Representing dynamic biological networks with multi-scale probabilistic models

From Data to Cure: A Comprehensive Exploration of Multi-omics Data Analysis for Targeted Therapies

In the dynamic landscape of targeted therapeutics, drug discovery has pivoted towards understanding underlying disease mechanisms, placing a strong emphasis on molecular perturbations and target identification. This paradigm shift, crucial for drug discovery, is underpinned by big data, a transformative force in the current era. Omics data, characterized by its heterogeneity and enormity, has ushered biological and biomedical research into the big data domain. Acknowledging the significance of integrating diverse omics data strata, known as multi-omics studies, researchers delve into the intricate interrelationships among various omics layers. This review navigates the expansive omics landscape, showcasing tailored assays for each molecular layer through genomes to metabolomes. The sheer volume of data generated necessitates sophisticated informatics techniques, with machine-learning (ML) algorithms emerging as robust tools. These datasets not only refine disease classification but also enhance diagnostics and foster the development of targeted therapeutic strategies. Through the integration of high-throughput data, the review focuses on targeting and modeling multiple disease-regulated networks, validating interactions with multiple targets, and enhancing therapeutic potential using network pharmacology approaches. Ultimately, this exploration aims to illuminate the transformative impact of multi-omics in the big data era, shaping the future of biological research.

Phenotypic consequences of logarithmic signaling in MAPK stress response

How cells respond to dynamic environmental changes is crucial for understanding fundamental biological processes and cell physiology. In this study, we developed an experimental and quantitative analytical framework to explore how dynamic stress gradients that change over time regulate cellular volume, signaling activation, and growth phenotypes. Our findings reveal that gradual stress conditions substantially enhance cell growth compared to conventional acute stress. This growth advantage correlates with a minimal reduction in cell volume dependent on the dynamic of stress. We explain the growth phenotype with our finding of a logarithmic signal transduction mechanism in the yeast Mitogen-Activated Protein Kinase (MAPK) osmotic stress response pathway. These insights into the interplay between gradual environments, cell volume change, dynamic cell signaling, and growth, advance our understanding of fundamental cellular processes in gradual stress environments.

Small Extracellular Vesicles Propagate the Inflammatory Response After Trauma

Trauma is the leading cause of death in individuals under 44 years of age. Thorax trauma (TxT) is strongly associated with trauma-related death, an unbalanced innate immune response, sepsis, acute respiratory distress syndrome, and multiple organ dysfunction. It is shown that different in vivo traumata, such as TxT or an in vitro polytrauma cytokine cocktail trigger secretion of small extracellular nanovesicles (sEVs) from endothelial cells with pro-inflammatory cargo. These sEVs transfer transcripts for ICAM-1, VCAM-1, E-selectin, and cytokines to systemically activate the endothelium, facilitate neutrophil-endothelium interactions, and destabilize barrier integrity. Inhibition of sEV-release after TxT in mice ameliorates local as well as systemic inflammation, neutrophil infiltration, and distant organ damage in kidneys (acute kidney injury, AKI). Vice versa, injection of TxT-plasma-sEVs into healthy animals is sufficient to trigger pulmonary and systemic inflammation as well as AKI. Accordingly, increased sEV concentrations and transfer of similar cargos are observed in polytrauma patients, suggesting a fundamental pathophysiological mechanism.

Reconstructing Boolean network ensembles from single-cell data for unraveling dynamics in the aging of human hematopoietic stem cells

Regulatory dependencies in molecular networks are the basis of dynamic behaviors affecting the phenotypical landscape. With the advance of high throughput technologies, the detail of omics data has arrived at the single-cell level. Nevertheless, new strategies are required to reconstruct regulatory networks based on populations of single-cell data. Here, we present a new approach to generate populations of gene regulatory networks from single-cell RNA-sequencing (scRNA-seq) data. Our approach exploits the heterogeneity of single-cell populations to generate pseudo-timepoints. This allows for the first time to uncouple network reconstruction from a direct dependency on time series measurements. The generated time series are then fed to a combined reconstruction algorithm. The latter allows a fast and efficient reconstruction of ensembles of gene regulatory networks. Since this approach does not require knowledge on time-related trajectories, it allows us to model heterogeneous processes such as aging. Applying the approach to the aging-associated NF-κB signaling pathway-based scRNA-seq data of human hematopoietic stem cells (HSCs), we were able to reconstruct eight ensembles, and evaluate their dynamic behavior. Moreover, we propose a strategy to evaluate the resulting attractor patterns. Interaction graph-based features and dynamic investigations of our model ensembles provide a new perspective on the heterogeneity and mechanisms related to human HSCs aging.

