A gibberellin-assisted study of the transcriptional and hormonal changes occurring at floral transition in peach buds (Prunus persica L. Batsch).

BACKGROUND: Flower load in peach is an important determinant of final fruit quality and is subjected to cost-effective agronomical practices, such as the thinning, to finely balance the sink-source relationships within the tree and drive the optimal amount of assimilates to the fruits. Floral transition in peach buds occurs as a result of the integration of specific environmental signals, such as light and temperature, into the endogenous pathways that induce the meristem to pass from vegetative to reproductive growth. The cross talk and integration of the different players, such as the genes and the hormones, are still partially unknown. In the present research, transcriptomics and hormone profiling were applied on bud samples at different developmental stages. A gibberellin treatment was used as a tool to identify the different phases of floral transition and characterize the bud sensitivity to gibberellins in terms of inhibition of floral transition. RESULTS: Treatments with gibberellins showed different efficacies and pointed out a timeframe of maximum inhibition of floral transition in peach buds. Contextually, APETALA1 gene expression was shown to be a reliable marker of gibberellin efficacy in controlling this process. RNA-Seq transcriptomic analyses allowed to identify specific genes dealing with ROS, cell cycle, T6P, floral induction control and other processes, which are correlated with the bud sensitivity to gibberellins and possibly involved in bud development during its transition to the reproductive stage. Transcriptomic data integrated with the quantification of the main bioactive hormones in the bud allowed to identify the main hormonal regulators of floral transition in peach, with a pivotal role played by endogenous gibberellins and cytokinins. CONCLUSIONS: The peach bud undergoes different levels of receptivity to gibberellin inhibition. The stage with maximum responsiveness corresponded to a transcriptional and hormonal crossroad, involving both flowering inhibitors and inductors. Endogenous gibberellin levels increased only at the latest developmental stage, when floral transition was already partially achieved, and the bud was less sensitive to exogenous treatments. A physiological model summarizes the main findings and suggests new research ideas to improve our knowledge about floral transition in peach.

Parkin R274W mutation affects muscle and mitochondrial physiology.

Recessive mutations in the Parkin gene (PRKN) are the most common cause of young-onset inherited parkinsonism. Parkin is a multifunctional E3 ubiquitin ligase that plays a variety of roles in the cell including the degradation of proteins and the maintenance of mitochondrial homeostasis, integrity, and biogenesis. In 2001, the R275W mutation in the PRKN gene was identified in two unrelated families with a multigenerational history of postural tremor, dystonia and parkinsonism. Drosophila models of Parkin R275W showed selective and progressive degeneration of dopaminergic neuronal clusters, mitochondrial abnormalities, and prominent climbing defects. In the Prkn mouse orthologue, the amino acid R274 corresponds to human R275. Here we described an age-related motor impairment and a muscle phenotype in R274W +/+ mice. In vitro, Parkin R274W mutation correlates with abnormal myoblast differentiation, mitochondrial defects, and alteration in mitochondrial mRNA and protein levels. Our data suggest that the Parkin R274W mutation may impact mitochondrial physiology and eventually myoblast proliferation and differentiation.

Grape Berry Responses to Sequential Flooding and Heatwave Events: A Physiological, Transcriptional, and Metabolic Overview.

Grapevine cultivation, such as the whole horticulture, is currently challenged by several factors, among which the extreme weather events occurring under the climate change scenario are the most relevant. Within this context, the present study aims at characterizing at the berry level the physiological response of Vitis vinifera cv. Sauvignon Blanc to sequential stresses simulated under a semi-controlled environment: flooding at bud-break followed by multiple summer stress (drought plus heatwave) occurring at pre-vèraison. Transcriptomic and metabolomic assessments were performed through RNASeq and NMR, respectively. A comprehensive hormone profiling was also carried out. Results pointed out a different response to the heatwave in the two situations. Flooding caused a developmental advance, determining a different physiological background in the berry, thus affecting its response to the summer stress at both transcriptional levels, with the upregulation of genes involved in oxidative stress responses, and metabolic level, with the increase in osmoprotectants, such as proline and other amino acids. In conclusion, sequential stress, including a flooding event at bud-break followed by a summer heatwave, may impact phenological development and berry ripening, with possible consequences on berry and wine quality. A berry physiological model is presented that may support the development of sustainable vineyard management solutions to improve the water use efficiency and adaptation capacity of actual viticultural systems to future scenarios.

Personal Heart Health Monitoring Based on 1D Convolutional Neural Network.

