整合分析多组学数据筛选疾病靶点的精准医学策略

随着高通量测序技术的不断发展与完善,对于不同层次和类型的生物组学数据的获取及分析方法也日趋成熟与完善。基于单组学数据的疾病研究已经发现了诸多新的疾病相关因子,而整合多组学数据研究疾病靶点的工作方兴未艾。生命体是一个复杂的调控系统,疾病的发生与发展涉及基因变异、表观遗传改变、基因表达异常以及信号通路紊乱等诸多层次的复杂调控机制,利用单一组学数据分析致病因子的局限性愈发显著。通过对多种层次和来源的高通量组学数据的整合分析,系统地研究临床发病机理、确定最佳疾病靶点已经成为精准医学研究的重要发展方向,将为疾病研究提供新的思路,并对疾病的早期诊断、个体化治疗和指导用药等提供新的理论依据。本文详细介绍了基因组、转录组和表观组等系统组学研究在疾病靶点筛选方面出现的新技术手段和研究进展,并对它们之间的整合分析新策略和优势进行了讨论。     

Drugging the undruggable proteins in cancer: A systems biology approach

In recent years, the research community has, with comprehensive systems biology approaches and related technologies, gained insight into the vast complexity of numerous cancers. These approaches allow an in-depth exploration that cannot be achieved solely using conventional low-throughput methods, which do not closely mimic the natural cellular environment. In this review, we discuss recent integrative multiple omics approaches for understanding and modulating previously identified ‘undruggable’ targets such as members of the RAS family, MYC, TP53, and various E3 ligases and deubiquitinases. We describe how these technologies have revolutionized drug discovery by overcoming an array of biological and technological challenges and how, in the future, they will be pivotal in assessing cancer states in individual patients, allowing for the prediction and application of personalized disease treatments.     

Molecular interaction networks for the analysis of human disease: Utility,limitations, and considerations

High‐throughput ‘‐omics’ data can be combined with large‐scale molecular interaction networks, for example, protein–protein interaction networks, to provide a unique framework for the investigation of human molecular biology. Interest in these integrative ‘‐omics’ methods is growing rapidly because of their potential to understand complexity and association with disease; such approaches have a focus on associations between phenotype and “network‐type.” The potential of this research is enticing, yet there remain a series of important considerations. Here, we discuss interaction data selection, data quality, the relative merits of using data from large high‐throughput studies versus a meta‐database of smaller literature‐curated studies, and possible issues of sociological or inspection bias in interaction data. Other work underway, especially international consortia to establish data formats, quality standards and address data redundancy, and the improvements these efforts are making to the field, is also evaluated. We present options for researchers intending to use large‐scale molecular interaction networks as a functional context for protein or gene expression data, including microRNAs, especially in the context of human disease.     

Prevention,diagnosis and treatment of high‐throughput sequencing data pathologies

High‐throughput sequencing (HTS) technologies generate millions of sequence reads from DNA/RNA molecules rapidly and cost‐effectively, enabling single investigator laboratories to address a variety of ‘omics’ questions in nonmodel organisms, fundamentally changing the way genomic approaches are used to advance biological research. One major challenge posed by HTS is the complexity and difficulty of data quality control (QC). While QC issues associated with sample isolation, library preparation and sequencing are well known and protocols for their handling are widely available, the QC of the actual sequence reads generated by HTS is often overlooked. HTS‐generated sequence reads can contain various errors, biases and artefacts whose identification and amelioration can greatly impact subsequent data analysis. However, a systematic survey on QC procedures for HTS data is still lacking. In this review, we begin by presenting standard ‘health check‐up’ QC procedures recommended for HTS data sets and establishing what ‘healthy’ HTS data look like. We next proceed by classifying errors, biases and artefacts present in HTS data into three major types of ‘pathologies’, discussing their causes and symptoms and illustrating with examples their diagnosis and impact on downstream analyses. We conclude this review by offering examples of successful ‘treatment’ protocols and recommendations on standard practices and treatment options. Notwithstanding the speed with which HTS technologies – and consequently their pathologies – change, we argue that careful QC of HTS data is an important – yet often neglected – aspect of their application in molecular ecology, and lay the groundwork for developing a HTS data QC ‘best practices’ guide.     

