A strategy for differential abundance analysis of sparse microbiome data with group-wise structured zeros.

Comparing the abundance of microbial communities between different groups or obtained under different experimental conditions using count sequence data is a challenging task due to various issues such as inflated zero counts, overdispersion, and non-normality. Several methods and procedures based on counts, their transformation and compositionality have been proposed in the literature to detect differentially abundant species in datasets containing hundreds to thousands of microbial species. Despite efforts to address the large numbers of zeros present in microbiome datasets, even after careful data preprocessing, the performance of existing methods is impaired by the presence of inflated zero counts and group-wise structured zeros (i.e. all zero counts in a group). We propose and validate using extensive simulations an approach combining two differential abundance testing methods, namely DESeq2-ZINBWaVE and DESeq2, to address the issues of zero-inflation and group-wise structured zeros, respectively. This combined approach was subsequently successfully applied to two plant microbiome datasets that revealed a number of taxa as interesting candidates for further experimental validation.

Intraintestinal fermentation of fructo- and galacto-oligosaccharides and the fate of short-chain fatty acids in humans.

Consumption of fructo- (FOS) and galacto-oligosaccharides (GOS) has health benefits which have been linked in part to short-chain fatty acids (SCFA) production by the gut microbiota. However, detailed knowledge of this process in the human intestine is lacking. We aimed to determine the acute fermentation kinetics of a FOS:GOS mixture in healthy males using a naso-intestinal catheter for sampling directly in the ileum or colon. We studied the fate of SCFA as substrates for glucose and lipid metabolism by the host after infusion of 13C-SCFA. In the human distal ileum, no fermentation of FOS:GOS, nor SCFA production, or bacterial cross-feeding was observed. The relative composition of intestinal microbiota changed rapidly during the test day, which demonstrates the relevance of postprandial intestinal sampling to track acute responses of the microbial community toward interventions. SCFA were vividly taken up and metabolized by the host as shown by incorporation of 13C in various host metabolites.

Age and Diet Modulate the Insulin-Sensitizing Effects of Exercise: A Tracer-Based Oral Glucose Tolerance Test.

Diet modulates the development of insulin resistance during aging. This includes tissue-specific alterations in insulin signaling and mitochondrial function, which ultimately affect glucose homeostasis. Exercise stimulates glucose clearance and mitochondrial lipid oxidation and also enhances insulin sensitivity (IS). It is not well known how exercise interacts with age and diet in the development of insulin resistance. To investigate this, oral glucose tolerance tests with tracers were conducted in mice ranging from 4 to 21 months of age, fed a low-fat diet (LFD) or high-fat diet (HFD) with or without life-long voluntary access to a running wheel (RW). We developed a computational model to derive glucose fluxes, which were commensurate with independent values from steady-state tracer infusions. Values for an IS index derived for peripheral tissues (IS-P) and one for the liver (IS-L) were steeply decreased by aging and an HFD. This preceded the age-dependent decline in the mitochondrial capacity to oxidize lipids. In young animals fed an LFD, RW access enhanced the IS-P concomitantly with the muscle ß-oxidation capacity. Surprisingly, RW access completely prevented the age-dependent IS-L decrease; however this only occurred in animals fed an LFD. Therefore, this study indicates that endurance exercise can improve the age-dependent decline in organ-specific IS if paired with a healthy diet. ARTICLE HIGHLIGHTS: Exercise is a known strategy to improve insulin sensitivity (IS), whereas aging and a lipid-rich diet decrease IS. Using a tracer-based oral glucose tolerance test, we investigated how exercise, age, and diet interact in the development of tissue-specific insulin resistance. Exercise (voluntary access to a running wheel) mainly improved IS in animals fed a low-fat diet. In these animals, exercise improved peripheral IS only at young age but fully prevented the age-dependent decline of hepatic IS. The prevention of age-dependent decline in IS by exercise is tissue-specific and blunted by a lipid-rich diet.

Age and diet modulate the insulin-sensitizing effects of exercise: a tracer-based oral glucose tolerance test.

