Environmental toxicants in breast milk of Norwegian mothers and gut bacteria composition and metabolites in their infants at 1 month.

BACKGROUND:Early disruption of the microbial community may influence life-long health. Environmental toxicants can contaminate breast milk and the developing infant gut microbiome is directly exposed. We investigated whether environmental toxicants in breastmilk affect the composition and function of the infant gut microbiome at 1 month. We measured environmental toxicants in breastmilk, fecal short-chain fatty acids (SCFAs), and gut microbial composition from 16S rRNA gene amplicon sequencing using samples from 267 mother-child pairs in the Norwegian Microbiota Cohort (NoMIC). We tested 28 chemical exposures: polychlorinated biphenyls (PCBs), polybrominated flame retardants (PBDEs), per- and polyfluoroalkyl substances (PFASs), and organochlorine pesticides. We assessed chemical exposure and alpha diversity/SCFAs using elastic net regression modeling and generalized linear models, adjusting for confounders, and variation in beta diversity (UniFrac), taxa abundance (ANCOM), and predicted metagenomes (PiCRUSt) in low, medium, and high exposed groups. RESULTS:PBDE-28 and the surfactant perfluorooctanesulfonic acid (PFOS) were associated with less microbiome diversity. Some sub-OTUs of Lactobacillus, an important genus in early life, were lower in abundance in samples from infants with relative "high" (> 80th percentile) vs. "low" (< 20th percentile) toxicant exposure in this cohort. Moreover, breast milk toxicants were associated with microbiome functionality, explaining up to 34% of variance in acetic and propionic SCFAs, essential signaling molecules. Per one standard deviation of exposure, PBDE-28 was associated with less propionic acid (- 24% [95% CI - 35% to - 14%] relative to the mean), and PCB-209 with less acetic acid (- 15% [95% CI - 29% to - 0.4%]). Conversely, PFOA and dioxin-like PCB-167 were associated with 61% (95% CI 35% to 87%) and 22% (95% CI 8% to 35%) more propionic and acetic acid, respectively. CONCLUSIONS:Environmental toxicant exposure may influence infant gut microbial function during a critical developmental window. Future studies are needed to replicate these novel findings and investigate whether this has any impact on child health

A multiple testing procedure for multi-dimensional pairwise comparisons with application to gene expression studies

Figure S1. A graphical display of various hypotheses of interest. (TIFF 149 kb

ERCIM 2013

In recent years there has been considerable interest in drawing inferences regarding order relationships among angular parameters. In particular, in biology the interest is to understand genes participating in cell cycle across multiple species and whether they are functionally conserved. The time to peak expression, known as phase angle, of such a gene can be mapped onto a unit circle. Biologists are not only interested in estimating the phase angles but in determining the relative order of expression of various genes. The final aim is to know whether the order of peak expression among cell cycle genes is conserved evolutionarily across species. These questions are challenging due to large variability among studies and to the circular nature of the data. A methodology to find the underlying circular order in a population is presented. We also propose a solution for the problem of testing equality of circular orders among two or more populations. Unbalanced samples and differences in distributions are taken into consideration. The proposed methodology is illustrated by analyzing data sets from three species: Schizosaccharomyces Pombe, Schizosaccharomyces Cerevisiae and Humans. As a result a set of genes is presented where the circular order is conserved across these three species

Order Restricted Inference for Oscillatory Systems for Detecting Rhythmic Signals

Many biological processes, such as cell cycle, circadian clock, menstrual cycles, are governed by oscillatory systems consisting of numerous components that exhibit rhythmic patterns over time. It is not always easy to identify such rhythmic components. For example, it is a challenging problem to identify circadian genes in a given tissue using time-course gene expression data. There is a great potential for misclassifying non-rhythmic as rhythmic genes and vice versa. This has been a problem of considerable interest in recent years. In this article we develop a constrained inference based methodology called Order Restricted Inference for Oscillatory Systems (ORIOS) to detect rhythmic signals. Instead of using mathematical functions (e.g. sinusoidal) to describe shape of rhythmic signals, ORIOS uses mathematical inequalities. Consequently, it is robust and not limited by the biologist’s choice of the mathematical model. We studied the performance of ORIOS using simulated as well as real data obtained from mouse liver, pituitary gland and data from NIH3T3, U2OS cell lines. Our results suggest that, for a broad collection of patterns of gene expression, ORIOS has substantially higher power to detect true rhythmic genes in comparison to some popular methods, while also declaring substantially fewer non-rhythmic genes as rhythmic.Spanish Ministerio de Ciencia e Innovación [MTM2015-71217-R]Spanish Ministerio de Educación, Cultura y Deporte [FPU14/04534]Intramural Research Program of the National Institute of Environmental Health Sciences (NIEHS) [Z01 ES101744-04

