Profiling the serum protein corona of fibrillar human islet amyloid polypeptide

Amyloids may be regarded as native nanomaterials that form in the presence of complex protein mixtures. By drawing an analogy with the physicochemical properties of nanoparticles in biological fluids, we hypothesized that amyloids should form a protein corona in vivo that would imbue the underlying amyloid with a modified biological identity. To explore this hypothesis we characterized the protein corona of human islet amyloid polypeptide (IAPP) fibrils in FBS using two complementary methodologies developed herein; quartz crystal microbalance and ‘centrifugal capture’, coupled with nano-liquid chromatography tandem mass spectroscopy. Clear evidence for a significant protein corona was obtained. No trends were identified for amyloid corona proteins based on their physicochemical properties, while strong binding with IAPP fibrils occurred for linear proteins or multi-domain proteins with structural plasticity. Proteomic analysis identified amyloid-enriched proteins that are known to play significant roles in mediating cellular machinery and processing, potentially leading to pathological outcomes and therapeutic targets

WorkflowHub: a registry for computational workflows

The rising popularity of computational workflows is driven by the need for repetitive and scalable data processing, sharing of processing know-how, and transparent methods. As both combined records of analysis and descriptions of processing steps, workflows should be reproducible, reusable, adaptable, and available. Workflow sharing presents opportunities to reduce unnecessary reinvention, promote reuse, increase access to best practice analyses for non-experts, and increase productivity. In reality, workflows are scattered and difficult to find, in part due to the diversity of available workflow engines and ecosystems, and because workflow sharing is not yet part of research practice. WorkflowHub provides a unified registry for all computational workflows that links to community repositories, and supports both the workflow lifecycle and making workflows findable, accessible, interoperable, and reusable (FAIR). By interoperating with diverse platforms, services, and external registries, WorkflowHub adds value by supporting workflow sharing, explicitly assigning credit, enhancing FAIRness, and promoting workflows as scholarly artefacts. The registry has a global reach, with hundreds of research organisations involved, and more than 700 workflows registered

Applying the FAIR Principles to Computational Workflows

Recent trends within computational and data sciences show an increasing recognition and adoption of computational workflows as tools for productivity, reproducibility, and democratized access to platforms and processing know-how. As digital objects to be shared, discovered, and reused, computational workflows benefit from the FAIR principles, which stand for Findable, Accessible, Interoperable, and Reusable. The Workflows Community Initiative's FAIR Workflows Working Group (WCI-FW), a global and open community of researchers and developers working with computational workflows across disciplines and domains, has systematically addressed the application of both FAIR data and software principles to computational workflows. We present our recommendations with commentary that reflects our discussions and justifies our choices and adaptations. Like the software and data principles on which they are based, these are offered to workflow users and authors, workflow management system developers, and providers of workflow services as guide rails for adoption and fodder for discussion. Workflows are becoming more prevalent as documented, automated instruments for data analysis, data collection, AI-based predictions, and simulations. The FAIR recommendations for workflows that we propose in this paper will maximize their value as research assets and facilitate their adoption by the wider community

Molecular characterization of metastatic ovarian cancer by MALDI imaging mass spectrometry.

