Biology subjects

Zucknick, M.

Publications and source records attributed to Zucknick, M..

7 recordsLinked to original sources

bioRxiv2025

Perturbational fitness analysis of CRISPR screens uncovers information-theoretic relation between gene function and selection

Despite major advances in genetic screening technology, a formal approach for quantifying gene function remains underdeveloped, thereby limiting the utility of these techniques in deciphering the complex behavior of human cells. In this study, we leverage information theory with a perturbational analysis of replicator dynamics to characterize functional drivers of selection in pooled CRISPR screens. Our approach challenges established methods for CRISPR screen analysis, while offering additional insight into selection dynamics through the Kullback-Leibler divergence (DKL) and cumulants of the fitness distribution. By modeling fluctuations in gene-fitness effects as a linear response to environmental perturbations, we derive a geometric measure for genomic information content based on a second-order approximation of the DKL. Our analysis reveals that functional information--encoded (or shared) between genes--can be quantified by analyzing the directions corresponding to maximal conditional selection within the space of decomposed gene-environment interactions. This geometric representation offers several advantages for the functional analysis of the human genome and its network architecture. Moreover, by constraining the space to cell-type-specific fluctuations, we uncover developmental and tissue-specific functional signatures. These findings represent significant progress in the dynamic analysis of gene function and in the functional wiring of the human genome.

systems biology↗

bioRxiv2025

Multimodal epigenetic and enhancer network remodeling shape the transcriptional landscape of beige adipocytes

Epigenetic regulation is a key determinant of adipocyte fate and function, conferring phenotypic plasticity to adipose tissue in response to metabolic and thermal challenges. To understand the spatiotemporal regulation of chromatin during the establishment of a beige thermogenic adipocyte phenotype, we analyzed the transcriptomic, epigenetic, and enhancer connectome dynamics during white and beige adipogenesis. Using a machine learning approach, we find that the white-specific transcriptional program is associated with promoter modulations of H3K2ac levels and chromatin accessibility. In contrast, beige-specific mitochondrial gene expression correlates with promoter changes in H3K4me3 levels. Adipocyte beiging is also mediated by a remodeling of the 3D genome involving the recruitment of short range enhancers targeting fatty acid oxidation and thermogenic genes. These increased promoter-enhancer contacts correlate with increased chromatin opening at sites enriched for C/EBP transcription factor motifs. We notably identify the C/EBP transcription factor NFIL3 as differentially bound between white and beige adipocytes at enhancers regulating PDK4, a key metabolic switch promoting fatty acid oxidation. Our results highlight a multimodal, pathway-specific regulation of the transcriptional program underlying the beige adipocyte phenotype.

genomics↗

bioRxiv2024

Genome-wide profiling of the hierarchical control of autophagy dynamics using deep learning

Recycling of cellular components through autophagy maintains homeostasis in dynamic nutrient environments, and its dysregulation is linked to several human disorders. Although extensive research has characterized the core mechanisms of autophagy, limited insight into its systems-wide dynamic control has hampered predictive modeling and effective in vivo manipulation. In this study, we mapped the genetic network that controls both the dynamic activation and inactivation of autophagy during nitrogen changes, using a combination of time-resolved high-content imaging, deep learning, and latent feature analysis. This approach generated a comprehensive genome-wide profiling repository, termed AutoDRY, categorizing 5919 mutants based on their nutrient response kinetics and differential contributions to autophagosome formation and clearance. Integrating these profiles with functional and genetic network data unveiled a hierarchical and multi-layered control of autophagy, identifying new regulatory aspects of the core machinery and established nutrient-sensing pathways. By leveraging multi-omics resources and explainable machine learning to predict genetic perturbation effects and infer new regulatory mechanisms, we identified the retrograde pathway as a pivotal, time-varying autophagy modulator through transcriptional tuning of core genes. By charting the systems-wide dynamical control of autophagy, we have laid the groundwork for connecting the complexity of genome-wide influences with specific core mechanisms. This represents a significant advancement in studying complex genetic phenotypes, guides functional genomics of dynamic cellular processes in any organism, and provides a powerful starting point for hypothesis-based research on autophagy.

systems biology↗

bioRxiv2022

metascreen: A modular tool for the design and analysis of drug combination screens