II Encuentro Galaico-Portugués de Biometría

The study of biological rhythms is receiving a lot of attention in the literature in recent years. At the core of this research lies the methodological problem of how to detect rhythmic signals in measured data. Night and day, or dark and light patterns impact on human health in many different ways. For this reason, researchers are studying the effect of sleep on the circadian clock in human body during various stages of life. Important components of this clock are the circadian genes which have rhythmic expression overtime with phases suitably matching the night and day. Consequently, the identification of rhythmic signals is a problem of considerable interest for biologists. In this work, we develop a novel statistical procedure to detect rhythmic signals in oscillatory systems based on Order Restricted Inference (ORI). This methodology is tested both on simulations and on real data bases. Moreover the obtained results are compared with the most widely extended rhythmicity detection algorithms in literature

Some advances in constrained inference for ordered circular parameters in oscillatory systems

Constraints on parameters arise naturally in many applications. Statistical methods that honor the underlying constraints tend to be more powerful and result in better interpretation of the underlying scientific data. In the context of Euclidean space data, there exists over five decades of statistical literature on constrained statistical inference and at least four books on the subject (e.g. Robertson et al. 1988; Silvapulle and Sen 2005). However, it was not until recently that these methods have been used extensively in applied research. For example, constrained statistical inference is gaining considerable interest among applied researchers in a variety of fields, such as, for example, toxicology (Peddada et al. 2007), genomics (Hoenerhoff et al. 2013; Perdivara et al. 2011; Peddada et al. 2003), epidemiology (Cao et al. 2011; Peddada et al. 2005), clinical trials (Conaway et al. 2004), or cancer trials (Conde et al. 2012, 2013).Ministerio de Ciencia e Innovación grant (MTM2012-37129)Junta de Castilla y León, Consejería de Educación and the European Social FundIntramural Research Program of the National Institute of Environmental Health Sciences (NIEHS) [Z01 ES101744-04

LASR 2015

This work is motivated by a problem encountered in Molecular Biology where researchers are interested in correlating angular data from two oscillatory systems. The observations are the time to peak expression (also known as phase angle) of periodic genes under two different conditions (dose levels, organs or even species). In particular, we deal here with expression data from genes participating in the cell-cycle. Cell-biologists are often interested in drawing inferences regarding the phase angle of cell-cycle genes since they are considered to be associated with the gene’s biological function (Jensen et al 2006)

A Minimal Set of Tissue-Specific Hypomethylated CpGs Constitute Epigenetic Signatures of Developmental Programming

Background: Cell specific states of the chromatin are programmed during mammalian development. Dynamic DNA methylation across the developing embryo guides a program of repression, switching off genes in most cell types. Thus, the majority of the tissue specific differentially methylated sites (TS-DMS) must be un-methylated CpGs. Methodology and Principal Findings Comparison of expanded Methyl Sensitive Cut Counting data (eMSCC) among four tissues (liver, testes, brain and kidney) from three C57BL/6J mice, identified 138,052 differentially methylated sites of which 23,270 contain CpGs un-methylated in only one tissue (TS-DMS). Most of these CpGs were located in intergenic regions, outside of promoters, CpG islands or their shores, and up to 20% of them overlapped reported active enhancers. Indeed, tissue-specific enhancers were up to 30 fold enriched in TS-DMS. Testis showed the highest number of TS-DMS, but paradoxically their associated genes do not appear to be specific to the germ cell functions, but rather are involved in organism development. In the other tissues the differentially methylated genes are associated with tissue-specific physiological or anatomical functions. The identified sets of TS-DMS quantify epigenetic distances between tissues, generated during development. We applied this concept to measure the extent of reprogramming in the liver of mice exposed to in utero or early postnatal nutritional stress. Different protocols of food restriction reprogrammed the liver methylome in different but reproducible ways. Conclusion and Significance Thus, each identified set of differentially methylated sites constituted an epigenetic signature that traced the developmental programing or the early nutritional reprogramming of each exposed mouse. We propose that our approach has the potential to outline a number of disease-associated epigenetic states. The composition of differentially methylated CpGs may vary with each situation, behaving as a composite variable, which can be used as a pre-symptomatic marker for disease