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Background The DNA methylation landscape is shaped by genetic and environmental factors and is modulate by aging. Here we evaluate the “aging methylome” in 12 recombinant inbred mouse strains from the BXD family that have more than two-fold variation in longevity. We examine relations among body weight, diet, lifespan, and DNA methylation-based rate of biological aging. We used affinity capture with the methyl-CpG binding domain (MBD) protein, followed by deep sequencing (MBD-seq), to assay DNA methylation in 70 mostly female liver samples, ranging in age from 6 to 25 months from mice maintained either on low fat chow or high fat diet (HFD). Results Genetic background among strains is a major source of variation in genome-wide DNA methylation patterns. Surprisingly, body weight at young adulthood had a stronger association with the methylome than age itself. Nonetheless, age also had a strong effect on methylation at well-defined CpG regions largely located within genes. We used subsets of age-informative CpGs to build versions of the epigenetic clock and as expected, these were strongly correlated with chronological age. Both high initial body weight and the HFD were associated with accelerated epigenetic aging. A DNA methylation clock model built using CpGs associated with body weight correlated with longevity of strains rather than chronological age, implying an underlying lifespan clock. Complementary mRNA clocks were also informative of chronological age. Conclusion Our results support the known association between body mass and lifespan, and indicate that the methylome provides a mechanistic link to accelerated aging.

In large-scale proteomic studies, logistics restrict the sample number that can be processed in one batch. This inevitably leads to systematic technical bias, known as batch effects. In this study, we analyse batch effects in proteomics. We test existing batch correction tools for their suitability in proteomic studies and introduce new methods for correction and quality control.

Cancer is mostly incurable when diagnosed at a metastatic stage, making its early detection via blood proteins of immense clinical interest. Proteomic changes in tumor tissue may lead to changes detectable in the protein composition of circulating blood plasma. Using a proteomic workflow combining N-glycosite enrichment and SWATH mass spectrometry, we generate a data resource of 284 blood samples derived from patients with different types of localized-stage carcinomas and from matched controls. We observe whether the changes in the patient’s plasma are specific to a particular carcinoma or represent a generic signature of proteins modified uniformly in a common, systemic response to many cancers. A quantitative comparison of the resulting N-glycosite profiles discovers that proteins related to blood platelets are common to several cancers (e.g., THBS1), whereas others are highly cancer-type specific. Available proteomics data, including a SWATH library to study N-glycoproteins, will facilitate follow-up biomarker research into early cancer detection., Cell Reports, 23 (9), ISSN:2666-3864, ISSN:2211-1247

In biomarker research, the goal is to construct an prediction rule on the basis of a small number of predictors. Formally, this means representing a macro-level response as a function of molecular features (DNA variants, transcript or protein abundancies) with minimal error. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 668858. This work was supported (in part) by the Swiss State Secretariat for Education, Research and Innovation (SERI) under contract number 15.0324-2. The opinions expressed and arguments employed therein do not necessarily reflect the official views of the Swiss Government., The information in this document is provided as is, and no guarantee or warranty is given that the information is fit for any particular purpose. The content of this document reflects only the author's view - the European Commission is not responsible for any use that may be made of the information it contains. The users use the information at their sole risk and liability.

The quantitative measurement of the proteome has been shown to yield new insights into physiology and cell biology that cannot be determined from the genome and transcriptome because the quantitative relationship between transcriptome and proteome is complex. MS-based proteomics techniques, such as SWATH-MS, have recently advanced to the point at which they may be reliably applied by biologists who are not specialists in mass spectrometry. Here we provide standard protocols for preparation of tissue samples for input into the SWATH-MS analytical pipeline. These protocols are designed for high-throughput processing of tissues with >/=5 mg of sample available for analysis. Studies with extremely limited amounts of tissue should consider PCT-SWATH. An experienced single user should be able to process 48 samples per day for injection into the mass spectrometer, or up to 144 samples a week. The machine time necessary for running these samples with SWATH is approximately 1.5 hr per sample. Data acquisition protocols are also provided. (c) 2017 by John Wiley & Sons, Inc.

In biomarker research, the goal is to construct an prediction rule on the basis of a small number of predictors. Formally, this means representing a macro-level response as a function of molecular features (DNA variants, transcript or protein abundancies) with minimal error. The aim is to develop a framework for selection of a composite biomarker: an ensemble of small number of predictors, that is able to predict the macro-level response. To benchmark the process of construction of the composite biomarker, we use a mouse model. Mouse model has an advantage over human samples, as many confounding factors are controlled. Here we use measurements of 35 murine strains from the BXD recombinant inbred strain panel exposed to high-fat and chow diets. As explanatory variable set we use molecular profile of liver, and as response variables, we have selected 7 phenotypic traits related to metabolism. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 668858. This work was supported (in part) by the Swiss State Secretariat for Education, Research and Innovation (SERI) under contract number 15.0324-2. The opinions expressed and arguments employed therein do not necessarily reflect the official views of the Swiss Government., The information in this document is provided as is, and no guarantee or warranty is given that the information is fit for any particular purpose. The content of this document reflects only the author's view - the European Commission is not responsible for any use that may be made of the information it contains. The users use the information at their sole risk and liability.