石彩鳳, 劉丹丹, 何愛(ài)琴, 吳小梅, 沈新佳, 朱雪婷, 薛穎, 楊俊偉, 周陽(yáng)
基于尿液外泌體的液體活檢結(jié)合代謝組學(xué)在評(píng)估糖尿病腎臟病中的應(yīng)用研究*
石彩鳳, 劉丹丹, 何愛(ài)琴, 吳小梅, 沈新佳, 朱雪婷, 薛穎, 楊俊偉△, 周陽(yáng)△
(南京醫(yī)科大學(xué)第二附屬醫(yī)院腎臟病中心,江蘇 南京 210003)
探討來(lái)源于腎小管的尿液外泌體中的代謝物在評(píng)估糖尿病腎臟?。―KD)中的應(yīng)用價(jià)值。采用橫斷面研究,選擇健康人群(13例)、單純2型糖尿病(T2D)患者(12例)及T2D合并DKD患者(12例)作為研究對(duì)象,收集人口學(xué)和實(shí)驗(yàn)室檢查等資料。培養(yǎng)原代腎小管上皮細(xì)胞。超速離心提取尿液及細(xì)胞培養(yǎng)上清液中的外泌體,免疫磁珠富集表達(dá)分化簇13(CD13)的外泌體,透射電鏡及納米顆粒跟蹤分析外泌體的形態(tài),Western blot和免疫組化染色檢測(cè)蛋白質(zhì)的表達(dá)和分布,ELISA檢測(cè)腎損傷因子1和中性粒細(xì)胞明膠酶相關(guān)脂質(zhì)運(yùn)載蛋白,超高效液相色譜-串聯(lián)質(zhì)譜方法靶向定量檢測(cè)外泌體中的代謝物。采用偏最小二乘判別分析進(jìn)行多維建模。代謝物的兩組間比較采用Wilcox檢驗(yàn),多組間比較采用Kruskal-Wallis檢驗(yàn),Spearman相關(guān)分析法分析代謝物與臨床指標(biāo)的相關(guān)性,受試者工作特征(ROC)曲線評(píng)價(jià)診斷效能。尿液外泌體中檢測(cè)到CD13的表達(dá)。腎組織中CD13分布在腎小管的頂端側(cè)。原代腎小管上皮細(xì)胞及其外泌體中均檢測(cè)到CD13。在表達(dá)CD13的尿液外泌體中定量檢測(cè)到144種代謝物。在單純T2D和T2D合并DKD兩組間篩選出差異具有統(tǒng)計(jì)學(xué)意義的15種代謝物(<0.05),包括賴氨酸、-乙酰丙氨酸、-乙酰絲氨酸、正亮氨酸、-苯乙酰苯丙氨酸、纈氨酸、鵝去氧膽酸、葡萄糖、肉豆蔻腦酸、油酸、辛二酸、十一烷酸、延胡索酸、酮亮氨酸和異戊酸,其中,-乙酰丙氨酸、十一烷酸、酮亮氨酸、賴氨酸和鵝去氧膽酸均與尿蛋白和血尿酸顯著相關(guān)(<0.05或<0.01)。肉豆蔻腦酸、-乙酰丙氨酸、纈氨酸和正亮氨酸的ROC曲線下面積分別為0.854(95% CI: 0.705~1.000)、0.840(95% CI: 0.677~1.000)、0.812(95% CI: 0.640~0.985)和0.806(95% CI: 0.630~0.982)。單純T2D與T2D合并DKD的腎小管外泌體中的代謝物存在顯著差異,從中篩選出的15種代謝物可作為臨床診斷DKD的新途徑。
糖尿病腎臟??;外泌體;代謝組學(xué);腎小管
糖尿病腎臟?。╠iabetic kidney disease, DKD)已成為我國(guó)終末期腎衰竭的主要病因[1-2]。然而,目前依據(jù)尿白蛋白/肌酐比值(urinary albumin-to-creatinine ratio, UACR)、估算的腎小球?yàn)V過(guò)率(estimated glomerular filtration rate, eGFR)及腎活檢的臨床診斷標(biāo)準(zhǔn)仍存在諸多缺陷[3]。尿液外泌體大都來(lái)自腎單位各段組織細(xì)胞,由復(fù)雜的分子系統(tǒng)調(diào)控其產(chǎn)生及成分[4],并攜帶來(lái)源細(xì)胞的特征[5]。這種主動(dòng)的分泌過(guò)程成為基于尿液外泌體對(duì)腎組織進(jìn)行無(wú)創(chuàng)性液體活檢的依據(jù)。本研究采用代謝組學(xué)方法,探討尿液外泌體中的代謝物在評(píng)估DKD中的應(yīng)用價(jià)值。
研究方案經(jīng)南京醫(yī)科大學(xué)第二附屬醫(yī)院倫理委員會(huì)審批(批準(zhǔn)號(hào):2019KY097)。所有研究對(duì)象均簽署知情同意書(shū)。選擇年齡、性別匹配的健康人群和確診2型糖尿?。╰ype 2 diabetes, T2D)的患者為研究對(duì)象。排除標(biāo)準(zhǔn)包括腎活檢提示非糖尿病性腎臟疾病,曾接受腎臟替代治療,惡性腫瘤,急性腎損傷及其他臟器的嚴(yán)重病變。健康對(duì)照組(13例)為不存在高血壓、肥胖、糖尿病、腎臟病、惡性腫瘤等的人群,且無(wú)長(zhǎng)期用藥史。單純T2D組(12例)為符合美國(guó)糖尿病學(xué)會(huì)2020年診斷標(biāo)準(zhǔn)的T2D患者。T2D合并DKD組(12例)的診斷依據(jù)為符合T2D診斷標(biāo)準(zhǔn),且具備明確的、與尿蛋白和腎功能變化存在因果關(guān)系的糖尿病病史,并符合隨機(jī)尿UACR≥30 mg/g和(或)eGFR<60 mL/(min·1.73 m2),其中UACR需排除感染等其他因素情況下,在6個(gè)月內(nèi)重復(fù)檢查3次中有兩次達(dá)到標(biāo)準(zhǔn)[1]。