Small extracellular vesicles (exosomes) and their cargo in pancreatic cancer: Key roles in the hallmarks of cancer

Pancreatic cancer (PC) is a devastating disease, offering poor mortality rates for patients. The current challenge being faced is the inability to diagnose patients in a timely manner, where potentially curative resection provides the best chance of survival. Recently, small/nanosized extracellular vesicles (sEVs), including exosomes, have gained significant preclinical and clinical attention due to their emerging roles in cancer progression and diagnosis. Extracellular vesicles (EVs) possess endogenous properties that offer stability and facilitate crossing of biological barriers for delivery of molecular cargo to cells, acting as a form of intercellular communication to regulate function and phenotype of recipient cells. This review provides an overview of the role of EVs, their subtypes and their oncogenic cargo (as characterised by targeted studies as well as agnostic ‘-omics’ analyses) in the pathobiology of pancreatic cancer. The discussion covers the progress of ‘omics technology’ that has enabled elucidation of the molecular mechanisms that mediate the role of EVs and their cargo in pancreatic cancer progression.     

The use of metabolomics as a tool to investigate hepatitis C

According to World Health Organization, an estimated 3 % of the global population is suffering from chronic hepatitis C. Furthermore, 60–70 % of chronically-infected patients develop liver diseases, of which 5–20 % of all cases advance to cirrhosis, and 1–5 % die from hepatitis C related hepatocellular carcinoma. This high incidence might be ascribed to the poor performance of the currently available diagnostic, treatment, and vaccination protocols, together with the lack of knowledge of the underlying disease mechanisms. In this review, we discuss the role that the relatively new research field termed metabolomics, alone or as part of an integrated ‘omics’ approach, has played in the investigation of hepatitis C and associated clinical manifestations. We also consider future research possibilities in this field, and the impact that these results might have on the fight against this global health predicament.     

Two-Way Horizontal and Vertical Omics Integration for Disease Subtype Discovery

Disease subtype discovery is an essential step in delivering personalized medicine. Disease subtyping via omics data has become a common approach for this purpose. With the advancement of technology and the lower price for generating omics data, multi-level and multi-cohort omics data are prevalent in the public domain, providing unprecedented opportunities to decrypt disease mechanisms. How to fully utilize multi-level/multi-cohort omics data and incorporate established biological knowledge toward disease subtyping remains a challenging problem. In this paper, we propose a meta-analytic integrative sparse Kmeans (MISKmeans) algorithm for integrating multi-cohort/multi-level omics data and prior biological knowledge. Compared with previous methods, MISKmeans shows better clustering accuracy and feature selection relevancy. An efficient R package, “MIS-Kmeans”, calling C++ is freely available on GitHub (https://github.com/Caleb-Huo/MIS-Kmeans).

     

Invitrogen & HUPO enter partnership

Neuroblastoma (NB) is one of the most common solid tumors of childhood and displays a remarkable diversity in both biologic characteristics and clinical outcomes. Availability of high-throughput ‘omics technologies and their subsequent application towards oncology has provided insight into the complex pathways of tumor formation and progression. Investigation of NB ‘omics profiles may better define tumor behavior and provide targeted therapy with the goal of improving outcomes in patients with high-risk disease. Utilization of these technologies in NB has already led to advances in classification and risk stratification. The gradual emergence of NB-directed proteomics adds a layer of intricacy to the analysis of biologic organization but may ultimately provide a better comprehension of this complex disease. In this review, we cite specific examples of how NB-directed proteomics has provided information regarding novel biomarkers and possible therapeutic targets. We finish by examining the impact of high-throughput ‘omics in the field of NB and speculate on how these emerging technologies may further be incorporated into the discipline.     

