Evolution of floral symmetry

Flowers can be classified into two basic types according to their symmetry: regular flowers have more than one plane of symmetry and irregular flowers have only a single plane of symmetry. The irregular condition is thought to have evolved many times independently from the regular one: most commonly through the appearance of asymmetry along the dorso-ventral axis of the flower. In most cases, the irregular condition is associated with a particular type of inflorescence architecture. To understand the molecular mechanism and evolutionary origin of irregular flowers, we have been investigating genes controlling asymmetry in Antirrhinum. Several mutations have been described in Antirrhinum, a species with irregular flowers, that reduce or eliminate asymmetry along the dorso-ventral axis. We describe the nature of these mutations and how they may be used to analyse the molecular mechanisms underlying floral evolution

Maturation of neuron types in nucleus of solitary tract associated with functional convergence during development of taste circuits

Late fetal through postnatal development in sheep is a period of increasing convergence of afferent taste fibers onto second-order neurons in the nucleus of the solitary tract (NST). To learn whether neuron morphology alters in concert with convergence and neurophysiological development in NST, three-dimensional neuron reconstructions were made of cells in a functionally defined region of gustatory NST from Golgi preparations of the brainstem. Elongate, multipolar, and ovoid neurons were studied in fetuses from 85 days of gestation through the perinatal period (term = 147 days of gestation), to postnatal stages. Somal size and form, and dendritic complexity and extent, increased markedly from 85 to about 110 days of gestation in both of the proposed NST projection neurons, elongate and multipolar. From 130 days of gestation to postnatal ages, growth of dendrites of elongate neurons plateaued or declined, whereas dendrites of multipolar neurons apparently continued to increase in size and extent. In addition, spine density decreased on elongate neurons but remained stable on multipolar neurons. Morphological variables of ovoid cells, proposed interneurons in NST, did not alter over this later period. The data suggest that multipolar, not elongate or ovoid, neurons are logical candidates to receive the increasing afferent fiber input onto NST cells during late gestation. Also, neural activity from taste afferent fibers is more likely to have a role in altering NST neuron morphology at later, rather than earlier, developmental periods. © 1994 Wiley-Liss, Inc.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/50062/1/903450304_ftp.pd

Treatment evolution in high-risk congenital diaphragmatic hernia: ten years\u27 experience with diaphragmatic agenesis.

OBJECTIVE: The objective of this study was to evaluate the impact of newer therapies on the highest risk patients with congenital diaphragmatic hernia (CDH), those with agenesis of the diaphragm. SUMMARY BACKGROUND DATA: CDH remains a significant cause of neonatal mortality. Many novel therapeutic interventions have been used in these infants. Those children with large defects or agenesis of the diaphragm have the highest mortality and morbidity. METHODS: Twenty centers from 5 countries collected data prospectively on all liveborn infants with CDH over a 10-year period. The treatment and outcomes in these patients were examined. Patients were followed until death or hospital discharge. RESULTS: A total of 1,569 patients with CDH were seen between January 1995 and December 2004 in 20 centers. A total of 218 patients (14%) had diaphragmatic agenesis and underwent repair. The overall survival for all patients was 68%, while survival was 54% in patients with agenesis. When patients with diaphragmatic agenesis from the first 2 years were compared with similar patients from the last 2 years, there was significantly less use of ECMO (75% vs. 52%) and an increased use of inhaled nitric oxide (iNO) (30% vs. 80%). There was a trend toward improved survival in patients with agenesis from 47% in the first 2 years to 59% in the last 2 years. The survivors with diaphragmatic agenesis had prolonged hospital stays compared with patients without agenesis (median, 68 vs. 30 days). For the last 2 years of the study, 36% of the patients with agenesis were discharged on tube feedings and 22% on oxygen therapy. CONCLUSIONS: There has been a change in the management of infants with CDH with less frequent use of ECMO and a greater use of iNO in high-risk patients with a potential improvement in survival. However, the mortality, hospital length of stay, and morbidity in agenesis patients remain significant

