Mycobacterium tuberculosis transmission in a country with low tuberculosis incidence: role of immigration and HIV infection.

Immigrants from high-burden countries and HIV-coinfected individuals are risk groups for tuberculosis (TB) in countries with low TB incidence. Therefore, we studied their role in transmission of Mycobacterium tuberculosis in Switzerland. We included all TB patients from the Swiss HIV Cohort and a sample of patients from the national TB registry. We identified molecular clusters by spoligotyping and mycobacterial interspersed repetitive-unit-variable-number tandem-repeat (MIRU-VNTR) analysis and used weighted logistic regression adjusted for age and sex to identify risk factors for clustering, taking sampling proportions into account. In total, we analyzed 520 TB cases diagnosed between 2000 and 2008; 401 were foreign born, and 113 were HIV coinfected. The Euro-American M. tuberculosis lineage dominated throughout the study period (378 strains; 72.7%), with no evidence for another lineage, such as the Beijing genotype, emerging. We identified 35 molecular clusters with 90 patients, indicating recent transmission; 31 clusters involved foreign-born patients, and 15 involved HIV-infected patients. Birth origin was not associated with clustering (adjusted odds ratio [aOR], 1.58; 95% confidence interval [CI], 0.73 to 3.43; P = 0.25, comparing Swiss-born with foreign-born patients), but clustering was reduced in HIV-infected patients (aOR, 0.49; 95% CI, 0.26 to 0.93; P = 0.030). Cavitary disease, male sex, and younger age were all associated with molecular clustering. In conclusion, most TB patients in Switzerland were foreign born, but transmission of M. tuberculosis was not more common among immigrants and was reduced in HIV-infected patients followed up in the national HIV cohort study. Continued access to health services and clinical follow-up will be essential to control TB in this population.

CD30-positive peripheral T-cell lymphomas share molecular and phenotypic features.

Peripheral T-cell lymphoma, not otherwise specified is a heterogeneous group of aggressive neoplasms with indistinct borders. By gene expression profiling we previously reported unsupervised clusters of peripheral T-cell lymphomas, not otherwise specified correlating with CD30 expression. In this work we extended the analysis of peripheral T-cell lymphoma molecular profiles to prototypical CD30(+) peripheral T-cell lymphomas (anaplastic large cell lymphomas), and validated mRNA expression profiles at the protein level. Existing transcriptomic datasets from peripheral T-cell lymphomas, not otherwise specified and anaplastic large cell lymphomas were reanalyzed. Twenty-one markers were selected for immunohistochemical validation on 80 peripheral T-cell lymphoma samples (not otherwise specified, CD30(+) and CD30(-); anaplastic large cell lymphomas, ALK(+) and ALK(-)), and differences between subgroups were assessed. Clinical follow-up was recorded. Compared to CD30(-) tumors, CD30(+) peripheral T-cell lymphomas, not otherwise specified were significantly enriched in ALK(-) anaplastic large cell lymphoma-related genes. By immunohistochemistry, CD30(+) peripheral T-cell lymphomas, not otherwise specified differed significantly from CD30(-) samples [down-regulated expression of T-cell receptor-associated proximal tyrosine kinases (Lck, Fyn, Itk) and of proteins involved in T-cell differentiation/activation (CD69, ICOS, CD52, NFATc2); upregulation of JunB and MUM1], while overlapping with anaplastic large cell lymphomas. CD30(-) peripheral T-cell lymphomas, not otherwise specified tended to have an inferior clinical outcome compared to the CD30(+) subgroups. In conclusion, we show molecular and phenotypic features common to CD30(+) peripheral T-cell lymphomas, and significant differences between CD30(-) and CD30(+) peripheral T-cell lymphomas, not otherwise specified, suggesting that CD30 expression might delineate two biologically distinct subgroups.

Multi-modal Change Detection, Application to the Detection of Flooded Areas: Outcome of the 2009-2010 data fusion contest

The 2009-2010 Data Fusion Contest organized by the Data Fusion Technical Committee of the IEEE Geoscience and Remote Sensing Society was focused on the detection of flooded areas using multi-temporal and multi-modal images. Both high spatial resolution optical and synthetic aperture radar data were provided. The goal was not only to identify the best algorithms (in terms of accuracy), but also to investigate the further improvement derived from decision fusion. This paper presents the four awarded algorithms and the conclusions of the contest, investigating both supervised and unsupervised methods and the use of multi-modal data for flood detection. Interestingly, a simple unsupervised change detection method provided similar accuracy as supervised approaches, and a digital elevation model-based predictive method yielded a comparable projected change detection map without using post-event data.

