Positive and negative feedback regulation of the TGF-ß1 explains two equilibrium states in skin aging.

During aging, skin homeostasis is essential for maintaining appearance, as well as biological defense of the human body. In this study, we identified thrombospondin-1 (THBS1) and fibromodulin (FMOD) as positive and negative regulators, respectively, of the TGF-ß1-SMAD4 axis in human skin aging, based on in vitro and in vivo omics analyses and mathematical modeling. Using transcriptomic and epigenetic analyses of senescent dermal fibroblasts, TGF-ß1 was identified as the key upstream regulator. Bifurcation analysis revealed a binary high-/low-TGF-ß1 switch, with THBS1 as the main controller. Computational simulation of the TGF-ß1 signaling pathway indicated that THBS1 expression was sensitively regulated, whereas FMOD was regulated robustly. Results of sensitivity analysis and validation showed that inhibition of SMAD4 complex formation was a promising method to control THBS1 production and senescence. Therefore, this study demonstrated the potential of combining data-driven target discovery with mathematical approaches to determine the mechanisms underlying skin aging.

Extending BioMASS to construct mathematical models from external knowledge.

Motivation: Mechanistic modeling based on ordinary differential equations has led to numerous findings in systems biology by integrating prior knowledge and experimental data. However, the manual curation of knowledge necessary when constructing models poses a bottleneck. As the speed of knowledge accumulation continues to grow, there is a demand for a scalable means of constructing executable models. Results: We previously introduced BioMASS-an open-source, Python-based framework-to construct, simulate, and analyze mechanistic models of signaling networks. With one of its features, Text2Model, BioMASS allows users to define models in a natural language-like format, thereby facilitating the construction of large-scale models. We demonstrate that Text2Model can serve as a tool for integrating external knowledge for mathematical modeling by generating Text2Model files from a pathway database or through the use of a large language model, and simulating its dynamics through BioMASS. Our findings reveal the tool's capabilities to encourage exploration from prior knowledge and pave the way for a fully data-driven approach to constructing mathematical models. Availability and implementation: The code and documentation for BioMASS are available at https://github.com/biomass-dev/biomass and https://biomass-core.readthedocs.io, respectively. The code used in this article are available at https://github.com/okadalabipr/text2model-from-knowledge.

A Combination of Conformation-Specific RAF Inhibitors Overcome Drug Resistance Brought about by RAF Overexpression.

Cancer cells often adapt to targeted therapies, yet the molecular mechanisms underlying adaptive resistance remain only partially understood. Here, we explore a mechanism of RAS/RAF/MEK/ERK (MAPK) pathway reactivation through the upregulation of RAF isoform (RAFs) abundance. Using computational modeling and in vitro experiments, we show that the upregulation of RAFs changes the concentration range of paradoxical pathway activation upon treatment with conformation-specific RAF inhibitors. Additionally, our data indicate that the signaling output upon loss or downregulation of one RAF isoform can be compensated by overexpression of other RAF isoforms. We furthermore demonstrate that, while single RAF inhibitors cannot efficiently inhibit ERK reactivation caused by RAF overexpression, a combination of two structurally distinct RAF inhibitors synergizes to robustly suppress pathway reactivation.

Early Effect of Calcitonin Gene-related Peptide Monoclonal Antibodies in Migraine with Medication Overuse: A Single-center Retrospective Study.

Objective Calcitonin gene-related peptide (CGRP)-(receptor) monoclonal antibody (mAb) has been reported to reduce the frequency of medication overuse in patients with migraine. The present study investigated whether or not CGRP-mAb treatment shows early effectiveness for medication overuse headache (MOH) in Japan. Methods We retrospectively reviewed 34 patients with MOH who received preventive treatment with CGRP-mAb from June 2021 to October 2022. The International Classification of Headache Disorders, 3rd edition was used to diagnose MOH. This study was conducted at the Department of Neurology, Saitama Medical University. Patients were recruited from this specialized headache outpatient center. Results In total, 69 patients with migraine had newly introduced CGRP-mAb, and 34 patients had MOH (49.3%). The mean±standard deviation patient age was 44±15.5 years old. The study population included 24 women (70.6%). The types of CGRP-mAb used were galcanezumab in 16 patients (47.0%), fremanezumab in 10 (29.4%), and erenumab in 8 (23.5%). The mean disease duration was 19.6±13.1 years. The types of migraine diagnosis were chronic migraine in 28 patients (82.4%) and migraine with aura in 11 patients (32.4%). The mean number of headache days in the month before administration of CGRP-mAb was 22±7.7 days; 1 month after administration, the MHD was 16.9±9.1 days. The change in MHD was -5.7 days (22.7%), indicating significant improvement (p<0.05). Conclusion CGRP-mAb has been suggested as a preventive treatment for patients with MOH. Further investigation of the long-term efficacy of CGRP-mAb for MOH is needed.

Quantitative Imaging Analysis of NF-κB for Mathematical Modeling Applications.

Mathematical models can integrate different types of experimental datasets, reconstitute biological systems in silico, and identify previously unknown molecular mechanisms. Over the past decade, mathematical models have been developed based on quantitative observations, such as live-cell imaging and biochemical assays. However, it is difficult to directly integrate next-generation sequencing (NGS) data. Although highly dimensional, NGS data mostly only provides a "snapshot" of cellular states. Nevertheless, the development of various methods for NGS analysis has led to much more accurate predictions of transcription factor activity and has revealed various concepts regarding transcriptional regulation. Therefore, fluorescence live-cell imaging of transcription factors can help alleviate the limitations in NGS data by supplementing temporal information, linking NGS to mathematical modeling. This chapter introduces an analytical method for quantifying dynamics of nuclear factor kappaB (NF-κB) which forms aggregates in the nucleus. The method may also be applicable to other transcription factors regulated in a similar fashion.

The Mediator subunit MED12 promotes formation of HSF1 condensates on heat shock response element arrays in heat-shocked cells.

Upon heat shock, activated heat shock transcription factor 1 (HSF1) binds to the heat shock response elements (HSEs) in the promoters of mammalian heat shock protein (HSP)-encoding genes and recruits the preinitiation complex and coactivators, including Mediator. These transcriptional regulators may be concentrated in phase-separated condensates around the promoters, but they are too minute to be characterized in detail. We herein established HSF1-/- mouse embryonic fibroblasts harbouring HSP72-derived multiple HSE arrays and visualized the condensates of fluorescent protein-tagged HSF1 with liquid-like properties upon heat shock. Using this experimental system, we demonstrate that endogenous MED12, a subunit of Mediator, is concentrated in artificial HSF1 condensates upon heat shock. Furthermore, the knockdown of MED12 markedly reduces the size of condensates, suggesting an important role for MED12 in HSF1 condensate formation.

Distinct but interchangeable subpopulations of colorectal cancer cells with different growth fates and drug sensitivity.

Dynamic changes in cell properties lead to intratumor heterogeneity; however, the mechanisms of nongenetic cellular plasticity remain elusive. When the fate of each cell from colorectal cancer organoids was tracked through a clonogenic growth assay, the cells showed a wide range of growth ability even within the clonal organoids, consisting of distinct subpopulations; the cells generating large spheroids and the cells generating small spheroids. The cells from the small spheroids generated only small spheroids (S-pattern), while the cells from the large spheroids generated both small and large spheroids (D-pattern), both of which were tumorigenic. Transition from the S-pattern to the D-pattern occurred by various extrinsic triggers, in which Notch signaling and Musashi-1 played a key role. The S-pattern spheroids were resistant to chemotherapy and transited to the D-pattern upon drug treatment through Notch signaling. As the transition is linked to the drug resistance, it can be a therapeutic target.

TGF-ß generates a population of cancer cells residing in G1 phase with high motility and metastatic potential via KRTAP2-3.

Transforming growth factor ß (TGF-ß) increases epithelial cancer cell migration and metastasis by inducing epithelial-mesenchymal transition (EMT). TGF-ß also inhibits cell proliferation by inducing G1 phase cell-cycle arrest. However, the correlation between these tumor-promoting and -suppressing effects remains unclear. Here, we show that TGF-ß confers higher motility and metastatic ability to oral cancer cells in G1 phase. Mechanistically, keratin-associated protein 2-3 (KRTAP2-3) is a regulator of these dual effects of TGF-ß, and its expression is correlated with tumor progression in patients with head and neck cancer and migratory and metastatic potentials of oral cancer cells. Furthermore, single-cell RNA sequencing reveals that TGF-ß generates two populations of mesenchymal cancer cells with differential cell-cycle status through two distinctive EMT pathways mediated by Slug/HMGA2 and KRTAP2-3. Thus, TGF-ß-induced KRTAP2-3 orchestrates cancer cell proliferation and migration by inducing EMT, suggesting motile cancer cells arrested in G1 phase as a target to suppress metastasis.

Protocol for stratification of triple-negative breast cancer patients using in silico signaling dynamics.

Personalized kinetic models can predict potential biomarkers and drug targets. Here, we provide a step-by-step approach for building an executable mathematical model from text and integrating transcriptomic datasets. We additionally describe the steps to personalize the mechanistic model and to stratify patients with triple-negative breast cancer (TNBC) based on in silico signaling dynamics. This protocol can also be applied to any signaling pathway for patient-specific modeling. For complete details on the use and execution of this protocol, please refer to Imoto et al. (2022).

ASURAT: functional annotation-driven unsupervised clustering of single-cell transcriptomes.

MOTIVATION: Single-cell RNA sequencing (scRNA-seq) analysis reveals heterogeneity and dynamic cell transitions. However, conventional gene-based analyses require intensive manual curation to interpret biological implications of computational results. Hence, a theory for efficiently annotating individual cells remains warranted. RESULTS: We present ASURAT, a computational tool for simultaneously performing unsupervised clustering and functional annotation of disease, cell type, biological process and signaling pathway activity for single-cell transcriptomic data, using a correlation graph decomposition for genes in database-derived functional terms. We validated the usability and clustering performance of ASURAT using scRNA-seq datasets for human peripheral blood mononuclear cells, which required fewer manual curations than existing methods. Moreover, we applied ASURAT to scRNA-seq and spatial transcriptome datasets for human small cell lung cancer and pancreatic ductal adenocarcinoma, respectively, identifying previously overlooked subpopulations and differentially expressed genes. ASURAT is a powerful tool for dissecting cell subpopulations and improving biological interpretability of complex and noisy transcriptomic data. AVAILABILITY AND IMPLEMENTATION: ASURAT is published on Bioconductor (https://doi.org/10.18129/B9.bioc.ASURAT). The codes for analyzing data in this article are available at Github (https://github.com/keita-iida/ASURATBI) and figshare (https://doi.org/10.6084/m9.figshare.19200254.v4). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

HSF1 phosphorylation establishes an active chromatin state via the TRRAP-TIP60 complex and promotes tumorigenesis.

Transcriptional regulation by RNA polymerase II is associated with changes in chromatin structure. Activated and promoter-bound heat shock transcription factor 1 (HSF1) recruits transcriptional co-activators, including histone-modifying enzymes; however, the mechanisms underlying chromatin opening remain unclear. Here, we demonstrate that HSF1 recruits the TRRAP-TIP60 acetyltransferase complex in HSP72 promoter during heat shock in a manner dependent on phosphorylation of HSF1-S419. TRIM33, a bromodomain-containing ubiquitin ligase, is then recruited to the promoter by interactions with HSF1 and a TIP60-mediated acetylation mark, and cooperates with the related factor TRIM24 for mono-ubiquitination of histone H2B on K120. These changes in histone modifications are triggered by phosphorylation of HSF1-S419 via PLK1, and stabilize the HSF1-transcription complex in HSP72 promoter. Furthermore, HSF1-S419 phosphorylation is constitutively enhanced in and promotes proliferation of melanoma cells. Our results provide mechanisms for HSF1 phosphorylation-dependent establishment of an active chromatin status, which is important for tumorigenesis.

Enhanced transcriptional heterogeneity mediated by NF-κB super-enhancers.

The transcription factor NF-κB, which plays an important role in cell fate determination, is involved in the activation of super-enhancers (SEs). However, the biological functions of the NF-κB SEs in gene control are not fully elucidated. We investigated the characteristics of NF-κB-mediated SE activity using fluorescence imaging of RelA, single-cell transcriptome and chromatin accessibility analyses in anti-IgM-stimulated B cells. The formation of cell stimulation-induced nuclear RelA foci was abolished in the presence of hexanediol, suggesting an underlying process of liquid-liquid phase separation. The gained SEs induced a switch-like expression and enhanced cell-to-cell variability in transcriptional response. These properties were correlated with the number of gained cis-regulatory interactions, while switch-like gene induction was associated with the number of NF-κB binding sites in SE. Our study suggests that NF-κB SEs have an important role in the transcriptional regulation of B cells possibly through liquid condensate formation consisting of macromolecular interactions.

A text-based computational framework for patient -specific modeling for classification of cancers.

Patient heterogeneity precludes cancer treatment and drug development; hence, development of methods for finding prognostic markers for individual treatment is urgently required. Here, we present Pasmopy (Patient-Specific Modeling in Python), a computational framework for stratification of patients using in silico signaling dynamics. Pasmopy converts texts and sentences on biochemical systems into an executable mathematical model. Using this framework, we built a model of the ErbB receptor signaling network, trained in cultured cell lines, and performed in silico simulation of 377 patients with breast cancer using The Cancer Genome Atlas (TCGA) transcriptome datasets. The temporal dynamics of Akt, extracellular signal-regulated kinase (ERK), and c-Myc in each patient were able to accurately predict the difference in prognosis and sensitivity to kinase inhibitors in triple-negative breast cancer (TNBC). Our model applies to any type of signaling network and facilitates the network-based use of prognostic markers and prediction of drug response.

Encoding and decoding NF-κB nuclear dynamics.

The NF-κB signaling pathway is crucial for cellular responses to environmental factors. Several studies have tried to decipher the mechanism of cells utilizing this pathway for information transfer and accurately encoding extracellular information that is translated into unique transcriptional programs. This fine-tuned encoding is possible owing to the complex regulatory mechanisms in the NF-κB pathway and is relayed through the nuclear dynamics of the NF-κB transcription factor. The "message" is then decoded through transcriptional and post-transcriptional regulation leading to stimulus-dependent phenotypes. Here, we reviewed recent advances on how different environmental stimuli are encoded and decoded by cells to produce distinct transcriptional responses and the important analytical techniques used in NF-κB research.

Systems approaches to investigate the role of NF-κB signaling in aging.

The nuclear factor-κB (NF-κB) signaling pathway is one of the most well-studied pathways related to inflammation, and its involvement in aging has attracted considerable attention. As aging is a complex phenomenon and is the result of a multi-step process, the involvement of the NF-κB pathway in aging remains unclear. To elucidate the role of NF-κB in the regulation of aging, different systems biology approaches have been employed. A multi-omics data-driven approach can be used to interpret and clarify unknown mechanisms but cannot generate mechanistic regulatory structures alone. In contrast, combining this approach with a mathematical modeling approach can identify the mechanistics of the phenomena of interest. The development of single-cell technologies has also helped clarify the heterogeneity of the NF-κB response and underlying mechanisms. Here, we review advances in the understanding of the regulation of aging by NF-κB by focusing on omics approaches, single-cell analysis, and mathematical modeling of the NF-κB network.

