Meet the Trainers – Raffaele Calogero, Jeroen Krijgsveld, Lennart Martens

This month we have not one but three trainers we would like to introduce you to. Meet Raffaele Calogero, Jeroen Krijgsveld and Lennart Martens, organisers and trainers at the EMBL Course: Analysis and Integration of Transcriptome and Proteome Data (2 – 7 February 2020).

What is your research focus and why did you choose to become a scientist?

RC: I have a background in molecular biology, but since 1999 I have been working in transcriptomics data analysis and tool development. I became a scientist because I loved the idea of investigating new unknown topics every day.

LM: My research focus is bioinformatics, which stems directly from becoming completely taken in by computers at an early age. Like many of my generation, I started out with a Commodore 64 when I was 8, and had written my first (very, very simple :)) program in BASIC after two weeks. But my link with science came about after I stumbled across an episode of Jacob Bronowski’s ‘The Ascent of Man’ a few years later (I think I was 10 or 11) on Saturday morning on television, and this inspired a lifelong interest in anything scientific. All in all, I consider myself extremely lucky to be able to combine my passion for computers with my passion for science, and make a living out of it!

JK: My research focus is on proteomics, where we develop and apply mass spectrometry-based technologies to understand the intricate machineries used by cells to respond to their environment. Curiosity to understand how things work is as an important motivation for our current research as it was for me during my studies. Becoming a scientist has always felt a natural path for me to follow, and I have never seriously considered doing anything else. Although at the same time, when looking back, I realise how fortunate I have been being given the opportunity to follow my interest and move between fields. This is due in no small part to having training opportunities and stimulating environments at the various stages of my career!

Where do you see this field heading in the future?

RC: Data integration is the new upcoming frontier for biological studies, and transcriptomics and proteomics are going to be important players in the game.

JK: Proteomics has come a long way in the last 2 decades, reaching its current ability to characterise thousands of proteins in a single experiment, and do this across multiple experiments. I expect that during the next 5-10 years we will see how these investments will pay off, helping to understand processes at the protein level very similar to the way this has been achieved in genomics. After transitioning from a technology exclusively for physicists to a useful tool used by biologists, the next big step to be made is to implement mass spectrometry in the clinical arena. Integration with genomic methods and data types will be key to drive this forward.

LM: Bioinformatics is slowly taking over much of large-scale biology. Of course, focused biochemical and molecular biology research will remain the staple of the life sciences, but our ability to process and interrogate very large amounts of (potentially even heterogeneous) data is transforming our ability to generate new leads, new ideas, and to shed a much more holistic view on life at the molecular level. And with the advent of extremely powerful parallel computing, the field of machine learning has received an enormous boost, further increasing our ability to spot previously hidden patterns in our data. So it’s an exciting time in bioinformatics, with lots of new areas of research opening up to us for the first time! We’re going to continue having a lot of fun for a long time!

How has training influenced your career?

JK: This has been fundamental, in different ways. One is in a formal way, where classes and courses have been key to shaping a foundation, and helping to fill specific knowledge gaps. The second is less formal, which to me has been at least equally important, where I got trained by working in different research environments together with people of different backgrounds.

RC: In my role as an associate professor, training is part of my everyday life. I am involved in many advanced courses through my university, both in Europe and in Asia, and I think that courses are useful for both participants and instructors. The former grab the knowledge from the latter and the latter get new fresh view points from the former.

LM: Many of the excellent teachers I have studied with have left a deep impression on me, and guided my career path indirectly. As to providing training, it is one of the aspects of my job that I most cherish. For me, the importance of sharing and spreading knowledge resonates very closely to the core principles of science: by sharing and exchanging knowledge, all participants in the teaching experience gain something. In all, the biggest contribution that training has had to my career, is in greatly enhancing my ability to be creative in a productive way.

What is your number one tip for people looking for scientific training?

LM: Go for it, and seek out courses taught by those rare experts that both intimately know their field, but can also clearly communicate about the essence of that field. You don’t want to only grasp the current ‘hot’ method at a user level, but rather want to understand how that field works, how it has evolved, and how the current methods fit in with this. Courses that focus on these aspects deliver knowledge that lasts for much longer!

