AI-driven discovery of blood xenobiotic biomarkers in neovascular age-related macular degeneration using iterative random forests.

PURPOSE: To investigate the xenobiotic profiles of patients with neovascular age-related macular degeneration (nAMD) undergoing anti-vascular endothelial growth factor (anti-VEGF) intravitreal therapy (IVT) to identify biomarkers indicative of clinical phenotypes through advanced AI methodologies. METHODS: In this cross-sectional observational study, we analyzed 156 peripheral blood xenobiotic features in a cohort of 46 nAMD patients stratified by choroidal neovascularization (CNV) control under anti-VEGF IVT. We employed Liquid Chromatography-Tandem Mass Spectrometry (LC-MS/MS) for measurement and leveraged an AI-driven iterative Random Forests (iRF) approach for robust pattern recognition and feature selection, aligning molecular profiles with clinical phenotypes. RESULTS: AI-augmented iRF models effectively refined the metabolite spectrum by discarding non-predictive elements. Perfluorooctanesulfonate (PFOS) and Ethyl ß-glucopyranoside were identified as significant biomarkers through this process, associated with various clinically relevant phenotypes. Unlike single metabolite classes, drug metabolites were distinctly correlated with subretinal fluid presence. CONCLUSIONS: This study underscores the enhanced capability of AI, particularly iRF, in dissecting complex metabolomic data to elucidate the xenobiotic landscape of nAMD and environmental impact on the disease. The preliminary biomarkers discovered offer promising directions for personalized treatment strategies, although further validation in broader cohorts is essential for clinical application.

Experimental validation of computerised models of clustering of platelet glycoprotein receptors that signal via tandem SH2 domain proteins.

The clustering of platelet glycoprotein receptors with cytosolic YxxL and YxxM motifs, including GPVI, CLEC-2 and PEAR1, triggers activation via phosphorylation of the conserved tyrosine residues and recruitment of the tandem SH2 (Src homology 2) domain effector proteins, Syk and PI 3-kinase. We have modelled the clustering of these receptors with monovalent, divalent and tetravalent soluble ligands and with transmembrane ligands based on the law of mass action using ordinary differential equations and agent-based modelling. The models were experimentally evaluated in platelets and transfected cell lines using monovalent and multivalent ligands, including novel nanobody-based divalent and tetravalent ligands, by fluorescence correlation spectroscopy. Ligand valency, receptor number, receptor dimerisation, receptor phosphorylation and a cytosolic tandem SH2 domain protein act in synergy to drive receptor clustering. Threshold concentrations of a CLEC-2-blocking antibody and Syk inhibitor act in synergy to block platelet aggregation. This offers a strategy for countering the effect of avidity of multivalent ligands and in limiting off-target effects.

Systemic Blood Proteome Patterns Reflect Disease Phenotypes in Neovascular Age-Related Macular Degeneration.

There is early evidence of extraocular systemic signals effecting function and morphology in neovascular age-related macular degeneration (nAMD). The prospective, cross-sectional BIOMAC study is an explorative investigation of peripheral blood proteome profiles and matched clinical features to uncover systemic determinacy in nAMD under anti-vascular endothelial growth factor intravitreal therapy (anti-VEGF IVT). It includes 46 nAMD patients stratified by the level of disease control under ongoing anti-VEGF treatment. Proteomic profiles in peripheral blood samples of every patient were detected with LC-MS/MS mass spectrometry. The patients underwent extensive clinical examination with a focus on macular function and morphology. In silico analysis includes unbiased dimensionality reduction and clustering, a subsequent annotation of clinical features, and non-linear models for recognition of underlying patterns. The model assessment was performed using leave-one-out cross validation. The findings provide an exploratory demonstration of the link between systemic proteomic signals and macular disease pattern using and validating non-linear classification models. Three main results were obtained: (1) Proteome-based clustering identifies two distinct patient subclusters with the smaller one (n = 10) exhibiting a strong signature for oxidative stress response. Matching the relevant meta-features on the individual patient's level identifies pulmonary dysfunction as an underlying health condition in these patients. (2) We identify biomarkers for nAMD disease features with Aldolase C as a putative factor associated with superior disease control under ongoing anti-VEGF treatment. (3) Apart from this, isolated protein markers are only weakly correlated with nAMD disease expression. In contrast, applying a non-linear classification model identifies complex molecular patterns hidden in a high number of proteomic dimensions determining macular disease expression. In conclusion, so far unconsidered systemic signals in the peripheral blood proteome contribute to the clinically observed phenotype of nAMD, which should be examined in future translational research on AMD.

