High-Resolution Temporal Profiling of Transcripts during Arabidopsis Leaf Senescence Reveals a Distinct Chronology of Processes and Regulation    

Abstract

Leaf senescence is an essential developmental process that impacts dramatically on crop yields and involves altered regulation of thousands of genes and many metabolic and signaling pathways, resulting in major changes in the leaf. The regulation of senescence is complex, and although senescence regulatory genes have been characterized, there is little information on how these function in the global control of the process. We used microarray analysis to obtain a highresolution time-course profile of gene expression during development of a single leaf over a 3-week period to senescence. A complex experimental design approach and a combination of methods were used to extract high-quality replicated data and to identify differentially expressed genes. The multiple time points enable the use of highly informative clustering to reveal distinct time points at which signaling and metabolic pathways change. Analysis of motif enrichment, as well as comparison of transcription factor (TF) families showing altered expression over the time course, identify clear groups of TFs active at different stages of leaf development and senescence. These data enable connection of metabolic processes, signaling pathways, and specific TF activity, which will underpin the development of network models to elucidate the process of senescence.

Update on the biochemistry of chlorophyll breakdown

“…Transcriptional regulation of Chl breakdown is an integral process within this network. Thus, genes for SGR and most of the CCEs are up-regulated during leaf senescence, independent of the mode of senescence induction (Buchanan-Wollaston et al 2005;Van der Graaff et al 2006;Breeze et al 2011). Furthermore, the transcriptionally regulated genes of the PAO pathway, i.e.…”

How to infer gene networks from expression profiles, revisited

“…Inference of GRNs from transcriptomic datasets is a notoriously difficult task, primarily because the number of probes (genes) on modern microarrays can be several orders of magnitude greater than the number of independent measurements, even for highly resolved and replicated time course studies [6]. The sheer number of genes, combined with the observation that a significant proportion of expression profiles can be correlated over time [6], means that inferring GRNs from microarray data alone may be inherently unidentifiable.…”

“…While measuring the level of protein present in biological systems remains difficult to do on a large scale, measuring the relative abundance of mRNA is comparatively straightforward. Consequently, the generation of highly resolved time-series measurement of transcript levels via multiple microarray experiments has become an increasingly powerful tool for investigating complex biological processes, including developmental programmes [6] and response to abiotic or biotic stress. The inference of GRNs from such time course data has proven useful for disentangling (suspected causal) relationships between genes [7,8], identifying network components important to the biological response, including highly connected genes (so-called 'hubs') and capturing the behaviour of the system under novel perturbations [9,10].…”

Delayed expression of SAGs correlates with longevity in CMS wheat plants compared to its fertile plants

“…It was reported recently that rubisco content in rice was regulated at transcription level in young and post-transcriptional level in old senescent leaves (Suzuki and Makino 2013). Expression of rbcL and rbcS generally declines during senescence (Breeze et al 2011) and decline in rbcL mRNA levels was lesser and slower than rbcS (Suzuki et al 2010). It is known that both cysteine and serine proteases help in protein mobilization in wheat leaves during monocarpic senescence (Table 2, Martínez et al 2007;Chauhan et al 2009).…”