Genome rearrangement with gene families

MOTIVATION: The theory and practice of genome rearrangement analysis breaks down in the biologically widespread contexts where each gene may be present in a number of copies, not necessarily contiguous. In some of these contexts it is, however, appropriate to ask which members of each gene family in two genomes G and H, lengths lG and lH, are its true exemplars, i.e. which best reflect the original position of the ancestral gene in the common ancestor genome. This entails a search for the two exemplar strings of same length n (= number of gene families, including singletons), having the smallest possible rearrangement distance: the exemplar distance. RESULTS: A branch and bound algorithm calculates these distances efficiently when based on easily calculated traditional rearrangement distances, such as signed reversals distance or breakpoint distance, which also satisfy a property of monotonicity in the number of genes. Simulations show that in two random genomes, the expected exemplar distance/n is sensitive to the number and size of gene families, but approaches 1 as the number of singleton families increases. When the basic rearrangement distance is just the number of breakpoints, the expected cost of computing the exemplar breakpoints distance (EBD), as measured by total calls to the underlying breakpoint distance routine, is highly dependent on both n and the configuration of gene families. On the other hand, basing exemplar distance on exemplar reversals distance (ERD), the expected computing cost depends on the configuration of gene families but is not sensitive to n. AVAILABILITY: Code for EBD and ERD is available from the author or may be accessed at http://www.crm.umontreal.ca/viart/exemplar_di s. html CONTACT: sankoff@ere.umontreal.ca.     

microRNAs相关新型调控技术与疾病治疗

MicroRNA（miRNA）是一类在进化中高度保守的非编码小分子单链RNA(19～25 nt）,在转录后水平调控中发挥重要作用. 越来越多的证据表明,miRNA广泛参与了机体生长发育、细胞增殖与凋亡等多种重要的生理过程,并在病毒感染、恶性肿瘤、心血管与内分泌疾病等多种病理状态下呈异常表达,影响许多疾病的发生、发展及预后. 对miRNA的表达水平进行可控性干预可能成为一种有效的治疗手段. 本文综述了目前用于干预miRNA水平的调控技术,以及疾病治疗相关给药系统的研究进展,提示miRNA相关新型调控技术具有较为广阔的临床应用前景.     

Aflatoxin conducive and non-conducive growth conditions reveal new gene associations with aflatoxin production

Research on aflatoxin (AF) production has traditionally focused on defining the AF biosynthetic pathway with the goal of identifying potential targets for intervention. To understand the effect of nitrogen source, carbon source, temperature, and pH on the regulation of AF biosynthesis, a targeted cDNA microarray consisting of genes associated with AF production over time was employed. Expression profiles for genes involved in AF biosynthesis grouped into five clades. A putative regulon was identified consisting of 20 genes that were induced in the conducive nitrogen and pH treatments and the non-conducive carbon and temperature treatments, as well as four other putative regulons corresponding to each of the four variables studied. Seventeen genes exhibited consistent induction/repression profiles across all the experiments. One of these genes was consistently downregulated with AF production. Overexpression of this gene resulted in repression of AF biosynthesis. The cellular function of this gene is currently unresolved.     

Molecular characterization of the glutamine synthetase gene in the Pacific oyster Crassostrea gigas: expression study in response to xenobiotic exposure and developmental stage

In this study, we characterized the full-length cDNA and genomic sequence of the gene encoding cytosolic glutamine synthetase (CgGSII) in the Pacific oyster, Crassostrea gigas. A phylogenetic analysis of GS sequences showed that CgGS clustered with the invertebrate group as expected. We analyzed the expression of mRNA CgGSII using RT-PCR to follow the expression of this gene in gills and digestive gland of oysters exposed, under experimental conditions, to hypoxia and to several contaminants (hydrocarbons and two pesticide treatments, glyphosate and a mixture of atrazine, diuron and isoproturon). We also investigated the expression of CgGSII in different developmental stages of C. gigas. Our results show that CgGSII expression was highly regulated in xenobiotic-exposed oysters compared to the control for all the treatments. Likewise, CgGSII expression was highly regulated according to the developmental stage of C. gigas. Finally, use of CgGSII as a possible marker to monitor xenobiotic exposure in disturbed ecosystems is discussed.     

