Single-cell atlas reveals multi-faced responses of losartan on tubular mitochondria in diabetic kidney disease

Zhu, Zhen; Luan, Guangxin; Wu, Song; Song, Yiyi; Shen, Shuang; Wu, Kaiyue; Qian, Shengnan; Jia, Weiping; Yin, Jun; Ren, Tao; Ye, Jianping; Wei, Li

doi:10.1186/s12967-025-06074-5

Research
Open access
Published: 21 January 2025

Single-cell atlas reveals multi-faced responses of losartan on tubular mitochondria in diabetic kidney disease

Zhen Zhu¹^na1,
Guangxin Luan²^na1,
Song Wu³^na1,
Yiyi Song¹^na1,
Shuang Shen⁴,
Kaiyue Wu⁴,
Shengnan Qian⁴,
Weiping Jia⁴,
Jun Yin⁵,
Tao Ren¹,
Jianping Ye⁴ &
…
Li Wei⁵

Journal of Translational Medicine volume 23, Article number: 90 (2025) Cite this article

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Abstract

Background and Objective

Mitochondria are crucial to the function of renal tubular cells, and their dynamic perturbation in many aspects is an important mechanism of diabetic kidney disease (DKD). Single-nucleus RNA sequencing (snRNA-seq) technology is a high-throughput sequencing analysis technique for RNA at the level of a single cell nucleus. Here, our DKD mouse kidney single-cell RNA sequencing conveys a more comprehensive mitochondrial profile, which helps us further understand the therapeutic response of this unique organelle family to drugs.

Methods

After high fat diet (HFD), mice were intraperitoneally injected with streptozotocin (STZ) to induce DKD, and then divided into three subsets: CON (healthy) subset, DKD (vehicle) subset, and LST (losartan; 25 mg/kg/day) subset. Divide HK-2 cell into LG (low glucose; 5 mM) and HG (high glucose; 30 mM) and HG + LST (losartan; 1 µ M) subsets. snRNA-seq was performed on the renal tissues of LST and DKD subset mice. To reveal the effects of losartan on gene function and pathway changes in renal tubular mitochondria, Gene Ontology (GO) enrichment analysis and GSEA/GSVA scoring were performed to analyze the specific response of proximal tubular (PT) cell mitochondria to losartan treatment, including key events in mitochondrial homeostasis such as mitochondrial morphology, dynamics, mitophagy, autophagic flux, mitochondrial respiratory chain, apoptosis, and ROS generation. Preliminary validation through in vitro and in vivo experiments, including observation of changes in mitochondrial morphology and dynamics using probes such as Mitotracker Red, and evaluation of the effect of losartan on key events of mitochondrial homeostasis perturbation using electron microscopy, laser confocal microscopy, immunofluorescence, and Western blotting. Detection of autophagic flux in cells by transfecting Ad-mCherry-GFP-LC3B dual fluorescence labeled adenovirus. Various fluorescent probes and energy detector are used to detect mitochondrial apoptosis, ROS, and respiration of mitochondrion.

Results

Through the single-cell atlas of DKD mouse kidneys, it was found that losartan treatment significantly increased the percentage of PT cells. Gene Ontology (GO) enrichment analysis of differentially expressed genes showed enrichment of autophagy of mitochondrion pathway. Further GSEA analysis and GSVA scoring revealed that mitophagy and other key mitochondrial perturbation events, such as ROS production, apoptosis, membrane potential, adenosine triphosphate (ATP) synthesis, and mitochondrial dynamics, were involved in the protective mechanism of losartan on PT cells, thereby improving mitochondrial homeostasis. Consistent results were also obtained in mice and cellular experiments. In addition, we highlighted a specific renal tubular subpopulation with mitophagy phenotype found in single-cell data, and preliminarily validated it with co-localization and increased expression of Pink1 and Gclc in kidney specimens of DKD patients treated with losartan.

Conclusions

Our research suggests that scRNA-seq can reflect the multifaceted mitochondrial landscape of DKD renal tubular cells after drug treatment, and these findings may provide new targets for DKD therapy at the organelle level.

Introduction

Mitochondria are known as “cellular dynamics” and produce ATP through oxidative phosphorylation (OXPHOS), synergistically regulating various cellular functions such as respiration, calcium homeostasis, and programmed cell death. Mitochondria have plasticity and can dynamically and reversibly adapt to energy, environment, and other pressures, maintaining cellular homeostasis [1,2,3,4,5,6,7]. Under external stimuli such as reactive oxygen species (ROS) stress, nutrient deficiency, and cellular aging, mitochondrial DNA (mtDNA) mutations gradually accumulate, and the intracellular mitochondrial membrane potential decreases and depolarization damage occurs. ROS refers to reactive oxygen species clusters, including superoxide anions, hydrogen peroxide, hydroxyl radicals, etc. In cells, ROS is mainly produced by mitochondrial respiratory chain and other enzymes. ROS further damages mitochondria, alters the expression of apoptosis related proteins such as cytochrome C, Bax1, Caspase3, and induces cell apoptosis. In order to maintain mitochondrial and cellular homeostasis and prevent damaged mitochondria from damaging cells, damaged mitochondria are specifically wrapped in autophagosomes and fused with lysosomes to complete lysosomal degradation, a process called mitophagy. The dynamic and continuous process of mitophagic flux includes the formation of mitophagosomes, fusion with lysosomes, and degradation of mitochondria.

Mitochondrial dynamics involves a continuous fusion fission cycle that alters mitochondrial morphology and function, from elongated interconnected mitochondria (i.e. mitochondrial fusion) to circular fragmented mitochondria (such as mitochondrial fission). This dynamic change leads to the mixing and exchange of mitochondrial contents, cristae alter, and regulation of and mitochondrial respiration. Many proteins are involved in the regulation of mitochondrial dynamics, among which Drp1 and Fis1 are important mediators of mitochondrial division, while MFN2 and OPA1 are the main mitochondrial fusion proteins [8]. Due to the abundant mitochondria in renal tubular epithelial cells, the mitochondrial dynamics of renal tubular epithelial cells play an important role in the process of DKD renal tubular injury [9, 10]. Compared with diabetes patients without kidney disease, there are specific mitochondrial fragments in renal tubular cells of DKD patients, but not in podocytes [11]. The expression of MFN2 in the kidneys of DKD rats induced by STZ was significantly reduced, and the overexpression of MFN2 inhibited the activation of P38 and the accumulation of ROS, thereby alleviating the pathological damage to the kidneys in DKD [12]. In addition, studies have shown that STZ induced mitochondrial fragmentation in DKD mice is significantly increased, accompanied by a decrease in MFN2 expression and an increase in renal cell apoptosis. Overexpression of antioxidant enzyme DsbA-L can increase MFN2 expression, alleviate mitochondrial fragmentation, and reduce renal tubular cell apoptosis [13]. These findings indicate the presence of mitochondrial dynamics abnormalities in DKD, and improving mitochondrial dynamics can alleviate kidney damage. However, the specific mechanism of mitochondrial dynamics regulation has not been fully elucidated, and the relationship between mitochondrial dynamics abnormalities and DKD kidney damage has not been clearly explored.

Despite the complexities in understanding mitochondrial dysfunction due to its multifaceted nature and elusive phenotype threshold, mitochondria remain a crucial therapeutic target, particularly as most renal mitochondria are found in the proximal tubules and TAL cells at the outer medulla-cortical medulla junction [14,15,16,17,18]. Mitochondria are closely related to the occurrence and development of renal tubular injury in DKD [19, 20]. For example, in DKD, hyperglycemia may lead to abnormal morphology and dynamic changes of renal tubular mitochondria, thus affecting the function of mitochondria, leading to the reduction of ATP production. Damaged mitochondria may be cleared through mitophagy to maintain cell stability. However, when the mitophagy is inhibited or blocked, the damaged mitochondria cannot be cleared in time, which may further aggravate the oxidative stress and inflammatory reaction of the kidney and promote the progress of DKD [19,20,21,22,23].

In recent decades, renin-angiotensin system (RAS) blockers have been the main treatment for DKD patients [19, 24,25,26]. Losartan is a type of angiotensin II receptor blocker that can reduce the increase in glomerular filtration pressure caused by angiotensin II, while also reducing kidney inflammation, fibrosis, and improving glomerular permeability [27]. Losartan is more accurate in the clinical efficacy of DKD, which can effectively reduce proteinuria and prevent structural lesions. For losartan, the potential renal protection mechanism involved in mitochondrial biosynthesis and mitochondrial respiratory function has been proposed [28, 29], but there is little research on whether losartan affects mitochondria in the treatment of DKD. Fan et al. found that losartan treatment can inhibit differential expression of mitochondrial ATP synthase subunits [30]. In the glomerulus of diabetes KKAy mice, it may play a renal protective role by reducing the production of mitochondrial ROS in the glomerulus and inhibiting oxidative stress. Su Jun et al. found that losartan increased podocyte mitophagy in STZ induced diabetes rats. This effect was accompanied by increased expression of LC3, PINK1, Parkin in podocytes of DKD rats, and decreased expression of p62 [31]. Our previous research also found the protective effect of losartan on renal tubular mitochondrial function in DKD mouse model [32]. We believe that its therapeutic mechanism in DKD is multifaceted and requires comprehensive exploration.

single-cell RNA sequencing (scRNA-seq) technology is a high-throughput sequencing analysis technique for RNA at the single-cell level [33, 34]. Traditional bulk RNA sequencing involves sequencing RNA from tissue samples composed of many cells to obtain average gene expression information of the cell population. scRNA-seq can distinguish gene expression heterogeneity between individual cells. The typical scRNA-seq workflow consists of three key stages: library generation, preprocessing, and post-processing. The library generation process includes isolation of individual cells or nuclei, mRNA capture, and sequencing. Preprocessing includes preliminary analysis of data counting and cleaning. In post-processing, dimensions are reduced, gene features and cell types are identified, and visualizations can be generated [35]. scRNA-seq provides the ability to examine different cell types and conditions, uniquely expressed genes or pathways, cell differentiation pathways, intercellular interactions, and gene regulation or co expression in different diseases. This technology provides a new perspective for the development and treatment of various diseases, including DKD [36,37,38,39,40,41].

scRNA-seq can be specifically applied to study mitochondrial dynamics by analyzing gene expression related to mitochondrial fission or fusion [42], mitochondrial DNA mutations [43], monitoring the quantity and quality of mitochondria [44], exploring intercellular mitochondrial transmission [45], and combining with other omics data. It is very important to study cellular heterogeneity such as mitochondrial dynamics at the single-cell level [46], which can reveal the potential mechanisms of mitochondrial dynamics changes in disease progression or therapeutic response, and further validate the results through in vivo and in vitro experiments. In addition, 50% of overall heterogeneity may come from two extreme situations: (1) each cell in the population has approximately 50% heterogeneity, or (2) half of the cells contain 100% wild-type mtDNA, while the other half contain 100% mutant mtDNA [42]. Currently, there is limited research on the use of scRNA-seq technology to analyze the effects and mechanisms of drugs on DKD. Wu et al. examined the scRNA-seq data from the kidneys of db/db mice treated with ARB and SGLT2 inhibitors, uncovering distinct mechanisms of action for these medications and identifying a novel group of renal tubular cells potentially crucial for kidney damage and healing [47]. A new investigation utilizing snRNA-seq data from one million kidney cells in DKD mice revealed varied responses among kidney cells to treatment, with the combined therapy of ARBs and SGLT2i proving more successful in reversing DKD-associated transcriptional alterations [48]. Balzer et al. performed scRNA-seq on the kidneys of ZSF1 rats and found that pharmacological soluble guanylate cyclase activation has significant benefits for DKD, and is mechanistically related to improving oxidative stress regulation, thereby enhancing the downstream action of cGMP [49]. The scRNA-seq technology not only provides a comprehensive atlas of renal biology at the single cell resolution, but also enables a complete and in-depth understanding of DKD from the perspective of organelle biology [50,51,52,53]. At present, there is no snRNA-seq to detect the multi-faceted perturbation of mitochondria in renal tubular cells of DKD with losartan treatment. In this study, we found and preliminarily confirmed the improvement effect of losartan treatment on mitochondrial dynamics, respiratory function, ATP production, ROS level, mitophagy and other key events related to mitochondrial homeostasis perturbation in renal tubules of DKD mice through snRNA-seq. The experiment on DKD mouse model preliminarily verified that losartan treatment promoted mitophagy in renal tubules, improved autophagic flux, and improved other key events of mitochondrial perturbation. Consistent results were also obtained in HK-2 cell in vitro experiments. We also found that after treatment with Losartan, a PT subcluster that plays a major role in kidney injury repair was significantly reduced and transformed into two subclusters with both anti apoptotic and oxidative stress phenotypes, as well as improved metabolism and mitophagy phenotypes. Finally, a renal tubular subpopulation with special mitophagy phenotype and increased co-localization expression of Pink1 and Gclc was found in kidney specimens of DKD patients treated with losartan, which also exhibited antioxidant phenotype.

Materials and methods

Mouse model of DKD

Animal research should be conducted following the Sixth People’s Hospital Ethics Committee guidelines (approval no.: 2020-022). Male C57BL/6 mice, six weeks old and weighing between 18 and 22 g, were sourced from Gempharmatech Co. Ltd. in Jiangsu, China, and kept in SPF-grade conditions at Shanghai Sixth People’s Hospital. Following a two-week acclimation period, the control group of mice (n = 8) received a standard diet containing 13.5% of calories from fat, provided by Shanghai Slac Laboratory Animal Co. Ltd. A group of 24 diabetic mice were given a diet high in fat (60% of calories from fat) for 8 weeks, followed by three intraperitoneal injections of Streptozotocin (40 mg/kg, administered every other day). Successful type 2 diabetes induction was confirmed by random blood glucose levels continuously more than 16.7mM for two days in a row and the presence of an albuminuric state. We randomly split the mice into two groups: one group received a phosphate-buffered saline (PBS) solution (DKD, n = 8), while the other was given an oral dose of 25 mg/kg of losartan daily (LST, n = 8). Study on dose optimization of losartan in “Supplementary Materials” and “Table S6”. Both treatments were administered daily via oral gavage for 12 weeks, and the mice were sacrificed at 28-weeks-old.

Losartan (Merck Sharp & Dohme, Australia) was prepared in PBS. STZ (Sigma Aldrich, USA) was solubilized in sodium citrate buffer of 0.1 M.

Evaluation of the physiology and functions of the kidneys

Urine samples and serum were collected before and after losartan treatment. Urine creatinine was detected using a QuantiChrom Creatinine Kit. As mentioned earlier, the LBIS Mouse Albumin ELISA Kit from Shibayagi was utilized to quantify albumin in urine [54]. The ACR (µg/mg) was determined by taking the product of urine albumin (µg/dl) and urine creatinine (µg/dl). The fasting blood glucose level was monitored twice a week, as well as the body weight. A portable blood glucose meter was used to check glucose levels in the blood.

Assessment of the kidney’s morphological features

Tubular and glomerular damage scores were calculated as described in reference [55]. Briefly, a semiquantitative criteria (0 to 4) was used to evaluate tubulointerstitial damage, including atrophy, casts, tubular dilation, inflammation, and interstitial fibrosis. Another semi-quantitative criterion (0 to 4) was used for assessing glomerular injury.

Morphological evaluation by toluidine blue staining and transmission electron microscopy

Five minutes of toluidine blue staining in 30% ethanol were performed on EPON sections. Microscopes with phase contrast inverted were used to observe the reaction after stopping it with double-distilled water, drying, and sealing it. The staining revealed purple mitochondria against a light blue background.

Kidney tissue specimens (1 mm³) were first preserved with an electron microscopy fixative from Servicebio and then sent at room temperature to the Instrumental Analysis Center at Shanghai Jiao Tong University. Upon arrival, the samples underwent fixation in 1% osmium tetroxide, followed by a graded ethanol dehydration process. Subsequently, the specimens were encased in epoxy resin and cut into extremely thin Sects. (60–80 nm) with a Leica UC7 ultramicrotome.

Initially, electron transmission microscope (TEM) was employed to examine morphological alterations in kidney slices, specifically comparing the deformations of kidney tubule and glomerulus among different subsets, including thickening of the basement membrane, dilation of mesangium, fusion and loss of the foot processes, and damage to the split diaphragms. Second, TEM images were taken to assess morphological changes in mitochondria. Fragmented mitochondria appeared as sphere-shaped structure less than 1 µM in length, while normal mitochondria are filamentous and longer than 2 µM. Additionally, high-magnification TEM was used to observe autophagic vacuole, autophagosome, mitophagosome, and autolysosome in renal tubules [7].

snRNA-seq and data quality control and analysis

Cell nucleus separation uses a cell nucleus separation kit (Shanghai biotechnology corporation, 52009-10). According to the instructions of the kit, frozen kidney tissue samples are quickly added to the lysis solution, ground, lysed, filtered, centrifuged, and resuspended. The cell nucleus suspension is stained with tryptophan blue for cell nucleus counting and microscopic observation, and the concentration of the cell nucleus suspension is adjusted. The Chromium Chip G was loaded with cell suspension, 10X barcode gel beads, and oil in distinct compartments, creating GEMs using the 10X Genomics Chromium platform. GEM was transferred into the PCR machine for reverse transcription. The gel beads were embedded with 30 nucleotide oligo dT primers for reverse transcription, enabling the poly-A RNA within cells to be reverse transcribed into the initial cDNA strand, incorporating Barcode and UMI data. Purification of the first strand cDNA using magnetic beads, followed by PCR amplification of the purified cDNA. The concentration of cDNA was measured with Qubit, and the fragment size was analyzed using the Agilent 2100. Following cDNA amplification, the ideal fragment was chosen through enzyme digestion and magnetic bead filtration. The cDNA library containing P5 and P7 connectors was constructed by end repair, addition of A, and connection of Read2 sequencing primers. The library was purified using magnetic beads, followed by concentration detection using Qubit and fragment size detection using Agilent 2100. Follow the Illumina User Guide to generate clusters and hybridize the first sequencing primer. Load the flow cell onto the computer and choose the paired-end program for dual-end sequencing. The Illumina software will control the sequencing and perform real-time data analysis.

