Ximena Bonilla, MD, PhD

Post Doc

E-Mail
ximena.bonilla@get-your-addresses-elsewhere.inf.ethz.ch
Address
Biomedical Informatics Group
Schmelzbergstrasse 26
8006 Zürich
Room
SHM 26 B 4

I’m interested in the study of diseases as complex systems using multidisciplinary approaches in the areas of 'omics, medicine, and analytical sciences. I focus on the understanding of key molecular pathways and the identification of drug targets that can ultimately impact patient management, survival, and quality of life.
I seek and enjoy cross-disciplinary experience and working in translational projects.

I obtained my PhD in human genetics at the University of Geneva as part of the NCCR-Frontiers in Genetics program (laboratory of Stylianos E. Antonarakis) for the genomic characterization of basal cell carcinoma of the skin, while also working in Mendelian genomics and somatic mosaicism as a disease mechanism. My background is in genetic diagnosis (MSc, Medical genetics, University of Glasgow, Scotland) and medicine (MD, General medicine, Universidad Autónoma de Coahuila, México). I joined the Rätsch lab in September 2018.

Abstract Mayer-Rokitansky-Küster-Hauser syndrome (MRKHS) is associated with congenital absence of the uterus, cervix, and the upper part of the vagina; it is a sex-limited trait. Disrupted development of the Müllerian ducts (MD)/Wölffian ducts (WD) through multifactorial mechanisms has been proposed to underlie MRKHS. In this study, exome sequencing (ES) was performed on a Chinese discovery cohort (442 affected subjects and 941 female control subjects) and a replication MRKHS cohort (150 affected subjects of mixed ethnicity from North America, South America, and Europe). Phenotypic follow-up of the female reproductive system was performed on an additional cohort of PAX8-associated congenital hypothyroidism (CH) (n = 5, Chinese). By analyzing 19 candidate genes essential for MD/WD development, we identified 12 likely gene-disrupting (LGD) variants in 7 genes: PAX8 (n = 4), BMP4 (n = 2), BMP7 (n = 2), TBX6 (n = 1), HOXA10 (n = 1), EMX2 (n = 1), and WNT9B (n = 1), while LGD variants in these genes were not detected in control samples (p = 1.27E−06). Interestingly, a sex-limited penetrance with paternal inheritance was observed in multiple families. One additional PAX8 LGD variant from the replication cohort and two missense variants from both cohorts were revealed to cause loss-of-function of the protein. From the PAX8-associated CH cohort, we identified one individual presenting a syndromic condition characterized by CH and MRKHS (CH-MRKHS). Our study demonstrates the comprehensive utilization of knowledge from developmental biology toward elucidating genetic perturbations, i.e., rare pathogenic alleles involving the same loci, contributing to human birth defects.

Authors Na Chen, Sen Zhao, Angad Jolly, Lianlei Wang, Hongxin Pan, Jian Yuan, Shaoke Chen, Andre Koch, Congcong Ma, Weijie Tian, Ziqi Jia, Jia Kang, Lina Zhao, Chenglu Qin, Xin Fan, Katharina Rall, Zeynep Coban-Akdemir, Zefu Chen, Shalini Jhangiani, Ze Liang, Yuchen Niu, Xiaoxin Li, Zihui Yan, Yong Wu, Shuangshuang Dong, Chengcheng Song, Guixing Qiu, Shuyang Zhang, Pengfei Liu, Jennifer E. Posey, Feng Zhang, Guangnan Luo, Zhihong Wu, Jianzhong Su, Jianguo Zhang, Eugenia Y. Chen, Konstantinos Rouskas, Stavros Glentis, Flora Bacopoulou, Efthymios Deligeoroglou, George Chrousos, Stanislas Lyonnet, Michel Polak, Carla Rosenberg, Irene Dingeldein, Ximena Bonilla, Christelle Borel, Richard A. Gibbs, Jennifer E. Dietrich, Antigone S. Dimas, Stylianos E. Antonarakis, Sara Y. Brucker, James R. Lupski, Nan WuLan Zhu

