Perspectives on human microbiome research ethics

study of ethical, legal, and social implications (ELSI) of human microbiome research has been integral to the Human Microbiome Project (HMP). This study explores core ELSI issues that arose during the first phase of the HMP from the perspective of individuals involved in the research. We conducted semi-structured in-depth interviews with investigators and NIH employees ("investigators") involved in the HMP, and with individuals recruited to participate in the HMP Healthy Cohort Study at Baylor College of Medicine ("recruits"). We report findings related to three major ELSI issues: informed consent, data sharing, and return of results. Our findings demonstrate that investigators and recruits were similarly sensitive to these issues yet generally comfortable with study design in light of current knowledge about the microbiome.     

Neuroethics and fMRI: mapping a fledgling relationship

Human functional magnetic resonance imaging (fMRI) informs the understanding of the neural basis of mental function and is a key domain of ethical enquiry. It raises questions about the practice and implications of research, and reflexively informs ethics through the empirical investigation of moral judgments. It is at the centre of debate surrounding the importance of neuroscience findings for concepts such as personhood and free will, and the extent of their practical consequences. Here, we map the landscape of fMRI and neuroethics, using citation analysis to uncover salient topics. We find that this landscape is sparsely populated: despite previous calls for debate, there are few articles that discuss both fMRI and ethical, legal, or social implications (ELSI), and even fewer direct citations between the two literatures. Recognizing that practical barriers exist to integrating ELSI discussion into the research literature, we argue nonetheless that the ethical challenges of fMRI, and controversy over its conceptual and practical implications, make this essential.     

Mining the Unknown: A Systems Approach to Metabolite Identification Combining Genetic and Metabolic Information

Recent genome-wide association studies (GWAS) with metabolomics data linked genetic variation in the human genome to differences in individual metabolite levels. A strong relevance of this metabolic individuality for biomedical and pharmaceutical research has been reported. However, a considerable amount of the molecules currently quantified by modern metabolomics techniques are chemically unidentified. The identification of these “unknown metabolites” is still a demanding and intricate task, limiting their usability as functional markers of metabolic processes. As a consequence, previous GWAS largely ignored unknown metabolites as metabolic traits for the analysis. Here we present a systems-level approach that combines genome-wide association analysis and Gaussian graphical modeling with metabolomics to predict the identity of the unknown metabolites. We apply our method to original data of 517 metabolic traits, of which 225 are unknowns, and genotyping information on 655,658 genetic variants, measured in 1,768 human blood samples. We report previously undescribed genotype–metabotype associations for six distinct gene loci (SLC22A2, COMT, CYP3A5, CYP2C18, GBA3, UGT3A1) and one locus not related to any known gene (rs12413935). Overlaying the inferred genetic associations, metabolic networks, and knowledge-based pathway information, we derive testable hypotheses on the biochemical identities of 106 unknown metabolites. As a proof of principle, we experimentally confirm nine concrete predictions. We demonstrate the benefit of our method for the functional interpretation of previous metabolomics biomarker studies on liver detoxification, hypertension, and insulin resistance. Our approach is generic in nature and can be directly transferred to metabolomics data from different experimental platforms.     

Mealtime,Temporal, and Daily Variability of the Human Urinary and Plasma Metabolomes in a Tightly Controlled Environment

While metabolomics has tremendous potential for diagnostic biomarker and therapeutic target discovery, its utility may be diminished by the variability that occurs due to environmental exposures including diet and the influences of the human circadian rhythm. For successful translation of metabolomics findings into the clinical setting, it is necessary to exhaustively define the sources of metabolome variation. To address these issues and to measure the variability of urinary and plasma metabolomes throughout the day, we have undertaken a comprehensive inpatient study in which we have performed non-targeted metabolomics analysis of blood and urine in 26 volunteers (13 healthy subjects with no known disease and 13 healthy subjects with autosomal dominant polycystic kidney disease not taking medication). These individuals were evaluated in a clinical research facility on two separate occasions, over three days, while on a standardized, weight-based diet. Subjects provided pre- and post-prandial blood and urine samples at the same time of day, and all samples were analyzed by “fast lane” LC-MS-based global metabolomics. The largest source of variability in blood and urine metabolomes was attributable to technical issues such as sample preparation and analysis, and less variability was due to biological variables, meals, and time of day. Higher metabolome variability was observed after the morning as compared to the evening meal, yet day-to-day variability was minimal and urine metabolome variability was greater than that of blood. Thus we suggest that blood and urine are suitable biofluids for metabolomics studies, though nontargeted mass spectrometry alone may not offer sufficient precision to reveal subtle changes in the metabolome. Additional targeted analyses may be needed to support the data from nontargeted mass spectrometric analyses. In light of these findings, future metabolomics studies should consider these sources of variability to allow for appropriate metabolomics testing and reliable clinical translation of metabolomics data.     

