ScerTF: a comprehensive database of benchmarked position weight matrices for Saccharomyces species

Saccharomyces cerevisiae is a primary model for studies of transcriptional control, and the specificities of most yeast transcription factors (TFs) have been determined by multiple methods. However, it is unclear which position weight matrices (PWMs) are most useful; for the roughly 200 TFs in yeast, there are over 1200 PWMs in the literature. To address this issue, we created ScerTF, a comprehensive database of 1226 motifs from 11 different sources. We identified a single matrix for each TF that best predicts in vivo data by benchmarking matrices against chromatin immunoprecipitation and TF deletion experiments. We also used in vivo data to optimize thresholds for identifying regulatory sites with each matrix. To correct for biases from different methods, we developed a strategy to combine matrices. These aligned matrices outperform the best available matrix for several TFs. We used the matrices to predict co-occurring regulatory elements in the genome and identified many known TF combinations. In addition, we predict new combinations and provide evidence of combinatorial regulation from gene expression data. The database is available through a web interface at http://ural.wustl.edu/ScerTF. The site allows users to search the database with a regulatory site or matrix to identify the TFs most likely to bind the input sequence

Sounding Situated Knowledges - Echo in Archaeoacoustics

This article proposes that feminist epistemologies via Donna Haraway's “Situated Knowledges” can be productively brought to bear upon theories of sonic knowledge production, as “sounding situated knowledges.” Sounding situated knowledges re-reads debates around the “nature of sound” with a Harawayan notion of the “natureculture of sound.” This aims to disrupt a traditional subject-object relation which I argue has perpetuated a pervasive “sonic naturalism” in sound studies. The emerging field of archaeoacoustics (acoustic archaeology), which examines the role of sound in human behaviour in archaeology, is theorized as an opening with potentially profound consequences for sonic knowledge production which are not currently being realized. The echo is conceived as a material-semiotic articulation, which akin to Haraway's infamous cyborg, serves as a feminist figuration which enables this renegotiation. Archaeoacoustics research, read following Haraway both reflectively and diffractively, is understood as a critical juncture for sound studies which exposes the necessity of both embodiedness and situatedness for sonic knowledge production. Given the potential opened up by archaeoacoustics through the figure of echo, a critical renegotiation of the subject-object relation in sound studies is suggested as central in further developing theories of sonic knowledge production

A Reply to My Critics: The Critical Spirit of Bourdieusian Language

Drawing on my article “Bourdieusian reflections on language: Unavoidable conditions of the real speech situation”, this paper provides a detailed response to the above commentaries by Lisa Adkins, Bridget Fowler, Michael Grenfell, David Inglis, Hans-Herbert Kögler, Steph Lawler, William Outhwaite, Derek Robbins and Bryan S. Turner. The main purpose of this “Reply to my critics” is to reflect upon the most important issues raised by these commentators and thereby contribute to a more nuanced understanding of key questions arising from Bourdieu’s analysis of language

Investigation of Combinatorial Gene Regulation in Saccharomyces Species

Transcriptional control of gene expression is a result of complex interactions between the cis-regulatory elements (CRE) at gene promoters. To understand the regulatory logic of a cell, we need to identify the CRE combinations that regulate gene expression. This dissertation describes a sensitive computational method to identify phylogenetically conserved CRE combinations for any species of interest. In contrast to previous methods, I do not need to align genomes to identify these combinations. I applied the method in 7 sensu stricto and sensu lato Saccharomyces species. 80% of the predictions displayed some evidence of combinatorial transcriptional behavior in several existing datasets including 1) ChIP-chip data for co-localization of transcription factors, 2) gene expression data for co-expression of predicted regulatory targets, and 3) gene ontology databases for common pathway membership of predicted regulatory targets. To establish definitive evidence that these CRE interactions influence TF occupancy, I performed ChIP-Seq experiments on transcription factors in a wild-type strain and strains in which a predicted cofactor was deleted. These experiments showed that TF occupancy at the promoters of the CRE combination target genes depends on the predicted cofactor while occupancy of other promoters is independent of the predicted cofactor. In addition to identifying phylogenetically conserved CRE combinations, the method can annotate potential regulatory differences between species. Previous studies of cis-regulatory rewiring between species assume that CREs act independently and have found that promoter divergence does not necessarily explain expression divergence. By analyzing the S. cerevisiae and S. bayanus genomes, I identified differences in combinatorial cis-regulation between the species and showed that the predicted changes in gene regulation explain several of the species-specific differences seen in gene expression datasets. In some instances, the same CRE combinations appear to regulate genes involved in distinct biological processes in the two different species. The results of this research demonstrate 1) that combinatorial cis-regulation can be inferred from similarities between species and 2) that combinatorial cis-regulation can explain differences between species

