The molecular basis of maternal control in seed development : genetic and molecular analysis of maternal effects in seed development: molecular mapping of cap2 and expression and functional analyses of AtLDC and AtHD2C

The female gametophyte of higher plants gives rise to the diploid embryo and the triploid endosperm which develop to produce the mature seed. Seed development is a concerted interplay of the embryo, endosperm and the surrounding diploid maternal tissue. In addition, it is highly dependent on the contribution from genetic programs executed in the gametophytic generations. What role the gametophytic maternal factors play in this process is still largely unknown. This thesis describes two approaches to identify novel genes involved in seed development. A forward genetic approach addresses the molecular nature of the maternal effect mutant capulet2 (cap2) by molecular mapping and a reverse genetics approach analyze the role in seed development of candidate genes from a promoter trap screen. The capulet2 gametophytic maternal-effect mutant was found in a linkage based screen preformed to identify gametophytic mutants in Arabidopsis (Grini et al., 1999). cap2 embryo and endosperm development is blocked at a very early stages, and heterozygous plants display a 50% reduced seed set. To investigate the molecular nature of the CAP2 gene, a map-based cloning approach was performed. Using PCR-based molecular markers the cap2 mutation was mapped to a genetic interval of 4238 basepairs, on the tip of the right arm of chromosome 1. This interval spanned parts of two genes, one involved in monoterpenoid biosynthesis and the other putatively involved in triterpenoid biosynthesis. Neither of these two genes could be verified to be responsible for the cap2 phenotype by complementation analysis. However the mapping interval of cap2 was reduced from more than 1 Mb to less than 100 kb. In a reverse genetic approach two candidate genes (AtHD2C and AtLDC) selected from a collection of promoter trap lines were analyzed to elucidate their role in seed development. Reporter gene expression studies, expression analysis, and the analysis of T-DNA insertion lines revealed that the candidate genes were expressed in the seed, but also in other organs. The promoter reporter line of AtLDC was found to have a similar but in some respects also different expression patterns in the seed than the original promoter trap line. The AtHD2C gene was found to be redundant as no phenotype could be observed in knock out alleles of the gene

Differential in vivo tumorigenicity of distinct subpopulations from a luminal-like breast cancer xenograft

Intratumor heterogeneity caused by genetic, phenotypic or functional differences between cancer cell subpopulations is a considerable clinical challenge. Understanding subpopulation dynamics is therefore central for both optimization of existing therapy and for development of new treatment. The aim of this study was to isolate subpopulations from a primary tumor and by comparing molecular characteristics of these subpopulations, find explanations to their differing tumorigenicity. Cell subpopulations from two patient derived in vivo models of primary breast cancer, ER+ and ER-, were identified. EpCAM+ cells from the ER+ model gave rise to tumors independently of stroma cell support. The tumorigenic fraction was further divided based on SSEA-4 and CD24 expression. Both markers were expressed in ER+ breast cancer biopsies. FAC-sorted cells based on EpCAM, SSEA-4 and CD24 expression were subsequently tested for differences in functionality by in vivo tumorigenicity assay. Three out of four subpopulations of cells were tumorigenic and showed variable ability to recapitulate the marker expression of the original tumor. Whole genome expression analysis of the sorted populations disclosed high similarity in the transcriptional profiles between the tumorigenic populations. Comparing the non-tumorigenic vs the tumorigenic populations, 44 transcripts were, however, significantly differentially expressed. A subset of these, 26 identified and named genes, highly expressed in the non-tumorigenic population, predicted longer overall survival (N = 737, p<0.0001) and distant metastasis free survival (DMFS) (N = 1379, p<0.0001) when performing Kaplan-Meier survival analysis using the GOBO online database. The 26 gene set correlated with longer DMFS in multiple breast cancer subgroups. Copy number profiling revealed no aberrations that could explain the observed differences in tumorigenicity. This study emphasizes the functional variability among cell populations that are otherwise genomically similar, and that the risk of breast cancer recurrence can only be eliminated if the tumorigenic abilities in multiple cancer cell subpopulations are inhibited

PREDICT: a method for inferring novel drug indications with application to personalized medicine

The authors present a new method, PREDICT, for the large-scale prediction of drug indications, and demonstrate its use on both approved drugs and novel molecules. They also provide a proof-of-concept for its potential utility in predicting patient-specific medications

Front Pharmacol

Drug misuse may happen when patients do not follow the prescriptions and do actions which lead to potentially harmful situations, such as intakes of incorrect dosage (overuse or underuse) or drug use for indications different from those prescribed. Although such situations are dangerous, patients usually do not report the misuse of drugs to their physicians. Hence, other sources of information are necessary for studying these issues. We assume that online health fora can provide such information and propose to exploit them. The general purpose of our work is the automatic detection and classification of drug misuses by analysing user-generated data in French social media. To this end, we propose a multi-step method, the main steps of which are: (1) indexing of messages with extended vocabulary adapted to social media writing; (2) creation of typology of drug misuses; and (3) automatic classification of messages according to whether they contain drug misuses or not. We present the results obtained at different steps and discuss them. The proposed method permit to detect the misuses with up to 0.773 F-measure

