Front. Mol. Neurosci. Frontiers in Molecular Neuroscience Front. Mol. Neurosci. 1662-5099 Frontiers Media S.A. 10.3389/fnmol.2024.1504424 Molecular Neuroscience Brief Research Report Urine miRNA signature as potential non-invasive diagnostic biomarker for Hirschsprung’s disease Sreepada Abhijit 1 2 † Khasanov Rasul 3 † Elkrewi Enas Zoheer 1 3 de la Torre Carolina 4 Felcht Judith 3 Al Abdulqader Ahmad A. 3 5 Martel Richard 3 Hoyos-Celis Nicolás Andrés 3 Boettcher Michael 3 Wessel Lucas M. 3 Schäfer Karl-Herbert 6 * Tapia-Laliena María Ángeles 3 * 1Translational Medical Research/International Master in Innovative Medicine Master Program, Medical Faculty of Mannheim, University of Heidelberg, Mannheim, Germany 2Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany 3Department of Pediatric Surgery, Medical Faculty of Mannheim, University of Heidelberg, Mannheim, Germany 4NGS Core Facility, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany 5Department of Surgery, College of Medicine, King Faisal University, Al Hofuf, Saudi Arabia 6Working Group Enteric Nervous Systems (AGENS), University of Applied Sciences Kaiserslautern, Campus Zweibrücken, Kaiserslautern, Germany

Edited by: Naho Fujiwara, Juntendo University, Japan

Reviewed by: Raquel Oliveira, King’s College London, United Kingdom

Bo Li, Hospital for Sick Children, University of Toronto, Canada

 *Correspondence: María Ángeles Tapia-Laliena, mariaangeles.tapia-laliena@medma.uni-heidelberg.de: Karl-Herbert Schäfer, KarlHerbert.Schaefer@hs-kl.de

†These authors have contributed equally to this work and share first authorship

 08 01 2025 2024 17 1504424 30 09 2024 28 11 2024 Copyright © 2025 Sreepada, Khasanov, Elkrewi, de la Torre, Felcht, Al Abdulqader, Martel, Hoyos-Celis, Boettcher, Wessel, Schäfer and Tapia-Laliena. 2025 Sreepada, Khasanov, Elkrewi, de la Torre, Felcht, Al Abdulqader, Martel, Hoyos-Celis, Boettcher, Wessel, Schäfer and Tapia-Laliena

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

Hirschsprung’s disease (HSCR) is characterized by congenital absence of ganglion cells in the gastrointestinal tract, which leads to impaired defecation, constipation and intestinal obstruction. The current diagnosis of HSCR is based on Rectal Suction Biopsies (RSBs), which could be complex in newborns. Occasionally, there is a delay in diagnosis that can increase the risk of clinical complications. Consequently, there is room for new non-invasive diagnostic methods that are objective, more logistically feasible and also deliver a far earlier base for a potential surgical intervention. In recent years, microRNA (miRNA) has come into the focus as a relevant early marker that could provide more insights into the etiology and progression of diseases. Therefore, in the search of a non-invasive HSCR biomarker, we analyzed miRNA expression in urine samples of HSCR patients. Results from 5 HSCR patients using microarrays, revealed hsa-miR-378 h, hsa-miR-210-5p, hsa-miR-6876-3p, hsa-miR-634 and hsa-miR-6883-3p as the most upregulated miRNAs; while hsa-miR-4443, hsa-miR-22-3p, hsa-miR-4732-5p, hsa-miR-3187-5p, and hsa-miR-371b-5p where the most downregulated miRNAs. Further search in miRNAwalk and miRDB databases showed that certainly most of these dysregulated miRNAs identified target HSCR associated genes, such as RET, GDNF, BDNF, EDN3, EDNRB, ERBB, NRG1, SOX10; and other genes implied in neuronal migration and neurogenesis. Finally, we could also validate some of these miRNA changes in HSCR urine by RT-qPCR. Altogether, our analyzed HSCR cohort presents a dysregulated miRNA expression presents that can be detected in urine. Our findings open the possibility of using specific urine miRNA signatures as non-invasive HSCR diagnosis method in the future.

 Hirschsprung’s disease (HSCR) enteric nervous system (ENS) microRNA (miRNA) non-invasive diagnostic urine extracellular vesicles section-at-acceptance Molecular Signalling and Pathways Introduction

Hirschsprung’s disease (HSCR) (incidence 1/5,000 births) is a congenital gastrointestinal disorder caused by aganglionosis of the distal colon, which in newborns causes impaired defecation, constipation, and intestinal obstruction due to a lack of relaxation (Heuckeroth, 2018). The latter can impact either only short segments, the whole colon, and in few cases the whole gut (Heuckeroth, 2018). Currently, the international evidence-based treatment is the surgical removal of the aganglionic bowel. However, after undergoing surgical resection patients may have a restricted quality of life, i.e., due to recurrent enterocolitis (Menezes and Puri, 2006), partial incontinence or persisting defecation problems (Calkins, 2018).

Although HSCR anatomy is well described, individual phenotypes are complex, often making the diagnosis difficult (Heuckeroth, 2018). The most reliable diagnosis method for HSCR diagnosis include methods such as Rectal Suction Biopsies (RSB) that include the submucous layer (Lewis et al., 2003; de Lorijn et al., 2006; Romero et al., 2024). Usually, RSBs are combined with acetylcholinesterase (AChE) staining. This habitually requires high quality thick tissue and several biopsies in order to confirm the absence of ganglion cells, which sometimes leads to clinical complications (Friedmacher and Puri, 2015; Neeser et al., 2024). Nonetheless, it is difficult to decipher the functionality of potential immature ganglia in newborns, where an experienced pathologist together with a good clinical team could be required for a correct, unambiguous HSCR diagnosis. Consequently, the complexity and potential human errors can further exacerbate the risk of erroneous diagnoses in complicated cases (Shayan et al., 2004; Erbersdobler, 2024).

Recent studies have attempted to improve RBSs-AChE’s diagnosis precision. Methods such as software-based quantification of the AChE-stained cholinergic hyperinnervation (Braun et al., 2024), or measuring neural fiber trunk diameter for precisely establishing the length of the aganglionic segment in patients (Talebi et al., 2024) have been described. Yet, the requirement of good quality RBSs for the AChE staining continues to remain the key-challenge limiting their applicability.

Calretinin IHC has emerged as an alternative, promising form of staining. It presents several advantages in comparison to AChE histochemistry, like the possibility to perform the test on paraffin-embedded tissue sections (AChE requires cryosections), a straightforward staining pattern of clear binary interpretation (negative or positive), cost-effectiveness, and being performable regardless of patient age (Muller et al., 2015; Romero et al., 2024). This can provide an earlier HSCR diagnosis than AChE, and help to avoid repeated RSBs. This would allow patients to undergo surgery in the first few months of life, thereby reducing the number of enterostomies and posterior complications (Romero et al., 2024). Nevertheless, high quality RSBs are still needed, and many can be inconclusive even using Calretinin staining (Korsager et al., 2023).

Despite their advantages, all these tests need highly specialized equipment, radiation exposure, additional hospital time and are challenging to perform in neonates (Osatakul et al., 1999; Diamond et al., 2007). Given the importance of an early diagnosis, there is thus a room for novel diagnostic techniques that are non-invasive, are relatively accessible to perform, and are easy to interpret, thus allowing to decide whether further biopsies are necessary or could be avoided.

Posttranscriptional regulation by microRNAs (miRNAs), small non-coding RNA molecules of 19–25 nucleotides, which constrain the expression of target genes by directly binding to their mRNAs, is an important regulatory mechanism of gene expression (Hosako et al., 2009). Usually, miRNAs are secreted from most cell types inside exosomal extracellular vesicles, which keep miRNA sequences stable (Mall et al., 2013). These vesicles and miRNAs can later be specifically detected in body fluids such as urine, blood, cerebrospinal fluid or saliva (Kroh et al., 2010; O’Brien et al., 2018; Salehi and Sharifi, 2018).

Given their ubiquitous presence and their associations with health and disease, in recent years, miRNAs have become popular as biomarkers, which refer to biological, objective markers representative of certain healthy or diseased states (Wang et al., 2016; Condrat et al., 2020). Some of the first published studies testing the diagnostic utility of miRNA in cancer were already published in around 2008 (Lawrie et al., 2008; Mitchell et al., 2008). Both studies used circulating miRNA in serum or plasma, which was a relatively non-invasive and feasible technique. Today there are multiple published studies that report the role of circulating miRNA as a diagnostic marker across several different types of cancers (Chen et al., 2008; Liu et al., 2012; Bertoli et al., 2015; Subramani et al., 2015; Fabris et al., 2016), cardiovascular diseases (Zhou et al., 2018), sepsis (Benz et al., 2016), gestational diabetes mellitus (Zhao et al., 2011), Parkinson’s disease (Gries et al., 2021) and several other diseases.

