Machine learning applied to atopic dermatitis transcriptome reveals distinct therapy‐dependent modification of the keratinocyte immunophenotype*

Artificial Intelligence: A Snapshot of Its Application in Chronic Inflammatory and Autoimmune Skin Diseases

Immuno-correlated dermatological pathologies refer to skin disorders that are closely associated with immune system dysfunction or abnormal immune responses. Advancements in the field of artificial intelligence (AI) have shown promise in enhancing the diagnosis, management, and assessment of immuno-correlated dermatological pathologies. This intersection of dermatology and immunology plays a pivotal role in comprehending and addressing complex skin disorders with immune system involvement. The paper explores the knowledge known so far and the evolution and achievements of AI in diagnosis; discusses segmentation and the classification of medical images; and reviews existing challenges, in immunological-related skin diseases. From our review, the role of AI has emerged, especially in the analysis of images for both diagnostic and severity assessment purposes. Furthermore, the possibility of predicting patients' response to therapies is emerging, in order to create tailored therapies.

Impaired expression of metallothioneins contributes to allergen-induced inflammation in patients with atopic dermatitis

Regulation of cutaneous immunity is severely compromised in inflammatory skin disease. To investigate the molecular crosstalk underpinning tolerance versus inflammation in atopic dermatitis, we utilise a human in vivo allergen challenge study, exposing atopic dermatitis patients to house dust mite. Here we analyse transcriptional programmes at the population and single cell levels in parallel with immunophenotyping of cutaneous immunocytes revealed a distinct dichotomy in atopic dermatitis patient responsiveness to house dust mite challenge. Our study shows that reactivity to house dust mite was associated with high basal levels of TNF-expressing cutaneous Th17 T cells, and documents the presence of hub structures where Langerhans cells and T cells co-localised. Mechanistically, we identify expression of metallothioneins and transcriptional programmes encoding antioxidant defences across all skin cell types, that appear to protect against allergen-induced inflammation. Furthermore, single nucleotide polymorphisms in the MTIX gene are associated with patients who did not react to house dust mite, opening up possibilities for therapeutic interventions modulating metallothionein expression in atopic dermatitis.

Artificial Intelligence: Exploring the Future of Innovation in Allergy Immunology

PURPOSE OF REVIEW

Artificial intelligence (AI) has increasingly been used in healthcare. Given the capacity of AI to handle large data and complex relationships between variables, AI is well suited for applications in healthcare. Recently, AI has been applied to allergy research.

RECENT FINDINGS

In this article, we review how AI technologies have been utilized in basic science and clinical allergy research for asthma, atopic dermatitis, rhinology, adverse reactions to drugs and vaccines, food allergy, anaphylaxis, urticaria, and eosinophilic gastrointestinal disorders. We discuss barriers for AI adoption to improve the care of patients with atopic diseases. These studies demonstrate the utility of applying AI to the field of allergy to help investigators expand their understanding of disease pathogenesis, improve diagnostic accuracy, enable prediction for treatments and outcomes, and for drug discovery.

Skin programming of inflammatory responses to Staphylococcus aureus is compartmentalized according to epidermal keratinocyte differentiation status

BACKGROUND

Acute cutaneous inflammation causes microbiome alterations as well as ultrastructural changes in epidermis stratification. However, the interactions between keratinocyte proliferation and differentiation status and the skin microbiome have not been fully explored.

OBJECTIVES

Hypothesizing that the skin microbiome contributes to regulation of keratinocyte differentiation and can modify antimicrobial responses, we examined the effect of exposure to commensal (Staphylococcus epidermidis, SE) or pathogenic (Staphylococcus aureus, SA) challenge on epidermal models.

METHODS

Explant biopsies were taken to investigate species-specific antimicrobial effects of host factors. Further investigations were performed in reconstituted epidermal models by bulk transcriptomic analysis alongside secreted protein profiling. Single-cell RNA sequencing analysis was performed to explore the keratinocyte populations responsible for SA inflammation. A dataset of 6391 keratinocytes from control (2044 cells), SE challenge (2028 cells) and SA challenge (2319 cells) was generated from reconstituted epidermal models.

RESULTS

Bacterial lawns of SA, not SE, were inhibited by human skin explant samples, and microarray analysis of three-dimensional epidermis models showed that host antimicrobial peptide expression was induced by SE but not SA. Protein analysis of bacterial cocultured models showed that SA exposure induced inflammatory mediator expression, indicating keratinocyte activation of other epidermal immune populations. Single-cell DropSeq analysis of unchallenged naive, SE-challenged and SA-challenged epidermis models was undertaken to distinguish cells from basal, spinous and granular layers, and to interrogate them in relation to model exposure. In contrast to SE, SA specifically induced a subpopulation of spinous cells that highly expressed transcripts related to epidermal inflammation and antimicrobial response. Furthermore, SA, but not SE, specifically induced a basal population that highly expressed interleukin-1 alarmins.

CONCLUSIONS

These findings suggest that SA-associated remodelling of the epidermis is compartmentalized to different keratinocyte populations. Elucidating the mechanisms regulating bacterial sensing-triggered inflammatory responses within tissues will enable further understanding of microbiome dysbiosis and inflammatory skin diseases, such as atopic eczema.

bSRWPSO-FKNN: A boosted PSO with fuzzy K-nearest neighbor classifier for predicting atopic dermatitis disease

Introduction

Atopic dermatitis (AD) is an allergic disease with extreme itching that bothers patients. However, diagnosing AD depends on clinicians' subjective judgment, which may be missed or misdiagnosed sometimes.

