A comprehensive SARS-CoV-2 and COVID-19 review, Part 2: host extracellular to systemic effects of SARS-CoV-2 infection

COVID-19, the disease caused by SARS-CoV-2, has caused significant morbidity and mortality worldwide. The betacoronavirus continues to evolve with global health implications as we race to learn more to curb its transmission, evolution, and sequelae. The focus of this review, the second of a three-part series, is on the biological effects of the SARS-CoV-2 virus on post-acute disease in the context of tissue and organ adaptations and damage. We highlight the current knowledge and describe how virological, animal, and clinical studies have shed light on the mechanisms driving the varied clinical diagnoses and observations of COVID-19 patients. Moreover, we describe how investigations into SARS-CoV-2 effects have informed the understanding of viral pathogenesis and provide innovative pathways for future research on the mechanisms of viral diseases.

SARS-CoV-2 Orphan Gene ORF10 Contributes to More Severe COVID-19 Disease

The orphan gene of SARS-CoV-2, ORF10, is the least studied gene in the virus responsible for the COVID-19 pandemic. Recent experimentation indicated ORF10 expression moderates innate immunity in vitro. However, whether ORF10 affects COVID-19 in humans remained unknown. We determine that the ORF10 sequence is identical to the Wuhan-Hu-1 ancestral haplotype in 95% of genomes across five variants of concern (VOC). Four ORF10 variants are associated with less virulent clinical outcomes in the human host: three of these affect ORF10 protein structure, one affects ORF10 RNA structural dynamics. RNA-Seq data from 2070 samples from diverse human cells and tissues reveals ORF10 accumulation is conditionally discordant from that of other SARS-CoV-2 transcripts. Expression of ORF10 in A549 and HEK293 cells perturbs immune-related gene expression networks, alters expression of the majority of mitochondrially-encoded genes of oxidative respiration, and leads to large shifts in levels of 14 newly-identified transcripts. We conclude ORF10 contributes to more severe COVID-19 clinical outcomes in the human host.

Core mitochondrial genes are down-regulated during SARS-CoV-2 infection of rodent and human hosts

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) viral proteins bind to host mitochondrial proteins, likely inhibiting oxidative phosphorylation (OXPHOS) and stimulating glycolysis. We analyzed mitochondrial gene expression in nasopharyngeal and autopsy tissues from patients with coronavirus disease 2019 (COVID-19). In nasopharyngeal samples with declining viral titers, the virus blocked the transcription of a subset of nuclear DNA (nDNA)-encoded mitochondrial OXPHOS genes, induced the expression of microRNA 2392, activated HIF-1α to induce glycolysis, and activated host immune defenses including the integrated stress response. In autopsy tissues from patients with COVID-19, SARS-CoV-2 was no longer present, and mitochondrial gene transcription had recovered in the lungs. However, nDNA mitochondrial gene expression remained suppressed in autopsy tissue from the heart and, to a lesser extent, kidney, and liver, whereas mitochondrial DNA transcription was induced and host-immune defense pathways were activated. During early SARS-CoV-2 infection of hamsters with peak lung viral load, mitochondrial gene expression in the lung was minimally perturbed but was down-regulated in the cerebellum and up-regulated in the striatum even though no SARS-CoV-2 was detected in the brain. During the mid-phase SARS-CoV-2 infection of mice, mitochondrial gene expression was starting to recover in mouse lungs. These data suggest that when the viral titer first peaks, there is a systemic host response followed by viral suppression of mitochondrial gene transcription and induction of glycolysis leading to the deployment of antiviral immune defenses. Even when the virus was cleared and lung mitochondrial function had recovered, mitochondrial function in the heart, kidney, liver, and lymph nodes remained impaired, potentially leading to severe COVID-19 pathology.

A comprehensive SARS-CoV-2 and COVID-19 review, Part 1: Intracellular overdrive for SARS-CoV-2 infection

COVID-19, the disease caused by SARS-CoV-2, has claimed approximately 5 million lives and 257 million cases reported globally. This virus and disease have significantly affected people worldwide, whether directly and/or indirectly, with a virulent pathogen that continues to evolve as we race to learn how to prevent, control, or cure COVID-19. The focus of this review is on the SARS-CoV-2 virus' mechanism of infection and its proclivity at adapting and restructuring the intracellular environment to support viral replication. We highlight current knowledge and how scientific communities with expertize in viral, cellular, and clinical biology have contributed to increase our understanding of SARS-CoV-2, and how these findings may help explain the widely varied clinical observations of COVID-19 patients.

TARGETED DOWN REGULATION OF CORE MITOCHONDRIAL GENES DURING SARS-COV-2 INFECTION

Defects in mitochondrial oxidative phosphorylation (OXPHOS) have been reported in COVID-19 patients, but the timing and organs affected vary among reports. Here, we reveal the dynamics of COVID-19 through transcription profiles in nasopharyngeal and autopsy samples from patients and infected rodent models. While mitochondrial bioenergetics is repressed in the viral nasopharyngeal portal of entry, it is up regulated in autopsy lung tissues from deceased patients. In most disease stages and organs, discrete OXPHOS functions are blocked by the virus, and this is countered by the host broadly up regulating unblocked OXPHOS functions. No such rebound is seen in autopsy heart, results in severe repression of genes across all OXPHOS modules. Hence, targeted enhancement of mitochondrial gene expression may mitigate the pathogenesis of COVID-19.

System-wide transcriptome damage and tissue identity loss in COVID-19 patients

The molecular mechanisms underlying the clinical manifestations of coronavirus disease 2019 (COVID-19), and what distinguishes them from common seasonal influenza virus and other lung injury states such as acute respiratory distress syndrome, remain poorly understood. To address these challenges, we combine transcriptional profiling of 646 clinical nasopharyngeal swabs and 39 patient autopsy tissues to define body-wide transcriptome changes in response to COVID-19. We then match these data with spatial protein and expression profiling across 357 tissue sections from 16 representative patient lung samples and identify tissue-compartment-specific damage wrought by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, evident as a function of varying viral loads during the clinical course of infection and tissue-type-specific expression states. Overall, our findings reveal a systemic disruption of canonical cellular and transcriptional pathways across all tissues, which can inform subsequent studies to combat the mortality of COVID-19 and to better understand the molecular dynamics of lethal SARS-CoV-2 and other respiratory infections.

The interplay between lncRNAs, RNA-binding proteins and viral genome during SARS-CoV-2 infection reveals strong connections with regulatory events involved in RNA metabolism and immune response

Rationale: Viral infections are complex processes based on an intricate network of molecular interactions. The infectious agent hijacks components of the cellular machinery for its profit, circumventing the natural defense mechanisms triggered by the infected cell. The successful completion of the replicative viral cycle within a cell depends on the function of viral components versus the cellular defenses. Non-coding RNAs (ncRNAs) are important cellular modulators, either promoting or preventing the progression of viral infections. Among these ncRNAs, the long non-coding RNA (lncRNA) family is especially relevant due to their intrinsic functional properties and ubiquitous biological roles. Specific lncRNAs have been recently characterized as modulators of the cellular response during infection of human host cells by single stranded RNA viruses. However, the role of host lncRNAs in the infection by human RNA coronaviruses such as SARS-CoV-2 remains uncharacterized.

Methods: In the present work, we have performed a transcriptomic study of a cohort of patients with different SARS-CoV-2 viral load and analyzed the involvement of lncRNAs in supporting regulatory networks based on their interaction with RNA-binding proteins (RBPs).

Results: Our results revealed the existence of a SARS-CoV-2 infection-dependent pattern of transcriptional up-regulation in which specific lncRNAs are an integral component. To determine the role of these lncRNAs, we performed a functional correlation analysis complemented with the study of the validated interactions between lncRNAs and RBPs. This combination of in silico functional association studies and experimental evidence allowed us to identify a lncRNA signature composed of six elements - NRIR, BISPR, MIR155HG, FMR1-IT1, USP30-AS1, and U62317.2 - associated with the regulation of SARS-CoV-2 infection.