研究對(duì)象在清晨空腹(禁食>10 h)采集靜脈血和尿液。由一名研究人員采用標(biāo)準(zhǔn)工具測(cè)量體重、身高等,并在靜坐休息15 min后,用歐姆龍HEM?7130血壓計(jì)在右上臂測(cè)量血壓和心率3次,每次間隔1 min,取3次平均值為最終的診室血壓和心率,羅氏P800全自動(dòng)生化分析儀檢測(cè)血、尿樣本。采用基于血肌酐的慢性腎臟病流行病學(xué)協(xié)作公式計(jì)算eGFR(http://www.nkdep.nih.gov)。
胎牛血清購(gòu)自Thermo Fisher;疊氮化鈉(S2002)、苯甲基磺酰氟(P7626)、亮抑肽酶(L8511)和二硫蘇糖醇(D9779)均購(gòu)自Sigma-Aldrich;磁珠(10608D)購(gòu)自Invitrogen;硝酸纖維膜購(gòu)自Amersham;生物素化的CD13抗體(5160823270)購(gòu)自MiltenyiBiotec;抗腫瘤易感基因101(tumor susceptibility gene 101, Tsg101)抗體(ab30871)、抗CD63抗體(ab213090)和抗CD13抗體(Ab108310)均購(gòu)自Abcam;抗CD9抗體(sc-13118)和抗CD13抗體(sc-166105)均購(gòu)自Santa Cruz;腎損傷因子1(kidney injury molecule-1, KIM-1)ELISA試劑盒(DKM100)和中性粒細(xì)胞明膠酶相關(guān)脂質(zhì)運(yùn)載蛋白(neutrophil gelatinase-associated lipocalin, NGAL)ELISA試劑盒(DLCN20)均購(gòu)自R&D Systems;靶向代謝組學(xué)的標(biāo)準(zhǔn)品購(gòu)自Sigma?Aldrich、Steraloids和TRC Chemicals。
DynaMag?-2磁力架(12321D; Thermo Fisher);Optima X Series低溫超速離心機(jī)(Beckman);JEM 1011透射電子顯微鏡(JEOL);NS 300系統(tǒng)(NanoSight);Xevo TQ?S超高效液相色譜?串聯(lián)質(zhì)譜聯(lián)用(ultraperformance liquid chromatography-tandem mass spectrometry, UPLC?MS)儀(Waters Corp)。
3.1原代腎小管上皮細(xì)胞培養(yǎng)采用110 000×離心90 min去除胎牛血清中的外泌體后,用于培養(yǎng)細(xì)胞。應(yīng)用改良的膠原酶消化-密度梯度離心法分離原代腎小管上皮細(xì)胞[6]:2~3周齡小鼠,處死后無(wú)菌取腎,剝離外膜去除髓質(zhì)后將皮質(zhì)剪碎,分別經(jīng)80、100和200目不銹鋼篩網(wǎng)過(guò)濾,收集200目網(wǎng)上物,經(jīng)0.25%胰蛋白酶消化,吸取高密度層細(xì)胞,用含10%胎牛血清的DMEM/F12培養(yǎng),細(xì)胞貼壁后定期換液,傳代3~4代后獲得形態(tài)穩(wěn)定的細(xì)胞并鑒定純度。
3.2外泌體的提取及富集用超速離心法提取細(xì)胞培養(yǎng)上清液或尿液中的外泌體[5],步驟簡(jiǎn)述如下:用去除外泌體的胎牛血清培養(yǎng)細(xì)胞,收集細(xì)胞培養(yǎng)上清液,4 ℃、500×離心15 min,10 000×離心30 min,110 000×離心90 min,沉淀細(xì)胞的外泌體。新鮮收集24 h尿液中加入防腐劑和蛋白酶抑制劑,4 ℃、500×離心15 min,10 000×離心30 min,110 000×離心90 min,沉淀物中加入二硫蘇糖醇,95 ℃加熱2 min去除Tamm-Horsfall蛋白,再次110 000×離心90 min沉淀外泌體。取1 × 107個(gè)磁珠,用無(wú)菌分選緩沖液(含0.1% 胎牛血清的磷酸緩沖鹽溶液)清洗,置于DynaMag?-2磁力架上1 min,去除上清,加入4 μg生物素化的CD13抗體,室溫60 min,用分選緩沖液清洗,置于磁力架上1 min,棄上清,制成CD13免疫磁珠。取20 000個(gè)CD13免疫磁珠與含200 μg蛋白的外泌體,4 ℃孵育18~22 h,用無(wú)菌磷酸緩沖鹽溶液清洗,置于磁力架上1 min,棄上清,進(jìn)行后續(xù)實(shí)驗(yàn)或-80 ℃暫存。
3.3透射電子顯微鏡外泌體置于2.5%戊二醛中4 ℃固定過(guò)夜,磷酸緩沖鹽溶液洗3次,室溫1%四氧化鋨固定60 min。梯度乙醇脫水后,室溫浸潤(rùn)在Epon樹(shù)脂(Ted Pella):環(huán)氧丙烷(1∶1)溶液中過(guò)夜。第2天置于新鮮Epon中60 ℃包埋過(guò)夜。用Leica EM UC7超微切片,收集在甲醛涂層格上,醋酸鈾酰和檸檬酸鉛染色,在80 kV下觀察和拍攝。
3.4納米顆粒跟蹤分析(nanoparticle tracking analysis, NTA)用配置有488 nm激光器和高靈敏度sCMOS相機(jī)的NanoSight NS 300系統(tǒng)[7]。外泌體以5 g/L蛋白濃度重懸于磷酸緩沖鹽溶液,稀釋100~500倍,以達(dá)到每幀20~100個(gè)物體。室溫下手動(dòng)注入樣品室,相機(jī)設(shè)置13下一式三次測(cè)量,采集時(shí)間30 s,檢測(cè)閾值7,每個(gè)視頻至少分析200個(gè)完整音軌。用NTA分析軟件2.3 版本進(jìn)行數(shù)據(jù)采集和分析。
