The RNA workbench: best practices for RNA and high-throughput sequencing bioinformatics in Galaxy

Genome-wide identification and expression analysis of heat shock protein gene family in cassava

Heat shock proteins are important molecular chaperones that are involved in plant growth and stress responses. However, members of the Hsp family have been poorly studied in cassava. In this study, 225 MeHsp genes were identified in the cassava genome, and their genetic structures exhibited relatively conserved features within each subfamily. The 225 MeHsp genes showed random chromosomal distribution, and at least 74 pairs of segmentally duplicated MeHsp genes. Eleven tandemly duplicated MeHsp genes were identified. Cis-element analysis revealed the importance of MeHsps in plant adaptations to the environment. The prediction of protein interactions suggested that MeHsp70-20 may play a critical regulatory role in the interactive network. Furthermore, the expression profiles of MeHsps in different tissues and cell subsets were analyzed using bulk transcriptomics and single-cell transcriptomic data. Several subfamily genes exhibited unique expression patterns in the transcriptome and were selected for detailed analysis of the single-cell transcriptome. Quantitative real-time polymerase chain reaction (qRT-PCR) revealed the expression patterns of these genes under temperature stress, further supporting the prediction of cis-acting elements. This study provides valuable information for understanding the functional characteristics of MeHsp genes and the evolutionary relationships between MeHsps.

Cytogenetics analysis as the central point of genetic testing in acute myeloid leukemia (AML): a laboratory perspective for clinical applications

Chromosomal abnormalities in acute myeloid leukemia (AML) have significantly contributed to scientific understanding of its molecular pathogenesis, which has aided in the development of therapeutic strategies and enhanced management of AML patients. The diagnosis, prognosis and treatment of AML have also rapidly transformed in recent years, improving initial response to treatment, remission rates, risk stratification and overall survival. Hundreds of rare chromosomal abnormalities in AML have been discovered thus far using chromosomal analysis and next-generation sequencing. As a result, the World Health Organization (WHO) has categorized AML into subgroups based on genetic, genomic and molecular characteristics, to complement the existing French-American classification which is solely based on morphology. In this review, we aim to highlight the most clinically relevant chromosomal aberrations in AML together with the technologies employed to detect these aberrations in laboratory settings.

A compilation of bioinformatic approaches to identify novel downstream targets for the detection and prophylaxis of cancer

The paradigm of cancer genomics has been radically changed by the development in next-generation sequencing (NGS) technologies making it possible to envisage individualized treatment based on tumor and stromal cells genome in a clinical setting within a short timeframe. The abundance of data has led to new avenues for studying coordinated alterations that impair biological processes, which in turn has increased the demand for bioinformatic tools for pathway analysis. While most of this work has been concentrated on optimizing certain algorithms to obtain quicker and more accurate results. Large volumes of these existing algorithm-based data are difficult for the biologists and clinicians to access, download and reanalyze them. In the present study, we have listed the bioinformatics algorithms and user-friendly graphical user interface (GUI) tools that enable code-independent analysis of big data without compromising the quality and time. We have also described the advantages and drawbacks of each of these platforms. Additionally, we emphasize the importance of creating new, more user-friendly solutions to provide better access to open data and talk about relevant problems like data sharing and patient privacy.

Laniakea@ReCaS: exploring the potential of customisable Galaxy on-demand instances as a cloud-based service

BACKGROUND

Improving the availability and usability of data and analytical tools is a critical precondition for further advancing modern biological and biomedical research. For instance, one of the many ramifications of the COVID-19 global pandemic has been to make even more evident the importance of having bioinformatics tools and data readily actionable by researchers through convenient access points and supported by adequate IT infrastructures. One of the most successful efforts in improving the availability and usability of bioinformatics tools and data is represented by the Galaxy workflow manager and its thriving community. In 2020 we introduced Laniakea, a software platform conceived to streamline the configuration and deployment of "on-demand" Galaxy instances over the cloud. By facilitating the set-up and configuration of Galaxy web servers, Laniakea provides researchers with a powerful and highly customisable platform for executing complex bioinformatics analyses. The system can be accessed through a dedicated and user-friendly web interface that allows the Galaxy web server's initial configuration and deployment.

