Method For High-throughput Identification Research Articles

Automatic and Accurate Calculation of Rice Seed Setting Rate Based on Image Segmentation and Deep Learning.

The rice seed setting rate (RSSR) is an important component in calculating rice yields and a key phenotype for its genetic analysis. Automatic calculations of RSSR through computer vision technology have great significance for rice yield predictions. The basic premise for calculating RSSR is having an accurate and high throughput identification of rice grains. In this study, we propose a method based on image segmentation and deep learning to automatically identify rice grains and calculate RSSR. By collecting information on the rice panicle, our proposed image automatic segmentation method can detect the full grain and empty grain, after which the RSSR can be calculated by our proposed rice seed setting rate optimization algorithm (RSSROA). Finally, the proposed method was used to predict the RSSR during which process, the average identification accuracy reached 99.43%. This method has therefore been proven as an effective, non-invasive method for high throughput identification and calculation of RSSR. It is also applicable to soybean yields, as well as wheat and other crops with similar characteristics.

An Account of Chiral Metal Surfaces and Their Enantiospecific Chemistry

ConspectusMolecular chirality has been of scientific interest since 1848 when Pasteur demonstrated its direct connection to the rotation of light by solutions of chiral compounds. In the 1960s the connection was made between the chirality of pharmaceutical compounds and their physiological impact; one enantiomer can be therapeutic while the other is toxic. That realization prompted enormous effort in the synthesis of enantiomerically pure compounds for bioactive use (a $300B/yr market). Until relatively recently, metals were ignored as potential substrates for asymmetric surface chemistry because metals have highly symmetric, achiral bulk structures and the premise was that they could not expose chiral surfaces. In 1996, we demonstrated that the high Miller index surfaces of metals can be chiral, existing in two enantiomeric forms M(hkl)R&S, and we hypothesized that they exhibit enantiospecific interactions with chiral adsorbates. Most such intrinsically chiral metal surfaces have ideal structural motifs based on low Miller index terraces separated by kinked monatomic steps. This Account begins with a short tutorial on the ideal and real structures of chiral metal surfaces to provide a firm basis for understanding the origin of their chirality. It then chronicles the evolution of our understanding of their enantiospecific interactions with chiral adsorbates.Detecting, quantifying, and understanding enantiospecific surface chemistry on intrinsically chiral metal surfaces has been far more challenging than coming to the realization that such surfaces exist. The first successes came from measurements and modeling of the enantiospecific adsorption energetics of small chiral molecules such as propylene oxide and trans-1,2-dimethylcyclopropane. These revealed one of the core challenges to observing enantiospecificity, the fact that the enantiospecificities of reaction energetics and barriers tend to be small, i.e., a few kJ/mol. Measurements of the enantiospecific adsorption energetics of R-3-methylcyclohexanone on seven different Cu(hkl)R&S surfaces demonstrated their sensitivity to surface structure, but again revealed variations of only a few kJ/mol. One of the most important advances in our understanding of chiral surface chemistry is that the limitations imposed by weakly enantiospecific interactions can be circumvented by processes with nonlinear kinetics or equilibria. As an example, the surface explosion mechanism of d- and l-tartaric acid decomposition on Cu(hkl)R&S surfaces leads to enantiospecific rates that differ by almost 2 orders of magnitude, in spite of the fact that the rate constants are only weakly enantiospecific. More surprising is the observation that equilibrium adsorption of nonracemic mixtures of d- and l-aspartic acid can lead to autoamplification of enantiomeric excess, even on achiral Cu(111) surfaces. Again, this arises from a nonlinear adsorption isotherm. Most recently, we have developed a high throughput method for identification of the most enantiospecific surface orientation for a given reaction from the continuum of Cu(hkl)R&S surface orientations. These developments, and others described in this Account, firmly establish some of the basic principles of chiral surface chemistry.

Limitations of 16S rRNA Gene Sequencing to Characterize Lactobacillus Species in the Upper Genital Tract.

The endometrial cavity is an upper genital tract site previously thought as sterile, however, advances in culture-independent, next-generation sequencing technology have revealed that this low-biomass site harbors a rich microbial community which includes multiple Lactobacillus species. These bacteria are considered to be the most abundant non-pathogenic genital tract commensals. Next-generation sequencing of the female lower genital tract has revealed significant variation amongst microbial community composition with respect to Lactobacillus sp. in samples collected from healthy women and women with urogenital conditions. The aim of this study was to evaluate our ability to characterize members of the genital tract microbial community to species-level taxonomy using variable regions of the 16S rRNA gene. Samples were interrogated for the presence of microbial DNA using next-generation sequencing technology that targets the V5–V8 regions of the 16S rRNA gene and compared to speciation using qPCR. We also performed re-analysis of published data using alternate variable regions of the 16S rRNA gene. In this analysis, we explore next-generation sequencing of clinical genital tract isolates as a method for high throughput identification to species-level of key Lactobacillus sp. Data revealed that characterization of genital tract taxa is hindered by a lack of a consensus protocol and 16S rRNA gene region target allowing comparison between studies.

Profiling of N-Linked Oligosaccharides of a Glycoprotein by UPLC-FLR-ESI-MS After Derivatization with Fluorescent Anthranilamide.

N-glycans are described to have a large influence on the properties of therapeutic proteins, including safety and efficacy. For this reason, the extent and type of glycosylation is a characterization parameter for the analysis of antibodies and other therapeutic proteins. The method described here is a fast and high-throughput method for identification and semiquantification of N-glycans by HILIC-FLR-ESI-MS. Sample preparation has been optimized and simultaneous preparation of a large number of samples can be achieved within a day. The use of MS coupled to fluorescence detection is an additional tool for identifying the N-glycan type.

Identification of Cancer Biomarkers in Human Body Fluids by Using Enhanced Physicochemical-incorporated Evolutionary Conservation Scheme.

Cancer is one of the most serious diseases affecting human health. Among all current cancer treatments, early diagnosis and control significantly help increase the chances of cure. Detecting cancer biomarkers in body fluids now is attracting more attention within oncologists. In-silico predictions of body fluid-related proteins, which can be served as cancer biomarkers, open a door for labor-intensive and time-consuming biochemical experiments. In this work, we propose a novel method for high-throughput identification of cancer biomarkers in human body fluids. We incorporate physicochemical properties into the weighted observed percentages (WOP) and position-specific scoring matrices (PSSM) profiles to enhance their attributes that reflect the evolutionary conservation of the body fluid-related proteins. The least absolute selection and shrinkage operator (LASSO) feature selection strategy is introduced to generate the optimal feature subset. The ten-fold cross-validation results on training datasets demonstrate the accuracy of the proposed model. We also test our proposed method on independent testing datasets and apply it to the identification of potential cancer biomarkers in human body fluids. The testing results promise a good generalization capability of our approach.

A high-throughput drug combination screen identifies an anti-glioma synergism between TH588 and PI3K inhibitors

BackgroundGlioblastoma multiforme (GBM) is the most common and lethal type of primary brain tumor. More than half of GBMs contain mutation(s) of PTEN/PI3K/AKT, making inhibitors targeting the PI3K pathway very attractive for clinical investigation. However, so far, PI3K/AKT/mTOR inhibitors have not achieved satisfactory therapeutic effects in clinical trials of GBM. In this study, we aimed to develop a high-throughput screening method for high-throughput identification of potential targeted agents that synergize with PI3K inhibitors in GBM.MethodsA Sensitivity Index (SI)-based drug combination screening method was established to evaluate the interactions between BKM120, a pan-PI3K inhibitor, and compounds from a library of 606 target-selective inhibitors. Proliferation, colony and 3D spheroid formation assays, western blotting, comet assay, γ-H2AX staining were used to evaluate the anti-glioma effects of the top-ranked candidates. The drug combination effects were analyzed by the Chou-Talalay method.ResultsSix compounds were successfully identified from the drug screen, including three previously reported compounds that cause synergistic antitumor effects with PI3K/mTOR inhibitors. TH588, an putative MTH1 inhibitor exhibited significant synergy with BKM120 in suppressing the proliferation, colony formation and 3D spheroid formation of GBM cells. Further investigation revealed that both DNA damage and apoptosis were markedly enhanced upon combination treatment with TH588 and BKM120. Finally, activation of PI3K or overexpression of AKT compromised the anti-glioma efficacy of TH588.ConclusionsThe screening method developed in this study demonstrated its usefulness in the rapid identification of synergistic drug combinations of PI3K inhibitors and targeted agents.

