The late Oligocene flora from the Río Leona Formation, Argentinian Patagonia

A late Oligocene plant macrofossil assemblage is described from the Río Leona Formation, Argentinian Patagonia. This includes a fern, “Blechnum turbioensis” Frenguelli, one species of conifer, and sixteen angiosperm taxa. Rosaceae, Myrtaceae, Proteaceae, Lauraceae, Anacardiaceae and Typhaceae are represented by one species in each family. Five species are considered to be members of the Fabales. Three leaf taxa together with Carpolithus seeds are placed in the Nothofagaceae. Palynomorphs and permineralized woods complete the floral record of the Río Leona Formation, which is considered early late Oligocene based on radiometric dating and palynofloras

Modules, networks and systems medicine for understanding disease and aiding diagnosis

Many common diseases, such as asthma, diabetes or obesity, involve altered interactions between thousands of genes. High-throughput techniques (omics) allow identification of such genes and their products, but functional understanding is a formidable challenge. Network-based analyses of omics data have identified modules of disease-associated genes that have been used to obtain both a systems level and a molecular understanding of disease mechanisms. For example, in allergy a module was used to find a novel candidate gene that was validated by functional and clinical studies. Such analyses play important roles in systems medicine. This is an emerging discipline that aims to gain a translational understanding of the complex mechanisms underlying common diseases. In this review, we will explain and provide examples of how network-based analyses of omics data, in combination with functional and clinical studies, are aiding our understanding of disease, as well as helping to prioritize diagnostic markers or therapeutic candidate genes. Such analyses involve significant problems and limitations, which will be discussed. We also highlight the steps needed for clinical implementation

The Evolving Transcriptome of Head and Neck Squamous Cell Carcinoma: A Systematic Review

BACKGROUND: Numerous studies were performed to illuminate mechanisms of tumorigenesis and metastases from gene expression profiles of Head and Neck Squamous Cell Carcinoma (HNSCC). The objective of this review is to conduct a network-based meta-analysis to identify the underlying biological signatures of the HNSCC transcriptome. METHODS AND FINDINGS: We included 63 HNSCC transcriptomic studies into three specific categories of comparisons: Pre, premalignant lesions v.s. normal; TvN, primary tumors v.s. normal; and Meta, metastatic or invasive v.s. primary tumors. Reported genes extracted from the literature were systematically analyzed. Participation of differential gene activities across three progressive stages deciphered the evolving nature of HNSCC. In total, 1442 genes were verified, i.e. reported at least twice, with ECM1, EMP1, CXCL10 and POSTN shown to be highly reported across all three stages. Knowledge-based networks of the HNSCC transcriptome were constructed, demonstrating integrin signaling and antigen presentation pathways as highly enriched. Notably, functional estimates derived from topological characteristics of integrin signaling networks identified such important genes as ITGA3 and ITGA5, which were supported by findings of invasiveness in vitro. Moreover, we computed genome-wide probabilities of reporting differential gene activities for the Pre, TvN, and Meta stages, respectively. Results highlighted chromosomal regions of 6p21, 19p13 and 19q13, where genomic alterations were shown to be correlated with the nodal status of HNSCC. CONCLUSIONS: By means of a systems-biology approach via network-based meta-analyses, we provided a deeper insight into the evolving nature of the HNSCC transcriptome. Enriched canonical signaling pathways, hot-spots of transcriptional profiles across the genome, as well as topologically significant genes derived from network analyses were highlighted for each of the three progressive stages, Pre, TvN, and Meta, respectively

An Integrative -omics Approach to Identify Functional Sub-Networks in Human Colorectal Cancer

Emerging evidence indicates that gene products implicated in human cancers often cluster together in “hot spots” in protein-protein interaction (PPI) networks. Additionally, small sub-networks within PPI networks that demonstrate synergistic differential expression with respect to tumorigenic phenotypes were recently shown to be more accurate classifiers of disease progression when compared to single targets identified by traditional approaches. However, many of these studies rely exclusively on mRNA expression data, a useful but limited measure of cellular activity. Proteomic profiling experiments provide information at the post-translational level, yet they generally screen only a limited fraction of the proteome. Here, we demonstrate that integration of these complementary data sources with a “proteomics-first” approach can enhance the discovery of candidate sub-networks in cancer that are well-suited for mechanistic validation in disease. We propose that small changes in the mRNA expression of multiple genes in the neighborhood of a protein-hub can be synergistically associated with significant changes in the activity of that protein and its network neighbors. Further, we hypothesize that proteomic targets with significant fold change between phenotype and control may be used to “seed” a search for small PPI sub-networks that are functionally associated with these targets. To test this hypothesis, we select proteomic targets having significant expression changes in human colorectal cancer (CRC) from two independent 2-D gel-based screens. Then, we use random walk based models of network crosstalk and develop novel reference models to identify sub-networks that are statistically significant in terms of their functional association with these proteomic targets. Subsequently, using an information-theoretic measure, we evaluate synergistic changes in the activity of identified sub-networks based on genome-wide screens of mRNA expression in CRC. Cross-classification experiments to predict disease class show excellent performance using only a few sub-networks, underwriting the strength of the proposed approach in discovering relevant and reproducible sub-networks

Using gene co-expression network analysis to predict biomarkers for chronic lymphocytic leukemia