RESULTS

"Laniakea@ReCaS", the first instance of a Laniakea-based service, is managed by ELIXIR-IT and was officially launched in February 2020, after about one year of development and testing that involved several users. Researchers can request access to Laniakea@ReCaS through an open-ended call for use-cases. Ten project proposals have been accepted since then, totalling 18 Galaxy on-demand virtual servers that employ ~ 100 CPUs, ~ 250 GB of RAM and ~ 5 TB of storage and serve several different communities and purposes. Herein, we present eight use cases demonstrating the versatility of the platform.

CONCLUSIONS

During this first year of activity, the Laniakea-based service emerged as a flexible platform that facilitated the rapid development of bioinformatics tools, the efficient delivery of training activities, and the provision of public bioinformatics services in different settings, including food safety and clinical research. Laniakea@ReCaS provides a proof of concept of how enabling access to appropriate, reliable IT resources and ready-to-use bioinformatics tools can considerably streamline researchers' work.

Distinct mechanisms mediate X chromosome dosage compensation in Anopheles and Drosophila

CRISPR knockout of msl-2 and epigenome analyses in Anopheles reveal that X chromosome dosage compensation in mosquitos and Drosophila is achieved by two different molecular mechanisms.

Sex chromosomes induce potentially deleterious gene expression imbalances that are frequently corrected by dosage compensation (DC). Three distinct molecular strategies to achieve DC have been previously described in nematodes, fruit flies, and mammals. Is this a consequence of distinct genomes, functional or ecological constraints, or random initial commitment to an evolutionary trajectory? Here, we study DC in the malaria mosquito Anopheles gambiae. The Anopheles and Drosophila X chromosomes evolved independently but share a high degree of homology. We find that Anopheles achieves DC by a mechanism distinct from the Drosophila MSL complex–histone H4 lysine 16 acetylation pathway. CRISPR knockout of Anopheles msl-2 leads to embryonic lethality in both sexes. Transcriptome analyses indicate that this phenotype is not a consequence of defective X chromosome DC. By immunofluorescence and ChIP, H4K16ac does not preferentially enrich on the male X. Instead, the mosquito MSL pathway regulates conserved developmental genes. We conclude that a novel mechanism confers X chromosome up-regulation in Anopheles. Our findings highlight the pluralism of gene-dosage buffering mechanisms even under similar genomic and functional constraints.

RNA nucleation by MSL2 induces selective X chromosome compartmentalization

Confinement of the X chromosome to a territory for dosage compensation is a prime example of how subnuclear compartmentalization is used to regulate transcription at the megabase scale. In Drosophila melanogaster, two sex-specific non-coding RNAs (roX1 and roX2) are transcribed from the X chromosome. They associate with the male-specific lethal (MSL) complex¹, which acetylates histone H4 lysine 16 and thereby induces an approximately twofold increase in expression of male X-linked genes^2,3. Current models suggest that X-over-autosome specificity is achieved by the recognition of cis-regulatory DNA high-affinity sites (HAS) by the MSL2 subunit^4,5. However, HAS motifs are also found on autosomes, indicating that additional factors must stabilize the association of the MSL complex with the X chromosome. Here we show that the low-complexity C-terminal domain (CTD) of MSL2 renders its recruitment to the X chromosome sensitive to roX non-coding RNAs. roX non-coding RNAs and the MSL2 CTD form a stably condensed state, and functional analyses in Drosophila and mammalian cells show that their interactions are crucial for dosage compensation in vivo. Replacing the CTD of mammalian MSL2 with that from Drosophila and expressing roX in cis is sufficient to nucleate ectopic dosage compensation in mammalian cells. Thus, the condensing nature of roX-MSL2^CTD is the primary determinant for specific compartmentalization of the X chromosome in Drosophila.

