Reporting p Values

Identification of lncRNAs involved in response to ionizing radiation in fibroblasts of long-term survivors of childhood cancer and cancer-free controls

Introduction

Long non-coding ribonucleic acids (lncRNAs) are involved in the cellular damage response following exposure to ionizing radiation as applied in radiotherapy. However, the role of lncRNAs in radiation response concerning intrinsic susceptibility to late effects of radiation exposure has not been examined in general or in long-term survivors of childhood cancer with and without potentially radiotherapy-related second primary cancers, in particular.

Methods

Primary skin fibroblasts (n=52 each) of long-term childhood cancer survivors with a first primary cancer only (N1), at least one second primary neoplasm (N2+), as well as tumor-free controls (N0) from the KiKme case-control study were matched by sex, age, and additionally by year of diagnosis and entity of the first primary cancer. Fibroblasts were exposed to 0.05 and 2 Gray (Gy) X-rays. Differentially expressed lncRNAs were identified with and without interaction terms for donor group and dose. Weighted co-expression networks of lncRNA and mRNA were constructed using WGCNA. Resulting gene sets (modules) were correlated to the radiation doses and analyzed for biological function.

Results

After irradiation with 0.05Gy, few lncRNAs were differentially expressed (N0: AC004801.4; N1: PCCA-DT, AF129075.3, LINC00691, AL158206.1; N2+: LINC02315). In reaction to 2 Gy, the number of differentially expressed lncRNAs was higher (N0: 152, N1: 169, N2+: 146). After 2 Gy, AL109976.1 and AL158206.1 were prominently upregulated in all donor groups. The co-expression analysis identified two modules containing lncRNAs that were associated with 2 Gy (module1: 102 mRNAs and 4 lncRNAs: AL158206.1, AL109976.1, AC092171.5, TYMSOS, associated with p53-mediated reaction to DNA damage; module2: 390 mRNAs, 7 lncRNAs: AC004943.2, AC012073.1, AC026401.3, AC092718.4, MIR31HG, STXBP5-AS1, TMPO-AS1, associated with cell cycle regulation).

Discussion

For the first time, we identified the lncRNAs AL158206.1 and AL109976.1 as involved in the radiation response in primary fibroblasts by differential expression analysis. The co-expression analysis revealed a role of these lncRNAs in the DNA damage response and cell cycle regulation post-IR. These transcripts may be targets in cancer therapy against radiosensitivity, as well as provide grounds for the identification of at-risk patients for immediate adverse reactions in healthy tissues. With this work we deliver a broad basis and new leads for the examination of lncRNAs in the radiation response.

A Brief Guide to Statistical Analysis and Presentation for the Plant Pathology Journal

Statistical analysis of data is an integral part of research projects in all scientific disciplines including the plant pathology. Appropriate design, application and interpretation of statistical analysis are also, therefore, at the center of publishing and properly evaluating studies in plant pathology. A survey of research works published in the Plant Pathology Journal, however, cast doubt on high standard of statistical analysis required for scientific rigor and reproducibility in the journal. Here I first describe, based on the survey of published works, what mistakes are commonly made and what components are often lacking during statistical analysis and interpretation of its results. Next, I provide possible remedies and suggestions to help guide researchers in preparing manuscript and reviewers in evaluating manuscripts submitted to the Plant Pathology Journal. This is not aiming at delineating technical and practical details of particular statistical methods or approaches.

Hypergraph-based connectivity measures for signaling pathway topologies

Characterizing cellular responses to different extrinsic signals is an active area of research, and curated pathway databases describe these complex signaling reactions. Here, we revisit a fundamental question in signaling pathway analysis: are two molecules “connected” in a network? This question is the first step towards understanding the potential influence of molecules in a pathway, and the answer depends on the choice of modeling framework. We examined the connectivity of Reactome signaling pathways using four different pathway representations. We find that Reactome is very well connected as a graph, moderately well connected as a compound graph or bipartite graph, and poorly connected as a hypergraph (which captures many-to-many relationships in reaction networks). We present a novel relaxation of hypergraph connectivity that iteratively increases connectivity from a node while preserving the hypergraph topology. This measure, B-relaxation distance, provides a parameterized transition between hypergraph connectivity and graph connectivity. B-relaxation distance is sensitive to the presence of small molecules that participate in many functionally unrelated reactions in the network. We also define a score that quantifies one pathway’s downstream influence on another, which can be calculated as B-relaxation distance gradually relaxes the connectivity constraint in hypergraphs. Computing this score across all pairs of 34 Reactome pathways reveals pairs of pathways with statistically significant influence. We present two such case studies, and we describe the specific reactions that contribute to the large influence score. Finally, we investigate the ability for connectivity measures to capture functional relationships among proteins, and use the evidence channels in the STRING database as a benchmark dataset. STRING interactions whose proteins are B-connected in Reactome have statistically significantly higher scores than interactions connected in the bipartite graph representation. Our method lays the groundwork for other generalizations of graph-theoretic concepts to hypergraphs in order to facilitate signaling pathway analysis.

Signaling pathways describe how cells respond to external signals through molecular interactions. As we gain a deeper understanding of these signaling reactions, it is important to understand how molecules may influence downstream responses and how pathways may affect each other. As the amount of information in signaling pathway databases continues to grow, we have the opportunity to analyze properties about pathway structure. We pose an intuitive question about signaling pathways: when are two molecules “connected” in a pathway? This answer varies dramatically based on the assumptions we make about how reactions link molecules. Here, examine four approaches for modeling the structural topology of signaling pathways, and present methods to quantify whether two molecules are “connected” in a pathway database. We find that existing approaches are either too permissive (molecules are connected to many others) or restrictive (molecules are connected to a handful of others), and we present a new measure that offers a continuum between these two extremes. We then expand our question to ask when an entire signaling pathway is “downstream” of another pathway, and show two case studies from the Reactome pathway database that uncovers pathway influence. Finally, we show that the strict notion of connectivity can capture functional relationships among proteins using an independent benchmark dataset. Our approach to quantify connectivity in pathways considers a biologically-motivated definition of connectivity, laying the foundation for more sophisticated analyses that leverage the detailed information in pathway databases.