Bayesian Inference in Large-scale Problems

Many modern applications fall into the category of "large-scale" statistical problems, in which both the number of observations n and the number of features or parameters p may be large. Many existing methods focus on point estimation, despite the continued relevance of uncertainty quantification in the sciences, where the number of parameters to estimate often exceeds the sample size, despite huge increases in the value of n typically seen in many fields. Thus, the tendency in some areas of industry to dispense with traditional statistical analysis on the basis that "n=all" is of little relevance outside of certain narrow applications. The main result of the Big Data revolution in most fields has instead been to make computation much harder without reducing the importance of uncertainty quantification. Bayesian methods excel at uncertainty quantification, but often scale poorly relative to alternatives. This conflict between the statistical advantages of Bayesian procedures and their substantial computational disadvantages is perhaps the greatest challenge facing modern Bayesian statistics, and is the primary motivation for the work presented here.

Two general strategies for scaling Bayesian inference are considered. The first is the development of methods that lend themselves to faster computation, and the second is design and characterization of computational algorithms that scale better in n or p. In the first instance, the focus is on joint inference outside of the standard problem of multivariate continuous data that has been a major focus of previous theoretical work in this area. In the second area, we pursue strategies for improving the speed of Markov chain Monte Carlo algorithms, and characterizing their performance in large-scale settings. Throughout, the focus is on rigorous theoretical evaluation combined with empirical demonstrations of performance and concordance with the theory.

One topic we consider is modeling the joint distribution of multivariate categorical data, often summarized in a contingency table. Contingency table analysis routinely relies on log-linear models, with latent structure analysis providing a common alternative. Latent structure models lead to a reduced rank tensor factorization of the probability mass function for multivariate categorical data, while log-linear models achieve dimensionality reduction through sparsity. Little is known about the relationship between these notions of dimensionality reduction in the two paradigms. In Chapter 2, we derive several results relating the support of a log-linear model to nonnegative ranks of the associated probability tensor. Motivated by these findings, we propose a new collapsed Tucker class of tensor decompositions, which bridge existing PARAFAC and Tucker decompositions, providing a more flexible framework for parsimoniously characterizing multivariate categorical data. Taking a Bayesian approach to inference, we illustrate empirical advantages of the new decompositions.

Latent class models for the joint distribution of multivariate categorical, such as the PARAFAC decomposition, data play an important role in the analysis of population structure. In this context, the number of latent classes is interpreted as the number of genetically distinct subpopulations of an organism, an important factor in the analysis of evolutionary processes and conservation status. Existing methods focus on point estimates of the number of subpopulations, and lack robust uncertainty quantification. Moreover, whether the number of latent classes in these models is even an identified parameter is an open question. In Chapter 3, we show that when the model is properly specified, the correct number of subpopulations can be recovered almost surely. We then propose an alternative method for estimating the number of latent subpopulations that provides good quantification of uncertainty, and provide a simple procedure for verifying that the proposed method is consistent for the number of subpopulations. The performance of the model in estimating the number of subpopulations and other common population structure inference problems is assessed in simulations and a real data application.

In contingency table analysis, sparse data is frequently encountered for even modest numbers of variables, resulting in non-existence of maximum likelihood estimates. A common solution is to obtain regularized estimates of the parameters of a log-linear model. Bayesian methods provide a coherent approach to regularization, but are often computationally intensive. Conjugate priors ease computational demands, but the conjugate Diaconis--Ylvisaker priors for the parameters of log-linear models do not give rise to closed form credible regions, complicating posterior inference. In Chapter 4 we derive the optimal Gaussian approximation to the posterior for log-linear models with Diaconis--Ylvisaker priors, and provide convergence rate and finite-sample bounds for the Kullback-Leibler divergence between the exact posterior and the optimal Gaussian approximation. We demonstrate empirically in simulations and a real data application that the approximation is highly accurate, even in relatively small samples. The proposed approximation provides a computationally scalable and principled approach to regularized estimation and approximate Bayesian inference for log-linear models.