3.5Western blot裂解細(xì)胞或外泌體后制備成相同蛋白濃度的樣本,取10 μg蛋白樣本在聚丙酰胺凝膠分離后,轉(zhuǎn)至硝酸纖維膜上,封閉后,與Ⅰ抗、Ⅱ抗分別孵育,采用美國(guó)國(guó)立衛(wèi)生研究院凝膠圖像分析程序分析信號(hào)強(qiáng)度。
3.6免疫組化染色選取DKD的腎活檢組織,對(duì)照為病理上僅表現(xiàn)為腎小球輕度系膜增生的腎活檢組織。將石蠟包埋組織切成 3 μm薄片,去石蠟化,二甲苯、乙醇和純水再水化后,室溫封閉 30 min,加入抗CD13抗體4 ℃過(guò)夜,Ⅱ抗室溫1 h,封片,用配備有DS-Ril(Nikon)數(shù)碼相機(jī)的Nikon Eclipse 80i顯微鏡觀察并拍照。
3.7ELISA采用ELISA檢測(cè)尿液KIM-1和NGAL:在包被有捕獲抗體的檢測(cè)孔中,分別加入50 μL梯度稀釋的標(biāo)準(zhǔn)品、尿液樣本及對(duì)照,室溫2 h后,去除液體并充分洗滌,每孔加入200 μL檢測(cè)抗體4 ℃ 2 h后,再次去除液體并充分洗滌,每孔加入200 μL底物反應(yīng)液,室溫30 min,加入50 μL 2 mol/L硫酸溶液,450 nm讀數(shù),根據(jù)標(biāo)準(zhǔn)曲線計(jì)算濃度。
3.8代謝組學(xué)采用UPLC?MS靶向定量檢測(cè)代謝物[8]。CD13+外泌體中加入氧化鋯珠和25 μL去離子水勻漿,加入150 μL含內(nèi)標(biāo)的甲醇溶液再次勻漿,18 000×離心20 min后將上清移入96孔板。后續(xù)步驟在Eppendorf epMotion工作站(Eppendorf Inc.)進(jìn)行,每孔加入20 μL衍生化試劑,加入50%甲醇,4 ℃下4 000×離心30 min,吸取上清液與內(nèi)標(biāo)混合,用于UPLC?MS分析。用MassLynx 4.1軟件處理原始數(shù)據(jù),用外泌體蛋白總量校正代謝物的定量。
采用SPSS 25.0和R軟件進(jìn)行統(tǒng)計(jì)分析。采用bartlett檢驗(yàn)確定變量分布特征。正態(tài)分布的連續(xù)性變量以均數(shù)±標(biāo)準(zhǔn)差(mean±SD)表示,兩組間比較采用檢驗(yàn),多組間比較采用方差分析。非正態(tài)分布的連續(xù)性變量用(1,3)描述,兩組間比較采用Mann-Whitney U檢驗(yàn),多組間比較采用秩和檢驗(yàn)。分類變量采用頻數(shù)和百分?jǐn)?shù)表示,組間比較采用2檢驗(yàn)。代謝物用(1,3)描述,兩組間比較采用Wilcox檢驗(yàn),多組間比較采用Kruskal-Wallis檢驗(yàn)。Spearman相關(guān)分析法評(píng)估代謝物與臨床指標(biāo)的相關(guān)性。用iMAP 1.0平臺(tái)(Metabo Profile)分析代謝物。偏最小二乘判別分析(partial least squares discriminant analysis, PLS-DA)進(jìn)行多維建模,進(jìn)行999次permutation置換檢驗(yàn)評(píng)估模型的過(guò)擬合風(fēng)險(xiǎn)。采用受試者工作特征(receiver operating characteristic, ROC)曲線評(píng)價(jià)診斷效能。以<0.05為差異有統(tǒng)計(jì)學(xué)意義。
如表1所示,三組研究對(duì)象的年齡、性別構(gòu)成比、體重指數(shù)、血紅蛋白、總膽固醇、甘油三酯、高密度脂蛋白膽固醇、低密度脂蛋白膽固醇、NGAL均無(wú)統(tǒng)計(jì)學(xué)意義(>0.05)。與對(duì)照組相比,單純T2D組和(或)T2D合并DKD組的收縮壓、舒張壓、空腹血糖、糖化血紅蛋白、UACR及KIM-1均顯著增高,血白蛋白顯著降低(<0.05)。與單純T2D組相比,T2D合并DKD組的血清肌酐、尿酸及UACR均顯著增高,eGFR顯著降低(<0.05),糖尿病病程、血糖、血壓、血白蛋白及KIM-1均無(wú)統(tǒng)計(jì)學(xué)意義(>0.05)。
表1 研究對(duì)象的臨床特征
DOT: duration of type 2 diabetes (T2D); BMI: body mass index; SBP: systolic blood pressure; DBP: diastolic blood pressure; Hb: hemoglobin; Alb: albumin; FBG: fast blood glucose; HbA1c: hemoglobin A1c; TC: total cholesterol; TG: triglyceride; HDL-C: high-density lipoprotein cholesterol; LDL-C: low-density lipoprotein cholesterol; SCr: serum creatinine; UA: uric acid; eGFR: estimated glomerular filtration rate; UACR: urinary albumin-to-creatinine ratio; KIM-1: kidney injury molecule-1; NGAL: neutrophil gelatinase associated lipocalin. 1 mmHg=0.133 kPa.*<0.05healthy control group;#<0.05simple T2D group.