A test of quantitative genetic theory using Drosophila- effects of inbreeding and rate of inbreeding on heritabilities and variance components

Inbreeding is expected to decrease the heritability within populations. However, results from empirical studies are inconclusive. In this study, we investigated the effects of three breeding treatments (fast and slow rate of inbreeding - inbred to the same absolute level - and a control) on heritability, phenotypic, genetic and environmental variances of sternopleural bristle number in Drosophila melanogaster. Heritability, and phenotypic, genetic and environmental variances were estimated in 10 replicate lines within each of the three treatments. Standard least squares regression models and Bayesian methods were used to analyse the data. Heritability and additive genetic variance within lines were higher in the control compared with both inbreeding treatments. Heritabilities and additive genetic variances within lines were higher in slow compared with fast inbred lines, indicating that slow inbred lines retain more evolutionary potential despite the same expected absolute level of inbreeding. The between line variance was larger with inbreeding and more than twice as large in the fast than in the slow inbred lines. The different pattern of redistribution of genetic variance within and between lines in the two inbred treatments cannot be explained invoking the standard model based on selective neutrality and additive gene action. Environmental variances were higher with inbreeding, and more so with fast inbreeding, indicating that inbreeding and the rate of inbreeding affect environmental sensitivity. The phenotypic variance decreased with inbreeding, but was not affected by the rate of inbreeding. No inbreeding depression for mean sternopleural bristle number was observed in this study. Considerable variance between lines in additive genetic variance within lines was observed, illustrating between line variation in evolutionary potential.     

Sequential allocation in clinical trials comparing two exponential survival curves.

The problem of comparing two medical treatments with respect to survival is considered. Treatment outcome is assumed to follow an exponential distribution. The ratio of expected survivals associated with the two treatments is the clinical parameter of interest. A nuisance parameter is present, but it is removed by an invariance reduction and a sequential probability ratio test is applied to the invariant likelihood ratio. A class of data-dependent treatment assignment rules is identified over which the probability of correct treatment selection at the termination of the trial is approximately constant. A cost function, the weighted sum of total patients in the trial and the number assigned to the inferior treatment is introduced, and a treatment allocation rule conjectured to minimize the expected cost is constructed. Both analytic and simulation results show that it is an improvement over rules previously proposed. The methodology contained herein can be used to construct near-optimal rules in other testing contexts.     

Optimal adaptive designs for binary response trials

Summary. We derive the optimal allocation between two treatments in a clinical trial based on the following optimality criterion: for fixed variance of the test statistic, what allocation minimizes the expected number of treatment failures? A sequential design is described that leads asymptotically to the optimal allocation and is compared with the randomized play‐the‐winner rule, sequential Neyman allocation, and equal allocation at similar power levels. We find that the sequential procedure generally results in fewer treatment failures than the other procedures, particularly when the success probabilities of treatments are smaller.     

The Nucleolus Organizer Region of Maize (ZEA MAYS L.): Tests for Ribosomal Gene Compensation or Magnification

Ribosomal gene compensation and magnification that might be detected on a whole-plant basis was not found in maize. Plants monosomic for chromosome 6 (the NOR chromosome) were compared with monosomic-8 and monosomic-10 plants, disomic sibs, and parental lines. Assuming no rDNA compensation, monosomic-6 plants showed approximately the decrease expected in rRNA cistron number. Monosomic-8 had a normal ribosomal gene number, while monosomic-10 showed a decrease; but further documentation is needed. Besides demonstrating the absence of gene compensation, the results document our previous conclusion that maize chromosome 6 carries DNA complementary to ribosomal RNA. Further documentation was provided from studies with trisomic chromosome 6 plants showing proportional increases in ribosomal gene number. Progeny of the monosomic plants crossed as males to a standard singlecross hybrid possessed expected ribosomal gene numbers suggesting the lack of ribosomal gene magnification.—The ragged (rgd) mutant of maize, suspected of being deficient in rRNA cistrons, had a normal number.     

Recent Advances in Intervention in Markovian Regulatory Networks

Markovian regulatory networks constitute a class of discrete state-space models used to study gene regulatory dynamics and discover methods that beneficially alter those dynamics. Thereby, this class of models provides a framework to discover effective drug targets and design potent therapeutic strategies. The salient translational goal is to design therapeutic strategies that desirably modify network dynamics via external signals that vary the expressions of a control gene. The objective of an intervention strategy is to reduce the likelihood of the pathological cellular function related to a disease. The task of finding an effective intervention strategy can be formulated as a sequential decision making problem for a pre-defined cost of intervention and a cost-per-stage function that discriminates the gene-activity profiles. An effective intervention strategy prescribes the actions associated with an external signal that result in the minimum expected cost. This strategy in turn can be used as a treatment that reduces the long-run likelihood of gene expressions favorable to the disease. In this tutorial, we briefly summarize the first method proposed to design such therapeutic interventions, and then move on to some of the recent refinements that have been proposed. Each of these recent intervention methods is motivated by practical or analytical considerations. The presentation of the key ideas is facilitated with the help of two case studies.Key Words: Regulatory networks, markovian decision processes, translational genomics, systems biology.     

