Secondary Evolve and Resequencing : An Experimental Confirmation of Putative Selection Targets without Phenotyping

Evolve and resequencing (E&R) studies investigate the genomic responses of adaptation during experimental evolution. Because replicate populations evolve in the same controlled environment, consistent responses to selection across replicates are frequently used to identify reliable candidate regions that underlie adaptation to a new environment. However, recent work demonstrated that selection signatures can be restricted to one or a few replicate(s) only. These selection signatures frequently have weak statistical support, and given the difficulties of functional validation, additional evidence is needed before considering them as candidates for functional analysis. Here, we introduce an experimental procedure to validate candidate loci with weak or replicate-specific selection signature(s). Crossing an evolved population from a primary E&R experiment to the ancestral founder population reduces the frequency of candidate alleles that have reached a high frequency. We hypothesize that genuine selection targets will experience a repeatable frequency increase after the mixing with the ancestral founders if they are exposed to the same environment (secondary E&R experiment). Using this approach, we successfully validate two overlapping selection targets, which showed a mutually exclusive selection signature in a primary E&R experiment of Drosophila simulans adapting to a novel temperature regime. We conclude that secondary E&R experiments provide a reliable confirmation of selection signatures that either are not replicated or show only a low statistical significance in a primary E&R experiment unless epistatic interactions predominate. Such experiments are particularly helpful to prioritize candidate loci for time-consuming functional follow-up investigations.Peer reviewe

Fusion (F) protein gene of newcastle disease virus: Sequence and hydrophobicity comparative analysis between virulent and avirulent strains

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Figure SI 1. Large genomic regions respond at both temperatures. ;Figure SI 2. Histograms of parental gene expression differences per gene at 18°C (blue) and at 29°C (purple). ;Figure SI 3. Influence of the window size (1, 50, 250, 500 SNP(s)) and FDR threshold (x-axis, 0.05, 0.1, 0.15) for the partitioning of the genomic windowś diagnostic (y-axis, color code indicated in the legend).;Figure SI 4. Correlation between ancestral gene expression and genomic response after 20 generations. ;Table SI 1. Partition of the genome per class.

We estimated the median size of genomic regions with pronounced linkage at F20 by the autocorrelation in allele frequency between non-overlapping windows of 250 SNPs as in Burny et al, 2021. The distance between windows where the autocorrelation is no longer significant is used as measure of association. In the jittered boxplots, one dot represents one chromosome and a given replicate (18°C, blue; 29°C, purple).;The dashed lines represent at 18°C and 29°C the median absolute logFC S/O (0.37 at 18°C and 0.50 at 29°C, B; p-value paired Wilcoxon one-sided test=5×10-148).;Individual chromosomes and genome-wide estimates are shown.;Left. The temperature effect on the gene expression differences between the two parental genotypes (Samarkand and Oregon-R, y-axis) is plotted against the temperature effect of the allele frequency changes (x-axis). Each data point corresponds to a gene. The Spearman correlation (ρ) coefficient is reported. Right. The correlation between gene expression differential (logFC S/O 29°C - logFC S/O 18°C) and allele frequency differential (AFC 29°C - AFC 18°C) are measured in bins containing an increasing number of genes, which are ranked by expression (red) or allele frequency (black) differential.;The class definition is indicated in column 3 where the conditioning is made on p-values corrected with the Benjamini-Hochberg procedure for either neutrality tests at 18°C and 29°C (p.adjw18°C neutral and p.adjw29°C neutral) or from the linear model on the non-neutral windows (p.adjwLM) (see Methods). The percentage of windows affected in each class is reported in column 4 per chromosome and average genome-wide (GW), obtained with an False Discovery Rate (FDR) threshold of 5%, 10% and 15% (1st, 2nd and 3rd sub-row) and for non-overlapping windows of 250 SNPs

Cell-to-cell expression dispersion of B-cell surface proteins is linked to genetic variants in humans

International audienceVariability in gene expression across a population of homogeneous cells is known to influence various biological processes. In model organisms, natural genetic variants were found that modify expression dispersion (variability at a fixed mean) but very few studies have detected such effects in humans. Here, we analyzed single-cell expression of four proteins (CD23, CD55, CD63 and CD86) across cell lines derived from individuals of the Yoruba population. Using data from over 30 million cells, we found substantial inter-individual variation of dispersion. We demonstrate, via de novo cell line generation and subcloning experiments, that this variation exceeds the variation associated with cellular immortalization. We detected a genetic association between the expression dispersion of CD63 and the rs971 SNP. Our results show that human DNA variants can have inherently-probabilistic effects on gene expression. Such subtle genetic effects may participate to phenotypic variation and disease outcome

