Generative artificial intelligence is revolutionizing digital twin development, enabling virtual patient representations that predict health trajectories, with large language models (LLMs) showcasing untapped clinical forecasting potential. We developed the Digital Twin—Generative Pretrained Transformer (DT-GPT), extending LLM-based forecasting solutions to clinical trajectory prediction. DT-GPT leverages electronic health records without requiring data imputation or normalization and overcomes real-world data challenges such as missingness, noise, and limited sample sizes. Benchmarking on non-small cell lung cancer, intensive care unit, and Alzheimer’s disease datasets, DT-GPT outperformed state-of-the-art machine learning models, reducing the scaled mean absolute error by 3.4%, 1.3% and 1.8%, respectively. It maintained distributions and cross-correlations of clinical variables, and demonstrated explainability through a human-interpretable interface. Additionally, DT-GPT’s ability to perform zero-shot forecasting highlights potential advantages of LLMs as clinical forecasting platforms, proposing a path towards digital twin applications in clinical trials, treatment selection, and adverse event mitigation.

Timely prognosis of type 2 diabetes (T2D) complications is critical for effective interventions and reducing economic burden. AI-driven large language models (LLMs) offer potential for extracting clinical insights but face challenges due to the sparse, high-dimensional nature of longitudinal medical records. This study demonstrates the utility of LLMs in medical time series prediction by preprocessing data with a missing mask, adding an embedding layer to a pretrained LLM, and fine-tuning both components. The fine-tuned model outperformed baselines in predicting both HbA1c and LDL levels using the DPV registry dataset of 449,185 T2D patients, achieving Pearson's correlations of 0.749 and 0.754, with a delta improvement of 0.253 and 0.259, respectively. The model also demonstrated robust long-term prediction for HbA1c over 554.3 days (95% CI: [547.0, 561.5]), with a 9% improvement in MSE over last-observation-based methods. Integrated gradient analysis identified significant clinical features and visits, revealing potential biomarkers for early intervention. Overall, the results showed the possibility to leverage the prediction power of LLM in T2D prognosis using sparse medical time series, assisting clinical prognosis and biomarker discovery, ultimately advancing precision medicine. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This study did not receive any funding ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The institutional ethics committee of Ulm University, Germany, approved the analysis of anonymized DPV data on August 25, 2021 (issue 314/21). I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes The data is not publicly accessible due to the patient's confidentiality. Researchers who would like to reproduce the results could contact the data access committee at the DPV Initiative in Ulm University. Detailed information can be found here: https://buster.zibmt.uni-ulm.de/.

Lichen planus (LP) is a chronic inflammatory disease affecting the skin, mucosa, nail, and hair. Previous studies demonstrated a pivotal role of type 1 immunity in LP because infiltrating T cells trigger apoptosis and necroptosis in the epidermis. In this study, we investigated the role of DAPK1 in LP with special focus on its role in mediating cell death and inflammation. Bulk RNA sequencing of skin biopsies revealed a high expression of DAPK1 in LP compared with that in psoriasis and atopic dermatitis. DAPK1 expression in human keratinocytes was induced by IFN-γ, TNF, and IL-32. CRISPR/Cas9-mediated DAPK1 knockout led to a decreased rate of cell death and induction of proapoptotic proteins (BAX, cPARP) in human keratinocytes upon stimulation with the supernatant T cells derived from LP skin biopsies. Meanwhile, DAPK1 knockout resulted in an induction of kinases involved in necroptosis (RIPK3) and an upregulation of inflammatory genes (CXCL9, CXCL10, CXCL11, IL32, CCL2) after stimulation with LP supernatant T cells. In summary, we demonstrate that DAPK1 mediates keratinocyte apoptosis under type 1 inflammatory conditions and thereby counteracts necroptosis and regulation of inflammatory genes. These findings point toward previously unreported therapeutic approaches for activating or stabilizing DAPK1 in LP.

