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

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

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

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

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

An evaluation of EDAM coverage in the Tools Ecosystem and prototype integration of Galaxy and WorkflowHub systems

https://biohackrxiv.org/79kje/Here we report the results of a project started at the BioHackathon Europe 2022. Its goals were to cross-compare and analyze the metadata centralized in the Tools Ecosystem, and linked to the EDAM ontology, as well as to explore methods for connecting tools used in registered Galaxy workflows (i.e. WorkflowHub entries) to the annotations available in bio.tools

A Galaxy of informatics resources for MS-based proteomics

International audienceIntroduction: Continuous advances in mass spectrometry (MS) technologies have enabled deeper and more reproducible proteome characterization and a better understanding of biological systems when integrated with other 'omics data. Bioinformatic resources meeting the analysis requirements of increasingly complex MS-based proteomic data and associated multi-omic data are critically needed. These requirements included availability of software that would span diverse types of analyses, scalability for large-scale, compute-intensive applications, and mechanisms to ease adoption of the software. Areas covered: The Galaxy ecosystem meets these requirements by offering a multitude of opensource tools for MS-based proteomics analyses and applications, all in an adaptable, scalable, and accessible computing environment. A thriving global community maintains these software and associated training resources to empower researcher-driven analyses. Expert opinion: The community-supported Galaxy ecosystem remains a crucial contributor to basic biological and clinical studies using MS-based proteomics. In addition to the current status of Galaxybased resources, we describe ongoing developments for meeting emerging challenges in MS-based proteomic informatics. We hope this review will catalyze increased use of Galaxy by researchers employing MS-based proteomics and inspire software developers to join the community and implement new tools, workflows, and associated training content that will add further value to this already rich ecosystem

The Galaxy platform for accessible, reproducible, and collaborative data analyses: 2024 update

International audienceAbstract Galaxy (https://galaxyproject.org) is deployed globally, predominantly through free-to-use services, supporting user-driven research that broadens in scope each year. Users are attracted to public Galaxy services by platform stability, tool and reference dataset diversity, training, support and integration, which enables complex, reproducible, shareable data analysis. Applying the principles of user experience design (UXD), has driven improvements in accessibility, tool discoverability through Galaxy Labs/subdomains, and a redesigned Galaxy ToolShed. Galaxy tool capabilities are progressing in two strategic directions: integrating general purpose graphical processing units (GPGPU) access for cutting-edge methods, and licensed tool support. Engagement with global research consortia is being increased by developing more workflows in Galaxy and by resourcing the public Galaxy services to run them. The Galaxy Training Network (GTN) portfolio has grown in both size, and accessibility, through learning paths and direct integration with Galaxy tools that feature in training courses. Code development continues in line with the Galaxy Project roadmap, with improvements to job scheduling and the user interface. Environmental impact assessment is also helping engage users and developers, reminding them of their role in sustainability, by displaying estimated CO2 emissions generated by each Galaxy job