Metagenomic sequencing provides evidence of the nasal microbiota’s influence on idiopathic orbital myositis: a case control study

doi:10.21203/rs.3.rs-955174/v1

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Metagenomic sequencing provides evidence of the nasal microbiota’s influence on idiopathic orbital myositis: a case control study

https://doi.org/10.21203/rs.3.rs-955174/v1

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Background: Nasal microbiota is a key factor in the development of chronic rhinosinusitis (CRS). Its influence on orbital autoimmune diseases remains unknown. This study aims to investigate the influence of CRS-related nasal dysbiosis on extraocular muscle (EOM) microbiota in patients with or without idiopathic orbital myositis (IOM).

Methods: Sixteen subjects were recruited, including 4 cases in each study group [IOM(+) CRS(+) group, IOM(–) CRS(+) group, IOM(+) CRS(–) group, IOM(–) CRS(–) group]. The EOM tissues and corresponding incision tissues were sampled and processed with metagenomic next-generation sequencing (mNGS). The nasal swaps from middle meatus were collected and analyzed with 16s rRNA sequencing. The sequencing data were calculated with Shannon index for microbial diversity, principal component analysis for microbial abundance and linear discriminant analysis effect size for dominant species.

Results: A total of 42 species of bacteria, 3 species of fungi and no virus were detected. Both microbial diversity and microbial abundance differed between the EOM and incision samples in IOM(–) CRS(+) and IOM(–) CRS(–) subjects, with Staphylococcus predominant in the EOM samples and Micrococcus luteus predominant in the incision samples. The microbiota also differed between EOM and swap samples in the IOM(–)CRS(+) subjects, and the most distinguishable species in the swab samples was Cutibacterium acnes. Further analysis of the swab samples confirmed nasal dysbiosis in the CRS(+) subjects with Staphylococcus haemolyticus representating as the dominant species. The microbiota of EOM samples differed between the IOM(–)CRS(+) and IOM(–)CRS(–) subjects, resulting into preponderance of Cutibacterium acnes in the IOM(–)CRS(+) subjects. Small discrepancies of EOM microbiota were detected between the IOM(+)CRS(+) and IOM(+)CRS(–) subjects, but data analysis showed no statistical significance. The above findings demonstrate an alteration of EOM microbiota in CRS patients, providing evidence that nasal dysbiosis may play a role in regulating microbial communities in EOM.

Conclusions: This study provided a reference spectrum of EOM microbiota in IOM patients and offered insights into the nasal microbiota’s influence on orbital autoimmune conditions.

(Trial registration: Chinese Clinical Trial Registry, ChiCTR2100047731. Registered 24 June 2021 - Retrospectively registered, https://www.chictr.org.cn/showproj.aspx?proj=127896)

microbiome

microbiota

dysbiosis

metagenomic

idiopathic orbital myositis

chronic rhinosinusitis

Orbital myositis, the second most common cause of extraocular muscle (EOM) diseases, is defined as nonspecific inflammation or pathogenic infection of one or more EOMs [1]. Infectious orbital myositis is typically attributed to direct spreading of pathogens from adjacent structures such as the paranasal sinuses. In patients with acute rhinosinusitis, orbital involvment accounts for approximately 95% of cases with complications [2]. Systemic antibiotic therapy, along with surgical removable of pathologic tissues, are considered as effective treatments [3]. Apart from infection, the cases with no known causative pathogens are referred to as idiopathic orbital myositis (IOM), and the autoimmune system plays a key role during IOM pathogenesis. Even though both infectious and noninfectious cases are characterized with pre-septal inflammation and acute onset of painful diplopia, the IOM cases are distinguishable by a lack of microbial evidence, limited inflammatory edema surrounding the affected EOMs and preponderance of chronic inflammatory cells within muscle fibers [4]. IOM patients generally respond to high dose corticosteroids, immunosuppressive drugs and orbital radiation therapy [1]. However, the recurrent rate was reported as high as 8.0-41.0% in different medical centers [5, 6]. To achieve better treatment outcome requires more targeted therapy and deeper understanding of the disease mechanism.

