Genetics in the age of systems biology

Pursuit of precision medicine: Systems biology approaches in Alzheimer's disease mouse models

Alzheimer's disease (AD) is a complex disease that is mediated by numerous factors and manifests in various forms. A systems biology approach to studying AD involves analyses of various body systems, biological scales, environmental elements, and clinical outcomes to understand the genotype to phenotype relationship that potentially drives AD development. Currently, there are many research investigations probing how modifiable and nonmodifiable factors impact AD symptom presentation. This review specifically focuses on how imaging modalities can be integrated into systems biology approaches using model mouse populations to link brain level functional and structural changes to disease onset and progression. Combining imaging and omics data promotes the classification of AD into subtypes and paves the way for precision medicine solutions to prevent and treat AD.

Identification of Biomarkers in Neuropsychiatric Disorders Based on Systems Biology and Epigenetics

Clinically useful biomarkers are available for some neuropsychiatric disorders like fragile X syndrome, Rett syndrome, and Huntington’s disease. Despite many decades of research on the pathogenesis of neuropsychiatric disorders like schizophrenia (SZ), bipolar disorder (BD), and major depressive disorder (MDD), the exact pathogenesis of these disorders remains unclear, and there are no clinically useful biomarkers for these disorders. However, there is increasing evidence that abnormal epigenetic mechanisms of gene expression contribute to the pathogenesis of SZ, BD, and MDD. Both systems (or network) biology and epigenetics (a component of systems biology) attempt to make sense of biological systems that are highly dynamic and multi-compartmental. This article suggests that systems biology, emphasizing the epigenetic component of systems biology, could help identify clinically useful biomarkers in neuropsychiatric disorders like SZ, BD, and MDD.

Commonalities in Biological Pathways, Genetics, and Cellular Mechanism between Alzheimer Disease and Other Neurodegenerative Diseases: An In Silico-Updated Overview

BACKGROUND

Alzheimer's disease (AD) is the most common and well-studied neurodegenerative disease (ND). Biological pathways, pathophysiology and genetics of AD show commonalities with other NDs viz. Parkinson's disease (PD), Amyotrophic lateral sclerosis (ALS), Huntington's disease (HD), Prion disease and Dentatorubral-pallidoluysian atrophy (DRPLA). Many of the NDs, sharing the common features and molecular mechanisms suggest that pathology may be directly comparable and be implicated in disease prevention and development of highly effective therapies.

METHOD

In this review, a brief description of pathophysiology, clinical symptoms and available treatment of various NDs have been explored with special emphasis on AD. Commonalities in these fatal NDs provide support for therapeutic advancements and enhance the understanding of disease manifestation.

CONCLUSION

The studies concentrating on the commonalities in biological pathways, cellular mechanisms and genetics may provide the scope to researchers to identify few novel common target(s) for disease prevention and development of effective common drugs for multi-neurodegenerative diseases.

Construction and analysis of a protein-protein interaction network related to self-renewal of mouse spermatogonial stem cells

Spermatogonial stem cells (SSCs) are responsible for sustained spermatogenesis throughout the reproductive life of the male. Extensive studies of SSCs have identified dozens of genes that play important roles in sustaining or controlling the pool of SSCs in the mammalian testis. However, there is still limited knowledge of whether or how these key genes interact with each other during SSC self-renewal. Here, we constructed a protein-protein interaction (PPI) network for SSC self-renewal based on interactions between 23 genes essential for SSC self-renewal, which were obtained from a text mining system, and the interacting partners of the 23 key genes, which were differentially expressed in SSCs. The SSC self-renewal PPI network consisted of 246 nodes connected by 844 edges. Topological analyses of the PPI network were conducted to identify genes essential for maintenance of SSC self-renewal. The subnetwork of the SSC self-renewal network suggested that the 23 key genes involved in SSC self-renewal were connected together through other 94 genes. Clustering of the whole network and subnetwork of SSC self-renewal revealed several densely connected regions, implying significant molecular interaction modules essential for SSC self-renewal. Notably, we found the 23 genes to be responsible for SSC self-renewal by forming a continuous PPI network centered on Pou5f1. Our study indicates that it is feasible to explore important proteins and regulatory pathways in biological activities by combining a PPI database with the high-throughput data of gene expression profiles.

