Transcriptomic signature of cancer cachexia by integration of machine learning, literature mining and meta-analysis.

BACKGROUND: Cancer cachexia is a severe metabolic syndrome marked by skeletal muscle atrophy. A successful clinical intervention for cancer cachexia is currently lacking. The study of cachexia mechanisms is largely based on preclinical animal models and the availability of high-throughput transcriptomic datasets of cachectic mouse muscles is increasing through the extensive use of next generation sequencing technologies. METHODS: Cachectic mouse muscle transcriptomic datasets of ten different studies were combined and mined by seven attribute weighting models, which analysed both categorical variables and numerical variables. The transcriptomic signature of cancer cachexia was identified by attribute weighting algorithms and was used to evaluate the performance of eleven pattern discovery models. The signature was employed to find the best combination of drugs (drug repurposing) for developing cancer cachexia treatment strategies, as well as to evaluate currently used cachexia drugs by literature mining. RESULTS: Attribute weighting algorithms ranked 26 genes as the transcriptomic signature of muscle from mice with cancer cachexia. Deep Learning and Random Forest models performed better in differentiating cancer cachexia cases based on muscle transcriptomic data. Literature mining revealed that a combination of melatonin and infliximab has negative interactions with 2 key genes (Rorc and Fbxo32) upregulated in the transcriptomic signature of cancer cachexia in muscle. CONCLUSIONS: The integration of machine learning, meta-analysis and literature mining was found to be an efficient approach to identifying a robust transcriptomic signature for cancer cachexia, with implications for improving clinical diagnosis and management of this condition.

Spatiotemporal deposition of cell wall polysaccharides in oat endosperm during grain development.

Oat (Avena sativa) is a cereal crop whose grains are rich in (1,3;1,4)-ß-D-glucan (mixed-linkage glucan or MLG), a soluble dietary fiber. In our study, we analyzed oat endosperm development in 2 Canadian varieties with differing MLG content and nutritional value. We confirmed that oat undergoes a nuclear type of endosperm development but with a shorter cellularization phase than barley (Hordeum vulgare). Callose and cellulose were the first polysaccharides to be detected in the early anticlinal cell walls at 11 days postemergence (DPE) of the panicle. Other polysaccharides such as heteromannan and homogalacturonan were deposited early in cellularization around 12 DPE after the first periclinal walls are laid down. In contrast to barley, heteroxylan deposition coincided with completion of cellularization and was detected from 14 DPE but was only detectable after demasking. Notably, MLG was the last polysaccharide to be laid down at 18 DPE within the differentiation phase, rather than during cellularization. In addition, differences in the spatiotemporal patterning of MLG were also observed between the 2 varieties. The lower MLG-containing cultivar AC Morgan (3.5% w/w groats) was marked by the presence of a discontinuous pattern of MLG labeling, while labeling in the same walls in CDC Morrison (5.6% w/w groats) was mostly even and continuous. RNA-sequencing analysis revealed higher transcript levels of multiple MLG biosynthetic cellulose synthase-like F (CSLF) and CSLH genes during grain development in CDC Morrison compared with AC Morgan that likely contributes to the increased abundance of MLG at maturity in CDC Morrison. CDC Morrison was also observed to have smaller endosperm cells with thicker walls than AC Morgan from cellularization onwards, suggesting the processes controlling cell size and shape are established early in development. This study has highlighted that the molecular processes influencing MLG content and deposition are more complex than previously imagined.

A Practical Guide to Inferring Multi-Omics Networks in Plant Systems.

The inference of gene regulatory networks can reveal molecular connections underlying biological processes and improve our understanding of complex biological phenomena in plants. Many previous network studies have inferred networks using only one type of omics data, such as transcriptomics. However, given more recent work applying multi-omics integration in plant biology, such as combining (phospho)proteomics with transcriptomics, it may be advantageous to integrate multiple omics data types into a comprehensive network prediction. Here, we describe a state-of-the-art approach for integrating multi-omics data with gene regulatory network inference to describe signaling pathways and uncover novel regulators. We detail how to download and process transcriptomics and (phospho)proteomics data for network inference, using an example dataset from the plant hormone signaling field. We provide a step-by-step protocol for inference, visualization, and analysis of an integrative multi-omics network using currently available methods. This chapter serves as an accessible guide for novice and intermediate bioinformaticians to analyze their own datasets and reanalyze published work.