The Distinct Molecular Signature Of Hepatosplenic T-Cell Lymphoma (Hstl) Identifies Oncogenic Pathways With Potential Therapeutic Relevance

Background: HSTL is a rare entity characterized by an infiltration of bone marrow, spleen and liver tissues by neoplastic gammadelta (gd) -more rarely alphabeta (ab)- T cells. Its pathogenesis is poorly understood. Our purpose was to identify the molecular signature of HSTL and explore molecular pathways implicated in its pathogenesis.Methods: Gene expression profiling and array CGH analysis of 10 HSTL samples (7gd, 3ab), 1 HSTL cell line (DERL2), 2 normal gd samples together with 16 peripheral T-cell lymphoma not otherwise specified (PTCL,NOS) and 7 nasal NK/T cell lymphomas were performed.Results: By unsupervised analysis, ab and gdHSTL clustered together remarkably separated from other lymphoma entities. Compared to PTCL, NOS, HSTL overexpresed genes encoding NK-associated molecules, oncogenes (VAV3) and the Sphingosine-1-phosphatase receptor 5 involved in cell trafficking. Compared to normal gd cells, HSTL overexpressed genes encoding NK-cell and multi drug resistance-associated molecules, transcription factors (RHOB), oncogenes (MAFB, FOS, JUN, VAV3) and the tyrosine kinase SYK whereas genes encoding cytotoxic molecules and the tumor suppressor gene AIM1 were among the most downregulated. By immunohistochemistry, SYK was demonstrated on HSTL cells with expression of its phosphorylated form in DERL2 cells by Western blot. Functional studies using a SYK inhibitor revealed a dose dependent increase of apoptotic DERL2 cells suggesting that SYK could be a candidate target for pharmacologic inhibition. Downexpression of AIM1 was validated by qRT-PCR. Methylation analysis of DERL2 genomic DNA treated by bisulfite demonstrated highly methylated CpG islands of AIM1. Genomic profiles confirmed recurrent isochromosome 7q (n=6/9) without alterations at 9q22 and 6q21 containing SYK and AIM1 genes, respectively.Conclusion: The current study identifies a distinct molecular signature for HSTL and highlights oncogenic pathways which offer rationale for exploring new therapeutic options such as SYK inhibitors. It supports the view of gd and ab HSTL as a single entity.

Evolutionary history of almond tree domestication in the Mediterranean basin.

Genetic diversity of contemporary domesticated species is shaped by both natural and human-driven processes. However, until now, little is known about how domestication has imprinted the variation of fruit tree species. In this study, we reconstruct the recent evolutionary history of the domesticated almond tree, Prunus dulcis, around the Mediterranean basin, using a combination of nuclear and chloroplast microsatellites [i.e. simple sequence repeat (SSRs)] to investigate patterns of genetic diversity. Whereas conservative chloroplast SSRs show a widespread haplotype and rare locally distributed variants, nuclear SSRs show a pattern of isolation by distance with clines of diversity from the East to the West of the Mediterranean basin, while Bayesian genetic clustering reveals a substantial longitudinal genetic structure. Both kinds of markers thus support a single domestication event, in the eastern side of the Mediterranean basin. In addition, model-based estimation of the timing of genetic divergence among those clusters is estimated sometime during the Holocene, a result that is compatible with human-mediated dispersal of almond tree out of its centre of origin. Still, the detection of region-specific alleles suggests that gene flow from relictual wild preglacial populations (in North Africa) or from wild counterparts (in the Near East) could account for a fraction of the diversity observed.

Genetic and phenotypic architecture of metabolic syndrome-associated components in dyslipidemic and normolipidemic subjects: the GEMS Study.

Atherogenic dyslipidemia, manifest by low HDL-cholesterol and high TG levels, is an important component of ATP-III defined metabolic syndrome. Here, we dissected the phenotypic and genetic architecture of these traits by assessing their relationships with other metabolically relevant measures, including plasma adipo-cytokines, highly sensitive C-reactive protein (hsCRP) and LDL particle size, in a large family data set (n=2800) and in an independent set of dyslipidemic cases (n=716) and normolipidemic controls (n=1073). We explored the relationships among these phenotypes using variable clustering and then estimated their genetic heritabilities and cross-trait correlations. In families, four clusters explained 61% of the total variance, with one adiposity-related cluster (including hsCRP), one BP-related cluster, and two lipid-related clusters (HDL-C, TG, adiponectin and LDL particle size; apoB and non-HDL-C). A similar structure was observed in dyslipidemic cases and normolipidemic controls. The genetic correlations in the families largely paralleled the phenotype clustering results, suggesting that common genes having pleiotropic effects contributed to the correlations observed. In summary, our analyses support a model of metabolic syndrome with two major components, body fat and lipids, each with two subcomponents, and quantifies their degree of overlap with each other and with metabolic-syndrome related measures (adipokines, LDL particle size and hsCRP).

