Project description:BackgroundNeighboring nucleotides exert a striking influence on mutation, with the hypermutability of CpG dinucleotides in many genomes being an exemplar. Among the approaches employed to measure the relative importance of sequence neighbors on molecular evolution have been continuous-time Markov process models for substitutions that treat sequences as a series of independent tuples. The most widely used examples are the codon substitution models. We evaluated the suitability of derivatives of the nucleotide frequency weighted (hereafter NF) and tuple frequency weighted (hereafter TF) models for measuring sequence context dependent substitution. Critical properties we address are their relationships to an independent nucleotide process and the robustness of parameter estimation to changes in sequence composition. We then consider the impact on inference concerning dinucleotide substitution processes from application of these two forms to intron sequence alignments from primates.ResultsWe prove that the NF form always nests the independent nucleotide process and that this is not true for the TF form. As a consequence, using TF to study context effects can be misleading, which is shown by both theoretical calculations and simulations. We describe a simple example where a context parameter estimated under TF is confounded with composition terms unless all sequence states are equi-frequent. We illustrate this for the dinucleotide case by simulation under a nucleotide model, showing that the TF form identifies a CpG effect when none exists. Our analysis of primate introns revealed that the effect of nucleotide neighbors is over-estimated under TF compared with NF. Parameter estimates for a number of contexts are also strikingly discordant between the two model forms.ConclusionOur results establish that the NF form should be used for analysis of independent-tuple context dependent processes. Although neighboring effects in general are still important, prominent influences such as the elevated CpG transversion rate previously identified using the TF form are an artifact. Our results further suggest as few as 5 parameters may account for approximately 85% of neighboring nucleotide influence.

Project description:The mouse is a useful preclinical species for evaluating disease etiology due to the availability of a wide variety of genetically modified strains and the ability to perform disease-modifying manipulations. In order to establish an atrial filtration (AF) model in our laboratory, we profiled several commonly used murine AF models. We initially evaluated a pharmacological model of acute carbachol (CCh) treatment plus atrial burst pacing in C57BL/6 mice. In an effort to observe micro-reentrant circuits indicative of authentic AF, we employed optical mapping imaging in isolated mouse hearts. While CCh reduced atrial refractoriness and increased atrial tachyarrhythmia vulnerability, the left atrial (LA) excitation patterns were rather regular without reentrant circuits or wavelets. Therefore, the atrial tachyarrhythmia resembled high frequency atrial flutter, not typical AF per se. We next examined both a chronic angiotensin II (Ang II) infusion model and the surgical model of transverse aortic constriction (TAC), which have both been reported to induce atrial and ventricular structural changes that serve as a substrates for micro-reentrant AF. Although we observed some extent of atrial remodeling such as fibrosis or enlarged LA diameter, burst pacing-induced atrial tachyarrhythmia vulnerability did not differ from control mice in either model. This again suggested that an AF-like pathophysiology is difficult to demonstrate in the mouse. To continue searching for a valid murine AF model, we studied mice with a cardiac-specific deficiency (KO) in liver kinase B1 (Cardiac-LKB1), which has been reported to exhibit spontaneous AF. Indeed, the electrocardiograms (ECG) of conscious Cardiac-LKB1 KO mice exhibited no P waves and had irregular RR intervals, which are characteristics of AF. Histological evaluation of Cardiac-LKB1 KO mice revealed dilated and fibrotic atria, again consistent with AF. However, atrial electrograms and optical mapping revealed that electrical activity was limited to the sino-atrial node area with no electrical conduction into the atrial myocardium beyond. Thus, Cardiac-LKB1 KO mice have severe atrial myopathy or atrial standstill, but not AF. In summary, the atrial tachyarrhythmias we observed in the four murine models were distinct from typical human AF, which often exhibits micro- or macro-reentrant atrial circuits. Our results suggest that the four murine AF models we examined may not reflect human AF well, and raise a cautionary note for use of those murine models to study AF.

