Adaptive modulation of antibiotic resistance through intragenomic coevolution

Bacteria gain antibiotic resistance genes by horizontal acquisition of mobile genetic elements (MGEs) from other lineages. Newly acquired MGEs are often poorly adapted causing intragenomic conflicts; these are resolved by either compensatory adaptation - of the chromosome or the MGE - or reciprocal coadaptation. The footprints of such intragenomic coevolution are present in bacterial genomes, suggesting an important role promoting genomic integration of horizontally acquired genes, but direct experimental evidence of the process is limited. Here we show adaptive modulation of tetracycline resistance via intragenomic coevolution between Escherichia coli and the multidrug resistant plasmid RK2. Tetracycline treatments, including monotherapy or combination therapies with ampicillin, favoured de novo chromosomal resistance mutations coupled with mutations on RK2 impairing the plasmid-encoded tetracycline efflux pump. These mutations together provided increased tetracycline resistance at reduced cost. Additionally, the chromosomal resistance mutations conferred cross-resistance to chloramphenicol. Reciprocal coadaptation was not observed under ampicillin-only or no antibiotic selection. Intragenomic coevolution can create genomes comprising multiple replicons that together provide high-level, low-cost resistance, but the resulting co-dependence may limit the spread of coadapted MGEs to other lineages

Genome-wide expression assay comparison across frozen and fixed postmortem brain tissue samples

<p>Abstract</p> <p>Background</p> <p>Gene expression assays have been shown to yield high quality genome-wide data from partially degraded RNA samples. However, these methods have not yet been applied to postmortem human brain tissue, despite their potential to overcome poor RNA quality and other technical limitations inherent in many assays. We compared cDNA-mediated annealing, selection, and ligation (DASL)- and <it>in vitro </it>transcription (IVT)-based genome-wide expression profiling assays on RNA samples from artificially degraded reference pools, frozen brain tissue, and formalin-fixed brain tissue.</p> <p>Results</p> <p>The DASL-based platform produced expression results of greater reliability than the IVT-based platform in artificially degraded reference brain RNA and RNA from frozen tissue-based samples. Although data associated with a small sample of formalin-fixed RNA samples were poor when obtained from both assays, the DASL-based platform exhibited greater reliability in a subset of probes and samples.</p> <p>Conclusions</p> <p>Our results suggest that the DASL-based gene expression-profiling platform may confer some advantages on mRNA assays of the brain over traditional IVT-based methods. We ultimately consider the implications of these results on investigations of neuropsychiatric disorders.</p

An anatomic gene expression atlas of the adult mouse brain

Studying gene expression provides a powerful means of understanding structure-function relationships in the nervous system. The availability of genome-scale in situ hybridization datasets enables new possibilities for understanding brain organization based on gene expression patterns. The Anatomic Gene Expression Atlas (AGEA) is a new relational atlas revealing the genetic architecture of the adult C57Bl/6J mouse brain based on spatial correlations across expression data for thousands of genes in the Allen Brain Atlas (ABA). The AGEA includes three discovery tools for examining neuroanatomical relationships and boundaries: (1) three-dimensional expression-based correlation maps, (2) a hierarchical transcriptome-based parcellation of the brain and (3) a facility to retrieve from the ABA specific genes showing enriched expression in local correlated domains. The utility of this atlas is illustrated by analysis of genetic organization in the thalamus, striatum and cerebral cortex. The AGEA is a publicly accessible online computational tool integrated with the ABA (http://mouse.brain-map.org/agea)

