Use it or average it: stochasticity in plant development

Getting it right: suppression and leveraging of noise in robust decision-making

Noise is a ubiquitous feature for all organisms growing in nature. Noise (defined here as stochastic variation) in the availability of nutrients, water and light profoundly impacts their growth and development. Not only is noise present as an external factor but cellular processes themselves are noisy. Therefore, it is remarkable that organisms can display robust control of growth and development despite noise. To survive, various mechanisms to suppress noise have evolved. However, it is also becoming apparent that noise is not just a nuisance that organisms must suppress but can be beneficial as low noise can facilitate the response of an organism to a sub-threshold input signal in a stochastic resonance mechanism. This review discusses mechanisms capable of noise suppression or noise leveraging that might play a significant role in robust temporal regulation of an organism's response to their noisy environment.

Robust organ size in Arabidopsis is primarily governed by cell growth rather than cell division patterns

Organ sizes and shapes are highly reproducible, or robust, within a species and individuals. Arabidopsis thaliana sepals, which are the leaf-like organs that enclose flower buds, have consistent size and shape, indicating robust development. Cell growth is locally heterogeneous due to intrinsic and extrinsic noise. To achieve robust organ shape, fluctuations in cell growth must average to an even growth rate, which requires that fluctuations are uncorrelated or anti-correlated in time and space. Here, we live image and quantify the development of sepals with an increased or decreased number of cell divisions (lgo mutant and LGO overexpression, respectively), a mutant with altered cell growth variability (ftsh4), and double mutants combining these. Changes in the number of cell divisions do not change the overall growth pattern. By contrast, in ftsh4 mutants, cell growth accumulates in patches of over- and undergrowth owing to correlations that impair averaging, resulting in increased organ shape variability. Thus, we demonstrate in vivo that the number of cell divisions does not affect averaging of cell growth, preserving robust organ morphogenesis, whereas correlated growth fluctuations impair averaging.

What Is a Plant Cell Type in the Age of Single-Cell Biology? It's Complicated

One of the fundamental questions in developmental biology is how a cell is specified to differentiate as a specialized cell type. Traditionally, plant cell types were defined based on their function, location, morphology, and lineage. Currently, in the age of single-cell biology, researchers typically attempt to assign plant cells to cell types by clustering them based on their transcriptomes. However, because cells are dynamic entities that progress through the cell cycle and respond to signals, the transcriptome also reflects the state of the cell at a particular moment in time, raising questions about how to define a cell type. We suggest that these complexities and dynamics of cell states are of interest and further consider the roles signaling, stochasticity, cell cycle, and mechanical forces play in plant cell fate specification. Once established, cell identity must also be maintained. With the wealth of single-cell data coming out, the field is poised to elucidate both the complexity and dynamics of cell states.

Noise reduction by upstream open reading frames

Gene expression is prone to burst production, making it a highly noisy process that requires additional controls. Upstream open reading frames (uORFs) are widely present in the 5' leader sequences of 30-50% of eukaryotic messenger RNAs^1-3. The translation of uORFs can repress the translation efficiency of the downstream main coding sequences. Whether the low translation efficiency leads to a different variation, or noise, in gene expression has not been investigated, nor has the direct biological impact of uORF-repressed translation. Here we show that uORFs achieve low but precise protein production in plant cells, possibly by reducing the protein production rate. We also demonstrate that, by buffering a stable TIMING OF CAB EXPRESSION 1 (TOC1) protein production level, uORFs contribute to the robust operation of the plant circadian clock. Our results provide both an action model and the biological impact of uORFs in translational control to mitigate transcriptional noise for precise protein production.

Noise suppression in stochastic genetic circuits using PID controllers

Inside individual cells, protein population counts are subject to molecular noise due to low copy numbers and the inherent probabilistic nature of biochemical processes. We investigate the effectiveness of proportional, integral and derivative (PID) based feedback controllers to suppress protein count fluctuations originating from two noise sources: bursty expression of the protein, and external disturbance in protein synthesis. Designs of biochemical reactions that function as PID controllers are discussed, with particular focus on individual controllers separately, and the corresponding closed-loop system is analyzed for stochastic controller realizations. Our results show that proportional controllers are effective in buffering protein copy number fluctuations from both noise sources, but this noise suppression comes at the cost of reduced static sensitivity of the output to the input signal. In contrast, integral feedback has no effect on the protein noise level from stochastic expression, but significantly minimizes the impact of external disturbances, particularly when the disturbance comes at low frequencies. Counter-intuitively, integral feedback is found to amplify external disturbances at intermediate frequencies. Next, we discuss the design of a coupled feedforward-feedback biochemical circuit that approximately functions as a derivate controller. Analysis using both analytical methods and Monte Carlo simulations reveals that this derivative controller effectively buffers output fluctuations from bursty stochastic expression, while maintaining the static input-output sensitivity of the open-loop system. In summary, this study provides a systematic stochastic analysis of biochemical controllers, and paves the way for their synthetic design and implementation to minimize deleterious fluctuations in gene product levels.

