Transcriptional mechanisms underlying sensitization of peripheral sensory neurons by Granulocyte-/Granulocyte-macrophage colony stimulating factors

Background: Cancer-associated pain is a major cause of poor quality of life in cancer patients and is frequently resistant to conventional therapy. Recent studies indicate that some hematopoietic growth factors, namely granulocyte macrophage colony stimulating factor (GMCSF) and granulocyte colony stimulating factor (GCSF), are abundantly released in the tumor microenvironment and play a key role in regulating tumor-nerve interactions and tumor-associated pain by activating receptors on dorsal root ganglion (DRG) neurons. Moreover, these hematopoietic factors have been highly implicated in postsurgical pain, inflammatory pain and osteoarthritic pain. However, the molecular mechanisms via which G-/GMCSF bring about nociceptive sensitization and elicit pain are not known. Results: In order to elucidate G-/GMCSF mediated transcriptional changes in the sensory neurons, we performed a comprehensive, genome-wide analysis of changes in the transcriptome of DRG neurons brought about by exposure to GMCSF or GCSF. We present complete information on regulated genes and validated profiling analyses and report novel regulatory networks and interaction maps revealed by detailed bioinformatics analyses. Amongst these, we validate calpain 2, matrix metalloproteinase 9 (MMP9) and a RhoGTPase Rac1 as well as Tumor necrosis factor alpha (TNFα) as transcriptional targets of G-/GMCSF and demonstrate the importance of MMP9 and Rac1 in GMCSF-induced nociceptor sensitization. Conclusion: With integrative approach of bioinformatics, in vivo pharmacology and behavioral analyses, our results not only indicate that transcriptional control by G-/GMCSF signaling regulates a variety of established pain modulators, but also uncover a large number of novel targets, paving the way for translational analyses in the context of pain disorders

Experiences From FAIRifying Community Data and FAIR Infrastructure in Biomedical Research Domains

FAIR data is considered good data. However, it can be difficult to quantify data FAIRness objectively, without appropriate tooling. To address this issue, FAIR metrics were developed in the early days of the FAIR era. However, to be truly informative, these metrics must be carefully interpreted in the context of a specific domain, and sometimes even of a project. Here, we share our experience with FAIR assessments and FAIRification processes in the biomedical domain. We aim to raise the awareness that “being FAIR” is not an easy goal, neither the principles are easily implemented. FAIR goes far beyond technical implementations: it requires time, expertise, communication and a shift in mindset.&nbsp

Clustering of cognate proteins among distinct proteomes derived from multiple links to a single seed sequence

<p>Abstract</p> <p>Background</p> <p>Modern proteomes evolved by modification of pre-existing ones. It is extremely important to comparative biology that related proteins be identified as members of the same cognate group, since a characterized putative homolog could be used to find clues about the function of uncharacterized proteins from the same group. Typically, databases of related proteins focus on those from completely-sequenced genomes. Unfortunately, relatively few organisms have had their genomes fully sequenced; accordingly, many proteins are ignored by the currently available databases of cognate proteins, despite the high amount of important genes that are functionally described only for these incomplete proteomes.</p> <p>Results</p> <p>We have developed a method to cluster cognate proteins from multiple organisms beginning with only one sequence, through connectivity saturation with that Seed sequence. We show that the generated clusters are in agreement with some other approaches based on full genome comparison.</p> <p>Conclusion</p> <p>The method produced results that are as reliable as those produced by conventional clustering approaches. Generating clusters based only on individual proteins of interest is less time consuming than generating clusters for whole proteomes. </p

Arena3D: visualization of biological networks in 3D

<p>Abstract</p> <p>Background</p> <p>Complexity is a key problem when visualizing biological networks; as the number of entities increases, most graphical views become incomprehensible. Our goal is to enable many thousands of entities to be visualized meaningfully and with high performance.</p> <p>Results</p> <p>We present a new visualization tool, Arena3D, which introduces a new concept of staggered layers in 3D space. Related data – such as proteins, chemicals, or pathways – can be grouped onto separate layers and arranged via layout algorithms, such as Fruchterman-Reingold, distance geometry, and a novel hierarchical layout. Data on a layer can be clustered via k-means, affinity propagation, Markov clustering, neighbor joining, tree clustering, or UPGMA ('unweighted pair-group method with arithmetic mean'). A simple input format defines the name and URL for each node, and defines connections or similarity scores between pairs of nodes. The use of Arena3D is illustrated with datasets related to Huntington's disease.</p> <p>Conclusion</p> <p>Arena3D is a user friendly visualization tool that is able to visualize biological or any other network in 3D space. It is free for academic use and runs on any platform. It can be downloaded or lunched directly from <url>http://arena3d.org</url>. Java3D library and Java 1.5 need to be pre-installed for the software to run.</p