合成生物学生物安全风险评价与管理

合成生物学(synthetic biology)已迅速发展为生命科学最具发展潜力的分支学科之一,但它同时也会给生态环境和人类健康带来潜在的风险。结合国内外合成生物学发展现状,本文综述了基因回路(DNA-based biocircuits)、最小基因组(minimal genome)、原型细胞(protocells)、化学合成生物学(chemical synthetic biology)等涉及的风险评价、合成生物学与生物安全工程(biosafety engineering)、合成生物学对社会伦理道德法律的影响以及当前热点议题,如生物朋(黑)客(biopunk(or biohackery))、家置生物学(garage biology)、DIY生物学(do-it-yourselfbiology)、生物恐怖主义(bioterrorism)等方面的新进展。分析讨论了世界各国合成生物学以自律监管或技术为主的安全管理原则和基于5个不同政策干预点的5P管理策略的合理性与潜在不足。同时结合我国合成生物学当前研究进展以及现有的安全管理规范,提出了建立以安全评价为核心的法规体系、生物学生物安全规范以及加强研发单位内部管理和生物安全科普宣传等我国合成生物学安全管理制度与措施等建议。     

乳酸菌代谢组学研究进展

代谢组学作为系统生物学的重要分支,近年来在微生物研究领域受到广泛关注,并取得了重要进展。目前乳酸菌代谢组学正日益成为研究的热点,就乳酸菌代谢组学研究中有关样品的制备、分析鉴定和数据分析等涉及的主要方法进行概述,并介绍一些乳酸菌代谢组学应用的典型实例,对乳酸菌代谢组学研究中潜在的问题和未来发展趋势进行讨论。     

代谢组学及其在微藻研究中的应用

代谢组学以完整的生物体为研究对象,运用合适的分析测试手段检测靶向或非靶向代谢物,结合统计模型进行分析解释。随着微藻研究的深入,微藻与代谢组学结合探究分子作用机理的研究日益增多。本文总结代谢组学的发展概况、研究流程及常用分析技术特点和代谢组学在微藻领域的研究进展,展望代谢组学在微藻研究的应用前景与发展趋势,并提出实际应用中所面临的困难与挑战。     

Production of transgenic rats by ooplasmic injection of spermatogenic cells exposed to exogenous DNA: a preliminary study

The aim of the present study was to investigate the efficiencies of producing transgenic rats by the ooplasmic injection of sperm heads (intracytoplasmic sperm injection: ICSI) and elongating spermatids (elongating spermatid injection: ELSI) exposed to the EGFP DNA solution. A slightly lower proportion of ICSI oocytes using sperm heads exposed to a concentration of 0.5 microg/ml DNA solution for 1 min developed into offspring (13.3%, 48/361) when compared to that of oocytes injected with nontreated sperm heads (19.4%, 32/165). Eight ICSI offspring were found to be EGFP-carrying transgenic rats (16.7% per offspring; 2.2% per embryo). After a 1-min exposure of the elongating spermatids to 5 microg/ml of DNA solution, 8.8% (45/511) of the ELSI oocytes developed into offspring while 12.7% (22/173) of the ELSI oocytes using nontreated spermatids developed. Six ELSI offspring carried the EGFP DNA (13.3% per offspring; 1.2% per embryo). The conventional pronuclear microinjection of 5 microg/ml of DNA solution resulted in the higher production of offspring (29.7%, 104/350) and the birth of three transgenic rats (2.9% per offspring; 0.9% per embryo). Thus, sperm heads and elongating spermatids were practically useful as the vector of exogenous DNA if the DNA-exposed spermatogenic cells were microinseminated into rat oocytes.     

An introduction to metabolomics and its potential application in veterinary science

Metabolomics has been found to be applicable to a wide range of fields, including the study of gene function, toxicology, plant sciences, environmental analysis, clinical diagnostics, nutrition, and the discrimination of organism genotypes. This approach combines high-throughput sample analysis with computer-assisted multivariate pattern-recognition techniques. It is increasingly being deployed in toxico- and pharmacokinetic studies in the pharmaceutical industry, especially during the safety assessment of candidate drugs in human medicine. However, despite the potential of this technique to reduce both costs and the numbers of animals used for research, examples of the application of metabolomics in veterinary research are, thus far, rare. Here we give an introduction to metabolomics and discuss its potential in the field of veterinary science.     

A metabolomics perspective of human brain tumours

During the past decade or so, a wealth of information about metabolites in various human brain tumour preparations (cultured cells, tissue specimens, tumours in vivo) has been accumulated by global profiling tools. Such holistic approaches to cellular biochemistry have been termed metabolomics. Inherent and specific metabolic profiles of major brain tumour cell types, as determined by proton nuclear magnetic resonance spectroscopy ((1)H MRS), have also been used to define metabolite phenotypes in tumours in vivo. This minireview examines the recent advances in the field of human brain tumour metabolomics research, including advances in MRS and mass spectrometry technologies, and data analysis.     