A framework for validating AI in precision medicine: considerations from the European ITFoC consortium.

BACKGROUND: Artificial intelligence (AI) has the potential to transform our healthcare systems significantly. New AI technologies based on machine learning approaches should play a key role in clinical decision-making in the future. However, their implementation in health care settings remains limited, mostly due to a lack of robust validation procedures. There is a need to develop reliable assessment frameworks for the clinical validation of AI. We present here an approach for assessing AI for predicting treatment response in triple-negative breast cancer (TNBC), using real-world data and molecular -omics data from clinical data warehouses and biobanks. METHODS: The European "ITFoC (Information Technology for the Future Of Cancer)" consortium designed a framework for the clinical validation of AI technologies for predicting treatment response in oncology. RESULTS: This framework is based on seven key steps specifying: (1) the intended use of AI, (2) the target population, (3) the timing of AI evaluation, (4) the datasets used for evaluation, (5) the procedures used for ensuring data safety (including data quality, privacy and security), (6) the metrics used for measuring performance, and (7) the procedures used to ensure that the AI is explainable. This framework forms the basis of a validation platform that we are building for the "ITFoC Challenge". This community-wide competition will make it possible to assess and compare AI algorithms for predicting the response to TNBC treatments with external real-world datasets. CONCLUSIONS: The predictive performance and safety of AI technologies must be assessed in a robust, unbiased and transparent manner before their implementation in healthcare settings. We believe that the consideration of the ITFoC consortium will contribute to the safe transfer and implementation of AI in clinical settings, in the context of precision oncology and personalized care.

Data and knowledge management in translational research: implementation of the eTRIKS platform for the IMI OncoTrack consortium.

BACKGROUND: For large international research consortia, such as those funded by the European Union's Horizon 2020 programme or the Innovative Medicines Initiative, good data coordination practices and tools are essential for the successful collection, organization and analysis of the resulting data. Research consortia are attempting ever more ambitious science to better understand disease, by leveraging technologies such as whole genome sequencing, proteomics, patient-derived biological models and computer-based systems biology simulations. RESULTS: The IMI eTRIKS consortium is charged with the task of developing an integrated knowledge management platform capable of supporting the complexity of the data generated by such research programmes. In this paper, using the example of the OncoTrack consortium, we describe a typical use case in translational medicine. The tranSMART knowledge management platform was implemented to support data from observational clinical cohorts, drug response data from cell culture models and drug response data from mouse xenograft tumour models. The high dimensional (omics) data from the molecular analyses of the corresponding biological materials were linked to these collections, so that users could browse and analyse these to derive candidate biomarkers. CONCLUSIONS: In all these steps, data mapping, linking and preparation are handled automatically by the tranSMART integration platform. Therefore, researchers without specialist data handling skills can focus directly on the scientific questions, without spending undue effort on processing the data and data integration, which are otherwise a burden and the most time-consuming part of translational research data analysis.

Network and systems biology: essential steps in virtualising drug discovery and development.

The biological processes that keep us healthy or cause disease, as well as the mechanisms of action of possible drugs are inherently complex. In the face of this complexity, attempts at discovering new drugs to treat diseases have alternated between trial-and-error (typically on experimental systems) and grand simplification, usually based on much too little information. We now have the chance to combine these strategies through establishment of 'virtual patient' models, centred on a detailed molecular characterisation of thousands or even, in the future, millions of patients. In doing so, we lay the foundations for truly personalised therapy, as well as a far-reaching virtualisation of drug discovery and development in oncology and other areas of medicine.

Proteomic and functional analysis of the mitotic Drosophila centrosome.

