Article A diabetic milieu increases ACE2 expression and cellular susceptibility to SARS-CoV-2 infections in human kidney organoids and patient cells Graphical abstract Authors Elena Garreta, Patricia Prado, Megan L. Stanifer, ..., Steeve Boulant, Josef M. Penninger, Nuria Montserrat Correspondence ali.mirazimi@ki.se (A.M.), s.boulant@ufl.edu (S.B.), josef.penninger@ubc.ca (J.M.P.), nmontserrat@ibecbarcelona.eu (N.M.) In brief Garreta et al. developed a human kidney organoid system mirroring early hallmarks of diabetic kidney disease development. This resulted in higher SARS-CoV-2 infections, which were critically dependent on ACE2. Diabetic patient renal cells also showed elevated ACE2 and altered energy metabolism that following chemical targeting resulted in a reduction of SARS-CoV-2 infection. Highlights d ACE2 is upregulated in hyperglycemia in kidney organoids and patient renal cells d Metabolic changes and increased ACE2 boost cellular infection by SARS-CoV-2 d Targeting of energy metabolism in patient renal cells lowers SARS-CoV-2 infection Garreta et al., 2022, Cell Metabolism 34, 857–873 June 7, 2022 ª 2022 The Authors. Published by Elsevier Inc. https://doi.org/10.1016/j.cmet.2022.04.009 ll ll OPEN ACCESS Article A diabetic milieu increases ACE2 expression and cellular susceptibility to SARS-CoV-2 infections in human kidney organoids and patient cells Elena Garreta,1,22 Patricia Prado,1,22 Megan L. Stanifer,2,3,4,22 Vanessa Monteil,5,22 Andrés Marco,1 Asier Ullate-Agote,6,7,8 Daniel Moya-Rull,1 Amaia Vilas-Zornoza,6,7,8 Carolina Tarantino,1 Juan Pablo Romero,6 Gustav Jonsson,9 Roger Oria,10 Alexandra Leopoldi,9 Astrid Hagelkruys,9 Maria Gallo,1 Federico González,1 Pere Domingo-Pedrol,11 Aleix Gavaldà,12 Carmen Hurtado del Pozo,1 Omar Hasan Ali,13,14,15 Pedro Ventura-Aguiar,16,17 Josep Marı́a Campistol,16,17 Felipe Prosper,6,7,8 Ali Mirazimi,5,18,* Steeve Boulant,3,4,19,* Josef M. Penninger,9,13,* and Nuria Montserrat1,20,21,23,* 1Pluripotency for Organ Regeneration, Institute for Bioengineering of Catalonia (IBEC), The Barcelona Institute of Science and Technology (BIST), Barcelona, Spain 2Department of Infectious Diseases, Molecular Virology, Heidelberg University Hospital, Heidelberg, Germany 3Research Group ‘‘Cellular Polarity and Viral Infection,’’ German Cancer Research Center (DKFZ), Heidelberg, Germany 4Department of Molecular Genetics and Microbiology, College of Medicine, University of Florida, Gainesville, FL, USA 5Karolinska Institute and Karolinska University Hospital, Unit of Clinical Microbiology, 17182 Stockholm, Sweden 6Área de Hemato-Oncologı́a, Centro de Investigación Médica Aplicada, Instituto de Investigación Sanitaria de Navarra (IDISNA), Universidad de Navarra, 31008 Pamplona, Spain 7Centro de Investigación Biomédica en Red de Cáncer (CIBERONC), 28029 Madrid, Spain 8Departamento de Hematologı́a, Clı́nica Universidad de Navarra, Universidad de Navarra, 31008 Pamplona, Spain 9IMBA, Institute of Molecular Biotechnology of the Austrian Academy of Sciences, Dr. Bohr-Gasse 3, 1030 Vienna, Austria 10Center for Bioengineering and Tissue Regeneration, UCSF, San Francisco, CA, USA 11Internal Medicine Department, Hospital Universitario de la Santa Creu i Sant Pau, Barcelona, Spain 12Departament de Bioquı́mica i Biomedicina Molecular, Institut de Biomedicina (IBUB), Universitat de Barcelona and CIBER Fisiopatologı́a de la Obesidad y Nutrición, Barcelona, Spain 13Department of Medical Genetics, Life Sciences Institute, University of British Columbia, Vancouver, BC, Canada 14Department of Dermatology, University Hospital Zurich, University of Zurich, Zurich, Switzerland 15Institute of Immunobiology, Cantonal Hospital St. Gallen, St. Gallen, Switzerland 16Nephrology and Kidney Transplant Department, Hospital Clı́nic Barcelona, Barcelona, Spain 17Laboratori Experimental de Nefrologia I Trasplantament (LENIT), Fundació Clı́nic per a la Recerca Biomèdica (FCRB), Barcelona, Spain 18National Veterinary Institute, Uppsala, Sweden 19Department of Infectious Diseases, Virology, Heidelberg University Hospital, Heidelberg, Germany 20Catalan Institution for Research and Advanced Studies (ICREA), Barcelona, Spain 21Centro de Investigación Biomédica en Red en Bioingenierı́a, Biomateriales y Nanomedicina, Madrid, Spain 22These authors contributed equally 23Lead contact *Correspondence: ali.mirazimi@ki.se (A.M.), s.boulant@ufl.edu (S.B.), josef.penninger@ubc.ca (J.M.P.), nmontserrat@ibecbarcelona.eu (N. M.) https://doi.org/10.1016/j.cmet.2022.04.009 SUMMARY It is not well understood why diabetic individuals are more prone to develop severe COVID-19. To this, we here established a human kidney organoid model promoting early hallmarks of diabetic kidney disease development. Upon SARS-CoV-2 infection, diabetic-like kidney organoids exhibited higher viral loads compared with their control counterparts. Genetic deletion of the angiotensin-converting enzyme 2 (ACE2) in kidney organoids under control or diabetic-like conditions prevented viral detection. Moreover, cells isolated from kidney biopsies from diabetic patients exhibited altered mitochondrial respiration and enhanced glycolysis, resulting in higher SARS-CoV-2 infections compared with non-diabetic cells. Conversely, the exposure of patient cells to dichloroacetate (DCA), an inhibitor of aerobic glycolysis, resulted in reduced SARS-CoV-2 infections. Our results provide insights into the identification of dia- betic-induced metabolic programming in the kidney as a critical event increasing SARS-CoV-2 infection susceptibility, opening the door to the identification of new interventions in COVID-19 pathogenesis target- ing energy metabolism. Cell Metabolism 34, 857–873, June 7, 2022 ª 2022 The Authors. Published by Elsevier Inc. 857 This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). ll OPEN ACCESS Article INTRODUCTION to generate diabetic-like kidney organoids from hPSCs by adapting our previous protocol (Monteil et al., 2020). We delin- Coronavirus disease 2019 (COVID-19) is an infectious disease eated a culture setup using continuous low glucose (5 mM, caused by severe acute respiratory syndrome coronavirus 2 termed ‘‘control’’ conditions) or high glucose in an oscillatory (SARS-CoV-2). COVID-19 patients display influenza-like symp- fashion (alternating 5–25 mM every 24 h, termed ‘‘diabetic’’ con- toms ranging from mild disease to severe lung injury. A high per- ditions) (Figure 1A). Kidney organoids under both control and centage of severe COVID-19 patients display symptoms in other diabetic culture conditions showed the presence of glomer- organs, most notably the gastrointestinal tract, cardiovascular ular-like and renal-tubular-like structures to a similar extent, as system, and the kidney. Several conditions have been linked to determined by immunodetection of PODXL+ podocyte-like cells the risk of developing severe COVID-19, including genetic pre- and Lotus Tetraglobus lectin proximal tubular-like cells (LTL)+ disposition (Ellinghaus et al., 2020; van der Made et al., 2020; (Figure S1A) and qPCR analysis for the proximal tubule marker Zhang et al., 2020b), immune-related responses (Bastard et al., gene SLC3A1 and the podocyte marker genes NPHS1, PODXL, 2021; Zhang et al., 2020a), obesity, or diabetes mellitus (DM) WT1, and MAFB (Figure S1B). Similarly, the mRNA expression (Lim et al., 2021). Both COVID-19 and DM are associated with levels of the endothelial marker genes Endoglin, VGFR, and acute and chronic inflammation and both disease conditions PDGFRa and the stromal marker genes vimentin and can impact each other in terms of clinical progression and dis- MEIS1/2/3 did not show significant variations between culture ease outcome (Ugwueze et al., 2020). Notably, SARS-CoV-2 in- conditions (Figure S1B). Transmission electron microscopy fections lead to acute kidney injury (AKI) in >20% of hospitalized (TEM) showed that tubular-like and podocyte-like cells in both patients (Nadim et al., 2020), and higher rates of mortality have control and diabetic conditions exhibited typical late-stage renal been reported in patients with pre-existing DM (Khalili et al., differentiation features (Figure S1C). 