Ins and outs of systems biology vis-à-vis molecular biology: continuation or clear cut?

The comprehension of living organisms in all their complexity poses a major challenge to the biological sciences. Recently, systems biology has been proposed as a new candidate in the development of such a comprehension. The main objective of this paper is to address what systems biology is and how it is practised. To this end, the basic tools of a systems biological approach are explored and illustrated. In addition, it is questioned whether systems biology 'revolutionizes' molecular biology and 'transcends' its assumed reductionism. The strength of this claim appears to depend on how molecular and systems biology are characterised and on how reductionism is interpreted. Doing credit to molecular biology and to methodological reductionism, it is argued that the distinction between molecular and systems biology is gradual rather than sharp. As such, the classical challenge in biology to manage, interpret and integrate biological data into functional wholes is further intensified by systems biology's use of modelling and bioinformatics, and by its scale enlargement.

Inference of RhoGAP/GTPase regulation using single-cell morphological data from a combinatorial RNAi screen

Biological networks are highly complex systems, consisting largely of enzymes that act as molecular switches to activate/inhibit downstream targets via post-translational modification. Computational techniques have been developed to perform signaling network inference using some high-throughput data sources, such as those generated from transcriptional and proteomic studies, but comparable methods have not been developed to use high-content morphological data, which are emerging principally from large-scale RNAi screens, to these ends. Here, we describe a systematic computational framework based on a classification model for identifying genetic interactions using high-dimensional single-cell morphological data from genetic screens, apply it to RhoGAP/GTPase regulation in Drosophila, and evaluate its efficacy. Augmented by knowledge of the basic structure of RhoGAP/GTPase signaling, namely, that GAPs act directly upstream of GTPases, we apply our framework for identifying genetic interactions to predict signaling relationships between these proteins. We find that our method makes mediocre predictions using only RhoGAP single-knockdown morphological data, yet achieves vastly improved accuracy by including original data from a double-knockdown RhoGAP genetic screen, which likely reflects the redundant network structure of RhoGAP/GTPase signaling. We consider other possible methods for inference and show that our primary model outperforms the alternatives. This work demonstrates the fundamental fact that high-throughput morphological data can be used in a systematic, successful fashion to identify genetic interactions and, using additional elementary knowledge of network structure, to infer signaling relations.

Understanding quantitative genetics in the systems biology era

Biology is now entering the new era of systems biology and exerting a growing influence on the future development of various disciplines within life sciences. In early classical and molecular periods of Biology, the theoretical frames of classical and molecular quantitative genetics have been systematically established, respectively. With the new advent of systems biology, there is occurring a paradigm shift in the field of quantitative genetics. Where and how the quantitative genetics would develop after having undergone its classical and molecular periods? This is a difficult question to answer exactly. In this perspective article, the major effort was made to discuss the possible development of quantitative genetics in the systems biology era, and for which there is a high potentiality to develop towards "systems quantitative genetics". In our opinion, the systems quantitative genetics can be defined as a new discipline to address the generalized genetic laws of bioalleles controlling the heritable phenotypes of complex traits following a new dynamic network model. Other issues from quantitative genetic perspective relating to the genetical genomics, the updates of network model, and the future research prospects were also discussed.

