A conceptual review on systems biology in health and diseases: from biological networks to modern therapeutics

Human physiology is an ensemble of various biological processes spanning from intracellular molecular interactions to the whole body phenotypic response. Systems biology endures to decipher these multi-scale biological networks and bridge the link between genotype to phenotype. The structure and dynamic properties of these networks are responsible for controlling and deciding the phenotypic state of a cell. Several cells and various tissues coordinate together to generate an organ level response which further regulates the ultimate physiological state. The overall network embeds a hierarchical regulatory structure, which when unusually perturbed can lead to undesirable physiological state termed as disease. Here, we treat a disease diagnosis problem analogous to a fault diagnosis problem in engineering systems. Accordingly we review the application of engineering methodologies to address human diseases from systems biological perspective. The review highlights potential networks and modeling approaches used for analyzing human diseases. The application of such analysis is illustrated in the case of cancer and diabetes. We put forth a concept of cell-to-human framework comprising of five modules (data mining, networking, modeling, experimental and validation) for addressing human physiology and diseases based on a paradigm of system level analysis. The review overtly emphasizes on the importance of multi-scale biological networks and subsequent modeling and analysis for drug target identification and designing efficient therapies.     

A cAMP signalosome in primary cilia drives gene expression and kidney cyst formation

The primary cilium constitutes an organelle that orchestrates signal transduction independently from the cell body. Dysregulation of this intricate molecular architecture leads to severe human diseases, commonly referred to as ciliopathies. However, the molecular underpinnings how ciliary signaling orchestrates a specific cellular output remain elusive. By combining spatially resolved optogenetics with RNA sequencing and imaging, we reveal a novel cAMP signalosome that is functionally distinct from the cytoplasm. We identify the genes and pathways targeted by the ciliary cAMP signalosome and shed light on the underlying mechanisms and downstream signaling. We reveal that chronic stimulation of the ciliary cAMP signalosome transforms kidney epithelia from tubules into cysts. Counteracting this chronic cAMP elevation in the cilium by small molecules targeting activation of phosphodiesterase‐4 long isoforms inhibits cyst growth. Thereby, we identify a novel concept of how the primary cilium controls cellular functions and maintains tissue integrity in a specific and spatially distinct manner and reveal novel molecular components that might be involved in the development of one of the most common genetic diseases, polycystic kidney disease.     

Hydrocephalus and red-disease in pike fry Esox lucius L.

Two diseases of pike fry have been investigated, both causing serious losses in Dutch pike culture. In one of these diseases the lump developing on the head of the fry was associated with an internal hydrocephalus.
The other one, 'red disease' after the haemorrhagic areas in the trunk, may be identical with red-sore disease of pike.
The etiologies of both diseases are unknown.     

Extracellular vesicles,the emerging mirrors of brain physiopathology

Extracellular vesicles are secreted by a wide variety of cells, and their primary functions include intercellular communication, immune responses, human reproduction, and synaptic plasticity. Their molecular cargo reflects the physiological processes that their cells of origin are undergoing. Thus, many studies have suggested that extracellular vesicles could be a promising biomarker tool for many diseases, mainly due to their biological relevance and easy accessibility to a broad range of body fluids. Moreover, since their biological composition leads them to cross the blood-brain barrier bidirectionally, growing evidence points to extracellular vesicles as emerging mirrors of brain diseases processes. In this regard, this review explores the biogenesis and biological functions of extracellular vesicles, their role in different physiological and pathological processes, their potential in clinical practice, and the recent outstanding studies about the role of exosomes in major human brain diseases, such as Alzheimer''s disease (AD), Parkinson''s disease (PD), multiple sclerosis (MS), amyotrophic lateral sclerosis (ALS), or brain tumors.     

外泌体miRNA的生物学功能及其在肺纤维化疾病中的调控作用

外泌体是一种微型纳米级细胞外囊泡,由于能够直接参与细胞间信息的传递和物质的运输,被认为是细胞间通讯、免疫调节、疾病诊断和预后循环生物学标志物的重要载体,其携带的核酸和蛋白质等内含物能够影响受体细胞的生理状态.作为一种内源性非编码微小RNA,microRNA (miRNA)对疾病诊断和治疗有着重要的研究价值,有大量证据表明该类分子对肺部疾病的发病进程起着控制调节作用.本文聚焦于近年来细胞外泌体来源miRNA的生物学特性和功能领域,综述了近年来生物医学研究中的热点分子外泌体miRNA在肺部疾病尤其是肺纤维化中调控功能和机制的研究,因此不仅能为肺纤维化疾病的诊断提供新的标志物分子,并且还能够为肺纤维化的外泌体干预治疗建议新的干预策略.     

