Sci Data. 2024 Dec 2;11(1):1313. doi: 10.1038/s41597-024-04047-9.

ABSTRACT

Lyme disease is caused by an infection with the spirochete Borrelia burgdorferi, and is the most common vector-borne disease in North America. B. burgdorferi isolates harbor extensive genomic and proteomic variability and further comparison of isolates is key to understanding the infectivity of the spirochetes and biological impacts of identified sequence variants. Here, we applied both transcriptome analysis and mass spectrometry-based proteomics to assemble peptide datasets of B. burgdorferi laboratory isolates B31, MM1, and the infective isolate B31-5A4, to provide a publicly available Borrelia PeptideAtlas. Included are total proteome, secretome, and membrane proteome identifications of the individual isolates. Proteomic data collected from 35 different experiment datasets, totaling 386 mass spectrometry runs, have identified 81,967 distinct peptides, which map to 1,113 proteins. The Borrelia PeptideAtlas covers 86% of the total B31 proteome of 1,291 protein sequences. The Borrelia PeptideAtlas is an extensible comprehensive peptide repository with proteomic information from B. burgdorferi isolates useful for Lyme disease research.

PMID:39622905 | DOI:10.1038/s41597-024-04047-9

Nat Commun. 2024 Dec 2;15(1):10480. doi: 10.1038/s41467-024-54727-8.

ABSTRACT

The overwhelming majority of participants in genome-wide association studies (GWAS) have European (EUR) ancestry, and polygenic scores (PGS) derived from EURs often perform poorly in non-EURs. Previous studies suggest that between-ancestry differences in allele frequencies and linkage disequilibrium are significant contributors to the poor portability of PGS in cross-ancestry prediction. We hypothesize that the portability of (local) PGS varies significantly over the genome. Therefore, we develop a method, MC-ANOVA, to estimate the loss of accuracy in cross-ancestry prediction attributable to allele frequency and linkage disequilibrium differences between ancestries. Using data from the UK Biobank we develop PGS relative accuracy (RA) maps quantifying the local portability of EUR-derived PGS in non-EUR ancestries. We report substantial variability in RA along the genome, suggesting that even in ancestries with low overall RA of EUR-derived effects (e.g., African), there are regions with high RA. We substantiate our findings using six complex traits, which show that EUR-derived effects from regions where MC-ANOVA predicts high RA also have high empirical RA in real PGS. We provide software implementing MC-ANOVA and RA maps for several non-EUR ancestries. These maps can be used to interpret similarities and differences in GWAS results between groups and to improve cross-ancestry prediction.

PMID:39622843 | DOI:10.1038/s41467-024-54727-8

PLoS Comput Biol. 2024 Dec 2;20(12):e1012555. doi: 10.1371/journal.pcbi.1012555. Online ahead of print.

ABSTRACT

Gene expression patterns in developing organisms are established by groups of cross-regulating target genes that are driven by morphogen gradients. As development progresses, morphogen activity is reduced, leaving the emergent pattern without stabilizing positional cues and at risk of rapid deterioration due to the inherently noisy biochemical processes at the cellular level. But remarkably, gene expression patterns remain spatially stable and reproducible over long developmental time spans in many biological systems. Here we combine spatial-stochastic simulations with an enhanced sampling method (Non-Stationary Forward Flux Sampling) and a recently developed stability theory to address how spatiotemporal integrity of a gene expression pattern is maintained in developing tissue lacking morphogen gradients. Using a minimal embryo model consisting of spatially coupled biochemical reactor volumes, we study a prototypical stripe pattern in which weak cross-repression between nearest neighbor expression domains alternates with strong repression between next-nearest neighbor domains, inspired by the gap gene system in the Drosophila embryo. We find that tuning of the weak repressive interactions to an optimal level can prolong stability of the expression patterns by orders of magnitude, enabling stable patterns over developmentally relevant times in the absence of morphogen gradients. The optimal parameter regime found in simulations of the embryo model closely agrees with the predictions of our coarse-grained stability theory. To elucidate the origin of stability, we analyze a reduced phase space defined by two measures of pattern asymmetry. We find that in the optimal regime, intact patterns are protected via restoring forces that counteract random perturbations and give rise to a metastable basin. Together, our results demonstrate that metastable attractors can emerge as a property of stochastic gene expression patterns even without system-wide positional cues, provided that the gene regulatory interactions shaping the pattern are optimally tuned.

