Investigating Topological and Functional Features of Multimodular Proteins

To generate functional modules as functionally and structurally cohesive formations in protein interaction networks (PINs) constitutes an important step towards understanding how modules communicate on a higher level of the PIN organisation that underlies cell functionality. However, we need to understand how individual modules communicate and are organized into the higher-order structure(s) of the PIN organization that underlies cell functionality. In an attempt to contribute to this understanding, we make an assumption that the proteins reappearing in several modules, termed here as multimodular proteins (MMPs), may be useful in building higher-order structure(s) as they may constitute communication points between different modules. In this paper, we investigate common properties shared by these proteins and compare them with the properties of so-called single-modular proteins (SMPs) by analyzing three aspects: functional aspect, that is, annotation of the proteins, topological aspect that is betweenness centrality of the proteins, and lethality. Furthermore, we investigate the interconnectivity role of some proteins that are identified as functionally and topologically important

Identifying functionally and topologically cohesive modules in protein interaction networks

Abstract unavailable please refer to PD

Network Properties for Ranking Predicted miRNA Targets in Breast Cancer

MicroRNAs control the expression of their target genes by translational repression and transcriptional cleavage. They are involved in various biological processes including development and progression of cancer. To uncover the biological role of miRNAs it is important to identify their target genes. The small number of experimentally validated target genes makes computer prediction methods very important. However, state-of-the-art prediction tools result in a great number of putative targets with an unpredictable number of false positives. In this paper, we propose and evaluate two approaches for ranking the biological relevance of putative targets of miRNAs which are associated with breast cancer

Using Expression Profiling to Understand the Effects of Chronic Cadmium Exposure on MCF-7 Breast Cancer Cells

Cadmium is a metalloestrogen known to activate the estrogen receptor and promote breast cancer cell growth. Previous studies have implicated cadmium in the development of more malignant tumors; however the molecular mechanisms behind this cadmium-induced malignancy remain elusive. Using clonal cell lines derived from exposing breast cancer cells to cadmium for over 6 months (MCF-7-Cd4, -Cd6, -Cd7, -Cd8 and -Cd12), this study aims to identify gene expression signatures associated with chronic cadmium exposure. Our results demonstrate that prolonged cadmium exposure does not merely result in the deregulation of genes but actually leads to a distinctive expression profile. The genes deregulated in cadmium-exposed cells are involved in multiple biological processes (i.e. cell growth, apoptosis, etc.) and molecular functions (i.e. cadmium/metal ion binding, transcription factor activity, etc.). Hierarchical clustering demonstrates that the five clonal cadmium cell lines share a common gene expression signature of breast cancer associated genes, clearly differentiating control cells from cadmium exposed cells. The results presented in this study offer insights into the cellular and molecular impacts of cadmium on breast cancer and emphasize the importance of studying chronic cadmium exposure as one possible mechanism of promoting breast cancer progression

The mitochondrial transporter SLC25A43 is frequently deleted and may influence cell proliferation in HER2-positive breast tumors

BACKGROUND: Overexpression of the human epidermal growth factor receptor (HER) 2 is associated with poor prognosis and shortened survival in breast cancer patients. HER2 is a potent activator of several signaling pathways that support cell survival, proliferation and metabolism. In HER2-positive breast cancer there are most likely unexplored proteins that act directly or indirectly downstream of well established pathways and take part in tumor development and treatment response. METHODS: In order to identify novel copy number variations (CNVs) in HER2-positive breast cancer whole-genome single nucleotide polymorphism (SNP) arrays were used. A PCR-based loss of heterozygosis (LOH) assay was conducted to verify presence of deletion in HER2-positive breast cancer cases but also in HER2 negative breast cancers, cervical cancers and lung cancers. Screening for mutations was performed using single-strand conformation polymorphism (SSCP) followed by PCR sequencing. Protein expression was evaluated with immunohistochemistry (IHC). RESULTS: A common deletion at chromosome Xq24 was found in 80% of the cases. This locus harbors the gene solute carrier (SLC) family 25A member 43 (SLC25A43) encoding for a mitochondrial transport protein. The LOH assay revealed presence of SLC25A43 deletion in HER2-positive (48%), HER2-negative (9%), cervical (42%) and lung (67%) cancers. HER2-positive tumors with negative or low SLC25A43 protein expression had significantly lower S-phase fraction compared to tumors with medium or high expression (P = 0.024). CONCLUSIONS: We have found deletion in the SLC25A43 gene to be a common event in HER2-positive breast cancer as well as in other cancers. In addition, the SLC25A43 protein expression was shown to be related to S-phase fraction in HER2-positive breast cancer. Our results indicate a possible role of SLC25A43 in HER2-positive breast cancer and support the hypothesis of altered mitochondrial function in cancer

Thesis Materials : Knowledge-based Methods for Identification of Functional Modules in Protein Interaction Networks

