The Non-coding Mammary Carcinoma Susceptibility Locus, Mcs5c, Regulates Pappa Expression via Age-Specific Chromatin Folding and Allele-Dependent DNA Methylation

The role of pregnancy associated plasma protein-A in triple negative breast cancer: a promising target for achieving clinical benefits

Pregnancy associated plasma protein-A (PAPP-A) plays an integral role in breast cancer (BC), especially triple negative breast cancer (TNBC). This subtype accounts for the most aggressive BC, possesses high tumor heterogeneity, is least responsive to standard treatments and has the poorest clinical outcomes. There is a critical need to address the lack of effective targeted therapeutic options available. PAPP-A is a protein that is highly elevated during pregnancy. Frequently, higher PAPP-A expression is detected in tumors than in healthy tissues. The increase in expression coincides with increased rates of aggressive cancers. In BC, PAPP-A has been demonstrated to play a role in tumor initiation, progression, metastasis including epithelial-mesenchymal transition (EMT), as well as acting as a biomarker for predicting patient outcomes. In this review, we present the role of PAPP-A, with specific focus on TNBC. The structure and function of PAPP-A, belonging to the pappalysin subfamily, and its proteolytic activity are assessed. We highlight the link of BC and PAPP-A with respect to the IGFBP/IGF axis, EMT, the window of susceptibility and the impact of pregnancy. Importantly, the relevance of PAPP-A as a TNBC clinical marker is reviewed and its influence on immune-related pathways are explored. The relationship and mechanisms involving PAPP-A reveal the potential for more treatment options that can lead to successful immunotherapeutic targets and the ability to assist with better predicting clinical outcomes in TNBC.

The Pregnancy-Associated Plasma Protein-A (PAPP-A) Story

Pregnancy-associated plasma protein-A (PAPP-A) was first identified in the early 1970s as a placental protein of unknown function, present at high concentrations in the circulation of pregnant women. In the mid-to-late 1990s, PAPP-A was discovered to be a metzincin metalloproteinase, expressed by many nonplacental cells, that regulates local insulin-like growth factor (IGF) activity through cleavage of high-affinity IGF binding proteins (IGFBPs), in particular IGFBP-4. With PAPP-A as a cell surface-associated enzyme, the reduced affinity of the cleavage fragments results in increased IGF available to bind and activate IGF receptors in the pericellular environment. This proteolytic regulation of IGF activity is important, since the IGFs promote proliferation, differentiation, migration, and survival in various normal and cancer cells. Thus, there has been a steady growth in investigation of PAPP-A structure and function outside of pregnancy. This review provides historical perspective on the discovery of PAPP-A and its structure and cellular function, highlights key studies of the first 50 years in PAPP-A research, and introduces new findings from recent years.

Identification of cortical interneuron cell markers in mouse embryos based on machine learning analysis of single-cell transcriptomics

Mammalian cortical interneurons (CINs) could be classified into more than two dozen cell types that possess diverse electrophysiological and molecular characteristics, and participate in various essential biological processes in the human neural system. However, the mechanism to generate diversity in CINs remains controversial. This study aims to predict CIN diversity in mouse embryo by using single-cell transcriptomics and the machine learning methods. Data of 2,669 single-cell transcriptome sequencing results are employed. The 2,669 cells are classified into three categories, caudal ganglionic eminence (CGE) cells, dorsal medial ganglionic eminence (dMGE) cells, and ventral medial ganglionic eminence (vMGE) cells, corresponding to the three regions in the mouse subpallium where the cells are collected. Such transcriptomic profiles were first analyzed by the minimum redundancy and maximum relevance method. A feature list was obtained, which was further fed into the incremental feature selection, incorporating two classification algorithms (random forest and repeated incremental pruning to produce error reduction), to extract key genes and construct powerful classifiers and classification rules. The optimal classifier could achieve an MCC of 0.725, and category-specified prediction accuracies of 0.958, 0.760, and 0.737 for the CGE, dMGE, and vMGE cells, respectively. The related genes and rules may provide helpful information for deepening the understanding of CIN diversity.