Regulation of centrosome structure, duplication and segregation is integrated into cellular pathways that control cell cycle progression and growth. As part of these pathways, numerous proteins with well-established non-centrosomal localization and function associate with the centrosome to fulfill regulatory functions. In turn, classical centrosomal components take up functional and structural roles as part of other cellular organelles and compartments. Thus, although a comprehensive inventory of centrosome components is missing, emerging evidence indicates that its molecular composition reflects the complexity of its functions. We analysed the Drosophila embryonic centrosomal proteome using immunoisolation in combination with mass spectrometry. The 251 identified components were functionally characterized by RNA interference. Among those, a core group of 11 proteins was critical for centrosome structure maintenance. Depletion of any of these proteins in Drosophila SL2 cells resulted in centrosome disintegration, revealing a molecular dependency of centrosome structure on components of the protein translation machinery, actin- and RNA-binding proteins. In total, we assigned novel centrosome-related functions to 24 proteins and confirmed 13 of these in human cells.

Exploring the Impact of Saccharin on Neovascular Age-Related Macular Degeneration: A Comprehensive Study in Patients and Mice.

Purpose: We aimed to determine the impact of artificial sweeteners (AS), especially saccharin, on the progression and treatment efficacy of patients with neovascular age-related macular degeneration (nAMD) under anti-vascular endothelial growth factor (anti-VEGF-A) treatment. Methods: In a cross-sectional study involving 46 patients with nAMD undergoing intravitreal anti-VEGF therapy, 6 AS metabolites were detected in peripheral blood using liquid chromatography - tandem mass spectrometry (LC-MS/MS). Disease features were statistically tested against these metabolite levels. Additionally, a murine choroidal neovascularization (CNV) model, induced by laser, was used to evaluate the effects of orally administered saccharin, assessing both imaging outcomes and gene expression patterns. Polymerase chain reaction (PCR) methods were used to evaluate functional expression of sweet taste receptors in a retinal pigment epithelium (RPE) cell line. Results: Saccharin levels in blood were significantly higher in patients with well-controlled CNV activity (P = 0.004) and those without subretinal hyper-reflective material (P = 0.015). In the murine model, saccharin-treated mice exhibited fewer leaking laser scars, lesser occurrence of bleeding, smaller fibrotic areas (P < 0.05), and a 40% decrease in mononuclear phagocyte accumulation (P = 0.06). Gene analysis indicated downregulation of inflammatory and VEGFR-1 response genes in the treated animals. Human RPE cells expressed taste receptor type 1 member 3 (TAS1R3) mRNA and reacted to saccharin stimulation with changes in mRNA expression. Conclusions: Saccharin appears to play a protective role in patients with nAMD undergoing intravitreal anti-VEGF treatment, aiding in better pathological lesion control and scar reduction. The murine study supports this observation, proposing saccharin's potential in mitigating pathological VEGFR-1-induced immune responses potentially via the RPE sensing saccharin in the blood stream.

DIPSBC--data integration platform for systems biology collaborations.

BACKGROUND: Modern biomedical research is often organized in collaborations involving labs worldwide. In particular in systems biology, complex molecular systems are analyzed that require the generation and interpretation of heterogeneous data for their explanation, for example ranging from gene expression studies and mass spectrometry measurements to experimental techniques for detecting molecular interactions and functional assays. XML has become the most prominent format for representing and exchanging these data. However, besides the development of standards there is still a fundamental lack of data integration systems that are able to utilize these exchange formats, organize the data in an integrative way and link it with applications for data interpretation and analysis. RESULTS: We have developed DIPSBC, an interactive data integration platform supporting collaborative research projects, based on Foswiki, Solr/Lucene, and specific helper applications. We describe the main features of the implementation and highlight the performance of the system with several use cases. All components of the system are platform independent and open-source developments and thus can be easily adopted by researchers. An exemplary installation of the platform which also provides several helper applications and detailed instructions for system usage and setup is available at http://dipsbc.molgen.mpg.de. CONCLUSIONS: DIPSBC is a data integration platform for medium-scale collaboration projects that has been tested already within several research collaborations. Because of its modular design and the incorporation of XML data formats it is highly flexible and easy to use.

Hsp90 inhibition differentially destabilises MAP kinase and TGF-beta signalling components in cancer cells revealed by kinase-targeted chemoproteomics.

