Systems biology approaches for the study of multiple sclerosis

Multiple sclerosis (MS) is a progressive neurological disease caused by an autoimmune attack to the central nervous system (CNS). MS is thought to result from a complex interaction between genetic and environmental factors. In this review we analyze the contribution of genomics, trancriptomics and proteomics in delineating these factors, as well as their utility for the monitoring of disease progression, the identification of new targets for therapeutic intervention and the early detection of individuals at risk.     

系统生物学与细胞发生系统动力学

系统生物学是系统理论和实验生物技术、计算机数学模型等方法整合的生物系统研究,系统遗传学研究基因组的稳态与进化、功能基因组和生物性状等复杂系统的结构、动态与发生演变等。合成生物学是系统生物学的工程应用,采用工程学方法、基因工程和计算机辅助设计等研究人工生物系统的生物技术。系统与合成生物学的结构理论,序列标志片段显示分析与微流控生物芯片,广泛用于研究细胞代谢、繁殖和应激的自组织进化、生物体形态发生等细胞分子生物系统原理等。     

Gene therapy for lung cancer

Lung cancer patients suffer a 15% overall survival despite advances in chemotherapy, radiation therapy, and surgery. This unacceptably low survival rate is due to the usual finding of advanced disease at diagnosis. However, multimodality strategies using conventional therapies only minimally improve survival rates even in early stages of lung cancer. Attempts to improve survival in advanced disease using various combinations of platinum-based chemotherapy have demonstrated that no regimen is superior, suggesting a therapeutic plateau and the need for novel, more specific, and less toxic therapeutic strategies. Over the past three decades, the genetic etiology of cancer has been gradually delineated, albeit not yet completely. Understanding the molecular events that occur during the multistep process of bronchogenic carcinogenesis may make these tasks more surmountable. During these same three decades, techniques have been developed which allow transfer of functional genes into mammalian cells. For example, blockade of activated tumor-promoting oncogenes or replacement of inactivated tumor-suppressing or apoptosis-promoting genes can be achieved by gene therapy. This article will discuss the therapeutic implications of these molecular changes associated with bronchogenic carcinomas and will then review the status of gene therapies for treatment of lung cancer.     

Systems and synthetic biology approaches to cell division

Cells proliferate by division into similar daughter cells, a process that lies at the heart of cell biology. Extensive research on cell division has led to the identification of the many components and control elements of the molecular machinery underlying cellular division. Here we provide a brief review of prokaryotic and eukaryotic cell division and emphasize how new approaches such as systems and synthetic biology can provide valuable new insight.     

Systems approaches to succinic acid-producing microorganisms

Succinic acid is a cellular metabolite belonging to the C4-dicarboxylic acid family, and the fermentative production of succinic acid via the use of recombinant microorganisms has recently become the focus of an increasing amount of attention. Considering the difficulty inherent to the direct application of natural succinic acid producers to the industrial process, a variety of systems biology studies have been conducted regarding the development of enhanced succinic acid production systems. This review shows how the metabolic processes of microorganisms, includingEscherichia coli andMannheimia succiniciproducens, have been optimized in order to achieve enhanced succinic acid production. First, their metabolic networks were constructed on the basis of complete genome sequences, after which their metabolic characteristics were estimated viain silico computer modeling. Metabolic engineering strategies were designed in accordance with the results ofin silico modeling and metabolically engineered versions of bothE. coli andM. succiniciproducens have been constructed. The succinic acid productivity and yield obtained using metabolically engineered bacteria was significantly higher than that obtained using wild-type bacteria.     

