A novel Fanconi anemia subtype associated with a dominant-negative mutation in RAD51

in Nature Communications (2015), 6(8829),

Fanconi anemia (FA) is a hereditary disease featuring hypersensitivity to DNA cross-linker-induced chromosomal instability in association with developmental abnormalities, bone marrow failure and a strong ... [more ▼]

Fanconi anemia (FA) is a hereditary disease featuring hypersensitivity to DNA cross-linker-induced chromosomal instability in association with developmental abnormalities, bone marrow failure and a strong predisposition to cancer. 17 FA disease genes have been reported, all of which act in a recessive mode of inheritance. Here we report on a de novo g.41022153G>A; p.Ala293Thr (NM_002875) missense mutation in one allele of the homologous recombination DNA repair gene RAD51 in an FA-like patient. This heterozygous mutation causes a novel FA subtype, “FA-R”, which appears to be the first subtype of FA caused by a dominant-negative mutation. The patient, who features microcephaly and mental retardation, has reached adulthood without the typical bone marrow failure and pediatric cancers. Together with the recent reports on RAD51-associated congenital mirror movement disorders our results point to an important role for RAD51-mediated homologous recombination in neurodevelopment, in addition to DNA repair and cancer susceptibility. [less ▲]

Biological data analysis as an information theory problem: multivariable dependence measures and the shadows algorithm

in Journal of Computational Biology (2015), 22(11), 1005-1024

Information theory is valuable in multiple-variable analysis for being model-free and nonparametric, and for the modest sensitivity to undersampling. We previously introduced a general approach to finding ... [more ▼]

Information theory is valuable in multiple-variable analysis for being model-free and nonparametric, and for the modest sensitivity to undersampling. We previously introduced a general approach to finding multiple dependencies that provides accurate measures of levels of dependency for subsets of variables in a data set, which is significantly nonzero only if the subset of variables is collectively dependent. This is useful, however, only if we can avoid a combinatorial explosion of calculations for increasing numbers of variables. The proposed dependence measure for a subset of variables, tau, differential interaction information, Delta(tau), has the property that for subsets of tau some of the factors of Delta(tau) are significantly nonzero, when the full dependence includes more variables. We use this property to suppress the combinatorial explosion by following the "shadows" of multivariable dependency on smaller subsets. Rather than calculating the marginal entropies of all subsets at each degree level, we need to consider only calculations for subsets of variables with appropriate "shadows." The number of calculations for n variables at a degree level of d grows therefore, at a much smaller rate than the binomial coefficient (n, d), but depends on the parameters of the "shadows" calculation. This approach, avoiding a combinatorial explosion, enables the use of our multivariable measures on very large data sets. We demonstrate this method on simulated data sets, and characterize the effects of noise and sample numbers. In addition, we analyze a data set of a few thousand mutant yeast strains interacting with a few thousand chemical compounds. [less ▲]

Systems genomics evaluation of the SH-SY5Y neuroblastoma cell line as a model for Parkinson’s disease

in BMC Genomics (2014), 15(1154),

Background: The human neuroblastoma cell line, SH-SY5Y, is a commonly used cell line in studies related to neurotoxicity, oxidative stress, and neurodegenerative diseases. Although this cell line is often ... [more ▼]

Background: The human neuroblastoma cell line, SH-SY5Y, is a commonly used cell line in studies related to neurotoxicity, oxidative stress, and neurodegenerative diseases. Although this cell line is often used as a cellular model for Parkinson’s disease, the relevance of this cellular model in the context of Parkinson’s disease (PD) and other neurodegenerative diseases has not yet been systematically evaluated. Results: We have used a systems genomics approach to characterize the SH-SY5Y cell line using whole-genome sequencing to determine the genetic content of the cell line and used transcriptomics and proteomics data to determine molecular correlations. Further, we integrated genomic variants using a network analysis approach to evaluate the suitability of the SH-SY5Y cell line for perturbation experiments in the context of neurodegenerative diseases, including PD. Conclusions: The systems genomics approach showed consistency across different biological levels (DNA, RNA and protein concentrations). Most of the genes belonging to the major Parkinson’s disease pathways and modules were intact in the SH-SY5Y genome. Specifically, each analysed gene related to PD has at least one intact copy in SH-SY5Y. The disease-specific network analysis approach ranked the genetic integrity of SH-SY5Y as higher for PD than for Alzheimer’s disease but lower than for Huntington’s disease and Amyotrophic Lateral Sclerosis for loss of function perturbation experiments. [less ▲]

Molecular and Clinical Evidence for an ARMC5 Tumor Syndrome: Concurrent Inactivating Germline and Somatic Mutations are Associated with both Primary Macronodular Adrenal Hyperplasia and Meningioma

in Journal of Clinical Endocrinology and Metabolism (2014)

