Dataset Information

FIT: statistical modeling tool for transcriptome dynamics under fluctuating field conditions.

ABSTRACT: Considerable attention has been given to the quantification of environmental effects on organisms. In natural conditions, environmental factors are continuously changing in a complex manner. To reveal the effects of such environmental variations on organisms, transcriptome data in field environments have been collected and analyzed. Nagano et al. proposed a model that describes the relationship between transcriptomic variation and environmental conditions and demonstrated the capability to predict transcriptome variation in rice plants. However, the computational cost of parameter optimization has prevented its wide application.: We propose a new statistical model and efficient parameter optimization based on the previous study. We developed and released FIT, an R package that offers functions for parameter optimization and transcriptome prediction. The proposed method achieves comparable or better prediction performance within a shorter computational time than the previous method. The package will facilitate the study of the environmental effects on transcriptomic variation in field conditions.Freely available from CRAN ( https://cran.r-project.org/web/packages/FIT/ ).: anagano@agr.ryukoku.ac.jp.Supplementary data are available at Bioinformatics online.

SUBMITTER: Iwayama K

PROVIDER: S-EPMC5447243 | biostudies-literature | 2017 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

FIT: statistical modeling tool for transcriptome dynamics under fluctuating field conditions.

Iwayama Koji K Aisaka Yuri Y Kutsuna Natsumaro N Nagano Atsushi J AJ

Bioinformatics (Oxford, England) 20170601 11

<h4>Motivation</h4>Considerable attention has been given to the quantification of environmental effects on organisms. In natural conditions, environmental factors are continuously changing in a complex manner. To reveal the effects of such environmental variations on organisms, transcriptome data in field environments have been collected and analyzed. Nagano et al. proposed a model that describes the relationship between transcriptomic variation and environmental conditions and demonstrated the ...[more]

PMID: 28158396

Similar Datasets

Project description:BackgroundClimate change threatens Earth's ice-based ecosystems which currently offer archives and eco-evolutionary experiments in the extreme. Arctic cryopeg brine (marine-derived, within permafrost) and sea ice brine, similar in subzero temperature and high salinity but different in temporal stability, are inhabited by microbes adapted to these extreme conditions. However, little is known about their viruses (community composition, diversity, interaction with hosts, or evolution) or how they might respond to geologically stable cryopeg versus fluctuating sea ice conditions.ResultsWe used long- and short-read viromics and metatranscriptomics to study viruses in Arctic cryopeg brine, sea ice brine, and underlying seawater, recovering 11,088 vOTUs (~species-level taxonomic unit), a 4.4-fold increase of known viruses in these brines. More specifically, the long-read-powered viromes doubled the number of longer (≥25 kb) vOTUs generated and recovered more hypervariable regions by >5-fold compared to short-read viromes. Distribution assessment, by comparing to known viruses in public databases, supported that cryopeg brine viruses were of marine origin yet distinct from either sea ice brine or seawater viruses, while 94% of sea ice brine viruses were also present in seawater. A virus-encoded, ecologically important exopolysaccharide biosynthesis gene was identified, and many viruses (~half of metatranscriptome-inferred "active" vOTUs) were predicted as actively infecting the dominant microbial genera Marinobacter and Polaribacter in cryopeg and sea ice brines, respectively. Evolutionarily, microdiversity (intra-species genetic variations) analyses suggested that viruses within the stable cryopeg brine were under significantly lower evolutionary pressures than those in the fluctuating sea ice environment, while many sea ice brine virus-tail genes were under positive selection, indicating virus-host co-evolutionary arms races.ConclusionsOur results confirmed the benefits of long-read-powered viromics in understanding the environmental virosphere through significantly improved genomic recovery, expanding viral discovery and the potential for biological inference. Evidence of viruses actively infecting the dominant microbes in subzero brines and modulating host metabolism underscored the potential impact of viruses on these remote and underexplored extreme ecosystems. Microdiversity results shed light on different strategies viruses use to evolve and adapt when extreme conditions are stable versus fluctuating. Together, these findings verify the value of long-read-powered viromics and provide foundational data on viral evolution and virus-microbe interactions in Earth's destabilized and rapidly disappearing cryosphere. Video Abstract.

Project description:Enteric pathogens cycle between nutrient-rich host and nutrient-poor external environment. These pathogens compete for nutrients while cycling between host and external environment, and often experience starvation. In this context, we have studied the role of a global regulator (NtrC) of Salmonella Typhimurium. The ntrC knockout mutation caused extended lag phase (8 h) and slow growth in the minimal medium. In lag phase, the wild-type cells showed ~60-fold more expression of ntrC gene. Gene expression studies and biochemical assays showed that the extended lag phase and slow growth is due to slow metabolism, instead of nitrogen transport. Further, we observed that ntrC knockout mutation led extended lag phase and slow growth, made ΔntrC mutant unable to compete with wild-type S. Typhimurium in both static and fluctuating nutrient condition. In addition to this, ΔntrC knockout mutant was unable to survive long-term nitrogen starvation (150 days). The nutrient recycling assays and gene expression studies revealed that ntrC gene is essential for rapid recycling of nutrients from the dead cells. Moreover, in the absence of ntrC gene, magnesium limits the nutrient recycling efficiency of S. Typhimurium. Therefore, the ntrC gene, which is often studied with respect to nitrogen scavenging in a low nitrogen growing condition, is required even in the adequate supply of nitrogen to maintain optimal growth and fast exit from the lag phase. Hence, we conclude that, the ntrC expression is essential for competitive fitness of S. Typhimurium under the low and fluctuating nutrient condition. IMPORTANCE S. Typhimurium, both in host and external environment, faces enormous competition from other microorganisms. The competition may take place either in static or in fluctuating nutrient conditions. Thus, how S. Typhimurium survives under such overlapping stress conditions remained unclear. Therefore, using S. Typhimurium as model organism we report that a global regulator NtrC, found in enteric bacteria like Escherichia coli and Salmonella, activates the set of genes and operons involved in rapid adaptation and efficient nutrient recycling/scavenging. These properties enable cells to compete with other microbes under the characteristic feast-or-famine lifestyle of S. Typhimurium. Therefore, this work helps us to understand the starvation physiology of the enteric bacterial pathogen S. Typhimurium.

Dataset Information

FIT: statistical modeling tool for transcriptome dynamics under fluctuating field conditions.

Publications

FIT: statistical modeling tool for transcriptome dynamics under fluctuating field conditions.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets