Nasal Dysbiosis in Cutaneous T-Cell Lymphoma Is Characterized by Shifts in Relative Abundances of Non-Staphylococcus Bacteria.

Conceptualization: XAZ, TML, FLV, MJH, JG; Data Curation: XAZ, TML, YP, KTB; Formal Analysis: GEC, LF, SJG, XAZ; Funding Acquisition: XAZ; Investigation: XAZ, TML, FLV, MJH, YP, KTB, DS; Methodology: GEC, LF, SJG; Project Administration: XAZ, JG; Resources: XAZ, KTB, JG, DS, SJG; Software: GEC, LF; Supervision: XAZ, PCS, JG; Validation: XAZ, MBB, GEC; Visualization: GEC, LF, XAZ, FLV; Writing - Original Draft Preparation: TML, FLV, MJH, XAZ; Writing - Review and Editing: XAZ, JG, MBB, SJG, GEC, FLV, MJH, TML

Madeline J. Hooper: http://orcid.org/0000-0003-1334-5342 Tessa M. LeWitt: http://orcid.org/0000-0001-8935-2165 Francesca L. Veon: http://orcid.org/0000-0002-0232-1671 Yanzhen Pang: http://orcid.org/0000-0003-1825-9603 George E. Chlipala: http://orcid.org/0000-0003-0203-3191 Leo Feferman: http://orcid.org/0000-0002-5821-3434 Stefan J. Green: http://orcid.org/0000-0003-2781-359X Dagmar Sweeney: http://orcid.org/0000-0001-6320-8931 Katherine T. Bagnowski: http://orcid.org/0000-0002-7482-4454 Michael B. Burns: http://orcid.org/0000-0001-9791-4359 Patrick C. Seed: http://orcid.org/0000-0001-8998-8374 Joan Guitart: http://orcid.org/0000-0001-7635-9237 Xiaolong A. Zhou: http://orcid.org/0000-0002-6177-2472

A total of 45 patients comprised the patient group with CTCL, of which 40 had been diagnosed with mycosis fungoides (MF), and 5 had been diagnosed with Sézary syndrome (SS) ( Table 1 and Supplementary Table S1 ). All patients and HCs were from the same geographical region (Chicago Metropolitan Area, Chicago, IL) to control for environmental influences on the microbiome ( Rothschild et al., 2018 ). Four HC‒CTCL pairs sharing a home were selected for even closer matching. To avoid bias in sample collection, manipulation, and analysis, we concurrently enrolled patients and controls rather than rely on publicly available human microbiome data. There was no significant difference in age, sex, race/ethnicity, or phototype between the two groups ( Table 1 ). Table 1 Characteristics of Patients (n = 45) and Healthy Controls (n = 20) Characteristics Patients Controls P- Value n 45 20 Sex 1 0.2797 2  Male 29 (64.4) 10 (50.0)  Female 16 (35.6) 10 (50.0) Age (y) 3 62.7 (17.5–83.4) 54.5 (24.4–79.1) 0.1393 2 Race/Ethnicity 1 0.9041 2  Asian 2 (4.4) 3 (15.0)  Black 5 (11.1) 0 (0.0)  White 30 (68.9) 15 (75.0) White/Hispanic 6 (13.3) 1 (5.0) Other/Hispanic 1 (2.2) 1 (5.0) Phototype 1 0.2398 2  Light (FST I‒III) 45 (100.0) 19 (95.0)  Dark (FST IV‒VI) 0 (0.0) 1 (5.0) Comorbidities 1  HTN 20 (44.4) 6 (30.0) 0.411 4  DLP 24 (53.3) 7 (35.0) 0.192 4  GERD 11 (24.4) 6 (30.0) 0.761 4 Diagnosis Subtype 1  MF 40 (88.9) —  SS 5 (11.1) — Clinical stage 1  Early (IA‒IIA) 26 (57.8) —  Advanced (IIB‒IVB) 19 (42.2) — Disease duration (y) 3 3.1 (0.2–30.0) — mSWAT 3 22 (3–100) — Abbreviations: DLP, dyslipidemia; FST, Fitzpatrick skin phototype; GERD, gastroesophageal reflux; HTN, hypertension; MF, mycosis fungoides; mSWAT, modified Severity Weighted Assessment Tool; SS, Sézary syndrome. 1 Data are presented as n (%). 2 Data were analyzed with two-tailed t -test. 3 Data are presented as median (range). 