Nevertheless, it was the presence of specific urine miRNAs described in other diseases, like nephrotic syndrome (Luo et al., 2013), prostate cancer (Korzeniewski et al., 2015) or to determine exposure to pesticides (Weldon et al., 2016), that led to our hypothesis that similar specific miRNA patterns probably also exist in urine of HSCR patients. Indeed, some studies have already identified dysregulated miRNA expression in intestinal tissue (Shen et al., 2016; Zhang et al., 2024) or serum (Tang et al., 2014) from HSCR patients.

However, bearing in mind the age of HSCR patients, we designed this “proof of concept” study in urine with the aim of identifying a non-invasive biomarker to complement the histology and clinical diagnosis in the future.

 Materials and methods Patient samples

The collection and use of patient material was performed according to informed consent signed by patients’ parents and approved by the “Medizinische Ethik-Kommission II” of the Medical Faculty Mannheim, University of Heidelberg (2011-237 N-MA). Samples were only identified by sequential code numbers with no other identifying details.

Urine from 5 patients diagnosed with HSCR and 5 healthy controls was collected “clean-catched” and stored at −80°C until analysis. The characteristics of the patient’s cohort is described in Supplementary Table S1.

 miRNA extraction from urine

miRNA was obtained using the extracellular vesicle extraction process. Urine samples were pre clarified by spinning for 15 min. at 3000 rpm. To isolate the extracellular vesicles, the supernatants were again ultra-centrifuged for 1 h at 28,000 g. Following this, the supernatant was removed, and the pellet was resuspended in TRIzol™ Reagent (Invitrogen, Thermofisher Scientific Inc., Waltham, MA, United States) to isolate RNA according to manufacturer’s protocol. Following this, the mirVana™ miRNA Isolation Kit (Invitrogen, Thermofisher Scientific Inc., Waltham, MA, United States) was utilized for the rest of the miRNA purification, also according to manufacturer’s protocol.

The miRNA concentration was measured with the infinite M200 micro plate reader (Tecan, Mainz-Kastel, Germany) and the miRNA quality was tested with the RNA 6000 Nano Kit (Agilent Technologies, Santa Clara, CA, United States) and the Agilent Bioanalyzer 2,100 (Agilent Technologies Inc., Santa Clara, CA, United States).

 miRNA microarrays

miRNA expression profiling was performed using the GeneChip™ miRNA 4.0 Arrays (Thermo Fisher Scientific Inc., Waltham, MA, United States) with the help of our NGS Core Facility (Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany). Biotinylated antisense cDNA was prepared according to the standard labeling protocol with the GeneChip® WT Plus Reagent Kit and the GeneChip® Hybridization, Wash and Stain Kit (both from Thermo Fisher Scientific Inc., Waltham, MA, United States). Afterwards, the hybridization on the chip was performed on a GeneChip Hybridization oven 640, then dyed in the GeneChip Fluidics Station 450 and thereafter scanned with a GeneChip Scanner 3,000.

Data was analyzed using a commercial software package SAS JMP15 Genomics. The raw fluorescence intensity values were normalized applying quantile normalization and RMA background correction. Thereafter, One Way ANOVA was performed to identify differential expressed genes. miRNAs with an adjusted p-value ≤0.05 were considered as significant.

The raw and normalized data are deposited in the Gene Expression Omnibus database (accession No. GSE277874).1

 miRNA selection and database target search

Data was sorted through, and 10 miRNAs (p-value ≤0.05) were selected based on their expression fold: the 5 that were most upregulated, and the 5 that were most down-regulated.

Based on this, the corresponding targets of each miRNA were searched using the miRWalk database (http://mirwalk.umm.uni-heidelberg.de/) (Sticht et al., 2018). First, the putative target list of each of the 10 selected miRNAs was downloaded, where the first top 10 targeted genes were listed. The resulting miRWalk list was cross-referenced with a second database, miRDB (mirdb.org) (Chen and Wang, 2020).

Additionally, already described HSCR-related genes (BDNF, ECE1, EDNRB, EDN3 and ERBB3, GDNF, RET, NRG1, SOX10, etc.) were searched in both databases for all the miRNAs. Ultimately, other neuronal- related genes (f.i.: linked to neuronal migration, neuronal crest development, neurogenesis, etc.) were also browsed in the same databases.

 RT-qPCR

cDNA synthesis was performed using the TaqMan™ MicroRNA Reverse Transcription Kit (Applied Biosystems Inc., Thermo Fisher Scientific Inc., Waltham, MA, United States) according to manufacturer’s instructions. The reaction was carried out using a peqSTAR Thermocycler (PeqLab Biotechnology GmbH, Erlangen, Germany) as follows: 5 min. Denaturation at 70°C, 10 min. Annealing at 20°C, 60 min. Elongation at 40°C, and 10 min. Inactivation at 70°C.

The Sensifast SYBR Low-ROX Kit (BIO-94020, Bioline, Meridian Biosciences, OH, United States) was used for the RT-qPCR following the manufacturer’s instructions. Briefly, amplification reactions were run using a Tm of 57°C, in the QuantStudio 5 device (Applied Biosystems Inc., Thermo Fisher Scientific Inc., Waltham, MA, United States) as follows: Hold stage: Step 1: 2 min. at 50°C, Step 2 (denaturation): 10 min. at 95°C; PCR Stage (40 cycles): Step 1 (denaturation): 15 s at 95°C, Step 2 (annealing): 1 min. at 57°C; followed by a final Melting Curve Stage: Step 1: 15 s at 95°C, Step 2: 1 min. at 57°C, Step 3 (Dissociation): 15 s at 95°C.

Primers’ sequences were purchased to OriGene Technologies GmbH, Herford, Germany. In total, 3 housekeeping control primers and 10 miRNA primers were used (see Supplementary Table S2).

The comparative 2−ΔΔCt method was used to calculate gene expression, where data were first normalized to the housekeeping standard (dCt: Target Ct—Housekeeping Ct). Then, for each gene sample ddCt (ddCt: Sample dCt—Calibrator dCt) was calculated using the average of the controls as a calibrator. Finally, fold 2−ΔΔCt was calculated for each miRNA.

 Statistical analysis

Statistical analysis was performed using the One Way ANOVA method for the miRNA microarray data analysis. The F.N. Test was used to compare differences in miRNA expression between controls and HSCR patients in the RT-qPCR test. Differences were considered statistically significant at p-value ≤0.05.

 Results miRNA microarray analysis of HSCR urine

In an initial “proof of concept” study, urinary miRNA from 5 HSCR patients and 5 healthy controls was analyzed using the GeneChip™ miRNA 4.0 Arrays (Thermo Fisher Scientific Inc., Waltham, MA, United States). The original array data is available on the NCBI Gene Expression Omnibus (GEO) browser (Reference number GSE277874).

Despite the limited size of our studied cohort, we could find significant differences in the expression of some urinary miRNAs between controls and HSCR patients. Though still preliminary results, they may be used as a basis for further research. The heatmap in Figure 1 shows the results of the clustering analysis based on the normalized expression values of up- (red) and down-regulated (blue) miRNAs (Figure 1). The distribution of the miRNAs is represented in the Volcano plot (Figure 1).

Overview of microarray analysis. (A) Heatmap of relative miRNAs expression. Differential expression is shown between Controls and HSCR samples, color scale represents up- (red), no threshold (gray), or downregulation (blue). (B) Volcano plot showing miRNAs distribution after ANOVA analysis.

After statistical analysis, data was sorted out based in fold changes in order to select the miRNAs that were most dysregulated. Results showed hsa-miR-378 h, hsa-miR-210-5p, hsa-miR-6876-3p, hsa-miR-634 and hsa-miR-6883-3p as the most upregulated miRNAs; while hsa-miR-4443, hsa-miR-22-3p, hsa-miR-4732-5p, hsa-miR-3187-5p, and hsa-miR-371b-5p were the most downregulated miRNAs in urine of HSCR patients compared to urine of healthy controls (p-value ≤0.05) (see Table 1).

List of the 10 selected most dysregulated miRNAs identified in HSCR urine.

ID Fold change P-val Probe set name Transcript ID
Upregulated 20518842 2.62 0.03 MIMAT0018984_st hsa-miR-378 h
20500464 1.45 0.01 MIMAT0026475_st hsa-miR-210-5p
20525714 1.39 0.01 MIMAT0027653_st hsa-miR-6876-3p
20504387 1.36 0.03 MIMAT0003304_st hsa-miR-634
20525728 1.34 0.01 MIMAT0027667_st hsa-miR-6883-3p
Downregulated 20518818 −7.3 0.05 MIMAT0018961_st hsa-miR-4443
20500144 −6.19 0.04 MIMAT0000077_st hsa-miR-22-3p
20519576 −2.68 0.03 MIMAT0019855_st hsa-miR-4732-5p
20515622 −1.92 0.02 MIMAT0019216_st hsa-miR-3187-5p
20519615 −1.66 0.05 MIMAT0019892_st hsa-miR-371b-5p
Most dysregulated urine miRNAs in HSCR patients target genes associated to HSCR pathology

With the aim of verifying the potential role of those miRNAs in HSCR development, we performed a target analysis exploration with the help of miRWalk (Sticht et al., 2018) and miRDB (Chen and Wang, 2020) databases.