Another challenging and somewhat non-standard joint modeling problem is inference on tail dependence in stochastic processes. In applications where extreme dependence is of interest, data are almost always time-indexed. Existing methods for inference and modeling in this setting often cluster extreme events or choose window sizes with the goal of preserving temporal information. In Chapter 5, we propose an alternative paradigm for inference on tail dependence in stochastic processes with arbitrary temporal dependence structure in the extremes, based on the idea that the information on strength of tail dependence and the temporal structure in this dependence are both encoded in waiting times between exceedances of high thresholds. We construct a class of time-indexed stochastic processes with tail dependence obtained by endowing the support points in de Haan's spectral representation of max-stable processes with velocities and lifetimes. We extend Smith's model to these max-stable velocity processes and obtain the distribution of waiting times between extreme events at multiple locations. Motivated by this result, a new definition of tail dependence is proposed that is a function of the distribution of waiting times between threshold exceedances, and an inferential framework is constructed for estimating the strength of extremal dependence and quantifying uncertainty in this paradigm. The method is applied to climatological, financial, and electrophysiology data.

The remainder of this thesis focuses on posterior computation by Markov chain Monte Carlo. The Markov Chain Monte Carlo method is the dominant paradigm for posterior computation in Bayesian analysis. It has long been common to control computation time by making approximations to the Markov transition kernel. Comparatively little attention has been paid to convergence and estimation error in these approximating Markov Chains. In Chapter 6, we propose a framework for assessing when to use approximations in MCMC algorithms, and how much error in the transition kernel should be tolerated to obtain optimal estimation performance with respect to a specified loss function and computational budget. The results require only ergodicity of the exact kernel and control of the kernel approximation accuracy. The theoretical framework is applied to approximations based on random subsets of data, low-rank approximations of Gaussian processes, and a novel approximating Markov chain for discrete mixture models.

Data augmentation Gibbs samplers are arguably the most popular class of algorithm for approximately sampling from the posterior distribution for the parameters of generalized linear models. The truncated Normal and Polya-Gamma data augmentation samplers are standard examples for probit and logit links, respectively. Motivated by an important problem in quantitative advertising, in Chapter 7 we consider the application of these algorithms to modeling rare events. We show that when the sample size is large but the observed number of successes is small, these data augmentation samplers mix very slowly, with a spectral gap that converges to zero at a rate at least proportional to the reciprocal of the square root of the sample size up to a log factor. In simulation studies, moderate sample sizes result in high autocorrelations and small effective sample sizes. Similar empirical results are observed for related data augmentation samplers for multinomial logit and probit models. When applied to a real quantitative advertising dataset, the data augmentation samplers mix very poorly. Conversely, Hamiltonian Monte Carlo and a type of independence chain Metropolis algorithm show good mixing on the same dataset.

The Role of TET Proteins in the Epigenetic Regulation of Neural Gene Expression and Behavior

Understanding how genes affect behavior is critical to develop precise therapies for human behavioral disorders. The ability to investigate the relationship between genes and behavior has been greatly advanced over the last few decades due to progress in gene-targeting technology. Recently, the Tet gene family was discovered and implicated in epigenetic modification of DNA methylation by converting 5-methylcytosine to 5-hydroxymethylcytosine (5hmC). 5hmC and its catalysts, the TET proteins, are highly abundant in the postnatal brain but with unclear functions. To investigate their neural functions, we generated new lines of Tet1 and Tet3 mutant mice using a gene targeting approach. We designed both mutations to cause a frameshift by deleting the largest coding exon of Tet1 (Tet1Δe4) and the catalytic domain of Tet3 (Tet3Δe7-9). As Tet1 is also highly expressed in embryonic stem cells (ESCs), we generated Tet1 homozygous deleted ESCs through sequential targeting to compare the function of Tet1 in the brain to its role in ESCs. To test our hypothesis that TET proteins epigenetically regulate transcription of key neural genes important for normal brain function, we examined transcriptional and epigenetic differences in the Tet1Δe4 mouse brain. The oxytocin receptor (OXTR), a neural gene implicated in social behaviors, is suggested to be epigenetically regulated by an unknown mechanism. Interestingly, several human studies have found associations between OXTR DNA hypermethylation and a wide spectrum of behavioral traits and neuropsychiatric disorders including autism spectrum disorders. Here we report the first evidence for an epigenetic mechanism of Oxtr transcription as expression of Oxtr is reduced in the brains of Tet1Δe4-/- mice. Likewise, the CpG island overlapping the promoter of Oxtr is hypermethylated during early embryonic development and persists into adulthood. We also discovered altered histone modifications at the hypermethylated regions, indicating the loss of TET1 has broad effects on the chromatin structure at Oxtr. Unexpectedly, we discovered an array of novel mRNA isoforms of Oxtr that are selectively reduced in Tet1Δe4-/- mice. Additionally, Tet1Δe4-/- mice display increased agonistic behaviors and impaired maternal care and short-term memory. Our findings support a novel role for TET1 in regulating Oxtr expression by preventing DNA hypermethylation and implicate TET1 in social behaviors, offering novel insight into Oxtr epigenetic regulation and its role in neuropsychiatric disorders.