More and more efforts have been devoted to unveil the immune triggers responsible for the development of idiopathic myositis. Recent findings provided insights into a potential interaction between the immune system and the exposure to microorganism antigens [7]. Notably, some particular IOM cases coexist with chronic rhinosinusitis (CRS), which is a common condition with high prevalence of 5.5-10.0% in general population of Asian countries [8]. Based on clinical experience, several case reports demonstrated that CRS treatment exerted positive effects on the coexisting IOM [9–11]. The role of nasal microbiota on CRS has been deeply explored, and a growing evidence supports the concept that dysfunction of the microbiota has a significant impact on the occurrence of CRS [12]. In general, pathogens of all kinds, including viruses, bacteria and parasites, not only cause direct damage to organs and tissues resulting in infectious diseases, but also interfere with the innate as well as the adaptive immune system leading to autoimmune diseases or even oncogenesis [13, 14]. The ocular surface microbiome is considered as a significant participant in regulating the pathophysiology of ocular surface diseases, such as Stevens-Johnson Syndrome and mucosa-associated lymphoid tissue lymphoma [15, 16]. The microbiome within aqueous humor and vitreous fluid is also closely related to intraocular diseases, including uveitis, retinitis, and vitreal lymphoma [17–19]. It is reasonable to speculate that a causal relationship exists between the nasal microbiota and the pathogenesis of IOM.

Before the development of metagenomic next-generation sequencing (mNGS), the progress of microbiome study was impeded by the culture-dependent techniques which bias towards easy culturable species [20]. The mNGS technology enables analysis of the entire microbiome as well as the human host genome in clinical samples, contributing to precision diagnosis of infectious diseases as well as detection of all potential pathogens in noninfectious conditions. For instance, mNGS analyses of neural tissues revealed a variety of fungal and bacterial species in samples obtained from Alzheimer’s disease, providing research basis for further exploration of the causative pathogens during disease evolution [21]. Since little is known about the taxonomic composition in IOM samples, mNGS technique may serve as an effective and efficient tool to assist demonstrating the microbiota in EOM tissues.

Our study hypothesized that the dysbiosis of nasal microbiota may play a role in IOM development. To test the hypothesis, we prospectively recruited 4 groups of patients, including the IOM(+) CRS(+) subjects, the IOM(–) CRS(+) subjects, the IOM(+) CRS(–) subjects and the IOM(–) CRS(–) subjects. Both the EOM samples and the nasal swap samples were collected and processed for microbiota analysis. The microbial diversity and microbial abundance were compared among the 4 study groups so as to evaluate the potential influence of nasal microbiota the pathogenesis of IOM.

Subject Recruitment

We prospectively recruited 16 age- and gender-matched subjects into 4 study groups [IOM(+) CRS(+) group, IOM(–) CRS(+) group, IOM(+) CRS(–) group, IOM(–) CRS(–) group] from January 2021 to May 2021 at Fudan Eye & ENT Hospital. The inclusion criteria of IOM(+) groups were acute onset of unilateral IOM within 1 month presenting with the typical symptoms such as conjunctival edema, eyelid erythema, orbital pain evoked by ocular motility, paretic or restrictive diplopia, and EOM enlargement on computed tomography (CT) scan. The inclusion criteria of IOM(–) groups were patients with concomitant strabismus who underwent muscle resection surgery. The inclusion criteria of CRS(+) groups were inflammation of the nose and paranasal sinuses for more than 3 months in accordance with the European Position Paper on Rhinosinusitis and Nasal Polyps guidelines (https://www.rhinologyjournal.com/Search.php; accessed October 4, 2021). The inclusion criteria of IOM(–) groups were CT and endoscopy-confirmed absence of inflammation within nasal cavity and paranasal sinuses. The exclusion criteria were ocular surface diseases such as conjunctivitis and keratitis, eyelid conditions such as Meibomian gland dysfunction and blepharitis, and specific causes of myositis including thyroid-associated ophthalmopathy, IgG4-related ophthalmic disease, other systemic autoimmune conditions such as sarcoidosis, myositis-specific autoantibodies, confirmed infections, drug reactions and paraneoplastic syndrome. The medical records of each subject were collected, including demographics, chief complaints, clinical symptoms and signs, medical treatments and 3-month follow-up results. The study was retrospectively registered on Chinese Clinical Trial Registry website with the registration number ChiCTR2100047731 on 24 June 2021 (https://www.chictr.org.cn/showproj.aspx?proj=127896; accessed October 4, 2021). The study protocol was in accordance with the Declaration of Helsinki, and informed consents were obtained from all patients.