Initially, we investigated the top 10 targeted genes in both databases with the highest score (see Table 2). Despite the high diversity of genes targeted by the selected miRNAs, it was possible to already find genes like ALKBH5, which was related to enteric neuronal crest migration in HSCR (Wang et al., 2021), or genes linked to neuronal migration, like DAB2IP (Lee et al., 2012) or NF1 (Sanchez-Ortiz et al., 2014), as well as to neuronal motility, like GMR5 (Turunen et al., 2018), or to other neuronal processes such as BTG2 (el-Ghissassi et al., 2002) or KIRREL3 (Hisaoka et al., 2021).

Target database analysis of the selected dysregulated miRNAS in HSCR urine.

Database
miRWalk miRDB miRWalk + miRDB
miRNA Gene symbol Score Gene symbol Score Gene symbol Score
Upregulated hsa-miR-378 h SLC12A6 1.00 KLK4 98 ERAP1 1.00
NF1 1.00 NR2C2 98 BAALC 1.00
CARHSP1 1.00 NKX3-1 97 PODXL 1.00
MLPH 1.00 KIAA1522 97 KCNMA1 1.00
TRAPPC5 1.00 PHC3 96 SHE 1.00
FILIP1L 1.00 ELAC1 92 TSPAN17 1.00
BPIFA3 1.00 ZNF124 92 PRLR 1.00
ST3GAL5 1.00 RAB10 91 GRIK3 1.00
SLC16A3 1.00 NCAPG 91 APOC2 1.00
RUFY2 1.00 KCNIP2 91 BRD4 1.00
hsa-miR-210-5p SLC12A6 1.00 BTG2 99 TMEM80 1.00
SMIM8 1.00 SH3BP4 96 FAM131B 1.00
FRMD6 1.00 BICD2 95 AP2B1 1.00
CEP85L 1.00 ZNF385B 95 LDLRAD2 1.00
ZNF565 1.00 FAM161A 94 PRAMEF6 1.00
ABHD12 1.00 EP300 94 ATP7B 1.00
MLPH 1.00 TENM2 93 PKP1 1.00
TMEM80 1.00 ANKRD13B 93 ATP2B3 1.00
ZNF205 1.00 KCNAB2 91 SLC11A1 1.00
C10orf53 1.00 KIRREL1 91 PKP1 1.00
hsa-miR-6876-3p DLGAP4 1.00 PAPOLB 99 SLAIN1 1.00
NUPR1 1.00 CDH13 96 SIRPA 1.00
FRMD6 1.00 BCL11A 95 RBFOX2 1.00
CEP85L 1.00 DPY19L3 95 KIAA1755 1.00
ACBD5 1.00 SLAMF7 95 ARGFX 1.00
MLPH 1.00 RNF6 94 CENPP 1.00
TMEM80 1.00 DUSP4 94 SLC7A2 1.00
TRAPPC5 1.00 SMCP 93 RAB37 1.00
FILIP1L 1.00 PIAS2 93 GPR179 1.00
SLC5A10 1.00 TIPARP 93 MITF 1.00
hsa-miR-634 SLC35A2 1.00 ARGLU1 98 PPP4R1 1.00
NF1 1.00 CRISPLD2 98 HEY1 1.00
TPK1 1.00 FBXW7 97 METTL17 1.00
ABHD12 1.00 HMGXB4 96 ABI1 1.00
TRAPPC5 1.00 CLIC5 96 NAA30 1.00
CYLD 1.00 NRXN3 95 ENAH 1.00
PPP4R1 1.00 FAM3D 94 UACA 1.00
KIAA0895L 1.00 IGDCC4 94 IRGQ 1.00
SYPL2 1.00 OAZ1 94 HMGXB4 1.00
HEY1 1.00 ATP6V0B 93 ATXN7 1.00
hsa-miR-6883-3p SLC35A2 1.00 MEX3D 98 TENT4B 1.00
NF1 1.00 GRIN2A 97 PAQR3 1.00
DLGAP4 1.00 NCAPG2 96 SCN3B 1.00
NUPR1 1.00 GID8 95 ATP6V1C2 1.00
ACBD5 1.00 ARFGEF1 95 C18orf32 1.00
TMEM80 1.00 OXR1 95 MYCL 1.00
TRAPPC5 1.00 HECA 94 AIPL1 1.00
SLC5A10 1.00 ZFX 94 DCAF4L1 1.00
C10orf53 1.00 SUMF1 94 RAB3IP 1.00
SLC16A3 1.00 LRP2 93 RAP1B 1.00
Downregulated hsa-miR-4443 SLC35A2 1.00 KDM2A 98 SUMF2 1.00
GSDMB 1.00 PTPRJ 97 GLIS3 1.00
SUMF2 1.00 PIK3C2B 97 PEG10 1.00
TRAPPC5 1.00 KRTAP4-12 97 SGSM1 1.00
TGFB1I1 1.00 AGPAT4 97 ACBD7 1.00
SLC5A10 1.00 RGS7 96 TYW5 1.00
CAST 1.00 MOGAT3 95 KREMEN1 1.00
ZNF205 1.00 EBF3 95 FER1L6 1.00
C10orf53 1.00 EIF4G3 95 KCNIP1 1.00
NSD2 1.00 ST3GAL1 95 RBFOX2 1.00
hsa-miR-22-3p ST8SIA2 1.00 GRM5 100 C17orf58 1.00
TLK2 1.00 FUT9 99 NEFM 1.00
CYB561 1.00 ESR1 97 MPZL3 1.00
DDX25 1.00 ELOVL6 97 COA7 1.00
TTYH2 1.00 EMILIN3 97 NPAS3 1.00
PIWIL2 1.00 LAMC1 97 MAT2A 1.00
B3GNT4 1.00 NET1 97 NPNT 1.00
HMBOX1 1.00 PDSS1 96 TYRO3 1.00
CENPX 1.00 RCOR1 96 COA7 1.00
RPGRIP1L 1.00 DDIT4 96 AGBL5 1.00
hsa-miR-4732-5p SLC12A6 1.00 ARSE 90 CDC42SE2 1.00
SMIM8 1.00 AMMECR1 90 VPS26A 1.00
NF1 1.00 ALKBH5 90 CALML4 1.00
DLGAP4 1.00 KLLN 89 FAM13A 1.00
NUPR1 1.00 TAF9B 89 DGKK 1.00
GEMIN8 1.00 WASL 88 ZNF766 1.00
CARHSP1 1.00 ANXA7 87 WDR33 1.00
CEP85L 1.00 PSMA5 87 GABRB3 1.00
ACBD5 1.00 FAM222B 86 STS 1.00
SUMF2 1.00 KIRREL3 86 LUC7L3 1.00
hsa-miR-3187-5p SLC12A6 1.00 TMEM41B 98 CEP44 1.00
NUPR1 1.00 AHI1 97 FOXL2NB 1.00
FRMD6 1.00 DAB2IP 97 SLC4A8 1.00
ZNF565 1.00 BTRC 96 CBFA2T2 1.00
ACBD5 1.00 FMO5 96 SCNN1G 1.00
SUMF2 1.00 SIKE1 96 KCNIP1 1.00
MLPH 1.00 SAR1A 95 CBFA2T2 1.00
TMEM80 1.00 SEC63 95 PCARE 1.00
TRAPPC5 1.00 YTHDF1 95 SC5D 1.00
STK26 1.00 ARRDC3 94 SH3PXD2B 1.00
hsa-miR-371b-5p DDX60L 1.00 ZNF845 100 MINDY2 1.00
ZDHHC3 1.00 ZC3H6 100 NKAIN2 1.00
HPS4 1.00 SPAG9 100 ZNF765 1.00
PRKCH 1.00 SLC35E2A 100 GLUL 1.00
TCF7L2 1.00 ZNF850 100 CRELD1 1.00
MARF1 1.00 AKR1D1 100 ARHGAP5 1.00
MEF2D 1.00 TMEM33 100 CELF2 1.00
PRMT2 1.00 ZBTB3 100 TNRC6B 1.00
GID8 1.00 S1PR2 100 TMCC1 1.00
REL 1.00 UQCC3 100 DCUN1D2 1.00

Further analysis in the same databases revealed that most of the dysregulated miRNAs in HSCR urine identified in the microarray regulate genes strongly associated to HSCR (see Table 3). For instance, genes like EDNRB (Puffenberger et al., 1994), RET (Luo et al., 1993; Emison et al., 2005), BDNF (Schriemer et al., 2016), GDNF (Parisi and Kapur, 2000), SOX10 (Prasad et al., 2011) etc. are targets of many of our detected miRNAs. In addition, a wide spectrum of different genes related to HSCR (Parisi and Kapur, 2000; Heuckeroth and Schäfer, 2016; Luzón-Toro et al., 2020) such as EDN3, ECE-1, ERBB3 (Gershon, 2021), NRG1 (Gui et al., 2013), NTKR3 (Sanchez-Mejias et al., 2009) or L1CAM (Jackson et al., 2009), have been also found in the databases (Table 3). Aside from those, other genes participating in signaling processes, like MAPK8 (Kuil et al., 2021), TMP3 (Camilleri et al., 2019) or PIK3C2B (Fu et al., 2020) have also recently been connected to ENS formation and HSCR.