JK: It is two-fold: find a course that gives you the theoretical foundation of an area or topic you would like to venture into. After this, I think it is essential to ‘internalise’ this knowledge, so I would advise you to find a collaborator or colleague to help you along the path to use the acquired knowledge. It is like driving a car: you need to get a driver’s license to be allowed on the road, but you really learn how to do it by practicing afterwards.

If you weren’t a scientist, what would you be?

RC: Definitively a Medical Doctor.

LM: Most likely a software developer or systems architect of some sort. I worked in industry for a few years after my Masters degree, and then chose to pursue a PhD rather than take a very nice position at Microsoft. So if I had made a different choice then, I’d probably still be in IT!

JK: I have never really been in a situation where I had to choose between being a scientist or do something else, but thinking about it now I would like to think I would be a writer, a novelist. The reason is that I like writing, and it is part of my every day job, however writing papers and proposals comes with a lot of constraints and requirements. Making up stories without the boundaries of page limits, allowing fantasy and imagination, in an expanded vocabulary and, above all, in my mother tongue sounds all very appealing to me. Maybe one day. 🙂

You are organising the EMBL Course: Analysis and Integration of Transcriptome and Proteome Data (2 – 7 February 2020). What is the greatest benefit of the course for the scientific community and what could the techniques in this course be used for in the bigger picture?

JK: The greatest benefit is that it allows participants to have a look ‘over the fence’: most participants usually have a background either in genomics or proteomics, and being exposed to both aspects – along with the technologies and methods used in these fields – allows them to ask questions they may not have thought about before. What we try to achieve is that they could even go a step further, and integrate the new knowledge in their research to gain a more complete picture of the biological systems they are using.

LM: The course is unique in that it brings together the two key fields of research to understand cellular functional: transcriptomics, which traces the usage of the information in the genome in a given cell, and proteomics, which shows the actual biochemical and signalling capability of a given cell. There is important information at each of these levels, and these are moreover more complementary than many might think.

Of course, gene regulation provides important insights into a cell’s internal processes, but proteins are not only made or broken down, they are also closely regulated at the protein level. A complete picture of a cellular process thus requires insight into (and thus data from) both the gene expression level as well as the protein level. This is where this course provides the right skills for researchers; teaching them how to integrate data from their transcriptomics and proteomics experiments.

The usefulness for the bigger picture is therefore more or less implicit: by combining transcriptomics and proteomics, a much more complete picture of cellular functioning can be obtained. And let’s be honest, isn’t that what all of us really want to obtain, after all? 🙂

RC: I think the phrase of Claudia Manzoni in the abstract of her paper in Briefing in Bioinformatics in 2016 (Volume 19, Issue 2, March 2018, Pages 286–302) “Our work intends to target students and researchers seeking knowledge outside of their field of expertise and fosters a leap from the reductionist to the global-integrative analytical approach in research” is a perfect explanation for our course.


Interested in this course? Apply by 10 November!

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Cooking for EMBL Events

Head of the EMBL Canteen and Cafeteria Michael Hansen (front, in grey) with his dedicated team. PHOTO: Marietta Schupp/EMBL

Anyone who has ever set foot in the EMBL Canteen is sure to go away wanting more. It’s no coincidence that the canteen has a reputation for serving some of the best food in the Heidelberg area.

So what is their secret?

Head chef Michael Hansen’s team of 29 (23 people in the canteen and 6 in the cafeteria) work tirelessly to cater for over 800 members of staff daily and over 6000 conference and course participants annually. Besides the great dedication of his staff – which involves regular evening and weekend shifts – he places great emphasis on the quality and freshness of the groceries they use.

“We buy our meat, fruit, vegetables, bread and eggs from local suppliers. For us it is important that the groceries have the shortest route so that they are as fresh as possible when they get to us. Our furthest supplier is 90 km away. For food that is not produced in Germany, such as olive oil, we do have to order from abroad, but we do that directly with the producers without going through a distributor.”

Everything is then freshly prepared and cooked before it is served, with close attention paid to nutritional value. This is especially important for the EMBL kindergarten, which caters for over 100 children of staff.

