A protein interaction based model for schizophrenia study

<p>Abstract</p> <p>Background</p> <p>Schizophrenia is a complex disease with multiple factors contributing to its pathogenesis. In addition to environmental factors, genetic factors may also increase susceptibility. In other words, schizophrenia is a highly heritable disease. Some candidate genes have been deduced on the basis of their known function with others found on the basis of chromosomal location. Individuals with multiple candidate genes may have increased risk. However it is not clear what kind of gene combinations may produce the disease phenotype. Their collective effect remains to be studied.</p> <p>Results</p> <p>Most pathways except metabolic pathways are rich in protein-protein interactions (PPIs). Thus, the PPI network contains pathway information, even though the upstream-downstream relation of PPI is yet to be explored. Here we have constructed a PPI sub-network by extracting the nearest neighbour of the 36 reported candidate genes described in the literature. Although these candidate genes were discovered by different approaches, most of the proteins formed a cluster. Two major protein interaction modules were identified on the basis of the pairwise distance among the proteins in this sub-network. The large and small clusters might play roles in synaptic transmission and signal transduction, respectively, based on gene ontology annotation. The protein interactions in the synaptic transmission cluster were used to explain the interaction between the NRG1 and CACNG2 genes, which was found by both linkage and association studies. This working hypothesis is supported by the co-expression analysis based on public microarray gene expression.</p> <p>Conclusion</p> <p>On the basis of the protein interaction network, it appears that the NRG1-triggered NMDAR protein internalization and the CACNG2 mediated AMPA receptor recruiting may act together in the glutamatergic signalling process. Since both the NMDA and AMPA receptors are calcium channels, this process may regulate the influx of Ca²⁺. Reducing the cation influx might be one of the disease mechanisms for schizophrenia. This PPI network analysis approach combined with the support from co-expression analysis may provide an efficient way to propose pathogenetic mechanisms for various highly heritable diseases.</p

Emerging strengths in Asia Pacific bioinformatics

The 2008 annual conference of the Asia Pacific Bioinformatics Network (APBioNet), Asia's oldest bioinformatics organisation set up in 1998, was organized as the 7th International Conference on Bioinformatics (InCoB), jointly with the Bioinformatics and Systems Biology in Taiwan (BIT 2008) Conference, Oct. 20–23, 2008 at Taipei, Taiwan. Besides bringing together scientists from the field of bioinformatics in this region, InCoB is actively involving researchers from the area of systems biology, to facilitate greater synergy between these two groups. Marking the 10th Anniversary of APBioNet, this InCoB 2008 meeting followed on from a series of successful annual events in Bangkok (Thailand), Penang (Malaysia), Auckland (New Zealand), Busan (South Korea), New Delhi (India) and Hong Kong. Additionally, tutorials and the Workshop on Education in Bioinformatics and Computational Biology (WEBCB) immediately prior to the 20th Federation of Asian and Oceanian Biochemists and Molecular Biologists (FAOBMB) Taipei Conference provided ample opportunity for inducting mainstream biochemists and molecular biologists from the region into a greater level of awareness of the importance of bioinformatics in their craft. In this editorial, we provide a brief overview of the peer-reviewed manuscripts accepted for publication herein, grouped into thematic areas. As the regional research expertise in bioinformatics matures, the papers fall into thematic areas, illustrating the specific contributions made by APBioNet to global bioinformatics efforts

Molecular analysis of Candidate genes at the 22q region in Schizophrenia subjects

22q11.2 deletion syndrome (22q11.2DS), also known as Velo-Cardio-Facial Syndrome (VCFS) or DiGeorge Syndrome, is a genetic disorder due to a micro deletion on chromosome 22q11.2. VCFS is associated with abnormalities in brain structure and with an increased risk of psychiatric disorders, particularly schizophrenia (SCZ). DNA copy  number is a largely unexplored source of human genetic variation that may contribute risk for complex disease like SCZ. The aim of this study was to assess Copy number variations (CNV) at candidate genes located in 22q11 region in SCZ subjects. We report aberrations in copy number  at PRODH and COMT gene loci supporting the hypothesis that dosage effects of 22q genes could lead to disruptions in neurotransmitter signaling and related neurobehavioral symptoms observed in SCZ subjects. The results support the hypothesis that the complex phenotype of 22qDS results either from the overlapping regulation of several genes within this region or from its concerted participation in a highly regulated process

