Identifying gene function and module connections by the integration of multispecies expression compendia.

The functions of many eukaryotic genes are still poorly understood. Here, we developed and validated a new method, termed GeneBridge, which is based on two linked approaches to impute gene function and bridge genes with biological processes. First, Gene-Module Association Determination (G-MAD) allows the annotation of gene function. Second, Module-Module Association Determination (M-MAD) allows predicting connectivity among modules. We applied the GeneBridge tools to large-scale multispecies expression compendia-1700 data sets with over 300,000 samples from human, mouse, rat, fly, worm, and yeast-collected in this study. G-MAD identifies novel functions of genes-for example, DDT in mitochondrial respiration and WDFY4 in T cell activation-and also suggests novel components for modules, such as for cholesterol biosynthesis. By applying G-MAD on data sets from respective tissues, tissue-specific functions of genes were identified-for instance, the roles of EHHADH in liver and kidney, as well as SLC6A1 in brain and liver. Using M-MAD, we identified a list of module-module associations, such as those between mitochondria and proteasome, mitochondria and histone demethylation, as well as ribosomes and lipid biosynthesis. The GeneBridge tools together with the expression compendia are available as an open resource, which will facilitate the identification of connections linking genes, modules, phenotypes, and diseases.

Mapping mitochondrial heteroplasmy in a Leydig tumor by laser capture micro-dissection and cycling temperature capillary electrophoresis.

BACKGROUND: The growth of tumor cells is accompanied by mutations in nuclear and mitochondrial genomes creating marked genetic heterogeneity. Tumors also contain non-tumor cells of various origins. An observed somatic mitochondrial mutation would have occurred in a founding cell and spread through cell division. Micro-anatomical dissection of a tumor coupled with assays for mitochondrial point mutations permits new insights into this growth process. More generally, the ability to detect and trace, at a histological level, somatic mitochondrial mutations in human tissues and tumors, makes these mutations into markers for lineage tracing. METHOD: A tumor was first sampled by a large punch biopsy and scanned for any significant degree of heteroplasmy in a set of sequences containing known mutational hotspots of the mitochondrial genome. A heteroplasmic tumor was sliced at a 12 µm thickness and placed on membranes. Laser capture micro-dissection was used to take 25000 µm2 subsamples or spots. After DNA amplification, cycling temperature capillary electrophoresis (CTCE) was used on the laser captured samples to quantify mitochondrial mutant fractions. RESULTS: Of six testicular tumors studied, one, a Leydig tumor, was discovered to carry a detectable degree of heteroplasmy for two separate point mutations: a C → T mutation at bp 64 and a T → C mutation found at bp 152. From this tumor, 381 spots were sampled with laser capture micro-dissection. The ordered distribution of spots exhibited a wide range of fractions of the mutant sequences from 0 to 100% mutant copies. The two mutations co-distributed in the growing tumor indicating they were present on the same genome copies in the founding cell. CONCLUSION: Laser capture microdissection of sliced tumor samples coupled with CTCE-based point mutation assays provides an effective and practical means to obtain maps of mitochondrial mutational heteroplasmy within human tumors.

Inference for binomial probability based on dependent Bernoulli random variables with applications to meta-analysis and group level studies.

We study bias arising as a result of nonlinear transformations of random variables in random or mixed effects models and its effect on inference in group-level studies or in meta-analysis. The findings are illustrated on the example of overdispersed binomial distributions, where we demonstrate considerable biases arising from standard log-odds and arcsine transformations of the estimated probability p̂, both for single-group studies and in combining results from several groups or studies in meta-analysis. Our simulations confirm that these biases are linear in ρ, for small values of ρ, the intracluster correlation coefficient. These biases do not depend on the sample sizes or the number of studies K in a meta-analysis and result in abysmal coverage of the combined effect for large K. We also propose bias-correction for the arcsine transformation. Our simulations demonstrate that this bias-correction works well for small values of the intraclass correlation. The methods are applied to two examples of meta-analyses of prevalence.

Improved statistical evaluation of group differences in connectomes by screening-filtering strategy with application to study maturation of brain connections between childhood and adolescence.

