Extended Multiplicative Signal Correction Based Model Transfer for Raman Spectroscopy in Biological Applications.

The chemometric analysis of Raman spectra of biological materials is hampered by spectral variations due to the instrumental setup that overlay the subtle biological changes of interest. Thus, an established statistical model may fail when applied to Raman spectra of samples acquired with a different device. Therefore, model transfer strategies are essential. Herein we report a model transfer approach based on extended multiplicative signal correction (EMSC). As opposed to existing model transfer methods, the EMSC based approach does not require group information on the secondary data sets, thus no extra measurements are required. The proposed model-transfer approach is a preprocessing procedure and can be combined with any method for regression and classification. The performance of EMSC as a model transfer method was demonstrated with a data set of Raman spectra of three Bacillus bacteria spore species ( B. mycoides, B. subtilis, and B. thuringiensis), which were acquired on four Raman spectrometers. A three-group classification by partial least-squares discriminant analysis (PLS-DA) with leave-one-device-out external cross-validation (LODCV) was performed. The mean sensitivities of the prediction on the independent device were considerably improved by the EMSC method. Besides the mean sensitivity, the model transferability was additionally benchmarked by the newly defined numeric markers: (1) relative Pearson's correlation coefficient and (2) relative Fisher's discriminant ratio. We show that these markers have led to consistent conclusions compared to the mean sensitivity of the classification. The advantage of our defined markers is that the evaluation is more effective and objective, because it is independent of the classification models.

The application of UV resonance Raman spectroscopy for the differentiation of clinically relevant Candida species.

Candida-related infections have become a major problem in hospitals. The species identification of yeast is the prerequisite for the initiation of adequate antifungal therapy. In the present study, the connection between inherent UV resonance Raman (RR) spectral profiles of Candida species and taxonomic differences was investigated for the first time. UV RR in combination with statistical modeling was applied to extract taxonomic information from the spectral fingerprints for subsequent differentiation. The identification accuracies of independent batch cultures were determined by applying a leave-one-batch-out cross validation. The quality of differentiation can be divided into three levels. Within a defined taxonomic group comprising the species C. glabrata, C. guilliermondii, and C. haemulonii, the identification accuracy was low. On the next level, the identification results of C. albicans and C. tropicalis were characterized by high sensitivities of 98 and 95% but simultaneously challenged by false-positive predictions due to the misallocation of C. spherica (as C. albicans) and C. viswanathii (as C. tropicalis). The highest level of identification accuracies was reached for the species C. dubliniensis, C. krusei, C. africana, C. novergica, and C. parapsilosis. Reliable identification results were observed with accuracies ranging from 93 up to 100%. The species allocation based on the UV RR spectral profiles could be reproduced by the identification of independent batch cultures. We conclude that the introduced spectroscopic approach is capable of transforming the high-dimensional UV RR data of Candida species into clinically useful decision parameters. Graphical abstract.

Investigating the effect of different pre-treatment methods on Raman spectra recorded with different excitation wavelengths.

Raman reference libraries can be used for identification of components in unknown samples as Raman spectroscopy offers fingerprint information of the measured samples. Since Raman libraries often contain many different and/or highly similar spectra, it is important that the spectra are a reliable fingerprint for each compound. However, Raman spectra are highly sensitive to the experimental conditions, and the Raman spectra will change in different conditions even though the same sample is measured. Raman data pre-treatment minimizes the differences between Raman spectra arising from different experimental conditions. In this study, different combinations of pre-treatment methods are used to quantify the effect of each pre-treatment step in minimizing the differences between Raman spectra of the same sample in different experimental conditions, e.g., different excitation wavelengths. These different pre-treatment processes are evaluated for six solvents. The spectra differences between spectra recorded with three excitation wavelengths (532 nm, 633 nm, and 830 nm) are evaluated by angular difference index and the influence on a classification model is tested. The angular difference index of each spectrum after every data pre-treatment step shows a decreasing behavior. It could be demonstrated that wavenumber calibration has the largest effect on the differences between the Raman spectra. However, ω4 correction doesn't have a significate effect in this dataset. The classification results show that the prediction accuracy is improving by doing data pre-treatment. In the dataset obtained in 633 nm a lower amount of pre-treatment steps is needed but in the dataset 830 nm more pre-treatment steps are needed for a high accuracy. The result shows that the choice of an optimal pre-treatment method or combination of methods strongly influences the analysis results, but is far from straightforward, since it depends on the characteristics of the data set and the goal of data analysis.

Computer- and structure-based lead design for epigenetic targets.

