Molekulare Ursachen und therapeutische Intervention bei der Alzheimer-Demenz

Ein charakteristisches, neuropathologisches Merkmal der Alzheimer-Demenz (AD), der am häufigsten vorkommenden Demenz-Form des Menschen, ist das Auftreten von senilen Plaques im Gehirn der Patienten. Hierbei stellt das neurotoxische A-beta Peptid den Hauptbestandteil dieser Ablagerungen dar. Einen Beitrag zu der pathologisch erhöhten A-beta Generierung liefert das verschobene Expressionsgleichgewicht der um APP-konkurrierenden Proteasen BACE-1 und ADAM10 zu Gunsten der beta-Sekretase BACE-1. In der vorliegenden Dissertation sollten molekulare Mechanismen identifiziert werden, die zu einem pathologisch veränderten Gleichgewicht der APP-Spaltung und somit zum Entstehen und Fortschritt der AD beitragen. Des Weiteren sollten Substanzen identifiziert werden, die durch Beeinflussung der Genexpression einer der beiden Proteasen das physiologische Gleichgewicht der APP-Prozessierung wiederherstellen können und somit therapeutisch einsetzbar sind.rnAnhand eines „Screenings“ von 704 Transkriptionsfaktoren wurden 23 Faktoren erhalten die das Verhältnis ADAM10- pro BACE-1-Promotor Aktivität beeinflussten. Exemplarisch wurden zwei der molekularen Faktoren auf ihren Wirkmechanismus untersucht: Der TF „X box binding protein-1“ (XBP-1), der die so genannte „unfolded protein response“ (UPR) reguliert, erhöhte die Expression von ADAM10 in Zellkultur-Experimenten. Die Menge dieses Faktors war in AD-Patienten im Vergleich zu gesunden, Alters-korrelierten Kontrollen signifikant erniedrigt. Im Gegensatz dazu verminderte der Seneszenz-assoziierte TF „T box 2“ (Tbx2) die Menge an ADAM10 in SH-SY5Y Zellen. Die Expression des Faktors selbst war in post-mortem Kortexgewebe von AD-Patienten erhöht. Zusätzlich zu den TFs konnten in einer Kooperation mit dem Helmholtz Zentrum München drei microRNAs (miRNA 103, 107, 1306) bioinformatisch prädiziert und experimentell validiert werden, die die Expression des humanen ADAM10 reduzierten.rnIm Rahmen dieser Arbeit konnten damit körpereigene Faktoren identifiziert werden, die die Menge an ADAM10 regulieren und folglich potenziell an der Entstehung der gestörten Homöostase der APP-Prozessierung beteiligt sind. Somit ist die AD auch im Hinblick auf eine A-beta-vermittelte Pathologie als multifaktorielle Krankheit zu verstehen, in der verschiedene Regulatoren zur gestörten APP-Prozessierung und somit zur pathologisch gesteigerten A-beta Generierung beitragen können. rnEine pharmakologische Erhöhung der ADAM10 Genexpression würde zu der Freisetzung von neuroprotektivem APPs-alpha und gleichzeitig zu einer reduzierten A-beta Generierung führen. Deshalb war ein weiteres Ziel dieser Arbeit die Evaluierung von Substanzen mit therapeutischem Potenzial im Hinblick auf eine erhöhte ADAM10 Expression. Von 640 FDA-zugelassenen Medikamenten einer Substanz-Bibliothek wurden 23 Substanzen identifiziert, die die Menge an ADAM10 signifikant steigerten während die Expression von BACE-1 und APP unbeeinflusst blieb. In Zusammenarbeit mit dem Institut für Pathologie (Johannes Gutenberg Universität Mainz) wurde ein Zellkultur-basiertes Modell etabliert, um die Permeationsfähigkeit der potenziellen Kandidaten-Substanzen über die Blut-Hirn Schranke (BHS) zu untersuchen. Von den 23 Medikamenten konnten neun im Rahmen des etablierten Modells als BHS-gängig charakterisiert werden. Somit erfüllen diese verbleibenden Medikamente die grundlegenden Anforderungen an ein AD-Therapeutikum. rnADAM10 spaltet neben APP eine Vielzahl anderer Substrate mit unterschiedlichen Funktionen in der Zelle. Zum Beispiel reguliert das Zelladhäsionsmolekül Neuroligin-1 (NL-1), das von ADAM10 prozessiert wird, die synaptische Funktion exzitatorischer Neurone. Aus diesem Grund ist die Abschätzung potenzieller, Therapie-bedingter Nebenwirkungen sehr wichtig. Im Rahmen eines Forschungsaufenthalts an der Universität von Tokio konnte in primären, kortikalen Neuronen der Ratte bei einer Retinoid-induzierten Erhöhung von ADAM10 neben einer vermehrten alpha-sekretorischen APP-Prozessierung auch eine gesteigerte Spaltung von NL-1 beobachtet werden. Dies lässt vermuten, dass bei einer Behandlung mit dem Retinoid Acitretin neben einer vermehrten APP-Spaltung durch ADAM10 auch die Regulation glutamaterger Neurone durch die Spaltung von NL-1 betroffen ist. Anhand eines geeigneten Alzheimer-Tiermodells sollten diese Befunde weiter analysiert werden, um so auf einen sicheren therapeutischen Ansatz bezüglich einer vermehrten ADAM10 Genexpression schließen zu können.rn

