A random mutation capture assay to detect genomic point mutations in mouse tissue

Herein, a detailed protocol for a random mutation capture (RMC) assay to measure nuclear point mutation frequency in mouse tissue is described. This protocol is a simplified version of the original method developed for human tissue that is easier to perform, yet retains a high sensitivity of detection. In contrast to assays relying on phenotypic selection of reporter genes in transgenic mice, the RMC assay allows direct detection of mutations in endogenous genes in any mouse strain. Measuring mutation frequency within an intron of a transcribed gene, we show this assay to be highly reproducible. We analyzed mutation frequencies from the liver tissue of animals with a mutation within the intrinsic exonuclease domains of the two major DNA polymerases, δ and ε. These mice exhibited significantly higher mutation frequencies than did wild-type animals. A comparison with a previous analysis of these genotypes in Big Blue mice revealed the RMC assay to be more sensitive than the Big Blue assay for this application. As RMC does not require analysis of a particular gene, simultaneous analysis of mutation frequency at multiple genetic loci is feasible. This assay provides a versatile alternative to transgenic mouse models for the study of mutagenesis in vivo

Quantitative evaluation of oligonucleotide surface concentrations using polymerization-based amplification

Quantitative evaluation of minimal polynucleotide concentrations has become a critical analysis among a myriad of applications found in molecular diagnostic technology. Development of high-throughput, nonenzymatic assays that are sensitive, quantitative and yet feasible for point-of-care testing are thus beneficial for routine implementation. Here, we develop a nonenzymatic method for quantifying surface concentrations of labeled DNA targets by coupling regulated amounts of polymer growth to complementary biomolecular binding on array-based biochips. Polymer film thickness measurements in the 20–220 nm range vary logarithmically with labeled DNA surface concentrations over two orders of magnitude with a lower limit of quantitation at 60 molecules/μm2 (∼106 target molecules). In an effort to develop this amplification method towards compatibility with fluorescence-based methods of characterization, incorporation of fluorescent nanoparticles into the polymer films is also evaluated. The resulting gains in fluorescent signal enable quantification using detection instrumentation amenable to point-of-care settings

Epidemiology of Doublet/Multiplet Mutations in Lung Cancers: Evidence that a Subset Arises by Chronocoordinate Events

BACKGROUND: Evidence strongly suggests that spontaneous doublet mutations in normal mouse tissues generally arise from chronocoordinate events. These chronocoordinate mutations sometimes reflect "mutation showers", which are multiple chronocoordinate mutations spanning many kilobases. However, little is known about mutagenesis of doublet and multiplet mutations (domuplets) in human cancer. Lung cancer accounts for about 25% of all cancer deaths. Herein, we analyze the epidemiology of domuplets in the EGFR and TP53 genes in lung cancer. The EGFR gene is an oncogene in which doublets are generally driver plus driver mutations, while the TP53 gene is a tumor suppressor gene with a more typical situation in which doublets derive from a driver and passenger mutation. METHODOLOGY/PRINCIPAL FINDINGS: EGFR mutations identified by sequencing were collected from 66 published papers and our updated EGFR mutation database (www.egfr.org). TP53 mutations were collected from IARC version 12 (www-p53.iarc.fr). For EGFR and TP53 doublets, no clearly significant differences in race, ethnicity, gender and smoking status were observed. Doublets in the EGFR and TP53 genes in human lung cancer are elevated about eight- and three-fold, respectively, relative to spontaneous doublets in mouse (6% and 2.3% versus 0.7%). CONCLUSIONS/SIGNIFICANCE: Although no one characteristic is definitive, the aggregate properties of doublet and multiplet mutations in lung cancer are consistent with a subset derived from chronocoordinate events in the EGFR gene: i) the eight frameshift doublets (present in 0.5% of all patients with EGFR mutations) are clustered and produce a net in-frame change; ii) about 32% of doublets are very closely spaced (< or =30 nt); and iii) multiplets contain two or more closely spaced mutations. TP53 mutations in lung cancer are very closely spaced (< or =30 nt) in 33% of doublets, and multiplets generally contain two or more very closely spaced mutations. Work in model systems is necessary to confirm the significance of chronocoordinate events in lung and other cancers

Bmi1 Is Down-Regulated in the Aging Brain and Displays Antioxidant and Protective Activities in Neurons

