Genome Biology

Journal: Genome Biology

Abbreviation

Genome Biol

Publisher

BioMed Central

ISSN

1474-760X

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Publications1 - 10 of 80

A depauperate immune repertoire precedes evolution of sociality in bees
Barribeau, Seth M.; Sadd, Ben M.; Du Plessis, Louis; et al. (2015)
Genome Biology
Background Sociality has many rewards, but can also be dangerous, as high population density and low genetic diversity, common in social insects, is ideal for parasite transmission. Despite this risk, honeybees and other sequenced social insects have far fewer canonical immune genes relative to solitary insects. Social protection from infection, including behavioral responses, may explain this depauperate immune repertoire. Here, based on full genome sequences, we describe the immune repertoire of two ecologically and commercially important bumblebee species that diverged approximately 18 million years ago, the North American Bombus impatiens and European Bombus terrestris. Results We find that the immune systems of these bumblebees, two species of honeybee, and a solitary leafcutting bee, are strikingly similar. Transcriptional assays confirm the expression of many of these genes in an immunological context and more strongly in young queens than males, affirming Bateman’s principle of greater investment in female immunity. We find evidence of positive selection in genes encoding antiviral responses, components of the Toll and JAK/STAT pathways, and serine protease inhibitors in both social and solitary bees. Finally, we detect many genes across pathways that differ in selection between bumblebees and honeybees, or between the social and solitary clades. Conclusions The similarity in immune complement across a gradient of sociality suggests that a reduced immune repertoire predates the evolution of sociality in bees. The differences in selection on immune genes likely reflect divergent pressures exerted by parasites across social contexts.
gespeR: a statistical model for deconvoluting off-target-confounded RNA interference screens
Schmich, Fabian; Szczurek, Ewa; Kreibich, Saskia; et al. (2015)
Genome Biology
Small interfering RNAs (siRNAs) exhibit strong off-target effects, which confound the gene-level interpretation of RNA interference screens and thus limit their utility for functional genomics studies. Here, we present gespeR, a statistical model for reconstructing individual, gene-specific phenotypes. Using 115,878 siRNAs, single and pooled, from three companies in three pathogen infection screens, we demonstrate that deconvolution of image-based phenotypes substantially improves the reproducibility between independent siRNA sets targeting the same genes. Genes selected and prioritized by gespeR are validated and shown to constitute biologically relevant components of pathogen entry mechanisms and TGF-β signaling. gespeR is available as a Bioconductor R-package.
Analysis of the Saccharomyces cerevisiae proteome with PeptideAtlas
King, Nichole L.; Deutsch, Eric W.; Ranish, Jeffrey A.; et al. (2006)
Genome Biology
We present the Saccharomyces cerevisiae PeptideAtlas composed from 47 diverse experiments and 4.9 million tandem mass spectra. The observed peptides align to 61% of Saccharomyces Genome Database (SGD) open reading frames (ORFs), 49% of the uncharacterized SGD ORFs, 54% of S. cerevisiae ORFs with a Gene Ontology annotation of 'molecular function unknown', and 76% of ORFs with Gene names. We highlight the use of this resource for data mining, construction of high quality lists for targeted proteomics, validation of proteins, and software development.
Arabidopsis replacement histone variant H3.3 occupies promoters of regulated genes
Shu, Huan; Nakamura, Miyuki; Siretskiy, Alexey; et al. (2014)
Genome Biology
Background Histone variants establish structural and functional diversity of chromatin by affecting nucleosome stability and histone-protein interactions. H3.3 is an H3 histone variant that is incorporated into chromatin outside of S-phase in various eukaryotes. In animals, H3.3 is associated with active transcription and possibly maintenance of transcriptional memory. Plant H3 variants, which evolved independently of their animal counterparts, are much less well understood. Results We profile the H3.3 distribution in Arabidopsis at mono-nucleosomal resolution using native chromatin immunoprecipitation. This results in the precise mapping of H3.3-containing nucleosomes, which are not only enriched in gene bodies as previously reported, but also at a subset of promoter regions and downstream of the 3′ ends of active genes. While H3.3 presence within transcribed regions is strongly associated with transcriptional activity, H3.3 at promoters is often independent of transcription. In particular, promoters with GA motifs carry H3.3 regardless of the gene expression levels. H3.3 on promoters of inactive genes is associated with H3K27me3 at gene bodies. In addition, H3.3-enriched plant promoters often contain RNA Pol II considerably upstream of the transcriptional start site. H3.3 and RNA Pol II are found on active as well as on inactive promoters and are enriched at strongly regulated genes. Conclusions In animals and plants, H3.3 organizes chromatin in transcribed regions and in promoters. The results suggest a function of H3.3 in transcriptional regulation and support a model that a single ancestral H3 evolved into H3 variants with similar sub-functionalization patterns in plants and animals.
CSS: cluster similarity spectrum integration of single-cell genomics data
He, Zhisong; Brazovskaja, Agnieska; Ebert, Sebastian; et al. (2020)
Genome Biology
It is a major challenge to integrate single-cell sequencing data across experiments, conditions, batches, time points, and other technical considerations. New computational methods are required that can integrate samples while simultaneously preserving biological information. Here, we propose an unsupervised reference-free data representation, cluster similarity spectrum (CSS), where each cell is represented by its similarities to clusters independently identified across samples. We show that CSS can be used to assess cellular heterogeneity and enable reconstruction of differentiation trajectories from cerebral organoid and other single-cell transcriptomic data, and to integrate data across experimental conditions and human individuals.
Systematic screening of polyphosphate (poly P) levels in yeast mutant cells reveals strong interdependence with primary metabolism
Freimoser, Florian M.; Hürlimann, Hans Caspar; Jakob, Claude A.; et al. (2006)
Genome Biology