PMID:39621779 | DOI:10.1371/journal.pcbi.1012555

PLoS One. 2024 Dec 2;19(12):e0310303. doi: 10.1371/journal.pone.0310303. eCollection 2024.

ABSTRACT

Peripheral glia are important regulators of diverse physiologic functions yet their molecular distinctions and locations in almost all visceral organs are not well-understood. We performed a systematic analysis of peripheral glia, focusing on the lung and leveraging single cell RNA sequencing (scRNA-seq) analysis to characterize their cellular and molecular features. Using in vivo lineage studies, we characterized the anatomic, cellular, and molecular features of the Sox10+ glial lineage of the mouse lung. Using high-resolution imaging, we quantified the distribution and cellular morphologies of myelinating, non-myelinating, satellite, and terminal glial cells with their intricate extensions along peripheral nerves, including terminals at specialized neurosensory structures within the lung. Spatial analysis of selectively expressed myelinating (periaxin/Prx, claudin 19/Cldn) or non-myelinating (sodium channel/Scn7a) glial cell genes identified by scRNA-seq analysis revealed molecularly distinct populations surrounding myelinated nerve fibers in the lung. To extend this analysis to primates and other organs, we extracted rare peripheral glial cells in whole organism scRNA-seq atlases of mouse lemur and human. Our cross-species data analysis and integration of scRNA-seq data of ~700 peripheral glial cells from mouse, mouse lemur, and human glial cells identified conserved gene expression of molecularly distinct peripheral glial cell populations. This foundational knowledge facilitates subsequent functional studies targeting molecularly distinct subsets of peripheral glia and integrating them into organ-specific disorders of autonomic dysregulation. In addition, our cross-species analysis identifying conserved gene expression patterns and glial networks in extrapulmonary organs provides a valuable resource for studying the functional role of peripheral glia in multiorgan human diseases.

PMID:39621665 | DOI:10.1371/journal.pone.0310303

PLoS One. 2024 Dec 2;19(12):e0313768. doi: 10.1371/journal.pone.0313768. eCollection 2024.

ABSTRACT

Rehabilitation from musculoskeletal injuries (MSKI) complicate healing dynamics typically by sustained disuse of bone and muscles. Microgravity naturally allows limb disuse and thus an effective model to understand MSKI. The current study examined epigenetic changes in a segmental bone defect (SBD) mouse model in a prolonged unloading condition after spaceflight (FLT). We further connected potential miRNA-mRNA regulatory pathways impacting bone healing. Here, SBD surgery was performed on nine-week-old male mice that were launched into space for approximately 4 weeks. Sham with no surgery and ground controls were included in the study. The midshaft of the ipsilateral femur (with callus on the surgical mice) as well as the ipsilateral quadriceps tissue were used for analysis. Femur and quadriceps had a distinct miRNA profile. There was a stronger surgery effect as observed by miRNA expression when compared to microgravity effects. Leukopoiesis, granulopoiesis, myelopoiesis of leukocytes, differentiation of myeloid leukocytes, and differentiation of progenitor cells were all altered because of surgery in the femur. The biological functions such as apoptosis, necrosis, and activation of cell migration and viability were altered because of surgery in quadriceps. Integrating the transcriptome and microRNA data indicated pronounced changes because of microgravity. According to pathway analysis, microgravity had a greater impact on the quadriceps tissue than the bone tissue in the absence of surgery. The altered biological functions resulting from microgravity were validated by integrating limited proteomics data to miRNA-mRNA. Thus, this study highlights the importance of dynamic interplay of gene-epigene regulations as they appear to be intrinsically interconnected and influence in combination for the biological outcome.

PMID:39621621 | DOI:10.1371/journal.pone.0313768

J Clin Invest. 2024 Dec 2;134(23):e185569. doi: 10.1172/JCI185569.

NO ABSTRACT

PMID:39621311 | DOI:10.1172/JCI185569

Hepatol Commun. 2024 Nov 29;8(12):e0586. doi: 10.1097/HC9.0000000000000586. eCollection 2024 Dec 1.