Inhaled Janus Kinase (JAK) inhibitors for the treatment of asthma

Multiple asthma-relevant cytokines including IL-4, IL-5, IL-13, and TSLP depend upon JAKs for signaling. JAK inhibition may, therefore, offer a novel intervention strategy for patients with disease refractory to current standards of care. Multiple systemically delivered JAK inhibitors have been approved for human use or are under clinical evaluation in autoimmune diseases such as rheumatoid arthritis. However, the on-target side effect profiles of these agents are likely not tolerable for many asthmatic patients. Limiting JAK inhibition to the lung is expected to improve therapeutic index relative to systemic inhibition. Thus, inhaled JAK inhibitors with lung-restricted exposure are of high interest as potential treatments for asthma.     

Target reproduction numbers for reaction-diffusion population models

A very important population threshold quantity is the target reproduction number, which is a measure of control effort required for a target prevention, intervention or control. This concept, as a generalization of type reproduction number, was first introduced in Shuai et al. (J Math Biol 67:1067–1082, 2013) for nonnegative matrices with immediate applications to compartmental population models of ordinary differential equations. The current paper is devoted to the study of all target reproduction numbers for reaction-diffusion population models with compartmental structure. It turns out that the target reproduction number can be regarded as the basic reproduction number of a modified system, where the state of newborn individuals is limited to the target control set and the offspring from the non-target set is regarded as a part of the transition. In other words, the target reproduction number can be interpreted as the expected number of offspring in a specific target set that a primary newborn individual of the same set would produce during its lifetime. We also characterize the target reproduction number so that it can be easily computed numerically for reaction-diffusion models. At the end, we demonstrate our theoretical observations using two examples.

     

Multi-class clustering and prediction in the analysis of microarray data

DNA microarray technology provides tools for studying the expression profiles of a large number of distinct genes simultaneously. This technology has been applied to sample clustering and sample prediction. Because of a large number of genes measured, many of the genes in the original data set are irrelevant to the analysis. Selection of discriminatory genes is critical to the accuracy of clustering and prediction. This paper considers statistical significance testing approach to selecting discriminatory gene sets for multi-class clustering and prediction of experimental samples. A toxicogenomic data set with nine treatments (a control and eight metals, As, Cd, Ni, Cr, Sb, Pb, Cu, and AsV with a total of 55 samples) is used to illustrate a general framework of the approach. Among four selected gene sets, a gene set omega(I) formed by the intersection of the F-test and the set of the union of one-versus-all t-tests performs the best in terms of clustering as well as prediction. Hierarchical and two modified partition (k-means) methods all show that the set omega(I) is able to group the 55 samples into seven clusters reasonably well, in which the As and AsV samples are considered as one cluster (the same group) as are the Cd and Cu samples. With respect to prediction, the overall accuracy for the gene set omega(I) using the nearest neighbors algorithm to predict 55 samples into one of the nine treatments is 85%.     

Estimating genetic variability in non-model taxa: a general procedure for discriminating sequence errors from actual variation

Genetic variation is the driving force of evolution and as such is of central interest for biologists. However, inadequate discrimination of errors from true genetic variation could lead to incorrect estimates of gene copy number, population genetic parameters, phylogenetic relationships and the deposition of gene and protein sequences in databases that are not actually present in any organism. Misincorporation errors in multi-template PCR cloning methods, still commonly used for obtaining novel gene sequences in non-model species, are difficult to detect, as no previous information may be available about the number of expected copies of genes belonging to multi-gene families. However, studies employing these techniques rarely describe in any great detail how errors arising in the amplification process were detected and accounted for. Here, we estimated the rate of base misincorporation of a widely-used PCR-cloning method, using a single copy mitochondrial gene from a single individual to minimise variation in the template DNA, as 1.62×10(-3) errors per site, or 9.26×10(-5) per site per duplication. The distribution of errors among sequences closely matched that predicted by a binomial distribution function. The empirically estimated error rate was applied to data, obtained using the same methods, from the Phospholipase A(2) toxin family from the pitviper Ovophis monticola. The distribution of differences detected closely matched the expected distribution of errors and we conclude that, when undertaking gene discovery or assessment of genetic diversity using this error-prone method, it will be informative to empirically determine the rate of base misincorporation.     