Relating simulation studies by provenance-Developing a family of Wnt signaling models

For many biological systems, a variety of simulation models exist. A new simulation model is rarely developed from scratch, but rather revises and extends an existing one. A key challenge, however, is to decide which model might be an appropriate starting point for a particular problem and why. To answer this question, we need to identify entities and activities that contributed to the development of a simulation model. Therefore, we exploit the provenance data model, PROV-DM, of the World Wide Web Consortium and, building on previous work, continue developing a PROV ontology for simulation studies. Based on a case study of 19 Wnt/β-catenin signaling models, we identify crucial entities and activities as well as useful metadata to both capture the provenance information from individual simulation studies and relate these forming a family of models. The approach is implemented in WebProv, a web application for inserting and querying provenance information. Our specialization of PROV-DM contains the entities Research Question, Assumption, Requirement, Qualitative Model, Simulation Model, Simulation Experiment, Simulation Data, and Wet-lab Data as well as activities referring to building, calibrating, validating, and analyzing a simulation model. We show that most Wnt simulation models are connected to other Wnt models by using (parts of) these models. However, the overlap, especially regarding the Wet-lab Data used for calibration or validation of the models is small. Making these aspects of developing a model explicit and queryable is an important step for assessing and reusing simulation models more effectively. Exposing this information helps to integrate a new simulation model within a family of existing ones and may lead to the development of more robust and valid simulation models. We hope that our approach becomes part of a standardization effort and that modelers adopt the benefits of provenance when considering or creating simulation models.

We revise a provenance ontology for simulation studies of cellular biochemical models. Provenance information is useful for understanding the creation of a simulation model because it not only contains information about the entities and activities that have led to a simulation model but also their relations, all of which can be visualized. It provides additional structure by explicitly recording research questions, assumptions, and requirements and relating them along with data, qualitative models, simulation models, and simulation experiments through a small set of predefined but extensible activities.

We have applied our concept to a family of 19 Wnt signaling models and implemented a web-based tool (WebProv) to store the provenance information from these studies. The resulting provenance graph visualizes the story line of simulation studies and demonstrates the creation and calibration of simulation models, the successive attempts of validation and extension, and shows, beyond an individual simulation study, how the Wnt models are related. Thereby, the steps and sources that contributed to a simulation model are made explicit.

Our approach complements other approaches aimed at facilitating the reuse and assessment of simulation products in systems biology such as model repositories as well as annotation and documentation guidelines.

Tools for time-course simulation in systems biology: a brief overview

Dynamic modeling of biological systems is essential for understanding all properties of a given organism as it allows us to look not only at the static picture of an organism but also at its behavior under various conditions. With the increasing amount of experimental data, the number of tools that enable dynamic analysis also grows. However, various tools are based on different approaches, use different types of data and offer different functions for analyses; so it can be difficult to choose the most suitable tool for a selected type of model. Here, we bring a brief overview containing descriptions of 50 tools for the reconstruction of biological models, their time-course simulation and dynamic analysis. We examined each tool using test data and divided them based on the qualitative and quantitative nature of the mathematical apparatus they use.

Genetic compensation prevents myopathy and heart failure in an in vivo model of Bag3 deficiency

Mutations in the molecular co-chaperone Bcl2-associated athanogene 3 (BAG3) are found to cause dilated cardiomyopathy (DCM), resulting in systolic dysfunction and heart failure, as well as myofibrillar myopathy (MFM), which is characterized by protein aggregation and myofibrillar disintegration in skeletal muscle cells. Here, we generated a CRISPR/Cas9-induced Bag3 knockout zebrafish line and found the complete preservation of heart and skeletal muscle structure and function during embryonic development, in contrast to morpholino-mediated knockdown of Bag3. Intriguingly, genetic compensation, a process of transcriptional adaptation which acts independent of protein feedback loops, was found to prevent heart and skeletal muscle damage in our Bag3 knockout model. Proteomic profiling and quantitative real-time PCR analyses identified Bag2, another member of the Bag protein family, significantly upregulated on a transcript and protein level in bag3^-/- mutants. This implied that the decay of bag3 mutant mRNA in homozygous bag3^-/- embryos caused the transcriptional upregulation of bag2 expression. We further demonstrated that morpholino-mediated knockdown of Bag2 in bag3^-/- embryos evoked severe functional and structural heart and skeletal muscle defects, which are similar to Bag3 morphants. However, Bag2 knockdown in bag3^+/+ or bag3^+/- embryos did not result in (cardio-)myopathy. Finally, we found that inhibition of the nonsense-mediated mRNA decay (NMD) machinery by knockdown of upf1, an essential NMD factor, caused severe heart and skeletal muscle defects in bag3^-/- mutants due to the blockade of transcriptional adaptation of bag2 expression. Our findings provide evidence that genetic compensation might vitally influence the penetrance of disease-causing bag3 mutations in vivo.