4 Data were analyzed with Fisher’s exact test. Characteristics of Patients (n = 45) and Healthy Controls (n = 20) Abbreviations: DLP, dyslipidemia; FST, Fitzpatrick skin phototype; GERD, gastroesophageal reflux; HTN, hypertension; MF, mycosis fungoides; mSWAT, modified Severity Weighted Assessment Tool; SS, Sézary syndrome. Data are presented as n (%). Data were analyzed with two-tailed t -test. Data are presented as median (range). Data were analyzed with Fisher’s exact test. A total of 26 patients had the early-stage disease (stages IA‒IIA; 57.8%), and 19 had the advanced-stage disease (stages IIB‒IVB; 42.2%); stage IB was the most common overall (n = 18, 40.0%). The median modified Severity-Weight Assessment Tool (mSWAT) score was 22 (range = 3‒100). The most common comorbidities reported were dyslipidemia (MF/SS = 53.3%, HCs = 35.0%), hypertension (MF/SS = 44.4%, HCs = 30.0%), and gastroesophageal reflux (MF/SS = 24.4%, HCs = 30.0%). There were no significant differences in comorbidities between the two groups for these three conditions (Fisher’s exact test: dyslipidemia P = 0.192; hypertension P = 0.411; and gastroesophageal reflux P = 0.761) or for any other comorbidity. The 16S rRNA gene amplicon sequence data identified a total of 720 genera, 285 families, 139 orders, 67 classes, and 28 phyla. Swab, reagent, and PCR controls were negative for any significant contamination. The most abundant phyla in both groups were the three most frequently encountered in the human nares: Proteobacteria , Actinobacteria , and Firmicutes . At the genus level, there was no significant difference in biodiversity between MF/SS and HC samples as assessed by Shannon diversity index ( P = 0.201) ( Figure 1 a). Notably, β-diversity revealed a small but globally significant difference in the microbial community structure between patients and controls on the basis of Adonis/permutational ANOVA (R 2 = 0.104, P = 0.023 for class level; R 2 = 0.0904, P = 0.038 for order level) ( Figure 1 b and c). The most abundant genera in both groups were Corynebacterium and Staphylococcus ( Figure 1 d). Figure 1 Distinct nasal bacterial communities were identified in patients with MF/SS versus in HCs. ( a ) α-Diversity was not significantly different between HCs and patients with MF/SS at the genus level (Shannon diversity index, P = 0.201). MDS plots using the Bray‒Curtis dissimilarity index of β-diversity analyses show significant differential clustering of HCs and patients with MF/SS at the taxonomic levels of ( b ) class (Adonis/PERMANOVA R 2 = 0.104, P = 0.023) and ( c ) order (R 2 = 0.0904, P = 0.038). ( d ) Relative abundance (%) of the 20 most abundant genera in nasal samples of HCs and patients with MF/SS (left, individual subjects; right, mean relative abundances per group [HC, MF/SS]). HC, healthy control; MDS, multidimensional scaling; MF, mycosis fungoides; PERMANOVA, permutational ANOVA; SS, Sézary syndrome. Distinct nasal bacterial communities were identified in patients with MF/SS versus in HCs. ( a ) α-Diversity was not significantly different between HCs and patients with MF/SS at the genus level (Shannon diversity index, P = 0.201). MDS plots using the Bray‒Curtis dissimilarity index of β-diversity analyses show significant differential clustering of HCs and patients with MF/SS at the taxonomic levels of ( b ) class (Adonis/PERMANOVA R 2 = 0.104, P = 0.023) and ( c ) order (R 2 = 0.0904, P = 0.038). ( d ) Relative abundance (%) of the 20 most abundant genera in nasal samples of HCs and patients with MF/SS (left, individual subjects; right, mean relative abundances per group [HC, MF/SS]). HC, healthy control; MDS, multidimensional scaling; MF, mycosis fungoides; PERMANOVA, permutational ANOVA; SS, Sézary syndrome. Specific taxa contributing to the distinct nasal microbiota of patients with MF/SS were then investigated. Several genera were significantly higher in patients than in HCs (q < 0.05): Roseomonas , Catenococcus , Vibrio , Marinobacter , Allorhizobium-Neorhizobium-Pararhizobium-Rhizobium , Acinetobacter , Alishewanella , Paracoccus , unclassified Clostridiales and unclassified Clostridiales family XIII, Atopobium , and Dietzia ( Table 2 and Figure 2 ). Meanwhile, Lachnospiraceae NK4A136 group was reduced in patient samples (q < 0.05). Regression analyses revealed a positive association between the relative abundance of Catenococcus , Vibrio , Roseomonas , unclassified Clostridiales , and Acinetobacter genera and increased mSWAT, an indicator of skin disease burden; reduced Lachnospiraceae NK4A136 group relative abundance was associated with higher mSWAT scores ( Figure 3 ). One-way ANOVA revealed significant differences in the mean relative abundance of Alishewanella , Allorhizobium-Neorhizobium-Pararhizobium-Rhizobium , Marinobacter , and Vibrio between HCs and patients as grouped by low versus high mSWAT and early versus advanced disease ( Supplementary Table S2 , Supplementary Table S3 , Supplementary Table S4 , Supplementary Table S5 ). Sidak test for pairwise comparisons showed that the mean relative abundance of Vibrio was significantly different between HCs and patients with low ( P = 0.034) and high ( P = 0.004) mSWAT and between HCs and patients with early-stage ( P = 0.011) and advanced-stage ( P = 0.007) disease. Table 2 Differential Taxonomic Analysis Shows Unique Microbial Signatures at the Genus Level in Nasal Samples from Patients with MF/SS Versus HCs Genus HC/Patient LogFC P -Value q-Value 1 Reduced abundance in MF/SS Lachnospiraceae NK4A136 group 0.51 0.005 0.04 Ruminococcus 0.12 0.02 0.14 Ruminoclostridium 0.48 0.03 0.15 Enriched abundance in MF/SS Catenococcus ‒1.41 <0.001 0.001 Alishewanella ‒1.07 <0.001 0.001 Vibrio ‒1.96 <0.001 0.001 Unclassified Clostridiales family XIII ‒0.35 <0.001 0.001 Roseomonas ‒0.89 <0.001 0.001 Unclassified Clostridiales ‒0.62 <0.001 0.001 Paracoccus ‒1.36 <0.001 0.001 Marinobacter ‒1.07 <0.001 0.001 Atopobium ‒0.38 <0.001 <0.005 Dietzia ‒0.62 <0.001 <0.005 Allorhizobium-Neorhizobium-Pararhizobium-Rhizobium ‒1.32 <0.001 <0.01 Acinetobacter ‒0.99 0.005 0.04 Unclassified bacteria ‒0.59 0.01 0.09 Christensenellaceae R-7 group ‒0.04 0.01 0.09 Cutibacterium ‒0.21 0.02 0.11 Escherichia/Shigella ‒1.04 0.02 0.13 Neisseria ‒0.81 0.03 0.14 Pseudoalteromonas ‒1.7 0.03 0.14 Subdoligranulum ‒0.53 0.04 0.15 Veillonella ‒0.93 0.04 0.17 Actinomyces ‒1.18 0.04 0.17 Unclassified Gammaproteobacteria ‒0.64 0.05 0.17 Abbreviations: FC, fold change; HC, healthy control; MF, mycosis fungoides; SS, Sézary syndrome. 1 The q-value is the FDR-adjusted P -value ( Benjamini and Hochberg, 1995 ). Figure 2 Changes in the abundance of specific bacterial genera present in the nares of patients with MF/SS compared with those in the nares of the HCs. Dot plots illustrate the relative sequence abundance (%) of genera that are ( a ) significantly enriched and ( b ) significantly reduced in patients with MF/SS versus in HCs. Mean relative abundances are indicated by black horizontal bars. Significance is determined by q ≤ 0.05; the q-value is the FDR-adjusted P -value ( Benjamini and Hochberg, 1995 ). FDR, false discovery rate; HC, healthy control; MF, mycosis fungoides; SS, Sézary syndrome. Figure 3 Relationship between the relative abundances of significantly enriched and depleted genera and skin disease burden in patients with MF/SS. The relative abundance (%) of each genus is plotted versus mSWAT score (an indicator of skin disease burden) with line of best fit. ( a ) Increased mSWAT score was associated with an increased relative abundance