13 Background: Optimal patient selection for first-line treatment targeting epithelial growth factor receptor (EGFR) in RAS-WT mCRC is based on primary tumor sidedness (PTS) with anti-EGFR being the preferred option for patients with left-sided mCRC (LC). Right-sided mCRCs (RC) are preferentially treated in combination with bevacizumab targeting vascular endothelial growth factor (VEGF). Here, improvement in patient selection was evaluated by combining clinical biomarkers beyond PTS using the randomized phase III trial FIRE-3. Methods: FIRE-3 evaluated first-line FOLFIRI (folinic acid, fluorouracil and irinotecan) plus cetuximab (FOLFIRI/Cet) versus FOLFIRI plus bevacizumab (FOLFIRI/Bev) in patients with RAS-WT mCRC. Besides PTS, further clinical biomarkers were evaluated in pairwise combinations using Cox regression models and model-based recursive partitioning with Weibull models to predict treatment benefit of either treatment arm regarding overall survival (OS): age, sex, liver-limited disease status (LLD) and baseline carcinoembryonic antigen serum level (CEA). The resulting P-values of second-order interactions were adjusted using Holm-Bonferroni correction. The model with the best test statistics and P-value was chosen for further evaluations. Results: In 400 patients with RAS-WT mCRC, a model combining PTS and LLD status best predicted treatment outcome of either treatment arm (c-index = 0.603, p=0.005). Here, a significant survival benefit of FOLFIRI/Cet over FOLFIRI/Bev was evident in patients with LC/non-LLD (HR 0.62, p=0.02) compared to LC/LLD (HR 0.83, p=0.40). In patients with RC, FOLFIRI/Bev was significantly associated with increased OS compared to FOLFIRI/Cet when patients suffered from non-LLD (HR 2.09, p=0.010). However, patients with RC/LLD rather had a benefit from FOLFIRI/Cet compared to FOLFIRI/Bev (HR 0.59, p=0.218). Conclusions: Combining clinical biomarkers PTS and LLD status might improve optimal patient selection for targeted first-line treatment in RAS-WT mCRC. Validation in further data sets is warranted. Clinical trial information: NCT00433927 .

The epithelial-mesenchymal transition (EMT) is characterised by the loss of cell-cell adhesion and cell polarity, which is often exploited by cancer cells to adopt a motile, invasive and metastatic phenotype. Whilst EMT is often linked with cancer progression and therapy resistance, strategies for its selective targeting remain limited. In order to address this, we infer EMT states of cancer cell lines from their molecular signatures and use predictive and causal modelling to estimate the effect of EMT on drug susceptibility in high-throughput drug screens. For example, we show that EMT signatures in melanoma cells can predict favourable responses to the HSP90 inhibitor luminespib and demonstrate that epithelial-like melanoma cells can be sensitised to luminespib upon stimulation of EMT by TGF-β. Thus, we provide an analysis that systematically yields a set of potent drugs by exploiting vulnerabilities of cancer cells undergoing EMT, which may pave the way for therapies to target these cells. ### Competing Interest Statement M.P.M. collaborates with GSK, Roche and AstraZeneca, and receives funding from Roche and GSK. M.P.M. is a former employee at AstraZeneca. The remaining authors declare no competing interest.

Abstract Background Distal sensorimotor polyneuropathy (DSPN) is a common neurological disorder in elderly adults and people with obesity, prediabetes and diabetes and is associated with high morbidity and premature mortality. DSPN is a multifactorial disease and not fully understood yet. Methods Here, we developed the Interpretable Multimodal Machine Learning (IMML) framework for predicting DSPN prevalence and incidence based on sparse multimodal data. Exploiting IMMLs interpretability further empowered biomarker identification. We leveraged the population-based KORA F4/FF4 cohort including 1091 participants and their deep multimodal characterisation, i.e. clinical data, genomics, methylomics, transcriptomics, proteomics, inflammatory proteins and metabolomics. Results Clinical data alone is sufficient to stratify individuals with and without DSPN (AUROC = 0.752), whilst predicting DSPN incidence 6.5 ± 0.2 years later strongly benefits from clinical data complemented with two or more molecular modalities (improved ΔAUROC > 0.1, achieved AUROC of 0.714). Important and interpretable features of incident DSPN prediction include up-regulation of proinflammatory cytokines, down-regulation of SUMOylation pathway and essential fatty acids, thus yielding novel insights in the disease pathophysiology. Conclusions These may become biomarkers for incident DSPN, guide prevention strategies and serve as proof of concept for the utility of IMML in studying complex diseases.

