In this work, we investigated sequence variation, evolutionary constraint, and selection at the CD163 gene in pigs. A functional CD163 protein is required for infection by porcine reproductive and ...respiratory syndrome virus, which is a serious pathogen with major impacts on pig production.
We used targeted pooled sequencing of the exons of CD163 to detect sequence variants in 35,000 pigs of diverse genetic backgrounds and to search for potential stop-gain and frameshift indel variants. Then, we used whole-genome sequence data from three pig lines to calculate: a variant intolerance score that measures the tolerance of genes to protein coding variation; an estimate of selection on protein-coding variation over evolutionary time; and haplotype diversity statistics to detect recent selective sweeps during breeding.
Using a deep survey of sequence variation in the CD163 gene in domestic pigs, we found no potential knockout variants. The CD163 gene was moderately intolerant to variation and showed evidence of positive selection in the pig lineage, but no evidence of recent selective sweeps during breeding.
Pisciricketssia salmonis is the causal agent of Salmon Rickettsial Syndrome (SRS), which affects salmon species and causes severe economic losses. Selective breeding for disease resistance represents ...one approach for controlling SRS in farmed Atlantic salmon. Knowledge concerning the architecture of the resistance trait is needed before deciding on the most appropriate approach to enhance artificial selection for P. salmonis resistance in Atlantic salmon. The purpose of the study was to dissect the genetic variation in the resistance to this pathogen in Atlantic salmon.
2,601 Atlantic salmon smolts were experimentally challenged against P. salmonis by means of intra-peritoneal injection. These smolts were the progeny of 40 sires and 118 dams from a Chilean breeding population. Mortalities were recorded daily and the experiment ended at day 40 post-inoculation. Fish were genotyped using a 50K Affymetrix® Axiom® myDesignTM Single Nucleotide Polymorphism (SNP) Genotyping Array. A Genome Wide Association Analysis was performed on data from the challenged fish. Linear regression and logistic regression models were tested.
Genome Wide Association Analysis indicated that resistance to P. salmonis is a moderately polygenic trait. There were five SNPs in chromosomes Ssa01 and Ssa17 significantly associated with the traits analysed. The proportion of the phenotypic variance explained by each marker is small, ranging from 0.007 to 0.045. Candidate genes including interleukin receptors and fucosyltransferase have been found to be physically linked with these genetic markers and may play an important role in the differential immune response against this pathogen.
Due to the small amount of variance explained by each significant marker we conclude that genetic resistance to this pathogen can be more efficiently improved with the implementation of genetic evaluations incorporating genotype information from a dense SNP array.
Disease resistance genes in livestock provide health benefits to animals and opportunities for farmers to meet the growing demand for affordable, high-quality protein. Previously, researchers used ...gene editing to modify the porcine CD163 gene and demonstrated resistance to a harmful virus that causes porcine reproductive and respiratory syndrome (PRRS). To maximize potential benefits, this disease resistance trait needs to be present in commercially relevant breeding populations for multiplication and distribution of pigs. Toward this goal, a first-of-its-kind, scaled gene editing program was established to introduce a single modified CD163 allele into four genetically diverse, elite porcine lines. This effort produced healthy pigs that resisted PRRS virus infection as determined by macrophage and animal challenges. This founder population will be used for additional disease and trait testing, multiplication, and commercial distribution upon regulatory approval. Applying CRISPR-Cas to eliminate a viral disease represents a major step toward improving animal health.
The immune system plays a pivotal role in the susceptibility to and progression of a variety of diseases. Due to a strong genetic basis, heritable differences in immune function may contribute to ...differential disease susceptibility between individuals. Genetic reference populations, such as the BXD (C57BL/6J × DBA/2J) panel of recombinant inbred (RI) mouse strains, provide unique models through which to integrate baseline phenotypes in healthy individuals with heritable risk for disease because of the ability to combine data collected from these populations across both multiple studies and time. We performed basic immunophenotyping (e.g., percentage of circulating B and T lymphocytes and CD4(+) and CD8(+) T cell subpopulations) in peripheral blood of healthy mice from 41 BXD RI strains to define the immunophenotypic variation in this strain panel and to characterize the genetic architecture that underlies these traits. Significant QTL models that explained the majority (50-77%) of phenotypic variance were derived for each trait and for the T:B cell and CD4(+):CD8(+) ratios. Combining QTL mapping with spleen gene expression data uncovered two quantitative trait transcripts, Ptprk and Acp1, as candidates for heritable differences in the relative abundance of helper and cytotoxic T cells. These data will be valuable in extracting genetic correlates of the immune system in the BXD panel. In addition, they will be a useful resource for prospective, phenotype-driven model selection to test hypotheses about differential disease or environmental susceptibility between individuals with baseline differences in the composition of the immune system.