The term epigenetics is defined as inheritable changes that influence the outcome of a phenotype without changes in the genome. Epigenetics is based upon DNA methylation and posttranslational histone modifications. While there is much known about reversible acetylation as a posttranslational modification, research on reversible histone methylation is still emerging, especially with regard to drug discovery. As aberrant epigenetic modifications have been linked to many diseases, inhibitors of histone modifying enzymes are very much in demand. This article will summarize the progress on small molecule epigenetic inhibitors identified by structure- and computer-based approaches.

Acyl derivatives of p-aminosulfonamides and dapsone as new inhibitors of the arginine methyltransferase hPRMT1.

Arginine methylation is an epigenetic modification that receives increasing interest as it plays an important role in several diseases. This is especially true for hormone-dependent cancer, seeing that histone methylation by arginine methyltransferase I (PRMT1) is involved in the activation of sexual hormone receptors. Therefore, PRMT inhibitors are potential drugs and interesting tools for cell biology. A dapsone derivative called allantodapsone previously identified by our group served as a lead structure for inhibitor synthesis. Acylated derivatives of p-aminobenzenesulfonamides and the antilepra drug dapsone were identified as new inhibitors of PRMT1 by in vitro testing. The bis-chloroacetyl amide of dapsone selectively inhibited human PRMT1 in the low micromolar region and was selective for PRMT1 as compared to the arginine methyltransferase CARM1 and the lysine methyltransferase Set7/9. It showed anticancer activity on MCF7a and LNCaP cells and blocked androgen dependent transcription specifically in a reporter gene system. Likewise, a transcriptional block was also demonstrated in LNCaP cells using quantitative RT-PCR on the mRNA of androgen dependent genes.

Target-based approach to inhibitors of histone arginine methyltransferases.

Lysine and arginine methyltransferases participate in the post-translational modification of histones and regulate key cellular functions. So far only one arginine methyltransferase inhibitor discovered by random screening was available. We present the first target-based approach to protein arginine methyltransferase (PRMT) inhibitors. Homology models of human and Aspergillus nidulans PRMT1 were generated from available X-ray structures of rat PRMTs. The NCI diversity set was filtered by a target-based virtual screening to identify PRMT inhibitors. Employing a fungal PRMT for screening and a human enzyme for validation, we have identified seven inhibitors of PRMTs in vitro. Hit validation was achieved for two new inhibitors by antibody mediated detection of histone hypomethylation as well as Western blotting in cancer cells. Functional activity was proven by an observed block of estrogen receptor activation. Thus, valuable chemical tools and potential drug candidates could be identified.

The emerging therapeutic potential of histone methyltransferase and demethylase inhibitors.

Epigenetics is defined as heritable changes to the transcriptome that are independent of changes in the genome. The biochemical modifications that govern epigenetics are DNA methylation and posttranslational histone modifications. Among the histone modifications, acetylation and deacetylation are well characterized, whereas the fields of histone methylation and especially demethylation are still in their infancy. This is particularly true with regard to drug discovery. There is strong evidence that these modifications play an important role in the maintenance of transcription as well as in the development of certain diseases. This article gives an overview of the mechanisms of action of histone methyltransferases and demethylases, their role in the formation of certain diseases, and available inhibitors. Special emphasis is placed on the strategies that led to the first inhibitors which are currently available and the screening approaches that were used in that process.

Virtual screening and biological characterization of novel histone arginine methyltransferase PRMT1 inhibitors.

Lysine and arginine methyltransferases participate in the posttranslational modification of histones and regulate key cellular functions. Protein arginine methyltransferase 1 (PRMT1) has been identified as an essential component of mixed lineage leukemia (MLL) oncogenic complexes, revealing its potential as a novel therapeutic target in human cancer. The first potent arginine methyltransferase inhibitors were recently discovered by random- and target-based screening approaches. Herein we report virtual and biological screening for novel inhibitors of PRMT1. Structure-based virtual screening (VS) of the Chembridge database composed of 328 000 molecules was performed with a combination of ligand- and target-based in silico approaches. Nine inhibitors were identified from the top-scored docking solutions; these were experimentally tested using human PRMT1 and an antibody-based assay with a time-resolved fluorescence readout. Among several aromatic amines, an aliphatic amine and an amide were also found to be active in the micromolar range.

A novel arginine methyltransferase inhibitor with cellular activity.

Via virtual screening we identified a thioglycolic amide as an arginine methyltransferase (PRMT) inhibitor and tested it and related compounds against the fungal PRMT RmtA and human PRMT1. Compound RM65 was the most potent druglike inhibitor (IC(50)-PRMT1: 55.4 microM) and showed histone hypomethylation in HepG2 cells. Docking studies proposed binding at the substrate and SAM cofactor binding pocket. It may serve as a lead for further PRMT inhibitors useful for the treatment for hormone dependent cancers.