Molecular signatures from omics data: From chaos to consensus

In the past 15 years, new "omics" technologies have made it possible to obtain high-resolution molecular snapshots of organisms, tissues, and even individual cells at various disease states and experimental conditions. It is hoped that these developments will usher in a new era of personalized medicine in which an individual's molecular measurements are used to diagnose disease, guide therapy, and perform other tasks more accurately and effectively than is possible using standard approaches. There now exists a vast literature of reported "molecular signatures". However, despite some notable exceptions, many of these signatures have suffered from limited reproducibility in independent datasets, insufficient sensitivity or specificity to meet clinical needs, or other challenges. In this paper, we discuss the process of molecular signature discovery on the basis of omics data. In particular, we highlight potential pitfalls in the discovery process, as well as strategies that can be used to increase the odds of successful discovery. Despite the difficulties that have plagued the field of molecular signature discovery, we remain optimistic about the potential to harness the vast amounts of available omics data in order to substantially impact clinical practice.     

Proteomic investigations into hypertension: what’s new and how might it affect clinical practice?

Introduction: Hypertension is a multifactorial disease that has, thus far, proven to be a difficult target for pharmacological intervention. The application of proteomic strategies may help to identify new biomarkers for the early diagnosis and prompt treatment of hypertension, in order to control blood pressure and prevent organ damage.

Areas covered: Advances in proteomics have led to the discovery of new biomarkers to help track the pathophysiological processes implicated in hypertension. These findings not only help to better understand the nature of the disease, but will also contribute to the clinical needs for a timely diagnosis and more precise treatment. In this review, we provide an overview of new biomarkers identified in hypertension through the application of proteomic techniques, and we also discuss the difficulties and challenges in identifying biomarkers in this clinical setting. We performed a literature search in PubMed with the key words ‘hypertension’ and ‘proteomics’, and focused specifically on the most recent literature on the utility of proteomics in hypertension research.

Expert opinion: There have been several promising biomarkers of hypertension identified by proteomics, but too few have been introduced to the clinic. Thus, further investigations in larger cohorts are necessary to test the feasibility of this strategy for patients. Also, this emerging field would profit from more collaboration between clinicians and researchers.     

From cancer big data to treatment: Artificial intelligence in cancer research

In recent years, developing the idea of “cancer big data” has emerged as a result of the significant expansion of various fields such as clinical research, genomics, proteomics and public health records. Advances in omics technologies are making a significant contribution to cancer big data in biomedicine and disease diagnosis. The increasingly availability of extensive cancer big data has set the stage for the development of multimodal artificial intelligence (AI) frameworks. These frameworks aim to analyze high-dimensional multi-omics data, extracting meaningful information that is challenging to obtain manually. Although interpretability and data quality remain critical challenges, these methods hold great promise for advancing our understanding of cancer biology and improving patient care and clinical outcomes. Here, we provide an overview of cancer big data and explore the applications of both traditional machine learning and deep learning approaches in cancer genomic and proteomic studies. We briefly discuss the challenges and potential of AI techniques in the integrated analysis of omics data, as well as the future direction of personalized treatment options in cancer.     

Dawn of ocular gene therapy: implications for molecular diagnosis in retinal disease

Personalized medicine aims to utilize genomic information about patients to tailor treatment. Gene replacement therapy for rare genetic disorders is perhaps the most extreme form of personalized medicine, in that the patients’ genome wholly determines their treatment regimen. Gene therapy for retinal disorders is poised to become a clinical reality. The eye is an optimal site for gene therapy due to the relative ease of precise vector delivery, immune system isolation, and availability for monitoring of any potential damage or side effects. Due to these advantages, clinical trials for gene therapy of retinal diseases are currently underway. A necessary precursor to such gene therapies is accurate molecular diagnosis of the mutation(s) underlying disease. In this review, we discuss the application of Next Generation Sequencing (NGS) to obtain such a diagnosis and identify disease causing genes, using retinal disorders as a case study. After reviewing ocular gene therapy, we discuss the application of NGS to the identification of novel Mendelian disease genes. We then compare current, array based mutation detection methods against next NGS-based methods in three retinal diseases: Leber’s Congenital Amaurosis, Retinitis Pigmentosa, and Stargardt’s disease. We conclude that next-generation sequencing based diagnosis offers several advantages over array based methods, including a higher rate of successful diagnosis and the ability to more deeply and efficiently assay a broad spectrum of mutations. However, the relative difficulty of interpreting sequence results and the development of standardized, reliable bioinformatic tools remain outstanding concerns. In this review, recent advances NGS based molecular diagnoses are discussed, as well as their implications for the development of personalized medicine.     