of several genera that were enriched in patients with MF/SS: Catenococcus , Vibrio , Roseomonas , unclassified Clostridiales, and Acinetobacter . ( b ) Lower relative abundances were associated with increased mSWAT scores for the remaining enriched genera: Paracoccus , Allorhizobium-Neorhizobium-Pararhizobium-Rhizobium , Alishewanella , Marinobacter , Dietzia , unclassified Clostridiales family XIII, and Atopobium. ( c ) Regression analysis of Lachnospiraceae NK4A136 group (reduced in patients with MF/SS compared with that in the HCs) revealed that lower relative abundances were associated with higher mSWAT scores. HC, healthy control; MF, mycosis fungoides; mSWAT, modified Severity-Weight Assessment Tool; SS, Sézary syndrome. Differential Taxonomic Analysis Shows Unique Microbial Signatures at the Genus Level in Nasal Samples from Patients with MF/SS Versus HCs Abbreviations: FC, fold change; HC, healthy control; MF, mycosis fungoides; SS, Sézary syndrome. The q-value is the FDR-adjusted P -value ( Benjamini and Hochberg, 1995 ). Changes in the abundance of specific bacterial genera present in the nares of patients with MF/SS compared with those in the nares of the HCs. Dot plots illustrate the relative sequence abundance (%) of genera that are ( a ) significantly enriched and ( b ) significantly reduced in patients with MF/SS versus in HCs. Mean relative abundances are indicated by black horizontal bars. Significance is determined by q ≤ 0.05; the q-value is the FDR-adjusted P -value ( Benjamini and Hochberg, 1995 ). FDR, false discovery rate; HC, healthy control; MF, mycosis fungoides; SS, Sézary syndrome. Relationship between the relative abundances of significantly enriched and depleted genera and skin disease burden in patients with MF/SS. The relative abundance (%) of each genus is plotted versus mSWAT score (an indicator of skin disease burden) with line of best fit. ( a ) Increased mSWAT score was associated with an increased relative abundance of several genera that were enriched in patients with MF/SS: Catenococcus , Vibrio , Roseomonas , unclassified Clostridiales, and Acinetobacter . ( b ) Lower relative abundances were associated with increased mSWAT scores for the remaining enriched genera: Paracoccus , Allorhizobium-Neorhizobium-Pararhizobium-Rhizobium , Alishewanella , Marinobacter , Dietzia , unclassified Clostridiales family XIII, and Atopobium. ( c ) Regression analysis of Lachnospiraceae NK4A136 group (reduced in patients with MF/SS compared with that in the HCs) revealed that lower relative abundances were associated with higher mSWAT scores. HC, healthy control; MF, mycosis fungoides; mSWAT, modified Severity-Weight Assessment Tool; SS, Sézary syndrome. Given the known role of S. aureus in worsening CTCL through its toxins ( Fujii, 2022 ), we next examined whether the relative abundances of Staphylococcus species differed between patients with MF/SS and HCs. We performed targeted sequencing of the bacterial tuf gene, which provides accurate species-level resolution of Staphylococcus communities ( Ahle et al., 2021 ) . S. epidermidis and S. aureus were the most abundant staphylococcal species in both groups: these species comprised 56.8% and 23.8% of all staphylococcal species for patients with MF/SS and 52.7% and 14.5% for HCs, respectively ( Supplementary Figure S1 ). There was no statistically significant difference between the relative abundance of any Staphylococcus species (including S. aureus and S. epidermidis ) between patients with MF/SS and HCs ( Supplementary Table S6 ).

The authors state no conflict of interest.