Versatile interactions and bioinformatics analysis of noncoding RNAs

Advances in RNA sequencing technologies and computational methodologies have provided a huge impetus to noncoding RNA (ncRNA) study. Once regarded as inconsequential results of transcriptional promiscuity, ncRNAs were later found to exert great roles in various aspects of biological functions. They are emerging as key players in gene regulatory networks by interacting with other biomolecules (DNA, RNA or protein). Here, we provide an overview of ncRNA repertoire and highlight recent discoveries of their versatile interactions. To better investigate the ncRNA-mediated regulation, it is necessary to make full use of innovative sequencing techniques and computational tools. We further describe a comprehensive workflow for in silico ncRNA analysis, providing up-to-date platforms, databases and tools dedicated to ncRNA identification and functional annotation.

The genomes uncoupled-dependent signalling pathway coordinates plastid biogenesis with the synthesis of anthocyanins

In recent years, it has become evident that plants perceive, integrate and communicate abiotic stress signals through chloroplasts. During the process of acclimation plastid-derived, retrograde signals control nuclear gene expression in response to developmental and environmental cues leading to complex genetic and metabolic reprogramming to preserve cellular homeostasis under challenging environmental conditions. Upon stress-induced dysfunction of chloroplasts, GENOMES UNCOUPLED (GUN) proteins participate in the repression of PHOTOSYNTHESIS-ASSOCIATED NUCLEAR GENES (PHANGs). Here, we show that the retrograde signal emitted by, or communicated through, GUN-proteins is also essential to induce the accumulation of photoprotective anthocyanin pigments when chloroplast development is attenuated. Comparative whole transcriptome sequencing and genetic analysis reveal GUN1 and GUN5-dependent signals as a source for the regulation of genes involved in anthocyanin biosynthesis. The signal transduction cascade includes well-known transcription factors for the control of anthocyanin biosynthesis, which are deregulated in gun mutants. We propose that regulation of PHANGs and genes contributing to anthocyanin biosynthesis are two, albeit oppositely, co-regulated processes during plastid biogenesis. This article is part of the theme issue 'Retrograde signalling from endosymbiotic organelles'.

GLP: A requirement in cell therapies - perspectives for the cardiovascular field

In biomedical research, enormous progress is being made and new candidates for putative medicinal products emerge. However, most published preclinical data are not conducted according to the standard Good Laboratory Practice (GLP). GLP is mandatory for preclinical analysis of Advanced Therapy Medicinal Products (ATMP) and thereby a prerequisite for planning and conduction of clinical trials. Not inconsiderable numbers of clinical trials are terminated earlier or fail - do inadequate testing strategies or missing specialized assays during the preclinical development contribute to this severe complex of problems? Unfortunately, there is also a lack of access to GLP testing results and OECD (Organisation for Economic Co-operation and Development) GLP guidelines are not yet adjusted to ATMP specialties. Ultimately, GLP offers possibilities to generate reliable and reproducible data. Therefore, this review elucidates different GLP aspects in drug development, speculates on reasons of putative low GLP acceptance in the scientific community and mentions solution proposals.

Laniakea: an open solution to provide Galaxy "on-demand" instances over heterogeneous cloud infrastructures

BACKGROUND

While the popular workflow manager Galaxy is currently made available through several publicly accessible servers, there are scenarios where users can be better served by full administrative control over a private Galaxy instance, including, but not limited to, concerns about data privacy, customisation needs, prioritisation of particular job types, tools development, and training activities. In such cases, a cloud-based Galaxy virtual instance represents an alternative that equips the user with complete control over the Galaxy instance itself without the burden of the hardware and software infrastructure involved in running and maintaining a Galaxy server.

RESULTS

We present Laniakea, a complete software solution to set up a "Galaxy on-demand" platform as a service. Building on the INDIGO-DataCloud software stack, Laniakea can be deployed over common cloud architectures usually supported both by public and private e-infrastructures. The user interacts with a Laniakea-based service through a simple front-end that allows a general setup of a Galaxy instance, and then Laniakea takes care of the automatic deployment of the virtual hardware and the software components. At the end of the process, the user gains access with full administrative privileges to a private, production-grade, fully customisable, Galaxy virtual instance and to the underlying virtual machine (VM). Laniakea features deployment of single-server or cluster-backed Galaxy instances, sharing of reference data across multiple instances, data volume encryption, and support for VM image-based, Docker-based, and Ansible recipe-based Galaxy deployments. A Laniakea-based Galaxy on-demand service, named Laniakea@ReCaS, is currently hosted at the ELIXIR-IT ReCaS cloud facility.