Specific miRNA targets associated with HSCR or neuronal processes (neuronal migration, neurogenesis, etc.).

Database
miRWalk miRWalk miRDB
HSCR-rel. genes Neuronal-rel. Genes HSCR-rel. Genes Neuronal-rel. Genes
miRNA Gene symbol Score Gene symbol Score References. Gene symbol Score Genesymbol Score References
Upregulated hsa-miR-378 h GDNF 1.00 NF2 1.00 Toledo et al. (2018) NPAS4 85 Kwon et al. (2024)
NGR1 1.00 NFASC 1.00 Zonta et al. (2008) NEUROD4 71
EGFR 0.92 TPM3 1.00 Camilleri et al. (2019) NEUROD1 63 Singh et al. (2022)
MAPK8 0.92 Kuil et al. (2021)
NEGR1 0.92 Kim et al. (2014)
hsa-miR-210-5p RET 1.00 ALKBH5 1.00 Wang et al. (2021) SOX10 76 BTG2 99
L1CAM 1.00 MAPK8 1.00 Kuil et al. (2021) BICD2 95 Tsai et al. (2020)
GDNF 0.92 NFASC 0.92 Zonta et al. (2008) KIRREL1 91
BDNF 0.85 BTG2 0.92 el-Ghissassi et al. (2002) RCOR2 50
SOX10 0.85 NEGR1 0.92
hsa-miR-6876-3p ERBB3 1.00 BCL11A 1.00 Wiegreffe et al. (2015) NRG1 60 BCL11A 95 Wiegreffe et al. (2015)
GDNF 1.00 MAPK8 1.00 Kuil et al. (2021) MAPK8 76 Kuil et al. (2021)
EDNRB 1.00 NTM 0,96 NETO1 71
ECE1 1.00 NEGR1 0.92 Kim et al. (2014) NEGR1 70 Kim et al. (2014)
BDNF 0.92 NFASC 0.92 Zonta et al. (2008) NPAS3 62 Liu et al. (2022)
RELA 0.92 Elkrewi et al. (2024) NTM 54 Salluzzo et al. (2023)
hsa-miR-634 ERBB3 0.92 NRXN3 1.00 Harkin et al. (2019) ERBB4 52 FBXW7 97 Jandke et al. (2011)
GDNF 0.85 RELA 1.00 Elkrewi et al. (2024) NFASC 66 Zonta et al. (2008)
ECE1 0.85 ALKBH5 0.92 Wang et al. (2021) NEUROG2 60 Heng et al. (2008)
MANF 0.92 Tseng et al. (2017) MANF 56 Tseng et al. (2017)
RELB 0.92 Denis-Donini et al. (2005) NAV3 55
hsa-miR-6883-3p BDNF 1.00 MAPK8 1.00 Kuil et al. (2021) CALN1 61
GDNF 0.92 NFASC 0.98 Zonta et al. (2008) ARFGEF1 95 You et al. (2022)
RET 0.85 NEGR1 0.92 Kim et al. (2014) MAPK8 69 Kuil et al. (2021)
EDNR3 0.85 NPAS3 0.92 Liu et al. (2022)
EDNRB 0.85 RELA 0.88 Elkrewi et al. (2024)
CALN1 61
Downregulated hsa-miR-4443 GDNF 1.00 CALN1 1.00 PIK3C2B 92 EBF3 95 Friocourt (2011)
NRG1 0.92 NF2 1.00 Toledo et al. (2018) NTRK3 85 NTRK2 84
ERBB3 0.85 NEGR1 0.92 Kim et al. (2014) ECE1 70 NFASC 66
ECE1 0.85 NEUROG2 0.92 Heng et al. (2008) CALN1 64
SOX10 0.85 NFASC 0.92 NDEL1 64
MAPK8 0.88 Kuil et al. (2021) PACSIN1 61
hsa-miR-22-3p NRG1 1.00 NFASC 1.00 ERBB3 95 GRM5 100 Turunen et al. (2018)
EDN3 0.85 NF2 1.00 Toledo et al. (2018) ERBB4 54 NET1 97 Robinson et al. (2021)
BDNF 0.92 GRM5 1.00 Turunen et al. (2018) RCOR1 97 Mandel et al. (2011)
RELA 1.00 Elkrewi et al. (2024) PHF8 96 Riveiro et al. (2017)
NEGR1 0.95 Kim et al. (2014) NEGR1 60
hsa-miR-4732-5p BDNF 1.00 MAPK8 1.00 Kuil et al. (2021) RET 61 ALKBH5 95 Wang et al. (2021)
GDNF 1.00 PHF8 1.00 Riveiro et al. (2017) GDNF 55 TAF9B 89 Herrera et al. (2014)
SOX10 1.00 TAF9B 0.96 Herrera et al. (2014) EDN3 65 ANXA7 87 Rick et al. (2005)
L1CAM 1.00 BCL11A 0.92 Wiegreffe et al. (2015) KIRREL3 86 Hisaoka et al. (2021)
NRG1 1.00 NEUROG2 0.92 Heng et al. (2008)
ECE1 0.92 NFASC 0.92
ERBB3 0.92 NF2 0.92 Toledo et al. (2018)
EDN3 0.85 NEGR1 0.90 Kim et al. (2014)
RELA 0.85 Elkrewi et al. (2024)
hsa-miR-3187-5p GDNF 1.00 NEGR1 1.00 Kim et al. (2014) GDNF 83 DAB2IP 97 Lee et al. (2012)
L1CAM 1.00 NF2 1.00 Toledo et al. (2018) RET 64 NRP1 92 Tillo et al. (2015)
NRG1 1.00 DAB2IP 0.92 Lee et al. (2012) BDNF 60 NF2 87 Toledo et al. (2018)
RET 0.92 NFASC 0.85 NRG1 58 LRRTM3 85
ECE1 0.85 NSMF 0.85 Xu et al. (2010) NSMF 75 Xu et al. (2010)
hsa-miR-371b-5p NRG1 1.00 DAB2IP 0.92 Lee et al. (2012) BDNF 60 MAPK8 99 Kuil et al. (2021)
BDNF 0.92 NF2 0.92 Toledo et al. (2018) NRG1 58 TPM3 99 Camilleri et al. (2019)
RET 0.92 ALKBH5 0.85 Wang et al. (2021) NF2 98 Toledo et al. (2018)
ECE1 0.85 MAPK8 0.85 Kuil et al. (2021) NET1 78 Robinson et al. (2021)
TPM3 0.85 Camilleri et al. (2019) NCALD 74 Upadhyay et al. (2019)

Further target genes of the selected miRNA, such as HSCR-related genes or other neuronal-related genes, were searched in mRNA databases (miRWALK and miRDB).

Following that, we also searched for other genes related to critical neuronal processes for a proper Enteric Nervous System (ENS) formation, like neurogenesis, neurodevelopment or neuronal migration (see Table 3). We found genes like ALKBH5 that could be critical for enteric neuronal crest migration in HSCR (Wang et al., 2021); in addition to others important for neuronal migration: as NF2 (Toledo et al., 2018), NEUROG2 (Heng et al., 2008) and NEUROD1 (Singh et al., 2022) or DAB2IP (Lee et al., 2012); as well as genes participating in neuronal proliferation NEGR1 (Kim et al., 2014), or in neuronal assembly NFASC (Zonta et al., 2008).

From all examined HSCR urinary downregulated miRNAs, miR-4732-5p was the one that was associated with the highest number of genes related to HSCR in both databases, 8 genes in miRWalk (BDNF, GDNF, SOX10, L1CAM, NRG1, ECE1, ERBB3, EDN3) together with 3 more genes in miRDB (RET, GDNF, EDN3); followed by hsa-miR-3187-5p with 5 genes in miRWalk (GDNF, L1CAM, NRG1, RET, ECE1) and 4 more in miRDB (GDNF, RET, BDNF, NRG1); and finally by hsa-miR-4443 with 5 genes in miRWalk (GDNF, NRG1, ERBB3, ECE1, SOX10) and 3 more in miRDB (PIK3C2B, NTRK3, ECE1) (Table 3).

Regarding the upregulated sequences, the miRNAs with more HSCR-associated gene targets were hsa-miR-210-5p with 5 genes in miRWalk (RET, L1CAM, GDNF, BDNF, SOX10) and 1 more in miRDB (SOX10); together with hsa-miR-6876-3p, also with 5 genes in miRWalk (ERBB3, GDNF, EDNRB, ECE1, BDNF) and one more located in miRDB (NRG1) (Table 3).

Altogether, the identified HSCR urinary-dysregulated miRNAs of interest targeted genes associated with HSCR pathology and also others related to neuronal processes that may be important also in ENS formation.

 RT-qPCR validation of urinary miRNA expression

Lastly, we evaluated the expression of the above selected miRNAs (see Table 1), in the urine of the HSCR patients and controls by RT-qPCR.

