The cycling and aging mouse female reproductive tract at single-cell resolution.

The female reproductive tract (FRT) undergoes extensive remodeling during reproductive cycling. This recurrent remodeling and how it shapes organ-specific aging remains poorly explored. Using single-cell and spatial transcriptomics, we systematically characterized morphological and gene expression changes occurring in ovary, oviduct, uterus, cervix, and vagina at each phase of the mouse estrous cycle, during decidualization, and into aging. These analyses reveal that fibroblasts play central-and highly organ-specific-roles in FRT remodeling by orchestrating extracellular matrix (ECM) reorganization and inflammation. Our results suggest a model wherein recurrent FRT remodeling over reproductive lifespan drives the gradual, age-related development of fibrosis and chronic inflammation. This hypothesis was directly tested using chemical ablation of cycling, which reduced fibrotic accumulation during aging. Our atlas provides extensive detail into how estrus, pregnancy, and aging shape the organs of the female reproductive tract and reveals the unexpected cost of the recurrent remodeling required for reproduction.

Do mammals have menopause?

Semantics and lack of data have clouded our understanding about menopause in non-human mammals. The traditional definition of menopause based on the last menstrual bleed is limited and hinders cross-species comparison. Here, we redefine it as the permanent cessation of ovulation and show menopause to be widespread across mammalian orders.

De novo identification of maximally deregulated subnetworks based on multi-omics data with DeRegNet.

BACKGROUND: With a growing amount of (multi-)omics data being available, the extraction of knowledge from these datasets is still a difficult problem. Classical enrichment-style analyses require predefined pathways or gene sets that are tested for significant deregulation to assess whether the pathway is functionally involved in the biological process under study. De novo identification of these pathways can reduce the bias inherent in predefined pathways or gene sets. At the same time, the definition and efficient identification of these pathways de novo from large biological networks is a challenging problem. RESULTS: We present a novel algorithm, DeRegNet, for the identification of maximally deregulated subnetworks on directed graphs based on deregulation scores derived from (multi-)omics data. DeRegNet can be interpreted as maximum likelihood estimation given a certain probabilistic model for de-novo subgraph identification. We use fractional integer programming to solve the resulting combinatorial optimization problem. We can show that the approach outperforms related algorithms on simulated data with known ground truths. On a publicly available liver cancer dataset we can show that DeRegNet can identify biologically meaningful subgraphs suitable for patient stratification. DeRegNet can also be used to find explicitly multi-omics subgraphs which we demonstrate by presenting subgraphs with consistent methylation-transcription patterns. DeRegNet is freely available as open-source software. CONCLUSION: The proposed algorithmic framework and its available implementation can serve as a valuable heuristic hypothesis generation tool contextualizing omics data within biomolecular networks.

Identification of Pparγ-modulated miRNA hubs that target the fibrotic tumor microenvironment.

Liver fibrosis interferes with normal liver function and facilitates hepatocellular carcinoma (HCC) development, representing a major threat to human health. Here, we present a comprehensive perspective of microRNA (miRNA) function on targeting the fibrotic microenvironment. Starting from a murine HCC model, we identify a miRNA network composed of 8 miRNA hubs and 54 target genes. We show that let-7, miR-30, miR-29c, miR-335, and miR-338 (collectively termed antifibrotic microRNAs [AF-miRNAs]) down-regulate key structural, signaling, and remodeling components of the extracellular matrix. During fibrogenic transition, these miRNAs are transcriptionally regulated by the transcription factor Pparγ and thus we identify a role of Pparγ as regulator of a functionally related class of AF-miRNAs. The miRNA network is active in human HCC, breast, and lung carcinomas, as well as in 2 independent mouse liver fibrosis models. Therefore, we identify a miRNA:mRNA network that contributes to formation of fibrosis in tumorous and nontumorous organs of mice and humans.