Using “Cellranger” to transfer scRNA-seq data from a “fastq” file to a software for cell expression matrix, with mm10 as the reference genome. The threshold for filtering each sample is determined by analyzing the distribution statistics of UMI, genes, mitochondria, and ribosomes for each sample. Given the elevated levels of mitochondria suggesting cellular death, and taking into account the substantial baseline mitochondria and mitochondrial impairment in PT cells in DKD, we have established a maximum mitochondria proportion threshold. In summary, cells are filtered if any of these conditions are met: genes expressed in fewer than 3 cells, cells with fewer than 200 genes, cells with over 5000 genes, mitochondrial genes exceeding 50%, or red blood cell gene ratios above 1%. Next, we will conduct multi-sample integration analysis, first identifying the Top 2000 variant gene. For subsequent integration analysis, each sample identifies the 2000 genes with the greatest variability, determined by their mean and dispersion (variance/mean). Apply the Louvain method to conduct cluster analysis on standardized data. Set the resolution parameter to 0.5 to optimize the model. UMAP algorithm is used for data visualization. From the obtained 10 clusters, we extracted PT-S1, PT-S2, and PT-S3. Since the resolution parameter determines the number of clusters obtained from downstream clustering analysis, that is, increasing or decreasing the resolution to increase or decrease the number of clusters obtained from clustering analysis. We increased the resolution parameter by 0.3 again, resulting in PT1 ~ PT7 subgroups. Analyze cluster marker genes with the Wilcoxon algorithm, scoring them using a one-vs-rest method. Select genes with high expression specificity (logFC > 0.25) and significant expression in at least 20% of cells per cluster. Employing “SingleR” software for single-cell sequencing and established marker genes to identify cell types. The transcriptome difference between DKD subset and LST subset was analyzed by using the “FindMarkers” function in the Seurat package. During the analysis, the standard criteria (| log2 FC |>0.25, P < 0.05) were chosen, and the Wilcoxon rank-sum test was applied. The “Clusterprofiler” package (v4.0.5) was utilized for GO enrichment analysis, focusing on ‘Biological process’ and applying Benjamin Hochberg’s method for multiple testing correction. Genes with FDR corrected P values less than 0.01 are considered significantly enriched. Build a mouse gene set using the “msigdbr” package and upload it to the GSEA official website for online GSEA analysis. Finally, load GSVA and ggplot2, extract signatures from the constructed mouse gene set, and then score the gene set. Cell trajectory analysis was conducted with Monocle. Differential expression analysis identified the top 1000 DEGs with the lowest q-values, which were used to construct the trajectory via “setOrderingFilter”. The process of dimensionality reduction was executed with the “reduceDimension” method. The initial state, characterized by the highest number of control cells, was identified, and cells were sequenced along the path using the “orderCells” method.

Treatment and culture of cells

HK-2 cells underwent a 24-hour incubation in three different environments: low glucose (LG; 5 mM), high glucose (HG; 30 mM), and high glucose with losartan (1 µM), resulting in LG, HG, and HG + LST groups. This experimental setup was designed to simulate high glucose-induced cellular injury and evaluate the potential therapeutic effects of losartan. Additionally, HK-2 cells were exposed to HG or LG for 48 h to study time-dependent effects, with a mannitol control subset included. We carried out an in vitro study to explore how losartan influences autophagic flux and mitophagy in tubules by pre-treating HK-2 cells under different conditions. HK-2 cells were pretreated with 40 µM chloroquine (Sigma Aldrich) for 4 h to pharmacologically inhibit autophagy.

Detection of morphology, ATP activity, and membrane voltage potential (ΔΨm) of mitochondria using fluorescent probes

To study mitochondrial morphology, cells were pretreated with low glucose, high glucose, or high glucose and losartan, followed by MitoTracker Red staining. After fixation and permeabilization, confocal microscopy assessed mitochondrial shapes in ten randomly selected regions (over 100 cells). Mitochondrial membrane potential (ΔΨm) was evaluated by treating cells with the same conditions, incubating them with 10 nmol/L TMRE dye for 10 min [56]. A laser confocal microscopy was used to measure ΔΨm at 582 nm.

Membrane potential of kidney tissue was measured by isolating mitochondria, mixing them with Rhodamine 123 solution, and evaluating the ΔΨm at 582 nm using a laser confocal microscopy. The average fluorescence intensity was measured in 10 randomly chosen areas utilizing Carl Zeiss software.

The ATP concentrations in mitochondria from HK-2 cells and renal tissue were determined using Byeotime’s ATP assay kit, adhering to the provided guidelines.

Assay for immunofluorescence

Samples of kidney tissue were obtained from biopsies of DKD patients, both treated and untreated with losartan, in accordance with the guidelines sanctioned by the Institutional Review Board at Shanghai Jiao Tong University Affiliated Sixth People’s Hospital. A 4 mm cryostat section was prepared on a slide, subsequently dried, and rehydrated using phosphate-buffered saline. Separately, we processed sections of mouse and human kidneys paraffin-embedded at a thickness of 3 mm by dewaxing and rehydration, followed by blocking with bovine serum albumin (BSA). Afterward, each segment was treated with primary antibodies against Drp1 (1:250, ab184247), Mfn2 (1:300, ab124773), LC3 (1:500, ab63817), and SQSTM1 (1:500, ab109012). Finally, we used Alexa Fluor-conjugated secondary antibodies for incubation.

HK-2 cells (approximately 2 × 10^5 cells/mL) were inoculated onto sterile slides in a plate with 24 wells. Following the intervention, the slides were gently shaken and washed. Next, the cells were treated with 500 µL of a 4% paraformaldehyde solution and left to incubate for 30 min to ensure fixation. After fixing, the cells were treated with 0.3% Triton X-100 (500 µL) for 15 min to permeabilize them. Subsequently, the cells were incubated with 300 µL of 1% BSA at ambient temperature for an hour to block them. Following the blocking step, an antigen-antibody incubation was performed using antibody targeting SQSTM1 (1:500, ab109012), LC3 (1:500, ab63817), Drp1 (1:250, ab184247), and Mfn2 (1:300, ab124773). Ultimately, the cells were labeled with Mitotracker Red and subsequently treated with a secondary antibody to enable the observation of mitophagy in a controlled environment. The samples were subsequently treated with 4′,6-Diamidino-2-phenylindole (DAPI) from Invitrogen, USA, and left to incubate in the dark at ambient temperature. Fluorescence signals were observed using a confocal microscopy. Number of cells exhibiting punctate LC3, as well as intensity of colocalization with Drp1, SQSTM1, Mfn2, LC3, and Mitotracker Red, were analyzed across a minimum of ten randomly selected fields of view (comprising over 100 cells per subset) using Carl Zeiss software.

Apoptosis and superoxide production detection

Apoptotic death and reactive oxygen species levels were assessed utilizing previously established methodologies [57]. Cells on slide glass and paraffin slices of renal tissue were subjected to staining utilizing Beyotime’s TUNEL apoptosis assay kit. Mitochondrial superoxide level was detected with MitoSOX Red (Invitrogen, USA) following the instructions. 2’,7’-dichlorodihydrofluorescein diacetic acid (h2-dcfda, Invitrogen) and dihydroethidium (DHE, Sigma Aldrich) were used to assess ROS generation in renal section and HK-2 cell via confocal microscope. Fluorescence intensity was measured by photographing ten random fields (approximately 200 cells per subset).

Mitochondrial respiration assay

Mitochondria were extracted from homogenized mouse kidney tissue using an MSE buffer containing 0.5% bovine serum albumin (BSA), 1 mM ethylene glycol tetraacetic acid (EGTA), 70 mM sucrose, and 210 mM mannitol, following a previously described protocol [58]. Briefly, the tissue fragments were homogenized and centrifuged at 800 × g, 8000 × g, and 8000 × g for 10 min, respectively, to obtain purified mitochondria to yield purified mitochondria.

