Methods
Study design and sample
collection
We included adult patients (> 18 years) with the diagnosis
of atopic dermatitis based on Hanifin and Rajka criteria made at least
12 months prior to the inclusion, who were indicated for systemic
therapy with dupilumab 300 mg s.c. The Ethical Committee of the Charité
Universitätsmedizin Berlin approved this non-interventional study
(EA1/112/19).
After obtaining informed consent, we gathered data on the clinical
presentation of AD, current symptoms before the initiation of the
therapy, and six months afterward, along with patient reported outcomes.
In addition, serum, and inter-scapular skin swabs from patients with
moderate to severe atopic dermatitis, before and 6 months after
initiating systemic therapy, were acquired. The microbiome sampling
location was chosen based on the relative stability of microbiota in
this anatomic location, precise and straightforward identification of
the sampling area, and sufficient surface for biomaterial collection.
Serum was prepared as described previously14.
Serum proteomic screening
Analysis of 440 proteins in sera of patients with moderate to severe
atopic dermatitis before systemic therapy and six months after
initiating dupilumab was done using Quantibody Human Cytokine Array Q440
chip (RayBiotech, GA, USA). Fluorescent protein arrays were scanned
using a PowerScanner (Tecan Group AG). Array microphotographs were
quantified using Protein Array Analyzer for ImageJ15.
Protein biomarker
measurement
Human serum samples were analyzed using ELISA kits provided by R&D
Systems, Minneapolis, USA (human CCL17, DY364; human CCL13, DY327; human
CCL27, DY376; CCL22, DY336; IL22, DY782; IL11, DY218; CD40L, DY617;
E-selectin, DY724; BDNF, DY248; Notch1, DY5317; CD25s, DY223; ADAM8,
DY1031; FGF1, DY232; CFD, DY1824 human), following the manufacturer’s
protocol.
Serum miRNA extraction, profiling and
validation
Serum miRNA isolation, library preparation and sequencing were done
following manufacturers instructions and are described in detail in the
supplement. The differentially expressed miRNAs were further validated
using reverse transcription quantitative real-time PCR (RT-qPCR) as
described before14 and in detail in the supplement.
Quantification of selected skin
microbiota
We used the data from next generation sequencing (NGS) platform Illumina
to identify the main bacterial species informative of the patient’s skin
status. Based on the results from our previously published
paper13, we decided to further investigate 3 bacterial
species in detail, using qPCR analysis.
Cutibacterium acnes (ATCC 6919), Staphylococcus
epidermidis (ATCC 12228) and Staphylococcus aureus (ATCC 29213)
cultures were grown as described elsewhere. Skin microbiota were
quantified using RT-qPCR as described in the supplement.
Data availability
The sequencing data presented in this study were be deposited in the
European Nucleotide Archive (ENA) under the accession number:
PRJEB59318.
Classification model
The supervised machine learning classification model was performed using
a random forest algorithm with the help of the caret
package16. All samples with complete observations
(without missing values) were included in the training set and divided
into good (super and high responders) and low responders groups.
Features for the final random forest model were selected based on
significant differences in each biomarker. The model fitness was
calculated using 10 sets of 10-fold repeated cross-validation.
Statistics
Mann Whitney U test was used for comparing values between unpaired
observations, with Holm’s p-value correction for multiple comparisons.
Paired data were analyzed using paired Wilcoxon’s test with Holm’s
correction where appropriate. Next-generation sequencing-derived data
were analyzed using the Wald test with Benjamini-Hochberg FDR to correct
for multiple comparisons. P values < 0.05 were considered
significant.