BACKGROUND: The heat shock protein 90 (Hsp90) is required for the stability of many signalling kinases. As a target for cancer therapy it allows the simultaneous inhibition of several signalling pathways. However, its inhibition in healthy cells could also lead to severe side effects. This is the first comprehensive analysis of the response to Hsp90 inhibition at the kinome level. METHODS: We quantitatively profiled the effects of Hsp90 inhibition by geldanamycin on the kinome of one primary (Hs68) and three tumour cell lines (SW480, U2OS, A549) by affinity proteomics based on immobilized broad spectrum kinase inhibitors ("kinobeads"). To identify affected pathways we used the KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway classification. We combined Hsp90 and proteasome inhibition to identify Hsp90 substrates in Hs68 and SW480 cells. The mutational status of kinases from the used cell lines was determined using next-generation sequencing. A mutation of Hsp90 candidate client RIPK2 was mapped onto its structure. RESULTS: We measured relative abundances of > 140 protein kinases from the four cell lines in response to geldanamycin treatment and identified many new potential Hsp90 substrates. These kinases represent diverse families and cellular functions, with a strong representation of pathways involved in tumour progression like the BMP, MAPK and TGF-beta signalling cascades. Co-treatment with the proteasome inhibitor MG132 enabled us to classify 64 kinases as true Hsp90 clients. Finally, mutations in 7 kinases correlate with an altered response to Hsp90 inhibition. Structural modelling of the candidate client RIPK2 suggests an impact of the mutation on a proposed Hsp90 binding domain. CONCLUSIONS: We propose a high confidence list of Hsp90 kinase clients, which provides new opportunities for targeted and combinatorial cancer treatment and diagnostic applications.

Characterization of C-strain "Riems" TAV-epitope escape variants obtained through selective antibody pressure in cell culture.

Classical swine fever virus (CSFV) C-strain "Riems" escape variants generated under selective antibody pressure with monoclonal antibodies and a peptide-specific antiserum in cell culture were investigated. Candidates with up to three amino acid exchanges in the immunodominant and highly conserved linear TAV-epitope of the E2-glycoprotein, and additional mutations in the envelope proteins ERNS and E1, were characterized both in vitro and in vivo.It was further demonstrated, that intramuscular immunization of weaner pigs with variants selected after a series of passages elicited full protection against lethal CSFV challenge infection. These novel CSFV C-strain variants with exchanges in the TAV-epitope present potential marker vaccine candidates. The DIVA (differentiating infected from vaccinated animals) principle was tested for those variants using commercially available E2 antibody detection ELISA. Moreover, direct virus differentiation is possible using a real-time RT-PCR system specific for the new C-strain virus escape variants or using differential immunofluorescence staining.

Mini-batch optimization enables training of ODE models on large-scale datasets.

Quantitative dynamic models are widely used to study cellular signal processing. A critical step in modelling is the estimation of unknown model parameters from experimental data. As model sizes and datasets are steadily growing, established parameter optimization approaches for mechanistic models become computationally extremely challenging. Mini-batch optimization methods, as employed in deep learning, have better scaling properties. In this work, we adapt, apply, and benchmark mini-batch optimization for ordinary differential equation (ODE) models, thereby establishing a direct link between dynamic modelling and machine learning. On our main application example, a large-scale model of cancer signaling, we benchmark mini-batch optimization against established methods, achieving better optimization results and reducing computation by more than an order of magnitude. We expect that our work will serve as a first step towards mini-batch optimization tailored to ODE models and enable modelling of even larger and more complex systems than what is currently possible.

Proposal of a population wide genome-based testing for Covid-19.

Our lives (and deaths) have by now been dominated for two years by COVID-19, a pandemic that has caused hundreds of millions of disease cases, millions of deaths, trillions in economic costs, and major restrictions on our freedom. Here we suggest a novel tool for controlling the COVID-19 pandemic. The key element is a method for a population-scale PCR-based testing, applied on a systematic and repeated basis. For this we have developed a low cost, highly sensitive virus-genome-based test. Using Germany as an example, we demonstrate by using a mathematical model, how useful this strategy could have been in controlling the pandemic. We show using real-world examples how this might be implemented on a mass scale and discuss the feasibility of this approach.

