Maternal emulsifier consumption programs offspring metabolic and neuropsychological health in mice

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Abstract

Modern lifestyle is associated with a major consumption of ultra–processed foods (UPF) due to their practicality and palatability. The ingestion of emulsifiers, a main additive in UPFs, has been related to gut inflammation, microbiota dysbiosis, adiposity, and obesity. Maternal unbalanced nutritional habits during embryonic and perinatal stages perturb offspring’s long–term metabolic health, thus increasing obesity and associated comorbidity risk. However, whether maternal emulsifier consumption influences developmental programming in the offspring remains unknown. Here, we show that, in mice, maternal consumption of dietary emulsifiers (1% carboxymethyl cellulose (CMC) and 1% P80 in drinking water), during gestation and lactation, perturbs the development of hypothalamic energy balance regulation centers of the progeny, leads to metabolic impairments, cognition deficits, and induces anxiety–like traits in a sex–specific manner. Our findings support the notion that maternal consumption of emulsifiers, common additives of UPFs, causes mild metabolic and neuropsychological malprogramming in the progeny. Our data call for nutritional advice during gestation.

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Matthew E. Ritchie, Belinda Phipson, Di Wu … (2015)

limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.

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Nicolas Bray, Harold Pimentel, Páll Melsted … (2016)

We present kallisto, an RNA-seq quantification program that is two orders of magnitude faster than previous approaches and achieves similar accuracy. Kallisto pseudoaligns reads to a reference, producing a list of transcripts that are compatible with each read while avoiding alignment of individual bases. We use kallisto to analyze 30 million unaligned paired-end RNA-seq reads in <10 min on a standard laptop computer. This removes a major computational bottleneck in RNA-seq analysis.