Chapter 7 Best Practices in Single-Cell RNA-seq Data Analysis

This cutting-edge reference book compiles standard operating procedures, protocols, and applications of next-generation sequencing (NGS). It discusses genomic testing applications through NGS. It pays special focus on the protocols for cataloguing variants of uncertain significance. Over the years, NGS and advanced bioinformatics approaches have allowed the transition of genomic assays into translational practices. The book covers visualisation of NGS datasets, investigation of early development impairment, and metagenome protocols. It also discusses the challenges in NGS methods. Key Points: Includes case studies of application of NGS in different taxa like humans, rodents, plants, and bacteria Compiles protocols from various reputed companies like Illumina, PacBio, and ThermoFisher Discusses the translational applications of NGS methods Reviews machine learning heuristics for NGS data interpretation Discusses emerging genomic assay technologies and characterising mechanisms of disease prevalence The book is meant for researchers and industry experts in genomics, computational biology, and bioinformatics. Chapter 7 and 9 of this book is freely available as a downloadable Open Access PDF at ""http://www.taylorfrancis.com"" "http://www.taylorfrancis.com under a Creative Commons [Attribution-Non Commercial-No Derivatives (CC BY-NC-ND)] 4.0 license

COBREXA 2: tidy and scalable construction of complex metabolic models.

peer reviewedSUMMARY: Constraint-based metabolic models offer a scalable framework to investigate biological systems using optimality principles. Construction and simulation of detailed models that utilize multiple kinds of constraint systems pose a significant coding overhead, complicating implementation of new types of analyses. We present an improved version of the constraint-based metabolic modeling package COBREXA, which utilizes a hierarchical model construction framework that decouples the implemented analysis algorithms into independent, yet re-combinable, building blocks. By removing the need to re-implement modeling components, assembly of complex metabolic models is simplified, which we demonstrate on use-cases of resource-balanced models, and enzyme-constrained flux balance models of interacting bacterial communities. Notably, these models show improved predictive capabilities in both monoculture and community settings. In perspective, the re-usable model-building components in COBREXA 2 provide a sustainable way to handle increasingly complex models in constraint-based modeling. AVAILABILITY AND IMPLEMENTATION: COBREXA 2 is available from https://github.com/COBREXA/COBREXA.jl, and from Julia package repositories. COBREXA 2 works on all major operating systems and computer architectures. Documentation is available at https://cobrexa.github.io/COBREXA.jl/

FAIR data management: what does it mean for drug discovery?

The drug discovery community faces high costs in bringing safe and effective medicines to market, in part due to the rising volume and complexity of data which must be generated during the research and development process. Fully utilising these expensively created experimental and computational data resources has become a key aim of scientists due to the clear imperative to leverage the power of artificial intelligence (AI) and machine learning-based analyses to solve the complex problems inherent in drug discovery. In turn, AI methods heavily rely on the quantity, quality, consistency, and scope of underlying training data. While pre-existing preclinical and clinical data cannot fully replace the need for de novo data generation in a project, having access to relevant historical data represents a valuable asset, as its reuse can reduce the need to perform similar experiments, therefore avoiding a “reinventing the wheel” scenario. Unfortunately, most suitable data resources are often archived within institutes, companies, or individual research groups and hence unavailable to the wider community. Hence, enabling the data to be Findable, Accessible, Interoperable, and Reusable (FAIR) is crucial for the wider community of drug discovery and development scientists to learn from the work performed and utilise the findings to enhance comprehension of their own research outcomes. In this mini-review, we elucidate the utility of FAIR data management across the drug discovery pipeline and assess the impact such FAIR data has made on the drug development process

Martini: using literature keywords to compare gene sets

Life scientists are often interested to compare two gene sets to gain insight into differences between two distinct, but related, phenotypes or conditions. Several tools have been developed for comparing gene sets, most of which find Gene Ontology (GO) terms that are significantly over-represented in one gene set. However, such tools often return GO terms that are too generic or too few to be informative. Here, we present Martini, an easy-to-use tool for comparing gene sets. Martini is based, not on GO, but on keywords extracted from Medline abstracts; Martini also supports a much wider range of species than comparable tools. To evaluate Martini we created a benchmark based on the human cell cycle, and we tested several comparable tools (CoPub, FatiGO, Marmite and ProfCom). Martini had the best benchmark performance, delivering a more detailed and accurate description of function. Martini also gave best or equal performance with three other datasets (related to Arabidopsis, melanoma and ovarian cancer), suggesting that Martini represents an advance in the automated comparison of gene sets. In agreement with previous studies, our results further suggest that literature-derived keywords are a richer source of gene-function information than GO annotations. Martini is freely available at http://martini.embl.de

SMASCH: Facilitating multi-appointment scheduling in longitudinal clinical research studies and care programs

Longitudinal clinical research studies require conducting various assessments over long periods of time. Such assessments comprise numerous stages, requiring different resources defined by multidisciplinary research staff and aligned with available infrastructure and equipment, altogether constrained by time. While it is possible to manage the allocation of resources manually, it is complex and error-prone. Efficient multi-appointment scheduling is essential to assist clinical teams, ensuring high participant retention and producing successful clinical studies, directly impacting patient throughput and satisfaction. We present Smart Scheduling (SMASCH) system [1], a web application for multi-appointment scheduling management aiming to reduce times, optimise resources and secure personal identifiable information. SMASCH facilitates clinical research and integrated care programs in Luxembourg, providing features to better manage multi-appointment scheduling problems (MASPs) characteristic of longitudinal clinical research studies and speed up management tasks. It is present in multiple clinical research and integrated care programs in Luxembourg since 2017, including Dementia Prevention Program, the study for Mild Cognitive Impairment and gut microbiome, and the National Centre of Excellence in Research on Parkinson’s disease [2] which encompasses the study for REM sleep behaviour disorder and the Luxembourg Parkinson’s Study. SMASCH is a free and open-source solution available both as a Linux package and Docker image