The protein translocation systems in plants - composition and variability on the example of Solanum lycopersicum

Background: Protein translocation across membranes is a central process in all cells. In the past decades the molecular composition of the translocation systems in the membranes of the endoplasmic reticulum, peroxisomes, mitochondria and chloroplasts have been established based on the analysis of model organisms. Today, these results have to be transferred to other plant species. We bioinformatically determined the inventory of putative translocation factors in tomato (Solanum lycopersicum) by orthologue search and domain architecture analyses. In addition, we investigated the diversity of such systems by comparing our findings to the model organisms Saccharomyces cerevisiae, Arabidopsis thaliana and 12 other plant species. Results: The literature search end up in a total of 130 translocation components in yeast and A. thaliana, which are either experimentally confirmed or homologous to experimentally confirmed factors. From our bioinformatic analysis (PGAP and OrthoMCL), we identified (co-)orthologues in plants, which in combination yielded 148 and 143 orthologues in A. thaliana and S. lycopersicum, respectively. Interestingly, we traced 82% overlap in findings from both approaches though we did not find any orthologues for 27% of the factors by either procedure. In turn, 29% of the factors displayed the presence of more than one (co-)orthologue in tomato. Moreover, our analysis revealed that the genomic composition of the translocation machineries in the bryophyte Physcomitrella patens resemble more to higher plants than to single celled green algae. The monocots (Z. mays and O. sativa) follow more or less a similar conservation pattern for encoding the translocon components. In contrast, a diverse pattern was observed in different eudicots. Conclusions: The orthologue search shows in most cases a clear conservation of components of the translocation pathways/machineries. Only the Get-dependent integration of tail-anchored proteins seems to be distinct. Further, the complexity of the translocation pathway in terms of existing orthologues seems to vary among plant species. This might be the consequence of palaeoploidisation during evolution in plants; lineage specific whole genome duplications in Arabidopsis thaliana and triplications in Solanum lycopersicum

Proton hyperpolarisation preserved in long-lived states.

International audienceThe polarisation of abundant protons, rather than dilute nuclei with low gyromagnetic ratios, can be enhanced in less than 10 min using dissolution DNP and converted into a long-lived state delocalised over an ensemble of three coupled protons. The process is more straightforward than the hyperpolarisation of heteronuclei followed by magnetisation transfer to protons

HyperSeed: An End-to-End Method to Process Hyperspectral Images of Seeds

High-throughput, nondestructive, and precise measurement of seeds is critical for the evaluation of seed quality and the improvement of agricultural productions. To this end, we have developed a novel end-to-end platform named HyperSeed to provide hyperspectral information for seeds. As a test case, the hyperspectral images of rice seeds are obtained from a high-performance line-scan image spectrograph covering the spectral range from 600 to 1700 nm. The acquired images are processed via a graphical user interface (GUI)-based open-source software for background removal and seed segmentation. The output is generated in the form of a hyperspectral cube and curve for each seed. In our experiment, we presented the visual results of seed segmentation on different seed species. Moreover, we conducted a classification of seeds raised in heat stress and control environments using both traditional machine learning models and neural network models. The results show that the proposed 3D convolutional neural network (3D CNN) model has the highest accuracy, which is 97.5% in seed-based classification and 94.21% in pixel-based classification, compared to 80.0% in seed-based classification and 85.67% in seed-based classification from the support vector machine (SVM) model. Moreover, our pipeline enables systematic analysis of spectral curves and identification of wavelengths of biological interest

HyperSeed: An End-to-End Method to Process Hyperspectral Images of Seeds

High-throughput, nondestructive, and precise measurement of seeds is critical for the evaluation of seed quality and the improvement of agricultural productions. To this end, we have developed a novel end-to-end platform named HyperSeed to provide hyperspectral information for seeds. As a test case, the hyperspectral images of rice seeds are obtained from a high-performance line-scan image spectrograph covering the spectral range from 600 to 1700 nm. The acquired images are processed via a graphical user interface (GUI)-based open-source software for background removal and seed segmentation. The output is generated in the form of a hyperspectral cube and curve for each seed. In our experiment, we presented the visual results of seed segmentation on different seed species. Moreover, we conducted a classification of seeds raised in heat stress and control environments using both traditional machine learning models and neural network models. The results show that the proposed 3D convolutional neural network (3D CNN) model has the highest accuracy, which is 97.5% in seed-based classification and 94.21% in pixel-based classification, compared to 80.0% in seed-based classification and 85.67% in seed-based classification from the support vector machine (SVM) model. Moreover, our pipeline enables systematic analysis of spectral curves and identification of wavelengths of biological interest