Detecting local differences between groups of connectomes is a great challenge in neuroimaging, because the large number of tests that have to be performed and the impact on multiplicity correction. Any available information should be exploited to increase the power of detecting true between-group effects. We present an adaptive strategy that exploits the data structure and the prior information concerning positive dependence between nodes and connections, without relying on strong assumptions. As a first step, we decompose the brain network, i.e., the connectome, into subnetworks and we apply a screening at the subnetwork level. The subnetworks are defined either according to prior knowledge or by applying a data driven algorithm. Given the results of the screening step, a filtering is performed to seek real differences at the node/connection level. The proposed strategy could be used to strongly control either the family-wise error rate or the false discovery rate. We show by means of different simulations the benefit of the proposed strategy, and we present a real application of comparing connectomes of preschool children and adolescents.

Innovative Biomarkers for Obesity and Type 1 Diabetes Based on Bifidobacterium and Metabolomic Profiling.

The role of Bifidobacterium species and microbial metabolites such as short-chain fatty acids (SCFAs) and human milk oligosaccharides in controlling intestinal inflammation and the pathogenesis of obesity and type 1 diabetes (T1D) has been largely studied in recent years. This paper discusses the discovery of signature biomarkers for obesity and T1D based on data from a novel test for profiling several Bifidobacterium species, combined with metabolomic analysis. Through the NUTRISHIELD clinical study, a total of 98 children were recruited: 40 healthy controls, 40 type 1 diabetics, and 18 obese children. Bifidobacterium profiles were assessed in stool samples through an innovative test allowing high taxonomic resolution and precise quantification, while SCFAs and branched amino acids were measured in urine samples through gas chromatography-mass spectrometry (GC-MS). KIDMED questionnaires were used to evaluate the children's dietary habits and correlate them with the Bifidobacterium and metabolomic profiles. We found that B. longum subs. infantis and B. breve were higher in individuals with obesity, while B. bifidum and B. longum subs. longum were lower compared to healthy individuals. In individuals with T1D, alterations were found at the metabolic level, with an overall increase in the level of the most measured metabolites. The high taxonomic resolution of the Bifidobacterium test used meant strong correlations between the concentrations of valine and isoleucine, and the relative abundance of some Bifidobacterium species such as B. longum subs. infantis, B. breve, and B. bifidum could be observed.

Comparing connectomes across subjects and populations at different scales.

Brain connectivity can be represented by a network that enables the comparison of the different patterns of structural and functional connectivity among individuals. In the literature, two levels of statistical analysis have been considered in comparing brain connectivity across groups and subjects: 1) the global comparison where a single measure that summarizes the information of each brain is used in a statistical test; 2) the local analysis where a single test is performed either for each node/connection which implies a multiplicity correction, or for each group of nodes/connections where each subset is summarized by one single test in order to reduce the number of tests to avoid a penalizing multiplicity correction. We comment on the different levels of analysis and present some methods that have been proposed at each scale. We highlight as well the possible factors that could influence the statistical results and the questions that have to be addressed in such an analysis.

Tracing of Human Tumor Cell Lineages by Mitochondrial Mutations.

BACKGROUND: Previous studies have shown the value in studying lineage tracing in slices of human tumors. However, a tumor is not a two-dimensional structure and to better understand how a tumor, and its corresponding metastasis grow, a three-dimensional (3-D) view is necessary. RESULTS: Using somatic mitochondrial mutations as a marker for lineage tracing, it is possible to identify and follow tumor specific cell lineages. Using cycling temperature capillary electrophoresis (CTCE) a total of 8 tissues from 5 patients (4 primary tumors and 4 metastasis) containing clear mitochondrial markers of tumor lineages were selected. From these 8 tissues over 9,500 laser capture microdisection (LCM) samples were taken and analyzed, in a way that allows 3-D rendering of the observations. CONCLUSION: Using CTCE combined with LCM makes it possible to study the 3-D patterns formed by tumors and metastasis as they grow. These results clearly show that the majority of the volume occupied by a tumor is not composed of tumor derived cells. These cells are most likely recruited from the neighboring tissue.

Heterogeneity in multistage carcinogenesis and mixture modeling.

Carcinogenesis is commonly described as a multistage process, in which stem cells are transformed into cancer cells via a series of mutations. In this article, we consider extensions of the multistage carcinogenesis model by mixture modeling. This approach allows us to describe population heterogeneity in a biologically meaningful way. We focus on finite mixture models, for which we prove identifiability. These models are applied to human lung cancer data from several birth cohorts. Maximum likelihood estimation does not perform well in this application due to the heavy censoring in our data. We thus use analytic graduation instead. Very good fits are achieved for models that combine a small high risk group with a large group that is quasi immune.

