Gene-based partial least-squares approaches for detecting rare variant associations with complex traits

Genome-wide association studies are largely based on single-nucleotide polymorphisms and rest on the common disease/common variants (single-nucleotide polymorphisms) hypothesis. However, it has been argued in the last few years and is well accepted now that rare variants are valuable for studying common diseases. Although current genome-wide association studies have successfully discovered many genetic variants that are associated with common diseases, detecting associated rare variants remains a great challenge. Here, we propose two partial least-squares approaches to aggregate the signals of many single-nucleotide polymorphisms (SNPs) within a gene to reveal possible genetic effects related to rare variants. The availability of the 1000 Genomes Project offers us the opportunity to evaluate the effectiveness of these two gene-based approaches. Compared to results from a SNP-based analysis, the proposed methods were able to identify some (rare) SNPs that were missed by the SNP-based analysis

Increased RPA1 gene dosage affects genomic stability potentially contributing to 17p13.3 duplication syndrome

A novel microduplication syndrome involving various-sized contiguous duplications in 17p13.3 has recently been described, suggesting that increased copy number of genes in 17p13.3, particularly PAFAH1B1, is associated with clinical features including facial dysmorphism, developmental delay, and autism spectrum disorder. We have previously shown that patient-derived cell lines from individuals with haploinsufficiency of RPA1, a gene within 17p13.3, exhibit an impaired ATR-dependent DNA damage response (DDR). Here, we show that cell lines from patients with duplications specifically incorporating RPA1 exhibit a different although characteristic spectrum of DDR defects including abnormal S phase distribution, attenuated DNA double strand break (DSB)-induced RAD51 chromatin retention, elevated genomic instability, and increased sensitivity to DNA damaging agents. Using controlled conditional over-expression of RPA1 in a human model cell system, we also see attenuated DSB-induced RAD51 chromatin retention. Furthermore, we find that transient over-expression of RPA1 can impact on homologous recombination (HR) pathways following DSB formation, favouring engagement in aberrant forms of recombination and repair. Our data identifies unanticipated defects in the DDR associated with duplications in 17p13.3 in humans involving modest RPA1 over-expression

SlimPLS: A Method for Feature Selection in Gene Expression-Based Disease Classification

A major challenge in biomedical studies in recent years has been the classification of gene expression profiles into categories, such as cases and controls. This is done by first training a classifier by using a labeled training set containing labeled samples from the two populations, and then using that classifier to predict the labels of new samples. Such predictions have recently been shown to improve the diagnosis and treatment selection practices for several diseases. This procedure is complicated, however, by the high dimensionality if the data. While microarrays can measure the levels of thousands of genes per sample, case-control microarray studies usually involve no more than several dozen samples. Standard classifiers do not work well in these situations where the number of features (gene expression levels measured in these microarrays) far exceeds the number of samples. Selecting only the features that are most relevant for discriminating between the two categories can help construct better classifiers, in terms of both accuracy and efficiency. In this work we developed a novel method for multivariate feature selection based on the Partial Least Squares algorithm. We compared the method's variants with common feature selection techniques across a large number of real case-control datasets, using several classifiers. We demonstrate the advantages of the method and the preferable combinations of classifier and feature selection technique

Seasonal dynamics of active SAR11 ecotypes in the oligotrophic Northwest Mediterranean Sea

A seven-year oceanographic time series in NW Mediterranean surface waters was combined with pyrosequencing of ribosomal RNA (16S rRNA) and ribosomal RNA gene copies (16S rDNA) to examine the environmental controls on SAR11 ecotype dynamics and potential activity. SAR11 diversity exhibited pronounced seasonal cycles remarkably similar to total bacterial diversity. The timing of diversity maxima was similar across narrow and broad phylogenetic clades and strongly associated with deep winter mixing. Diversity minima were associated with periods of stratification that were low in nutrients and phytoplankton biomass and characterised by intense phosphate limitation (turnover time80%) by SAR11 Ia. A partial least squares (PLS) regression model was developed that could reliably predict sequence abundances of SAR11 ecotypes (Q2=0.70) from measured environmental variables, of which mixed layer depth was quantitatively the most important. Comparison of clade-level SAR11 rRNA:rDNA signals with leucine incorporation enabled us to partially validate the use of these ratios as an in-situ activity measure. However, temporal trends in the activity of SAR11 ecotypes and their relationship to environmental variables were unclear. The strong and predictable temporal patterns observed in SAR11 sequence abundance was not linked to metabolic activity of different ecotypes at the phylogenetic and temporal resolution of our study

