Effective genome editing with an enhanced ISDra2 TnpB system and deep learning-predicted ωRNAs.

Transposon (IS200/IS605)-encoded TnpB proteins are predecessors of class 2 type V CRISPR effectors and have emerged as one of the most compact genome editors identified thus far. Here, we optimized the design of Deinococcus radiodurans (ISDra2) TnpB for application in mammalian cells (TnpBmax), leading to an average 4.4-fold improvement in editing. In addition, we developed variants mutated at position K76 that recognize alternative target-adjacent motifs (TAMs), expanding the targeting range of ISDra2 TnpB. We further generated an extensive dataset on TnpBmax editing efficiencies at 10,211 target sites. This enabled us to delineate rules for on-target and off-target editing and to devise a deep learning model, termed TnpB editing efficiency predictor (TEEP; https://www.tnpb.app ), capable of predicting ISDra2 TnpB guiding RNA (ωRNA) activity with high performance (r > 0.8). Employing TEEP, we achieved editing efficiencies up to 75.3% in the murine liver and 65.9% in the murine brain after adeno-associated virus (AAV) vector delivery of TnpBmax. Overall, the set of tools presented in this study facilitates the application of TnpB as an ultracompact programmable endonuclease in research and therapeutics.

Innate acting memory Th1 cells modulate heterologous diseases.

Through immune memory, infections have a lasting effect on the host. While memory cells enable accelerated and enhanced responses upon rechallenge with the same pathogen, their impact on susceptibility to unrelated diseases is unclear. We identify a subset of memory T helper 1 (Th1) cells termed innate acting memory T (TIA) cells that originate from a viral infection and produce IFN-γ with innate kinetics upon heterologous challenge in vivo. Activation of memory TIA cells is induced in response to IL-12 in combination with IL-18 or IL-33 but is TCR independent. Rapid IFN-γ production by memory TIA cells is protective in subsequent heterologous challenge with the bacterial pathogen Legionella pneumophila. In contrast, antigen-independent reactivation of CD4+ memory TIA cells accelerates disease onset in an autoimmune model of multiple sclerosis. Our findings demonstrate that memory Th1 cells can acquire additional TCR-independent functionality to mount rapid, innate-like responses that modulate susceptibility to heterologous challenges.

COL10A1 expression distinguishes a subset of cancer-associated fibroblasts present in the stroma of high-risk basal cell carcinoma.

BACKGROUND: Basal cell carcinoma (BCC) is the most frequently diagnosed skin cancer and the most common malignancy in humans. Different morphological subtypes of BCC are associated with low- or high-risk of recurrence and aggressiveness, but the underlying biology of how the individual subtypes arise remains largely unknown. Because the majority of BCCs appear to arise from mutations in the same pathway, we hypothesized that BCC development, growth and invasive potential is also influenced by the tumor microenvironment and in particular by cancer-associated fibroblasts (CAFs) and their secreted factors. OBJECTIVE: We aimed to characterize the stroma of the different BCC subtypes with a focus on CAF populations. METHODS: To investigate the stromal features of the different BCC subtypes, we applied laser-capture microdissection (LCM) followed by RNA sequencing. A cohort of 15 BCC samples from 5 different "pure" subtypes (superficial, nodular, micronodular, sclerosing and basosquamous; n=3 each) were selected and included in the analysis. Healthy skin was used as a control (n=6). We confirmed the results by immunohistochemistry. We validated our findings in two independent, public single-cell RNA sequencing (scRNAseq) datasets and by RNAscope. RESULTS: The stroma of the different BCC subtypes have distinct gene expression signatures. Nodular and micronodular seem to have the most similar signatures, while superficial and sclerosing the most different. By comparing low- and high-risk BCC subtypes, we observed that Collagen 10A1 (COL10A1) is overexpressed in the stroma of sclerosing/infiltrative and basosquamous but not micronodular high-risk subtypes. Those findings were confirmed by immunohistochemistry in a cohort of 89 different BCC and 13 healthy skin samples. Moreover, scRNAseq analysis of BCCs of two independent datasets showed that the COL10A1-expressing population of cells is associated with the stroma adjacent to invasive BCC and shows extracellular matrix remodeling features. CONCLUSION: We identified COL10A1 as a marker of high-risk BCC, in particular of the sclerosing/infiltrative and basosquamous subtypes. We demonstrated at the single cell level that COL10A1 is expressed by a specific CAF population associated with the stroma of invasive BCC. This opens up new tailored treatment options as well as a new prognostic biomarker for BCC progression.