Fetal-juvenile origins of point mutations in the adult human tracheal-bronchial epithelium: absence of detectable effects of age, gender or smoking status.

Allele-specific mismatch amplification mutation assays (MAMA) of anatomically distinct sectors of the upper bronchial tracts of nine nonsmokers revealed many numerically dispersed clusters of the point mutations C742T, G746T, G747T of the TP53 gene, G35T of the KRAS gene and G508A of the HPRT1 gene. Assays of these five mutations in six smokers have yielded quantitatively similar results. One hundred and eighty four micro-anatomical sectors of 0.5-6x10(6) tracheal-bronchial epithelial cells represented en toto the equivalent of approximately 1.7 human smokers' bronchial trees to the fifth bifurcation. Statistically significant mutant copy numbers above the 95% upper confidence limits of historical background controls were found in 198 of 425 sector assays. No significant differences (P=0.1) for negative sector fractions, mutant fractions, distributions of mutant cluster size or anatomical positions were observed for smoking status, gender or age (38-76 year). Based on the modal cluster size of mitochondrial point mutants, the size of the adult bronchial epithelial maintenance turnover unit was estimated to be about 32 cells. When data from all 15 lungs were combined the log2 of nuclear mutant cluster size plotted against log2 of the number of clusters of a given cluster size displayed a slope of approximately 1.1 over a range of cluster sizes from approximately 2(6) to 2(15) mutant copies. A parsimonious interpretation of these nuclear and previously reported data for lung epithelial mitochondrial point mutant clusters is that they arose from mutations in stem cells at a high but constant rate per stem cell doubling during at least ten stem cell doublings of the later fetal-juvenile period. The upper and lower decile range of summed point mutant fractions among lungs was about 7.5-fold, suggesting an important source of stratification in the population with regard to risk of tumor initiation.

An Integrated Systems Genetics and Omics Toolkit to Probe Gene Function.

Identifying genetic and environmental factors that impact complex traits and common diseases is a high biomedical priority. Here, we developed, validated, and implemented a series of multi-layered systems approaches, including (expression-based) phenome-wide association, transcriptome-/proteome-wide association, and (reverse-) mediation analysis, in an open-access web server (systems-genetics.org) to expedite the systems dissection of gene function. We applied these approaches to multi-omics datasets from the BXD mouse genetic reference population, and identified and validated associations between genes and clinical and molecular phenotypes, including previously unreported links between Rpl26 and body weight, and Cpt1a and lipid metabolism. Furthermore, through mediation and reverse-mediation analysis we established regulatory relations between genes, such as the co-regulation of BCKDHA and BCKDHB protein levels, and identified targets of transcription factors E2F6, ZFP277, and ZKSCAN1. Our multifaceted toolkit enabled the identification of gene-gene and gene-phenotype links that are robust and that translate well across populations and species, and can be universally applied to any populations with multi-omics datasets.

A strategy to discover genes that carry multi-allelic or mono-allelic risk for common diseases: a cohort allelic sums test (CAST).

A method is described to discover if a gene carries one or more allelic mutations that confer risk for any specified common disease. The method does not depend upon genetic linkage of risk-conferring mutations to high frequency genetic markers such as single nucleotide polymorphisms. Instead, the sums of allelic mutation frequencies in case and control cohorts are determined and a statistical test is applied to discover if the difference in these sums is greater than would be expected by chance. A statistical model is presented that defines the ability of such tests to detect significant gene-disease relationships as a function of case and control cohort sizes and key confounding variables: zygosity and genicity, environmental risk factors, errors in diagnosis, limits to mutant detection, linkage of neutral and risk-conferring mutations, ethnic diversity in the general population and the expectation that among all exonic mutants in the human genome greater than 90% will be neutral with regard to any effect on disease risk. Means to test the null hypothesis for, and determine the statistical power of, each test are provided. For this "cohort allelic sums test" or "CAST", the statistical model and test are provided as an Excel program, CASTAT(c) at . Based on genetics, technology and statistics, a strategy of enumerating the mutant alleles carried in the exons and splice sites of the estimated approximately 25,000 human genes in case cohort samples of 10,000 persons for each of 100 common diseases is proposed and evaluated: A wide range of possible conditions of multi-allelic or mono-allelic and monogenic, multigenic or polygenic (including epistatic) risk are found to be detectable using the statistical criteria of 1 or 10 "false positive" gene associations approximately 25,000 gene-disease pair-wise trials and a statistical power of >0.8. Using estimates of the distribution of both neutral and gene-inactivating nondeleterious mutations in humans and the sensitivity of the test to multigenic or multicausal risk, it is estimated that about 80% of nullizygous, heterozygous and functionally dominant gene-common disease associations may be discovered. Limitations include relative insensitivity of CAST to about 60% of possible associations given homozygous (wild type) risk and, more rarely, other stochastic limits when the frequency of mutations in the case cohort approaches that of the control cohort and biases such as absence of genetic risk masked by risk derived from a shared cultural environment.

