The Biofuel Feedstock Genomics Resource: a web-based portal and database to enable functional genomics of plant biofuel feedstock species

Exploration of Bioinformatics on Microbial Fuel Cell Technology: Trends, Challenges, and Future Prospects

Microbial fuel cells (MFCs) are a cost-effective and environmentally friendly alternative energy method. MFC technology has gained much interest in recent decades owing to its effectiveness in remediating wastewater and generating bioelectricity. The microbial fuel cell generates energy mainlybecause of oxidation-reduction reactions. In this reaction, electrons were transferred between two reactants. Bioinformatics is expanding across a wide range of microbial fuel cell technology. Electroactive species in the microbial community were evaluated using bioinformatics methodologies in whole genome sequencing, RNA sequencing, transcriptomics, metagenomics, and phylogenetics. Technology advancements in microbial fuel cells primarily produce power from organic and inorganic waste from various sources. Reduced chemical oxygen demand and waste degradation are two added advantages for microbial fuel cells. From plants, bacteria, and algae, microbial fuel cells were developed. Due to the rapid advancement of sequencing techniques, bioinformatics approaches are currently widely used in the technology of microbial fuel cells. In addition, they play an important role in determining the composition of electroactive species in microorganisms. The metabolic pathway is also possible to determine with bioinformatics resources. A computational technique that reveals the nature of the mediators and the substrate was also used to predict the electrochemical properties. Computational strategies were used to tackle significant challenges in experimental procedures, such as optimization and understanding microbiological systems. The main focus of this review is on utilizing bioinformatics techniques to improve microbial fuel cell technology.

dEMBF v2.0: An Updated Database of Enzymes for Microalgal Biofuel Feedstock

In light of increasing algal genomics data and knowledge of biosynthetic pathways responsible for biofuel production, an integrated resource for easy access to all information is essential to improve our understanding of algal lipid metabolism. Against this backdrop, dEMBF v2.0, a significantly updated and improved version of our database of microalgae lipid biosynthetic enzymes for biofuel production, has been developed. dEMBF v2.0 now contains a comprehensive annotation of 2018 sequences encoding 35 enzymes, an increase of over 7-fold as compared with the first version. Other improved features include an increase in species coverage to 32 algal genomes, analysis of additional metabolic pathways, expanded annotation thoroughly detailing sequence and structural features, including enzyme-ligand interactions, and integration of supporting experimental evidence to demonstrate the role of enzymes in increasing lipid content. Along with a complete redesign of the interface, the updated database provides several inbuilt tools and user-friendly functionalities for more interactive and dynamic visualization of data.

Rice Genome-Scale Network Integration Reveals Transcriptional Regulators of Grass Cell Wall Synthesis

Grasses have evolved distinct cell wall composition and patterning relative to dicotyledonous plants. However, despite the importance of this plant family, transcriptional regulation of its cell wall biosynthesis is poorly understood. To identify grass cell wall-associated transcription factors, we constructed the Rice Combined mutual Ranked Network (RCRN). The RCRN covers >90% of annotated rice (Oryza sativa) genes, is high quality, and includes most grass-specific cell wall genes, such as mixed-linkage glucan synthases and hydroxycinnamoyl acyltransferases. Comparing the RCRN and an equivalent Arabidopsis network suggests that grass orthologs of most genetically verified eudicot cell wall regulators also control this process in grasses, but some transcription factors vary significantly in network connectivity between these divergent species. Reverse genetics, yeast-one-hybrid, and protoplast-based assays reveal that OsMYB61a activates a grass-specific acyltransferase promoter, which confirms network predictions and supports grass-specific cell wall synthesis genes being incorporated into conserved regulatory circuits. In addition, 10 of 15 tested transcription factors, including six novel Wall-Associated regulators (WAP1, WACH1, WAHL1, WADH1, OsMYB13a, and OsMYB13b), alter abundance of cell wall-related transcripts when transiently expressed. The results highlight the quality of the RCRN for examining rice biology, provide insight into the evolution of cell wall regulation, and identify network nodes and edges that are possible leads for improving cell wall composition.

PoplarGene: poplar gene network and resource for mining functional information for genes from woody plants

Poplar is not only an important resource for the production of paper, timber and other wood-based products, but it has also emerged as an ideal model system for studying woody plants. To better understand the biological processes underlying various traits in poplar, e.g., wood development, a comprehensive functional gene interaction network is highly needed. Here, we constructed a genome-wide functional gene network for poplar (covering ~70% of the 41,335 poplar genes) and created the network web service PoplarGene, offering comprehensive functional interactions and extensive poplar gene functional annotations. PoplarGene incorporates two network-based gene prioritization algorithms, neighborhood-based prioritization and context-based prioritization, which can be used to perform gene prioritization in a complementary manner. Furthermore, the co-functional information in PoplarGene can be applied to other woody plant proteomes with high efficiency via orthology transfer. In addition to poplar gene sequences, the webserver also accepts Arabidopsis reference gene as input to guide the search for novel candidate functional genes in PoplarGene. We believe that PoplarGene (http://bioinformatics.caf.ac.cn/PoplarGene and http://124.127.201.25/PoplarGene) will greatly benefit the research community, facilitating studies of poplar and other woody plants.