Photooxidation of 2-methyl-3-buten-2-ol (MBO) as a potential source of secondary organic aerosol

2-Methyl-3-buten-2-ol (MBO) is an important biogenic hydrocarbon emitted in large quantities by pine forests. Atmospheric photooxidation of MBO is known to lead to oxygenated compounds, such as glycolaldehyde, which is the precursor to glyoxal. Recent studies have shown that the reactive uptake of glyoxal onto aqueous particles can lead to formation of secondary organic aerosol (SOA). In this work, MBO photooxidation under high- and low-NO_x conditions was performed in dual laboratory chambers to quantify the yield of glyoxal and investigate the potential for SOA formation. The yields of glycolaldehyde and 2-hydroxy-2-methylpropanal (HMPR), fragmentation products of MBO photooxidation, were observed to be lower at lower NO_x concentrations. Overall, the glyoxal yield from MBO photooxidation was 25% under high-NO_x and 4% under low-NO_x conditions. In the presence of wet ammonium sulfate seed and under high-NO_x conditions, glyoxal uptake and SOA formation were not observed conclusively, due to relatively low (<30 ppb) glyoxal concentrations. Slight aerosol formation was observed under low-NO_x and dry conditions, with aerosol mass yields on the order of 0.1%. The small amount of SOA was not related to glyoxal uptake, but is likely a result of reactions similar to those that generate isoprene SOA under low-NO_x conditions. The difference in aerosol yields between MBO and isoprene photooxidation under low-NO_x conditions is consistent with the difference in vapor pressures between triols (from MBO) and tetrols (from isoprene). Despite its structural similarity to isoprene, photooxidation of MBO is not expected to make a significant contribution to SOA formation

\u3ci\u3ePhenoImage\u3c/i\u3e: An open-source graphical user interface for plant image analysis

High-throughput genotyping coupled with molecular breeding approaches have dramatically accelerated crop improvement programs. More recently, improved plant phenotyping methods have led to a shift from manual measurements to automated platforms with increased scalability and resolution. Considerable effort has also gone into developing large-scale downstream processing of the imaging datasets derived from high-throughput phenotyping (HTP) platforms. However, most available tools require some programming skills.We developed PhenoImage, an open-source graphical user interface (GUI) based cross-platform solution for HTP image processing intending to make image analysis accessible to users with either little or no programming skills. The open-source nature provides the possibility to extend its usability to meet user-specific requirements. The availability of multiple functions and filtering parameters provides flexibility to analyze images from a wide variety of plant species and platforms. PhenoImage can be run on a personal computer as well as on high-performance computing clusters. To test the efficacy of the application, we analyzed the LemnaTec Imaging system derived red, green, and blue (RGB) color intensity and plant pigmentation-based fluorescence shoot images from two plant species: sorghum [Sorghum bicolor (L.) Moench] and wheat (Triticum aestivum L.) differing in their physical attributes. In the study, we discuss the development, implementation, and working of the PhenoImage

Divergent phenotypic response of rice accessions to transient heat stress during early seed development

Increasing global surface temperatures is posing a major food security challenge. Part of the solution to address this problem is to improve crop heat resilience, especially during grain development, along with agronomic decisions such as shift in planting time and increasing crop diversification. Rice is a major food crop consumed by more than 3 billion people. For rice, thermal sensitivity of reproductive development and grain filling is well-documented, while knowledge concerning the impact of heat stress (HS) on early seed development is limited. Here, we aim to study the phenotypic variation in a set of diverse rice accessions for elucidating the HS response during early seed development. To explore the variation in HS sensitivity, we investigated aus (1), indica (2), temperate japonica (2), and tropical japonica (4) accessions for their HS (39/35°C) response during early seed development that accounts for transition of endosperm from syncytial to cellularization, which broadly corresponds to 24 and 96 hr after fertilization (HAF), respectively, in rice. The two indica and one of the tropical japonica accessions exhibited severe heat sensitivity with increased seed abortion; three tropical japonicas and an aus accession showed moderate heat tolerance, while temperate japonicas exhibited strong heat tolerance. The accessions exhibiting extreme heat sensitivity maintain seed size at the expense of number of fully developed mature seeds, while the accessions showing relative resilience to the transient HS maintained number of fully developed seeds but compromised on seed size, especially seed length. Further, histochemical analysis revealed that all the tested accessions have delayed endosperm cellularization upon exposure to the transient HS by 96 HAF; however, the rate of cellularization was different among the accessions. These findings were further corroborated by upregulation of cellularization associated marker genes in the developing seeds from the heat-stressed samples