Preparation and functional characterisation of recombinant silicatein-alpha from the sponge Suberites domuncula

The marine world is an immense source of biodiversity that provides substances with striking potentials in medicinal chemistry and biotechnology. Sponges (Porifera) are marine animals that represent the most impressive example of organisms possessing the ability to metabolise silica through a family of enzymes known as silicateins. Complex skeletal structures (spicules) made of pure biogenic silica (biosilica) are produced under physiological conditions. Biosilica is a natural material comprising inorganic and organic components with unique mechanical, optical, and physico-chemical properties, including promising potential to be used for development of therapeutic agents in regenerative medicine. Unravelling the intimate physiological mechanisms occurring in sponges during the construction of their siliceous spicules is an on-going project, and several questions have been addressed by the studies proposed by our working group. In this doctoral work, the recombinant DNA technology is exploited for functional and structural characterisation of silicatein. Its precursors are produced as fusion proteins with a chaperone tag (named TF-Ps), and a robust method for the overexpression of native soluble proteins in high concentrations has been developed. In addition, it is observed and proven experimentally that the maturation of silicatein is an autocatalytic event that: (i) can be modulated by rational use of protease inhibitors; (ii) is influenced by the temperature of the environment; (iii) only slightly depends on the pH. In the same experimental framework, observations on the dynamics in the maturation of silicateins allow a better understanding of how the axial filaments form during the early stages of spicule construction. In addition, the definition of new distinct properties of silicatein (termed “structure-guiding” and “structure-forming”) is introduced. By homology models and through comparisons with similar proteins (the cathepsins), domains with significant surface hydrophobicity are identified as potential self-assembly mediators. Moreover, a high-throughput screening showed that TF-Ps could generate crystals under certain conditions, becoming promising for further structural studies. With the goal of optimise the properties of the recombinant silicatein, implementation of new production systems are tried for the first time. Success in the expression of silicatein-type proteins in insect and yeast cells, constitute a promising basis for further development, towards the establishment of an efficient method for the production of a high-value pure and soluble protein.

Algorithmen zur labelfreien quantitativen Proteomanalyse auf Basis datenunabhängig-akquirierter LC-MS-Daten