Bayesian Emulation for Sequential Modeling, Inference and Decision Analysis

The advances in three related areas of state-space modeling, sequential Bayesian learning, and decision analysis are addressed, with the statistical challenges of scalability and associated dynamic sparsity. The key theme that ties the three areas is Bayesian model emulation: solving challenging analysis/computational problems using creative model emulators. This idea defines theoretical and applied advances in non-linear, non-Gaussian state-space modeling, dynamic sparsity, decision analysis and statistical computation, across linked contexts of multivariate time series and dynamic networks studies. Examples and applications in financial time series and portfolio analysis, macroeconomics and internet studies from computational advertising demonstrate the utility of the core methodological innovations.

Chapter 1 summarizes the three areas/problems and the key idea of emulating in those areas. Chapter 2 discusses the sequential analysis of latent threshold models with use of emulating models that allows for analytical filtering to enhance the efficiency of posterior sampling. Chapter 3 examines the emulator model in decision analysis, or the synthetic model, that is equivalent to the loss function in the original minimization problem, and shows its performance in the context of sequential portfolio optimization. Chapter 4 describes the method for modeling the steaming data of counts observed on a large network that relies on emulating the whole, dependent network model by independent, conjugate sub-models customized to each set of flow. Chapter 5 reviews those advances and makes the concluding remarks.

In vivo inhibition of elevated myocardial beta-adrenergic receptor kinase activity in hybrid transgenic mice restores normal beta-adrenergic signaling and function.

BACKGROUND: The clinical syndrome of heart failure (HF) is characterized by an impaired cardiac beta-adrenergic receptor (betaAR) system, which is critical in the regulation of myocardial function. Expression of the betaAR kinase (betaARK1), which phosphorylates and uncouples betaARs, is elevated in human HF; this likely contributes to the abnormal betaAR responsiveness that occurs with beta-agonist administration. We previously showed that transgenic mice with increased myocardial betaARK1 expression had impaired cardiac function in vivo and that inhibiting endogenous betaARK1 activity in the heart led to enhanced myocardial function. METHODS AND RESULTS: We created hybrid transgenic mice with cardiac-specific concomitant overexpression of both betaARK1 and an inhibitor of betaARK1 activity to study the feasibility and functional consequences of the inhibition of elevated betaARK1 activity similar to that present in human HF. Transgenic mice with myocardial overexpression of betaARK1 (3 to 5-fold) have a blunted in vivo contractile response to isoproterenol when compared with non-transgenic control mice. In the hybrid transgenic mice, although myocardial betaARK1 levels remained elevated due to transgene expression, in vitro betaARK1 activity returned to control levels and the percentage of betaARs in the high-affinity state increased to normal wild-type levels. Furthermore, the in vivo left ventricular contractile response to betaAR stimulation was restored to normal in the hybrid double-transgenic mice. CONCLUSIONS: Novel hybrid transgenic mice can be created with concomitant cardiac-specific overexpression of 2 independent transgenes with opposing actions. Elevated myocardial betaARK1 in transgenic mouse hearts (to levels seen in human HF) can be inhibited in vivo by a peptide that can prevent agonist-stimulated desensitization of cardiac betaARs. This may represent a novel strategy to improve myocardial function in the setting of compromised heart function.

Huntingtin is required for normal excitatory synapse development in cortical and striatal circuits.