Systemic Maternal Inflammation and Neonatal Hyperoxia Induces Remodeling and Left Ventricular Dysfunction in Mice

The impact of the neonatal environment on the development of adult cardiovascular disease is poorly understood. Systemic maternal inflammation is linked to growth retardation, preterm birth, and maturation deficits in the developing fetus. Often preterm or small-for-gestational age infants require medical interventions such as oxygen therapy. The long-term pathological consequences of medical interventions on an immature physiology remain unknown. In the present study, we hypothesized that systemic maternal inflammation and neonatal hyperoxia exposure compromise cardiac structure, resulting in LV dysfunction during adulthood.Pregnant C3H/HeN mice were injected on embryonic day 16 (E16) with LPS (80 µg/kg; i.p.) or saline. Offspring were placed in room air (RA) or 85% O(2) for 14 days and subsequently maintained in RA. Cardiac echocardiography, cardiomyocyte contractility, and molecular analyses were performed. Echocardiography revealed persistent lower left ventricular fractional shortening with greater left ventricular end systolic diameter at 8 weeks in LPS/O(2) than in saline/RA mice. Isolated cardiomyocytes from LPS/O(2) mice had slower rates of contraction and relaxation, and a slower return to baseline length than cardiomyocytes isolated from saline/RA controls. α-/β-MHC ratio was increased and Connexin-43 levels decreased in LPS/O(2) mice at 8 weeks. Nox4 was reduced between day 3 and 14 and capillary density was lower at 8 weeks of life in LPS/O(2) mice.These results demonstrate that systemic maternal inflammation combined with neonatal hyperoxia exposure induces alterations in cardiac structure and function leading to cardiac failure in adulthood and supports the importance of the intrauterine and neonatal milieu on adult health

Observation of Quantum Interference in Molecular Charge Transport

As the dimensions of a conductor approach the nano-scale, quantum effects will begin to dominate its behavior. This entails the exciting possibility of controlling the conductance of a device by direct manipulation of the electron wave function. Such control has been most clearly demonstrated in mesoscopic semiconductor structures at low temperatures. Indeed, the Aharanov-Bohm effect, conductance quantization and universal conductance fluctuations are direct manifestations of the electron wave nature. However, an extension of this concept to more practical emperatures has not been achieved so far. As molecules are nano-scale objects with typical energy level spacings (~eV) much larger than the thermal energy at 300 K (~25 meV), they are natural candidates to enable such a break-through. Fascinating phenomena including giant magnetoresistance, Kondo effects and conductance switching, have previously been demonstrated at the molecular level. Here, we report direct evidence for destructive quantum interference in charge transport through two-terminal molecular junctions at room temperature. Furthermore, we show that the degree of interference can be controlled by simple chemical modifications of the molecule. Not only does this provide the experimental demonstration of a new phenomenon in quantum charge transport, it also opens the road for a new type of molecular devices based on chemical or electrostatic control of quantum interference

Comparative genomics of Escherichia coli isolated from patients with inflammatory bowel disease

<p>Abstract</p> <p>Background</p> <p>Inflammatory bowel disease (IBD) is used to describe a state of idiopathic, chronic inflammation of the gastrointestinal tract. The two main phenotypes of IBD are Crohn's disease (CD) and ulcerative colitis (UC). The major cause of IBD-associated mortality is colorectal cancer. Although both host-genetic and exogenous factors have been found to be involved, the aetiology of IBD is still not well understood. In this study we characterized thirteen <it>Escherichia coli </it>strains from patients with IBD by comparative genomic hybridization employing a microarray based on 31 sequenced <it>E. coli </it>genomes from a wide range of commensal and pathogenic isolates.</p> <p>Results</p> <p>The IBD isolates, obtained from patients with UC and CD, displayed remarkably heterogeneous genomic profiles with little or no evidence of group-specific determinants. No IBD-specific genes were evident when compared with the prototypic CD isolate, LF82, suggesting that the IBD-inducing effect of the strains is multifactorial. Several of the IBD isolates carried a number of extraintestinal pathogenic <it>E. coli </it>(ExPEC)-related virulence determinants such as the <it>pap</it>, <it>sfa</it>, <it>cdt </it>and <it>hly </it>genes. The isolates were also found to carry genes of ExPEC-associated genomic islands.</p> <p>Conclusions</p> <p>Combined, these data suggest that <it>E. coli </it>isolates obtained from UC and CD patients represents a heterogeneous population of strains, with genomic profiles that are indistinguishable to those of ExPEC isolates. Our findings indicate that IBD-induction from <it>E. coli </it>strains is multifactorial and that a range of gene products may be involved in triggering the disease.</p