Explainable deep learning for disease activity prediction in chronic inflammatory joint diseases.

Analysing complex diseases such as chronic inflammatory joint diseases (CIJDs), where many factors influence the disease evolution over time, is a challenging task. CIJDs are rheumatic diseases that cause the immune system to attack healthy organs, mainly the joints. Different environmental, genetic and demographic factors affect disease development and progression. The Swiss Clinical Quality Management in Rheumatic Diseases (SCQM) Foundation maintains a national database of CIJDs documenting the disease management over time for 19'267 patients. We propose the Disease Activity Score Network (DAS-Net), an explainable multi-task learning model trained on patients' data with different arthritis subtypes, transforming longitudinal patient journeys into comparable representations and predicting multiple disease activity scores. First, we built a modular model composed of feed-forward neural networks, long short-term memory networks and attention layers to process the heterogeneous patient histories and predict future disease activity. Second, we investigated the utility of the model's computed patient representations (latent embeddings) to identify patients with similar disease progression. Third, we enhanced the explainability of our model by analysing the impact of different patient characteristics on disease progression and contrasted our model outcomes with medical expert knowledge. To this end, we explored multiple feature attribution methods including SHAP, attention attribution and feature weighting using case-based similarity. Our model outperforms temporal and non-temporal neural network, tree-based, and naive static baselines in predicting future disease activity scores. To identify similar patients, a k-nearest neighbours regression algorithm applied to the model's computed latent representations outperforms baseline strategies that use raw input features representation.

Machine learning prediction of prime editing efficiency across diverse chromatin contexts.

The success of prime editing depends on the prime editing guide RNA (pegRNA) design and target locus. Here, we developed machine learning models that reliably predict prime editing efficiency. PRIDICT2.0 assesses the performance of pegRNAs for all edit types up to 15 bp in length in mismatch repair-deficient and mismatch repair-proficient cell lines and in vivo in primary cells. With ePRIDICT, we further developed a model that quantifies how local chromatin environments impact prime editing rates.

Longitudinal cell-free DNA characterization by low-coverage whole-genome sequencing in patients undergoing high-dose radiotherapy.

BACKGROUND AND PURPOSE: Current radiotherapy guidelines rely heavily on imaging-based monitoring. Liquid biopsy monitoring promises to complement imaging by providing frequent systemic information about the tumor. In particular, cell-free DNA (cfDNA) sequencing offers a tumor-agnostic approach, which lends itself to monitoring heterogeneous cohorts of cancer patients. METHODS: We collected plasma cfDNA from oligometastatic patients (OMD) and head-and-neck cancer patients (SCCHN) at six time points before, during, and after radiotherapy, and compared them to the plasma samples of healthy and polymetastatic volunteers. We performed low-pass (on average 7x) whole-genome sequencing on 93 plasma cfDNA samples and correlated copy number alterations and fragment length distributions to clinical and imaging findings. RESULTS: We observed copy number alterations in 4/7 polymetastatic cancer patients, 1/7 OMD and 1/7 SCCHN patients, these patients' imaging showed progression following radiotherapy. Using unsupervised learning, we identified cancer-specific fragment length features that showed a strong correlation with copy number-based tumor fraction estimates. In 4/4 HPV-positive SCCHN patient samples, we detected viral DNA that enabled the monitoring of very low tumor fraction samples. CONCLUSIONS: Our results indicate that an elevated tumor fraction is associated with tumor aggressiveness and systemic tumor spread. This information may be used to adapt treatment strategies. Further, we show that by detecting specific sequences such as viral DNA, the sensitivity of detecting cancer from cell-free DNA sequencing data can be greatly increased.

Former smoking, but not active smoking, is associated with delirium in postoperative ICU patients: a matched case-control study.