Background: Computational simulation of complex biological networks lies at the heart of systems biology since it can confirm the conclusions drawn by experimental studies of biological networks and ...guide researchers to produce fresh hypotheses for further experimental validation. Since this iterative process helps in development of more realistic system models a variety of computational tools have been developed. In the absence of a common format for representation of models these tools were developed in different formats. As a result these tools became unable to exchange models amongst them, leading to development of SBML, a standard exchange format for computational models of biochemical networks. Here the formats of SBML and one of the commercial tools of systems biology are being compared to study the issues which may arise during conversion between their respective formats. A tool StoP has been developed to convert the format of SBML to the format of the selected tool. Results: The basic format of SBML representation which is in the form of listings of various elements of a biochemical reaction system differs from the representation of the selected tool which is location oriented. In spite of this difference the various components of biochemical pathways including multiple compartments, global parameters, reactants, products, modifiers, reactions, kinetic formulas and reaction parameters could be converted from the SBML representation to the representation of the selected tool. The MathML representation of the kinetic formula in an SBML model can be converted to the string format of the selected tool. Some features of the SBML are not present in the selected tool. Similarly, the ability of the selected tool to declare parameters for locations, which are global to those locations and their children, is not present in the SBML. Conclusions: Differences in representations of pathway models may include differences in terminologies, basic architecture, differences in capabilities of software’s, and adoption of different standards for similar things. But the overall similarity of domain of pathway models enables us to interconvert these representations. The selected tool should develop support for unit definitions, events and rules. Development of facility for parameter declaration at compartment level by SBML and facility for function declaration by the selected tool is recommended.
Självständigt arbete på avancerad nivå (magisterexamen)
20 poäng / 30 hp
Background: Computational simulation of complex biological networks lies at the heart of systems biology since it can confirm ...the conclusions drawn by experimental studies of biological networks and guide researchers to produce fresh hypotheses for further experimental validation. Since this iterative process helps in development of more realistic system models a variety of computational tools have been developed. In the absence of a common format for representation of models these tools were developed in different formats. As a result these tools became unable to exchange models amongst them, leading to development of SBML, a standard exchange format for computational models of biochemical networks. Here the formats of SBML and one of the commercial tools of systems biology are being compared to study the issues which may arise during conversion between their respective formats. A tool StoP has been developed to convert the format of SBML to the format of the selected tool.
Results: The basic format of SBML representation which is in the form of listings of various elements of a biochemical reaction system differs from the representation of the selected tool which is location oriented. In spite of this difference the various components of biochemical pathways including multiple compartments, global parameters, reactants, products, modifiers, reactions, kinetic formulas and reaction parameters could be converted from the SBML representation to the representation of the selected tool. The MathML representation of the kinetic formula in an SBML model can be converted to the string format of the selected tool. Some features of the SBML are not present in the selected tool. Similarly, the ability of the selected tool to declare parameters for locations, which are global to those locations and their children, is not present in the SBML.
Conclusions: Differences in representations of pathway models may include differences in terminologies, basic architecture, differences in capabilities of software’s, and adoption of different standards for similar things. But the overall similarity of domain of pathway models enables us to interconvert these representations. The selected tool should develop support for unit definitions, events and rules. Development of facility for parameter declaration at compartment level by SBML and facility for function declaration by the selected tool is recommended.
Background: Computational simulation of complex biological networks lies at the heart of systems biology since it can confirm the conclusions drawn by experimental studies of biological networks and guide researchers to produce fresh hypotheses for further experimental validation. Since this iterative process helps in development of more realistic system models a variety of computational tools have been developed. In the absence of a common format for representation of models these tools were developed in different formats. As a result these tools became unable to exchange models amongst them, leading to development of SBML, a standard exchange format for computational models of biochemical networks. Here the formats of SBML and one of the commercial tools of systems biology are being compared to study the issues which may arise during conversion between their respective formats. A tool StoP has been developed to convert the format of SBML to the format of the selected tool.
Results: The basic format of SBML representation which is in the form of listings of various elements of a biochemical reaction system differs from the representation of the selected tool which is location oriented. In spite of this difference the various components of biochemical pathways including multiple compartments, global parameters, reactants, products, modifiers, reactions, kinetic formulas and reaction parameters could be converted from the SBML representation to the representation of the selected tool. The MathML representation of the kinetic formula in an SBML model can be converted to the string format of the selected tool. Some features of the SBML are not present in the selected tool. Similarly, the ability of the selected tool to declare parameters for locations, which are global to those locations and their children, is not present in the SBML.
Conclusions: Differences in representations of pathway models may include differences in terminologies, basic architecture, differences in capabilities of software’s, and adoption of different standards for similar things. But the overall similarity of domain of pathway models enables us to interconvert these representations. The selected tool should develop support for unit definitions, events and rules. Development of facility for parameter declaration at compartment level by SBML and facility for function declaration by the selected tool is recommended.