In the noisy cellular environment, biochemical species such as genes, RNAs and proteins that often occur at low molecular counts, are subject to considerable stochastic fluctuations in copy numbers over time. How cellular biochemical processes function reliably in the face of such randomness is an intriguing fundamental problem. Increasing evidence suggests that random fluctuations (noise) in protein copy numbers play important functional roles, such as driving genetically identical cells to different cell fates. Moreover, many disease states have been attributed to elevated noise levels in specific proteins. Here we systematically investigate design of biochemical systems that function as proportional, integral and derivative-based feedback controllers to suppress protein count fluctuations arising from bursty expression of the protein and external disturbance in protein synthesis. Our results show that different controllers are effective in buffering different noise components, and identify ranges of feedback gain for minimizing deleterious fluctuations in protein levels.

Developmental stochasticity and variation in floral phyllotaxis

Floral phyllotaxis is a relatively robust phenotype; trimerous and pentamerous arrangements are widely observed in monocots and core eudicots. Conversely, it also shows variability in some angiosperm clades such as 'ANA' grade (Amborellales, Nymphaeales, and Austrobaileyales), magnoliids, and Ranunculales. Regardless of the phylogenetic relationship, however, phyllotactic pattern formation appears to be a common process. What are the causes of the variability in floral phyllotaxis and how has the variation of floral phyllotaxis contributed to floral diversity? In this review, I summarize recent progress in studies on two related fields to develop answers to these questions. First, it is known that molecular and cellular stochasticity are inevitably found in biological systems, including plant development. Organisms deal with molecular stochasticity in several ways, such as dampening noise through gene networks or maintaining function through cellular redundancy. Recent studies on molecular and cellular stochasticity suggest that stochasticity is not always detrimental to plants and that it is also essential in development. Second, studies on vegetative and inflorescence phyllotaxis have shown that plants often exhibit variability and flexibility in phenotypes. Three types of phyllotaxis variations are observed, namely, fluctuation around the mean, transition between regular patterns, and a transient irregular organ arrangement called permutation. Computer models have demonstrated that stochasticity in the phyllotactic pattern formation plays a role in pattern transitions and irregularities. Variations are also found in the number and positioning of floral organs, although it is not known whether such variations provide any functional advantages. Two ways of diversification may be involved in angiosperm floral evolution: precise regulation of organ position and identity that leads to further specialization of organs and organ redundancy that leads to flexibility in floral phyllotaxis.

Using single-plant-omics in the field to link maize genes to functions and phenotypes

Most of our current knowledge on plant molecular biology is based on experiments in controlled laboratory environments. However, translating this knowledge from the laboratory to the field is often not straightforward, in part because field growth conditions are very different from laboratory conditions. Here, we test a new experimental design to unravel the molecular wiring of plants and study gene-phenotype relationships directly in the field. We molecularly profiled a set of individual maize plants of the same inbred background grown in the same field and used the resulting data to predict the phenotypes of individual plants and the function of maize genes. We show that the field transcriptomes of individual plants contain as much information on maize gene function as traditional laboratory-generated transcriptomes of pooled plant samples subject to controlled perturbations. Moreover, we show that field-generated transcriptome and metabolome data can be used to quantitatively predict individual plant phenotypes. Our results show that profiling individual plants in the field is a promising experimental design that could help narrow the lab-field gap.

Does Gene Expression Noise Play a Functional Role in Plants?

Gene expression in individual cells can be surprisingly noisy. In unicellular organisms this noise can be functional; for example, by allowing a subfraction of the population to prepare for environmental stress. The role of gene expression noise in multicellular organisms has, however, remained unclear. In this review, we discuss how new techniques are revealing an unexpected level of variability in gene expression between and within genetically identical plants. We describe recent progress as well as speculate on the function of transcriptional noise as a mechanism for generating functional phenotypic diversity in plants.

Nanoscale structure, mechanics and growth of epidermal cell walls

This article briefly reviews recent advances in nano-scale and micro-scale assessments of primary cell wall structure, mechanical behaviors and expansive growth. Cellulose microfibrils have hydrophobic and hydrophilic faces which may selectively bind different matrix polysaccharides and adjacent microfibrils. These distinctive binding interactions may guide partially aligned cellulose microfibrils in primary cell walls to form a planar, load-bearing network within each lamella of polylamellate walls. Consideration of expansive growth of cross-lamellate walls leads to a surprising inference: side-by-side sliding of microfibrils may be a key rate-limiting physical step, potentially targeted by specific wall loosening agents. Atomic force microscopy shows different patterns of microfibril movement during force-driven extension versus enzymatic loosening. Consequently, simulations of cell growth as elastic deformation of isotropic cell walls may need to be augmented to incorporate the distinctive behavior of growing cell walls.