Space radiation damage rescued by inhibition of key spaceflight associated miRNAs.

Our previous research revealed a key microRNA signature that is associated with spaceflight that can be used as a biomarker and to develop countermeasure treatments to mitigate the damage caused by space radiation. Here, we expand on this work to determine the biological factors rescued by the countermeasure treatment. We performed RNA-sequencing and transcriptomic analysis on 3D microvessel cell cultures exposed to simulated deep space radiation (0.5 Gy of Galactic Cosmic Radiation) with and without the antagonists to three microRNAs: miR-16-5p, miR-125b-5p, and let-7a-5p (i.e., antagomirs). Significant reduction of inflammation and DNA double strand breaks (DSBs) activity and rescue of mitochondria functions are observed after antagomir treatment. Using data from astronaut participants in the NASA Twin Study, Inspiration4, and JAXA missions, we reveal the genes and pathways implicated in the action of these antagomirs are altered in humans. Our findings indicate a countermeasure strategy that can potentially be utilized by astronauts in spaceflight missions to mitigate space radiation damage.

African Americans and European Americans exhibit distinct gene expression patterns across tissues and tumors associated with immunologic functions and environmental exposures.

The COVID-19 pandemic has affected African American populations disproportionately with respect to prevalence, and mortality. Expression profiles represent snapshots of combined genetic, socio-environmental (including socioeconomic and environmental factors), and physiological effects on the molecular phenotype. As such, they have potential to improve biological understanding of differences among populations, and provide therapeutic biomarkers and environmental mitigation strategies. Here, we undertook a large-scale assessment of patterns of gene expression between African Americans and European Americans, mining RNA-Seq data from 25 non-diseased and diseased (tumor) tissue-types. We observed the widespread enrichment of pathways implicated in COVID-19 and integral to inflammation and reactive oxygen stress. Chemokine CCL3L3 expression is up-regulated in African Americans. GSTM1, encoding a glutathione S-transferase that metabolizes reactive oxygen species and xenobiotics, is upregulated. The little-studied F8A2 gene is up to 40-fold more highly expressed in African Americans; F8A2 encodes HAP40 protein, which mediates endosome movement, potentially altering the cellular response to SARS-CoV-2. African American expression signatures, superimposed on single cell-RNA reference data, reveal increased number or activity of esophageal glandular cells and lung ACE2-positive basal keratinocytes. Our findings establish basal prognostic signatures that can be used to refine approaches to minimize risk of severe infection and improve precision treatment of COVID-19 for African Americans. To enable dissection of causes of divergent molecular phenotypes, we advocate routine inclusion of metadata on genomic and socio-environmental factors for human RNA-sequencing studies.

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/.

Comparative Proteomics of Rubber Latex Revealed Multiple Protein Species of REF/SRPP Family Respond Diversely to Ethylene Stimulation among Different Rubber Tree Clones.

Rubber elongation factor (REF) and small rubber particle protein (SRPP) are two key factors for natural rubber biosynthesis. To further understand the roles of these proteins in rubber formation, six different genes for latex abundant REF or SRPP proteins, including REF138,175,258 and SRPP117,204,243, were characterized from Hevea brasiliensis Reyan (RY) 7-33-97. Sequence analysis showed that REFs have a variable and long N-terminal, whereas SRPPs have a variable and long C-terminal beyond the REF domain, and REF258 has a ß subunit of ATPase in its N-terminal. Through two-dimensional electrophoresis (2-DE), each REF/SRPP protein was separated into multiple protein spots on 2-DE gels, indicating they have multiple protein species. The abundance of REF/SRPP proteins was compared between ethylene and control treatments or among rubber tree clones with different levels of latex productivity by analyzing 2-DE gels. The total abundance of each REF/SRPP protein decreased or changed a little upon ethylene stimulation, whereas the abundance of multiple protein species of the same REF/SRPP changed diversely. Among the three rubber tree clones, the abundance of the protein species also differed significantly. Especially, two protein species of REF175 or REF258 were ethylene-responsive only in the high latex productivity clone RY 8-79 instead of in RY 7-33-97 and PR 107. Some individual protein species were positively related to ethylene stimulation and latex productivity. These results suggested that the specific protein species could be more important than others for rubber production and post-translational modifications might play important roles in rubber biosynthesis.

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 of antioxidant, 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.

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.

Characterizing the metabolic fingerprint and anti-inflammatory activity of Hypericum gentianoides.