The majority of the current methods for identifying modules in protein interaction networks are based solely on analysing topological features of the networks. In contrast, the main idea that underpins the planned thesis is that combining topological information with knowledge about protein function will result in more biologically plausible modules than using approaches based solely on topology. We here propose approaches that use a combination of domain-specific knowledge, derived from Gene Ontology, and topological properties, to generate functional modules from protein interaction networks. By using yeast two hybrid (Y2H) interactions from /S/. /Cerevisiae/ and knowledge in terms of Gene Ontology (GO) annotations, we have elucidated functional modules of interacting proteins. In this report, a summary of the proposed approaches is presented. The methods with the same rationale but slightly different designs have been implemented, tested and evaluated. The first approach, where we combine clusters of proteins based on their mutual neighbours profiles with the corresponding clusters based on GO semantic similarity profiles, treats each of the aspects (functional knowledge and topology) separately to obtain functional clusters, and thereafter merges the clusters into one single structure. In contrast, the other approaches integrate both aspects from the beginning. The two other approaches are two versions of a method named SWEMODE (Semantic WEights for MODule Elucidation), which uses knowledge-based clustering coefficient to identify network modules. The first one is uses the original protein interaction graph, and the second one is a recently designed extension of SWEMODE where the /k/-cores of the graph are emphasised. We demonstrate that all three methods are able to identify the key functional modules in protein interaction networks. The first method was applied to smaller well-studied networks, that are known to contain modules of signalling pathways, while SWEMODE was applied on a large network containing 2 231 proteins and 6 379 interactions. The methods were also used to study intermodule connections, which is a step towards revealing a higher order hierarchy between modules. In this report, we describe and discuss the proposed approaches, along with their strengths and weaknesses. We also propose further extensions and improvements of the proposed methods, some of which may be attempted as the final steps in the implementation phase of the dissertatio

Är tiden mogen för Intelligenta Agenter? : en studie av acceptans för agenttekniken

Den snabba utvecklingen av informationsteknik har medfört stora förändringar i modernt arbetsliv. Nya verktyg uppkommer ständigt i syfte att effektivisera arbete. Intelligenta Agenter är en sådan artefakt vars syfte är bl a att effektivisera olika arbetsuppgifter. Detta verktyg har gjort sitt intåg i och förväntas revolutionera vårt IT- samhälle. När arbetet effektiviseras är det dock inte ovanligt att det sker på bekostnad av andra värden, såsom arbetets innehåll och karaktär. Det är minst lika viktigt att beakta de värden som kan gå förlorade, t ex arbetstillfredsställelse och personlig kontakt, eftersom dessa är ofta kritiska för en människas acceptans av sin arbetssituation. Det här arbetet tar upp viktiga frågor i samband med användarnas acceptans av Intelligenta Agenter. Jag undersöker bl a vilka yttre egenskaper hos agenter skulle kunna bidra till att underlätta acceptans av detta verktyg. En annan fråga som är central i mitt arbete går ut på att undersöka hur acceptans kan uppnås vid införandet av Intelligenta Agenter. I detta arbete fästs även vikt vid agenternas påverkan på kommunikation människor emellan Den metod som används för att undersöka valt ämne är kombination av en litteraturstudie och intervjuer. Undersökning visar bl a att användarna vill ha kontroll över agenten. Därmed bör de ges möjlighet att anpassa agent efter sina smak och tycke och på det viset känna att de har kontrollen över agentapplikationen. Resultat tyder också på att människor befarar minskning av den sociala kontakten, om agenttekniken får sin stora genombrott. Detta skulle i sin tur ha negativ inverkan på användarnas acceptans av agenter. Vidare tyder resultatet på att det skulle vara nödvändigt att engagera alla berörda i processen kring införande av Intelligenta Agenter, för att uppnå deras acceptans

Towards reverse engineering of genetic regulatory networks

The major goal of computational biolo gy is to derive regulatory interactions between genes from large-scale gene expression data and other biological sources. There have been many attemp ts to reach this goal, but the field needs more research before we can claim that we have reached a complete understanding of reverse engineering of regulatory networks. One of the aspects that have not been considered to a great extent in the development of reverse engineering approaches is combinatorial regulation. Combinatorial regulation can be obtained by the presence of modular architectures in regulation, where multiple binding sites for multiple transcription factors are combined into modular units. When modelling regulatory networks, genes are often considered as “black boxes”, where gene expression level is an input signal and changed level of expression is the output. We need to shed light on reverse engineering of regulatory networks by modelling the gene “boxes ” at a more detailed level of information, e.g., by using regulatory elements as input to gene boxes as a complement to expression levels. Another problem in the context of inferring regulatory networks is the difficulty of validating inferred interactions because it is practically impossible to test and experimentally confirm hundreds to thousands of predicted interactions. Therefore, we need to develop an artificial network to evaluate the developed method for reverse engineering. One of the major research questions that will be proposed in this work is: Can we reverse engineer the cis-regulatory logic controlling the network organised by modular units? This work is aiming to give an overview of possible research directions in this field as well as the chosen direction for the future work where more research is needed. It also gives a theoretical foundation for the reverse engineering problem, where key aspects are reviewed

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