The automated detection of suspicious anomalies in electrocardiogram (ECG) recordings allows frequent personal heart health monitoring and can drastically reduce the number of ECGs that need to be manually examined by the cardiologists, excluding those classified as normal, facilitating healthcare decision-making and reducing a considerable amount of time and money. In this paper, we present a system able to automatically detect the suspect of cardiac pathologies in ECG signals from personal monitoring devices, with the aim to alert the patient to send the ECG to the medical specialist for a correct diagnosis and a proper therapy. The main contributes of this work are: (a) the implementation of a binary classifier based on a 1D-CNN architecture for detecting the suspect of anomalies in ECGs, regardless of the kind of cardiac pathology; (b) the analysis was carried out on 21 classes of different cardiac pathologies classified as anomalous; and (c) the possibility to classify anomalies even in ECG segments containing, at the same time, more than one class of cardiac pathologies. Moreover, 1D-CNN based architectures can allow an implementation of the system on cheap smart devices with low computational complexity. The system was tested on the ECG signals from the MIT-BIH ECG Arrhythmia Database for the MLII derivation. Two different experiments were carried out, showing remarkable performance compared to other similar systems. The best result showed high accuracy and recall, computed in terms of ECG segments and even higher accuracy and recall in terms of patients alerted, therefore considering the detection of anomalies with respect to entire ECG recordings.

TGFß signaling curbs cell fusion and muscle regeneration.

Muscle cell fusion is a multistep process involving cell migration, adhesion, membrane remodeling and actin-nucleation pathways to generate multinucleated myotubes. However, molecular brakes restraining cell-cell fusion events have remained elusive. Here we show that transforming growth factor beta (TGFß) pathway is active in adult muscle cells throughout fusion. We find TGFß signaling reduces cell fusion, regardless of the cells' ability to move and establish cell-cell contacts. In contrast, inhibition of TGFß signaling enhances cell fusion and promotes branching between myotubes in mouse and human. Exogenous addition of TGFß protein in vivo during muscle regeneration results in a loss of muscle function while inhibition of TGFßR2 induces the formation of giant myofibers. Transcriptome analyses and functional assays reveal that TGFß controls the expression of actin-related genes to reduce cell spreading. TGFß signaling is therefore requisite to limit mammalian myoblast fusion, determining myonuclei numbers and myofiber size.

Nasal cytology with deep learning techniques.

BACKGROUND: In recent years, cytological observations in the Rhinology field are being increasingly utilized. This development has taken place over the last two decades and has proven to be fundamental in defining new nosological entities and in driving changes in the previous classification of rhinitis. The simplicity of the technique and its low invasiveness make nasal cytology a practical diagnostic tool for all rhino-allergology services. Furthermore, since it allows the monitoring of responses to treatment, this method plays an important role in guiding a more effective and less expensive diagnostic program. Microscopic observation requires prolonged effort by a specialist, but the modern scanning systems for cytological preparations and the new affordable digital microscopes allow to design a software support system, based on deep learning techniques, to relieve specialist's tiring activity. BASIC PROCEDURE: By means of the system presented in this paper, it is possible to automatically identify and classify cells present on a nasal cytological preparation based on a digital image of the preparation itself. Thus, an interesting diagnostic support has been made available to the rhino-cytologist, who can quickly verify that the cells have been correctly classified by the software system: any few unclassified or incorrectly classified cells can be quickly sorted by the specialist itself, then one or more diagnosis can be suggested by this system, taking into consideration also the anamnesis of each patient. The final diagnosis can be defined by the specialist, also based on the result of the prick test and the observation of the nasal cavity. FINDINGS: In the system presented herein, image processing and image segmentation techniques have been used to find images of cellular elements within the preparation. Cell classification is based on a convolutional neural network composed of three blocks of main layers. Cell identification (first step, image segmentation) exhibits sensitivity greater than 97%, while cell classification (second step, seven cytotypes) attained a mean accuracy of approximately 99% on the test set and 94% on the validation set. CONCLUSIONS: This complete system supports clinicians in the preparation of a rhino-cytogram report.

Wnt Signaling in Skeletal Muscle Development and Regeneration.

Wnt is a family of signaling molecules involved in embryogenesis, adult tissue repair, and cancer. They activate canonical and noncanonical Wnt signaling cascades in target cells. Several studies, within the last decades, showed that several Wnt ligands are involved in myogenesis and both canonical and noncanonical Wnt pathways regulate muscle formation and the maintenance of adult tissue homeostasis. In this review, we provide a comprehensive overview of the roles of Wnt signaling during muscle development and an updated description of Wnt functions during muscle repair. Lastly, we discuss the crosstalk between Wnt and TGFß signaling pathways in skeletal muscle.