Spatially resolved transcriptomic analysis of the germinating barley grain.

Seeds are a vital source of calories for humans and a unique stage in the life cycle of flowering plants. During seed germination, the embryo undergoes major developmental transitions to become a seedling. Studying gene expression in individual seed cell types has been challenging due to the lack of spatial information or low throughput of existing methods. To overcome these limitations, a spatial transcriptomics workflow was developed for germinating barley grain. This approach enabled high-throughput analysis of spatial gene expression, revealing specific spatial expression patterns of various functional gene categories at a sub-tissue level. This study revealed over 14 000 genes differentially regulated during the first 24 h after imbibition. Individual genes, such as the aquaporin gene family, starch degradation, cell wall modification, transport processes, ribosomal proteins and transcription factors, were found to have specific spatial expression patterns over time. Using spatial autocorrelation algorithms, we identified auxin transport genes that had increasingly focused expression within subdomains of the embryo over time, suggesting their role in establishing the embryo axis. Overall, our study provides an unprecedented spatially resolved cellular map for barley germination and identifies specific functional genomics targets to better understand cellular restricted processes during germination. The data can be viewed at https://spatial.latrobe.edu.au/.

Transcription Factor Dynamics in Cross-Regulation of Plant Hormone Signaling Pathways.

Cross-regulation between hormone signaling pathways is indispensable for plant growth and development. However, the molecular mechanisms by which multiple hormones interact and co-ordinate activity need to be understood. Here, we generated a cross-regulation network explaining how hormone signals are integrated from multiple pathways in etiolated Arabidopsis (Arabidopsis thaliana) seedlings. To do so we comprehensively characterized transcription factor activity during plant hormone responses and reconstructed dynamic transcriptional regulatory models for six hormones; abscisic acid, brassinosteroid, ethylene, jasmonic acid, salicylic acid and strigolactone/karrikin. These models incorporated target data for hundreds of transcription factors and thousands of protein-protein interactions. Each hormone recruited different combinations of transcription factors, a subset of which were shared between hormones. Hub target genes existed within hormone transcriptional networks, exhibiting transcription factor activity themselves. In addition, a group of MITOGEN-ACTIVATED PROTEIN KINASES (MPKs) were identified as potential key points of cross-regulation between multiple hormones. Accordingly, the loss of function of one of these (MPK6) disrupted the global proteome, phosphoproteome and transcriptome during hormone responses. Lastly, we determined that all hormones drive substantial alternative splicing that has distinct effects on the transcriptome compared with differential gene expression, acting in early hormone responses. These results provide a comprehensive understanding of the common features of plant transcriptional regulatory pathways and how cross-regulation between hormones acts upon gene expression.

Emerging trends in genomic and epigenomic regulation of plant specialised metabolism.

Regulation of specialised metabolism genes is multilayered and complex, influenced by an array of genomic, epigenetic and epigenomic mechanisms. Here, we review the most recent knowledge in this field, drawing from discoveries in several plant species. Our aim is to improve understanding of how plant genome structure and function influence specialised metabolism. We also highlight key areas for future exploration. Gene regulatory mechanisms influencing specialised metabolism include gene duplication and neo-functionalization, conservation of operon-like clusters of specialised metabolism genes, local chromatin modifications, and the organisation of higher order chromatin structures within the nucleus. Genomic and epigenomic research to-date in the discipline have focused on a relatively small number of plant species, primarily at whole organ or tissue level. This is largely due to the technical demands of the experimental methods needed. However, a high degree of cell-type specificity of function exists in specialised metabolism, driven by similarly specific gene regulation. In this review we focus on the genomic characteristics of genes that are found in different types of clusters within the genome. We propose that acquisition of cell-resolution epigenomic datasets in emerging models, such as the glandular trichomes of Cannabis sativa, will yield important advances. Data such as chromatin accessibility and histone modification profiles can pinpoint which regulatory sequences are active in individual cell types and at specific times in development. These could provide fundamental biological insight as well as novel targets for genetic engineering and crop improvement.

The retrograde signaling regulator ANAC017 recruits the MKK9-MPK3/6, ethylene, and auxin signaling pathways to balance mitochondrial dysfunction with growth.