透射電鏡下超速離心的沉淀物為直徑50~100 nm的雙層膜結(jié)構(gòu)囊泡(圖1A),NTA分析粒徑分布表明囊泡的直徑約100 nm(圖1B),符合外泌體的形態(tài)和大小。Western blot表明三組研究對(duì)象的尿液外泌體均表達(dá)標(biāo)志蛋白Tsg101、CD63和CD9,同時(shí)還表達(dá)CD13(圖1C)。免疫組化染色表明CD13在對(duì)照及DKD的腎組織中均有表達(dá),且都分布在腎小管頂端側(cè)(圖1D),腎小球和腎間質(zhì)中幾乎檢測(cè)不到。原代腎小管上皮細(xì)胞及其外泌體中均檢測(cè)到CD13及外泌體標(biāo)志蛋白Tsg101、CD63和CD9(圖1E)。由此可見(jiàn),健康對(duì)照及T2D的尿液中均存在表達(dá)CD13并來(lái)源于腎小管的外泌體。
Figure 1. Expression of CD13 in kidney tissue and exosomes. A: representative image showed the structure and size of isolated exosomes from urine assessed by transmission electron microscopy (arrows indicate exosomes; scale bar=100 nm); B: size distribution demonstrated by finite track length adjustment concentration (left) and intensity (right) of isolated exosomes from urine assessed by NTA (n=3); C: the protein expression in isolated urinary exosomes from subjects as indicated was assessed by Western blot (n=12); D: representative images showed the expression of CD13 in the brush border of kidney tubules from control individuals and patients with DKD assessed by immunohistochemical staining (scale bar=100 μm); E: the protein expression in primarily cultured tubular cells and isolated cellular exosomes was assessed by Western blot (n=3).
免疫磁珠富集表達(dá)CD13的外泌體(CD13+外泌體),靶向代謝組學(xué)定量其中的代謝物,測(cè)得144種代謝物的含量。PLS-DA表明單純T2D組與T2D合并DKD組的代謝物存在顯著差異(圖2A),提示以CD13+外泌體中的代謝物評(píng)估T2D是否合并DKD是可行的。從單純T2D組與T2D合并DKD組間篩選出15種差異具有統(tǒng)計(jì)學(xué)意義(<0.05)的代謝物,比較它們?cè)谌M間的差異(表2),并按照分類繪制的濃度中位數(shù)熱圖(圖2B)均顯示,與單純T2D組相比,T2D合并DKD組CD13+外泌體中僅-乙酰絲氨酸增多,其余14種代謝物均減少(<0.05);與健康對(duì)照組相比,單純T2D組CD13+外泌體中-乙酰丙氨酸、正亮氨酸、葡萄糖、肉豆蔻腦酸、酮亮氨酸增多,僅-乙酰絲氨酸減少(<0.05);健康對(duì)照組和T2D合并DKD組的CD13+外泌體中,上述15種代謝物的差異均無(wú)統(tǒng)計(jì)學(xué)意義(>0.05)。相關(guān)性網(wǎng)絡(luò)圖表明15種代謝物之間存在廣泛的相關(guān)關(guān)系(圖2C)。
Figure 2. Identification of potential metabolite biomarkers in urinary CD13+ exosomes. A: PLS-DA score plot for healthy control individuals (n=13), patients with simple T2D (n=12) and patients with DKD (n=12); B: heatmap classification of samples based on the 15 significantly altered metabolites between the simple T2D group and the T2D with DKD group; C: correlation network of significantly altered metabolites. Each node represents a metabolite, and each edge represents the strength of the correlation between two compounds. The size of each circle represents the significance of the compound in the metabolic network.
表2 研究對(duì)象尿液CD13+外泌體中15種代謝物水平的比較結(jié)果
DKD: diabetic kidney disease; T2D: type 2 diabetes.*<0.05healthy control group;#<0.05simple T2D group.
如圖3所示,Spearman相關(guān)分析表明,葡萄糖、-乙酰丙氨酸、正亮氨酸、肉豆蔻腦酸、纈氨酸、十一烷酸、酮亮氨酸、賴氨酸、鵝去氧膽酸和延胡索酸均與尿蛋白負(fù)相關(guān);-乙酰絲氨酸與尿蛋白正相關(guān)(<0.05);-乙酰丙氨酸、十一烷酸、酮亮氨酸、賴氨酸、鵝去氧膽酸和-苯乙酰苯丙氨酸均與血尿酸負(fù)相關(guān)(<0.05)。此外,葡萄糖與糖化血紅蛋白正相關(guān),與血肌酐負(fù)相關(guān)(<0.05);酮亮氨酸與低密度脂蛋白膽固醇正相關(guān)(<0.05);賴氨酸與體重指數(shù)負(fù)相關(guān)(<0.05);鵝去氧膽酸與低密度脂蛋白膽固醇及總膽固醇均正相關(guān)(<0.05);僅延胡索酸與年齡正相關(guān)(<0.05);僅-苯乙酰苯丙氨酸與性別有關(guān)(<0.05)。
Figure 3. Heatmap based on the correlations between the 15 significantly altered metabolites and clinical features of subjects with T2D. Alb: albumin; BMI: body mass index; DBP: diastolic blood pressure; DOT: duration of T2D; eGFR: estimated glomerular filtration rate; FBG: fasting blood glucose; Hb: hemoglobin; HbA1c: hemoglobin A1c; HDL-C: high-density lipoprotein cholesterol; LDL-C: low-density lipoprotein cholesterol; KIM-1: kidney injury molecule-1; NGAL: neutrophil gelatinase-associated lipocalin; SBP: systolic blood pressure; SCr: serum creatinine; TC: total cholesterol; TG: triglyceride; UA: uric acid; UACR: urinary albumin-to-creatinine ratio. *P<0.05; #P<0.01.