Huntington's disease (HD) is a neurodegenerative disease caused by the expansion of a poly-glutamine (poly-Q) stretch in the huntingtin (Htt) protein. Gain-of-function effects of mutant Htt have been extensively investigated as the major driver of neurodegeneration in HD. However, loss-of-function effects of poly-Q mutations recently emerged as potential drivers of disease pathophysiology. Early synaptic problems in the excitatory cortical and striatal connections have been reported in HD, but the role of Htt protein in synaptic connectivity was unknown. Therefore, we investigated the role of Htt in synaptic connectivity in vivo by conditionally silencing Htt in the developing mouse cortex. When cortical Htt function was silenced, cortical and striatal excitatory synapses formed and matured at an accelerated pace through postnatal day 21 (P21). This exuberant synaptic connectivity was lost over time in the cortex, resulting in the deterioration of synapses by 5 weeks. Synaptic decline in the cortex was accompanied with layer- and region-specific reactive gliosis without cell loss. To determine whether the disease-causing poly-Q mutation in Htt affects synapse development, we next investigated the synaptic connectivity in a full-length knock-in mouse model of HD, the zQ175 mouse. Similar to the cortical conditional knock-outs, we found excessive excitatory synapse formation and maturation in the cortices of P21 zQ175, which was lost by 5 weeks. Together, our findings reveal that cortical Htt is required for the correct establishment of cortical and striatal excitatory circuits, and this function of Htt is lost when the mutant Htt is present.

Preservation of myocardial beta-adrenergic receptor signaling delays the development of heart failure after myocardial infarction.

When the heart fails, there is often a constellation of biochemical alterations of the beta-adrenergic receptor (betaAR) signaling system, leading to the loss of cardiac inotropic reserve. betaAR down-regulation and functional uncoupling are mediated through enhanced activity of the betaAR kinase (betaARK1), the expression of which is increased in ischemic and failing myocardium. These changes are widely viewed as representing an adaptive mechanism, which protects the heart against chronic activation. In this study, we demonstrate, using in vivo intracoronary adenoviral-mediated gene delivery of a peptide inhibitor of betaARK1 (betaARKct), that the desensitization and down-regulation of betaARs seen in the failing heart may actually be maladaptive. In a rabbit model of heart failure induced by myocardial infarction, which recapitulates the biochemical betaAR abnormalities seen in human heart failure, delivery of the betaARKct transgene at the time of myocardial infarction prevents the rise in betaARK1 activity and expression and thereby maintains betaAR density and signaling at normal levels. Rather than leading to deleterious effects, cardiac function is improved, and the development of heart failure is delayed. These results appear to challenge the notion that dampening of betaAR signaling in the failing heart is protective, and they may lead to novel therapeutic strategies to treat heart disease via inhibition of betaARK1 and preservation of myocardial betaAR function.

Metabolism Regulates the Fate and Function of T Lymphocytes

Proper balancing of the activities of metabolic pathways to meet the challenge of providing necessary products for biosynthetic and energy demands of the cell is a key requirement for maintaining cell viability and allowing for cell proliferation. Cell metabolism has been found to play a crucial role in numerous cell settings, including in the cells of the immune system, where a successful immune response requires rapid proliferation and successful clearance of dangerous pathogens followed by resolution of the immune response. Additionally, it is now well known that cell metabolism is markedly altered from normal cells in the setting of cancer, where tumor cells rapidly and persistently proliferate. In both settings, alterations to the metabolic profile of the cells play important roles in promoting cell proliferation and survival.

It has long been known that many types of tumor cells and actively proliferating immune cells adopt a metabolic phenotype of aerobic glycolysis, whereby the cell, even under normoxic conditions, imports large amounts of glucose and fluxes it through the glycolytic pathway and produces lactate. However, the metabolic programs utilized by various immune cell subsets have only recently begun to be explored in detail, and the metabolic features and pathways influencing cell metabolism in tumor cells in vivo have not been studied in detail. The work presented here examines the role of metabolism in regulating the function of an important subset of the immune system, the regulatory T cell (Treg) and the role and regulation of metabolism in the context of malignant T cell acute lymphoblastic leukemia (T-ALL). We show that Treg cells, in order to properly function to suppress auto-inflammatory disease, adopt a metabolic program that is characterized by oxidative metabolism and active suppression of anabolic signaling and metabolic pathways. We found that the transcription factor FoxP3, which is highly expressed in Treg cells, drives this phenotype. Perturbing the metabolic phenotype of Treg cells by enforcing increased glycolysis or driving proliferation and anabolic signaling through inflammatory signaling pathways results in a reduction in suppressive function of Tregs.