2021; Legrand et al., 2021). Changes in kidney extracellular matrix (ECM) proteins are We have previously shown that both kidney and vascular associated with renal fibrosis and chronic kidney disease organoids derived from human pluripotent stem cells (hPSCs) (CKD) in diabetes (Bu €low and Boor, 2019; Yang et al., 2015). support SARS-CoV-2 infections, which was blocked in the Importantly, high oscillatory glucose treatment resulted in presence of clinical-grade human recombinant soluble angio- increased collagen fiber deposition in kidney organoids tensin-converting enzyme 2 (ACE2) (Monteil et al., 2020). How- compared with low-glucose controls (Figures 1B–1D; Data S1). ever, besides the great utility of organoids in SARS-CoV-2 We also observed an upregulation of collagen III and collagen research, organoids have not yet been developed that model IV mRNA expression by qPCR (Figure 1E). Similarly, fibronectin human co-morbidities associated with severe COVID-19, deposits were detected in the tubulointerstitial areas within such as DM. kidney organoids (Figure 1F; Data S1). PAS staining and Here, we show that high glucose oscillations in engineered immunohistochemistry for the basement membrane proteins human kidney organoids led to phenotypic, transcriptional, laminin and collagen IV in combination with the podocyte marker and metabolic alterations reminiscent to human kidney dis- nephrin and proximal tubule marker LTL showed that the ease development in a diabetic milieu. These diabetic condi- composition and integrity of basement membranes were largely tions enhanced ACE2 expression and SARS-CoV-2 infection, preserved under diabetic conditions (Figures S1D and S1E; Data which was validated in human proximal tubular cells isolated S1). The glycolysis-associated genes HK2 and LDHA were upre- from diabetic kidney biopsies. Genetic deletion of ACE2, but gulated in the diabetic kidney organoids (Figure S1F), whereas not BSG/CD147 or NRP1, other candidate receptors for PGC1a (a key regulator of mitochondrial biogenesis involved in SARS-CoV-2, completely abrogated SARS-CoV-2 infections diabetic nephropathy) (Li and Susztak, 2018) was downregu- under normal and diabetic conditions. This study provides lated (Figure S1G). mechanistic evidence on metabolic alterations that can in- To assess whether the diabetic milieu altered cellular meta- crease cellular susceptibility to SARS-CoV-2 infections bolism, we isolated proximal tubular-like cells from kidney or- and unequivocally establishes ACE2 as the critical SARS- ganoids by fluorescent-activated cell sorting of the LTL+ cell CoV-2 receptor in the human kidney, even under diabetic fraction (Figures S2A and S2B). LTL+ cells isolated from conditions. both experimental conditions were expanded in 5 mM glucose medium for 2 months until passage 5–7 in culture. After this RESULTS time, PGC1a expression in LTL+ cells was significantly decreased in cells isolated from diabetic organoids compared Establishment of diabetic human kidney organoids with control organoids as shown by qPCR (Figure S2C) and Diabetic patients exhibit oscillatory levels in glucose with daily immunofluorescence (Figure S2D). The expression of the episodes of hypo- and hyperglycemia, which is thought to be tubular markers LTL and Na-K ATPase was unchanged (Fig- important for driving DM-associated pathologies (Vasquez- ure S2D). The reduction in PGC1a levels was concomitant Muñoz et al., 2021). Indeed, the term ‘‘metabolic memory’’ coins with increases in the maximum oxygen consumption rate the pathogenic alterations induced by hyperglycemia long after (OCR), a measurement of mitochondrial respiration, basal accomplishment of glycemic control (Genuth et al., 2002; Writing and maximal respiration, as well ATP synthesis (Figure 1G). Team for the Diabetes Control and Complications Trial/ Of note, our results are in line with other studies at early Epidemiology of Diabetes Interventions and Complications phases of diabetes in animal models (1–4 weeks after induc- Research Group, 2003). To emulate in vitro diabetic-patient- tion of diabetes) (Friederich et al., 2008; Friederich-Persson like oscillations in glucose levels, we established a procedure and Persson, 2020). In summary, our data show that exposure 858 Cell Metabolism 34, 857–873, June 7, 2022 ll Article OPEN ACCESS A B D C F E G Figure 1. High oscillatory glucose conditions induce early hallmarks of the diabetic kidney disease in human kidney organoids (A) Experimental scheme for the generation of human kidney organoids from hPSCs. (B) Trichrome Masson staining of control or diabetic kidney organoids. Glomerular (*) and tubular (**) structures are shown. Scale bars, 250 and 100 mm (magnified views). (C) Corresponding quantification of collagen fibers (B). y axis represents integrated intensity. Data are mean ± SD of at least n = 5 independent experimental replicates per condition. *p < 0.05, unpaired Student’s t test. (D) Representative immunofluorescence staining for COLLAGEN-I (green), LTL (gray), and DAPI (blue) in control or diabetic kidney organoids. Scale bars, 250 and 100 mm (magnified views). (E) mRNA expression levels of COL3A1 and COL4A1 in control or diabetic kidney organoids. Data are mean ± SD. n = 3 independent biological replicates from a pool of 12 organoids/group with two technical replicates each. **p < 0.01, unpaired Student’s t test. (F) Representative immunofluorescence staining for FIBRONECTIN (green), E-CADHERIN (ECAD; red), LTL (gray), and DAPI (blue) in control or diabetic kidney organoids. Scale bars, 250 and 100 mm (magnified views). Yellow arrows highlight sites of fibronectin deposits. (G) Seahorse analysis in LTL+ cells isolated from control or diabetic kidney organoids. The oxygen consumption rate (OCR) data are normalized to total protein. Data are mean ± SD. n = 10 biological replicates/group. ****p < 0.0001, two-way ANOVA, followed by Bonferroni post-test. Basal respiration and spare respiratory capacity, cellular ATP production, and maximal respiration are shown as mean ± SD. n = 10 biological replicates/group. *p < 0.05; **p < 0.005, unpaired Student’s t test. See also Figures S1 and S2 and Data S1. Cell Metabolism 34, 857–873, June 7, 2022 859 ll OPEN ACCESS Article A B C D E F I G H J K L M Figure 2. Diabetic conditions induce ACE2 expression in human kidney organoids and enhance SARS-CoV-2 infections (A) Representative bright-field images and hematoxylin and eosin staining of control or diabetic kidney organoids. Asterisks highlight podocyte-like cells (*) or tubular-like structures (**). Consecutive sections were stained for ACE2 (green), PODOCIN (red), and using Lotus Tetraglobus lectin (LTL, tubular cell marker, gray) and DAPI (blue). Scale bars, 250 and 100 mm (magnified views). (legend continued on next page) 860 Cell Metabolism 34, 857–873, June 7, 2022 ll Article OPEN ACCESS of human kidney organoids to high oscillatory glucose leads to half-life of ACE2 mRNA. To test this idea, kidney organoids transcriptional changes, ECM alterations, and metabolic mito- were challenged with the RNA transcription inhibitor actinomycin chondrial rewiring in tubular cells, early hallmarks of kidney D leading to an increase of ACE2 mRNA