# paper.pdf ## Metadata - PDFFormatVersion=1.7 - Language=en - IsLinearized=true - IsAcroFormPresent=false - IsXFAPresent=false - IsCollectionPresent=false - IsSignaturesPresent=false - Custom.CrossMarkDomains[2]=elsevier.com - Custom.CrossmarkMajorVersionDate=2010-04-23 - Custom.EPSprocessor=PStill version 1.84.42 - Custom.ElsevierWebPDFSpecifications=7.0.1 - Custom.CrossmarkDomainExclusive=true - Custom.robots=noindex - Custom.doi=10.1016/j.waojou.2026.101389 - Custom.CrossMarkDomains[1]=sciencedirect.com - CreationDate=D:20260428121603+05'30' - Subject=World Allergy Organization Journal, 19 (2026) 101389. doi:10.1016/j.waojou.2026.101389 - Author=Ying Zhou M.S. - Creator=Elsevier - Producer=PDFlib+PDI 10.0.2p2 (C++/Win64) - ModDate=D:20260428122108+05'30' - Title=Integrated transcriptomic analysis identifies tsRNA-mRNA regulatory axis in asthma pathogenesis - dc:format=application/pdf - dc:identifier=doi:10.1016/j.waojou.2026.101389 - dc:title=Integrated transcriptomic analysis identifies tsRNA-mRNA regulatory axis in asthma pathogenesis - dc:creator=Ying Zhou M.S.,Hang Lin M.D.,Dongmei Zhou M.S.,Xinhui Gong B.S.,Ximeng Ma B.S.,Lei Shi M.S.,Yubao Cui Ph.D. - dc:subject=Asthma,Bronchial epithelial cells,tsRNA,mRNA,Biomarkers - dc:description=World Allergy Organization Journal, 19 (2026) 101389. doi:10.1016/j.waojou.2026.101389 - dc:publisher=The Author(s) - prism:aggregationtype=journal - prism:publicationname=World Allergy Organization Journal - prism:copyright=© 2026 The Author(s). Published by Elsevier Inc. on behalf of World Allergy Organization. - prism:issn=1939-4551 - prism:volume=19 - prism:number=5 - prism:coverdisplaydate=May 2026 - prism:pagerange=101389 - prism:startingpage=101389 - prism:doi=10.1016/j.waojou.2026.101389 - prism:url=https://doi.org/10.1016/j.waojou.2026.101389 - crossmark:majorversiondate=2010-04-23 - crossmark:crossmarkdomainexclusive=true - crossmark:doi=10.1016/j.waojou.2026.101389 - crossmark:crossmarkdomains=elsevier.comsciencedirect.com - jav:journal_article_version=VoR - pdfx:elsevierwebpdfspecifications=7.0.1 - pdfx:doi=10.1016/j.waojou.2026.101389 - pdfx:robots=noindex - pdfx:crossmarkmajorversiondate=2010-04-23 - pdfx:crossmarkdomainexclusive=true - pdfx:epsprocessor=PStill version 1.84.42 - pdfx:crossmarkdomains=sciencedirect.comelsevier.com - xap:creatortool=Elsevier - xap:createdate=2026-04-28T12:16:03+05:30 - xap:modifydate=2026-04-28T12:21:08+05:30 - xap:metadatadate=2026-04-28T12:21:08+05:30 - xaprights:marked=True - tdm:reservation=1 - tdm:policy=https://www.elsevier.com/tdm/tdmrep-policy.json - pdf:producer=PDFlib+PDI 10.0.2p2 (C++/Win64) - xapmm:documentid=uuid:a1c6a678-2177-45ce-acff-0cfcc7c05025 - xapmm:instanceid=uuid:7a4d6c52-fb34-481b-b199-20d7c4b09b98 - ali:license_ref=http://creativecommons.org/licenses/by/4.0/ ## Contents ### Page 1 Zhou et al. World Allergy Organization Journal (2026) 19:101389 Open Access # Integrated transcriptomic analysis identifiestsRNA–mRNA regulatory axis in asthmapathogenesis Ying Zhou, M.S.a,e, Hang Lin, M.D.b,e, Dongmei Zhou, M.S.c,c, Ximeng Ma, B.S.cLei Shi, M.S.d and Yubao Cui, Ph.D.c , ## ABSTRACT Background: Asthma is a complex respiratory disorder with incompletely understood molecularmechanisms. tRNA-derived small RNAs (tsRNAs), a newly discovered class of non-coding RNAs,have been implicated in various diseases, but their role in asthma pathogenesis remains largelyunexplored. Methods: We performed an integrated analysis of tsRNA and mRNA profiles in bronchialepithelial cells from severe asthma patients (GSE85215, GSE85214) and healthy controls.Differentially expressed tsRNAs were identified using MINTmap and DESeq2 (P < 0.05,|FC| > 1.5), and their target mRNAs were predicted via miRanda and TargetScan. Validation wasconducted in an independent cohort (GSE201955). Functional interactions were assessed using atsRNA–mRNA regulatory network, and key findings were experimentally confirmed in house dustmite (HDM)-stimulated BEAS-2B cells via qRT-PCR and dual-luciferase reporter assays. Results: We identified 58 dysregulated tsRNAs and 961 differentially expressed mRNAs inasthma. A regulatory network revealed 343 negatively correlated tsRNA–mRNA pairs, with 36 hubmRNAs validated in GSE201955. Three candidate tsRNAs (tRF-18-SX73IE03, tRF-22-BZBZOS4Y1,tRF-22-MI7O3B1N4) showed consistent dysregulation in HDM-exposed cells. Mechanistically,tRF-21-Z4NY2V7KE directly suppressed MYBL1 expression, as confirmed by luciferase assays. Conclusions: Our study provides the first comprehensive tsRNA–mRNA regulatory network inasthma, uncovering novel post-transcriptional