Sickle cell anemia and vascular dysfunction: The nitric oxide connection

Endothelial dysfunction and impaired nitric oxide bioavailability have been implicated in the pathogenesis of sickle cell anemia. Nitric oxide is a diatomic gas with a role in vascular homeostasis. Hemoglobin polymerization resulting from the HbS mutation produces erythrocyte deformation and hemolysis. Free hemoglobin, released into plasma by hemolysis scavenges on nitric oxide, and leads to reduced nitric oxide bioavailability. Pulmonary hypertension is a known consequence of sickle cell anemia. It occurs in 30–40% of patients with sickle cell anemia, and is associated with increased mortality. Several studies have implicated intravascular hemolysis, and impaired nitric oxide bioavailability in the pathogenesis of pulmonary hypertension. In this review, we summarize the mechanisms of altered nitric oxide bioavailability in sickle cell anemia and its possible role in the pathogenesis of pulmonary hypertension. J. Cell. Physiol. 224: 620–625, 2010. © 2010 Wiley‐Liss, Inc.     

Proteomic analysis of red blood cells and the potential for the clinic: what have we learned so far?

Introduction: Red blood cells (RBC) are the most abundant host cells in the human body. Mature erythrocytes are devoid of nuclei and organelles and have always been regarded as circulating ‘bags of hemoglobin’. The advent of proteomics has challenged this assumption, revealing unanticipated complexity and novel roles for RBCs not just in gas transport, but also in systemic metabolic homeostasis in health and disease.

Areas covered: In this review we will summarize the main advancements in the field of discovery mode and redox/quantitative proteomics with respect to RBC biology. We thus focus on translational/clinical applications, such as transfusion medicine, hematology (e.g. hemoglobinopathies) and personalized medicine. Synergy of omics technologies – especially proteomics and metabolomics – are highlighted as a hallmark of clinical metabolomics applications for the foreseeable future.

Expert commentary: The introduction of advanced proteomics technologies, especially quantitative and redox proteomics, and the integration of proteomics data with omics information gathered through orthogonal technologies (especially metabolomics) promise to revolutionize many biomedical areas, from hematology and transfusion medicine to personalized medicine and clinical biochemistry.     

Systems Metabolic Engineering Meets Machine Learning: A New Era for Data‐Driven Metabolic Engineering

The recent increase in high‐throughput capacity of ‘omics datasets combined with advances and interest in machine learning (ML) have created great opportunities for systems metabolic engineering. In this regard, data‐driven modeling methods have become increasingly valuable to metabolic strain design. In this review, the nature of ‘omics is discussed and a broad introduction to the ML algorithms combining these datasets into predictive models of metabolism and metabolic rewiring is provided. Next, this review highlights recent work in the literature that utilizes such data‐driven methods to inform various metabolic engineering efforts for different classes of application including product maximization, understanding and profiling phenotypes, de novo metabolic pathway design, and creation of robust system‐scale models for biotechnology. Overall, this review aims to highlight the potential and promise of using ML algorithms with metabolic engineering and systems biology related datasets.     

Systemizing the structures and structuring the system

This Keystone symposium, entitled ‘Biomolecular Interactions and Networks: function and disease’, was held in Quebec City, Canada, 7–12 March 2010. The conference was distinctive in that it bridged two fields that may be perceived as having little in common: structural and systems biology. However, the growth in structural and omics data brings these two fields closer and closer. Indeed, in two sections of this article we cover talks on systematic analyses of protein structures, as well as systems level approaches that incorporate structural information. In two other sections, we report studies that aim at charting and analyzing cellular systems, and finally we discuss talks that pointed to the issue of promiscuity in biological networks.