Andreas Beyer

On the Dependency of Cellular Protein Levels on mRNA Abundance

The question of how genomic information is expressed to determine phenotypes is of central importance for basic and translational life science research and has been studied by transcriptomic and proteomic profiling. Here, we review the relationship between protein and mRNA levels under various scenarios, such as steady state, long-term state changes, and short-term adaptation, demonstrating the complexity of gene expression regulation, especially during dynamic transitions. The spatial and temporal variations of mRNAs, as well as the local availability of resources for protein biosynthesis, strongly influence the relationship between protein levels and their coding transcripts. We further discuss the buffering of mRNA fluctuations at the level of protein concentrations. We conclude that transcript levels by themselves are not sufficient to predict protein levels in many scenarios and to thus explain genotype-phenotype relationships and that high-quality data quantifying different levels of gene expression are indispensable for the complete understanding of biological processes.

The African Turquoise Killifish Genome Provides Insights into Evolution and Genetic Architecture of Lifespan

Lifespan is a remarkably diverse trait ranging from a few days to several hundred years in nature, but the mechanisms underlying the evolution of lifespan differences remain elusive. Here we de novo assemble a reference genome for the naturally short-lived African turquoise killifish, providing a unique resource for comparative and experimental genomics. The identification of genes under positive selection in this fish reveals potential candidates to explain its compressed lifespan. Several aging genes are under positive selection in this short-lived fish and long-lived species, raising the intriguing possibility that the same gene could underlie evolution of both compressed and extended lifespans. Comparative genomics and linkage analysis identify candidate genes associated with lifespan differences between various turquoise killifish strains. Remarkably, these genes are clustered on the sex chromosome, suggesting that short lifespan might have co-evolved with sex determination. Our study provides insights into the evolutionary forces that shape lifespan in nature.

Assessing Computational Methods for Transcription Factor Target Gene Identification Based on ChIP-seq Data

Chromatin immunoprecipitation coupled with deep sequencing (ChIP-seq) has great potential for elucidating transcriptional networks, by measuring genome-wide binding of transcription factors (TFs) at high resolution. Despite the precision of these experiments, identification of genes directly regulated by a TF (target genes) is not trivial. Numerous target gene scoring methods have been used in the past. However, their suitability for the task and their performance remain unclear, because a thorough comparative assessment of these methods is still lacking. Here we present a systematic evaluation of computational methods for defining TF targets based on ChIP-seq data. We validated predictions based on 68 ChIP-seq studies using a wide range of genomic expression data and functional information. We demonstrate that peak-to-gene assignment is the most crucial step for correct target gene prediction and propose a parameter-free method performing most consistently across the evaluation tests.

Dietary restriction protects from age-associated DNA methylation and induces epigenetic reprogramming of lipid metabolism

BackgroundDietary restriction (DR), a reduction in food intake without malnutrition, increases most aspects of health during aging and extends lifespan in diverse species, including rodents. However, the mechanisms by which DR interacts with the aging process to improve health in old age are poorly understood. DNA methylation could play an important role in mediating the effects of DR because it is sensitive to the effects of nutrition and can affect gene expression memory over time.ResultsHere, we profile genome-wide changes in DNA methylation, gene expression and lipidomics in response to DR and aging in female mouse liver. DR is generally strongly protective against age-related changes in DNA methylation. During aging with DR, DNA methylation becomes targeted to gene bodies and is associated with reduced gene expression, particularly of genes involved in lipid metabolism. The lipid profile of the livers of DR mice is correspondingly shifted towards lowered triglyceride content and shorter chain length of triglyceride-associated fatty acids, and these effects become more pronounced with age.ConclusionsOur results indicate that DR remodels genome-wide patterns of DNA methylation so that age-related changes are profoundly delayed, while changes at loci involved in lipid metabolism affect gene expression and the resulting lipid profile.

Impact of natural genetic variation on gene expression dynamics.