Conclusions: We propose a competition mechanism between the viral RNA genome and the regulatory lncRNAs in the sequestering of specific RBPs that modulates the interferon response and the regulation of RNA surveillance by nonsense-mediated decay (NMD).

Foster thy young: enhanced prediction of orphan genes in assembled genomes

Proteins encoded by newly-emerged genes ('orphan genes') share no sequence similarity with proteins in any other species. They provide organisms with a reservoir of genetic elements to quickly respond to changing selection pressures. Here, we systematically assess the ability of five gene prediction pipelines to accurately predict genes in genomes according to phylostratal origin. BRAKER and MAKER are existing, popular ab initio tools that infer gene structures by machine learning. Direct Inference is an evidence-based pipeline we developed to predict gene structures from alignments of RNA-Seq data. The BIND pipeline integrates ab initio predictions of BRAKER and Direct inference; MIND combines Direct Inference and MAKER predictions. We use highly-curated Arabidopsis and yeast annotations as gold-standard benchmarks, and cross-validate in rice. Each pipeline under-predicts orphan genes (as few as 11 percent, under one prediction scenario). Increasing RNA-Seq diversity greatly improves prediction efficacy. The combined methods (BIND and MIND) yield best predictions overall, BIND identifying 68% of annotated orphan genes, 99% of ancient genes, and give the highest sensitivity score regardless dataset in Arabidopsis. We provide a light weight, flexible, reproducible, and well-documented solution to improve gene prediction.

Role of miR-2392 in driving SARS-CoV-2 infection

MicroRNAs (miRNAs) are small non-coding RNAs involved in post-transcriptional gene regulation that have a major impact on many diseases and provide an exciting avenue toward antiviral therapeutics. From patient transcriptomic data, we determined that a circulating miRNA, miR-2392, is directly involved with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) machinery during host infection. Specifically, we show that miR-2392 is key in driving downstream suppression of mitochondrial gene expression, increasing inflammation, glycolysis, and hypoxia, as well as promoting many symptoms associated with coronavirus disease 2019 (COVID-19) infection. We demonstrate that miR-2392 is present in the blood and urine of patients positive for COVID-19 but is not present in patients negative for COVID-19. These findings indicate the potential for developing a minimally invasive COVID-19 detection method. Lastly, using in vitro human and in vivo hamster models, we design a miRNA-based antiviral therapeutic that targets miR-2392, significantly reduces SARS-CoV-2 viability in hamsters, and may potentially inhibit a COVID-19 disease state in humans.

The Great Deceiver: miR-2392's Hidden Role in Driving SARS-CoV-2 Infection

MicroRNAs (miRNAs) are small non-coding RNAs involved in post-transcriptional gene regulation that have a major impact on many diseases and provides an exciting avenue towards antiviral therapeutics. From patient transcriptomic data, we have discovered a circulating miRNA, miR-2392, that is directly involved with SARS-CoV-2 machinery during host infection. Specifically, we show that miR-2392 is key in driving downstream suppression of mitochondrial gene expression, increasing inflammation, glycolysis, and hypoxia as well as promoting many symptoms associated with COVID-19 infection. We demonstrate miR-2392 is present in the blood and urine of COVID-19 positive patients, but not detected in COVID-19 negative patients. These findings indicate the potential for developing a novel, minimally invasive, COVID-19 detection method. Lastly, using in vitro human and in vivo hamster models, we have developed a novel miRNA-based antiviral therapeutic that targets miR-2392, significantly reduces SARS-CoV-2 viability in hamsters and may potentially inhibit a COVID-19 disease state in humans.

Landscape of the Dark Transcriptome Revealed Through Re-mining Massive RNA-Seq Data

The "dark transcriptome" can be considered the multitude of sequences that are transcribed but not annotated as genes. We evaluated expression of 6,692 annotated genes and 29,354 unannotated open reading frames (ORFs) in the Saccharomyces cerevisiae genome across diverse environmental, genetic and developmental conditions (3,457 RNA-Seq samples). Over 30% of the highly transcribed ORFs have translation evidence. Phylostratigraphic analysis infers most of these transcribed ORFs would encode species-specific proteins ("orphan-ORFs"); hundreds have mean expression comparable to annotated genes. These data reveal unannotated ORFs most likely to be protein-coding genes. We partitioned a co-expression matrix by Markov Chain Clustering; the resultant clusters contain 2,468 orphan-ORFs. We provide the aggregated RNA-Seq yeast data with extensive metadata as a project in MetaOmGraph (MOG), a tool designed for interactive analysis and visualization. This approach enables reuse of public RNA-Seq data for exploratory discovery, providing a rich context for experimentalists to make novel, experimentally testable hypotheses about candidate genes.

pyrpipe: a Python package for RNA-Seq workflows

The availability of terabytes of RNA-Seq data and continuous emergence of new analysis tools, enable unprecedented biological insight. There is a pressing requirement for a framework that allows for fast, efficient, manageable, and reproducible RNA-Seq analysis. We have developed a Python package, (pyrpipe), that enables straightforward development of flexible, reproducible and easy-to-debug computational pipelines purely in Python, in an object-oriented manner. pyrpipe provides access to popular RNA-Seq tools, within Python, via high-level APIs. Pipelines can be customized by integrating new Python code, third-party programs, or Python libraries. Users can create checkpoints in the pipeline or integrate pyrpipe into a workflow management system, thus allowing execution on multiple computing environments, and enabling efficient resource management. pyrpipe produces detailed analysis, and benchmark reports which can be shared or included in publications. pyrpipe is implemented in Python and is compatible with Python versions 3.6 and higher. To illustrate the rich functionality of pyrpipe, we provide case studies using RNA-Seq data from GTEx, SARS-CoV-2-infected human cells, and Zea mays. All source code is freely available at https://github.com/urmi-21/pyrpipe; the package can be installed from the source, from PyPI (https://pypi.org/project/pyrpipe), or from bioconda (https://anaconda.org/bioconda/pyrpipe). Documentation is available at (http://pyrpipe.rtfd.io).

Systemic Tissue and Cellular Disruption from SARS-CoV-2 Infection revealed in COVID-19 Autopsies and Spatial Omics Tissue Maps

The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) virus has infected over 115 million people and caused over 2.5 million deaths worldwide. Yet, the molecular mechanisms underlying the clinical manifestations of COVID-19, as well as what distinguishes them from common seasonal influenza virus and other lung injury states such as Acute Respiratory Distress Syndrome (ARDS), remains poorly understood. To address these challenges, we combined transcriptional profiling of 646 clinical nasopharyngeal swabs and 39 patient autopsy tissues, matched with spatial protein and expression profiling (GeoMx) across 357 tissue sections. These results define both body-wide and tissue-specific (heart, liver, lung, kidney, and lymph nodes) damage wrought by the SARS-CoV-2 infection, evident as a function of varying viral load (high vs. low) during the course of infection and specific, transcriptional dysregulation in splicing isoforms, T cell receptor expression, and cellular expression states. In particular, cardiac and lung tissues revealed the largest degree of splicing isoform switching and cell expression state loss. Overall, these findings reveal a systemic disruption of cellular and transcriptional pathways from COVID-19 across all tissues, which can inform subsequent studies to combat the mortality of COVID-19, as well to better understand the molecular dynamics of lethal SARS-CoV-2 infection and other viruses.

orfipy: a fast and flexible tool for extracting ORFs

SUMMARY

Searching for open reading frames is a routine task and a critical step prior to annotating protein coding regions in newly sequenced genomes or de novo transcriptome assemblies. With the tremendous increase in genomic and transcriptomic data, faster tools are needed to handle large input datasets. These tools should be versatile enough to fine-tune search criteria and allow efficient downstream analysis. Here we present a new python based tool, orfipy, which allows the user to flexibly search for open reading frames in genomic and transcriptomic sequences. The search is rapid and is fully customizable, with a choice of FASTA and BED output formats.

AVAILABILITY AND IMPLEMENTATION

orfipy is implemented in python and is compatible with python v3.6 and higher. Source code: https://github.com/urmi-21/orfipy. Installation: from the source, or via PyPi (https://pypi.org/project/orfipy) or bioconda (https://anaconda.org/bioconda/orfipy).