PhyteByte: identification of foods containing compounds with specific pharmacological properties

BackgroundPhytochemicals and other molecules in foods elicit positive health benefits, often by poorly established or unknown mechanisms. While there is a wealth of data on the biological and biophysical properties of drugs and therapeutic compounds, there is a notable lack of similar data for compounds commonly present in food. Computational methods for high-throughput identification of food compounds with specific biological effects, especially when accompanied by relevant food composition data, could enable more effective and more personalized dietary planning. We have created a machine learning-based tool (PhyteByte) to leverage existing pharmacological data to predict bioactivity across a comprehensive molecular database of foods and food compounds.ResultsPhyteByte uses a cheminformatic approach to structure-based activity prediction and applies it to uncover the putative bioactivity of food compounds. The tool takes an input protein target and develops a random forest classifier to predict the effect of an input molecule based on its molecular fingerprint, using structure and activity data available from the ChEMBL database. It then predicts the relevant bioactivity of a library of food compounds with known molecular structures from the FooDB database. The output is a list of food compounds with high confidence of eliciting relevant biological effects, along with their source foods and associated quantities in those foods, where available. Applying PhyteByte to the human PPARG gene, we identified irigenin, sesamin, fargesin, and delta-sanshool as putative agonists of PPARG, along with previously identified agonists of this important metabolic regulator.ConclusionsPhyteByte identifies food-based compounds that are predicted to interact with specific protein targets. The identified relationships can be used to prioritize food compounds for experimental or epidemiological follow-up and can contribute to the rapid development of precision approaches to new nutraceuticals as well as personalized dietary planning.

TALEN mediated targeted mutagenesis of the caffeic acid O-methyltransferase in highly polyploid sugarcane improves cell wall composition for production of bioethanol.

Sugarcane (Saccharum spp. hybrids) is a prime crop for commercial biofuel production. Advanced conversion technology utilizes both, sucrose accumulating in sugarcane stems as well as cell wall bound sugars for commercial ethanol production. Reduction of lignin content significantly improves the conversion of lignocellulosic biomass into ethanol. Conventional mutagenesis is not expected to confer reduction in lignin content in sugarcane due to its high polyploidy (x = 10–13) and functional redundancy among homo(eo)logs. Here we deploy transcription activator-like effector nuclease (TALEN) to induce mutations in a highly conserved region of the caffeic acid O-methyltransferase (COMT) of sugarcane. Capillary electrophoresis (CE) was validated by pyrosequencing as reliable and inexpensive high throughput method for identification and quantitative characterization of TALEN mediated mutations. Targeted COMT mutations were identified by CE in up to 74 % of the lines. In different events 8–99 % of the wild type COMT were converted to mutant COMT as revealed by pyrosequencing. Mutation frequencies among mutant lines were positively correlated to lignin reduction. Events with a mutation frequency of 99 % displayed a 29–32 % reduction of the lignin content compared to non-transgenic controls along with significantly reduced S subunit content and elevated hemicellulose content. CE analysis displayed similar peak patterns between primary COMT mutants and their vegetative progenies suggesting that TALEN mediated mutations were faithfully transmitted to vegetative progenies. This is the first report on genome editing in sugarcane. The findings demonstrate that targeted mutagenesis can improve cell wall characteristics for production of lignocellulosic ethanol in crops with highly complex genomes.Electronic supplementary materialThe online version of this article (doi:10.1007/s11103-016-0499-y) contains supplementary material, which is available to authorized users.