Moderne ESI-LC-MS/MS-Techniken erlauben in Verbindung mit Bottom-up-Ansätzen eine qualitative und quantitative Charakterisierung mehrerer tausend Proteine in einem einzigen Experiment. Für die labelfreie Proteinquantifizierung eignen sich besonders datenunabhängige Akquisitionsmethoden wie MSE und die IMS-Varianten HDMSE und UDMSE. Durch ihre hohe Komplexität stellen die so erfassten Daten besondere Anforderungen an die Analysesoftware. Eine quantitative Analyse der MSE/HDMSE/UDMSE-Daten blieb bislang wenigen kommerziellen Lösungen vorbehalten. rn| In der vorliegenden Arbeit wurden eine Strategie und eine Reihe neuer Methoden zur messungsübergreifenden, quantitativen Analyse labelfreier MSE/HDMSE/UDMSE-Daten entwickelt und als Software ISOQuant implementiert. Für die ersten Schritte der Datenanalyse (Featuredetektion, Peptid- und Proteinidentifikation) wird die kommerzielle Software PLGS verwendet. Anschließend werden die unabhängigen PLGS-Ergebnisse aller Messungen eines Experiments in einer relationalen Datenbank zusammengeführt und mit Hilfe der dedizierten Algorithmen (Retentionszeitalignment, Feature-Clustering, multidimensionale Normalisierung der Intensitäten, mehrstufige Datenfilterung, Proteininferenz, Umverteilung der Intensitäten geteilter Peptide, Proteinquantifizierung) überarbeitet. Durch diese Nachbearbeitung wird die Reproduzierbarkeit der qualitativen und quantitativen Ergebnisse signifikant gesteigert.rn| Um die Performance der quantitativen Datenanalyse zu evaluieren und mit anderen Lösungen zu vergleichen, wurde ein Satz von exakt definierten Hybridproteom-Proben entwickelt. Die Proben wurden mit den Methoden MSE und UDMSE erfasst, mit Progenesis QIP, synapter und ISOQuant analysiert und verglichen. Im Gegensatz zu synapter und Progenesis QIP konnte ISOQuant sowohl eine hohe Reproduzierbarkeit der Proteinidentifikation als auch eine hohe Präzision und Richtigkeit der Proteinquantifizierung erreichen.rn| Schlussfolgernd ermöglichen die vorgestellten Algorithmen und der Analyseworkflow zuverlässige und reproduzierbare quantitative Datenanalysen. Mit der Software ISOQuant wurde ein einfaches und effizientes Werkzeug für routinemäßige Hochdurchsatzanalysen labelfreier MSE/HDMSE/UDMSE-Daten entwickelt. Mit den Hybridproteom-Proben und den Bewertungsmetriken wurde ein umfassendes System zur Evaluierung quantitativer Akquisitions- und Datenanalysesysteme vorgestellt.

TILLING for Mutations in Model Plants and Crops

A growing world population, changing climate and limiting fossil fuels will provide new pressures on human production of food, medicine, fuels and feed stock in the twenty-first century. Enhanced crop production promises to ameliorate these pressures. Crops can be bred for increased yields of calories, starch, nutrients, natural medicinal compounds, and other important products. Enhanced resistance to biotic and abiotic stresses can be introduced, toxins removed, and industrial qualities such as fibre strength and biofuel per mass can be increased. Induced and natural mutations provide a powerful method for the generation of heritable enhanced traits. While mainly exploited in forward, phenotype driven, approaches, the rapid accumulation of plant genomic sequence information and hypotheses regarding gene function allows the use of mutations in reverse genetic approaches to identify lesions in specific target genes. Such gene-driven approaches promise to speed up the process of creating novel phenotypes, and can enable the generation of phenotypes unobtainable by traditional forward methods. TILLING (Targeting Induced Local Lesions IN Genome) is a high-throughput and low cost reverse genetic method for the discovery of induced mutations. The method has been modified for the identification of natural nucleotide polymorphisms, a process called Ecotilling. The methods are general and have been applied to many species, including a variety of different crops. In this chapter the current status of the TILLING and Ecotilling methods and provide an overview of progress in applying these methods to different plant species, with a focus on work related to food production for developing nations.

