Familial hypercholesterolemia and elevated lipoprotein(a) : double heritable risk and new therapeutic opportunities

Vuorio A, Watts GF, Schneider WJ, Tsimikas S, Kovanen PT (Mehilainen Airport Health Centre, Vantaa; University of Helsinki, Helsinki, Finland; University of Western Australia, Perth, Australia; Royal Perth Hospital, Perth, Australia; Medical University of Vienna, Vienna, Austria; University of California San Diego, La Jolla, CA, USA; Wihuri Research Institute, Helsinki, Finland). Familial hypercholesterolemia and elevated lipoprotein(a): double heritable risk and new therapeutic opportunities (Review). J Intern Med 2020; 287: 2-18. There is compelling evidence that the elevated plasma lipoprotein(a) [Lp(a)] levels increase the risk of atherosclerotic cardiovascular disease (ASCVD) in the general population. Like low-density lipoprotein (LDL) particles, Lp(a) particles contain cholesterol and promote atherosclerosis. In addition, Lp(a) particles contain strongly proinflammatory oxidized phospholipids and a unique apoprotein, apo(a), which promotes the growth of an arterial thrombus. At least one in 250 individuals worldwide suffer from the heterozygous form of familial hypercholesterolemia (HeFH), a condition in which LDL-cholesterol (LDL-C) is significantly elevated since birth. FH-causing mutations in the LDL receptor gene demonstrate a clear gene-dosage effect on Lp(a) plasma concentrations and elevated Lp(a) levels are present in 30-50% of patients with HeFH. The cumulative burden of two genetically determined pro-atherogenic lipoproteins, LDL and Lp(a), is a potent driver of ASCVD in HeFH patients. Statins are the cornerstone of treatment of HeFH, but they do not lower the plasma concentrations of Lp(a). Emerging therapies effectively lower Lp(a) by as much as 90% using RNA-based approaches that target the transcriptional product of the LPA gene. We are now approaching the dawn of an era, in which permanent and significant lowering of the high cholesterol burden of HeFH patients can be achieved. If outcome trials of novel Lp(a)-lowering therapies prove to be safe and cost-effective, they will provide additional risk reduction needed to effectively treat HeFH and potentially lower the CVD risk in these high-risk patients even more than currently achieved with LDL-C lowering alone.Peer reviewe

Network Geometry and Complexity

(28 pages, 11 figures)Higher order networks are able to characterize data as different as functional brain networks, protein interaction networks and social networks beyond the framework of pairwise interactions. Most notably higher order networks include simplicial complexes formed not only by nodes and links but also by triangles, tetrahedra, etc. More in general, higher-order networks can be cell-complexes formed by gluing convex polytopes along their faces. Interestingly, higher order networks have a natural geometric interpretation and therefore constitute a natural way to explore the discrete network geometry of complex networks. Here we investigate the rich interplay between emergent network geometry of higher order networks and their complexity in the framework of a non-equilibrium model called Network Geometry with Flavor. This model, originally proposed for capturing the evolution of simplicial complexes, is here extended to cell-complexes formed by subsequently gluing different copies of an arbitrary regular polytope. We reveal the interplay between complexity and geometry of the higher order networks generated by the model by studying the emergent community structure and the degree distribution as a function of the regular polytope forming its building blocks. Additionally, we discuss the underlying hyperbolic nature of the emergent geometry and we relate the spectral dimension of the higher-order network to the dimension and nature of its building blocks

Integrated Analysis of Gene Expression, CpG Island Methylation, and Gene Copy Number in Breast Cancer Cells by Deep Sequencing

We used deep sequencing technology to profile the transcriptome, gene copy number, and CpG island methylation status simultaneously in eight commonly used breast cell lines to develop a model for how these genomic features are integrated in estrogen receptor positive (ER+) and negative breast cancer. Total mRNA sequence, gene copy number, and genomic CpG island methylation were carried out using the Illumina Genome Analyzer. Sequences were mapped to the human genome to obtain digitized gene expression data, DNA copy number in reference to the non-tumor cell line (MCF10A), and methylation status of 21,570 CpG islands to identify differentially expressed genes that were correlated with methylation or copy number changes. These were evaluated in a dataset from 129 primary breast tumors. Gene expression in cell lines was dominated by ER-associated genes. ER+ and ER− cell lines formed two distinct, stable clusters, and 1,873 genes were differentially expressed in the two groups. Part of chromosome 8 was deleted in all ER− cells and part of chromosome 17 amplified in all ER+ cells. These loci encoded 30 genes that were overexpressed in ER+ cells; 9 of these genes were overexpressed in ER+ tumors. We identified 149 differentially expressed genes that exhibited differential methylation of one or more CpG islands within 5 kb of the 5′ end of the gene and for which mRNA abundance was inversely correlated with CpG island methylation status. In primary tumors we identified 84 genes that appear to be robust components of the methylation signature that we identified in ER+ cell lines. Our analyses reveal a global pattern of differential CpG island methylation that contributes to the transcriptome landscape of ER+ and ER− breast cancer cells and tumors. The role of gene amplification/deletion appears to more modest, although several potentially significant genes appear to be regulated by copy number aberrations

Mammary epithelial cell transformation: insights from cell culture and mouse models