stability in diabetic orga- disease development induced by hyperglycemia. Our culture noids compared with control counterparts (Figure 2I). system also allowed for the generation of proximal tubule- like cells (LTL+) recapitulating hallmarks of an early diabeto- Increased SARS-CoV-2 infection in diabetic kidney genic-like phenotype after removal of the diabetogenic insult, organoids indicative of metabolic memory. Next, control and diabetic kidney organoids were infected with SARS-CoV-2, recovered, and analyzed at 1 day post-infection Diabetic conditions induce ACE2 expression in human (1 dpi) (Figure 2J). Control organoids were productively infected kidney organoids as detected by immunostaining for the viral nuclear protein (NP) ACE2 has been previously identified as a key host cell surface re- (Figure 2K). Infected cells within organoids were primarily LTL+ ceptor sufficient for SARS-CoV-2 entry into host cells (Walls et al., (Figure 2L; Data S1). Remarkably, diabetic organoids showed 2020; Wan et al., 2020; Wrapp et al., 2020). Of note, ACE2 has also significantly enhanced SARS-CoV-2 infections compared with been previously shown to control the progression of CKD in mul- control organoids, as quantified by the percentage of cells pos- tiple animal models (Maksimowski et al., 2020). Although it has itive for NP expression (NP+) by confocal microscopy (Figure 2M; been reported that glucose can induce ACE2 expression in cell Data S1). To assess potential transcriptional changes induced by lines (Ha€rdtner et al., 2013), it is still controversial whether DM re- SARS-CoV-2, single-cell RNA sequencing (scRNA-seq) was sults in up- or downregulation of ACE2 (Mizuiri et al., 2008; Reich performed across 4 biological conditions (mock and 1 dpi, in et al., 2008; Sandooja et al., 2020), making it paramount to test the both 5 mM glucose- and high oscillatory glucose-treated kidney effects of glucose on ACE2 expression in complex human tissue- organoids). Cell types in kidney organoids were assigned using like cultures as organoids. Our analysis indicated that in the kidney unsupervised clustering after integrating control versus diabetic organoids, ACE2-expressing cells (ACE2+) are predominantly de- infected conditions and the uniform manifold approximation and tected in LTL+ (Figure 2A; Data S1). Image quantification of projection (UMAP) algorithm to visualize the scRNA-seq data ACE2+LTL+ was evaluated together with markers of different renal (Figure 3A). We retrieved renal endothelial-like, mesenchymal, compartments including the podocyte marker WT1, the endothe- proliferating, podocyte, and tubule cell populations in all four lial cell marker CD31, and the stroma marker MEIS1/2/3 experimental samples, indicating that apparently neither dia- (Figures 2B–2D; Data S1), overall showing a similar expression betic conditions nor SARS-CoV-2 infections altered cell compo- pattern of ACE2 to that found in the native human and mouse kid- sitions in the kidney organoids (Figure S3A). Organoids from the ney (Deng et al., 2020; Lin et al., 2021). Importantly, the oscillatory all four conditions contained cells representative of a developing glucose treatment promoted a significant upregulation of ACE2 nephron, including ENG+ and PECAM1+ (endothelial-like cells), expression compared with control conditions at the protein NPHS1+ and NPHS2+ (podocyte-like cells), LRP2+ and (Figures 2E–2G and S2E–S2G) and mRNA (Figure 2H) levels. SLC3A1+ (tubule proximal-like cells), and CDH1+ (distal-like Since mRNA stability plays a major role in gene expression tubular cells) (Figure S3B), also resembling second trimester hu- regulation, we hypothesized that hyperglycemia might affect the man fetal kidney cell populations (Figure S3C). Diabetic kidney (B) Quantification of ACE2 colocalization with LTL+ and WT1+ cells in control or diabetic kidney organoids. Data are mean ± SD. n = 13 (control) and n = 16 (diabetic) organoids per condition. ****p < 0.0001, one-way ANOVA, Tukey’s multiple comparisons test. (C) Quantification of ACE2 colocalization with LTL+ and CD31+ cells in control or diabetic kidney organoids. Data are mean ± SD. n = 3 (control) and n = 4 (diabetic) organoids per condition. ****p < 0.0001, one-way ANOVA, Tukey’s multiple comparisons test. (D) Quantification of ACE2 colocalization with LTL+ and MEIS+ cells in control or diabetic kidney organoids. Data are mean ± SD. n = 4 (control) and n = 4 (diabetic) organoids per condition. ****p < 0.0001, one-way ANOVA, Tukey’s multiple comparisons test. (E) Quantification of the area of ACE2+ cells in control or diabetic kidney organoids. Data are mean ± SD. n = 20 (control) and n = 24 (diabetic) organoids. *p < 0.05, unpaired Student’s t test. (F) Quantification of the mean fluorescence intensity (MFI as arbitrary units [AU]) of ACE2+ cells in control or diabetic kidney organoids. Data are mean ± SD. n = 6 (control) and n = 6 (diabetic) organoids. ****p < 0.0001, unpaired Student’s t test. (G) Protein levels of ACE2 in control or diabetic kidney organoids are shown by western blot. b-actin was used as loading control. The correspondent quanti- fication is shown. Data are mean ± SD. n = 3 independent biological replicates from a pool of 12 organoids/group; *p < 0.05, unpaired Student’s t test. (H) mRNA expression levels of ACE2 in control or diabetic kidney organoids. Data are mean ± SD. n = 4 independent experimental replicates from a pool of 12 organoids/group with two technical replicates each. **p < 0.005, unpaired Student’s t test. (I) mRNA expression levels of ACE2 in control or diabetic kidney organoids upon actinomycin D treatment. Data are mean ± SD. n = 2 independent biological replicates from a pool of 12 organoids/group with three technical replicates each. ns, no statistical significance; ****p < 0.0001, two-way ANOVA followed by Bonferroni post-test. (J) Experimental scheme for the infection of control or diabetic kidney organoids with SARS-CoV-2. (K) Immunofluorescence of control and diabetic kidney organoids at 1 dpi with SARS-CoV-2 for ACE2 (green), viral nuclear protein (NP, red), LTL (magenta), and DAPI (blue). Scale bars, 250 and 50 mm (magnified views). n = 2 organoids per condition. (L) Quantification of viral NP colocalization with LTL+, CD31+, and WT1+ cells in control or diabetic kidney organoids. Data are mean ± SD. n = 3 control and n = 3 diabetic organoids. ****p < 0.0001, one-way ANOVA, Tukey’s multiple comparisons test. (M) Quantification of the area of NP+ cells from images in (K). Data are mean ± SD. n = 5 control and n = 5 diabetic kidney organoids performing two technical replicates each. *p < 0.05, unpaired Student’s t test. See also Figure S2 and Data S1. Cell Metabolism 34, 857–873, June 7, 2022 861 ll OPEN ACCESS Article Figure 3. Diabetic-induced metabolic programming in human kidney organoids enhances SARS-CoV-2 infection (A) Uniform manifold approximation and projection (UMAP) of control or diabetic kidney organoids at 1 dpi with SARS-CoV-2. Clusters are colored by annotated cell types. (B) UMAPs for SARS-CoV-2 expression in control or diabetic kidney organoids at 1 dpi. For SARS-CoV-2, expression is considered as undetectable for cells expressing <5 unique molecular identifiers (UMIs). Cells are colored based on expression level. The violin plots in the bottom panels represent R5 UMI expression levels for the different cell types indicated. (C) A hallmark GSEA was performed separately for control and diabetic conditions, comparing SARS-CoV-2-infected versus mock organoids. The ten gene sets per direction and sample with lowest adjusted p values (p < 0.05) are shown. Each column corresponds to one of the comparisons. Circles are coded by color (direction), size (NES), and transparency (log10(p value)). (D) Differentially expressed genes (DEGs) in the comparison of the SARS-CoV-2-infected against mock organoids in diabetic conditions considering only renal- like cell types. In the volcano plot, the x axis indicates log fold change (FC), and the y axis indicates statistical significance with the log10(p value). Genes with an adjusted p value < 0.05 are considered upregulated (red) if the logFC > 0.1 and downregulated (blue) if the logFC < 0.1. Non-DEGs are shown in gray. See also Figures S3 and S4. organoids at 1 dpi again showed increased numbers of cells injury, and inflammatory responses driven by SARS-CoV-2 containing viral RNA compared with control organoids (Fig- infection (Jansen et al., 2021). Our results also showed that ure 3B). As expected from an active infection, SARS-CoV-2 SARS-CoV-2 infections were also associated with downregula- infections resulted in an increase of inflammatory-related tion of glycolysis-related processes in diabetic organoids (Fig- processes in kidney organoids under control and diabetic ure 3C). In addition to altered glycolysis, differential gene expres- conditions (Figure 3C) in agreement with previous findings in kid- sion analysis showed increased inflammation (e.g., CXCL family ney organoids showing increased profibrotic signaling, cellular genes) and diabetic-related pathways (e.g., CEBPD and STAT3) 862 Cell Metabolism 34, 857–873, June 7, 2022 ll Article OPEN ACCESS A B C D E F Figure 4. SARS-CoV-2 infection in human kidney organoids depends on ACE2 (A) mRNA expression levels of ACE2 in mock or SARS-CoV-2-infected kidney organoids. Data are mean ± SD. n = 2 independent biological replicates from a pool of 12 organoids/group with two technical replicates each. *p < 0.05, unpaired Student’s t test. (B) Immunofluorescence staining for ACE2 (green) and LTL (magenta) in WT and ACE2 KO kidney organoids. Scale bars, 50 mm. (C) Experimental scheme for the infection with SARS-CoV-2 of WT and ACE2 KO kidney organoids. Immunofluorescence of mock or SARS-CoV-2-infected WT and ACE2 KO kidney organoids at 3 dpi for ACE2 (green), viral nuclear protein (NP, red), LTL (gray), and DAPI (blue). Scale bars, 250 mm. (D) mRNA expression of SARS-CoV-2 and ACE2 in mock or SARS-CoV-2-infected WT and ACE2 KO kidney organoids at 3 dpi by qPCR. Data are mean ± SD. n = 2 independent biological replicates from a pool of 12 organoids/group with two technical replicates each. ****p < 0.0001; ns, no statistical significance; one- way ANOVA, Tukey’s multiple comparisons test. (E) TEM analysis of WT and ACE2 KO kidney organoids infected with SARS-CoV-2 and recovered at 3 dpi. Representative images of infected ACE2 WT specimen show numerous viral particles (asterisks) inside a vesicle near the plasma membrane of a dying cell (1) and in the apical microvilli (amv) of a tubular-like cell (2). Details for podocyte-like cells exhibiting podocyte-related structures including primary processes (pp) (3), the deposition of a basement membrane (bm) (3), (legend continued on next page) Cell Metabolism 34, 857–873, June 7, 2022 863 ll OPEN ACCESS Article in SARS-CoV-2-infected diabetic kidney organoids compared WT organoids as shown by gene set enrichment analysis (GSEA) with non-diabetic counterparts (Figures 3D and S3D–S3F). (Figure S6D). These results are in line with previous observations Recent findings showed that glycolysis sustains SARS-CoV-2 that genetic deletion of ACE2 or pharmacological inhibition of replication in human monocytes exposed to high glucose ACE2 induces renal oxidative stress and promotes diabetic renal (11 mM) (Codo et al., 2020). Considering our findings that injury (Soler et al., 2007; Wysocki et al., 2014). SARS-CoV-2 infection in diabetic kidney organoids led to a Next, control WT and ACE2 KO kidney organoids were in- decrease in glycolysis, we also investigated the impact of fected with SARS-CoV-2 and virus infection was monitored at SARS-CoV-2 infection in kidney organoids exposed to 11 mM 3 dpi (Figure 4C). Downregulation of ACE2 expression upon glucose culture conditions. SARS-CoV-2 infections did not alter infection in WT organoids was re-confirmed at the protein level cell clustering or cell-type proportions across mock and 1 dpi (Figure S6E). SARS-CoV-2 infection in ACE2 WT organoids kidney organoids grown in 11 mM glucose (Figures S4A and was shown based in viral NP detection in LTL+ cells by immuno- S4B). Moreover, cells presenting high expression of viral RNA fluorescence (Figure 4C) and the expression of SARS-CoV-2 were mainly located in the loop of Henle and proximal tubule mRNA by qPCR (Figures 4D and S7). In contrast, ACE2 deletion cell clusters, again co-expressing ACE2 (Figure S4C). Impor- in ACE2 KO kidney organoids resulted in a complete absence of tantly, following SARS-CoV-2 infections, we observed a switch NP+ cells and almost undetectable levels of SARS-CoV-2 mRNA from oxidative phosphorylation (OXPHOS) to a glycolytic-based (Figures 4C, 4D, and S7). TEM analysis corroborated the metabolism in the kidney organoids exposed to 11 mM glucose absence of SARS-CoV-2 viral particles in ACE2 KO kidney orga- in agreement with recent findings in human monocytes (Fig- noids (Figure 4E), whereas viral particles were present in the WT ure S4D) (Codo et al., 2020). Analysis for differentially expressed background (Figure 4F). No significant changes in the levels of genes confirmed those findings together with enrichment in IFN expression of podocyte marker genes (WT1, PODXL, NPHS1, and TNF, IL-2/6, and mTORC1 signaling (Figures S4D–S4F). NPHS2, and MAFB) or for tubular marker genes (SLC3A1 and SLC16A1) were found after infection comparing WT and ACE2 ACE2 is essential for SARS-CoV-2 infections in kidney KO kidney organoids (Figure S7). organoids The ACE2 receptor is sufficient for SARS-CoV-2 entry into hu- Impact of SARS-CoV-2 infections in BSG and NRP1 KO man cells, though other receptors have been proposed (Hoff- kidney organoids mann et al., 2020). Whether ACE2 is essential for SARS-CoV-2 To further validate the unique role of ACE2 in SARS-CoV-2 infec- infections is still not known. We and others have previously tion in kidney organoids, we generated BSG KO hPSCs using shown that ACE2 expression protects the lung from injury and CRISPR-Cas9 (Figures 5A and 5B). Upon differentiation, both that its expression is downregulated in vivo and in vitro upon the respective WT and BSG KO kidney organoids exhibited the SARS-CoV and SARS-CoV-2 spike protein engagement on the presence of nephron-like structures containing podocyte-like cell membrane (Haga et al., 2008; Imai et al., 2005; Kuba et al., cells and tubular structures (Figure 5C). BSG deletion in BSG 2005; Triana et al., 2021). Here, we found that ACE2 mRNA KO kidney organoids was confirmed using qPCR (Figure 5D). levels were significantly downregulated in kidney organoids We also generated NRP1 KO hPSCs (Figures S8A and S8B), after SARS-CoV-2 infection compared with mock conditions confirming NRP1 deletion in NRP1 KO kidney organoids by (Figure 4A). western blotting (Figure S8C). To determine if BSG or NRP1 To test whether ACE2 is indeed essential for SARS-CoV-2 in- also plays a role in SARS-CoV-2 infection, as previously pro- fections in kidney organoids, we generated ACE2 knockout (KO) posed (Cantuti-Castelvetri et al., 2020; Daly et al., 2020; Wang hPSC lines using CRISPR-Cas9 genome editing (Figures S5A et al., 2020), WT and KO kidney organoids for BSG and NRP1 and S5B). Upon differentiation, H&E staining showed that both were infected with SARS-CoV-2 and analyzed at 3 dpi wild-type (WT) and ACE2 KO kidney organoids exhibited similar (Figures 