Non-genomic transgenerational inheritance of disease risk

That there is a heritable or familial component of susceptibility to chronic non-communicable diseases such as type 2 diabetes, obesity and cardiovascular disease is well established, but there is increasing evidence that some elements of such heritability are transmitted non-genomically and that the processes whereby environmental influences act during early development to shape disease risk in later life can have effects beyond a single generation. Such heritability may operate through epigenetic mechanisms involving regulation of either imprinted or non-imprinted genes but also through broader mechanisms related to parental physiology or behaviour. We review evidence and potential mechanisms for non-genomic transgenerational inheritance of 'lifestyle' disease and propose that the 'developmental origins of disease' phenomenon is a maladaptive consequence of an ancestral mechanism of developmental plasticity that may have had adaptive value in the evolution of generalist species such as Homo sapiens.     

Ranking candidate disease genes from gene expression and protein interaction: a Katz-centrality based approach

Many diseases have complex genetic causes, where a set of alleles can affect the propensity of getting the disease. The identification of such disease genes is important to understand the mechanistic and evolutionary aspects of pathogenesis, improve diagnosis and treatment of the disease, and aid in drug discovery. Current genetic studies typically identify chromosomal regions associated specific diseases. But picking out an unknown disease gene from hundreds of candidates located on the same genomic interval is still challenging. In this study, we propose an approach to prioritize candidate genes by integrating data of gene expression level, protein-protein interaction strength and known disease genes. Our method is based only on two, simple, biologically motivated assumptions--that a gene is a good disease-gene candidate if it is differentially expressed in cases and controls, or that it is close to other disease-gene candidates in its protein interaction network. We tested our method on 40 diseases in 58 gene expression datasets of the NCBI Gene Expression Omnibus database. On these datasets our method is able to predict unknown disease genes as well as identifying pleiotropic genes involved in the physiological cellular processes of many diseases. Our study not only provides an effective algorithm for prioritizing candidate disease genes but is also a way to discover phenotypic interdependency, cooccurrence and shared pathophysiology between different disorders.     

Genetic disorders of the skeleton: a developmental approach

Although disorders of the skeleton are individually rare, they are of clinical relevance because of their overall frequency. Many attempts have been made in the past to identify disease groups in order to facilitate diagnosis and to draw conclusions about possible underlying pathomechanisms. Traditionally, skeletal disorders have been subdivided into dysostoses, defined as malformations of individual bones or groups of bones, and osteochondrodysplasias, defined as developmental disorders of chondro-osseous tissue. In light of the recent advances in molecular genetics, however, many phenotypically similar skeletal diseases comprising the classical categories turned out not to be based on defects in common genes or physiological pathways. In this article, we present a classification based on a combination of molecular pathology and embryology, taking into account the importance of development for the understanding of bone diseases.     

Integrated Genomic and Network-Based Analyses of Complex Diseases and Human Disease Network

A disease phenotype generally reflects various pathobiological processes that interact in a complex network. The highly interconnected nature of the human protein interaction network(interactome) indicates that, at the molecular level, it is difficult to consider diseases as being independent of one another. Recently, genome-wide molecular measurements, data mining and bioinformatics approaches have provided the means to explore human diseases from a molecular basis. The exploration of diseases and a system of disease relationships based on the integration of genome-wide molecular data with the human interactome could offer a powerful perspective for understanding the molecular architecture of diseases. Recently, subnetwork markers have proven to be more robust and reliable than individual biomarker genes selected based on gene expression profiles alone, and achieve higher accuracy in disease classification. We have applied one of these methodologies to idiopathic dilated cardiomyopathy(IDCM) data that we have generated using a microarray and identified significant subnetworks associated with the disease. In this paper, we review the recent endeavours in this direction, and summarize the existing methodologies and computational tools for network-based analysis of complex diseases and molecular relationships among apparently different disorders and human disease network. We also discuss the future research trends and topics of this promising field.     