Ethical approval was obtained from the Northwestern University Institutional Review Board (STU00209226). Written informed consent, nasal samples, and personal data were obtained at the Northwestern University Cutaneous Lymphoma Clinic (Chicago, Illinois) between 2019 and 2021 in compliance with the Declaration of Helsinki. Each patient had clinically and biopsy-proven CTCL, as reviewed by an expert dermatopathologist (JG). At the time of sample collection, patients were receiving standard-of-care therapies, including skin-directed (n = 36, 80.0%) and select systemic (n = 13, 28.9%) treatments or were treatment naive (n = 9, 18.9%) ( Supplementary Table S1 ). Subjects on any antibiotics within the preceding 4 weeks were excluded. Clinical staging and mSWAT were assessed by the study’s principal investigator (XAZ) at sample collection. The HC group (n = 20) was composed of age-matched volunteers without CTCL or other skin diseases from the same geographical region. Nasal samples were obtained through sterile swabs (FLOQSwabs, Copan Diagnostics, Murrieta, CA) with hands covered in sterile gloves. All specimens were placed immediately in sterile cryovials and promptly stored at ‒80°C until DNA extraction. Genomic DNA was extracted using a Maxwell 16 LEV Blood DNA Kit (Promega, Madison, WI) implemented on a Maxwell 16 Instrument, following the manufacturer’s instructions with minor modifications: a lysozyme incubation (10 ng/μl lysozyme; Thermo Fisher Scientific, Waltham, MA) for 30 minutes at 37°C and bead beating (40 seconds at 6 min/sec) using a FastPrep-24 System (MP Biomedicals, Irvine, CA). Homogenized samples were transferred to the Maxwell cartridges for final DNA purification. Genomic DNA was prepared for sequencing using a two-stage amplicon sequencing workflow, as described previously ( Naqib et al., 2018 ), using primers targeting the V4 (fourth hypervariable) region of microbial 16S rRNA genes. The 515 forward modified and 806 reverse modified primers contained 5 ′ linker sequences compatible with access array primers for Illumina sequencers (Fluidigm, South San Francisco, CA ) ( Walters et al., 2015 ). PCRs were performed in a total volume of 10 μl using MyTaq HS 2X Mix (Meridian Bioscience, Cincinnati, OH) primers at 500 nM concentration and approximately 1,000 copies per reaction of a synthetic double-stranded DNA template (described below). Extraction blanks and PCR blanks were treated as independent samples and sequenced with unique barcodes. Thermocycling conditions were 95 °C for 5 minutes (initial denaturation), followed by 28 cycles of 95°C for 30 seconds, 55°C for 45 seconds, and 72°C for 30 seconds. Second-stage reactions contained 1 μl of PCR product and a unique primer pair of access array primers; thermocycling conditions consisted of 95°C for 5 minutes (initial denaturation), followed by 8 cycles of 95°C for 30 seconds, 60°C for 30 seconds, and 72°C for 30 seconds. Libraries were pooled and sequenced on an Illumina MiniSeq sequencer (Illumina, San Diego, CA) with 15% phiX spike-in and paired-end 2 × 153 base sequencing reads. A synthetic double-stranded DNA spike-in was synthesized as a gBLOCK by Integrated DNA Technologies (Coralville, IA). The basis of the design was a 999 base pairs region of the 16S rRNA gene of Rhodanobacter denitrificans strain 2APBS1T ( NC_020541 ) ( Prakash et al., 2012 ). Portions of V1, V2, and V4 variable regions were replaced by eukaryotic mRNA sequences ( Apostichopus japonicus Gapdh mRNA, HQ292612 ; and Strongylocentrotus intermedius Gapdh mRNA, KC775387 ). Primer sites were preserved, and the overall length in the base pair of the synthetic DNA did not differ from the equivalent R. denitrificans fragment. PCR amplicons generated from this synthetic DNA do not differ in size from bacterial amplicons and can only be identified and removed through postsequencing bioinformatics analysis. The sequence can be accessed through GenBank using the accession number OK324963 . Genomic DNA was PCR amplified with primers ACACTGACGACATGGTTCTACAACAGGCCGTGTTGAACGTG for CS1_ tuf2 forward and TACGGTAGCAGAGACTTGGTCTACAGTACGTCCACCTTCACG for CS2_ tuf2 reverse ( Ahle et al., 2021 , 2020 ) targeting the Staphylococcus tuf gene. Amplicons were generated using a two-stage PCR amplification protocol as previously described ( Naqib et al., 2018 ). First-stage PCR amplifications were performed in 10 μl reactions in 96-well plates