Submitted American Journal of Human Genetics

Link DOI

Abstract Allele-specific gene expression can influence disease traits. Non-coding germline genetic variants that alter regulatory elements can cause allele-specific gene expression and contribute to cancer susceptibility. In tumors, both somatic copy number alterations and somatic single nucleotide variants have been shown to lead to allele-specific expression of genes, many of which are considered drivers of tumor growth. Here, we review recent studies revealing the pervasive presence of this phenomenon in cancer susceptibility and progression. Furthermore, we underscore the importance of careful experimental design and computational analysis for accurate allelic expression quantification and avoidance of false positives. Finally, we discuss additional methodological challenges encountered in cancer studies and in the burgeoning field of single-cell transcriptomics.

Authors Carla Daniela Robles-Espinoza, Pejman Mohammadi, Ximena Bonilla, Maria Gutierrez-Arcelus

Submitted Current Opinion in Genetics & Development

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Abstract The application and integration of molecular profiling technologies create novel opportunities for personalized medicine. Here, we introduce the Tumor Profiler Study, an observational trial combining a prospective diagnostic approach to assess the relevance of in-depth tumor profiling to support clinical decision-making with an exploratory approach to improve the biological understanding of the disease.

Authors Anja Irmisch, Ximena Bonilla, Stéphane Chevrier, Kjong-Van Lehmann, Franziska Singer, Nora C. Toussaint, Cinzia Esposito, Julien Mena, Emanuela S. Milani, Ruben Casanova, Daniel J. Stekhoven, Rebekka Wegmann, Francis Jacob, Bettina Sobottka, Sandra Goetze, Jack Kuipers, Jacobo Sarabia del Castillo, Michael Prummer, Mustafa A. Tuncel, Ulrike Menzel, Andrea Jacobs, Stefanie Engler, Sujana Sivapatham, Anja L. Frei, Gabriele Gut, Joanna Ficek, Nicola Miglino, Melike Ak, Faisal S. Al-Quaddoomi, Jonas Albinus, Ilaria Alborelli, Sonali Andani, Per-Olof Attinger, Daniel Baumhoer, Beatrice Beck-Schimmer, Lara Bernasconi, Anne Bertolini, Natalia Chicherova, Maya D'Costa, Esther Danenberg, Natalie Davidson, Monica-Andreea Drăgan, Martin Erkens, Katja Eschbach, André Fedier, Pedro Ferreira, Bruno Frey, Linda Grob, Detlef Günther, Martina Haberecker, Pirmin Haeuptle, Sylvia Herter, Rene Holtackers, Tamara Huesser, Tim M. Jaeger, Katharina Jahn, Alva R. James, Philip M. Jermann, André Kahles, Abdullah Kahraman, Werner Kuebler, Christian P. Kunze, Christian Kurzeder, Sebastian Lugert, Gerd Maass, Philipp Markolin, Julian M. Metzler, Simone Muenst, Riccardo Murri, Charlotte K.Y. Ng, Stefan Nicolet, Marta Nowak, Patrick G.A. Pedrioli, Salvatore Piscuoglio, Mathilde Ritter, Christian Rommel, María L. Rosano-González, Natascha Santacroce, Ramona Schlenker, Petra C. Schwalie, Severin Schwan, Tobias Schär, Gabriela Senti, Vipin T. Sreedharan, Stefan Stark, Tinu M. Thomas, Vinko Tosevski, Marina Tusup, Audrey Van Drogen, Marcus Vetter, Tatjana Vlajnic, Sandra Weber, Walter P. Weber, Michael Weller, Fabian Wendt, Norbert Wey, Mattheus H.E. Wildschut, Shuqing Yu, Johanna Ziegler, Marc Zimmermann, Martin Zoche, Gregor Zuend, Rudolf Aebersold, Marina Bacac, Niko Beerenwinkel, Christian Beisel, Bernd Bodenmiller, Reinhard Dummer, Viola Heinzelmann-Schwarz, Viktor H. Koelzer, Markus G. Manz, Holger Moch, Lucas Pelkmans, Berend Snijder, Alexandre P.A. Theocharides, Markus Tolnay, Andreas Wicki, Bernd Wollscheid, Gunnar Rätsch, Mitchell P. Levesque

