological roles. The goal is to develop integrative data-mining platforms that allow the scientific community at large to access and utilize this rich source of experimental knowledge. To this end, we review recent advances in generating whole-genome experimental datasets, where this data can be accessed, and how it can be used to drive prediction of gene function.

Synergistic use of plant-prokaryote comparative genomics for functional annotations

BACKGROUND

Identifying functions for all gene products in all sequenced organisms is a central challenge of the post-genomic era. However, at least 30-50% of the proteins encoded by any given genome are of unknown or vaguely known function, and a large number are wrongly annotated. Many of these 'unknown' proteins are common to prokaryotes and plants. We set out to predict and experimentally test the functions of such proteins. Our approach to functional prediction integrates comparative genomics based mainly on microbial genomes with functional genomic data from model microorganisms and post-genomic data from plants. This approach bridges the gap between automated homology-based annotations and the classical gene discovery efforts of experimentalists, and is more powerful than purely computational approaches to identifying gene-function associations.

RESULTS

Among Arabidopsis genes, we focused on those (2,325 in total) that (i) are unique or belong to families with no more than three members, (ii) occur in prokaryotes, and (iii) have unknown or poorly known functions. Computer-assisted selection of promising targets for deeper analysis was based on homology-independent characteristics associated in the SEED database with the prokaryotic members of each family. In-depth comparative genomic analysis was performed for 360 top candidate families. From this pool, 78 families were connected to general areas of metabolism and, of these families, specific functional predictions were made for 41. Twenty-one predicted functions have been experimentally tested or are currently under investigation by our group in at least one prokaryotic organism (nine of them have been validated, four invalidated, and eight are in progress). Ten additional predictions have been independently validated by other groups. Discovering the function of very widespread but hitherto enigmatic proteins such as the YrdC or YgfZ families illustrates the power of our approach.

CONCLUSIONS

Our approach correctly predicted functions for 19 uncharacterized protein families from plants and prokaryotes; none of these functions had previously been correctly predicted by computational methods. The resulting annotations could be propagated with confidence to over six thousand homologous proteins encoded in over 900 bacterial, archaeal, and eukaryotic genomes currently available in public databases.

Folate biosynthesis, turnover, and transport in plants

Folates are essential cofactors for one-carbon transfer reactions and are needed in the diets of humans and animals. Because plants are major sources of dietary folate, plant folate biochemistry has long been of interest but progressed slowly until the genome era. Since then, genome-enabled approaches have brought rapid advances: We now know (a) all the plant folate synthesis genes and some genes of folate turnover and transport, (b) certain mechanisms governing folate synthesis, and (c) the subcellular locations of folate synthesis enzymes and of folates themselves. Some of this knowledge has been applied, simply and successfully, to engineer folate-enriched food crops (i.e., biofortification). Much remains to be discovered about folates, however, particularly in relation to homeostasis, catabolism, membrane transport, and vacuolar storage. Understanding these processes, which will require both biochemical and -omics research, should lead to improved biofortification strategies based on transgenic or conventional approaches.

'Unknown' proteins and 'orphan' enzymes: the missing half of the engineering parts list--and how to find it

Like other forms of engineering, metabolic engineering requires knowledge of the components (the 'parts list') of the target system. Lack of such knowledge impairs both rational engineering design and diagnosis of the reasons for failures; it also poses problems for the related field of metabolic reconstruction, which uses a cell's parts list to recreate its metabolic activities in silico. Despite spectacular progress in genome sequencing, the parts lists for most organisms that we seek to manipulate remain highly incomplete, due to the dual problem of 'unknown' proteins and 'orphan' enzymes. The former are all the proteins deduced from genome sequence that have no known function, and the latter are all the enzymes described in the literature (and often catalogued in the EC database) for which no corresponding gene has been reported. Unknown proteins constitute up to about half of the proteins in prokaryotic genomes, and much more than this in higher plants and animals. Orphan enzymes make up more than a third of the EC database. Attacking the 'missing parts list' problem is accordingly one of the great challenges for post-genomic biology, and a tremendous opportunity to discover new facets of life's machinery. Success will require a co-ordinated community-wide attack, sustained over years. In this attack, comparative genomics is probably the single most effective strategy, for it can reliably predict functions for unknown proteins and genes for orphan enzymes. Furthermore, it is cost-efficient and increasingly straightforward to deploy owing to a proliferation of databases and associated tools.