CONCLUSIONS

Laniakea offers to scientific e-infrastructures a complete and easy-to-use software solution to provide a Galaxy on-demand service to their users. Laniakea-based cloud services will help in making Galaxy more accessible to a broader user base by removing most of the burdens involved in deploying and running a Galaxy service. In turn, this will facilitate the adoption of Galaxy in scenarios where classic public instances do not represent an optimal solution. Finally, the implementation of Laniakea can be easily adapted and expanded to support different services and platforms beyond Galaxy.

GraphClust2: Annotation and discovery of structured RNAs with scalable and accessible integrative clustering

BACKGROUND

RNA plays essential roles in all known forms of life. Clustering RNA sequences with common sequence and structure is an essential step towards studying RNA function. With the advent of high-throughput sequencing techniques, experimental and genomic data are expanding to complement the predictive methods. However, the existing methods do not effectively utilize and cope with the immense amount of data becoming available.

RESULTS

Hundreds of thousands of non-coding RNAs have been detected; however, their annotation is lagging behind. Here we present GraphClust2, a comprehensive approach for scalable clustering of RNAs based on sequence and structural similarities. GraphClust2 bridges the gap between high-throughput sequencing and structural RNA analysis and provides an integrative solution by incorporating diverse experimental and genomic data in an accessible manner via the Galaxy framework. GraphClust2 can efficiently cluster and annotate large datasets of RNAs and supports structure-probing data. We demonstrate that the annotation performance of clustering functional RNAs can be considerably improved. Furthermore, an off-the-shelf procedure is introduced for identifying locally conserved structure candidates in long RNAs. We suggest the presence and the sparseness of phylogenetically conserved local structures for a collection of long non-coding RNAs.

CONCLUSIONS

By clustering data from 2 cross-linking immunoprecipitation experiments, we demonstrate the benefits of GraphClust2 for motif discovery under the presence of biological and methodological biases. Finally, we uncover prominent targets of double-stranded RNA binding protein Roquin-1, such as BCOR's 3' untranslated region that contains multiple binding stem-loops that are evolutionary conserved.

The de.NBI / ELIXIR-DE training platform - Bioinformatics training in Germany and across Europe within ELIXIR

The German Network for Bioinformatics Infrastructure (de.NBI) is a national and academic infrastructure funded by the German Federal Ministry of Education and Research (BMBF). The de.NBI provides (i) service, (ii) training, and (iii) cloud computing to users in life sciences research and biomedicine in Germany and Europe and (iv) fosters the cooperation of the German bioinformatics community with international network structures. The de.NBI members also run the German node (ELIXIR-DE) within the European ELIXIR infrastructure. The de.NBI / ELIXIR-DE training platform, also known as special interest group 3 (SIG 3) ‘Training & Education’, coordinates the bioinformatics training of de.NBI and the German ELIXIR node. The network provides a high-quality, coherent, timely, and impactful training program across its eight service centers. Life scientists learn how to handle and analyze biological big data more effectively by applying tools, standards and compute services provided by de.NBI. Since 2015, more than 300 training courses were carried out with about 6,000 participants and these courses received recommendation rates of almost 90% (status as of July 2020). In addition to face-to-face training courses, online training was introduced on the de.NBI website in 2016 and guidelines for the preparation of e-learning material were established in 2018. In 2016, ELIXIR-DE joined the ELIXIR training platform. Here, the de.NBI / ELIXIR-DE training platform collaborates with ELIXIR in training activities, advertising training courses via TeSS and discussions on the exchange of data for training events essential for quality assessment on both the technical and administrative levels. The de.NBI training program trained thousands of scientists from Germany and beyond in many different areas of bioinformatics.

CMV: visualization for RNA and protein family models and their comparisons

Summary

A standard method for the identification of novel RNAs or proteins is homology search via probabilistic models. One approach relies on the definition of families, which can be encoded as covariance models (CMs) or Hidden Markov Models (HMMs). While being powerful tools, their complexity makes it tedious to investigate them in their (default) tabulated form. This specifically applies to the interpretation of comparisons between multiple models as in family clans. The Covariance model visualization tools (CMV) visualize CMs or HMMs to: I) Obtain an easily interpretable representation of HMMs and CMs; II) Put them in context with the structural sequence alignments they have been created from; III) Investigate results of model comparisons and highlight regions of interest.