Quantifying mitochondrial DNA copy number using robust regression to interpret real time PCR results.

BACKGROUND: Real time PCR (rtPCR) is a quantitative assay to determine the relative DNA copy number in a sample versus a reference. The [Formula: see text] method is the standard for the analysis of the output data generated by an rtPCR experiment. We developed an alternative based on fitting a robust regression to the rtPCR signal. This new data analysis tool reduces potential biases and does not require all of the compared DNA fragments to have the same PCR efficiency. RESULTS: Comparing the two methods when analysing 96 identical PCR preparations showed similar distributions of the estimated copy numbers. Estimating the efficiency with the [Formula: see text] method, however, required a dilution series, which is not necessary for the robust regression method. We used rtPCR to quantify mitochondrial DNA (mtDNA) copy numbers in three different tissues types: breast, colon and prostate. For each type, normal tissue and a tumor from the same three patients were analysed. This gives a total of six samples. The mitochondrial copy number is estimated to lie between 200 and 300 copies per cell. Similar results are obtained when using the robust regression or the [Formula: see text] method. Confidence ratios were slightly narrower for the robust regression. The new data analysis method has been implemented as an R package.

Maintenance of Voluntary Self-regulation Learned through Real-Time fMRI Neurofeedback.

Neurofeedback based on real-time functional magnetic resonance imaging (fMRI) is an emerging technique that allows for learning voluntary control over brain activity. Such brain training has been shown to cause specific behavioral or cognitive enhancements, and even therapeutic effects in neurological and psychiatric patient populations. However, for clinical applications it is important to know if learned self-regulation can be maintained over longer periods of time and whether it transfers to situations without neurofeedback. Here, we present preliminary results from five healthy participants who successfully learned to control their visual cortex activity and who we re-scanned 6 and 14 months after the initial neurofeedback training to perform learned self-regulation. We found that participants achieved levels of self-regulation that were similar to those achieved at the end of the successful initial training, and this without further neurofeedback information. Our results demonstrate that learned self-regulation can be maintained over longer periods of time and causes lasting transfer effects. They thus support the notion that neurofeedback is a promising therapeutic approach whose effects can last far beyond the actual training period.

Cycling temperature capillary electrophoresis: A quantitative, fast and inexpensive method to detect mutations in mixed populations of human mitochondrial DNA.

Cycling temperature capillary electrophoresis has been optimised for mutation detection in 76% of the mitochondrial genome. The method was tested on a mixed sample and compared to mutation detection by next generation sequencing. Out of 152 fragments 90 were concordant, 51 discordant and in 11 were semi-concordant. Dilution experiments show that cycling capillary electrophoresis has a detection limit of 1-3%. The detection limit of routine next generation sequencing was in the ranges of 15 to 30%. Cycling temperature capillary electrophoresis detect and accurate quantify mutations at a fraction of the cost and time required to perform a next generation sequencing analysis.

Prediction of long-term memory scores in MCI based on resting-state fMRI.

Resting-state functional MRI (rs-fMRI) opens a window on large-scale organization of brain function. However, establishing relationships between resting-state brain activity and cognitive or clinical scores is still a difficult task, in particular in terms of prediction as would be meaningful for clinical applications such as early diagnosis of Alzheimer's disease. In this work, we employed partial least square regression under cross-validation scheme to predict episodic memory performance from functional connectivity (FC) patterns in a set of fifty-five MCI subjects for whom rs-fMRI acquisition and neuropsychological evaluation was carried out. We show that a newly introduced FC measure capturing the moments of anti-correlation between brain areas, discordance, contains key information to predict long-term memory scores in MCI patients, and performs better than standard measures of correlation to do so. Our results highlighted that stronger discordance within default mode network (DMN) areas, as well as across DMN, attentional and limbic networks, favor episodic memory performance in MCI.

