One-Pot Methods for the Protection and Assembly of Sugars

In recent years, there has been rapid expansion of glycan synthesis, fueled by the recognition that the structural complexity of sugars translates to a myriad of biological functions. Such chemical syntheses involve many challenges, mostly due to the regio- and stereochemical aspects of glycosidic bond formation. One-pot strategies were developed to assist in attaining faster and more economical access to the glycan constructs. In this front, achievements in protecting group manipulation, glycosylation, and combinations of these have been reported. Protecting group manipulations in one pot take advantage of the reaction compatibility of commonly used transformations, many of which occur in high regioselectivity. Sequential glycosylations, on the other hand, rely on leaving group orthogonalities and reactivity tuning, as well as the preactivation technique. Altogether, these approaches offer attractive means to the much needed glycan structures and, consequently, help usher in advances in glycoscience.     

Effect of the cultivation mode on red pigments production from Monascus ruber

In submerged cultures performed in chemically defined fermentation medium containing glucose and glutamate, the growth and production of water‐soluble red pigments and citrinin by the filamentous fungus Monascus ruber were studied under various carbon/nitrogen (C/N) ratios. The specific production of the red pigments was optimal at a glucose/glutamate ratio of about 10 and then steadily decreased at higher C/N ratio. In contrast, the production of the mycotoxin increased with increased C/N with an optimum in the range of 30–45. In a fed‐batch mode, it was also found that the production of pigments was not favoured in fed‐batch mode by feeding the medium with glucose while keeping the C/N ratio lower than 10. This low production likely resulted from concurrent high accumulation of L‐malic acid that was reported to inhibit this production. In contrast, this mode of cultivation was rather favourable for the production of the mycotoxin.     

Histone Methylation Marks on Circulating Nucleosomes as Novel Blood-Based Biomarker in Colorectal Cancer

Circulating nucleic acids (CNAs) are under investigation as a liquid biopsy in cancer as potential non-invasive biomarkers, as stable structure in circulation nucleosomes could be valuable sources for detection of cancer-specific alterations in histone modifications. Our interest is in histone methylation marks with a focus on colorectal cancer, one of the leading cancers respective the incidence and mortality. Our previous work included the analysis of trimethylations of lysine 9 on histone 3 (H3K9me3) and of lysine 20 on histone 4 (H4K20me3) by chromatin immuno- precipitation-related PCR in circulating nucleosomes. Here we asked whether global immunologic measurement of histone marks in circulation could be a suitable approach to show their potential as biomarkers. In addition to H3K9me3 and H4K20me3 we also measured H3K27me3 in plasma samples from CRC patients (n = 63) and cancer free individuals (n = 40) by ELISA-based methylation assays. Our results show that of three marks, the amounts of H3K27me3 (p = 0.04) and H4K20me3 (p < 0.001) were significantly lower in CRC patients than in healthy controls. For H3K9me3 similar amounts were measured in both groups. Areas under the curve (AUC) in receiver operating characteristic (ROC) curves indicating the power of CRC detection were 0.620 for H3K27me3, 0.715 for H4K20me3 and 0.769 for the combination of both markers. In conclusion, findings of this preliminary study reveal the potential of blood-based detection of CRC by quantification of histone methylation marks and the additive effect of the marker combination.     

The Spatial LASSO With Applications to Unmixing Hyperspectral Biomedical Images

Hyperspectral imaging (HSI) is a spectroscopic method that uses densely sampled measurements along the electromagnetic spectrum to identify the unique molecular composition of an object. Traditionally HSI has been associated with remote sensing-type applications, but recently has found increased use in biomedicine, from investigations at the cellular to the tissue level. One of the main challenges in the analysis of HSI is estimating the proportions, also called abundance fractions of each of the molecular signatures. While there is great promise for HSI in the area of biomedicine, large variability in the measurements and artifacts related to the instrumentation has slow adoption into more widespread practice. In this article, we propose a novel regularization and variable selection method called the spatial LASSO (SPLASSO). The SPLASSO incorporates spatial information via a graph Laplacian-based penalty to help improve the model estimation process for multivariate response data. We show the strong performance of this approach on a benchmark HSI dataset with considerable improvement in predictive accuracy over the standard LASSO. Supplementary materials for this article are available online.