Transcriptional analysis of left-sided colitis, pancolitis, and ulcerative colitis-associated dysplasia

BACKGROUND: It is unknown why patients with extensive ulcerative colitis (UC) have a higher risk of colorectal cancer compared with patients with left-sided UC. This study characterizes the inflammatory processes in left-sided UC, pancolitis, and UC-associated dysplasia at the transcriptional level to identify potential biomarkers and transcripts of importance for the carcinogenic behavior of chronic inflammation. METHODS: The Affymetrix GeneChip Human Genome U133 Plus 2.0 was applied on colonic biopsies from UC patients with left-sided UC, pancolitis, dysplasia, and controls. Reverse transcription polymerase chain reaction and immunohistochemistry were performed for validating selected transcripts in the initial cohort and in 2 independent cohorts of patients with UC. Microarray data were analyzed by principal component analysis, and reverse transcription polymerase chain reaction and immunohistochemistry data by the Wilcoxon's rank-sum test. RESULTS: The principal component analysis results revealed separate clusters for left-sided UC, pancolitis, dysplasia, and controls. Close clustering of dysplastic and pancolitic samples indicated similarities in gene expression. Indeed, 101 and 656 parallel upregulated and downregulated transcripts, respectively, were identified in specimens from dysplasia and pancolitis. Validation of selected transcripts hereof identified insulin receptor alpha (INSRA) and MAP kinase interacting serine/threonine kinase 2 (MKNK2) with an enhanced expression in dysplasia compared with left-sided UC and controls, whereas laminin γ2 (LAMC2) was found with a lower expression in dysplasia compared with the remaining 3 groups. CONCLUSIONS: This study demonstrates pancolitis and left-sided UC as distinct inflammatory processes at the transcriptional level, and identifies INSRA, MKNK2, and LAMC2 as potential critical transcripts in the inflammation-driven preneoplastic process of UC.

Constructing brain functional networks from EEG: partial and unpartial correlations.

We consider electroencephalograms (EEGs) of healthy individuals and compare the properties of the brain functional networks found through two methods: unpartialized and partialized cross-correlations. The networks obtained by partial correlations are fundamentally different from those constructed through unpartial correlations in terms of graph metrics. In particular, they have completely different connection efficiency, clustering coefficient, assortativity, degree variability, and synchronization properties. Unpartial correlations are simple to compute and they can be easily applied to large-scale systems, yet they cannot prevent the prediction of non-direct edges. In contrast, partial correlations, which are often expensive to compute, reduce predicting such edges. We suggest combining these alternative methods in order to have complementary information on brain functional networks.

Exome sequencing identifies DYNC2H1 mutations as a common cause of asphyxiating thoracic dystrophy (Jeune syndrome) without major polydactyly, renal or retinal involvement.

BACKGROUND: Jeune asphyxiating thoracic dystrophy (JATD) is a rare, often lethal, recessively inherited chondrodysplasia characterised by shortened ribs and long bones, sometimes accompanied by polydactyly, and renal, liver and retinal disease. Mutations in intraflagellar transport (IFT) genes cause JATD, including the IFT dynein-2 motor subunit gene DYNC2H1. Genetic heterogeneity and the large DYNC2H1 gene size have hindered JATD genetic diagnosis. AIMS AND METHODS: To determine the contribution to JATD we screened DYNC2H1 in 71 JATD patients JATD patients combining SNP mapping, Sanger sequencing and exome sequencing. RESULTS AND CONCLUSIONS: We detected 34 DYNC2H1 mutations in 29/71 (41%) patients from 19/57 families (33%), showing it as a major cause of JATD especially in Northern European patients. This included 13 early protein termination mutations (nonsense/frameshift, deletion, splice site) but no patients carried these in combination, suggesting the human phenotype is at least partly hypomorphic. In addition, 21 missense mutations were distributed across DYNC2H1 and these showed some clustering to functional domains, especially the ATP motor domain. DYNC2H1 patients largely lacked significant extra-skeletal involvement, demonstrating an important genotype-phenotype correlation in JATD. Significant variability exists in the course and severity of the thoracic phenotype, both between affected siblings with identical DYNC2H1 alleles and among individuals with different alleles, which suggests the DYNC2H1 phenotype might be subject to modifier alleles, non-genetic or epigenetic factors. Assessment of fibroblasts from patients showed accumulation of anterograde IFT proteins in the ciliary tips, confirming defects similar to patients with other retrograde IFT machinery mutations, which may be of undervalued potential for diagnostic purposes.

Probabilistic base calling of Solexa sequencing data.