Here we could verify an increased expression of hsa-miR-6883-3p and hsa-miR-6876-3p, as well as a decreased expression of hsa-miR-4732-5p (F.N Test p = 0.000015) and hsa-miR-3187-5p (F.N Test p = 0.000002) (Figure 2) in HSCR urine compared to the controls, which confirmed the previous results of those miRNAs in the microarrays.

(A) Overview of RT-qPCR analysis of the selected miRNAs in HSCR urine samples compared to controls: miR3187-5p (F.N Test p = 0.000002), miR4732-5p (F.N Test p = 0.000015), miR6876-3p, miR-6883-3p. (B) Fold change comparison of 2 − ∆∆Ct from HSCR to control samples (HSCR/Controls).

 Discussion

Our study demonstrates that specific dysregulated miRNAs can be detected in the urine of HSCR patients when compared to healthy controls. In our HSCR patients’ cohort, we could identify several upregulated and downregulated miRNAs by microarray analysis that are, related to key functional aspects in ENS development. Analyzing the predicted targets of the top 5 up and downregulated miRNAs in data bases, we could verify that they do regulate genes related to HSCR pathology, and also to neuronal processes that are maybe necessary for the proper development of the Enteric Nervous System (ENS) formation, like neurogenesis, neurodevelopment or neuronal migration (Table 3).

When we compare our results with other miRNA studies in HSCR, our identified miRNAs in HSCR urine differ from those found by others in HSCR serum (Tang et al., 2014), HSCR intestine (Shen et al., 2016; Zhang et al., 2024), or in developing ENS cells (Pai et al., 2023). However, the miRNAs found by these studies are also different between each other, possibly because of differential expression between tissue and body fluids and the specific cohorts studied.

Some of the selected miRNAs found in HSCR urine regulate RELA and ERBB3 genes according to the search in miRNA databases. We recently published a downregulated expression of ERBB3 and RELA mRNAs in distal aganglionic segments of HSCR patients in a cohort of 25 patients (Elkrewi et al., 2024). ERBB3, a member of the EGF receptor tyrosine kinase family, is known to play an important role in neural crest development (Prasad et al., 2011), while RELA, the main subunit of the NF-κB pathway (Perkins, 2007), participates in embryonic neurogenesis and neural progenitor migration and differentiation (Mémet, 2006; Zhang and Hu, 2012). Therefore, it could be interesting to further validate the miRNA targets at protein level in tissue or blood HSCR samples in the future.

Regarding the potential causes of miRNA dysregulation in HSCR, miRNAs expression can be regulated at transcriptional (changes in gene expression) or post-transcriptional (changes in miRNA processing) levels (Gulyaeva and Kushlinskiy, 2016). Prospective investigation is needed to assess if already known HSCR mutations mutations, or other HSCR disease’s characteristics, i.e., alterations in intestinal tissue, are somehow responsible for these miRNA modifications.

In addition, cellular pathways (Treiber et al., 2019) and various physiological and pathological stimuli, such as hormones, stress, inflammatory cytokines, DNA methylation status, etc. have been related to affect miRNA expression (Gulyaeva and Kushlinskiy, 2016; Juźwik et al., 2019). Of these, processes like inflammation, and neurodegeneration are certainly consistent with HSCR pathology and could be considered as possible miRNA dysregulation mechanisms for future investigation.

Given that our chosen list of dysregulated miRNAs in HSCR urine regulate the expression of genes related to HSCR and to neuronal and cell migration processes (see Table 3), we think they may be good candidates for HSCR biomarkers. In particular, sequences like miR-4732-5p, hsa-miR-3187-5p and hsa-miR-6876-3p target several HSCR-related genes and were validated in our RT-qPCR analysis. As an example, high levels of hsa-miR-6876-3p, which can target GDNF, ERBB3, EDNRB and BDNF mRNAs, will lead to a lower expression of those genes and thus impair neurogenesis and neuronal migration, which finally may impact the proper gut innervation in those patients.

Importantly, the expression of specific candidate miRNAs can also be detected using nanophotonic biosensors integrated into portable lab-on-a-chip platforms, which are applicable in clinical and environmental diagnostics (Tarasov et al., 2016; Smith et al., 2017; Rebelo et al., 2019; García-Chamé et al., 2020). Thus, when at some point a defined miRNA pattern would be determined, the miRNA detection could easily be implemented in a microanalytical chip system and thereafter be used routinely to complete HSCR diagnosis, potentially helping future patients.

In summary, this work provides initial results that are promising in the search of a new complementary diagnosis method for HSCR. They show they show that indeed some miRNAs have dysregulated expression as predicted. However, further research is still necessary to validate a specific miRNA signature in a larger cohort of patients that could be used as HSCR biomarker in the future. After validation, these can be used for the creation of a non-invasive, easy to interpret and relatively simple novel diagnostic tool, complementary to histology, patient history, and pathology.

This preliminary study serves as a foundation for future investigation. A larger number of patients are required to increase the robustness of the results. Ideally, patients may be recruited from a network of hospitals at national level. Once verified, selected miRNAs must be correlated with clinical history, histology and surgery outcome. Based on that, the best miRNAs may be initially added to other diagnosis approaches until their application is fully demonstrated, and finally be implemented in a regular manner.

 Conclusion

HSCR patients present a detectable miRNA dysregulated signature in urine that could work as an easier, non-invasive and more affordable method to complement current Hirschsprung’s disease diagnosis tests. Therefore, further research is necessary to validate this preliminary results in a larger cohort of patients.

 Data availability statement

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found at: https://www.ncbi.nlm.nih.gov/, GSE277874.

 Ethics statement

The studies involving humans were approved by the “Medizinische Ethik-Kommission II” of the Medical Faculty Mannheim, University of Heidelberg (2011-237N-MA). The studies were conducted in accordance with the local legislation and institutional requirements. Written informed consent for participation in this study was provided by the participants’ legal guardians/next of kin.

 Author contributions

AS: Data curation, Formal analysis, Investigation, Visualization, Writing – original draft. RK: Investigation, Resources, Writing – review & editing. EE: Investigation, Writing – review & editing. CT: Data curation, Formal analysis, Writing – review & editing. JF: Resources, Writing – review & editing. AA: Resources, Writing – review & editing. RM: Resources, Writing – review & editing. NAH-C: Resources, Writing – review & editing. MB: Funding acquisition, Resources, Writing – review & editing. LW: Funding acquisition, Resources, Writing – review & editing. K-HS: Funding acquisition, Resources, Writing – review & editing. MAT-L: Conceptualization, Data curation, Project administration, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing.

 Funding

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. The work of Abhijit Sreepada and Enas Zoheer Elkrewi was supported through funding from the Medical Faculty Mannheim, University of Heidelberg, under the auspices of the Master’s program “Translational Medical Research” (TMR). Note: This Master’s program has no funding registration code for their students’ practicum. For the publication fee we acknowledged financial support by Heidelberg University.

We would like to thank Camela Jost (Manager of General Core Equipment unite, Medical Faculty of Mannheim, University of Heidelberg) for her kind support and technical help.

 Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

 Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

 Supplementary material

The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fnmol.2024.1504424/full#supplementary-material