There is a rapidly growing interest in high-throughput drug combination screening to identify synergizing drug interactions for treatment of various maladies, such as cancer and infectious disease. This creates the need for pipelines that can be used to design such screens, perform quality control on the data, and generate data files that can be analyzed by synergy-finding bioinformatics applications. metascreen is an open source, end-to-end modular tool available as an R-package for the design and analysis of drug combination screens. The tool allows for a customized build of pipelines through its modularity and provides a flexible approach to quality control and data analysis. metascreen is adaptable to various experimental requirements with an emphasis on precision medicine. It can be coupled to other R packages, such as bayesynergy, to identify synergistic and antagonistic drug interactions in cell lines or patient samples. metascreen is scalable and provides a complete solution for setting up drug sensitivity screens, read raw measurements and consolidate different datasets, perform various types of quality control, and analyze, report and visualize the results of drug sensitivity screens. Availability and implementationThe R-package and technical documentation is available at https://github.com/Enserink-lab; the R source code is publicly available at https://github.com/Enserink-lab/metascreen under GNU General Public License v3.0; bayesynergy is accessible at https://github.com/ocbe-uio/bayesynergy/ Selected modules will be available through Galaxy, an open-source platform for FAIR data analysis, Norway: https://usegalaxy.no

bioinformatics↗

bioRxiv2021

bayesynergy: flexible Bayesian modelling of synergistic interaction effects in in-vitro drug combination experiments

The effect of cancer therapies is often tested pre-clinically via in-vitro experiments, where the post-treatment viability of the cancer cell population is measured through assays estimating the number of viable cells. In this way, large libraries of compounds can be tested, comparing the efficacy of each treatment. Drug interaction studies focus on the quantification of the additional effect encountered when two drugs are combined, as opposed to using the treatments separately. In the bayesynergy R package, we implement a probabilistic approach for the description of the drug combination experiment, where the observed dose response curve is modelled as a sum of the expected response under a zero-interaction model and an additional interaction effect (synergistic or antagonistic). The interaction is modelled in a flexible manner, using a Gaussian process formulation. Since the proposed approach is based on a statistical model, it allows the natural inclusion of replicates, handles missing data and uneven concentration grids, and provides uncertainty quantification around the results. The model is implemented in the Stan programming language providing a computationally efficient sampler, a fast approximation of the posterior through variational inference, and features parallel processing for working with large drug combination screens.

bioinformatics↗

bioRxiv2020

An extension to: Systematic assessment of commercially available low-input miRNA library preparation kits

High-throughput sequencing has emerged as the favoured method to study microRNA (miRNA) expression, but biases introduced during library preparation have been reported. To assist researchers choose the most appropriate library preparation kit, we recently compared the performance of six commercially-available kits on synthetic miRNAs and human RNA, where library preparation was performed by the vendors. We hereby supplement this study with data from two further commonly used kits (NEBNext, NEXTflex) whose manufacturers initially declined to participate. As before, performance was assessed with respect to sensitivity, reliability, titration response and differential expression. Despite NEXTflex employing partially-randomised adapter sequences to minimise bias, we reaffirm that biases in miRNA abundance are kit-specific, complicating the comparison of miRNA datasets generated using different kits.

molecular biology↗

bioRxiv2019

Systematic assessment of commercially available low-input miRNA library preparation kits

High-throughput sequencing is increasingly favoured to assay the presence and abundance of micro RNAs (miRNAs) in biological samples, even from low RNA amounts, and a number of commercial vendors now offer kits that allow miRNA sequencing from sub-nanogram (ng) inputs. However, although biases introduced during library preparation have been documented, the relative performance of current reagent kits has not been investigated in detail. Here, six commercial kits capable of handling <100ng total RNA input were used for library preparation, performed by kit manufactures, on synthetic miRNAs of known quantities and human biological total RNA samples. We compared the performance of miRNA detection sensitivity, reliability, titration response and the ability to detect differentially expressed miRNAs. In addition, we assessed the use of unique molecular identifiers sequence (UMI) tags in one kit. We observed differences in detection sensitivity and ability to identify differentially expressed miRNAs between the kits, but none were able to detect the full repertoire of expected miRNAs. The reliability within the replicates of all kits was good, while larger differences were observed between the kits, although none could accurately quantify the majority of miRNAs. UMI tags, at least within the input ranges tested, offered little advantage to improve data utility. In conclusion, biases in miRNA abundance are heavily influenced by the kit used for library preparation, suggesting that comparisons of datasets prepared by different procedures should be made with caution. This article is intended to assist researchers select the most appropriate kit for their experimental conditions.

bioinformatics↗