Information integration and decision making in flowering time control

CIRCADIAN CLOCK-ASSOCIATED1 Delays Flowering by Directly Inhibiting the Transcription of BcSOC1 in Pak-choi

Flowering is critical to the success of plant propagation. The MYB family transcription factor CIRCADIAN CLOCK-ASSOCIATED1 (CCA1) is an essential component of the core loop of the circadian clock and plays a crucial role in regulating plant flowering time. In this study, we found that photoperiod affects the expression pattern and expression level of BcCCA1, which is delayed flowering time under short-day conditions in Pak-choi [Brassica campestris (syn. Brassica rapa) ssp. chinensis]. We detected overexpression and silencing of BcCCA1 in Pak-choi, resulting in delayed and promoted flowering time, respectively. Furthermore, we also discovered that FLOWERING LOCUS C (BcFLC) and SUPPRESSOR OF CONSTANS1 (BcSOC1) were expressed significantly differently in BcCCA1 overexpression and silencing plants compared with control plants. Therefore, we further investigated the interaction relationship between BcCCA1, BcFLC, and BcSOC1, and the results showed that BcCCA1 and BcFLC as a complex interacted with each other. Moreover, both BcCCA1 and BcFLC can directly bind to the promoter of BcSOC1 and repress its transcription, and BcCCA1 can form a complex with BcFLC to enhance the transcriptional inhibition of BcSOC1 by BcFLC. This study reveals a new mechanism by which the circadian clock regulates flowering time.

Plant environmental memory: implications, mechanisms and opportunities for plant scientists and beyond

Plants are extremely plastic organisms. They continuously receive and integrate environmental information and adjust their growth and development to favour fitness and survival. When this integration of information affects subsequent life stages or the development of subsequent generations, it can be considered an environmental memory. Thus, plant memory is a relevant mechanism by which plants respond adaptively to different environments. If the cost of maintaining the response is offset by its benefits, it may influence evolutionary trajectories. As such, plant memory has a sophisticated underlying molecular mechanism with multiple components and layers. Nonetheless, when mathematical modelling is combined with knowledge of ecological, physiological, and developmental effects as well as molecular mechanisms as a tool for understanding plant memory, the combined potential becomes unfathomable for the management of plant communities in natural and agricultural ecosystems. In this review, we summarize recent advances in the understanding of plant memory, discuss the ecological requirements for its evolution, outline the multilayered molecular network and mechanisms required for accurate and fail-proof plant responses to variable environments, point out the direct involvement of the plant metabolism and discuss the tremendous potential of various types of models to further our understanding of the plant's environmental memory. Throughout, we emphasize the use of plant memory as a tool to unlock the secrets of the natural world.

Identification of Flowering Regulatory Networks and Hub Genes Expressed in the Leaves of Elymus sibiricus L. Using Comparative Transcriptome Analysis

Flowering is a significant stage from vegetative growth to reproductive growth in higher plants, which impacts the biomass and seed yield. To reveal the flowering time variations and identify the flowering regulatory networks and hub genes in Elymus sibiricus, we measured the booting, heading, and flowering times of 66 E. sibiricus accessions. The booting, heading, and flowering times varied from 136 to 188, 142 to 194, and 148 to 201 days, respectively. The difference in flowering time between the earliest- and the last-flowering accessions was 53 days. Furthermore, transcriptome analyses were performed at the three developmental stages of six accessions with contrasting flowering times. A total of 3,526 differentially expressed genes (DEGs) were predicted and 72 candidate genes were identified, including transcription factors, known flowering genes, and plant hormone-related genes. Among them, four candidate genes (LATE, GA2OX6, FAR3, and MFT1) were significantly upregulated in late-flowering accessions. LIMYB, PEX19, GWD3, BOR7, PMEI28, LRR, and AIRP2 were identified as hub genes in the turquoise and blue modules which were related to the development time of flowering by weighted gene co-expression network analysis (WGCNA). A single-nucleotide polymorphism (SNP) of LIMYB found by multiple sequence alignment may cause late flowering. The expression pattern of flowering candidate genes was verified in eight flowering promoters (CRY, COL, FPF1, Hd3, GID1, FLK, VIN3, and FPA) and four flowering suppressors (CCA1, ELF3, Ghd7, and COL4) under drought and salt stress by qRT-PCR. The results suggested that drought and salt stress activated the flowering regulation pathways to some extent. The findings of the present study lay a foundation for the functional verification of flowering genes and breeding of new varieties of early- and late-flowering E. sibiricus.

Feeling Every Bit of Winter - Distributed Temperature Sensitivity in Vernalization

Temperature intrinsically influences all aspects of biochemical and biophysical processes. Organisms have therefore evolved strategies to buffer themselves against thermal perturbations. Many organisms also use temperature signals as cues to align behavior and development with certain seasons. These developmentally important thermosensory mechanisms have generally been studied in constant temperature conditions. However, environmental temperature is an inherently noisy signal, and it has been unclear how organisms reliably extract specific temperature cues from fluctuating temperature profiles. In this context, we discuss plant thermosensory responses, focusing on temperature sensing throughout vernalization in Arabidopsis. We highlight many different timescales of sensing, which has led to the proposal of a distributed thermosensing paradigm. Within this paradigm, we suggest a classification system for thermosensors. Finally, we focus on the longest timescale, which is most important for sensing winter, and examine the different mechanisms in which memory of cold exposure can be achieved.