Early diagnosis of gestational trophoblastic neoplasia based on trajectory classification with compartment modeling

Abstract Background In randomized clinical trials or observational studies, it is common to collect biomarker values longitudinally on a cohort of individuals. The investigators may be interested in grouping individuals that share similar changes of biomarker values and use these groups for diagnosis or therapeutic purposes. However, most classical model-based classification methods rely mainly on empirical models such as splines or polynomials and do not reflect the physiological processes. Methods A model-based classification method was developed for longitudinal biomarker measurements through a pharmacokinetic model that describes biomarker changes over time. The method is illustrated using data on human Chorionic Gonadotrophic Hormone measurements after curettage of hydatidiform moles. Results The resulting classification was linked to the evolution toward gestational trophoblastic neoplasia and may be used as a tool for early diagnosis. The diagnostic accuracy of the pharmacokinetic model was more reproducible than the one of a purely mathematical model that did not take into account the biological processes. Conclusion The use of pharmacokinetic models in model-based classification approaches can lead to clinically useful classifications

Identification and characterization of a functional E-box motif in the R region of the Bovine Leukemia Virus Long Terminal repeat

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Host cell surfaces induce a Type IV pili-dependent alteration of bacterial swimming

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Single-particle tracking photoactivated localization microscopy of membrane proteins in living plant tissues

International audienceSuper-resolution microscopy techniques have pushed the limit of optical imaging to unprecedented spatial resolutions. However, one of the frontiers in nanoscopy is its application to intact living organisms. Here we describe the implementation and application of super-resolution single-particle tracking photoactivated localization microscopy (sptPALM) to probe single-molecule dynamics of membrane proteins in live roots of the model plant Arabidopsis thaliana. We first discuss the advantages and limitations of sptPALM for studying the diffusion properties of membrane proteins and compare this to fluorescence recovery after photobleaching (FRAP) and fluorescence correlation spectroscopy (FCS). We describe the technical details for handling and imaging the samples for sptPALM, with a particular emphasis on the specificity of imaging plant cells, such as their thick cell walls or high degree of autofluorescence. We then provide a practical guide from data collection to image analyses. In particular, we introduce our sptPALM_viewer software and describe how to install and use it for analyzing sptPALM experiments. Finally, we report an R statistical analysis pipeline to analyze and compare sptPALM experiments. Altogether, this protocol should enable plant researchers to perform sptPALM using a benchmarked reproducible protocol. Routinely, the procedure takes 3-4 h of imaging followed by 3-4 d of image processing and data analysis

Identification and characterization of a PU.1/Spi-B binding site in the bovine leukemia virus long terminal repeat.

Bovine leukemia virus (BLV) is a B-lymphotropic oncogenic retrovirus whose transcriptional promoter is located in the viral 5' long terminal repeat (LTR). To date, no B-lymphocyte-specific cis-regulatory element has been identified in this region. Since ETS proteins are known to regulate transcription of numerous retroviruses, we searched for the presence in the BLV promoter region of binding sites for PU.1/Spi-1, a B-cell- and macrophage-specific ETS family member. In this report, nucleotide sequence analysis of the viral LTR identified a PUbox located at -95/-84 bp. We demonstrated by gel shift and supershift assays that PU.1 and the related Ets transcription factor Spi-B interacted specifically with this PUbox. A 2-bp mutation (GGAA-->CCAA) within this motif abrogated PU.1/Spi-B binding. This mutation caused a marked decrease in LTR-driven basal gene expression in transient transfection assays of B-lymphoid cell lines, but did not impair the responsiveness of the BLV promoter to the virus-encoded transactivator Tax(BLV). Moreover, ectopically expressed PU.1 and Spi-B proteins transactivated the BLV promoter in a PUbox-dependent manner. Taken together, our results provide the first demonstration of regulation of the BLV promoter by two B-cell-specific Ets transcription factors, PU.1 and Spi-B. The PU.1/Spi-B binding site identified here could play an important role in BLV replication and B-lymphoid tropism.In VitroJournal ArticleResearch Support, Non-U.S. Gov'tinfo:eu-repo/semantics/publishe

Role of the transcription factor PU.1 and role of chromatin organization in the transcriptional regulation of the Bovine Leukemia Virus (BLV)

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