Sample Collection

The sequencing samples were collected in Class 1000 surgical operating room by an experienced surgeon. After topical anesthesia, both the conjunctival sacs and the skin of upper face were disinfected with iodophor. For IOM(+) subjects, surgical incisions of the affected eyes were made along limbal conjunctiva, and the surrounding conjunctival tissues were collected as the Incision samples. The most severely affected EOMs were sampled at muscle belly, and the EOM samples were kept no more than 10 mm³ to minimize iatrogenic injury. For IOM(–) subjects, both the incision tissues and the resected muscle tissues were collected during strabismus surgery. A small part of each EOM sample was processed with hematoxylin-eosin (H&E) stain, and the remaining part was subjected to mNGS analysis. The ocular motility function was examined immediately after surgery to ensure no presence of new onset diplopia. For both CRS(+) and CRS(–) subjects, nasal swab samples were immediately collected prior to the removal of nasal secretions and the application of topical mucosal vasoconstrictors. Sterile swabs were endoscopically guided to the middle meatus region to avoid contamination from the anterior nasal cavity, rotated for 5 full turns and kept in place for at least one minute until fully saturated. Any swabs that were contaminated through contact with a nontarget region were discarded. All collected samples were placed in sterile Eppendorf tubes without enzyme on ice temporarily, transported to the laboratory within 2 h and stored at − 80°C in liquid nitrogen until DNA extraction was performed.

DNA Extraction and mNGS

DNA was extracted using the TIANamp Micro DNA Kit (DP316, TIANGEN BIOTECH ,Beijing, China) according to the manufacturer’s instructions. HeLa cell lines with or without Acinetobacter baumannii were subjected to the same procedure as positive and negative controls. The DNA samples, along with the negative and positive controls, were processed with mNGS according to the MGISeq2000 sequencing protocol. Briefly, samples were purified using AmPure beads (Qiagen, Germany) and quantitated by Qubit Fluorometer 3.0 (Life Technologies, USA) and Agilent 2100 Bioanalyzer (Agilent Technologies Inc. USA). A total of 100 ng DNA was fragmented into 100-150 bps by Bioruptor® Pico ( Diagenode, Belgium) and was constructed into the library following the standard protocol provided by the manufacture using PMseq DNA/RNA Library Preparation Kit (BGI-Wuhan, China). DNA libraries were sequenced to a depth of at leats 20 million reads per sample on the MGISeq-2000 platform.

16S rRNA sequencing

Microbial DNA was extracted from the swab samples using the DNA Kit (Omega Bio-tek, Norcross, GA, USA) according to the manufacturer’s protocols. Amplification of the V3-V4 hypervariable region of the bacterial 16S rRNA gene was performed using the extracted DNA template with the following primers: 338F (5’- ACTCCTACGGGAGGCAGCAG-3’) and 806R (5’-GGACTACHVGGGTWTCTAAT-3’). The purified products were sequenced (2 × 250) on an Illumina MiSeq platform (Illumina, San Diego, USA) according to the standard protocols. The raw reads were deposited into the National Center for Biotechnology Information Sequence Read Archive database.

Data Analysis

After quality control by FastQC, the sequence reads were preprocessed with removal of human reads by Scalable Nucleotide Alignment Program to obtain clean nonhuman sequences. The nonhuman sequences were aligned to the National Center for Biotechnology Information nucleotide reference database (updated 2021.1) and grouped into distinct taxonomic units according to their species level classification. The relative abundance of each species was calculated by the ratio of the total mapped reads of each species, normalized by their genome size and the total mapped microbial reads within each sample. Microbial diversity (Shannon index) was calculated according to the method described in the Mothur program after using a subsampling cutoff of 500 bacterial sequences per sample [22]. Microbial abundance was calculated with principal component analysis (PCA) using Ade4 package in R statistical software (v3.1.1). The differences in microbiome characterizing the groups were evaluated by linear discriminant analysis (LDA) effect size [23].

Statistics

Data analysis was carried out with Statistical Product and Service Solutions. Continuous variables were described as mean and range and compared with the two-tailed t-test. Categorical variables were described as frequencies (number (%)) and compared with the two-tailed chi-square test or Fisher’s exact test as appropriate. The Shannon index was analyzed by Mann-Whitney U test, and the PCA results were compared with Wilcoxson Rank-Sum test. A value of P < 0.05 was considered statistically significant.

