Abstract

   Understanding asymptomatic moyamoya disease (aMMD), for which treatment
   options are currently limited, is key to the development of therapeutic
   strategies that will slow down the progression of this disease, as well
   as facilitate the discovery of therapeutic targets for symptomatic MMD.
   Newly found transfer RNA-derived small RNAs (tsRNAs) perform potential
   regulatory functions in neovascularization, which is a well-known
   pathological manifestation of MMD. In this study, the neutrophilic
   tsRNA transcriptome in aMMD was profiled using next-generation RNA
   sequencing in five patients and five matched healthy subjects. A
   negative binominal generalized log-linear regression was used to
   identify differentially expressed (DE)-tsRNAs in aMMD. Gene Ontology
   and functional pathway analyses were used to identify biological
   pathways involved with the targeted genes of the DE-tsRNAs. Four tsRNAs
   were selected and validated using quantitative reverse transcription
   polymerase chain reaction. In total, 186 tsRNAs were DE between the two
   groups. Pathophysiological events, including immune response,
   angiogenesis, axon guidance, and metabolism adjustment, were enriched
   for the DE-tsRNAs. The expression levels of the four DE-tsRNAs were
   consistent with those in the neutrophilic transcriptome. These
   aberrantly expressed tsRNAs and their targeted pathophysiological
   processes provide a basis for potential future interventions for aMMD.

   Keywords: asymptomatic moyamoya disease, inflammation, neutrophil,
   next-generation RNA sequencing, transfer RNA-derived small RNA

1. Introduction

   Moyamoya disease (MMD) is a chronic progressive cerebrovascular
   steno-occlusion disorder that affects the bilateral or unilateral main
   trunks of the circle of Willis, with concomitant abnormal collateral
   networks [[46]1]. Epidemiological studies have confirmed that MMD has a
   higher incidence in East Asian countries, including China, than in
   other countries, showing wide regional variations [[47]2,[48]3]. The
   initial clinical manifestations of MMD include transient ischemic
   attack, ischemic and hemorrhagic stroke, headaches, seizures, and
   cognitive impairment, although those affected can also be asymptomatic
   [[49]4].

   With the increasing availability of non-invasive diagnostic techniques
   and their popularization in regular medical checkups, the incidence of
   asymptomatic MMD (aMMD) has been found to be higher than that
   previously considered [[50]5]. However, our understanding of its
   pathophysiological progress remains limited, which is reflected in the
   lack of interventions available for aMMD [[51]6,[52]7]. Previous
   studies investigating the genetic background and acquired conditions of
   symptomatic MMD have substantially contributed to our understanding of
   the pathogenesis of MMD [[53]8,[54]9]. In this study, by focusing on
   patients with aMMD, especially those without any vascular risk factors,
   we hope to provide important insights into the natural course of MMD
   and help develop prophylactic intervention measures to prevent MMD.

   Transfer RNA (tRNA)-derived small RNA (tsRNA) is a newly discovered
   class of non-coding RNAs, with lengths of 15–45 nt
   [[55]10,[56]11,[57]12]. They are functional RNA populations, implicated
   in stress and viral infections, cancer, neurodegeneration, metabolic
   syndromes, and microbiome dysregulation [[58]13]. Recently, studies
   have identified the role of tsRNAs in cerebral ischemic injury,
   inflammation, and neovascularization [[59]14,[60]15,[61]16,[62]17],
   which conforms with the pathological manifestations of MMD.

   In the present study, we focused on whether tsRNAs in neutrophils
   influence aMMD progression, and whether promising therapeutic targets
   could be identified during this process. To this end, we sequenced
   neutrophilic tsRNAs in patients with aMMD and probed differentially
   expressed (DE)-tsRNAs and their targeted pathophysiological processes.