Systems genetics: From GWAS to disease pathways

Most common diseases are complex, involving multiple genetic and environmental factors and their interactions. In the past decade, genome-wide association studies (GWAS) have successfully identified thousands of genetic variants underlying susceptibility to complex diseases. However, the results from these studies often do not provide evidence on how the variants affect downstream pathways and lead to the disease. Therefore, in the post-GWAS era the greatest challenge lies in combining GWAS findings with additional molecular data to functionally characterize the associations. The advances in various ~omics techniques have made it possible to investigate the effect of risk variants on intermediate molecular levels, such as gene expression, methylation, protein abundance or metabolite levels. As disease aetiology is complex, no single molecular analysis is expected to fully unravel the disease mechanism. Multiple molecular levels can interact and also show plasticity in different physiological conditions, cell types and disease stages. There is therefore a great need for new integrative approaches that can combine data from different molecular levels and can help construct the causal inference from genotype to phenotype. Systems genetics is such an approach; it is used to study genetic effects within the larger scope of systems biology by integrating genotype information with various ~omics datasets as well as with environmental and physiological variables. In this review, we describe this approach and discuss how it can help us unravel the molecular mechanisms through which genetic variation causes disease. This article is part of a Special Issue entitled: From Genome to Function.     

High resolution proteome/peptidome analysis of body fluids by capillary electrophoresis coupled with MS

All organisms contain thousands of proteins and peptides in their body fluids. A deeper insight into the functional relevance of these polypeptides under different physiological and pathophysiological conditions and the discovery of specific peptide biomarkers would greatly enhance both diagnosis and therapy of specific diseases. Proteomic methods can provide means to accomplish this grand medical vision. In this review, we will focus on the potential use of proteome analysis for clinical applications, such as disease diagnosis and assessment of response to therapy. We focus on CE coupled with MS (CE-MS) and review in detail different aspects of CE-MS coupling and the results obtained using CE-MS analysis of clinically relevant samples. We also discuss clinical applications of the technology for the diagnosis of renal diseases, urogenital cancer, and arteriosclerosis as well as monitoring the responses to therapeutic interventions.     

PET/CT影像技术在神经退行性疾病动物模型研究中的应用

PET和CT影像技术是肿瘤、痴呆症、心脑血管病等的重要临床诊断技术,近年发展的小动物PET/CT成为对人类疾病动物模型,尤其是小动物模型进行比较医学研究的最新工具,能够活体无创的、动态的、定量的从分子水平观察动物的生理生化变化,进行代谢显像、受体显像、基因表达显像等。已经广泛应用于对神经退行性疾病的研究中.本文介绍了近年来一些小动物PET/CT在退行性疾病的研究中的最新应用。     

Defining the role of essential genes in human disease

A greater understanding of the causes of human disease can come from identifying characteristics that are specific to disease genes. However, a full understanding of the contribution of essential genes to human disease is lacking, due to the premise that these genes tend to cause developmental abnormalities rather than adult disease. We tested the hypothesis that human orthologs of mouse essential genes are associated with a variety of human diseases, rather than only those related to miscarriage and birth defects. We segregated human disease genes according to whether the knockout phenotype of their mouse ortholog was lethal or viable, defining those with orthologs producing lethal knockouts as essential disease genes. We show that the human orthologs of mouse essential genes are associated with a wide spectrum of diseases affecting diverse physiological systems. Notably, human disease genes with essential mouse orthologs are over-represented among disease genes associated with cancer, suggesting links between adult cellular abnormalities and developmental functions. The proteins encoded by essential genes are highly connected in protein-protein interaction networks, which we find correlates with an over-representation of nuclear proteins amongst essential disease genes. Disease genes associated with essential orthologs also are more likely than those with non-essential orthologs to contribute to disease through an autosomal dominant inheritance pattern, suggesting that these diseases may actually result from semi-dominant mutant alleles. Overall, we have described attributes found in disease genes according to the essentiality status of their mouse orthologs. These findings demonstrate that disease genes do occupy highly connected positions in protein-protein interaction networks, and that due to the complexity of disease-associated alleles, essential genes cannot be ignored as candidates for causing diverse human diseases.     