An Agilent Technologies Seahorse XFe24 Analyzer was used to quantify mitochondrial oxygen consumption rate (OCR) in a 24-well plate format, with each well containing 5 µg of renal mitochondria. Centrifuge the cell plate at 2200 ×G for 20 min at 4℃ to isolate mitochondria. For the coupling experiment, employ 2 µM rotenone and 10 mM succinate as MAS components. Administer in order 4 mM ADP, 2 µM oligomycin, 4 µM FCCP, and 4 µM antimycin A to the cell culture dish.

To conduct the mitochondrial stress test, HK-2 cells were grown in RPMI 1640 medium with 5% fetal bovine serum and seeded at a density of 2 × 10^4 cells per well in 24-well Seahorse plates. The Seahorse XFe24 measured the changes in dissolved oxygen. Once washed and cultured within basic mediums for 60 min, an OCR test kit with mitochondrial stress testing was used. The oxygen consumption rate of HK-2 cells was measured following consecutive injections of Oligomycin A (1 M), FCCP (2 M), and a combination of Rotenone and Antimycin A (0.5 M).

Western blotting

Total cell lysates and cytoplasmic extracts were analyzed via western blotting [59]. Proteins were isolated via 10–15% SDS-PAGE, moved onto a PVDF membrane, and then blocked with 5% BSA. The membrane was then incubated overnight at 4℃ with various primary antibodies. The next day, the membrane was treated with a secondary antibody (β-actin 1:5000, ab8227) at ambient temperature for one hour. The signal was obtained using the chemiluminescence-based detector and captured with the CCD camera. Analyze band intensity through ImageJ.

Primary antibodies used contain LC3 (1:2000, ab192890), Atg5 (1:5000, ab108327), VDAC1 (1:5000, ab154856), SQSTM1, (1:10000, ab109012), TOMM20 (1:5000, ab186735), Bax1 (1:2000, ab32503), Beclin (1:2000, ab207612), Mfn2 (1:2000, ab124773), Drp1 (1:1000, ab184247), cytochrome c (1:5000, ab133504), Opa1 (1:5000, ab157457), and caspase-3 (1:5000, ab32351).

Autophagic flux analysis

Autophagic flux was measured as described earlier [60]. Adenovirus Adplus-mCherry-GFP-LC3B was transfected into HK-2 cell for a duration of 2 days, then treated the cells with low glucose, high glucose or losartan for 24 h, and CQ (40 µM) for 4 h. Yellow and red spots were examined utilizing confocal microscopy across a minimum of 10 microfields per experiment.

Statistical analyses

Data are described as mean ± SE. A two-tailed t-test analyzed the two subsets, while differences among subsets were assessed using one-way ANOVA with Bonferroni post hoc test. A p-value below 0.05 was deemed to indicate statistical significance.

Results

Normalization of renal function and morphology in mice with DKD by losartan

The control mice were maintained on standard diet (CON subset, n = 8). DKD was induced in the mice through the administration of a high-fat diet (HFD) followed by streptozotocin (STZ) treatment, and then treated with PBS (DKD subset, n = 8) or losartan (LST subset, n = 8). Regularly record the urinary albumin creatinine ratio (ACR) and basic parameters. Compared with control mice, DKD mice have heavier weight, higher fasting blood glucose, more significantly increased urinary ACR (Fig. 1A, B, D and E), and slightly increased serum creatinine levels, but not statistically significant (Fig. 1C). After treatment with losartan, urinary ACR levels significantly decreased (Fig. 1D), while the decrease in fasting blood glucose concentrations and serum creatinine level didn’t reach statistical significance (Fig. 1B and C).

The kidney volume of the DKD subset was significantly increased compared to the CON subset, and larger than that of the losartan treatment subset (Fig. 1E). The brush edge of the renal tubules in the DKD subset showed PAS staining positivity, with necrosis of the renal tubular epithelium, glomerular hypertrophy, increased mesangium matrix, and inflammation. Renal tubular necrosis was significantly reduced in the LST subset (Fig. 1F). Electron transmission microscope confirmed that losartan treatment improved expansion of mesangium matrix, thickening of basement membrane, and disappearance and fusion of foot processes in DKD mouse (Fig. 1G). Figure 1H and I show the corresponding injury scores of glomeruli and tubules.

Single-cell landscape of DKD kidney with or without losartan treatment

According to the steps described in the experimental method and the process shown in Fig. 2A, we mixed the kidneys of 8 losartan treated DKD mice (LST group) into an average of 2 samples, while mixing the kidneys of 8 untreated DKD control mice (DKD group) into an average of 2 samples. Then, we performed snRNA-seq on a total of 4 samples. In RStudio software, data filtering such as quality control and screening (Figure S1A-C) was performed, and then “LogNormalize” and “Scale data” were used to generate a total of 38,699 single-cell nuclear transcriptomes (Fig. 2B). Load the Seurat software package, collect all samples together, eliminate heterogeneity between multiple samples using anchoring method (Figure S1D-H), select 2000 highly variable genes (Figure S1I), and use non-linear dimensionality reduction (UMAP: Unified Manifold Estimation and Projection) to identify a total of 10 independent cell clusters (Fig. 2C-D). According to the known marker genes (Fig. 2E), 10 different cell types include three segments of proximal tubule cells (S1-S3) (20699), podocytes (733), mesangial cells (4519), distal tubules (5484), loops (1199), collecting tubes (1210), one immune cell type (1093) and one endothelial cell type (3294), while the heat map represents the top 10 differentially expressed genes (DEG: the top 3 in text labeling) with the highest expression in each annotation type of cell (Fig. 2F). Table S1 provides a complete list of marker genes in each cluster. Subsequently, in order to determine the main cell type changes caused by losartan treatment, the number changes of 10 cell clusters in the LST group and DKD group were compared. As shown in Fig. 2D, proximal tubular cells (PT) (including PT-S1, PT-S2, PT-S3) are the main component of all cell clusters, accounting for a large proportion of all cells. Compared with the DKD group mice, the percentage of PT cells in the LST group increased, especially in the PT-S1 and PT-S3 cell clusters. On the contrary, the percentage of renal macrophages in the LST group significantly decreased. Although there was a difference in the number of cells in other cell clusters between the two groups, it lacked statistical significance. The effect of losartan treatment on PT cells and renal macrophages is more prominent.

Specific response of PT cells to losartan treatment

PT cells are the main component in our single-cell atlas and show significant quantitative changes in response to losartan treatment. In addition, considering the direct action of losartan on PT [61], we focused on analyzing the specific response of PT cells to losartan treatment for DKD. We compared the differentially expressed genes (DEGs) between the DKD group and the LST group (Fig. 3A, Figure S4A), and then determined how losartan alters these DEGs. Table S2 provides a complete list of each group of DEGs. Among 262 DEGs (Fig. 3A), 141 genes were down-regulated by losartan treatment, including Dscan, Bcl2a1a, Prlr, Fancd2, C3, Tlr2, Fcgr1, Child1, Fndc4, Dscaml1, etc. Enrichment analysis of Gene Ontology (GO) terms showed that losartan treatment enriched regulation of apoptotic signaling pathway, response to oxidative stress, regulation of inflammatory response, regulation of fibroblast proliferation, and regulation of reactive oxygen species metabolic process (Fig. 3B, Figure S2A). Figure S2B illustrates the functional grouping of GO pathways enriched with down-regulated DEGs. Among all enriched pathways, the regulation of apoptotic signaling pathway and regulation of reactive oxygen species metabolic process is the core, which interacts extensively with other pathways such as response to oxidative stress, regulation of inflammatory response, and regulation of fibroblast proliferation (see Figure S2C). Figure S2D identifies closely related constituent genes in the network through heat map. Then, we constructed a mouse gene set using the “msigdbr” package and uploaded it to the official GSEA website for online GSEA analysis, further verifying the results of GO enrichment analysis (Fig. 3C). These results indicate that losartan treatment plays a crucial role in PT cell resistance to inflammation, oxidative stress, cell apoptosis, reactive oxygen species metabolism, and fibrosis. Losartan treatment up-regulated 121 DEGs (Fig. 3A), including Fmo2, Slc8a1, Ndufb6, Tmem207, Mb21d1, Cyp4b1, Hmgcr, Tmem173, Atg5, and Ppargc1a. GO analysis showed that most of the enriched pathways were related to metabolism, and in addition, the autophagy of mitochondrion pathway was also enriched (Fig. 3D, Figure S3A). Figure S3B depicts the functional grouping of GO enriched pathways, while Figure S3C illustrates the interaction network of pathways enriched by up-regulated DEGs. Among all enriched pathways, the response to xenobiotic stimulus is central and extensively interacts with other pathways such as fatty acid metabolic process, alpha-amino acid metabolic process, autophagy of mitochondrion, and mitochondrion disassembly (Figure S3C). Figure S3D identifies closely related constituent genes in the network through heat map. GSEA analysis further validated the results of GO enrichment analysis (Fig. 3E). Table S3 provides a complete list of each group of GO terms. These results indicate that losartan treatment can increase mitophagy levels in PT cells and improve various metabolic processes. In addition, GSEA also enriched two core pathways closely related to mitochondria: regulation of membrane potential and ATP metabolic process.

