Transcriptome and morphological analyses of double flower formation in Dianthus chinensis

The double flower developmental process is regulated via a complex transcriptional regulatory network.To understand this highly dynamic and complex developmental process of Dianthus spp.,we performed a comparative analysis of floral morphology and transcriptome dynamics in simple flowers and double flowers.We found that the primordium of double flowers of‘X’was larger in size compared to that of simple flowers of‘L’in Dianthus chinensis.RNA-seq and Weighted Gene Co-expression Network Analysis(WGCNA)during flower development,identified stage-specific gene network modules.Expression analysis by RNA-seq indicated that a group of genes related to floral meristem identity,primordia position and polarity were highly expressed in double flowers genotypes compared to simple flowers genotypes,suggesting their roles in double-petal formation.A total of 21 DEGs related to petal number were identified between simple and double flowers.The experiments of in situ hybridization revealed that DcaAP2L,DcaLFY and DcaUFO genes were expressed in the intra-sepal boundary and petal boundary.We proposed a potential transcriptional regulatory network for simple and double flower development.This study provides novel insights into the molecular mechanism underlying double flower formation in Dianthus spp.

SlZF3 regulates tomato plant height by directly repressing SlGA20ox4 in the gibberellic acid biosynthesis pathway

Plant height is an important target trait for crop genetic improvement.Our previous work has identified a salt-tolerant C2H2 zinc finger,SlZF3,and its overexpression lines also showed a semi-dwarf phenotype,but themolecular mechanism remains to be elucidated.Here,we characterized the dwarf phenotype in detail.The dwarfism is caused by a decrease in stem internode cell elongation and deficiency of bioactive gibberellic acids(GAs),and can be rescued by exogenous GA3 treatment.Gene expression assays detected reduced expression of genes in the GA biosynthesis pathway of the overexpression lines,including SlGA20ox4.Several protein-DNA interaction methods confirmed that SlZF3 can directly bind to the SlGA20ox4 promoter and inhibit its expression,and the interaction can also occur for SlKS and SlKO.Overexpression of SlGA20ox4 in the SlZF3-overexpressing line can recover the dwarf phenotype.Therefore,SlZF3 regulates plant height by directly repressing genes in the tomato GA biosynthesis pathway.

A 21-bp InDel in the promoter of STP1 selected during tomato improvement accounts for soluble solid content in fruits

Domestication and improvement are important processes that generate the variation in genome and phonotypes underlying crop improvement.Unfortunately,during selection for certain attributes,other valuable traits may be inadvertently discarded.One example is the decline in fruit soluble solids content(SSC)during tomato breeding.Several genetic loci for SSC have been identified,but few reports on the underlying mechanisms are available.In this study we performed a genome-wide association study(GWAS)for SSC of the red-ripe fruits in a population consisting of 481 tomato accessions with large natural variations and found a new quantitative trait locus,STP1,encoding a sugar transporter protein.The causal variation of STP1,a 21-bp InDel located in the promoter region 1124 bp upstream of the start codon,alters its expression.STP1 Insertion accessions with an 21-bp insertion have higher SSC than STP1Deletion accessions with the 21-bp deletion.Knockout of STP1 in TS-23 with high SSC using CRISPR/Cas9 greatly decreased SSC in fruits.In vivo and in vitro assays demonstrated that ZAT10-LIKE,a zinc finger protein transcription factor(ZFP TF),can specifically bind to the promoter of STP1Insertion to enhance STP1 expression,but not to the promoter of STP1Deletion,leading to lower fruit SSC in modern tomatoes.Diversity analysis revealed that STP1 was selected during tomato improvement.Taking these results together,we identified a naturally occurring causal variation underlying SSC in tomato,and a new role for ZFP TFs in regulating sugar transporters.The findings enrich our understanding of tomato evolution and domestication,and provide a genetic basis for genome design for improving fruit taste.

Isolation and comparative proteomic analysis of mitochondria from the pulp of ripening citrus fruit

