O receptor scavenger classe B tipo I (SR-BI) media a captação seletiva do colesterol da lipoproteina de alta densidade (HDL) e participa no effluxo do colesterol livre para aceptores lipoprotéicos. A HDL tem um importante rol aterogênico associado com sua participação no transporte reverso do colesterol. Polimorfismos no gene que codifica para o SR-BI (SCARB1) foram relacionados com alterações do perfil lipídico sérico e outros fatores de risco associados com doença cardiovascular. As estatinas são inibidores da síntese do colesterol utilizados no tratamento da dislipidemia. Vários polimorfismos em genes envolvidos no metabolismo intermediario de lipideos foram relacionados com diferenças na resposta a hipolipemiantes. Com a finalidade de avaliar o efeito de polimorfismos do SCARB1 sobre o perfil lipídico sérico, expressão gênica e a resposta a estatinas, foram selecionados 185 indivíduos normolipidêmicos (NL) e 147 pacientes hipercolesterolêmicos (HC). Os pacientes HC foram tratados com atorvastatina (10 mg/dia/4 semanas). DNA e RNA foram extraídos de amostras de sangue periférico. Os polimorfismos de nucleotídeo único (SNP) G4A, In5C>T e Ex8C>T foram detectados por PCR-RFLP. A expressão de RNAm do SCARB1 em células mononucleares de sangue periférico (CMSP) foi analisada por PCR em tempo real usando o gene da Ubiquitina c (UBC) como referência endógena. Nos indivíduos HC, as freqüências dos alelos raros G4A (12%), In5C>T (7%) e Ex8C>T (40%), no grupo HC, foram similares às encontradas no grupo NL (4A: 15%, In5T: 7%, e Ex8T: 35%, p>0,05). O alelo SCARB1 4A (genótipos GA + AA) foi associado com valores diminuídos de apoAI no grupo NL. O alelo In5T foi associado com maior concentração LDL-C sérico (p=0,029), em NL, e com apoB e razão apoB/apoAI elevadas (p>0,05) no grupo HC. O SNP SCARB1 Ex8C>T não foi relacionado com o perfil lipídico sérico basal, embora os portadores do genótipo Ex8CC foram associados com resposta reduzida ao tratamento com atorvastatina mostrando menor variação de colesterol total, LDL-C, apoB e razão apoB/apoAI. O SNP Ex8C>T foi associado com maior probabilidade (OR=3,1; 95% IC: 1,00-9,5; p=0,044) de ter uma resposta à atorvastatina diminuída. Os SNPs SCARB1 In5C>T e Ex8C>T estão em desequilíbrio de ligação. O haplótipo G1C5C8/G1T5C8 foi associado com concentrações basais elevadas de triglicérides e VLDL-C em NL e diminuídas de HDL-C e apoAI em HC. Os haplótipos G1C5C8/A1C5C8 e C5C8/C5C8 tiveram variação diminuída da apoB quando comparados com os outros haplótipos, G1C5C8/A1C5C8 e o diplótipo C5C8/C5C8 também apresentou uma variação reduzida da razão apoB/apoAI. Os SNPs G4A e In5C>T estão associados com diminuição da expressão gênica do SCARB1 em NL. O tratamento com atorvastatina não modifica a expressão de RNAm do SCARB1 em CMSP nos HC. Esses resultados são sugestivos de que os polimorfismos no SCARB1 estão associados com valores basais do perfil lipídico sérico e de expressão de RNAm do SCARB1, assim como de resposta à atorvastatina. / The scavenger receptor class B type I (SR-BI) mediates the selective uptake of the high density lipoprotein (HDL) cholesterol and it participates in the free cholesterol efflux to lipoprotein acceptors. HDL has an important antiatherogenic role associated with important activity in the cholesterol reverse transport. Polymorphisms in the SR-BI gene (SCARB1) have been related to variations on plasma lipoprotein profile and other risk factors for cardiovascular disease. Statins are potent inhibitors of cholesterol synthesis prescribed for treatment of the dislipidemia. Several polymorphisms in genes involved in intermediary metabolism of lipids have been related to differences in response to lowering-cholesterol drugs. In order to evaluate the effect of SCARB1 polymorphisms on serum lipids, gene expression and lipid-lowering response to atorvastatin, 185 normolipidemic (NL) and 147 hypercholesterolemic (HC) individuals were selected. HC individuals were treated with atorvastatin (10 mg/day/4 weeks). DNA and RNA were extracted from peripheric blood mononuclear cells (PBMC). SCARB1 mRNA expression was analyzed by real time PCR using ubiquitin c gene (UBC) as endogenous reference. The frequencies of the rare alleles in HC group (G4A: 12%; In5C>T: 7%, and ExC>T: 39%) were similar to those found in NL individuals (4A: 15%, In5T: 7%, and Ex8T: 35%, p>0.05). The SCARB1 4A allele (GA+AA genotypes) was associated with lower apoAI concentration in NL. The In5T allele was associated with higher serum LDL-C (p=0,029) in NL individuals, and with higher apoB and apoB/apoAI ratio (p>0,05) in HC group. SCARB1 Ex8C>T SNP was not related to serum lipids profile, however Ex8CC genotype carriers had lower variation of total cholesterol, LDL-C, apoB and apoB/apoAI ratio in response to atorvastatin. SCARB1 Ex8C>T was associated with higher chance to have a lower atorvastatin response (OR=3.1, 95% CI: 1.00-9.5; p=0.044). SCARB1 In5C>T and ExC>T were in linkage disequilibrium. G1C5C8/G1T5C8 SCARB1 haplotype was associated with higher level of triglycerides and VLDL-C in NL and lower HDL-C and apoAI levels in HC individuals. G1C5C8/A1C5C8 haplotype and C5C8/C5C8 diplotype had lower variations on apoB than the other haplotypes, and G1C5C8/A1C5C8 had also lower variation on apoB/apoAI ratio. G4A and In5C>T SNPs are associated with lower SCARB1 mRNA expression in PBMC of NL individuals. Atorvastatin therapy did not modify the expression level of the SCARB1 transcript in HC. Our results suggest that SCARB1 polymorphisms are associated with basal serum lipids profile, mRNA SCARB1 expression and atorvastatin response.