如圖4所示,15種代謝物ROC曲線下面積(area under the curve, AUC)的中位數(shù)均高于0.700;其中,肉豆蔻腦酸、-乙酰丙氨酸、纈氨酸和正亮氨酸的AUC分別為0.854 (95% CI: 0.705~1.000)、0.840 (95% CI: 0.677~1.000)、0.812 (95% CI: 0.640~0.985)和0.806 (95% CI: 0.630~0.982),表明上述代謝物可作為診斷DKD的新標(biāo)志物。
Figure 4. Fifteen significantly altered metabolites discriminating T2D with DKD from simple T2D samples. AUC: area under the receiver operating characteristic curve. The position of each node corresponds to the AUC on the horizontal axis. The black lines on either side of the node represent the 95% confidence interval of the AUC. The colour of the node indicates the specificity, and the size of the circle indicates the sensitivity.
我國(guó)是世界上糖尿病患病人數(shù)最多的國(guó)家[9],其中20%~40%并發(fā)DKD[10-11],不僅成為終末期腎衰竭的主要病因,而且顯著增高心血管事件和死亡風(fēng)險(xiǎn)[12]。然而,DKD的診斷幾乎停滯不前,仍依據(jù)UACR、eGFR和腎活檢。個(gè)體尿蛋白的排泄變異系數(shù)近40%[13],運(yùn)動(dòng)、發(fā)熱、感染等均造成尿蛋白增高,甚至有超過(guò)半數(shù)的已出現(xiàn)腎功能損害的T2D患者卻始終無(wú)臨床意義的蛋白尿[14]。肌酐水平同樣受運(yùn)動(dòng)、飲食、肌肉總量等影響,采用計(jì)算公式估計(jì)腎小球?yàn)V過(guò)率的準(zhǔn)確性也備受爭(zhēng)議[15]。腎活檢對(duì)不典型的DKD存在漏診風(fēng)險(xiǎn),其在糖尿病患者中的適應(yīng)癥還存在爭(zhēng)議[16-17]。視網(wǎng)膜病變僅可作為診斷依據(jù),但并非必要條件,也與DKD的發(fā)生發(fā)展并不平行[18]。學(xué)者們一直致力于探索新的診斷標(biāo)志物,但其臨床應(yīng)用價(jià)值尚缺乏有力證據(jù)[19-22]。
UACR和eGFR主要反映腎小球?yàn)V過(guò)功能,DKD的病理特征也側(cè)重于腎小球的變化。近年來(lái)的遺傳學(xué)研究表明絕大多數(shù)eGFR相關(guān)基因在腎小管而非腎小球中表達(dá),提示腎小管才是決定腎小球?yàn)V過(guò)功能的關(guān)鍵[23]。腎小管病變促成DKD早期的高濾過(guò)狀態(tài)和細(xì)胞肥大,晚期eGFR下降與腎小管萎縮有關(guān)[24]。然而尚缺乏特異性指標(biāo)評(píng)估DKD的腎小管損傷[25]。腎小管代謝模式轉(zhuǎn)變及代謝中間產(chǎn)物蓄積成為DKD的關(guān)鍵機(jī)制[26],血液及尿液中發(fā)生相應(yīng)變化的代謝物已成為診斷DKD的潛在標(biāo)志物[8, 27-28]。循環(huán)中代謝物的來(lái)源廣泛,與腎臟病變之間難以建立直接的病理生理聯(lián)系;由于組織結(jié)構(gòu)的高度異質(zhì)性及尿液產(chǎn)生過(guò)程的復(fù)雜性,盡管尿液代謝物與腎臟關(guān)系緊密,但仍然無(wú)法精準(zhǔn)的反映DKD的病變部位,因而在現(xiàn)階段的臨床研究中代謝組學(xué)尚未展現(xiàn)出足夠的優(yōu)越性[29]。本研究聚焦腎小管,立足于糖尿病引發(fā)代謝改變的角度,旨在尋找診斷DKD的新策略。
尿液外泌體主要來(lái)源于泌尿道上皮細(xì)胞,如足細(xì)胞、腎小管上皮細(xì)胞等。外泌體的形成經(jīng)歷一系列復(fù)雜調(diào)控,也使其攜帶來(lái)源細(xì)胞的特征標(biāo)志,并實(shí)時(shí)反映該細(xì)胞的生理和病理狀態(tài)[5]。CD13,又稱氨基肽酶N,是腎臟近端小管細(xì)胞和腸道粘膜細(xì)胞刷緣膜的主要成分[30],在子宮內(nèi)膜、脾臟、腦組織及免疫細(xì)胞中也有表達(dá)。既往研究表明尿液外泌體表達(dá)CD13[31-34],并將其作為腎小管外泌體的標(biāo)志蛋白[35]。本研究進(jìn)一步證實(shí)原代腎小管上皮細(xì)胞及其外泌體均表達(dá)CD13,首次借助CD13從尿液中篩選腎小管外泌體,并采用高通量的代謝組學(xué)技術(shù)定量其中的代謝物。與尿蛋白、尿酸等功能學(xué)指標(biāo)和外泌體代謝物之間的廣泛關(guān)聯(lián)相比,KIM-1和NGAL這類腎臟損傷指標(biāo)與代謝物之間并不存在相關(guān)性,可能與腎小管主動(dòng)的產(chǎn)生外泌體與其被動(dòng)的遭受損傷破壞分別屬于不同的生物學(xué)過(guò)程有關(guān),推測(cè)前者在反映腎小管病變上的價(jià)值更高。ROC曲線證實(shí)了腎小管外泌體中代謝物的診斷效能。因此,探索腎小管來(lái)源外泌體中的代謝物可能成為尋找DKD診斷標(biāo)志物的新方向。