5E and S8D, respectively). TEM analysis revealed the nephron-like structures containing podocyte-like and tubule presence of viral particles in both WT and BSG KO organoids proximal-like cells (Figure S5C). ACE2 deficiency was confirmed (Figure 5F). These results were confirmed by immunofluores- by immunofluorescence (Figure 4B) and western blotting (Fig- cence for the detection of NP+ (Figure 5G). Interestingly, we ure S5D). qPCR analysis of WT and ACE2 KO kidney organoids found decreased levels of SARS-CoV-2 mRNA expression in showed the detection of prototypic renal markers (Figure S5E) the infected BSG KO kidney organoids compared with WT coun- with no apparent changes in renal cell populations as revealed terparts (Figure 5H), suggesting that BSG deletion might have an by scRNA-seq (Figures S6A and S6B). In addition, the expres- impact on the infection susceptibility of the organoids. In sion of basigin (BSG, also known as CD147), proposed as contrast, the presence of NP+ and SARS-CoV-2 mRNA expres- another putative receptor for SARS-CoV-2 (Wang et al., 2020), sion levels were comparable between WT and NRP1 KO kidney was not affected by ACE2 deficiency as shown by immunofluo- organoids upon infection (Figures S8E and S8F). Overall, these rescence (Figure S6C). Interestingly, ACE2 KO kidney organoids findings do not support an indispensable role for BSG or NRP1 exhibited an upregulation in OXPHOS processes compared with in SARS-CoV-2 infections in human kidney organoids. and apical microvilli (amv) (4) are shown. Scale bars, 10 and 5 mm, 200 nm (magnified views in 1 and 2), and 2 mm (magnified views in 3 and 4). Representative images of infected ACE2 KO specimen show epithelial tubular-like cells with brush borders (bb) (5) and tight junctions (tj) (6). Details for podocyte-like cells with podocyte-related structures including primary (pp), the deposition of a basement membrane (bm), and cell processes (sp) are shown. Scale bars, 5 mm, 500 nm (magnified views in 5 and 6), and 2 mm (magnified views in 7 and 8). See also Figures S5–S7. 864 Cell Metabolism 34, 857–873, June 7, 2022 ll Article OPEN ACCESS A B C D E F G H Figure 5. SARS-CoV-2 infections in BSG KO kidney organoids (A) Schematic of Cas9/gRNA-targeting sites (pink arrows) in BSG locus showing exon structure (blue boxes) and PCR amplicons (light gray boxes in all figures in this study). Histogram shows allelic sequence distribution after the transfection of the different gRNAs in undifferentiated ES[4] cells expressing an inducible Cas9 (iCas9). WT, wild type; mut, mutation; FS, frameshift. (B) Representative sequence of the wild type (+/+) or BSG mutant clones generated with the different gRNAs. (C) Representative bright-field images of WT and BSG KO kidney organoids. Scale bars, 250 mm. Hematoxylin and eosin staining show tubular-like (**) and glomerular-like (*) structures. Scale bars, 250 and 50 mm (magnified views). (D) mRNA expression levels of BSG in WT and BSG KO kidney organoids by qPCR. Data are mean ± SD. n = 1 independent experiment from a pool of 12 organoids/group with at least two technical replicates each. (E) Experimental scheme for the infection with SARS-CoV-2 of WT and BSG KO kidney organoids. (F) TEM analysis of WT and BSG KO kidney organoids infected with SARS-CoV-2 at 3 dpi. Representative images of infected BSG WT specimens (left) show numerous viral particles in the cell surface of a dying cell (1 and 2). Details for podocyte-like cells (3) exhibiting podocyte-related structures including primary (pp) (legend continued on next page) Cell Metabolism 34, 857–873, June 7, 2022 865 ll OPEN ACCESS Article ACE2 is essential for SARS-CoV-2 infections in diabetic betic kidney organoid-isolated LTL+ (Figures S1G and S2C). In kidney organoids addition, diabetic HPTCs exhibited higher OCR, basal respira- We next assessed whether ACE2 is essential for the observed tion, ATP production, and maximal respiratory capacity increase in SARS-CoV-2 infections under our diabetic culture compared with control counterparts (Figure 7B), as well as conditions. To address this question, WT and ACE2 KO kidney increased LDHA mRNA levels (Figure S9C). Of note, diabetic organoids were exposed to control and diabetic conditions and HPTCs showed higher ACE2 expression levels compared analyzed at 1 dpi (Figure 6A). Importantly, infected ACE2 KO with control HPTCs (Figures S9D and S9E). Upon SARS- kidney organoids were negative for NP detection irrespective CoV-2 infection, we detected increased numbers of NP+ cells of the control or diabetic treatment. On the contrary, ACE2 (Figures 7C and 7D) and elevated SARS-CoV-2 mRNA expres- WT kidney organoids again displayed significantly enhanced sion levels in diabetic HPTCs compared with non-diabetic viral NP expression within the LTL+ structures under high oscil- controls (Figure 7E). latory glucose conditions (Figure 6B) as confirmed by qPCR The diabetic-induced metabolic programming observed in analysis (Figure 6C). Similarly, TEM analysis also confirmed HPTCs from diabetic patients prompted us to explore the molec- the presence of viral particles in ACE2 WT kidney organoids ular mechanisms at the interface between glycolysis and compared with ACE2 KO counterparts (Data S1). As expected, OXPHOS during SARS-CoV-2 infections. To assess the role of ACE2 mRNA and protein levels remained undetectable in the OXPHOS in viral infection, we treated the cells with DCA, an in- diabetic ACE2 KO kidney organoids (Figures 6D and 6E). To hibitor of mitochondrial pyruvate dehydrogenase kinase (PDK) further explore the role of ACE2 in SARS-CoV-2 infections, (Figure S9F), which results in activation of mitochondrial we tested whether restoration of ACE2 expression in the OXPHOS at the expense of glycolysis (Bonnet et al., 2007; ACE2 KO kidney organoids through lentiviral transduction Cook et al., 2021; Eleftheriadis et al., 2013). Importantly, when was sufficient to rescue SARS-CoV-2 infectivity in control and diabetic and non-diabetic patient-derived HPTCs were treated diabetic culture conditions. ACE2 transduced (ACE2t) organo- with DCA, we observed a significant decrease in SARS-CoV-2 ids were subsequently infected with SARS-CoV-2 (Figure 6F). mRNA expression levels (Figures 7F and 7G). Therefore, our re- At 1 dpi, ACE2t kidney organoids showed similar levels of sults show that both altered energy metabolism and increased ACE2 expression (Figures 6F and 6G), and we were able to ACE2 impact SARS-CoV-2 infections in kidney patient cells. detect NP+ (Figure 6F). Quantification of NP+ in confocal im- ages revealed that organoids exposed to diabetic conditions DISCUSSION showed a significant increase of NP+ compared with their con- trol counterparts (Figure 6H). Thus, diabetic conditions sustain There is an urgent need to better understand the complex rela- enhanced SARS-CoV-2 infections in kidney organoids, which is tionships between pre-existing conditions in COVID-19 patients, critically dependent on ACE2 expression. which may exacerbate viral infection and disease outcomes. Diabetes has emerged as one of the most frequent co-morbid- Enhanced SARS-CoV-2 infection in kidney cells from ities associated with severity and mortality of COVID-19. Here, diabetic patients we developed a unique culture system to generate diabetic hu- Although several studies could not detect direct SARS-CoV-2 man kidney organoids, based on oscillation of glucose levels infection in kidneys (Golmai et al., 2020; el Jamal et al., 2021; also observed in DM patients. Our approach preserved renal Santoriello et al., 2020; Volbeda et al., 2021), other recent in- cell types while recapitulating early hallmarks of diabetic kidney vestigations