PI‑Plat: a high‑resolution image‑based 3D reconstruction method to estimate growth dynamics of rice inflorescence traits

Background: Recent advances in image-based plant phenotyping have improved our capability to study vegetative stage growth dynamics. However, more complex agronomic traits such as inflorescence architecture (IA), which predominantly contributes to grain crop yield are more challenging to quantify and hence are relatively less explored. Previous efforts to estimate inflorescence-related traits using image-based phenotyping have been limited to destructive end-point measurements. Development of non-destructive inflorescence phenotyping platforms could accelerate the discovery of the phenotypic variation with respect to inflorescence dynamics and mapping of the underlying genes regulating critical yield components. Results: The major objective of this study is to evaluate post-fertilization development and growth dynamics of inflorescence at high spatial and temporal resolution in rice. For this, we developed the Panicle Imaging Platform (PI-Plat) to comprehend multi-dimensional features of IA in a non-destructive manner. We used 11 rice genotypes to capture multi-view images of primary panicle on weekly basis after the fertilization. These images were used to reconstruct a 3D point cloud of the panicle, which enabled us to extract digital traits such as voxel count and color intensity. We found that the voxel count of developing panicles is positively correlated with seed number and weight at maturity. The voxel count from developing panicles projected overall volumes that increased during the grain filling phase, wherein quantification of color intensity estimated the rate of panicle maturation. Our 3D based phenotyping solution showed superior performance compared to conventional 2D based approaches. Conclusions: For harnessing the potential of the existing genetic resources, we need a comprehensive understanding of the genotype-to-phenotype relationship. Relatively low-cost sequencing platforms have facilitated high-throughput genotyping, while phenotyping, especially for complex traits, has posed major challenges for crop improvement. PI-Plat offers a low cost and high-resolution platform to phenotype inflorescence-related traits using 3D reconstruction-based approach. Further, the non-destructive nature of the platform facilitates analyses of the same panicle at multiple developmental time points, which can be utilized to explore the genetic variation for dynamic inflorescence traits in cereals

Factors associated with delays in treatment initiation after tuberculosis diagnosis in two districts of India.

BACKGROUND: Excessive time between diagnosis and initiation of tuberculosis (TB) treatment contributes to ongoing TB transmission and should be minimized. In India, Revised National TB Control Programme (RNTCP) focuses on indicator start of treatment within 7 days of diagnosis for patients with sputum smear-positive PTB for monitoring DOTS implementation. OBJECTIVES: To determine length of time between diagnosis and initiation of treatment and factors associated with delays of more than 7 days in smear-positive pulmonary TB. METHODS: Using existing programme records such as the TB Register, treatment cards, and the laboratory register, we conducted a retrospective cohort study of all patients with smear-positive pulmonary TB registered from July-September 2010 in two districts in India. A random sample of patients with pulmonary TB who experienced treatment delay of more than 7 days was interviewed using structured questionnaire. RESULTS: 2027 of 3411 patients registered with pulmonary TB were smear-positive. 711(35%) patients had >7 days between diagnosis and treatment and 262(13%) had delays >15 days. Mean duration between TB diagnosis and treatment initiation was 8 days (range = 0-128 days). Odds of treatment delay >7 days was 1.8 times more likely among those who had been previously treated (95% confidence interval [CI] 1.5-2.3) and 1.6 (95% CI 1.3-1.8) times more likely among those diagnosed in health facilities without microscopy centers. The main factors associated with a delay >7 days were: patient reluctance to start a re-treatment regimen, patients seeking second opinions, delay in transportation of drugs to the DOT centers and delay in initial home visits. To conclude, treatment delay >7 days was associated with a number of factors that included history of previous treatment and absence of TB diagnostic services in the local health facility. Decentralized diagnostic facilities and improved referral procedures may reduce such treatment delays