Somatic mosaicism of an intragenic FANCB duplication in both fibroblast and peripheral blood cells observed in a Fanconi anemia patient leads to milder phenotype

© 2017 The Authors. Molecular Genetics & Genomic Medicine published by Wiley Periodicals, Inc. Background: Fanconi anemia (FA) is a rare disorder characterized by congenital malformations, progressive bone marrow failure, and predisposition to cancer. Patients harboring X-linked FANCB pathogenic variants usually present with severe congenital malformations resembling VACTERL syndrome with hydrocephalus. Methods: We employed the diepoxybutane (DEB) test for FA diagnosis, arrayCGH for detection of duplication, targeted capture and next-gen sequencing for defining the duplication breakpoint, PacBio sequencing of full-length FANCB aberrant transcript, FANCD2 ubiquitination and foci formation assays for the evaluation of FANCB protein function by viral transduction of FANCB- cells with lentiviral FANCB WT and mutant expression constructs, and droplet digital PCR for quantitation of the duplication in the genomic DNA and cDNA. Results: We describe here an FA-B patient with a mild phenotype. The DEB diagnostic test for FA revealed somatic mosaicism. We identified a 9154 bp intragenic duplication in FANCB, covering the first coding exon 3 and the flanking regions. A four bp homology (GTAG) present at both ends of the breakpoint is consistent with microhomology-mediated duplication mechanism. The duplicated allele gives rise to an aberrant transcript containing exon 3 duplication, predicted to introduce a stop codon in FANCB protein (p.A319*). Duplication levels in the peripheral blood DNA declined from 93% to 7.9% in the span of eleven years. Moreover, the patient fibroblasts have shown 8% of wild-type (WT) allele and his carrier mother showed higher than expected levels of WT allele (79% vs. 50%) in peripheral blood, suggesting that the duplication was highly unstable. Conclusion: Unlike sequence point variants, intragenic duplications are difficult to precisely define, accurately quantify, and may be very unstable, challenging the proper diagnosis. The reversion of genomic duplication to the WT allele results in somatic mosaicism and may explain the relatively milder phenotype displayed by the FA-B patient described here

Searching for network modules

When analyzing complex networks a key target is to uncover their modular structure, which means searching for a family of modules, namely node subsets spanning each a subnetwork more densely connected than the average. This work proposes a novel type of objective function for graph clustering, in the form of a multilinear polynomial whose coefficients are determined by network topology. It may be thought of as a potential function, to be maximized, taking its values on fuzzy clusterings or families of fuzzy subsets of nodes over which every node distributes a unit membership. When suitably parametrized, this potential is shown to attain its maximum when every node concentrates its all unit membership on some module. The output thus is a partition, while the original discrete optimization problem is turned into a continuous version allowing to conceive alternative search strategies. The instance of the problem being a pseudo-Boolean function assigning real-valued cluster scores to node subsets, modularity maximization is employed to exemplify a so-called quadratic form, in that the scores of singletons and pairs also fully determine the scores of larger clusters, while the resulting multilinear polynomial potential function has degree 2. After considering further quadratic instances, different from modularity and obtained by interpreting network topology in alternative manners, a greedy local-search strategy for the continuous framework is analytically compared with an existing greedy agglomerative procedure for the discrete case. Overlapping is finally discussed in terms of multiple runs, i.e. several local searches with different initializations.Comment: 10 page

Community Structure Characterization

This entry discusses the problem of describing some communities identified in a complex network of interest, in a way allowing to interpret them. We suppose the community structure has already been detected through one of the many methods proposed in the literature. The question is then to know how to extract valuable information from this first result, in order to allow human interpretation. This requires subsequent processing, which we describe in the rest of this entry

A meta-analysis of state-of-the-art electoral prediction from Twitter data

Electoral prediction from Twitter data is an appealing research topic. It seems relatively straightforward and the prevailing view is overly optimistic. This is problematic because while simple approaches are assumed to be good enough, core problems are not addressed. Thus, this paper aims to (1) provide a balanced and critical review of the state of the art; (2) cast light on the presume predictive power of Twitter data; and (3) depict a roadmap to push forward the field. Hence, a scheme to characterize Twitter prediction methods is proposed. It covers every aspect from data collection to performance evaluation, through data processing and vote inference. Using that scheme, prior research is analyzed and organized to explain the main approaches taken up to date but also their weaknesses. This is the first meta-analysis of the whole body of research regarding electoral prediction from Twitter data. It reveals that its presumed predictive power regarding electoral prediction has been rather exaggerated: although social media may provide a glimpse on electoral outcomes current research does not provide strong evidence to support it can replace traditional polls. Finally, future lines of research along with a set of requirements they must fulfill are provided.Comment: 19 pages, 3 table