Imaging mass spectrometry (IMS) is a novel technology which measures the spatial distribution of drugs, lipids, peptides and proteins across tissue sections by application of mass spectrometry (MS) directly to the section surface. Several hundred analytes can be measured across a tissue in a single IMS experiment, without the need for antibodies and without prior knowledge of tissue composition or structure. In the context of human cancers, the molecular information collected by IMS approaches has been used to grade cancers and predict patient survival. IMS is thus a potentially technology capable of providing valuable complementary information to classical histology and immuno-histochemistry. Ovarian cancers have the highest mortality of any gynaecological cancer. The high mortality results from late diagnosis due to the asymptomatic nature of ovarian malignancies. Advanced stage ovarian tumours will shed cancer cells into the abdominal cavity, where they subsequently implant into the peritoneum and form metastatic tumour nodules. Despite invasive surgery and adjuvant chemotherapy, there is a large increase in patient morbidity following peritoneal metastasis. Compounding this issue further is the absence of reliable grading systems for ovarian cancer and a subsequent lack of individualized treatments for specific cancer sub-types. As a result of the potential ability to grade tumours and provide patient prognoses based on IMS data, the molecular composition of ovarian metastatic tumours was investigated by IMS. The novelty of IMS required set up of a robust and reproducible workflow. Methods were thus optimized for IMS analysis of both frozen and formalin-fixed paraffin-embedded (FFPE) ovarian tumour tissue. Subsequently it was shown that optimization of available antigen retrieval and tryptic digest methods for accessing FFPE tissues could achieve higher tryptic peptide signal to noise at a better spatial resolution than methods available in the literature. As such, a complete tryptic peptide IMS workflow was developed alongside liquid chromatography (LC) and MS/MS based peptide identification. In conjunction with this workflow, methods for improving the matching of IMS peptides to LC-MS/MS identified peptides using internal calibrants and development of an in-house software tool were described. As a result of the work presented in this thesis, a complete tryptic peptide IMS workflow which could be applied to virtually any cancer tissue was developed. The application of this workflow, and exploratory k-means clustering, to ovarian peritoneal metastases showed that key tryptic peptides could be found which distinguish cancer tissue from the surrounding peritoneal stroma. This represented the first step in characterizing these metastatic tumours at the molecular level. The results in this thesis are a precursor to future work which will validate these peptide markers and develop a classification system for metastatic ovarian cancers based on patient survival and response to chemotherapy.Thesis (Ph.D.) -- University of Adelaide, School of Molecular and Biomedical Science, 201

Building community data assets for life sciences through ABLeS - the Australian BioCommons Leadership Share

The intention of ABLeS (the Australia BioCommons Leadership Share) is to grow and simultaneously accelerate community capacity to construct, maintain and gain insights from community-defined and developed data assets (e.g. reference genome assemblies). ABLeS aims to provide these communities with access to the tailored mix of infrastructure and computational resources that is necessary to create these assets. This document provides (1) an introductory overview of ABLeS as well as (2) Resources, onboarding and support that are available within ABLeS and (3) Acceptable use and expectations of ABLeS resources

Enabling national step changes in bioinformatics through ABLeS, the Australian BioCommons Leadership Share

<p>ABLeS aims to provide Australian life science research communities with access to the tailored mix of infrastructure and computational resources that is necessary to respond to challenges in bioinformatics.</p><p>This document provides:</p><ol><li>An introductory overview of ABLeS</li><li>Access schemes available for (1) Creation of reference data assets, (2) Production analysis and (3) Optimisation, testing and/or benchmarking of bioinformatics software</li><li>An overview of onboarding and support that are available within ABLeS</li><li>Acceptable use and expectations of ABLeS resources.</li></ol&gt

Galaxy CoDex for finding tools, workflows, and training

International audienceGalaxy offers an ecosystem containing thousands of tools, hundreds of tutorials, and a currently unknown number of workflows. The abundance of locations where these resources can be found, coupled with their diverse and fragmented nature, makes it incredibly difficult for Galaxy users to find and reuse tools, or to filter for all resources available for a specific research community, domain, or research area. By extension, it is also difficult for Special Interest Groups (SIG) to give visibility to their collective works.To improve the findability of tools, a pipeline (Galaxy Tool Metadata Extractor) was developed at the BioHackathon Europe 2023 to collect Galaxy suites from different locations, automatically extract their metadata (including bio.tools identifier and EDAM ontology concepts), and display this information as an interactive list that can be filtered to display tools that are relevant to a specific research community or domain (DOI: 10.37044/osf.io/qjbxc). In developing this pipeline, two challenges were apparent: 1) many tools are missing proper bio.tools or EDAM annotations, and 2) a Galaxy SIG offers more resources than just tools. In fact, SIGs also offer training materials and workflows, which are often dispersed and poorly annotated. During the BioHackathon Europe 2023, and in a second community-hosted online hackathon in 2024, the microGalaxy SIG tackled the first challenge by working on tool annotation, thereby improving the EDAM annotations for more than 200 tools, and annotating more than 30 tutorials with EDAM concepts. Additional communities, including single-cell and imaging SIGs, have also started similar annotation efforts.To address the second challenge, and aggregate the sum of resources available to a SIG, the Galaxy Tool Metadata Extractor is now being extended to create the Galaxy Communities Dock or Galaxy CoDex. Galaxy CoDex includes a centralised webpage template and files that will enable domain communities to rapidly aggregate, curate, integrate, display, and launch relevant tools, workflows, and training on different Galaxy servers. This catalog and its implementation form the foundation of a wider initiative - spearheaded by the Galaxy Community Board and two communities in particular - to unify resources across Galaxy servers.In this talk, we present the work done over the last year to build this catalog. Importantly, we also want to make the wider community aware of this ongoing effort and invite additional contributors: (i) new communities or SIGs that can be included in the catalog and give feedback on its structure and function, and (ii) Galaxy developers to help us establish and implement best practice recommendations for resource annotation at different levels in the Galaxy ecosystem (e.g. adding an EDAM Topics field to the workflow best practices dialogue in the Galaxy interface)

