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The relationships between the levels of transcripts and the levels of the proteins they encode have not been examined comprehensively in mammals, although previous work in plants and yeast suggest a surprisingly modest correlation. We have examined this issue using a genetic approach in which natural variations were used to perturb both transcript levels and protein levels among inbred strains of mice. We quantified over 5,000 peptides and over 22,000 transcripts in livers of 97 inbred and recombinant inbred strains and focused on the 7,185 most heritable transcripts and 486 most reliable proteins. The transcript levels were quantified by microarray analysis in three replicates and the proteins were quantified by Liquid Chromatography-Mass Spectrometry using O(18)-reference-based isotope labeling approach. We show that the levels of transcripts and proteins correlate significantly for only about half of the genes tested, with an average correlation of 0.27, and the correlations of transcripts and proteins varied depending on the cellular location and biological function of the gene. We examined technical and biological factors that could contribute to the modest correlation. For example, differential splicing clearly affects the analyses for certain genes; but, based on deep sequencing, this does not substantially contribute to the overall estimate of the correlation. We also employed genome-wide association analyses to map loci controlling both transcript and protein levels. Surprisingly, little overlap was observed between the protein- and transcript-mapped loci. We have typed numerous clinically relevant traits among the strains, including adiposity, lipoprotein levels, and tissue parameters. Using correlation analysis, we found that a low number of clinical trait relationships are preserved between the protein and mRNA gene products and that the majority of such relationships are specific to either the protein levels or transcript levels. Surprisingly, transcript levels were more strongly correlated with clinical traits than protein levels. In light of the widespread use of high-throughput technologies in both clinical and basic research, the results presented have practical as well as basic implications.

Bacteriophages and their potential contribution to antimicrobial resistance (AMR) have attracted growing attention in the context of medicine and pharmaceutics. A major objective of the CAMDA challenge is to acquire more knowledge about the relationship between viruses, their hosts and AMR genes in determining if AMR indeed can spread through phages. This study is focused on exploring the relationship and possible dependencies between bacteriophages and AMR based on the data collected from urban environments all over the world. The samples in the data are classified into two categories: high and low, according to the observed levels of AMR genes. The approach used in our analyses consists of several different methods which assess the differential abundance of phages, their diversity across samples, the impact on AMR categories and associations with AMR genes. The relationship between phages, their hosts and AMR is also explored by a Bayesian spatial model, using the AMR category (low vs high) as a factor. We found a higher relative risk for phages known to infect Staphylococcus aureus alone or both S. aureus and Acinetobacter baumannii in the high AMR group, which implies that these phages may have a role in the dissemination of antimicrobial genes.

Oligodendrogliomas are defined by mutation in isocitrate dehydrogenase (NADP(+)) ( IDH )1/2 genes and chromosome 1p/19q codeletion. World Health Organisation diagnosis endorses testing for 1p/19q codeletion to distinguish IDH mutant (Mut) oligodendrogliomas from astrocytomas because these gliomas require different treatments and they have different outcomes. Several methods have been used to identify 1p/19q status; however, these techniques are not routinely available and require substantial infrastructure investment. Two recent studies reported reduced immunostaining for trimethylation at lysine 27 on histone H3 (H3K27me3) in IDH Mut 1p/19q codeleted oligodendroglioma. However, the specificity of H3K27me3 immunostaining in this setting is controversial. Therefore, we developed an easy-to-implement immunohistochemical surrogate for IDH Mut glioma subclassification and evaluated a validated adult glioma cohort. We screened 145 adult glioma cases, consisting of 45 IDH Mut and 1p/19q codeleted oligodendrogliomas, 30 IDH Mut astrocytomas, 16 IDH wild-type (Wt) astrocytomas, and 54 IDH Wt glioblastomas (GBMs). We compared immunostaining with DNA sequencing and fluorescent in situ hybridization analysis and assessed differences in H3K27me3 staining between oligodendroglial and astrocytic lineages and between IDH1-R132H and non-canonical (non-R132H) IDH1/2 Mut oligodendroglioma. A loss of H3K27me3 was observed in 36/40 (90%) of IDH1-R132H Mut oligodendroglioma. In contrast, loss of H3K27me3 was never seen in IDH1-R132L or IDH2-mutated 1p/19q codeleted oligodendrogliomas. IDH Mut astrocytoma, IDH Wt astrocytoma and GBM showed preserved nuclear staining in 87%, 94%, and 91% of cases, respectively. A high recursive partitioning model predicted probability score (0.9835) indicated that the loss of H3K27me3 is frequent to IDH1-R132H Mut oligodendroglioma. Our results demonstrate H3K27me3 immunohistochemical evaluation to be a cost-effective and reliable method for defining 1p/19q codeletion along with IDH1-R132H and ATRX immunostaining, even in the absence of 1p/19q testing.

