Identifying differentially expressed genes in unreplicated multiple-treatment microarray timecourse experiments

Microarray technology has become widespread as a means to investigate gene function and metabolic pathways in an organism. A common experiment involves probing, at each of several time points, the gene expression of experimental units subjected to different treatments. Due to the high cost of microarrays, such experiments may be performed without replication and therefore provide a gene expression measurement of only one experimental unit for each combination of treatment and time point. Though an experiment with replication would provide more powerful conclusions, it is still possible to identify differentially expressed genes and to estimate the number of false positives for a specified rejection region when the data is unreplicated. We present a method for identifying differentially expressed genes in this situation that utilizes polynomial regression models to approximate underlying expression patterns. In the first stage of a two-stage permutation approach, we choose a ‘best’ model at each gene after considering all possible regression models involving treatment effects, terms polynomial in time, and interactions between treatments and polynomial terms. In the second stage, we identify genes whose ‘best’ model differs significantly from the overall mean model as differentially expressed. The number of expected false positives in the chosen rejection region and the overall proportion of differentially expressed genes are both estimated using a method presented by Storey and Tibshirani (2003). For illustration, the proposed method is applied to an Arabidopsis thaliana microarray data set

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 [13C]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

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

<p>Abstract</p> <p>Background</p> <p>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.</p> <p>Results</p> <p>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.</p> <p>Conclusions</p> <p>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 <url>http://www.metnetonline.org/api</url>.</p

host extracellular to systemic effects of SARS-CoV-2 infection

Funding Information: The opinions expressed in this article are those of the authors and do not reflect the view of the National Institutes of Health, the Department of Health and Human Services, the National Science Foundation, or the United States government. APK’s research is supported by the Department of Defense and Swezey & Jewell, Moore and MacKenzie Research Fund. Funding Information: The opinions expressed in this article are those of the authors and do not reflect the view of the National Institutes of Health, the Department of Health and Human Services, the National Science Foundation, or the United States government. APK’s research is supported by the Department of Defense and Swezey & Jewell, Moore and MacKenzie Research Fund. Funding Information: This work was supported by supplemental funds for COVID-19 research from Translational Research Institute of Space Health through NASA Cooperative Agreement NNX16AO69A (T-0404) to AB, and by a NASA Space Biology Postdoctoral Fellowship (80NSSC19K0426) and Human Research Program Augmentation Award (80NSSC19K1322) to SAN. Publisher Copyright: © 2023, The Author(s).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.publishersversionpublishe

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 \u3e 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

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.This preprint is made available through medRxiv at doi:https://doi.org/10.1101/2023.11.27.23298847. It is made available under a CC-BY 4.0 International license

Microbial Community and Chemical Characteristics of Swine Manure during Maturation

Swine diet formulations have the potential to lower animal emissions, including odor and ammonia (NH3). The purpose of this study was to determine the impact of manure storage duration on manure chemical and microbial properties in swine feeding trials. Three groups of 12 pigs were fed a standard corn–soybean meal diet over a 13-wk period. Urine and feces were collected at each feeding and transferred to 12 manure storage tanks. Manure chemical characteristics and headspace gas concentrations were monitored for NH3, hydrogen sulfide (H2S), volatile fatty acids, phenols, and indoles. Microbial analysis of the stored manure included plate counts, community structure (denaturing gradient gel electrophoresis), and metabolic function (Biolog). All odorants in manure and headspace gas concentrations were significantly (p \u3c 0.01) correlated for length of storage using quadratic equations, peaking after Week 5 for all headspace gases and most manure chemical characteristics. Microbial community structure and metabolic utilization patterns showed continued change throughout the 13-wk trial. Denaturing gradient gel electrophoresis species diversity patterns declined significantly (p \u3c 0.01) with time as substrate utilization declined for sugars and certain amino acids, but functionality increased in the utilization of short chain fatty acids as levels of these compounds increased in manure. Studies to assess the effect of swine diet formulations on manure emissions for odor need to be conducted for a minimum of 5 wk. Efforts to determine the impact of diets on greenhouse gas emissions will require longer periods of study (\u3e13 wk)

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.This article is published as McDonald, J.T., Kim, J., Farmerie, L. et al. Space radiation damage rescued by inhibition of key spaceflight associated miRNAs. Nat Commun 15, 4825 (2024). https://doi.org/10.1038/s41467-024-48920-y. Works produced by employees of the U.S. Government as part of their official duties are not copyrighted within the U.S. The content of this document is not copyrighted

Cloning, mapping, and analyses of expression of the Em-like gene family in soybean [Glycine max (L). Merr.]

The entire Em-like Group-1 late embryogenesis abundant (Lea) gene family from soybean was cloned and characterized. The five Group-1 Lea genes (Sle1-5) were divided into two classes based on sequence identity. Sle1-4 were genetically mapped to four different linkage groups. Nucleotide sequencing indicated that Sle1, Sle2, Sle3, and Sle5 encode polypeptides differing primarily by the presence of a repeated 20-amino acid motif. Sle1 and Sle5 were shown by Northern analysis to be expressed in developing embryos weeks earlier than Sle2 and Sle3. Sle4 was shown to be a pseudogene. Maximal levels of mRNA for all functional Sle genes accumulated in maturation-phase seeds, before significant desiccation had occurred, and declined rapidly upon seed imbibition. Desiccation did not induce Sle expression in seeds or vegetative tissue. Sle expression was confined to embryo tissues and Sle mRNA accumulated at similar levels in both the embryo axis and in the cotyledons.This article is from Theoretical and Applied Genetics 94 (1997): 957, doi: 10.1007/s001220050501.</p