Fitting a geometric graph to a protein-protein interaction network

Finding a good network null model for protein-protein interaction (PPI) networks is a fundamental issue. Such a model would provide insights into the interplay between network structure and biological function as well as into evolution. Also, network (graph) models are used to guide biological experiments and discover new biological features. It has been proposed that geometric random graphs are a good model for PPI networks. In a geometric random graph, nodes correspond to uniformly randomly distributed points in a metric space and edges (links) exist between pairs of nodes for which the corresponding points in the metric space are close enough according to some distance norm. Computational experiments have revealed close matches between key topological properties of PPI networks and geometric random graph models. In this work, we push the comparison further by exploiting the fact that the geometric property can be tested for directly. To this end, we develop an algorithm that takes PPI interaction data and embeds proteins into a low-dimensional Euclidean space, under the premise that connectivity information corresponds to Euclidean proximity, as in geometric-random graphs.We judge the sensitivity and specificity of the fit by computing the area under the Receiver Operator Characteristic (ROC) curve. The network embedding algorithm is based on multi-dimensional scaling, with the square root of the path length in a network playing the role of the Euclidean distance in the Euclidean space. The algorithm exploits sparsity for computational efficiency, and requires only a few sparse matrix multiplications, giving a complexity of O(N2) where N is the number of proteins.The algorithm has been verified in the sense that it successfully rediscovers the geometric structure in artificially constructed geometric networks, even when noise is added by re-wiring some links. Applying the algorithm to 19 publicly available PPI networks of various organisms indicated that: (a) geometric effects are present and (b) two-dimensional Euclidean space is generally as effective as higher dimensional Euclidean space for explaining the connectivity. Testing on a high-confidence yeast data set produced a very strong indication of geometric structure (area under the ROC curve of 0.89), with this network being essentially indistinguishable from a noisy geometric network. Overall, the results add support to the hypothesis that PPI networks have a geometric structure

Fitting a geometric graph to a protein-protein interaction network

Finding a good network null model for protein-protein interaction (PPI) networks is a fundamental issue. Such a model would provide insights into the interplay between network structure and biological function as well as into evolution. Also, network (graph) models are used to guide biological experiments and discover new biological features. It has been proposed that geometric random graphs are a good model for PPI networks. In a geometric random graph, nodes correspond to uniformly randomly distributed points in a metric space and edges (links) exist between pairs of nodes for which the corresponding points in the metric space are close enough according to some distance norm. Computational experiments have revealed close matches between key topological properties of PPI networks and geometric random graph models. In this work, we push the comparison further by exploiting the fact that the geometric property can be tested for directly. To this end, we develop an algorithm that takes PPI interaction data and embeds proteins into a low-dimensional Euclidean space, under the premise that connectivity information corresponds to Euclidean proximity, as in geometric-random graphs.We judge the sensitivity and specificity of the fit by computing the area under the Receiver Operator Characteristic (ROC) curve. The network embedding algorithm is based on multi-dimensional scaling, with the square root of the path length in a network playing the role of the Euclidean distance in the Euclidean space. The algorithm exploits sparsity for computational efficiency, and requires only a few sparse matrix multiplications, giving a complexity of O(N2) where N is the number of proteins.The algorithm has been verified in the sense that it successfully rediscovers the geometric structure in artificially constructed geometric networks, even when noise is added by re-wiring some links. Applying the algorithm to 19 publicly available PPI networks of various organisms indicated that: (a) geometric effects are present and (b) two-dimensional Euclidean space is generally as effective as higher dimensional Euclidean space for explaining the connectivity. Testing on a high-confidence yeast data set produced a very strong indication of geometric structure (area under the ROC curve of 0.89), with this network being essentially indistinguishable from a noisy geometric network. Overall, the results add support to the hypothesis that PPI networks have a geometric structure

Anion-Dependent Construction of Two Hexanuclear 3D-4F Complexes with a Flexible Schiff Base Ligand