Real-time traffic event detection using Twitter data

Incident detection is an important component of intelligent transport systems and plays a key role in urban traffic management and provision of traveller information services. Due to its importance, a wide number of researchers have developed different algorithms for real-time incident detection. However, the main limitation of existing techniques is that they do not work well in conditions where random factors could influence traffic flows. Twitter is a valuable source of information as its users post events as they happen or shortly after. Therefore, Twitter data have been used to predict a wide variety of real-time outcomes. This paper aims to present a methodology for a real-time traffic event detection using Twitter. Tweets are obtained through the Twitter streaming application programming interface in real time with a geolocation filter. Then, the author used natural language processing techniques to process the tweets before they are fed into a text classification algorithm that identifies if it is traffic related or not. The authors implemented their methodology in the West Midlands region in the UK and obtained an overall accuracy of 92·86%

Diffusion Model Based Spectral Clustering for Protein-Protein Interaction Networks

BACKGROUND: A goal of systems biology is to analyze large-scale molecular networks including gene expressions and protein-protein interactions, revealing the relationships between network structures and their biological functions. Dividing a protein-protein interaction (PPI) network into naturally grouped parts is an essential way to investigate the relationship between topology of networks and their functions. However, clear modular decomposition is often hard due to the heterogeneous or scale-free properties of PPI networks. METHODOLOGY/PRINCIPAL FINDINGS: To address this problem, we propose a diffusion model-based spectral clustering algorithm, which analytically solves the cluster structure of PPI networks as a problem of random walks in the diffusion process in them. To cope with the heterogeneity of the networks, the power factor is introduced to adjust the diffusion matrix by weighting the transition (adjacency) matrix according to a node degree matrix. This algorithm is named adjustable diffusion matrix-based spectral clustering (ADMSC). To demonstrate the feasibility of ADMSC, we apply it to decomposition of a yeast PPI network, identifying biologically significant clusters with approximately equal size. Compared with other established algorithms, ADMSC facilitates clear and fast decomposition of PPI networks. CONCLUSIONS/SIGNIFICANCE: ADMSC is proposed by introducing the power factor that adjusts the diffusion matrix to the heterogeneity of the PPI networks. ADMSC effectively partitions PPI networks into biologically significant clusters with almost equal sizes, while being very fast, robust and appealing simple

Radiation-Induced Bystander Effects in Cultured Human Stem Cells

The radiation-induced "bystander effect" (RIBE) was shown to occur in a number of experimental systems both in vitro and in vivo as a result of exposure to ionizing radiation (IR). RIBE manifests itself by intercellular communication from irradiated cells to non-irradiated cells which may cause DNA damage and eventual death in these bystander cells. It is known that human stem cells (hSC) are ultimately involved in numerous crucial biological processes such as embryologic development; maintenance of normal homeostasis; aging; and aging-related pathologies such as cancerogenesis and other diseases. However, very little is known about radiation-induced bystander effect in hSC. To mechanistically interrogate RIBE responses and to gain novel insights into RIBE specifically in hSC compartment, both medium transfer and cell co-culture bystander protocols were employed.Human bone-marrow mesenchymal stem cells (hMSC) and embryonic stem cells (hESC) were irradiated with doses 0.2 Gy, 2 Gy and 10 Gy of X-rays, allowed to recover either for 1 hr or 24 hr. Then conditioned medium was collected and transferred to non-irradiated hSC for time course studies. In addition, irradiated hMSC were labeled with a vital CMRA dye and co-cultured with non-irradiated bystander hMSC. The medium transfer data showed no evidence for RIBE either in hMSC and hESC by the criteria of induction of DNA damage and for apoptotic cell death compared to non-irradiated cells (p>0.05). A lack of robust RIBE was also demonstrated in hMSC co-cultured with irradiated cells (p>0.05).These data indicate that hSC might not be susceptible to damaging effects of RIBE signaling compared to differentiated adult human somatic cells as shown previously. This finding could have profound implications in a field of radiation biology/oncology, in evaluating radiation risk of IR exposures, and for the safety and efficacy of hSC regenerative-based therapies

The Ionizing Radiation-Induced Bystander Effect: Evidence, Mechanism, and Significance

It has long been considered that the important biological effects of ionizing radiation are a direct consequence of unrepaired or misrepaired DNA damage occurring in the irradiated cells. It was presumed that no effect would occur in cells in the population that receive no direct radiation exposure. However, in vitro evidence generated over the past two decades has indicated that non-targeted cells in irradiated cell cultures also experience significant biochemical and phenotypic changes that are often similar to those observed in the targeted cells. Further, nontargeted tissues in partial body-irradiated rodents also experienced stressful effects, including oxidative and oncogenic effects. This phenomenon, termed the “bystander response,” has been postulated to impact both the estimation of health risks of exposure to low doses/low fluences of ionizing radiation and the induction of second primary cancers following radiotherapy. Several mechanisms involving secreted soluble factors, oxidative metabolism, gap-junction intercellular communication, and DNA repair, have been proposed to regulate radiation-induced bystander effects. The latter mechanisms are major mediators of the system responses to ionizing radiation exposure, and our knowledge of the biochemical and molecular events involved in these processes is reviewed in this chapter