Induced mammary cancer in rat models: pathogenesis, genetics, and relevance to female breast cancer

Mammary cancer, or breast cancer in women, is a polygenic disease with a complex etiopathogenesis. While much remains elusive regarding its origin, it is well established that chemical carcinogens and endogenous estrogens contribute significantly to the initiation and progression of this disease. Rats have been useful models to study induced mammary cancer. They develop mammary tumors with comparable histopathology to humans and exhibit differences in resistance or susceptibility to mammary cancer depending on strain. While some rat strains (e.g., Sprague-Dawley) readily form mammary tumors following treatment with the chemical carcinogen, 7,12-dimethylbenz[a]-anthracene (DMBA), other strains (e.g., Copenhagen) are resistant to DMBA-induced mammary carcinogenesis. Genetic linkage in inbred strains has identified strain-specific quantitative trait loci (QTLs) affecting mammary tumors, via mechanisms that act together to promote or attenuate, and include 24 QTLs controlling the outcome of chemical induction, 10 QTLs controlling the outcome of estrogen induction, and 4 QTLs controlling the outcome of irradiation induction. Moreover, and based on shared factors affecting mammary cancer etiopathogenesis between rats and humans, including orthologous risk regions between both species, rats have served as useful models for identifying methods for breast cancer prediction and treatment. These studies in rats, combined with alternative animal models that more closely mimic advanced stages of breast cancer and/or human lifestyles, will further improve our understanding of this complex disease.

Deciphering the Role of 3D Genome Organization in Breast Cancer Susceptibility

Cancer risk by environmental exposure is modulated by an individual's genetics and age at exposure. This age-specific period of susceptibility is referred to as the "Window of Susceptibility" (WOS). Rats have a similar WOS for developing breast cancer. A previous study in rat identified an age-specific long-range regulatory interaction for the cancer gene, Pappa, that is associated with breast cancer susceptibility. However, the global role of three-dimensional genome organization and downstream gene expression programs in the WOS is not known. Therefore, we generated Hi-C and RNA-seq data in rat mammary epithelial cells within and outside the WOS. To systematically identify higher-order changes in 3D genome organization, we developed NE-MVNMF that combines network enhancement followed by multitask non-negative matrix factorization. We examined three-dimensional genome organization dynamics at the level of individual loops as well as higher-order domains. Differential chromatin interactions tend to be associated with differentially up-regulated genes with the WOS and recapitulate several human SNP-gene interactions associated with breast cancer susceptibility. Our approach identified genomic blocks of regions with greater overall differences in contact count between the two time points when the cluster assignments change and identified genes and pathways implicated in early carcinogenesis and cancer treatment. Our results suggest that WOS-specific changes in 3D genome organization are linked to transcriptional changes that may influence susceptibility to breast cancer.

Rat models of human diseases and related phenotypes: a systematic inventory of the causative genes

The laboratory rat has been used for a long time as the model of choice in several biomedical disciplines. Numerous inbred strains have been isolated, displaying a wide range of phenotypes and providing many models of human traits and diseases. Rat genome mapping and genomics was considerably developed in the last decades. The availability of these resources has stimulated numerous studies aimed at discovering causal disease genes by positional identification. Numerous rat genes have now been identified that underlie monogenic or complex diseases and remarkably, these results have been translated to the human in a significant proportion of cases, leading to the identification of novel human disease susceptibility genes, helping in studying the mechanisms underlying the pathological abnormalities and also suggesting new therapeutic approaches. In addition, reverse genetic tools have been developed. Several genome-editing methods were introduced to generate targeted mutations in genes the function of which could be clarified in this manner [generally these are knockout mutations]. Furthermore, even when the human gene causing a disease had been identified without resorting to a rat model, mutated rat strains (in particular KO strains) were created to analyze the gene function and the disease pathogenesis. Today, over 350 rat genes have been identified as underlying diseases or playing a key role in critical biological processes that are altered in diseases, thereby providing a rich resource of disease models. This article is an update of the progress made in this research and provides the reader with an inventory of these disease genes, a significant number of which have similar effects in rat and humans.