The development of functional neurons is a complex orchestration of multiple signaling pathways controlling cell proliferation and differentiation. Because the balance of antioxidants is important for neuronal survival and development, we hypothesized that ferroptosis must be suppressed to gain neurons. We find that removal of antioxidants diminishes neuronal development and laminar organization of cortical organoids, which is fully restored when ferroptosis is inhibited by ferrostatin-1 or when neuronal differentiation occurs in the presence of vitamin A. Furthermore, iron-overload-induced developmental growth defects in C. elegans are ameliorated by vitamin E and A. We determine that all-trans retinoic acid activates the Retinoic Acid Receptor, which orchestrates the expression of anti-ferroptotic genes. In contrast, retinal and retinol show radical-trapping antioxidant activity. Together, our study reveals an unexpected function of vitamin A in coordinating the expression of essential cellular gatekeepers of ferroptosis, and demonstrates that suppression of ferroptosis by radical-trapping antioxidants or by vitamin A is required to obtain mature neurons and proper laminar organization in cortical organoids.

Representation learning for tumor gene expression (GEx) data with deep neural networks is limited by the large gene feature space and the scarcity of available clinical and preclinical data. The translation of the learned representation between these data sources is further hindered by inherent molecular differences. To address these challenges, we propose GExMix ( G ene Ex pression Mix up), a data augmentation method, which extends the Mixup concept to generate training samples accounting for the imbalance in both data classes and data sources. We leverage the GExMix-augmented training set in encoder-decoder models to learn a GEx latent representation. Subsequently, we combine the learned representation with drug chemical features in a dual-objective enhanced gene-centric drug response prediction, i.e., reconstruction of GEx latent embeddings and drug response classification. This dual-objective design strategically prioritizes gene-centric information to enhance the final drug response prediction. We demonstrate that augmenting training samples improves the GEx representation, benefiting the gene-centric drug response prediction model. Our findings underscore the effectiveness of our proposed GExMix in enriching GEx data for deep neural networks. Moreover, our proposed gene-centricity further improves drug response prediction when translating preclinical to clinical datasets. This highlights the untapped potential of the proposed framework for GEx data analysis, paving the way toward precision medicine.### Competing Interest StatementThe authors have declared no competing interest.

Introduction: The concept of Digital Twins (DTs) translated to drug development and clinical trials describes virtual representations of systems of various complexities, ranging from individual cells to entire humans, and enables in silico simulations and experiments. DTs increase the efficiency of drug discovery and development by digitalizing processes associated with high economic, ethical, or social burden. The impact is multifaceted: DT models sharpen disease understanding, support biomarker discovery and accelerate drug development, thus advancing precision medicine. One way to realize DTs is by generative artificial intelligence (AI), a cutting-edge technology that enables the creation of novel, realistic and complex data with desired properties.Areas covered: The authors provide a brief introduction to generative AI and describe how it facilitates the modeling of DTs. In addition, they compare existing implementations of generative AI for DTs in drug discovery and clinical trials. Finally, they discuss technical and regulatory challenges that should be addressed before DTs can transform drug discovery and clinical trials.Expert opinion: The current state of DTs in drug discovery and clinical trials does not exploit the entire power of generative AI yet and is limited to simulation of a small number of characteristics. Nonetheless, generative AI has the potential to transform the field by leveraging recent developments in deep learning and customizing models for the needs of scientists, physicians and patients.

BACKGROUND:Diabetic sensorimotor polyneuropathy (DSPN) is often asymptomatic and remains undiagnosed. The ability of clinical and anthropometric variables to identify individuals likely to have DSPN might be limited. Here, we aimed to integrate protein biomarkers for reliably predicting present DSPN. METHODS:Using the proximity extension assay, we measured 135 neurological and protein biomarkers of inflammation in blood samples of 423 individuals with recent-onset diabetes from the German Diabetes Study (GDS). DSPN was diagnosed based on the Toronto Consensus Criteria. We constructed (i) a protein-based prediction model using LASSO logistic regression, (ii) an optimised traditional risk model with age, sex, waist circumference, height and diabetes type and (iii) a model combining both. All models were bootstrapped to assess the robustness, and optimism-corrected AUCs (95% CI) were reported. RESULTS:DSPN was present in 16% of the study population. LASSO logistic regression selected the neurofilament light chain (NFL) and fibroblast growth factor-19 (FGF-19) as the most predictive protein biomarkers for detecting DSPN in individuals with recent-onset diabetes. The protein-based model achieved an AUC of 0.66 (0.59, 0.73), while the traditional risk model had an AUC of 0.66 (0.61, 0.74). However, combined features boosted the model performance to an AUC of 0.72 (0.67, 0.79). CONCLUSION:We developed a prediction model for DSPN in recent-onset diabetes based on two protein biomarkers and five standard anthropometric, demographic and clinical variables. The model has a fair discrimination performance and might be used to inform the referral of patients for further testing.

