Molecular organization of neuronal cell types and neuromodulatory systems in the zebrafish telencephalon.

The function of neuronal networks is determined not only by synaptic connectivity but also by neuromodulatory systems that broadcast information via distributed connections and volume transmission. To understand the molecular constraints that organize neuromodulatory signaling in the telencephalon of adult zebrafish, we used transcriptomics and additional approaches to delineate cell types, to determine their phylogenetic conservation, and to map the expression of marker genes at high granularity. The combinatorial expression of GPCRs and cell-type markers indicates that all neuronal cell types are subject to modulation by multiple monoaminergic systems and distinct combinations of neuropeptides. Individual cell types were associated with multiple (typically >30) neuromodulatory signaling networks but expressed only a few diagnostic GPCRs at high levels, suggesting that different neuromodulatory systems act in combination, albeit with unequal weights. These results provide a detailed map of cell types and brain areas in the zebrafish telencephalon, identify core components of neuromodulatory networks, highlight the cell-type specificity of neuropeptides and GPCRs, and begin to decipher the logic of combinatorial neuromodulation.

Galaxy Training: A powerful framework for teaching!

There is an ongoing explosion of scientific datasets being generated, brought on by recent technological advances in many areas of the natural sciences. As a result, the life sciences have become increasingly computational in nature, and bioinformatics has taken on a central role in research studies. However, basic computational skills, data analysis, and stewardship are still rarely taught in life science educational programs, resulting in a skills gap in many of the researchers tasked with analysing these big datasets. In order to address this skills gap and empower researchers to perform their own data analyses, the Galaxy Training Network (GTN) has previously developed the Galaxy Training Platform (https://training.galaxyproject.org), an open access, community-driven framework for the collection of FAIR (Findable, Accessible, Interoperable, Reusable) training materials for data analysis utilizing the user-friendly Galaxy framework as its primary data analysis platform. Since its inception, this training platform has thrived, with the number of tutorials and contributors growing rapidly, and the range of topics extending beyond life sciences to include topics such as climatology, cheminformatics, and machine learning. While initially aimed at supporting researchers directly, the GTN framework has proven to be an invaluable resource for educators as well. We have focused our efforts in recent years on adding increased support for this growing community of instructors. New features have been added to facilitate the use of the materials in a classroom setting, simplifying the contribution flow for new materials, and have added a set of train-the-trainer lessons. Here, we present the latest developments in the GTN project, aimed at facilitating the use of the Galaxy Training materials by educators, and its usage in different learning environments.

Expanding the Galaxy's reference data.

Summary: Properly and effectively managing reference datasets is an important task for many bioinformatics analyses. Refgenie is a reference asset management system that allows users to easily organize, retrieve and share such datasets. Here, we describe the integration of refgenie into the Galaxy platform. Server administrators are able to configure Galaxy to make use of reference datasets made available on a refgenie instance. In addition, a Galaxy Data Manager tool has been developed to provide a graphical interface to refgenie's remote reference retrieval functionality. A large collection of reference datasets has also been made available using the CVMFS (CernVM File System) repository from GalaxyProject.org, with mirrors across the USA, Canada, Europe and Australia, enabling easy use outside of Galaxy. Availability and implementation: The ability of Galaxy to use refgenie assets was added to the core Galaxy framework in version 22.01, which is available from https://github.com/galaxyproject/galaxy under the Academic Free License version 3.0. The refgenie Data Manager tool can be installed via the Galaxy ToolShed, with source code managed at https://github.com/BlankenbergLab/galaxy-tools-blankenberg/tree/main/data_managers/data_manager_refgenie_pull and released using an MIT license. Access to existing data is also available through CVMFS, with instructions at https://galaxyproject.org/admin/reference-data-repo/. No new data were generated or analyzed in support of this research.

A single-cell RNA-sequencing training and analysis suite using the Galaxy framework.

BACKGROUND: The vast ecosystem of single-cell RNA-sequencing tools has until recently been plagued by an excess of diverging analysis strategies, inconsistent file formats, and compatibility issues between different software suites. The uptake of 10x Genomics datasets has begun to calm this diversity, and the bioinformatics community leans once more towards the large computing requirements and the statistically driven methods needed to process and understand these ever-growing datasets. RESULTS: Here we outline several Galaxy workflows and learning resources for single-cell RNA-sequencing, with the aim of providing a comprehensive analysis environment paired with a thorough user learning experience that bridges the knowledge gap between the computational methods and the underlying cell biology. The Galaxy reproducible bioinformatics framework provides tools, workflows, and trainings that not only enable users to perform 1-click 10x preprocessing but also empower them to demultiplex raw sequencing from custom tagged and full-length sequencing protocols. The downstream analysis supports a range of high-quality interoperable suites separated into common stages of analysis: inspection, filtering, normalization, confounder removal, and clustering. The teaching resources cover concepts from computer science to cell biology. Access to all resources is provided at the singlecell.usegalaxy.eu portal. CONCLUSIONS: The reproducible and training-oriented Galaxy framework provides a sustainable high-performance computing environment for users to run flexible analyses on both 10x and alternative platforms. The tutorials from the Galaxy Training Network along with the frequent training workshops hosted by the Galaxy community provide a means for users to learn, publish, and teach single-cell RNA-sequencing analysis.