<p>Abstract</p> <p>Background</p> <p>Chronic lymphocytic leukemia (CLL) is the most common adult leukemia. It is a highly heterogeneous disease, and can be divided roughly into indolent and progressive stages based on classic clinical markers. Immunoglobin heavy chain variable region (IgV_H) mutational status was found to be associated with patient survival outcome, and biomarkers linked to the IgV_H status has been a focus in the CLL prognosis research field. However, biomarkers highly correlated with IgV_H mutational status which can accurately predict the survival outcome are yet to be discovered.</p> <p>Results</p> <p>In this paper, we investigate the use of gene co-expression network analysis to identify potential biomarkers for CLL. Specifically we focused on the co-expression network involving ZAP70, a well characterized biomarker for CLL. We selected 23 microarray datasets corresponding to multiple types of cancer from the Gene Expression Omnibus (GEO) and used the frequent network mining algorithm CODENSE to identify highly connected gene co-expression networks spanning the entire genome, then evaluated the genes in the co-expression network in which ZAP70 is involved. We then applied a set of feature selection methods to further select genes which are capable of predicting IgV_H mutation status from the ZAP70 co-expression network.</p> <p>Conclusions</p> <p>We have identified a set of genes that are potential CLL prognostic biomarkers IL2RB, CD8A, CD247, LAG3 and KLRK1, which can predict CLL patient IgV_H mutational status with high accuracies. Their prognostic capabilities were cross-validated by applying these biomarker candidates to classify patients into different outcome groups using a CLL microarray datasets with clinical information.</p

Gene expression profiling integrated into network modelling reveals heterogeneity in the mechanisms of BRCA1 tumorigenesis

Background: gene expression profiling has distinguished sporadic breast tumour classes with genetic and clinical differences. Less is known about the molecular classification of familial breast tumours, which are generally considered to be less heterogeneous. Here, we describe molecular signatures that define BRCA1 subclasses depending on the expression of the gene encoding for oestrogen receptor, ESR1. Methods: for this purpose, we have used the Oncochip v2, a cancer-related cDNA microarray to analyze 14 BRCA1-associated breast tumours. Results: signatures were found to be molecularly associated with different biological processes and transcriptional regulatory programs. The signature of ESR1-positive tumours was mainly linked to cell proliferation and regulated by ER, whereas the signature of ESR1-negative tumours was mainly linked to the immune response and possibly regulated by transcription factors of the REL/NFκB family. These signatures were then verified in an independent series of familial and sporadic breast tumours, which revealed a possible prognostic value for each subclass. Over-expression of immune response genes seems to be a common feature of ER-negative sporadic and familial breast cancer and may be associated with good prognosis. Interestingly, the ESR1-negative tumours were substratified into two groups presenting slight differences in the magnitude of the expression of immune response transcripts and REL/NFκB transcription factors, which could be dependent on the type of BRCA1 germline mutation. Conclusion: this study reveals the molecular complexity of BRCA1 breast tumours, which are found to display similarities to sporadic tumours, and suggests possible prognostic implications

Heterogeneity and Cancer-Related Features in Lymphangioleiomyomatosis Cells and Tissue

Lymphangioleiomyomatosis (LAM) is a rare, low-grade metastasizing disease characterized by cystic lung destruction. LAM can exhibit extensive heterogeneity at the molecular, cellular, and tissue levels. However, the molecular similarities and differences among LAM cells and tissue, and their connection to cancer features are not fully understood. By integrating complementary gene and protein LAM signatures, and single-cell and bulk tissue transcriptome profiles, we show sources of disease heterogeneity, and how they correspond to cancer molecular portraits. Subsets of LAM diseased cells differ with respect to gene expression profiles related to hormones, metabolism, proliferation, and stemness. Phenotypic diseased cell differences are identified by evaluating lumican (LUM) proteoglycan and YB1 transcription factor expression in LAM lung lesions. The RUNX1 and IRF1 transcription factors are predicted to regulate LAM cell signatures, and both regulators are expressed in LAM lung lesions, with differences between spindle-like and epithelioid LAM cells. The cancer single-cell transcriptome profiles most similar to those of LAM cells include a breast cancer mesenchymal cell model and lines derived from pleural mesotheliomas. Heterogeneity is also found in LAM lung tissue, where it is mainly determined by immune system factors. Variable expression of the multifunctional innate immunity protein LCN2 is linked to disease heterogeneity. This protein is found to be more abundant in blood plasma from LAM patients than from healthy women.This research was partially supported by AELAM (ICO-IDIBELL agreement, to M.A. Pujana), The LAM Foundation Seed Grant 2019, to M.A. Pujana, Carlos III Institute of Health grant PI18/01029, to M.A. Pujana and ICI19/00047 to M. Molina-Molina [co-funded by European Regional Development Fund (ERDF), a way to build Europe], Generalitat de Catalunya SGR grant 2017-449, to M.A. Pujana, the CERCA Program for IDIBELL institutional support, and ZonMW-TopZorg grant 842002003, to C.H.M. van Moorsel. M. Plass was supported by a “Ramón y Cajal” contract of the Spanish Ministry of Science and Innovation (RYC2018-024564-I) and J. Moss was supported by the Intramural Research Program of NIH/NHLBI