Background Inorganic polyphosphate (poly P) occurs universally in all organisms from bacteria to man. It functions, for example, as a phosphate and energy store, and is involved in the activation and regulation of proteins. Despite its ubiquitous occurrence and important functions, it is unclear how poly P is synthesized or how poly P metabolism is regulated in higher eukaryotes. This work describes a systematic analysis of poly P levels in yeast knockout strains mutated in almost every non-essential gene. Results After three consecutive screens, 255 genes (almost 4% of the yeast genome) were found to be involved in the maintenance of normal poly P content. Many of these genes encoded proteins functioning in the cytoplasm, the vacuole or in transport and transcription. Besides reduced poly P content, many strains also exhibited reduced total phosphate content, showed altered ATP and glycogen levels and were disturbed in the secretion of acid phosphatase. Conclusion Cellular energy and phosphate homeostasis is suggested to result from the equilibrium between poly P, ATP and free phosphate within the cell. Poly P serves as a buffer for both ATP and free phosphate levels and is, therefore, the least essential and consequently most variable component in this network. However, strains with reduced poly P levels are not only affected in their ATP and phosphate content, but also in other components that depend on ATP or free phosphate content, such as glycogen or secreted phosphatase activity.
Eleven grand challenges in single-cell data science
Lähnemann, David; Beerenwinkel, Niko; Jahn, Katharina; et al. (2020)
Genome Biology
The recent boom in microfluidics and combinatorial indexing strategies, combined with low sequencing costs, has empowered single-cell sequencing technology. Thousands—or even millions—of cells analyzed in a single experiment amount to a data revolution in single-cell biology and pose unique data science problems. Here, we outline eleven challenges that will be central to bringing this emerging field of single-cell data science forward. For each challenge, we highlight motivating research questions, review prior work, and formulate open problems. This compendium is for established researchers, newcomers, and students alike, highlighting interesting and rewarding problems for the coming years.
Bioconductor: open software development for computational biology and bioinformatics
Gentleman, Robert C.; Carey, Vincent J.; Bates, Douglas M.; et al. (2004)
Genome Biology
The Bioconductor project is an initiative for the collaborative creation of extensible software for computational biology and bioinformatics. The goals of the project include: fostering collaborative development and widespread use of innovative software, reducing barriers to entry into interdisciplinary scientific research, and promoting the achievement of remote reproducibility of research results. We describe details of our aims and methods, identify current challenges, compare Bioconductor to other open bioinformatics projects, and provide working examples.
The global and promoter-centric 3D genome organization temporally resolved during a circadian cycle
Furlan-Magaril, Mayra; Ando-Kuri, Masami; Arzate-Mejía, Rodrigo G.; et al. (2021)
Genome Biology
Background: Circadian gene expression is essential for organisms to adjust their physiology and anticipate daily changes in the environment. The molecular mechanisms controlling circadian gene transcription are still under investigation. In particular, how chromatin conformation at different genomic scales and regulatory elements impact rhythmic gene expression has been poorly characterized. Results: Here we measure changes in the spatial chromatin conformation in mouse liver using genome-wide and promoter-capture Hi-C alongside daily oscillations in gene transcription. We find topologically associating domains harboring circadian genes that switch assignments between the transcriptionally active and inactive compartment at different hours of the day, while their boundaries stably maintain their structure over time. To study chromatin contacts of promoters at high resolution over time, we apply promoter capture Hi-C. We find circadian gene promoters displayed a maximal number of chromatin contacts at the time of their peak transcriptional output. Furthermore, circadian genes, as well as contacted and transcribed regulatory elements, reach maximal expression at the same timepoints. Anchor sites of circadian gene promoter loops are enriched in DNA binding sites for liver nuclear receptors and other transcription factors, some exclusively present in either rhythmic or stable contacts. Finally, by comparing the interaction profiles between core clock and output circadian genes, we show that core clock interactomes are more dynamic compared to output circadian genes. Conclusion: Our results identify chromatin conformation dynamics at different scales that parallel oscillatory gene expression and characterize the repertoire of regulatory elements that control circadian gene transcription through rhythmic or stable chromatin configurations.
The shaky foundations of simulating single-cell RNA sequencing data
Crowell, Helena L.; Morillo Leonardo, Sarah X.; Soneson, Charlotte; et al. (2023)
Genome Biology
Background With the emergence of hundreds of single-cell RNA-sequencing (scRNA-seq) datasets, the number of computational tools to analyze aspects of the generated data has grown rapidly. As a result, there is a recurring need to demonstrate whether newly developed methods are truly performant—on their own as well as in comparison to existing tools. Benchmark studies aim to consolidate the space of available methods for a given task and often use simulated data that provide a ground truth for evaluations, thus demanding a high quality standard results credible and transferable to real data. Results Here, we evaluated methods for synthetic scRNA-seq data generation in their ability to mimic experimental data. Besides comparing gene- and cell-level quality control summaries in both one- and two-dimensional settings, we further quantified these at the batch- and cluster-level. Secondly, we investigate the effect of simulators on clustering and batch correction method comparisons, and, thirdly, which and to what extent quality control summaries can capture reference-simulation similarity. Conclusions Our results suggest that most simulators are unable to accommodate complex designs without introducing artificial effects, they yield over-optimistic performance of integration and potentially unreliable ranking of clustering methods, and it is generally unknown which summaries are important to ensure effective simulation-based method comparisons.

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Journal: Genome Biology

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