Monthly Archives: February 2014

The disruption of trace element homeostasis due to aneuploidy as a unifying theme in the etiology of cancer

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The disruption of trace element homeostasis due to aneuploidy as a unifying theme in the etiology of cancer

Johannes Engelken, Matthias Altmeyer, Renty Franklin

#### #### Abstract for Scientists: While decades of cancer research have firmly established multiple “hallmarks of cancer”, cancer’s genomic landscape remains to be fully understood. Particularly, the phenomenon of aneuploidy – gains and losses of large genomic regions, i.e. whole chromosomes or chromosome arms – and why most cancer cells are aneuploid remains enigmatic. This is despite the achievements of cytogenomics and whole genome sequencing which have successfully pinpointed focal amplifications and focal deletions as well as point mutations affecting numerous genes involved in carcinogenesis. A characteristic of many different cancers is the deregulation of the homeostasis of trace elements, such as copper (Cu), zinc (Zn) and iron (Fe). Concentrations of copper are markedly increased in cancer tissue and the blood plasma of cancer patients, while zinc levels are typically decreased. Here we discuss the hypothesis that the disruption of trace element homeostasis and the phenomenon of aneuploidy might be linked. Our tentative analysis of genomic data from diverse tumor types mainly from The Cancer Genome Atlas (TCGA) project suggests that gains and losses of metal transporter genes occur frequently and correlate well with transporter gene expression levels. Hereby they may confer a cancer-driving selective growth advantage at early and possibly also later stages during cancer development. This idea is consistent with recent observations in yeast, which suggest that through chromosomal gains and losses cells can adapt quickly to new carbon sources, nutrient starvation as well as to copper toxicity. In human cancer development, candidate driving events may include, among others, the gains of zinc transporter genes SLC39A1 and SLC39A4 on chromosome arms 1q and 8q, respectively, and the losses of zinc transporter genes SLC30A5, SLC39A14 and SLC39A6 on 5q, 8p and 18q. The recurrent gain of 3q might be associated with the iron transporter gene TFRC and the loss of 13q with the copper transporter gene ATP7B. By altering cellular trace element homeostasis (especially fluctuations in labile and total zinc) such events might contribute to the initiation of the malignant transformation. Consistently, it has been shown that zinc affects a number of the observed hallmark characteristics including DNA repair, inflammation and apoptosis. We term this model the “aneuploidy metal transporter cancer” (AMTC) hypothesis. While the AMTC hypothesis does not contradict the cancer-promoting role of point and focal mutations in established tumor suppressor genes and oncogenes (e.g. MYC, MYCN, TP53, PIK3CA, BRCA1, ERBB2), it seems possible that some of these mutations may be a response to the prior disruption of trace element homeostasis. We suggest a number of approaches for how this hypothesis could be tested experimentally and briefly touch on possible implications for cancer etiology, metastasis, drug resistance and therapy.

Nonparametric inference of the distribution of fitness effects across functional categories in humans

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Nonparametric inference of the distribution of fitness effects across functional categories in humans

Fernando Racimo, Joshua G Schraiber

Quantifying the proportion of polymorphic mutations that are deleterious or neutral is of fundamental importance to our understanding of evolution, disease genetics and the maintenance of variation genome-wide. Here, we develop an approximation to the distribution of fitness effects (DFE) of segregating single-nucleotide mutations in humans. Unlike previous methods, we do not assume that synonymous mutations are neutral, or rely on fitting the DFE of new nonsynonymous mutations to a particular parametric probability distribution, which is poorly motivated on a biological level. We rely on a previously developed method that utilizes a variety of published annotations (including conservation scores, protein deleteriousness estimates and regulatory data) to score all mutations in the human genome based on how likely they are to be affected by negative selection, controlling for mutation rate. We map this score to a scale of fitness coefficients via maximum likelihood using diffusion theory and a Poisson random field model. We then use our coefficient mapping to quantify the distribution of all scored single-nucleotide polymorphisms in Yoruba and Europeans. Our method serves to approximate the DFE of any type of segregating mutations, regardless of its genomic consequence, and so allows us to compare the proportion of mutations that are negatively selected or neutral across various genomic categories, including different types of regulatory sites. We observe that the distribution of intergenic polymorphisms is highly leptokurtic, with a strong peak at neutrality, while the distribution of nonsynonymous polymorphisms is bimodal, with a neutral peak and a second peak at s ≈ −10^(−4). Other types of polymorphisms have shapes that fall roughly in between these two.