Modulation of L-α-lysophosphatidylinositol/GPR55 mitogen-activated protein kinase (MAPK) signaling by cannabinoids

GPR55 is activated by l-α-lysophosphatidylinositol (LPI) but also by certain cannabinoids. In this study, we investigated the GPR55 pharmacology of various cannabinoids, including analogues of the CB1 receptor antagonist Rimonabant®, CB2 receptor agonists, and Cannabis sativa constituents. To test ERK1/2 phosphorylation, a primary downstream signaling pathway that conveys LPI-induced activation of GPR55, a high throughput system, was established using the AlphaScreen® SureFire® assay. Here, we show that CB1 receptor antagonists can act both as agonists alone and as inhibitors of LPI signaling under the same assay conditions. This study clarifies the controversy surrounding the GPR55-mediated actions of SR141716A; some reports indicate the compound to be an agonist and some report antagonism. In contrast, we report that the CB2 ligand GW405833 behaves as a partial agonist of GPR55 alone and enhances LPI signaling. GPR55 has been implicated in pain transmission, and thus our results suggest that this receptor may be responsible for some of the antinociceptive actions of certain CB2 receptor ligands. The phytocannabinoids Δ9-tetrahydrocannabivarin, cannabidivarin, and cannabigerovarin are also potent inhibitors of LPI. These Cannabis sativa constituents may represent novel therapeutics targeting GPR55.

An integrated nano-scale approach to profile miRNAs in limited clinical samples

Profiling miRNA expression in cells that directly contribute to human disease pathogenesis is likely to aid the discovery of novel drug targets and biomarkers. However, tissue heterogeneity and the limited amount of human diseased tissue available for research purposes present fundamental difficulties that often constrain the scope and potential of such studies. We established a flow cytometry-based method for isolating pure populations of pathogenic T cells from bronchial biopsy samples of asthma patients, and optimized a high-throughput nano-scale qRT-PCR method capable of accurately measuring 96 miRNAs in as little as 100 cells. Comparison of circulating and airway T cells from healthy and asthmatic subjects revealed asthma-associated and tissue-specific miRNA expression patterns. These results establish the feasibility and utility of investigating miRNA expression in small populations of cells involved in asthma pathogenesis, and set a precedent for application of our nano-scale approach in other human diseases. The microarray data from this study (Figure 7) has been submitted to the NCBI Gene Expression Omnibus (GEO; http://ncbi.nlm.nih.gov/geo) under accession no. GSE31030.

16S rRNA terminal restriction fragment length polymorphism for the characterization of the nasopharyngeal microbiota

A novel non-culture based 16S rRNA Terminal Restriction Fragment Length Polymorphism (T-RFLP) method using the restriction enzymes Tsp509I and Hpy166II was developed for the characterization of the nasopharyngeal microbiota and validated using recently published 454 pyrosequencing data. 16S rRNA gene T-RFLP for 153 clinical nasopharyngeal samples from infants with acute otitis media (AOM) revealed 5 Tsp509I and 6 Hpy166II terminal fragments (TFs) with a prevalence of >10%. Cloning and sequencing identified all TFs with a prevalence >6% allowing a sufficient description of bacterial community changes for the most important bacterial taxa. The conjugated 7-valent pneumococcal polysaccharide vaccine (PCV-7) and prior antibiotic exposure had significant effects on the bacterial composition in an additive main effects and multiplicative interaction model (AMMI) in concordance with the 16S rRNA 454 pyrosequencing data. In addition, the presented T-RFLP method is able to discriminate S. pneumoniae from other members of the Mitis group of streptococci, which therefore allows the identification of one of the most important human respiratory tract pathogens. This is usually not achieved by current high throughput sequencing protocols. In conclusion, the presented 16S rRNA gene T-RFLP method is a highly robust, easy to handle and a cheap alternative to the computationally demanding next-generation sequencing analysis. In case a lot of nasopharyngeal samples have to be characterized, it is suggested to first perform 16S rRNA T-RFLP and only use next generation sequencing if the T-RFLP nasopharyngeal patterns differ or show unknown TFs.

