Structure and assembly of the α-carboxysome in the marine cyanobacterium Prochlorococcus.

Carboxysomes are bacterial microcompartments that encapsulate the enzymes RuBisCO and carbonic anhydrase in a proteinaceous shell to enhance the efficiency of photosynthetic carbon fixation. The self-assembly principles of the intact carboxysome remain elusive. Here we purified α-carboxysomes from Prochlorococcus and examined their intact structures using single-particle cryo-electron microscopy to solve the basic principles of their shell construction and internal RuBisCO organization. The 4.2 Å icosahedral-like shell structure reveals 24 CsoS1 hexamers on each facet and one CsoS4A pentamer at each vertex. RuBisCOs are organized into three concentric layers within the shell, consisting of 72, 32 and up to 4 RuBisCOs at the outer, middle and inner layers, respectively. We uniquely show how full-length and shorter forms of the scaffolding protein CsoS2 bind to the inner surface of the shell via repetitive motifs in the middle and C-terminal regions. Combined with previous reports, we propose a concomitant 'outside-in' assembly principle of α-carboxysomes: the inner surface of the self-assembled shell is reinforced by the middle and C-terminal motifs of the scaffolding protein, while the free N-terminal motifs cluster to recruit RuBisCO in concentric, three-layered spherical arrangements. These new insights into the coordinated assembly of α-carboxysomes may guide the rational design and repurposing of carboxysome structures for improving plant photosynthetic efficiency.

DNA looping mediates cooperative transcription activation.

Transcription factors respond to multilevel stimuli and co-occupy promoter regions of target genes to activate RNA polymerase (RNAP) in a cooperative manner. To decipher the molecular mechanism, here we report two cryo-electron microscopy structures of Anabaena transcription activation complexes (TACs): NtcA-TAC composed of RNAP holoenzyme, promoter and a global activator NtcA, and NtcA-NtcB-TAC comprising an extra context-specific regulator, NtcB. Structural analysis showed that NtcA binding makes the promoter DNA bend by ∼50°, which facilitates RNAP to contact NtcB at the distal upstream NtcB box. The sequential binding of NtcA and NtcB induces looping back of promoter DNA towards RNAP, enabling the assembly of a fully activated TAC bound with two activators. Together with biochemical assays, we propose a 'DNA looping' mechanism of cooperative transcription activation in bacteria.

Fine structure and assembly pattern of a minimal myophage Pam3.

The myophage possesses a contractile tail that penetrates its host cell envelope. Except for investigations on the bacteriophage T4 with a rather complicated structure, the assembly pattern and tail contraction mechanism of myophage remain largely unknown. Here, we present the fine structure of a freshwater Myoviridae cyanophage Pam3, which has an icosahedral capsid of ~680 Å in diameter, connected via a three-section neck to an 840-Å-long contractile tail, ending with a three-module baseplate composed of only six protein components. This simplified baseplate consists of a central hub-spike surrounded by six wedge heterotriplexes, to which twelve tail fibers are covalently attached via disulfide bonds in alternating upward and downward configurations. In vitro reduction assays revealed a putative redox-dependent mechanism of baseplate assembly and tail sheath contraction. These findings establish a minimal myophage that might become a user-friendly chassis phage in synthetic biology.

Structural Insights into the Chaperone-Assisted Assembly of a Simplified Tail Fiber of the Myocyanophage Pam3.

