Small -cell neuroendocrine carcinoma (SCNEC) of the cervix is a rare disease characterized by a high incidence of mixed tumors with other types of cancer. The mechanism underlying this mixed phenotype is not well understood. This study established a panel of organoid lines from patients with SCNEC of the cervix and ultimately focused on one line, which retained a mixed tumor phenotype, both in vitro and in vivo. Histologically, both organoids and xenograft tumors showed distinct differentiation into either SCNEC or adenocarcinoma in some regions and ambiguous differentiation in others. Tracking single cells indicated the existence of cells with bipotential differentiation toward SCNEC and adenocarcinomas. Single -cell transcriptional analysis identified three distinct clusters: SCNEC-like, adenocarcinoma-like, and a cluster lacking specific differentiation markers. The expression of neuroendocrine markers was enriched in the SCNEC-like cluster but not exclusively. Human papillomavirus 18 E6 was enriched in the SCNEC-like cluster, which showed higher proliferation and lower levels of the p53 pathway. After treatment with anticancer drugs, the expression of adenocarcinoma markers increased, whereas that of SCNEC decreased. Using a reporter system for keratin 19 expression, changes in the differentiation of each cell were shown to be associated with the shift in differentiation induced by drug treatment. These data suggest that mixed SCNEC/cervical tumors have a clonal origin and are characterized by an ambiguous and flexible differentiation state. (Am J Pathol 2024, 194: 415-429; https://doi.org/ 10.1016/j.ajpath.2023.11.013)

Mammalian genomes encode large number of long noncoding RNAs (lncRNAs) that play key roles in various biological processes, including proliferation, differentiation, and stem cell pluripotency. Recent studies have addressed that some lncRNAs are dysregulated in human cancers and may play crucial roles in tumor development and progression. Here, we show that the lncRNA ZNNT1 is required for the proliferation and tumorigenicity of colon cancer cells with wild-type p53. ZNNT1 knockdown leads to decreased ubiquitination and stabilization of p53 protein. Moreover, we demonstrate that ZNNT1 needs to interact with SART3 to destabilize p53 and to promote the proliferation and tumorigenicity of colon cancer cells. We further show that SART3 is associated with the ubiquitin-specific peptidase USP15 and that ZNNT1 may induce p53 destabilization by inhibiting this interaction. These results suggest that ZNNT1 interferes with the SART3-USP15 complex-mediated stabilization of p53 protein and thereby plays important roles in the proliferation and tumorigenicity of colon cancer cells. Our findings suggest that ZNNT1 may be a promising molecular target for the therapy of colon cancer.

Anaphylaxis caused by allergen sensitization and vaccination is a serious health concern. The severity of anaphylaxis is associated with the presence of immunoglobulin E (IgE) antibodies that bind to allergens with a high affinity. Here, we report the development of a unique Interleukin-13 (IL-13)-producing follicular helper T (TFH) cell subset, designated TFH2 cells, which is tightly associated with the production of high-affinity IgE antibodies. TFH2 cells had a transcriptionally hybrid phenotype between TH2 and TFH cells, which express GATA-binding protein 3 (Gata3) and B-cell/CLL lymphoma 6 (BCL-6), respectively. Adaptive transfer experiments demonstrated that type 2 helper T (TH2) cells were capable of differentiating into TFH2 cells upon secondary antigen stimulation. The type 2 TFH (TFH2) conversion process was entirely attenuated in T cell-specific deficient mice of Bcl6 (Bcl6f/f cd4-cre; Bcl6ΔT). Moreover, the transfer of IL-13 defective TH2 cells partially inhibited IgE responses and significantly decreased the high-affinity IgE, even though TFH development was intact. Therefore, IL-13 from TFH2 cells controlled the selective enrichment of IgE+ B-cells with a high affinity for allergens. A previously undefined unique TFH subset, TFH2, likely contributes to IgE responses. This suggests that the accumulation of antigen-specific TH2 cells in human patients with allergies is a high-risk factor for IgE-dependent allergic diseases.