mechanisms in bronchial epithelial cells. Theidentified tsRNAs and their targets may be potential biomarkers; moreover, they warrant furtherinvestigation as therapeutic targets for asthma. Keywords: Asthma, Bronchial epithelial cells, tsRNA, mRNA, Biomarkers aDepartment of Pediatrics Laboratory, The Affiliated Children’s Hospital ofJiangnan University, Wuxi 214023, China *Corresponding author. E-mail: **Corresponding author. Emailes:; e Ying Zhou and Hang Lin contributed equally to this article. Full list of author information is available at the end of the article Received 26 October 2025; Received in revised from 22 March 2026;Accepted 3 April 2026 Online publication date xxx 1939-4551/© 2026 The Author(s). Published by Elsevier Inc. on behalf ofWorld Allergy Organization. This is an open access article under the CC BYlicense (). ### Page 2 2 Zhou et al. World Allergy Organization Journal (2026) 19:101389 ## INTRODUCTION Asthma is a chronic inflammatory disordermarked by airway inflammation, bronchial hyperresponsiveness, and reversible airflow obstruction, with global prevalence varying significantlyacross regions, ranging from 3.44% in Asia and3.67% in Africa to 4.90% in South America, 5.69%in Europe, 8.29% in North America, and 8.33% inOceania.1 It is triggered by environmental factorssuch as allergens (eg, pollen, dust mites, petdander), respiratory infections, tobacco smoke,and air pollutants. Genetic predisposition alsosignificantly contributes to the development ofasthma, as certain genetic variants can increasean individual’s susceptibility to the condition.2,3This complex interplay of environmental andgenetic factors results in diverse asthmamanifestations, including variations in symptomtriggers, disease severity, and treatmentresponses, posing challenges in effectivediagnosis and management.4 Non-coding RNAs (ncRNAs) have emerged askey regulators of many biological processes,including inflammation and immune responses.They can be categorized into 3 groups based ontheir length: long ncRNAs (>200 nt), such as ribosomal RNA (rRNA) and long non-coding RNA(lncRNA); medium-length ncRNAs (40–200 nt), liketransfer RNA (tRNA) fragments, small nucleolarRNA (snoRNA), and small nuclear RNA (snRNA);and short ncRNAs (<40 nt), including microRNA(miRNA), piwi-interacting RNA (piRNA), and tRNAderived small RNAs (tsRNAs). Research has highlighted the roles of lncRNAs, circRNAs, and miRNAsin asthma pathogenesis, demonstrating theirinvolvement in immune modulation and airwayinflammation.5,6 Among these, macrophages andT helper cells are crucial in asthma development.Macrophages influence airway inflammation,immune regulation, and airway function throughncRNA-mediated polarization., tsRNAs, a novelsubclass of small non-coding RNAs, regulatediverse cellular processes including gene expressioncontrol, epigenetic modulation, and intercellularcommunication, critically influencing cell fate determination and metabolic homeostasis.11–13 WhiletsRNA dysregulation has been implicated in variousdiseases, their expression and function in asthmahave yet to be investigated. Bronchial epithelial cells (BECs), key players inthe airway defense system, protect the airwaysfrom harmful substances and facilitate theremoval of particulates through mucociliaryclearance.14–16 These cells also participate inimmune responses by releasing lipid mediators,cytokines, and growth factors involved in asthmapathogenesis.17–19 Despite the knowndysregulation of various ncRNAs in asthma, therole of tsRNAs in BECs during diseasepathogenesis remains largely unexplored.Therefore, this is a study aimed to investigatethe tsRNA expression profile in BECs fromsevere asthma patients and to elucidate thepotential tsRNA-mRNA regulatory networksinvolved in disease development. ## MATERIALS AND METHODS Datasets and samples We searched the GEO database for “asthma”and “Bronchial Epithelial Cells” and successfullydownloaded the GSE85216 and GSE201955datasets. GSE85216 includes RNA-Seq datafrom GSE85214 and miRNA-Seq data fromGSE85215, both derived from bronchial epithelialcells of patients with severe asthma and healthycontrols. Specifically, the mRNA expression profile wasanalyzed using GSE85214 (16 severe asthma patients vs. 10 healthy controls). For tsRNA analysis,the original FASTQ files from GSE85215 (8 severeasthma patients vs. 5 healthy controls) were processed. Validation of mRNA expression was performed using GSE201955, an independent cohortcomprising 79 asthma patients (including individuals with clinical features associated with severe asthma endotypes, as characterized