Genomic Basis of Transcriptome Dynamics in Rice under Field Conditions.

How genetic variations affect gene expression dynamics of field-grown plants remains unclear. Expression quantitative trait loci (eQTL) analysis is frequently used to find genomic regions underlying gene expression polymorphisms. This approach requires transcriptome data for the complete set of the QTL mapping population under the given conditions. Therefore, only a limited range of environmental conditions is covered by a conventional eQTL analysis. We sampled sparse time series of field-grown rice from chromosome segment substitution lines (CSSLs) and conducted RNA sequencing (RNA-Seq). Then, by using statistical analysis integrating meteorological data and the RNA-Seq data, we identified 1,675 eQTLs leading to polymorphisms in expression dynamics under field conditions. A genomic region on chromosome 11 influences the expression of several defense-related genes in a time-of-day- and scaled-age-dependent manner. This includes the eQTLs that possibly influence the time-of-day- and scaled-age-dependent differences in the innate immunity between Koshihikari and Takanari. Based on the eQTL and meteorological data, we successfully predicted gene expression under environments different from training environments and in rice cultivars with more complex genotypes than the CSSLs. Our novel approach of eQTL identification facilitated the understanding of the genetic architecture of expression dynamics under field conditions, which is difficult to assess by conventional eQTL studies. The prediction of expression based on eQTLs and environmental information could contribute to the understanding of plant traits under diverse field conditions.

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

MOTIVATION: 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. RESULTS: : 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. AVAILABILITY AND IMPLEMENTATION: Freely available from CRAN ( https://cran.r-project.org/web/packages/FIT/ ). CONTACT: : anagano@agr.ryukoku.ac.jp. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Alternative stable states, nonlinear behavior, and predictability of microbiome dynamics.

BACKGROUND: Microbiome dynamics are both crucial indicators and potential drivers of human health, agricultural output, and industrial bio-applications. However, predicting microbiome dynamics is notoriously difficult because communities often show abrupt structural changes, such as "dysbiosis" in human microbiomes. METHODS: We integrated theoretical frameworks and empirical analyses with the aim of anticipating drastic shifts of microbial communities. We monitored 48 experimental microbiomes for 110 days and observed that various community-level events, including collapse and gradual compositional changes, occurred according to a defined set of environmental conditions. We analyzed the time-series data based on statistical physics and non-linear mechanics to describe the characteristics of the microbiome dynamics and to examine the predictability of major shifts in microbial community structure. RESULTS: We confirmed that the abrupt community changes observed through the time-series could be described as shifts between "alternative stable states" or dynamics around complex attractors. Furthermore, collapses of microbiome structure were successfully anticipated by means of the diagnostic threshold defined with the "energy landscape" analysis of statistical physics or that of a stability index of nonlinear mechanics. CONCLUSIONS: The results indicate that abrupt microbiome events in complex microbial communities can be forecasted by extending classic ecological concepts to the scale of species-rich microbial systems. Video Abstract.

Duration of cold exposure defines the rate of reactivation of a perennial FLC orthologue via H3K27me3 accumulation.

Vernalisation is the process in which long-term cold exposure makes plants competent to flower. In vernalisation of Arabidopsis thaliana, a floral repressor, AtFLC, undergoes epigenetic silencing. Although the silencing of AtFLC is maintained under warm conditions after a sufficient duration of cold, FLC orthologues are reactivated under the same conditions in perennial plants, such as A. halleri. In contrast to the abundant knowledge on cold requirements in AtFLC silencing, it has remained unknown how cold duration affects the reactivation of perennial FLC. Here, we analysed the dynamics of A. halleri FLC (AhgFLC) mRNA, H3K4me3, and H3K27me3 over 8 weeks and 14 weeks cold followed by warm conditions. We showed that the minimum levels of AhgFLC mRNA and H3K4me3 were similar between 8 and 14 weeks vernalisation; however, the maximum level of H3K27me3 was higher after 14 weeks than after 8 weeks vernalisation. Combined with mathematical modelling, we showed that H3K27me3 prevents a rapid increase in AhgFLC expression in response to warm temperatures after vernalisation, which controls AhgFT expression and the initiation of flowering. Thus, the duration of cold defines the rate of AhgFLC reactivation via the buffering function of H3K27me3 against temperature increase.

Repressive chromatin modification underpins the long-term expression trend of a perennial flowering gene in nature.