High‐throughput quantification of stabilizers in polymeric materials by flow injection tandem mass spectrometry

High-throughput methods for identification and quantification of stabilizers in plastic materials are of significant importance in order to evaluate the suitability of materials of unknown origin for specific application areas, to clarify reasons for failure of materials, or for comparison of materials from different sources. In the present study, a highly sensitive and rapid flow injection method coupled to selected reaction monitoring mass spectrometry (MS) for comprehensive analysis of 21 polymer stabilizers in polyolefins is demonstrated. A critical factor for this approach is the choice of ionization mode, as no separation was performed prior to MS detection. Differences between several ionization techniques regarding matrix effects are reported. Atmospheric pressure chemical ionization was found to be the most suitable ionization technique, with no significant matrix effects observed. The developed method has a linear dynamic range over two to three orders of magnitude with correlation coefficients better than 0.99 for all studied analytes. Following a multistep sample preparation protocol, the method allowed quantification down to minimum values of between 0.0001 and 0.04 wt% depending on the type of stabilizer. Results were compared to an established chromatographic approach and showed very good correlation (bias below 7.5%). The applicability of the optimized method could be demonstrated for both the qualitative and quantitative determination of polymer stabilizers in polyolefins. Furthermore, the described approach yields a complete analysis in a much shorter time than can be achieved with commonly applied chromatographic methods.

The role of gut microbiota in the gut-brain axis: current challenges and perspectives

Brain and the gastrointestinal (GI) tract are intimately connected to form a bidirectional neurohumoral communication system. The communication between gut and brain, knows as the gut-brain axis, is so well established that the functional status of gut is always related to the condition of brain. The researches on the gut-brain axis were traditionally focused on the psychological status affecting the function of the GI tract. However, recent evidences showed that gut microbiota communicates with the brain via the gut-brain axis to modulate brain development and behavioral phenotypes. These recent findings on the new role of gut microbiota in the gut-brain axis implicate that gut microbiota could associate with brain functions as well as neurological diseases via the gut-brain axis. To elucidate the role of gut microbiota in the gut-brain axis, precise identification of the composition of microbes constituting gut microbiota is an essential step. However, identification of microbes constituting gut microbiota has been the main technological challenge currently due to massive amount of intestinal microbes and the difficulties in culture of gut microbes. Current methods for identification of microbes constituting gut microbiota are dependent on omics analysis methods by using advanced high tech equipment. Here, we review the association of gut microbiota with the gut-brain axis, including the pros and cons of the current high throughput methods for identification of microbes constituting gut microbiota to elucidate the role of gut microbiota in the gut-brain axis.

ISsaga is an ensemble of web-based methods for high throughput identification and semi-automatic annotation of insertion sequences in prokaryotic genomes

Insertion sequences (ISs) play a key role in prokaryotic genome evolution but are seldom well annotated. We describe a web application pipeline, ISsaga (http://issaga.biotoul.fr/ISsaga/issaga_index.php), that provides computational tools and methods for high-quality IS annotation. It uses established ISfinder annotation standards and permits rapid processing of single or multiple prokaryote genomes. ISsaga provides general prediction and annotation tools, information on genome context of individual ISs and a graphical overview of IS distribution around the genome of interest.

High Throughput Minor Histocompatibility Antigen Discovery by Whole Genome Association Scanning.