byMagnaye et al)20 and 39 healthy controls(). Differential expression tsRNA analysis The original fastq data were downloaded fromthe GSE85215 with 8 severe asthma and 5 healthcontrol individuals. The MINTmap (v2.0) was usedto screen and quantify tsRNA,21 and thendifferentially expressed tsRNAs were identifiedbetween asthma and control using the DESeq2package in R, with a filtering threshold ofP < 0.05 and |FC| > 1.5. ### Page 3 Volume 19, No. 5, Month 2026 Construction of tsRNA-mRNA regulatory network To construct the tsRNA-mRNA regulatorynetwork, we first analyzed RNA-seq data frombronchial epithelial cells (GSE85214: 16 severeasthma vs. 10 healthy controls) using DESeq2(v1.38.3) with |log2FC| > 1.5 and adjusted P < 0.05(Benjamini-Hochberg correction) to identifydifferentially expressed mRNAs (DEmRNAs), whiletsRNA targets were predicted by integratingmiRanda (v3.3a; ΔG ≤ 20 kcal/mol, seedcomplementarity) and TargetScan (v7.0; context++ score >50) to ensure high-confidence interactions, followed by filtering for anti-correlatedpairs (Pearson’s r < 0.5, P < 0.05) betweentsRNA and mRNA expression levels, with theresulting network visualized in Cytoscape (v3.9.1)and functionally annotated using DAVID (v6.8) forGO/KEGG enrichment (P < 0.05, Fisher’s exacttest) to elucidate asthma-related pathways. In-silico validation of mRNA expression levels To validate the mRNA expression levels in theGSE85214 dataset, a total of 36 mRNA with a Pvalue lower than 0.05 were selected, which wasthen detected in the GSE201955 dataset. qRT-PCR validation of tsRNA in BEAS-2B cellsstimulated with mite extract qRT-PCR was performed to validate theexpression of differentially expressed tsRNAs inBEAS-2B cells (American Type Culture Collection,Rockville, MD) treated with house dust mite (HDM)extract. The cells were cultured in RPMI-1640medium supplemented with 10% (v/v) heatinactivated FBS (HyClone, Logan, UT, USA).BEAS-2B cells were seeded in 6-well plates at adensity of 150,000 cells per well, and after 24 h,40 μg/mL of HDM extract (Dermatophagoidespteronyssinus, Greer Laboratories, Lenoir, NC,USA) was added. After another 24 h, total RNAwas extracted using TRI Reagent (Sigma: T9424,Sigma-Aldrich, Saint Louis, MO, USA). RNA pretreatment and cDNA synthesis were then performed using the rtStar� tRF&tiRNA PretreatmentKit (Cat# AS-FS-005) and rtStar� First-StrandcDNA Synthesis Kit (Cat# AS-FS-003). RNA wasfirst deacetylated by mixing it with the deacetylation reaction buffer and incubating at 37◦ C for40 min, followed by the addition of Deacylation 3 Stop Buffer and a 5-min room temperature incubation. Next, 3′ -cP was removed and 5′ -P wasadded, and the mixture was incubated at 37◦ C for40 min, followed by a 5-min incubation at 70◦ C toterminate the reaction. The RNA was extractedagain, and demethylation was performed by mixing the Demethylation Reaction Buffer withDemethylase and incubating at 37◦ C for 2 h. Thereaction was then terminated with the addition ofthe termination solution, followed by RNAextraction. For 3′ adaptor ligation, RNA was mixedwith the 3′ adaptor, incubated at 70◦ C for 2 min,and then cooled on ice before adding the reactionmixture, which was incubated at 25◦ C for 1 h.Reverse transcription primer hybridization wascarried out by adding the reverse transcriptionprimer and incubating at specified temperatures.Next, 5′ adaptor ligation was performed by mixingthe 5′ adaptor with the appropriate reagents andincubating at 25◦ C for 1 h. Finally, reverse transcription was performed by adding the adaptorligated RNA and reverse transcription reagents,incubating at 50◦ C for 1 h, and cooling the reaction for subsequent PCR amplification. Real-time PCR was conducted using theApplied Biosystems QuantStudio� 5 Real-TimePCR System with 2X PCR master mix (Arraystar)and Primer 5.0 software. To prepare the standardcurve, cDNA templates for both target andhousekeeping genes were selected. PCR reactions were set up using 2 × Master Mix, 10 μMforward and reverse primers, cDNA, and water toa final volume of 10 μl. The PCR conditions were95◦ C for 10 min, followed by 40 cycles of 95◦ Cfor 10 s and 60◦ C for 60 s for fluorescencecollection. PCR products were analyzed on a 2%agarose gel with a 100 bp DNA Ladder to confirmthe amplification specificity. The PCR productswere serially diluted (1 × 10 1 to 1 × 10 9) tocreate the standard curve. For real-time PCR,cDNA samples were mixed with 2 × Master Mix,primers, and water, then briefly centrifuged. Thereaction mixture was added to a 384-well PCRplate, followed by 2 μl of cDNA, sealed withSealing Film, briefly centrifuged, and