Dominant-negative mutations associated with signal transducer and activator of transcription 3 (STAT3) signaling, which controls epithelial proliferation in various tissues, lead to atopic dermatitis in hyper IgE syndrome. This dermatitis is thought to be attributed to defects in STAT3 signaling in type 17 helper T cell specification. However, the role of STAT3 signaling in skin epithelial cells remains unclear. We found that STAT3 signaling in keratinocytes is required to maintain skin homeostasis by negatively controlling the expression of hair follicle-specific keratin genes. These expression patterns correlated with the onset of dermatitis, which was observed in specific pathogen-free conditions but not in germ-free conditions, suggesting the involvement of Toll-like receptor-mediated inflammatory responses. Thus, our study suggests that STAT3-dependent gene expression in keratinocytes plays a critical role in maintaining the homeostasis of skin, which is constantly exposed to microorganisms.

NF-κB is a transcription factor that activates super enhancers (SEs) and typical enhancers (TEs) and triggers threshold and graded gene expression, respectively. However, the mechanisms by which NF-κB selectively participates in these enhancers remain unclear. Here we show using mouse primary B lymphocytes that SE activity simultaneously associates with chromatin opening and enriched NF-κB binding, resulting in a higher fold change and threshold expression upon B cell receptor (BCR) activation. The higher fold change results from longer DNA, whereas the threshold response is explained by synergy in DNA-NF-κB binding and is supported by the coexistence of PU.1 and NF-κB in a SE before cell stimulation. This model indicates that the pre-existing NF-κB functions as a seed and triggers its processive binding upon BCR activation. Our mathematical modeling of the single-cell transcriptome reveals an additional role for SEs in divergent clonal responses in B cells.

Regnase-1-mediated mRNA decay (RMD), in which inflammatory mRNAs harboring specific stem-loop structures are degraded, is a critical part of proper immune homeostasis. Prior to initial translation, Regnase-1 associates with target stem-loops but does not carry out endoribonucleolytic cleavage. Single molecule imaging revealed that UPF1 is required to first unwind the stem-loops, thus licensing Regnase-1 to proceed with RNA degradation. Following translation, Regnase-1 physically associates with UPF1 using two distinct points of interaction: The Regnase-1 RNase domain binds to SMG1-phosphorylated residue T28 in UPF1; in addition, an intrinsically disordered segment in Regnase-1 binds to the UPF1 RecA domain, enhancing the helicase activity of UPF1. The SMG1-UPF1-Regnase-1 axis targets pioneer rounds of translation and is critical for rapid resolution of inflammation through restriction of the number of proteins translated by a given mRNA. Furthermore, small-molecule inhibition of SMG1 prevents RNA unwinding in dendritic cells, allowing post-transcriptional control of innate immune responses.

In using gene expression levels for genetic network inference, we believe that two measurements that are similar to each other are less informative than two measurements that differ from each other. Given, for example, that gene expression levels measured at two adjacent time points in a time-series experiment are often similar to each other, we assume that each measurement in the time-series experiment will be less informative than each measurement in a steady-state experiment. Based on this idea, we propose a new inference method that relies heavily on informative gene expression data. Through numerical experiments, we prove that the quality of an inferred genetic network is slightly improved by heavily weighting informative gene expression data. In this study, we develop a new method by modifying the existing random-forest-based inference method to take advantage of its ability to analyze both time-series and static gene expression data. The idea we propose can be similarly applied to many of the other existing inference methods, as well.