Elevated mitophagy in PT cells response to losartan treatment

Given that the results of cellular heterogeneity analysis and functional enrichment analysis of our single-cell data suggest changes in multiple mitochondrial related cellular processes after losartan treatment, and combined with the characteristic of abundant distribution of mitochondria in PT cells, we further performed GSVA analysis on single-cell data to evaluate the changes in mitophagy, mitochondrial dynamics, mitochondrial respiratory function, ROS levels, ATP synthesis, apoptosis, membrane potential of PT cells treated with losartan, as these are key events in mitochondrial homeostasis. The complete gene list of the gene set is listed in Table S4. Firstly, we analyzed the changes in mitophagy in PT cells treated with losartan, which is involved in the core mechanism of mitochondrial homeostasis. We extracted features of autophagy of mitochondrion from the constructed mouse gene set and performed GSVA scoring (Fig. 3F, G), and found that the score increased in PT cells treated with losartan (Fig. 3F, G), which is consistent with our GO enrichment and GSEA analysis.

In order to verify the single-cell transcriptomic response and determine whether losartan treatment can improve the mitophagy activity of PT cells in DKD. We observed the kidney samples of mice with electron microscope and found that quantity of autophagic vacuoles within tubular cell of DKD mice significantly increased after losartan treatment (Fig. 4A and B), while the number of autophagosomes decreased (Fig. 4C and D). High power electron microscopy revealed that losartan treatment significantly reduced mitophagosomes and autolysosomes in the tubular cells of DKD mice. This suggests that DKD impairs the degradation of autophagosomes containing damaged mitochondria, leading to their accumulation, while losartan improves mitophagic activity and autophagic flux (Fig. 4E). Immunofluorescence and Western blotting of mouse kidney samples also indicate that losartan treatment enhances mitophagy in tubular cells. Post-treatment, LC3 green fluorescence intensifies, VDAC1 red fluorescence diminishes, co-localization (yellow) increases, and autophagic spots multiply (Fig. 5A-C). Additionally, the LC3-II/LC3-I ratio and the protein levels of ATG5, Beclin, and TOMM20 increased, whereas SQSTM1 and VDAC1 levels decreased (Fig. 5D).

We used mitochondrial tracers and immunofluorescence to analyze mitophagy in HK-2 cells. Confocal microscopy showed that Losartan treatment increased MitoTracker Red and LC3 co-localization (Fig. 6A) and decreased MitoTracker Red and SQSTM1 co-localization (Fig. 6B), thereby restoring mitophagy in high glucose-exposed HK-2 cells (Fig. 6C-D). Western blot analysis of HK-2 cells yielded consistent results (Fig. 6E). To confirm this, we treated HK-2 cells with a recombinant adenovirus engineered to express fused mCherry-GFP-LC3B, observing stable red mCherry dots. High glucose reduced the yellow dots co-localized with mCherry and GFP, but losartan treatment restored mature autophagosomal degradation and normalized autophagic flux disrupted by high glucose (Fig. 6F).

Losartan treatment improves other key events of mitochondrial homeostasis perturbation in PT cells

Next, we further performed GSVA scoring on single-cell data in various aspects of mitochondrial biology, including oxidative stress, ROS level, cell apoptosis, membrane potential, ATP synthesis, and mitochondrial dynamics. Firstly, we scored regulation of oxidative stress, cellular response to ROS, and intrinsic apoptotic signaling pathway, which are crucial for PT function. We found that all scores decreased after treatment with losartan. Next, we examined the positive regulation of membrane potential and ATP biosynthetic process, and found that the scores increased after treatment. Then, to evaluate the effect of losartan treatment on the mitochondrial dynamics of PT cells, we performed gene set scoring for mitochondrial fission and mitochondrial fusion. In mice treated with losartan, the mitochondrial fusion score increased while the mitochondrial fission score decreased (Fig. 3F, G). In summary, these results indicate that oxidative stress, ROS level, apoptosis, membrane potential, ATP synthesis, and mitochondrial dynamics are involved in the therapeutic response mechanism of losartan to PT cells, thereby overall improving mitochondrial homeostasis perturbation, which is consistent with our enrichment analysis of single-cell data.

We validated the single-cell data with animal and cellular experiments. After staining with toluidine blue on kidney slices of DKD mice, it was observed that treatment with losartan restored scattered fragmented mitochondrial to elongated and well-organized tissue in tubular cells (Fig. 7A). Consistent findings were also observed under electron microscopy (Fig. 7B, upper panel), and the percentage of renal tubular cells containing fragmented mitochondria was significantly reduced (Fig. 7C). Further high-power electron microscopy revealed that treatment with losartan improved the poor condition of mitochondrial cristae dissolution and local rupture of the endometrium in renal tubules of DKD mice (Fig. 7B, lower panel). Mitotracker Red staining showed that after 24 h of treatment with losartan, the mitochondria of HK-2 cells exposed to high glucose returned from short rod-shaped to filamentous form (Figs. 6A and B and 8A). Confocal microscopy revealed that losartan treatment decreased Mitotracker Red and Drp1 co-localization fluorescence, which was elevated in HG-exposed HK-2 cells, and increased Mitotracker Red and Mfn2 co-localization fluorescence, which was reduced in HG-exposed HK-2 cells (Fig. 8A). Western blot analysis revealed that losartan treatment significantly decreased the mitochondrial pro-fission protein Drp1, which was elevated in DKD mice kidneys and HG-treated HK-2 cells, whereas the mitochondrial pro-fusion protein Mfn2 showed an opposite trend (Figs. 7D and 8B). These results confirm that losartan affects mitochondrial morphology and dynamics of DKD kidney tubule.

Next, we’ll assess if losartan treatment enhances membrane potential, ATP production, and respiratory function, while reducing ROS and apoptosis, to verify its impact on renal tubular mitochondrial homeostasis perturbation. The TUNEL fluorescent probe showed that losartan treatment significantly reduced apoptosis, the incidence of which was elevated in HK-2 cell treated with high glucose and DKD mouse kidneys (Figs. 9B and 10C), accompanied by down-regulation of Cytochrome c, Bax1, and Caspase-3, as demonstrated by western blot (Figs. 9A and 10G).

The DHE fluorescent probe showed that losartan treatment significantly reduced oxidative stress in the renal tubular cells of DKD mice (Fig. 9C). Similarly, the results of MitoSox staining (Fig. 10B, red) and H2-DCFDA fluorescent probe (Fig. 10A, green) also showed that losartan reduced mitochondrial ROS and intracellular ROS in HK-2 cells exposed to HG. Quantitative analysis confirms the above results (Fig. 10E, F).

In addition, losartan treatment increased the renal tubular ATP production and membrane potential (ΔΨm) in mitochondria in DKD mice (Fig. 9D, E). The TMRE fluorescent probe also confirmed an increase ΔΨm in HK-2 cells exposed to HG treated with losartan (Fig. 10D).

The OCR of purified mitochondria from fresh mice kidney and mitochondria from HK-2 cell were assayed utilizing Seahorse XFe-24 energy detector (Figs. 9F and 10H). The results showed that losartan treatment significantly increased the respiratory OCRs of spare renal mitochondrial capacity, production of ATP, basal and maximum respiration of DKD mice and HK-2 cells exposed to HG, while reducing proton leakage. The above results confirm that losartan treatment can restore mitochondrial respiration in kidney tubule in DKD.

Losartan treatment activates a specific PT subpopulation with mitophagy phenotype

We categorized PT cells into S1, S2, and S3 segments based on known specific markers (Fig. 2B, C, E, Figure S4B). Post-treatment analysis revealed an overall increasing trend in the number of cells within each segment. Notably, within the PT-S1 segment, two distinct subclusters were identified. One of these subclusters exhibited a significant decrease in cell number following treatment, contrary to the general trend observed (Figure S4B). We divided PT cells into 7 subclusters with higher resolution (Fig. 11A, Figure S4C). Figure 11B highlights potential markers for each subcluster, and Fig. 11C shows their DEGs. Differential ratio analysis revealed a significant decrease in the number and proportion of the PT4 subcluster after treatment, while PT5 and PT6 subclusters significantly increased (Fig. 11D, E).