單純T2D腎小管外泌體中的代謝物大都較健康對(duì)照顯著增高的變化趨勢(shì)并未在T2D合并DKD時(shí)更加顯著,反之,T2D合并DKD的代謝物較單純T2D卻顯著減少。DKD是否存在腎小管中外泌體的產(chǎn)生、分泌及成分篩選調(diào)控的變化仍不清楚。與尿蛋白、血尿酸顯著相關(guān)的代謝物中,-乙酰丙氨酸和賴氨酸屬于氨基酸類,前者包含的-乙?;鶇⑴c蛋白質(zhì)的翻譯后修飾,血中-乙酰丙氨酸的水平與eGFR及腎臟病有關(guān)[36]。采用尿液賴氨酸等多種代謝物構(gòu)建的代謝分?jǐn)?shù)模型能夠預(yù)測(cè)鹽皮質(zhì)激素受體拮抗劑緩解T2D患者白蛋白尿的療效[37]。酮亮氨酸為有機(jī)酸,是亮氨酸分解代謝的中間產(chǎn)物,血清酮亮氨酸及亮氨酸的增高能預(yù)測(cè)T2D的發(fā)生[38]。十一烷酸和鵝去氧膽酸分別屬于脂肪酸類和膽汁酸類,兩者與T2D或DKD的關(guān)聯(lián)尚未見(jiàn)報(bào)道。此外,外泌體中顯著性改變的代謝物在腎小管中如何變化,潛在的臨床意義為何、是否參與糖尿病腎小管病變的發(fā)生發(fā)展,以及能否成為臨床診治的新靶標(biāo)[39]等,均有待深入研究。
本研究采用病例對(duì)照研究設(shè)計(jì),因而無(wú)法得出因果關(guān)系的結(jié)論。但篩選出的具有統(tǒng)計(jì)學(xué)意義的代謝物及其與臨床特征之間的關(guān)聯(lián)是可靠的,因此可以作為未來(lái)探討DKD發(fā)病機(jī)制的實(shí)驗(yàn)室研究及尋找早期診斷標(biāo)志物的前瞻性研究的基礎(chǔ)。
綜上所述,腎小管外泌體中的肉豆蔻腦酸、-乙酰丙氨酸、纈氨酸、正亮氨酸、十一烷酸、葡萄糖、酮亮氨酸、鵝去氧膽酸、延胡索酸、油酸、-乙酰絲氨酸、-苯乙酰苯丙氨酸、賴氨酸、異戊酸和辛二酸可作為診斷T2D合并DKD的新途徑。
[1]中華醫(yī)學(xué)會(huì)糖尿病學(xué)分會(huì)微血管并發(fā)癥學(xué)組. 中國(guó)糖尿病腎臟病防治指南(2021年版)[J]. 中華糖尿病雜志, 2021, 13(8):762-784.
Microvascular Complications Group of Chinese Diabetes Society. Clinical guideline for the prevention and treatment of diabetic kidney disease in China (2021 edition) [J]. Chin J Diabetes Mellitus, 2021, 13(8):762-784.
[2]中華醫(yī)學(xué)會(huì)腎臟病學(xué)分會(huì)專家組. 糖尿病腎臟疾病臨床診療中國(guó)指南[J]. 中華腎臟病雜志, 2021, 37(3):255-304.
Chinese Medical Association. Expert Group of Chinese Society of Nephrology. Chinese guidelines for diagnosis and treatment of diabetic kidney disease[J]. Chin J Nephrol, 2021, 37(3):255-304.
[3] Elsayed NA, Aleppo G, Aroda VR, et al. 11. Chronic kidney disease and risk management: standards of care in diabetes-2023[J]. Diabetes Care, 2023, 46(Suppl 1):S191-S202.
[4] Colombo M, Raposo G, Théry C. Biogenesis, secretion, and intercellular interactions of exosomes and other extracellular vesicles[J]. Annu Rev Cell Dev Biol, 2014, 30:255-289.
[5] Erdbrugger U, Blijdorp CJ, Bijnsdorp IV, et al. Urinary extracellular vesicles: a position paper by the urine task force of the international society for extracellular vesicles [J]. J Extracell Vesicles, 2021, 10(7):e12093.
[6] Terryn S, Jouret F, Vandenabeele F, et al. A primary culture of mouse proximal tubular cells, established on collagen-coated membranes[J]. Am J Physiol Renal Physiol, 2007, 293(2):F476-F485.
[7] Felicetti F, De Feo A, Coscia C, et al. Exosome-mediated transfer of miR-222 is sufficient to increase tumor malignancy in melanoma[J]. J Transl Med, 2016, 14:56.