Systems biology approaches to metabolic and cardiovascular disorders: network perspectives of cardiovascular metabolism

In this review, we examine cardiovascular metabolism from three different, but highly complementary, perspectives. First, from the abstract perspective of a metabolite network, composed of nodes and links. We present fundamental concepts in network theory, including emergence, to illustrate how nature has designed metabolism with a hierarchal modular scale-free topology to provide a robust system of energy delivery. Second, from the physical perspective of a modular spatially compartmentalized network. We review evidence that cardiovascular metabolism is functionally compartmentalized, such that oxidative phosphorylation, glycolysis, and glycogenolysis preferentially channel ATP to ATPases in different cellular compartments, using creatine kinase and adenylate kinase to maximize efficient energy delivery. Third, from the dynamics perspective, as a network of dynamically interactive metabolic modules capable of self-oscillation. Whereas normally, cardiac metabolism exists in a regime in which excitation-metabolism coupling closely matches energy supply and demand, we describe how under stressful conditions, the network can be pushed into a qualitatively new dynamic regime, manifested as cell-wide oscillations in ATP levels, in which the coordination between energy supply and demand is lost. We speculate how this state of "metabolic fibrillation" leads to cell death if not corrected and discuss the implications for cardioprotection.     

Epigenetic therapy approaches in non-small cell lung cancer: Update and perspectives

Non-small cell lung cancer (NSCLC) still constitutes the most common cancer-related cause of death worldwide. All efforts to introduce suitable treatment options using chemotherapeutics or targeted therapies have, up to this point, failed to exhibit a substantial effect on the 5-year-survival rate. The involvement of epigenetic alterations in the evolution of different cancers has led to the development of epigenetics-based therapies, mainly targeting DNA methyltransferases (DNMTs) and histone-modifying enzymes. So far, their greatest success stories have been registered in hematologic neoplasias. As the effects of epigenetic single agent treatment of solid tumors have been limited, the investigative focus now lies on combination therapies of epigenetically active agents with conventional chemotherapy, immunotherapy, or kinase inhibitors. This review includes a short overview of the most important preclinical approaches as well as an extensive discussion of clinical trials using epigenetic combination therapies in NSCLC, including ongoing trials. Thus, we are providing an overview of what lies ahead in the field of epigenetic combinatory therapies of NSCLC in the coming years.     

A herbal medicine for the treatment of lung cancer

Lung cancer is the leading cause of cancer-related deaths throughout the world. Extracts of medicinal plants are believed to contain different chemopreventive or chemotherapeutic compounds. In this study, we determined the anti-cancer property of one of the traditional Indian medicine Rasagenthi Lehyam (RL) for the treatment of lung cancer. Two lung cancer cell lines (A-549 and H-460) and one normal bronchial epithelial (BEAS-2B) cell line were used to test the chemotherapeutic effect of RL. Out of five fractions of RL, chloroform fraction of RL (cRL) demonstrated a significant inhibition of cell proliferation and induction of apoptosis in A-549 and H-460 cells but not in normal BEAS-2B cells. The cRL fraction up-regulated the pro-apoptotic genes p53 and Bax and induced caspase-3 activation, and down-regulated the pro-survival gene Bcl-2 in both the lung cancer cell lines. Also, nuclear export of p53 was seen in cRL-treated lung cancer cells. In addition, cRL induced G₂/M arrest of cell cycle and enhanced the radio-sensitivity of both the lung cancer cell lines. This study suggests that cRL may prove to be a potent anti-cancer agent that may be used for the treatment of lung cancer. However, further studies are required to bring cRL into the mainstream of medicine in the treatment of lung cancer. (Mol Cell Biochem xxx: 125–133, 2005)     

Strategies to develop radiomics and machine learning models for lung cancer stage and histology prediction using small data samples

