Clonal Origin and Lineage Ambiguity in Mixed Neuroendocrine Carcinoma of the Uterine Cervix.

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.

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.

Synthesis of Hydrocarbazole Derivatives by Oxidative Dearomative Cyclization of Diarylamines Using a Hypervalent Iodine Reagent.

Hydrocarbazole derivatives bearing a quaternary stereogenic center at C4a were synthesized by means of intramolecular oxidative dearomatization of diarylamines using hypervalent iodine in moderate to good yields. The hydrocarbazole bearing a cyclohexadienone moiety was further converted into the tetracyclic skeletons of Aspidosperma and akuammiline-type alkaloids via regioselective aza-Michael reaction at C4 and at C9a, respectively.

Quantifying heterogeneity of stochastic gene expression.

The heterogeneity of stochastic gene expression, which refers to the temporal fluctuation in a gene product and its cell-to-cell variation, has attracted considerable interest from biologists, physicists, and mathematicians. The dynamics of protein production and degradation have been modeled as random processes with transition probabilities. However, there is a gap between theory and phenomena, particularly in terms of analytical formulation and parameter estimation. In this study, we propose a theoretical framework in which we present a basic model of a gene regulatory system, derive a steady-state solution, and provide a Bayesian approach for estimating the model parameters from single-cell experimental data. The proposed framework is demonstrated to be applicable for various scales of single-cell experiments at both the mRNA and protein levels and is useful for comparing kinetic parameters across species, genomes, and cell strains.

Vaginal LPS changed gene transcriptional regulation response to ischemic reperfusion and increased vulnerability of fetal brain hemorrhage.

During pregnancy, both ischemic reperfusion and bacterial agent LPS are known risk factors for fetal brain damage. However, there is a lack of evidence to explain whether vaginal LPS affects the fetus response to ischemic reperfusion. Here we reported that there was more than 2 folds higher vulnerability of fetal brain hemorrhage response to ischemic reperfusion when mother mouse was treated with vaginal LPS. As our previously reported, ischemic reperfusion induces P53-dependent fetal brain damage was based on a molecular mechanism: the transcriptional pattern was changed from HIF-1alpha-dependent to P53-dependent immediately. In the present work, only with vaginal LPS precondition, phosphorylation of activated transcriptional factor (ATF) 2 at Thr71 appeared in response to ischemic reperfusion. Moreover, this phosphorylation was completely blocked by pre-treatment with a P53 inhibitor, pifithrin-α. We concluded that vaginal LPS precondition trigged the p53-dependent phosphorylation of ATF2 in response to ischemic reperfusion, which played an important role of increasing vulnerability to hemorrhage in fetus.

Identifying Key Regulatory Genes in Drug Resistance Acquisition: Modeling Pseudotime Trajectories of Breast Cancer Single-Cell Transcriptome.

Single-cell RNA-sequencing (scRNA-seq) technology has provided significant insights into cancer drug resistance at the single-cell level. However, understanding dynamic cell transitions at the molecular systems level remains limited, requiring a systems biology approach. We present an approach that combines mathematical modeling with a pseudotime analysis using time-series scRNA-seq data obtained from the breast cancer cell line MCF-7 treated with tamoxifen. Our single-cell analysis identified five distinct subpopulations, including tamoxifen-sensitive and -resistant groups. Using a single-gene mathematical model, we discovered approximately 560-680 genes out of 6000 exhibiting multistable expression states in each subpopulation, including key estrogen-receptor-positive breast cancer cell survival genes, such as RPS6KB1. A bifurcation analysis elucidated their regulatory mechanisms, and we mapped these genes into a molecular network associated with cell survival and metastasis-related pathways. Our modeling approach comprehensively identifies key regulatory genes for drug resistance acquisition, enhancing our understanding of potential drug targets in breast cancer.

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.

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.

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.

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.