Trends and monthly variations in the historical record of suicide in Japan from 1976 to 1994.

Monthly suicide rates in Japan were analyzed for the period from 1976 to 1994 to clarify trends and recurring effects. The data were separated by sex, and the least-squares method was used. The major findings were (1) a significant positive correlation between unemployment rate and suicide rate for both sexes, (2) the suicide rate was highest in April for both sexes, and (3) an upsurge in male suicide mortality was noted from 1983 to 1990. These findings may well be associated with socioeconomic factors as well as neurobehavioral variables.

Enterprise failures correlate positively with suicide rate for both sexes in Japan.

Correlations among suicide rate, unemployment rate, and enterprise failure were examined from 1976 to 1994 by using a Loess smoothing method. Significant positive correlations were found between suicide rate in men and unemployment and between suicide rate in both sexes and enterprise failure (p < .001).

Mutator/Hypermutable fetal/juvenile metakaryotic stem cells and human colorectal carcinogenesis.

Adult age-specific colorectal cancer incidence rates increase exponentially from maturity, reach a maximum, then decline in extreme old age. Armitage and Doll (1) postulated that the exponential increase resulted from "n" mutations occurring throughout adult life in normal "cells at risk" that initiated the growth of a preneoplastic colony in which subsequent "m" mutations promoted one of the preneoplastic "cells at risk" to form a lethal neoplasia. We have reported cytologic evidence that these "cells at risk" are fetal/juvenile organogenic, then preneoplastic metakaryotic stem cells. Metakaryotic cells display stem-like behaviors of both symmetric and asymmetric nuclear divisions and peculiarities such as bell shaped nuclei and amitotic nuclear fission that distinguish them from embryonic, eukaryotic stem cells. Analyses of mutant colony sizes and numbers in adult lung epithelia supported the inferences that the metakaryotic organogenic stem cells are constitutively mutator/hypermutable and that their contributions to cancer initiation are limited to the fetal/juvenile period. We have amended the two-stage model of Armitage and Doll and incorporated these several inferences in a computer program CancerFit v.5.0. We compared the expectations of the amended model to adult (15-104 years) age-specific colon cancer rates for European-American males born 1890-99 and observed remarkable concordance. When estimates of normal colonic fetal/juvenile APC and OAT gene mutation rates (∼2-5 × 10(-5) per stem cell doubling) and preneoplastic colonic gene loss rates (∼8 × 10(-3)) were applied, the model was in accordance only for the values of n = 2 and m = 4 or 5.

Adaptive strategy for the statistical analysis of connectomes.

We study an adaptive statistical approach to analyze brain networks represented by brain connection matrices of interregional connectivity (connectomes). Our approach is at a middle level between a global analysis and single connections analysis by considering subnetworks of the global brain network. These subnetworks represent either the inter-connectivity between two brain anatomical regions or by the intra-connectivity within the same brain anatomical region. An appropriate summary statistic, that characterizes a meaningful feature of the subnetwork, is evaluated. Based on this summary statistic, a statistical test is performed to derive the corresponding p-value. The reformulation of the problem in this way reduces the number of statistical tests in an orderly fashion based on our understanding of the problem. Considering the global testing problem, the p-values are corrected to control the rate of false discoveries. Finally, the procedure is followed by a local investigation within the significant subnetworks. We contrast this strategy with the one based on the individual measures in terms of power. We show that this strategy has a great potential, in particular in cases where the subnetworks are well defined and the summary statistics are properly chosen. As an application example, we compare structural brain connection matrices of two groups of subjects with a 22q11.2 deletion syndrome, distinguished by their IQ scores.

Combining the evidence using stable weights.

In a meta-analysis one seeks to combine the results of several studies in order to improve the accuracy of decisions. Here we compare by simulation four methods for combining estimates of the risk difference, namely the Cochran and Mantel-Haenszel (MH) methods, the inverse-variance weights approach and a recent variance-stabilized weights approach. Both the level and power of corresponding test statistics, as well as the coverage of related confidence intervals are compared over a wide range of sample size and parameter configurations. We found that the inverse-variance weights methodology is unreliable and is not recommended, while for equal risks, the Cochran test and the associated confidence intervals are the most reliable. Under alternatives of unequal risks, the coverage probabilities of the variance-stabilized confidence intervals are almost uniformly more reliable than those based on other methods except when the average risk is small in which case the MH confidence intervals are preferable. Copyright © 2011 John Wiley & Sons, Ltd.