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

phylostratr: a framework for phylostratigraphy

MOTIVATION

The goal of phylostratigraphy is to infer the evolutionary origin of each gene in an organism. This is done by searching for homologs within increasingly broad clades. The deepest clade that contains a homolog of the protein(s) encoded by a gene is that gene's phylostratum.

RESULTS

We have created a general R-based framework, phylostratr, to estimate the phylostratum of every gene in a species. The program fully automates analysis: selecting species for balanced representation, retrieving sequences, building databases, inferring phylostrata and returning diagnostics. Key diagnostics include: detection of genes with inferred homologs in old clades, but not intermediate ones; proteome quality assessments; false-positive diagnostics, and checks for missing organellar genomes. phylostratr allows extensive customization and systematic comparisons of the influence of analysis parameters or genomes on phylostrata inference. A user may: modify the automatically generated clade tree or use their own tree; provide custom sequences in place of those automatically retrieved from UniProt; replace BLAST with an alternative algorithm; or tailor the method and sensitivity of the homology inference classifier. We show the utility of phylostratr through case studies in Arabidopsis thaliana and Saccharomyces cerevisiae.

AVAILABILITY AND IMPLEMENTATION

Source code available at https://github.com/arendsee/phylostratr.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

MetaOmGraph: a workbench for interactive exploratory data analysis of large expression datasets

The diverse and growing omics data in public domains provide researchers with tremendous opportunity to extract hidden, yet undiscovered, knowledge. However, the vast majority of archived data remain unused. Here, we present MetaOmGraph (MOG), a free, open-source, standalone software for exploratory analysis of massive datasets. Researchers, without coding, can interactively visualize and evaluate data in the context of its metadata, honing-in on groups of samples or genes based on attributes such as expression values, statistical associations, metadata terms and ontology annotations. Interaction with data is easy via interactive visualizations such as line charts, box plots, scatter plots, histograms and volcano plots. Statistical analyses include co-expression analysis, differential expression analysis and differential correlation analysis, with significance tests. Researchers can send data subsets to R for additional analyses. Multithreading and indexing enable efficient big data analysis. A researcher can create new MOG projects from any numerical data; or explore an existing MOG project. MOG projects, with history of explorations, can be saved and shared. We illustrate MOG by case studies of large curated datasets from human cancer RNA-Seq, where we identify novel putative biomarker genes in different tumors, and microarray and metabolomics data from Arabidopsis thaliana. MOG executable and code: http://metnetweb.gdcb.iastate.edu/ and https://github.com/urmi-21/MetaOmGraph/.

How new genes are born

Analysis of yeast, fly and human genomes suggests that sequence divergence is not the main source of orphan genes.

fagin: synteny-based phylostratigraphy and finer classification of young genes

Background

With every new genome that is sequenced, thousands of species-specific genes (orphans) are found, some originating from ultra-rapid mutations of existing genes, many others originating de novo from non-genic regions of the genome. If some of these genes survive across speciations, then extant organisms will contain a patchwork of genes whose ancestors first appeared at different times. Standard phylostratigraphy, the technique of partitioning genes by their age, is based solely on protein similarity algorithms. However, this approach relies on negative evidence ─ a failure to detect a homolog of a query gene. An alternative approach is to limit the search for homologs to syntenic regions. Then, genes can be positively identified as de novo orphans by tracing them to non-coding sequences in related species.

Results

We have developed a synteny-based pipeline in the R framework. Fagin determines the genomic context of each query gene in a focal species compared to homologous sequence in target species. We tested the fagin pipeline on two focal species, Arabidopsis thaliana (plus four target species in Brassicaseae) and Saccharomyces cerevisiae (plus six target species in Saccharomyces). Using microsynteny maps, fagin classified the homology relationship of each query gene against each target genome into three main classes, and further subclasses: AAic (has a coding syntenic homolog), NTic (has a non-coding syntenic homolog), and Unknown (has no detected syntenic homolog). fagin inferred over half the “Unknown” A. thaliana query genes, and about 20% for S. cerevisiae, as lacking a syntenic homolog because of local indels or scrambled synteny.

Conclusions

fagin augments standard phylostratigraphy, and extends synteny-based phylostratigraphy with an automated, customizable, and detailed contextual analysis. By comparing synteny-based phylostrata to standard phylostrata, fagin systematically identifies those orphans and lineage-specific genes that are well-supported to have originated de novo. Analyzing within-species genomes should distinguish orphan genes that may have originated through rapid divergence from de novo orphans. Fagin also delineates whether a gene has no syntenic homolog because of technical or biological reasons. These analyses indicate that some orphans may be associated with regions of high genomic perturbation.

Electronic supplementary material

The online version of this article (10.1186/s12859-019-3023-y) contains supplementary material, which is available to authorized users.

Comprehensive transcriptome analyses correlated with untargeted metabolome reveal differentially expressed pathways in response to cell wall alterations

This research provides new insights into plant response to cell wall perturbations through correlation of transcriptome and metabolome datasets obtained from transgenic plants expressing cell wall-modifying enzymes. Plants respond to changes in their cell walls in order to protect themselves from pathogens and other stresses. Cell wall modifications in Arabidopsis thaliana have profound effects on gene expression and defense response, but the cell signaling mechanisms underlying these responses are not well understood. Three transgenic Arabidopsis lines, two with reduced cell wall acetylation (AnAXE and AnRAE) and one with reduced feruloylation (AnFAE), were used in this study to investigate the plant responses to cell wall modifications. RNA-Seq in combination with untargeted metabolome was employed to assess differential gene expression and metabolite abundance. RNA-Seq results were correlated with metabolite abundances to determine the pathways involved in response to cell wall modifications introduced in each line. The resulting pathway enrichments revealed the deacetylation events in AnAXE and AnRAE plants induced similar responses, notably, upregulation of aromatic amino acid biosynthesis and changes in regulation of primary metabolic pathways that supply substrates to specialized metabolism, particularly those related to defense responses. In contrast, genes and metabolites of lipid biosynthetic pathways and peroxidases involved in lignin polymerization were downregulated in AnFAE plants. These results elucidate how primary metabolism responds to extracellular stimuli. Combining the transcriptomics and metabolomics datasets increased the power of pathway prediction, and demonstrated the complexity of pathways involved in cell wall-mediated signaling.

Raising orphans from a metadata morass: A researcher's guide to re-use of public 'omics data

More than 15 petabases of raw RNAseq data is now accessible through public repositories. Acquisition of other 'omics data types is expanding, though most lack a centralized archival repository. Data-reuse provides tremendous opportunity to extract new knowledge from existing experiments, and offers a unique opportunity for robust, multi-'omics analyses by merging metadata (information about experimental design, biological samples, protocols) and data from multiple experiments. We illustrate how predictive research can be accelerated by meta-analysis with a study of orphan (species-specific) genes. Computational predictions are critical to infer orphan function because their coding sequences provide very few clues. The metadata in public databases is often confusing; a test case with Zea mays mRNA seq data reveals a high proportion of missing, misleading or incomplete metadata. This metadata morass significantly diminishes the insight that can be extracted from these data. We provide tips for data submitters and users, including specific recommendations to improve metadata quality by more use of controlled vocabulary and by metadata reviews. Finally, we advocate for a unified, straightforward metadata submission and retrieval system.