Clinical Characteristics

Sixteen cases were prospectively recruited in the study (Table 1). The clinical symptoms of IOM(+) subjects included painful diplopia (87.5%, 7/8), congestion and edema of ocular surface (75.0%, 6/8), eyelid swelling and erythema (62.5%, 5/8), proptosis (37.5%, 3/8), and ptosis (25.0%, 2/8). The oculomotor disorders initially started with paretic dysfunction within the first week, moved into a combined paretic and restrictive phase during 2-3 weeks, and finally ended as restrictive strabismus after 1 month (Fig. 1a). Based on CT scan, the most frequently affected EOMs were medial rectus (75.0%, 6/8) and inferior rectus (62.5%, 5/8) (Fig. 1b), and the superior and inferior oblique muscles were not affected in our case series. Compared with the IOM(–) subjects, the pathologic findings of the IOM(+) subjects typically presented as focal infiltration of inflammatory cells, perimysial edema within muscle fibers and mild accumulation of collagen strands (Fig. 1c). Oral corticosteroids were prescribed for all IOM(+) cases. The IOM(+)CRS(+) subjects were concurrently treated with intranasal corticosteroids to manage rhinosinusitis. Two patients (25.0%) in the IOM(+)CRS(–) group underwent orbital radiotherapy to correct muscle dysfunction. All patients responded to treatments, and 3 patients (37.5%) achieved full recovery after 3 months of follow-up.

Table 1

Clinical characteristics of the recruited subjects
			IOM(+) CRS(+) (n=4)	IOM(–) CRS(+) (n=4)	IOM(+) CRS(–) (n=4)	IOM(–) CRS(–) (n=4)
Male			3	3	3	3
Age (yr)			47.8 ± 11.6	48.0 ± 12.7	46.5 ± 15.0	47.8 ± 4.9
General medical history
	Previous antibiotics		0	1	0	0
	Smoking		2	1	0	1
	Allergic rhinitis		3	2	1	0
	Asthma		0	0	0	0
	Blood eosinophil(%)		2.6 ± 1.3	4.5 ± 0.4	1.8 ± 1.5	0.9 ± 0.1
	Sampled extraocular muscle
		Medial rectus	2	3	3	2
		Inferior rectus	1	0	1	0
		Superior rectus	1	0	0	0
		Lateral rectus	0	1	0	2
CRS-related medical history
	Nasal polys		1	2	–	–
	Lund-Mackay CT score		11.3 ± 3.6	13.8 ± 0.9	–	–
	Treatment
		Corticosteroids	4	4	–	–
		Functional endoscopic sinus surgery	1	2	–	–
	Follow-up at 3 months
		Full recovery	1	2	–	–
		Partial recovery	3	2	–	–
IOM-related medical history
	Affected orbit
		Left	3	–	2	–
		Right	1	–	2	–
	Affected extraocular muscle
		Medial rectus	2	–	4	–
		Inferior rectus	2	–	3	–
		Superior rectus	1	–	1	–
		Lateral rectus	0	–	0	–
	Muscle dysfunction
		Paretic	1	–	2	–
		Mixed paretic-restrictive	1	–	1	–
		Restrictive	2	–	1	–
	Treatment
		Corticosteroids	4	–	4	–
		Radiotherapy	0	–	2	–
	Follow-up at 3 months
		Full recovery	2	–	1	–
		Partial recovery	2	–	3	–
IOM idiopathic orbital myositis, CRS chronic rhinosinusitis, CT computer tomography

Microbiome Results

The distribution of microbial species in the EOM samples, the surgical incision samples and the nasal swab samples were processed with mNGS or 16s rRNA sequencing in each subject (Fig. 2). Taken together, 42 species (18 genera) of bacteria and 3 species (2 genera) of fungi were detected, and no virus was present in any sample. Regarding to the IOM(+) subjects, the top three microorganisms were Cutibacterium acnes, Malassezia restricta and Acinetobacter johnsonii in the EOM samples, and Cutibacterium acnes, Brevundimonas and Sphingomonas echinoides in the Incision samples. Regarding to the IOM(–) subjects, the top three microorganisms were Cutibacterium acnes, Staphylococcus and Acinetobacter johnsonii in the EOM samples, and Ralstonia, Cutibacterium acnes and Micrococcus luteus in the Incision samples. In terms of the Swab samples, the predominant species were Cutibacterium acnes and Staphylococcus haemolyticus in the CRS(+) subjects, and Cutibacterium acnes and Staphylococcus hominis in the CRS(–) subjects.