In 2018, the EMBL Canteen cooked for 6430 course and conference participants, and for this purpose used:

  • 32 crates of salad
  • 160 kg onions/garlic (imagine how many tears must have been shed!)
  • 225 kg fish
  • 225 kg potatoes
  • 290 kg meat
  • 803 kg fruit
  • 935 kg vegan/vegetarian dishes
  • 1,607 kg vegetables
  • 1,376,020 l coffee was served

“In the EMBL spirit, the canteen team is truly international, employing people from 12 nations who, despite their differences, have one thing in common – their love for cooking!  One of the reasons I became a cook is because of food’s power to unite people. And here I see this every day. Preparing one meal requires real team work. Everybody gets together and takes one step of the process so that all is done in the most efficient way, but still has great taste.”

Here is one of the canteen’s most popular recipes, named after Thomas Graf, EMBL Alumnus (1983 – 1998) and currently Senior Scientist at the Centre for Genomics Regulation in Barcelona, Spain:

Thomas Graf potatoes

1kg potatoes

100 ml oyster sauce

1 clove of garlic (pressed)

1 tsp honey

Pinch of salt

Black pepper

1 tbsp oil

Wash the potatoes and cut them into wedges without peeling them. Add all the ingredients and mix well. Preheat the oven to 180°C, place the potatoes on a baking sheet and bake for 40 min.


PHOTO: Marietta Schupp/EMBL
PHOTO: Marietta Schupp/EMBL
PHOTO: Marietta Schupp/EMBL
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Best Poster Awards – EMBO|EMBL Symposium: Systems Genetics: From Genomes to Complex Traits

The first EMBO|EMBL Symposium: Systems Genetics: From Genomes to Complex Traits (29 Sep – 2 Oct 2019) brought together over 150 international researchers to discuss how genetic variation alters molecular mechanisms to cause phenotypic changes amongst individuals, including quantitative traits and human disease. 

From the 77 posters that were presented on-site, 3 were selected as the winners after a shortlist round by popular vote, followed by a selection round by the conference organisers.

Resolving noise-control conflict by gene duplication

Michal Chapal is a PhD student in Naama Barkai’s lab at the Weizmann Institute of Science in Israel. PHOTO: Michal Chapal

Authors: Michal Chapal, Sefi Mintzer, Sagie Brodsky, Miri Carmi, Naama Barkai, Weizmann Institute of Science, Israel

Gene duplication promotes adaptive evolution in two principle ways: allowing one duplicate to evolve a new function and resolving adaptive conflicts by splitting ancestral functions between the duplicates. In an apparent departure from both scenarios, low-expressing transcription factor duplicates commonly regulate similar sets of genes and act in overlapping conditions. To examine for possible benefits of such apparently redundant duplicates, we examined the budding yeast duplicated stress regulators Msn2 and Msn4. We show that Msn2,4 indeed function as one unit, inducing the same set of target genes in overlapping conditions, yet this two-factor composition allows its expression to be both  environmental-responsive and with low-noise, thereby resolving an adaptive conflict that inherently limits expression of single genes. Our study exemplified a new model for evolution by gene duplication whereby duplicates provide adaptive benefit through cooperation, rather than functional divergence: attaining two-factor dynamics with beneficial properties that cannot be achieved by a single gene.

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Deep learning on single-cell ATAC-seq data to decipher enhancer logic

Ibrahim Taskiran is a PhD student in Stein Aerts’ lab at the VIB & KU Leuven Center for Brain & Disease Research. PHOTO: Ibrahim Taskiran

Authors: Ibrahim Ihsan Taskiran, Liesbeth Minnoye, Carmen Bravo Gonzalez-Blas, Sara Aibar Santos, Gert Hulselmans, Valerie Christiaens, Stein Aerts
KU Leuven – VIB, Belgium

Single-cell ATAC-seq provides new opportunities to study gene regulation in heterogeneous cell populations such as complex tissues or dynamic processes. We recently developed a probabilistic topic modeling approach, called cisTopic, to predict regulatory topics and sets of co-accessible enhancers from scATAC-seq data. Here, we apply deep learning approaches to analyze these sets of co-accessible enhancers, with the goal to predict the spatiotemporal pattern of enhancer accessibility directly from the enhancer sequence. We trained different types of Artificial Neural Networks, including a hybrid model that combines Convolutional and Recurrent Neural Networks. By applying this approach to a cohort of melanoma patient samples and Drosophila eye disc, we show that key transcription factors can be identified from the convolutional filters. In addition, we use the trained model to analyze the motif architecture in enhancers, such as motif combinations and relationship to nucleosome preferences. We furthermore exploit network explaining methods to predict the impact of somatic mutations, using publicly available SNP databases and in-house whole genome sequencing of inbred fly lines. Currently, to validate our models we are testing (mutated) synthetic cell state specific enhancers using massively parallel enhancer reporter assays (MPRA). In conclusion, training deep learning models on single-cell epigenomics data sets has multiple applications to understand the underlying enhancer logic and decipher gene expression programs.