Mitochondria are crucial for the production of primary and secondary metabolites,which largely determine the quality of fruit.However,a method for isolating high-quality mitochondria is currently not available in citrus fruit,preventing high-throughput characterization of mitochondrial functions.Here,based on differential and discontinuous Percoll density gradient centrifugation,we devised a universal protocol for isolating mitochondria from the pulp of four major citrus species,including satsuma mandarin,ponkan mandarin,sweet orange,and pummelo.Western blot analysis and microscopy confirmed the high purity and intactness of the isolated mitochondria.By using this protocol coupled with a label-free proteomic approach,a total of 3353 nonredundant proteins were identified.Comparison of the four mitochondrial proteomes revealed that the proteins commonly detected in all proteomes participate in several typical metabolic pathways(such as tricarboxylic acid cycle,pyruvate metabolism,and oxidative phosphorylation)and pathways closely related to fruit quality(such asγ-aminobutyric acid(GABA)shunt,ascorbate metabolism,and biosynthesis of secondary metabolites).In addition,differentially abundant proteins(DAPs)between different types of species were also identified;these were found to be mainly involved in fatty acid and amino acid metabolism and were further confirmed to be localized to the mitochondria by subcellular localization analysis.In summary,the proposed protocol for the isolation of highly pure mitochondria from different citrus fruits may be used to obtain high-coverage mitochondrial proteomes,which can help to establish the association between mitochondrial metabolism and fruit storability or quality characteristics of different species and lay the foundation for discovering novel functions of mitochondria in plants.

Individual automatic detection and identification of big cats with the combination of different body parts

The development of facial recognition technology has become an increasingly powerful tool in wild animal indi-vidual recognition.In this paper,we develop an automatic detection and recognition method with the combinations of body features of big cats based on the deep convolutional neural network(CNN).We collected dataset including 12244 images from 47 individual Amur tigers(Panthera tigris altaica)at the Siberian Tiger Park by mobile phones and digital camera and 1940 images and videos of 12 individual wild Amur leopard(Panthera pardus orientalis)by infrared cameras.First,the single shot multibox detector algorithm is used to perform the automatic detection process of feature regions in each image.For the different feature regions of the image,like face stripe or spots,CNNs and multi-layer perceptron models were applied to automatically identify tiger and leopard individuals,in-dependently.Our results show that the identification accuracy of Amur tiger can reach up to 93.27%for face front,93.33%for right body stripe,and 93.46%for left body stripe.Furthermore,the combination of right face,left body stripe,and right body stripe achieves the highest accuracy rate,up to 95.55%.Consequently,the combination of different body parts can improve the individual identification accuracy.However,it is not the higher the number of body parts,the higher the accuracy rate.The combination model with 3 body parts has the highest accuracy.The identification accuracy of Amur leopard can reach up to 86.90%for face front,89.13%for left body spots,and 88.33%for right body spots.The accuracy of different body parts combination is lower than the independent part.For wild Amur leopard,the combination of face with body spot part is not helpful for the improvement of identification accuracy.The most effective identification part is still the independent left or right body spot part.It can be applied in long-term monitoring of big cats,including big data analysis for animal behavior,and be helpful for the individual identification of other wildlife species.

Amur tiger stripes:individual identification based on deep convolutional neural network

The automatic individual identification of Amur tigers(Panthera tigris altaica)is important for population monitoring and making effective conservation strategies.Most existing research primarily relies on manual identifi-cation,which does not scale well to large datasets.In this paper,the deep convolution neural networks algorithm is constructed to implement the automatic individual identification for large numbers of Amur tiger images.The experimental data were obtained from 40 Amur tigers in Tieling Guaipo Tiger Park,China.The number of images collected from each tiger was approximately 200,and a total of 8277 images were obtained.The experiments were carried out on both the left and right side of body.Our results suggested that the recognition accuracy rate of left and right sides are 90.48%and 93.5%,respectively.The accuracy of our network has achieved the similar level compared to other state of the art networks like LeNet,ResNet34,and ZF_Net.The running time is much shorter than that of other networks.Consequently,this study can provide a new approach on automatic individual identification technology in the case of the Amur tiger.

Estimation of body weight in captive Amur tigers(Panthera tigris altaica)

So far,there has been no safe and convenient method to weigh the largefierce animals,like Amur tigers.To address this problem,we built models to predict the body weight of Amur tigers based on the fact that body weight is proportional to body measurements or age.Using the method of body measurements,we extracted the body measurements from 4 different kinds of the lateral body image of tigers,that is,total lateral image,central lateral image,ellipsefitting image,and rectanglefitting image,and then we respectively used artificial neural network(ANN)and power regression model to analyze the predictive relationships between body weight and body measurements.Our results demonstrated that,among all ANN models,the model built with rectanglefitting image had the smallest mean square error.Comparatively,we screened power regression models which had the smallest Akakai information criteria(AIC).In addition,using the method of age,wefitted nonlinear regression models for the relationship between body weight and age and found that,for male tigers,logistic model had the smallest AIC.For female tigers,Gompertz model had the smallest AIC.Consequently,this study could be applied to estimate body weight of captive,or even wild,Amur tigers safely and conveniently,helping to monitor individual health and growth of the Amur tiger populations.