Dynamics of uS19 C-Terminal Tail during the Translation Elongation Cycle in Human Ribosomes.

Ribosomes undergo multiple conformational transitions during translation elongation. Here, we report the high-resolution cryoelectron microscopy (cryo-EM) structure of the human 80S ribosome in the post-decoding pre-translocation state (classical-PRE) at 3.3-Å resolution along with the rotated (hybrid-PRE) and the post-translocation states (POST). The classical-PRE state ribosome structure reveals a previously unobserved interaction between the C-terminal region of the conserved ribosomal protein uS19 and the A- and P-site tRNAs and the mRNA in the decoding site. In addition to changes in the inter-subunit bridges, analysis of different ribosomal conformations reveals the dynamic nature of this domain and suggests a role in tRNA accommodation and translocation during elongation. Furthermore, we show that disease-associated mutations in uS19 result in increased frameshifting. Together, this structure-function analysis provides mechanistic insights into the role of the uS19 C-terminal tail in the context of mammalian ribosomes.

Community-Driven Data Analysis Training for Biology.

The primary problem with the explosion of biomedical datasets is not the data, not computational resources, and not the required storage space, but the general lack of trained and skilled researchers to manipulate and analyze these data. Eliminating this problem requires development of comprehensive educational resources. Here we present a community-driven framework that enables modern, interactive teaching of data analytics in life sciences and facilitates the development of training materials. The key feature of our system is that it is not a static but a continuously improved collection of tutorials. By coupling tutorials with a web-based analysis framework, biomedical researchers can learn by performing computation themselves through a web browser without the need to install software or search for example datasets. Our ultimate goal is to expand the breadth of training materials to include fundamental statistical and data science topics and to precipitate a complete re-engineering of undergraduate and graduate curricula in life sciences. This project is accessible at https://training.galaxyproject.org.

SYK inhibition blocks proliferation and migration of glioma cells and modifies the tumor microenvironment.

Background: Glioblastoma (GBM) is one of the most aggressive human brain tumors, with a median survival of 15-18 months. There is a desperate need to find novel therapeutic targets. Various receptor protein kinases have been identified as potential targets; however, response rates in clinical studies have been somewhat disappointing. Targeting the spleen tyrosine kinase (SYK), which acts downstream of a range of oncogenic receptors, may therefore show more promising results. Methods: Kinase expression of brain tumor samples including GBM and low-grade tumors were compared with normal brain and normal human astrocytes by microarray analysis. Furthermore, SYK, LYN, SLP76, and PLCG2 protein expressions were analyzed by immunohistochemistry, western blot, and immunofluorescence of additional GBM patient samples, murine glioma samples, and cell lines. SYK was then blocked chemically and genetically in vitro and in vivo in 2 different mouse models. Multiphoton intravital imaging and multicolor flow cytometry were performed in a syngeneic immunocompetent C57BL/6J mouse GL261 glioma model to study the effect of these inhibitors on the tumor microenvironment. Results: SYK, LYN, SLP76, and PLCG2 were found expressed in human and murine glioma samples and cell lines. SYK inhibition blocked proliferation, migration, and colony formation. Flow cytometric and multiphoton imaging imply that targeting SYK in vivo attenuated GBM tumor growth and invasiveness and reduced B and CD11b+ cell mobility and infiltration. Conclusions: Our data suggest that gliomas express a SYK signaling network important in glioma progression, inhibition of which results in reduced invasion with slower tumor progression.

Dynamic expression of chromatin modifiers during developmental transitions in mouse preimplantation embryos.

During mouse preimplantation development, major changes in cell fate are accompanied by extensive alterations of gene expression programs. Embryos first transition from a maternal to zygotic program and subsequently specify the pluripotent and the trophectodermal cell lineages. These processes are regulated by key transcription factors, likely in cooperation with chromatin modifiers that control histone and DNA methylation. To characterize the spatiotemporal expression of chromatin modifiers in relation to developmental transitions, we performed gene expression profiling of 156 genes in individual oocytes and single blastomeres of developing mouse embryos until the blastocyst stage. More than half of the chromatin modifiers displayed either maternal or zygotic expression. We also detected lineage-specific expression of several modifiers, including Ezh1, Prdm14, Scmh1 and Tet1 underscoring possible roles in cell fate decisions. Members of the SET-domain containing SMYD family showed differential gene expression during preimplantation development. We further observed co-expression of genes with opposing biochemical activities, such as histone methyltransferases and demethylases, suggesting the existence of a dynamic chromatin steady-state during preimplantation development.