생물학적 네트워크를 이용하여 유전자로부터 패스웨이, 표현형까지의 전사체 공간을 탐색하는 정보학 기법

학위논문(박사)--서울대학교 대학원 :자연과학대학 협동과정 생물정보학전공,2019. 8. 김선.Transcriptome data, genome-wide measurement of transcripts, has been used to increase our understandings of biological processes at transcription level significantly. Analysis of transcriptome data involves a series of steps from identification of differentially expressed genes (DEGs) to pathway enrichment analysis to association with phenotypes. There exist several hurdles at each step that need to be addressed with state of the art bioinformatics techniques. For example, the complex nature of living organisms can be represented as a network where the nodes are the interacting entities such as genes or pathways and the edges are the interactions between the nodes. Network analysis is crucial in that it can reveal the hidden associations between transcriptome data and phenotypes. In addition, network propagation has emerged as a technique to measure the influential power of nodes in a network. Network propagation has demonstrated its utility on biological context by many studies and has been contributing to invaluable discoveries in biological and medical science fields. In my doctoral study, I explored and analyzed trasncriptome at various levels using machine learning, network information and network propagation techniques. My thesis consists of three studies. The first study was to develop an accurate and stable method for determining differentially expressed genes using machine learning techniques. The second study was to develop a novel method to investigate interactions among biological pathways using explicit gene expression information from RNA-seq. The last study was to perform analysis of xenotransplant transcriptome data using various methods including the network propagation technique. In the first study, MLDEG, a machine learning approach to identify DEGs using network property and network propagation, was developed. Currently available DEG detection methods have widely been used and contributed to new biological discoveries. Most of the methods use their own models to define DEGs. However, because the traits of transcriptome data vary significantly depending on the experimental designs and sequencing technologies, a single model can hardly fit all transcriptome data of different traits. In addition, setting cutoff values of p-values and fold change is arbitrary. Thus, the results yielded by the methods are often inconsistent and heterogeneous. MLDEG addresses these issues by building a model that uses network information and network propagation results as features. The goal of MLDEG is to train a model by using network-based features extracted from more likely true and false DEGs and use the model to classify DEGs from the genes that cannot be clearly defined as DEGs by existing methods. Tested on 10 high-throughput RNA-seq data, MLDEG showed better performances than the competing methods. In the second study, I developed a Pathway INTeraction network construction method (PINTnet) that can construct a condition-specific pathway interaction network by computing shortest paths on protein-protein interaction (PPI) networks. Because pathways usually function in a coordinated and cooperative fashion, understanding interactions, or crosstalks, between pathways becomes as important as identifying perturbed single pathway. However, existing methods do not take into account the topological features, treating the pathways just as a set of genes. To solve the problem, PINTnet computes shortest paths on PPI networks mapped to each pair of pathways and creates subnetworks using the shortest paths. It then measures the activation status of pathway interaction using the product of closeness centrality and explicit gene expression quantity. The performance of PINTnet was evaluated using three high-throughput RNAseq data and successfully reproduced the findings in the original papers of the data. In the last study, I participated in a xenotransplantation study to elucidate the cause of chronic phase islet graft loss. Clinical islet transplantation is one of the promising options for type 1 diabetes but long-term outcome of graft function is not yet satisfactory. To reveal the mechanism of the graft loss in chronic phase, I carried out pathway interaction network analysis using PINTnet on a time-series porcine islet-transplanted rhesus monkey RNA-seq data and identified the activation of T cell receptor signaling pathway. The analysis results were supported by the biopsy result of liver sample that CD3+ T cell heavily infiltrated the porcine islet. Additionally, I carried out gene prioritization using network propagation to verify five graft loss-relevant scenarios. The result suggested that T cell-mediated long-term graft loss was the most probable scenario. In summary, my doctoral study used network information, network property, and network propagation to identify DEGs and predict pathway interactions. In addition, I participated in a xenotransplantation research and carried out pathway interaction network analysis and network propagation to reveal the possible cause of chronic phase islet graft loss. Utilizing network information and network propagation was very effective to discover the relationships among biological entities and analyze the complex phenotypes.