Natural environments require organisms to possess robust mechanisms allowing responses to seasonal trends. In Arabidopsis halleri, the flowering regulator AhgFLC shows upregulation and downregulation phases along with long-term past temperature, but the underlying machinery remains elusive. Here, we investigate the seasonal dynamics of histone modifications, H3K27me3 and H3K4me3, at AhgFLC in a natural population. Our advanced modelling and transplant experiments reveal that H3K27me3-mediated chromatin regulation at AhgFLC provides two essential properties. One is the ability to respond to the long-term temperature trends via bidirectional interactions between H3K27me3 and H3K4me3; the other is the ratchet-like character of the AhgFLC system, i.e. reversible in the entire perennial life cycle but irreversible during the upregulation phase. Furthermore, we show that the long-term temperature trends are locally indexed at AhgFLC in the form of histone modifications. Our study provides a more comprehensive understanding of H3K27me3 function at AhgFLC in a complex natural environment.

Publisher Correction: Annual transcriptome dynamics in natural environments reveals plant seasonal adaptation.

In Fig. 3b of the version of this Article originally published, a number of arrows indicating repression of downstream processes were mistakenly formatted as arrows indicating activation of downstream processes. This has now been amended in all versions of the Article.

Annual transcriptome dynamics in natural environments reveals plant seasonal adaptation.

As most organisms have evolved in seasonal environments, their environmental responses should be adapted to seasonal transitions. Here we show that the combination of temperature and day length shapes the seasonal dynamics of the transcriptome and adaptation to seasonal environments in a natural habitat of a perennial plant Arabidopsis halleri subsp. gemmifera. Weekly transcriptomes for two years and bihourly diurnal transcriptomes on the four equinoxes/solstices, identified 2,879 and 7,185 seasonally- and diurnally-oscillating genes, respectively. Dominance of annual temperature changes for defining seasonal oscillations of gene expressions was indicated by controlled environment experiments manipulating the natural 1.5-month lag of temperature behind day length. We found that plants have higher fitness in 'natural' chambers than in 'unnatural' chambers simulating in-phase and anti-phase oscillations between temperature and day length. Seasonal temperature responses were disturbed in unnatural chambers. Our results demonstrate how plants use multiple types of environmental information to adapt to seasonal environments.

Possibility of short-term probabilistic forecasts for large earthquakes making good use of the limitations of existing catalogs.

Earthquakes are quite hard to predict. One of the possible reasons can be the fact that the existing catalogs of past earthquakes are limited at most to the order of 100 years, while their characteristic time scale is sometimes greater than that time span. Here we rather use these limitations positively and characterize some large earthquake events as abnormal events that are not included there. When we constructed probabilistic forecasts for large earthquakes in Japan based on similarity and difference to their past patterns-which we call known and unknown abnormalities, respectively-our forecast achieved probabilistic gains of 5.7 and 2.4 against a time-independent model for main shocks with the magnitudes of 7 or above. Moreover, the two abnormal conditions covered 70% of days whose maximum magnitude was 7 or above.

Decision-making ability of Physarum polycephalum enhanced by its coordinated spatiotemporal oscillatory dynamics.

An amoeboid unicellular organism, a plasmodium of the true slime mold Physarum polycephalum, exhibits complex spatiotemporal oscillatory dynamics and sophisticated information processing capabilities while deforming its amorphous body. We previously devised an 'amoeba-based computer (ABC),' that implemented optical feedback control to lead this amoeboid organism to search for a solution to the traveling salesman problem (TSP). In the ABC, the shortest TSP route (the optimal solution) is represented by the shape of the organism in which the body area (nutrient absorption) is maximized while the risk of being exposed to aversive light stimuli is minimized. The shortness of the TSP route found by ABC, therefore, serves as a quantitative measure of the optimality of the decision made by the organism. However, it remains unclear how the decision-making ability of the organism originates from the oscillatory dynamics of the organism. We investigated the number of coexisting traveling waves in the spatiotemporal patterns of the oscillatory dynamics of the organism. We show that a shorter TSP route can be found when the organism exhibits a lower number of traveling waves. The results imply that the oscillatory dynamics are highly coordinated throughout the global body. Based on the results, we discuss the fact that the decision-making ability of the organism can be enhanced not by uncorrelated random fluctuations, but by its highly coordinated oscillatory dynamics.

Characterizing global evolutions of complex systems via intermediate network representations.

Recent developments in measurement techniques have enabled us to observe the time series of many components simultaneously. Thus, it is important to understand not only the dynamics of individual time series but also their interactions. Although there are many methods for analysing the interaction between two or more time series, there are very few methods that describe global changes of the interactions over time. Here, we propose an approach to visualise time evolution for the global changes of the interactions in complex systems. This approach consists of two steps. In the first step, we construct a meta-time series of networks. In the second step, we analyse and visualise this meta-time series by using distance and recurrence plots. Our two-step approach involving intermediate network representations elucidates the half-a-day periodicity of foreign exchange markets and a singular functional network in the brain related to perceptual alternations.