Whole-body metabolic homeostasis is tightly controlled by hormone-like factors with systemic or paracrine effects that are derived from nonendocrine organs, including adipose tissue (adipokines) and liver (hepatokines). Fibroblast growth factor 21 (FGF21) is a hormone-like protein, which is emerging as a major regulator of whole-body metabolism and has therapeutic potential for treating metabolic syndrome. However, the mechanisms that control FGF21 levels are not fully understood. Herein, we demonstrate that FGF21 production in the liver is regulated via a posttranscriptional network consisting of the CCR4–NOT deadenylase complex and RNA-binding protein tristetraprolin (TTP). In response to nutrient uptake, CCR4–NOT cooperates with TTP to degrade AU-rich mRNAs that encode pivotal metabolic regulators, including FGF21. Disruption of CCR4–NOT activity in the liver, by deletion of the catalytic subunit CNOT6L, increases serum FGF21 levels, which ameliorates diet-induced metabolic disorders and enhances energy expenditure without disrupting bone homeostasis. Taken together, our study describes a hepatic CCR4–NOT/FGF21 axis as a hitherto unrecognized systemic regulator of metabolism and suggests that hepatic CCR4–NOT may serve as a target for devising therapeutic strategies in metabolic syndrome and related morbidities.

Adenosine deaminases acting on RNA (ADARs) catalyze the deamination of adenosine (A) to inosine (I). A-to-I RNA editing targets double-stranded RNA (dsRNA), and increases the complexity of gene regulation by modulating base pairing-dependent processes such as splicing, translation, and microRNA (miRNA)-mediated gene silencing. This study investigates the genome-wide binding preferences of the nuclear constitutive isoforms ADAR1-p110 and ADAR2 on human miRNA species by RNA immunoprecipitation of ADAR-bound small RNAs (RIP-seq). Our results suggest that secondary structure predicted by base-pairing probability in the mainly double-stranded region of a pre-miRNA or mature miRNA duplex may determine ADAR isoform preference for binding distinct subpopulations of miRNAs. Furthermore, we identify 31 unique editing sites with statistical significance, 19 sites of which are novel editing sites. Editing sites are enriched in the seed region responsible for target recognition by miRNAs, and isoform-specific nucleotide motifs in the immediate vicinity and opposite of editing sites are consistent with previous studies, and further reveal that ADAR2 may edit A/C bulges more frequently than ADAR1-p110 in the context of miRNA.

Paired-end RNA sequencing (RNA-seq) is usually applied to the quantification of long transcripts such as messenger or long non-coding RNAs, in which case overlapping pairs are discarded. In contrast, RNA-seq on short RNAs (<= 200 nt) is typically carried out in single-end mode, as the additional cost associated with paired-end would only translate into redundant sequence information. Here, we exploit paired-end sequencing of short RNAs as a strategy to filter out sequencing errors and apply this method to the identification of adenosine-to-inosine (A-to-I) RNA editing events on human precursor microRNA (pre-miRNA) and mature miRNA. Combined with RNA immunoprecipitation sequencing (RIP-seq) of A-to-I RNA editing enzymes, this method takes full advantage of deep sequencing technology to identify RNA editing sites with unprecedented resolution in terms of editing efficiency.

A single transcription factor (TF) potentially induces both threshold and graded transcription responses via super enhancers (SEs) and typical enhancers (TEs). However, the mechanism by which a TF binds to different types of enhancers to determine gene expression quantity remains to be elucidated. To clarify the mechanism, we performed an integrated analysis of chromatin accessibility, RelA-DNA binding and single-cell transcription for each enhancer in anti-IgM-stimulated mouse primary B lymphocytes. Here we show that SE activity, defined by acetylated histone H3 lysine 27 (H3K27Ac) modifications, was simultaneously quantitatively controlled by chromatin opening and enriched RelA binding, resulting in threshold transcription. Our analysis indicated that the number of RelA binding in open chromatin regions at SEs and TEs was on average five and one, respectively, and that a mathematical model having these differences in TF numbers as parameters could explain enhancer-mediated quantitative transcription differences of individual genes. This study indicates that highly specific on-off expression of TF-enriched SE-targeted genes results in a highly selective epigenetic landscape for cell specificity, in contract to broadly responsive TE-regulated genes that may be responsible for cellular homeostasis.