2. Materials and Methods

2.1. Patients and Controls

   This study complied with the Declaration of Helsinki and was approved
   by the Research Ethics Committee Review Board of Beijing Xuanwu
   Hospital, Capital Medical University (Beijing, China). Written informed
   consent was obtained from all individuals involved in the study of
   blood samples. Patients diagnosed with aMMD using a high-resolution MRI
   at Xuanwu Hospital were enrolled in our study. Patients diagnosed with
   moyamoya syndrome (also named secondary moyamoya disease,
   quasi-moyamoya disease, akin-moyamoya disease, etc.) were denoted as
   patients with MMD, either bilateral or unilateral, and patients with
   relations to other underlying diseases, such as autoimmune diseases,
   atherosclerosis, thyroid disease, neurofibromatosis type 1, tuberous
   sclerosis, meningitis, down syndrome, cranial irradiation, von
   Recklinghausen disease, and sickle cell anemia, were noticed and
   excluded. Patients with hypertension, heart disease, hyperlipidemia, or
   diabetes, and those taking certain drugs (including anti-inflammatory
   drugs, antibiotics, antiplatelet drugs, and antiepileptic drugs, among
   others) were excluded from the study to prevent potential interference
   with the study. Overall, three male and two female adult patients with
   aMMD (age range, 45–58 years) and five age- and sex-matched healthy
   volunteers (age range, 45–58 years) were included in the tsRNA
   sequencing (seq) study. Peripheral blood samples were extracted into
   EDTA vacutainer tubes from patients and controls and then used for the
   purification and isolation of neutrophils, using density gradient
   centrifugation.

2.2. Library Preparation and tsRNA Sequencing

   The procedure for RNA extraction from neutrophils has been described
   elsewhere. The quantity and quality of RNA were determined using a
   spectrophotometer. Subsequently, a NanoDrop^® ND-1000 instrument
   (NanoDrop; Thermo Fisher Scientific, Inc., Wilmington, DE, USA) was
   used to accurately measure the concentration (abs260) and protein
   contamination (ratio abs260/abs280) of the total RNA samples. The
   optical density (OD) A260/A280 and A260/A230 ratios of all the samples
   were within the normal range. Agarose gel electrophoresis was used to
   assess the integrity of the total RNA samples. No smearing of the
   ribosomal RNA band indicated that the RNA samples were not degraded.

   For efficient reverse transcription, the total RNA samples were then
   treated using the rtStar™ tsRNA pretreatment kit (cat. no. AS-FS-005;
   Arraystar Inc., Rockville, MD, USA), which may cause an unavoidable
   degradation of total RNA, but it could remove possible RNA
   modifications that may otherwise interfere with the library
   construction for tsRNA-seq, without a noticeable effect on tRNA quality
   [[63]18]. The pretreated samples were then used for tsRNA-seq library
   preparation, using a commercial kit (NEBNext^® Multiplex Small RNA
   Library Prep Set for Illumina, cat. no. 7580; New England Bio Labs,
   Inc., Ipswich, MA, USA). Library preparation procedures included
   3′-adapter and 5′-adapter ligation, cDNA synthesis, polymerase chain
   reaction (PCR) amplification, and the screening of 134–160 bp
   PCR-amplified fragments (corresponding to 14–40 nt small RNAs). The
   completed libraries were quantified using an Agilent 2100 Bioanalyzer
   (Agilent Technologies, Inc., Santa Clara, CA, USA). According to the
   quantification results, the libraries were mixed in equal amounts and
   used for sequencing on an Illumina NextSeq 500 instrument (Illumina,
   Inc., San Diego, CA, USA).

Sequencing Data Analysis

   Fast QC (v.0.11.5) software was used to assess the quality of the
   sequencing. Sequencing fragments were analyzed using the Solexa
   pipeline (offline Base Caller software, v.1.8). Trimmed fragments were
   first compared in the tRNA genome database
   ([64]http://gtrnadb.ucsc.edu/ (v.Release 17, accessed on 30 April
   2021)) with Novo Align software (v.2.07.11) to identify known mature
   tRNA and tRNA precursors. The unrecognized fragments were further
   compared with other small RNA databases, including those for mRNA,
   rRNA, snRNA, snoRNA, miRNA, and piRNA. The abundance of tsRNAs was
   evaluated using sequencing counts and normalized as counts per million
   of the total aligned reads (CPM). The tsRNAs were filtered if the CPM
   was < 20 in all the samples. The fold change was then calculated
   between the aMMD group and the control group to form an expression
   profile.

