Juan Henao

A key problem in systems biology is the discovery of regulatory mechanisms that drive phenotypic behaviour of complex biological systems in the form of multi-level networks. Modern multi-omics profiling techniques probe these fundamental regulatory networks but are often hampered by experimental restrictions leading to missing data or partially measured omics types for subsets of individuals due to cost restrictions. In such scenarios, in which missing data is present, classical computational approaches to infer regulatory networks are limited. In recent years, approaches have been proposed to infer sparse regression models in the presence of missing information. Nevertheless, these methods have not been adopted for regulatory network inference yet. In this study, we integrated regression-based methods that can handle missingness into KiMONo, a Knowledge guided Multi-Omics Network inference approach, and benchmarked their performance on commonly encountered missing data scenarios in single- and multi-omics studies. Overall, two-step approaches that explicitly handle missingness performed best for a wide range of random- and block-missingness scenarios on imbalanced omics-layers dimensions, while methods implicitly handling missingness performed best on balanced omics-layers dimensions. Our results show that robust multi-omics network inference in the presence of missing data with KiMONo is feasible and thus allows users to leverage available multi-omics data to its full extent.

One hallmark of cancer is the upregulation and dependency on glucose metabolism to fuel macromolecule biosynthesis and rapid proliferation. Despite significant pre-clinical effort to exploit this pathway, additional mechanistic insights are necessary to prioritize the diversity of metabolic adaptations upon acute loss of glucose metabolism. Here, we investigated a potent small molecule inhibitor to Class I glucose transporters, KL-11743, using glycolytic leukemia cell lines and patient-based model systems. Our results reveal that while several metabolic adaptations occur in response to acute glucose uptake inhibition, the most critical is increased mitochondrial oxidative phosphorylation. KL-11743 treatment efficiently blocks the majority of glucose uptake and glycolysis, yet markedly increases mitochondrial respiration via enhanced Complex I function. Compared to partial glucose uptake inhibition, dependency on mitochondrial respiration is less apparent suggesting robust blockage of glucose uptake is essential to create a metabolic vulnerability. When wild-type and oncogenic RAS patient-derived induced pluripotent stem cell acute myeloid leukemia (AML) models were examined, KL-11743 mediated induction of mitochondrial respiration and dependency for survival associated with oncogenic RAS. Furthermore, we examined the therapeutic potential of these observations by treating a cohort of primary AML patient samples with KL-11743 and witnessed similar dependency on mitochondrial respiration for sustained cellular survival. Together, these data highlight conserved adaptations to acute glucose uptake inhibition in diverse leukemic models and AML patient samples, and position mitochondrial respiration as a key determinant of treatment success.

Abstract A key problem in systems biology is the discovery of regulatory mechanisms that drive phenotypic behaviour of complex biological systems in the form of multi-level networks. Modern multi-omics profiling techniques probe these fundamental regulatory networks but are often hampered by experimental restrictions leading to missing data or partially measured omics types for subsets of individuals due to cost restrictions. In such scenarios, in which missing data is present, classical computational approaches to infer regulatory networks are limited. In recent years, approaches have been proposed to infer sparse regression models in the presence of missing information. Nevertheless, these methods have not been adopted for regulatory network inference yet. In this study, we integrated regression-based methods that can handle missingness into KiMONo, a K nowledge gu I ded M ulti- O mics N etw o rk inference approach, and benchmarked their performance on commonly encountered missing data scenarios in single- and multi-omics studies. Overall, two-step approaches that explicitly handle missingness performed best for a wide range of random- and block-missingness scenarios on imbalanced omics-layers dimensions, while methods implicitly handling missingness performed best on balanced omics-layers dimensions. Our results show that robust multi-omics network inference in the presence of missing data with KiMONo is feasible and thus allows users to leverage available multi-omics data to its full extent. Juan Henao is a 3rd year PhD candidate at Computational Health Center at Helmholtz Center Munich working on multi-omics and clinical data integration using both, bulk and single-cell data. Michael Lauber is a PhD Candidate at the Chair of Experimental Bioinformatics at the Technical University Munich. Currently, he is working on an approach for inference of reprogramming transcription factors for trans-differentiation. Manuel Azevedo is a Master’s student at the Technical University of Munich in Mathematics with a focus on Biomathematics and Biostatistics. Currently, he is working as a Student Assistant at Helmholtz Munich, where he is also doing his master’s thesis. Anastasiia Grekova is a Master’s student of bioinformatics at the Technical University of Munich and the Ludwig-Maximilians-University Munich, working on multi-omics data integration in Marsico Lab at HMGU. Fabian Theis is the Head of the Institute of Computational Biology and leading the group for Machine Learning at Helmholtz Center Munich. He also holds the chair of ‘Mathematical modelling of biological systems’, Department of Mathematics, Technical University of Munich as an Associate Professor. Markus List obtained his PhD at the University of Southern Denmark and worked as a postdoctoral fellow at the Max Planck Institute for Informatics before starting his group Big Data in BioMedicine at the Technical University of Munich. Christoph Ogris holds a PostDoc position in the Marsico Lab at Helmholtz-Center Munich. His research focuses on predicting and exploiting multi-modal biological networks to identify disease-specific cross-omic interactions. Benjamin Schubert obtained his PhD at the University of Tübingen and worked as a postdoctoral fellow at Harvard Medical School and Dana-Farber Cancer Institute USA before starting his group for Translational Immmunomics at the Helmholtz Center Munich.

Multiomics Regulatory Network Inference in the Presence of Missing Data We present a benchmarking of six different lasso models integrated within the KiMONo approach looking for network inference using multi-omics data dealing with missing information.