1http://www.ncbi.nlm.nih.gov/geo/

References Benz F. Roy S. Trautwein C. Roderburg C. Luedde T. (2016). Circulating MicroRNAs as biomarkers for Sepsis. Int. J. Mol. Sci. 17:78. doi: 10.3390/ijms17010078, PMID: 26761003 Bertoli G. Cava C. Castiglioni I. (2015). MicroRNAs: new biomarkers for diagnosis, prognosis, therapy prediction and therapeutic tools for breast cancer. Theranostics 5, 1122–1143. doi: 10.7150/thno.11543, PMID: 26199650 Braun Y. Friedmacher F. Theilen T. Fiegel H. C. Weber K. Harter P. N. . (2024). Diagnosis of Hirschsprung disease by analyzing acetylcholinesterase staining using artificial intelligence. J. Pediatr. Gastroenterol. Nutr. 79, 729–737. doi: 10.1002/jpn3.12339, PMID: 39118474 Calkins C. (2018). Hirschsprung disease beyond infancy. Clin. Colon Rectal Surg. 31, 051–060. doi: 10.1055/s-0037-1604034 Camilleri M. Wieben E. Eckert D. Carlson P. Hurley O’Dwyer R. Gibbons D. . (2019). Familial chronic megacolon presenting in childhood or adulthood: seeking the presumed gene association. Neurogastroenterol. Motil. 31:e13550. doi: 10.1111/nmo.13550, PMID: 30663199 Chen X. Ba Y. Ma L. Cai X. Yin Y. Wang K. . (2008). Characterization of microRNAs in serum: a novel class of biomarkers for diagnosis of cancer and other diseases. Cell Res. 18, 997–1006. doi: 10.1038/cr.2008.282, PMID: 18766170 Chen Y. Wang X. (2020). miRDB: an online database for prediction of functional microRNA targets. Nucleic Acids Res. 48, D127–D131. doi: 10.1093/nar/gkz757, PMID: 31504780 Condrat C. E. Thompson D. C. Barbu M. G. Bugnar O. L. Boboc A. Cretoiu D. . (2020). miRNAs as biomarkers in disease: latest findings regarding their role in diagnosis and prognosis. Cells 9:276. doi: 10.3390/cells9020276, PMID: 31979244 de Lorijn F. Kremer L. C. M. Reitsma J. B. Benninga M. A. (2006). Diagnostic tests in Hirschsprung disease. J. Pediatr. Gastroenterol. Nutr. 42, 496–505. doi: 10.1097/01.mpg.0000214164.90939.92, PMID: 16707970 Denis-Donini S. Caprini A. Frassoni C. Grilli M. (2005). Members of the NF-κB family expressed in zones of active neurogenesis in the postnatal and adult mouse brain. Dev. Brain Res. 154, 81–89. doi: 10.1016/j.devbrainres.2004.10.010, PMID: 15617758 Diamond I. R. Casadiego G. Traubici J. Langer J. C. Wales P. W. (2007). The contrast enema for Hirschsprung disease: predictors of a false-positive result. J. Pediatr. Surg. 42, 792–795. doi: 10.1016/j.jpedsurg.2006.12.031, PMID: 17502185 el-Ghissassi F. Valsesia-Wittmann S. Falette N. Duriez C. Walden P. D. Puisieux A. (2002). BTG2TIS21/PC3 induces neuronal differentiation and prevents apoptosis of terminally differentiated PC12 cells. Oncogene 21, 6772–6778. doi: 10.1038/sj.onc.1205888 Elkrewi E. Z. Al Abdulqader A. A. Khasanov R. Maas-Omlor S. Boettcher M. Wessel L. M. . (2024). Role of inflammation and the NF-κB signaling pathway in Hirschsprung’s disease. Biomol. Ther. 14:992. doi: 10.3390/biom14080992, PMID: 39199380 Emison E. S. McCallion A. S. Kashuk C. S. Bush R. T. Grice E. Lin S. . (2005). A common sex-dependent mutation in a RET enhancer underlies Hirschsprung disease risk. Nature 434, 857–863. doi: 10.1038/nature03467, PMID: 15829955 Erbersdobler A. (2024). The Pathologist’s role in the diagnosis of Hirschsprung’s disease. Eur. J. Pediatr. Surg. doi: 10.1055/s-0044-1788562, PMID: 38996443 Fabris L. Ceder Y. Chinnaiyan A. M. Jenster G. W. Sorensen K. D. Tomlins S. . (2016). The potential of MicroRNAs as prostate cancer biomarkers. Eur. Urol. 70, 312–322. doi: 10.1016/j.eururo.2015.12.054, PMID: 26806656 Friedmacher F. Puri P. (2015). Rectal suction biopsy for the diagnosis of Hirschsprung’s disease: a systematic review of diagnostic accuracy and complications. Pediatr. Surg. Int. 31, 821–830. doi: 10.1007/s00383-015-3742-8, PMID: 26156878 Friocourt G. (2011). Identification of Arx targets unveils new candidates for controlling cortical interneuron migration and differentiation. Front. Cell. Neurosci. 5:28. doi: 10.3389/fncel.2011.00028, PMID: 22355284 Fu A. X. Lui K. N.-C. Tang C. S.-M. Ng R. K. Lai F. P.-L. Lau S.-T. . (2020). Whole-genome analysis of noncoding genetic variations identifies multiscale regulatory element perturbations associated with Hirschsprung disease. Genome Res. 30, 1618–1632. doi: 10.1101/gr.264473.120, PMID: 32948616 García-Chamé M.-Á. Gutiérrez-Sanz Ó. Ercan-Herbst E. Haustein N. Filipiak M. S. Ehrnhöfer D. E. . (2020). A transistor-based label-free immunosensor for rapid detection of tau protein. Biosens. Bioelectron. 159:112129. doi: 10.1016/j.bios.2020.112129, PMID: 32364931 Gershon M. D. (2021). Hirschsprung disease and more: dysregulation of ERBB2 and ERBB3. J. Clin. Invest. 131:e146389. doi: 10.1172/JCI146389, PMID: 33720042 Gries M. Christmann A. Schulte S. Weyland M. Rommel S. Martin M. . (2021). Parkinson mice show functional and molecular changes in the gut long before motoric disease onset. Mol. Neurodegener. 16:34. doi: 10.1186/s13024-021-00439-2 Gui H. Tang W.-K. So M.-T. Proitsi P. Sham P. C. Tam P. K. . (2013). RET and NRG1 interplay in Hirschsprung disease. Hum. Genet. 132, 591–600. doi: 10.1007/s00439-013-1272-9, PMID: 23400839 Gulyaeva L. F. Kushlinskiy N. E. (2016). Regulatory mechanisms of microRNA expression. J. Transl. Med. 14:143. doi: 10.1186/s12967-016-0893-x Harkin L. F. Lindsay S. J. Xu Y. Alzu’bi A. Ferrera A. Gullon E. A. . (2019). Corrigendum: Neurexins 1–3 each have a distinct pattern of expression in the early developing human cerebral cortex. Cereb. Cortex 29:1705. doi: 10.1093/cercor/bhz012 Heng J. I.-T. Nguyen L. Castro D. S. Zimmer C. Wildner H. Armant O. . (2008). Neurogenin 2 controls cortical neuron migration through regulation of Rnd2. Nature 455, 114–118. doi: 10.1038/nature07198, PMID: 18690213 Herrera F. J. Yamaguchi T. Roelink H. Tjian R. (2014). Core promoter factor TAF9B regulates neuronal gene expression. eLife 3:e02559. doi: 10.7554/eLife.02559, PMID: 25006164 Heuckeroth R. O. (2018). Hirschsprung disease — integrating basic science and clinical medicine to improve outcomes. Nat. Rev. Gastroenterol. Hepatol. 15, 152–167. doi: 10.1038/nrgastro.2017.149, PMID: 29300049 Heuckeroth R. O. Schäfer K.-H. (2016). Gene-environment interactions and the enteric nervous system: neural plasticity and Hirschsprung disease prevention. Dev. Biol. 417, 188–197. doi: 10.1016/j.ydbio.2016.03.017, PMID: 26997034 Hisaoka T. Komori T. Fujimoto K. Kitamura T. Morikawa Y. (2021). Comprehensive expression pattern of kin of irregular chiasm-like 3 in the adult mouse brain. Biochem. Biophys. Res. Commun. 563, 66–72. doi: 10.1016/j.bbrc.2021.05.063, PMID: 34062388 Hosako H. Martin G. S. Barrier M. Chen Y. A. Ivanov I. V. Mirkes P. E. (2009). Gene and microRNA expression in p53 -deficient day 8.5 mouse embryos. Birth Defects Res. A Clin. Mol. Teratol. 85, 546–555. doi: 10.1002/bdra.20565, PMID: 19229884 Jackson S.-R. Guner Y. S. Woo R. Randolph L. M. Ford H. Shin C. E. (2009). L1CAM mutation in association with X-linked hydrocephalus and Hirschsprung’s disease. Pediatr. Surg. Int. 25, 823–825. doi: 10.1007/s00383-009-2420-0, PMID: 19641926 Jandke A. Da Costa C. Sancho R. Nye E. Spencer-Dene B. Behrens A. (2011). The F-box protein Fbw7 is required for cerebellar development. Dev. Biol. 358, 201–212. doi: 10.1016/j.ydbio.2011.07.030, PMID: 21827743 Juźwik C. A. Drake S. Zhang Y. Paradis-Isler N. Sylvester A. Amar-Zifkin A. . (2019). microRNA dysregulation in neurodegenerative diseases: A systematic review. Prog. Neurobiol. 182:101664. doi: 10.1016/j.pneurobio.2019.101664, PMID: 31356849 Kim H. Hwang J.