DNA sequence variation causes changes in gene expression, which in turn has profound effects on cellular states. These variations affect tissue development and may ultimately lead to pathological phenotypes. A genetic locus containing a sequence variation that affects gene expression is called an “expression quantitative trait locus” (eQTL). Whereas the impact of cellular context on expression levels in general is well established, a lot less is known about the cell-state specificity of eQTL. Previous studies differed with respect to how “dynamic eQTL” were defined. Here, we propose a unified framework distinguishing static, conditional and dynamic eQTL and suggest strategies for mapping these eQTL classes. Further, we introduce a new approach to simultaneously infer eQTL from different cell types. By using murine mRNA expression data from four stages of hematopoiesis and 14 related cellular traits, we demonstrate that static, conditional and dynamic eQTL, although derived from the same expression data, represent functionally distinct types of eQTL. While static eQTL affect generic cellular processes, non-static eQTL are more often involved in hematopoiesis and immune response. Our analysis revealed substantial effects of individual genetic variation on cell type-specific expression regulation. Among a total number of 3,941 eQTL we detected 2,729 static eQTL, 1,187 eQTL were conditionally active in one or several cell types, and 70 eQTL affected expression changes during cell type transitions. We also found evidence for feedback control mechanisms reverting the effect of an eQTL specifically in certain cell types. Loci correlated with hematological traits were enriched for conditional eQTL, thus, demonstrating the importance of conditional eQTL for understanding molecular mechanisms underlying physiological trait variation. The classification proposed here has the potential to streamline and unify future analysis of conditional and dynamic eQTL as well as many other kinds of QTL data.

Natural genetic variation impacts expression levels of coding, non-coding, and antisense transcripts in fission yeast.

Our current understanding of how natural genetic variation affects gene expression beyond well‐annotated coding genes is still limited. The use of deep sequencing technologies for the study of expression quantitative trait loci (eQTLs) has the potential to close this gap. Here, we generated the first recombinant strain library for fission yeast and conducted an RNA‐seq‐based QTL study of the coding, non‐coding, and antisense transcriptomes. We show that the frequency of distal effects (trans‐eQTLs) greatly exceeds the number of local effects (cis‐eQTLs) and that non‐coding RNAs are as likely to be affected by eQTLs as protein‐coding RNAs. We identified a genetic variation of swc5 that modifies the levels of 871 RNAs, with effects on both sense and antisense transcription, and show that this effect most likely goes through a compromised deposition of the histone variant H2A.Z. The strains, methods, and datasets generated here provide a rich resource for future studies.

Testing and Validation of Computational Methods for Mass Spectrometry

High-throughput methods based on mass spectrometry (proteomics, metabolomics, lipidomics, etc.) produce a wealth of data that cannot be analyzed without computational methods. The impact of the choice of method on the overall result of a biological study is often underappreciated, but different methods can result in very different biological findings. It is thus essential to evaluate and compare the correctness and relative performance of computational methods. The volume of the data as well as the complexity of the algorithms render unbiased comparisons challenging. This paper discusses some problems and challenges in testing and validation of computational methods. We discuss the different types of data (simulated and experimental validation data) as well as different metrics to compare methods. We also introduce a new public repository for mass spectrometric reference data sets ( http://compms.org/RefData ) that contains a collection of publicly available data sets for performance evaluation for a wide range of different methods.

