A consensus-based ensemble approach to improve transcriptome assembly

Background Systems-level analyses, such as differential gene expression analysis, co-expression analysis, and metabolic pathway reconstruction, depend on the accuracy of the transcriptome. Multiple tools exist to perform transcriptome assembly from RNAseq data. However, assembling high quality transcriptomes is still not a trivial problem. This is especially the case for non-model organisms where adequate reference genomes are often not available. Different methods produce different transcriptome models and there is no easy way to determine which are more accurate. Furthermore, having alternative-splicing events exacerbates such difficult assembly problems. While benchmarking transcriptome assemblies is critical, this is also not trivial due to the general lack of true reference transcriptomes. Results In this study, we first provide a pipeline to generate a set of the simulated benchmark transcriptome and corresponding RNAseq data. Using the simulated benchmarking datasets, we compared the performance of various transcriptome assembly approaches including both de novo and genome-guided methods. The results showed that the assembly performance deteriorates significantly when alternative transcripts (isoforms) exist or for genome-guided methods when the reference is not available from the same genome. To improve the transcriptome assembly performance, leveraging the overlapping predictions between different assemblies, we present a new consensus-based ensemble transcriptome assembly approach, ConSemble. Conclusions Without using a reference genome, ConSemble using four de novo assemblers achieved an accuracy up to twice as high as any de novo assemblers we compared. When a reference genome is available, ConSemble using four genome-guided assemblies removed many incorrectly assembled contigs with minimal impact on correctly assembled contigs, achieving higher precision and accuracy than individual genome-guided methods. Furthermore, ConSemble using de novo assemblers matched or exceeded the best performing genome-guided assemblers even when the transcriptomes included isoforms. We thus demonstrated that the ConSemble consensus strategy both for de novo and genome-guided assemblers can improve transcriptome assembly. The RNAseq simulation pipeline, the benchmark transcriptome datasets, and the script to perform the ConSemble assembly are all freely available from: http://bioinfolab.unl.edu/emlab/consemble/.

Performance Assessment of the Network Reconstruction Approaches on Various Interactomes

Beyond the list of molecules, there is a necessity to collectively consider multiple sets of omic data and to reconstruct the connections between the molecules. Especially, pathway reconstruction is crucial to understanding disease biology because abnormal cellular signaling may be pathological. The main challenge is how to integrate the data together in an accurate way. In this study, we aim to comparatively analyze the performance of a set of network reconstruction algorithms on multiple reference interactomes. We first explored several human protein interactomes, including PathwayCommons, OmniPath, HIPPIE, iRefWeb, STRING, and ConsensusPathDB. The comparison is based on the coverage of each interactome in terms of cancer driver proteins, structural information of protein interactions, and the bias toward well-studied proteins. We next used these interactomes to evaluate the performance of network reconstruction algorithms including all-pair shortest path, heat diffusion with flux, personalized PageRank with flux, and prize-collecting Steiner forest (PCSF) approaches. Each approach has its own merits and weaknesses. Among them, PCSF had the most balanced performance in terms of precision and recall scores when 28 pathways from NetPath were reconstructed using the listed algorithms. Additionally, the reference interactome affects the performance of the network reconstruction approaches. The coverage and disease- or tissue-specificity of each interactome may vary, which may result in differences in the reconstructed networks.

A Rickettsiella Endosymbiont Is a Potential Source of Essential B-Vitamins for the Poultry Red Mite, Dermanyssus gallinae

Many obligate blood-sucking arthropods rely on symbiotic bacteria to provision essential B vitamins that are either missing or at sub-optimal levels in their nutritionally challenging blood diet. The poultry red mite Dermanyssus gallinae, an obligate blood-feeding ectoparasite, is a serious threat to the hen egg industry. Poultry red mite infestation has a major impact on hen health and welfare and causes a significant reduction in both egg quality and production. Thus far, the identity and biological role of nutrient provisioning bacterial mutualists from D. gallinae are little understood. Here, we demonstrate that an obligate intracellular bacterium of the Rickettsiella genus is detected in D. gallinae mites collected from 63 sites (from 15 countries) across Europe. In addition, we report the genome sequence of Rickettsiella from D. gallinae (Rickettsiella – D. gallinae endosymbiont; Rickettsiella DGE). Rickettsiella DGE has a circular 1.89Mbp genome that encodes 1,973 proteins. Phylogenetic analysis confirms the placement of Rickettsiella DGE within the Rickettsiella genus, related to a facultative endosymbiont from the pea aphid and Coxiella-like endosymbionts (CLEs) from blood feeding ticks. Analysis of the Rickettsiella DGE genome reveals that many protein-coding sequences are either pseudogenized or lost, but Rickettsiella DGE has retained several B vitamin biosynthesis pathways, suggesting the importance of these pathways in evolution of a nutritional symbiosis with D. gallinae. In silico metabolic pathway reconstruction revealed that Rickettsiella DGE is unable to synthesize protein amino acids and, therefore, amino acids are potentially provisioned by the host. In contrast, Rickettsiella DGE retains biosynthetic pathways for B vitamins: thiamine (vitamin B1) via the salvage pathway; riboflavin (vitamin B2) and pyridoxine (vitamin B6) and the cofactors: flavin adenine dinucleotide (FAD) and coenzyme A (CoA) that likely provision these nutrients to the host.