In our studies focused on the metabolism of T-ALL, we observed that while T-ALL cells use and require aerobic glycolysis, the glycolytic metabolism of T-ALL is restrained compared to that of an antigen activated T cell. The metabolism of T-ALL is instead balanced, with mitochondrial metabolism also being increased. We observed that the pro-anabolic growth mTORC1 signaling pathway was limited in primary T-ALL cells as a result of AMPK pathway activity. AMPK pathway signaling was elevated as a result of oncogene induced metabolic stress. AMPK played a key role in the regulation of T-ALL cell metabolism, as genetic deletion of AMPK in an in vivo murine model of T-ALL resulted in increased glycolysis and anabolic metabolism, yet paradoxically increased cell death and increased mouse survival time. AMPK acts to promote mitochondrial oxidative metabolism in T-ALL through the regulation of Complex I activity, and loss of AMPK reduced mitochondrial oxidative metabolism and resulted in increased metabolic stress. Confirming a role for mitochondrial metabolism in T-ALL, we observed that the direct pharmacological inhibition of Complex I also resulted in a rapid loss of T-ALL cell viability in vitro and in vivo. Taken together, this work establishes an important role for AMPK to both balance the metabolic pathways utilized by T-ALL to allow for cell proliferation and to also promote tumor cell viability by controlling metabolic stress.

Overall, this work demonstrates the importance of the proper coupling of metabolic pathway activity with the function needs of particular types of immune cells. We show that Treg cells, which mainly act to keep immune responses well regulated, adopt a metabolic program where glycolytic metabolism is actively repressed, while oxidative metabolism is promoted. In the setting of malignant T-ALL cells, metabolic activity is surprisingly balanced, with both glycolysis and mitochondrial oxidative metabolism being utilized. In both cases, altering the metabolic balance towards glycolytic metabolism results in negative outcomes for the cell, with decreased Treg functionality and increased metabolic stress in T-ALL. In both cases, this work has generated a new understanding of how metabolism couples to immune cell function, and may allow for selective targeting of immune cell subsets by the specific targeting of metabolic pathways.

Survival expectations of the obese: Is excess mortality reflected in perceptions?

OBJECTIVE: This study compared self-reported subjective life expectancy (i.e., probability of living to age 75) for normal-weight, overweight, and obese weight groups to examine whether individuals are internalizing information about the health risks due to excessive weight. RESEARCH METHODS AND PROCEDURES: Using data from the Health and Retirement Study, a total of 9035 individuals 51 to 61 years old were analyzed by BMI category. The primary outcome measure was individuals' reports about their own expectations of survival to age 75. Absolute and relative risks of survival were compared with published estimates of survival to age 75. RESULTS: Consistently, higher levels of BMI were associated with lower self-estimated survival probabilities. Differences relative to normal weight ranged from 4.9% (p < 0.01) for male nonsmokers to 8.8% (p < 0.001) for female nonsmokers. However, these differences were substantially less than those obtained from published survival curve estimates, suggesting that obese individuals tended to underestimate mortality risks. DISCUSSION: Individuals appeared to underestimate the mortality risks of excessive weight; thus, knowledge campaigns about the risks of obesity should remain a top priority.

The effects of osmotic stress on the structure and function of the cell nucleus.

Osmotic stress is a potent regulator of the normal function of cells that are exposed to osmotically active environments under physiologic or pathologic conditions. The ability of cells to alter gene expression and metabolic activity in response to changes in the osmotic environment provides an additional regulatory mechanism for a diverse array of tissues and organs in the human body. In addition to the activation of various osmotically- or volume-activated ion channels, osmotic stress may also act on the genome via a direct biophysical pathway. Changes in extracellular osmolality alter cell volume, and therefore, the concentration of intracellular macromolecules. In turn, intracellular macromolecule concentration is a key physical parameter affecting the spatial organization and pressurization of the nucleus. Hyper-osmotic stress shrinks the nucleus and causes it to assume a convoluted shape, whereas hypo-osmotic stress swells the nucleus to a size that is limited by stretch of the nuclear lamina and induces a smooth, round shape of the nucleus. These behaviors are consistent with a model of the nucleus as a charged core/shell structure pressurized by uneven partition of macromolecules between the nucleoplasm and the cytoplasm. These osmotically-induced alterations in the internal structure and arrangement of chromatin, as well as potential changes in the nuclear membrane and pores are hypothesized to influence gene transcription and/or nucleocytoplasmic transport. A further understanding of the biophysical and biochemical mechanisms involved in these processes would have important ramifications for a range of fields including differentiation, migration, mechanotransduction, DNA repair, and tumorigenesis.

Stream segregation on a single electrode as a function of pulse rate in cochlear implant listeners.