Aldehyde Dehydrogenase (ALDH) Activity Does Not Select for Cells with Enhanced Aggressive Properties in Malignant Melanoma

Malignant melanoma is an exceptionally aggressive, drug-resistant and heterogeneous cancer. Recently it has been shown that melanoma cells with high clonogenic and tumourigenic abilities are common, but markers distinguishing such cells from cells lacking these abilities have not been identified. There is therefore no definite evidence that an exclusive cell subpopulation, i.e. cancer stem cells (CSC), exists in malignant melanoma. Rather, it is suggested that multiple cell populations are implicated in initiation and progression of the disease, making it of importance to identify subpopulations with elevated aggressive properties.. Furthermore, both subpopulations showed similar sensitivity to the anti-melanoma drugs, dacarbazine and lexatumumab.These findings suggest that ALDH does not distinguish tumour-initiating and/or therapy-resistant cells, implying that the ALDH phenotype is not associated with more-aggressive subpopulations in malignant melanoma, and arguing against ALDH as a “universal” marker. Besides, it was shown that the ability to reestablish tumour heterogeneity is not necessarily linked to the more aggressive phenotype

Co-morbidity and drug treatment in Alzheimer's disease. A cross sectional study of participants in the Dementia Study in Northern Norway

Inappropriate medical treatment of co-morbidities in Alzheimer’s disease (AD) is an increasing concern in geriatric medicine. The objective of this study was to compare current drug use related to co-morbidity between individuals with a recent diagnosis of AD and a cognitively healthy control group in a population based clinical trial in Northern Norway. Setting: Nine rural municipalities with 70 000 inhabitants in Northern Norway. Participants: Participants with and without AD recruited in general practice and by population based screening. 187 participants with a recent diagnosis of AD were recruited among community dwellers. Of 791 respondents without cognitive symptoms, 500 were randomly selected and invited to further clinical and cognitive testing. The final control group consisted of 200 cognitively healthy individuals from the same municipalities. Demographic characteristics, data on medical history and current medication were included, and a physical and cognitive examination was performed. The statistical analyses were carried out by independent sample t-test, chi-square, ANCOVA and logistic regression. A co-morbidity score was significantly higher in AD participants compared to controls. The mean number of drugs was higher for AD participants compared to controls (5.1 ± 3.6 and 2.9 ± 2.4 respectively, p < 0.001 age and gender adjusted), also when adjusted for co-morbidity. AD participants used significantly more anticholinergic, sedative and antidepressant drugs. For nursing home residents with AD the mean number of drugs was significantly higher compared to AD participants living at home (6.9 ± 3.9 and 4.5 ± 3.3, respectively, p < 0.001). AD participants were treated with a significantly higher number of drugs as compared to cognitively healthy controls, even after adjustment for co-morbidity. An inappropriate use of anticholinergic and sedative drugs was identified, especially among nursing home residents with AD. The drug burden and the increased risk of adverse reactions among individuals suffering from AD need more attention from prescribing doctors

Machine learning in the aviation industry and the potential of using air traffic as a real-time indicator of GDP : a study of how useful machine learning is to predict Norwegian air traffic and investigating the causal relationship between air traffic and GDP

Travel by air is an essential part of both the Norwegian society and its infrastructure, where Norway has one of the highest number of flights per capita in Europe. Nonetheless, the aviation industry is characterized by high uncertainty, with the Covid-19 pandemic being the most recent one. This thesis has sought to investigate the use of machine learning in the Norwegian aviation industry and how the number of air passengers potentially can be used as a real-time indicator of GDP. Therefore, the thesis has been divided into two parts. The first part has aimed to use machine learning to predict the number of domestic and total passengers per capita in Norway. More precisely, we applied the methods OLS, elastic net, and random forest. The purpose of the second part has been to investigate the causal relationship between air passengers and GDP by conducting a strict linear Granger causality test. We particularly questioned whether air passengers could be used as a real-time indicator of GDP. The findings suggest that machine learning is applicable for predicting the number of air passengers per capita in Norway, where elastic net yield the best results. In relation to the second part of the thesis, the findings reveal a causal relationship running from air passengers to GDP. Consequently, we find that there is a potential of using the number of air passengers as a real-time indicator of GDP in Norway. Keywords – Machine learning, the Norwegian aviation industry, economic growth, causality, real-time indicatorsnhhma