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Biology subjects

Lindstrom, S.

Publications and source records attributed to Lindstrom, S..

2 recordsLinked to original sources

Development of an orthotopic medulloblastoma zebrafish model for rapid drug testing.

Medulloblastoma (MB) is one of the most common malignant brain tumors in children. Current preclinical in vivo model systems for MB have increased our understanding of molecular mechanisms regulating MB development; however, they may not be suitable for high-throughput screening efforts. We demonstrate here that transplantation of seven different MB cell lines or patient-derived cells into the blastula stage of zebrafish embryos leads to orthotopic tumor cell growth that can be observed within 24 hours after transplantation. Importantly, the homing of transplanted cells to the hindbrain region and the aggressiveness of tumor growth are enhanced by pre-culturing cells in a neural stem cell-like medium. The change in culture conditions rewires the transcriptome towards a more migratory and neuronal progenitor phenotype, including the expression of guidance molecules SEMA3A and EFNB1, both of which correlate with lower overall survival in MB patients. Furthermore, we highlight that the orthotopic zebrafish MB xenograft model has the potential to be used for high-throughput drug screening. Key pointsO_LIMedulloblastoma cells home to the hindbrain region in developing zebrafish embryos. C_LIO_LINeural stem cell culture conditions improve the homing capacity of MB tumor cells. C_LIO_LIMedulloblastoma-transplanted zebrafish embryos can be used as a high-throughput in vivo model for drug screening. C_LI Importance of the StudyOne of the challenges of accurately modeling medulloblastoma is the large heterogeneity in tumor characteristics. To accurately model this heterogeneous disease, patient-derived xenograft mouse models are currently the standard. However, such mouse models are labor intensive, time-consuming, and not suitable for high-throughput studies. Here, we describe a quick and straightforward zebrafish xenograft model that provides a promising alternative to these existing mouse models. We demonstrate that this model can be utilized to study tumor cell growth of several major medulloblastoma subgroups. More importantly, our model facilitates high-throughput drug testing, providing a scalable opportunity for in vivo drug screenings that will support the discovery of novel therapeutic compounds against medulloblastoma.

cancer biology↗

A comprehensive survey of genetic variation in 20,691 subjects from four large cohorts

The Nurses Health Study (NHS), Nurses Health Study II (NHSII), Health Professionals Follow Up Study (HPFS) and the Physicians Health Study (PHS) have collected detailed longitudinal data on multiple exposures and traits for approximately 310,000 study participants over the last 35 years. Over 160,000 study participants across the cohorts have donated a DNA sample and to date, 20,691 subjects have been genotyped as part of genome-wide association studies (GWAS) of twelve primary outcomes. However, these studies utilized six different GWAS arrays making it difficult to conduct analyses of secondary phenotypes or share controls across studies. To allow for secondary analyses of these data, we have created three new datasets merged by platform family and performed imputation using a common reference panel, the 1,000 Genomes Phase I release. Here, we describe the methodology behind the data merging and imputation and present imputation quality statistics and association results from two GWAS of secondary phenotypes (body mass index (BMI) and venous thromboembolism (VTE)).\n\nWe observed the strongest BMI association for the FTO SNP rs55872725 ({beta}=0.45, p=3.48x10-22), and using a significance level of p=0.05, we replicated 19 out of 32 known BMI SNPs. For VTE, we observed the strongest association for the rs2040445 SNP (OR=2.17, 95% CI: 1.79-2.63, p=2.70x10-15), located downstream of F5 and also observed significant associations for the known ABO and F11 regions. This pooled resource can be used to maximize power in GWAS of phenotypes collected across the cohorts and for studying gene-environment interactions as well as rare phenotypes and genotypes.

epidemiology↗