In plant cells, mitochondria are ideally positioned to sense and balance changes in energy metabolism in response to changing environmental conditions. Retrograde signaling from mitochondria to the nucleus is crucial for adjusting the required transcriptional responses. We show that ANAC017, the master regulator of mitochondrial stress, directly recruits a signaling cascade involving the plant hormones ethylene and auxin as well as the MAP KINASE KINASE (MKK) 9-MAP KINASE (MPK) 3/6 pathway in Arabidopsis thaliana. Chromatin immunoprecipitation followed by sequencing and overexpression demonstrated that ANAC017 directly regulates several genes of the ethylene and auxin pathways, including MKK9, 1-AMINO-CYCLOPROPANE-1-CARBOXYLATE SYNTHASE 2, and YUCCA 5, in addition to genes encoding transcription factors regulating plant growth and stress responses such as BASIC REGION/LEUCINE ZIPPER MOTIF (bZIP) 60, bZIP53, ANAC081/ATAF2, and RADICAL-INDUCED CELL DEATH1. A time-resolved RNA-seq experiment established that ethylene signaling precedes the stimulation of auxin signaling in the mitochondrial stress response, with a large part of the transcriptional regulation dependent on ETHYLENE-INSENSITIVE 3. These results were confirmed by mutant analyses. Our findings identify the molecular components controlled by ANAC017, which integrates the primary stress responses to mitochondrial dysfunction with whole plant growth via the activation of regulatory and partly antagonistic feedback loops.

scCloudMine: A cloud-based app for visualization, comparison, and exploration of single-cell transcriptomic data.

scCloudMine is a cloud-based application for visualization, comparison, and exploration of single-cell transcriptome data. It does not require an on-site, high-power computing server, installation, or associated expertise and expense. Users upload their own or publicly available scRNA-seq datasets after pre-processing for visualization using a web browser. The data can be viewed in two color modes-Cluster, representing cell identity, and Values, showing levels of expression-and data can be queried using keywords or gene identification number(s). Using the app to compare studies, we determined that some genes frequently used as cell-type markers are in fact study specific. The apparent cell-specific expression of PHO1;H3 differed between GFP-tagging and scRNA-seq studies. Some phosphate transporter genes were induced by protoplasting, but they retained cell specificity, suggesting that cell-specific responses to stress (i.e., protoplasting) can occur. Examination of the cell specificity of hormone response genes revealed that 132 hormone-responsive genes display restricted expression and that the jasmonate response gene TIFY8 is expressed in endodermal cells, in contrast to previous reports. It also appears that JAZ repressors have cell-type-specific functions. These features identified using scCloudMine highlight the need for resources to enable biological researchers to compare their datasets of interest under a variety of parameters. scCloudMine enables researchers to form new hypotheses and perform comparative studies and allows for the easy re-use of data from this emerging technology by a wide variety of users who may not have access or funding for high-performance on-site computing and support.

How 'omics technologies can drive plant engineering, ecosystem surveillance, human and animal health.

'Omics describes a broad collection of research tools and techniques that enable researchers to collect data about biological systems at a very large, or near-complete, scale. These include sequencing of individual and community genomes (genomics, metagenomics), characterization and quantification of gene expression (transcriptomics), metabolite abundance (metabolomics), protein content (proteomics) and phosphorylation (phospho-proteomics), amongst many others. Though initially exploited as tools for fundamental discovery, 'omics techniques are now used extensively in applied and translational research, for example in plant and animal breeding, biomarker development and drug discovery. In this collection of reviews, we aimed to introduce readers to current and future applications of 'omics technologies to solve real-world problems.

Applications of cell- and tissue-specific 'omics to improve plant productivity.

The individual tissues and cell types of plants each have characteristic properties that contribute to the function of the plant as a whole. These are reflected by unique patterns of gene expression, protein and metabolite content, which enable cell-type-specific patterns of growth, development and physiology. Gene regulatory networks act within the cell types to govern the production and activity of these components. For the broader organism to grow and reproduce successfully, cell-type-specific activity must also function within the context of surrounding cell types, which is achieved by coordination of signalling pathways. We can investigate how gene regulatory networks are constructed and function using integrative 'omics technologies. Historically such experiments in plant biological research have been performed at the bulk tissue level, to organ resolution at best. In this review, we describe recent advances in cell- and tissue-specific 'omics technologies that allow investigation at much improved resolution. We discuss the advantages of these approaches for fundamental and translational plant biology, illustrated through the examples of specialised metabolism in medicinal plants and seed germination. We also discuss the challenges that must be overcome for such approaches to be adopted widely by the community.