using MyTaq HS 2X mastermix (Meridian Bioscience). PCR conditions were 95°C for 5 minutes, followed by 28 cycles of 95°C for 30 seconds, 55°C for 30 seconds, and 72°C for 60 seconds. Second-stage reactions using access array primers were performed as described earlier. Samples were pooled, purified, and sequenced on an Illumina MiSeq with 10% phiX spike-in and paired-end 2 × 300 base sequencing reads (i.e., V3 chemistry). Library preparation, pooling, and sequencing were performed at the Genome Research Core within the Research Resources Center at the University of Illinois Chicago (Chicago, IL). To check for contamination, control swab, PCR, and reagent/kit samples were performed. In total, 22 PCR and 32 extraction controls were analyzed, all of which yielded very low sequence counts (mean ± SD: 122.5 ± 61.4), far below the 5,000 counts per sample threshold needed for inclusion in data analyses. 16S rRNA gene amplicon reads were merged using PEAR, version 0.9.6 ( Zhang et al., 2014 ), and trimmed using cutadapt, version 1.18, to remove ambiguous nucleotides and primer sequences on the basis of a quality threshold of P = 0.01 ( Martin, 2011 ). Reads lacking the primer sequence and/or sequences <225 base pairs after merging and quality trimming were discarded. Chimeric sequences were identified and removed using the USEARCH algorithm with a comparison with Silva (version 132) reference sequence ( Edgar, 2010 ; Glöckner et al., 2017 ). Amplicon sequence variants were identified using DADA2, version 1.18 ( Callahan et al., 2016 ), and annotated taxonomically using the Naive Bayesian classifier included in DADA2 with the Silva (version 132) training set. Synthetic spike-in sequences were removed before proceeding with downstream bioinformatics analyses. Diversity analyses were performed in R using the vegan library, version 2.5-6 ( Okansen et al., 2018 ). Biodiversity (α-diversity) was calculated using the Shannon index modeled with the sample covariates using a generalized linear model assuming Gaussian distribution. Bray‒Curtis indices were calculated to assess sample dissimilarity (β-diversity). For the tuf2 next-generation sequencing, merged reads that lacked either primer sequence or were <400 base pairs were discarded. Chimeric sequences were identified and removed in a de novo fashion using USEARCH, version 8.1.1861 ( Edgar, 2010 ). Amplicon sequence variants were identified using the protocol described earlier and taxonomically annotated using alignment from BLAST (blastn) with the RefSeq Prokaryotic Genomes reference, downloaded on 1 December 2021 ( NCBI Resource Coordinators, 2017 ). Differential analyses of taxa as compared with experimental covariates were performed using edgeR (version 3.28.1) on raw sequence counts ( McCarthy et al., 2012 ). The 16S data were filtered to remove sequences of chloroplast, mitochondrial, or eukaryotic origin and taxa present in <30% of all samples and with <500 total sequence counts across all samples. The tuf2 next-generation sequencing data were filtered to retain only species belonging to the genus Staphylococcus and to remove taxa following the same parameters as mentioned earlier. Data were normalized as counts per million and fit using a negative binomial generalized linear model using experimental covariates. Statistical analyses were performed in R and STATA SE. Significance of the α-diversity model (ANOVA) was tested using the F-test. Posthoc, pairwise analyses were performed using the Mann‒Whitney test ( Wickham, 2009 ). The dissimilarity indices were tested for significance using Adonis/permutational ANOVA, and additional comparisons of the individual covariates were performed using analysis of similarities. Statistical tests for the differential analyses were performed using a likelihood ratio test. Adjusted P -values (q-values) were calculated using the Benjamini‒Hochberg false discovery rate correction ( Benjamini and Hochberg, 1995 ). Significant taxa were determined on the basis of a false discovery rate threshold of 5.0% (0.05). Plots were generated using GraphPad Prism, version 9.2, (GraphPad Software, San Diego, CA) and the ggplot2 library in R ( Wickham, 2009 ). Datasets related to this article can be found at https://dataview.ncbi.nlm.nih.gov/object/PRJNA768111?reviewer=pd94ec0d6iurp8k0gbs5evtjhj (National Center for Biotechnology Information Short Read Archive, accession number PRJNA768111).