Context:Primary macronodular adrenal hyperplasia (PMAH) is a rare cause of Cushing's syndrome (CS), which may present in the context of different familial multitumor syndromes. Heterozygous inactivating ... [more ▼]

Context:Primary macronodular adrenal hyperplasia (PMAH) is a rare cause of Cushing's syndrome (CS), which may present in the context of different familial multitumor syndromes. Heterozygous inactivating germline mutations of armadillo repeat containing 5 (ARMC5) have very recently been described as cause for sporadic PMAH. Whether this genetic condition also causes familial PMAH in association with other neoplasias is unclear. Objective: The aim of the present study was to delineate the molecular cause in a large family with PMAH and other neoplasias. Patients and Methods: Whole genome sequencing and comprehensive clinical and biochemical phenotyping was performed in members of a PMAH affected family. Nodules derived from adrenal surgery and pancreatic and meningeal tumor tissue were analysed for accompanying somatic mutations in the identified target genes. Results: PMAH presenting either as overt or subclinical CS was accompanied by a heterozygous germline mutation in ARMC5 (p.A110fs*9) located on chromosome 16. Analysis of tumor tissue showed different somatic ARMC5 mutations in adrenal nodules supporting a “second hit” hypothesis with inactivation of a tumor suppressor gene. A damaging somatic ARMC5 mutation was also found in a concomitant meningioma (p.R502fs) but not in a pancreatic tumor suggesting biallelic inactivation of ARMC5 as causal also for the intracranial meningioma. Conclusions: Our analysis further confirms inherited inactivating ARMC5 mutations as a cause of familial PMAH and suggests an additional role for the development of concomitant intracranial meningiomas. [less ▲]

Discovering pair-wise genetic interactions: an information theory-based approach.

in PloS one (2014), 9(3), 92310

Phenotypic variation, including that which underlies health and disease in humans, results in part from multiple interactions among both genetic variation and environmental factors. While diseases or ... [more ▼]

Phenotypic variation, including that which underlies health and disease in humans, results in part from multiple interactions among both genetic variation and environmental factors. While diseases or phenotypes caused by single gene variants can be identified by established association methods and family-based approaches, complex phenotypic traits resulting from multi-gene interactions remain very difficult to characterize. Here we describe a new method based on information theory, and demonstrate how it improves on previous approaches to identifying genetic interactions, including both synthetic and modifier kinds of interactions. We apply our measure, called interaction distance, to previously analyzed data sets of yeast sporulation efficiency, lipid related mouse data and several human disease models to characterize the method. We show how the interaction distance can reveal novel gene interaction candidates in experimental and simulated data sets, and outperforms other measures in several circumstances. The method also allows us to optimize case/control sample composition for clinical studies. [less ▲]

An evaluation of high-throughput approaches to QTL mapping in Saccharomyces cerevisiae

in Genetics (2014), 196(3), 853-65

Dissecting the molecular basis of quantitative traits is a significant challenge and is essential for understanding complex diseases. Even in model organisms, precisely determining causative genes and ... [more ▼]

Dissecting the molecular basis of quantitative traits is a significant challenge and is essential for understanding complex diseases. Even in model organisms, precisely determining causative genes and their interactions has remained elusive, due in part to difficulty in narrowing intervals to single genes and in detecting epistasis or linked quantitative trait loci. These difficulties are exacerbated by limitations in experimental design, such as low numbers of analyzed individuals or of polymorphisms between parental genomes. We address these challenges by applying three independent high-throughput approaches for QTL mapping to map the genetic variants underlying 11 phenotypes in two genetically distant Saccharomyces cerevisiae strains, namely (1) individual analysis of >700 meiotic segregants, (2) bulk segregant analysis, and (3) reciprocal hemizygosity scanning, a new genome-wide method that we developed. We reveal differences in the performance of each approach and, by combining them, identify eight polymorphic genes that affect eight different phenotypes: colony shape, flocculation, growth on two nonfermentable carbon sources, and resistance to two drugs, salt, and high temperature. Our results demonstrate the power of individual segregant analysis to dissect QTL and address the underestimated contribution of interactions between variants. We also reveal confounding factors like mutations and aneuploidy in pooled approaches, providing valuable lessons for future designs of complex trait mapping studies. [less ▲]

The spectrum of circulating RNA: A window into systems toxicology

in Toxicological Sciences (2013), 132(2), 478492

Adverse effects caused by therapeutic drugs are a serious and costly health concern. Despite the body’s systemic responses to therapeutics, the liver is often the focus of damage and is usually the focus ... [more ▼]