kept on icebefore PCR. The PCR program included 95◦ C for10 min, followed by 40 cycles of 95◦ C for 10 s and60◦ C for 60 s with fluorescence collection. Afteramplification, a melt curve was generated byheating from 60◦ C to 99◦ C. The system ### Page 4 | tsRNA | log2FoldChange | style | P value | | - | - | - | - | | MINTbase_ID | sequence | | tRF-29-PSQP4PW3FJFL | GCCCGGCTAGCTCAGTCGGTAGAGCATGA | 2.237056712 | Down | 0.000246017 | | tRF-30-PSQP4PW3FJI0 | GCCCGGCTAGCTCAGTCGGTAGAGCATGAG | 2.319316189 | Down | 0.000421713 | | tRF-23-WJ9X0UD304 | TCGGCTGTTAACCGAAAGGTTGG | 3.811301691 | Down | 0.001466312 | | tRF-23-BZBZOS4YV | AACTTAACTTGACCGCTCTGACC | 2.026751696 | Up | 0.001638739 | | tRF-22-MI7O3B1N4 | CGGCTGTTAACCGAAAGGTTGG | 3.056880169 | Down | 0.001866166 | | tRF-38-HMI8W47W1R7HFEV | ATATCATTGGTCGTGGTTGTAGTCCGTGCGAGAATACC | 3.623880688 | Up | 0.002438001 | | tRF-27-HJYJRPFQZDP | ATAGCTTAGCGGTAGAGCATTTGACTG | 3.635608675 | Up | 0.003127083 | | tRF-20-R2IP4OQ3 | GGGGAATTAGCTCAAGCGGT | 2.479625852 | Up | 0.004078512 | | tRF-29-PS5P4PW3FJF2 | GCCCGGATAGCTCAGTCGGTAGAGCATCA | 2.769672856 | Down | 0.004147541 | | tRF-22-BZBZOS4Y1 | AACTTAACTTGACCGCTCTGAC | 2.487907694 | Up | 0.004457195 | | tRF-31-M2OSRNLNKSEK0 | CGGGAGACCGGGGTTCGATTCCCCGACGGGG | 2.123681434 | Down | 0.005010545 | | tRF-28-WS3V2VR0PSDZ | TCTCGCCTGCCACGCGGGAGGCCCGGGT | 2.0629518 | Down | 0.005199811 | | tRF-30-623K7SIR3DR2 | GGAGACCGGGGTTCGATTCCCCGACGGGGA | 3.051878437 | Down | 0.006135064 | | tRF-18-SX73IE03 | GTCTAGTGGTATGATTCT | 3.458903754 | Up | 0.006226498 | | tRF-22-79M89P9NI | GTTTCCGTGGTGTAGTGGTTAT | 3.041543093 | Down | 0.007618769 | | tRF-31-2YU04DYJIO3ZE | CACTGTAAAGCTAACTTAGCATTAACCTTTT | 2.160897127 | Down | 0.007706691 | | tRF-40VEX0K3XN13QXQWUI | TAGATTGAAGCCAGTTGATTAGGGTGCTTAGCTGTTAACT | 2.822629124 | Up | 0.008886318 | | tRF-17-QK11M3Q | GCGGGAGGCCCGGGTTT | 2.509487698 | Up | 0.010021056 | | tRF-21-P2PSSELQB | GCAGAGCCCGGTAATCGCATA | 3.157411323 | Up | 0.010522779 | | tRF-418L8NRS9NS334L2H1B | TCATATCATTGGTCGTGGTTGTAGTCCGTGCGAGAATACCA | 2.363698707 | Up | 0.010698882 | | tRF-20-P42U6R93 | GCATGCACGAGGCCCTGGGT | 3.499451928 | Up | 0.010931776 | | tRF-41U5YKFN8DYDZDL9X1B | TACACTTAGGAGATTTCAACTTAACTTGACCGCTCTGACCA | 2.952835454 | Up | 0.011588804 | | tRF-28-I3VF4YO9XED2 | ATGGTTAGCACTCTGGACTCTGAATCCA | 2.184612733 | Down | 0.012404392 | | tRF-32-MIF91SS2P46I3 | CGGCTAGCTCAGTCGGTAGAGCATGGGACTCT | 2.017163523 | Down | 0.013074589 | | tRF-23-2IUIX1Q7DR | CAACTTAACTTGACCGCTCTGAC | 3.307885712 | Up | 0.013116275 | | tRF-22-8XF6RE98 N | TCCTAAGCCAGGGATTGTGGGT | 2.56829377 | Down | 0.014052447 | | tRF-27-W3FJI0E7UM5 | TCGGTAGAGCATGAGACTCTTAATCTC | 2.393448977 | Down | 0.015314791 | 4 Zhou et al. World Allergy Organization Journal (2026) 19:101389 ### Page 5 | tRF-29-4S14IZJQXEJU | CTCCGAGGTGATTTTCATATTGAATTGCA | 2.570921148 | Down | 0.016629727 | | - | - | - | - | - | | tRF-30-QKF1R3WE8RO8 | GCGGGAGACCGGGGTTCGATTCCCCGACGG | 2.161384893 | Down | 0.017478753 | | tRF-32-389MV47P596V5 | CCTGGTGGTCTAGTGGTTAGGATTCGGCGCTC | 2.122703863 | Down | 0.017799856 | | tRF-31-PIR8YP9LON4VD | GCACTGGTGGTTCAGTGGTAGAATTCTCGCC | 2.081762507 | Down | 0.018212393 | | tRF-31-6XQ6S8V0J8O9E | GGCTAGCTCAGTCGGTAGAGCATGGGACTCT | 2.344294423 | Down | 0.018392052 | | tRF-29-5DMKYUYRLHIX | GAGACCGGGGTTCGATTCCCCGACGGGGA | 2.014030483 | Down | 0.01840148 | | tRF-22-WD8SS46D2 | TCGACTCCCGGTATGGGAACCA | 2.068479477 | Down | 0.019257342 | | tRF-21-S79PVOEOE | GTCCTTGTAGTATAAACTAAT | 2.064471302 | Down | 0.020264274 | | tRF-31-FSXMSL73VL4YD | AGCCGTGATCGTATAGTGGTTAGTACTCTGC | 2.353283973 | Down | 0.02048367 | | tRF-19-18VUY9IQ | AGTGGTATGATTCTCGCTT | 2.413774246 | Up | 0.021500326 | | tRF-21-Z4NY2V7KE | TTGTGCTTTGCACGCGTGGGT | 2.457420207 | Down | 0.021642073 | | tRF-19-IRMJ6VE2 | ATTCCCGGGCGGCGCACCA | 2.148014499 | Up | 0.022174462 | | tRF-29-08F4BDNZ8OIR | ACCGGAGATGAAAACCTTTTTCCAAGGAC | 2.408449035 | Down | 0.023887304 | | tRF-18-43RQ590K | CTAGTGGTATGATTCTCG | 2.02458771 | Up | 0.024214957 | | tRF-25-YONONU3IND | TTCAACTTAACTTGACCGCTCTGAC | 2.558763424 | Up | 0.02478296 | | tRF-31-K84J83ML5FX2D | CCCGGCTAGCTCAGTCGGTAGAGCATGAGAC | 2.057152992 | Down | 0.025374427 | | tRF-30-32VIJMRPFQJD | CCGGATAGCTCAGTCGGTAGAGCATCAGAC | 2.179503679 | Down | 0.026146605 | | tRF-40-B9I1KQSX0DIJZ726 | AACTCATGCCCCCATGTCTAACAACATGGCTTTCTCACCA | 2.233331282 | Up | 0.02631471 | | tRF-28-H4SXQ3V2Y7DZ | ATATGGTCTAGCGGTTAGGATTCCTGGT | 2.16710546 | Down | 0.026412491 | | tRF-45BZ0IV25Z2IUIX1Q7O6 | AACTTACACTTAGGAGATTTCAACTTAACTTGACCGCTCTGACCA | 2.682135899 | Up | 0.030493817 | | tRF-26-IK9NJ4S2I7D | ATGGGTGGTTCAGTGGTAGAATTCTC | 2.13392983 | Down | 0.031145948 | | tRF-28-MQ18Y3E7QN00 | CGTATAGTGGTTAGTACTCTGCGTTGTG | 2.010816348 | Down | 0.035847172 | | tRF-21-VKS4I7LZE | TAGGGGTATGATTCTCGCTTT | 2.570463757 | Up | 0.037271046 | | tRF-17-8Z1MQ8Q | TCCTTAGGTCGCTGGTT | 2.022022763 | Down | 0.039077026 | | tRF-27-U5XBINVDRI2 | TACACTGAAAATGTTTAGACGGGCTCA | 2.319668237 | Down | 0.0395488 | | tRF-32-M1M3WD8S746D2 | CGGCCCGGGTTCGACTCCCGGTGTGGGAACCA | 2.303735617 | Down | 0.040428234 | | tRF-27-3JVIJMRPFQL | CCGGCTAGCTCAGTCGGTAGAGCATGA | 2.128434561 | Down | 0.040476564 | | tRF-29-H4SXQ3V2Y72E | ATATGGTCTAGCGGTTAGGATTCCTGGTT | 2.245440821 | Down | 0.040570509 | (continued) Volume 19, No. 5, Month 2026 5 ### Page 6 6 Zhou et al. World Allergy Organization Journal (2026) 19:101389 | 2.846945402 Up 0.049174852 | | - | | sequence log2FoldChange style P value | TGAATCTGACAACAGAGGCTTACGACCCCTTATTTACCCCA 2.107607172 Up 0.040735393 | tRF-29-7EMQ18Y3E7IN GTGATCGTATAGTGGTTAGTACTCTGCGT 2.115583704 Down 0.048812002 | GAGAAAGCTCACAAGA | ACTGCTAACTCATGCCC | CCATGTCTAACAAC | | tsRNA | MINTbase_ID | tRF-41XENDBP1IUUK7VZ0RB | tRF-475BF900BY4D84KRIMUF1 | automatically calculated the relative geneexpression by normalizing the target gene concentrations to housekeeping gene levels basedon the standard curve. The qPCR primer sequences are provided in. Prior to the formal tsRNA validation experiments, the responsiveness of BEAS-2B cells toHDM stimulation under the current conditions wasconfirmed in our laboratory through preliminaryassays (eg, observing typical morphologicalchanges and increased expression of inflammatory markers, data not shown). Furthermore, thismodel has been widely adopted and validated innumerous studies.22,23 The consistency of ourpreliminary findings with this extensive body ofliterature confirms the suitability of this wellestablished model for the current study. Dual-luciferase reporter gene assay The 3ʹ-untranslated regions (UTRs) of MYBL1(containing a tRF-21-Z4NY2V7KE binding site)were synthetized and cloned into pmirGLO vector(Promega) to construct wild-type plasmids. The 3ʹ-UTRs of MYBL1 was mutated from “TCTATGTATGGAAAGCACA” to “CTCGCACACAGGGGATGTG”, which was synthesized and cloned intopmirGLO vector (Promega) to construct mutationtype plasmids. The Mimics of tRF-21-Z4NY2V7KEwas synthesized, too. As we previously described in detail,24 the HEK293T cells (the Cell Bank of the Chinese Academyof Sciences, Shanghai, China) were cultured on a96-well culture plate 24 h before transfection.The culture medium was Dulbecco’s ModifiedEagle Medium (DMEM) + 10% Fetal bovine serum(FBS). The plasmids were subjected to transfectionat 60%–70% confluency. After 48 h, a DualLuciferase Reporter Gene Assay Kit (11402ES60,Yeasen Biotechnology, Shanghai, China) was usedto assay luciferase activity to verify the effect oftRF-21-Z4NY2V7KE on MYBL1. Statistical analysis Numerical data are presented asmean ± standard deviation (SD). Differentialexpression analysis of tsRNAs and mRNAs between asthmatic patients and healthy controls(GSE85214, GSE85215) was performed usingDESeq2 (v1.38.3) with thresholds of |log2FC| > 1.5 ### Page 7 Volume 19, No. 5, Month 2026 7 and adjusted P < 0.05 (Benjamini-Hochbergcorrection for multiple testing). For validation inthe independent cohort (GSE201955), Student’s ttest was applied to compare expression levels of36 hub mRNAs, with significance set at P < 0.05.Differential expression of tsRNAs in HDMstimulated BEAS-2B cells was assessed via Student’s t-test (version 22.0 SPSS, Chicago, USA),and P < 0.05 was deemed statistically significant.The Pearson correlation analysis for tsRNA-mRNApairs (|cor| > 0.5, P < 0.05) was adjusted for multiple comparisons using the false discovery rate(FDR) method. All tests were two-tailed, and variance homogeneity was confirmed via Levene’stest. ## RESULTS Construction of tsRNA-mRNA regulatorynetwork 24 severe asthmatics and 15 health controls inthe GSE85216 dataset (including GSE85214 andGSE85215)25 were enrolled in the construction oftsRNA-mRNA regulatory network. We initiallyanalyzed the differential expression of tsRNAsbetween 8 severe asthmatic patients and 5 healthcontrol individuals in the GSE85215 dataset, followed by an analysis of mRNA expression levelsbetween 16 severe asthma and 10 health controlin the GSE85214 dataset. The construction andvalidation process of the tsRNA-mRNA regulatorynetwork is illustrated in. Intotal, 58 differentially expressed tsRNAs ()and 961 differentially expressed mRNAs wereobserved in bronchial epithelial cells. Accordingto the detection results, we constructed thenegative