Sugarcane genes associated with sucrose content

<p>Abstract</p> <p>Background -</p> <p>Sucrose content is a highly desirable trait in sugarcane as the worldwide demand for cost-effective biofuels surges. Sugarcane cultivars differ in their capacity to accumulate sucrose and breeding programs routinely perform crosses to identify genotypes able to produce more sucrose. Sucrose content in the mature internodes reach around 20% of the culms dry weight. Genotypes in the populations reflect their genetic program and may display contrasting growth, development, and physiology, all of which affect carbohydrate metabolism. Few studies have profiled gene expression related to sugarcane's sugar content. The identification of signal transduction components and transcription factors that might regulate sugar accumulation is highly desirable if we are to improve this characteristic of sugarcane plants.</p> <p>Results -</p> <p>We have evaluated thirty genotypes that have different Brix (sugar) levels and identified genes differentially expressed in internodes using cDNA microarrays. These genes were compared to existing gene expression data for sugarcane plants subjected to diverse stress and hormone treatments. The comparisons revealed a strong overlap between the drought and sucrose-content datasets and a limited overlap with ABA signaling. Genes associated with sucrose content were extensively validated by qRT-PCR, which highlighted several protein kinases and transcription factors that are likely to be regulators of sucrose accumulation. The data also indicate that aquaporins, as well as lignin biosynthesis and cell wall metabolism genes, are strongly related to sucrose accumulation. Moreover, sucrose-associated genes were shown to be directly responsive to short term sucrose stimuli, confirming their role in sugar-related pathways.</p> <p>Conclusion -</p> <p>Gene expression analysis of sugarcane populations contrasting for sucrose content indicated a possible overlap with drought and cell wall metabolism processes and suggested signaling and transcriptional regulators to be used as molecular markers in breeding programs. Transgenic research is necessary to further clarify the role of the genes and define targets useful for sugarcane improvement programs based on transgenic plants.</p

The mechanisms of boronate ester formation and fluorescent turn-on in ortho-aminomethylphenylboronic acids

ortho-Aminomethylphenylboronic acids are used in receptors for carbohydrates and various other compounds containing vicinal diols. The presence of the o-aminomethyl group enhances the affinity towards diols at neutral pH, and the manner in which this group plays this role has been a topic of debate. Further, the aminomethyl group is believed to be involved in the turn-on of the emission properties of appended fluorophores upon diol binding. In this treatise, a uniform picture emerges for the role of this group: it primarily acts as an electron-withdrawing group that lowers the pK(a) of the neighbouring boronic acid thereby facilitating diol binding at neutral pH. The amine appears to play no role in the modulation of the fluorescence of appended fluorophores in the protic-solvent-inserted form of the boronic acid/boronate ester. Instead, fluorescence turn-on can be consistently tied to vibrational-coupled excited-state relaxation (a loose-bolt effect). Overall, this Review unifies and discusses the existing data as of 2019 whilst also highlighting why o-aminomethyl groups are so widely used, and the role they play in carbohydrate sensing using phenylboronic acids

Detection of coliform bacteria in water by polymerase chain reaction and gene probes.

Polymerase chain reaction (PCR) amplification and gene probe detection of regions of two genes, lacZ and lamB, were tested for their abilities to detect coliform bacteria. Amplification of a segment of the coding region of Escherichia coli lacZ by using a PCR primer annealing temperature of 50 degrees C detected E. coli and other coliform bacteria (including Shigella spp.) but not Salmonella spp. and noncoliform bacteria. Amplification of a region of E. coli lamB by using a primer annealing temperature of 50 degrees C selectively detected E. coli and Salmonella and Shigella spp. PCR amplification and radiolabeled gene probes detected as little as 1 to 10 fg of genomic E. coli DNA and as a few as 1 to 5 viable E. coli cells in 100 ml of water. PCR amplification of lacZ and lamB provides a basis for a method to detect indicators of fecal contamination of water, and amplification of lamB in particular permits detection of E. coli and enteric pathogens (Salmonella and Shigella spp.) with the necessary specificity and sensitivity for monitoring the bacteriological quality of water so as to ensure the safety of water supplies

Multivariate Data Analysis Methodology to Solve Data Challenges Related to Scale‐Up Model Validation and Missing Data on a Micro‐Bioreactor System

Multivariate data analysis (MVDA) is a highly valuable and significantly underutilised resource in biomanufacturing. It offers the opportunity to enhance our understanding and leverage useful information from complex high-dimensional data sets, recorded throughout all stages of therapeutic drug manufacture. To help standardise the application and promote this resource within the biopharmaceutical industry, this paper outlines a novel MVDA methodology describing the necessary steps for efficient and effective data analysis. The MVDA methodology was followed to solve two case studies: a 'small data' and a 'big data' challenge. In the 'small data' example, a large-scale data set was compared to data from a scale-down model. This methodology enabled a new quantitative metric for equivalence to be established by combining a Two One-Sided Test (TOST) with principal component analysis. In the 'big data' example, this methodology enabled accurate predictions of critical missing data essential to a cloning study performed in the ambr15TM system. These predictions were generated by exploiting the underlying relationship between the off-line missing values and the on-line measurements through the generation of a partial least squares model. In summary, the proposed MVDA methodology highlights the importance of data pre-processing, restructuring and visualisation during data analytics to solve complex biopharmaceutical challenges. This article is protected by copyright. All rights reserved