Childhood neuroblastoma with MYCN amplification is classified as high risk and often relapses after intensive treatments. Immune checkpoint blockade therapy against the PD-1/L1 axis shows limited efficacy in patients with neuroblastoma, and the cancer intrinsic immune regulatory network is poorly understood. Here, we leverage genome-wide CRISPR/Cas9 screens and identify H2AFYas a resistance gene to the clinically approved PD-1 blocking antibody nivolumab. Analysis of single- cell RNA-Seq datasets reveals that H2AFYmRNA is enriched in adrenergic cancer cells and is associated with worse patient survival. Genetic deletion of H2afy in MYCN-driven neuroblastoma cells reverts in vivo resistance to PD-1 blockade by eliciting activation of the adaptive and innate immunity. Mapping of the epigenetic and translational landscape demonstrates that H2afy deletion promotes cell transition to a mesenchymal-like state. With a multiomics approach, we uncovered H2AFY- associated genes that are functionally relevant and prognostic in patients. Altogether, our study elucidates the role of H2AFY as an epigenetic gatekeeperfor cell states and immunogenicity in high-risk neuroblastoma.

Mast cells (MCs) play critical roles in allergic disease, canonically by activation through the IgE-dependent pathway. However, MCs can also be activated by IgE-independent mechanisms with the Mas-related G protein-coupled receptor X2 (MRGPRX2) being extensively studied. MRGPRX2 is found on connective tissue MCs with abundant expression in the skin. Human MRGPRX2 is activated by a range of endogenous polycationic inflammatory peptides such as host defence peptides and neuropeptides (e.g., LL-37 and substance P) but with relatively low potency.1 Thus, it is plausible that additional endogenous agonists at MRGPRX2 have important physiological/pathophysiological roles. The novel chemokine CXCL17 is expressed in mucosal tissues and has suggested antimicrobial roles besides being a regulator of cell chemotaxis and inflammation.2 A previous study suggested that the orphan G protein-coupled receptor (GPCR) GPR35 acts as the receptor for CXCL17, but this observation remains controversial.2 Given the presence of polycationic regions within CXCL17, we hypothesized that it might act as a MRGPRX2 agonist and that this action might be of importance in inflammatory conditions where CXCL17 expression is upregulated. To determine whether CXCL17 activates human MCs via the MRGPRX2 pathway, we utilized the LAD2 MC line that natively express MRGPRX2 and FcεRI receptors, and MRGPRX2 knockdown LAD2 cells (MRGPRX2-KD; Figure S1A). CXCL17, MRGPRX2 agonists LL-37 and compound 48/80 (C48/80), induced calcium mobilization (Figure 1A) and degranulation measured by β-hexosaminidase release (Figure 1B) and enhanced surface expression of CD63 (Figure S1B) in wild-type (WT) LAD2 cells with responses being markedly dampened in MRGPRX2-KD cells. Antigen (NIP-BSA)-induced IgE-dependent responses were unaltered in MRGPRX2-KD LAD2 cells (Figure 1A,B; Figure S1). The MRGPRX2 dependency of this effect was also confirmed through the inhibitory action of the MRGPRX2 inverse agonist compound C93 (Figure 1C). In addition, CXCL17 strongly synergized with the alarmin IL-33, a known MC activator that is upregulated in psoriasis, to induce CCL2 release from LAD2 cells (Figure 1D). CXCL17 also triggered β-hexosaminidase and histamine release from purified rat peritoneal MCs, presumably via the rat homologue of MRGPRX2, MrgprB3 (Figure S2). The mRNA levels of CXCL17 and MRGPRX2 have been reported to be increased in psoriatic skin compared with healthy skin biopsies.4, 5 We therefore performed immunohistochemistry and conducted an analysis of spatial transcriptomic data from biopsies from non-lesional and lesional psoriatic skin to establish the in vivo connection between CXCL17 and MRGPRX2. By immunohistochemistry, the expression of CXCL17 was increased in psoriatic lesional skin compared with non-lesional skin, mainly in the epidermis (Figure 2). Mast cells, as identified by tryptase and MRGPRX2 staining, were clearly observed in both lesional and non-lesional skin sections (Figure 2A). In addition, spatial transcriptomics analysis (Figure 2B; Figure S3) showed enhanced expression of the MC marker tryptase β2 (TPSB2) in lesional psoriatic