전사 과정에서의 생물학적 프로세스에 대한 이해를 높이는 데 사용되는 전사체 데이터의 분석은 차별 발현 유전자를 찾아내는 것에서부터 표현형에 연관된 패스 웨이 증폭 분석까지의 일련의 단계를 포함한다. 각 단계마다, 넘어야 할 장애물들이 존재하며 이를 극복하기 위한 새로운 생물정보학 기술의 개발은 필수적이다. 예를 들어, 생명체의 복잡한 특성은 유전자 또는 패스웨이가 노드, 그 개체 사이의 상호 작용이 엣지인 네트워크로 나타낼 수 있다. 이 때, 네트워크 분석 기법은 전사체 데이터와 표현형 간의 숨겨진 연관성을 찾는 데 중요한 역할을 할 수 있다. 한 편, 네트워크 전파는 네트워크에서 노드의 영향력을 측정하는 기술로 주목받고 있으며 새로운 생물학적 발견에 기여하는 등, 생물학 및 의학 분야의 많은 연구에서 그 유용성을 입증하였다. 본 논문에서는 이러한 기계 학습, 네트워크 정보 및 네트워크 전파를 이용한 전사체 데이터 분석에 관한 연구에 대해 다룬다. 첫 번째 연구에서는, 네트워크 정보와 네트워크 전파를 이용하여 차별 발현 유전자를 식별하는 기계 학습 접근법(MLDEG)에 관한 연구를 다룬다. 차별 발현 유전자 분석은 생물학 연구에서 새로운 생물학적 지식의 발견에 중요한 역할을 하고 있으나 이를 위한 기존의 분석 도구들이 도출하는 결과는 각기 다르다. 본 연구에서는 네트워크 정보 및 네트워크 전파 결과를 활용하는 모델을 구축하여 이러한 문제를 해결하였다. 본 연구의 목표는 차별 발현 유전자 및 비차별 발현 유전자로서 가장 가능성이 있는 유전자를 선정하여 네트워크 기반 특징을 추출하고 이 특징을 바탕으로 모델을 학습하여 차별 발현 유전자를 분류하는 것이다. 열개의 RNA-seq 데이터를 이용하여 검증한 결과, 기존의 분석 도구들보다 우수한 성능을 보임을 확인하였다. 두 번째 연구에서는 단백질 상호 작용 네트워크상의 최단 경로를 계산하여 특정 실험 조건하에서 패스웨이 상호 작용 네트워크를 구축할 수 있는 패스웨이 상호 작용 네트워크 구축 방법(PINTnet)에 대한 내용을 다룬다. 기존의 방법들은 유전자 사이의 관계를 고려하지 않고 패스웨이를 단순히 유전자의 집합으로만 다루는 문제를 가지고 있다. 본 연구에서는 유전자 사이의 관계를 고려하여 각 패스웨이 쌍에 매핑된 단백질 상호작용 네트워크에서 최단 경로를 계산하고, 이를 통해 만들어진 서브네트워크에서 근접중심성과 유전자 발현량의 곱을 바탕으로 패스웨이 상호작용의 활성화 상태를 측정함으로 문제를 해결하였다. 세 개의 RNA-seq 데이터를 이용하여 PINTnet의 성능을 평가한 결과, 각 데이터의 원 논문에서 주장한 결과를 성공적으로 재현함을 확인하였다. 마지막 연구는 만성 췌도 이식편 소실의 원인을 밝히기 위한 이종장기이식 데이터 분석에 관한 내용을 다룬다. 만성 단계에서의 이식편 소실의 기작을 밝히기 위해, PINTnet을 사용하여 돼지 췌도가 이식된 원숭이의 RNA-seq 데이터를 분석하였고 T 세포 수용체 신호 전달 패스웨이(T cell receptor signalling pathway)가 활성화 되었음을 확인하였다. 해당 원숭이의 간 샘플을 생검하여 CD3+ T 세포가 이식된 췌도에 침투하였음을 확인함으로써 분석 결과가 실제 결과와 일치함을 확인하였다. 한편, 네트워크 전파를 이용하여 다섯 가지 거부 반응 시나리오를 검증하였고 T 세포로 인한 거부반응이 가장 가능성이 높음을 확인하였다. 결론적으로, 본 논문에서는 다양한 전사체 데이터 분석을 수행함에 있어서 네트워크 정보, 네트워크 특성 및 네트워크 전파를 이용한 네트워크 분석 및 기계학습 기법이 유용함을 보였다.Abstract Chapter 1. Introduction 1 1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.1.1 An introduction to network theory and its application to the fields of biology . . . . . . . . . . . . . . . . . . . . . 2 1.1.2 An introduction to machine learning . . . . . . . . . . . . 5 1.2 Three problems in my doctoral study . . . . . . . . . . . . . . . . 6 1.2.1 Problem 1: DEG detection . . . . . . . . . . . . . . . . . 6 1.2.2 Problem 2: Pathway interaction analysis . . . . . . . . . . 8 1.2.3 Problem 3: Analysis of transcriptome from pig-to-nonhuman primate islet xenotransplantation . . . . . . . . . . . . . . 10 1.3 My network-based approaches to three research problems . . . . 11 1.4 Outline of thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 Chapter 2. A machine learning approach to identify differentially expressed genes using network property and network propagation 14 2.1 Background of differential expression analysis methods . . . . . . 15 2.1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . 17 2.1.2 My machine learning approach . . . . . . . . . . . . . . . 17 2.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 2.2.1 Training and Test Data . . . . . . . . . . . . . . . . . . . 19 2.2.2 Features . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 2.2.3 Network Property . . . . . . . . . . . . . . . . . . . . . . 22 2.2.4 Network Propagation . . . . . . . . . . . . . . . . . . . . 23 2.2.5 Machine Learning Algorithm . . . . . . . . . . . . . . . . 24 2.3 Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . 26 2.3.1 Experimental Data Description . . . . . . . . . . . . . . . 26 2.3.2 Performance of Network Information Features . . . . . . . 30 2.3.3 Performance Evaluation and Discussion . . . . . . . . . . 34 2.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 Chapter 3. Construction of condition-specific pathway interaction network by computing shortest paths on weighted PPI 38 3.1 Background of pathway interaction network construction . . . . . 39 3.1.1 The importance of finding perturbed interaction between pathways . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 3.1.2 Challenges in pathway interaction network construction . 40 3.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 3.2.1 Preparation of PPI and pathway information . . . . . . . 41 3.2.2 Defining edges in the pathway network . . . . . . . . . . . 42 3.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 3.3.1 Data description . . . . . . . . . . . . . . . . . . . . . . . 47 3.3.2 Evaluation criteria . . . . . . . . . . . . . . . . . . . . . . 49 3.3.3 Performance comparison to other methods . . . . . . . . . 51 3.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 3.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 Chapter 4. Bioinformatics analyses with peripheral blood RNA-sequencing unveiled the cause of the graft loss after pig-to-nonhuman primate islet xenotransplantation model 63 4.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64 4.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65 4.2.1 Peripheral blood RNA sequencing . . . . . . . . . . . . . 65 4.2.2 Graft loss period-related activated pathways (GLPAPs) defined by TRAP (Time-series RNA-seq analysis package) 66 4.2.3 Pathway interaction network analysis . . . . . . . . . . . 72 4.2.4 Hypothesis evaluation using network propagation . . . . . 75 4.3 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80 Chapter 5 Conclusion 83 초록 103Docto