Objective: To examine the relationship between current and former smoking and the occurrence of delirium in surgical Intensive Care Unit (ICU) patients. Methods: We conducted a single center, case-control study involving 244 delirious and 251 non-delirious patients that were admitted to our ICU between 2018 and 2022. Using propensity score analysis, we obtained 115 pairs of delirious and non-delirious patients matched for age and Simplified Acute Physiology Score II (SAPS II). Both groups of patients were further stratified into non-smokers, active smokers and former smokers, and logistic regression was performed to further investigate potential confounders. Results: Our study revealed a significant association between former smoking and the incidence of delirium in ICU patients, both in unmatched (adjusted odds ratio (OR): 1.82, 95% confidence interval (CI): 1.17-2.83) and matched cohorts (OR: 3.0, CI: 1.53-5.89). Active smoking did not demonstrate a significant difference in delirium incidence compared to non-smokers (unmatched OR = 0.98, CI: 0.62-1.53, matched OR = 1.05, CI: 0.55-2.0). Logistic regression analysis of the matched group confirmed former smoking as an independent risk factor for delirium, irrespective of other variables like surgical history (p = 0.010). Notably, also respiratory and vascular surgeries were associated with increased odds of delirium (respiratory: OR: 4.13, CI: 1.73-9.83; vascular: OR: 2.18, CI: 1.03-4.59). Medication analysis showed that while Ketamine and Midazolam usage did not significantly correlate with delirium, Morphine use was linked to a decreased likelihood (OR: 0.27, 95% CI: 0.13-0.55). Discussion: Nicotine's complex neuropharmacological impact on the brain is still not fully understood, especially its short-term and long-term implications for critically ill patients. Although our retrospective study cannot establish causality, our findings suggest that smoking may induce structural changes in the brain, potentially heightening the risk of postoperative delirium. Intriguingly, this effect seems to be obscured in active smokers, potentially due to the recognized neuroprotective properties of nicotine. Our results motivate future prospective studies, the results of which hold the potential to substantially impact risk assessment procedures for surgeries.

Fragmentstein-facilitating data reuse for cell-free DNA fragment analysis.

SUMMARY: Method development for the analysis of cell-free DNA (cfDNA) sequencing data is impeded by limited data sharing due to the strict control of sensitive genomic data. An existing solution for facilitating data sharing removes nucleotide-level information from raw cfDNA sequencing data, keeping alignment coordinates only. This simplified format can be publicly shared and would, theoretically, suffice for common functional analyses of cfDNA data. However, current bioinformatics software requires nucleotide-level information and cannot process the simplified format. We present Fragmentstein, a command-line tool for converting non-sensitive cfDNA-fragmentation data into alignment mapping (BAM) files. Fragmentstein complements fragment coordinates with sequence information from a reference genome to reconstruct BAM files. We demonstrate the utility of Fragmentstein by showing the feasibility of copy number variant (CNV), nucleosome occupancy, and fragment length analyses from non-sensitive fragmentation data. AVAILABILITY AND IMPLEMENTATION: Implemented in bash, Fragmentstein is available at https://github.com/uzh-dqbm-cmi/fragmentstein, licensed under GNU GPLv3.

Exploring the Latest Highlights in Medical Natural Language Processing across Multiple Languages: A Survey.

OBJECTIVES: This survey aims to provide an overview of the current state of biomedical and clinical Natural Language Processing (NLP) research and practice in Languages other than English (LoE). We pay special attention to data resources, language models, and popular NLP downstream tasks. METHODS: We explore the literature on clinical and biomedical NLP from the years 2020-2022, focusing on the challenges of multilinguality and LoE. We query online databases and manually select relevant publications. We also use recent NLP review papers to identify the possible information lacunae. RESULTS: Our work confirms the recent trend towards the use of transformer-based language models for a variety of NLP tasks in medical domains. In addition, there has been an increase in the availability of annotated datasets for clinical NLP in LoE, particularly in European languages such as Spanish, German and French. Common NLP tasks addressed in medical NLP research in LoE include information extraction, named entity recognition, normalization, linking, and negation detection. However, there is still a need for the development of annotated datasets and models specifically tailored to the unique characteristics and challenges of medical text in some of these languages, especially low-resources ones. Lastly, this survey highlights the progress of medical NLP in LoE, and helps at identifying opportunities for future research and development in this field.