Combinatorial Cis-regulation in Saccharomyces Species

Transcriptional control of gene expression requires interactions between the cis-regulatory elements (CREs) controlling gene promoters. We developed a sensitive computational method to identify CRE combinations with conserved spacing that does not require genome alignments. When applied to seven sensu stricto and sensu lato Saccharomyces species, 80% of the predicted interactions displayed some evidence of combinatorial transcriptional behavior in several existing datasets including: (1) chromatin immunoprecipitation data for colocalization of transcription factors, (2) gene expression data for coexpression of predicted regulatory targets, and (3) gene ontology databases for common pathway membership of predicted regulatory targets. We tested several predicted CRE interactions with chromatin immunoprecipitation experiments in a wild-type strain and strains in which a predicted cofactor was deleted. Our experiments confirmed that transcription factor (TF) occupancy at the promoters of the CRE combination target genes depends on the predicted cofactor while occupancy of other promoters is independent of the predicted cofactor. Our method has the additional advantage of identifying regulatory differences between species. By analyzing the S. cerevisiae and S. bayanus genomes, we identified differences in combinatorial cis-regulation between the species and showed that the predicted changes in gene regulation explain several of the species-specific differences seen in gene expression datasets. In some instances, the same CRE combinations appear to regulate genes involved in distinct biological processes in the two different species. The results of this research demonstrate that (1) combinatorial cis-regulation can be inferred by multi-genome analysis and (2) combinatorial cis-regulation can explain differences in gene expression between species

Metabolomics reveals the origins of antimicrobial plant resins collected by honey bees

The deposition of antimicrobial plant resins in honey bee, Apis mellifera, nests has important physiological benefits. Resin foraging is difficult to approach experimentally because resin composition is highly variable among and between plant families, the environmental and plant-genotypic effects on resins are unknown, and resin foragers are relatively rare and often forage in unobservable tree canopies. Subsequently, little is known about the botanical origins of resins in many regions or the benefits of specific resins to bees. We used metabolomic methods as a type of environmental forensics to track individual resin forager behavior through comparisons of global resin metabolite patterns. The resin from the corbiculae of a single bee was sufficient to identify that resin’s botanical source without prior knowledge of resin composition. Bees from our apiary discriminately foraged for resin from eastern cottonwood (Populus deltoides), and balsam poplar (P. balsamifera) among many available, even closely related, resinous plants. Cottonwood and balsam poplar resin composition did not show significant seasonal or regional changes in composition. Metabolomic analysis of resin from 6 North American Populus spp. and 5 hybrids revealed peaks characteristic to taxonomic nodes within Populus, while antimicrobial analysis revealed that resin from different species varied in inhibition of the bee bacterial pathogen, Paenibacillus larvae. We conclude that honey bees make discrete choices among many resinous plant species, even among closely related species. Bees also maintained fidelity to a single source during a foraging trip. Furthermore, the differential inhibition of P. larvae by Populus spp., thought to be preferential for resin collection in temperate regions, suggests that resins from closely related plant species many have different benefits to bees

Compositional differences in <i>Populus spp.</i> resin.

<p>PCA scores plot of resin ‘fingerprints’ from 11 different <i>Populus spp.</i> and hybrids grown under greenhouse conditions. Pure species are indicated by closed shapes, while hybrids are indicated by open shapes. 49.11% of the total variation in the data set is shown. d x m  =  <i>P. deltoides x maximowiczii</i> (N = 8), d x n = <i>P. deltoides x nigra</i> (N = 4), d x t = <i>P. deltoides x trichocapra</i> (N = 6), t x d = <i>P. trichocarpa x deltoides</i> (N = 5), (t x d) x d = <i>P. (trichocarpa x deltoides) x deltoides</i> (N = 14). N = 6 for <i>P. deltoides</i> and <i>P. nigra</i>, N = 12 for <i>P. fremontii</i>, N = 14 for <i>P. trichocarpa</i>, N = 18 for <i>P. angustifolia</i>, N = 5 for <i>P. balsamifera</i>.</p