BackgroundMajor secondary metabolites, including flavonoids, caffeine, and theanine, are important components of tea products and are closely related to the taste, flavor, and health benefits of tea. Secondary metabolite biosynthesis in Camellia sinensis is differentially regulated in different tissues during growth and development. Until now, little was known about the expression patterns of genes involved in secondary metabolic pathways or their regulatory mechanisms. This study aimed to generate expression profiles for C. sinensis tissues and to build a gene regulation model of the secondary metabolic pathways.ResultsRNA sequencing was performed on 13 different tissue samples from various organs and developmental stages of tea plants, including buds and leaves of different ages, stems, flowers, seeds, and roots. A total of 43.7 Gbp of raw sequencing data were generated, from which 347,827 unigenes were assembled and annotated. There were 46,693, 8446, 3814, 10,206, and 4948 unigenes specifically expressed in the buds and leaves, stems, flowers, seeds, and roots, respectively. In total, 1719 unigenes were identified as being involved in the secondary metabolic pathways in C. sinensis, and the expression patterns of the genes involved in flavonoid, caffeine, and theanine biosynthesis were characterized, revealing the dynamic nature of their regulation during plant growth and development. The possible transcription factor regulation network for the biosynthesis of flavonoid, caffeine, and theanine was built, encompassing 339 transcription factors from 35 families, namely bHLH, MYB, and NAC, among others. Remarkably, not only did the data reveal the possible critical check points in the flavonoid, caffeine, and theanine biosynthesis pathways, but also implicated the key transcription factors and related mechanisms in the regulation of secondary metabolite biosynthesis.ConclusionsOur study generated gene expression profiles for different tissues at different developmental stages in tea plants. The gene network responsible for the regulation of the secondary metabolic pathways was analyzed. Our work elucidated the possible cross talk in gene regulation between the secondary metabolite biosynthetic pathways in C. sinensis. The results increase our understanding of how secondary metabolic pathways are regulated during plant development and growth cycles, and help pave the way for genetic selection and engineering for germplasm improvement.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-015-1773-0) contains supplementary material, which is available to authorized users.

Cerebral cortical brain networks possess a number of conspicuous features of structure and dynamics. First, these networks have an intricate, non-random organization. In particular, they are structured in a hierarchical modular fashion, from large-scale regions of the whole brain, via cortical areas and area subcompartments organized as structural and functional maps to cortical columns, and finally circuits made up of individual neurons. Second, the networks display self-organized sustained activity, which is persistent in the absence of external stimuli. At the systems level, such activity is characterized by complex rhythmical oscillations over a broadband background, while at the cellular level, neuronal discharges have been observed to display avalanches, indicating that cortical networks are at the state of self-organized criticality (SOC). We explored the relationship between hierarchical neural network organization and sustained dynamics using large-scale network modeling. Previously, it was shown that sparse random networks with balanced excitation and inhibition can sustain neural activity without external stimulation. We found that a hierarchical modular architecture can generate sustained activity better than random networks. Moreover, the system can simultaneously support rhythmical oscillations and SOC, which are not present in the respective random networks. The mechanism underlying the sustained activity is that each dense module cannot sustain activity on its own, but displays SOC in the presence of weak perturbations. Therefore, the hierarchical modular networks provide the coupling among subsystems with SOC. These results imply that the hierarchical modular architecture of cortical networks plays an important role in shaping the ongoing spontaneous activity of the brain, potentially allowing the system to take advantage of both the sensitivity of critical states and the predictability and timing of oscillations for efficient information processing.

One of the most prominent architecture properties of neural networks in the brain is the hierarchical modular structure. How does the structure property constrain or improve brain function? It is thought that operating near criticality can be beneficial for brain function. Here, we find that networks with modular structure can extend the parameter region of coupling strength over which critical states are reached compared to non-modular networks. Moreover, we find that one aspect of network function-dynamical range-is highest for the same parameter region. Thus, hierarchical modularity enhances robustness of criticality as well as function. However, too much modularity constrains function by preventing the neural networks from reaching critical states, because the modular structure limits the spreading of avalanches. Our results suggest that the brain may take advantage of the hierarchical modular structure to attain criticality and enhanced function.

Self-organized critical states (SOCs) and stochastic oscillations (SOs) are simultaneously observed in neural systems, which appears to be theoretically contradictory since SOCs are characterized by scale-free avalanche sizes but oscillations indicate typical scales. Here, we show that SOs can emerge in SOCs of small size systems due to temporal correlation between large avalanches at the finite-size cutoff, resulting from the accumulation-release process in SOCs. In contrast, the critical branching process without accumulation-release dynamics cannot exhibit oscillations. The reconciliation of SOCs and SOs is demonstrated both in the sandpile model and robustly in biologically plausible neuronal networks. The oscillations can be suppressed if external inputs eliminate the prominent slow accumulation process, providing a potential explanation of the widely studied Berger effect or event-related desynchronization in neural response. The features of neural oscillations and suppression are confirmed during task processing in monkey eye-movement experiments. Our results suggest that finite-size, columnar neural circuits may play an important role in generating neural oscillations around the critical states, potentially enabling functional advantages of both SOCs and oscillations for sensitive response to transient stimuli. DOI: 10.1103/PhysRevLett.116.018101 Self-organized criticality [1] is a key concept for describing the emergence of complexity in many natural systems [2]. The fingerprint of self-organized critical states (SOCs), the power-law distribution of avalanche sizes, means that the activity has no characteristic scale in the thermodynamic limit. As excellent functional complex systems in nature, neural systems in the brain have been supposed to operate at SOCs. Indeed, SOCs of neuronal firing activity have been observed in experiments with electrode arrays [2-4] and have been studied intensively in computational models [2,5,6]. It has been shown that critical states have functional advantages for both the sensory system [7] and memory [8], and they play an important role in the development of neural systems [9].On the other hand, stochastic oscillation (SO) in brain activity has been observed for more than 80 years [10]. Oscillations characterized by repetition of activities with typical scales are believed to be essential to brain functions, especially to provide timing, predictability, coherence, and integration in neural information processing [11]. Several different oscillation bands exist and appear in different states of the brain [10]. The synchronization between inhibitory neurons has been found to be crucial for gamma oscillations (30-70 Hz) [12]. Neural field models [13] indicated that resonance between thalamus and cortex can generate alpha oscillations (8-13 Hz). Despite many modeling studies, a commonly accepted mechanism of alpha rhythm is still lacking [14]. This slow oscillation is particularly obvious during the resting states without systematic external stimuli (i.e., eyes closed). I...