ABSTRACT

BACKGROUND: Reliable, noninvasive tools to diagnose at-risk metabolic dysfunction-associated steatohepatitis (MASH) are urgently needed to improve management. We developed a risk stratification score incorporating proteomics-derived serum markers with clinical variables to identify high-risk patients with MASH (NAFLD activity score >4 and fibrosis score >2).

METHODS: In this 3-phase proteomic study of biopsy-proven metabolic dysfunction-associated steatotic fatty liver disease, we first developed a multi-protein predictor for discriminating NAFLD activity score >4 based on SOMAscan proteomics quantifying 1305 serum proteins from 57 US patients. Four key predictor proteins were verified by ELISA in the expanded US cohort (N = 168) and enhanced by adding clinical variables to create the 9-feature MASH Dx score, which predicted MASH and also high-risk MASH (F2+). The MASH Dx score was validated in 2 independent, external cohorts from Germany (N = 139) and Brazil (N = 177).

RESULTS: The discovery phase identified a 6-protein classifier that achieved an AUC of 0.93 for identifying MASH. Significant elevation of 4 proteins (THBS2, GDF15, SELE, and IGFBP7) was verified by ELISA in the expanded discovery and independently in the 2 external cohorts. MASH Dx score incorporated these proteins with established MASH risk factors (age, body mass index, ALT, diabetes, and hypertension) to achieve good discrimination between MASH and metabolic dysfunction-associated steatotic fatty liver disease without MASH (AUC: 0.87-discovery; 0.83-pooled external validation cohorts), with similar performance when evaluating high-risk MASH F2-4 (vs. MASH F0-1 and metabolic dysfunction-associated steatotic fatty liver disease without MASH).

CONCLUSIONS: The MASH Dx score offers the first reliable noninvasive approach combining novel, biologically plausible ELISA-based fibrosis markers and clinical parameters to detect high-risk MASH in patient cohorts from the United States, Brazil, and Europe.

PMID:39621304 | DOI:10.1097/HC9.0000000000000586

Gastric Cancer. 2024 Dec 2. doi: 10.1007/s10120-024-01569-4. Online ahead of print.

ABSTRACT

BACKGROUND: Gastric cancer is a major oncological challenge, ranking highly among causes of cancer-related mortality worldwide. This study was initiated to address the variability in patient responses to combination chemotherapy, highlighting the need for personalized treatment strategies based on genomic data.

METHODS: We analyzed whole-genome and RNA sequences from biopsy specimens of 65 advanced gastric cancer patients before their chemotherapy treatment. Using machine learning techniques, we developed a model with 123 omics features, such as immune signatures and copy number variations, to predict their chemotherapy outcomes.

RESULTS: The model demonstrated a prediction accuracy of 70-80% in forecasting chemotherapy responses in both test and validation cohorts. Notably, tumor-associated neutrophils emerged as significant predictors of treatment efficacy. Further single-cell analyses from cancer tissues revealed different neutrophil subgroups with potential antitumor activities suggesting their usefulness as biomarkers for treatment decisions.

CONCLUSIONS: This study confirms the utility of machine learning in advancing personalized medicine for gastric cancer by identifying tumor-associated neutrophils and their subgroups as key indicators of chemotherapy response. These findings could lead to more tailored and effective treatment plans for patients.

PMID:39621213 | DOI:10.1007/s10120-024-01569-4

J Cell Biol. 2025 Feb 3;224(2):e202406107. doi: 10.1083/jcb.202406107. Epub 2024 Dec 2.

ABSTRACT

Cell division in Escherichia coli is intricately regulated by the MinD and MinE proteins, which form oscillatory waves between cell poles. These waves manifest as concentration gradients that reduce MinC inhibition at the cell center, thereby influencing division site placement. This study explores the plasticity of the MinD gradients resulting from the interdependent interplay between molecular interactions and diffusion in the system. Through live cell imaging, we observed that as cells elongate, the gradient steepens, the midcell concentration decreases, and the oscillation period stabilizes. A one-dimensional model investigates kinetic rate constants representing various molecular interactions, effectively recapitulating our experimental findings. The model reveals the nonlinear dynamics of the system and a dynamic equilibrium among these constants, which underlie variable concentration gradients in growing cells. This study enhances quantitative understanding of MinD oscillations within the cellular environment. Furthermore, it emphasizes the fundamental role of concentration gradients in cellular processes.