   DE-tsRNA analysis was performed using the R package edgeR. A tsRNA with
   a two-tailed p < 0.05, after false discovery rate (FDR) correction,
   according to Benjamini and Hochberg, and a fold change ≥ 2, was defined
   as DE-one between the two groups. Volcano plots and hierarchical
   clustering plots were performed in R or Perl for the statistical
   computing and graphics of DE-tsRNAs.

2.3. Bioinformatics Analysis

   tsRNAs can interact with Argonaute (AGO) proteins and target RNAs in a
   manner similar to miRNAs, with seed sequences that have matching
   profiles similar to those of miRNAs [[65]19]. Based on the presence of
   binding sites in the 3′-untranslated region, we adopted miRNA
   target-predicting algorithms, including miRanda
   ([66]http://www.microrna.org/microrna/home.do (v.3.3a, accessed on 30
   April 2021)) and TargetScan ([67]http://www.targetscan.org/vert_71/
   (v.7.2, accessed on 30 April 2021)), to identify the potential target
   genes of the DE-tsRNAs. Subsequently, the predicted target genes were
   input into the Database for Annotation, Visualization, and Integrated
   Discovery (DAVID) ([68]http://david.abcc.ncifcrf.gov/ (v.6.8, accessed
   on 30 April 2021)) with Gene Ontology (GO)
   ([69]http://www.geneontology.org/ (v.3.5, accessed on 30 April 2021))
   to analyze their molecular functions. The Kyoto Encyclopedia of Genes
   and Genomes (KEGG) ([70]http://www.genome.ad.jp/kegg/ (v.89.1, accessed
   on 30 April 2021)) database was used for signaling pathway enrichment
   analysis of the predicted target genes. Similar to GO analysis,
   statistical algorithms were used in KEGG analysis to analyze the
   association between a group of target genes and specific signaling
   pathway entries, and the p-value was used to indicate the significance
   of the association.

2.4. Validation by Quantitative Reverse Transcription PCR (qRT-PCR)

   The expression of the four tsRNAs selected through bioinformatics
   analysis was validated using a qRT-PCR assay. In brief, RNA was treated
   using a rtStar™ tsRNA pretreatment kit (cat. no. AS-FS-005; Arraystar
   Inc., Rockville, MD, USA) and then converted to cDNA using a rtStar™
   First-Strand cDNA Synthesis kit (3′- and 5′-adapters) (cat. no.
   AS-FS-003; Arraystar Inc., Rockville, MD, USA), according to the
   manufacturer’s instructions. We performed qRT-PCR in the ViiA 7
   Real-time PCR System (Applied Biosystems) using a 2× PCR master mix
   (AS-MR-006-5; Arraystar Inc., Rockville, MD, USA). The relative tsRNA
   expression levels were calculated using the relative standard curve
   method and normalized to U6 levels. All reactions were performed in
   triplicate. The primers for the tested tsRNAs are listed in [71]Table
   1.

Table 1.