Normal human mammary epithelial cells (HMECs) have a finite life span and do not undergo spontaneous immortalization in culture. Critical to oncogenic transformation is the ability of cells to overcome the senescence checkpoints that define their replicative life span and to multiply indefinitely – a phenomenon referred to as immortalization. HMECs can be immortalized by exposing them to chemicals or radiation, or by causing them to overexpress certain cellular genes or viral oncogenes. However, the most efficient and reproducible model of HMEC immortalization remains expression of high-risk human papillomavirus (HPV) oncogenes E6 and E7. Cell culture models have defined the role of tumor suppressor proteins (pRb and p53), inhibitors of cyclin-dependent kinases (p16(INK4a), p21, p27 and p57), p14(ARF), telomerase, and small G proteins Rap, Rho and Ras in immortalization and transformation of HMECs. These cell culture models have also provided evidence that multiple epithelial cell subtypes with distinct patterns of susceptibility to oncogenesis exist in the normal mammary tissue. Coupled with information from distinct molecular portraits of primary breast cancers, these findings suggest that various subtypes of mammary cells may be precursors of different subtypes of breast cancers. Full oncogenic transformation of HMECs in culture requires the expression of multiple gene products, such as SV40 large T and small t, hTERT (catalytic subunit of human telomerase), Raf, phosphatidylinositol 3-kinase, and Ral-GEFs (Ral guanine nucleotide exchange factors). However, when implanted into nude mice these transformed cells typically produce poorly differentiated carcinomas and not adenocarcinomas. On the other hand, transgenic mouse models using ErbB2/neu, Ras, Myc, SV40 T or polyomavirus T develop adenocarcinomas, raising the possibility that the parental normal cell subtype may determine the pathological type of breast tumors. Availability of three-dimensional and mammosphere models has led to the identification of putative stem cells, but more studies are needed to define their biologic role and potential as precursor cells for distinct breast cancers. The combined use of transformation strategies in cell culture and mouse models together with molecular definition of human breast cancer subtypes should help to elucidate the nature of breast cancer diversity and to develop individualized therapies

ARRDC3 suppresses breast cancer progression by negatively regulating integrin β4

Large-scale genetic analyses of human tumor samples have been used to identify novel oncogenes, tumor suppressors and prognostic factors, but the functions and molecular interactions of many individual genes have not been determined. In this study we examined the cellular effects and molecular mechanism of the arrestin family member, ARRDC3, a gene preferentially lost in a subset of breast cancers. Oncomine data revealed that the expression of ARRDC3 decreases with tumor grade, metastases and recurrences. ARRDC3 overexpression represses cancer cell proliferation, migration, invasion, growth in soft agar and in vivo tumorigenicity, whereas downregulation of ARRCD3 has the opposite effects. Mechanistic studies showed that ARRDC3 functions in a novel regulatory pathway that controls the cell surface adhesion molecule, β-4 integrin (ITGβ4), a protein associated with aggressive tumor behavior. Our data indicates ARRDC3 directly binds to a phosphorylated form of ITGβ4 leading to its internalization, ubiquitination and ultimate degradation. The results identify the ARRCD3-ITGβ4 pathway as a new therapeutic target in breast cancer and show the importance of connecting genetic arrays with mechanistic studies in the search for new treatments

Parkinson’s disease mouse models in translational research

Animal models with high predictive power are a prerequisite for translational research. The closer the similarity of a model to Parkinson’s disease (PD), the higher is the predictive value for clinical trials. An ideal PD model should present behavioral signs and pathology that resemble the human disease. The increasing understanding of PD stratification and etiology, however, complicates the choice of adequate animal models for preclinical studies. An ultimate mouse model, relevant to address all PD-related questions, is yet to be developed. However, many of the existing models are useful in answering specific questions. An appropriate model should be chosen after considering both the context of the research and the model properties. This review addresses the validity, strengths, and limitations of current PD mouse models for translational research

Pan-cancer analysis of whole genomes

Cancer is driven by genetic change, and the advent of massively parallel sequencing has enabled systematic documentation of this variation at the whole-genome scale(1-3). Here we report the integrative analysis of 2,658 whole-cancer genomes and their matching normal tissues across 38 tumour types from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). We describe the generation of the PCAWG resource, facilitated by international data sharing using compute clouds. On average, cancer genomes contained 4-5 driver mutations when combining coding and non-coding genomic elements; however, in around 5% of cases no drivers were identified, suggesting that cancer driver discovery is not yet complete. Chromothripsis, in which many clustered structural variants arise in a single catastrophic event, is frequently an early event in tumour evolution; in acral melanoma, for example, these events precede most somatic point mutations and affect several cancer-associated genes simultaneously. Cancers with abnormal telomere maintenance often originate from tissues with low replicative activity and show several mechanisms of preventing telomere attrition to critical levels. Common and rare germline variants affect patterns of somatic mutation, including point mutations, structural variants and somatic retrotransposition. A collection of papers from the PCAWG Consortium describes non-coding mutations that drive cancer beyond those in the TERT promoter(4); identifies new signatures of mutational processes that cause base substitutions, small insertions and deletions and structural variation(5,6); analyses timings and patterns of tumour evolution(7); describes the diverse transcriptional consequences of somatic mutation on splicing, expression levels, fusion genes and promoter activity(8,9); and evaluates a range of more-specialized features of cancer genomes(8,10-18).Peer reviewe