Diet modulates the development of insulin resistance during aging. This includes tissue-specific alterations in insulin signaling and mitochondrial function, which ultimately affect glucose homeostasis. Exercise stimulates glucose clearance, mitochondrial lipid oxidation and enhances insulin sensitivity. It is not well known how exercise interacts with age and diet in the development of insulin resistance. To investigate this, oral glucose tolerance tests (OGTT) with a tracer were conducted in mice ranging from 4 to 21 months of age, fed a low- (LFD) or high-fat diet (HFD), with or without life-long voluntary access to a running wheel (RW). We developed a computational model to derive glucose fluxes, which were commensurate with independent values from steady-state tracer infusions. Both insulin sensitivity indices derived for peripheral tissues and liver (IS-P and IS-L, respectively) were steeply decreased by aging and a HFD. This preceded the age-dependent decline in the mitochondrial capacity to oxidize lipids. In LFD young animals, RW access enhanced the IS-P concomitantly with the muscle ß- oxidation capacity. Surprisingly, RW access completely prevented the age-dependent IS-L decrease, but only in LFD animals. This study indicates, therefore, that endurance exercise can improve the age-dependent decline in organ-specific IS mostly in the context of a healthy diet.

Seroprevalence and Associated Risk Factors of Lumpy Skin Disease of Cattle in Selected Districts of Afar Region, Ethiopia.

Background: Lumpy skin disease (LSD) is one of Ethiopia's most economically significant transboundary livestock illnesses. The disease has a significant economic impact on pastoral household livestock owners, who rely significantly on their cattle as a source of income. Methods: A cross-sectional study was undertaken in selected districts of Afar region from November 2018 to May 2019 primarily intended to estimate the prevalence of lumpy skin disease serologically in local Afar cattle as well as identify potential associated factors. A multistage sampling method was employed to select study districts, peasant association, herd size and study units. A total of 384 sera were processed using serum neutralization test (SNT) method to detect antibodies against lumpy skin disease virus. Relevant data were refined and further analyzed using stata version 14. Results: In the study districts, the overall animal level seroprevalence was found to be 7.6% (N = 29/384; 95% confidence interval: 4.90-10.20) and the overall herd level prevalence was found to be 20.8% (n = 15/72; 95% confidence interval: 11.42-30.18). Only district was shown to be statistically significant (P = 0.004) in terms of LSD occurrence among the relevant factors studied. Cattle in Chifra district were 20.18 times more likely to contract LSD infection than cattle in Dubti district, when Asayita district was used as the reference group. Conclusion: The present study finding confirmed the presence of the disease in the study districts of afar region and coordinated intervention set to be in place.

Bistability in fatty-acid oxidation resulting from substrate inhibition.

In this study we demonstrated through analytic considerations and numerical studies that the mitochondrial fatty-acid ß-oxidation can exhibit bistable-hysteresis behavior. In an experimentally validated computational model we identified a specific region in the parameter space in which two distinct stable and one unstable steady state could be attained with different fluxes. The two stable states were referred to as low-flux (disease) and high-flux (healthy) state. By a modular kinetic approach we traced the origin and causes of the bistability back to the distributive kinetics and the conservation of CoA, in particular in the last rounds of the ß-oxidation. We then extended the model to investigate various interventions that may confer health benefits by activating the pathway, including (i) activation of the last enzyme MCKAT via its endogenous regulator p46-SHC protein, (ii) addition of a thioesterase (an acyl-CoA hydrolysing enzyme) as a safety valve, and (iii) concomitant activation of a number of upstream and downstream enzymes by short-chain fatty-acids (SCFA), metabolites that are produced from nutritional fibers in the gut. A high concentration of SCFAs, thioesterase activity, and inhibition of the p46Shc protein led to a disappearance of the bistability, leaving only the high-flux state. A better understanding of the switch behavior of the mitochondrial fatty-acid oxidation process between a low- and a high-flux state may lead to dietary and pharmacological intervention in the treatment or prevention of obesity and or non-alcoholic fatty-liver disease.

Age-related susceptibility to insulin resistance arises from a combination of CPT1B decline and lipid overload.