Molecular pharmacology of AMPA receptor trafficking proteins - TARPs - evidence for an association with 5HT(_2c) receptors

There has been an increasing awareness of the involvement of neurotransmitters other than serotonin in depression, with new antidepressants possessing effects on other receptor types, such as Agomelatine®, an antagonist of 5HT(_2c) receptors that functions as an agonist at melatonin receptors. AMPA receptors are one of the families of ionotropic glutamate receptors and another neurotransmitter receptor type that have been demonstrated to be important in the function of new antidepressants. AMPA receptors possess effects upon brain-derived neurotrophic factor (BDNF) expression, a protein involved in neurogenesis in the hippocampus, which is believed to be pivotal to antidepressant efficacy, with BDNF expression diminishing in critical brain areas in response to chronic stress, but increasing in the hippocampus in response to treatment with both antidepressants and/or AMPA receptor modulators. AMPA receptors interact with a family of accessory proteins, the transmembrane AMPA receptor regulatory proteins (TARPs), which not only possess important roles in the trafficking and targeting of AMPA receptors, but also function as auxiliary subunits to AMPA receptors. Present in several isoforms, each individual TARP also directly modifies AMPA receptor kinetics. As such TARPs and their effects must be taken into account for any pathophysiological or drug-induced change involving AMPA receptors. Despite the vast literature on AMPA receptors, there is comparatively little information regarding how TARPs modify AMPA receptor function, largely due to the absence of the necessary tools. We developed polyclonal antibodies specific to each of the known TARP isoforms (y2, y4, y8) mapping the distribution of the TARPs in the mouse CNS, displaying a different distribution profile for each of the TARP isoforms. TARP y8 is of particular interest, being shown to have a wide expression in the CNS from the frontal cortex to the spinal cord, but also a regional distribution in the forebrain that shares similarities to a positive allosteric modulator of AMPA receptors. There is also some evidence of a strain dependent distribution of TARP 78, possibly contributing to some of the behavioural differences between strains. With the extensive distribution of TARP y8 in the forebrain, particularly in those structures shown to experience severe neuronal atrophy in depression, such as the hippocampus, we focused on the antibodies generated to this isoform to generate immunoaffinity columns and immunopurify TARP y8 and its interacting proteins from Triton X-100’_™’ solubilised, so effectively non-synaptic, cerebral cortex. Examination of the purified TARP y8 and its interacting partners by both immunological and proteomic techniques revealed a range of proteins previously not implicated as TARP interacting proteins important in several pathophysiological situations, including several isoforms of actin. Furthermore, the immunopurified TARP y8 material also contained a protein identified with multiple 5HT(_2c) receptor antibodies at -60 kDa, the molecular weight correlating to fully glycosylated 5HT(_2c) receptor. Further study of TARP and AMPA receptor levels in the forebrain of mice with either forebrain-specific over-expression, or forebrain-specific knockdown of 5HT(_2c) receptors, identified several differences in total protein levels of the TARPs and AMPA receptor subunits. TARP y8 was shown to possess higher levels of expression in both of the mice strains with altered 5HT2c receptor expression, suggesting a complex functional interaction between TARPs/AMPA receptors and 5HT(_2c) receptors. These results, in addition to providing evidence of strain variations with regard to TARP distributions, have also identified several previously unknown TARP y8 interacting proteins, including, but not limited to cytoskeletal proteins. The results also show evidence of both a physical and functional interaction of TARP y8 and AMPA receptors with 5HT(_2c) receptors in the forebrain, particularly the cerebral cortex - findings of potential importance regarding the role of AMPA receptors in mood disorders