In this paper we characterize the metabolic fingerprint and first reported anti-inflammatory activity of Hypericum gentianoides. H. gentianoides has a history of medical use by Native Americans, but it has been studied very little for biological activity. High-performance liquid chromatography (HPLC) and liquid chromatography-electrospray ionization-mass spectrometry (LC-ESI-MS) analyses of a methanol extract show that H. gentianoides contains a family of over nine related compounds that have retention times, mass spectra, and a distinctive UV absorption spectra characteristic of certain acyl-phloroglucinols. These metabolites are abundant relative to other secondary products present in H. gentianoides, accounting for approximately 0.2 g per gram of dry plant tissue. H. gentianoides methanol extracts and a specific semipreparative HPLC fraction from these extracts containing the putative acyl-phloroglucinols reduce prostaglandin E 2 synthesis in mammalian macrophages.

Genome wide co-expression among the starch debranching enzyme genes AtISA1, AtISA2, and AtISA3 in Arabidopsis thaliana.

Each of four starch debranching enzymes (DBE) is distinct and highly conserved across the plant kingdom; however, the specific functions of these proteins in carbohydrate metabolism are not well understood. DBEs function in both biosynthesis and degradation of starch, and two have been shown to function as multimers in various quarternary structures that can contain one or more DBE proteins, i.e. ISA1 homomultimers and ISA1/ISA2 heteromultimers. This study characterizes potential functional relationships between the three isoamylase-type DBE proteins (ISA) of Arabidopsis using a comprehensive bioinformatics analysis and promoter fusion approach to determine tissue-, subcellular-, and temporal specificity of gene expression. The results reveal complementary sets of expression patterns, in particular that AtISA1 (known to be involved in starch biosynthesis) and AtISA2 (a non-catalytic polypeptide) are co-expressed in some conditions in the absence of AtISA3 (known to be involved in starch degradation), whereas in other conditions AtISA2 is co-expressed with AtISA3 in the absence of AtISA1 (AtISA2 and AtISA3, but not AtISA1, are co-expressed specially in root columella cells and leaf hydathodes). Thus, AtISA2 may function in starch degradation, in addition to its role in starch biosynthesis. AtISA3 and several other potential regulatory genes, starch metabolic genes, and transcription factors, are specifically induced during cold acclimation; these transcription factors are candidates for involvement of cold-induced changes in starch metabolism. Finally, bioinformatics analysis using MetaOmGraph (http://www.metnetdb.org/MetNet_MetaOmGraph.htm) identifies Arabidopsis genes of unknown function that might be involved in starch metabolism in the cold.

Molecular characterization of a heteromeric ATP-citrate lyase that generates cytosolic acetyl-coenzyme A in Arabidopsis.

Acetyl-coenzyme A (CoA) is used in the cytosol of plant cells for the synthesis of a diverse set of phytochemicals including waxes, isoprenoids, stilbenes, and flavonoids. The source of cytosolic acetyl-CoA is unclear. We identified two Arabidopsis cDNAs that encode proteins similar to the amino and carboxy portions of human ATP-citrate lyase (ACL). Coexpression of these cDNAs in yeast (Saccharomyces cerevisiae) confers ACL activity, indicating that both the Arabidopsis genes are required for ACL activity. Arabidopsis ACL is a heteromeric enzyme composed of two distinct subunits, ACLA (45 kD) and ACLB (65 kD). The holoprotein has a molecular mass of 500 kD, which corresponds to a heterooctomer with an A(4)B(4) configuration. ACL activity and the ACLA and ACLB polypeptides are located in the cytosol, consistent with the lack of targeting peptides in the ACLA and ACLB sequences. In the Arabidopsis genome, three genes encode for the ACLA subunit (ACLA-1, At1g10670; ACLA-2, At1g60810; and ACLA-3, At1g09430), and two genes encode the ACLB subunit (ACLB-1, At3g06650 and ACLB-2, At5g49460). The ACLA and ACLB mRNAs accumulate in coordinated spatial and temporal patterns during plant development. This complex accumulation pattern is consistent with the predicted physiological needs for cytosolic acetyl-CoA, and is closely coordinated with the accumulation pattern of cytosolic acetyl-CoA carboxylase, an enzyme using cytosolic acetyl-CoA as a substrate. Taken together, these results indicate that ACL, encoded by the ACLA and ACLB genes of Arabidopsis, generates cytosolic acetyl-CoA. The heteromeric organization of this enzyme is common to green plants (including Chlorophyceae, Marchantimorpha, Bryopsida, Pinaceae, monocotyledons, and eudicots), species of fungi, Glaucophytes, Chlamydomonas, and prokaryotes. In contrast, all known animal ACL enzymes have a homomeric structure, indicating that a evolutionary fusion of the ACLA and ACLB genes probably occurred early in the evolutionary history of this kingdom.