Associations between DNA methylation and schizophrenia-related intermediate phenotypes: A gene set enrichment analysis

Multiple genetic approaches have identified microRNAs as key effectors in psychiatric disorders as they post-transcriptionally regulate expression of thousands of target genes. However, their role in specific psychiatric diseases remains poorly understood. In addition, epigenetic mechanisms such as DNA methylation, which affect the expression of both microRNAs and coding genes, are critical for our understanding of molecular mechanisms in schizophrenia. Using clinical, imaging, genetic, and epigenetic data of 103 patients with schizophrenia and 111 healthy controls of the Mind Clinical Imaging Consortium (MCIC) study of schizophrenia, we conducted gene set enrichment analysis to identify markers for schizophrenia-associated intermediate phenotypes. Genes were ranked based on the correlation between DNA methylation patterns and each phenotype, and then searched for enrichment in 221 predicted microRNA target gene sets. We found the predicted hsa-miR-219a-5p target gene set to be significantly enriched for genes (EPHA4, PKNOX1, ESR1, among others) whose methylation status is correlated with hippocampal volume independent of disease status. Our results were strengthened by significant associations between hsa-miR-219a-5p target gene methylation patterns and hippocampus-related neuropsychological variables. IPA pathway analysis of the respective predicted hsa-miR-219a-5p target genes revealed associated network functions in behavior and developmental disorders. Altered methylation patterns of predicted hsa-miR-219a-5p target genes are associated with a structural aberration of the brain that has been proposed as a possible biomarker for schizophrenia. The (dys)regulation of microRNA target genes by epigenetic mechanisms may confer additional risk for developing psychiatric symptoms. Further study is needed to understand possible interactions between microRNAs and epigenetic changes and their impact on risk for brain-based disorders such as schizophrenia.

The footprint of polygenic adaptation on stress-responsive cis-regulatory divergence in the Arabidopsis genus

Adaptation of a complex trait often requires the accumulation of many modifications to finely tune its underpinning molecular components to novel environmental requirements. The investigation of cis-acting regulatory modifications can be used to pinpoint molecular systems partaking in such complex adaptations. Here, we identify cis-acting modifications with the help of an interspecific crossing scheme designed to distinguish modifications derived in each of the two sister species, Arabidopsis halleri and A. lyrata Allele-specific expression levels were assessed in three environmental conditions chosen to reflect interspecific ecological differences: cold exposure, dehydration, and standard conditions. The functions described by Gene Ontology categories enriched in cis-acting mutations are markedly different in A. halleri and A. lyrata, suggesting that polygenic adaptation reshaped distinct polygenic molecular functions in the two species. In the A. halleri lineage, an excess of cis-acting changes affecting metal transport and homeostasis was observed, confirming that the well-known heavy metal tolerance of this species is the result of polygenic selection. In A. lyrata, we find a marked excess of cis-acting changes among genes showing a transcriptional response to cold stress in the outgroup species A. thaliana The adaptive relevance of these changes will have to be validated. We finally observed that polygenic molecular functions enriched in derived cis-acting changes are more constrained at the amino acid level. Using the distribution of cis-acting variation to tackle the polygenic basis of adaptation thus reveals the contribution of mutations of small effect to Darwinian adaptation.

Systematic proteome and proteostasis profiling in human Trisomy 21 fibroblast cells

Down syndrome (DS) is mostly caused by a trisomy of the entire Chromosome 21 (Trisomy 21, T21). Here, we use SWATH mass spectrometry to quantify protein abundance and protein turnover in fibroblasts from a monozygotic twin pair discordant for T21, and to profile protein expression in 11 unrelated DS individuals and matched controls. The integration of the steady-state and turnover proteomic data indicates that protein-specific degradation of members of stoichiometric complexes is a major determinant of T21 gene dosage outcome, both within and between individuals. This effect is not apparent from genomic and transcriptomic data. The data also reveal that T21 results in extensive proteome remodeling, affecting proteins encoded by all chromosomes. Finally, we find broad, organelle-specific post-transcriptional effects such as significant downregulation of the mitochondrial proteome contributing to T21 hallmarks. Overall, we provide a valuable proteomic resource to understand the origin of DS phenotypic manifestations.Trisomy 21 (T21) is a major cause of Down syndrome but little is known about its impact on the cellular proteome. Here, the authors define the proteome of T21 fibroblasts and its turnover and also map proteomic differences in monozygotic T21-discordant twins, revealing extensive, organelle-specific changes caused by T21.