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ROADdt: Regulation network remodeling along disease development trajectories

Celine Sin is a Postdoctoral Fellow in Jörg Menche’s Group at the CeMM Research Center for Molecular Medicine, Austria. PHOTO: Celine Sin

Authors: Celine Sin, Jörg Menche
CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Austria

The human body is comprised of over 200 different cell types varying in size, shape, and function. The differentiation and subsequent maintenance of these different phenotypic states are governed by complex gene regulatory networks that dynamically orchestrate the activation and deactivation of genes. Abnormalities in these networks may lead to dysfunctional expression programs, e.g. uncontrolled cell proliferation. In order to understand the conditions resulting in disease, we must understand the underlying gene regulatory networks governing
the gene expression program. As cells move through the differentiation space, the networks that govern gene regulation are remodeled in order to achieve the appropriate gene expression program. While statistical physics and network theory have demonstrated numerous relationships between the structure of networks and the dynamic processes that act on them,
few studies link these mathematically rigorous principles to gene regulatory networks, none at the level of cell-trajectory-states. The overall goal of this project is to understand the fundamental architecture of gene regulatory networks associated with cell differentiation processes in disease. We hypothesize that the gene regulatory networks of different
cell-trajectory-states along the differentiation trajectory – e.g. transitory, branching, or terminal states – are each characterized by distinct structural features. I will present our first steps in this direction, starting from single-cell RNA seq profiles of tumors. Ultimately, we expect that detailed characterization of the gene regulatory networks in these disease processes will reveal basic principles applicable to other diseases and cell  developmental processes.

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Working on your own conference poster? Then check out 10 tips to create a scientific poster people want to stop by .

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New EMBL Course and Conference postcards

We know how much our course and conference participants love the postcards at the registration desk, so we have created a new batch. These will be available from October on for you to take as a souvenir or send to your loved ones. EMBL offers a postal service on-site, so you can even send them right on that day!

A pop-art vision of yeast cells. Design by Petra Riedinger

EMBO Conference Series: Protein Synthesis and Translational Control; Graphics: Petra Riedinger · Image: Marietta Schupp
EMBO Workshop: Imaging Mouse Development; Image: Manuel Eguren/EMBL
EMBO Workshop: Integrating Systems Biology: From Networks to Mechanisms to Models; Graphics: Beata Science Art
EMBL Conference: The Human Microbiome; Graphics: Petra Riedinger
EMBO Conference: Quantitative Principles in Biology; Graphics: EMBL Design Team
EMBO | EMBL Symposium: Organoids: Modelling Organ Development and Disease in 3D Culture; Graphics: Beata Science Art
EMBO | EMBL Symposium: Systems Genetics: From Genomes to Complex Traits; Graphics: EMBO Design Team
EMBO | EMBL Symposium: The Four-Dimensional Genome; Graphics: EMBO Design Team
EMBO | EMBL Symposium: Quality Control – From Molecules to Organelles; Graphics: EMBO Design Team
EMBO Conference Series: Protein Synthesis and Translational Control; Graphics: Petra Riedinger · Image: Marietta Schupp
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Multiomics to Mechanisms Symposium – Best Poster Awards

The recent EMBO|EMBL Symposium: Multiomics to Mechanisms – Challenges in Data Integration (11-13 Sep 2019) addressed ways of integrating large-scale biological data across the different omics fields.

258 researchers from various fields gathered in Heidelberg last week to listen to 36 talks and engage with 146 poster presenters. Here we present the posters of 5 scientists who received best poster awards at the conference by popular vote.

Benchmarking of multi-omics joint  dimensionality reduction (DR) approaches for cancer study

Laura Cantini is a CNRS Research Scientist at IBENS in France.