One form of genetic compensation is described as transcriptional adaptation of gene expression triggered by deleterious gene mutations. Although the precise molecular mechanism that induces genetic compensation needs to be defined, it represents a powerful biological phenomenon that warrants genetic robustness. We find that antisense-mediated knockdown of Bag3 in zebrafish embryos causes heart failure and myopathy. By contrast, CRISPR/Cas9-induced depletion of Bag3 does not result in the abrogation of heart and skeletal muscle function in zebrafish embryos. We find here that transcriptional activation of the Bag family member bag2 is capable of restoring heart and skeletal muscle function in bag3 mutant embryos, whereas this compensatory mechanism is not present in the bag3 morphants. Furthermore, we show that nonsense-mediated decay of bag3 mRNA is the molecular trigger for the compensatory upregulation of bag2. Our study provides evidence that genetic compensation via transcriptional adaptation is a vital modulator of disease peculiarity and penetrance in bag3 mutant zebrafish and that this biological phenomenon might also be active in certain human BAG3 mutation carriers.

Diverse Cell Stimulation Kinetics Identify Predictive Signal Transduction Models

Computationally understanding the molecular mechanisms that give rise to cell signaling responses upon different environmental, chemical, and genetic perturbations is a long-standing challenge that requires models that fit and predict quantitative responses for new biological conditions. Overcoming this challenge depends not only on good models and detailed experimental data but also on the rigorous integration of both. We propose a quantitative framework to perturb and model generic signaling networks using multiple and diverse changing environments (hereafter "kinetic stimulations") resulting in distinct pathway activation dynamics. We demonstrate that utilizing multiple diverse kinetic stimulations better constrains model parameters and enables predictions of signaling dynamics that would be impossible using traditional dose-response or individual kinetic stimulations. To demonstrate our approach, we use experimentally identified models to predict signaling dynamics in normal, mutated, and drug-treated conditions upon multitudes of kinetic stimulations and quantify which proteins and reaction rates are most sensitive to which extracellular stimulations.

The ParaHox gene Cdx4 induces acute erythroid leukemia in mice

Acute erythroid leukemia (AEL) is a rare and aggressive form of acute leukemia, the biology of which remains poorly understood. Here we demonstrate that the ParaHox gene CDX4 is expressed in patients with acute erythroid leukemia, and that aberrant expression of Cdx4 induced homogenously a transplantable acute erythroid leukemia in mice. Gene expression analyses demonstrated upregulation of genes involved in stemness and leukemogenesis, with parallel downregulation of target genes of Gata1 and Gata2 responsible for erythroid differentiation. Cdx4 induced a proteomic profile that overlapped with a cluster of proteins previously defined to represent the most primitive human erythroid progenitors. Whole-exome sequencing of diseased mice identified recurrent mutations significantly enriched for transcription factors involved in erythroid lineage specification, as well as TP53 target genes partly identical to the ones reported in patients with AEL. In summary, our data indicate that Cdx4 is able to induce stemness and inhibit terminal erythroid differentiation, leading to the development of AEL in association with co-occurring mutations.

Protein Kinase D1, Reduced in Human Pancreatic Tumors, Increases Secretion of Small Extracellular Vesicles From Cancer Cells That Promote Metastasis to Lung in Mice

BACKGROUND & AIMS

Pancreatic tumor cells release small extracellular vesicles (sEVs, exosomes) that contain lipids and proteins, RNA, and DNA molecules that might promote formation of metastases. It is not clear what cargo these vesicles contain and how they are released. Protein kinase D1 (PRKD1) inhibits cell motility and is believed to be dysregulated in pancreatic ductal adenocarcinomas. We investigated whether it regulates production of sEVs in pancreatic cancer cells and their ability to form premetastatic niches for pancreatic cancer cells in mice.

METHODS

We analyzed data from UALCAN and human pancreatic tissue microarrays to compare levels of PRKD1 between tumor and nontumor tissues. We studied mice with pancreas-specific disruption of Prkd1 (PRKD1^KO mice), mice that express oncogenic KRAS (KC mice), and KC mice with disruption of Prkd1 (PRKD1^KO-KC mice). Subcutaneous xenograft tumors were grown in NSG mice from Panc1 cells; some mice were then given injections of sEVs. Pancreata and lung tissues from mice were analyzed by histology, immunohistochemistry, and/or quantitative polymerase chain reaction; we performed nanoparticle tracking analysis of plasma sEVs. The Prkd1 gene was disrupted in Panc1 cells using CRISPR-Cas9 or knocked down with small hairpin RNAs, or PRKD1 activity was inhibited with the selective inhibitor CRT0066101. Pancreatic cancer cell lines were analyzed by gene-expression microarray, quantitative polymerase chain reaction, immunoblot, and immunofluorescence analyses. sEVs secreted by Panc1 cell lines were analyzed by flow cytometry, transmission electron microscopy, and mass spectrometry.