Acquired drug resistance is the major reason why patients fail to respond to cancer therapies.It is a challenging task to determine the tipping point of endocrine resistance and detect the associated molecules.Derived from new systems biology theory,the dynamic network biomarker (DNB) method is designed to quantitatively identify the tipping point of a drastic system transition and can theoretically identify DNB genes that play key roles in acquiring drug resistance.We analyzed time-course mRNA sequence data generated from the tamoxifen-treated estrogen receptor (ER)-positive MCF-7 cell line,and identified the tipping point of endocrine resistance with its leading molecules.The results show that there is interplay between gene mutations and DNB genes,in which the accumulated mutations eventually affect the DNB genes that subsequently cause the change of transcriptional landscape,enabling full-blown drug resistance.Survival analyses based on clinical datasets validated that the DNB genes were associated with the poor survival of breast cancer patients.The results provided the detection for the pre-resistance state or early signs of endocrine resistance.Our predictive method may greatly benefit the scheduling of treatments for complex diseases in which patients are exposed to considerably different drugs and may become drug resistant.

The tumor microenvironment is fundamental to cancer progression, and the influence of its mechanical properties is increasingly being appreciated. Tamoxifen has been used for many years to treat estrogen-positive breast cancer. Here we report that tamoxifen regulates the level and activity of collagen cross-linking and degradative enzymes, and hence the organization of the extracellular matrix, via a mechanism involving both the G protein-coupled estrogen receptor (GPER) and hypoxia-inducible factor-1 alpha (HIF-1A). We show that tamoxifen reduces HIF-1A levels by suppressing myosin-dependent contractility and matrix stiffness mechanosensing. Tamoxifen also downregulates hypoxia-regulated genes and increases vascularization in PDAC tissues. Our findings implicate the GPER/HIF-1A axis as a master regulator of peri-tumoral stromal remodeling and the fibrovascular tumor microenvironment and offer a paradigm shift for tamoxifen from a well-established drug in breast cancer hormonal therapy to an alternative candidate for stromal targeting strategies in PDAC and possibly other cancers.

Our previous study proposed a positive quadratic system representation for molecular interaction in a cell, in-cluding a signal transduction pathway and a gene regulatory network, and also presented a method for estimating a positive invariant set depending on the initial state. As an extension towards wider applications of this approach, this paper proposes a system representation called here a singularly perturbed positive quadratic system, and shows that every positive rational system, which is used as a mathematical model expressing biological behavior, can be approximately represented by a quasi-steady state system of a singularly perturbed positive quadratic system. In addition, we prove that the singularly perturbed positive quadratic system preserves stability at an equilibrium point of the positive rational system.

The ErbB receptor signaling pathway plays an important role in the regulation of cellular proliferation, survival and differentiation, and dysregulation of the pathway is linked to various types of human cancer. Mathematical models have been developed as a practical complementary approach to deciphering the complexity of ErbB receptor signaling and elucidating how the pathways discriminate between ligands to induce different cell fates. In this study, we developed a simulator to accurately calculate the dynamic sensitivity of extracellular-signal-regulated kinase (ERK) activity (ERK*) and Akt activity (Akt*), downstream of the ErbB receptors stimulated with epidermal growth factor (EGF) and heregulin (HRG). To demonstrate the feasibility of this simulator, we estimated how the reactions critically responsible for ERK* and Akt* change with time and in response to different doses of EGF and HRG, and predicted that only a small number of reactions determine ERK* and Akt*. ERK* increased steeply with increasing HRG dose until saturation, while showing a gently rising response to EGF. Akt* had a gradual wide-range response to HRG and a blunt response to EGF. Akt* was sensitive to perturbations of intracellular kinetics, while ERK* was more robust due to multiple, negative feedback loops. Overall, the simulator predicted reactions that were critically responsible for ERK* and Akt* in response to the dose of EGF and HRG, illustrated the response characteristics of ERK* and Akt*, and estimated mechanisms for generating robustness in the ErbB signaling network.