Authors: Laura Cantini (1), Pooya Zakeri (2), Aurelien Naldi (1), Denis Thieffry (1), Elisabeth Remy (3), Anaïs Baudot (2)

Dimensionality Reduction (DR), decomposing data into low-dimensional spaces while preserving most of their information content, is among the most prevalent machine learning techniques in data mining. With the advent of high-throughput technologies, high-dimensional data have become a standard in biology, emphasizing the use of DR. This phenomenon is particularly pronounced in cancer biology, where consortia have profiled thousands of patients for multiple molecular assays (“multi-omics”), including at the emerging single-cell scale. DR approaches have been mainly applied to single omics data leading to cancer subtyping, tumor sub-clones quantification and immune infiltration quantification. Recently, DR approaches designed to jointly analyze multiple omics have been proposed. Integrative DR methods are based on various mathematical assumptions, ranging from extensions of CCA, tensors, or more general data fusion approaches, which makes difficult to chose which method to apply.
In this context, we here in-depth benchmark multi-omics DR approaches using: i) artificial multi-omics cancer data ii) multi-omics bulk data from 10 different cancer types downloaded from TCGA iii) multi-omics single-cell data from cancer cell lines In (i), the capability of the various methods to predict the clustering ground truth was found strongly sensible to the size of the clusters, with intNMF, RGCCA, MCIA and JIVE being the more robust methods. For (ii), MCIA, RGCCA, MOFA and JIVE more consistently identified factors associated to survival, clinical annotations and biological annotations. Finally in (iii), despite never being applied to single-cell data, tICA and MSFA outperformed other methods for their ability to cluster  single cells based on their cell line of origin. Overall, our results show that RGCCA, MCIA and JIVE perform consistently better across the three scenarios. This suggests that a mathematical formulation, based on the search of omic-specific factors whose inter-dependence is maximized, better approximates the nature of multi-omics data.

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(1) Institut de Biologie de l’Ecole Normale Superieure IBENS, France, (2) Aix Marseille University, INSERM, MMG, CNRS, France, (3) Aix Marseille University, CNRS, France


Single-cell transcriptome and chromatin accessibility data integration reveals cell specific signatures

Andrés Felipe is a PhD student at the German Cancer Research Center in Heidelberg, Germany.

Authors: Andres Quintero (1), Anne-Claire Kröger (2), Carl Herrmann (2)

The ability to integrate multiple layers of omics data will play an essential role in understanding the complex interplay of different molecular mechanisms that give rise to cellular diversity. In particular, single-cell multi-omics studies provide an enormously valuable source of information, allowing the characterization of different cell states under different biological contexts. However, the integration of distinct cellular modalities to disentangle the regulatory networks and pathways that explain cell identity is still a challenge.Here we introduce Integrative Iterative Non-negative Matrix Factorization (i2NMF), a computational method to dissect cell type associated signatures from multi-omics data sets. i2NMF takes full advantage of data sets with multiple modalities for the same sample or cell, defining cell type-specific features and discerning the shared and specific contribution of each omics type to the identification of different cell types. We applied i2NMF to an early human embryo single-cell multi-omics data set for which scRNA-seq and scATAC-seq profiles were available for every single cell, identifying master transcription factors at the morula and blastocyst stages. Finally, i2NMF is also able to integrate different modalities across multiple experiments. We used this functionality to extract cell-type specific molecular signatures from two complementary datasets of the mouse visual cortex, comprising scATAC-seq and scRNA-seq data. i2NMF was implemented on TensorFlow, presenting a scalable framework and allowing its efficient execution under multiple systems. Our results demonstrate that i2NMF is a useful tool to identify cell-type specific signatures and dissect their underlying molecular features.

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(1) German Cancer Research Center (DKFZ), Germany, (2) University Hospital Heidelberg, Germany


Linking signalling and metabolomic footprints with causal networks

Aurélien Dugourd is a PhD student in mechanistic modelling at JRC Combine, RWTH Aachen, Germany.

Aurélien Dugourd (1), Christoph Kuppe (1), Rafael Kramann (1), Julio Saez-Rodriguez (2)