The PT4 subcluster represents approximately 15–18% of PT cells of untreated DKD kidney and expresses both PT-S1 fragment markers (Slc5a2, Slc5a12, Slc12a1) and unique genes like Prlr, Cd44, Cd24h, Pdgfa, Pdgfb, Serpine1, Cp, etc. (Fig. 11C). GO term enrichment analysis indicates that PT4 genes are related to urinary or renal development pathways and healing pathways (Fig. 12A). The PT5 subcluster, comprising 10–12% of PT cells of Losartan-treated DKD kidney, expresses PT-S2 fragment markers (Fxyd2, Hrsp12) and unique genes like Gclc, Nrp1, Parl, Cbs, Acox1, Opa1, Meis2, etc. (Fig. 11C). GO analysis shows PT5 genes are associated with apoptosis and oxidative stress pathways (Fig. 12A). The PT6 subcluster accounts for 9–10% of PT cells of Losartan-treated DKD kidney, expressing not only PT-S2 fragment markers (Fxyd2, Hrsp12), but also a unique set of genes such as Cyp2f2, Cyp4a12a, Ass1, Adhfe1, Pink1, Mfn2, Becn1, etc. (Fig. 11C). GO analysis showed that the PT6 subcluster is rich in genes related to metabolic process and regulation of autophagy of mitochondrion (Fig. 12A). GSVA scoring was performed on each PT subcluster, and the results were consistent with GO analysis (Fig. 12B).

The above analysis suggests that the PT4 subcluster may contain potential renal progenitor cells for repairing damage or undifferentiated cells. We speculate that the PT4 subcluster plays a major role in kidney injury or repair. In order to further investigate the cell type specific mechanisms that lead to the above therapeutic effects, we performed cell trajectory analysis on PT4 to PT6 subclusters using Monocle 2 package. We found that the PT4 subcluster (red scatter) evolved towards PT5 (green scatter) and PT6 subclusters (blue scatter) as losartan treatment progressed (Fig. 12C). Figure 12D-E illustrate changes in specifically expressed genes during transformation, while Fig. 12F highlights differentially expressed transcripts across three subclusters. In the PT4 subcluster, genes like Cp, Prlr, Pdgfa, and Serpine1 significantly decrease in expression, whereas in the PT5 subcluster, genes such as Gclc, Cbs, Nrp1, and Parl, and in the PT6 subcluster, genes like Cyp2f2, Phactr1, Pink1, and Becn, significantly increase in expression. Our preliminary results suggest that after LST treatment, PT4 subcluster with injury or repair phenotype transform into PT5 subcluster with anti-apoptotic-oxidative stress phenotype, and PT6 subcluster with pro-metabolic-mitophagy phenotype. PT4, PT5, and PT6 subclusters represent three phenotypes in the phenotype spectrum of renal tubular cells, which may undergo cellular transformation to response to losartan therapy.

Finally, we performed immunofluorescence staining on the above representative DEGs in human kidney samples from untreated DKD patients (DKD group) and losartan treated DKD patients (ARB group) (Fig. 12G). The clinical characteristics of these patients are shown in Table S5. Consistent with the results of the cell trajectory analysis, we found that the expression of Cp, Prlr, Pdgfa, and Serpine1 in the renal tubules of ARB group was significantly decreased than that of DKD group. In contrast, significantly increased gene expression of Gclc, Cbs, Nrp1, Parl, Cyp2f2, Phactr1, Pink1, Becn1 was found in ARB group (red fluorescence). Interestingly, we unexpectedly discovered a renal tubular subpopulation co-localized with Pink1 and Gclc in the ARB group, suggesting that it is a special subpopulation of tubular cells with mitophagy phenotype, as they also possess antioxidant phenotype. It may be derived from a subpopulation with injury and repair phenotype. The result is partially different from the results we obtained in the DKD mouse model, possibly due to racial differences or differences in medication duration, but the overall trend is consistent.

Discussion

DKD impacts between 20% and 40% of individuals with diabetes, and there are few available treatments. RAS inhibitors, including ACE inhibitors and angiotensin receptor blockers (ARBs), help lower glomerular pressure, widen renal efferent arterioles, enhance permeability, and consequently decrease albumin excretion. Various studies have demonstrated that inhibiting the RAS can effectively decrease kidney failure in both early and advanced stages of chronic kidney disease. Therefore, these drugs are common first-line treatment options for DKD patients.

Losartan belongs to the class of angiotensin II receptor blockers, which has significant renal benefits in patients with type 2 diabetes and nephropathy and is generally well tolerated [62, 63]. The drug has a long half-life and is characterized by stability and high efficacy; At the same time, the drug can also reduce the levels of aldosterone in plasma and alleviate clinical symptoms in patients. Some patients may experience mild gastrointestinal discomfort, dizziness, insomnia, eczema like skin diseases and other adverse reactions after taking medication, so clinical medication is safe and reliable. However, there are still some issues or adverse reactions in the study of losartan in mouse models, such as hypotension [64, 65], hyperkalemia [66, 67], renal dysfunction, immobility [68], interference with fetal development, allergic reactions, and so on. Wu et al. treated mice with losartan (4 µ g/min/kg) in their study and continuously infused them for 3 days using a subcutaneous osmotic pump. Finally, the application of AT2R antagonists reduced the renal K excretion capacity of wild-type mice and led to hyperkalemia [66, 67]. Vijayapandi et al. studied the effects of losartan potassium on immobility in mouse forced swim test. In mice, losartan potassium triggered a biphasic response in the forced swimming test, showing a positive reaction at lower doses (0.1, 1.0, and 5 mg/kg, i.p.) and a negative reaction at higher doses (20 and 100 mg/kg, i.p.). In chronic studies, it was observed that losartan potassium (3 and 30 mg/kg, orally, 21 days) led to increased immobility. In acute combination studies, losartan potassium (1 and 5 mg/kg) significantly reversed immobility, but the opposite was true at 100 mg/kg. The biphasic effect of losartan potassium on immobility in mice may be due to the inhibitory effect of low-dose on AT1 receptors and the significant effect of high-dose on AT2 receptors (high concentrations of losartan potassium can displace angiotensin II (Ang II) from AT1 receptors to AT2 receptors) [68].Sauge et al.‘s research team recently demonstrated that metabolites of losartan, including EXP3179 and EXP3174, can block AT1 in rodents and lower blood pressure [64]. In our previous experiments, two mice in the LST50 group who received daily gavage of 50 mg/kg were found to have died, exhibiting symptoms such as head rotation before death, which may be related to insufficient cerebral perfusion caused by lowering blood pressure. Therefore, in this study, we used the dose point that achieves a balance between effectiveness and safety as the optimal dose, which is 25 mg/kg. And closely monitor the physiology of mice during the use of losartan, including regularly measuring blood pressure, pulse, blood potassium, creatinine, urinary protein, and observing changes in mouse behavior and appearance. Similar to our results, Nagasawa et al. investigated the effect of losartan treatment on the development of renal injury in gddY mice (a spontaneous IgAN mouse model) at doses resulting in similar blood pressure lowering. It was found that in 4-week-old gddY mice, treatment with comparable blood pressure for 8 or 16 weeks and administration of losartan at doses of 10 and 30 mg/kg/day in drinking water could decrease ACR and protect the kidneys from damage [65].

Mitochondrial impairment is recognized as a crucial factor in the advancement of DKD, and early pharmacological intervention to restore mitochondrial function could serve as a promising therapeutic approach to halt disease progression. Many studies have shown that diabetes directly damages the renal tubules, leading to the destruction of mitochondrial homeostasis, including the reduction of bioenergy, excessive production of mitochondrial reactive oxygen species (mtROS), mitophagy defects and dynamic disorders. From the perturbation of mitochondrial homeostasis to the significant disorder, all these will lead to a series of metabolic abnormalities [69,70,71,72,73]. However, the research of exact mechanism of mitochondrial dysfunction in renal tubules is still in its infancy [74], and there is limited research on the mitochondrial efficacy response of drugs. Currently, it is believed that RAS inhibitors can reverse Ang II induced mitochondrial negative effects. In diabetic rats administered losartan, an AT1 receptor antagonist, the levels of TCA cycle enzymes citrate synthase and succinate dehydrogenase were altered, while the expression of the superoxide-producing enzyme NADPH oxidase 2 was normalized. Research on diabetic rats treated with STZ revealed that losartan, an AT1 receptor antagonist, safeguarded kidney mitochondria from alterations in mtMP, hydrogen peroxide, and pyruvate levels. Another study on db/db mice showed inhibition of lipid accumulation and an increase in PGC1 α after treatment with losartan, suggesting a potential regulatory effect of losartan on mitochondrial biogenesis [28]. In addition, Zhu et al. Administered mitochondrial quinone, an antioxidant, to podocytes, safeguarding them against Ang II-induced mitochondrial impairment [75]. These studies suggest that restoring mitochondrial function in the DKD model has therapeutic effects on the kidneys, and the effects of RAS blockers, including drugs such as losartan, on renal mitochondria may be multifaceted. However, there are currently no systematic reports on the multifaceted effects of losartan on the DKD renal tubular mitochondrial homeostasis perturbations.