Reconstitution of monoterpene indole alkaloid biosynthesis in Nicotiana benthamiana

Monoterpene indole alkaloids (MIAs) are a diverse and important class of plant natural products that include a number of medicinally significant compounds, often present at low concentrations within their native plant species. The complex biosynthesis of MIAs requires the assembly of tryptamine with a secoiridoid to produce the central intermediate, strictosidine, from which all known MIAs derive. Structural complexity makes chemical synthesis challenging, but recent efforts to identify the biosynthetic enzymes provide options for pathway reconstruction in a heterologous host. Previous attempts have had limited success, with yield in microorganisms limited by the poor expression of some enzymes. Here, we reconstitute the pathway for strictosidine biosynthesis from central metabolism without the need for supplementation of any metabolite precursors or intermediates in Nicotiana benthamiana. The best yields were obtained by the co-expression of 14 enzymes, of which a major latex protein-like enzyme (MLPL) from Nepeta (catmint) was critical for improving flux through the secoiridoid pathway. The production of strictosidine in planta expands the range of MIA products amenable to biological synthesis.

Building Biofoundry India: Challenges and Path Forward

ABSTRACT Biofoundry is a place where biomanufacturing meets automation. The highly modular structure of a biofoundry helps accelerate the design-build-test-learn (DBTL) workflow to deliver products fast and in a streamlined fashion. In this perspective, we describe our efforts to build Biofoundry India (BI) and where we see the facility add substantial value in supporting research, innovation, and entrepreneurship. We describe three key areas of our focus, harnessing the potential of non-expressing parts of the sequenced genomes, using deep learning in pathway reconstruction, and synthesis of enzymes and metabolites. Towards the end, we describe specific challenges in building such a facility in India and the path forward to mitigate some of those in working with the other biofoundries worldwide.

Review of Machine Learning Methods for the Prediction and Reconstruction of Metabolic Pathways

Prediction and reconstruction of metabolic pathways play significant roles in many fields such as genetic engineering, metabolic engineering, drug discovery, and are becoming the most active research topics in synthetic biology. With the increase of related data and with the development of machine learning techniques, there have many machine leaning based methods been proposed for prediction or reconstruction of metabolic pathways. Machine learning techniques are showing state-of-the-art performance to handle the rapidly increasing volume of data in synthetic biology. To support researchers in this field, we briefly review the research progress of metabolic pathway reconstruction and prediction based on machine learning. Some challenging issues in the reconstruction of metabolic pathways are also discussed in this paper.

A Rickettsiella endosymbiont is a potential source of essential B-vitamins for the poultry red mite, Dermanyssus gallinae

Obligate blood-sucking arthropods rely on symbiotic bacteria to provision essential B vitamins that are either missing or at sub-optimal amounts in their nutritionally challenging blood diet. The poultry red mite Dermanyssus gallinae, an obligate blood-feeding ectoparasite, is primarily associated with poultry and a serious threat to the hen egg industry. Thus far, the identity and biological role of nutrient provisioning bacterial mutualists from D. gallinae are little understood. Here, we demonstrate that a Rickettsiella Gammaproteobacteria in maternally transmitted in D. gallinae and universally present in D. gallinae mites collected at different sites throughout Europe. In addition, we report the genome sequence of uncultivable endosymbiont “Candidatus Rickettsiella rubrum” from D. gallinae eggs. The endosymbiont has a circular 1. 89 Mbp genome that encodes 1973 protein. Phylogenetic analysis confirms the placement R. rubrum within the Rickettsiella genus, closely related to a facultative endosymbiont from the pea aphid and Coxiella-like endosymbionts from blood feeding ticks. Analysis of the R. rubrum genome reveals many protein-coding sequences are either pseudogenized or lost, but R. rubrum has retained several B vitamin biosynthesis pathways, confirming the importance of these pathways in evolution of its nutritional symbiosis with D. gallinae. In silico metabolic pathway reconstruction revealed that R. rubrum is unable to synthesise protein amino acids and therefore these nutrients are likely provisioned by the host. In contrast R. rubrum retains biosynthetic pathways for B vitamins: thiamine (vitamin B1) via the salvage pathway; riboflavin (vitamin B2) and pyridoxine (vitamin B6) and the cofactors: flavin adenine dinucleotide (FAD) and coenzyme A (CoA) that likely provision these nutrients to the host. We propose that bacterial symbionts which are essential to blood-feeding arthropod survival provide attractive targets for the development of novel control methods.