[8]石彩鳳, 周陽(yáng), 何愛(ài)琴, 等. 新型尿液代謝標(biāo)志物在糖尿病腎臟病診斷中的價(jià)值[J]. 中華糖尿病雜志, 2022, 14(5):456-464.
Shi CF, Zhou Y, He AQ, et al. Novel urinary metabolite biomarkers in diagnosis of diabetic kidney disease[J]. Chin J Diabetes Mellitus, 2022, 14(5):456-464.
[9] Li Y, Teng D, Shi X, et al. Prevalence of diabetes recorded in mainland China using 2018 diagnostic criteria from the American Diabetes Association: national cross sectional study[J]. BMJ, 2020, 369:m997.
[10] Zhang XX, Kong J, Yun K. Prevalence of diabetic nephropathy among patients with type 2 diabetes mellitus in China: a meta-analysis of observational studies[J]. J Diabetes Res, 2020, 2020:2315607.
[11] Johansen KL, Chertow GM, Foley RN, et al. US renal data system 2020 annual data report: epidemiology of kidney disease in the United States[J]. Am J Kidney Dis, 2021, 77(4 Suppl 1):A7-A8.
[12] Fox CS, Matsushita K, Woodward M, et al. Associations of kidney disease measures with mortality and end-stage renal disease in individuals with and without diabetes: a meta-analysis[J]. Lancet, 2012, 380(9854):1662-1673.
[13] Kdoqi. KDOQI clinical practice guidelines and clinical practice recommendations for diabetes and chronic kidney disease[J]. Am J Kidney Dis, 2007, 49(2 Suppl 2):S12-154.
[14] Oshima M, Shimizu M, Yamanouchi M, et al. Trajectories of kidney function in diabetes: a clinicopathological update [J]. Nat Rev Nephrol, 2021, 17(11):740-750.
[15] Elsayed NA, Aleppo G, Aroda VR, et al. 2. Classification and diagnosis of diabetes: standards of care in diabetes-2023[J]. Diabetes Care, 2023, 46(Suppl 1):S19-S40.
[16] Bermejo S, Pascual J, Soler MJ. The current role of renal biopsy in diabetic patients[J]. Minerva Med, 2018, 109(2):116-125.
[17] Gonzalez Suarez ML, Thomas DB, Barisoni L, et al. Diabetic nephropathy: is it time yet for routine kidney biopsy?[J]. World J Diabetes, 2013, 4(6):245-255.
[18] He F, Xia X, Wu XF, et al. Diabetic retinopathy in predicting diabetic nephropathy in patients with type 2 diabetes and renal disease: a meta-analysis[J]. Diabetologia, 2013, 56(3):457-466.
[19] Ye X, Luo T, Wang K, et al. Circulating TNF receptors 1 and 2 predict progression of diabetic kidney disease: a meta-analysis[J]. Diabetes Metab Res Rev, 2019, 35(8):e3195.
[20] Coca SG, Nadkarni GN, Huang Y, et al. Plasma biomarkers and kidney function decline in early and established diabetic kidney disease[J]. J Am Soc Nephrol, 2017, 28(9):2786-2793.
[21] Looker HC, Colombo M, Hess S, et al. Biomarkers of rapid chronic kidney disease progression in type 2 diabetes[J]. Kidney Int, 2015, 88(4):888-896.
[22] Niewczas MA, Pavkov ME, Skupien J, et al. A signature of circulating inflammatory proteins and development of end-stage renal disease in diabetes[J]. Nat Med, 2019, 25(5):805-813.
[23] Qiu C, Huang S, Park J, et al. Renal compartment-specific genetic variation analyses identify new pathways in chronic kidney disease[J]. Nat Med, 2018, 24(11):1721-1731.
[24] Vallon V, Thomson SC. The tubular hypothesis of nephron filtration and diabetic kidney disease[J]. Nat Rev Nephrol, 2020, 16(6):317-336.
[25] Satirapoj B, Pooluea P, Nata N, et al. Urinary biomarkers of tubular injury to predict renal progression and end stage renal disease in type 2 diabetes mellitus with advanced nephropathy: a prospective cohort study[J]. J Diabetes Complications, 2019, 33(9):675-681.
[26] Tanaka S, Sugiura Y, Saito H, et al. Sodium-glucose cotransporter 2 inhibition normalizes glucose metabolism and suppresses oxidative stress in the kidneys of diabetic mice[J]. Kidney Int, 2018, 94(5):912-925.
[27] Wu IW, Tsai TH, Lo CJ, et al. Discovery of a biomarker signature that reveals a molecular mechanism underlying diabetic kidney disease via organ cross talk[J]. Diabetes Care, 2022, 45(6):e102-e104.
[28] Kwan B, Fuhrer T, Zhang J, et al. Metabolomic markers of kidney function decline in patients with diabetes: evidence from the chronic renal insufficiency cohort (CRIC) study[J]. Am J Kidney Dis, 2020, 76(4):511-520.
[29] Kammer M, Heinzel A, Willency JA, et al. Integrative analysis of prognostic biomarkers derived from multiomics panels helps discrimination of chronic kidney disease trajectories in people with type 2 diabetes[J]. Kidney Int, 2019, 96(6):1381-1388.
[30] Olsen J, Kokholm K, Noren O, et al. Structure and expression of aminopeptidase N[J]. Adv Exp Med Biol, 1997, 421:47-57.
[31] Pisitkun T, Shen RF, Knepper MA. Identification and proteomic profiling of exosomes in human urine[J]. Proc Natl Acad Sci U S A, 2004, 101(36):13368-13373.