Predictive models based on radiomics and machine-learning (ML) need large and annotated datasets for training, often difficult to collect. We designed an operative pipeline for model training to exploit data already available to the scientific community. The aim of this work was to explore the capability of radiomic features in predicting tumor histology and stage in patients with non-small cell lung cancer (NSCLC).We analyzed the radiotherapy planning thoracic CT scans of a proprietary sample of 47 subjects (L-RT) and integrated this dataset with a publicly available set of 130 patients from the MAASTRO NSCLC collection (Lung1). We implemented intra- and inter-sample cross-validation strategies (CV) for evaluating the ML predictive model performances with not so large datasets.We carried out two classification tasks: histology classification (3 classes) and overall stage classification (two classes: stage I and II). In the first task, the best performance was obtained by a Random Forest classifier, once the analysis has been restricted to stage I and II tumors of the Lung1 and L-RT merged dataset (AUC = 0.72 ± 0.11). For the overall stage classification, the best results were obtained when training on Lung1 and testing of L-RT dataset (AUC = 0.72 ± 0.04 for Random Forest and AUC = 0.84 ± 0.03 for linear-kernel Support Vector Machine).According to the classification task to be accomplished and to the heterogeneity of the available dataset(s), different CV strategies have to be explored and compared to make a robust assessment of the potential of a predictive model based on radiomics and ML.     

Reinforcement learning strategies for clinical trials in nonsmall cell lung cancer

Typical regimens for advanced metastatic stage IIIB/IV nonsmall cell lung cancer (NSCLC) consist of multiple lines of treatment. We present an adaptive reinforcement learning approach to discover optimal individualized treatment regimens from a specially designed clinical trial (a "clinical reinforcement trial") of an experimental treatment for patients with advanced NSCLC who have not been treated previously with systemic therapy. In addition to the complexity of the problem of selecting optimal compounds for first- and second-line treatments based on prognostic factors, another primary goal is to determine the optimal time to initiate second-line therapy, either immediately or delayed after induction therapy, yielding the longest overall survival time. A reinforcement learning method called Q-learning is utilized, which involves learning an optimal regimen from patient data generated from the clinical reinforcement trial. Approximating the Q-function with time-indexed parameters can be achieved by using a modification of support vector regression that can utilize censored data. Within this framework, a simulation study shows that the procedure can extract optimal regimens for two lines of treatment directly from clinical data without prior knowledge of the treatment effect mechanism. In addition, we demonstrate that the design reliably selects the best initial time for second-line therapy while taking into account the heterogeneity of NSCLC across patients.     

Human Cytokinome: a new challenge for systems biology

Cytokines play an important role in the evolution of inflammatory processes. Therefore, they are also key components of the cancer evolution, a disease recognized to depend on chronic inflammation. On the whole, we define cytokinome as the totality of these proteins and their interactions in and around biological cells. Understanding the complex interaction network of cytokines in patients affected from cancers should be very useful both to follow the cancer evolution from its early steps and to define innovative therapeutic strategies by using systems biology approaches.     

Role of exosomal microRNAs in lung cancer biology and clinical applications

Exosomes, small extracellular vesicles ranging from 30 to 150 nm, are secreted by various cell types, including tumour cells. Recently, microRNAs (miRNAs) were identified to be encapsulated and hence protected from degradation within exosomes. These exosomal miRNAs can be horizontally transferred to target cells, in which they subsequently modulate biological processes. Increasing evidence indicates that exosomal miRNAs play a critical role in modifying the microenvironment of lung cancers, possibly facilitating progression, invasion, angiogenesis, metastasis and drug resistance. In this review, we summarize the novel findings on exosomal miRNA functions during lung cancer initiation and progression. In addition, we highlight their potential role and challenges as biomarkers in lung cancer diagnosis, prognosis and drug resistance and as therapeutic agents.     

New insights into small‐cell lung cancer development and therapy

Small‐cell lung cancer (SCLC) accounts for approximately 15% of lung cancer cases; however, it is characterized by easy relapse and low survival rate, leading to one of the most intractable diseases in clinical practice. Despite decades of basic and clinical research, little progress has been made in the management of SCLC. The current standard first‐line regimens of SCLC still remain to be cisplatin or carboplatin combined with etoposide, and the adverse events of chemotherapy are by no means negligible. Besides, the immunotherapy on SCLC is still in an early stage and novel studies are urgently needed. In this review, we describe SCLC development and current therapy, aiming at providing useful advices on basic research and clinical strategy.     

