Lei Mao

A Large Number of Protein Expression Changes Occur Early in Life and Precede Phenotype Onset in a Mouse Model for Huntington Disease

Huntington disease (HD) is fatal in humans within 15–20 years of symptomatic disease. Although late stage HD has been studied extensively, protein expression changes that occur at the early stages of disease and during disease progression have not been reported. In this study, we used a large two-dimensional gel/mass spectrometry-based proteomics approach to investigate HD-induced protein expression alterations and their kinetics at very early stages and during the course of disease. The murine HD model R6/2 was investigated at 2, 4, 6, 8, and 12 weeks of age, corresponding to absence of disease and early, intermediate, and late stage HD. Unexpectedly the most HD stage-specific protein changes (71–100%) as well as a drastic alteration (almost 6% of the proteome) in protein expression occurred already as early as 2 weeks of age. Early changes included mainly the up-regulation of proteins involved in glycolysis/gluconeogenesis and the down-regulation of the actin cytoskeleton. This suggests a period of highly variable protein expression that precedes the onset of HD phenotypes. Although an up-regulation of glycolysis/gluconeogenesis-related protein alterations remained dominant during HD progression, late stage alterations at 12 weeks showed an up-regulation of proteins involved in proteasomal function. The early changes in HD coincide with a peak in protein alteration during normal mouse development at 2 weeks of age that may be responsible for these massive changes. Protein and mRNA data sets showed a large overlap on the level of affected pathways but not single proteins/mRNAs. Our observations suggest that HD is characterized by a highly dynamic disease pathology not represented by linear protein concentration alterations over the course of disease.

Proteomic shifts in embryonic stem cells with gene dose modifications suggest the presence of balancer proteins in protein regulatory networks

Large numbers of protein expression changes are usually observed in mouse models for neurodegenerative diseases, even when only a single gene was mutated in each case. To study the effect of gene dose alterations on the cellular proteome, we carried out a proteomic investigation on murine embryonic stem cells that either overexpressed individual genes or displayed aneuploidy over a genomic region encompassing 14 genes. The number of variant proteins detected per cell line ranged between 70 and 110, and did not correlate with the number of modified genes. In cell lines with single gene mutations, up and down-regulated proteins were always in balance in comparison to parental cell lines regarding number as well as concentration of differentially expressed proteins. In contrast, dose alteration of 14 genes resulted in an unequal number of up and down-regulated proteins, though the balance was kept at the level of protein concentration. We propose that the observed protein changes might partially be explained by a proteomic network response. Hence, we hypothesize the existence of a class of “balancer” proteins within the proteomic network, defined as proteins that buffer or cushion a system, and thus oppose multiple system disturbances. Through database queries and resilience analysis of the protein interaction network, we found that potential balancer proteins are of high cellular abundance, possess a low number of direct interaction partners, and show great allelic variation. Moreover, balancer proteins contribute more heavily to the network entropy, and thus are of high importance in terms of system resilience. We propose that the “elasticity” of the proteomic regulatory network mediated by balancer proteins may compensate for changes that occur under diseased conditions.

Reverse engineering a mouse embryonic stem cell-specific transcriptional network reveals a new modulator of neuronal differentiation

Gene expression profiles can be used to infer previously unknown transcriptional regulatory interaction among thousands of genes, via systems biology ‘reverse engineering’ approaches. We ‘reverse engineered’ an embryonic stem (ES)-specific transcriptional network from 171 gene expression profiles, measured in ES cells, to identify master regulators of gene expression (‘hubs’). We discovered that E130012A19Rik (E13), highly expressed in mouse ES cells as compared with differentiated cells, was a central ‘hub’ of the network. We demonstrated that E13 is a protein-coding gene implicated in regulating the commitment towards the different neuronal subtypes and glia cells. The overexpression and knock-down of E13 in ES cell lines, undergoing differentiation into neurons and glia cells, caused a strong up-regulation of the glutamatergic neurons marker Vglut2 and a strong down-regulation of the GABAergic neurons marker GAD65 and of the radial glia marker Blbp. We confirmed E13 expression in the cerebral cortex of adult mice and during development. By immuno-based affinity purification, we characterized protein partners of E13, involved in the Polycomb complex. Our results suggest a role of E13 in regulating the division between glutamatergic projection neurons and GABAergic interneurons and glia cells possibly by epigenetic-mediated transcriptional regulation.