PMID:39621132 | DOI:10.1083/jcb.202406107

iScience. 2024 Oct 25;27(11):111076. doi: 10.1016/j.isci.2024.111076. eCollection 2024 Nov 15.

ABSTRACT

The potential of ancient DNA analyses to provide independent sources of information about events in the historical record remains to be demonstrated. Here we apply palaeogenomic analysis to human remains excavated from a medieval well at the ruins of Sverresborg Castle in central Norway. In Sverris Saga, the Old Norse saga of King Sverre Sigurdsson, one passage details a 1197-CE raid on the castle and mentions a dead man thrown into the well. Radiocarbon dating supports that these are that individual's remains. We sequenced the Well-man's nuclear genome to 3.4× and compared it to Scandinavian populations, revealing he was closely related to inhabitants of southern Norway. This was surprising because King Sverre's defeated army was assumed to be recruited from parts of central Norway, whereas the raiders were from the south. The findings also indicate that the unique genetic drift seen in present-day southern Norwegians already existed 800 years ago.

PMID:39620136 | PMC:PMC11607536 | DOI:10.1016/j.isci.2024.111076

J Tissue Eng. 2024 Nov 29;15:20417314241295332. doi: 10.1177/20417314241295332. eCollection 2024 Jan-Dec.

ABSTRACT

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.

PMID:39620099 | PMC:PMC11605762 | DOI:10.1177/20417314241295332

Eng Comput. 2024;40(3):1469-1499. doi: 10.1007/s00366-023-01873-0. Epub 2023 Aug 7.

ABSTRACT

Neoadjuvant chemotherapy (NAC) is a standard-of-care treatment for locally advanced triple negative breast cancer (TNBC) before surgery. The early assessment of TNBC response to NAC would enable an oncologist to adapt the therapeutic plan of a non-responding patient, thereby improving treatment outcomes while preventing unnecessary toxicities. To this end, a promising approach consists of obtaining in silico personalized forecasts of tumor response to NAC via computer simulation of mechanistic models constrained with patient-specific magnetic resonance imaging (MRI) data acquired early during NAC. Here, we present a new mechanistic model of TNBC growth and response to NAC, including an explicit description of drug pharmacodynamics and pharmacokinetics. As longitudinal in vivo MRI data for model calibration is limited, we perform a sensitivity analysis to identify the model mechanisms driving the response to two NAC drug combinations: doxorubicin with cyclophosphamide, and paclitaxel with carboplatin. The model parameter space is constructed by combining patient-specific MRI-based in silico parameter estimates and in vitro measurements of pharmacodynamic parameters obtained using time-resolved microscopy assays of several TNBC lines. The sensitivity analysis is run in two MRI-based scenarios corresponding to a well-perfused and a poorly perfused tumor. Out of the 15 parameters considered herein, only the baseline tumor cell net proliferation rate along with the maximum concentrations and effects of doxorubicin, carboplatin, and paclitaxel exhibit a relevant impact on model forecasts (total effect index, S T 0.1). These results dramatically limit the number of parameters that require in vivo MRI-constrained calibration, thereby facilitating the clinical application of our model.

SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00366-023-01873-0.

PMID:39620056 | PMC:PMC11607094 | DOI:10.1007/s00366-023-01873-0

RSC Med Chem. 2024 Nov 29. doi: 10.1039/d4md00735b. Online ahead of print.

ABSTRACT

Target 2035 is a global initiative that seeks to identify a pharmacological modulator of most human proteins by the year 2035. As part of an ongoing series of annual updates of this initiative, we summarise here the efforts of the EUbOPEN project whose objectives and results are making a strong contribution to the goals of Target 2035. EUbOPEN is a public-private partnership with four pillars of activity: (1) chemogenomic library collections, (2) chemical probe discovery and technology development for hit-to-lead chemistry, (3) profiling of bioactive compounds in patient-derived disease assays, and (4) collection, storage and dissemination of project-wide data and reagents. The substantial outputs of this programme include a chemogenomic compound library covering one third of the druggable proteome, as well as 100 chemical probes, both profiled in patient derived assays, as well as hundreds of data sets deposited in existing public data repositories and a project-specific data resource for exploring EUbOPEN outputs.

