Liebig's law of the minimum in the TGF-ß/SMAD pathway.

Cells use signaling pathways to sense and respond to their environments. The transforming growth factor-ß (TGF-ß) pathway produces context-specific responses. Here, we combined modeling and experimental analysis to study the dependence of the output of the TGF-ß pathway on the abundance of signaling molecules in the pathway. We showed that the TGF-ß pathway processes the variation of TGF-ß receptor abundance using Liebig's law of the minimum, meaning that the output-modifying factor is the signaling protein that is most limited, to determine signaling responses across cell types and in single cells. We found that the abundance of either the type I (TGFBR1) or type II (TGFBR2) TGF-ß receptor determined the responses of cancer cell lines, such that the receptor with relatively low abundance dictates the response. Furthermore, nuclear SMAD2 signaling correlated with the abundance of TGF-ß receptor in single cells depending on the relative expression levels of TGFBR1 and TGFBR2. A similar control principle could govern the heterogeneity of signaling responses in other signaling pathways.

Machine learning unveils RNA polymerase II binding as a predictor for SMAD2-dependent transcription dynamics in response to Actvin signalling.

The transforming growth factor-ß (TGF-ß) superfamily, including Nodal and Activin, plays a critical role in various cellular processes. Understanding the intricate regulation and gene expression dynamics of TGF-ß signalling is of interest due to its diverse biological roles. A machine learning approach is used to predict gene expression patterns induced by Activin using features, such as histone modifications, RNA polymerase II binding, SMAD2-binding, and mRNA half-life. RNA sequencing and ChIP sequencing datasets were analysed and differentially expressed SMAD2-binding genes were identified. These genes were classified into activated and repressed categories based on their expression patterns. The predictive power of different features and combinations was evaluated using logistic regression models and their performances were assessed. Results showed that RNA polymerase II binding was the most informative feature for predicting the expression patterns of SMAD2-binding genes. The authors provide insights into the interplay between transcriptional regulation and Activin signalling and offers a computational framework for predicting gene expression patterns in response to cell signalling.

PhyloVelo enhances transcriptomic velocity field mapping using monotonically expressed genes.

Single-cell RNA sequencing (scRNA-seq) is a powerful approach for studying cellular differentiation, but accurately tracking cell fate transitions can be challenging, especially in disease conditions. Here we introduce PhyloVelo, a computational framework that estimates the velocity of transcriptomic dynamics by using monotonically expressed genes (MEGs) or genes with expression patterns that either increase or decrease, but do not cycle, through phylogenetic time. Through integration of scRNA-seq data with lineage information, PhyloVelo identifies MEGs and reconstructs a transcriptomic velocity field. We validate PhyloVelo using simulated data and Caenorhabditis elegans ground truth data, successfully recovering linear, bifurcated and convergent differentiations. Applying PhyloVelo to seven lineage-traced scRNA-seq datasets, generated using CRISPR-Cas9 editing, lentiviral barcoding or immune repertoire profiling, demonstrates its high accuracy and robustness in inferring complex lineage trajectories while outperforming RNA velocity. Additionally, we discovered that MEGs across tissues and organisms share similar functions in translation and ribosome biogenesis.

Absolute Quantification of TGF-ß Signaling Proteins Using Quantitative Western Blot.

Cell signaling governs the basic functions of cells by molecular interactions that involve of many proteins. The abundance of signaling proteins can directly influence cellular responses to external signal, contributing to cellular heterogeneity. Absolute quantification of proteins is important for modeling and understanding the complex signaling network. Here, we introduce how to measure the amount of TGF-ß signaling proteins using quantitative immunoblotting. In addition, we discuss how to convert the measurements of protein abundance to the quantities of absolute molecules per cell. This method is generally applicable to the absolute quantification of other proteins.

Optogenetic Control of TGF-ß Signaling.

Cells employ signaling pathways to make decisions in response to changes in their immediate environment. The Transforming Growth Factor ß (TGF-ß) signaling pathway plays pivotal roles in regulating many cellular processes, including cell proliferation, differentiation, and migrations. In order to manipulate and explore the dynamic behavior of TGF-ß signaling at high spatiotemporal resolution, we developed an optogenetic system (the optoTGFBRs system), in which light is used to control TGF-ß signaling precisely in time and space. Here, we describe about experimental details of how to build the optoTGFBRs system and utilize it to manipulate TGF-ß signaling in a single cell or a cell population using microscope or LED array, respectively.

Revisiting the Role of TGFß Receptor Internalization for Smad Signaling: It is Not Required in Optogenetic TGFß Signaling Systems.

Endocytosis is an important process by which many signaling receptors reach their intracellular effectors. Accumulating evidence suggests that internalized receptors play critical roles in triggering cellular signaling, including transforming growth factor ß (TGFß) signaling. Despite intensive studies on the TGFß pathway over the last decades, the necessity of TGFß receptor endocytosis for downstream TGFß signaling responses is a subject of debate. In this study, mathematical modeling and synthetic biology approaches are combined to re-evaluate whether TGFß receptor internalization is indispensable for inducing Smad signaling. It is found that optogenetic systems with plasma membrane-tethered TGFß receptors can induce fast and sustained Smad2 activation upon light stimulations. Modeling analysis suggests that endocytosis is precluded for the membrane-anchored optogenetic TGFß receptors. Therefore, this study provides new evidence to support that TGFß receptor internalization is not required for Smad2 activation.

Programmable Extracellular Vesicles for Macromolecule Delivery and Genome Modifications.

Getting large macromolecules through the plasma membrane and endosomal barriers remains a major challenge. Here, we report a generalizable method of delivering proteins and ribonucleoproteins (RNPs) to cells in vitro and mouse liver tissue in vivo with engineered ectosomes. These ectosomes, referred to as "Gectosomes," are designed to co-encapsulate vesicular stomatitis virus G protein (VSV-G) with bioactive macromolecules via split GFP complementation. We found that this method enables active cargo loading, improves the specific activity of cargo delivery, and facilitates Gectosome purification. Experimental and mathematical modeling analyses suggest that active cargo loading reduces non-specific encapsulation of cellular proteins, particularly nucleic-acid-binding proteins. Using Gectosomes that encapsulate Cre, Ago2, and SaCas9, we demonstrate their ability to execute designed modifications of endogenous genes in cell lines in vitro and mouse liver tissue in vivo, paving the way toward applications of this technology for the treatment of a wide range of human diseases.

Cell-type-specific role of CHK2 in mediating DNA damage-induced G2 cell cycle arrest.

Cancer is a life-threatening disease that affects one in three people. Although most cases are sporadic, cancer risk can be increased by genetic factors. It remains unknown why certain genes predispose for specific forms of cancer only, such as checkpoint protein 2 (CHK2), in which gene mutations convey up to twofold higher risk for breast cancer but do not increase lung cancer risk. We have investigated the role of CHK2 and the related kinase checkpoint protein 1 (CHK1) in cell cycle regulation in primary breast and lung primary epithelial cells. At the molecular level, CHK1 activity was higher in lung cells, whereas CHK2 was more active in breast cells. Inhibition of CHK1 profoundly disrupted the cell cycle profile in both lung and breast cells, whereas breast cells were more sensitive toward inhibition of CHK2. Finally, we provide evidence that breast cells require CHK2 to induce a G2-M cell cycle arrest in response of DNA damage, whereas lung cells can partially compensate for the loss of CHK2. Our results provide an explanation as to why CHK2 germline mutations predispose for breast cancer but not for lung cancer.

Molecular Engineering of the TGF-ß Signaling Pathway.

Transforming growth factor beta (TGF-ß) is an important growth factor that plays essential roles in regulating tissue development and homeostasis. Dysfunction of TGF-ß signaling is a hallmark of many human diseases. Therefore, targeting TGF-ß signaling presents broad therapeutic potential. Since the discovery of the TGF-ß ligand, a collection of engineered signaling proteins have been developed to probe and manipulate TGF-ß signaling responses. In this review, we highlight recent progress in the engineering of TGF-ß signaling for different applications and discuss how molecular engineering approaches can advance our understanding of this important pathway. In addition, we provide a future outlook on the opportunities and challenges in the engineering of the TGF-ß signaling pathway from a quantitative perspective.

eDetect: A Fast Error Detection and Correction Tool for Live Cell Imaging Data Analysis.

Live cell imaging has been widely used to generate data for quantitative understanding of cellular dynamics. Various applications have been developed to perform automated imaging data analysis, which often requires tedious manual correction. It remains a challenge to develop an efficient curation method that can analyze massive imaging datasets with high accuracy. Here, we present eDetect, a fast error detection and correction tool that provides a powerful and convenient solution for the curation of live cell imaging analysis results. In eDetect, we propose a gating strategy to distinguish correct and incorrect image analysis results by visualizing image features based on principal component analysis. We demonstrate that this approach can substantially accelerate the data correction process and improve the accuracy of imaging data analysis. eDetect is well documented and designed to be user friendly for non-expert users. It is freely available at https://sites.google.com/view/edetect/ and https://github.com/Zi-Lab/eDetect.

Tissue-Specific Chk1 Activation Determines Apoptosis by Regulating the Balance of p53 and p21.

The DNA damage response (DDR) protects cells against genomic instability. Surprisingly, little is known about the differences in DDR across tissues, which may affect cancer evolutionary trajectories and chemotherapy response. Using mathematical modeling and quantitative experiments, we found that the DDR is regulated differently in human breast and lung primary cells. Equal levels of cisplatin-DNA lesions caused stronger Chk1 activation in lung cells, leading to resistance. In contrast, breast cells were more resistant and showed more Chk2 activation in response to doxorubicin. Further analyses indicate that Chk1 activity played a regulatory role in p53 phosphorylation, whereas Chk2 activity was essential for p53 activation and p21 expression. We propose a novel "friction model," in which the balance of p53 and p21 levels contributes to the apoptotic response in different tissues. Our results suggest that modulating the balance of p53 and p21 dynamics could optimize the response to chemotherapy.

Spatiotemporal Control of TGF-ß Signaling with Light.

Cells employ signaling pathways to make decisions in response to changes in their immediate environment. Transforming growth factor beta (TGF-ß) is an important growth factor that regulates many cellular functions in development and disease. Although the molecular mechanisms of TGF-ß signaling have been well studied, our understanding of this pathway is limited by the lack of tools that allow the control of TGF-ß signaling with high spatiotemporal resolution. Here, we developed an optogenetic system (optoTGFBRs) that enables the precise control of TGF-ß signaling in time and space. Using the optoTGFBRs system, we show that TGF-ß signaling can be selectively and sequentially activated in single cells through the modulation of the pattern of light stimulations. By simultaneously monitoring the subcellular localization of TGF-ß receptor and Smad2 proteins, we characterized the dynamics of TGF-ß signaling in response to different patterns of blue light stimulations. The spatial and temporal precision of light control will make the optoTGFBRs system as a powerful tool for quantitative analyses of TGF-ß signaling at the single cell level.

Measuring TGF-ß Ligand Dynamics in Culture Medium.

TGF-ß plays an important role in a myriad of cell activities including differentiation, proliferation, and growth arrest. These effects are influenced by the concentration of TGF-ß in the surrounding milieu, which is interpreted by mammalian cells and subsequently translated into meaningful signals that guide their proliferation, survival, or death. To predict cellular responses to TGF-ß signaling based on molecular mechanisms, it is important to consider how cells respond to different ligand doses and how variations in ligand exposure impact Smad signaling dynamics and subsequent gene expression. Here we describe methods to measure TGF-ß concentration in the environment and approaches to perturb cellular TGF-ß exposure to gain a quantitative understanding of signaling dynamics of this pathway.

Inferring cellular regulatory networks with Bayesian model averaging for linear regression (BMALR).

Bayesian network and linear regression methods have been widely applied to reconstruct cellular regulatory networks. In this work, we propose a Bayesian model averaging for linear regression (BMALR) method to infer molecular interactions in biological systems. This method uses a new closed form solution to compute the posterior probabilities of the edges from regulators to the target gene within a hybrid framework of Bayesian model averaging and linear regression methods. We have assessed the performance of BMALR by benchmarking on both in silico DREAM datasets and real experimental datasets. The results show that BMALR achieves both high prediction accuracy and high computational efficiency across different benchmarks. A pre-processing of the datasets with the log transformation can further improve the performance of BMALR, leading to a new top overall performance. In addition, BMALR can achieve robust high performance in community predictions when it is combined with other competing methods. The proposed method BMALR is competitive compared to the existing network inference methods. Therefore, BMALR will be useful to infer regulatory interactions in biological networks. A free open source software tool for the BMALR algorithm is available at https://sites.google.com/site/bmalr4netinfer/.

Characterization of early autophagy signaling by quantitative phosphoproteomics.

Under conditions of nutrient shortage autophagy is the primary cellular mechanism ensuring availability of substrates for continuous biosynthesis. Subjecting cells to starvation or rapamycin efficiently induces autophagy by inhibiting the MTOR signaling pathway triggering increased autophagic flux. To elucidate the regulation of early signaling events upon autophagy induction, we applied quantitative phosphoproteomics characterizing the temporal phosphorylation dynamics after starvation and rapamycin treatment. We obtained a comprehensive atlas of phosphorylation kinetics within the first 30 min upon induction of autophagy with both treatments affecting widely different cellular processes. The identification of dynamic phosphorylation already after 2 min demonstrates that the earliest events in autophagy signaling occur rapidly after induction. The data was subjected to extensive bioinformatics analysis revealing regulated phosphorylation sites on proteins involved in a wide range of cellular processes and an impact of the treatments on the kinome. To approach the potential function of the identified phosphorylation sites we performed a screen for MAP1LC3-interacting proteins and identified a group of binding partners exhibiting dynamic phosphorylation patterns. The data presented here provide a valuable resource on phosphorylation events underlying early autophagy induction.

Dynamics of TGF-ß/Smad signaling.