skin compared with non-lesional skin, suggesting increased MC numbers or increased TPSB2 gene expression perhaps indicative of MC activation. Concordant with the immunohistochemistry data, spatial transcriptomics also showed localization of CXCL17 expression to tissue areas containing MCs (as identified by TPSB2 expression) largely within the epidermis (see supplementary information for a more comprehensive description of the spatial transcriptomics data analysis). In summary, we demonstrate that CXCL17 activates human MCs in a concentration-dependent manner, via the MRGPRX2 pathway. CXCL17 is one of the more potent MRGPRX2 agonists identified among the family of known MRGPRX2-activating antimicrobial peptides. We also demonstrate that CXCL17 expression is proximal to that of MRGPRX2-positive MCs in psoriatic skin. Our data suggest that in psoriasis, CXCL17 release from keratinocytes causes the activation of human MCs via MRGPRX2. CXCL17 might also act synergistically with other MC stimuli enhancing the importance of the CXCL17-MC axis in features of psoriasis such as itch in which MC MRGPRX2 has been highlighted.6 Other psoriasis-associated antimicrobial peptide mediators7 that are known MRGPRX2 agonists (e.g., LL-37 and β-defensins) may also contribute to this pathway. Further studies are required to characterize the unique pathobiological importance of CXCL17-induced MC activation in psoriasis and other non-communicable inflammatory skin diseases. GAM and JD designed and planned the study; JD conducted experiments and collected the majority of data; CH performed spatial transcriptomics analysis; CWW, NAF, HA, JSK and MPM generated key tools, reagents and clinical samples and provided expertise on their use; JD and GAM wrote the draft manuscript; CWW, CH, MPM, HA and JSK provided critical insight into the generated data and revised the draft manuscript. All authors approved the final version of the manuscript. This work was partially supported by a grant from The Australian and New Zealand College of Anaesthetists and supported by the Deutsche Forschungsgemeinschaft through TUM International Graduate School of Science and Engineering (CH, MPM). We acknowledge the Melbourne Histology Platform, The University of Melbourne, for assisting with the immunohistochemistry study, and the Melbourne Cytometry Platform (Melbourne Brain Centre node) for provision of flow cytometry services. Open access publishing facilitated by The University of Melbourne, as part of the Wiley - The University of Melbourne agreement via the Council of Australian University Librarians. The authors declare that they have no conflicts of interest. CWW is now an employee of Dimerix Ltd, which had no involvement in or contribution to the project. The data that support the findings of this study are available from the corresponding author upon reasonable request. Appendix S1. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.

Immune checkpoint blockade therapy aims to activate the immune system to eliminate cancer cells. However, clinical benefits are only recorded in a subset of patients. Here, we leverage genome-wide CRISPR/Cas9 screens in a Tumor-Immune co-Culture System focusing on triple-negative breast cancer (TNBC). We reveal that NEDD8 loss in cancer cells causes a vulnerability to nivolumab (anti-PD-1). Genetic deletion of NEDD8 only delays cell division initially but cell proliferation is unaffected after recovery. Since the NEDD8 gene is commonly essential, we validate this observation with additional CRISPR screens and uncover enhanced immunogenicity in NEDD8 deficient cells using proteomics. In female immunocompetent mice, PD-1 blockade lacks efficacy against established EO771 breast cancer tumors. In contrast, we observe tumor regression mediated by CD8+ T cells against Nedd8 deficient EO771 tumors after PD-1 blockade. In essence, we provide evidence that NEDD8 is conditionally essential in TNBC and presents as a synergistic drug target for PD-1/L1 blockade therapy. NEDD8 is a ubiquitin-like protein that governs protein neddylation, previously demonstrated to be essential for cell survival. Here the authors show that NEDD8 loss in breast cancer cells is associated with enhanced immunogenicity and increased sensitivity to PD-1 blockade in preclinical cancer models.