Acinetobacter baylyi ADP1 as a model for metabolic system biology

Information produced by the annotation of an 'average bacterial genome' can be separated into three parts. One-third represents what we know, another third what we think we know, and the last third what we know we do not know. Knowledge of metabolism is also described by this three thirds rule. Understanding how a cell operates will require a better knowledge of the two ignored thirds of its parts. Moreover, metabolism needs to be further investigated using organisms whose life styles are different from those of model organisms. In this short review, we present Acinetobacter baylyi ADP1 as an environmental model especially suitable for large-scale genetic manipulation. Resources have been constructed in the past few years that can form the basis for diverse metabolic studies: the genome sequence, a single gene mutant collection, and a genome-scale metabolic model.

Orthogenomics of photosynthetic organisms: bioinformatic and experimental analysis of chloroplast proteins of endosymbiont origin in Arabidopsis and their counterparts in Synechocystis

Chloroplasts are descendents of a cyanobacterial endosymbiont, but many chloroplast protein genes of endosymbiont origin are encoded by the nucleus. The chloroplast-cyanobacteria relationship is a typical target of orthogenomics, an analytical method that focuses on the relationship of orthologous genes. Here, we present results of a pilot study of functional orthogenomics, combining bioinformatic and experimental analyses, to identify nuclear-encoded chloroplast proteins of endosymbiont origin (CPRENDOs). Phylogenetic profiling based on complete clustering of all proteins in 17 organisms, including eight cyanobacteria and two photosynthetic eukaryotes, was used to deduce 65 protein groups that are conserved in all oxygenic autotrophs analyzed but not in non-oxygenic organisms. With the exception of 28 well-characterized protein groups, 56 Arabidopsis proteins and 43 Synechocystis proteins in the 37 conserved homolog groups were analyzed. Green fluorescent protein (GFP) targeting experiments indicated that 54 Arabidopsis proteins were targeted to plastids. Expression of 39 Arabidopsis genes was promoted by light. Among the 40 disruptants of Synechocystis, 22 showed phenotypes related to photosynthesis. Arabidopsis mutants in 21 groups, including those reported previously, showed phenotypes. Characteristics of pulse amplitude modulation fluorescence were markedly different in corresponding mutants of Arabidopsis and Synechocystis in most cases. We conclude that phylogenetic profiling is useful in finding CPRENDOs, but the physiological functions of orthologous genes may be different in chloroplasts and cyanobacteria.

Genome sequence of the beta-rhizobium Cupriavidus taiwanensis and comparative genomics of rhizobia

We report the first complete genome sequence of a beta-proteobacterial nitrogen-fixing symbiont of legumes, Cupriavidus taiwanensis LMG19424. The genome consists of two chromosomes of size 3.42 Mb and 2.50 Mb, and a large symbiotic plasmid of 0.56 Mb. The C. taiwanensis genome displays an unexpected high similarity with the genome of the saprophytic bacterium C. eutrophus H16, despite being 0.94 Mb smaller. Both organisms harbor two chromosomes with large regions of synteny interspersed by specific regions. In contrast, the two species host highly divergent plasmids, with the consequence that C. taiwanensis is symbiotically proficient and less metabolically versatile. Altogether, specific regions in C. taiwanensis compared with C. eutrophus cover 1.02 Mb and are enriched in genes associated with symbiosis or virulence in other bacteria. C. taiwanensis reveals characteristics of a minimal rhizobium, including the most compact (35-kb) symbiotic island (nod and nif) identified so far in any rhizobium. The atypical phylogenetic position of C. taiwanensis allowed insightful comparative genomics of all available rhizobium genomes. We did not find any gene that was both common and specific to all rhizobia, thus suggesting that a unique shared genetic strategy does not support symbiosis of rhizobia with legumes. Instead, phylodistribution analysis of more than 200 Sinorhizobium meliloti known symbiotic genes indicated large and complex variations of their occurrence in rhizobia and non-rhizobia. This led us to devise an in silico method to extract genes preferentially associated with rhizobia. We discuss how the novel genes we have identified may contribute to symbiotic adaptation.

Genome-enabled approaches shed new light on plant metabolism

Plant metabolism research has experienced a second golden age resulting from synergies between genome-enabled technologies and classical biochemistry. The rapid rate at which genomics data are being accumulated creates increased needs for robust metabolomic technologies and fast and accurate methods for identifying the activities of enzymes.