Building patient-specific models for receptor tyrosine kinase signaling networks.

Cancer progresses due to changes in the dynamic interactions of multidimensional factors associated with gene mutations. Cancer research has actively adopted computational methods, including data-driven and mathematical model-driven approaches, to identify causative factors and regulatory rules that can explain the complexity and diversity of cancers. A data-driven, statistics-based approach revealed correlations between gene alterations and clinical outcomes in many types of cancers. A model-driven mathematical approach has elucidated the dynamic features of cancer networks and identified the mechanisms of drug efficacy and resistance. More recently, machine learning methods have emerged that can be used for mining omics data and classifying patient. However, as the strengths and weaknesses of each method becoming apparent, new analytical tools are emerging to combine and improve the methodologies and maximize their predictive power for classifying cancer subtypes and prognosis. Here, we introduce recent advances in cancer systems biology aimed at personalized medicine, with focus on the receptor tyrosine kinase signaling network.

Systems biology of protein network.

The development of comprehensive measurement technology for biomolecules and the development of computer hardware and algorithms, enhanced by human genome research, have greatly contributed to the advancement of predictive biology. However, to make that happen, it was essential to have a common concept of sharing data in a standard format. In this article, I would like to briefly review how such concepts were developed in Japan for the foundation of today's biological sciences.

IκBα is required for full transcriptional induction of some NFκB-regulated genes in response to TNF in MCF-7 cells.

Inflammatory stimuli triggers the degradation of three inhibitory κB (IκB) proteins, allowing for nuclear translocation of nuclear factor-κB (NFκB) for transcriptional induction of its target genes. Of these three, IκBα is a well-known negative feedback regulator that limits the duration of NFκB activity. We sought to determine whether IκBα's role in enabling or limiting NFκB activation is important for tumor necrosis factor (TNF)-induced gene expression in human breast cancer cells (MCF-7). Contrary to our expectations, many more TNF-response genes showed reduced induction than enhanced induction in IκBα knockdown cells. Mathematical modeling was used to investigate the underlying mechanism. We found that the reduced activation of some NFκB target genes in IκBα-deficient cells could be explained by the incoherent feedforward loop (IFFL) model. In addition, for a subset of genes, prolonged NFκB activity due to loss of negative feedback control did not prolong their transient activation; this implied a multi-state transcription cycle control of gene induction. Genes encoding key inflammation-related transcription factors, such as JUNB and KLF10, were found to be best represented by a model that contained both the IFFL and the transcription cycle motif. Our analysis sheds light on the regulatory strategies that safeguard inflammatory gene expression from overproduction and repositions the function of IκBα not only as a negative feedback regulator of NFκB but also as an enabler of NFκB-regulated stimulus-responsive inflammatory gene expression. This study indicates the complex involvement of IκBα in the inflammatory response to TNF that is induced by radiation therapy in breast cancer.

A combination approach of pseudotime analysis and mathematical modeling for understanding drug-resistant mechanisms.

Cancer cells acquire drug resistance through the following stages: nonresistant, pre-resistant, and resistant. Although the molecular mechanism of drug resistance is well investigated, the process of drug resistance acquisition remains largely unknown. Here we elucidate the molecular mechanisms underlying the process of drug resistance acquisition by sequential analysis of gene expression patterns in tamoxifen-treated breast cancer cells. Single-cell RNA-sequencing indicates that tamoxifen-resistant cells can be subgrouped into two, one showing altered gene expression related to metabolic regulation and another showing high expression levels of adhesion-related molecules and histone-modifying enzymes. Pseudotime analysis showed a cell transition trajectory to the two resistant subgroups that stem from a shared pre-resistant state. An ordinary differential equation model based on the trajectory fitted well with the experimental results of cell growth. Based on the established model, it was predicted and experimentally validated that inhibition of transition to both resistant subtypes would prevent the appearance of tamoxifen resistance.