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.     

Schwannomas of the peroneal nerves: Clinical and functional results of surgical treatment

Objectives:Peroneal nerves Schwannomas are rare benign tumors. Literature is still poor of studies about clinical and functional outcomes after surgical treatment. We evaluated the pre-operative presentation of the disease and assessed clinical and functional outcomes after surgery.Methods:We collected all the cases of peroneal nerves’ neurinoma treated surgically between June 2016 and June 2020. We analyzed each patients’ personal data and carried out accurate clinical examinations before and after surgery. MRI was performed both pre-operatively and post-operatively.Results:We reported 9 cases of peroneal nerves schwannomas: five arising from the common peroneal nerve and four arising from the deep or superficial branches alone. Their mean size was 22.6 mm. Each patient showed sensation deficits before surgery; pre-operative MRC score was 4.2. Pre-Operative MSTS and LEFS scores were 23.6 and 64.4. Surgery was successful in each case. No local recurrence nor major complication occurred. Tumor size was significantly associated with both diagnostic delay and development of pre-operative deficits. Surgery was proven to be globally successful: post-operative evaluations highlighted a marked reduction of neurological signs and overall functional limitations.Conclusions:Surgical treatment at early stages of the disease represents a reliable and relatively safe therapeutic option.     

GREEN-DB: a framework for the annotation and prioritization of non-coding regulatory variants from whole-genome sequencing data

Non-coding variants have long been recognized as important contributors to common disease risks, but with the expansion of clinical whole genome sequencing, examples of rare, high-impact non-coding variants are also accumulating. Despite recent advances in the study of regulatory elements and the availability of specialized data collections, the systematic annotation of non-coding variants from genome sequencing remains challenging. Here, we propose a new framework for the prioritization of non-coding regulatory variants that integrates information about regulatory regions with prediction scores and HPO-based prioritization. Firstly, we created a comprehensive collection of annotations for regulatory regions including a database of 2.4 million regulatory elements (GREEN-DB) annotated with controlled gene(s), tissue(s) and associated phenotype(s) where available. Secondly, we calculated a variation constraint metric and showed that constrained regulatory regions associate with disease-associated genes and essential genes from mouse knock-outs. Thirdly, we compared 19 non-coding impact prediction scores providing suggestions for variant prioritization. Finally, we developed a VCF annotation tool (GREEN-VARAN) that can integrate all these elements to annotate variants for their potential regulatory impact. In our evaluation, we show that GREEN-DB can capture previously published disease-associated non-coding variants as well as identify additional candidate disease genes in trio analyses.     

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.