At the first step of phage infection, the receptor-binding proteins (RBPs) such as tail fibers are responsible for recognizing specific host surface receptors. The proper folding and assembly of tail fibers usually requires a chaperone encoded by the phage genome. Despite extensive studies on phage structures, the molecular mechanism of phage tail fiber assembly remains largely unknown. Here, using a minimal myocyanophage, termed Pam3, isolated from Lake Chaohu, we demonstrate that the chaperone gp25 forms a stable complex with the tail fiber gp24 at a stoichiometry of 3:3. The 3.1-Å cryo-electron microscopy structure of this complex revealed an elongated structure with the gp25 trimer embracing the distal moieties of gp24 trimer at the center. Each gp24 subunit consists of three domains: the N-terminal α-helical domain required for docking to the baseplate, the tumor necrosis factor (TNF)-like and glycine-rich domains responsible for recognizing the host receptor. Each gp25 subunit consists of two domains: a non-conserved N-terminal ß-sandwich domain that binds to the TNF-like and glycine-rich domains of the fiber, and a C-terminal α-helical domain that mediates trimerization/assembly of the fiber. Structural analysis enabled us to propose the assembly mechanism of phage tail fibers, in which the chaperone first protects the intertwined and repetitive distal moiety of each fiber subunit, further ensures the proper folding of these highly plastic structural elements, and eventually enables the formation of the trimeric fiber. These findings provide the structural basis for the design and engineering of phage fibers for biotechnological applications.

Rubisco accumulation factor 1 (Raf1) plays essential roles in mediating Rubisco assembly and carboxysome biogenesis.

Carboxysomes are membrane-free organelles for carbon assimilation in cyanobacteria. The carboxysome consists of a proteinaceous shell that structurally resembles virus capsids and internal enzymes including ribulose 1,5-bisphosphate carboxylase/oxygenase (Rubisco), the primary carbon-fixing enzyme in photosynthesis. The formation of carboxysomes requires hierarchical self-assembly of thousands of protein subunits, initiated from Rubisco assembly and packaging to shell encapsulation. Here we study the role of Rubisco assembly factor 1 (Raf1) in Rubisco assembly and carboxysome formation in a model cyanobacterium, Synechococcus elongatus PCC7942 (Syn7942). Cryo-electron microscopy reveals that Raf1 facilitates Rubisco assembly by mediating RbcL dimer formation and dimer-dimer interactions. Syn7942 cells lacking Raf1 are unable to form canonical intact carboxysomes but generate a large number of intermediate assemblies comprising Rubisco, CcaA, CcmM, and CcmN without shell encapsulation and a low abundance of carboxysome-like structures with reduced dimensions and irregular shell shapes and internal organization. As a consequence, the Raf1-depleted cells exhibit reduced Rubisco content, CO2-fixing activity, and cell growth. Our results provide mechanistic insight into the chaperone-assisted Rubisco assembly and biogenesis of carboxysomes. Advanced understanding of the biogenesis and stepwise formation process of the biogeochemically important organelle may inform strategies for heterologous engineering of functional CO2-fixing modules to improve photosynthesis.

Molecular basis for the assembly of RuBisCO assisted by the chaperone Raf1.

The folding and assembly of RuBisCO, the most abundant enzyme in nature, needs a series of chaperones, including the RuBisCO accumulation factor Raf1, which is highly conserved in cyanobacteria and plants. Here, we report the crystal structures of Raf1 from cyanobacteria Anabaena sp. PCC 7120 and its complex with RuBisCO large subunit RbcL. Structural analyses and biochemical assays reveal that each Raf1 dimer captures an RbcL dimer, with the C-terminal tail inserting into the catalytic pocket, and further mediates the assembly of RbcL dimers to form the octameric core of RuBisCO. Furthermore, the cryo-electron microscopy structures of the RbcL-Raf1-RbcS assembly intermediates enable us to see a dynamic assembly process from RbcL8Raf18 to the holoenzyme RbcL8RbcS8. In vitro assays also indicate that Raf1 can attenuate and reverse CcmM-mediated cyanobacterial RuBisCO condensation. Combined with previous findings, we propose a putative model for the assembly of cyanobacterial RuBisCO coordinated by the chaperone Raf1.

Zebrafish Embryo Toxicity Microscale Model for Ichthyotoxicity Evaluation of Marine Natural Products.