Taxonomic Changes

The microbial diversity in each group was measured with Shannon index (Fig. 3a). Both the Incision samples and the Swab samples exhibited higher microbial diversity than the EOM samples. In terms of the Incision samples, no significant difference was detected among the four study groups, suggesting that the microbiome within conjunctival tissues was probably not affected by IOM or CRS conditions. The microbial diversity of the EOM samples was statistically higher in the IOM(+)CRS(+) subjects compared with the IOM(–) subjects, but no statistical difference was detected between the IOM(+)CRS(–) subjects and the IOM(–) subjects, indicating that microbiotal dysbiosis may be responsible for myositis development in the IOM(+)CRS(+) group but not in the IOM(+)CRS(–) group.

The taxonomic changes were further analyzed with PCA and LDA effect size. PCA analysis revealed that microbial abundance significantly differed between the EOM and Incision samples in the IOM(–) subjects (Fig. 3b, Wilcoxson Rank-Sum test, P = 0.011) but not in the IOM(+) subjects. LDA effect size screened out the most distinguishable species (LDA score > 3) as Staphylococcus in the EOM samples and Micrococcus luteus in the Incision samples (Fig. 3b). Comparison of the EOM and Swab samples detected statistical differences of microbial abundance in the IOM(–)CRS(+) subjects (Fig. 3c, Wilcoxson Rank-Sum test, P = 0.003) but not in the IOM(+)CRS(+) or CRS(–) subjects, and the most distinguishable species (LDA score > 3) were Cutibacterium acnes, Malassezia restricta and Staphylococcus haemolyticus in the Swab samples (Fig. 3c). Further analysis of the Swab samples confirmed distinctive microbiome between the CRS(+) and CRS(–) subjects (Fig. 3d, Wilcoxson Rank-Sum test, P = 0.006), with Staphylococcus haemolyticus predominant in the CRS(+) subjects and Staphylococcus hominis predominant in the CRS(–) subjects (Fig. 3d). Notably, the microbiota in EOM samples was statistically different between the IOM(–)CRS(+) and IOM(–)CRS(–) subjects (Fig. 3d, Wilcoxson Rank-Sum test, P = 0.018), and LDA effect size revealed preponderance of Cutibacterium acnes in the IOM(–)CRS(+) subjects. We also noticed discrepancies of EOM microbiota between the IOM(+)CRS(+) and IOM(+)CRS(–) subjects, but data analysis showed no statistical significance (Fig. 3d). The above findings collectively demonstrated an alteration of microbiota in both nasal cavity and EOMs of the CRS subjects, providing evidence that the microbiotal dysbiosis may contribute to IOM pathogenesis in patients with concurrent IOM and CRS.

Microbiome, which refers to the entirety of organisms that colonize individual sites in the human body, has been considered as an essential participant in the the surveillance and monitoring of immune system. Microbiota encompasses a relatively narrower range of microorganisms than microbiome, and the prokaryotes and eukaryotes in microbiota are active within the range of microbial structures, metabolites and movable genetic elements [12]. Many studies have confirmed that dysfunctional microbiota inhabiting the airway is a key factor in the pathology of upper respiratory tract diseases such as CRS [24, 25]. Consistent with previous studies, our data also demonstrated microbiotal dysbiosis in the nasal cavities of CRS subjects. Furthermore, we discovered an alteration of microbiota in EOM tissues obtained from IOM(–)CRS(+) subjects compared with the IOM(–)CRS(–) subjects. Our results indicate that the nasal microbiota may interfere with the balance of microbial communities in EOM tissues and the imbalanced microbiota within muscle fibers in turn contributes to the development of IOM. This new finding provides insights into the role of nasal microbiome in the development of orbital autoimmune diseases. Further study is necessary to investigate the interaction between microbiotal dysbiosis in nasal cavity and autoimmune reaction within orbit.