Confero: an integrated contrast data and gene set platform for computational analysis and biological interpretation of omics data.

BACKGROUND: High-throughput omics technologies such as microarrays and next-generation sequencing (NGS) have become indispensable tools in biological research. Computational analysis and biological interpretation of omics data can pose significant challenges due to a number of factors, in particular the systems integration required to fully exploit and compare data from different studies and/or technology platforms. In transcriptomics, the identification of differentially expressed genes when studying effect(s) or contrast(s) of interest constitutes the starting point for further downstream computational analysis (e.g. gene over-representation/enrichment analysis, reverse engineering) leading to mechanistic insights. Therefore, it is important to systematically store the full list of genes with their associated statistical analysis results (differential expression, t-statistics, p-value) corresponding to one or more effect(s) or contrast(s) of interest (shortly termed as " contrast data") in a comparable manner and extract gene sets in order to efficiently support downstream analyses and further leverage data on a long-term basis. Filling this gap would open new research perspectives for biologists to discover disease-related biomarkers and to support the understanding of molecular mechanisms underlying specific biological perturbation effects (e.g. disease, genetic, environmental, etc.). RESULTS: To address these challenges, we developed Confero, a contrast data and gene set platform for downstream analysis and biological interpretation of omics data. The Confero software platform provides storage of contrast data in a simple and standard format, data transformation to enable cross-study and platform data comparison, and automatic extraction and storage of gene sets to build new a priori knowledge which is leveraged by integrated and extensible downstream computational analysis tools. Gene Set Enrichment Analysis (GSEA) and Over-Representation Analysis (ORA) are currently integrated as an analysis module as well as additional tools to support biological interpretation. Confero is a standalone system that also integrates with Galaxy, an open-source workflow management and data integration system. To illustrate Confero platform functionality we walk through major aspects of the Confero workflow and results using the Bioconductor estrogen package dataset. CONCLUSION: Confero provides a unique and flexible platform to support downstream computational analysis facilitating biological interpretation. The system has been designed in order to provide the researcher with a simple, innovative, and extensible solution to store and exploit analyzed data in a sustainable and reproducible manner thereby accelerating knowledge-driven research. Confero source code is freely available from http://sourceforge.net/projects/confero/.

Noncoding RNAs prevent spreading of a repressive histone mark.

Transcription of eukaryotic genomes is more widespread than was previously anticipated and results in the production of many non-protein-coding RNAs (ncRNAs) whose functional relevance is poorly understood. Here we demonstrate that ncRNAs can counteract the encroachment of heterochromatin into neighboring euchromatin. We have identified a long ncRNA (termed BORDERLINE) that prevents spreading of the HP1 protein Swi6 and histone H3 Lys9 methylation beyond the pericentromeric repeat region of Schizosaccharomyces pombe chromosome 1. BORDERLINE RNAs act in a sequence-independent but locus-dependent manner and are processed by Dicer into short RNAs referred to as brdrRNAs. In contrast to canonical centromeric short interfering RNAs, brdrRNAs are rarely loaded onto Argonaute. Our analyses reveal an unexpected regulatory activity of ncRNAs in demarcating an epigenetically distinct chromosomal domain that could also be operational in other eukaryotes.

RNAi keeps Atf1-bound stress response genes in check at nuclear pores.

RNAi pathways are prevalent throughout the eukaryotic kingdom and are well known to regulate gene expression on a post-transcriptional level in the cytoplasm. Less is known about possible functions of RNAi in the nucleus. In the fission yeast Schizosaccharomyces pombe, RNAi is crucial to establish and maintain centromeric heterochromatin and functions to repress genome activity by a chromatin silencing mechanism referred to as cotranscriptional gene silencing (CTGS). Mechanistic details and the physiological relevance of CTGS are unknown. Here we show that RNAi components interact with chromatin at nuclear pores to keep stress response genes in check. We demonstrate that RNAi-mediated CTGS represses stress-inducible genes by degrading mRNAs under noninduced conditions. Under chronic heat stress conditions, a Dicer thermoswitch deports Dicer to the cytoplasm, thereby disrupting CTGS and enabling expression of genes implicated in the acquisition of thermotolerance. Taken together, our work highlights a role for nuclear pores and the stress response transcription factor Atf1 in coordinating the interplay between the RNAi machinery and the S. pombe genome and uncovers a novel mode of RNAi regulation in response to an environmental cue.

An extended dsRBD with a novel zinc-binding motif mediates nuclear retention of fission yeast Dicer.