Utilizing scRNA-Seq technology in diabetic kidney disease (DKD) has led to a rapidly evolving area of research, with numerous studies highlighting the diverse transcriptional, genetic, and epigenomic profiles among kidney cells. Nonetheless, in contrast to these facets of cellular diversity, there is comparatively limited data gathered from single-cell analyses about mitochondrial variability in renal cells. ScRNA-seq technology offers insights into organelles like mitochondria, allowing scientists to identify single-cell heterogeneity and examine their interactions with cellular functions [76]. A new investigation explored the transcriptome variability at the single-cell level concerning mitochondrial balance in db/db mice through scRNA-Seq, revealing that renal collecting duct principal cells and B cells consist of two distinct subgroups: one predominantly healthy and the other primarily associated with diabetic kidney disease. Additionally, the primary genes showing differential expression between these two groups were pinpointed using pseudo-temporal analysis, cell signaling, and transcription factor forecasting, uncovering that the hierarchical regulatory network of receptor-TF-target genes is activated by mitochondrial decay. Moreover, research revealed that a reduction in Fzd7 affects the main cells of the collecting duct, resulting in hindered cell growth and development, increased apoptosis, a stalled cell cycle, and reduced transport efficiency, highlighting the variability of mitochondrial issues across various cell types in DKD [50].

In our study, it was found through snRNA-seq mapping that losartan treatment significantly increased the percentage of PT cells in the kidneys of DKD mice (Fig. 2C-D). In order to further explore the key genes and pathways of renal tubular epithelial cells affected by losartan and understand its pharmacological mechanism, we performed GO enrichment analysis on upregulated DEGs. Gene Ontology (GO) enrichment analysis and GSEA/GSVA scoring provide powerful tools for revealing gene function, pathway changes, and drug effects on cells. GO enrichment analysis mainly targets individual genes and is commonly used for preliminary understanding of gene function. Including cellular components (CC), molecular functions (MF), and biological processes (BP), achieving a correlation between function and phenotype. Through GO enrichment analysis, genes related to mitochondrial function can be preliminarily identified, such as those involved in mitochondrial dynamics, electron transport chain, apoptosis, oxidative stress, and mitophagy processes. Our results showed that upregulated DEGs enriched genes related to the mitophagy pathway (Fig. 3D, Figure S3A-D). GSEA (Gene Set Enrichment Analysis) is based on pre-defined gene sets, analyzing whether there are enrichment differences in these gene sets under physiological and pathological conditions, which helps to discover pathways or gene sets related to specific phenotypes. GSVA (Gene Set Variation Analysis) is a method for evaluating the degree of variation of a gene set between samples. The GSEA/GSVA score can analyze the effect of losartan on the mitochondrial pathway in renal tubular epithelial cells, further understand its pharmacological mechanism, and identify key genes and pathways affected by losartan, which may become potential therapeutic targets and provide new ideas for drug development. Therefore, we further conducted GSEA/GSVA scoring. GSEA analysis confirmed that losartan treatment can increase the mitophagy level of PT cells (Fig. 3E). In addition, GSEA enriches two core pathways closely related to mitochondria: membrane potential regulation and ATP metabolism. These results suggest that the therapeutic response mechanism of losartan to DKD involves multiple aspects of mitochondria. Therefore, we further evaluated other key events of mitochondrial homeostasis in single-cell data through GSVA analysis, such as mitophagy, oxidative stress, ROS levels, apoptosis, membrane potential, ATP synthesis, and mitochondrial dynamics. We found that these events were involved in the protective mechanism of losartan on PT cells, thereby improving mitochondrial perturbation (Fig. 3F, G). This is partially inconsistent with the research results of Wu et al. [47]. They used scRNA-seq to analyze the transcriptome of renal cells in db/db mice treated with ARBs, SGLT2i, or ARBs + SGLT2i, and found that ARBs mainly have anti-inflammatory and anti-fibrotic effects, while SGLT2i has a greater impact on the mitochondrial function of proximal renal tubular cells. The difference in this result may be related to the choice of scRNA-seq method. In order to avoid the loss of a large number of tubular epithelial cells during scRNA-seq enzymatic hydrolysis and the potential low activity of gene expression products after enzymatic hydrolysis, we used snRNA-seq to minimize the bias caused by scRNA-seq and provide highly accurate single-cell profiles. In addition, snRNA-seq has additional advantages in sequencing frozen tissue samples [38, 77]. Through snRNA-seq, our research results not only indicate that losartan has anti-inflammatory and anti-fibrotic effects, but also show that losartan has significant effects on multiple aspects of PT mitochondria, including increasing mitophagy, promoting mitochondrial dynamics from fission to fusion, enhancing mitochondrial respiratory function, reducing oxidative stress, ROS levels and apoptosis, increasing ATP biosynthesis and membrane potential. These suggest that losartan has an improving effect on the mitochondrial homeostasis perturbation in DKD renal tubules, and we further preliminarily confirmed the above results in DKD mouse models and high glucose exposed HK-2 cell lines (Figs. 4, 5, 6, 7, 8, 9 and 10).

Meanwhile, through differential ratio analysis and cell trajectory analysis, we found that after treatment with losartan, PT4 subcluster with injury or repair phenotype was significantly reduced, and transformed into PT5 subcluster with anti-apoptotic-oxidative stress phenotype, and PT6 subcluster with pro-metabolic-mitophagy phenotype (Figs. 11 and 12A-F). Finally, to substantiate the aforementioned findings, we identified a distinct renal tubular subpopulation exhibiting a mitophagy phenotype co-localized with Pink1 and Gclc in kidney specimens from DKD patients treated with losartan, which also demonstrated an antioxidant phenotype (Fig. 12F). Pink1 is the most extensively studied pathway involved in mitophagy, activated on the surface of depolarized mitochondria, resulting in the degradation of mitochondrial proteins. In the context of diabetes, there is a Pink1-dependent inhibition of mitophagy in renal tubular cells [78]. Gclc, a catalytic subunit of glutamate cysteine ligase, functions as a downstream signaling molecule within the NRF2 antioxidant defense system. Accumulating evidence underscores the significance of the NRF2 signaling pathway in the pathogenesis of diabetic kidney disease (DKD), with GCLC acting as a critical downstream target that mitigating oxidative stress [79]. Research indicates that under hyperglycemic conditions, the expression and activity of nuclear Nrf2 in renal tubular cells are diminished, resulting in decreased PINK1 transcription and subsequent defects in mitophagy. Conversely, upregulation of Nrf2 has been shown to positively regulate PINK1 transcription and mitigate oxidative stress-induced cell death [56]. This may partially explain our findings, further supporting the existence of this particular subpopulation with mitophagy phenotype, and this specific renal tubular subpopulation possibly derived from a subpopulation with injury or repair phenotype with the therapy of losartan. This is partially similar to the previous study by Wu et al., who identified a subpopulation of proximal tubular cells in db/db mice transfected with renin expressing adeno-associated virus after nephrectomy through scRNA-seq. This subpopulation is in an injured state and expresses typical injury PT markers such as Havcr1, Vcam1, and C3. The combined drug treatment of ACE inhibitor and SGLT2 inhibitor significantly reduced the abnormal up regulation of the PT injury marker Havcr1 in DKD, and also corrected the changes in the damaged PT gene regulatory network, as well as the dysfunctional pathways such as fatty acid oxidation in diabetes [48]. Our research findings further highlight a renal tubular subpopulation exhibiting a distinct mitophagy phenotype that emerges following losartan treatment, which may be related to specific drug types, treatment duration, and species differences.

Overall, we performed snRNA-seq analysis on the kidneys of DKD mice and discovered that losartan influences multiple critical processes involved in maintaining mitochondrial homeostasis in PT cells. These processes include mitophagy, mitochondrial dynamics, mitochondrial respiratory function, ATP synthesis, ROS levels, oxidative stress, and apoptosis. Additionally, we corroborated the snRNA-seq findings through both in vivo and in vitro experiments, with a particular focus on a subpopulation exhibiting a distinct mitophagy phenotype in the renal tubules of DKD patients treated with losartan.