Identification and functional characterization of HbOsmotin from Hevea brasiliensis

Latex in the laticiferous cell network of Hevea brasiliensis tree is composed of cytoplasm that synthesizes natural rubber. Ethylene stimulation of the tree bark enhances latex production partly by prolonging the duration of latex flow during the tapping process. Here, we identified an osmotin-like cDNA sequence (HbOsmotin) from H. brasiliensis that belongs to the pathogenesis-related 5 (PR-5) gene family. The HbOsmotin protein is present in the lutoids of latex in H. brasiliensis, whereas in onion epidermal cells, this protein is predominantly distributed around the cell wall, suggesting that it may be secreted from the cytoplasm. We investigated the effects of exogenous ethylene on HbOsmotin transcription and protein accumulation in rubber latex, and further determined the protein function after osmotic stress in Arabidopsis. In regularly tapped trees, HbOsmotin expression was drastically inhibited in rubber latex after tapping, although the expression was subsequently recovered by ethylene stimulation. However, in virgin plants that had never been tapped, exogenous ethylene application slightly decreased HbOsmotin expression. HbOsmotin overexpression in Arabidopsis showed that HbOsmotin reduced the osmotic stress tolerance of the plant, which likely occurred by raising the water potential. These data indicated that HbOsmotin may contribute to osmotic regulation in laticiferous cells.

QQS orphan gene regulates carbon and nitrogen partitioning across species via NF-YC interactions

The allocation of carbon and nitrogen resources to the synthesis of plant proteins, carbohydrates, and lipids is complex and under the control of many genes; much remains to be understood about this process. QQS (Qua-Quine Starch; At3g30720), an orphan gene unique to Arabidopsis thaliana, regulates metabolic processes affecting carbon and nitrogen partitioning among proteins and carbohydrates, modulating leaf and seed composition in Arabidopsis and soybean. Here the universality of QQS function in modulating carbon and nitrogen allocation is exemplified by a series of transgenic experiments. We show that ectopic expression of QQS increases soybean protein independent of the genetic background and original protein content of the cultivar. Furthermore, transgenic QQS expression increases the protein content of maize, a C4 species (a species that uses 4-carbon photosynthesis), and rice, a protein-poor agronomic crop, both highly divergent from Arabidopsis. We determine that QQS protein binds to the transcriptional regulator AtNF-YC4 (Arabidopsis nuclear factor Y, subunit C4). Overexpression of AtNF-YC4 in Arabidopsis mimics the QQS-overexpression phenotype, increasing protein and decreasing starch levels. NF-YC, a component of the NF-Y complex, is conserved across eukaryotes. The NF-YC4 homologs of soybean, rice, and maize also bind to QQS, which provides an explanation of how QQS can act in species where it does not occur endogenously. These findings are, to our knowledge, the first insight into the mechanism of action of QQS in modulating carbon and nitrogen allocation across species. They have major implications for the emergence and function of orphan genes, and identify a nontransgenic strategy for modulating protein levels in crop species, a trait of great agronomic significance.

The QQS orphan gene of Arabidopsis modulates carbon and nitrogen allocation in soybean

The genome of each species contains as high as 8% of genes that are uniquely present in that species. Little is known about the functional significance of these so-called species specific or orphan genes. The Arabidopsis thaliana gene Qua-Quine Starch (QQS) is species specific. Here, we show that altering QQS expression in Arabidopsis affects carbon partitioning to both starch and protein. We hypothesized QQS may be conserved in a feature other than primary sequence, and as such could function to impact composition in another species. To test the potential of QQS in affecting composition in an ectopic species, we introduced QQS into soybean. Soybean T1 lines expressing QQS have up to 80% decreased leaf starch and up to 60% increased leaf protein; T4 generation seeds from field-grown plants contain up to 13% less oil, while protein is increased by up to 18%. These data broaden the concept of QQS as a modulator of carbon and nitrogen allocation, and demonstrate that this species-specific gene can affect the seed composition of an agronomic species thought to have diverged from Arabidopsis 100 million years ago.

A systems biology approach toward understanding seed composition in soybean

BACKGROUND

The molecular, biochemical, and genetic mechanisms that regulate the complex metabolic network of soybean seed development determine the ultimate balance of protein, lipid, and carbohydrate stored in the mature seed. Many of the genes and metabolites that participate in seed metabolism are unknown or poorly defined; even more remains to be understood about the regulation of their metabolic networks. A global omics analysis can provide insights into the regulation of seed metabolism, even without a priori assumptions about the structure of these networks.

RESULTS

With the future goal of predictive biology in mind, we have combined metabolomics, transcriptomics, and metabolic flux technologies to reveal the global developmental and metabolic networks that determine the structure and composition of the mature soybean seed. We have coupled this global approach with interactive bioinformatics and statistical analyses to gain insights into the biochemical programs that determine soybean seed composition. For this purpose, we used Plant/Eukaryotic and Microbial Metabolomics Systems Resource (PMR, http://www.metnetdb.org/pmr, a platform that incorporates metabolomics data to develop hypotheses concerning the organization and regulation of metabolic networks, and MetNet systems biology tools http://www.metnetdb.org for plant omics data, a framework to enable interactive visualization of metabolic and regulatory networks.

CONCLUSIONS

This combination of high-throughput experimental data and bioinformatics analyses has revealed sets of specific genes, genetic perturbations and mechanisms, and metabolic changes that are associated with the developmental variation in soybean seed composition. Researchers can explore these metabolomics and transcriptomics data interactively at PMR.

Modifications of membrane lipids in response to wounding of Arabidopsis thaliana leaves

Mechanical wounding of Arabidopsis thaliana leaves results in modifications of most membrane lipids within 6 hours. Here, we discuss the lipid changes, their underlying biochemistry, and possible relationships among activated pathways. New evidence is presented supporting the role of the processive galactosylating enzyme SENSITIVE TO FREEZING2 in the wounding response.

Coming of age: orphan genes in plants

Sizable minorities of protein-coding genes from every sequenced eukaryotic and prokaryotic genome are unique to the species. These so-called ‘orphan genes’ may evolve de novo from non-coding sequence or be derived from older coding material. They are often associated with environmental stress responses and species-specific traits or regulatory patterns. However, difficulties in studying genes where comparative analysis is impossible, and a bias towards broadly conserved genes, have resulted in underappreciation of their importance. We review here the identification, possible origins, evolutionary trends, and functions of orphans with an emphasis on their role in plant biology. We exemplify several evolutionary trends with an analysis of Arabidopsis thaliana and present QQS as a model orphan gene.

Metabolomic Characterization of Knockout Mutants in Arabidopsis: Development of a Metabolite Profiling Database for Knockout Mutants in Arabidopsis

Despite recent intensive research efforts in functional genomics, the functions of only a limited number of Arabidopsis (Arabidopsis thaliana) genes have been determined experimentally, and improving gene annotation remains a major challenge in plant science. As metabolite profiling can characterize the metabolomic phenotype of a genetic perturbation in the plant metabolism, it provides clues to the function(s) of genes of interest. We chose 50 Arabidopsis mutants, including a set of characterized and uncharacterized mutants, that resemble wild-type plants. We performed metabolite profiling of the plants using gas chromatography-mass spectrometry. To make the data set available as an efficient public functional genomics tool for hypothesis generation, we developed the Metabolite Profiling Database for Knock-Out Mutants in Arabidopsis (MeKO). It allows the evaluation of whether a mutation affects metabolism during normal plant growth and contains images of mutants, data on differences in metabolite accumulation, and interactive analysis tools. Nonprocessed data, including chromatograms, mass spectra, and experimental metadata, follow the guidelines set by the Metabolomics Standards Initiative and are freely downloadable. Proof-of-concept analysis suggests that MeKO is highly useful for the generation of hypotheses for genes of interest and for improving gene annotation. MeKO is publicly available at http://prime.psc.riken.jp/meko/.

Automatic extraction of biomolecular interactions: an empirical approach

BACKGROUND

We describe a method for extracting data about how biomolecule pairs interact from texts. This method relies on empirically determined characteristics of sentences. The characteristics are efficient to compute, making this approach to extraction of biomolecular interactions scalable. The results of such interaction mining can support interaction network annotation, question answering, database construction, and other applications.

RESULTS

We constructed a software system to search MEDLINE for sentences likely to describe interactions between given biomolecules. The system extracts a list of the interaction-indicating terms appearing in those sentences, then ranks those terms based on their likelihood of correctly characterizing how the biomolecules interact. The ranking process uses a tf-idf (term frequency-inverse document frequency) based technique using empirically derived knowledge about sentences, and was applied to the MEDLINE literature collection. Software was developed as part of the MetNet toolkit (http://www.metnetdb.org).

CONCLUSIONS

Specific, efficiently computable characteristics of sentences about biomolecular interactions were analyzed to better understand how to use these characteristics to extract how biomolecules interact.The text empirics method that was investigated, though arising from a classical tradition, has yet to be fully explored for the task of extracting biomolecular interactions from the literature. The conclusions we reach about the sentence characteristics investigated in this work, as well as the technique itself, could be used by other systems to provide evidence about putative interactions, thus supporting efforts to maximize the ability of hybrid systems to support such tasks as annotating and constructing interaction networks.

Comprehensive analysis of correlation coefficients estimated from pooling heterogeneous microarray data

BACKGROUND

The synthesis of information across microarray studies has been performed by combining statistical results of individual studies (as in a mosaic), or by combining data from multiple studies into a large pool to be analyzed as a single data set (as in a melting pot of data). Specific issues relating to data heterogeneity across microarray studies, such as differences within and between labs or differences among experimental conditions, could lead to equivocal results in a melting pot approach.

RESULTS

We applied statistical theory to determine the specific effect of different means and heteroskedasticity across 19 groups of microarray data on the sign and magnitude of gene-to-gene Pearson correlation coefficients obtained from the pool of 19 groups. We quantified the biases of the pooled coefficients and compared them to the biases of correlations estimated by an effect-size model. Mean differences across the 19 groups were the main factor determining the magnitude and sign of the pooled coefficients, which showed largest values of bias as they approached ±1. Only heteroskedasticity across the pool of 19 groups resulted in less efficient estimations of correlations than did a classical meta-analysis approach of combining correlation coefficients. These results were corroborated by simulation studies involving either mean differences or heteroskedasticity across a pool of N > 2 groups.

CONCLUSIONS

The combination of statistical results is best suited for synthesizing the correlation between expression profiles of a gene pair across several microarray studies.

Identification and biosynthesis of acylphloroglucinols in Hypericum gentianoides

Species of the genus Hypericum contain a rich array of unusual polyketides, however, only a small proportion of the over 450 Hypericum species, other than the popular medicinal supplement St. John's Wort (Hypericum perforatum), have even been chemically characterized. Hypericum gentianoides, a small annual used medicinally by Cherokee Americans, contains bioactive acylphloroglucinols. Here, we identify acylphloroglucinol constituents of H. gentianoides and determine a potential pathway to their synthesis. Liquid chromatography/electrospray ionization-mass spectrometry (LC/ESI-MS) and HPLC-UV indicate that the level of accumulation and profile of acylphloroglucinols in H. gentianoides vary little seasonally when grown in a greenhouse, but do vary with development and are highly dependent on the accession, highlighting the importance of the selection of plant material for study. We identify the chemical structures of the nine prevalent polyketides, based on LC/ESI-MS and hybrid quadrupole orthogonal time-of-flight (Q-TOF) mass spectrometry; these metabolites include one monomeric phlorisobutyrophenone (PIB) derivative and eight dimeric acylphloroglucinols. Q-TOF spectrometry was used to identify eight additional PIB derivatives that were not detected by LC/ESI-MS. These data lead us to propose that diacylphloroglucinols are synthesized via modification of PIB to yield diverse phloroglucinol and filicinic acids moieties, followed by dimerization of a phloroglucinol and a filicinic acid monomer to yield the observed complement of diacylphloroglucinols. The metabolomics data from H. gentianoides are accessible in plant metabolomics resource (PMR) (http://www.metnetdb.org/pmr), a public metabolomics database with analysis software for plants and microbial organisms.

A global approach to analysis and interpretation of metabolic data for plant natural product discovery

Discovering molecular components and their functionality is key to the development of hypotheses concerning the organization and regulation of metabolic networks. The iterative experimental testing of such hypotheses is the trajectory that can ultimately enable accurate computational modelling and prediction of metabolic outcomes. This information can be particularly important for understanding the biology of natural products, whose metabolism itself is often only poorly defined. Here, we describe factors that must be in place to optimize the use of metabolomics in predictive biology. A key to achieving this vision is a collection of accurate time-resolved and spatially defined metabolite abundance data and associated metadata. One formidable challenge associated with metabolite profiling is the complexity and analytical limits associated with comprehensively determining the metabolome of an organism. Further, for metabolomics data to be efficiently used by the research community, it must be curated in publicly available metabolomics databases. Such databases require clear, consistent formats, easy access to data and metadata, data download, and accessible computational tools to integrate genome system-scale datasets. Although transcriptomics and proteomics integrate the linear predictive power of the genome, the metabolome represents the nonlinear, final biochemical products of the genome, which results from the intricate system(s) that regulate genome expression. For example, the relationship of metabolomics data to the metabolic network is confounded by redundant connections between metabolites and gene-products. However, connections among metabolites are predictable through the rules of chemistry. Therefore, enhancing the ability to integrate the metabolome with anchor-points in the transcriptome and proteome will enhance the predictive power of genomics data. We detail a public database repository for metabolomics, tools and approaches for statistical analysis of metabolomics data, and methods for integrating these datasets with transcriptomic data to create hypotheses concerning specialized metabolisms that generate the diversity in natural product chemistry. We discuss the importance of close collaborations among biologists, chemists, computer scientists and statisticians throughout the development of such integrated metabolism-centric databases and software.

Holocarboxylase synthetase 1 physically interacts with histone h3 in Arabidopsis

Biotin is a water-soluble vitamin required by all organisms, but only synthesized by plants and some bacterial and fungal species. As a cofactor, biotin is responsible for carbon dioxide transfer in all biotin-dependent carboxylases, including acetyl-CoA carboxylase, methylcrotonyl-CoA carboxylase, and pyruvate carboxylase. Adding biotin to carboxylases is catalyzed by the enzyme holocarboxylase synthetase (HCS). Biotin is also involved in gene regulation, and there is some indication that histones can be biotinylated in humans. Histone proteins and most histone modifications are highly conserved among eukaryotes. HCS1 is the only functional biotin ligase in Arabidopsis and has a high homology with human HCS. Therefore, we hypothesized that HCS1 also biotinylates histone proteins in Arabidopsis. A comparison of the catalytic domain of HCS proteins was performed among eukaryotes, prokaryotes, and archaea, and this domain is highly conserved across the selected organisms. Biotinylated histones could not be identified in vivo by using avidin precipitation or two-dimensional gel analysis. However, HCS1 physically interacts with Arabidopsis histone H3 in vitro, indicating the possibility of the role of this enzyme in the regulation of gene expression.

Functional identification of valerena-1,10-diene synthase, a terpene synthase catalyzing a unique chemical cascade in the biosynthesis of biologically active sesquiterpenes in Valeriana officinalis

Valerian is an herbal preparation from the roots of Valeriana officinalis used as an anxiolytic and sedative and in the treatment of insomnia. The biological activities of valerian are attributed to valerenic acid and its putative biosynthetic precursor valerenadiene, sesquiterpenes, found in V. officinalis roots. These sesquiterpenes retain an isobutenyl side chain whose origin has been long recognized as enigmatic because a chemical rationalization for their biosynthesis has not been obvious. Using recently developed metabolomic and transcriptomic resources, we identified seven V. officinalis terpene synthase genes (VoTPSs), two that were functionally characterized as monoterpene synthases and three that preferred farnesyl diphosphate, the substrate for sesquiterpene synthases. The reaction products for two of the sesquiterpene synthases exhibiting root-specific expression were characterized by a combination of GC-MS and NMR in comparison to the terpenes accumulating in planta. VoTPS7 encodes for a synthase that biosynthesizes predominately germacrene C, whereas VoTPS1 catalyzes the conversion of farnesyl diphosphate to valerena-1,10-diene. Using a yeast expression system, specific labeled [(13)C]acetate, and NMR, we investigated the catalytic mechanism for VoTPS1 and provide evidence for the involvement of a caryophyllenyl carbocation, a cyclobutyl intermediate, in the biosynthesis of valerena-1,10-diene. We suggest a similar mechanism for the biosynthesis of several other biologically related isobutenyl-containing sesquiterpenes.