Exploring the interaction network of a synthetic gut bacterial community

A key challenge in microbiome research is to predict functionality from microbial community composition. As central microbiota functions are determined by bacterial community networks it is important to gain insight into the principles that govern bacteria-bacteria interactions. Here, we focused on growth and metabolic interactions of the Oligo-Mouse-Microbiota (OMM12) synthetic bacterial community, which is increasingly used as model system in gut microbiome research. Using a bottom-up approach, we uncovered the directionality of strain-strain interactions in mono- and pairwise co-culture experiments, as well as in community batch culture. Metabolomics analysis of spent culture supernatant of individual strains in combination with genome-informed pathway reconstruction provided insights into the metabolic potential of the individual community members. Thereby, we could show that the OMM12 interaction network is shaped by both, exploitative and interference competition in vitro. In particular, Enterococcus faecalis KB1 was identified as important driver of community composition by affecting the abundance of several other consortium members. Together, this study gives fundamental insight into key drivers and mechanistic basis of the OMM12 interaction network, which serves as knowledge base for future mechanistic studies.

Automating parameter selection to avoid implausible biological pathway models

A common way to integrate and analyze large amounts of biological “omic” data is through pathway reconstruction: using condition-specific omic data to create a subnetwork of a generic background network that represents some process or cellular state. A challenge in pathway reconstruction is that adjusting pathway reconstruction algorithms’ parameters produces pathways with drastically different topological properties and biological interpretations. Due to the exploratory nature of pathway reconstruction, there is no ground truth for direct evaluation, so parameter tuning methods typically used in statistics and machine learning are inapplicable. We developed the pathway parameter advising algorithm to tune pathway reconstruction algorithms to minimize biologically implausible predictions. We leverage background knowledge in pathway databases to select pathways whose high-level structure resembles that of manually curated biological pathways. At the core of this method is a graphlet decomposition metric, which measures topological similarity to curated biological pathways. In order to evaluate pathway parameter advising, we compare its performance in avoiding implausible networks and reconstructing pathways from the NetPath database with other parameter selection methods across four pathway reconstruction algorithms. We also demonstrate how pathway parameter advising can guide reconstruction of an influenza host factor network. Pathway parameter advising is method agnostic; it is applicable to any pathway reconstruction algorithm with tunable parameters.

Bornyl Diphosphate Synthase From Cinnamomum burmanni and Its Application for (+)-Borneol Biosynthesis in Yeast

(+)-Borneol is a desirable monoterpenoid with effective anti-inflammatory and analgesic effects that is known as soft gold. (+)-bornyl diphosphate synthase is the key enzyme in the (+)-borneol biosynthesis pathway. Despite several reported (+)-bornyl diphosphate synthase genes, relatively low (+)-borneol production hinders the attempts to synthesize it using microbial fermentation. Here, we identified the highly specific (+)-bornyl diphosphate synthase CbTPS1 from Cinnamomum burmanni. An in vitro assay showed that (+)-borneol was the main product of CbTPS1 (88.70% of the total products), and the Km value was 5.11 ± 1.70 μM with a kcat value of 0.01 s–1. Further, we reconstituted the (+)-borneol biosynthetic pathway in Saccharomyces cerevisiae. After tailored truncation and adding Kozak sequences, the (+)-borneol yield was improved by 96.33-fold to 2.89 mg⋅L–1 compared with the initial strain in shake flasks. This work is the first reported attempt to produce (+)-borneol by microbial fermentation. It lays a foundation for further pathway reconstruction and metabolic engineering production of this valuable natural monoterpenoid.