Insulin Sensitivity Is Reflected by Characteristic Metabolic Fingerprints - A Fourier Transform Mass Spectrometric Non-Targeted Metabolomics Approach

BACKGROUND: A decline in body insulin sensitivity in apparently healthy individuals indicates a high risk to develop type 2 diabetes. Investigating the metabolic fingerprints of individuals with different whole body insulin sensitivity according to the formula of Matsuda, et al. (ISI(Matsuda)) by a non-targeted metabolomics approach we aimed a) to figure out an unsuspicious and altered metabolic pattern, b) to estimate a threshold related to these changes based on the ISI, and c) to identify the metabolic pathways responsible for the discrimination of the two patterns. METHODOLOGY AND PRINCIPAL FINDINGS: By applying infusion ion cyclotron resonance Fourier transform mass spectrometry, we analyzed plasma of 46 non-diabetic subjects exhibiting high to low insulin sensitivities. The orthogonal partial least square model revealed a cluster of 28 individuals with alterations in their metabolic fingerprints associated with a decline in insulin sensitivity. This group could be separated from 18 subjects with an unsuspicious metabolite pattern. The orthogonal signal correction score scatter plot suggests a threshold of an ISI(Matsuda) of 15 for the discrimination of these two groups. Of note, a potential subgroup represented by eight individuals (ISI(Matsuda) value between 8.5 and 15) was identified in different models. This subgroup may indicate a metabolic transition state, since it is already located within the cluster of individuals with declined insulin sensitivity but the metabolic fingerprints still show some similarities with unaffected individuals (ISI >15). Moreover, the highest number of metabolite intensity differences between unsuspicious and altered metabolic fingerprints was detected in lipid metabolic pathways (arachidonic acid metabolism, metabolism of essential fatty acids and biosynthesis of unsaturated fatty acids), steroid hormone biosyntheses and bile acid metabolism, based on data evaluation using the metabolic annotation interface MassTRIX. CONCLUSIONS: Our results suggest that altered metabolite patterns that reflect changes in insulin sensitivity respectively the ISI(Matsuda) are dominated by lipid-related pathways. Furthermore, a metabolic transition state reflected by heterogeneous metabolite fingerprints may precede severe alterations of metabolism. Our findings offer future prospects for novel insights in the pathogenesis of the pre-diabetic phase

Concept drift over geological times : predictive modeling baselines for analyzing the mammalian fossil record

Fossils are the remains organisms from earlier geological periods preserved in sedimentary rock. The global fossil record documents and characterizes the evidence about organisms that existed at different times and places during the Earth's history. One of the major directions in computational analysis of such data is to reconstruct environmental conditions and track climate changes over millions of years. Distribution of fossil animals in space and time make informative features for such modeling, yet concept drift presents one of the main computational challenges. As species continuously go extinct and new species originate, animal communities today are different from the communities of the past, and the communities at different times in the past are different from each other. The fossil record is continuously increasing as new fossils and localities are being discovered, but it is not possible to observe or measure their environmental contexts directly, because the time is gone. Labeled data linking organisms to climate is available only for the present day, where climatic conditions can be measured. The approach is to train models on the present day and use them to predict climatic conditions over the past. But since species representation is continuously changing, transfer learning approaches are needed to make models applicable and climate estimates to be comparable across geological times. Here we discuss predictive modeling settings for such paleoclimate reconstruction from the fossil record. We compare and experimentally analyze three baseline approaches for predictive paleoclimate reconstruction: (1) averaging over habitats of species, (2) using presence-absence of species as features, and (3) using functional characteristics of species communities as features. Our experiments on the present day African data and a case study on the fossil data from the Turkana Basin over the last 7 million of years suggest that presence-absence approaches are the most accurate over short time horizons, while species community approaches, also known as ecometrics, are the most informative over longer time horizons when, due to ongoing evolution, taxonomic relations between the present day and fossil species become more and more uncertain.Peer reviewe