Abstract 685Allogeneic stem cell transplantation (allo-SCT) followed by donor lymphocyte infusion (DLI) is used as a curative treatment for patients with malignant diseases. Donor derived T cells mediate graft versus tumor responses by targeting minor histocompatibility antigens (mHags) that are encoded by patient specific single nucleotide polymorphisms (SNPs). Various approaches have been applied for mHag discovery resulting in the characterization of more than 20 mHags. However, restriction to specific HLA types and unfavourable gene expression strongly limits the number of clinically relevant mHags. Recently, it has been demonstrated that whole genome association scanning (WGAs) can be a tool for mHag identification. Here, we present WGAs as a powerful method for high throughput identification of new mHags. From 2 patients that entered complete remission with limited graft versus host disease after allo-SCT and DLI, activated T cells were cloned by flowcytometric cell sorting. After expansion, 232 stably growing T cell clones were obtained. Patient specific recognition in the absence of donor recognition was demonstrated for 78 clones. By using blocking antibodies and a test panel consisting of partially HLA matched EBV-transformed B cell lines (EBV-LCL), we demonstrated that these 78 T cell clones comprised 20 unique mHag reactivities which could be identified to be restricted to HLA-A*02 or B*07. Since WGAs is based on a balanced segregation of test cells in a positive and a negative group, 15 T cell clones were selected recognizing mHags with population frequencies between 20% to 80% for further analysis. To perform WGAs, a test panel was generated containing 80 HLA-A*02 and B*07 positive EBV-LCL for testing of recognition by all selected T cell clones using Interferon-γ Elisa. In parallel, all EBV-LCL were genotyped for 1 million SNPs using bead arrays. All SNP genotype patterns were combined with each individual T cell recognition pattern. The level of matching between both patterns was statistically analyzed using Fisher's exact test, resulting in p-values indicating the significance of association. Significant association (p-value<10-12) between SNP genotypes and a T cell recognition pattern identified a single genomic region for 12 out of 15 T cell clones. In 2 cases no clear discrimination between positive and negative EBV-LCL could be made, suggesting that these T cell clones may not recognize mHags. Incomplete coverage of a genomic region by SNPs on the bead array may explain the lack of association for 1 T cell clone. For 7 T cell clones, significant association was found with array SNPs located in exons of the genes WNK1, SSR1, PRCP, ARHGDIB, PDCD11, EBI3 and APOBEC3B. For 3 other T cell clones, the genes ERAP1, BCAT2 and GEMIN4 were identified based on significant association with SNPs located in non coding regions. Sequence analysis of the coding regions of these genes from patient and donor revealed additional patient specific SNPs that were not included in the bead array. For the remaining associating TTK and ERGIC1 genes, the coding regions were identical between patient and donor, showing that these mHags are not encoded by exon SNPs in the identified TTK and ERGIC1 genes. Differential mHag expression may be induced in these cases by SNPs in adjacent genes that were not identified by SNPs on the bead array or may be the result of SNPs in non coding regulatory regions or in mRNA splice variants. According to the BioGPS gene expression database, a number of genes as identified by WGAs were predominantly expressed in hematopoietic cells, and may therefore encode relevant targets for T cell therapy. Next, we investigated the amino acid polymorphisms encoded by all identified coding SNPs. Peptides spanning the patient type amino acid polymorphism were submitted to HLA binding prediction algorithms. Candidate peptides were synthesized and T cell recognition was demonstrated at concentrations varying from 0.5 to 5000 nM. Recognition of donor type peptides was absent in all cases, validating the identification of 10 novel mHags. In conclusion, these data demonstrate that activation marker based T cell selection and cloning combined with WGAs results in high throughput discovery of multiple mHags. This strategy therefore allows broad characterization of mHags in donor derived T cell responses and selection of clinically relevant mHags for development of T cell based immunotherapy. Disclosures:No relevant conflicts of interest to declare.

'Calling Cards' method for high-throughput identification of targets of yeast DNA-binding proteins

We present a protocol for a novel method for identifying the targets of DNA-binding proteins in the genome of the yeast Saccharomyces cerevisiae. This is accomplished by engineering a DNA-binding protein so that it leaves behind in the genome a permanent mark -- a 'calling card' -- that provides a record of that protein's visit to that region of the genome. The calling card is the yeast Ty5 retrotransposon, whose integrase interacts with the Sir4 protein. If Sir4 is fused to a DNA-binding protein, it recruits the Ty5 integrase, which directs insertion of a Ty5 calling card into the genome. The calling card along with the flanking genomic DNA is harvested by inverse PCR and its genomic location is determined by hybridization of the product to a DNA microarray. This method provides a straightforward alternative to the 'ChIP-chip' method for determining the targets of DNA-binding proteins. This protocol takes approximately 2 weeks to complete.