Determination of risperidone and its major metabolite 9-hydroxyrisperidone in human plasma by reversed-phase liquid chromatography with ultraviolet detection

A simple and sensitive high-performance liquid chromatographic (HPLC) method with UV absorbance detection is described for the quantitation of risperidone and its major metabolite 9-hydroxyrisperidone in human plasma, using clozapine as internal standard. After sample alkalinization with 1 ml of NaOH (2 M) the test compounds were extracted from plasma using diisopropyl ether–isoamylalcohol (99:1, v/v). The organic phase was back-extracted with 150 μl potassium phosphate (0.1 M, pH 2.2) and 60 μl of the acid solution was injected into a C₁₈ BDS Hypersil analytical column (3 μm, 100×4.6 mm I.D.). The mobile phase consisted of phosphate buffer (0.05 M, pH 3.7 with 25% H₃PO₄)–acetonitrile (70:30, v/v), and was delivered at a flow-rate of 1.0 ml/min. The peaks were detected using a UV detector set at 278 nm and the total time for a chromatographic separation was about 4 min. The method was validated for the concentration range 5–100 ng/ml. Mean recoveries were 98.0% for risperidone and 83.5% for 9-hydroxyrisperidone. Intra- and inter-day relative standard deviations were less than 11% for both compounds, while accuracy, expressed as percent error, ranged from 1.6 to 25%. The limit of quantitation was 2 ng/ml for both analytes. The method shows good specificity with respect to commonly prescribed psychotropic drugs, and it has successfully been applied for pharmacokinetic studies and therapeutic drug monitoring.     

New insights on the sialidase protein family revealed by a phylogenetic analysis in metazoa

Sialidases are glycohydrolytic enzymes present from virus to mammals that remove sialic acid from oligosaccharide chains. Four different sialidase forms are known in vertebrates: the lysosomal NEU1, the cytosolic NEU2 and the membrane-associated NEU3 and NEU4. These enzymes modulate the cell sialic acid content and are involved in several cellular processes and pathological conditions. Molecular defects in NEU1 are responsible for sialidosis, an inherited disease characterized by lysosomal storage disorder and neurodegeneration. The studies on the biology of sialic acids and sialyltransferases, the anabolic counterparts of sialidases, have revealed a complex picture with more than 50 sialic acid variants selectively present in the different branches of the tree of life. The gain/loss of specific sialoconjugates have been proposed as key events in the evolution of deuterostomes and Homo sapiens, as well as in the host-pathogen interactions. To date, less attention has been paid to the evolution of sialidases. Thus we have conducted a survey on the state of the sialidase family in metazoan. Using an in silico approach, we identified and characterized sialidase orthologs from 21 different organisms distributed among the evolutionary tree: Metazoa relative (Monosiga brevicollis), early Deuterostomia, precursor of Chordata and Vertebrata (teleost fishes, amphibians, reptiles, avians and early and recent mammals). We were able to reconstruct the evolution of the sialidase protein family from the ancestral sialidase NEU1 and identify a new form of the enzyme, NEU5, representing an intermediate step in the evolution leading to the modern NEU3, NEU4 and NEU2. Our study provides new insights on the mechanisms that shaped the substrate specificity and other peculiar properties of the modern mammalian sialidases. Moreover, we further confirm findings on the catalytic residues and identified enzyme loop portions that behave as rapidly diverging regions and may be involved in the evolution of specific properties of sialidases.     

From correlation to causation: analysis of metabolomics data using systems biology approaches

Introduction

Metabolomics is a well-established tool in systems biology, especially in the top–down approach. Metabolomics experiments often results in discovery studies that provide intriguing biological hypotheses but rarely offer mechanistic explanation of such findings. In this light, the interpretation of metabolomics data can be boosted by deploying systems biology approaches.

Objectives

This review aims to provide an overview of systems biology approaches that are relevant to metabolomics and to discuss some successful applications of these methods.

Methods

We review the most recent applications of systems biology tools in the field of metabolomics, such as network inference and analysis, metabolic modelling and pathways analysis.

Results

We offer an ample overview of systems biology tools that can be applied to address metabolomics problems. The characteristics and application results of these tools are discussed also in a comparative manner.

Conclusions

Systems biology-enhanced analysis of metabolomics data can provide insights into the molecular mechanisms originating the observed metabolic profiles and enhance the scientific impact of metabolomics studies.

     

Profiling microbial strains in urban environments using metagenomic sequencing data

Background

The microbial communities populating human and natural environments have been extensively characterized with shotgun metagenomics, which provides an in-depth representation of the microbial diversity within a sample. Microbes thriving in urban environments may be crucially important for human health, but have received less attention than those of other environments. Ongoing efforts started to target urban microbiomes at a large scale, but the most recent computational methods to profile these metagenomes have never been applied in this context. It is thus currently unclear whether such methods, that have proven successful at distinguishing even closely related strains in human microbiomes, are also effective in urban settings for tasks such as cultivation-free pathogen detection and microbial surveillance. Here, we aimed at a) testing the currently available metagenomic profiling tools on urban metagenomics; b) characterizing the organisms in urban environment at the resolution of single strain and c) discussing the biological insights that can be inferred from such methods.

Results

We applied three complementary methods on the 1614 metagenomes of the CAMDA 2017 challenge. With MetaMLST we identified 121 known sequence-types from 15 species of clinical relevance. For instance, we identified several Acinetobacter strains that were close to the nosocomial opportunistic pathogen A. nosocomialis. With StrainPhlAn, a generalized version of the MetaMLST approach, we inferred the phylogenetic structure of Pseudomonas stutzeri strains and suggested that the strain-level heterogeneity in environmental samples is higher than in the human microbiome. Finally, we also probed the functional potential of the different strains with PanPhlAn. We further showed that SNV-based and pangenome-based profiling provide complementary information that can be combined to investigate the evolutionary trajectories of microbes and to identify specific genetic determinants of virulence and antibiotic resistances within closely related strains.

Conclusion

We show that strain-level methods developed primarily for the analysis of human microbiomes can be effective for city-associated microbiomes. In fact, (opportunistic) pathogens can be tracked and monitored across many hundreds of urban metagenomes. However, while more effort is needed to profile strains of currently uncharacterized species, this work poses the basis for high-resolution analyses of microbiomes sampled in city and mass transportation environments.

Reviewers

This article was reviewed by Alexandra Bettina Graf, Daniel Huson and Trevor Cickovski.