Sebaceous glands drive acne, however, their role in other inflammatory skin diseases remains unclear. To shed light on their potential contribution to disease development, we investigated the spatial transcriptome of sebaceous glands in psoriasis and atopic dermatitis patients across lesional and non-lesional human skin samples. Both atopic dermatitis and psoriasis sebaceous glands expressed genes encoding key proteins for lipid metabolism and transport such as ALOX15B, APOC1, FABP7, FADS1/2, FASN, PPARG, and RARRES1. Also, inflammation-related SAA1 was identified as a common spatially variable gene. In atopic dermatitis, genes mainly related to lipid metabolism (e.g. ACAD8, FADS6, or EBP) as well as disease-specific genes, i.e., Th2 inflammation-related lipid-regulating HSD3B1 were differentially expressed. On the contrary, in psoriasis, more inflammation-related spatially variable genes (e.g. SERPINF1, FKBP5, IFIT1/3, DDX58) were identified. Other psoriasis-specific enriched pathways included lipid metabolism (e.g. ACOT4, S1PR3), keratinization (e.g. LCE5A, KRT5/7/16), neutrophil degranulation, and antimicrobial peptides (e.g. LTF, DEFB4A, S100A7-9). In conclusion, our results show that sebaceous glands contribute to skin homeostasis with a cell type-specific lipid metabolism, which is influenced by the inflammatory microenvironment. These findings further support that sebaceous glands are not bystanders in inflammatory skin diseases, but can actively and differentially modulate inflammation in a disease-specific manner.

Highly specific and efficient drugs have been developed during the last two decades to treat non-communicable chronic inflammatory skin diseases (ncISD). Due to their specificity, these drugs are asking for precise diagnostic measures to attribute the most efficient treatment to each patient. Diagnosis, however, is complicated by the complex pathogenesis of ncISD and their clinical and histological overlap. Especially, precise diagnosis of psoriasis and eczema is difficult in special cases and molecular diagnostic tools need to be developed to support gold standard diagnosis of patients. In this line, we have developed a real-time based molecular classifier to distinguish psoriasis from eczema in RNA-later fixed skin samples. However, this type of skin sample is not regularly used in routine diagnostics. Therefore, we evaluated if the combination of NOS2 and CCL27 expression in lesional skin can be transferred to formalin-fixed paraffin embedded (FFPE) tissue. We present a FFPE-based molecular classifier (MC) that determines the probability for psoriasis with a specificity and sensitivity of 100% and 92%, respectively, and an area under the curve (AUC) of 0.97 delivering comparable results to the RNA-later based MC. The probability for psoriasis as well as the PCR result of NOS2 expression correlated positive with disease hallmarks of psoriasis and negative with eczema hallmarks. This tool now offers broad usage in pathology laboratories and can support diagnostic decision making on a molecular level.### Competing Interest StatementThe authors have declared no competing interest.### Funding StatementThis work was supported by the Medical Valley Award.### Author DeclarationsI confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.YesThe details of the IRB/oversight body that provided approval or exemption for the research described are given below:Ethical approval was granted by the ethical committee of the Klinikum rechts der Isar, Technical University Munich (project number 2773/10 and 515/17)I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.YesI understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).YesI have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable.YesGenerated data and codes can be provided upon request

The nasal epithelium is an important target for drug delivery to the nose and secondary organs such as the brain via the olfactory bulb. For both topical and brain delivery, the targeting of specific nasal regions such as the olfactory epithelium (brain) is essential, yet challenging. In this study, a numerical model was developed to predict the regional dose as mass per surface area (for an inhaled mass of 2.5 mg), which is the biologically most relevant dose metric for drug delivery in the respiratory system. The role of aerosol diameter (particle diameter: 1 nm to 30 µm) and inhalation flow rate (4, 15 and 30 L/min) in optimal drug delivery to the vestibule, nasal valve, olfactory and nasopharynx is assessed. To obtain the highest doses in the olfactory region, we suggest aerosols with a diameter of 20 µm and a medium inlet air flow rate of 15 L/min. High deposition on the olfactory epithelium was also observed for nanoparticles below 1 nm, as was high residence time (slow flow rate of 4 L/min), but the very low mass of 1 nm nanoparticles is prohibitive for most therapeutic applications. Moreover, high flow rates (30 L/min) and larger micro-aerosols lead to highest doses in the vestibule and nasal valve regions. On the other hand, the highest drug doses in the nasopharynx are observed for nano-aerosol (1 nm) and fine microparticles (1–20 µm) with a relatively weak dependence on flow rate. Furthermore, using the 45 different inhalation scenarios generated by numerical models, different machine learning models with five-fold cross-validation are trained to predict the delivered dose and avoid partial differential equation solvers for future predictions. Random forest and gradient boosting models resulted in R2 scores of 0.89 and 0.96, respectively. The aerosol diameter and region of interest are the most important features affecting delivered dose, with an approximate importance of 42% and 47%, respectively.