Gene Expression in the Rodent Brain is Associated with Its Regional Connectivity

The putative link between gene expression of brain regions and their neural connectivity patterns is a fundamental question in neuroscience. Here this question is addressed in the first large scale study of a prototypical mammalian rodent brain, using a combination of rat brain regional connectivity data with gene expression of the mouse brain. Remarkably, even though this study uses data from two different rodent species (due to the data limitations), we still find that the connectivity of the majority of brain regions is highly predictable from their gene expression levels–the outgoing (incoming) connectivity is successfully predicted for 73% (56%) of brain regions, with an overall fairly marked accuracy level of 0.79 (0.83). Many genes are found to play a part in predicting both the incoming and outgoing connectivity (241 out of the 500 top selected genes, p-value<1e-5). Reassuringly, the genes previously known from the literature to be involved in axon guidance do carry significant information about regional brain connectivity. Surveying the genes known to be associated with the pathogenesis of several brain disorders, we find that those associated with schizophrenia, autism and attention deficit disorder are the most highly enriched in the connectivity-related genes identified here. Finally, we find that the profile of functional annotation groups that are associated with regional connectivity in the rodent is significantly correlated with the annotation profile of genes previously found to determine neural connectivity in C. elegans (Pearson correlation of 0.24, p<1e-6 for the outgoing connections and 0.27, p<1e-5 for the incoming). Overall, the association between connectivity and gene expression in a specific extant rodent species' brain is likely to be even stronger than found here, given the limitations of current data

Using Network Component Analysis to Dissect Regulatory Networks Mediated by Transcription Factors in Yeast

Understanding the relationship between genetic variation and gene expression is a central question in genetics. With the availability of data from high-throughput technologies such as ChIP-Chip, expression, and genotyping arrays, we can begin to not only identify associations but to understand how genetic variations perturb the underlying transcription regulatory networks to induce differential gene expression. In this study, we describe a simple model of transcription regulation where the expression of a gene is completely characterized by two properties: the concentrations and promoter affinities of active transcription factors. We devise a method that extends Network Component Analysis (NCA) to determine how genetic variations in the form of single nucleotide polymorphisms (SNPs) perturb these two properties. Applying our method to a segregating population of Saccharomyces cerevisiae, we found statistically significant examples of trans-acting SNPs located in regulatory hotspots that perturb transcription factor concentrations and affinities for target promoters to cause global differential expression and cis-acting genetic variations that perturb the promoter affinities of transcription factors on a single gene to cause local differential expression. Although many genetic variations linked to gene expressions have been identified, it is not clear how they perturb the underlying regulatory networks that govern gene expression. Our work begins to fill this void by showing that many genetic variations affect the concentrations of active transcription factors in a cell and their affinities for target promoters. Understanding the effects of these perturbations can help us to paint a more complete picture of the complex landscape of transcription regulation. The software package implementing the algorithms discussed in this work is available as a MATLAB package upon request

Gene expression in the rat brain: High similarity but unique differences between frontomedial-, temporal- and occipital cortex

<p>Abstract</p> <p>Background</p> <p>The six-layered neocortex of the mammalian brain may appear largely homologous, but is in reality a modular structure of anatomically and functionally distinct areas. However, global gene expression seems to be almost identical across the cerebral cortex and only a few genes have so far been reported to show regional enrichment in specific cortical areas.</p> <p>Results</p> <p>In the present study on adult rat brain, we have corroborated the strikingly similar gene expression among cortical areas. However, differential expression analysis has allowed for the identification of 30, 24 and 11 genes enriched in frontomedial -, temporal- or occipital cortex, respectively. A large proportion of these 65 genes appear to be involved in signal transduction, including the ion channel <it>Fxyd6</it>, the neuropeptide <it>Grp </it>and the nuclear receptor <it>Rorb</it>. We also find that the majority of these genes display increased expression levels around birth and show distinct preferences for certain cortical layers and cell types in rodents.</p> <p>Conclusions</p> <p>Since specific patterns of expression often are linked to equally specialised biological functions, we propose that these cortex sub-region enriched genes are important for proper functioning of the cortical regions in question.</p