BACKGROUND: The skeletal muscle plays a central role in glucose homeostasis through the uptake of glucose from the extracellular medium in response to insulin. A number of factors are known to disrupt the normal response to insulin leading to the emergence of insulin resistance (IR). Advanced age and a high-fat diet are factors that increase the susceptibility to IR, with lipid accumulation in the skeletal muscle being a key driver of this phenomenon. It is debated, however, whether lipid accumulation arises due to dietary lipid overload or from a decline of mitochondrial function. To gain insights into the interplay of diet and age in the flexibility of muscle lipid and glucose handling, we combined lipidomics, proteomics, mitochondrial function analysis and computational modelling to investigate young and aged mice on a low- or high-fat diet (HFD). RESULTS: As expected, aged mice were more susceptible to IR when given a HFD than young mice. The HFD induced intramuscular lipid accumulation specifically in aged mice, including C18:0-containing ceramides and diacylglycerols. This was reflected by the mitochondrial ß-oxidation capacity, which was upregulated by the HFD in young, but not in old mice. Conspicuously, most ß-oxidation proteins were upregulated by the HFD in both groups, but carnitine palmitoyltransferase 1B (CPT1B) declined in aged animals. Computational modelling traced the flux control mostly to CPT1B, suggesting a CPT1B-driven loss of flexibility to the HFD with age. Finally, in old animals, glycolytic protein levels were reduced and less flexible to the diet. CONCLUSION: We conclude that intramuscular lipid accumulation and decreased insulin sensitivity are not due to age-related mitochondrial dysfunction or nutritional overload alone, but rather to their combined effects. Moreover, we identify CPT1B as a potential target to counteract age-dependent intramuscular lipid accumulation and thereby IR.

Performance of model-based multifactor dimensionality reduction methods for epistasis detection by controlling population structure.

BACKGROUND: In genome-wide association studies the extent and impact of confounding due to population structure have been well recognized. Inadequate handling of such confounding is likely to lead to spurious associations, hampering replication, and the identification of causal variants. Several strategies have been developed for protecting associations against confounding, the most popular one is based on Principal Component Analysis. In contrast, the extent and impact of confounding due to population structure in gene-gene interaction association epistasis studies are much less investigated and understood. In particular, the role of nonlinear genetic population substructure in epistasis detection is largely under-investigated, especially outside a regression framework. METHODS: To identify causal variants in synergy, to improve interpretability and replicability of epistasis results, we introduce three strategies based on a model-based multifactor dimensionality reduction approach for structured populations, namely MBMDR-PC, MBMDR-PG, and MBMDR-GC. RESULTS: Simulation results comparing the performance of various approaches show that in the presence of population structure MBMDR-PC and MBMDR-PG consistently better control type I error rate at the nominal level than MBMDR-GC. Moreover, our proposed three methods of population structure correction outperform MDR-SP in terms of statistical power. CONCLUSION: We demonstrate through extensive simulation studies the effect of various degrees of genetic population structure and relatedness on epistasis detection and propose appropriate remedial measures based on linear and nonlinear sample genetic similarity.

Sparse relative risk regression models.

Clinical studies where patients are routinely screened for many genomic features are becoming more routine. In principle, this holds the promise of being able to find genomic signatures for a particular disease. In particular, cancer survival is thought to be closely linked to the genomic constitution of the tumor. Discovering such signatures will be useful in the diagnosis of the patient, may be used for treatment decisions and, perhaps, even the development of new treatments. However, genomic data are typically noisy and high-dimensional, not rarely outstripping the number of patients included in the study. Regularized survival models have been proposed to deal with such scenarios. These methods typically induce sparsity by means of a coincidental match of the geometry of the convex likelihood and a (near) non-convex regularizer. The disadvantages of such methods are that they are typically non-invariant to scale changes of the covariates, they struggle with highly correlated covariates, and they have a practical problem of determining the amount of regularization. In this article, we propose an extension of the differential geometric least angle regression method for sparse inference in relative risk regression models. A software implementation of our method is available on github (https://github.com/LuigiAugugliaro/dgcox).

A different view on fine-scale population structure in Western African populations.

Due to its long genetic evolutionary history, Africans exhibit more genetic variation than any other population in the world. Their genetic diversity further lends itself to subdivisions of Africans into groups of individuals with a genetic similarity of varying degrees of granularity. It remains challenging to detect fine-scale structure in a computationally efficient and meaningful way. In this paper, we present a proof-of-concept of a novel fine-scale population structure detection tool with Western African samples. These samples consist of 1396 individuals from 25 ethnic groups (two groups are African American descendants). The strategy is based on a recently developed tool called IPCAPS. IPCAPS, or Iterative Pruning to CApture Population Structure, is a genetic divisive clustering strategy that enhances iterative pruning PCA, is robust to outliers and does not require a priori computation of haplotypes. Our strategy identified in total 12 groups and 6 groups were revealed as fine-scale structure detected in the samples from Cameroon, Gambia, Mali, Southwest USA, and Barbados. Our finding helped to explain evolutionary processes in the analyzed West African samples and raise awareness for fine-scale structure resolution when conducting genome-wide association and interaction studies.

