Absolute quantification of transcription factors during cellular differentiation using multiplexed targeted proteomics

The cellular abundance of transcription factors (TFs) is an important determinant of their regulatory activities. Deriving TF copy numbers is therefore crucial to understanding how these proteins control gene expression. We describe a sensitive selected reaction monitoring-based mass spectrometry assay that allowed us to determine the copy numbers of up to ten proteins simultaneously. We applied this approach to profile the absolute levels of key TFs, including PPARγ and RXRα, during terminal differentiation of mouse 3T3-L1 pre-adipocytes. Our analyses revealed that individual TF abundance differs dramatically (from ∼250 to >300,000 copies per nucleus) and that their dynamic range during differentiation can vary up to fivefold. We also formulated a DNA binding model for PPARγ based on TF copy number, binding energetics and local chromatin state. This model explains the increase in PPARγ binding sites during the final differentiation stage that occurs despite a concurrent saturation in PPARγ copy number.

Dissecting Gene Regulatory Networks Using Targeted Quantitative Proteomics

Jovan Simicevic

Gene regulatory networks control gene expression levels, and therefore play an essential role in mammalian development and function. Regulation of gene expression is the result of a complex interplay between DNA regulatory elements and their binding partners, known as transcription factors (TFs). Due to their vital role in development, intercellular signalling, cell cycle and disease development, elucidating the mechanisms by which TFs regulate gene expression is of crucial importance in the vast majority of biological processes. In particular, understanding how each TF contributes to the expression output of its respective target gene in space and time will help to elucidate how gene regulatory networks (GRNs) behave under different physiological or pathological conditions. Although extensive work has been accomplished in characterizing the key TFs involved in many biological processes, almost no quantitative information is currently available in the literature. To get a deep insight into the complex mechanisms underlying the regulation of gene expression, we need to acquire quantitative information, since TF abundance within the cell can be linked to their transcriptional capabilities. Such information would be of utmost importance to build accurate in silico quantitative DNA binding models that could predict and explain the particular properties of gene regulatory mechanisms. The quantification of TFs is a difficult task due their natural low abundance in cells, and their reliable detection is therefore very much dependent on the overall sensitivity of current technologies. In recent years, a new MS-based technology termed selected reaction monitoring (SRM) has gained popularity due to the targeted nature of its approach that allows the detection and quantification of proteins in complex samples with an exceptional sensitivity and specificity. I will show in this thesis, this approach is particularly well suited for targeting low abundant proteins such as TFs, which are otherwise difficult to identify with conventional shotgun proteomics experiments. Consequently, the main focus of my thesis research project entailed the development of an SRM-based platform aimed at quantifying TFs in absolute amounts based on in vitro protein expression during the terminal stage of adipogenesis, using the pre-adipocyte 3T3-L1 cell line. Interestingly, our initial efforts led to the creation of an atlas of TF-specific peptide data, which could be readily used for the design of quantitative assays. In the first phase, abundance measurements in terms of copies per cell were derived at precise differentiation time-points for two major adipogenic players, PPARγ and RXRα. In the second phase, we expanded the number of adipogenic TFs that can be monitored in one assay, allowing for the quantification of up to 10 TFs in one single, integrated SRM run. Such upscale increases the practical usefulness of the methodology while reducing the associated costs, and ultimately allows for non-negligible time-savings. The availability of absolute protein copy number data permitted us ultimately to examine the relationship between the number of genome-wide DNA binding events and TF molecules. We derived a quantitative DNA binding model that allowed the prediction of the number of PPARγ ChIP-seq binding events given its nuclear abundance, chromatin state, and DNA binding energetics. As such, we were able to explain the paradoxical observation of a significant increase in PPARγ binding sites despite a saturation in the number of PPARγ molecules. We thus demonstrate how TF abundance data can be modeled in conjunction with large-scale DNA occupancy and chromatin state data to further our understanding of gene regulatory mechanisms mediating cellular differentiation. We are now starting to build on our pioneering work to quantify in absolute terms key players of the entire, core adipogenic GRN, as such aiming to provide a quantitative explanation of the regulatory mechanisms at play during the terminal phase of adipocyte differentiation. Moreover, to increase the explicative power of our methodology and to alleviate the throughput limitation that comes with obtaining absolute protein measurements, we decided to perform copy-number estimates for a larger set of adipogenic TFs utilizing a modified version of our original approach. At the cost of a modest loss of accuracy, we are now aiming to develop a sensitive and robust methodology that will allow the quantification of entire GRNs at low cost and in a time-effective manner. This is consistent with the overall goal in life sciences or clinical research to improve our ability to accurately and reproducibly quantify entire pathways or biological networks to improve our systems understanding of biological processes.