-S. Lee B. Hong J. Lee S. (2014). Newly identified Cancer-associated role of human neuronal growth regulator 1 (NEGR1). J. Cancer 5, 598–608. doi: 10.7150/jca.8052, PMID: 25057311 Korsager L. E. H. Bjørn N. Ellebæk M. B. Christensen L. G. Qvist N. (2023). Full-thickness rectal biopsy in children suspected of having Hirschsprung’s disease: the inconclusive biopsy. Children 10:1619. doi: 10.3390/children10101619, PMID: 37892282 Korzeniewski N. Tosev G. Pahernik S. Hadaschik B. Hohenfellner M. Duensing S. (2015). Identification of cell-free microRNAs in the urine of patients with prostate cancer. Urol. Oncol. 33, 16.e17–16.e22. doi: 10.1016/j.urolonc.2014.09.015, PMID: 25445383 Kroh E. M. Parkin R. K. Mitchell P. S. Tewari M. (2010). Analysis of circulating microRNA biomarkers in plasma and serum using quantitative reverse transcription-PCR (qRT-PCR). Methods 50, 298–301. doi: 10.1016/j.ymeth.2010.01.032, PMID: 20146939 Kuil L. E. MacKenzie K. C. Tang C. S. Windster J. D. Le T. L. Karim A. . (2021). Size matters: large copy number losses in Hirschsprung disease patients reveal genes involved in enteric nervous system development. PLoS Genet. 17:e1009698. doi: 10.1371/journal.pgen.1009698, PMID: 34358225 Kwon O.-H. Choe J. Kim D. Kim S. Moon C. (2024). Sensory stimulation-dependent Npas4 expression in the olfactory bulb during early postnatal development. Exp. Neurobiol. 33, 77–98. doi: 10.5607/en23037, PMID: 38724478 Lawrie C. H. Gal S. Dunlop H. M. Pushkaran B. Liggins A. P. Pulford K. . (2008). Detection of elevated levels of tumour-associated microRNAs in serum of patients with diffuse large B-cell lymphoma. Br. J. Haematol. 141, 672–675. doi: 10.1111/j.1365-2141.2008.07077.x, PMID: 18318758 Lee G. H. Kim S. H. Homayouni R. D’Arcangelo G. (2012). Dab2ip regulates neuronal migration and neurite outgrowth in the developing neocortex. PLoS One 7:e46592. doi: 10.1371/journal.pone.0046592, PMID: 23056358 Lewis N. A. Levitt M. A. Zallen G. S. Zafar M. S. Iacono K. L. Rossman J. E. . (2003). Diagnosing Hirschsprung’s disease: increasing the odds of a positive rectal biopsy result. J. Pediatr. Surg. 38, 412–416. doi: 10.1053/jpsu.2003.50070, PMID: 12632358 Liu J.-W. Li H. Zhang Y. (2022). Npas3 regulates stemness maintenance of radial glial cells and neuronal migration in the developing mouse cerebral cortex. Front. Cell. Neurosci. 16:865681. doi: 10.3389/fncel.2022.865681, PMID: 36313621 Liu R. Chen X. Du Y. Yao W. Shen L. Wang C. . (2012). Serum MicroRNA expression profile as a biomarker in the diagnosis and prognosis of pancreatic Cancer. Clin. Chem. 58, 610–618. doi: 10.1373/clinchem.2011.172767, PMID: 22194634 Luo Y. Ceccherini I. Pasini B. Matera I. Bicocchi M. P. Barone V. . (1993). Close linkage with the RET protooncogene and boundaries of deletion mutations in autosomal dominant Hirschsprung disease. Hum. Mol. Genet. 2, 1803–1808. doi: 10.1093/hmg/2.11.1803, PMID: 7904208 Luo Y. Wang C. Chen X. Zhong T. Cai X. Chen S. . (2013). Increased serum and urinary MicroRNAs in children with idiopathic nephrotic syndrome. Clin. Chem. 59, 658–666. doi: 10.1373/clinchem.2012.195297, PMID: 23344497 Luzón-Toro B. Villalba-Benito L. Torroglosa A. Fernández R. M. Antiñolo G. Borrego S. (2020). What is new about the genetic background of Hirschsprung disease? Clin. Genet. 97, 114–124. doi: 10.1111/cge.13615, PMID: 31355911 Mall C. Rocke D. M. Durbin-Johnson B. Weiss R. H. (2013). Stability of miRNA in human urine supports its biomarker potential. Biomark. Med 7, 623–631. doi: 10.2217/bmm.13.44, PMID: 23905899 Mandel G. Fiondella C. G. Covey M. V. Lu D. D. LoTurco J. J. Ballas N. (2011). Repressor element 1 silencing transcription factor (REST) controls radial migration and temporal neuronal specification during neocortical development. Proc. Natl. Acad. Sci. U.S.A. 108, 16789–16794. doi: 10.1073/pnas.1113486108, PMID: 21921234 Mémet S. (2006). NF-κB functions in the nervous system: from development to disease. Biochem. Pharmacol. 72, 1180–1195. doi: 10.1016/j.bcp.2006.09.003, PMID: 16997282 Menezes M. Puri P. (2006). Long-term outcome of patients with enterocolitis complicating Hirschsprung’s disease. Pediatr. Surg. Int. 22, 316–318. doi: 10.1007/s00383-006-1639-2, PMID: 16463033 Mitchell P. S. Parkin R. K. Kroh E. M. Fritz B. R. Wyman S. K. Pogosova-Agadjanyan E. L. . (2008). Circulating microRNAs as stable blood-based markers for cancer detection. Proc. Natl. Acad. Sci. 105, 10513–10518. doi: 10.1073/pnas.0804549105, PMID: 18663219 Muller C. Hobeika C. Montalva L. Berrebi D. Bonnard A. (2015). Calretinin variant in Hirschsprung disease: Pretransitional sign and surgical planning. Eur. J. Pediatr. Surg. 26, 449–453. doi: 10.1055/s-0035-1566106 Neeser H. R. Robbiani I. Rodewald A.-K. Nigbur T. di Natale A. Moehrlen U. . (2024). Enough is enough: how many rectal suction biopsies do you need to diagnose Hirschsprung’s disease? Pediatr. Surg. Int. 40:206. doi: 10.1007/s00383-024-05793-y O’Brien J. Hayder H. Zayed Y. Peng C. (2018). Overview of MicroRNA biogenesis, mechanisms of actions, and circulation. Front. Endocrinol. 9:402. doi: 10.3389/fendo.2018.00402 Osatakul S. Patrapinyokul S. Osatakul N. (1999). The diagnostic value of anorectal manometry as a screening test for Hirschsprung’s disease. J. Med. Assoc. Thail. 82, 1100–1105, PMID: 10659544 Pai C. Sengupta R. Heuckeroth R. O. (2023). Sequencing reveals miRNAs enriched in the developing mouse enteric nervous system. Noncoding RNA 10:1. doi: 10.3390/ncrna10010001 Parisi M. A. Kapur R. P. (2000). Genetics of Hirschsprung disease. Curr. Opin. Pediatr. 12, 610–617. doi: 10.1097/00008480-200012000-00017, PMID: 11106284 Perkins N. D. (2007). Integrating cell-signalling pathways with NF-κB and IKK function. Nat. Rev. Mol. Cell Biol. 8, 49–62. doi: 10.1038/nrm2083, PMID: 17183360 Prasad M. K. Reed X. Gorkin D. U. Cronin J. C. McAdow A. R. Chain K. . (2011). SOX10 directly modulates ERBB3 transcription via an intronic neural crest enhancer. BMC Dev. Biol. 11:40. doi: 10.1186/1471-213X-11-40 Puffenberger E. G. Hosoda K. Washington S. S. Nakao K. deWit D. Yanagisawa M. . (1994). A missense mutation of the endothelin-B receptor gene in multigenic hirschsprung’s disease. Cell 79, 1257–1266. doi: 10.1016/0092-8674(94)90016-7, PMID: 8001158 Rebelo A. R. Liu C. Schäfer K.-H. Saumer M. Yang G. Liu Y. (2019). Poly(4-vinylaniline)/polyaniline bilayer-functionalized bacterial cellulose for flexible electrochemical biosensors. Langmuir 35, 10354–10366. doi: 10.1021/acs.langmuir.9b01425, PMID: 31318565 Rick M. Ramos Garrido S. I. Herr C. Thal D. R. Noegel A. A. Clemen C. S. (2005). Nuclear localization of Annexin A7 during murine brain development. BMC Neurosci. 6:25. doi: 10.1186/1471-2202-6-25 Riveiro A. R. Mariani L. Malmberg E. Amendola P. G. Peltonen J. Wong G. . (2017). JMJD-1.2/PHF8 controls axon guidance by regulating hedgehog-like signaling. Development. 144, 856–856. doi: 10.1242/dev.142695, PMID: 28126843 Robinson R. A. Griffiths S. C. van de Haar L. L. Malinauskas T. van Battum E. Y. Zelina P. . (2021). Simultaneous binding of guidance cues NET1 and RGM blocks extracellular NEO1 signaling. Cell 184, 2103–2120.e31. doi: 10.1016/j.cell.2021.02.045, PMID: 33740419 Romero P. Burger A. Wennberg E. Schmitteckert S. Holland-Cunz S. Schwab C. . (2024). Clinical relevance of pathological diagnosis of Hirschsprung’s disease with acetylcholine-esterase Histochemistry or Calretinin immunohistochemistry. Children 11:428. doi: 10.3390/children11040428, PMID: 38671645 Salehi M. Sharifi M. (2018). Exosomal miRNAs as novel cancer biomarkers: challenges and opportunities. J. Cell. Physiol. 