Search

Non-healing bone defects are a pressing public health concern accounting for one main cause for decreased life expectancy and quality. An aging population accompanied with increasing incidence of comorbidities, foreshadows a worsening of this socio-economic problem. Conventional treatments for non-healing bone defects prove ineffective for 5%–10% of fractures. Those challenges not only increase the patient’s burden but also complicate medical intervention, underscoring the need for more effective treatment strategies and identification of patients at risk before treatment selection. To address this, our proteomic meta-analysis aims to identify universally affected proteins and functions in the context of bone regeneration that can be utilized as novel bioactive biomaterial functionalizations, drug targets or therapeutics as well as analytical endpoints, or biomarkers in implant design and testing, respectively. We compiled 29 proteomic studies covering cellular models, extracellular vesicles, extracellular matrix, bone tissue, and liquid-biopsies to address different tissue hierarchies and species. An innovative, integrated framework consisting of data harmonization, candidate protein selection, network construction, and functional enrichment as well as drug repurposing and protein scoring metrics was developed. To make this framework widely applicable to other research questions, we have published a detailed tutorial of our meta-analysis process. We identified 51 proteins that are potentially important for bone healing. This includes well-known ECM components such as collagens, fibronectin and periostin, and proteins less explored in bone biology like YWHAE, HSPG2, CCN1, HTRA1, IGFBP7, LGALS1, TGFBI, C3, SERPINA1, and ANXA1 that might be utilized in future bone biomaterial development. Furthermore, we discovered the compounds trifluoperazine, phenethyl isothiocyanate, quercetin, and artenimol, which target key proteins such as S100A4, YWHAZ, MMP2, and TPM4 providing the option to manipulate undesired processes in bone regeneration. This may open new ways for treatment options to face the increasing socio-economic pressure of non-healing bone defects.

Abstract Proteomics technologies, which include a diverse range of approaches such as mass spectrometry-based, array-based, and others, are key technologies for the identification of biomarkers and disease mechanisms, referred to as mechanotyping. Despite over 15,000 published studies in 2022 alone, leveraging publicly available proteomics data for biomarker identification, mechanotyping and drug target identification is not readily possible. Proteomic data addressing similar biological/biomedical questions are made available by multiple research groups in different locations using different model organisms. Furthermore, not only various organisms are employed but different assay systems, such as in vitro and in vivo systems, are used. Finally, even though proteomics data are deposited in public databases, such as ProteomeXchange, they are provided at different levels of detail. Thus, data integration is hampered by non-harmonized usage of identifiers when reviewing the literature or performing meta-analyses to consolidate existing publications into a joint picture. To address this problem, we present ProHarMeD, a tool for harmonizing and comparing proteomics data gathered in multiple studies and for the extraction of disease mechanisms and putative drug repurposing candidates. It is available as a website, Python library and R package. ProHarMeD facilitates ID and name conversions between protein and gene levels, or organisms via ortholog mapping, and provides detailed logs on the loss and gain of IDs after each step. The web tool further determines IDs shared by different studies, proposes potential disease mechanisms as well as drug repurposing candidates automatically, and visualizes these results interactively. We apply ProHarMeD to a set of four studies on bone regeneration. First, we demonstrate the benefit of ID harmonization which increases the number of shared genes between studies by 50%. Second, we identify a potential disease mechanism, with five corresponding drug targets, and the top 20 putative drug repurposing candidates, of which Fondaparinux, the candidate with the highest score, and multiple others are known to have an impact on bone regeneration. Hence, ProHarMeD allows users to harmonize multi-centric proteomics research data in meta-analyses, evaluates the success of the ID conversions and remappings, and finally, it closes the gaps between proteomics, disease mechanism mining and drug repurposing. It is publicly available at https://apps.cosy.bio/proharmed/ .

BackgroundArtificial intelligence (AI) and game-based methods such as serious games or gamification are both emerging technologies and methodologies in health care. The merging of the two could provide greater advantages, particularly in the field of therapeutic interventions in medicine. ObjectiveThis scoping review sought to generate an overview of the currently existing literature on the connection of AI and game-based approaches in health care. The primary objectives were to cluster studies by disease and health topic addressed, level of care, and AI or games technology. MethodsFor this scoping review, the databases PubMed, Scopus, IEEE Xplore, Cochrane Library, and PubPsych were comprehensively searched on February 2, 2022. Two independent authors conducted the screening process using Rayyan software (Rayyan Systems Inc). Only original studies published in English since 1992 were eligible for inclusion. The studies had to involve aspects of therapy or education in medicine and the use of AI in combination with game-based approaches. Each publication was coded for basic characteristics, including the population, intervention, comparison, and outcomes (PICO) criteria; the level of evidence; the disease and health issue; the level of care; the game variant; the AI technology; and the function type. Inductive coding was used to identify the patterns, themes, and categories in the data. Individual codings were analyzed and summarized narratively. ResultsA total of 16 papers met all inclusion criteria. Most of the studies (10/16, 63%) were conducted in disease rehabilitation, tackling motion impairment (eg, after stroke or trauma). Another cluster of studies (3/16, 19%) was found in the detection and rehabilitation of cognitive impairment. Machine learning was the main AI technology applied and serious games the main game-based approach used. However, direct interaction between the technologies occurred only in 3 (19%) of the 16 studies. The included studies all show very limited quality evidence. From the patients’ and healthy individuals’ perspective, generally high usability, motivation, and satisfaction were found. ConclusionsThe review shows limited quality of evidence for the combination of AI and games in health care. Most of the included studies were nonrandomized pilot studies with few participants (14/16, 88%). This leads to a high risk for a range of biases and limits overall conclusions. However, the first results present a broad scope of possible applications, especially in motion and cognitive impairment, as well as positive perceptions by patients. In future, the development of adaptive game designs with direct interaction between AI and games seems promising and should be a topic for future reviews.