IPCAPS: an R package for iterative pruning to capture population structure.

BACKGROUND: Resolving population genetic structure is challenging, especially when dealing with closely related or geographically confined populations. Although Principal Component Analysis (PCA)-based methods and genomic variation with single nucleotide polymorphisms (SNPs) are widely used to describe shared genetic ancestry, improvements can be made especially when fine-scale population structure is the target. RESULTS: This work presents an R package called IPCAPS, which uses SNP information for resolving possibly fine-scale population structure. The IPCAPS routines are built on the iterative pruning Principal Component Analysis (ipPCA) framework that systematically assigns individuals to genetically similar subgroups. In each iteration, our tool is able to detect and eliminate outliers, hereby avoiding severe misclassification errors. CONCLUSIONS: IPCAPS supports different measurement scales for variables used to identify substructure. Hence, panels of gene expression and methylation data can be accommodated as well. The tool can also be applied in patient sub-phenotyping contexts. IPCAPS is developed in R and is freely available from http://bio3.giga.ulg.ac.be/ipcaps.

Principals about principal components in statistical genetics.

Principal components (PCs) are widely used in statistics and refer to a relatively small number of uncorrelated variables derived from an initial pool of variables, while explaining as much of the total variance as possible. Also in statistical genetics, principal component analysis (PCA) is a popular technique. To achieve optimal results, a thorough understanding about the different implementations of PCA is required and their impact on study results, compared to alternative approaches. In this review, we focus on the possibilities, limitations and role of PCs in ancestry prediction, genome-wide association studies, rare variants analyses, imputation strategies, meta-analysis and epistasis detection. We also describe several variations of classic PCA that deserve increased attention in statistical genetics applications.

An approximate marginal logistic distribution for the analysis of longitudinal ordinal data.

Subject-specific and marginal models have been developed for the analysis of longitudinal ordinal data. Subject-specific models often lack a population-average interpretation of the model parameters due to the conditional formulation of random intercepts and slopes. Marginal models frequently lack an underlying distribution for ordinal data, in particular when generalized estimating equations are applied. To overcome these issues, latent variable models underneath the ordinal outcomes with a multivariate logistic distribution can be applied. In this article, we extend the work of O'Brien and Dunson (2004), who studied the multivariate t-distribution with marginal logistic distributions. We use maximum likelihood, instead of a Bayesian approach, and incorporated covariates in the correlation structure, in addition to the mean model. We compared our method with GEE and demonstrated that it performs better than GEE with respect to the fixed effect parameter estimation when the latent variables have an approximately elliptical distribution, and at least as good as GEE for other types of latent variable distributions.

An alternative parameterization of Bayesian logistic hierarchical models for mixed treatment comparisons.

Mixed treatment comparison (MTC) models rely on estimates of relative effectiveness from randomized clinical trials so as to respect randomization across treatment arms. This approach could potentially be simplified by an alternative parameterization of the way effectiveness is modeled. We introduce a treatment-based parameterization of the MTC model that estimates outcomes on both the study and treatment levels. We compare the proposed model to the commonly used MTC models using a simulation study as well as three randomized clinical trial datasets from published systematic reviews comparing (i) treatments on bleeding after cirrhosis, (ii) the impact of antihypertensive drugs in diabetes mellitus, and (iii) smoking cessation strategies. The simulation results suggest similar or sometimes better performance of the treatment-based MTC model. Moreover, from the real data analyses, little differences were observed on the inference extracted from both models. Overall, our proposed MTC approach performed as good, or better, than the commonly applied indirect and MTC models and is simpler, fast, and easier to implement in standard statistical software.

Sparse time series chain graphical models for reconstructing genetic networks.

We propose a sparse high-dimensional time series chain graphical model for reconstructing genetic networks from gene expression data parametrized by a precision matrix and autoregressive coefficient matrix. We consider the time steps as blocks or chains. The proposed approach explores patterns of contemporaneous and dynamic interactions by efficiently combining Gaussian graphical models and Bayesian dynamic networks. We use penalized likelihood inference with a smoothly clipped absolute deviation penalty to explore the relationships among the observed time course gene expressions. The method is illustrated on simulated data and on real data examples from Arabidopsis thaliana and mammary gland time course microarray gene expressions.