The structural impact of cancer-associated missense mutations in oncogenes and tumor suppressors.

BACKGROUND: Current large-scale cancer sequencing projects have identified large numbers of somatic mutations covering an increasing number of different cancer tissues and patients. However, the characterization of these mutations at the structural and functional level remains a challenge. RESULTS: We present results from an analysis of the structural impact of frequent missense cancer mutations using an automated method. We find that inactivation of tumor suppressors in cancer correlates frequently with destabilizing mutations preferably in the core of the protein, while enhanced activity of oncogenes is often linked to specific mutations at functional sites. Furthermore, our results show that this alteration of oncogenic activity is often associated with mutations at ATP or GTP binding sites. CONCLUSIONS: With our findings we can confirm and statistically validate the hypotheses for the gain-of-function and loss-of-function mechanisms of oncogenes and tumor suppressors, respectively. We show that the distinct mutational patterns can potentially be used to pre-classify newly identified cancer-associated genes with yet unknown function.

Integration of the centrosome in cell cycle control, stress response and signal transduction pathways.

The identification of cell cycle control and signal transduction components on the centrosome has fostered the idea that the centrosome is more than a microtubule-organizing center. Indeed, recent molecular evidence suggests that the centrosome plays an active role not only in the regulation of microtubule nucleation activity, but also in the coordination of centrosome duplication with cell cycle progression, in stress response and in cell cycle checkpoint control. To achieve these roles, it interacts with a multitude of signal transduction molecules. The specificity of the interactions is mediated through anchoring proteins that bring centrosomal components and regulatory proteins into close proximity. The molecular composition and organization of the centrosome thus reflects its multiple functions.

Health technology assessment in the era of personalized health care.

OBJECTIVES: This article examines the challenges for health technology assessment (HTA) in the light of new developments of personalized health care, focusing on European HTA perspectives. METHODS: Using the example of the Integrated Genome Research Network - Mutanom (IG Mutanom) project, with focus on personalized cancer diagnostics and treatment, we assess the scope of current HTA and examine it prospectively in the context of the translation of basic and clinical research into public health genomics and personalized health care. RESULTS: The approaches developed within the IG-Mutanom project are based on innovative technology potentially providing targeted therapies for cancer; making translation into clinical practice requires a novel course of action, however. New models of HTA are needed that can account for the unique types of evidence inherent to individualized targeted therapies. Using constructive health technology assessment (CTA) models is an option, but further suitable models should be developed. CONCLUSIONS: Integrative, systems biology-based approaches toward personalized medicine call for novel assessment methods. The translation of their highly innovative technologies into the practice of health care requires the development of new HTA concepts.

Asterless is a centriolar protein required for centrosome function and embryo development in Drosophila.

BACKGROUND: Centrosomes, the major organizers of the microtubule network in most animal cells, are composed of centrioles embedded in a web of pericentriolar material (PCM). Recruitment and stabilization of PCM on the centrosome is a centriole-dependent function. Compared to the considerable number of PCM proteins known, the molecular characterization of centrioles is still very limited. Only a few centriolar proteins have been identified so far in Drosophila, most related to centriole duplication. RESULTS: We have cloned asterless (asl) and found that it encodes a 120 kD highly coiled-coil protein that is a constitutive pancentriolar and basal body component. Loss of asl function impedes the stabilization/maintenance of PCM at the centrosome. In embryos deficient for Asl, development is arrested right after fertilization. Asl shares significant homology with Cep 152, a protein described as a component of the human centrosome for which no functional data is yet available. CONCLUSIONS: The cloning of asl offers new insight into the molecular composition of Drosophila centrioles and a possible model for the role of its human homolog. In addition, the phenotype of asl-deficient flies reveals that a functional centrosome is required for Drosophila embryo development.

Pea-derived vaccines demonstrate high immunogenicity and protection in rabbits against rabbit haemorrhagic disease virus.