Classification and selection of biomarkers in genomic data using LASSO

High-throughput gene expression technologies such as microarrays have been utilized in a variety of scientific applications. Most of the work has been on assessing univariate associations between gene expression with clinical outcome (variable selection) or on developing classification procedures with gene expression data (supervised learning). We consider a hybrid variable selection/classification approach that is based on linear combinations of the gene expression profiles that maximize an accuracy measure summarized using the receiver operating characteristic curve. Under a specific probability model, this leads to consideration of linear discriminant functions. We incorporate an automated variable selection approach using LASSO. An equivalence between LASSO estimation with support vector machines allows for model fitting using standard software. We apply the proposed method to simulated data as well as data from a recently published prostate cancer study.

Data Quality Assessment of Ungated Flow Cytometry Data in High

Background: The recent development of semi-automated techniques for staining and analyzing flow cytometry samples has presented new challenges. Quality control and quality assessment are critical when developing new high throughput technologies and their associated information services. Our experience suggests that significant bottlenecks remain in the development of high throughput flow cytometry methods for data analysis and display. Especially, data quality control and quality assessment are crucial steps in processing and analyzing high throughput flow cytometry data. Methods: We propose a variety of graphical exploratory data analytic tools for exploring ungated flow cytometry data. We have implemented a number of specialized functions and methods in the Bioconductor package rflowcyt. We demonstrate the use of these approaches by investigating two independent sets of high throughput flow cytometry data. Results: We found that graphical representations can reveal substantial non-biological differences in samples. Empirical Cumulative Distribution Function and summary scatterplots were especially useful in the rapid identification of problems not identified by manual review. Conclusions: Graphical exploratory data analytic tools are quick and useful means of assessing data quality. We propose that the described visualizations should be used as quality assessment tools and where possible, be used for quality control.

EXPLORATION, NORMALIZATION, AND GENOTYPE CALLS OF HIGH DENSITY OLIGONUCLEOTIDE SNP ARRAY DATA

In most microarray technologies, a number of critical steps are required to convert raw intensity measurements into the data relied upon by data analysts, biologists and clinicians. These data manipulations, referred to as preprocessing, can influence the quality of the ultimate measurements. In the last few years, the high-throughput measurement of gene expression is the most popular application of microarray technology. For this application, various groups have demonstrated that the use of modern statistical methodology can substantially improve accuracy and precision of gene expression measurements, relative to ad-hoc procedures introduced by designers and manufacturers of the technology. Currently, other applications of microarrays are becoming more and more popular. In this paper we describe a preprocessing methodology for a technology designed for the identification of DNA sequence variants in specific genes or regions of the human genome that are associated with phenotypes of interest such as disease. In particular we describe methodology useful for preprocessing Affymetrix SNP chips and obtaining genotype calls with the preprocessed data. We demonstrate how our procedure improves existing approaches using data from three relatively large studies including one in which large number independent calls are available. Software implementing these ideas are avialble from the Bioconductor oligo package.

The in vitro evidence for an effect of high homeopathic potencies--a systematic review of the literature

OBJECTIVE: Systematic assessment of the in vitro research on high potency effects. METHOD: Publications of experiments were collected through databases, experts, previous reviews, citation tracking. Inclusion criteria: stepwise agitated dilutions <10(-23); cells or molecules from human or animal. Experiments were assessed with the modified SAPEH score. RESULTS: From 75 publications, 67 experiments (1/3 of them replications) were evaluated. Nearly 3/4 of them found a high potency effect, and 2/3 of those 18 that scored 6 points or more and controlled contamination. Nearly 3/4 of all replications were positive. Design and experimental models of the reviewed experiments were inhomogenous, most were performed on basophiles. CONCLUSIONS: Even experiments with a high methodological standard could demonstrate an effect of high potencies. No positive result was stable enough to be reproduced by all investigators. A general adoption of succussed controls, randomization and blinding would strengthen the evidence of future experiments.