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

Relationships between Gene Expression and Brain Wiring in the Adult Rodent Brain

We studied the global relationship between gene expression and neuroanatomical connectivity in the adult rodent brain. We utilized a large data set of the rat brain “connectome” from the Brain Architecture Management System (942 brain regions and over 5000 connections) and used statistical approaches to relate the data to the gene expression signatures of 17,530 genes in 142 anatomical regions from the Allen Brain Atlas. Our analysis shows that adult gene expression signatures have a statistically significant relationship to connectivity. In particular, brain regions that have similar expression profiles tend to have similar connectivity profiles, and this effect is not entirely attributable to spatial correlations. In addition, brain regions which are connected have more similar expression patterns. Using a simple optimization approach, we identified a set of genes most correlated with neuroanatomical connectivity, and find that this set is enriched for genes involved in neuronal development and axon guidance. A number of the genes have been implicated in neurodevelopmental disorders such as autistic spectrum disorder. Our results have the potential to shed light on the role of gene expression patterns in influencing neuronal activity and connectivity, with potential applications to our understanding of brain disorders. Supplementary data are available at http://www.chibi.ubc.ca/ABAMS

Genome-Wide Assessments Reveal Extremely High Levels of Polymorphism of Two Active Families of Mouse Endogenous Retroviral Elements

Endogenous retroviral elements (ERVs) in mice are significant genomic mutagens, causing ∼10% of all reported spontaneous germ line mutations in laboratory strains. The majority of these mutations are due to insertions of two high copy ERV families, the IAP and ETn/MusD elements. This significant level of ongoing retrotranspositional activity suggests that inbred mice are highly variable in content of these two ERV groups. However, no comprehensive genome-wide studies have been performed to assess their level of polymorphism. Here we compared three test strains, for which sufficient genomic sequence is available, to each other and to the reference C57BL/6J genome and detected very high levels of insertional polymorphism for both ERV families, with an estimated false discovery rate of only 0.4%. Specifically, we found that at least 60% of IAP and 25% of ETn/MusD elements detected in any strain are absent in one or more of the other three strains. The polymorphic nature of a set of 40 ETn/MusD elements found within gene introns was confirmed using genomic PCR on DNA from a panel of mouse strains. For some cases, we detected gene-splicing abnormalities involving the ERV and obtained additional evidence for decreased gene expression in strains carrying the insertion. In total, we identified nearly 700 polymorphic IAP or ETn/MusD ERVs or solitary LTRs that reside in gene introns, providing potential candidates that may contribute to gene expression differences among strains. These extreme levels of polymorphism suggest that ERV insertions play a significant role in genetic drift of mouse lines

Genome-Wide Assessments Reveal Extremely High Levels of Polymorphism of Two Active Families of Mouse Endogenous Retroviral Elements

Endogenous retroviral elements (ERVs) in mice are significant genomic mutagens, causing ∼10% of all reported spontaneous germ line mutations in laboratory strains. The majority of these mutations are due to insertions of two high copy ERV families, the IAP and ETn/MusD elements. This significant level of ongoing retrotranspositional activity suggests that inbred mice are highly variable in content of these two ERV groups. However, no comprehensive genome-wide studies have been performed to assess their level of polymorphism. Here we compared three test strains, for which sufficient genomic sequence is available, to each other and to the reference C57BL/6J genome and detected very high levels of insertional polymorphism for both ERV families, with an estimated false discovery rate of only 0.4%. Specifically, we found that at least 60% of IAP and 25% of ETn/MusD elements detected in any strain are absent in one or more of the other three strains. The polymorphic nature of a set of 40 ETn/MusD elements found within gene introns was confirmed using genomic PCR on DNA from a panel of mouse strains. For some cases, we detected gene-splicing abnormalities involving the ERV and obtained additional evidence for decreased gene expression in strains carrying the insertion. In total, we identified nearly 700 polymorphic IAP or ETn/MusD ERVs or solitary LTRs that reside in gene introns, providing potential candidates that may contribute to gene expression differences among strains. These extreme levels of polymorphism suggest that ERV insertions play a significant role in genetic drift of mouse lines