BACKGROUND: Solexa/Illumina short-read ultra-high throughput DNA sequencing technology produces millions of short tags (up to 36 bases) by parallel sequencing-by-synthesis of DNA colonies. The processing and statistical analysis of such high-throughput data poses new challenges; currently a fair proportion of the tags are routinely discarded due to an inability to match them to a reference sequence, thereby reducing the effective throughput of the technology. RESULTS: We propose a novel base calling algorithm using model-based clustering and probability theory to identify ambiguous bases and code them with IUPAC symbols. We also select optimal sub-tags using a score based on information content to remove uncertain bases towards the ends of the reads. CONCLUSION: We show that the method improves genome coverage and number of usable tags as compared with Solexa's data processing pipeline by an average of 15%. An R package is provided which allows fast and accurate base calling of Solexa's fluorescence intensity files and the production of informative diagnostic plots.

The association of aldosterone with obesity-related hypertension and the metabolic syndrome.

Overweight and obesity are associated with arterial hypertension. Given the large increase in the obesity prevalence worldwide, the number of obese patients with hypertension is likely to increase substantially in the near future. Overweight and obese patients are exposed to an important metabolic and cardiovascular risk. The understanding of the mechanisms linking obesity to hypertension is important for specific prevention and therapy in this population. There is some evidence that obesity is associated with an increased aldosterone level. To date, 2 mechanisms may explain the interaction of fat tissue with the renin-angiotensin-aldosterone system, and therefore explain, in part, obesity-related hypertension. First, human adipose tissue produces several components of the renin-angiotensin-aldosterone system, mainly adipose tissue-derived angiotensinogen. Second, increased fatty acid production in the obese patient, especially nonesterified fatty acids, might stimulate aldosterone production, independent of renin. A better understanding of these mechanisms might have implications for the management of hypertension in overweight and obese patients. Because aldosterone also is associated with blood glucose and blood lipids, selective aldosterone blockade may represent a particularly attractive therapeutic strategy in obese patients with a clustering of cardiovascular risk factors.

Kernel-based methods for change detection in remote sensing images

Nowadays, the joint exploitation of images acquired daily by remote sensing instruments and of images available from archives allows a detailed monitoring of the transitions occurring at the surface of the Earth. These modifications of the land cover generate spectral discrepancies that can be detected via the analysis of remote sensing images. Independently from the origin of the images and of type of surface change, a correct processing of such data implies the adoption of flexible, robust and possibly nonlinear method, to correctly account for the complex statistical relationships characterizing the pixels of the images. This Thesis deals with the development and the application of advanced statistical methods for multi-temporal optical remote sensing image processing tasks. Three different families of machine learning models have been explored and fundamental solutions for change detection problems are provided. In the first part, change detection with user supervision has been considered. In a first application, a nonlinear classifier has been applied with the intent of precisely delineating flooded regions from a pair of images. In a second case study, the spatial context of each pixel has been injected into another nonlinear classifier to obtain a precise mapping of new urban structures. In both cases, the user provides the classifier with examples of what he believes has changed or not. In the second part, a completely automatic and unsupervised method for precise binary detection of changes has been proposed. The technique allows a very accurate mapping without any user intervention, resulting particularly useful when readiness and reaction times of the system are a crucial constraint. In the third, the problem of statistical distributions shifting between acquisitions is studied. Two approaches to transform the couple of bi-temporal images and reduce their differences unrelated to changes in land cover are studied. The methods align the distributions of the images, so that the pixel-wise comparison could be carried out with higher accuracy. Furthermore, the second method can deal with images from different sensors, no matter the dimensionality of the data nor the spectral information content. This opens the doors to possible solutions for a crucial problem in the field: detecting changes when the images have been acquired by two different sensors.

A better understanding of ecological conditions for Leontopodium alpinum Cassini in the Swiss Alps

Although Leontopodium alpinum is considered to be threatened in many countries, only limited scientific information about its autecology is available. In this study, we aim to define the most important ecological factors which influence the distribution of L. alpinum in the Swiss Alps. These were assessed at the national scale using species distribution models based on topoclimatic predictors and at the community scale using exhaustive plant inventories. The latter were analysed using hierarchical clustering and principal component analysis, and the results were interpreted using ecological indicator values. L. alpinum was found almost exclusively on base-rich bedrocks (limestone and ultramaphic rocks). The species distribution models showed that the available moisture (dry regions, mostly in the Inner Alps), elevation (mostly above 2000 m.a.s.l.) and slope (mostly >30°) were the most important predictors. The relevés showed that L. alpinum is present in a wide range of plant communities, all subalpine-alpine open grasslands, with a low grass cover. As a light-demanding and short species, L. alpinum requires light at ground level; hence, it can only grow in open, nutrient-poor grasslands. These conditions are met in dry conditions (dry, summer-warm climate, rocky and draining soil, south-facing aspect and/or steep slope), at high elevations, on oligotrophic soils and/or on windy ridges. Base-rich soils appear to also be essential, although it is still unclear if this corresponds to physiological or ecological (lower competition) requirements.