Successes and prospects of molecular diagnosis of the most widespread inherited diseases]

Current state of molecular diagnosis of hereditary diseases most common in the former USSR such as cystic fibrosis, Duchenne muscular dystrophy, haemophilia A and B as well as phenylketonuria is reviewed. Basic results of prenatal diagnosis and carrier detection of the above mentioned diseases in St.-Petersbourg and somewhere else in Russia are presented. The urgent necessity to start an efficient molecular diagnosis of some other widespread hereditary diseases (von Willebrand's disease, Martin-Bell syndrome, polycystic kidney. Huntington chorea, myotonic dystrophy, etc.) is emphasized. Creation of new diagnostic centers dealing with most common diseases as well as complementing each other as to molecular diagnosis of more rare hereditary diseases is substantiated. Prospects of implementation of new molecular methods and novel technical approaches (preimplantation embryos, fetal cells selected from maternal blood) for more efficient diagnosis of hereditary diseases are briefly outlined.     

From Cellular Characteristics to Disease Diagnosis: Uncovering Phenotypes with Supercells

Cell heterogeneity and the inherent complexity due to the interplay of multiple molecular processes within the cell pose difficult challenges for current single-cell biology. We introduce an approach that identifies a disease phenotype from multiparameter single-cell measurements, which is based on the concept of “supercell statistics”, a single-cell-based averaging procedure followed by a machine learning classification scheme. We are able to assess the optimal tradeoff between the number of single cells averaged and the number of measurements needed to capture phenotypic differences between healthy and diseased patients, as well as between different diseases that are difficult to diagnose otherwise. We apply our approach to two kinds of single-cell datasets, addressing the diagnosis of a premature aging disorder using images of cell nuclei, as well as the phenotypes of two non-infectious uveitides (the ocular manifestations of Behçet''s disease and sarcoidosis) based on multicolor flow cytometry. In the former case, one nuclear shape measurement taken over a group of 30 cells is sufficient to classify samples as healthy or diseased, in agreement with usual laboratory practice. In the latter, our method is able to identify a minimal set of 5 markers that accurately predict Behçet''s disease and sarcoidosis. This is the first time that a quantitative phenotypic distinction between these two diseases has been achieved. To obtain this clear phenotypic signature, about one hundred CD8⁺ T cells need to be measured. Although the molecular markers identified have been reported to be important players in autoimmune disorders, this is the first report pointing out that CD8⁺ T cells can be used to distinguish two systemic inflammatory diseases. Beyond these specific cases, the approach proposed here is applicable to datasets generated by other kinds of state-of-the-art and forthcoming single-cell technologies, such as multidimensional mass cytometry, single-cell gene expression, and single-cell full genome sequencing techniques.     

Site-directed targeting of plasminogen activator inhibitor-1 as an example for a novel approach in rational drug design

As plasminogen activator inhibitor-1 (PAI-1), the physiological inhibitor of tissue-type plasminogen activator, is considered to be an important risk factor in several (patho)physiological conditions, many research activities focus on attempts to inhibit this serpin. The approach illustrated in the current study focuses on elucidating important interaction sites allowing the inhibition of PAI-1. Since monoclonal antibodies are in most cases not ideal for therapeutic use, the question of whether smaller molecules exert comparable effects is a hot issue. To answer this question, Cys residues were introduced in PAI-1 at positions previously identified as determining the epitope of a PAI-1-inhibiting antibody, MA-8H9D4, resulting in PAI-1-R300C, PAI-1-Q303C, and PAI-1-D305C. Subsequently, low molecular mass sulfhydryl-specific reagents (i.e. BODIPY 530/550 IA (molecular mass 626 Da) and BODIPY FL C(1)-IA (molecular mass 417 Da)) were allowed to react covalently with the cysteine. The functional distribution (inhibitory versus substrate) toward tissue-type plasminogen activator was determined for the labeled and the unlabeled samples. Labeling at position 300 leads to a 1.7- and 2.2-fold increase in SI value for BODIPY 530/550 IA and BODIPY FL C(1)-IA, respectively. Labeling at position 303 results in a 3.3- and 1.9-fold increase of the SI value for the large and the small label, respectively. At position 305, the SI values are 3.1-fold increased for both labels. The effect (on SI and on serpin activity) of the manipulations at these positions is in good agreement with the effect exerted by MA-8H9D4. In conclusion, our study provides proof of concept for the proposed approach in evaluating whether targeting a functional epitope with a small synthetic compound may be a feasible strategy in rational drug design.     

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.