The genomic Echinococcus microsatellite EmsB sequences: from a molecular marker to the epidemiological tool

In the field of molecular and epidemiological parasitology, characterization of fast evolving genetic markers appears as an important challenge to consider the diversity and genetic structure of parasites. The study of respective populations can help us to understand their adaptive strategies to survive and perpetuate the species within different host populations, all trying to resist infection. In the past, the relative monomorphic features of Echinococcus multilocularis, the causative agent of alveolar echinococcosis and a severe human parasitic disease, did not stimulate studies dealing with the genetic variability of Echinococcus species or respective populations. A recently developed, characterized and validated original multilocus microsatellite, named EmsB, tandemly repeated in the genome, offered an additional opportunity for this line of investigation. We have compiled in this review new insights brought by this molecular tracker on the transmission activity of Echinococcus among different hosts and at different geographical scales.

Genomic Data Reveal a Complex Making of Humans

In the last few years, two paradigms underlying human evolution have crumbled. Modern humans have not totally replaced previous hominins without any admixture, and the expected signatures of adaptations to new environments are surprisingly lacking at the genomic level. Here we review current evidence about archaic admixture and lack of strong selective sweeps in humans. We underline the need to properly model differential admixture in various populations to correctly reconstruct past demography. We also stress the importance of taking into account the spatial dimension of human evolution, which proceeded by a series of range expansions that could have promoted both the introgression of archaic genes and background selection.

Evolutionary forces shaping genomic islands of population differentiation in humans

Background Levels of differentiation among populations depend both on demographic and selective factors: genetic drift and local adaptation increase population differentiation, which is eroded by gene flow and balancing selection. We describe here the genomic distribution and the properties of genomic regions with unusually high and low levels of population differentiation in humans to assess the influence of selective and neutral processes on human genetic structure. Methods Individual SNPs of the Human Genome Diversity Panel (HGDP) showing significantly high or low levels of population differentiation were detected under a hierarchical-island model (HIM). A Hidden Markov Model allowed us to detect genomic regions or islands of high or low population differentiation. Results Under the HIM, only 1.5% of all SNPs are significant at the 1% level, but their genomic spatial distribution is significantly non-random. We find evidence that local adaptation shaped high-differentiation islands, as they are enriched for non-synonymous SNPs and overlap with previously identified candidate regions for positive selection. Moreover there is a negative relationship between the size of islands and recombination rate, which is stronger for islands overlapping with genes. Gene ontology analysis supports the role of diet as a major selective pressure in those highly differentiated islands. Low-differentiation islands are also enriched for non-synonymous SNPs, and contain an overly high proportion of genes belonging to the 'Oncogenesis' biological process. Conclusions Even though selection seems to be acting in shaping islands of high population differentiation, neutral demographic processes might have promoted the appearance of some genomic islands since i) as much as 20% of islands are in non-genic regions ii) these non-genic islands are on average two times shorter than genic islands, suggesting a more rapid erosion by recombination, and iii) most loci are strongly differentiated between Africans and non-Africans, a result consistent with known human demographic history.

Genomic differences between type strain PG1 and field strains of Mycoplasma mycoides subsp. mycoides small-colony type

The recently accomplished complete genomic sequence analysis of the type strain PG1 of Mycoplasma mycoides subsp. mycoides small-colony type revealed four large repeated segments of 24, 13, 12, and 8 kb that are flanked by insertion sequence (IS) elements. Genetic analysis of type strain PG1 and African, European, and Australian field and vaccine strains revealed that the 24-kb genetic locus is repeated only in PG1 and not in other M. mycoides subsp. mycoides SC strains. In contrast, the 13-kb genetic locus was found duplicated in some strains originating from Africa and Australia but not in strains that were isolated from the European outbreaks. The 12- and 8-kb genetic loci were found in two and three copies, respectively, in all 28 strains analyzed. The flanking IS elements are assumed to lead to these tandem duplications, thus contributing to genomic plasticity. This aspect must be considered when designing novel diagnostic approaches and recombinant vaccines.

Classification and selection of biomarkers in genomic data using LASSO

High-throughput gene expression technologies such as microarrays have been utilized in a variety of scientific applications. Most of the work has been on assessing univariate associations between gene expression with clinical outcome (variable selection) or on developing classification procedures with gene expression data (supervised learning). We consider a hybrid variable selection/classification approach that is based on linear combinations of the gene expression profiles that maximize an accuracy measure summarized using the receiver operating characteristic curve. Under a specific probability model, this leads to consideration of linear discriminant functions. We incorporate an automated variable selection approach using LASSO. An equivalence between LASSO estimation with support vector machines allows for model fitting using standard software. We apply the proposed method to simulated data as well as data from a recently published prostate cancer study.

Visualizing Genomic Data

The advent of experimental techniques capable of probing biomolecules and cells at high levels of resolution has led to a rapid change in the methods used for the analysis of experimental molecular biology data. In this article we give an overview over visualization techniques and methods that can be used to assess various aspects of genomic data.

A graph theoretic approach to testing associations between disparate sources of functional genomic data

The last few years have seen the advent of high-throughput technologies to analyze various properties of the transcriptome and proteome of several organisms. The congruency of these different data sources, or lack thereof, can shed light on the mechanisms that govern cellular function. A central challenge for bioinformatics research is to develop a unified framework for combining the multiple sources of functional genomics information and testing associations between them, thus obtaining a robust and integrated view of the underlying biology. We present a graph theoretic approach to test the significance of the association between multiple disparate sources of functional genomics data by proposing two statistical tests, namely edge permutation and node label permutation tests. We demonstrate the use of the proposed tests by finding significant association between a Gene Ontology-derived "predictome" and data obtained from mRNA expression and phenotypic experiments for Saccharomyces cerevisiae. Moreover, we employ the graph theoretic framework to recast a surprising discrepancy presented in Giaever et al. (2002) between gene expression and knockout phenotype, using expression data from a different set of experiments.

A Pseudolikelihood Approach for Simultaneous Analysis of Array Comparative Genomic Hybridizations (aCGH)

DNA sequence copy number has been shown to be associated with cancer development and progression. Array-based Comparative Genomic Hybridization (aCGH) is a recent development that seeks to identify the copy number ratio at large numbers of markers across the genome. Due to experimental and biological variations across chromosomes and across hybridizations, current methods are limited to analyses of single chromosomes. We propose a more powerful approach that borrows strength across chromosomes and across hybridizations. We assume a Gaussian mixture model, with a hidden Markov dependence structure, and with random effects to allow for intertumoral variation, as well as intratumoral clonal variation. For ease of computation, we base estimation on a pseudolikelihood function. The method produces quantitative assessments of the likelihood of genetic alterations at each clone, along with a graphical display for simple visual interpretation. We assess the characteristics of the method through simulation studies and through analysis of a brain tumor aCGH data set. We show that the pseudolikelihood approach is superior to existing methods both in detecting small regions of copy number alteration and in accurately classifying regions of change when intratumoral clonal variation is present.

State affective instability in borderline personality disorder assessed by ambulatory monitoring

BACKGROUND: Although affective instability is an essential criterion for borderline personality disorder (BPD), it has rarely been reported as an outcome criterion. To date, most of the studies assessing state affective instability in BPD using paper-pencil diaries did not find indications of this characteristic, whereas in others studies, the findings were conflicting. Furthermore, the pattern of instability that characterizes BPD has not yet been identified. METHOD: We assessed the affective states of 50 female patients with BPD and 50 female healthy controls (HC) during 24 hours of their everyday life using electronic diaries. RESULTS: In contrast to previous paper-and-pencil diary studies, heightened affective instability for both emotional valence and distress was clearly exhibited in the BPD group but not in the HC group. Inconsistencies in previous papers can be explained by the methods used to calculate instability (see Appendix). In additional, we were able to identify a group-specific pattern of instability in the BPD group characterized by sudden large decreases from positive mood states. Furthermore, 48% of the declines from a very positive mood state in BPD were so large that they reached a negative mood state. This was the case in only 9% of the HC group, suggesting that BPD patients, on average, take less time to fluctuate from a very positive mood state to a negative mood state. CONCLUSION: Future ambulatory monitoring studies will be useful in clarifying which events lead to the reported, sudden decrease in positive mood in BPD patients.