Identifying Keystone Species in the Human Gut Microbiome from Metagenomic Timeseries using Sparse Linear Regression

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Identifying Keystone Species in the Human Gut Microbiome from Metagenomic Timeseries using Sparse Linear Regression

Charles K. Fisher, Pankaj Mehta
(Submitted on 3 Feb 2014)

Human associated microbial communities exert tremendous influence over human health and disease. With modern metagenomic sequencing methods it is possible to follow the relative abundance of microbes in a community over time. These microbial communities exhibit rich ecological dynamics and an important goal of microbial ecology is to infer the interactions between species from sequence data. Any algorithm for inferring species interactions must overcome three obstacles: 1) a correlation between the abundances of two species does not imply that those species are interacting, 2) the sum constraint on the relative abundances obtained from metagenomic studies makes it difficult to infer the parameters in timeseries models, and 3) errors due to experimental uncertainty, or mis-assignment of sequencing reads into operational taxonomic units, bias inferences of species interactions. Here we introduce an approach, Learning Interactions from MIcrobial Time Series (LIMITS), that overcomes these obstacles. LIMITS uses sparse linear regression with boostrap aggregation to infer a discrete-time Lotka-Volterra model for microbial dynamics. We tested LIMITS on synthetic data and showed that it could reliably infer the topology of the inter-species ecological interactions. We then used LIMITS to characterize the species interactions in the gut microbiomes of two individuals and found that the interaction networks varied significantly between individuals. Furthermore, we found that the interaction networks of the two individuals are dominated by distinct “keystone species”, Bacteroides fragilis and Bacteroided stercosis, that have a disproportionate influence on the structure of the gut microbiome even though they are only found in moderate abundance. Based on our results, we hypothesize that the abundances of certain keystone species may be responsible for individuality in the human gut microbiome.

Genetic variants associated with motion sickness point to roles for inner ear development, neurological processes, and glucose homeostasis

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Genetic variants associated with motion sickness point to roles for inner ear development, neurological processes, and glucose homeostasis

Bethann S Hromatka, Joyce Y Tung, Amy K Kiefer, Chuong B Do, David A Hinds, Nicholas Eriksson

Roughly one in three individuals is highly susceptible to motion sickness and yet the underlying causes of this condition are not well understood. Despite high heritability, no associated genetic factors have been discovered to date. Here, we conducted the first genome-wide association study on motion sickness in 80,494 individuals from the 23andMe database who were surveyed about car sickness. Thirty-five single-nucleotide polymorphisms (SNPs) were associated with motion sickness at a genome-wide-significant level (p< 5e-8). Many of these SNPs are near genes involved in balance, and eye, ear, and cranial development (e.g., PVRL3, TSHZ1, MUTED, HOXB3, HOXD3). Other SNPs may affect motion sickness through nearby genes with roles in the nervous system, glucose homeostasis, or hypoxia. We show that several of these SNPs display sex-specific effects, with as much as three times stronger effects in women. We searched for comorbid phenotypes with motion sickness, confirming associations with known comorbidities including migraines, postoperative nausea and vomiting (PONV), vertigo, and morning sickness, and observing new associations with altitude sickness and many gastrointestinal conditions. We also show that two of these related phenotypes (PONV and migraines) share underlying genetic factors with motion sickness. These results point to the importance of the nervous system in motion sickness and suggest a role for glucose levels in motion-induced nausea and vomiting, a finding that may provide insight into other nausea-related phenotypes such as PONV. They also highlight personal characteristics (e.g., being a poor sleeper) that correlate with motion sickness, findings that could help identify risk factors or treatments.

motifDiverge: a model for assessing the statistical significance of gene regulatory motif divergence between two DNA sequences

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motifDiverge: a model for assessing the statistical significance of gene regulatory motif divergence between two DNA sequences
Dennis Kostka, Tara Friedrich, Alisha K. Holloway, Katherine S. Pollard
(Submitted on 1 Feb 2014)

Next-generation sequencing technology enables the identification of thousands of gene regulatory sequences in many cell types and organisms. We consider the problem of testing if two such sequences differ in their number of binding site motifs for a given transcription factor (TF) protein. Binding site motifs impart regulatory function by providing TFs the opportunity to bind to genomic elements and thereby affect the expression of nearby genes. Evolutionary changes to such functional DNA are hypothesized to be major contributors to phenotypic diversity within and between species; but despite the importance of TF motifs for gene expression, no method exists to test for motif loss or gain. Assuming that motif counts are Binomially distributed, and allowing for dependencies between motif instances in evolutionarily related sequences, we derive the probability mass function of the difference in motif counts between two nucleotide sequences. We provide a method to numerically estimate this distribution from genomic data and show through simulations that our estimator is accurate. Finally, we introduce the R package {\tt motifDiverge} that implements our methodology and illustrate its application to gene regulatory enhancers identified by a mouse developmental time course experiment. While this study was motivated by analysis of regulatory motifs, our results can be applied to any problem involving two correlated Bernoulli trials.

Most viewed on Haldane’s Sieve: January 2014

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The most viewed preprints on Haldane’s Sieve this month were: