Generalized Borcea-Voisin Construction

C. Voisin and C. Borcea have constructed mirror pairs of families of Calabi-Yau threefolds by taking the quotient of the product of an elliptic curve with a K3 surface endowed with a non-symplectic involution. In this paper, we generalize the construction of Borcea and Voisin to any prime order and build three and four dimensional Calabi-Yau orbifolds. We classify the topological types that are obtained and show that, in dimension 4, orbifolds built with an involution admit a crepant resolution and come in topological mirror pairs. We show that for odd primes, there are generically no minimal resolutions and the mirror pairing is lost.Comment: 15 pages, 2 figures. v2: typos corrected & references adde

Comparison of normalization methods for differential gene expression analysis in RNA-Seq experiments: A matter of relative size of studied transcriptomes

In recent years, RNA-Seq technologies became a powerful tool for transcriptome studies. However, computational methods dedicated to the analysis of high-throughput sequencing data are yet to be standardized. In particular, it is known that the choice of a normalization procedure leads to a great variability in results of differential gene expression analysis. The present study compares the most widespread normalization procedures and proposes a novel one aiming at removing an inherent bias of studied transcriptomes related to their relative size. Comparisons of the normalization procedures are performed on real and simulated data sets. Real RNA-Seq data sets analyses, performed with all the different normalization methods, show that only 50% of significantly differentially expressed genes are common. This result highlights the influence of the normalization step on the differential expression analysis. Real and simulated data sets analyses give similar results showing 3 different groups of procedures having the same behavior. The group including the novel method named “Median Ratio Normalization” (MR N) gives the lower number of false discoveries. Within this group the MR N method is less sensitive to the modification of parameters related to the relative size of transcriptomes such as the number of down- and upregulated genes and the gene expression levels. The newly proposed MR N method efficiently deals with intrinsic bias resulting from relative size of studied transcriptomes. Validation with real and simulated data sets confirmed that MR N is more consistent and robust than existing methods

Normalizing single-cell RNA sequencing data: challenges and opportunities

Single-cell transcriptomics is becoming an important component of the molecular biologist's toolkit. A critical step when analyzing data generated using this technology is normalization. However, normalization is typically performed using methods developed for bulk RNA sequencing or even microarray data, and the suitability of these methods for single-cell transcriptomics has not been assessed. We here discuss commonly used normalization approaches and illustrate how these can produce misleading results. Finally, we present alternative approaches and provide recommendations for single-cell RNA sequencing users

Benchmarking of cell type deconvolution pipelines for transcriptomics data

Many computational methods have been developed to infer cell type proportions from bulk transcriptomics data. However, an evaluation of the impact of data transformation, pre-processing, marker selection, cell type composition and choice of methodology on the deconvolution results is still lacking. Using five single-cell RNA-sequencing (scRNA-seq) datasets, we generate pseudo-bulk mixtures to evaluate the combined impact of these factors. Both bulk deconvolution methodologies and those that use scRNA-seq data as reference perform best when applied to data in linear scale and the choice of normalization has a dramatic impact on some, but not all methods. Overall, methods that use scRNA-seq data have comparable performance to the best performing bulk methods whereas semi-supervised approaches show higher error values. Moreover, failure to include cell types in the reference that are present in a mixture leads to substantially worse results, regardless of the previous choices. Altogether, we evaluate the combined impact of factors affecting the deconvolution task across different datasets and propose general guidelines to maximize its performance. Inferring cell type proportions from transcriptomics data is affected by data transformation, normalization, choice of method and the markers used. Here, the authors use single-cell RNAseq datasets to evaluate the impact of these factors and propose guidelines to maximise deconvolution performance

Cis-regulatory evolution spotlights species differences in the adaptive potential of gene expression plasticity

Plasticity allows organisms to respond to environmental change. Here the authors compare the distribution of cis-regulatory variants in the transcriptomes of Arabidopsis lyrata and A. halleriafter exposure to stress, to trace the role of polygenic selection in the evolution of gene expression plasticity

Gene expression clines reveal local adaptation and associated trade-offs at a continental scale

Local adaptation, where fitness in one environment comes at a cost in another, should lead to spatial variation in trade-offs between life history traits and may be critical for population persistence. Recent studies have sought genomic signals of local adaptation, but often have been limited to laboratory populations representing two environmentally different locations of a species' distribution. We measured gene expression, as a proxy for fitness, in males of Drosophila subobscura, occupying a 20° latitudinal and 11 °C thermal range. Uniquely, we sampled six populations and studied both common garden and semi-natural responses to identify signals of local adaptation. We found contrasting patterns of investment: transcripts with expression positively correlated to latitude were enriched for metabolic processes, expressed across all tissues whereas negatively correlated transcripts were enriched for reproductive processes, expressed primarily in testes. When using only the end populations, to compare our results to previous studies, we found that locally adaptive patterns were obscured. While phenotypic trade-offs between metabolic and reproductive functions across widespread species are well-known, our results identify underlying genetic and tissue responses at a continental scale that may be responsible for this. This may contribute to understanding population persistence under environmental change

Life in an arsenic-containing gold mine: Genome and physiology of the autotrophic arsenite-oxidizing bacterium Rhizobium sp. NT-26:

Arsenic is widespread in the environment and its presence is a result of natural or anthropogenic activities. Microbes have developed different mechanisms to deal with toxic compounds such as arsenic and this is to resist or metabolize the compound. Here, we present the first reference set of genomic, transcriptomic and proteomic data of an Alphaproteobacterium isolated from an arseniccontaining goldmine: Rhizobium sp. NT-26. Although phylogenetically related to the plant-associated bacteria, this organism has lost the major colonizing capabilities needed for symbiosis with legumes. In contrast, the genome of Rhizobium sp. NT-26 comprises a megaplasmid containing the various genes, which enable it to metabolize arsenite. Remarkably, although the genes required for arsenite oxidation and flagellar motility/biofilm formation are carried by the megaplasmid and the chromosome, respectively, a coordinate regulation of these two mechanisms was observed. Taken together, these processes illustrate the impact environmental pressure can have on the evolution of bacterial genomes, improving the fitness of bacterial strains by the acquisition of novel functions. © The Author(s) 2013. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution

Adaptation in toxic environments: Arsenic genomic islands in the bacterial genus Thiomonas:

Acid mine drainage (AMD) is a highly toxic environment for most living organisms due to the presence of many lethal elements including arsenic (As). Thiomonas (Tm.) bacteria are found ubiquitously in AMD and can withstand these extreme conditions, in part because they are able to oxidize arsenite. In order to further improve our knowledge concerning the adaptive capacities of these bacteria, we sequenced and assembled the genome of six isolates derived from the Carnoulès AMD, and compared them to the genomes of Tm. arsenitoxydans 3As (isolated from the same site) and Tm. intermedia K12 (isolated from a sewage pipe). A detailed analysis of the Tm. sp. CB2 genome revealed various rearrangements had occurred in comparison to what was observed in 3As and K12 and over 20 genomic islands (GEIs) were found in each of these three genomes. We performed a detailed comparison of the two arsenic-related islands found in CB2, carrying the genes required for arsenite oxidation and As resistance, with those found in K12, 3As, and five other Thiomonas strains also isolated from Carnoulès (CB1, CB3, CB6, ACO3 and ACO7). Our results suggest that these arsenic-related islands have evolved differentially in these closely related Thiomonas strains, leading to divergent capacities to survive in As rich environments

Docosahexaenoic acid inhibits Helicobacter pylori growth in vitro and mice gastric mucosa colonization

H. pylori drug-resistant strains and non-compliance to therapy are the major causes of H. pylori eradication failure. For some bacterial species it has been demonstrated that fatty acids have a growth inhibitory effect. Our main aim was to assess the ability of docosahexaenoic acid (DHA) to inhibit H. pylori growth both in vitro and in a mouse model. The effectiveness of standard therapy (ST) in combination with DHA on H. pylori eradication and recurrence prevention success was also investigated. The effects of DHA on H. pylori growth were analyzed in an in vitro dose-response study and n in vivo model. We analized the ability of H. pylori to colonize mice gastric mucosa following DHA, ST or a combination of both treatments. Our data demonstrate that DHA decreases H. pylori growth in vitro in a dose-dependent manner. Furthermore, DHA inhibits H. pylori gastric colonization in vivo as well as decreases mouse gastric mucosa inflammation. Addition of DHA to ST was also associated with lower H. pylori infection recurrence in the mouse model. In conclusion, DHA is an inhibitor of H. pylori growth and its ability to colonize mouse stomach. DHA treatment is also associated with a lower recurrence of H. pylori infection in combination with ST. These observations pave the way to consider DHA as an adjunct agent in H. pylori eradication treatment.publishe