代谢组学及其在环境毒理学中的应用

代谢组学作为系统生物学的一部分,因其具有分析速度快的特点,被广泛用于生物医学等方面的研究。目前代谢组学在环境毒理学方面的研究主要针对单一污染物,但也需要考虑到被污染地的复杂情况。通过介绍代谢组学及其发展历程,总结了目前主流代谢组学技术的各自特点,讨论了代谢组学在环境重金属、有机污染物和抗生素中的毒性评估以及环境胁迫耐受性中的评价等方面内容,综述了其在环境毒理学中的应用,并指出其应用不足,旨在为代谢组学应用于环境毒理学的研究提供参考。     

Metabolomics across the globe

This article highlights some of the larger and more recent metabolomics activities which are funded and organised at local (mostly national) level. While being just a snap-shot, and far from exhaustive, the details clearly illustrate the extent to which metabolomics has already become established and integrated in both basic fundamental and more applied research covering a wide range of organisms. Many national (service) centres for metabolomics already exist and additional ones are envisaged.     

Integrating metabolomics into a systems biology framework to exploit metabolic complexity: strategies and applications in microorganisms

As an important functional genomic tool, metabolomics has been illustrated in detail in recent years, especially in plant science. However, the microbial category also has the potential to benefit from integration of metabolomics into system frameworks. In this article, we first examine the concepts and brief history of metabolomics. Next, we summarize metabolomic research processes and analytical platforms in strain improvements. The application cases of metabolomics in microorganisms answer what the metabolomics can do in strain improvements. The position of metabolomics in this systems biology framework and the real cases of integrating metabolomics into a system framework to explore the microbial metabolic complexity are also illustrated in this paper.     

Metabolomics in the fight against malaria

Metabolomics uses high-resolution mass spectrometry to provide a chemical fingerprint of thousands of metabolites present in cells, tissues or body fluids. Such metabolic phenotyping has been successfully used to study various biologic processes and disease states. High-resolution metabolomics can shed new light on the intricacies of host-parasite interactions in each stage of the Plasmodium life cycle and the downstream ramifications on the host’s metabolism, pathogenesis and disease. Such data can become integrated with other large datasets generated using top-down systems biology approaches and be utilised by computational biologists to develop and enhance models of malaria pathogenesis relevant for identifying new drug targets or intervention strategies. Here, we focus on the promise of metabolomics to complement systems biology approaches in the quest for novel interventions in the fight against malaria. We introduce the Malaria Host-Pathogen Interaction Center (MaHPIC), a new systems biology research coalition. A primary goal of the MaHPIC is to generate systems biology datasets relating to human and non-human primate (NHP) malaria parasites and their hosts making these openly available from an online relational database. Metabolomic data from NHP infections and clinical malaria infections from around the world will comprise a unique global resource.     

Metabolomics meets lipidomics: Assessing the small molecule component of metabolism

Metabolomics, including lipidomics, is emerging as a quantitative biology approach for the assessment of energy flow through metabolism and information flow through metabolic signaling; thus, providing novel insights into metabolism and its regulation, in health, healthy ageing and disease. In this forward-looking review we provide an overview on the origins of metabolomics, on its role in this postgenomic era of biochemistry and its application to investigate metabolite role and (bio)activity, from model systems to human population studies. We present the challenges inherent to this analytical science, and approaches and modes of analysis that are used to resolve, characterize and measure the infinite chemical diversity contained in the metabolome (including lipidome) of complex biological matrices. In the current outbreak of metabolic diseases such as cardiometabolic disorders, cancer and neurodegenerative diseases, metabolomics appears to be ideally situated for the investigation of disease pathophysiology from a metabolite perspective.     

Metabolomics – an overview. From basic principles to potential biomarkers (part 2)

The metabolome, which represents the complete set of molecules (metabolites) of a biological sample (cell, tissue, organ, organism), is the final downstream product of the metabolic cell process that involves the genome and exogenous sources. The metabolome is characterized by a large number of small molecules with a huge diversity of chemical structures and abundances. Exploring the metabolome requires complementary analytical platforms to reach its extensive coverage. The metabolome is continually evolving, reflecting the continuous flux of metabolic and signaling pathways. Metabolomic research aims to study the biochemical processes by detecting and quantifying metabolites to obtain a metabolic picture able to give a functional readout of the physiological state. Recent advances in mass spectrometry (one of the mostly used technologies for metabolomics studies) have given the opportunity to determine the spatial distribution of metabolites in tissues. In a two-part article, we describe the usual metabolomics technologies, workflows and strategies leading to the implementation of new clinical biomarkers. In this second part, we first develop the steps of a metabolomic analysis from sample collection to biomarker validation. Then with two examples, autism spectrum disorders and Alzheimer's disease, we illustrate the contributions of metabolomics to clinical practice. Finally, we discuss the complementarity of in vivo (positron emission tomography) and in vitro (metabolomics) molecular explorations for biomarker research.