Two hexanuclear 3d-4f Ni-Eu and Cu-Eu complexes [Eu4Ni2L2(OAc)(12)(EtOH)(2)] (1) and [Eu4Cu2L2(OAc)(12)]center dot 2H(2)O (2) are reported which are formed from the salen type Schiff-base ligand H2L (H2L = N,N'-bis(3-methoxysalicylidene)butane-1,4-diamine). In both complexes, four Eu3+ cations are bridged by eight OAc- groups and the chain is terminated at each end by two ML (M = Ni and Cu) units. The structures of 1 and 2 were determined by single crystal X-ray crystallographic studies and the luminescence properties of the free ligand and metal complexes in solution were measured.HHMI Undergraduate Science Education Award 52005907National Science Foundation CHE-0629136, CHE-0741973, CHE-0847763Welch Foundation F-1631, F-816Hong Kong Baptist University FRG/06-07/II-16Hong Kong Research Grants Council HKBU 202407Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD)Open Foundation of Jiangsu Province Key Laboratory of Fine Petrochemical Technology KF1005UT-CNM and UT-AustinChemistr

Evolution of flower color pattern through selection on regulatory small RNAs

Small RNAs (sRNAs) regulate genes in plants and animals. Here, we show that population-wide differences in color patterns in snapdragon flowers are caused by an inverted duplication that generates sRNAs. The complexity and size of the transcripts indicate that the duplication represents an intermediate on the pathway to microRNA evolution. The sRNAs repress a pigment biosynthesis gene, creating a yellow highlight at the site of pollinator entry. The inverted duplication exhibits steep clines in allele frequency in a natural hybrid zone, showing that the allele is under selection. Thus, regulatory interactions of evolutionarily recent sRNAs can be acted upon by selection and contribute to the evolution of phenotypic diversity

Selection and gene flow shape genomic islands that control floral guides

Genomes of closely-related species or populations often display localized regions of enhanced relative sequence divergence, termed genomic islands. It has been proposed that these islands arise through selective sweeps and/or barriers to gene flow. Here, we genetically dissect a genomic island that controls flower color pattern differences between two subspecies of Antirrhinum majus, A.m.striatum and A.m.pseudomajus, and relate it to clinal variation across a natural hybrid zone. We show that selective sweeps likely raised relative divergence at two tightly-linked MYB-like transcription factors, leading to distinct flower patterns in the two subspecies. The two patterns provide alternate floral guides and create a strong barrier to gene flow where populations come into contact. This barrier affects the selected flower color genes and tightlylinked loci, but does not extend outside of this domain, allowing gene flow to lower relative divergence for the rest of the chromosome. Thus, both selective sweeps and barriers to gene flow play a role in shaping genomic islands: sweeps cause elevation in relative divergence, while heterogeneous gene flow flattens the surrounding "sea," making the island of divergence stand out. By showing how selective sweeps establish alternative adaptive phenotypes that lead to barriers to gene flow, our study sheds light on possible mechanisms leading to reproductive isolation and speciation

Cauliflower fractal forms arise from perturbations of floral gene networks

[EN] Throughout development, plant meristems regularly produce organs in defined spiral, opposite, or whorl patterns. Cauliflowers present an unusual organ arrangement with a multitude of spirals nested over a wide range of scales. How such a fractal, self-similar organization emerges from developmental mechanisms has remained elusive. Combining experimental analyses in an Arabidopsis thaliana cauliflower-like mutant with modeling, we found that curd self-similarity arises because the meristems fail to form flowers but keep the "memory" of their transient passage in a floral state. Additional mutations affecting meristem growth can induce the production of conical structures reminiscent of the conspicuous fractal Romanesco shape. This study reveals how fractal-like forms may emerge from the combination of key, defined perturbations of floral developmental programs and growth dynamics.This work was supported by the INRAE Caulimodel project (to F.P. and C.Go.); Inria Project Lab Morphogenetics (to C.Go., E.A., and F.P.); the ANR BBSRC Flower model project (to F.P. and C.Go.); the GRAL LabEX (ANR-10-LABX-49-01) within the framework of the CBH-EUR-GS (ANR-17-EURE-0003) (to F.P., G.T., M.L.M., and J.L.); the EU H2020 773875 ROMI project (to C.Go.); and the Spanish Ministerio de Ciencia Innovacion and FEDER (grant no. PGC2018-099232-B-I00 to F.M.).Azpeitia, E.; Tichtinsky, G.; Le Masson, M.; Serrano-Mislata, A.; Lucas, J.; Gregis, V.; Gimenez, C.... (2021). Cauliflower fractal forms arise from perturbations of floral gene networks. Science. 373(6551):1-6. https://doi.org/10.1126/science.abg5999S16373655