Interactions between stressful environment and gene deletions alleviate the expected average loss of fitness in yeast

The conjecture that the deleterious effects of mutations are amplified by stress or interaction with one another remains unsatisfactorily tested. It is now possible to reapproach this problem systematically by using genomic collections of mutants and applying stress-inducing conditions with a well-recognized impact on metabolism. We measured the maximum growth rate of single- and double-gene deletion strains of yeast in several stress-inducing treatments, including poor nutrients, elevated temperature, high salinity, and the addition of caffeine. The negative impact of deletions on the maximum growth rate was relatively smaller in stressful than in favorable conditions. In both benign and harsh environments, double-deletion strains grew on average slightly faster than expected from a multiplicative model of interaction between single growth effects, indicating positive epistasis for the rate of growth. This translates to even higher positive epistasis for fitness defined as the number of progeny. We conclude that the negative impact of metabolic disturbances, regardless of whether they are of environmental or genetic origin, is absolutely and relatively highest when growth is fastest. The effect of further damages tends to be weaker. This results in an average alleviating effect of interactions between stressful environment and gene deletions and among gene deletions.     

The expected number of heterozygous sites in a subdivided population.

A simple, exact formula is derived for the expected number of heterozygous sites per individual at equilibrium in a subdivided population. The model of infinitely many neutral sites is posited; the linkage map is arbitrary. The monoecious, diploid population is subdivided into a finite number of panmictic colonies that exchange gametes. The backward migration matrix is arbitrary, but time independent and ergodic (i.e., irreducible and aperiodic). With suitable weighting, the expected number of heterozygous sites is 4Neu, where Ne denotes the migration effective population number and u designates the total mutation rate per gene (or DNA sequence). For diploid migration, this formula is a good approximation if Ne >> 1.     

Experiment specific expression patterns

The differential analysis of genes between microarrays from several experimental conditions or treatments routinely estimates which genes change significantly between groups. As genes are never regulated individually, observed behavior may be a consequence of changes in other genes. Existing approaches like co-expression analysis aim to resolve such patterns from a wide range of experiments. The knowledge of such a background set of experiments can be used to compute expected gene behavior based on known links. It is particularly interesting to detect previously unseen specific effects in other experiments. Here, a new method to spot genes deviating from expected behavior (PAttern DEviation SCOring--Padesco) is devised. It uses linear regression models learned from a background set to arrive at gene specific prediction accuracy distributions. For a given experiment, it is then decided whether each gene is predicted better or worse than expected. This provides a novel way to estimate the experiment specificity of each gene. We propose a validation procedure to estimate the detection of such specific candidates and show that these can be identified with an average accuracy of about 85%.     

Computational and Theoretical Analysis of the Association Between Gender and HSV-2 Treatment Adherence

Herpes simplex virus type 2 (HSV-2) is the most prevalent sexually transmitted infection in the world, despite the availability of effective anti-viral treatments. A mathematical model to explore the association between gender and HSV-2 treatment adherence is developed. Threshold parameters are determined and stabilities analyzed. Sensitivity analysis of the reproduction number and the numerical simulations suggest that treatment adherence for both females and males are equally important in keeping the reproduction as low as possible. The basic model is then extended to incorporate time-dependent intervention strategies. The Pontryagin’s Maximum Principle is used to characterize the optimal level of the controls, and the resulting optimality system is solved numerically.

     

The number of repeats expected in random nucleic acid sequences and found in genes

Many nucleic acid sequences contain local repeats. These are often considered as traces of evolutionary events such as gene duplications. However, every random sequence of four characters contains a rather large amount of chance repeats. To assess the significance of repeats found in a gene it is necessary to know how large a background of chance repeats has to be expected. Equations are derived that allow the computation of the number of repeats of different lengths and frequencies expected in any random sequence of known chain length and base composition. Tandem repeats are considered as well as repeats interspersed with other sequences. Sample calculations on viral, messenger, ribosomal, and transfer RNA sequences show that some contain no more than the expected background of random repeats, whereas others contain an excess. In the latter case, the distribution of distances between the repeats, as well as their number, can give clues as to the evolutionary events that may have produced them.