regulatory networks with a cutoff|cor|>0.5 using the Cytoscape software (fordetails, see the Supplementary Methods in theOnline Repository). Consequently, the interactionsbetween tsRNAs and mRNAs were identified, andsubsequent statistical analysis revealed 343 pairsof tsRNA-mRNA negative regulatory relationships,encompassing 47 tsRNAs and 254 mRNAs (and). To further investigate the potential functionalimplications of the tsRNA, we performed functional enrichment analyses for the 961 mRNAs and343 tsRNA-mRNA pairs by respectively using GOenrichment analyses and KEGG-Pathway analyses. As shown in, the GOenrichment highlighted the comprehensiveregulatory functions of these tsRNAs on mRNAs.Furthermore, the KEGG-Pathway analysesdetailed the pathway of tsRNAs in regulatingmRNAs (). GO/KEGGanalyses of this network implicated these tsRNAmRNA axes in key asthma pathways, includingIL-17 signaling and airway remodeling. In-silico validation of differentially expressedmRNAs Based on the constructed tsRNA-mRNA negative regulatory relationships, we selected 36 hubmRNAs with a P value lower than 0.05 from thediscovery cohort (GSE85214, severe asthma patients vs. healthy controls). We then examined theexpression levels of these candidates in an independent validation cohort (GSE201955), whichcomprises 79 asthmatics (including individualswith clinical features associated with severeasthma endotypes) and 39 healthy controls.Notably, all 36 selected mRNAs exhibited significant differential expression in this validationcohort (all P < 0.05,), confirming thereproducibility of our findings across differentasthma populations. qRT-PCR validation of tsRNAs in BEAS-2B cellsstimulated with Dermatophagoides pteronyssinusextract To verify the accuracy of tsRNA expression levelspresented in the above transcriptome sequencingresults, we used the human bronchial epithelialcells being exposed to house dust mite extract asthe cell model of asthma. We cultured BEAS-2Bcells with 40 μg/mL of house dust mite extracts,and then amplified tsRNAs for validation viaquantitative real-time PCR (qRT-PCR). The detection results confirmed significant differences in theexpression levels of tRF-18-SX73IE03 (P < 0.01),tRF-22-BZBZOS4Y1 (P < 0.05), and tRF-22MI7O3B1N4 (P < 0.05) compared to controls(). Verification of tRF-21-Z4NY2V7KE regulatingmRNA expression of MYBL1 by luciferasereporter assay Using a dual-luciferase reporter gene systemand site-directed mutagenesis, we investigated ### Page 8 8 Zhou et al. World Allergy Organization Journal (2026) 19:101389 the potential regulatory effect of tRF-21Z4NY2V7KE on the MYBL1 gene. The resultsshowed that tRF-21-Z4NY2V7KE down-regulated luciferase expression in the MYBL1 context(P < 0.01,). The altered base sequencessuggest that tRF-21-Z4NY2V7KE modulates ### Page 9 Volume 19, No. 5, Month 2026 9 MYBL1 mRNA expression through its affinity for 3ʹURT binding sites, indicating a specific posttranscriptional regulatory mechanism. ## DISCUSSION In this study, we investigated the role of tsRNAs inasthma pathogenesis by conducting an integratedtranscriptomic analysis of bronchial epithelial cellsfrom severe asthma patients. Our analysis revealeda previously uncharacterized tsRNA-mRNA regulatory network, identifying 58 differentially expressedtsRNAs and 961 differentially expressed mRNAs.Notably, we constructed 343 negatively correlatedregulatory pairs and validated the dysregulation of key tsRNAs (tRF-18-SX73IE03, tRF-22-BZBZOS4Y1,and tRF-22-MI7O3B1N4) in an HDM-stimulatedcellular model. Furthermore, mechanistic studiesconfirmed that tRF-21-Z4NY2V7KE directly suppresses MYBL1 expression via 3′ UTR binding.Thesefindings position tsRNAs as critical posttranscriptional regulators in asthma and provide afoundational framework for understanding theirrole in airway inflammation. BECs play a crucial role in the airway defensesystem, preventing harmful substances fromentering the airways and facilitating the removal ofparticulates through mucociliary clearance andother mechanical barriers.24 Beyond their ### Page 10 10 Zhou et al. World Allergy Organization Journal (2026) 19:101389 protective functions, BECs actively participate inimmune responses by producing and releasingbiologically active compounds such as lipidmediators, growth factors, endothelin, and avariety of cytokines/chemokines. These mediatorsare integral to the pathogenesis of airwaydisorders, including asthma.18 Previous studieshave demonstrated that gene expression profilesin BECs differ significantly between healthyindividuals and those with severe asthma. Forinstance, transcriptomic