Marine organisms often protect themselves against their predators by chemical defensive strategy. The second metabolites isolated from marine organisms and their symbiotic microbes have been proven to play a vital role in marine chemical ecology, such as ichthyotoxicity, allelopathy, and antifouling. It is well known that the microscale models for marine chemoecology assessment are urgently needed for trace quantity of marine natural products. Zebrafish model has been widely used as a microscale model in the fields of environment ecological evaluation and drug safety evaluation, but seldom reported for marine chemoecology assessment. In this work, zebrafish embryo toxicity microscale model was established for ichthyotoxicity evaluation of marine natural products by using 24-well microplate based on zebrafish embryo. Ichthyotoxicity was evaluated by observation of multiple toxicological endpoints, including coagulation egg, death, abnormal heartbeat, no spontaneous movement, delayed hatch, and malformation of the different organs during zebrafish embryogenesis periods at 24, 48, and 72 h post-fertilization (hpf). 3,4-Dichloroaniline was used as the positive control for method validation. Subsequently, the established model was applied to test the ichthyotoxic activity of the compounds isolated from corals and their symbiotic microbes and to isolate the bioactive secondary metabolites from the gorgonian Subergorgia mollis under bioassay guidance. It was suggested that zebrafish embryo toxicity microscale model is suitable for bioassay-guided isolation and preliminary bioactivity screening of marine natural products.

[Determination of Soluble Solid Content in Strawberry Using Hyperspectral Imaging Combined with Feature Extraction Methods].

Hyperspectral imaging combined with feature extraction methods were applied to determine soluble sugar content (SSC) in mature and scatheless strawberry. Hyperspectral images of 154 strawberries covering the spectral range of 874-1,734 nm were captured and the spectral data were extracted from the hyperspectral images, and the spectra of 941~1,612 nm were preprocessed by moving average (MA). Nineteen samples were defined as outliers by the residual method, and the remaining 135 samples were divided into the calibration set (n = 90) and the prediction set (n = 45). Successive projections algorithm (SPA), genetic algorithm partial least squares (GAPLS) combined with SPA, weighted regression coefficient (Bw) and competitive adaptive reweighted sampling (CARS) were applied to select 14, 17, 24 and 25 effective wavelengths, respectively. Principal component analysis (PCA) and wavelet transform (WT) were applied to extract feature information with 20 and 58 features, respectively. PLS models were built based on the full spectra, the effective wavelengths and the features, respectively. All PLS models obtained good results. PLS models using full-spectra and features extracted by WT obtained the best results with correlation coefficient of calibration (r(c)) and correlation coefficient of prediction (r(p)) over 0.9. The overall results indicated that hyperspectral imaging combined with feature extraction methods could be used for detection of SSC in strawberry.

[Application of visible/near-infrared spectroscopy to the determination of catalase and peroxidase content in barley leaves].

Visible/near-infrared spectroscopy was applied to determine the content of catalase (CAT) and peroxidase (POD) in barley leaves under the herbicide stress of propyl 4-(2-(4, 6-dimethoxypyrimidin-2-yloxy) benzylamino) benzoate (ZJ0273). The spectral data of the barley leaves in the range of 500-900 nm were preprocessed by moving average with 11 points. Seven outlier samples for CAT and 8 outlier samples for POD were detected and removed by Monte Carlo-partial least squares (MCPLS). PLS, least-squares support vector machine (LS-SVM) and extreme learning machine (ELM) models were built for both CAT and POD. ELM model obtained best results for CAT, with correlation coefficient of calibration (Rc) of 0.916 and correlation co-efficient of prediction (Rp) of 0.786. PLS model obtained best prediction results for POD, with Rc of 0.984 and Rp of 0.876. Successive projections algorithm (SPA) was applied to select 8 and 19 effective wavelengths for CAT and POD, respectively. PLS, LS-SVM and ELM models were built using the selected effective wavelengths of CAT and POD. ELM model performed best for CAT and POD prediction, with Rc of 0.928 and Rp of 0.790 for CAT and Rp of 0.965 and Rp of 0.941 for POD. The prediction results using the full spectral data and the effective wavelengths were quite close, and the prediction performance for POD was much better than the prediction performance for CAT, and the studies should be further explored to build more precise and more robust models for CAT and POD determination. The overall results indicated that it was feasible to use visible/near-infrared spectroscopy for CAT and POD content determination in barley leaves under the stress of ZJ0273.

[Variety recognition of Chinese cabbage seeds by hyperspectral imaging combined with machine learning].