Our results show that the nasal microbiomes of patients with MF/SS and HCs are different. These data add to the existing body of knowledge that supports the importance of the nasal microbiome in skin disease ( McCarthy et al., 2022 ; Olesen et al., 2021 ; Totté et al., 2019 ). Nasal microbiota are already known to be important in atopic dermatitis, in which increased relative abundance of nasal Staphylococcus and Moraxella and decreased Dolosigranulum are associated with disease severity ( Totté et al., 2019 ), and increased nasal S. hominis is linked to skin S. hominis abundance and disease improvement ( Olesen et al., 2021 ). The nares of patients with hidradenitis suppurativa are characterized by enriched Proteus communities and reduced Corynebacterium ( McCarthy et al., 2022 ). In addition, loss of nasal Proteobacteria has been associated with skin and soft tissue infections ( Johnson et al., 2015 ), and nasal S. aureus colonization has been implicated in disease activity in various inflammatory skin conditions, including CTCL ( Ng et al., 2017 ; Nørreslet et al., 2020 ; Talpur et al., 2008 ). The nasal microbiome could also feasibly serve as a source for bacterial recolonization of the skin after systemic antibiotic treatment ( Lindahl et al., 2021 ), if not also increase the risk of recurrent infections. Although the exact mechanisms for how the nasal microbiome influences CTCL pathogenesis and vice versa are unclear, the data included in this study provide greater context from which ongoing CTCL skin microbiome research can be understood. We found that enrichment of the genera Vibrio , Roseomonas , and Acinetobacter and depletion of Paracoccus are associated with increased skin severity. From the literature, we know that Vibrio , Roseomonas , and Acinetobacter bacteria are important in causing necrotizing fasciitis, aggravating atopic dermatitis, and instigating skin and soft tissue infections, respectively ( Table 3 and Supplementary Discussion) ( Cerqueira and Peleg, 2011 ; Janda et al., 1988 ; Myles et al., 2018 ); however, their role as nasal bacteria requires further study because there remains an extreme paucity of knowledge on the biological relationships shared by non‒ Staphylococcus species in the nasal microbiome in healthy and disease states. Table 3 Summary of Human Disease Associations of Significantly Enriched/Reduced Genera Found in the Anterior Nares of Patients with MF/SS Genus Associations with Human Disease Associations with Human Cutaneous Disease Acinetobacter Hospital- and community-acquired pneumonia, invasive bloodstream infections, urinary tract infections, hospital-acquired meningitis, osteomyelitis, pericarditis Skin and soft tissue infections Alishewanella None available None available Allorhizobium-Neorhizobium-Pararhizobium-Rhizobium None available None available Atopobium Bacteremia, dental infections, bacterial vaginosis Bacteremia in the setting of Fournier’s gangrene Catenococcus None available None available Dietzia Bacteremia, prosthetic hip infection, pacemaker infection, pleural fluid isolate Confluent and reticulated papillomatosis Lachnospiraceae NK4A136 group Decreased abundance in the gut microbiome after Trichinella spiralis infection and in patients with dementia; biomarker for lean body habitus None available Marinobacter None available None available Paracoccus P. yeei : myocarditis, peritonitis, bacteremia None available Roseomonas Septicemia R. mucosa : catheter-related infections, dialysis and surgical wound infections, bacteremia Skin and soft tissue infections, atopic dermatitis Unclassified Clostridiales Mediates allergic immune activity Reduced in the gut microbiome of alopecia areata and pediatric atopic dermatitis Unclassified Clostridiales family XIII Mediates mood disorders None available Vibrio Cholera, gastroenteritis, sepsis, less commonly otitis media, meningitis, peritonitis, and pneumonia Necrotizing fasciitis Abbreviations: MF, mycosis fungoides; SS, Sézary syndrome. Summary of Human Disease Associations of Significantly Enriched/Reduced Genera Found in the Anterior Nares of Patients with MF/SS Abbreviations: MF, mycosis fungoides; SS, Sézary syndrome. Importantly, our data showed that the nasal relative abundances of Staphylococcus species in patients with CTCL did not differ significantly from those of HCs. Although the pathogenic role of S. aureus toxins in CTCL has been well established