Project description:Background: Despite advances in therapeutics, outcomes for hepatocellular carcinoma (HCC) remain poor and there is an urgent need for efficacious systemic therapy. Unfortunately, drugs that are successful in preclinical studies often fail in the clinical setting, and we hypothesize that this is due to functional differences between primary tumors and commonly used preclinical models. In this study, we attempt to answer this question by comparing tumor morphology and gene expression profiles between primary tumors, xenografts and HCC cell lines. Methods: Hep G2 cell lines and tumor cells from patient tumor explants were subcutaneously (ectopically) injected into the flank and orthotopically into liver parenchyma of Mus Musculus SCID mice. The mice were euthanized after two weeks. RNA was extracted from the tumors, and gene expression profiling was performed using the Gene Chip Human Genome U133 Plus 2.0. Principal component analyses (PCA) and construction of dendrograms were conducted using Partek genomics suite. Results: PCA showed that the commonly used HepG2 cell line model and its xenograft counterparts were vastly different from all fresh, primary tumors. Expression profiles of primary tumors were also significantly divergent from their counterpart patient-derived xenograft (PDX) models, regardless of the site of implantation. Xenografts from the same primary tumors were more likely to cluster together regardless of site of implantation, although heat maps showed distinct differences in gene expression profiles between orthotopic and ectopic models. Conclusions: The data presented here challenges the utility of routinely used preclinical models. Models using HepG2 were vastly different from primary tumors and PDXs, suggesting that this is not clinically representative. Surprisingly, site of implantation (orthotopic versus ectopic) resulted in limited impact on gene expression profiles, and in both scenarios xenografts differed significantly from the original primary tumors, challenging the long-held notion that orthotopic PDX model is the gold standard preclinical model for HCC.

Project description:BackgroundEcological communities tend to be spatially structured due to environmental gradients and/or spatially contagious processes such as growth, dispersion and species interactions. Data transformation followed by usage of algorithms such as Redundancy Analysis (RDA) is a fairly common approach in studies searching for spatial structure in ecological communities, despite recent suggestions advocating the use of Generalized Linear Models (GLMs). Here, we compared the performance of GLMs and RDA in describing spatial structure in ecological community composition data. We simulated realistic presence/absence data typical of many β-diversity studies. For model selection we used standard methods commonly used in most studies involving RDA and GLMs.MethodsWe simulated communities with known spatial structure, based on three real spatial community presence/absence datasets (one terrestrial, one marine and one freshwater). We used spatial eigenvectors as explanatory variables. We varied the number of non-zero coefficients of the spatial variables, and the spatial scales with which these coefficients were associated and then compared the performance of GLMs and RDA frameworks to correctly retrieve the spatial patterns contained in the simulated communities. We used two different methods for model selection, Forward Selection (FW) for RDA and the Akaike Information Criterion (AIC) for GLMs. The performance of each method was assessed by scoring overall accuracy as the proportion of variables whose inclusion/exclusion status was correct, and by distinguishing which kind of error was observed for each method. We also assessed whether errors in variable selection could affect the interpretation of spatial structure.ResultsOverall GLM with AIC-based model selection (GLM/AIC) performed better than RDA/FW in selecting spatial explanatory variables, although under some simulations the methods performed similarly. In general, RDA/FW performed unpredictably, often retaining too many explanatory variables and selecting variables associated with incorrect spatial scales. The spatial scale of the pattern had a negligible effect on GLM/AIC performance but consistently affected RDA's error rates under almost all scenarios.ConclusionWe encourage the use of GLM/AIC for studies searching for spatial drivers of species presence/absence patterns, since this framework outperformed RDA/FW in situations most likely to be found in natural communities. It is likely that such recommendations might extend to other types of explanatory variables.

Project description:BackgroundDespite advances in therapeutics, outcomes for hepatocellular carcinoma (HCC) remain poor and there is an urgent need for efficacious systemic therapy. Unfortunately, drugs that are successful in preclinical studies often fail in the clinical setting, and we hypothesize that this is due to functional differences between primary tumors and commonly used preclinical models. In this study, we attempt to answer this question by comparing tumor morphology and gene expression profiles between primary tumors, xenografts and HCC cell lines.MethodsHep G2 cell lines and tumor cells from patient tumor explants were subcutaneously (ectopically) injected into the flank and orthotopically into liver parenchyma of Mus Musculus SCID mice. The mice were euthanized after two weeks. RNA was extracted from the tumors, and gene expression profiling was performed using the Gene Chip Human Genome U133 Plus 2.0. Principal component analyses (PCA) and construction of dendrograms were conducted using Partek genomics suite.ResultsPCA showed that the commonly used HepG2 cell line model and its xenograft counterparts were vastly different from all fresh primary tumors. Expression profiles of primary tumors were also significantly divergent from their counterpart patient-derived xenograft (PDX) models, regardless of the site of implantation. Xenografts from the same primary tumors were more likely to cluster together regardless of site of implantation, although heat maps showed distinct differences in gene expression profiles between orthotopic and ectopic models.ConclusionsThe data presented here challenges the utility of routinely used preclinical models. Models using HepG2 were vastly different from primary tumors and PDXs, suggesting that this is not clinically representative. Surprisingly, site of implantation (orthotopic versus ectopic) resulted in limited impact on gene expression profiles, and in both scenarios xenografts differed significantly from the original primary tumors, challenging the long-held notion that orthotopic PDX model is the gold standard preclinical model for HCC.

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Pitfalls of the most commonly used models of context dependent substitution.

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