Pronounced alterations of cellular Metabolism and structure due to hyper- or hypo-osmosis

Cell volume alteration represents an important factor contributing to the pathology of late-onset diseases. Previously, it was reported that protein biosynthesis and degradation are inversely (trans) regulated during cell volume regulation. Upon cell shrinkage, protein biosynthesis was up-regulated and protein degradation down-regulated. Cell swelling showed opposite regulation. Recent evidence suggests a decrease of protein biodegradation activity in many neurodegenerative diseases and even during aging; both also show prominent cell shrinkage. To clarify the effect of cell volume regulation on the overall protein turnover dynamics, we investigated mouse embryonic stem cells under hyper- and hypotonic osmotic conditions using a 2-D gel based proteomics approach. These conditions cause cell swelling and shrinkage, respectively. Our results demonstrate that the adaption to altered osmotic conditions and therefore cell volume alterations affects a broad spectrum of cellular pathways, including stress response, cytoskeleton remodeling and importantly, cellular metabolism and protein degradation. Interestingly, protein synthesis and degradation appears to be cis-regulated (same direction) on a global level. Our findings also support the hypothesis that protein alterations due to osmotic stress contribute to the pathology of neurodegenerative diseases due to a 60% expression overlap with proteins found altered in Alzheimers, Huntingtons, or Parkinsons disease. Eighteen percent of the proteins altered are even shared with all three disorders.

Aging in mouse brain is a cell/tissue-level phenomenon exacerbated by proteasome loss.

Biological aging is often described by its phenotypic effect on individuals. Still, its causes are more likely found on the molecular level. Biological organisms can be considered as reliability-engineered, robust systems and applying reliability theory to their basic nonaging components, proteins, could provide insight into the aging mechanism. Reliability theory suggests that aging is an obligatory trade-off in a fault-tolerant system such as the cell which is constructed based on redundancy design. Aging is the inevitable redundancy loss of functional system components, that is proteins, over time. In our study, we investigated mouse brain development, adulthood, and aging from embryonic day 10 to 100 weeks. We determined redundancy loss of different protein categories with age using reliability theory. We observed a near-linear decrease of protein redundancy during aging. Aging may therefore be a phenotypic manifestation of redundancy loss caused by nonfunctional protein accumulation. This is supported by a loss of proteasome system components faster than dictated by reliability theory. This loss is highly detrimental to biological self-renewal and seems to be a key contributor to aging and therefore could represent a major target for therapies for aging and age-related diseases.

Kainate promotes alterations in neuronal RNA splicing machinery.

Kainate, a glutamate analogue, activates kainate and AMPA receptors inducing strong synaptic activation. Systemic kainate application to rodents results in seizures, neurodegeneration, and neuronal remodeling in the brain. It is therefore used to investigate molecular mechanisms responsible for these conditions. We analyzed proteome alterations in murine primary cortical neurons after 24 h of kainate treatment. Our 2-D gel based proteomics approach revealed 91 protein alterations, some already associated with kainate-induced pathology. In addition, we found a large number of proteins which have not previously been reported to be associated with kainate-induced pathology. Functional classification of altered proteins revealed that they predominantly participate in mRNA splicing and cytoskeleton remodeling.

PROTEOMER: A workflow-optimized laboratory information management system for 2-D electrophoresis-centered proteomics.

In recent years proteomics became increasingly important to functional genomics. Although a large amount of data is generated by high throughput large‐scale techniques, a connection of these mostly heterogeneous data from different analytical platforms and of different experiments is limited. Data mining procedures and algorithms are often insufficient to extract meaningful results from large datasets and therefore limit the exploitation of the generated biological information. In our proteomic core facility, which almost exclusively focuses on 2‐DE/MS‐based proteomics, we developed a proteomic database custom tailored to our needs aiming at connecting MS protein identification information to 2‐DE derived protein expression profiles. The tools developed should not only enable an automatic evaluation of single experiments, but also link multiple 2‐DE experiments with MS‐data on different levels and thereby helping to create a comprehensive network of our proteomics data. Therefore the key feature of our “PROTEOMER” database is its high cross‐referencing capacity, enabling integration of a wide range of experimental data. To illustrate the workflow and utility of the system, two practical examples are provided to demonstrate that proper data cross‐referencing can transform information into biological knowledge.

Tracking the Structure of Protein Interaction Network via Multiple Genetic Perturbations on Mouse Embryonic Stem Cells — Implementation of the Entropy Maximization Principle

Lei Mao1, Rossella De Cegli2, Mario Lauria2, Grit Nebrich1, Jean Maurice Delabar3, Yann Herault4, Gilda Cobellis2 and Joachim Klose1 1Institute for Medical Genetics, Charite – Universitatsmedizin, Berlin, 2Telethon Institute of Genetics and Medicine, Napoli, 3Universite Paris, Paris, 4Institut de Genetique Biologie Moleculaire et Cellulaire, IGBMC, Institut clinique de la Souris, ICS, CNRS, INSERM, Universite de Strasbourg, UMR7104, UMR964, Illkirch 1Germany 2Italy 3,4France