IntroductionThe aryl hydrocarbon receptor (AhR) is a ligand-activated transcription factor that regulates a broad range of target genes involved in the xenobiotic response, cell cycle control and circadian rhythm. AhR is constitutively expressed in macrophages (Mϕ), acting as key regulator of cytokine production. While proinflammatory cytokines, i.e., IL-1β, IL-6, IL-12, are suppressed through AhR activation, anti-inflammatory IL-10 is induced. However, the underlying mechanisms of those effects and the importance of the specific ligand structure are not yet completely understood.MethodsTherefore, we have compared the global gene expression pattern in activated murine bone marrow-derived macrophages (BMMs) subsequently to exposure with either benzo[a]pyrene (BaP) or indole-3-carbinol (I3C), representing high-affinity vs. low-affinity AhR ligands, respectively, by means of mRNA sequencing. AhR dependency of observed effects was proved using BMMs from AhR-knockout (Ahr-/-) mice.Results and discussionIn total, more than 1,000 differentially expressed genes (DEGs) could be mapped, covering a plethora of AhR-modulated effects on basal cellular processes, i.e., transcription and translation, but also immune functions, i.e., antigen presentation, cytokine production, and phagocytosis. Among DEGs were genes that are already known to be regulated by AhR, i.e., Irf1, Ido2, and Cd84. However, we identified DEGs not yet described to be AhR-regulated in Mϕ so far, i.e., Slpi, Il12rb1, and Il21r. All six genes likely contribute to shifting the Mϕ phenotype from proinflammatory to anti-inflammatory. The majority of DEGs induced through BaP were not affected through I3C exposure, probably due to higher AhR affinity of BaP in comparison to I3C. Mapping of known aryl hydrocarbon response element (AHRE) sequence motifs in identified DEGs revealed more than 200 genes not possessing any AHRE, and therefore being not eligible for canonical regulation. Bioinformatic approaches modeled a central role of type I and type II interferons in the regulation of those genes. Additionally, RT-qPCR and ELISA confirmed a AhR-dependent expressional induction and AhR-dependent secretion of IFN-γ in response to BaP exposure, suggesting an auto- or paracrine activation pathway of Mϕ.

Tuberculosis (TB), which is caused by the bacterium Mycobacterium tuberculosis (Mtb), is still one of the deadliest infectious diseases. Understanding how the host and pathogen interact in active TB will have a significant impact on global TB control efforts. Exosomes are increasingly recognized as a means of cell-to-cell contact and exchange of soluble mediators. In the case of TB, exosomes are released from the bacillus and infected cells. In the present study, a comprehensive lipidomics and proteomics analysis of size exclusion chromatography-isolated plasma-derived exosomes from patients with TB lymphadenitis (TBL) and treated as well as untreated pulmonary TB (PTB) was performed to elucidate the possibility to utilize exosomes in diagnostics and knowledge building. According to our findings, exosome-derived lipids and proteins originate from both the host and Mtb in the plasma of active TB patients. Exosomes from all patients are mostly composed of sphingomyelins (SM), phosphatidylcholines, phosphatidylinositols, free fatty acids, triacylglycerols (TAG), and cholesterylesters. Relative proportions of, e.g., SMs and TAGs, vary depending on the disease or treatment state and could be linked to Mtb pathogenesis and dormancy. We identified three proteins of Mtb origin: DNA-directed RNA polymerase subunit beta (RpoC), Diacyglycerol O-acyltransferase (Rv2285), and Formate hydrogenase (HycE), the latter of which was discovered to be differently expressed in TBL patients. Furthermore, we discovered that Mtb infection alters the host protein composition of circulating exosomes, significantly affecting a total of 37 proteins. All TB patients had low levels of apolipoproteins, as well as the antibacterial proteins cathelicidin, Scavenger Receptor Cysteine Rich Family Member (SSC5D), and Ficolin 3 (FCN3). When compared to healthy controls, the protein profiles of PTB and TBL were substantially linked, with 14 proteins being co-regulated. However, adhesion proteins (integrins, Intercellular adhesion molecule 2 (ICAM2), CD151, Proteoglycan 4 (PRG4)) were shown to be more prevalent in PTB patients, while immunoglobulins, Complement component 1r (C1R), and Glutamate receptor-interacting protein 1 (GRIP1) were found to be more abundant in TBL patients, respectively. This study could confirm findings from previous reports and uncover novel molecular profiles not previously in focus of TB research. However, we applied a minimally invasive sampling and analysis of circulating exosomes in TB patients. Based on the findings given here, future studies into host–pathogen interactions could pave the way for the development of new vaccines and therapies.