Submitted Cancer Cell (Commentary)

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Abstract Motivation Recent technological advances have led to an increase in the production and availability of single-cell data. The ability to integrate a set of multi-technology measurements would allow the identification of biologically or clinically meaningful observations through the unification of the perspectives afforded by each technology. In most cases, however, profiling technologies consume the used cells and thus pairwise correspondences between datasets are lost. Due to the sheer size single-cell datasets can acquire, scalable algorithms that are able to universally match single-cell measurements carried out in one cell to its corresponding sibling in another technology are needed. Results We propose Single-Cell data Integration via Matching (SCIM), a scalable approach to recover such correspondences in two or more technologies. SCIM assumes that cells share a common (low-dimensional) underlying structure and that the underlying cell distribution is approximately constant across technologies. It constructs a technology-invariant latent space using an autoencoder framework with an adversarial objective. Multi-modal datasets are integrated by pairing cells across technologies using a bipartite matching scheme that operates on the low-dimensional latent representations. We evaluate SCIM on a simulated cellular branching process and show that the cell-to-cell matches derived by SCIM reflect the same pseudotime on the simulated dataset. Moreover, we apply our method to two real-world scenarios, a melanoma tumor sample and a human bone marrow sample, where we pair cells from a scRNA dataset to their sibling cells in a CyTOF dataset achieving 90% and 78% cell-matching accuracy for each one of the samples, respectively.

Authors Stefan G Stark, Joanna Ficek, Francesco Locatello, Ximena Bonilla, Stéphane Chevrier, Franziska Singer, Tumor Profiler Consortium, Gunnar Rätsch, Kjong-Van Lehmann

Submitted Bioinformatics

Link DOI

Abstract We call upon the research community to standardize efforts to use daily self-reported data about COVID-19 symptoms in the response to the pandemic and to form a collaborative consortium to maximize global gain while protecting participant privacy. The rapid and global spread of COVID-19 led the World Health Organization to declare it a pandemic on 11 March 2020. One factor contributing to the spread of the pandemic is the lack of information about who is infected, in large part because of the lack of testing. This facilitated the silent spread of the causative coronavirus (SARS-CoV-2), which led to delays in public-health and government responses and an explosion in cases. In countries that have tested more aggressively and that had the capacity to transparently share this data, such as South Korea and Singapore, the spread of disease has been greatly slowed1. Although efforts are underway around the world to substantially ramp up testing capacity, technology-driven approaches to collecting self-reported information can fill an immediate need and complement official diagnostic results. This type of approach has been used for tracking other diseases, notably influenza2. The information collected may include health status that is self-reported through surveys, including those from mobile apps; results of diagnostic laboratory tests; and other static and real-time geospatial data. The collection of privacy-protected information from volunteers about health status over time may enable researchers to leverage these data to predict, respond to and learn about the spread of COVID-19. Given the global nature of the disease, we aim to form an international consortium, tentatively named the ‘Coronavirus Census Collective’, to serve as a hub for amassing this type of data and to create a unified platform for global epidemiological data collection and analysis.

Authors Segal E, Zhang F, Lin X, King G, Shalem O, Shilo S, Allen WE, Alquaddoomi F, Altae-Tran H, Anders S, Balicer R, Bauman T, Ximena Bonilla, Booman G, Chan AT, Cohen O, Coletti S, Natalie R Davidson, Dor Y, Drew DA, Elemento O, Evans G, Ewels P, Gale J, Gavrieli A, Geiger B, Grad YH, Greene CS, Hajirasouliha I, Jerala R, Kahles Andre, Kallioniemi O, Keshet A, Kocarev L, Landua G, Meir T, Muller A, Nguyen LH, Oresic M, Ovchinnikova S, Peterson H, Prodanova J, Rajagopal J, Rätsch Gunnar, Rossman H, Rung J, Sboner A, Sigaras A, Spector T, Steinherz R, Stevens I, Vilo J, Wilmes P.

Submitted Nature Medicine

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