Aging increases the risk to develop several neurodegenerative diseases, although the underlying mechanisms are poorly understood. Inactivation of the Polycomb group gene Bmi1 in mice results in growth retardation, cerebellar degeneration, and development of a premature aging-like phenotype. This progeroid phenotype is characterized by formation of lens cataracts, apoptosis of cortical neurons, and increase of reactive oxygen species (ROS) concentrations, owing to p53-mediated repression of antioxidant response (AOR) genes. Herein we report that Bmi1 expression progressively declines in the neurons of aging mouse and human brains. In old brains, p53 accumulates at the promoter of AOR genes, correlating with a repressed chromatin state, down-regulation of AOR genes, and increased oxidative damages to lipids and DNA. Comparative gene expression analysis further revealed that aging brains display an up-regulation of the senescence-associated genes IL-6, p19Arf and p16Ink4a, along with the pro-apoptotic gene Noxa, as seen in Bmi1-null mice. Increasing Bmi1 expression in cortical neurons conferred robust protection against DNA damage-induced cell death or mitochondrial poisoning, and resulted in suppression of ROS through activation of AOR genes. These observations unveil that Bmi1 genetic deficiency recapitulates aspects of physiological brain aging and that Bmi1 over-expression is a potential therapeutic modality against neurodegeneration

Evidence of Genetic Instability in Tumors and Normal Nearby Tissues

We have analyzed the sequence heterogeneity of the transcripts of the human HPRT and G6PD single copy genes that are not considered tumor markers. Analyses have been performed on different colon cancers and on the nearby histologically normal tissues of two male patients. Several copies of each cDNA, which were produced by cloning the RT-PCR-amplified fragments of the specific mRNA, have been sequenced. Similar analyses have been performed on blood samples of two ostensibly healthy males as reference controls. The sequence heterogeneity of the HPRT and G6PD genes was also determined on DNA from tumor tissues. The employed analytical approach revealed the presence of low-frequency mutations not detectable by other procedures. The results show that genetic heterogeneity is detectable in HPRT and G6PD transcripts in both tumors and nearby healthy tissues of the two studied colon tumors. Similar frequencies of mutations are observed in patient genomic DNA, indicating that mutations have a somatic origin. HPRT transcripts show genetic heterogeneity also in healthy individuals, in agreement with previous results on human T-cells, while G6PD transcript heterogeneity is a characteristic of the patient tissues. Interestingly, data on TP53 show little, if any, heterogeneity in the same tissues. CONCLUSIONS/SIGNIFICANCE: These findings show that genetic heterogeneity is a peculiarity not only of cancer cells but also of the normal tissue where a tumor arises

Ultradeep Sequencing of a Human Ultraconserved Region Reveals Somatic and Constitutional Genomic Instability

Ultradeep sequencing of genomes permits the detection of very low-level genomic instability in non-neoplastic tissues of patients with the most common form of inherited colorectal cancer

Disease Gene Characterization through Large-Scale Co-Expression Analysis

In the post genome era, a major goal of biology is the identification of specific roles for individual genes. We report a new genomic tool for gene characterization, the UCLA Gene Expression Tool (UGET).Celsius, the largest co-normalized microarray dataset of Affymetrix based gene expression, was used to calculate the correlation between all possible gene pairs on all platforms, and generate stored indexes in a web searchable format. The size of Celsius makes UGET a powerful gene characterization tool. Using a small seed list of known cartilage-selective genes, UGET extended the list of known genes by identifying 32 new highly cartilage-selective genes. Of these, 7 of 10 tested were validated by qPCR including the novel cartilage-specific genes SDK2 and FLJ41170. In addition, we retrospectively tested UGET and other gene expression based prioritization tools to identify disease-causing genes within known linkage intervals. We first demonstrated this utility with UGET using genetically heterogeneous disorders such as Joubert syndrome, microcephaly, neuropsychiatric disorders and type 2 limb girdle muscular dystrophy (LGMD2) and then compared UGET to other gene expression based prioritization programs which use small but discrete and well annotated datasets. Finally, we observed a significantly higher gene correlation shared between genes in disease networks associated with similar complex or Mendelian disorders.UGET is an invaluable resource for a geneticist that permits the rapid inclusion of expression criteria from one to hundreds of genes in genomic intervals linked to disease. By using thousands of arrays UGET annotates and prioritizes genes better than other tools especially with rare tissue disorders or complex multi-tissue biological processes. This information can be critical in prioritization of candidate genes for sequence analysis

Analysis of Cancer Mutation Signatures in Blood by a Novel Ultra-Sensitive Assay: Monitoring of Therapy or Recurrence in Non-Metastatic Breast Cancer