Medicinal plants: a public resource for metabolomics and hypothesis development

Specialized compounds from photosynthetic organisms serve as rich resources for drug development. From aspirin to atropine, plant-derived natural products have had a profound impact on human health. Technological advances provide new opportunities to access these natural products in a metabolic context. Here, we describe a database and platform for storing, visualizing and statistically analyzing metabolomics data from fourteen medicinal plant species. The metabolomes and associated transcriptomes (RNAseq) for each plant species, gathered from up to twenty tissue/organ samples that have experienced varied growth conditions and developmental histories, were analyzed in parallel. Three case studies illustrate different ways that the data can be integrally used to generate testable hypotheses concerning the biochemistry, phylogeny and natural product diversity of medicinal plants. Deep metabolomics analysis of Camptotheca acuminata exemplifies how such data can be used to inform metabolic understanding of natural product chemical diversity and begin to formulate hypotheses about their biogenesis. Metabolomics data from Prunella vulgaris, a species that contains a wide range ofantioxidant, antiviral, tumoricidal and anti-inflammatory constituents, provide a case study of obtaining biosystematic and developmental fingerprint information from metabolite accumulation data in a little studied species. Digitalis purpurea, well known as a source of cardiac glycosides, is used to illustrate how integrating metabolomics and transcriptomics data can lead to identification of candidate genes encoding biosynthetic enzymes in the cardiac glycoside pathway. Medicinal Plant Metabolomics Resource (MPM) [1] provides a framework for generating experimentally testable hypotheses about the metabolic networks that lead to the generation of specialized compounds, identifying genes that control their biosynthesis and establishing a basis for modeling metabolism in less studied species. The database is publicly available and can be used by researchers in medicine and plant biology.

BirdsEyeView (BEV): graphical overviews of experimental data

Background

Analyzing global experimental data can be tedious and time-consuming. Thus, helping biologists see results as quickly and easily as possible can facilitate biological research, and is the purpose of the software we describe.

Results

We present BirdsEyeView, a software system for visualizing experimental transcriptomic data using different views that users can switch among and compare. BirdsEyeView graphically maps data to three views: Cellular Map (currently a plant cell), Pathway Tree with dynamic mapping, and Gene Ontology http://www.geneontology.org Biological Processes and Molecular Functions. By displaying color-coded values for transcript levels across different views, BirdsEyeView can assist users in developing hypotheses about their experiment results.

Conclusions

BirdsEyeView is a software system available as a Java Webstart package for visualizing transcriptomic data in the context of different biological views to assist biologists in investigating experimental results. BirdsEyeView can be obtained from http://metnetdb.org/MetNet_BirdsEyeView.htm.

MetNet Online: a novel integrated resource for plant systems biology

BACKGROUND

Plants are important as foods, pharmaceuticals, biorenewable chemicals, fuel resources, bioremediation tools and general tools for recombinant technology. The study of plant biological pathways is advanced by easy access to integrated data sources. Today, various plant data sources are scattered throughout the web, making it increasingly complicated to build comprehensive datasets.

RESULTS

MetNet Online is a web-based portal that provides access to a regulatory and metabolic plant pathway database. The database and portal integrate Arabidopsis, soybean (Glycine max) and grapevine (Vitis vinifera) data. Pathways are enriched with known or predicted information on sub cellular location. MetNet Online enables pathways, interactions and entities to be browsed or searched by multiple categories such as sub cellular compartment, pathway ontology, and GO term. In addition to this, the "My MetNet" feature allows registered users to bookmark content and track, import and export customized lists of entities. Users can also construct custom networks using existing pathways and/or interactions as building blocks.

CONCLUSION

The site can be reached at http://www.metnetonline.org. Extensive video tutorials on how to use the site are available through http://www.metnetonline.org/tutorial/.

Massive human co-expression network and its medical applications

Network-based analysis is indispensable in analyzing high-throughput biological data. Based on the assumption that the variation of gene interactions under given biological conditions could be better interpreted in the context of a large-scale and wide variety of developmental, tissue, and disease conditions, we leverage the large quantity of publicly available transcriptomic data >40,000 HG U133A Affymetrix microarray chips stored in ArrayExpress (http://www.ebi.ac.uk/arrayexpress/) using MetaOmGraph (http://metnet.vrac.iastate.edu/MetNet_MetaOmGraph.htm). From this data, 18,637 chips encompassing over 500 experiments containing high-quality data (18637 Hu-dataset) were used to create a globally stable gene co-expression network (18637 Hu-co-expression-network). Regulons, groups of highly and consistently co-expressed genes, were obtained by partitioning the 18637 Hu-co-expression-network using an Markov clustering algorithm (MCL). The regulons were demonstrated to be statistically significant using a gene ontology (GO) term overrepresentation test combined with evaluation of the effects of gene permutations. The regulons include ca. 12% of human genes, interconnected by 31,471 correlations. All network data and metadata are publically available (http://metnet.vrac.iastate.edu/MetNet_MetaOmGraph.htm). Text mining of these metadata, GO term overrepresentation analysis, and statistical analysis of transcriptomic experiments across multiple environmental, tissue, and disease conditions, has revealed novel fingerprints distinguishing central nervous system (CNS)-related conditions. This study demonstrates the value of mega-scale network-based analysis for biologists to further refine transcriptomic data, derived from a particular condition, to study the global relationships between genes and diseases, and to develop hypotheses that can inform future research.

MetNet: Software to Build and Model the Biogenetic Lattice of Arabidopsis

MetNet (http://www.botany.iastate.edu/∼mash/metnetex/metabolicnetex.html) is publicly available software in development for analysis of genome-wide RNA, protein and metabolite profiling data. The software is designed to enable the biologist to visualize, statistically analyse and model a metabolic and regulatory network map of Arabidopsis, combined with gene expression profiling data. It contains a JAVA interface to an interactions database (MetNetDB) containing information on regulatory and metabolic interactions derived from a combination of web databases (TAIR, KEGG, BRENDA) and input from biologists in their area of expertise. FCModeler captures input from MetNetDB in a graphical form. Sub-networks can be identified and interpreted using simple fuzzy cognitive maps. FCModeler is intended to develop and evaluate hypotheses, and provide a modelling framework for assessing the large amounts of data captured by high-throughput gene expression experiments. FCModeler and MetNetDB are currently being extended to three-dimensional virtual reality display. The MetNet map, together with gene expression data, can be viewed using multivariate graphics tools in GGobi linked with the data analytic tools in R. Users can highlight different parts of the metabolic network and see the relevant expression data highlighted in other data plots. Multi-dimensional expression data can be rotated through different dimensions. Statistical analysis can be computed alongside the visual. MetNet is designed to provide a framework for the formulation of testable hypotheses regarding the function of specific genes, and in the long term provide the basis for identification of metabolic and regulatory networks that control plant composition and development.

AtRabD2b and AtRabD2c have overlapping functions in pollen development and pollen tube growth

BACKGROUND

Rab GTPases are important regulators of endomembrane trafficking, regulating exocytosis, endocytosis and membrane recycling. Many Rab-like proteins exist in plants, but only a subset have been functionally characterized.

RESULTS

Here we report that AtRabD2b and AtRabD2c play important roles in pollen development, germination and tube elongation. AtrabD2b and AtrabD2c single mutants have no obvious morphological changes compared with wild-type plants across a variety of growth conditions. An AtrabD2b/2c double mutant is also indistinguishable from wild-type plants during vegetative growth; however its siliques are shorter than those in wild-type plants. Compared with wild-type plants, AtrabD2b/2c mutants produce deformed pollen with swollen and branched pollen tube tips. The shorter siliques in the AtrabD2b/2c double mutant were found to be primarily due to the pollen defects. AtRabD2b and AtRabD2c have different but overlapping expression patterns, and they are both highly expressed in pollen. Both AtRabD2b and AtRabD2c protein localize to Golgi bodies.

CONCLUSIONS

These findings support a partially redundant role for AtRabD2b and AtRabD2c in vesicle trafficking during pollen tube growth that cannot be fulfilled by the remaining AtRabD family members.