The physiological responses to TGF-ß stimulation are diverse and vary amongst different cell types and environmental conditions. Even though the principal molecular components of the canonical and the non-canonical TGF-ß signaling pathways have been largely identified, the mechanism that underlies the well-established context dependent physiological responses remains a mystery. Understanding how the components of TGF-ß signaling function as a system and how this system functions in the context of the global cellular regulatory network requires a more quantitative and systematic approach. Here, we review the recent progress in understanding TGF-ß biology using integration of mathematical modeling and quantitative experimental analysis. These studies reveal many interesting dynamics of TGF-ß signaling and how cells quantitatively decode variable doses of TGF-ß stimulation.

A tutorial on mathematical modeling of biological signaling pathways.

Mathematical models have been widely used in the studies of biological signaling pathways. Among these studies, two systems biology approaches have been applied: top-down and bottom-up systems biology. The former approach focuses on X-omics researches involving the measurement of experimental data in a large scale, for example proteomics, metabolomics, or fluxomics and transcriptomics. In contrast, the bottom-up approach studies the interaction of the network components and employs mathematical models to gain some insights about the mechanisms and dynamics of biological systems. This chapter introduces how to use the bottom-up approach to establish mathematical models for cell signaling studies.

Time-dependent quantitative multicomponent control of the G1-S network by the stress-activated protein kinase Hog1 upon osmostress.

Control of cell cycle progression by stress-activated protein kinases (SAPKs) is essential for cell adaptation to extracellular stimuli. Exposure of yeast to hyperosmotic stress activates the SAPK Hog1, which delays cell cycle progression through G1 by direct phosphorylation of the cyclin-dependent kinase (CDK) inhibitor Sic1 and by inhibition of the transcription of the genes encoding the G1 cyclins Cln1 and 2. Additional targets of Hog1 may also play a role in this response. We used mathematical modeling and quantitative in vivo experiments to define the contributions of individual components of the G1-S network downstream of Hog1 to this stress-induced delay in the cell cycle. The length of the arrest depended on the degree of stress and the temporal proximity of the onset of the stress to the commitment to cell division, called "Start." Hog1-induced inhibition of the transcription of the gene encoding cyclin Clb5, rather than that of the gene encoding Cln2, prevented entry into S phase upon osmostress. By controlling the accumulation of specific cyclins, Hog1 delayed bud morphogenesis (through Clns) and delayed DNA replication (through Clb5). Hog1-mediated phosphorylation and degradation of Sic1 at Start prevented residual activity of the cyclin/CDK complex Clb5/Cdc28 from initiating DNA replication before adaptation to the stress. Thus, our work defines distinct temporal roles for the actions of Hog1 on Sic1 and cyclins in mediating G1 arrest upon hyperosmotic stress.

Quantitative analysis of transient and sustained transforming growth factor-ß signaling dynamics.

Mammalian cells can decode the concentration of extracellular transforming growth factor-ß (TGF-ß) and transduce this cue into appropriate cell fate decisions. How variable TGF-ß ligand doses quantitatively control intracellular signaling dynamics and how continuous ligand doses are translated into discontinuous cellular fate decisions remain poorly understood. Using a combined experimental and mathematical modeling approach, we discovered that cells respond differently to continuous and pulsating TGF-ß stimulation. The TGF-ß pathway elicits a transient signaling response to a single pulse of TGF-ß stimulation, whereas it is capable of integrating repeated pulses of ligand stimulation at short time interval, resulting in sustained phospho-Smad2 and transcriptional responses. Additionally, the TGF-ß pathway displays different sensitivities to ligand doses at different time scales. While ligand-induced short-term Smad2 phosphorylation is graded, long-term Smad2 phosphorylation is switch-like to a small change in TGF-ß levels. Correspondingly, the short-term Smad7 gene expression is graded, while long-term PAI-1 gene expression is switch-like, as is the long-term growth inhibitory response. Our results suggest that long-term switch-like signaling responses in the TGF-ß pathway might be critical for cell fate determination.

SBML-PET-MPI: a parallel parameter estimation tool for Systems Biology Markup Language based models.

UNLABELLED: Parameter estimation is crucial for the modeling and dynamic analysis of biological systems. However, implementing parameter estimation is time consuming and computationally demanding. Here, we introduced a parallel parameter estimation tool for Systems Biology Markup Language (SBML)-based models (SBML-PET-MPI). SBML-PET-MPI allows the user to perform parameter estimation and parameter uncertainty analysis by collectively fitting multiple experimental datasets. The tool is developed and parallelized using the message passing interface (MPI) protocol, which provides good scalability with the number of processors. AVAILABILITY: SBML-PET-MPI is freely available for non-commercial use at http://www.bioss.uni-freiburg.de/cms/sbml-pet-mpi.html or http://sites.google.com/site/sbmlpetmpi/.