   Sequence of primers used for qRT-PCR.
   Gene Name Primer Sequence Tm (°C) Product Length (bp)
   U6 F: 5′GCTTCGGCAGCACATATACTAAAAT3′
   R: 5′CGCTTCACGAATTTGCGTGTCAT3′ 60 89
   tsRNA-Glu-TTC-010 F: 5′AGTCCGACGATCTCCCACAT3′
   R: 5′TCCGATCTAGGAATCCTAACCG3′ 60 49
   tsRNA-Glu-CTC-003 F: 5′TCTACAGTCCGACGATCTCCCT3′
   R: 5′GCTCTTCCGATCTACTAGACCACC 3′ 60 46
   tsRNA-Val-AAC-017 F: 5′GACGATCGTTTCCGTAGTGTAGTG3′
   R: 5′TGCTCTTCCGATCTAAACGTGA3′ 60 50
   tsRNA-Arg-CCT-008 F: 5′CTACAGTCCGACGATCGCCT3′
   R: 5′TGTGCTCTTCCGATCTCAAAAGT3′ 60 46
   [72]Open in a new tab

3. Results

   The characteristics of the whole neutrophilic tsRNAs transcriptome of
   aMMD and controls are provided in [73]Data Supplement I. The heatmap of
   the correlation coefficient and the hierarchical heatmap from all
   samples ([74]Data Supplement II) confirmed the reliability and
   appropriateness of the sample selection, and explicitly showed the
   extent of intra-case and intra-control variability in both groups.

3.1. Differential Expression Analysis

   From the comparative tsRNA sequencing analysis of the neutrophil
   samples from the patients with aMMD and healthy subjects, 186 tsRNAs
   were DE between the two groups (fold change ≥ 2.0 and FDR-adjusted p <
   0.05). Of these, 151 were upregulated and 35 were downregulated
   ([75]Figure 1). Notably, the top 10 tsRNAs were significantly DE by at
   least 60-fold between the two groups (FC ≥ 60; FDR-adjusted p < 0.05);
   all of these tsRNAs were upregulated ([76]Table 2). Among the DE-tRNAs,
   tRF-1 derived from tRNA^Glu(TTC) and tRF-5b from tRNA^Leu(TAA) were the
   most upregulated, whereas tRF-2 from tRNA^Gly(GCC) was the most
   downregulated.

Figure 1.

   [77]Figure 1
   [78]Open in a new tab

   Volcano plot. Red dots represent significantly (fold change ≥ 2.0,
   false discovery rate (FDR)-adjusted p < 0.05) upregulated tsRNAs in
   aMMD and green dots represent significantly downregulated tsRNAs
   compared with the control samples. Black indicates tsRNAs that were not
   differentially expressed.

Table 2.

   Top 10 DE-tsRNAs with the highest fold change.
   TsRNA_ID Type Length FC (abs) FDR-Adjusted p-Value Regulation TsRNA
   Sequence
   tsRNA-Glu-TTC-003 tRF-1 22 122.3993273 0.00029169 up
   ATGATGTATGCTTTGTTTCTGTT
   tsRNA-Leu-TAA-037 tRF-5b 22 92.58353218 0.00043738 up
   GTTAAGATGGCAGAGCCCGGTA
   tsRNA-Gly-GCC-022 tRF-1 16 89.6665225 0.01625079 up GCACGCCCTCCCATTT
   tsRNA-Gly-TCC-035 tRF-1 15 83.29000089 0.00332883 up GCGGGCGGACCTTTT
   tsRNA-Leu-TAA-012 tRF-1 16 81.97503369 0.01469735 up AAGAGGAGTTGTTTTT
   tsRNA-Gln-TTG-047 tRF-1 18 81.75096387 0.00300796 up TTCAAAGGTGAACGTTTT
   tsRNA-Val-CAC-007 tRF-5c 28 76.86797098 0.00077968 up
   GCTTCTGTAGTGTAGTGGTTATCACGTT
   tsRNA-Gly-CCC-050 tRF-1 31 74.16577619 0.00836043 up
   AAAGGGTCTTTTTCACCCCGCTGTTGCTCTT
   tsRNA-Ala-TGC-001 tRF-1 14 72.54554601 0.03955729 up AACGGTGACTTTTT
   tsRNA-Glu-TTC-003 tRF-5b 22 64.48624603 0.00029169 up
   AGTAAGGTCAGCTAAATAAGCT
   [79]Open in a new tab

   FC: fold change; FDR: false discovery rate.