have clearly shown that SARS-CoV-2 can directly disease (Sas et al., 2016). infect renal cells (Braun et al., 2020; Diao et al., 2021; Jansen When analyzing kidney organoids for the expression of the et al., 2021; Puelles et al., 2020). However, whether the kidney SARS-CoV-2 entry receptor ACE2, we consistently observed damage observed results from direct infection of target renal ACE2 positivity in tubular-like cells by confocal microscopy, con- cells or indirect injury responses is not yet fully understood firming our previous observations using single-cell profiling in (Braun et al., 2020; el Jamal et al., 2021; Puelles et al., 2020; human kidney organoids (Monteil et al., 2020) and earlier reports Volbeda et al., 2021). We therefore isolated kidney human using single-cell profiling (Zou et al., 2020) and immunohisto- proximal tubular cells (HPTCs) from kidney biopsies of non-dia- chemistry in the mouse (Soler et al., 2013; Ye et al., 2006) and hu- betic (control) and diabetic patients (Figures 7A and S9A). The man kidney (Lely et al., 2004). Recently, single-cell profiling from mRNA levels of PGC1a were significantly downregulated in dia- 436 patients suggested that increases in ACE2 expression within betic HPTCs compared with control counterparts (Figure S9B), lungs and kidneys may increase the risk of SARS-CoV-2 infec- paralleling our findings in diabetic kidney organoids and in dia- tions (Jiang et al., 2020). Our results now show that a high and the deposition of a basement membrane (bm). Scale bars, 2 mm, 200 nm (magnified views in 1 and 2), and 2 mm (magnified views in 3). Representative images of infected BSG KO specimens (right) show numerous viral particles in the intercellular space (4) and in the cell surface (5). Details for podocyte-like cells (6) exhibiting podocyte-related structures including primary (pp) and the deposition of a basement membrane (bm). Scale bars, 2 and 5 mm, 200 nm (magnified views in 4 and 5), and 1 mm (magnified view in 6). (G) SARS-CoV-2 mRNA expression levels in mock-treated or SARS-CoV-2-infected WT and BSG KO kidney organoids at 3 dpi by qPCR. Data are mean ± SD. n = 1 independent experiment from a pool of 12 organoids/group with at least two technical replicates each. (H) Immunofluorescence of mock or SARS-CoV-2-infected WT and BSG KO kidney organoids at 3 dpi for ACE2 (green), viral nuclear protein (NP, red), LTL (gray), and DAPI (blue). Scale bars, 250 mm. See also Figure S8. 866 Cell Metabolism 34, 857–873, June 7, 2022 ll Article OPEN ACCESS A B C D E F G H Figure 6. ACE2 expression in diabetic human kidney organoids sustains SARS-CoV-2 infections (A) Experimental scheme for the infection with SARS-CoV-2 of WT and ACE2 KO kidney organoids under control or diabetic conditions. (B) Immunofluorescence of mock or SARS-CoV-2-infected specimens under control or diabetic conditions at 1 dpi for ACE2 (green), virus nuclear protein (NP, red), LTL (magenta), and DAPI (blue). Scale bars, 250 mm. n = 3 organoids per condition. (legend continued on next page) Cell Metabolism 34, 857–873, June 7, 2022 867 ll OPEN ACCESS Article oscillatory glucose regime induces the expression of ACE2 at SARS-CoV-2 infection, such as immune activation or altered both the mRNA and protein levels, consistent with previous find- coagulation, or whether it is also due to a direct SARS-CoV-2 ings showing increased ACE2 levels in the kidney cortex from infection of specific organs. We first described that kidney orga- db/db mice and STZ diabetic mice (Ye et al., 2004) and proximal noids can shed progeny SARS-CoV-2 viruses (Monteil et al., tubular cells in kidney biopsies from patients with diabetic kidney 2020, 2021), and later investigations confirmed SARS-CoV-2 disease (Menon et al., 2020). Importantly, concurrent with kidney tropism (Braun et al., 2020; Diao et al., 2021; Jansen enhanced ACE2 expression, we observed increased viral loads et al., 2021; Puelles et al., 2020), including the ability to replicate at the mRNA and protein levels in the diabetic kidney organoids. in human kidney cells, thereby establishing an association of kid- Following infection, we detected a marked decrease in ACE2 ney infection by SARS-CoV-2 with shorter survival time and mRNA expression in accordance with previous results using co- increased incidence of AKI in COVID-19 patients (Braun et al., lon- and ileum-derived human intestinal organoids (Triana et al., 2020; Puelles et al., 2020). However, it should be noted that other 2021). Single-cell profiling showed a decrease in OXPHOS and studies failed to detect SARS-CoV-2 in kidneys (Golmai et al., decrease in glycolytic-based metabolism as well as a hypoxic 2020; el Jamal et al., 2021; Santoriello et al., 2020; Volbeda signature in response to SARS-CoV-2 infection in diabetic et al., 2021). Our results now indicate that imbalances in cellular organoids. metabolism and ACE2 expression in kidney organoids due to Nearly two decades ago, we and others showed in ace2 elevated glucose levels directly lead to higher viral loads upon mutant mice that ACE2 is a negative regulator of the RAS system infection, potentially leading to a switch from an OXPHOS to and genetically controls cardiovascular function and damage of an aerobic glycolytic state that could further contribute to higher multiple organs such as the lung, liver, and kidney (Clarke and susceptibility to SARS-CoV-2 infection. Similar results were Turner, 2012; Crackower et al., 2002). From the beginning of observed in kidney proximal tubular cells isolated from diabetic the pandemic, ACE2 took center stage in the COVID-19 patients, which exhibited increased altered mitochondrial respi- outbreak as a receptor for the spike glycoprotein of SARS- ration and enhanced glycolysis that correlated with higher CoV-2 (Wrapp et al., 2020; Zhou et al., 2020), which spurred SARS-CoV-2 infections compared with non-diabetic samples. the development of vaccines and therapies targeting the Interestingly, the exposure of kidney patient cells to a metabolic ACE2-SARS-CoV-2 spike interaction. Since various other candi- modulator that boosts mitochondrial OXPHOS at the expense of date receptors have been reported (and considering the drug glycolysis resulted in decreased SARS-CoV-2 infection, sug- and vaccine development landscape), it is therefore paramount gesting that both ACE2 expression and altered metabolism to establish whether ACE2 is not only sufficient for infection affect the outcome of SARS-CoV-2 infection in human kidney but is in fact essential. To evaluate the key role of ACE2 in cells, which is in line with findings in our ACE2 re-expression SARS-CoV-2 infection, we therefore generated ACE2 KO hPSCs experiments. derived kidney organoids, which showed preserved renal differ- Overall, our results provide insights into the identification of entiation; though, in line with the previously reported functions of diabetic-induced metabolic programming as a critical event ACE2, these ACE2 mutant organoids displayed differences with that increases the susceptibility of kidney cells to SARS-CoV-2 regard to OXPHOS, lipid metabolism, as well as angiogenesis/ infection. We hope that our observation will open the door to endothelium (Peña-Silva et al., 2012). To further demonstrate the identification of new interventions in COVID-19 pathogenesis the unique role of ACE2 for viral entry, we also generated BSG/ targeting energy metabolism. CD147 KO and NRP1 KO hPSC lines as others have highlighted its role in SARS-CoV-2 infection (Cantuti-Castelvetri et al., 2020; Limitations of study Daly et al., 2020; Shilts et al., 2021; Wang et al., 2020). However, Future work will be needed to address limitations of our study. (1) BSG