Biochemical Conversions of Lignocellulosic Biomass for Sustainable Fuel-Ethanol Production in the Upper Midwest

Biofuels are an increasingly important component of worldwide energy supply. This research aims to understand the pathways and impacts of biofuels production, and to improve these processes to make them more efficient. In Chapter 2, a life cycle assessment (LCA) is presented for cellulosic ethanol production from five potential feedstocks of regional importance to the upper Midwest - hybrid poplar, hybrid willow, switchgrass, diverse prairie grasses, and logging residues - according to the requirements of Renewable Fuel Standard (RFS). Direct land use change emissions are included for the conversion of abandoned agricultural land to feedstock production, and computer models of the conversion process are used in order to determine the effect of varying biomass composition on overall life cycle impacts. All scenarios analyzed here result in greater than 60% reduction in greenhouse gas emissions relative to petroleum gasoline. Land use change effects were found to contribute significantly to the overall emissions for the first 20 years after plantation establishment. Chapter 3 is an investigation of the effects of biomass mixtures on overall sugar recovery from the combined processes of dilute acid pretreatment and enzymatic hydrolysis. Biomass mixtures studied were aspen, a hardwood species well suited to biochemical processing; balsam, a high-lignin softwood species, and switchgrass, an herbaceous energy crop with high ash content. A matrix of three different dilute acid pretreatment severities and three different enzyme loading levels was used to characterize interactions between pretreatment and enzymatic hydrolysis. Maximum glucose yield for any species was 70% oftheoretical for switchgrass, and maximum xylose yield was 99.7% of theoretical for aspen. Supplemental β-glucosidase increased glucose yield from enzymatic hydrolysis by an average of 15%, and total sugar recoveries for mixtures could be predicted to within 4% by linear interpolation of the pure species results. Chapter 4 is an evaluation of the potential for producing Trichoderma reesei cellulose hydrolases in the Kluyveromyces lactis yeast expression system. The exoglucanases Cel6A and Cel7A, and the endoglucanase Cel7B were inserted separately into the K. lactis and the enzymes were analyzed for activity on various substrates. Recombinant Cel7B was found to be active on carboxymethyl cellulose and Avicel powdered cellulose substrates. Recombinant Cel6A was also found to be active on Avicel. Recombinant Cel7A was produced, but no enzymatic activity was detected on any substrate. Chapter 5 presents a new method for enzyme improvement studies using enzyme co-expression and yeast growth rate measurements as a potential high-throughput expression and screening system in K. lactis yeast. Two different K. lactis strains were evaluated for their usefulness in growth screening studies, one wild-type strain and one strain which has had the main galactose metabolic pathway disabled. Sequential transformation and co-expression of the exoglucanase Cel6A and endoglucanase Cel7B was performed, and improved hydrolysis rates on Avicel were detectable in the cell culture supernatant. Future work should focus on hydrolysis of natural substrates, developing the growth screening method, and utilizing the K. lactis expression system for directed evolution of enzymes.

NON-CHROMATOGRAPHIC PURIFICATION OF SYNTHETIC BIO-OLIGOMERS

Synthetic oligonucleotides and peptides have found wide applications in industry and academic research labs. There are ~60 peptide drugs on the market and over 500 under development. The global annual sale of peptide drugs in 2010 was estimated to be $13 billion. There are three oligonucleotide-based drugs on market; among them, the FDA newly approved Kynamro was predicted to have a $100 million annual sale. The annual sale of oligonucleotides to academic labs was estimated to be $700 million. Both bio-oligomers are mostly synthesized on automated synthesizers using solid phase synthesis technology, in which nucleoside or amino acid monomers are added sequentially until the desired full-length sequence is reached. The additions cannot be complete, which generates truncated undesired failure sequences. For almost all applications, these impurities must be removed. The most widely used method is HPLC. However, the method is slow, expensive, labor-intensive, not amendable for automation, difficult to scale up, and unsuitable for high throughput purification. It needs large capital investment, and consumes large volumes of harmful solvents. The purification costs are estimated to be more than 50% of total production costs. Other methods for bio-oligomer purification also have drawbacks, and are less favored than HPLC for most applications. To overcome the problems of known biopolymer purification technologies, we have developed two non-chromatographic purification methods. They are (1) catching failure sequences by polymerization, and (2) catching full-length sequences by polymerization. In the first method, a polymerizable group is attached to the failure sequences of the bio-oligomers during automated synthesis; purification is achieved by simply polymerizing the failure sequences into an insoluble gel and extracting full-length sequences. In the second method, a polymerizable group is attached to the full-length sequences, which are then incorporated into a polymer; impurities are removed by washing, and pure product is cleaved from polymer. These methods do not need chromatography, and all drawbacks of HPLC no longer exist. Using them, purification is achieved by simple manipulations such as shaking and extraction. Therefore, they are suitable for large scale purification of oligonucleotide and peptide drugs, and also ideal for high throughput purification, which currently has a high demand for research projects involving total gene synthesis. The dissertation will present the details about the development of the techniques. Chapter 1 will make an introduction to oligodeoxynucleotides (ODNs), their synthesis and purification. Chapter 2 will describe the detailed studies of using the catching failure sequences by polymerization method to purify ODNs. Chapter 3 will describe the further optimization of the catching failure sequences by polymerization ODN purification technology to the level of practical use. Chapter 4 will present using the catching full-length sequence by polymerization method for ODN purification using acid-cleavable linker. Chapter 5 will make an introduction to peptides, their synthesis and purification. Chapter 6 will describe the studies using the catching full-length sequence by polymerization method for peptide purification.

Assay of β-hematin formation by malaria parasite

Novel leads are urgently required for designing antimalarials due to the reduced efficacy of presently available drugs. The malaria parasite has a unique reaction of heme polymerization, which has attracted much attention in the recent past as a target for the design of antimalarial drugs. The process is hampered by non-availability of a proper assay method. Currently available methods are cumbersome and require advanced instrumentation or radioactive substrates. Here, we are describing an assay for hemozoin formation that is simple and reproducible. This assay has routinely been used by us for the identification of potential compounds with antimalarial activity.

NETWORK TOPOLOGY IN HUMAN PROTEIN INTERACTION DATA PREDICTS FUNCTIONAL ASSOCIATION

High-throughput assays, such as yeast two-hybrid system, have generated a huge amount of protein-protein interaction (PPI) data in the past decade. This tremendously increases the need for developing reliable methods to systematically and automatically suggest protein functions and relationships between them. With the available PPI data, it is now possible to study the functions and relationships in the context of a large-scale network. To data, several network-based schemes have been provided to effectively annotate protein functions on a large scale. However, due to those inherent noises in high-throughput data generation, new methods and algorithms should be developed to increase the reliability of functional annotations. Previous work in a yeast PPI network (Samanta and Liang, 2003) has shown that the local connection topology, particularly for two proteins sharing an unusually large number of neighbors, can predict functional associations between proteins, and hence suggest their functions. One advantage of the work is that their algorithm is not sensitive to noises (false positives) in high-throughput PPI data. In this study, we improved their prediction scheme by developing a new algorithm and new methods which we applied on a human PPI network to make a genome-wide functional inference. We used the new algorithm to measure and reduce the influence of hub proteins on detecting functionally associated proteins. We used the annotations of the Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) as independent and unbiased benchmarks to evaluate our algorithms and methods within the human PPI network. We showed that, compared with the previous work from Samanta and Liang, our algorithm and methods developed in this study improved the overall quality of functional inferences for human proteins. By applying the algorithms to the human PPI network, we obtained 4,233 significant functional associations among 1,754 proteins. Further comparisons of their KEGG and GO annotations allowed us to assign 466 KEGG pathway annotations to 274 proteins and 123 GO annotations to 114 proteins with estimated false discovery rates of <21% for KEGG and <30% for GO. We clustered 1,729 proteins by their functional associations and made pathway analysis to identify several subclusters that are highly enriched in certain signaling pathways. Particularly, we performed a detailed analysis on a subcluster enriched in the transforming growth factor β signaling pathway (P<10-50) which is important in cell proliferation and tumorigenesis. Analysis of another four subclusters also suggested potential new players in six signaling pathways worthy of further experimental investigations. Our study gives clear insight into the common neighbor-based prediction scheme and provides a reliable method for large-scale functional annotations in this post-genomic era.