3.2. Functional Pathway Analysis

   To probe the biological functions of these aberrantly expressed tsRNAs,
   we used miRanda and TargetScan to predict the putative target genes of
   the top 20 tsRNAs (ranked by FDR-adjusted p-value). The predicted
   targets of DE-tsRNAs in aMMD were mainly distributed in the nucleus,
   and their molecular functions were correspondingly implicated in DNA
   binding transcription factor activity, and the top enriched GO
   biological processes mainly included the regulation of cellular
   metabolic processes ([80]Data Supplement III). KEGG pathway analysis
   revealed 23 significantly enriched pathways (p < 0.05) corresponding to
   the target genes ([81]Data Supplement IV). These pathways were mainly
   involved in immune response, angiogenesis, metabolism adjustment and
   axon outgrowth-related pathways, including “axon guidance”, “natural
   killer cell mediated cytotoxicity”, “MAPK signaling pathway”, “Hedgehog
   signaling pathway”, “AMPK signaling pathway” and “HIF-1 signaling
   pathway” ([82]Figure 2).

Figure 2.

   [83]Figure 2
   [84]Open in a new tab

   Pathway analysis of targeted mRNAs regulated by DE-tsRNAs. The bar plot
   showed the top 15 pathways with the highest enrichment score
   (−log[10](p-value)) and the number of involved genes.

3.3. qRT-PCR Validation

   To further validate the tsRNAs-Seq results, we detected the expression
   levels of the four candidate DE-tsRNAs, using qRT-PCR in samples from
   five aMMD patients and five healthy volunteers. tsRNA-Glu-CTC-003,
   tsRNA-Glu-TTC-010, and tsRNA-Val-AAC-017 were all upregulated, and
   tsRNA-Arg-CCT-008 was downregulated, showing a similar expression trend
   as their counterparts in the tsRNAs transcriptome ([85]Figure 3).
   Additionally, lists of the mRNA targets of the four validated DE-tsRNAs
   were shown in [86]Data Supplement V.

Figure 3.

   [87]Figure 3
   [88]Open in a new tab

   Validation of DE-tsRNAs in aMMD and controls. The comparative bars
   represented the relative expression level of tsRNA-Glu-TTC-010,
   tsRNA-Glu-CTC-003, tsRNA-Val-AAC-017, and tsRNA-Arg-CCT-008 in control
   and aMMD detected by qRT-PCR. *, p < 0.05; **, p < 0.01.

4. Discussion

   This study first profiled the transcriptome of neutrophilic tsRNAs in
   aMMD. Compared with neutrophils in controls, we identified 109 tsRNAs
   specifically expressed in the aMMD group. This suggested that aMMD had
   an unstable status, and the altered expression profile of tsRNAs in
   neutrophils may partly contribute to disease progression. More
   importantly, functional pathway analysis showed that the targeted genes
   of DE-tsRNAs were involved in immune response and angiogenesis, which
   coincided with the previously accepted pathological manifestation of
   MMD. In addition, axon guidance and metabolism adjustment, mediated by
   the HIF-1 signaling pathway, as well as other cascades, also played
   critical roles, which may explain the specificity of the recruited aMMD
   patients. In order to strengthen the results deduced from the analyses
   from the DE-tsRNAs, here we compared the functional pathway analysis of
   the targeted genes of DE-tsRNAs with the mRNA sequencing data ([89]Data
   Supplement VI) obtained from the same sample of aMMD and control
   subjects [[90]20], and we found that consistent with the results from
   the prediction of putative targeted mRNA ([91]Data Supplement V), the
   main pathophysiological processes were highlighted in the in-depth
   analyses result of mRNA sequencing data as well, which supported the
   roles of the four main pathophysiologic processes in aMMD. As to the
   specific mRNA targets of DE-tsRNAs, we also compared the mRNA targets
   of the four tsRNAs validated by qRT-PCR, with the DE-mRNA, detected by
   the mRNA sequencing to further reinforce the results ([92]Figure 4).

Figure 4.

   [93]Figure 4
   [94]Open in a new tab

   Regulatory networks of the four validated DE-tsRNAs, mRNA targets and
   pathophysiological processes involved in aMMD. The oblong on the left
   represented the four RT-PCR validated DE-tsRNAs, the oval in the middle
   represented the mRNA targets, the oblong on the right represented the
   four main pathophysiological processes involved in aMMD, and the line
   represented the predictive connection. The blue indicated the
   downregulated DE-tsRNA in aMMD (tsRNA-Arg-CCT-008) or the red indicated
   the upregulated DE-ones in aMMD (tsRNA-Glu-CTC-003, tsRNA-Glu-TTC-010,
   and tsRNA-Val-AAC-017); similarly, the blue indicted the downregulated
   mRNA targets, the red indicted upregulated ones, and the grey indicated
   the mRNA targets regulated by the DE-tsRNA but not differentially
   expressed in mRNA sequencing data ([95]Data Supplement VI).

4.1. Immune Response

   Among the pathophysiologic pathways enriched by DE-tsRNAs, immune cell
   activation and the release of inflammatory factors were the most widely
   regulated, highlighting the pivotal role of the immune response in MMD.
   In line with the results of surgical specimens showing some
   infiltration of macrophages and T cells in the thickened intima of MMD
   [[96]21], here we found evidence for possible monocyte, macrophage, and
   T cell involvement. Key genes such as HLA-G, HLA-DPA1 and HLA-DQA1 (MHC
   family), and CD28 (costimulatory molecule on T cell), IL-2R, CD209
   (M2-like markers), CD109, TLR2/4, and P2RY6 were all predicted to be
   regulated by DE-tsRNAs in aMMD, among which, CD109 was the common
   target of tsRNA-Glu-TTC-010 and tsRNA-Glu-CTC-003 and upregulated in
   the neutrophilic mRNA sequencing data; P2RY6 was the target of
   tsRNA-Arg-CCT-008 and upregulated in the mRNA sequencing result; and
   TLR4 was the target of tsRNA-Val-AAC-017 and downregulated in the mRNA
   sequencing result ([97]Figure 4 and [98]Data Supplement IV–VI).
   Moreover, the differentially altered targeted mRNA FAS and FAIMS were
   indicative of the possible role of natural killer cells in aMMD.
   Consistent with previous studies that showed elevated serum and CSF
   levels of endothelial adhesion molecules in MMD [[99]22], in the
   present study, ICAM1/2, CXCR1/2, and CCR6 were predicted and regulated,
   and there was also one pathway highlighted as “cell adhesion molecules”
   in the in-depth analyses of the DE-mRNA, implicating the transmigration
   process of leucocytes in aMMD. In particular, CXCR1/2 were detected as
   differentially expressed mRNAs from the mRNA sequencing data, and the
   common targets of tsRNA-Glu-CTC-003 and tsRNA-Arg-CCT-008, and CCR6 was
   also targeted by tsRNA-Glu-TTC-010 and upregulated in the mRNA
   sequencing data ([100]Figure 4 and [101]Data Supplement V–VI). The
   implication of a possible complex interplay between these immune cells
   via some targeted genes, such as IL10RB, IL13RA1 and IL22RA, predicted
   to be targeted by tsRNA-Glu-CTC-003 and upregulated in the mRNA
   sequencing data, and the BCL9 target of tsRNA-Glu-TTC-010, which was
   downregulated in the mRNA sequencing data ([102]Figure 4 and [103]Data
   Supplement V–VI), provides a basis for further exploration in MMD
   [[104]23]. Meanwhile, immunosuppression may occur, along with immune
   activation in aMMD, manifesting in processes that inhibit T cell
   proliferation and Th1/2 differentiation, mediated by cell cycle
   retardation in the KEGG analyses of the mRNA targets of DE-tsRNA; the
   negative regulation of T cell activation, enriched by DE-mRNAs,
   involved genes such as cyclins, predicted to be targeted by
   tsRNA-Val-AAC-017 and differentially expressed in the mRNA sequencing
   data. Moreover, the FOXO signaling pathway, shown both in the analyses
   of mRNA targets of DE-tsRNA and DE-tsRNA ([105]Figure 4 and [106]Data
   Supplement IV–VI), may also contribute to immunosuppression by
   mediating cellular apoptosis in aMMD [[107]24]. It is worth noting
   that, in our data, thyroid hormone signaling was enriched for targeted
   genes of DE-tsRNAs, which supports the theory that increased thyroid
   function or underlying thyroid autoimmunity is associated with MMD
   [[108]25].