BACKGROUND: Tumor DNA has been shown to be present both in circulating tumor cells in blood and as fragments in the plasma of metastatic cancer patients. The identification of ultra-rare tumor-specific mutations in blood would be the ultimate marker to measure efficacy of cancer therapy and/or early recurrence. Herein we present a method for detecting microinsertions/deletions/indels (MIDIs) at ultra-high analytical selectivity. MIDIs comprise about 15% of mutations. METHODS AND FINDINGS: We describe MIDI-Activated Pyrophosphorolysis (MAP), a method of ultra-high analytical selectivity for detecting MIDIs. The high analytical selectivity of MAP is putatively due to serial coupling of two rare events: heteroduplex slippage and mis-pyrophosphorolysis. MAP generally has an analytical selectivity of one mutant molecule per >1 billion wild type molecules and an analytical sensitivity of one mutant molecule per reaction. The analytical selectivity of MAP is about 100,000-fold better than that of our previously described method of Pyrophosphorolysis Activated Polymerization-Allele specific amplification (PAP-A) for detecting MIDIs. The utility of this method is illustrated in two ways. 1) We demonstrate that two EGFR deletions commonly found in lung cancers are not present in tissue from four normal human lungs (10(7) copies of gDNA each) or in blood samples from 10 healthy individuals (10(7) copies of gDNA each). This is inconsistent, at least at an analytical sensitivity of 10(-7), with the hypotheses of (a) hypermutation or (b) strong selection of these growth factor-mutated cells during normal lung development leads to accumulation of pre-neoplastic cells with these EGFR mutations, which sometimes can lead to lung cancer in late adulthood. Moreover, MAP was used for large scale, high throughput "gene pool" analysis. No germline or early embryonic somatic mosaic mutation was detected (at a frequency of >0.3%) for the 15/18 bp EGFR deletion mutations in 6,400 individuals, suggesting that early embryonic EGFR somatic mutation is very rare, inconsistent with hypermutation or strong selection of these deletions in the embryo. 2) The second illustration of MAP utility is in personalized monitoring of therapy and early recurrence in cancer. Tumor-specific p53 mutations identified at diagnosis in the plasma of six patients with stage II and III breast cancer were undetectable after therapy in four women, consistent with clinical remission, and continued to be detected after treatment in two others, reflecting tumor progression. CONCLUSIONS: MAP has an analytical selectivity of one part per billion for detection of MIDIs and an analytical sensitivity of one molecule. MAP provides a general tool for monitoring ultra-rare mutations in tissues and blood. As an example, we show that the personalized cancer signature in six out of six patients with non-metastatic breast cancer can be detected and that levels over time are correlated with the clinical course of disease

Global Developmental Gene Expression and Pathway Analysis of Normal Brain Development and Mouse Models of Human Neuronal Migration Defects

Heterozygous LIS1 mutations are the most common cause of human lissencephaly, a human neuronal migration defect, and DCX mutations are the most common cause of X-linked lissencephaly. LIS1 is part of a protein complex including NDEL1 and 14-3-3ε that regulates dynein motor function and microtubule dynamics, while DCX stabilizes microtubules and cooperates with LIS1 during neuronal migration and neurogenesis. Targeted gene mutations of Lis1, Dcx, Ywhae (coding for 14-3-3ε), and Ndel1 lead to neuronal migration defects in mouse and provide models of human lissencephaly, as well as aid the study of related neuro-developmental diseases. Here we investigated the developing brain of these four mutants and wild-type mice using expression microarrays, bioinformatic analyses, and in vivo/in vitro experiments to address whether mutations in different members of the LIS1 neuronal migration complex lead to similar and/or distinct global gene expression alterations. Consistent with the overall successful development of the mutant brains, unsupervised clustering and co-expression analysis suggested that cell cycle and synaptogenesis genes are similarly expressed and co-regulated in WT and mutant brains in a time-dependent fashion. By contrast, focused co-expression analysis in the Lis1 and Ndel1 mutants uncovered substantial differences in the correlation among pathways. Differential expression analysis revealed that cell cycle, cell adhesion, and cytoskeleton organization pathways are commonly altered in all mutants, while synaptogenesis, cell morphology, and inflammation/immune response are specifically altered in one or more mutants. We found several commonly dysregulated genes located within pathogenic deletion/duplication regions, which represent novel candidates of human mental retardation and neurocognitive disabilities. Our analysis suggests that gene expression and pathway analysis in mouse models of a similar disorder or within a common pathway can be used to define novel candidates for related human diseases

Predicting cancer involvement of genes from heterogeneous data

<p>Abstract</p> <p>Background</p> <p>Systematic approaches for identifying proteins involved in different types of cancer are needed. Experimental techniques such as microarrays are being used to characterize cancer, but validating their results can be a laborious task. Computational approaches are used to prioritize between genes putatively involved in cancer, usually based on further analyzing experimental data.</p> <p>Results</p> <p>We implemented a systematic method using the PIANA software that predicts cancer involvement of genes by integrating heterogeneous datasets. Specifically, we produced lists of genes likely to be involved in cancer by relying on: (i) protein-protein interactions; (ii) differential expression data; and (iii) structural and functional properties of cancer genes. The integrative approach that combines multiple sources of data obtained positive predictive values ranging from 23% (on a list of 811 genes) to 73% (on a list of 22 genes), outperforming the use of any of the data sources alone. We analyze a list of 20 cancer gene predictions, finding that most of them have been recently linked to cancer in literature.</p> <p>Conclusion</p> <p>Our approach to identifying and prioritizing candidate cancer genes can be used to produce lists of genes likely to be involved in cancer. Our results suggest that differential expression studies yielding high numbers of candidate cancer genes can be filtered using protein interaction networks. </p