抗凋亡蛋白survivin研究与癌治疗新策略的进展

survivin是凋亡抑制蛋白家族中的一个新成员,在近乎所有的人类肿瘤中都显示高表达,而在终末分化的正常组织中未能检测出表达。survivin表达的另一个独特性质是,其表达受细胞周期调控,在G2／M期有异常高的特异性表达。survivin的功能意义在于参与细胞凋亡调控和细胞分裂调控。引人兴趣的是,新近癌生物学基础与应用研究则更关注它是一个枢纽癌基因。本文将重点讨论与survivin在细胞死亡和细胞分裂功能相联系的、当前一些新的癌治疗策略的进展。     

Development of GRAS strains for nutraceutical production using systems and synthetic biology approaches: advances and prospects

Nutraceuticals are food substances with medical and health benefits for humans. Limited by complicated procedures, high cost, low yield, insufficient raw materials, resource waste, and environment pollution, chemical synthesis and extraction are being replaced by microbial synthesis of nutraceuticals. Many microbial strains that are generally regarded as safe (GRAS) have been identified and developed for the synthesis of nutraceuticals, and significant nutraceutical production by these strains has been achieved. In this review, we systematically summarize recent advances in nutraceutical research in terms of physiological effects on health, potential applications, drawbacks of traditional production processes, characteristics of production strains, and progress in microbial fermentation. Recent advances in systems and synthetic biology techniques have enabled comprehensive understanding of GRAS strains and its wider applications. Thus, these microbial strains are promising cell factories for the commercial production of nutraceuticals.     

突变p53功能研究新进展与个性化的肿瘤治疗新策略

p53是迄今为止研究最多的一种抑癌蛋白,最新研究仍在不断地揭示p53在调控机体代谢、生殖方面的新功能。同时,也揭示了不同p53突变蛋白的获得性新功能在肿瘤发生中的促进作用。这些研究对于了解p53突变的个性化新功能,寻找再激活野生型p53,校正突变p53的新途径奠定了基础,不同突变p53蛋白的个性化治疗将是未来肿瘤治疗的热点。文章综述了已发现的一些突变p53的获得性新功能,及针对不同的p53功能缺陷进行的p53蛋白功能再激活的策略:通过小分子或多肽再激活肿瘤细胞中的p53突变蛋白的野生型功能;通过重组的腺病毒在肿瘤细胞中表达野生型p53蛋白;通过抑制MDM2与p53的相互作用稳定野生型p53蛋白。对p53不同位点突变的深入研究可以帮助我们制定更合理的个性化治疗方案,寻求更有效的肿瘤治疗新途径。     

Thematic review series: systems biology approaches to metabolic and cardiovascular disorders. Reverse engineering gene networks to identify key drivers of complex disease phenotypes

Diseases such as obesity, diabetes, and atherosclerosis result from multiple genetic and environmental factors, and importantly, interactions between genetic and environmental factors. Identifying susceptibility genes for these diseases using genetic and genomic technologies is accelerating, and the expectation over the next several years is that a number of genes will be identified for common diseases. However, the identification of single genes for disease has limited utility, given that diseases do not originate in complex systems from single gene changes. Further, the identification of single genes for disease may not lead directly to genes that can be targeted for therapeutic intervention. Therefore, uncovering single genes for disease in isolation of the broader network of molecular interactions in which they operate will generally limit the overall utility of such discoveries. Several integrative approaches have been developed and applied to reconstructing networks. Here we review several of these approaches that involve integrating genetic, expression, and clinical data to elucidate networks underlying disease. Networks reconstructed from these data provide a richer context in which to interpret associations between genes and disease. Therefore, these networks can lead to defining pathways underlying disease more objectively and to identifying biomarkers and more-robust points for therapeutic intervention.