Applications of hyperspectral imaging in plant phenotyping.

Our ability to interrogate and manipulate the genome far exceeds our capacity to measure the effects of genetic changes on plant traits. Much effort has been made recently by the plant science research community to address this imbalance. The responses of plants to environmental conditions can now be defined using a variety of imaging approaches. Hyperspectral imaging (HSI) has emerged as a promising approach to measure traits using a wide range of wavebands simultaneously in 3D to capture information in lab, glasshouse, or field settings. HSI has been applied to define abiotic, biotic, and quality traits for optimisation of crop management.

Insights into opium poppy (Papaver spp.) genetic diversity from genotyping-by-sequencing analysis.

Opium poppy (Papaver somniferum) is one of the world's oldest medicinal plants and a versatile model system to study secondary metabolism. However, our knowledge of its genetic diversity is limited, restricting utilization of the available germplasm for research and crop improvement. We used genotyping-by-sequencing to investigate the extent of genetic diversity and population structure in a collection of poppy germplasm consisting of 91 accessions originating in 30 countries of Europe, North Africa, America, and Asia. We identified five genetically distinct subpopulations using discriminate analysis of principal components and STRUCTURE analysis. Most accessions obtained from the same country were grouped together within subpopulations, likely a consequence of the restriction on movement of poppy germplasm. Alkaloid profiles of accessions were highly diverse, with morphine being dominant. Phylogenetic analysis identified genetic groups that were largely consistent with the subpopulations detected and that could be differentiated broadly based on traits such as number of branches and seed weight. These accessions and the associated genotypic data are valuable resources for further genetic diversity analysis, which could include definition of poppy core sets to facilitate genebank management and use of the diversity for genetic improvement of this valuable crop.

RNA-seq analysis of laser microdissected Arabidopsis thaliana leaf epidermis, mesophyll and vasculature defines tissue-specific transcriptional responses to multiple stress treatments.

Acclimation of plants to adverse conditions requires the coordination of gene expression and signalling pathways between tissues and cell types. As the energy and carbon capturing organs, leaves are significantly affected by abiotic and biotic stresses. However, tissue- or cell type-specific analyses of stress responses have focussed on the Arabidopsis root. Here, we comparatively explore the transcriptomes of three leaf tissues (epidermis, mesophyll, vasculature) after induction of diverse stress pathways by chemical stimuli (antimycin A, 3-amino-1,2,4-triazole, methyl viologen, salicylic acid) and ultraviolet light in Arabidopsis using laser capture microdissection followed by RNA sequencing. Stimulation of stress pathways caused an overall reduction in the number of genes expressed in a tissue-specific manner, though a small subset gained or changed their tissue specificity. We find no evidence of a common stress response, with only a few genes consistently responsive to two or more treatments in the analysed tissues. However, differentially expressed genes overlap between tissues for individual treatments. A focussed analysis provided evidence for an interaction of auxin and ethylene that mediates retrograde signalling during mitochondrial dysfunction specifically in the epidermis, and a gene regulatory network defined the hierarchy of interactions. Taken together, we have generated an extensive reference dataset that will be valuable for future experiments analysing transcriptional responses on a tissue or single-cell level. Our results will enable the tailoring of the tissue-specific engineering of stress-tolerant plants.

Recent advances in Cannabis sativa genomics research.

Cannabis (Cannabis sativa L.) is one of the oldest cultivated plants purported to have unique medicinal properties. However, scientific research of cannabis has been restricted by the Single Convention on Narcotic Drugs of 1961, an international treaty that prohibits the production and supply of narcotic drugs except under license. Legislation governing cannabis cultivation for research, medicinal and even recreational purposes has been relaxed recently in certain jurisdictions. As a result, there is now potential to accelerate cultivar development of this multi-use and potentially medically useful plant species by application of modern genomics technologies. Whilst genomics has been pivotal to our understanding of the basic biology and molecular mechanisms controlling key traits in several crop species, much work is needed for cannabis. In this review we provide a comprehensive summary of key cannabis genomics resources and their applications. We also discuss prospective applications of existing and emerging genomics technologies for accelerating the genetic improvement of cannabis.