Indiana University-Purdue University Indianapolis (IUPUI) / Traditional treatment strategy development for diseases involves the identification of target proteins related to disease states, and the interference of these proteins with drug molecules. Computational drug discovery and virtual screening from thousands of chemical compounds have accelerated this process. The thesis presents a comprehensive framework of computational drug discovery using system biology approaches. The thesis mainly consists of two parts: disease biomarker identification and disease treatment discoveries. The first part of the thesis focuses on the research in biomarker identification for human diseases in the post-genomic era with an emphasis in system biology approaches such as using the protein interaction networks. There are two major types of biomarkers: Diagnostic Biomarker is expected to detect a given type of disease in an individual with both high sensitivity and specificity; Predictive Biomarker serves to predict drug response before treatment is started. Both are essential before we even start seeking any treatment for the patients. In this part, we first studied how the coverage of the disease genes, the protein interaction quality, and gene ranking strategies can affect the identification of disease genes. Second, we addressed the challenge of constructing a central database to collect the system level data such as protein interaction, pathway, etc. Finally, we built case studies for biomarker identification for using dabetes as a case study. The second part of the thesis mainly addresses how to find treatments after disease identification. It specifically focuses on computational drug repositioning due to its low lost, few translational issues and other benefits. First, we described how to implement literature mining approaches to build the disease-protein-drug connectivity map and demonstrated its superior performances compared to other existing applications. Second, we presented a valuable drug-protein directionality database which filled the research gap of lacking alternatives for the experimental CMAP in computational drug discovery field. We also extended the correlation based ranking algorithms by including the underlying topology among proteins. Finally, we demonstrated how to study drug repositioning beyond genomic level and from one dimension to two dimensions with clinical side effect as prediction features.

Patients not responding to therapy as expected is one of the most pressing healthcare concerns of today. It causes economical, medical and societal issues along with suffering for patients. This project aimed to address this problem and evaluate how to find the best suited drug treatments for individual patients to treat Covid-19. This project was carried out in collaboration with the company AB Mavatar, that have two networks, one experimental and one predicted, which produce drug treatment rankings differently. Different methods are used to connect drug targets to disease associated genes and thus evaluate what drugs are best suited for specific patients to treat Covid-19. The aim of this project is to examine how network, method and drug category affect the ranking of a drug treatment for four mapped Covid-19 patients. Which drug category a drug belongs to did not seem to significantly affect the drug ranking. Yet, certain drug subcategories were closely correlated. However, these subcategories were not those that are typically associated with Covid-19. The method used to connect drug targets to disease associated genes heavily impacts the ranking of the drug treatment. The methods should be further evaluated to see if some should be excluded or weighted less in drug ranking calculations. The two networks are similar in how they rank different drugs, especially in severely ill patients. Through this project and the evaluation of the impact of method choice, one can start to figure out what should be prioritized among disease related changes. Also, important parameters for personalized treatment can be evaluated. / Patienter som inte svarar på terapi som förväntat är en av de största utmaningarna inom hälso- och sjukvård idag. Det orsakar ekonomiska, medicinska och samhälleliga problem samt lidande för patienter. Det här projektet adresserade detta problem och evaluerade hur man kan hitta det bäst lämpade läkemedlet för specifika patienter för att behandla Covid-19. Projektet gjordes tillsammans med företaget AB Mavatar, som har två interaktom, en experimentell och en datadriven, som rangordnar läkemedelsrekommendationer på olika sätt. Olika metoder används för att koppla samman läkemedelsmål med sjukdomsrelaterade gener och således evaluera vilka läkemedel som är bäst lämpade för specifika patienter för behandling av Covid-19. Syftet med projektet var att undersöka hur nätverk, metod och läkemedelskategori påverkar hur läkemedel rangordnas för fyra kartlagda Covid-19-patienter.  Vilken läkemedelskategori ett läkemedel tillhör tycks inte märkbart påverka läkemedelsrangordning. Trots detta var vissa läkemedelsunderkategorier nära korrelerade. Dock var dessa underkategorier inte typiskt associerade med Covid-19. Metoden för att koppla samman läkemedelsmål med sjukdomsassocierade gener påverkade läkemedelsrangordningen väsentligt. Metoderna borde dock evalueras ytterligare för att eventuellt exkludera eller vikta vissa mindre i uträkningar av läkemedelsrang. De två nätverken är lika i hur de rangordnar olika läkemedel, särskilt för svårt sjuka patienter. Genom detta projekt och genom evaluering av metodvalets påverkan kan man börja begripa hur man borde priorita bland sjukdomsrelaterade förändringar. Dessutom kunde viktiga parametrar inom personlig behandling evalueras.