Prioritizing genes for pathway impact using network analysis

Prioritization, or ranking, of gene lists is becoming increasingly important for analyzing data generated from high-throughput assays like expression profiling and RNAi-based screening. This is especially the case when specific genes in a list need to be further validated using low-throughput experiments. In addition to gene set overlap enrichment methods, a complementary approach is to examine molecular interaction networks. These can provide putative functional insights based on gene connectivity, especially when many genes contain little or no annotation. For bench and computational biologists alike, using networks requires an informed selection of interaction data for network construction and strategies for managing network complexity. Moreover, graph theory and social network analysis methods can be used to isolate critical subnetworks and quantify network properties. Here, I discuss the basic components of networks, implications of their structure for functional interpretation, and common metrics used for prioritization. Although this is still an ongoing area of research, networks are providing new ways for gauging pathway impact in large-scale data sets.

Integrating computational biology and forward genetics in Drosophila

Genetic screens are powerful methods for the discovery of gene-phenotype associations. However, a systems biology approach to genetics must leverage the massive amount of "omics" data to enhance the power and speed of functional gene discovery in vivo. Thus far, few computational methods for gene function prediction have been rigorously tested for their performance on a genome-wide scale in vivo. In this work, we demonstrate that integrating genome-wide computational gene prioritization with large-scale genetic screening is a powerful tool for functional gene discovery. To discover genes involved in neural development in Drosophila, we extend our strategy for the prioritization of human candidate disease genes to functional prioritization in Drosophila. We then integrate this prioritization strategy with a large-scale genetic screen for interactors of the proneural transcription factor Atonal using genomic deficiencies and mutant and RNAi collections. Using the prioritized genes validated in our genetic screen, we describe a novel genetic interaction network for Atonal. Lastly, we prioritize the whole Drosophila genome and identify candidate gene associations for ten receptor-signaling pathways. This novel database of prioritized pathway candidates, as well as a web application for functional prioritization in Drosophila, called Endeavour-HighFly, and the Atonal network, are publicly available resources. A systems genetics approach that combines the power of computational predictions with in vivo genetic screens strongly enhances the process of gene function and gene-gene association discovery.

Constructing disease-specific gene networks using pair-wise relevance metric: application to colon cancer identifies interleukin 8, desmin and enolase 1 as the central elements

Background

With the advance of large-scale omics technologies, it is now feasible to reversely engineer the underlying genetic networks that describe the complex interplays of molecular elements that lead to complex diseases. Current networking approaches are mainly focusing on building genetic networks at large without probing the interaction mechanisms specific to a physiological or disease condition. The aim of this study was thus to develop such a novel networking approach based on the relevance concept, which is ideal to reveal integrative effects of multiple genes in the underlying genetic circuit for complex diseases.

Results

The approach started with identification of multiple disease pathways, called a gene forest, in which the genes extracted from the decision forest constructed by supervised learning of the genome-wide transcriptional profiles for patients and normal samples. Based on the newly identified disease mechanisms, a novel pair-wise relevance metric, adjusted frequency value, was used to define the degree of genetic relationship between two molecular determinants. We applied the proposed method to analyze a publicly available microarray dataset for colon cancer. The results demonstrated that the colon cancer-specific gene network captured the most important genetic interactions in several cellular processes, such as proliferation, apoptosis, differentiation, mitogenesis and immunity, which are known to be pivotal for tumourigenesis. Further analysis of the topological architecture of the network identified three known hub cancer genes [interleukin 8 (IL8) (p ≈ 0), desmin (DES) (p = 2.71 × 10^-6) and enolase 1 (ENO1) (p = 4.19 × 10^-5)], while two novel hub genes [RNA binding motif protein 9 (RBM9) (p = 1.50 × 10^-4) and ribosomal protein L30 (RPL30) (p = 1.50 × 10^-4)] may define new central elements in the gene network specific to colon cancer. Gene Ontology (GO) based analysis of the colon cancer-specific gene network and the sub-network that consisted of three-way gene interactions suggested that tumourigenesis in colon cancer resulted from dysfunction in protein biosynthesis and categories associated with ribonucleoprotein complex which are well supported by multiple lines of experimental evidence.