PMID:39618964 | PMC:PMC11605244 | DOI:10.1039/d4md00735b

Biophys Rev. 2024 Oct 21;16(5):515-517. doi: 10.1007/s12551-024-01226-1. eCollection 2024 Oct.

ABSTRACT

As one of the twelve newly elected councillors, it is my pleasure to provide a brief biographical sketch for the readers of Biophys. Rev. and the members of the Biophysical Societies. I have been actively involved in the German Biophysical Society (DGfB) since 2008, initially as the speaker for the "Membrane Biophysics" section and, since 2015, as the secretary. Within the IUPAB council I follow Prof. Hans-Joachim Galla, former Secretary and President of the German Biophysical Society, who served as a councillor for two terms from 2018 to 2024. Thus, a direct continuation of the German contribution to the IUPAB is guaranteed. My journey in biophysics began during my studies of physics at the University of Kiel, where I specialized in physiology and biophysics. After earning my doctorate in the lab of Ulrich Seydel at the Research Center Borstel, I spent two years at the University of California, Santa Barbara, working in Paul Hansma's lab on the development and application of atomic force microscopy. During my time at UCSB, I also collaborated with Jacob Israelachvili's lab on membrane properties. Since 2008, I have been leading the Biophysics Research Group at the Research Center Borstel, Leibniz Lung Center. In 2010, I was appointed as a professor at the University of Lübeck. Additionally, since 2023, I have been serving as an associate member at the Centre for Structural Systems Biology (CSSB) in Hamburg.

PMID:39618781 | PMC:PMC11604882 | DOI:10.1007/s12551-024-01226-1

iScience. 2024 Oct 22;27(12):111218. doi: 10.1016/j.isci.2024.111218. eCollection 2024 Dec 20.

ABSTRACT

Our aim is to predict how often genic and non-genic promoters fire within a cell. We first review a parsimonious pan-genomic model for genome organization and gene regulation, where transcription rate is determined by proximity in 3D space of promoters to clusters containing appropriate factors and RNA polymerases. This model reconciles conflicting results indicating that regulatory mammalian networks are both simple (as over-expressing just 4 transcription factors switches cell state) and complex (as genome-wide association studies show phenotypes like cell type are determined by thousands of loci rarely encoding such factors). We then present 3D polymer simulations, and a proximity formula based on our biological model that enables prediction of transcriptional activities of all promoters in three human cell types. This simple fitting-free formula contains just one variable (distance on the genetic map to the nearest active promoter), and we suggest it can in principle be applied to any organism.

PMID:39618494 | PMC:PMC11607604 | DOI:10.1016/j.isci.2024.111218

J Drug Target. 2024 Dec 2:1-6. doi: 10.1080/1061186X.2024.2434911. Online ahead of print.

ABSTRACT

Drug-induced photosensitivity is a potential adverse event of many drugs and chemicals used across a wide range of specialties in clinical medicine. In the present study, we investigated the feasibility of predicting the photosensitising effects of drugs and chemical compounds via state-of-the-art artificial intelligence-based workflows. A dataset of 2200 drugs was used to train three distinct models (logistic regression, XGBoost and a deep learning model (Chemprop)) to predict photosensitising attributes. Labels were obtained from a list of previously published photosensitisers by string matching and manual validation. External evaluation of the different models was performed using the tox21 dataset. ROC-AUC ranged between 0.8939 (Chemprop) and 0.9525 (XGBoost) during training, while in the test partition it ranged between 0.7785 (Chemprop) and 0.7927 (XGBoost). Analysis of the top 200 compounds of each model resulted in 55 overlapping molecules in the external validation set. Prediction scores in fluoroquinolones within this subset corresponded well with culprit substructures such as fluorinated aryl halides suspected of mediating photosensitising effects. All three models appeared capable of predicting photosensitising effects of chemical compounds. However, compared to the simpler model, the complex models appeared to be more confident in their predictions as exhibited by their distribution of prediction scores.