4.2. Cell Proliferation and Axon Guidance

   Abnormal cerebral vessel function causes massive neuronal apoptosis,
   while uncompromised brain tissue invokes many compensatory mechanisms,
   such as cell proliferation, axon guidance, and angiogenesis [[109]26].
   Here, we identified enriched terms implicated in EGFR tyrosine kinase
   inhibitor resistance and the hedgehog signaling pathway, highlighted
   both in the GO and KEGG pathway analyses of mRNA targets of DE-tsRNAs
   and DE-mRNAs, which are critical cascades responsible for cell
   proliferation, growth, survival, and angiogenesis, in line with the
   previous discovery in surgical specimens that smooth muscle cells
   proliferate in the occlusive cerebrovascular lesions in MMD [[110]21].
   Moreover, our data implicated DE-tsRNAs target genes in axon guidance
   with the highest enrichment score ([111]Figure 2) and it was also
   underlined in the GO analyses of the DE-mRNA as the term “response to
   axon injury”, where the targeted genes included TAFI, EIF2AK2, NLGN3,
   LHX6, SCD5, ISLR2, and AP3M2 ([112]Figure 4 and [113]Data Supplement
   IV). These genes play critical roles during axon outgrowth, axon
   repulsion, and axon attraction, and may regulate the regenerative and
   reparative capacity of neurons during the progression of abnormal
   vascular networks in MMD. This was also consistent with the results of
   neuroimaging studies, which showed that chronic ischemia in aMMD
   decreased the axon density in the white matter and the dendrite density
   in the cortex [[114]27]. It is worth noticing that SCD5 and ISLR were
   the predicted targets of tsRNA-Glu-TTC-010 and tsRNA-Glu-CTC-003, and
   were aberrantly expressed in the mRNA sequencing data ([115]Figure 4).

4.3. Angiogenesis and Metabolism Adjustment

   In addition, to deal with energy shortages, brain tissues prioritize
   the supply of energy to the most needed locations to maintain
   homeostasis, defending against the onset of ischemic or hemorrhagic
   stroke. This can be deduced from GO terms such as “cellular response to
   environment stimulus” in the mRNA sequencing and “regulation of
   cellular metabolic process” in the tsRNA sequencing. Notably, HIF-1
   signaling was shown to be activated by DE-tsRNAs ([116]Figure 2), which
   acted to sense oxygen shortages and redistribute oxygen supply in
   patients with aMMD. This is particularly important for abnormal blood
   vessels supplying oxygen and energy to the brain tissues involved
   [[117]28]. Surgical revascularization can only relieve ischemic
   symptoms in a minority of patients with MMD, while the activation of
   the HIF-1 pathway may exert compensatory adjustment to protect MMD
   patients from symptom attack. However, potential hemorrhagic symptoms,
   induced by the activation of genes regulating vascular tone and
   angiogenesis in HIF-1 pathways, should also be considered. In addition,
   our data indicate that some targeted genes are involved in galactose
   metabolism. G6Pase, AdipoR, IGF1R, INSR, and INSRR were predicted to be
   targeted by DE-tsRNAs ([118]Data Supplement IV); particularly,
   tsRNA-Glu-CTC-003 targeted G6PC2 and tsRNA-Val-AAC-017 targeted IGF1R
   were both differentially altered in the mRNA sequencing results
   ([119]Figure 4 and [120]Data Supplement V and VI), which jointly
   pointed to the conclusion drawn from a previous study, in which an
   elevated supply of energy protected patients with MMD from brain
   dysfunction [[121]29].