EPFL2012

Development and application of experimental tools to elucidate the gene regulatory network underlying adipogenesis

Carine Delattre-Gubelmann

Excess fat mass in obese individuals, which is characterized by an increase of white adipocyte cell size and number, significantly raises the risk of developing metabolic syndrome symptoms as well as cancer. A detailed understanding of the transcriptional mechanisms mediating white adipocyte differentiation (i.e., adipogenesis) is required to counteract the growing obesity epidemics. In this thesis, we have implemented two combinatory approaches to detect new protein-DNA interactions in the mouse, one of which targets the adipogenic gene regulatory network (GRN) that underlies the white adipocyte differentiation process. As a first approach to shed light on GRNs, we have developed and validated a powerful approach that combines a high-throughput yeast one-hybrid-based (Y1H) system to detect transcription factors (TFs)-DNA element interactions, together with a novel microfluidics-based technique, enabling the identification and characterization of individual binding sites for detected TFs within the respective regulatory sequences at unprecedented throughput and resolution. Using well-described regulatory elements as well as an uncharacterized enhancer which can play also a role in adipogenesis, we show that our approach in a first phase uncovers many known as well as novel TF-DNA interactions, of which a large proportion were independently validated. In addition, we further demonstrate how our approach can be used in a second phase to characterize detected interactions by enabling the generation of a relative DNA occupancy landscape over the length of the respective DNA element. Furthermore, we provide evidence that this system enables the detection of novel interactions that may have in vivo relevance. Given the strict requirements for direct and well-characterized protein-DNA interaction data to generate and model gene regulatory networks, the presented two-tiered approach should be of great value to enhance our understanding of the regulatory principles underlying mouse gene expression. To identify transcription factors involved in white adipocyte differentiation, we overexpressed TFs in preadipocytes and quantified their effect on differentiation. To this end, we have created a mouse open-reading frame resource consisting of more than 750 fully sequence-verified TF clones. We transferred these TF clones to a Tet-On lentiviral vector and used them to infect white preadipocytes. Based on microscopy-assisted quantification of lipids after induction of differentiation, we identified putatively novel TF candidates that strongly enhanced differentiation based on the increased lipid accumulation, indicating a potential role in adipogenesis. Gene expression and ChIP-seq experiments have been performed for the three top candidates (ZEB1, ZFP30 and ZFP277) to further study their function within the adipogenic regulatory network. To assess whether the effect of these TFs on adipogenesis could be reproduced in vivo, murine adipose stromal-vascular fraction containing adipocyte precursor cells were implanted into mice fed a high-fat diet, after each of the three TF candidates were knockdown or overexpressed in these cells. This experiment and the RNA-seq analyses are currently being processed while this thesis is being written. Furthermore, due to the absence of quantitative real-time PCR (qPCR) primer designing tools that would allow for gene-specific expression profiling in a high-throughput fashion, we design a user-friendly platform: GETPrime. This platform uniquely combines and automates several features critical to optimal qPCR primer design for all annotated Homo sapiens, Mus musculus, Caenorhabditis elegans, Drosophila melanogaster and Danio rerio genes in the Ensembl database. GETPrime primers have been extensively validated experimentally, demonstrating their high quality as well as high transcript specificity in complex samples.

EPFL2013

Computational study of transcription factor binding sites

Romain Fernand Pietro Groux

Any living organism contains a whole set of instructions encoded as genes on the DNA. This set of instructions contains all the necessary information that the organism will ever need, from its development to a mature individual to environment specific responses. Since all these instructions are not needed at the same time, the gene expression needs to be regulated. Eukaryotic genomes are stored inside nuclei as chromatin. The chromatin is the association of DNA with dedicated storage proteins - the histones - and the necessary machinery to regulate and express genes (RNA polymerases or RNAPs). In the nuclei, histones are assembled into octamers around which are wrapped ~148bp of DNA. This structure is known as the nucleosome. The repetition of nucleosomes along the genome allows to drastically compact the genome, eventually allowing to fit it inside the nucleus. However, this comes at the cost of rendering the DNA sequence inaccessible to DNA readers, such as the RNAPs and transcription factors (TFs). TFs are a class of proteins that have the remarkable property of recognizing and binding specific DNA sequences. More striking, each TF can recognize a multitude of different - but similar - DNA sequences providing TFs with a wide sequence specificity range. Eventually, this allows the cell to recruit TFs at dedicated locations in the genome called regulatory elements (REs). The action of TFs at REs is crucial to gene expression. Indeed, TFs are involved in many processes such as the opening of the chromatin structure or the recruitment of RNAPs. However if TFs can influence the chromatin structure, the opposite is also true as histones impede TF binding on DNA. Thus the regulation of genes relies on a subtle and complex crosstalk between the chromatin and TFs. To better understand how TFs and chromatin interact together to regulate gene expression, I lead several projects prospecting TF binding specificity and the chromatin structure at REs in human. First, I used ENCODE next generation sequencing (NGS) data to explore how TF binding influences the nearby nucleosome organization and the propensity of some TFs to bind together. The results suggest that regular nucleosome arrays are found near all TFs. They also point out two special cases. When CTCF binds with the cohesin complex, it seems to drive the nucleosome organization, which is a unique feature among all TFs investigated. Additionally I present evidence supporting that EBF1 is a pioneer factor - a special class of TFs able to bind nucleosome. Secondly, I developed several clustering algorithms and software to partition genomic regions according to NGS data and/or on their DNA sequences. These methods allow to discover important trends, for instance different nucleosome architectures . I illustrated the usefulness of these methods for the study of chromatin accessibility data and the identification of REs. Thirdly, I participated to the assessment of SMiLE-seq, a new microfluidic device that generates TF specificity data. The creation of TF specificity models and their comparison with other publicly available models demonstrated the value of SMiLE-seq to study TF specificity. Finally, I participated in the development of a software that predicts TF binding sites. A careful benchmarking suggested that this software is - at the time of writing - the best available software in terms of speed while showing other performances similar to its competitors.

EPFL2020