In the FANTOM5 project, transcription initiation events across the human and mouse genomes were mapped at a single base-pair resolution and their frequencies were monitored by CAGE (Cap Analysis of Gene Expression) coupled with single-molecule sequencing. Approximately three thousands of samples, consisting of a variety of primary cells, tissues, cell lines, and time series samples during cell activation and development, were subjected to a uniform pipeline of CAGE data production. The analysis pipeline started by measuring RNA extracts to assess their quality, and continued to CAGE library production by using a robotic or a manual workflow, single molecule sequencing, and computational processing to generate frequencies of transcription initiation. Resulting data represents the consequence of transcriptional regulation in each analyzed state of mammalian cells. Non-overlapping peaks over the CAGE profiles, approximately 200,000 and 150,000 peaks for the human and mouse genomes, were identified and annotated to provide precise location of known promoters as well as novel ones, and to quantify their activities.

Abstract Motivation Drug combination therapy for treatment of cancers and other multifactorial diseases has the potential of increasing the therapeutic effect, while reducing the likelihood of drug resistance. In order to reduce time and cost spent in comprehensive screens, methods are needed which can model additive effects of possible drug combinations. Results We here show that the transcriptional response to combinatorial drug treatment at promoters, as measured by single molecule CAGE technology, is accurately described by a linear combination of the responses of the individual drugs at a genome wide scale. We also find that the same linear relationship holds for transcription at enhancer elements. We conclude that the described approach is promising for eliciting the transcriptional response to multidrug treatment at promoters and enhancers in an unbiased genome wide way, which may minimize the need for exhaustive combinatorial screens. Availability and implementation The CAGE sequence data used in this study is available in the DDBJ Sequence Read Archive (http://trace.ddbj.nig.ac.jp/index_e.html), accession number DRP001113. Supplementary information Supplementary data are available at Bioinformatics online.

Several researchers have focused on the inference of genetic networks as a process for extracting useful information from gene expression data. Their work has led to the proposal of a number of methods for genetic network inference. Yet the genetic networks inferred by these methods often contain large numbers of false-positive regulations along with the true-positives. One effective way to reduce the number of erroneous regulations is to apply inference methods that use a priori knowledge on the properties of the genetic networks. The existing inference methods adopting this approach generally use a priori knowledge and the observed gene expression data simultaneously to determine whether or not the target genetic network actually contains each of the candidate regulations. In this study, we establish a new framework for “using a priori knowledge after genetic network inference.” The framework uses a priori knowledge only to modify the genetic network that has already been inferred by the other inference method. Based on this framework, we propose a new inference method that uses multiple kinds of a priori knowledge about genetic networks. The proposed method effectively combines multiple kinds of knowledge and computes the confidence values of regulations. Here, we confirm the effectiveness of the proposed method by applying it to artificial and actual genetic network inference problems. While only a small improvement is gained from the use of multiple kinds of a priori knowledge, we can improve the performance of many other existing inference methods by combining them with the method we propose here.

Cancer phenotypes, referred to as hallmarks of cancer, are acquired by dysregulation of biochemical networks, in addition to genetic and epigenetic alterations. For the last decade, systems biology approaches, a combination of mathematical modeling and quantitative experiments, have attempted to reveal the structure and spatiotemporal dynamics of signal transduction networks in cancer. In this review, we discuss recent systems biology studies of dynamic regulation associated with well-studied sub-networks of the hallmarks of cancer: cell cycle regulation abnormalities, resistance to cell death, angiogenesis, invasion and metastasis, dysregulation of immune responses, and metabolism. Using mathematical modeling to understand these dynamic circuitries in the complete cancer network should enable us to formulate basic principles in the hallmarks of cancer.