[32] Gonzales PA, Pisitkun T, Hoffert JD, et al. Large-scale proteomics and phosphoproteomics of urinary exosomes[J]. J Am Soc Nephrol, 2009, 20(2):363-379.
[33] Raj DA, Fiume I, Capasso G, et al. A multiplex quantitative proteomics strategy for protein biomarker studies in urinary exosomes[J]. Kidney Int, 2012, 81(12):1263-1272.
[34] Oeyen E, Van Mol K, Baggerman G, et al. Ultrafiltration and size exclusion chromatography combined with asymmetrical-flow field-flow fractionation for the isolation and characterisation of extracellular vesicles from urine[J]. J Extracell Vesicles, 2018, 7(1):1490143.
[35] Zhang W, Zhou X, Zhang H, et al. Extracellular vesicles in diagnosis and therapy of kidney diseases[J]. Am J Physiol Renal Physiol, 2016, 311(5):F844-F851.
[36] Sekula P, Goek ON, Quaye L, et al. A metabolome-wide association study of kidney function and disease in the general population[J]. J Am Soc Nephrol, 2016, 27(4):1175-1188.
[37] Mulder S, Perco P, Oxlund C, et al. Baseline urinary metabolites predict albuminuria response to spironolactone in type 2 diabetes [J]. Transl Res, 2020, 222: 17-27.
[38] Zeng Y, Mtintsilana A, Goedecke JH, et al. Alterations in the metabolism of phospholipids, bile acids and branched-chain amino acids predicts development of type 2 diabetes in black South African women: a prospective cohort study[J]. Metabolism, 2019, 95:57-64.
[39] 王艷麗, 劉春花, 潘潔, 等. 基于細(xì)胞代謝組學(xué)的藥物研究方法及應(yīng)用[J]. 中國(guó)病理生理雜志, 2022, 38(12):2258-2267.
Wang Y, Liu C, Pan J, et al. Methods and application of cell metabolomics in drug research[J]. Chin J Pathophysiol, 2022, 38(12):2258-2267.
Application of urinary exosome-based liquid biopsy combined with metabolomics in clinical diagnosis of diabetic kidney disease
SHI Caifeng, LIU Dandan, HE Aiqin, WU Xiaomei, SHEN Xinjia, ZHU Xueting, XUE Ying, YANG Junwei△, ZHOU Yang△
(,,210003,)
To investigate the application of metabolites in urinary exosomes from kidney tubules in the assessment of diabetic kidney disease (DKD).Healthy individuals (=13), simple type 2 diabetes (T2D) patients (=12) and T2D patients with DKD (=12) were enrolled in this cross-sectional study. Demographic and laboratory data were collected. Primary renal tubular epithelial cells were cultured. Exosomes were extracted by ultracentrifugation. Exosomes expressing cluster of differentiation 13 (CD13) were enriched by immunomagnetic beads. The morphology of exosomes was tracked by transmission electron microscopy and nanoparticle tracking analysis. Western blot and immunohistochemical staining were applied. Kidney injury molecule-1 and neutrophil gelatinase-associated lipocalin were detected by ELISA. Metabolites in exosomes were quantified by ultraperformance liquid chromatography coupled to tandem mass spectrometry. Partial least squares discriminant analysis was used for multidimensional modelling. The Wilcox test was used for comparison of metabolite data between two groups, and the Kruskal-Wallis test was used for comparison between multiple groups. Spearman correlation analysis was used to analyse the correlation between metabolites and clinical indicators. The receiver operating characteristic (ROC) curve was used to evaluate diagnostic efficacy.Expression of CD13 was detected in urinary exosomes. Immunostaining showed that CD13 was located in the apical membrane of kidney tubules. Moreover, CD13 was expressed in primary renal tubular epithelial cells and their exosomes. A total of 144 metabolites were detected in urinary exosomes expressing CD13 by targeted quantitative metabolomics. Fifteen metabolites with statistical significance were selected between the simple T2D group and the T2D with DKD group (<0.05). These metabolites included lysine,-acetylalanine,-acetylserine, norleucine,-phenylacetylphenylalanine, valine, chenodeoxycholic acid, glucose, myristoleic acid, oleic acid, suberic acid, undecanoic acid, fumaric acid, ketoleucine, and isovaleric acid.-acetylalanine, undecanoic acid, ketoleucine, lysine and chenodeoxycholic acid in urinary CD13+exosomes were all significantly correlated with urinary albumin and serum uric acid levels in T2D patients (<0.05 or<0.01). The areas under the ROC curves of myristoleic acid,-acetylalanine, valine, and norleucine were 0.854 (95% CI: 0.705~1.000), 0.840 (95% CI: 0.677~1.000), 0.812 (95% CI: 0.640~0.985), and 0.806 (95% CI: 0.630~0.982), respectively.There are significant differences in metabolites encapsulated in kidney tubular cell-derived exosomes between simple T2D patients and T2D patients with DKD. The 15 metabolites with statistical significance could become novel biomarkers for the clinical diagnosis of DKD.
diabetic kidney disease; exosome; metabolomics; kidney tubules
1000-4718(2023)07-1244-09
2023-05-12
2023-06-26
025-58509741; E-mail: zhouyang@njmu.edu.cn(周陽(yáng)); jwyang@njmu.edu.cn(楊俊偉)
R363; R587
A
10.3969/j.issn.1000-4718.2023.07.011
[基金項(xiàng)目]國(guó)家自然科學(xué)基金資助項(xiàng)目(No. 82270760);江蘇省自然科學(xué)基金資助項(xiàng)目(No. BK20201497)
(責(zé)任編輯:宋延君,李淑媛)