Gene expression time course data can be used not only to detect differentially expressed genes but also to find temporal associations among genes. The problem of reconstructing generalized logical networks to account for temporal dependencies among genes and environmental stimuli from transcriptomic data is addressed. A network reconstruction algorithm was developed that uses statistical significance as a criterion for network selection to avoid false-positive interactions arising from pure chance. The multinomial hypothesis testing-based network reconstruction allows for explicit specification of the false-positive rate, unique from all extant network inference algorithms. The method is superior to dynamic Bayesian network modeling in a simulation study. Temporal gene expression data from the brains of alcohol-treated mice in an analysis of the molecular response to alcohol are used for modeling. Genes from major neuronal pathways are identified as putative components of the alcohol response mechanism. Nine of these genes have associations with alcohol reported in literature. Several other potentially relevant genes, compatible with independent results from literature mining, may play a role in the response to alcohol. Additional, previously unknown gene interactions were discovered that, subject to biological verification, may offer new clues in the search for the elusive molecular mechanisms of alcoholism.

The relationships between the levels of transcripts and the levels of the proteins they encode have not been examined comprehensively in mammals, although previous work in plants and yeast suggest a surprisingly modest correlation. We have examined this issue using a genetic approach in which natural variations were used to perturb both transcript levels and protein levels among inbred strains of mice. We quantified over 5,000 peptides and over 22,000 transcripts in livers of 97 inbred and recombinant inbred strains and focused on the 7,185 most heritable transcripts and 486 most reliable proteins. The transcript levels were quantified by microarray analysis in three replicates and the proteins were quantified by Liquid Chromatography-Mass Spectrometry using O(18)-reference-based isotope labeling approach. We show that the levels of transcripts and proteins correlate significantly for only about half of the genes tested, with an average correlation of 0.27, and the correlations of transcripts and proteins varied depending on the cellular location and biological function of the gene. We examined technical and biological factors that could contribute to the modest correlation. For example, differential splicing clearly affects the analyses for certain genes; but, based on deep sequencing, this does not substantially contribute to the overall estimate of the correlation. We also employed genome-wide association analyses to map loci controlling both transcript and protein levels. Surprisingly, little overlap was observed between the protein- and transcript-mapped loci. We have typed numerous clinically relevant traits among the strains, including adiposity, lipoprotein levels, and tissue parameters. Using correlation analysis, we found that a low number of clinical trait relationships are preserved between the protein and mRNA gene products and that the majority of such relationships are specific to either the protein levels or transcript levels. Surprisingly, transcript levels were more strongly correlated with clinical traits than protein levels. In light of the widespread use of high-throughput technologies in both clinical and basic research, the results presented have practical as well as basic implications.

Gene set enrichment analysis for analyzing large profiling and screening experiments can reveal unifying biological schemes based on previously accumulated knowledge represented as \"gene sets\". Most of the existing implementations use a fixed fold-change or P value cutoff to generate regulated gene lists. However, the threshold selection in most cases is arbitrary, and has a significant effect on the test outcome and interpretation of the experiment. We developed a new gene set enrichment analysis method, ie, FDR-FET, which dynamically optimizes the threshold choice and improves the sensitivity and selectivity of gene set enrichment analysis. The procedure translates experimental results into a series of regulated gene lists at multiple false discovery rate (FDR) cutoffs, and computes the P value of the overrepresentation of a gene set using a Fisher's exact test (FET) in each of these gene lists. The lowest P value is retained to represent the significance of the gene set. We also implemented improved methods to define a more relevant global reference set for the FET. We demonstrate the validity of the method using a published microarray study of three protease inhibitors of the human immunodeficiency virus and compare the results with those from other popular gene set enrichment analysis algorithms. Our results show that combining FDR with multiple cutoffs allows us to control the error while retaining genes that increase information content. We conclude that FDR-FET can selectively identify significant affected biological processes. Our method can be used for any user-generated gene list in the area of transcriptome, proteome, and other biological and scientific applications.

We show the existence of drifting orbits for certain perturbations of non-convex Hamiltonian systems with several degrees of freedom. These orbits remain in the vicinity of resonant surfaces where the action variables can undergo changes \\(O(1)\\) infinitely often although the size of perturbations \\(O(\\epsilon)\\) can be arbitrarily small. The first drifts occur in a period of time \\(O(1/\\epsilon)\\) and then reoccur with frequencies independent of \\(\\epsilon\\). We also perform numerical simulations to compare the effects of two conditions for instability in two four-dimensional examples with random parameters.

This thesis uses Markov chain decomposition techniques to extract informative traits from arterial pressure recordings of hypertensive rats and humans. The feedback mechanisms, such as baroreceptor and renin angiotensin system, acting on blood pressure are quantified by the Cheeger ratios of specific levels and patterns of blood pressure. Statistical properties of Cheeger ratios and related quantities are established in a central limit theorem. The results show the dependence of the error on the spectral gap, length of recordings and relative frequencies of the estimated sets. These quantities are shown to be statistically different between normal and hypertensive rats, and heritable for the human population. New lower and upper bounds for the convergence rate (spectral gap) of the chain are derived via decomposition techniques. Linkage analysis and multi-trait linkage are used to find QTL for the estimated Cheeger ratios. Principal components are used to reduce the number of tests and increase the linkage power. Conditions for maximizing the power are derived and applied for humans.