Renal clear cell carcinomas (RCCC) are the result of a system-wide dysregulation of signaling and metabolic functions  originating from multiple factors. Characterizing cellular molecular machineries across multiple omic layers is a very powerful strategy to understand the cellular effects of such dysregulations. In this study, we performed metabolomics and phosphoproteomics from RCCC tissue in comparison to the non-cancerous kidney tissue in a cohort of 20 patients. In order to extract mechanistic information from these observations and to integrate both datasets, we developed a novel analysis pipeline. Phosphoproteomic abundance changes are used to estimate kinase activity changes across patients. Kinase activity estimations are then correlated with metabolite abundance changes. This points at possible interactions between signaling pathways and metabolism. We subsequently build a generic network integrating signaling pathways and metabolic reaction networks based on literature knowledge and databases. We use this signaling/metabolic network to identify paths across kinases and metabolic enzymes to link the correlated kinase activities and metabolites.
This provides potential mechanisms to explain the effect of deregulation of signaling on metabolism. Our approach was able to recover the structure canonical signaling pathway topologies and highlight specific connections between kinases and metabolite abundance deregulated in kidney tumor tissues. This pipeline allows to extract and compare mechanistic
information from metabolomic, phosphoproteomic (and potentially transcriptomic) data across many kidney cancer patients. This information can be used to select potential therapeutic targets to disrupt cancer specific cellular mechanisms, such as the SP1 kinase. Furthermore, the pipeline offers the advantage of being easily transferable in many different biological contexts.

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(1) RWTH Uniklinikum Aachen, Germany, (2) Heidelberg University, Germany


A network-based approach for the identification of multi-omics modules associated with complex human diseases

Maria Anna Wörheide is a PhD student at the Helmholtz Zentrum München in Germany.

Authors: Maria Anna Wörheide (1), Jan Krumsiek (2), Gabi Kastenmüller (1), Matthias Arnold (1)

Application of advanced high-throughput omics technologies have provided us with vast amounts of quantitative, highly valuable data. For complex, heterogeneous, and untreatable diseases such as Alzheimer’s disease (AD), the integration of different omics levels and their interconnections is desperately needed to understand the underlying molecular pathomechanisms and identify potential therapeutic targets. However, integrated, multivariable analyses of cross-omics data are not straightforward, and even if successfully applied, often lack a human comprehensible representation. Graph databases provide an intuitive and mathematically well defined framework to store and interconnect diverse biological domains in accessible network structures. Here, we propose a network-based, multi-omics framework
developed with the graph database Neo4j, that allows the large-scale integration and analysis of data on biological entities across omics, as well as results from association analysis with specific (endo) phenotypes. The backbone of this framework comes from known biological relationships and functional/pathway annotations available in public databases. It is augmented with experimental, quantitative data for single omics (e.g. tissue-specific gene expression) and across omics (e.g. eQTLs or mQTLs) derived in population-based studies. To identify modules within this network that are potentially relevant to a disease such as AD, we extend the
framework using large-scale association data for AD (e.g. from case-control GWASs). The resulting network is comprised of over 50 million nodes (entities), representing more than 30 different data types, and more than 80 million edges (relationships). We mined this comprehensive catalogue of biological information using established graph algorithms to
identify potentially disease-related modules of tightly interlinked entities, and were able to obtain several subnetworks significantly enriched for AD-associations.

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(1) Helmholtz Zentrum München, Germany, (2) Weill Cornell Medicine, United States of America


Mechanistic insights into transcription factor cooperativity and its impact on protein-phenotype interactions

Ignacio Ibarra is a PhD student in Judith Zaugg’s lab at EMBL Heidelberg, Germany.

Authors: Ignacio Ibarra, Nele Hollmann, Bernd Klaus, Sandra Augsten, Britta Velten, Janosch Hennig, Judith Zaugg (EMBL Heidelberg)

Recent high-throughput transcription factor (TF) binding assays revealed that TF cooperativity
is a widespread phenomenon. However, we still miss global mechanistic and functional understanding of TF cooperativity. To close this gap we introduce a statistical learning framework that provides structural insight into TF cooperativity and its functional consequences based on next generation sequencing data. We identify DNA shape as driver for cooperativity, with a particularly strong effect for Forkhead-Ets pairs. Follow-up experiments revealed a local shape preference at the Ets-DNA-Forkhead interface and a decreased cooperativity once the interaction is lost. Additionally, we discovered many novel functional associations for cooperatively bound TFs. Examining the novel link between FOXO1:ETV6 and lymphomas revealed that their joint expression levels improve patient survival stratification.
Altogether, our results demonstrate that inter-family cooperative TF binding is driven by position-specific DNA readout mechanisms, which provides an additional regulatory layer for downstream biological functions.

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Working on your own conference poster? Then check out 10 tips to create a scientific poster people want to stop by .

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