ROS Induction Targets Persister Cancer Cells with Low Metabolic Activity in NRAS-Mutated Melanoma.

Clinical management of melanomas with NRAS mutations is challenging. Targeting MAPK signaling is only beneficial to a small subset of patients due to resistance that arises through genetic, transcriptional, and metabolic adaptation. Identification of targetable vulnerabilities in NRAS-mutated melanoma could help improve patient treatment. Here, we used multiomics analyses to reveal that NRAS-mutated melanoma cells adopt a mesenchymal phenotype with a quiescent metabolic program to resist cellular stress induced by MEK inhibition. The metabolic alterations elevated baseline reactive oxygen species (ROS) levels, leading these cells to become highly sensitive to ROS induction. In vivo xenograft experiments and single-cell RNA sequencing demonstrated that intratumor heterogeneity necessitates the combination of a ROS inducer and a MEK inhibitor to inhibit both tumor growth and metastasis. Ex vivo pharmacoscopy of 62 human metastatic melanomas confirmed that MEK inhibitor-resistant tumors significantly benefited from the combination therapy. Finally, oxidative stress response and translational suppression corresponded with ROS-inducer sensitivity in 486 cancer cell lines, independent of cancer type. These findings link transcriptional plasticity to a metabolic phenotype that can be inhibited by ROS inducers in melanoma and other cancers. SIGNIFICANCE: Metabolic reprogramming in drug-resistant NRAS-mutated melanoma cells confers sensitivity to ROS induction, which suppresses tumor growth and metastasis in combination with MAPK pathway inhibitors.

Predicting prime editing efficiency and product purity by deep learning.

Prime editing is a versatile genome editing tool but requires experimental optimization of the prime editing guide RNA (pegRNA) to achieve high editing efficiency. Here we conducted a high-throughput screen to analyze prime editing outcomes of 92,423 pegRNAs on a highly diverse set of 13,349 human pathogenic mutations that include base substitutions, insertions and deletions. Based on this dataset, we identified sequence context features that influence prime editing and trained PRIDICT (prime editing guide prediction), an attention-based bidirectional recurrent neural network. PRIDICT reliably predicts editing rates for all small-sized genetic changes with a Spearman's R of 0.85 and 0.78 for intended and unintended edits, respectively. We validated PRIDICT on endogenous editing sites as well as an external dataset and showed that pegRNAs with high (>70) versus low (<70) PRIDICT scores showed substantially increased prime editing efficiencies in different cell types in vitro (12-fold) and in hepatocytes in vivo (tenfold), highlighting the value of PRIDICT for basic and for translational research applications.

Vision transformer assisting rheumatologists in screening for capillaroscopy changes in systemic sclerosis: an artificial intelligence model.

OBJECTIVES: The first objective of this study was to implement and assess the performance and reliability of a vision transformer (ViT)-based deep-learning model, an 'off-the-shelf' artificial intelligence solution, for identifying distinct signs of microangiopathy in nailfold capilloroscopy (NFC) images of patients with SSc. The second objective was to compare the ViT's analysis performance with that of practising rheumatologists. METHODS: NFC images of patients prospectively enrolled in our European Scleroderma Trials and Research group (EUSTAR) and Very Early Diagnosis of Systemic Sclerosis (VEDOSS) local registries were used. The primary outcome investigated was the ViT's classification performance for identifying disease-associated changes (enlarged capillaries, giant capillaries, capillary loss, microhaemorrhages) and the presence of the scleroderma pattern in these images using a cross-fold validation setting. The secondary outcome involved a comparison of the ViT's performance vs that of rheumatologists on a reliability set, consisting of a subset of 464 NFC images with majority vote-derived ground-truth labels. RESULTS: We analysed 17 126 NFC images derived from 234 EUSTAR and 55 VEDOSS patients. The ViT had good performance in identifying the various microangiopathic changes in capillaries by NFC [area under the curve (AUC) from 81.8% to 84.5%]. In the reliability set, the rheumatologists reached a higher average accuracy, as well as a better trade-off between sensitivity and specificity compared with the ViT. However, the annotators' performance was variable, and one out of four rheumatologists showed equal or lower classification measures compared with the ViT. CONCLUSIONS: The ViT is a modern, well-performing and readily available tool for assessing patterns of microangiopathy on NFC images, and it may assist rheumatologists in generating consistent and high-quality NFC reports; however, the final diagnosis of a scleroderma pattern in any individual case needs the judgement of an experienced observer.