Impaired reinforcement learning and Bayesian inference in psychiatric disorders: from maladaptive decision making to psychosis in schizophrenia

Computational modelling has been gaining an increasing amount of support from the neuroscience community as a tool to assay cognition and computational processes in the brain. Lately, scientists have started to apply computational methods from neuroscience to the study of psychiatry to gain further insight into the mechanisms leading to mental disorders. In fact, only recently has psychiatry started to move away from categorising illnesses using behavioural symptoms in an attempt for a more biologically driven diagnosis. To date, several neurobiological anomalies have been found in schizophrenia and led to a multitude of conceptual framework attempting to link the biology to the patients’ symptoms. Computational modelling can be applied to formalise these conceptual frameworks in an effort to test the validity or likelihood of each hypothesis. Recently, a novel conceptual model has been proposed to describe how positive symptoms (delusions, hallucinations and thought disorder) and cognitive symptoms (poor decision-making, i.e. “executive functioning”) might arise in schizophrenia. This framework however, has not been tested experimentally or against computational models. The focus of this thesis was to use a combination of behavioural experiments and computational models to independently assess the validity of each component that make up this framework. The first study of this thesis focused on the computational analysis of a disrupted prediction-error signalling and its implications for decision-making performances in complex tasks. Briefly, we used a reinforcement-learning model of a gambling task in rodents and disrupted the prediction-error signal known to be critical for learning. We found that this disruption can account for poor performances in decision-making due to an incorrect acquisition of the model of the world. This study illustrates how disruptions in prediction-error signalling (known to be present in schizophrenia) can lead to the acquisition of an incorrect world model which can lead to poor executive functioning or false beliefs (delusions) as seen in patients. The second study presented in this thesis addressed spatial working memory performances in chronic schizophrenia, bipolar disorder, first episode psychosis and family relatives of DISC1 translocation carriers. We build a probabilistic inference model to solve the working memory task optimally and then implemented various alterations of this model to test commonly debated hypotheses of cognitive deficiency in schizophrenia. Our goal was to find which of these hypotheses accounts best for the poor performance observed in patients. We found that while the performance at the task was significantly different for most patients groups in comparison to controls, this effect disappeared after controlling for IQ in one group. The models were nonetheless fitted to the experimental data and suggest that working memory maintenance is most likely to account for the poor performances observed in patients. We propose that the maintenance of information in working memory might have indirect implications for measures of general cognitive performance, as these rely on a correct filtering of information against distractions and cortical noise. Finally the third study presented in this thesis assessed the performance of medicated chronic schizophrenia patients in a statistical learning task of visual stimuli and measured how the acquired statistics influenced their perception. We find that patient with chronic schizophrenia appear to be unimpaired at statistical learning of visual stimuli. The acquired statistics however appear to induce less expectation-driven ‘hallucinations’ of the stimuli in the patients group than in controls. We find that this is in line with previous literature showing that patients are less susceptible to expectation-driven illusions than controls. This study highlights however the idea that perceptual processes during sensory integration diverge from this of healthy controls. In conclusion, this thesis suggests that impairments in reinforcement learning and Bayesian inference appear to be able to account for the positive and cognitive symptoms observed in schizophrenia, but that further work is required to merge these findings. Specifically, while our studies addressed individual components such as associative learning, working memory, implicit learning & perceptual inference, we cannot conclude that deficits of reinforcement learning and Bayesian inference can collectively account for symptoms in schizophrenia. We argue however that the studies presented in this thesis provided evidence that impairments of reinforcement learning and Bayesian inference are compatible with the emergence of positive and cognitive symptoms in schizophrenia