Analysis of Spatio-Temporal Transcriptome Profiles of Soybean (Glycine max) Tissues during Early Seed Development.

Soybean (Glycine max) is an important crop providing oil and protein for both human and animal consumption. Knowing which biological processes take place in specific tissues in a temporal manner will enable directed breeding or synthetic approaches to improve seed quantity and quality. We analyzed a genome-wide transcriptome dataset from embryo, endosperm, endothelium, epidermis, hilum, outer and inner integument and suspensor at the global, heart and cotyledon stages of soybean seed development. The tissue specificity of gene expression was greater than stage specificity, and only three genes were differentially expressed in all seed tissues. Tissues had both unique and shared enriched functional categories of tissue-specifically expressed genes associated with them. Strong spatio-temporal correlation in gene expression was identified using weighted gene co-expression network analysis, with the most co-expression occurring in one seed tissue. Transcription factors with distinct spatiotemporal gene expression programs in each seed tissue were identified as candidate regulators of expression within those tissues. Gene ontology (GO) enrichment of orthogroup clusters revealed the conserved functions and unique roles of orthogroups with similar and contrasting expression patterns in transcript abundance between soybean and Arabidopsis during embryo proper and endosperm development. Key regulators in each seed tissue and hub genes connecting those networks were characterized by constructing gene regulatory networks. Our findings provide an important resource for describing the structure and function of individual soybean seed compartments during early seed development.

Developmental normalization of phenomics data generated by high throughput plant phenotyping systems.

BACKGROUND: Sowing time is commonly used as the temporal reference for Arabidopsis thaliana (Arabidopsis) experiments in high throughput plant phenotyping (HTPP) systems. This relies on the assumption that germination and seedling establishment are uniform across the population. However, individual seeds have different development trajectories even under uniform environmental conditions. This leads to increased variance in quantitative phenotyping approaches. We developed the Digital Adjustment of Plant Development (DAPD) normalization method. It normalizes time-series HTPP measurements by reference to an early developmental stage and in an automated manner. The timeline of each measurement series is shifted to a reference time. The normalization is determined by cross-correlation at multiple time points of the time-series measurements, which may include rosette area, leaf size, and number. RESULTS: The DAPD method improved the accuracy of phenotyping measurements by decreasing the statistical dispersion of quantitative traits across a time-series. We applied DAPD to evaluate the relative growth rate in Arabidopsis plants and demonstrated that it improves uniformity in measurements, permitting a more informative comparison between individuals. Application of DAPD decreased variance of phenotyping measurements by up to 2.5 times compared to sowing-time normalization. The DAPD method also identified more outliers than any other central tendency technique applied to the non-normalized dataset. CONCLUSIONS: DAPD is an effective method to control for temporal differences in development within plant phenotyping datasets. In principle, it can be applied to HTPP data from any species/trait combination for which a relevant developmental scale can be defined.

Laser-Capture Microdissection RNA-Sequencing for Spatial and Temporal Tissue-Specific Gene Expression Analysis in Plants.

The development of a complex multicellular organism is governed by distinct cell types that have different transcriptional profiles. To identify transcriptional regulatory networks that govern developmental processes it is necessary to measure the spatial and temporal gene expression profiles of these individual cell types. Therefore, insight into the spatio-temporal control of gene expression is essential to gain understanding of how biological and developmental processes are regulated. Here, we describe a laser-capture microdissection (LCM) method to isolate small number of cells from three barley embryo organs over a time-course during germination followed by transcript profiling. The method consists of tissue fixation, tissue processing, paraffin embedding, sectioning, LCM and RNA extraction followed by real-time PCR or RNA-seq. This method has enabled us to obtain spatial and temporal profiles of seed organ transcriptomes from varying numbers of cells (tens to hundreds), providing much greater tissue-specificity than typical bulk-tissue analyses. From these data we were able to define and compare transcriptional regulatory networks as well as predict candidate regulatory transcription factors for individual tissues. The method should be applicable to other plant tissues with minimal optimization.