The Use and Ethics of Digital Twins in Medicine.

Digital Health Technologies (DHTs) are currently the subject of much debate both in terms of their technological frontiers as well as their ethical, legal and societal implications (ELSI). Regulation of such technologies as medical devices currently lacks behind their level of adoption. Digital Twins are the next evolution step of such DHTs and provide an opportunity to anticipate and act on ELSI before adoption again leaps before the necessary review. This paper introduces the concept and use cases of digital twins in medicine, then frames the debate through the lens of related technologies, machine learning and personalized medicine, and maps ethical challenges stemming from those. Finally, we lay out how digital twins may change and challenge the future practice of medicine.

Live slow-frozen human tumor tissues viable for 2D, 3D, ex vivo cultures and single-cell RNAseq.

Biobanking of surplus human healthy and disease-derived tissues is essential for diagnostics and translational research. An enormous amount of formalin-fixed and paraffin-embedded (FFPE), Tissue-Tek OCT embedded or snap-frozen tissues are preserved in many biobanks worldwide and have been the basis of translational studies. However, their usage is limited to assays that do not require viable cells. The access to intact and viable human material is a prerequisite for translational validation of basic research, for novel therapeutic target discovery, and functional testing. Here we show that surplus tissues from multiple solid human cancers directly slow-frozen after resection can subsequently be used for different types of methods including the establishment of 2D, 3D, and ex vivo cultures as well as single-cell RNA sequencing with similar results when compared to freshly analyzed material.

Prospective observational study of the role of the microbiome in BCG responsiveness prediction (SILENT-EMPIRE): a study protocol.

INTRODUCTION: The human microbiota, the community of micro-organisms in different cavities, has been increasingly linked with inflammatory and neoplastic diseases. While investigation into the gut microbiome has been robust, the urinary microbiome has only recently been described. Investigation into the relationship between bladder cancer (BC) and the bladder and the intestinal microbiome may elucidate a pathophysiological relationship between the two. The bladder or the intestinal microbiome or the interplay between both may also act as a non-invasive biomarker for tumour behaviour. While these associations have not yet been fully investigated, urologists have been manipulating the bladder microbiome for treatment of BC for more than 40 years, treating high grade non-muscle invasive BC (NMIBC) with intravesical BCG immunotherapy. Neither the association between the microbiome sampled directly from bladder tissue and the response to BCG-therapy nor the association between response to BCG-therapy with the faecal microbiome has been studied until now. A prognostic tool prior to initiation of BCG-therapy is still needed. METHODS AND ANALYSIS: In patients with NMIBC bladder samples will be collected during surgery (bladder microbiome assessment), faecal samples (microbiome assessment), instrumented urine and blood samples (biobank) will also be taken. We will analyse the microbial community by 16S rDNA gene amplicon sequencing. The difference in alpha diversity (diversity of species within each sample) and beta diversity (change in species diversity) between BCG-candidates will be assessed. Subgroup analysis will be performed which will lead to the development of a clinical prediction model estimating risk of BCG-response. ETHICS AND DISSEMINATION: The study has been approved by the Cantonal Ethics Committee Zurich (2021-01783) and it is being conducted in accordance with the Declaration of Helsinki and Good Clinical Practice. Study results will be disseminated through peer-reviewed journals and national and international scientific conferences. TRIAL REGISTRATION NUMBER: NCT05204199.

AI support for ethical decision-making around resuscitation: proceed with care.