dEMBF: A Comprehensive Database of Enzymes of Microalgal Biofuel Feedstock

Microalgae have attracted wide attention as one of the most versatile renewable feedstocks for production of biofuel. To develop genetically engineered high lipid yielding algal strains, a thorough understanding of the lipid biosynthetic pathway and the underpinning enzymes is essential. In this work, we have systematically mined the genomes of fifteen diverse algal species belonging to Chlorophyta, Heterokontophyta, Rhodophyta, and Haptophyta, to identify and annotate the putative enzymes of lipid metabolic pathway. Consequently, we have also developed a database, dEMBF (Database of Enzymes of Microalgal Biofuel Feedstock), which catalogues the complete list of identified enzymes along with their computed annotation details including length, hydrophobicity, amino acid composition, subcellular location, gene ontology, KEGG pathway, orthologous group, Pfam domain, intron-exon organization, transmembrane topology, and secondary/tertiary structural data. Furthermore, to facilitate functional and evolutionary study of these enzymes, a collection of built-in applications for BLAST search, motif identification, sequence and phylogenetic analysis have been seamlessly integrated into the database. dEMBF is the first database that brings together all enzymes responsible for lipid synthesis from available algal genomes, and provides an integrative platform for enzyme inquiry and analysis. This database will be extremely useful for algal biofuel research. It can be accessed at http://bbprof.immt.res.in/embf.

RiceNet v2: an improved network prioritization server for rice genes

Rice is the most important staple food crop and a model grass for studies of bioenergy crops. We previously published a genome-scale functional network server called RiceNet, constructed by integrating diverse genomics data and demonstrated the use of the network in genetic dissection of rice biotic stress responses and its usefulness for other grass species. Since the initial construction of the network, there has been a significant increase in the amount of publicly available rice genomics data. Here, we present an updated network prioritization server for Oryza sativa ssp. japonica, RiceNet v2 (http://www.inetbio.org/ricenet), which provides a network of 25 765 genes (70.1% of the coding genome) and 1 775 000 co-functional links. Ricenet v2 also provides two complementary methods for network prioritization based on: (i) network direct neighborhood and (ii) context-associated hubs. RiceNet v2 can use genes of the related subspecies O. sativa ssp. indica and the reference plant Arabidopsis for versatility in generating hypotheses. We demonstrate that RiceNet v2 effectively identifies candidate genes involved in rice root/shoot development and defense responses, demonstrating its usefulness for the grass research community.

Fuelling genetic and metabolic exploration of C3 bioenergy crops through the first reference transcriptome of Arundo donax L

The development of inexpensive and highly productive biomass sources of biofuel is a priority in global climate change biology. Arundo donax, also known as the giant reed, is recognized as one of the most promising nonfood bioenergy crops in Europe. Despite its relevance, to date no genomic resources are available to support the characterization of the developmental, adaptive and metabolic traits underlying the high productivity of this nonmodel species. We hereby present the first report on the de novo assembly of bud, culm, leaf and root transcriptomes of A. donax, which can be accessed through a customized BLAST server (http://ecogenomics.fmach.it/arundo/) for mining and exploring the genetic potential of this species. Based on functional annotation and homology comparison to 19 prospective biofuel Poaceae species, we provide the first genomic view of this so far unexplored crop and indicate the model species with highest potential for comparative genomics approaches. The analysis of the transcriptome reveals strong differences in the enrichment of the Gene Ontology categories and the relative expression among different organs, which can guide future efforts for functional genomics or genetic improvement of A. donax. A set of homologs to key genes involved in lignin, cellulose, starch, lipid metabolism and in the domestication of other crops is discussed to provide a platform for possible enhancement of productivity and saccharification efficiency in A. donax.

Agrigenomics for microalgal biofuel production: an overview of various bioinformatics resources and recent studies to link OMICS to bioenergy and bioeconomy

Microalgal biofuels offer great promise in contributing to the growing global demand for alternative sources of renewable energy. However, to make algae-based fuels cost competitive with petroleum, lipid production capabilities of microalgae need to improve substantially. Recent progress in algal genomics, in conjunction with other "omic" approaches, has accelerated the ability to identify metabolic pathways and genes that are potential targets in the development of genetically engineered microalgal strains with optimum lipid content. In this review, we summarize the current bioeconomic status of global biofuel feedstocks with particular reference to the role of "omics" in optimizing sustainable biofuel production. We also provide an overview of the various databases and bioinformatics resources available to gain a more complete understanding of lipid metabolism across algal species, along with the recent contributions of "omic" approaches in the metabolic pathway studies for microalgal biofuel production.

Comparative transcriptomics of three Poaceae species reveals patterns of gene expression evolution

The Poaceae family, also known as the grasses, includes agronomically important cereal crops such as rice, maize, sorghum, and wheat. Previous comparative studies have shown that much of the gene content is shared among the grasses; however, functional conservation of orthologous genes has yet to be explored. To gain an understanding of the genome-wide patterns of evolution of gene expression across reproductive tissues, we employed a sequence-based approach to compare analogous transcriptomes in species representing three Poaceae subgroups including the Pooideae (Brachypodium distachyon), the Panicoideae (sorghum), and the Ehrhartoideae (rice). Our transcriptome analyses reveal that only a fraction of orthologous genes exhibit conserved expression patterns. A high proportion of conserved orthologs include genes that are upregulated in physiologically similar tissues such as leaves, anther, pistil, and embryo, while orthologs that are highly expressed in seeds show the most diverged expression patterns. More generally, we show that evolution of gene expression profiles and coding sequences in the grasses may be linked. Genes that are highly and broadly expressed tend to be conserved at the coding sequence level while genes with narrow expression patterns show accelerated rates of sequence evolution. We further show that orthologs in syntenic genomic blocks are more likely to share correlated expression patterns compared with non-syntenic orthologs. These findings are important for agricultural improvement because sequence information is transferred from model species, such as Brachypodium, rice, and sorghum to crop plants without sequenced genomes.