Dicer proteins function in RNA interference (RNAi) pathways by generating small RNAs (sRNAs). Here, we report the solution structure of the C-terminal domain of Schizosaccharomyces pombe Dicer (Dcr1). The structure reveals an unusual double-stranded RNA binding domain (dsRBD) fold embedding a novel zinc-binding motif that is conserved among dicers in yeast. Although the C-terminal domain of Dcr1 still binds nucleic acids, this property is dispensable for proper functioning of Dcr1. In contrast, disruption of zinc coordination renders Dcr1 mainly cytoplasmic and leads to remarkable changes in gene expression and loss of heterochromatin assembly. In summary, our results reveal novel insights into the mechanism of nuclear retention of Dcr1 and raise the possibility that this new class of dsRBDs might generally function in nucleocytoplasmic trafficking and not substrate binding. The C-terminal domain of Dcr1 constitutes a novel regulatory module that might represent a potential target for therapeutic intervention with fungal diseases.

Tenascin-C triggers fibrin accumulation by downregulation of tissue plasminogen activator.

We explored novel functions of tenascin-C by comparing mouse embryonic fibroblasts (MEFs) proficient or deficient in tenascin-C expression. Transcript profiling analysis identified tissue plasminogen activator (tPA) as the most consistently over-expressed gene in all tenascin-C deficient MEFs. This was confirmed by real-time PCR as well as by protein expression analysis. In agreement with these observations, tenascin-C deficient MEFs had an increased capacity to digest fibrin in situ. Consistently, tenascin-C expression in vivo was found to correlate with fibrin deposition in several diseases associated with tenascin-C overexpression such as fibrosis, asthma and cancer. In conclusion, the present study suggests a new role of tenascin-C as a regulator of the fibrinolytic system.

The Quaking family of RNA-binding proteins: coordinators of the cell cycle and differentiation.

Transcriptional and epigenetic control of gene expression is critical for cell fate specification, commitment and terminal differentiation during development. However, also regulatory RNAs and RNA-binding proteins have emerged as critical developmental regulators. They control various aspects of mRNA metabolism such as stability, translation, and localization, and similar to some transcriptional regulators, such as PAX5 and MYC, they can affect gene expression on a massive scale. Consistently, defects in many mRNA regulators have been implicated in a number of human disorders, including cancer. Quaking-related (QR) proteins are conserved RNA-binding proteins of the STAR (signal transduction and activation of RNA) family. QR proteins regulate expression of diverse mRNA targets by various mechanisms, play essential roles in a whole host of developmental decisions, and function as tumor suppressors. This review discusses several best-studied members of the QR family, their developmental functions, molecular mechanisms, representative mRNA targets, and their intriguing ability to coordinately control the cell cycle and a wide range of differentiation pathways.

The Pfam protein families database.

Pfam is a comprehensive collection of protein domains and families, represented as multiple sequence alignments and as profile hidden Markov models. The current release of Pfam (22.0) contains 9318 protein families. Pfam is now based not only on the UniProtKB sequence database, but also on NCBI GenPept and on sequences from selected metagenomics projects. Pfam is available on the web from the consortium members using a new, consistent and improved website design in the UK (http://pfam.sanger.ac.uk/), the USA (http://pfam.janelia.org/) and Sweden (http://pfam.sbc.su.se/), as well as from mirror sites in France (http://pfam.jouy.inra.fr/) and South Korea (http://pfam.ccbb.re.kr/).

The Ensembl Web site: mechanics of a genome browser.

The Ensembl Web site (http://www.ensembl.org/) is the principal user interface to the data of the Ensembl project, and currently serves >500,000 pages (approximately 2.5 million hits) per week, providing access to >80 GB (gigabyte) of data to users in more than 80 countries. Built atop an open-source platform comprising Apache/mod_perl and the MySQL relational database management system, it is modular, extensible, and freely available. It is being actively reused and extended in several different projects, and has been downloaded and installed in companies and academic institutions worldwide. Here, we describe some of the technical features of the site, with particular reference to its dynamic configuration that enables it to handle disparate data from multiple species.

An overview of Ensembl.

Ensembl (http://www.ensembl.org/) is a bioinformatics project to organize biological information around the sequences of large genomes. It is a comprehensive source of stable automatic annotation of individual genomes, and of the synteny and orthology relationships between them. It is also a framework for integration of any biological data that can be mapped onto features derived from the genomic sequence. Ensembl is available as an interactive Web site, a set of flat files, and as a complete, portable open source software system for handling genomes. All data are provided without restriction, and code is freely available. Ensembl's aims are to continue to "widen" this biological integration to include other model organisms relevant to understanding human biology as they become available; to "deepen" this integration to provide an ever more seamless linkage between equivalent components in different species; and to provide further classification of functional elements in the genome that have been previously elusive.