The variety of Chinese cabbage seeds were recognized using hyperspectral imaging with 256 bands from 874 to 1,734 nm in the present paper. A total of 239 Chinese cabbage seed samples including 8 varieties were acquired by hyperspectral image system, 158 for calibration and the rest 81 for validation. A region of 15 pixel x 15 pixel was selected as region of interest (ROI) and the average spectral information of ROI was obtained as sample spectral information. Multiplicative scatter correction was selected as pretreatment method to reduce the noise of spectrum. The performance of four classification algorithms including Ada-boost algorithm, extreme learning machine (ELM), random forest (RF) and support vector machine (SVM) were examined in this study. In order to simplify the input variables, 10 effective wavelengths (EMS) including 1,002, 1,005, 1,015, 1,019, 1,022, 1,103, 1,106, 1,167, 1,237 and 1,409 nm were selected by analysis of variable load distribution in PLS model. The reflectance of effective wavelengths was taken as the input variables to build effective wavelengths based models. The results indicated that the classification accuracy of the four models based on full-spectral were over 90%, the optimal models were extreme learning machine and random forest, and the classification accuracy achieved 100%. The classification accuracy of effective wavelengths based models declined slightly but the input variables compressed greatly, the efficiency of data processing was improved, and the classification accuracy of EW-ELM model achieved 100%. ELM performed well both in full-spectral model and in effective wavelength based model in this study, it was proven to be a useful tool for spectral analysis. So rapid and nondestructive recognition of Chinese cabbage seeds by hyperspectral imaging combined with machine learning is feasible, and it provides a new method for on line batch variety recognition of Chinese cabbage seeds.

[Application of near-infrared spectroscopy to distinguish brands of soy milk powder and fake soy milk powder].

Near-infrared spectroscopy combined with chemometrics was used to investigate the feasibility of identifying different brands of soymilk powder and the counterfeit soymilk powder products. For this purpose, partial least squares-discriminant analysis (PLS-DA), linear discriminant analysis (LDA) and back-propagation neural network (BPNN) were employed as pattern recognition methods to class ify soymilk powder samples. The performances of different pretreatments of raw spectra were also compared by PLS-DA. PLS-DA models based on De-trending and multiplicative scatter correction (MSC)combined with De-trending(MSC+De-trending) spectra obtained best results with 100% prediction accuracy, respectively. Six and seven optimal wavenumbers selected by chi-loading weights of the best two PLS-DA models were used to build LDA and BPNN models. Results showed that BPNN performed best and correctly classified 100% of the soymilk powder samples for both the calibration and the prediction set. The overall results indicated that NIR spectroscopy could accurately identify branded and counterfeit soymilk powder products.

[Protein content determination of shiitake mushroom (Lentinus edodes) using mid-infrared spectroscopy and chemometeics].

The feasibility of protein determination of shiitake mushroom (Lentinus edodes) using mid-infrared spectroscopy (MIR) was studied in the present paper. Wavenumbers 3 581-689 cm(-1) were used for quantitative analysis of protein content after removing of the part of obvious noises. Five points Savitzky-Golay smoothing was applied to pretreat the MIR spectra and partial least squares (PLS) model was built based on the pretreated spectra. The full spectra PLS model obtained poor performance with the ratio of prediction to deviation (RPD) of only 1.77. Successive projections algorithm (SPA) was applied to select 7 sensitive wavenumbers from the full spectra, and PLS model, multiple linear regression (MLR), back-propagation neural network (BPNN) and extreme learning machine (ELM) model were built using the selected sensitive wavenumbers. SPA-PLS model and SPA-MLR model obtained relatively worse results than SPA-BPNN model and SPA-ELM model. SPA-ELM obtained the best results with correlation coefficient of prediction (R(p)) of 0.899 5, root mean square error of prediction (RMSEP) of 1.431 3 and RPD of 2.18. The overall results indicated that MIR combined with chemometrics could be used for protein content determination of shiitake mushroom, and SPA could select sensitive wavenumbers to build more accurate models instead of the full spectra.

[Producing area identification of Letinus edodes using mid-infrared spectroscopy].