It is disappointing that no significant differences were detected between the EOM samples obtained from IOM(+)CRS(+) subjects and IOM(+)CRS(–) subjects. This result is seemingly paradoxical to the hypothesis that nasal microbiota influences the pathogenesis of IOM. It is reasonable to presume that the negative results are attributed to small sample size and the drawbacks of mNGS technique. The major challenge of mNGS is immature validation of sequencing process and lack of experience in bioinformatic analysis [26]. Many efforts have been made to develop analytical tools to aid in all aspects of data analysis. We believe that the rapid progress of bioinformatics may remarkably improve the performance of mNGS in future studies. Up to date, mNGS has been successfully applied in dozens of sample types, such as cerebrospinal fluid, respiratory secretions, feces, urine, blood and other human tissues [27, 28]. The advantages of high-throughput, culture-independence and unbiased performance have enabled mNGS as a routine diagnostic test in some medical centers [29]. The mNGS technology not only opens another window for the diagnosis of infectious diseases but also offers a precious opportunity to identify the whole genome of pathogens within a certain sample. Many researchers have utilized mNGS to screen out the potential causative pathogens in previously-considered idiopathic inflammatory conditions. For instance, in inflammatory bowel disease, mNGS analysis identified a common skin resident fungus Malassezia restricta as a specific pathogen, and mouse model experiments confirmed its pathogenic roles as eliciting innate inflammatory responses and exacerbating colitis [30]. The mNGS technique has also been applied to investigate periocular and intraocular conditions [31]. However, few studies focus on intraorbital diseases. To this point, our study provides a reference spectrum for further experiments to explore the microbiome within orbital tissues.

Another drawback of our study is unbalanced confounding factors. Many studies have proved that the ocular surface microbiome differs by age, sex, geographic area, ocular surface conditions and antibiotic drugs [22, 32–34]. Since the conjunctival incision may affect the results of EOM tissues, our study recruited age- and gender-matched subjects to minimize the confounding effects of ocular surface microbiome. Other potential confounding factors include the sampled EOM, the histologic endotype of CRS, the affected sinuses, previous usage of antibiotics and so forth. Owing to a small sample size, these factors were not balanced in our study. We anticipate large sample study with case-control design to balance more confounding effects and clarify the diverse microbiota during different clinical phases of IOM.

Our study recruited a cohort of age- and gender-matched subjects with IOM and/or CRS, performed mNGS analyses of the EOM tissues and 16s rRNA sequencing of the nasal swap samples, and explored the influence of CRS on EOM microbiota in subjects with/without IOM. Our data demonstrated distinctive microbiota in EOM tissues between subjects with CRS and without CRS, providing evidence that nasal dysbiosis may play a role in the development of IOM.

CRS

Chronic rhinosinusitis

Computed tomography

EOM

Extraocular muscle

H&E

Hematoxylin-eosin

IOM

Idiopathic orbital myositis

LDA

Linear discriminant analysis

mNGS

Metagenomic next-generation sequencing

PCA

Principal component analysis

Ethics approval and consent to participate: The study protocol was approved by the Ethics Committee of Fudan EENT Hospital. Written informed consents were obtained from all patients.

Consent for publication: All authors have approved the manuscript for publication. The patients depicted in the figures have given written consent for their personal or clinical details along with any identifying images to be published in this study.

Availability of data and materials: The original dataset is available upon request to the corresponding authors.

Competing interests: None of the authors have any conflicts of interest to report.

Funding: This work was supported by the National Natural Science Foundation of China (grant number 82000940, 81970835, 81800867); and the Natural Science Foundation of Shanghai (grant number 20ZR1409500).

Authors’ contributions: XL and RM were involved in analysis and interpretation of data and drafting the manuscript. LG and ZP made contribution to acquisition of data and drafting. JQ and RZ contributed to conception and design, analysis and interpretation of data, drafting and revising the manuscript. All authors read and approved the final manuscript.

Acknowledgements: The authors thank Dr. Yingwen Bi (Fudan Eye & ENT Hospital) for performing the pathologic analysis.

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Metagenomic sequencing provides evidence of the nasal microbiota’s influence on idiopathic orbital myositis: a case control study

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Abstract

Figures

Background

Methods

Subject Recruitment

Sample Collection

DNA Extraction and mNGS

16S rRNA sequencing

Data Analysis

Statistics

Results

Clinical Characteristics

Microbiome Results

Taxonomic Changes

Discussion

Conclusions

Abbreviations

Declarations

References

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