PMID:39618307 | DOI:10.1080/1061186X.2024.2434911

Mol Cell Proteomics. 2024 Nov 29:100888. doi: 10.1016/j.mcpro.2024.100888. Online ahead of print.

ABSTRACT

Cells have many protective mechanisms against background levels of ionizing radiation (IR) orchestrated by molecular changes in expression, post-translational modifications and subcellular localization. Radiotherapeutic treatment in oncology attempts to overwhelm such mechanisms, but radio-resistance is an ongoing challenge. Here, global subcellular proteomics combined with Bayesian modeling identified 544 differentially localized proteins in A549 cells upon 6 Gy x-ray exposure, revealing subcellular-specific changes of proteins involved in ferroptosis, an iron-dependent cell death, suggestive of potential radio-resistance mechanisms. These observations were independent of expression changes, emphasizing the utility of global subcellular proteomics and the promising prospect of ferroptosis-inducing therapies for combatting radioresistance.

PMID:39617061 | DOI:10.1016/j.mcpro.2024.100888

J Biomol Struct Dyn. 2024 Dec 1:1-16. doi: 10.1080/07391102.2024.2435033. Online ahead of print.

ABSTRACT

Hearing loss is the incapability to hear sound, either partially or fully. One potential natural remedy for hearing loss is the use of Carthamus tinctorius, commonly known as safflower, contains bioactive compounds, including flavonoids, phenolic acids, and terpenoids, which possess potent antioxidant and anti-inflammatory activities. This study uses network pharmacology to identify the potential therapeutic effects of these compounds on hearing loss. We identified 17 active compounds of C. tinctorius with favorable ADME properties through a litrature search. Potential targets for these compounds were found using databases like STITCH and Swiss Target Prediction, as well as the GeneCards database to retrieve hearing loss-related potential targets. A Venn diagram was drawn to determine shared targets, and GO enrichment and KEGG pathways analysis of 64 key targets were conducted DAVID database. Protein-protein interactions (PPIs) were analyzed via STRING database, and a compound-target-pathway network was created in Cytoscape. Molecular docking studies focused on one hub gene, namely, tumor necrosis factor (TNF) revealing that C. tinctorius's compounds have high affinity for TNF, suggesting a potential therapeutic link to hearing loss. Finally, molecular dynamics simulations and calculations of binding free energy showed TNF_Beta Sitosterol and TNF_Campesterol were more stable than TNF_Cholesterol. Our findings indicate that C. tinctorius may have therapeutic potential for hearing loss by targeting key genes and pathways. However, further in-vivo and in-vitro studies are needed to confirm these findings and determine optimal treatment parameters.

PMID:39616544 | DOI:10.1080/07391102.2024.2435033

Cell Discov. 2024 Nov 30;10(1):122. doi: 10.1038/s41421-024-00753-1.

NO ABSTRACT

PMID:39616169 | DOI:10.1038/s41421-024-00753-1

NPJ Syst Biol Appl. 2024 Nov 30;10(1):141. doi: 10.1038/s41540-024-00450-5.

ABSTRACT

Digital twins represent a key technology for precision health. Medical digital twins consist of computational models that represent the health state of individual patients over time, enabling optimal therapeutics and forecasting patient prognosis. Many health conditions involve the immune system, so it is crucial to include its key features when designing medical digital twins. The immune response is complex and varies across diseases and patients, and its modelling requires the collective expertise of the clinical, immunology, and computational modelling communities. This review outlines the initial progress on immune digital twins and the various initiatives to facilitate communication between interdisciplinary communities. We also outline the crucial aspects of an immune digital twin design and the prerequisites for its implementation in the clinic. We propose some initial use cases that could serve as "proof of concept" regarding the utility of immune digital technology, focusing on diseases with a very different immune response across spatial and temporal scales (minutes, days, months, years). Lastly, we discuss the use of digital twins in drug discovery and point out emerging challenges that the scientific community needs to collectively overcome to make immune digital twins a reality.

PMID:39616158 | DOI:10.1038/s41540-024-00450-5