BoF: Establishing a AU-NZ bioinformatics software accelerator program

The creation of quality and performant bioinformatics software is a growing need as the field of data-driven omics continues to rapidly grow and expand. A proposed response to this challenge is the creation of a software accelerator program that supports the development, optimisation and sharing of software for bioinformatics, by providing the authors with expertise, best practice guidelines, and access to HPC and cloud facilities. This idea arises from the existing Australian BioCommons Leadership Share (ABLeS) program, by providing environments on peak compute systems to test, scale, debug and enhance bioinformatics software (tools and workflows) in lock step with support and expertise in making software findable, understandable and citable (i.e. FAIR). The ultimate vision is to accelerate the transition towards a culture of best practice bioinformatics at scale on peak infrastructures. In this BoF we will collectively discuss and shape this proposed program, by exploring what the RSE community considers to be critical, and how we might share the outcomes of this effort across infrastructure partners in the region and beyond. These observations are broadly applicable to all domains, and we invite people of diverse domain backgrounds to the BoF. This concept is being actively pursued as a collaborative effort by the Australian BioCommons, Pawsey Supercomputing Centre, New Zealand eScience Infrastructure and the National Computational Infrastructure

How to increase the findability, visibility, and impact of Galaxy tools for your scientific community

The scale and diversity of available software options in the Galaxy ecosystem can make domain or community specific discovery of software challenging. Here, we present a semi-automated and reusable pipeline for creating tailored interactive tables that list the identity and metadata (e.g. bio.tools, EDAM) available for Galaxy tools in a specific community (e.g. microGalaxy, imaging). In addition, we also describe an annotation framework to improve the quality of the table contents, and training material to support the reuse of both the pipeline and table by additional communities. The sum of these contributions is expected to make it easier for Galaxy users to discover and understand the software within their research area, improve the annotation of these software resources, and allow other domains to enable equivalent discovery processes for their community.This work is the outcome of a BioHackathon Europe 2023 project

Mapping insoluble indole metabolites in the gastrointestinal environment of a murine colorectal cancer model using desorption/ionisation on porous silicon imaging

Indole derivatives are a structurally diverse group of compounds found in food, toxins, medicines, and produced by commensal microbiota. On contact with acidic stomach conditions, indoles undergo condensation to generate metabolites that vary in solubility, activity and toxicity as they move through the gut. Here, using halogenated ions, we map promising chemo-preventative indoles, i) 6-bromoisatin (6Br), ii) the mixed indole natural extract (NE) 6Br is found in, and iii) the highly insoluble metabolites formed in vivo using desorption/ionisation on porous silicon-mass spectrometry imaging (DIOS-MSI). The functionalised porous silicon architecture allowed insoluble metabolites to be detected that would otherwise evade most analytical platforms, providing direct evidence for identifying the therapeutic component, 6Br, from the mixed indole NE. As a therapeutic lead, 0.025 mg/g 6Br acts as a chemo-preventative compound in a 12 week genotoxic mouse model; at this dose 6Br significantly reduces epithelial cell proliferation, tumour precursors (aberrant crypt foci; ACF); and tumour numbers while having minimal effects on liver, blood biochemistry and weight parameters compared to controls. The same could not be said for the NE where 6Br originates, which significantly increased liver damage markers. DIOS-MSI revealed a large range of previously unknown insoluble metabolites that could contribute to reduced efficacy and increased toxicity