analysis has identifiedover 1200 differentially expressed genes betweencentral and peripheral airways in asthmapatients.26 Further investigation combiningtranscriptomic data from severe asthma patientsand healthy controls revealed a total of 1703differentially expressed genes and 71 microRNAs,offering insights into novel inflammatory andstructural mechanisms contributing to asthma.27While these studies have established BECs as avaluable model for asthma research and identifiednumerous protein-coding transcripts involved indisease pathogenesis, the regulatory roles of noncoding RNAs, particularly transfer RNA-derivedsmall RNAs (tsRNAs), remain largely unexplored inasthma. To address this knowledge gap and build uponexisting transcriptomic findings, we conducted anintegrated analysis of mRNA and tsRNA profiles in bronchial epithelial cells using the GSE85216 dataset (GSE85214 and GSE85215) created by Rocio T.Martinez-Nunez et al (2018).25 Our study revealed961 differentially expressed mRNAs and 58 tsRNAsin severe asthma patients compared to healthycontrols. Significantly, we established the firstcomprehensive tsRNA-mRNA interactome forasthma, identifying 343 negatively correlated regulatory pairs (Pearson’s r < 0.5, P < 0.05). Amongthese, 36 hub mRNAs demonstrated consistentdysregulation in an independent validation cohort(GSE201955; 79 asthma vs 39 controls). GO andpathway analyses further confirmed these tsRNAmRNA interactions participate in asthma-relatedbiological processes. The reproducibility of thesefindings across cohorts and their convergence withknown asthma mechanisms not only validates priorwork on BEC dysregulation but importantly expandsour understanding of ncRNA regulation in asthmapathogenesis. These robust molecular signatureshighlight promising diagnostic biomarkers andprovide new insights into asthma’s complex regulatory networks. Building upon recent breakthroughs in tsRNAdiagnostics, such as the identification of tRF-AlaAGC-2-M4 as a specific serum biomarker for lupusnephritis,28 our study demonstrates paralleldiagnostic potential for asthma through thediscovery of 3 clinically relevant tsRNAs. Using an ### Page 11 Volume 19, No. 5, Month 2026 11 established HDM-stimulated bronchial epithelialcell model, we identified and validated tRF-18SX73IE03, tRF-22-BZBZOS4Y1, and tRF-22MI7O3B1N4 through rigorous qRT-PCR confirmation, mirroring the validation approach that successfully established tRF-Ala-AGC-2-M4 as a lupusnephritis biomarker.28 Importantly, our findingsshow these asthma-associated tsRNAs share keycharacteristics with their lupus nephritis counterparts: (1) disease-specific expression patterns(consistent with GSE85215 dataset observations),(2) robust detectability by standard qRT-PCR protocols, and (3) strong correlation with diseasepathogenesis.The reliability of our in vitro findings issupported by the use of a well-established HDMstimulated BEAS-2B cell model, whose inflammatory response characteristics have been amplydocumented.22,23 This parallel suggests that,similar to the diagnostic utility demonstrated fortRF-Ala-AGC-2-M4 in lupus nephritis,28 ouridentified tsRNAs could be further explored ascandidates for minimally invasive asthmadiagnostic tests using readily accessible biologicalsamples like serum or saliva. However, extensivevalidation in larger clinical cohorts and functionalstudies are needed before any clinical applicationcan be considered. Our study highlighted the significant regulatoryrole of tRF-21-Z4NY2V7KE on MYBL1 mRNAexpression, as confirmed by a dual-fluorescentreporter gene system and site-directed mutagenesis. This finding underscores the specific interactions between tsRNAs and their targetmRNAs, thus contributing to the understanding ofpost-transcriptional regulatory mechanismsinvolved in asthma pathogenesis, which alignswith insights from previous studies demonstratingthe impact of small RNA fragments on geneexpression and inflammatory processes (eg, thesuppression of pro-inflammatory factors andmodulation of signaling pathways, as illustratedby the work on tRF-3023b).29 From a biologicalperspective, MYBL1 is a transcription factorinvolved in cell cycle regulation anddifferentiation, and its dysregulation has beenlinked to airway epithelial remodeling in asthma.The tRF-21-Z4NY2V7KE-mediated suppression ofMYBL1 may therefore contribute to asthma pathogenesis by modulating epithelial repair processes and inflammatory responses. This interaction may intersect with known asthmarelated pathways, such as NF-κB signaling andepithelial barrier function, although the precisemolecular