KO and NPR1 KO kidney organoids clearly supported viral Utilizing more complex culture systems, including the assembly infection as demonstrated by confocal microscopy and TEM of a vascular component to kidney organoids, would help to analysis, thus excluding an essential role of BSG or NRP1 in renal further explore the impact of a high oscillatory glucose regimen SARS-CoV-2 infections. in a more physiologically relevant context while also allowing For many of the systemic manifestations of COVID-19, it is un- us to better mirror diabetic endothelial cell dysfunction in the clear whether the pathology is a secondary ‘‘side effect’’ of the renal context. (2) The design of our studies focused on the early (C) SARS-CoV-2 mRNA expression levels of mock or SARS-CoV-2-infected WT and ACE2 KO kidney organoids in control or diabetic conditions by qPCR. Data are mean ± SD. n = 1 independent experiment from a pool of 12 organoids/group with two technical replicates each. (D) ACE2 mRNA expression levels of mock or SARS-CoV-2-infected WT and ACE2 KO kidney organoids in control or diabetic conditions by qPCR. Data are mean ± SD. n = 1 independent experiment from a pool of 12 organoids/group with two technical replicates each. (E) Protein levels of ACE2 in WT and ACE2 KO kidney organoids under control or diabetic conditions by western blot analysis. b-actin was used as loading control. Data from a pool of 12 organoids/group are shown. (F) Lentiviral transduction of ACE2 in ACE2 KO kidney organoids (ACE2t) under control or diabetic conditions. Immunofluorescence of mock or SARS-CoV-2- infected specimens at 1 dpi for the viral nuclear protein (NP, red), ACE2 (green), LTL (white), and DAPI (blue). Scale bars, 250 and 50 mm (magnified views). n = 1 organoid (mock) and n = 2 organoids (SARS-CoV-2 infected). (G) Quantification of ACE2 expression (shown as integrated density-IntD) in (F). Data are mean ± SD. n = 2 organoid per condition performing two technical replicates. No statistically significant differences were observed. One-way ANOVA, Tukey’s multiple comparisons test. (H) Quantification of the area of NP+ cells in (F). Data are mean ± SD. n = 2 organoid per condition performing two technical replicates. *p < 0.05, one-way ANOVA, Tukey’s multiple comparisons test. See also Data S1. 868 Cell Metabolism 34, 857–873, June 7, 2022 ll Article OPEN ACCESS A B C D E F G Figure 7. SARS-CoV-2 infection in tubular epithelial cells derived from diabetic human kidney biopsies (A) Representative bright-field images of HPTCs from non-diabetic (control) or diabetic patient kidney biopsies. Scale bars, 100 mm. (B) Seahorse analysis of control and diabetic HPTCs. The oxygen consumption rate (OCR) data are normalized to total protein. Basal respiration, cellular ATP production, and maximal respiration are shown. Data are mean ± SD from at least n = 3 biological replicates/group. *p < 0.05; **p < 0.005; ***p < 0.0005; ****p < 0.0001, one-way ANOVA, Tukey’s multiple comparisons test. (C) Experimental scheme for SARS-CoV-2 infection in control or diabetic HPTCs. Immunofluorescence of infected control or diabetic HPTCs at 1 dpi for the viral nuclear protein (NP, red) and DAPI (blue). Scale bars, 100 mm. (D) Quantification of NP+ cells in (C). Data are mean ± SD. n = 2 independent biological replicates per condition performing at least six technical replicates. ****p < 0.0001, one-way ANOVA, Tukey’s multiple comparisons test. (E) qPCR analysis of SARS-CoV-2-infected control or diabetic HPTCs at 1 dpi for the detection of SARS-CoV-2 mRNA. Data are mean ± SD. n = 2 independent biological replicates per condition with at least three technical replicates. **p < 0.01; ****p < 0.0001, one-way ANOVA, Tukey’s multiple comparisons test. (F) Experimental scheme for SARS-CoV-2 infection in control or diabetic HPTCs treated with DCA or vehicle for 16 h prior infection. (G) qPCR analysis of mock or SARS-CoV-2-infected control or diabetic HPTCs exposed to DCA or vehicle at 1 dpi. Data are mean ± SD. n = 1 independent exper- iment with at least two technical replicates. **p < 0.005; ***p < 0.001; ****p < 0.0001, one-way ANOVA, Tukey’s multiple comparisons test. See also Figure S9. Cell Metabolism 34, 857–873, June 7, 2022 869 ll OPEN ACCESS Article stages of infection. As such, we cannot make any predictions N.M. N.M. is also supported by the MCIN/AEI/ 10.13039/501100011033 with respect to the observed changes at later stages of the dis- (PID2020-119929RB-I00) and MCIN/European Union—‘‘Next Generation EU/PRTR’’ (PLEC2021-008127) and the Generalitat de Catalunya and CERCA ease process. In this regard, inflammation represents a complex Programme (2017 SGR 1306), and the IBEC Faster Future program (A por la network of pathways that are influenced by external processes, COVID-19). This work was supported in part by the ISCIII and co-financed which currently cannot be simulated in our model. (3) We did not by FEDER (TERCEL RETIC RD16/0011/0005, RD16/0011/0027, PTC20/ study pancreatic organoids, and the pancreas is one of the major 00013, and PTC20/00130 to N.M.), CIBERONC (CB16/12/00489 to F.P., metabolic organs that could explain later complications of A.V.-Z., and A.U.-A.), Gobierno de Navarra Departamento de Desarrollo COVID-19. Económico y Empresarial AGATA (0011-1411-2020-000011), and DIANA (0011-1411-2017-000029) to F.P., A.V.-Z., and A.U.-A. F.G. is supported by the Ramon y Cajal Grant—Biomedicine (RYC-2014-16751) from the Ministry STAR+METHODS of Economy and Competitivity (MINECO), Spain. A.M. is supported by the IBEC International PhD Programme ‘‘La Caixa’’ Severo Ochoa fellowships Detailed methods are provided in the online version of this paper (LCF/BQ/SO16/52270019). This project has received funding from the Euro- and include the following: pean Union’s Horizon 2020 research and innovation program under grant agreement no. 874827 BRAV3 to N.M. and C.T. M.L.S. and S.B. are supported d KEY RESOURCES TABLE by start-up funds from the College of Medicine at the University of Florida, d RESOURCE AVAILABILITY Gainesville. J.M.P. and the research leading to these results have received B Lead contact funding from the T. von Zastrow Foundation, the FWF Wittgenstein award (Z 271-B19), the Austrian Academy of Sciences and the Canada 150 Research B Materials availability Chairs Program F18-01336, as well as the Canadian Institutes of Health B Data and code availability Research COVID-19 grants F20-02343 and F20-02015. O.H.A. is supported d EXPERIMENTAL MODEL AND SUBJECT DETAILS by a Swiss National Science Foundation fellowship (P400PM_194473/1). B Cells A.M. is supported by Swedish Research Council (2018-05766). This project B SARS-CoV-2 isolate has received funding from the Innovative Medicines Initiative 2 Joint Undertak- d METHOD DETAILS ing (JU) under grant agreement no. 101005026. The JU receives support from the European Union’s Horizon 2020 research and innovation program and B Generation of genome edited hPSC lines EFPIA (A.M., J.M.P., and N.M.). This research has been supported by the proj- B Kidney organoid differentiation ect Ayudas SARS-CoV-2/COVID-19 (ACE2-ORG-COV20/00278) from Insti- B Glucose challenge in kidney organoids tuto de Salud Carlos III/FEDER to A.U.