While cochlear implants (CIs) usually provide high levels of speech recognition in quiet, speech recognition in noise remains challenging. To overcome these difficulties, it is important to understand how implanted listeners separate a target signal from interferers. Stream segregation has been studied extensively in both normal and electric hearing, as a function of place of stimulation. However, the effects of pulse rate, independent of place, on the perceptual grouping of sequential sounds in electric hearing have not yet been investigated. A rhythm detection task was used to measure stream segregation. The results of this study suggest that while CI listeners can segregate streams based on differences in pulse rate alone, the amount of stream segregation observed decreases as the base pulse rate increases. Further investigation of the perceptual dimensions encoded by the pulse rate and the effect of sequential presentation of different stimulation rates on perception could be beneficial for the future development of speech processing strategies for CIs.

Growth factor erv1-like modulates Drp1 to preserve mitochondrial dynamics and function in mouse embryonic stem cells.

The relationship of mitochondrial dynamics and function to pluripotency are rather poorly understood aspects of stem cell biology. Here we show that growth factor erv1-like (Gfer) is involved in preserving mouse embryonic stem cell (ESC) mitochondrial morphology and function. Knockdown (KD) of Gfer in ESCs leads to decreased pluripotency marker expression, embryoid body (EB) formation, cell survival, and loss of mitochondrial function. Mitochondria in Gfer-KD ESCs undergo excessive fragmentation and mitophagy, whereas those in ESCs overexpressing Gfer appear elongated. Levels of the mitochondrial fission GTPase dynamin-related protein 1 (Drp1) are highly elevated in Gfer-KD ESCs and decreased in Gfer-overexpressing cells. Treatment with a specific inhibitor of Drp1 rescues mitochondrial function and apoptosis, whereas expression of Drp1-dominant negative resulted in the restoration of pluripotency marker expression in Gfer-KD ESCs. Altogether, our data reveal a novel prosurvival role for Gfer in maintaining mitochondrial fission-fusion dynamics in pluripotent ESCs.

Ras controls melanocyte expansion during zebrafish fin stripe regeneration.

Regenerative medicine for complex tissues like limbs will require the provision or activation of precursors for different cell types, in the correct number, and with the appropriate instructions. These strategies can be guided by what is learned from spectacular events of natural limb or fin regeneration in urodele amphibians and teleost fish. Following zebrafish fin amputation, melanocyte stripes faithfully regenerate in tandem with complex fin structures. Distinct populations of melanocyte precursors emerge and differentiate to pigment regenerating fins, yet the regulation of their proliferation and patterning is incompletely understood. Here, we found that transgenic increases in active Ras dose-dependently hyperpigmented regenerating zebrafish fins. Lineage tracing and marker analysis indicated that increases in active Ras stimulated the in situ amplification of undifferentiated melanocyte precursors expressing mitfa and kita. Active Ras also hyperpigmented early fin regenerates of kita mutants, which are normally devoid of primary regeneration melanocytes, suppressing defects in precursor function and survival. By contrast, this protocol had no noticeable impact on pigmentation by secondary regulatory melanocyte precursors in late-stage kita regenerates. Our results provide evidence that Ras activity levels control the repopulation and expansion of adult melanocyte precursors after tissue loss, enabling the recovery of patterned melanocyte stripes during zebrafish appendage regeneration.

Quantifiable biomarkers of normal aging in the Japanese medaka fish (Oryzias latipes).

BACKGROUND: Small laboratory fish share many anatomical and histological characteristics with other vertebrates, yet can be maintained in large numbers at low cost for lifetime studies. Here we characterize biomarkers associated with normal aging in the Japanese medaka (Oryzias latipes), a species that has been widely used in toxicology studies and has potential utility as a model organism for experimental aging research. PRINCIPAL FINDINGS: The median lifespan of medaka was approximately 22 months under laboratory conditions. We performed quantitative histological analysis of tissues from age-grouped individuals representing young adults (6 months old), mature adults (16 months old), and adults that had survived beyond the median lifespan (24 months). Livers of 24-month old individuals showed extensive morphologic changes, including spongiosis hepatis, steatosis, ballooning degeneration, inflammation, and nuclear pyknosis. There were also phagolysosomes, vacuoles, and residual bodies in parenchymal cells and congestion of sinusoidal vessels. Livers of aged individuals were characterized by increases in lipofuscin deposits and in the number of TUNEL-positive apoptotic cells. Some of these degenerative characteristics were seen, to a lesser extent, in the livers of 16-month old individuals, but not in 6-month old individuals. The basal layer of the dermis showed an age-dependent decline in the number of dividing cells and an increase in senescence-associated β-galactosidase. The hearts of aged individuals were characterized by fibrosis and lipofuscin deposition. There was also a loss of pigmented cells from the retinal epithelium. By contrast, age-associated changes were not apparent in skeletal muscle, the ocular lens, or the brain. SIGNIFICANCE: The results provide a set of markers that can be used to trace the process of normal tissue aging in medaka and to evaluate the effect of environmental stressors.