Vaccines against rabbit haemorrhagic disease virus (RHDV) are commercially produced in experimentally infected rabbits. A genetically engineered and manufactured version of the major structural protein of RHDV (VP60) is considered to be an alternative approach for vaccine production. Plants have the potential to become an excellent recombinant production system, but the low expression level and insufficient immunogenic potency of plant-derived VP60 still hamper its practical use. In this study, we analysed the expression of a novel multimeric VP60-based antigen in four different plant species, including Nicotiana tabacum L., Solanum tuberosum L., Brassica napus L. and Pisum sativum L. Significant differences were detected in the expression patterns of the novel fusion antigen cholera toxin B subunit (CTB)::VP60 (ctbvp60(SEKDEL)) at the mRNA and protein levels. Pentameric CTB::VP60 molecules were only detected in N. tabacum and P. sativum, and displayed equal levels of CTB, at approximately 0.01% of total soluble protein (TSP), and traces of detectable VP60. However, strong enhancement of the CTB protein content via self-fertilization was only observed in P. sativum, where it reached up to 0.7% of TSP. In rabbits, a strong decrease in the protective vaccine dose required from 48-400 microg potato-derived VP60 [Castanon, S., Marin, M.S., Martin-Alonso, J.M., Boga, J.A., Casais, R., Humara, J.M., Ordas, R.J. and Parra, F. (1999) Immunization with potato plants expressing VP60 protein protects against rabbit hemorrhagic disease virus. J. Virol. 73, 4452-4455; Castanon, S., Martin-Alonso, J.M., Marin, M.S., Boga, J.A., Alonso, P., Parra, F. and Ordas, R.J. (2002) The effect of the promoter on expression of VP60 gene from rabbit hemorrhagic disease virus in potato plants. Plant Sci. 162, 87-95] to 0.56-0.28 microg antigenic VP60 (measured with VP60 enzyme-linked immunosorbent assay) of crude CTB::VP60 pea extracts was demonstrated. Rabbits immunized with pea-derived CTB::VP60 showed anti-VP60-specific antibodies, similar to RikaVacc((R))-immunized rabbits, and survived RHDV challenge.

The micromechanical behavior of implant-abutment connections under a dynamic load protocol.

BACKGROUND: The implant-abutment connection (IAC) is known to be a key factor for the long-term stability of peri-implant tissue. PURPOSE: The aim of the present in vitro study was to detect and measure the mechanical behavior of different IACs by X-ray imaging. MATERIALS AND METHODS: A total of 20 different implant systems with various implant dimensions and IACs (13 conical-, 6 flat-, and 1 gable-like IAC) have been tested using a chewing device simulating dynamic and static loading up to 200 N. Micromovements have been recorded with a high-resolution, high-speed X-ray camera, and gap length and gap width between implant and abutment have been calculated. Furthermore, X-ray video sequences have been recorded to investigate the sealing capacity of different IACs. RESULTS: Out of the 20 implant systems, eight implant systems with a conical IAC showed no measurable gaps under static and dynamic loading (200 N). By contrast, all investigated implant systems with a flat IAC showed measurable gaps under dynamic and static loading. X-ray video sequences revealed that a representative conical IAC had sufficient sealing capacity. CONCLUSION: Within the limits of the present in vitro study, X-ray imaging showed reduced formation of microgaps and consecutive micromovements in implants with conical IAC compared to flat IACs.

Efficient Parameter Estimation Enables the Prediction of Drug Response Using a Mechanistic Pan-Cancer Pathway Model.

Mechanistic models are essential to deepen the understanding of complex diseases at the molecular level. Nowadays, high-throughput molecular and phenotypic characterizations are possible, but the integration of such data with prior knowledge on signaling pathways is limited by the availability of scalable computational methods. Here, we present a computational framework for the parameterization of large-scale mechanistic models and its application to the prediction of drug response of cancer cell lines from exome and transcriptome sequencing data. This framework is over 104 times faster than state-of-the-art methods, which enables modeling at previously infeasible scales. By applying the framework to a model describing major cancer-associated pathways (>1,200 species and >2,600 reactions), we could predict the effect of drug combinations from single drug data. This is the first integration of high-throughput datasets using large-scale mechanistic models. We anticipate this to be the starting point for development of more comprehensive models allowing a deeper mechanistic insight.