Availability and implementation

Source code (http://www.github.com/eggzilla/cmv), web-service (http://rna.informatik.uni-freiburg.de/CMVS).

Supplementary information

Supplementary data are available at Bioinformatics online.

The RNA workbench 2.0: next generation RNA data analysis

RNA has become one of the major research topics in molecular biology. As a central player in key processes regulating gene expression, RNA is in the focus of many efforts to decipher the pathways that govern the transition of genetic information to a fully functional cell. As more and more researchers join this endeavour, there is a rapidly growing demand for comprehensive collections of tools that cover the diverse layers of RNA-related research. However, increasing amounts of data, from diverse types of experiments, addressing different aspects of biological questions need to be consolidated and integrated into a single framework. Only then is it possible to connect findings from e.g. RNA-Seq experiments and methods for e.g. target predictions. To address these needs, we present the RNA Workbench 2.0 , an updated online resource for RNA related analysis. With the RNA Workbench we created a comprehensive set of analysis tools and workflows that enables researchers to analyze their data without the need for sophisticated command-line skills. This update takes the established framework to the next level, providing not only a containerized infrastructure for analysis, but also a ready-to-use platform for hands-on training, analysis, data exploration, and visualization. The new framework is available at https://rna.usegalaxy.eu , and login is free and open to all users. The containerized version can be found at https://github.com/bgruening/galaxy-rna-workbench.

Developmental programming of the HPA axis and related behaviours: epigenetic mechanisms

It has been approximately 30 years since the seminal discoveries of David Barker and his colleagues, and research is beginning to unravel the mechanisms that underlie developmental programming. The early environment of the embryo, foetus and newborn have been clearly linked to altered hypothalamic-pituitary-adrenal (HPA) function and related behaviours through the juvenile period and into adulthood. A number of recent studies have shown that these effects can pass across multiple generations. The HPA axis is highly responsive to the environment, impacts both central and peripheral systems and is critical to health in a wide variety of contexts. Mechanistic studies in animals are linking early exposures to adversity with changes in gene regulatory mechanisms, including modifications of DNA methylation and altered levels of miRNA. Similar associations are emerging from recent human studies. These findings suggest that epigenetic mechanisms represent a fundamental link between adverse early environments and developmental programming of later disease. The underlying biological mechanisms that connect the perinatal environment with modified long-term health outcomes represent an intensive area of research. Indeed, opportunities for early interventions must identify the relevant environmental factors and their molecular targets. This new knowledge will likely assist in the identification of individuals who are at risk of developing poor outcomes and for whom early intervention is most effective.

RNA-sequencing in ophthalmology research: considerations for experimental design and analysis

High-throughput, massively parallel sequence analysis has revolutionized the way that researchers design and execute scientific investigations. Vast amounts of sequence data can be generated in short periods of time. Regarding ophthalmology and vision research, extensive interrogation of patient samples for underlying causative DNA mutations has resulted in the discovery of many new genes relevant to eye disease. However, such analysis remains functionally limited. RNA-sequencing accurately snapshots thousands of genes, capturing many subtypes of RNA molecules, and has become the gold standard for transcriptome gene expression quantification. RNA-sequencing has the potential to advance our understanding of eye development and disease; it can reveal new candidates to improve our molecular diagnosis rates and highlight therapeutic targets for intervention. But with a wide range of applications, the design of such experiments can be problematic, no single optimal pipeline exists, and therefore, several considerations must be undertaken for optimal study design. We review the key steps involved in RNA-sequencing experimental design and the downstream bioinformatic pipelines used for differential gene expression. We provide guidance on the application of RNA-sequencing to ophthalmology and sources of open-access eye-related data sets.