Structure, function, and phylogeny of the mating locus in the Rhizopus oryzae complex.

The Rhizopus oryzae species complex is a group of zygomycete fungi that are common, cosmopolitan saprotrophs. Some strains are used beneficially for production of Asian fermented foods but they can also act as opportunistic human pathogens. Although R. oryzae reportedly has a heterothallic (+/-) mating system, most strains have not been observed to undergo sexual reproduction and the genetic structure of its mating locus has not been characterized. Here we report on the mating behavior and genetic structure of the mating locus for 54 isolates of the R. oryzae complex. All 54 strains have a mating locus similar in overall organization to Phycomyces blakesleeanus and Mucor circinelloides (Mucoromycotina, Zygomycota). In all of these fungi, the minus (-) allele features the SexM high mobility group (HMG) gene flanked by an RNA helicase gene and a TP transporter gene (TPT). Within the R. oryzae complex, the plus (+) mating allele includes an inserted region that codes for a BTB/POZ domain gene and the SexP HMG gene. Phylogenetic analyses of multiple genes, including the mating loci (HMG, TPT, RNA helicase), ITS1-5.8S-ITS2 rDNA, RPB2, and LDH genes, identified two distinct groups of strains. These correspond to previously described sibling species R. oryzae sensu stricto and R. delemar. Within each species, discordant gene phylogenies among multiple loci suggest an outcrossing population structure. The hypothesis of random-mating is also supported by a 50:50 ratio of plus and minus mating types in both cryptic species. When crossed with tester strains of the opposite mating type, most isolates of R. delemar failed to produce zygospores, while isolates of R. oryzae produced sterile zygospores. In spite of the reluctance of most strains to mate in vitro, the conserved sex locus structure and evidence for outcrossing suggest that a normal sexual cycle occurs in both species.

Elevated C-peptide and insulin predict increased risk of colorectal adenomas in normal mucosa.

BACKGROUND: Lower concentrations of the insulin-like growth factor binding protein-1 (IGFBP-1) and elevated concentrations of insulin or C-peptide have been associated with an increase in colorectal cancer risk (CRC). However few studies have evaluated IGFBP-1 and C-peptide in relation to adenomatous polyps, the only known precursor for CRC. METHODS: Between November 2001 and December 2002, we examined associations between circulating concentrations of insulin, C-peptide, IGFBP-1 and apoptosis among 190 individuals with one or more adenomatous polyps and 488 with no adenomatous polyps using logistic regression models. RESULTS: Individuals with the highest concentrations of C-peptide were more likely to have adenomas (OR = 2.2, 95% CI 1.4-4.0) than those with the lowest concentrations; associations that appeared to be stronger in men (OR = 4.4, 95% CI 1.7-10.9) than women. Individuals with high insulin concentrations also had a higher risk of adenomas (OR = 3.5, 95% CI 1.7-7.4), whereas higher levels of IGFBP-1 were associated with a reduced risk of adenomas in men only (OR = 0.3, 95% CI 0.1-0.7). Overweight and obese individuals with higher C-peptide levels (>1(st) Q) were at increased risk for lower apoptosis index (OR = 2.5, 95% CI 0.9-7.1), an association that remained strong in overweight and obese men (OR = 6.3, 95% CI 1.0-36.7). Higher levels of IGFBP-1 in overweight and obese individuals were associated with a reduced risk of low apoptosis (OR = 0.3, 95% CI 0.1-1.0). CONCLUSIONS: Associations between these peptides and the apoptosis index in overweight and obese individuals, suggest that the mechanism by which C-peptide could induce adenomas may include its anti-apoptotic properties. This study suggests that hyperinsulinemia and IGF hormones predict adenoma risk, and that outcomes associated with colorectal carcinogenesis maybe modified by gender.