This study investigates the application of scRNA-seq technology to elucidate the response mechanism of losartan on the therapeutic efficacy of renal tubular mitochondrial organelles in DKD. The objective is to uncover the molecular mechanisms underlying the dysfunction of renal tubular mitochondrial organelles in DKD by utilizing high-resolution gene expression profiles. We recognize that current research on mitochondrial dysfunction in DKD is mostly based on a large amount of RNA levels, which to some extent masks the heterogeneity of mitochondrial function among different cell types. Therefore, the core value and goal of this study is to: ⑴ strengthen research on cell heterogeneity: through scRNA-seq, we can distinguish the mitochondrial gene expression patterns between different subtypes of renal tubular epithelial cells in the kidney, identify different subtypes of renal tubular cells based on mitochondrial heterogeneity, and reveal the multi-faced mechanism of losartan treatment for DKD. ⑵ advancing the multi-faced mechanisms of mitochondrial dysfunction: Our research not only focuses on changes in mitochondrial homeostasis related gene expression, but also delves into how these changes affect key events such as mitochondrial morphology, dynamics, mitophagy, autophagic flux, mitochondrial respiratory chain, apoptosis, and ROS production, providing potential targets for future precision therapy and laying the foundation for personalized treatment of DKD. ⑶ identifying a specific renal tubular subpopulation with a mitophagy phenotype: This provides us with a more precise therapeutic target, contributes to the development of new treatment strategies. For example, drugs targeting the regulation of mitophagy can be designed to treat DKD by enhancing or inhibiting the mitophagy activity of specific renal tubular subgroups.

However, there are still some limitations and gaps in our research, for example: ⑴ although we have some understanding of the changes in renal tubular epithelial cells under DKD disease status and after losartan treatment, we know little about the efficacy response mechanisms of other relatively less studied cell types at the single-cell level, such as immune cell subpopulations or renal stem/progenitor cells in the renal interstitium, or podocytes with a small number of cells. ⑵ lack of resolution in intercellular communication: Intercellular interactions are crucial in the occurrence and development of DKD, as well as in response to drug therapy. At present, although our scRNA-seq data can identify different cell types, there is a lack of in-depth research on the inter-cellular communication network in the mechanism of losartan treatment. ⑶ There is insufficient research on the dynamic changes of single cell in different stages of DKD progression: DKD is a progressive disease, ranging from early microalbuminuria to late renal failure. At present, our scRNA-seq research is only at one stage of the disease, lacking comprehensive research on the dynamic changes of single-cell levels at different stages of the entire disease process. The differences in the interference effects of losartan on the mitochondrial homeostasis of these renal tubules at different stages still need further exploration. ⑷ experimental grouping design and sample size: Our study involved different populations (CON, DKD, LST), and the healthy control group (CON) provided a reference standard for comparing the results between the DKD model group and the losartan treatment group. The diabetes model group (DKD) revealed the mitochondrial atlas of renal tubules under the condition of diabetic kidney disease. The Losartan Treatment Group (LST) is mainly used to evaluate the therapeutic effect of Losartan on DKD renal tubular mitochondria. However, due to cost reasons, the sample size is still limited and further research is needed to increase the sample size to reduce differences between animals. Due to limitations in data usage permissions, only the DKD and LST groups of scRNA-seq data were included in this study, while the CON group only appeared in the validation experiments. ⑸ limitations of the experimental model: We used C57BL/6J mice, first inducing insulin resistance with HFD, and then injecting STZ to cause damage to pancreatic beta cells, forming the DKD model. However, our animal models cannot fully reproduce the characteristics of human DKD, and we still need to use more types of animal models or induce more complex disease models to better simulate. ⑹ limitations of technology and data interpretation: we use snRNA-seq, which will lose the RNA information in the cytoplasm and affect the comprehensive understanding of post transcriptional regulation of genes. More advanced bioinformatics methods and algorithms need to be developed, integrating more sequencing technologies such as network-based, multi omics based, and path based methods [80]. In addition, the unprecedented progress in computing power and strategies of artificial intelligence (AI) algorithms such as machine learning (ML) and deep learning (DL) combined with single-cell RNA technology has begun to produce better results [81, 82] to overcome these limitations, such as using metaheuristic algorithms (feature selection based on quantum squirrel search algorithm) to extract the optimal gene set, which can greatly ensure the performance of cell clustering and enhance scRNA-seq data clustering analysis [83], to compensate for our limitations in this area. However, this technology still requires us to expand the sample size to achieve it, which will be the direction of our future work efforts.

Data availability

The accession number for the RNA sequencing data reported in this paper is NCBI GEO: GSE280259 for snRNA-seq. The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

We thank Academician Jia Weiping for providing us with experimental infrastructure for our research.

Funding

This work was supported by a grant from the National Natural Science Foundation of China (grant no. 82004432) and a grant from Shanghai Municipal Science and Technology Commission Project (20ZR1442500).

Author information

Zhen Zhu, Guangxin Luan, Song Wu and Yiyi Song contributed equally to this work.

Authors and Affiliations

Department of Respiratory Medicine, Shanghai Sixth People’s Hospital, Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 201306, China
Zhen Zhu, Yiyi Song & Tao Ren
Department of Clinical Laboratory, Shanghai Sixth People’s Hospital, Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 201306, China
Guangxin Luan
Department of Cardiothoracic Surgery, Shanghai Sixth People’s Hospital, Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 201306, China
Song Wu
Shanghai Diabetes Institute, Shanghai Sixth People’s Hospital, Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 201306, China
Shuang Shen, Kaiyue Wu, Shengnan Qian, Weiping Jia & Jianping Ye
Department of Endocrine Medicine, Shanghai Sixth People’s Hospital, Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 201306, Chin, China
Jun Yin & Li Wei

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Zhen Zhu
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Guangxin Luan
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Song Wu
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Yiyi Song
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Shuang Shen
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Kaiyue Wu
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Shengnan Qian
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Weiping Jia
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Jun Yin
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Tao Ren
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Jianping Ye
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Li Wei
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Contributions

Jianping Ye and Li Wei conceived the project, designed the study, and interpreted the results. Weiping Jia, Jun Yin and Tao Ren provided analytical and experiment support. Zhen Zhu, Guangxin Luan, Song Wu and Yiyi Song performed the experiments, analyzed the data. Kaiyue Wu, Shuang Shen, Shengnan Qian assisted in experiment, data collection and statistics. Li Wei, Zhen Zhu and Guangxin Luan wrote the manuscript with feedback from all other authors. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Jun Yin, Tao Ren, Jianping Ye or Li Wei.

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All procedures in mice adhered to the “Animal Research: Reporting of In Vivo Experiments” (ARRIVE) standards and were approved by the Sixth People’s Hospital Ethics Committee guidelines (approval no.: 2020-022). In accordance with local and national laws, ethics approval and consent to participate were exempted due to the retrospective/observational and non-interventional nature of the human study. However, all patients involved in this study provided written informed consent before undergoing treatment administration and molecular assessments.

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Informed consent was obtained from all individual participants included in the study.

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The authors declare no competing interests.

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Zhu, Z., Luan, G., Wu, S. et al. Single-cell atlas reveals multi-faced responses of losartan on tubular mitochondria in diabetic kidney disease. J Transl Med 23, 90 (2025). https://doi.org/10.1186/s12967-025-06074-5

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Received: 02 August 2024
Accepted: 06 January 2025
Published: 21 January 2025
DOI: https://doi.org/10.1186/s12967-025-06074-5

Single-cell atlas reveals multi-faced responses of losartan on tubular mitochondria in diabetic kidney disease

Abstract

Background and Objective

Methods

Results

Conclusions

Introduction

Materials and methods

Mouse model of DKD

Evaluation of the physiology and functions of the kidneys

Assessment of the kidney’s morphological features

Morphological evaluation by toluidine blue staining and transmission electron microscopy

snRNA-seq and data quality control and analysis

Treatment and culture of cells

Detection of morphology, ATP activity, and membrane voltage potential (ΔΨm) of mitochondria using fluorescent probes

Assay for immunofluorescence

Apoptosis and superoxide production detection

Mitochondrial respiration assay

Western blotting

Autophagic flux analysis

Statistical analyses

Results

Normalization of renal function and morphology in mice with DKD by losartan

Single-cell landscape of DKD kidney with or without losartan treatment

Specific response of PT cells to losartan treatment

Elevated mitophagy in PT cells response to losartan treatment

Losartan treatment improves other key events of mitochondrial homeostasis perturbation in PT cells

Losartan treatment activates a specific PT subpopulation with mitophagy phenotype

Discussion

Data availability

References

Acknowledgements

Funding

Author information

Authors and Affiliations

Contributions

Corresponding authors

Ethics declarations

Ethics approval and consent to participate

Consent for publication

Competing interests

Additional information

Publisher’s note

Electronic supplementary material

Supplementary Material 1

Supplementary Material 2

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About this article

Cite this article

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Keywords

Journal of Translational Medicine

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