Methods

This paper establishes a medical prediction model for the first time on the basis of the enhanced particle swarm optimization (SRWPSO) algorithm and the fuzzy K-nearest neighbor (FKNN), called bSRWPSO-FKNN, which is practiced on a dataset related to patients with AD. In SRWPSO, the Sobol sequence is introduced into particle swarm optimization (PSO) to make the particle distribution of the initial population more uniform, thus improving the population's diversity and traversal. At the same time, this study also adds a random replacement strategy and adaptive weight strategy to the population updating process of PSO to overcome the shortcomings of poor convergence accuracy and easily fall into the local optimum of PSO. In bSRWPSO-FKNN, the core of which is to optimize the classification performance of FKNN through binary SRWPSO.

Results

To prove that the study has scientific significance, this paper first successfully demonstrates the core advantages of SRWPSO in well-known algorithms through benchmark function validation experiments. Secondly, this article demonstrates that the bSRWPSO-FKNN has practical medical significance and effectiveness through nine public and medical datasets.

Discussion

The 10 times 10-fold cross-validation experiments demonstrate that bSRWPSO-FKNN can pick up the key features of AD, including the content of lymphocytes (LY), Cat dander, Milk, Dermatophagoides Pteronyssinus/Farinae, Ragweed, Cod, and Total IgE. Therefore, the established bSRWPSO-FKNN method practically aids in the diagnosis of AD.

In vitro models for investigating itch

Itch (pruritus) is a sensation that drives a desire to scratch, a behavior observed in many animals. Although generally short-lasting and not causing harm, there are several pathological conditions where chronic itch is a hallmark symptom and in which prolonged scratching can induce damage. Finding medications to counteract the sensation of chronic itch has proven difficult due to the molecular complexity that involves a multitude of triggers, receptors and signaling pathways between skin, immune and nerve cells. While much has been learned about pruritus from in vivo animal models, they have limitations that corroborate the necessity for a transition to more human disease-like models. Also, reducing animal use should be encouraged in research. However, conducting human in vivo experiments can also be ethically challenging. Thus, there is a clear need for surrogate models to be used in pre-clinical investigation of the mechanisms of itch. Most in vitro models used for itch research focus on the use of known pruritogens. For this, sensory neurons and different types of skin and/or immune cells are stimulated in 2D or 3D co-culture, and factors such as neurotransmitter or cytokine release can be measured. There are however limitations of such simplistic in vitro models. For example, not all naturally occurring cell types are present and there is also no connection to the itch-sensing organ, the central nervous system (CNS). Nevertheless, in vitro models offer a chance to investigate otherwise inaccessible specific cell–cell interactions and molecular pathways. In recent years, stem cell-based approaches and human primary cells have emerged as viable alternatives to standard cell lines or animal tissue. As in vitro models have increased in their complexity, further opportunities for more elaborated means of investigating itch have been developed. In this review, we introduce the latest concepts of itch and discuss the advantages and limitations of current in vitro models, which provide valuable contributions to pruritus research and might help to meet the unmet clinical need for more refined anti-pruritic substances.

Implication of Human Bacterial Gut Microbiota on Immune-Mediated and Autoimmune Dermatological Diseases and Their Comorbidities: A Narrative Review

During the last decade, the advent of modern sequencing methods (next generation techniques, NGS) has helped describe the composition of the human gut microbiome, enabling us to understand the main characteristics of a healthy gut microbiome and, conversely, the magnitude of its disease-related changes. This new knowledge has revealed that healthy gut microbiota allow the maintenance of several crucial physiological functions, such as the ability to regulate the innate and adaptive immune systems. Increasing evidence has pointed out a condition of dysbiosis in several autoimmune/immune mediated dermatological conditions and specific gut microbial signatures have also been reported to correlate with clinical and prognostic parameters of such diseases. Based on a literature search of relevant published articles, this review debates the current knowledge and the possible pathogenic implications of bacterial gut microbiota composition assessed through NGS techniques in systemic lupus erythematosus, atopic dermatitis, psoriasis, and alopecia areata. Evidence of a potential role of specific gut microbiota signatures in modulating the clinical course of such diseases and their main comorbidities has been also reviewed.

EAACI Biologicals Guidelines-dupilumab for children and adults with moderate-to-severe atopic dermatitis

Atopic dermatitis imposes a significant burden on patients, families and healthcare systems. Management is difficult, due to disease heterogeneity, co-morbidities, complexity in care pathways and differences between national or regional healthcare systems. Better understanding of the mechanisms has enabled a stratified approach to the management of atopic dermatitis, supporting the use of targeted treatments with biologicals. However, there are still many issues that require further clarification. These include the definition of response, strategies to enhance the responder rate, the duration of treatment and its regimen (in the clinic or home-based), its cost-effectiveness and long-term safety. The EAACI Guidelines on the use of dupilumab in atopic dermatitis follow the GRADE approach in formulating recommendations for each outcome and age group. In addition, future approaches and research priorities are discussed.