Tools for Understanding miRNA-mRNA Interactions for Reproducible RNA Analysis

MicroRNAs (miRNAs) are an integral part of gene regulation at the post-transcriptional level. The use of RNA data in gene expression analysis has become increasingly important to gain insights into the regulatory mechanisms behind miRNA-mRNA interactions. As a result, we are confronted with a growing landscape of tools, while standards for reproducibility and benchmarking lag behind. This work identifies the challenges for reproducible RNA analysis, and highlights best practices on the processing and dissemination of scientific results. We found that the success of a tool does not solely depend on its performances: equally important is how a tool is received, and then supported within a community. This leads us to a detailed presentation of the RNA workbench, a community effort for sharing workflows and processing tools, built on top of the Galaxy framework. Here, we follow the community guidelines to extend its portfolio of RNA tools with the integration of the TriplexRNA ( https://triplexrna.org ). Our findings provide the basis for the development of a recommendation system, to guide users in the choice of tools and workflows.

Proof of concept web application for understanding the energetic basis of oligonucleotide unfolding

Measuring and quantifying thermodynamic parameters that determine both the stability of and interactions between biological macromolecules are an essential and necessary complement to structural studies. Although basic thermodynamic parameters for an observed process can be readily obtained, the data interpretation is often slow and analysis quality can be extremely variable. We have started to develop a web application that will help users to perform thermodynamic characterizations of oligonucleotide unfolding. The application can perform global fitting of calorimetric and spectroscopic data, and uses a three-state equilibrium model to obtain thermodynamic parameters for each transition step – namely, the Gibbs energy, the enthalpy, and the heat capacity. In addition, the application can define the number of K⁺ ions and the number of water molecules being released or taken up during unfolding. To test our application, we used UV spectroscopy, circular dichroism, and differential scanning calorimetry to monitor folding and unfolding of a model 22-nucleotide-long sequence of a human 3′-telomeric overhang, known as Tel22. The obtained data were uploaded to the web application and the global fit revealed that unfolding of Tel22 involves at least one intermediate state, and that K⁺ ions are released during the unfolding, whereas water molecules are taken up.

A novel web application: performing global fitting of oligonucleotide unfolding experimental data in style.

Workflow Development for the Functional Characterization of ncRNAs

During the last decade, ncRNAs have been investigated intensively and revealed their regulatory role in various biological processes. Worldwide research efforts have identified numerous ncRNAs and multiple RNA subtypes, which are attributed to diverse functionalities known to interact with different functional layers, from DNA and RNA to proteins. This makes the prediction of functions for newly identified ncRNAs challenging. Current bioinformatics and systems biology approaches show promising results to facilitate an identification of these diverse ncRNA functionalities. Here, we review (a) current experimental protocols, i.e., for Next Generation Sequencing, for a successful identification of ncRNAs; (b) sequencing data analysis workflows as well as available computational environments; and (c) state-of-the-art approaches to functionally characterize ncRNAs, e.g., by means of transcriptome-wide association studies, molecular network analyses, or artificial intelligence guided prediction. In addition, we present a strategy to cover the identification and functional characterization of unknown transcripts by using connective workflows.

FOXG1 Regulates PRKAR2B Transcriptionally and Posttranscriptionally via miR200 in the Adult Hippocampus

Rett syndrome is a complex neurodevelopmental disorder that is mainly caused by mutations in MECP2. However, mutations in FOXG1 cause a less frequent form of atypical Rett syndrome, called FOXG1 syndrome. FOXG1 is a key transcription factor crucial for forebrain development, where it maintains the balance between progenitor proliferation and neuronal differentiation. Using genome-wide small RNA sequencing and quantitative proteomics, we identified that FOXG1 affects the biogenesis of miR200b/a/429 and interacts with the ATP-dependent RNA helicase, DDX5/p68. Both FOXG1 and DDX5 associate with the microprocessor complex, whereby DDX5 recruits FOXG1 to DROSHA. RNA-Seq analyses of Foxg1^cre/+ hippocampi and N2a cells overexpressing miR200 family members identified cAMP-dependent protein kinase type II-beta regulatory subunit (PRKAR2B) as a target of miR200 in neural cells. PRKAR2B inhibits postsynaptic functions by attenuating protein kinase A (PKA) activity; thus, increased PRKAR2B levels may contribute to neuronal dysfunctions in FOXG1 syndrome. Our data suggest that FOXG1 regulates PRKAR2B expression both on transcriptional and posttranscriptional levels.

