New Phytologist最新文献

Alternative splicing (AS) is a mechanism by which cells generate abundant protein diversity from a limited number of genes (Baralle & Giudice, 2017). AS plays a crucial role in regulating various life activities such as growth, development, and aging in plants (Zhu et al., 2017; Godoy Herz & Kornblihtt, 2019; Jabre et al., 2019; Chen et al., 2020; Reddy et al., 2020; Zhang et al., 2020), where it greatly influences plant growth, development, and response to biotic and abiotic stresses (Motion et al., 2015; Laloum et al., 2018; Chaudhary et al., 2019; Chen et al., 2021; Ganie & Reddy, 2021; Saini et al., 2021; Zhu et al., 2023; Supporting Information Fig. S1). The traditional method for the identification of AS is semi-quantitative RT-PCR, which is easy to perform (Palusa et al., 2007; Li et al., 2020; Riegler et al., 2021; Han et al., 2022). Quantitative PCR (qPCR) is also widely used in AS research, as it enables real-time monitoring of fluorescence signals and accurate quantification of isoform copy numbers through the use of specific primers (Hefti et al., 2018; Liu et al., 2018; Huang et al., 2021). With the emergence of digital PCR (dPCR), the identification methods of AS have become more diversified, which disperses each single target fragment into separate droplets as much as possible through the calculation of positive droplets (Fig. S2; Gao et al., 2021).

Based on the urgent need for the accurate quantification of various isoforms, an AS detection method called QuantAS was established (Fig. 1), which allows us to accurately quantify all isoforms of genes based on absolute quantification technology and specific primer design. The method utilizes isoform-specific primers to overcome the isoform identification difficulty caused by different AS events and is designed by using the functional coding region as the isoform structure classification unit to ensure isoform independence (Fig. 2a). RT-qPCR enables real-time monitoring of changes in the fluorescence signal, quantification of differences between expression levels, and simultaneous detection of multiple in a single reaction. According to the copy number of different isoforms, isoform expression patterns can be identified by combining with absolute quantitative techniques. This method greatly increases the accuracy of identification and reduces the cost of repeated experiments. Furthermore, the absolute quantification of AS isoforms employing the combination of qPCR and dPCR could provide their respective advantages, thus rapidly obtaining all isoform info

Iron is an essential micronutrient for plant growth and development, as well as for crop productivity and the quality of their derived products (Briat et al., 2015). This is because iron is a co-factor for several metalloproteins involved in essential physiological processes such as respiration in mitochondria or photosynthesis in chloroplasts. In most soils, iron is present in the form of insoluble oxides/hydroxides rendering it poorly available to plants. To cope with this poor bioavailability, plants have evolved sophisticated strategies to take up iron from soils (Berger et al., 2023; Li et al., 2023). Arabidopsis plants preferentially use the reduction-based strategy (the so-called Strategy I), as do most dicots and nongraminous monocots. This strategy relies on the secretion of protons into the rhizosphere by AHA2 (ARABIDOPSIS H⁺ ATPASE 2) to decrease the pH of the soil solution and solubilize oxidized iron (Fe³⁺), which is then reduced to Fe²⁺ by FRO2 (ferric reduction oxidases 2), and subsequently taken up into the root by IRT1 (iron-regulated transporter 1). This process is tightly regulated since, in excess, iron is also detrimental to the plant because of its capacity to generate reactive oxygen species (ROS) via the Fenton reaction.

The regulation of Iron homeostasis is well-conserved in plants, and is primarily controlled at the transcriptional level (Berger et al., 2023; Li et al., 2023). It relies on an intricate regulatory network involving several regulatory proteins, among which the bHLH (basic helix–loop–helix) transcription factors play a preponderant role (Fig. 1). For instance, Arabidopsis has 17 different bHLH proteins (from six bHLH clades) that regulate iron homeostasis. This regulatory network is composed of two modules. The first module relies on FIT/bHLH29 (FER-LIKE IRON DEFICIENCY INDUCED TRANSCRIPTION FACTOR; clade IIIa). FIT is a direct positive regulator of FRO2 and IRT1 expression (Fig. 1). To achieve its function, FIT interacts with bHLH38, bHLH39, bHLH100, and bHLH101 (clade Ib), forming heterodimers with partially overlapping roles. In the second module, another set of bHLH transcription factors positively regulate the expression of FIT and clade Ib bHLHs (Fig. 1). It involves ILR3/bHLH105 (iaa-leucine resistant 3), IDT1/bHLH34 (iron deficiency tolerant 1), bHLH104, bHLH115 from clade IVc, and URI/bLHL121 (UPSTREAM REGULATOR OF IRT1) from clade IVb (Tissot et al., 2019; Gao et al., 2020). By contrast, PYE/bHLH47 (POPEYE; clade IVb) is a negative regulator of clade Ib bHLH expression (Pu & Liang, 2023).

In their study, Yang et al. demonstrated that the H2B histone variant HTB4 negatively regulates leaf senescence in an iron-dependent manner. For instance, HTB4 expression is in

There is much circumstantial evidence that flowering plants were diverse by the Lower Cretaceous and were pollinated by insects (Arber & Parkin, 1907; Crepet & Friis, 1987). Arguments supporting this come from extant and fossil flower morphology, fossilized traces of interactions, and the pollination modes of surviving early lineages. First, some extinct gymnosperms had bisporangiate cones (with both micro- and megasporangia) surrounded by bracts (Fig. 1), and many such cones show traces of having been chewed by mandibulate insects (Peris et al., 2017). Fossils of flower-associated flies also provide evidence of the existence of strobilus–pollinator interactions from the Permian to the Jurassic (Ren, 1998; Ren et al., 2009; Khramov et al., 2023). Second, if flowers evolved from bisporangiate strobili, they were not well suited for wind pollination because simultaneous optimization for pollen export and pollen capture is structurally difficult. The angiosperms' defining enclosure of the megasporangium inside surrounding structures may also point to ancestral insect pollination, as argued by Arber & Parkin (1907: 73), ‘In the case of the angiosperms such primitive entomophily was preserved and rendered permanent by a transference of the pollen-collecting mechanism from the ovule itself to the carpel or megasporophyll and by the closure of this organ.’ Third, all angiosperms, but no living gymnosperm, produce pollenkitt, an oily substance on the surface of pollen that serves as a glue to attach pollen to animal vectors (Hesse, 1980). In wind-pollinated plants, pollenkitt abundance is secondarily reduced. Lastly, the oldest lineages of flowering plants that still survive today are pollinated by flies, moths, and beetles (Luo et al., 2018).

While insect pollination thus undoubtedly played a decisive role in the evolution of flowers, a phylogenetically informed analysis of pollination by insects, vertebrates, wind, and water across a full modern phylogeny of plants has been lacking. This is what Stephens et al. now provide in an article published in this issue of New Phytologist (2023; 880–891). Using a time-calibrated phylogeny with 1201 species representing the major lineages of flowering plants, together with geographic occurrence data, Stephens et al. quantified the timing and environmental associations of pollination shifts. Where possible, they scored pollination at the species level, either from published fieldwork (n = 432) or from the pollinator syndrome approach (n = 728). Where no information was available for a particular species, taxa were scored at genus (n = 131) or family (n = 4) level. In some analyses, 180 taxa with missing or polymorphic data were excluded from the analyses.

All major angiosperm clades (