Conclusion

This study demonstrated that IL8, DES and ENO1 act as the central elements in colon cancer susceptibility, and protein biosynthesis and the ribosome-associated function categories largely account for the colon cancer tumuorigenesis. Thus, the newly developed relevancy-based networking approach offers a powerful means to reverse-engineer the disease-specific network, a promising tool for systematic dissection of complex diseases.

Toward an interaction map of the two-component signaling pathway of Arabidopsis thaliana

Among the signal transduction pathways in higher eukaryotes, the two-component system (TCS) is unique to plants. In the model plant Arabidopsis thaliana, it consists of more than 30 proteins, including eight receptors, five phosphotransmitters and 23 response regulators. One of its important functions is to perceive and transduce the signal of the plant hormone cytokinin. The basic signal flow within the TCS is well-understood, but it is unclear how this pathway is integrated with the remainder of the proteome. Thus, knowledge about the interactions of TCS proteins should contribute to the understanding of their mode of action. Therefore, we conducted medium-scale yeast two-hybrid screens focusing on those members of the TCS, which are thought to be involved in cytokinin signaling. In total, more than 6.3 x 10 (7) transformants were screened resulting in the identification of 160 different interactions, of which 136 were novel. Most of the interacting proteins belong to the functional categories of signal transduction and protein metabolism. TCS proteins and their interactors localized to the same subcellular compartment in many cases, a prerequisite to being of biological relevance. The resulting interaction network map revealed large differences in the connectivity. Cytokinin receptors (AHK2, CRE1/AHK4) showed the highest numbers of different interaction partners. This study is the first systematic protein-protein interaction experiment for a plant signal system and provides numerous starting points for further analysis of the molecular mechanisms used to convert the signal carried by the TCS into biological processes.

A computational analysis of protein-protein interaction networks in neurodegenerative diseases

BACKGROUND

Recent developments have meant that network theory is making an important contribution to the topological study of biological networks, such as protein-protein interaction (PPI) networks. The identification of differentially expressed genes in DNA array experiments is a source of information regarding the molecular pathways involved in disease. Thus, considering PPI analysis and gene expression studies together may provide a better understanding of multifactorial neurodegenerative diseases such as Multiple Sclerosis (MS) and Alzheimer disease (AD). The aim of this study was to assess whether the parameters of degree and betweenness, two fundamental measures in network theory, are properties that differentiate between implicated (seed-proteins) and non-implicated nodes (neighbors) in MS and AD. We used experimentally validated PPI information to obtain the neighbors for each seed group and we studied these parameters in four networks: MS-blood network; MS-brain network; AD-blood network; and AD-brain network.

RESULTS

Specific features of seed-proteins were revealed, whereby they displayed a lower average degree in both diseases and tissues, and a higher betweenness in AD-brain and MS-blood networks. Additionally, the heterogeneity of the processes involved indicate that these findings are not pathway specific but rather that they are spread over different pathways.

CONCLUSION

Our findings show differential centrality properties of proteins whose gene expression is impaired in neurodegenerative diseases.

Cancer initiation and progression: an unsimplifiable complexity

BACKGROUND

Cancer remains one of the most complex diseases affecting humans and, despite the impressive advances that have been made in molecular and cell biology, how cancer cells progress through carcinogenesis and acquire their metastatic ability is still widely debated.

CONCLUSION

There is no doubt that human carcinogenesis is a dynamic process that depends on a large number of variables and is regulated at multiple spatial and temporal scales. Viewing cancer as a system that is dynamically complex in time and space will, however, probably reveal more about its underlying behavioural characteristics. It is encouraging that mathematicians, biologists and clinicians continue to contribute together towards a common quantitative understanding of cancer complexity. This way of thinking may further help to clarify concepts, interpret new and old experimental data, indicate alternative experiments and categorize the acquired knowledge on the basis of the similarities and/or shared behaviours of very different tumours.