   There are several limitations to this study. First, despite further
   analysis of DE-tsRNAs in our sequencing results, validation in larger
   samples was not conducted due to the difficulty of recruiting more
   asymptomatic patients. Relatively small sample size and different
   samples from different subjects might also be partially responsible for
   the fact that the rank of FC value for the four candidate DE-tsRNAs by
   the qRT-PCR validation did not match their counterparts in the tsRNAs
   transcriptome. Second, there was no symptomatic MMD group as a
   reference. However, we plan to perform a follow-up of these
   asymptomatic patients and the alterations in their tsRNAs, the possible
   clinical outcomes of which may help us to better understand the natural
   course of MMD and its pathological mechanism. Finally, we were unable
   to conduct further research into the DE-tsRNAs due to a lack of matched
   animal models and cell lines for MMD.

5. Conclusions

   This study conducted neutrophilic tsRNA profiling in patients with aMMD
   and healthy subjects. Pathophysiological processes, including immune
   response, axon guidance, angiogenesis, and metabolism adjustment, were
   highlighted by DE-tsRNAs and DE-mRNA in aMMD patients, which supports
   the potential receptivity of aMMD to medical therapeutics, such as
   immune-modifying drugs. Further studies will be needed to elucidate the
   complex mechanism of these tsRNAs and their associated pathways, in
   order to better understand the pathogenesis of MMD.

Supplementary Materials

   The following are available online at
   [122]https://www.mdpi.com/article/10.3390/cells10051086/s1, Data
   Supplement I:The characteristics of the whole neutrophilic tsRNAs
   transcriptome of aMMD and controls, Data Supplement II: The heatmap of
   the correlation coefficient and the hierarchical heatmap of all
   involved samples, Data Supplement III: GO analyses results of top
   twenty DE-tsRNAs, Data Supplement IV: KEGG analyses results of top
   twenty DE-tsRNAs, Data Supplement V: Lists of mRNA targets of PCR
   validated DE-tsRNA, Data Supplement VI: The characteristics of the
   neutrophilic DE-mRNA transcriptome of aMMD and controls. Online Data
   Supplement I–VI.
   [123]Click here for additional data file.^ (19.7MB, zip)

Author Contributions

   Conceptualization, Z.H., H.Z. and P.L.; methodology, R.W. and Y.H.;
   software, Y.H.; validation, L.L. and R.W.; formal analysis, Z.T.;
   investigation, Z.T. and Y.Z.; resources, J.L. and L.J.; data curation,
   Y.Z.; writing—original draft preparation, L.L.; writing—review and
   editing, Z.H. and Y.L.; visualization, J.F.; supervision, H.Z.; project
   administration, P.L. and J.F.; funding acquisition, Y.L. All authors
   have read and agreed to the published version of the manuscript.

Funding

   This research was funded by the National Natural Science Foundation of
   China (81801299, 81971222, and 82001268) and the Beijing Municipal
   Commission of Education (KM201910025019).

Institutional Review Board Statement

   The study was conducted according to the guidelines of the Declaration
   of Helsinki, and approved by the Ethics Committee of Xuanwu Hospital,
   Capital Medical university. APPROVAL NUMBER: Clinical Research [2018]
   No. 18.

Informed Consent Statement

   Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

   The data presented in this study are available in [Online [124]Data
   Supplement I–VI].

Conflicts of Interest

   The authors declare no conflict of interest.

Footnotes

   Publisher’s Note: MDPI stays neutral with regard to jurisdictional
   claims in published maps and institutional affiliations.

References