Artificial intelligence (AI) systems are increasingly being used in healthcare, thanks to the high level of performance that these systems have proven to deliver. So far, clinical applications have focused on diagnosis and on prediction of outcomes. It is less clear in what way AI can or should support complex clinical decisions that crucially depend on patient preferences. In this paper, we focus on the ethical questions arising from the design, development and deployment of AI systems to support decision-making around cardiopulmonary resuscitation and the determination of a patient's Do Not Attempt to Resuscitate status (also known as code status). The COVID-19 pandemic has made us keenly aware of the difficulties physicians encounter when they have to act quickly in stressful situations without knowing what their patient would have wanted. We discuss the results of an interview study conducted with healthcare professionals in a university hospital aimed at understanding the status quo of resuscitation decision processes while exploring a potential role for AI systems in decision-making around code status. Our data suggest that (1) current practices are fraught with challenges such as insufficient knowledge regarding patient preferences, time pressure and personal bias guiding care considerations and (2) there is considerable openness among clinicians to consider the use of AI-based decision support. We suggest a model for how AI can contribute to improve decision-making around resuscitation and propose a set of ethically relevant preconditions-conceptual, methodological and procedural-that need to be considered in further development and implementation efforts.

Analyzing Patient Trajectories With Artificial Intelligence.

In digital medicine, patient data typically record health events over time (eg, through electronic health records, wearables, or other sensing technologies) and thus form unique patient trajectories. Patient trajectories are highly predictive of the future course of diseases and therefore facilitate effective care. However, digital medicine often uses only limited patient data, consisting of health events from only a single or small number of time points while ignoring additional information encoded in patient trajectories. To analyze such rich longitudinal data, new artificial intelligence (AI) solutions are needed. In this paper, we provide an overview of the recent efforts to develop trajectory-aware AI solutions and provide suggestions for future directions. Specifically, we examine the implications for developing disease models from patient trajectories along the typical workflow in AI: problem definition, data processing, modeling, evaluation, and interpretation. We conclude with a discussion of how such AI solutions will allow the field to build robust models for personalized risk scoring, subtyping, and disease pathway discovery.

Predicting base editing outcomes with an attention-based deep learning algorithm trained on high-throughput target library screens.

Base editors are chimeric ribonucleoprotein complexes consisting of a DNA-targeting CRISPR-Cas module and a single-stranded DNA deaminase. They enable transition of C•G into T•A base pairs and vice versa on genomic DNA. While base editors have great potential as genome editing tools for basic research and gene therapy, their application has been hampered by a broad variation in editing efficiencies on different genomic loci. Here we perform an extensive analysis of adenine- and cytosine base editors on a library of 28,294 lentivirally integrated genetic sequences and establish BE-DICT, an attention-based deep learning algorithm capable of predicting base editing outcomes with high accuracy. BE-DICT is a versatile tool that in principle can be trained on any novel base editor variant, facilitating the application of base editing for research and therapy.

AttentionDDI: Siamese attention-based deep learning method for drug-drug interaction predictions.

BACKGROUND: Drug-drug interactions (DDIs) refer to processes triggered by the administration of two or more drugs leading to side effects beyond those observed when drugs are administered by themselves. Due to the massive number of possible drug pairs, it is nearly impossible to experimentally test all combinations and discover previously unobserved side effects. Therefore, machine learning based methods are being used to address this issue. METHODS: We propose a Siamese self-attention multi-modal neural network for DDI prediction that integrates multiple drug similarity measures that have been derived from a comparison of drug characteristics including drug targets, pathways and gene expression profiles. RESULTS: Our proposed DDI prediction model provides multiple advantages: (1) It is trained end-to-end, overcoming limitations of models composed of multiple separate steps, (2) it offers model explainability via an Attention mechanism for identifying salient input features and (3) it achieves similar or better prediction performance (AUPR scores ranging from 0.77 to 0.92) compared to state-of-the-art DDI models when tested on various benchmark datasets. Novel DDI predictions are further validated using independent data resources. CONCLUSIONS: We find that a Siamese multi-modal neural network is able to accurately predict DDIs and that an Attention mechanism, typically used in the Natural Language Processing domain, can be beneficially applied to aid in DDI model explainability.