In the present study, Mid-infrared spectroscopy was used to identify the producing area of Letinus edodes, and relevance vector machine (RVM) was put forward to build classification models as a novel classification technique, and they obtained good performances. The head and the tail of the acquired mid-infrared spectra with the absolute noise were cut off, and the remaining spectra in the range of 3,581-689 cm(-1) (full spectra) of Letinus edodes were preprocessed by multiplicative scatter correction (MSC). Five classification techniques, including partial least Squares-discriminant analysis (PLS-DA), soft independent modeling of class analogy (SIMCA), K-nearest neighbor algorithm (KNN), support vector machine (SVM) and RVM, were applied to build classification models based on the preprocessed full spectra. All classification models obtained classification accuracy over 80%, KNN, SVM and RVM models based on full spectra obtained similar and good performances with classification accuracy over 90% in both the calibration set and the prediction set. The weighted regression coefficients (Bw) were used to select effective wave numbers of mid-infrared spectra and 6 effective wave numbers in total were selected on the basis of the weighted regression coefficients of PLS-DA model based on full spectra. PLS-DA, KNN, SVM and RVM models were built using these effective wave numbers. Compared with the classification models based on full spectra, PLS-DA models based on effective wave numbers obtained relatively worse results with classification accuracy less than 80%, and KNN, SVM and RVM obtained similar results in both calibration set and prediction set with classification accuracy over 90%. RVM performed well with classification rate over 90% based on full spectra and effective wave numbers. The overall results indicated that producing area of Letinus edodes could be identified by mid-infrared spectroscopy, while wave number selection and the RVM algorithm could be effectively used in mid-infrared spectroscopy analysis. In this study, mid-infrared spectroscopy was successfully applied to identify the producing area of Letinus edodes, which could provide a new concept for quality analysis of Letinus edodes and other agricultural products, and the application of mid-infrared spectroscopy had practical significance.

[Identification of varieties of black bean using ground based hyperspectral imaging].

In the present study, hyperspectral imaging combined with chemometrics was successfully proposed to identify different varieties of black bean. The varieties of black bean were defined based on the three different colors of the bean core. The hy-perspectral images in the spectral range of 380-1,030 nm of black bean were acquired using the developed hyperspectral imaging system, and the reflectance spectra were extracted from the region of interest (ROD) in the images. The average spectrum of a ROI of the sample in the images was used to represent the spectrum of the sample and build classification models. In total, 180 spectra of 180 samples were extracted. The wavelengths from 440 to 943 nm were used for analysis after the removal of the spec- tral region with absolute noises, and 440-943 nm spectra were preprocessed by multiplicative scatter correction (MSC). Five classification methods, including partial least squares discriminant analysis (PLS-DA), soft independent modeling of class analogy (SIMCA), K-nearest neighbor algorithm (KNN), support vector machine (SVM) and extreme learning machine (ELM), were used to build discriminant models using the preprocessed full spectra, the feature information extracted by principal component analysis (PCA) and the feature information extracted by wavelet transform (WT) from the preprocessed spectra, respectively. Among all the classification models using the preprocessed full spectra, ELM models obtained the best performance; among all the classification models using the feature information extracted from the preprocessed spectra by PCA, ELM model also obtained the best classification accuracy; and among all the classification models using the feature information extracted from the preprocessed spectra by WT, ELM models obtained the best classification performance with 100% accuracy in both the calibration set and the prediction set. Among all classification models, WT-ELM model obtained the best classification accuracy. The overall results indicated that it was feasible to identify black bean varieties nondestructively by using hyperspectral imaging, and WT could effectively extract feature information from spectra and ELM algorithm was effective to build high performance classification models.

[Identification of transmission fluid based on NIR spectroscopy by combining sparse representation method with manifold learning].