Reverse-genetic analysis of the two biotin-containing subunit genes of the heteromeric acetyl-coenzyme A carboxylase in Arabidopsis indicates a unidirectional functional redundancy

The heteromeric acetyl-coenzyme A carboxylase catalyzes the first and committed reaction of de novo fatty acid biosynthesis in plastids. This enzyme is composed of four subunits: biotin carboxyl-carrier protein (BCCP), biotin carboxylase, α-carboxyltransferase, and β-carboxyltransferase. With the exception of BCCP, single-copy genes encode these subunits in Arabidopsis (Arabidopsis thaliana). Reverse-genetic approaches were used to individually investigate the physiological significance of the two paralogous BCCP-coding genes, CAC1A (At5g16390, codes for BCCP1) and CAC1B (At5g15530, codes for BCCP2). Transfer DNA insertional alleles that completely eliminate the accumulation of BCCP2 have no perceptible effect on plant growth, development, and fatty acid accumulation. In contrast, transfer DNA insertional null allele of the CAC1A gene is embryo lethal and deleteriously affects pollen development and germination. During seed development the effect of the cac1a null allele first becomes apparent at 3-d after flowering, when the synchronous development of the endosperm and embryo is disrupted. Characterization of CAC1A antisense plants showed that reducing BCCP1 accumulation to 35% of wild-type levels, decreases fatty acid accumulation and severely affects normal vegetative plant growth. Detailed expression analysis by a suite of approaches including in situ RNA hybridization, promoter:reporter transgene expression, and quantitative western blotting reveal that the expression of CAC1B is limited to a subset of the CAC1A-expressing tissues, and CAC1B expression levels are only about one-fifth of CAC1A expression levels. Therefore, a likely explanation for the observed unidirectional redundancy between these two paralogous genes is that whereas the BCCP1 protein can compensate for the lack of BCCP2, the absence of BCCP1 cannot be tolerated as BCCP2 levels are not sufficient to support heteromeric acetyl-coenzyme A carboxylase activity at a level that is required for normal growth and development.

Echinacea-induced cytosolic Ca2+ elevation in HEK293

Background

With a traditional medical use for treatment of various ailments, herbal preparations of Echinacea are now popularly used to improve immune responses. One likely mode of action is that alkamides from Echinacea bind to cannabinoid type 2 (CB2) receptors and induce a transient increase in intracellular Ca²⁺. Here, we show that unidentified compounds from Echinacea purpurea induce cytosolic Ca²⁺ elevation in non-immune-related cells, which lack CB2 receptors and that the Ca²⁺ elevation is not influenced by alkamides.

Methods

A non-immune human cell line, HEK293, was chosen to evaluate E. purpurea root extracts and constituents as potential regulators of intracellular Ca²⁺ levels. Changes in cytosolic Ca²⁺ levels were monitored and visualized by intracellular calcium imaging. U73122, a phospholipase C inhibitor, and 2-aminoethoxydiphenyl borate (2-APB), an antagonist of inositol-1,4,5-trisphosphate (IP₃) receptor, were tested to determine the mechanism of this Ca²⁺ signaling pathway. E. purpurea root ethanol extracts were fractionated by preparative HPLC, screened for bioactivity on HEK293 cells and by GC-MS for potential constituent(s) responsible for this bioactivity.

Results

A rapid transient increase in cytosolic Ca²⁺ levels occurs when E. purpurea extracts are applied to HEK293 cells. These stimulatory effects are phospholipase C and IP₃ receptor dependent. Echinacea-evoked responses could not be blocked by SR 144528, a specific CB2 receptor antagonist, indicating that CB2 is not involved. Ca²⁺ elevation is sustained after the Echinacea-induced Ca²⁺ release from intracellular Ca²⁺ stores; this longer-term effect is abolished by 2-APB, indicating a possible store operated calcium entry involvement. Of 28 HPLC fractions from E. purpurea root extracts, six induce cytosolic Ca²⁺ increase. Interestingly, GC-MS analysis of these fractions, as well as treatment of HEK293 cells with known individual and combined chemicals, indicates the components thought to be responsible for the major immunomodulatory bioactivity of Echinacea do not explain the observed Ca²⁺ response. Rather, lipophilic constituents of unknown structures are associated with this bioactivity.

Conclusions

Our data indicate that as yet unidentified constituents from Echinacea stimulate an IP₃ receptor and phospholipase C mediation of cytosolic Ca²⁺ levels in non-immune mammalian cells. This pathway is distinct from that induced in immune associated cells via the CB2 receptor.

MetNetAPI: A flexible method to access and manipulate biological network data from MetNet

Background

Convenient programmatic access to different biological databases allows automated integration of scientific knowledge. Many databases support a function to download files or data snapshots, or a webservice that offers "live" data. However, the functionality that a database offers cannot be represented in a static data download file, and webservices may consume considerable computational resources from the host server.

Results

MetNetAPI is a versatile Application Programming Interface (API) to the MetNetDB database. It abstracts, captures and retains operations away from a biological network repository and website. A range of database functions, previously only available online, can be immediately (and independently from the website) applied to a dataset of interest. Data is available in four layers: molecular entities, localized entities (linked to a specific organelle), interactions, and pathways. Navigation between these layers is intuitive (e.g. one can request the molecular entities in a pathway, as well as request in what pathways a specific entity participates). Data retrieval can be customized: Network objects allow the construction of new and integration of existing pathways and interactions, which can be uploaded back to our server. In contrast to webservices, the computational demand on the host server is limited to processing data-related queries only.

Conclusions

An API provides several advantages to a systems biology software platform. MetNetAPI illustrates an interface with a central repository of data that represents the complex interrelationships of a metabolic and regulatory network. As an alternative to data-dumps and webservices, it allows access to a current and "live" database and exposes analytical functions to application developers. Yet it only requires limited resources on the server-side (thin server/fat client setup). The API is available for Java, Microsoft.NET and R programming environments and offers flexible query and broad data- retrieval methods. Data retrieval can be customized to client needs and the API offers a framework to construct and manipulate user-defined networks. The design principles can be used as a template to build programmable interfaces for other biological databases. The API software and tutorials are available at http://www.metnetonline.org/api.

PlantMetabolomics.org: a web portal for plant metabolomics experiments

PlantMetabolomics.org (PM) is a web portal and database for exploring, visualizing, and downloading plant metabolomics data. Widespread public access to well-annotated metabolomics datasets is essential for establishing metabolomics as a functional genomics tool. PM integrates metabolomics data generated from different analytical platforms from multiple laboratories along with the key visualization tools such as ratio and error plots. Visualization tools can quickly show how one condition compares to another and which analytical platforms show the largest changes. The database tries to capture a complete annotation of the experiment metadata along with the metabolite abundance databased on the evolving Metabolomics Standards Initiative. PM can be used as a platform for deriving hypotheses by enabling metabolomic comparisons between genetically unique Arabidopsis (Arabidopsis thaliana) populations subjected to different environmental conditions. Each metabolite is linked to relevant experimental data and information from various annotation databases. The portal also provides detailed protocols and tutorials on conducting plant metabolomics experiments to promote metabolomics in the community. PM currently houses Arabidopsis metabolomics data generated by a consortium of laboratories utilizing metabolomics to help elucidate the functions of uncharacterized genes. PM is publicly available at http://www.plantmetabolomics.org.

PathBinder--text empirics and automatic extraction of biomolecular interactions

Motivation

The increasingly large amount of free, online biological text makes automatic interaction extraction correspondingly attractive. Machine learning is one strategy that works by uncovering and using useful properties that are implicit in the text. However these properties are usually not reported in the literature explicitly. By investigating specific properties of biological text passages in this paper, we aim to facilitate an alternative strategy, the use of text empirics, to support mining of biomedical texts for biomolecular interactions. We report on our application of this approach, and also report some empirical findings about an important class of passages. These may be useful to others who may also wish to use the empirical properties we describe.