Structure and Interaction Prediction in Prokaryotic RNA Biology

Many years of research in RNA biology have soundly established the importance of RNA-based regulation far beyond most early traditional presumptions. Importantly, the advances in "wet" laboratory techniques have produced unprecedented amounts of data that require efficient and precise computational analysis schemes and algorithms. Hence, many in silico methods that attempt topological and functional classification of novel putative RNA-based regulators are available. In this review, we technically outline thermodynamics-based standard RNA secondary structure and RNA-RNA interaction prediction approaches that have proven valuable to the RNA research community in the past and present. For these, we highlight their usability with a special focus on prokaryotic organisms and also briefly mention recent advances in whole-genome interactomics and how this may influence the field of predictive RNA research.

Intestinal in vitro and ex vivo Models to Study Host-Microbiome Interactions and Acute Stressors

The gut microbiome is extremely important for maintaining homeostasis with host intestinal epithelial, neuronal, and immune cells and this host-microbe interaction is critical during times of stress or disease. Environmental, nutritional, and cognitive stress are just a few factors known to influence the gut microbiota and are thought to induce microbial dysbiosis. Research on this bidirectional relationship as it pertains to health and disease is extensive and rapidly expanding in both in vivo and in vitro/ex vivo models. However, far less work has been devoted to studying effects of host-microbe interactions on acute stressors and performance, the underlying mechanisms, and the modulatory effects of different stressors on both the host and the microbiome. Additionally, the use of in vitro/ex vivo models to study the gut microbiome and human performance has not been researched extensively nor reviewed. Therefore, this review aims to examine current evidence concerning the current status of in vitro and ex vivo host models, the impact of acute stressors on gut physiology/microbiota as well as potential impacts on human performance and how we can parlay this information for DoD relevance as well as the broader scientific community. Models reviewed include widely utilized intestinal cell models from human and animal models that have been applied in the past for stress or microbiology research as well as ex vivo organ/tissue culture models and new innovative models including organ-on-a-chip and co-culture models.

Interactive implementations of thermodynamics-based RNA structure and RNA-RNA interaction prediction approaches for example-driven teaching

The investigation of RNA-based regulation of cellular processes is becoming an increasingly important part of biological or medical research. For the analysis of this type of data, RNA-related prediction tools are integrated into many pipelines and workflows. In order to correctly apply and tune these programs, the user has to have a precise understanding of their limitations and concepts. Within this manuscript, we provide the mathematical foundations and extract the algorithmic ideas that are core to state-of-the-art RNA structure and RNA–RNA interaction prediction algorithms. To allow the reader to change and adapt the algorithms or to play with different inputs, we provide an open-source web interface to JavaScript implementations and visualizations of each algorithm. The conceptual, teaching-focused presentation enables a high-level survey of the approaches, while providing sufficient details for understanding important concepts. This is boosted by the simple generation and study of examples using the web interface available at http://rna.informatik.uni-freiburg.de/Teaching/. In combination, we provide a valuable resource for teaching, learning, and understanding the discussed prediction tools and thus enable a more informed analysis of RNA-related effects.

RNA molecules are central players in many cellular processes. Thus, the analysis of RNA-based regulation has provided valuable insights and is often pivotal to biological and medical research. In order to correctly select appropriate algorithms and apply available RNA structure and RNA–RNA interaction prediction software, it is crucial to have a good understanding of their limitations and concepts. Such an overview is hard to achieve by end users, since most state-of-the-art tools are introduced on expert level and are not discussed in text books. Within this manuscript, we provide the mathematical means and extract the algorithmic concepts that are core to state-of-the-art RNA structure and RNA–RNA interaction prediction algorithms. The conceptual, teaching-focused presentation enables a detailed understanding of the approaches using a simplified model for didactic purposes. We support this process by providing clear examples using the web interface of our algorithm implementation. In summary, we have compiled material and web applications for teaching—and the self-study of—several state-of-the-art algorithms commonly used to investigate the role of RNA in regulatory processes.