An identification method based on sparse representation (SR) combined with autoencoder network (AN) manifold learning was proposed for discriminating the varieties of transmission fluid by using near infrared (NIR) spectroscopy technology. NIR transmittance spectra from 600 to 1 800 nm were collected from 300 transmission fluid samples of five varieties (each variety consists of 60 samples). For each variety, 30 samples were randomly selected as training set (totally 150 samples), and the rest 30 ones as testing set (totally 150 samples). Autoencoder network manifold learning was applied to obtain the characteristic information in the 600-1800 nm spectra and the number of characteristics was reduced to 10. Principal component analysis (PCA) was applied to extract several relevant variables to represent the useful information of spectral variables. All of the training samples made up a data dictionary of the sparse representation (SR). Then the transmission fluid variety identification problem was reduced to the problem as how to represent the testing samples from the data dictionary (training samples data). The identification result thus could be achieved by solving the L-1 norm-based optimization problem. We compared the effectiveness of the proposed method with that of linear discriminant analysis (LDA), least squares support vector machine (LS-SVM) and sparse representation (SR) using the relevant variables selected by principal component analysis (PCA) and AN. Experimental results demonstrated that the overall identification accuracy of the proposed method for the five transmission fluid varieties was 97.33% by AN-SR, which was significantly higher than that of LDA or LS-SVM. Therefore, the proposed method can provide a new effective method for identification of transmission fluid variety.

[Nondestructive discrimination of waxed apples based on hyperspectral imaging technology].

The potential of hyperspectral imaging technology was evaluated for discriminating three types of waxed apples. Three types of apples smeared with fruit wax, with industrial wax, and not waxed respectively were imaged by a hyperspectral imaging system with a spectral range of 308-1 024 nm. ENVI software processing platform was used for extracting hyperspectral image object of diffuse reflection spectral response characteristics. Eighty four of 126 apple samples were selected randomly as calibration set and the rest were prediction set. After different preprocess, the related mathematical models were established by using the partial least squares (PLS), the least squares support vector machine (LS-SVM) and BP neural network methods and so on. The results showed that the model of MSC-SPA-LSSVM was the best to discriminate three kinds of waxed apples with 100%, 100% and 92.86% correct prediction respectively.

[Early diagnosis of gray mold on tomato stalks based on hyperspectral data].

Early diagnosis of gray mold on tomato stalks based on hyperspectral data was studied in the present paper. A total of 112 samples' hyperspectral data were collected by hyperspectral imaging system. The study spectral region was from 400 to 1,030 nm. Combined with image processing and chemometric methods, the tomato stalk gray mold diagnosis models were built. Seven effective wavelengths were selected by analysis of variable load distribution in PLS model. The experimental results showed that the excellent results were achieved by EW-LS-SVM model with standard normal variate (SNV) spectral and multiplicative scatter correction (MSC) spectral, and the accuracy of diagnosing gray mold on tomato stalks was satisfied and better than PLS model with whole band. Hence, it is feasible to early diagnose gray mold on tomato stalks using hyperspectral imaging technology, which provides a new early diagnosis and warning method for tomato disease.

[Fast determination of malondialdehyde in oilseed rape leaves using near infrared spectroscopy].

Near infrared (NIR) spectroscopy was applied for the fast and nondestructive determination of malondialdehyde (MDA) content in oilseed rape leaves. A total of 90 leaf samples were collected, the calibration set was composed of 60 samples, and the prediction set was composed of 30 samples. Different preprocessing methods were used before the calibration stage, including smoothing, standard normal variate, first and second derivative, and detrending. Then partial least squares (PLS) models were developed for the prediction of MDA content in oilseed rape leaves. The latent variables selected by PLS and effective wavelengths selected by successive projections algorithm (SPA) were used as the inputs of least square-support vector machine (LS-SVM) to develop LV-LS-SVM and SPA-LS-SVM models. The correlation coefficients (r) and root mean square error of prediction (RMSEP) were used as the model evaluation indices. Excellent results were achieved by LV-LS-SVM model, and the prediction results by LS-SVM model using detrending spectra were r = 0.999 9 and RMSEP = 0.530 2, and those by LS-SVM model using 2-Der spectra were r = 0.999 9 and RMSEP = 0.395 7. The results showed that NIR spectroscopy could be used for determination of MDA content in oilseed rape leaves, and an excellent prediction precision was achieved. This study supplied a new approach to the dynamic and continuous field monitoring of growing status of oilseed rape.