Formal Concept Analysis Applied to Transcriptomic Data

International audienceIdentifying functions or pathways shared by genes responsible for cancer is still a challenging task. This paper describes the preparation work for applying Formal Concept Analysis (FCA) to biological data. After gene transcription experiments, we integrate various annotations of selected genes in a database along with relevant domain knowledge. The database subsequently allows to build formal contexts in a flexible way. We present here a preliminary experiment using these data on a core context with the addition of domain knowledge by context apposition. The resulting concept lattices are pruned and we discuss some interesting concepts. Our study shows how data integration and FCA can help the domain expert in the exploration of complex data

Revisiting Numerical Pattern Mining with Formal Concept Analysis

In this paper, we investigate the problem of mining numerical data in the framework of Formal Concept Analysis. The usual way is to use a scaling procedure --transforming numerical attributes into binary ones-- leading either to a loss of information or of efficiency, in particular w.r.t. the volume of extracted patterns. By contrast, we propose to directly work on numerical data in a more precise and efficient way, and we prove it. For that, the notions of closed patterns, generators and equivalent classes are revisited in the numerical context. Moreover, two original algorithms are proposed and used in an evaluation involving real-world data, showing the predominance of the present approach

A proteomic atlas of senescence-associated secretomes for aging biomarker development.

The senescence-associated secretory phenotype (SASP) has recently emerged as a driver of and promising therapeutic target for multiple age-related conditions, ranging from neurodegeneration to cancer. The complexity of the SASP, typically assessed by a few dozen secreted proteins, has been greatly underestimated, and a small set of factors cannot explain the diverse phenotypes it produces in vivo. Here, we present the "SASP Atlas," a comprehensive proteomic database of soluble proteins and exosomal cargo SASP factors originating from multiple senescence inducers and cell types. Each profile consists of hundreds of largely distinct proteins but also includes a subset of proteins elevated in all SASPs. Our analyses identify several candidate biomarkers of cellular senescence that overlap with aging markers in human plasma, including Growth/differentiation factor 15 (GDF15), stanniocalcin 1 (STC1), and serine protease inhibitors (SERPINs), which significantly correlated with age in plasma from a human cohort, the Baltimore Longitudinal Study of Aging (BLSA). Our findings will facilitate the identification of proteins characteristic of senescence-associated phenotypes and catalog potential senescence biomarkers to assess the burden, originating stimulus, and tissue of origin of senescent cells in vivo

Functional Effects of let-7g Expression in Colon Cancer Metastasis.

MicroRNA regulation is crucial for gene expression and cell functions. It has been linked to tumorigenesis, development and metastasis in colorectal cancer (CRC). Recently, the let-7 family has been identified as a tumor suppressor in different types of cancers. However, the function of the let-7 family in CRC metastasis has not been fully investigated. Here, we focused on analyzing the role of let-7g in CRC. The Cancer Genome Atlas (TCGA) genomic datasets of CRC and detailed data from a Taiwanese CRC cohort were applied to study the expression pattern of let-7g. In addition, in vitro as well as in vivo studies have been performed to uncover the effects of let-7g on CRC. We found that the expression of let-7g was significantly lower in CRC specimens. Our results further supported the inhibitory effects of let-7g on CRC cell migration, invasion and extracellular calcium influx through store-operated calcium channels. We report a critical role for let-7g in the pathogenesis of CRC and suggest let-7g as a potential therapeutic target for CRC treatment

Adaptive response of neonatal sepsis-derived Group B Streptococcus to bilirubin

This work was funded by the Neonatal Unit Endowment Fund, Aberdeen Maternity Hospital. RH is funded by a career researcher fellowship from NHS Research Scotland. SG was funded by the MRC Flagship PhD programme. We are grateful for the support of Dr Phil Cash and Aberdeen Proteomics, at University of Aberdeen, in completing this project. Supplementary information accompanies this paper at https://doi.org/10.1038/s41598-018-24811-3.Peer reviewedPublisher PD

An innovative index to incorporate transcriptomic data into weight of evidence approaches for environmental risk assessment

The sharp decrease in the cost of RNA-sequencing and the rapid improvement in computational analysis of eco-toxicogenomic data have brought new insights into the adverse effects of chemicals on aquatic organisms. Yet, transcriptomics is generally applied qualitatively in environmental risk assessments, hampering more effective exploitation of this evidence through multidisciplinary studies. In view of this limitation, a methodology is here presented to quantitatively elaborate transcriptional data in support to environmental risk assessment. The proposed methodology makes use of results from the application of Gene Set Enrichment Analysis to recent studies investigating the response of Mytilus galloprovincialis and Ruditapes philippinarum exposed to contaminants of emerging concern. The degree of changes in gene sets and the relevance of physiological reactions are integrated in the calculation of a hazard index. The outcome is then classified according to five hazard classes (from absent to severe), providing an evaluation of whole-transcriptome effects of chemical exposure. The application to experimental and simulated datasets proved that the method can effectively discriminate different levels of altered transcriptomic responses when compared to expert judgement (Spearman correlation coefficient of 0.96). A further application to data collected in two independent studies of Salmo trutta and Xenopus tropicalis exposed to contaminants confirmed the potential extension of the methodology to other aquatic species. This methodology can serve as a proof of concept for the integration of “genomic tools” in environmental risk assessment based on multidisciplinary investigations. To this end, the proposed transcriptomic hazard index can now be incorporated into quantitative Weight of Evidence approaches and weighed, with results from other types of analysis, to elucidate the role of chemicals in adverse ecological effects