RESULTS

Levels of PRKD1 were reduced in human pancreatic ductal adenocarcinoma tissues compared with nontumor tissues. PRKD1^KO-KC mice developed more pancreatic intraepithelial neoplasia, at a faster rate, than KC mice, and had more lung metastases and significantly shorter average survival time. Serum from PRKD1^KO-KC mice had increased levels of sEVs compared with KC mice. Pancreatic cancer cells with loss or inhibition of PRKD1 increased secretion of sEVs; loss of PRKD1 reduced phosphorylation of its substrate, cortactin, resulting in increased F-actin levels at the plasma membrane. sEVs from cells with loss or reduced expression of PRKD1 had altered content, and injection of these sEVs into mice increased metastasis of xenograft tumors to lung, compared with sEVs from pancreatic cells that expressed PRKD1. PRKD1-deficient pancreatic cancer cells showed increased loading of integrin α6β4 into sEVs-a process that required CD82.

CONCLUSIONS

Human pancreatic ductal adenocarcinoma has reduced levels of PRKD1 compared with nontumor pancreatic tissues. Loss of PRKD1 results in reduced phosphorylation of cortactin in pancreatic cancer cell lines, resulting in increased in F-actin at the plasma membrane and increased release of sEVs, with altered content. These sEVs promote metastasis of xenograft and pancreatic tumors to lung in mice.

Noncatalytic Bruton's tyrosine kinase activates PLCγ2 variants mediating ibrutinib resistance in human chronic lymphocytic leukemia cells

Treatment of patients with chronic lymphocytic leukemia (CLL) with inhibitors of Bruton's tyrosine kinase (BTK), such as ibrutinib, is limited by primary or secondary resistance to this drug. Examinations of CLL patients with late relapses while on ibrutinib, which inhibits BTK's catalytic activity, revealed several mutations in BTK, most frequently resulting in the C481S substitution, and disclosed many mutations in PLCG2, encoding phospholipase C-γ₂ (PLCγ₂). The PLCγ₂ variants typically do not exhibit constitutive activity in cell-free systems, leading to the suggestion that in intact cells they are hypersensitive to Rac family small GTPases or to the upstream kinases spleen-associated tyrosine kinase (SYK) and Lck/Yes-related novel tyrosine kinase (LYN). The sensitivity of the PLCγ₂ variants to BTK itself has remained unknown. Here, using genetically-modified DT40 B lymphocytes, along with various biochemical assays, including analysis of PLCγ₂-mediated inositol phosphate formation, inositol phospholipid assessments, fluorescence recovery after photobleaching (FRAP) static laser microscopy, and determination of intracellular calcium ([Ca²⁺] _i ), we show that various CLL-specific PLCγ₂ variants such as PLCγ₂S707Y are hyper-responsive to activated BTK, even in the absence of BTK's catalytic activity and independently of enhanced PLCγ₂ phospholipid substrate supply. At high levels of B-cell receptor (BCR) activation, which may occur in individual CLL patients, catalytically-inactive BTK restored the ability of the BCR to mediate increases in [Ca²⁺] _i Because catalytically-inactive BTK is insensitive to active-site BTK inhibitors, the mechanism involving the noncatalytic BTK uncovered here may contribute to preexisting reduced sensitivity or even primary resistance of CLL to these drugs.

Computational Approaches in Theranostics: Mining and Predicting Cancer Data

The ability to understand the complexity of cancer-related data has been prompted by the applications of (1) computer and data sciences, including data mining, predictive analytics, machine learning, and artificial intelligence, and (2) advances in imaging technology and probe development. Computational modelling and simulation are systematic and cost-effective tools able to identify important temporal/spatial patterns (and relationships), characterize distinct molecular features of cancer states, and address other relevant aspects, including tumor detection and heterogeneity, progression and metastasis, and drug resistance. These approaches have provided invaluable insights for improving the experimental design of therapeutic delivery systems and for increasing the translational value of the results obtained from early and preclinical studies. The big question is: Could cancer theranostics be determined and controlled in silico? This review describes the recent progress in the development of computational models and methods used to facilitate research on the molecular basis of cancer and on the respective diagnosis and optimized treatment, with particular emphasis on the design and optimization of theranostic systems. The current role of computational approaches is providing innovative, incremental, and complementary data-driven solutions for the prediction, simplification, and characterization of cancer and intrinsic mechanisms, and to promote new data-intensive, accurate diagnostics and therapeutics.