mechanisms remain to be elucidated.Ultimately, our findings may pave the way fornovel therapeutic strategies targeting tsRNAs inthe management of asthma-related inflammation. Several limitations of this study should beacknowledged. First, phenotypic heterogeneitybetween the discovery (severe asthma) and validation (GSE201955, n = 79, including severe asthmaendotype features) cohorts may introduce bias,warranting confirmation in uniformly phenotypedpopulations. Second, the tRF-21-Z4NY2V7KE/MYBL1 axis was validated only in BEAS-2B cells,necessitating future in vivo and primary cell studies.Third, our focus on negatively correlated tsRNAmRNA pairs may overlook other regulatory mechanisms (eg, epigenetic modifications), highlightingthe need for multi-omics investigations. Finally,beyond the validated axis, 3 additional dysregulated tsRNAs (tRF-18-SX73IE03, tRF-22BZBZOS4Y1, tRF-22-MI7O3B1N4) were identified,whose functional roles warrant future investigation. Despite these limitations, this study presents thefirst comprehensive tsRNA-mRNA regulatorynetwork in bronchial epithelial cells from severeasthma patients. Our findings reveal novel posttranscriptional mechanisms that may contribute toasthma pathogenesis and identify candidate tsRNAsand their mRNA targets as potential biomarkers,thereby establishing a foundational resource forfuture mechanistic and therapeutic studies. Furtherinvestigations are warranted to elucidate the preciseroles of these tsRNAs and assess their translationalrelevance. ## Funding This study was supported by the Taihu Lake talent plan(Top-Level, no. 2020THRC-GD-7), the Medical KeyStrategic Project of Wuxi Health Commission (2025TH-LJCYB), the “333 Project” of Jiangsu Province in 2022(ZUZHIBU 202221001), Jiangsu Maternal and Child HealthResearch Project (no. F202068), and Jiangsu ProvincialResearch Project on the Prevention and Control ofSchistosomiasis and Parasitic Diseases (No. x202316 ## Data availability All data generated or analyzed during this study areincluded in this article. The datasets used and/or analyzedduring the current study are available from thecorresponding author on reasonable request. ### Page 12 12 Zhou et al. World Allergy Organization Journal (2026) 19:101389 Authors contributions ## Conceptualization: Lei Shi and Yubao Cui. Investigation:Ying Zhou, Hang Lin, Dongmei Zhou, Xinhui Gong andXimeng Ma. Methodology: Ying Zhou, Hang Lin andDongmei Zhou. Data curation: Xinhui Gong and XimengMa. Formal analysis: Lei Shi. Funding acquisition: YingZhou and Yubao Cui. Project administration: Yubao Cui.Resources: Ying Zhou and Hang Lin. Software: Lei Shi.Validation: Dongmei Zhou. Visualization: Xinhui Gong.Writing original draft: Ying Zhou. Writing – review &editing: Yubao Cui. The manuscript was written throughcontributions of all authors. All authors have givenapproval to the final version of the manuscript. Ethics approval and consent to participateNot applicable. Abbreviations ncRNA: Non-coding RNA; circRNAs: Circular RNAs; lncRNAs:Long non-coding RNAs; miRNAs: MicroRNAs; nt: nucleotides; rRNA: Ribosomal RNA; snoRNA: Small nucleolar RNA;piRNA: Piwi-interacting RNA; tRNA: Transfer RNA; tsRNA:tRNA-derived small RNA; mRNA: Messenger RNA; BEC:Bronchial epithelial cells; HDM: House dust mite; GO: GeneOntology; KEGG: Kyoto Encyclopedia of Genes and Genomes Pathway; cor: Correlation; qRT-PCR: Quantitativereal-time PCR; URT: Untranslated Region; MYBL1: MYBproto-oncogene like 1. Consent for publication Authors approved the publication of the work. Competing interest The authors declare no competing interests. Appendix A. Supplementary data Supplementary data to this article can be found online at. Disclosure of the use of generative AI and AI-assistedtechnologies in the writing process Nothing to disclose, the author(s) declare that nogenerative AI was used in the creation of this manuscript. Author details aDepartment of Pediatrics Laboratory, The AffiliatedChildren’s Hospital of Jiangnan University, Wuxi 214023,China.bDepartment of Allergy, The Affiliated Hospital ofQingdao University, Qingdao 266003, China.cClinicalResearch Center, The Affiliated Wuxi People’s Hospital ofNanjing Medical University, Wuxi 214023, China. dDepartment of Clinical Laboratory, Shuguang HospitalAffiliated to Shanghai University of Chinese TraditionalMedicine, Shanghai 201203, China. REFERENCES ## 1. . - 2.. - 3.. - 4.. - 5.. - 6.. - 7.. - 8.. - 9.. - 10.. - 11.. - 12.. - 13.. - 14.. - 15.. - 16.. - 17.. - 18.. - 19.. ### Page 13 Volume 19, No. 5, Month 2026 ## 20. . - 21.. - 22.. - 23.. - 24.. 13 - 25.. - 26.. - 27.. - 28.. - 29..