over the years ( Fujii, 2022 ), it had been unclear whether this translates to a higher relative abundance of S. aureus in the skin and/or nose. Previous culture-based studies have either failed to show a statistically significant difference in nasal S. aureus colonization rates between patients with CTCL and HCs ( Nguyen et al., 2008 ) or did not include a matched comparison group ( Talpur et al., 2008 ). Our nasal data, together with recent CTCL skin microbiome data ( Harkins et al., 2021 ), suggest that the effects of S. aureus in CTCL may not translate to the actual increased relative abundance of S. aureus. Instead, it remains possible that S. aureus toxin production—and not relative abundance—differs between patients with CTCL and HCs. These differences between patients and controls may be mediated by shifts in the abundances of the other bacterial taxa. In fact, in atopic dermatitis, quorum sensing between bacterial species in the skin revealed that coagulase-negative staphylococci species produce autoinducing peptides that inhibit S. aureus phenol-soluble modulin α, a proinflammatory virulence factor capable of mediating epidermal injury ( Williams et al., 2019 ). Nasal dysbiosis carries intriguing insights for pathophysiology in a disease where advanced-stage patients often suffer from recurrent skin infections ( Blaizot et al., 2018 ). Through the accurate characterization of the nasal microbial profiles associated with worse disease, we can conceivably intervene by altering the nasal microbiome through the decolonization of high-risk bacteria or reconstitution with bacteria associated with healthy individuals. The nasal microbiome may have the potential to serve as an additional and accessible biomarker for the determination of disease progression risk. Eventual matched patient skin and nasal microbiome analyses can further elucidate these relationships. In this study, we establish that CTCL is characterized by nasal dysbiosis composed of shifts in specific non‒ Staphylococcus taxa compared with that of healthy individuals. Because bacterial activity perpetuates CTCL disease progression and because infection is the most common cause of death in this patient population ( Tsambiras, 2001 ; Willerslev-Olsen et al., 2013 ), attention to the nasal microbiome and its relationship with other microbial reservoirs is crucial to our understanding of the CTCL disease state and pathogenesis.

Cutaneous T-cell lymphoma (CTCL) comprises a heterogeneous group of T-lymphocyte malignancies that infiltrate the skin. Patients with advanced, progressive disease often suffer from profound immune dysregulation and recurrent skin infections. Previous research suggests that the microbiome may influence CTCL pathogenesis, flares, and progression ( Harkins et al., 2021 ; Lindahl et al., 2019 ; Willerslev-Olsen et al., 2013 ). Moreover, distinct microbe-precipitated metabolic and immunologic pathways have been linked to the pathobiology of atopic dermatitis ( Nørreslet et al., 2020 ; Paller et al., 2019 ), psoriasis ( Hidalgo-Cantabrana et al., 2019 ), hidradenitis suppurativa ( McCarthy et al., 2022 ), and various malignancies ( Goodman and Gardner, 2018 )—conditions similarly known to be associated with immune dysregulation. The ecosystem encompassing the nares may be a principal reservoir for self-contamination through nose-to-skin bacterial spread or vice versa. The importance of the nasal microbiome is further emphasized by recent literature suggesting that altered nasal bacterial diversity is associated with gut and skin dysbiosis in hidradenitis suppurativa ( McCarthy et al., 2022 ). Although early culture-based studies have suggested that higher rates of Staphylococcus aureus skin and nasal colonization occur in patients with CTCL ( Nguyen et al., 2008 ; Talpur et al., 2008 ), the complete nasal microbiome in CTCL has yet to be described. Although the CTCL skin microbiota is currently being investigated ( Harkins et al., 2021 ; Salava et al., 2020 ), its nasal microbial profile is a missing piece of data because CTCL dysbiosis likely extends beyond the skin. To better understand the CTCL nasal microbiome, we conducted a cross-sectional analysis of the nasal microbiota present in patients with CTCL and healthy controls (HCs) using 16S ribosomal RNA (rRNA) gene sequencing and further determined staphylococcal species relative abundances using tuf2 gene amplicon sequencing.