Results

We manually analyzed syntactic and semantic properties of sentences likely to describe interactions between biomolecules. The resulting empirical data were used to design an algorithm for the PathBinder system to extract biomolecular interactions from texts. PathBinder searches PubMed for sentences describing interactions between two given biomolecules. PathBinder then uses probabilistic methods to combine evidence from multiple relevant sentences in PubMed to assess the relative likelihood of interaction between two arbitrary biomolecules. A biomolecular interaction network was constructed based on those likelihoods.

Conclusion

The text empirics approach used here supports computationally friendly, performance competitive, automatic extraction of biomolecular interactions from texts.

Availability

http://www.metnetdb.org/pathbinder.

Identification of the novel protein QQS as a component of the starch metabolic network in Arabidopsis leaves

Little is known about the role of proteins that lack primary sequence homology with any known motifs (proteins with unknown functions, PUFs); these comprise more than 10% of all proteins. This paper offers a generalized experimental strategy for identifying the functions of such proteins, particularly in relation to metabolism. Using this strategy, we have identified a novel regulatory function for Arabidopsis locus At3g30720 (which we term QQS for qua-quine starch). QQS expression, revealed through global mRNA profiling, is up-regulated in an Arabidopsis Atss3 mutant that lacks starch synthase III and has increased leaf starch content. Analysis of public microarray data using MetaOmGraph (metnetdb.org), in combination with transgenic Arabidopsis lines containing QQS promoter-GUS transgenes, indicated that QQS expression responds to a variety of developmental/genetic/environmental perturbations. In addition to the increase in the Atss3 mutant, QQS is up-regulated in the carbohydrate mutants mex1 and sis8. A 586 nt sequence for the QQS mRNA was identified by 5' and 3' RACE experiments. The QQS transcript is predicted to encode a protein of 59 amino acids, whose expression was confirmed by immunological Western blot analysis. The QQS gene is recognizable in sequenced Arabidopsis ecotypes, but is not identifiable in any other sequenced species, including the closely related Brassica napus. Transgenic RNA interference lines in which QQS expression is reduced show excess leaf starch content at the end of the illumination phase of a diurnal cycle. Taken together, the data identify QQS as a potential novel regulator of starch biosynthesis.

Metabolic profiling of echinacea genotypes and a test of alternative taxonomic treatments

The genus Echinacea is used as an herbal medicine to treat a variety of ailments. To better understand its potential chemical variation, 40 Echinacea accessions encompassing broad geographical and morphological diversity were evaluated under controlled conditions. Metabolites of roots from these accessions were analyzed by HPLC-photo diode array (HPLC-PDA), GC-MS, and multivariate statistical methods. In total, 43 lipophilic metabolites, including 24 unknown compounds, were detected. Weighted principal component analysis (WPCA) and clustering analysis of the levels of these metabolites across Echinacea accessions, based on Canberra distances, allowed us to test two alternative taxonomic treatments of the genus, with the further goal of facilitating accession identification. A widely used system developed by McGregor based primarily on morphological features was more congruent with the dendrogram generated from the lipophilic metabolite data than the system more recently developed by Binns et al. Our data support the hypothesis that Echinacea pallida is a diverse allopolyploid, incorporating the genomes of Echinacea simulata and another taxon, possibly Echinacea sanguinea. Finally, most recognized taxa of Echinacea can be identified by their distinct lipophilic metabolite fingerprints.

Quantitative analysis of short-chain acyl-coenzymeAs in plant tissues by LC-MS-MS electrospray ionization method

Because acyl-CoAs play major roles in numerous anabolic and catabolic pathways, the quantitative determination of these metabolites in biological tissues is paramount to understanding the regulation of these metabolic processes. Here, we report a method for the analysis of a collection of short-chain acyl-CoAs (<6 carbon chain length) from plant extracts. Identification of each individual acyl-CoA was conducted by monitoring specific mass-fragmentation ions that are derived from common chemical moieties of all Coenzyme A (CoA) derivatives, namely the adenosine triphosphate nucleotide, pantothenate and acylated cysteamine. This method is robust and quick, enabling the quantitative analysis of up to 12 different acyl-CoAs in plant metabolite extracts with minimal post-extraction processing, using a 30min chromatographic run-time.

Regulon organization of Arabidopsis

BACKGROUND

Despite the mounting research on Arabidopsis transcriptome and the powerful tools to explore biology of this model plant, the organization of expression of Arabidopsis genome is only partially understood. Here, we create a coexpression network from a 22,746 Affymetrix probes dataset derived from 963 microarray chips that query the transcriptome in response to a wide variety of environmentally, genetically, and developmentally induced perturbations.

RESULTS

Markov chain graph clustering of the coexpression network delineates 998 regulons ranging from one to 1623 genes in size. To assess the significance of the clustering results, the statistical over-representation of GO terms is averaged over this set of regulons and compared to the analogous values for 100 randomly-generated sets of clusters. The set of regulons derived from the experimental data scores significantly better than any of the randomly-generated sets. Most regulons correspond to identifiable biological processes and include a combination of genes encoding related developmental, metabolic pathway, and regulatory functions. In addition, nearly 3000 genes of unknown molecular function or process are assigned to a regulon. Only five regulons contain plastomic genes; four of these are exclusively plastomic. In contrast, expression of the mitochondrial genome is highly integrated with that of nuclear genes; each of the seven regulons containing mitochondrial genes also incorporates nuclear genes. The network of regulons reveals a higher-level organization, with dense local neighborhoods articulated for photosynthetic function, genetic information processing, and stress response.

CONCLUSION

This analysis creates a framework for generation of experimentally testable hypotheses, gives insight into the concerted functions of Arabidopsis at the transcript level, and provides a test bed for comparative systems analysis.

Articulation of three core metabolic processes in Arabidopsis: fatty acid biosynthesis, leucine catabolism and starch metabolism

BACKGROUND

Elucidating metabolic network structures and functions in multicellular organisms is an emerging goal of functional genomics. We describe the co-expression network of three core metabolic processes in the genetic model plant Arabidopsis thaliana: fatty acid biosynthesis, starch metabolism and amino acid (leucine) catabolism.

RESULTS

These co-expression networks form modules populated by genes coding for enzymes that represent the reactions generally considered to define each pathway. However, the modules also incorporate a wider set of genes that encode transporters, cofactor biosynthetic enzymes, precursor-producing enzymes, and regulatory molecules. We tested experimentally the hypothesis that one of the genes tightly co-expressed with starch metabolism module, a putative kinase AtPERK10, will have a role in this process. Indeed, knockout lines of AtPERK10 have an altered starch accumulation. In addition, the co-expression data define a novel hierarchical transcript-level structure associated with catabolism, in which genes performing smaller, more specific tasks appear to be recruited into higher-order modules with a broader catabolic function.

CONCLUSION

Each of these core metabolic pathways is structured as a module of co-expressed transcripts that co-accumulate over a wide range of environmental and genetic perturbations and developmental stages, and represent an expanded set of macromolecules associated with the common task of supporting the functionality of each metabolic pathway. As experimentally demonstrated, co-expression analysis can provide a rich approach towards understanding gene function.

Direct profiling and imaging of plant metabolites in intact tissues by using colloidal graphite-assisted laser desorption ionization mass spectrometry

Laser desorption/ionization (LDI)-based imaging mass spectrometry (MS) has been applied to several biological systems to obtain information about both the identities of the major chemical species and their localization. Colloidal graphite-assisted LDI (GALDI) MS imaging was introduced for the imaging of small molecules such as phospholipids, cerebrosides, oligosaccharides, flavonoids, and other secondary metabolites with high spatial homogeneity due to finely dispersed particles. Mass profiles and images of Arabidopsis thaliana have been recorded directly from various plant surfaces and cross sections. The main targeted metabolites were flavonoids and cuticular waxes, both of which are important in many aspects of functional genomics, proteomics, and metabolomics. The mass spectral profiles revealed tissue-specific accumulation of flavonoids in flowers and petals. In addition, many other location-specific ions were observed. The location and the degree of light-induced accumulation of flavonoids in stem sections were successfully probed by GALDI MS.