Muscle function is an important denominator of energy balance and metabolic health. Adapting the proteome to energetic challenges, in response to diet or fasting, is facilitated by programs of proteostasis, but the adaptive role of the ubiquitin-proteasome system (UPS) in muscle remains unclear. Here, using a multi-omics approach, we uncover that the distinct metabolic condition of obesity is associated with recalibration of the UPS in muscle. Interestingly, obesity is associated with the activation of the transcription factor Nuclear factor, erythroid derived 2,- like 1 (Nfe2l1, also known as Nrf1), and loss of myocyte Nfe2l1 diminishes proteasomal activity and leads to hyperubiquitylation. Mice lacking Nfe2l1 display hormetic energy metabolism and resistance to diet-induced obesity, associated with a lean phenotype and muscle fiber type switching. In conclusion, we define a new adaptive role for UPS in remolding of muscle proteome and function, which is controlled by fine-tuning of proteasome function by Nfe2l1.

Aberrant DNA methylation accompanies genetic alterations during oncogenesis and tumour homeostasis and contributes to the transcriptional deregulation of key signalling pathways in cancer. Despite increasing efforts in DNA methylation profiling of cancer patients, there is still a lack of epigenetic biomarkers to predict treatment efficacy. To address this, we analyse 721 cancer cell lines across 22 cancer types treated with 453 anti-cancer compounds. We systematically detect the predictive component of DNA methylation in the context of transcriptional and mutational patterns, i.e., in total 19 DNA methylation biomarkers across 17 drugs and five cancer types. DNA methylation constitutes drug sensitivity biomarkers by mediating the expression of proximal genes, thereby enhancing biological signals across multi-omics data modalities. Our method reproduces anticipated associations, and in addition, we find that the NEK9 promoter hypermethylation may confer sensitivity to the NEDD8-activating enzyme (NAE) inhibitor pevonedistat in melanoma through downregulation of NEK9. In summary, we envision that epigenomics will refine existing patient stratification, thus empowering the next generation of precision oncology.

Highly effective targeted therapies are available to treat noncommunicable chronic inflammatory skin diseases. In contrast, the exact diagnosis of noncommunicable chronic inflammatory skin diseases is complicated by its complex pathogenesis and clinical and histological overlap. Particularly, the differential diagnosis of psoriasis and eczema can be challenging in some cases, and molecular diagnostic tools need to be developed to support a gold standard diagnosis. The aim of this work was to develop a real-time PCR-based molecular classifier to distinguish psoriasis from eczema in formalin-fixed and paraffin-embedded-fixed skin samples and to evaluate the use of minimally invasive microbiopsies and tape strips for molecular diagnosis. In this study, we present a formalin-fixed and paraffin-embedded-based molecular classifier that determines the probability for psoriasis with a sensitivity/specificity of 92%/100%, respectively, and an area under the curve of 0.97, delivering comparable results to our previous published RNAprotect-based molecular classifier. The psoriasis probability, as well as levels of NOS2 expression, positively correlated with the disease hallmarks of psoriasis and negatively with eczema hallmarks. Furthermore, minimally invasive tape strips and microbiopsies were effectively used to differentiate psoriasis from eczema. In summary, the molecular classifier offers broad usage in pathology laboratories as well as outpatient settings and can support the differential diagnosis of noncommunicable chronic inflammatory skin diseases on a molecular level using formalin-fixed and paraffin-embedded tissue, microbiopsies, and tape strips.