In silico prediction of high-resolution Hi-C interaction matrices

The three-dimensional (3D) organization of the genome plays an important role in gene regulation bringing distal sequence elements in 3D proximity to genes hundreds of kilobases away. Hi-C is a powerful genome-wide technique to study 3D genome organization. Owing to experimental costs, high resolution Hi-C datasets are limited to a few cell lines. Computational prediction of Hi-C counts can offer a scalable and inexpensive approach to examine 3D genome organization across multiple cellular contexts. Here we present HiC-Reg, an approach to predict contact counts from one-dimensional regulatory signals. HiC-Reg predictions identify topologically associating domains and significant interactions that are enriched for CCCTC-binding factor (CTCF) bidirectional motifs and interactions identified from complementary sources. CTCF and chromatin marks, especially repressive and elongation marks, are most important for HiC-Reg’s predictive performance. Taken together, HiC-Reg provides a powerful framework to generate high-resolution profiles of contact counts that can be used to study individual locus level interactions and higher-order organizational units of the genome.

Existing computational approaches to predict long-range regulatory interactions do not fully exploit high-resolution Hi-C datasets. Here the authors present a Random Forests regression-based approach to predict high-resolution Hi-C counts using one-dimensional regulatory genomic signals.

Mapping Mammary Tumor Traits in the Rat

For nearly a century, the rat has served as a key model for studying the pathophysiology and genetic risk modifiers of breast cancer. Rat mammary tumors that initiate after exposure to carcinogens or estrogens closely resemble the etiological, histopathological, and genomic features of human breast cancer. Recent developments in genome-editing techniques in the rat have also enabled the development of sophisticated models for identifying the genetic modifiers of the nonmalignant tumor microenvironment that contribute to the formation, progression, and outcome of breast cancer. In this protocol review, we discuss the current methodologies for the three genetic mapping techniques in the rat that are widely used for identifying and testing the heritable genetic modifiers of breast cancer.

PAPP-A and cancer

The zinc metalloproteinase, PAPP-A, enhances local insulin-like growth factor (IGF) action through cleavage of inhibitory IGF-binding proteins, thereby increasing IGF available for IGF receptor-mediated cell proliferation, migration and survival. In many tumors, enhanced IGF receptor signaling is associated with tumor growth, invasion and metastasis. We will first discuss PAPP-A structure and function, and post-translational inhibitors of PAPP-A expression or proteolytic activity. We will then review the evidence supporting an important role for PAPP-A in many cancers, including breast, ovarian and lung cancer, and Ewing sarcoma.

Inducible knockdown of pregnancy-associated plasma protein-A gene expression in adult female mice extends life span

Pregnancy-associated plasma protein-A (PAPP-A) knockout (KO) mice, generated through homologous recombination in embryonic stem cells, have a significantly increased lifespan compared to wild-type littermates. However, it is unknown whether this longevity advantage would pertain to PAPP-A gene deletion in adult animals. In the present study, we used tamoxifen (Tam)-inducible Cre recombinase-mediated excision of the floxed PAPP-A (fPAPP-A) gene in mice at 5 months of age. fPAPP-A mice, which were either positive (pos) or negative (neg) for Tam-Cre, received Tam treatment with quarterly boosters. Only female mice could be used with this experimental design. fPAPP-A/neg and fPAPP-A/pos mice had similar weights at the start of the experiment and showed equivalent weight gain. We found that fPAPP-A/pos mice had a significant extension of life span (P = 0.005). The median life span was increased by 21% for fPAPP-A/pos compared to fPAPP-A/neg mice. Analysis of mortality in life span quartiles indicated that the proportion of deaths of fPAPP-A/pos mice were lower than fPAPP-A/neg mice at young adult ages (P = 0.002 for 601-800 days) and higher than fPAPP-A/neg mice at older ages (P = 0.004 for >1000 days). Thus, survival curves and age-specific mortality indicate that female mice with knockdown of PAPP-A gene expression as adults have an extended healthy life span.