Självständigt arbete på avancerad nivå (magisterexamen)
20 poäng / 30 hp
Purpose
To determine the prognostic value of
68
Ga-DOTANOC PET/CT in patients with well-differentiated neuroendocrine tumor (NET), and to compare the prognostic value with that of
18
F-FDG PET/CT and ...other conventional clinicopathological prognostic factors.
Methods
Data from 37 consecutive patients (age 46.6 ± 13.5 years, 51 % men) with well-differentiated NET who underwent
68
Ga-DOTANOC PET/CT and
18
F-FDG PET/CT were analyzed. All patients underwent a baseline visit with laboratory and radiological examinations. Clinical and imaging follow-up was performed in all patients. Progression-free survival (PFS) was measured from the date of the first PET/CT scan to the first documentation of progression of disease.
Results
68
Ga-DOTANOC PET/CT was positive in 37 of the 37 patients and
18
F-FDG PET/CT was positive in 21. During follow-up 10 patients (27 %) showed progression of disease and 27 (73 %) showed no progression (24 stable disease, 3 partial response). The median follow-up was 25 months (range 2 – 52 months). Among the variables evaluated none was significantly different between the progressive disease and nonprogressive disease groups, with only SUVmax on
68
Ga-DOTANOC PET/CT being borderline significant (
P
= 0.073). In the univariate analysis for PFS outcome, SUVmax on
68
Ga-DOTANOC PET/CT (HR 0.122, 95 % CI 0.019 – 0.779;
P
= 0.026) and histopathological tumor grade (HR 4.238, 95 % CI 1.058 – 16.976;
P
= 0.041) were found to be associated with PFS. Other factors including age, sex, primary site, Ki-67 index, TNM stage,
18
F-FDG PET/CT status (positive/negative), SUVmax on
18
F-FDG PET/CT and type of treatment were not significant. In multivariable analysis, only SUVmax on
68
Ga-DOTANOC PET/CT was found to be an independent positive predictor of PFS (HR 0.122, 95 % CI 0.019 – 0.779;
P
= 0.026).
Conclusion
SUVmax measured on
68
Ga-DOTANOC PET/CT is an independent, positive prognostic factor in patients with well-differentiated NET and is superior to SUVmax on
18
F-FDG PET/CT and conventional clinicopathological factors for predicting PFS.
To determine the prognostic value of ^sup 68^Ga-DOTANOC PET/CT in patients with well-differentiated neuroendocrine tumor (NET), and to compare the prognostic value with that of ^sup 18^F-FDG PET/CT ...and other conventional clinicopathological prognostic factors. Data from 37 consecutive patients (age 46.6±13.5 years, 51 % men) with well-differentiated NET who underwent ^sup 68^Ga-DOTANOC PET/CT and ^sup 18^F-FDG PET/CT were analyzed. All patients underwent a baseline visit with laboratory and radiological examinations. Clinical and imaging follow-up was performed in all patients. Progression-free survival (PFS) was measured from the date of the first PET/CT scan to the first documentation of progression of disease. ^sup 68^Ga-DOTANOC PET/CT was positive in 37 of the 37 patients and ^sup 18^F-FDG PET/CT was positive in 21. During follow-up 10 patients (27 %) showed progression of disease and 27 (73 %) showed no progression (24 stable disease, 3 partial response). The median follow-up was 25 months (range 2 - 52 months). Among the variables evaluated none was significantly different between the progressive disease and nonprogressive disease groups, with only SUVmax on ^sup 68^Ga-DOTANOC PET/CT being borderline significant (P=0.073). In the univariate analysis for PFS outcome, SUVmax on ^sup 68^Ga-DOTANOC PET/CT (HR 0.122, 95 % CI 0.019 - 0.779; P=0.026) and histopathological tumor grade (HR 4.238, 95 % CI 1.058 - 16.976; P=0.041) were found to be associated with PFS. Other factors including age, sex, primary site, Ki-67 index, TNM stage, ^sup 18^F-FDG PET/CT status (positive/negative), SUVmax on ^sup 18^F-FDG PET/CT and type of treatment were not significant. In multivariable analysis, only SUVmax on ^sup 68^Ga-DOTANOC PET/CT was found to be an independent positive predictor of PFS (HR 0.122, 95 % CI 0.019 - 0.779; P=0.026). SUVmax measured on ^sup 68^Ga-DOTANOC PET/CT is an independent, positive prognostic factor in patients with well-differentiated NET and is superior to SUVmax on ^sup 18^F-FDG PET/CT and conventional clinicopathological factors for predicting PFS.PUBLICATION ABSTRACT
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