The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2018 update

Galaxy (homepage: https://galaxyproject.org, main public server: https://usegalaxy.org) is a web-based scientific analysis platform used by tens of thousands of scientists across the world to analyze large biomedical datasets such as those found in genomics, proteomics, metabolomics and imaging. Started in 2005, Galaxy continues to focus on three key challenges of data-driven biomedical science: making analyses accessible to all researchers, ensuring analyses are completely reproducible, and making it simple to communicate analyses so that they can be reused and extended. During the last two years, the Galaxy team and the open-source community around Galaxy have made substantial improvements to Galaxy's core framework, user interface, tools, and training materials. Framework and user interface improvements now enable Galaxy to be used for analyzing tens of thousands of datasets, and >5500 tools are now available from the Galaxy ToolShed. The Galaxy community has led an effort to create numerous high-quality tutorials focused on common types of genomic analyses. The Galaxy developer and user communities continue to grow and be integral to Galaxy's development. The number of Galaxy public servers, developers contributing to the Galaxy framework and its tools, and users of the main Galaxy server have all increased substantially.

ASaiM: a Galaxy-based framework to analyze microbiota data

Background

New generations of sequencing platforms coupled to numerous bioinformatics tools have led to rapid technological progress in metagenomics and metatranscriptomics to investigate complex microorganism communities. Nevertheless, a combination of different bioinformatic tools remains necessary to draw conclusions out of microbiota studies. Modular and user-friendly tools would greatly improve such studies.

Findings

We therefore developed ASaiM, an Open-Source Galaxy-based framework dedicated to microbiota data analyses. ASaiM provides an extensive collection of tools to assemble, extract, explore, and visualize microbiota information from raw metataxonomic, metagenomic, or metatranscriptomic sequences. To guide the analyses, several customizable workflows are included and are supported by tutorials and Galaxy interactive tours, which guide users through the analyses step by step. ASaiM is implemented as a Galaxy Docker flavour. It is scalable to thousands of datasets but also can be used on a normal PC. The associated source code is available under Apache 2 license at https://github.com/ASaiM/framework and documentation can be found online (http://asaim.readthedocs.io).

Conclusions

Based on the Galaxy framework, ASaiM offers a sophisticated environment with a variety of tools, workflows, documentation, and training to scientists working on complex microorganism communities. It makes analysis and exploration analyses of microbiota data easy, quick, transparent, reproducible, and shareable.

Stem cells and heart disease - Brake or accelerator?

After two decades of intensive research and attempts of clinical translation, stem cell based therapies for cardiac diseases are not getting closer to clinical success. This review tries to unravel the obstacles and focuses on underlying mechanisms as the target for regenerative therapies. At present, the principal outcome in clinical therapy does not reflect experimental evidence. It seems that the scientific obstacle is a lack of integration of knowledge from tissue repair and disease mechanisms. Recent insights from clinical trials delineate mechanisms of stem cell dysfunction and gene defects in repair mechanisms as cause of atherosclerosis and heart disease. These findings require a redirection of current practice of stem cell therapy and a reset using more detailed analysis of stem cell function interfering with disease mechanisms. To accelerate scientific development the authors suggest intensifying unified computational data analysis and shared data knowledge by using open-access data platforms.

(Re-)programming of subtype specific cardiomyocytes

Adult cardiomyocytes (CMs) possess a highly restricted intrinsic regenerative potential - a major barrier to the effective treatment of a range of chronic degenerative cardiac disorders characterized by cellular loss and/or irreversible dysfunction and which underlies the majority of deaths in developed countries. Both stem cell programming and direct cell reprogramming hold promise as novel, potentially curative approaches to address this therapeutic challenge. The advent of induced pluripotent stem cells (iPSCs) has introduced a second pluripotent stem cell source besides embryonic stem cells (ESCs), enabling even autologous cardiomyocyte production. In addition, the recent achievement of directly reprogramming somatic cells into cardiomyocytes is likely to become of great importance. In either case, different clinical scenarios will require the generation of highly pure, specific cardiac cellular-subtypes. In this review, we discuss these themes as related to the cardiovascular stem cell and programming field, including a focus on the emergent topic of pacemaker cell generation for the development of biological pacemakers and in vitro drug testing.