233, 6370–6380. doi: 10.1002/jcp.26481, PMID: 29323722 Salluzzo M. Vianello C. Abdullatef S. Rimondini R. Piccoli G. Carboni L. (2023). The role of IgLON cell adhesion molecules in neurodegenerative diseases. Genes (Basel) 14:1886. doi: 10.3390/genes14101886, PMID: 37895235 Sanchez-Mejias A. Fernandez R. M. Lopez-Alonso M. Antinolo G. Borrego S. (2009). Contribution of RET, NTRK3 and EDN3 to the expression of Hirschsprung disease in a multiplex family. J. Med. Genet. 46, 862–864. doi: 10.1136/jmg.2009.067819, PMID: 19556619 Sanchez-Ortiz E. Cho W. Nazarenko I. Mo W. Chen J. Parada L. F. (2014). NF1 regulation of RAS/ERK signaling is required for appropriate granule neuron progenitor expansion and migration in cerebellar development. Genes Dev. 28, 2407–2420. doi: 10.1101/gad.246603.114, PMID: 25367036 Schriemer D. Sribudiani Y. IJpma A. Natarajan D. MacKenzie K. C. Metzger M. . (2016). Regulators of gene expression in enteric neural crest cells are putative Hirschsprung disease genes. Dev. Biol. 416, 255–265. doi: 10.1016/j.ydbio.2016.06.004, PMID: 27266404 Shayan K. Smith C. Langer J. C. (2004). Reliability of intraoperative frozen sections in the management of Hirschsprung’s disease. J. Pediatr. Surg. 39, 1345–1348. doi: 10.1016/j.jpedsurg.2004.05.009, PMID: 15359388 Shen Z. Du C. Zang R. Xie H. Lv W. Li H. . (2016). Microarray expression profiling of dysregulated long non-coding RNAs in Hirschsprung’s disease reveals their potential role in molecular diagnosis. Neurogastroenterol. Motil. 28, 266–273. doi: 10.1111/nmo.12722 Singh A. Mahesh A. Noack F. Cardoso de Toledo B. Calegari F. Tiwari V. K. (2022). Tcf12 and NeuroD1 cooperatively drive neuronal migration during cortical development. Development 149:dev200250. doi: 10.1242/dev.200250, PMID: 35147187 Smith D. A. Newbury L. J. Drago G. Bowen T. Redman J. E. (2017). Electrochemical detection of urinary microRNAs via sulfonamide-bound antisense hybridisation. Sens Actuators B Chem 253, 335–341. doi: 10.1016/j.snb.2017.06.069, PMID: 29200659 Sticht C. De La Torre C. Parveen A. Gretz N. (2018). miRWalk: an online resource for prediction of microRNA binding sites. PLoS One 13:e0206239. doi: 10.1371/journal.pone.0206239, PMID: 30335862 Subramani R. Gangwani L. Nandy S. B. Arumugam A. Chattopadhyay M. Lakshmanaswamy R. (2015). Emerging roles of microRNAs in pancreatic cancer diagnosis, therapy and prognosis (review). Int. J. Oncol. 47, 1203–1210. doi: 10.3892/ijo.2015.3129, PMID: 26314882 Talebi A. Kahdouei M. T. Memar E. H. Ashjaei B. Raji H. Adel M. G. (2024). Can the diameter of nerve fibers be effectively utilized to enhance the accuracy of determining the length of the aganglionic segment compared to intraoperative biopsy in patients with Hirschsprung’s disease? BMC. Res. Notes 17:220. doi: 10.1186/s13104-024-06873-x Tang W. Li H. Tang J. Wu W. Qin J. Lei H. . (2014). Specific serum microRNA profile in the molecular diagnosis of Hirschsprung’s disease. J. Cell. Mol. Med. 18, 1580–1587. doi: 10.1111/jcmm.12348, PMID: 24974861 Tarasov A. Gray D. W. Tsai M.-Y. Shields N. Montrose A. Creedon N. . (2016). A potentiometric biosensor for rapid on-site disease diagnostics. Biosens. Bioelectron. 79, 669–678. doi: 10.1016/j.bios.2015.12.086, PMID: 26765531 Tillo M. Erskine L. Cariboni A. Fantin A. Joyce A. Denti L. . (2015). VEGF189 binds NRP1 and is sufficient for VEGF/NRP1-dependent neuronal patterning in the developing brain. Development. 142, 314–9. doi: 10.1242/dev.115998, PMID: 25519242 Toledo A. Grieger E. Karram K. Morrison H. Baader S. L. (2018). Neurofibromatosis type 2 tumor suppressor protein is expressed in oligodendrocytes and regulates cell proliferation and process formation. PLoS One 13:e0196726. doi: 10.1371/journal.pone.0196726, PMID: 29715273 Treiber T. Treiber N. Meister G. (2019). Regulation of microRNA biogenesis and its crosstalk with other cellular pathways. Nat. Rev. Mol. Cell Biol. 20, 5–20. doi: 10.1038/s41580-018-0059-1, PMID: 30228348 Tsai M.-H. Cheng H.-Y. Nian F.-S. Liu C. Chao N.-H. Chiang K.-L. . (2020). Impairment in dynein-mediated nuclear translocation by BICD2 C-terminal truncation leads to neuronal migration defect and human brain malformation. Acta Neuropathol. Commun. 8:106. doi: 10.1186/s40478-020-00971-0 Tseng K.-Y. Danilova T. Domanskyi A. Saarma M. Lindahl M. Airavaara M. (2017). MANF is essential for neurite extension and neuronal migration in the developing cortex. eNeuro 4:ENEURO.0214-17.2017. doi: 10.1523/ENEURO.0214-17.2017, PMID: 29082311 Turunen P. M. Louhivuori L. M. Louhivuori V. Kukkonen J. P. Åkerman K. E. (2018). Endocannabinoid signaling in embryonic neuronal motility and cell–cell contact – role of mGluR5 and TRPC3 channels. Neuroscience 375, 135–148. doi: 10.1016/j.neuroscience.2018.02.005, PMID: 29438802 Upadhyay A. Hosseinibarkooie S. Schneider S. Kaczmarek A. Torres-Benito L. Mendoza-Ferreira N. . (2019). Neurocalcin Delta knockout impairs adult neurogenesis whereas half reduction is not pathological. Front. Mol. Neurosci. 12:19. doi: 10.3389/fnmol.2019.00019, PMID: 30853885 Wang B. Fang X. Sun X. Du C. Zhou L. Lv X. . (2021). m6A demethylase ALKBH5 suppresses proliferation and migration of enteric neural crest cells by regulating TAGLN in Hirschsprung’s disease. Life Sci. 278:119577. doi: 10.1016/j.lfs.2021.119577, PMID: 33984358 Wang J. Chen J. Sen S. (2016). MicroRNA as biomarkers and diagnostics. J. Cell. Physiol. 231, 25–30. doi: 10.1002/jcp.25056, PMID: 26031493 Weldon B. A. Shubin S. P. Smith M. N. Workman T. Artemenko A. Griffith W. C. . (2016). Urinary microRNAs as potential biomarkers of pesticide exposure. Toxicol. Appl. Pharmacol. 312, 19–25. doi: 10.1016/j.taap.2016.01.018, PMID: 26826490 Wiegreffe C. Simon R. Peschkes K. Kling C. Strehle M. Cheng J. . (2015). Bcl11a (Ctip1) controls migration of cortical projection neurons through regulation of Sema3c. Neuron 87, 311–325. doi: 10.1016/j.neuron.2015.06.023, PMID: 26182416 Xu N. Bhagavath B. Kim H.-G. Halvorson L. Podolsky R. S. Chorich L. P. . (2010). NELF is a nuclear protein involved in hypothalamic GnRH neuronal migration. Mol. Cell. Endocrinol. 319, 47–55. doi: 10.1016/j.mce.2009.11.016, PMID: 20025934 You Z. Yang Z. Cao S. Deng S. Chen Y. (2022). The novel KLF4/BIG1 regulates LPS-mediated neuro-inflammation and migration in BV2 cells via PI3K/Akt/NF-kB signaling pathway. Neuroscience 488, 102–111. doi: 10.1016/j.neuroscience.2022.01.014, PMID: 35090882 Zhang B. Yang J. Song A. Feng W. Guo Z. (2024). miR-146b-5p regulates the enteric nervous system development in Hirschsprung disease via targeting RET pathway. Biochem. Genet. doi: 10.1007/s10528-024-10852-z, PMID: 38844639 Zhang Y. Hu W. (2012). NFκB signaling regulates embryonic and adult neurogenesis. Front Biol (Beijing) 7, 277–291. doi: 10.1007/s11515-012-1233-z, PMID: 24324484 Zhao C. Dong J. Jiang T. Shi Z. Yu B. Zhu Y. . (2011). Early second-trimester serum MiRNA profiling predicts gestational diabetes mellitus. PLoS One 6:e23925. doi: 10.1371/journal.pone.0023925, PMID: 21887347 Zhou S. Jin J. Wang J. Zhang Z. Freedman J. H. Zheng Y. . (2018). miRNAS in cardiovascular diseases: potential biomarkers, therapeutic targets and challenges. Acta Pharmacol. Sin. 39, 1073–1084. doi: 10.1038/aps.2018.30, PMID: 29877320 Zonta B. Tait S. Melrose S. Anderson H. Harroch S. Higginson J. . (2008). Glial and neuronal isoforms of Neurofascin have distinct roles in the assembly of nodes of Ranvier in the central nervous system. J. Cell Biol. 181, 1169–1177. doi: 10.1083/jcb.200712154, PMID: 18573915