Adverse effects caused by therapeutic drugs are a serious and costly health concern. Despite the body’s systemic responses to therapeutics, the liver is often the focus of damage and is usually the focus of studies of toxic effects due to its active roles in the metabolism of xenobiotics. It is extremely difficult, however, to assess systemic responses with currently available methods. Comprehensive cataloging of cell-free circulating RNAs using next-generation sequencing technology may open a window to assess drug-associated adverse effects at the systems level. To explore this potential, we conducted an RNA profiling study using the well-characterized acetaminophen overdose mouse model on liver and plasma with microarray and next-generation sequencing platforms, respectively. After drug treatment, the levels of a number of transcripts, both endogenous and exogenous RNAs, showed significant changes in plasma, reflecting not only the classical liver injury induced by acetaminophen overdose but also damage in tissues other than the liver. The changes in exogenous RNAs also reflect alteration on dieting behavior after acetaminophen overdose. Besides reporting an extensive list of circulating RNAbased biomarker candidates, this study illustrates the possibility of using circulating RNAs to assess global effects of therapeutics. This could also lead to a new approach for a more comprehensive assessment of the efficacy and safety of therapeutics. [less ▲]

Comparing the MicroRNA Spectrum between Serum and Plasma

in PLoS ONE (2012), 7(7), 41561

MicroRNAs (miRNAs) are small, non-coding RNAs that regulate various biological processes, primarily through interaction with messenger RNAs. The levels of specific, circulating miRNAs in blood have been ... [more ▼]

MicroRNAs (miRNAs) are small, non-coding RNAs that regulate various biological processes, primarily through interaction with messenger RNAs. The levels of specific, circulating miRNAs in blood have been shown to associate with various pathological conditions including cancers. These miRNAs have great potential as biomarkers for various pathophysiological conditions. In this study we focused on different sample types’ effects on the spectrum of circulating miRNA in blood. Using serum and corresponding plasma samples from the same individuals, we observed higher miRNA concentrations in serum samples compared to the corresponding plasma samples. The difference between serum and plasma miRNA concentration showed some associations with miRNA from platelets, which may indicate that the coagulation process may affect the spectrum of extracellular miRNA in blood. Several miRNAs also showed platform dependent variations in measurements. Our results suggest that there are a number of factors that might affect the measurement of circulating miRNA concentration. Caution must be taken when comparing miRNA data generated from different sample types or measurement platforms [less ▲]

Relations between the set-complexity and the structure of graphs and their sub-graphs

in EURASIP Journal on Bioinformatics and Systems Biology (2012), 13

We describe some new conceptual tools for the rigorous, mathematical description of the “set-complexity” of graphs. This set-complexity has been shown previously to be a useful measure for analyzing some ... [more ▼]

We describe some new conceptual tools for the rigorous, mathematical description of the “set-complexity” of graphs. This set-complexity has been shown previously to be a useful measure for analyzing some biological networks, and in discussing biological information in a quantitative fashion. The advances described here allow us to define some significant relationships between the set-complexity measure and the structure of graphs, and of their component sub-graphs. We show here that modular graph structures tend to maximize the set-complexity of graphs. We point out the relationship between modularity and redundancy, and discuss the significance of set-complexity in this regard. We specifically discuss the relationship between complexity and entropy in the case of complete-bipartite graphs, and present a new method for constructing highly complex, binary graphs. These results can be extended to the case of ternary graphs, and to other multi-edge graphs, which are fundamentally more relevant to biological structures and systems. Finally, our results lead us to an approach for extracting high complexity modular graphs from large, noisy graphs with low information content. We illustrate this approach with two examples. [less ▲]

Complexity of Networks II: The Set Complexity of Edge-Colored Graphs

in Complexity (2012), 17(5), 23-36

We previously introduced the concept of “set-complexity”, based on a context-dependent measure of information, and used this concept to describe the complexity of gene interaction networks. In the ... [more ▼]

We previously introduced the concept of “set-complexity”, based on a context-dependent measure of information, and used this concept to describe the complexity of gene interaction networks. In the previous paper in this series we analyzed the set-complexity of binary graphs. Here we extend this analysis to graphs with multi-colored edges that more closely match biological structures like the gene interaction networks. All highly complex graphs by this measure exhibit a modular structure. A principal result of this work is that for the most complex graphs of a given size the number of edge colors is equal to the number of “modules” of the graph. Complete multipartite graphs (CMGs) are defined and analyzed, and the relation between complexity and structure of these graphs is examined in detail. We establish that the mutual information between any two nodes in a CMG can be fully expressed in terms of entropy, and present an explicit expression for the set complexity of CMGs (Theorem 3). An algorithm for generating highly complex graphs from CMGs is described. We establish several theorems relating these concepts and connecting complex graphs with a variety of practical network properties. In exploring the relation between symmetry and complexity we use the idea of a similarity matrix and its spectrum for highly complex graphs. [less ▲]