-A., F.P., and N.M. and from Instituto B Isolation of proximal tubular-like cells from kidney or- de Salud Carlos III and European Union—Next Generation EU, Plan de Recu- ganoids peración Transformación y Resiliencia (TERAV/ISCIII RD21/0017/0018) to B ACE2 mRNA half-life in kidney organoids exposed to N.M. This project has received funding from Ayudas Fundación BBVA a Equi- 5mM and 5-25 mM glucose conditions pos de Investigación Cientı́fica SARS-CoV-2 y COVID-19 through the project ‘‘Identifying SARS-CoV-2-host cell interactions exploiting CRISPR-Cas9-engi- B ACE2 lentiviral production and transduction of kidney neered human organoids: through the development of specific therapies organoids against COVID19’’ (A.M., J.M.P., and N.M.) and Fundació la Marató de TV3 B SARS-CoV-2 infections of kidney organoids and prox- (201910-31 and 202125-3) to N.M. imal tubular kidney cells B SARS-CoV-2 infections of proximal tubular kidney cells exposed to DCA AUTHOR CONTRIBUTIONS B Single-cell RNA sequencing This study was conceived and designed by N.M. N.M. and J.M.P. wrote the B Protein extraction and western blot analysis in kidney paper. E.G. and P.P. performed all cell culture experiments in wild-type cell organoids lines. E.G. and P.P. performed all cell culture experiments in genome-edited B Histology and Immunocytochemistry cell lines. E.G. and P.P. performed all histological analysis. E.G. performed B Electron microscopy TEM analysis. E.G., P.P., A.M., G.J., A.L., A.H., R.O., D.M.-R., C.T., P.D.-P., B qRT-PCR O.H.A., J.M.P., and N.M. performed organoid data analysis including qPCR, B Oxygen consumption rate (OCR) western blot, and image quantification. M.L.S., V.M., S.B., and A.M. performed all the experiments involving SARS-CoV-2, including isolation, and helped with d QUANTIFICATION AND STATISTICAL ANALYSIS manuscript editing. F.G. and A.M. performed CRISPR-Cas9 lines. M.L.S., B scRNA-Seq data analysis S.B., and A.V.-Z. prepared scRNA-seq samples. A.U.-A., J.P.R., and F.P. per- B Image analysis formed all the single-cell data analysis on genome-edited derived organoids. B Data representation and statistical analysis A.G. contributed to Seahorse analysis in LTL+ cells and HPTCs. C.H.P. per- formed one cell culture experiment to isolate LTL+ cells from kidney organoids SUPPLEMENTAL INFORMATION for seahorse analysis and performed experiments to detect COL3A1, COL4A1, and PGC1a by qPCR in kidney organoids and western blot to detect ACE2 and Supplemental information can be found online at https://doi.org/10.1016/j. NPR1. C.H.P. performed inmunofluorescence analysis to detect PODXL and cmet.2022.04.009. LTL in kidney organoids in Figure S1A and PGC1a, LTL, and NaK (sodium po- tassium ATPase) in LTL cells in Figure S2D. D.M.-R., P.-V.A., and J.M.C. per- ACKNOWLEDGMENTS formed patient cell isolation and characterization. We thank all members from our laboratories for helpful discussions and tech- nical support. This work has received funding from the European Research DECLARATION OF INTERESTS Council (ERC) under the European Union’s Horizon 2020 Research and Inno- vation Programme (StG-2014-640525_REGMAMKID to P.P. and N.M. and A patent has been submitted to use human organoids to study SARS-CoV-2 CoG-2020_101002478_ENGINORG to N.M.). This research has been sup- infections and possibly develop new therapies. J.M.P. is a shareholder of ported by EIT Health under grant ID 20366 (R2U-Tox-Assay) to E.G. and Apeiron Biologics, which is developing ACE2 decoys for COVID-19 therapy. 870 Cell Metabolism 34, 857–873, June 7, 2022 ll Article OPEN ACCESS Received: September 2, 2021 Daly, J.L., Simonetti, B., Klein, K., Chen, K.E., Williamson, M.K., Antón- Revised: February 2, 2022 Plágaro, C., Shoemark, D.K., Simón-Gracia, L., Bauer, M., Hollandi, R., et al. Accepted: April 19, 2022 (2020). Neuropilin-1 is a host factor for SARS-CoV-2 infection. 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Cell Metabolism 34, 857–873, June 7, 2022 873 ll OPEN ACCESS Article STAR+METHODS KEY RESOURCES TABLE REAGENT or RESOURCE SOURCE IDENTIFIER Antibodies Human ACE-2 Bio-Techne R&D Systems S.L Cat# AF933-SP; RRID: AB_355722 Recombinant Anti-Neuropilin 1 antibody Abcam Cat# ab81321; RRID: AB_1640739 a-Tubulin Abcam Cat# ab4074; RRID: AB_2288001 b-Actin Sigma Aldrich Cat# A5316; RRID: AB_476743 IRDye 800CW donkey anti-Goat IgG Licor Cat# 925-32214; RRID: AB_2687553 Secondary Antibody IRDye 800CW Goat anti-Rabbit IgG Licor Cat# 925-32211; RRID: AB_2651127 Secondary Antibody Human ACE2 Bio-Techne R&D Systems Cat# AF933; RRID: AB_355722 Laminin Merck Cat# L9393; RRID: AB_477163 Collagen I Abcam Cat# ab34710; RRID: AB_731684 Collagen IV Merck Cat# AB769; RRID: AB_92262 Fibronectin Abcam Cat# ab2413; RRID: AB_2262874 Lotus Tetragonolobus Lectin (LTL), Biotinylated Vector laboratories Cat# B-1325; RRID: AB_2336558 Podocin Merck Cat# P0372; RRID: AB_261982 E-Cadherin BD Bioscience Cat# 610181; RRID: AB_397580 Human Podocalyxin Biotinylated R&D Systems Cat# BAF1658; RRID: AB_356080 SARS-CoV/SARS-CoV-2 Nucleocapsid Abyntek Biopharma Cat# 40143-MM05; RRID: AB_2827977 CD147 Abcam Cat# ab666; RRID: AB_305632 Human Nephrin R&D Systems Cat# AF4269 RRID: AB_2154851 Recombinant PE Anti-Sodium Potassium ATPase Abcam Cat# ab209299 PGC1a R&D Systems Cat# NBP1-04676; RRID: AB_1522118 CD31 Abcam Cat#ab28364; RRID: AB_726362 MEIS1/2/3 Thermo Fisher Cat#39795; RRID: AB_2750570 Anti-Goat Alexa Fluor 488-conjugated Jackson ImmunoResearch Cat# 705-545-147; RRID: AB_2336933 Anti-Goat IgG Alexa Fluor 555-conjugated Fisher Scientific Cat# A-21432; RRID: AB_2535853 Anti-rabbit IgG Alexa fluor 488-conjugated Fisher Scientific Cat# A21206; RRID: AB_2535792 Anti-rabbit IgG Alexa fluor 555-conjugated Fisher Scientific Cat# A-31572; RRID: AB_162543 Anti-Mouse IgG CyTM3-conjugated Jackson ImmunoResearch Cat# 715-165-151; RRID: AB_2315777 Anti-Sheep IgG Alexa Fluor 555-conjugated Fisher Scientific Cat# A-21436; RRID: AB_2535857 Dylight 649 Streptavidin Vector Labs Cat# SA-5649; RRID: AB_2336421 Fluorescein-conjugated LTL Vector Labs Cat# FL-1321; RRID: AB_2336559 Anti- human ACE2-APC LS Bio Cat# LS-C275129 Normal Rabbit IgG APC-conjugated Control R&D systems Cat#. IC105A; RRID: AB_10174791 Recombinant Anti-Wilms Tumor Protein N/A Cat# ab89901; RRID: AB_2043201 antibody [CAN-R9(IHC)-56-2] Biological samples Human kidney tubular cells N/A N/A Chemicals, peptides, and recombinant proteins Essential 8 medium ThermoFisher Cat# A1517001 Vitronectin ThermoFisher Cat# A14700 Trypan blue solution Sigma Cat# T8154 EDTA solution ThermoFisher Cat# 15575-038 Accumax Stem Cell Technologies Cat# 07921 (Continued on next page) e1 Cell Metabolism 34, 857–873.e1–e9, June 7, 2022 ll Article OPEN ACCESS Continued REAGENT or RESOURCE SOURCE IDENTIFIER RPMI 1640 ThermoFisher Cat#21875-034 Advanced RPMI 1640 ThermoFisher Cat#12633020 GlutaMAX (200 mM) ThermoFisher Cat# 35050–038 Penicillin/Streptomicin ThermoFisher Cat# 15140122 Dulbecco’s Modified Eagle Medium (DMEM) ThermoFisher Cat# 11966025 glucose-free D-Glucose Merck Cat# G7021 D-Manitol Merck Cat# M9647 ITS Merck Cat#I3146 Fetal Bovine Serum (FBS) Gibco Cat# 10270-106 Human EGF R&D systems Cat# 236-EG-01M CHIR99021 Merck Cat#SML1046; CAS: 2 52917-06-9 Recombinant human FGF9 PeproTech Cat# 100-23 Heparin Merck Cat# H3149; CAS: 9041-08-1 Activin A R&D systems Cat# 338-AC-050 Cell culture grade distilled water ThermoFisher Cat# 15230-089 Protease inhibitor cocktail Roche Cat#11836153001 Phosphate buffered saline (PBS) pH 7.4 (1x) ThermoFisher Cat#1001–015 RIPA buffer Cell signaling Cat#9806 4–15% Mini-PROTEAN TGX Precast Protein Gels BioRad Cat# 4561083 SYBR Green PCR Master Mix Applied Biosystem Cat# KK4605 Fluoromount-G Southern Biotech Cat# 0100-01 Triton X-100 Sigma Cat# T8787 40 ,6-Diamidino-2-Phenylindole, Dihydrochloride (DAPI) ThermoFisher Cat# D1306 DNA dye Draq5 Abcam Cat# ab108410 Dimethyl Sulfoxide (DMSO) Merck Cat# D2650; CAS: 67-68-5 Paraformaldehyde Aname Cat# sc-281692 Donkey serum Millipore Cat# S30 Glutaraldehyde Sigma-Aldrich Cat# G7776 Sodium phosphate dibasic dihydrate R99.0% Sigma-Aldrich Cat# 71643 Sodium phosphate monobasic R98% Sigma-Aldrich Cat# S3139 Trizol ThermoFisher Cat# 15596018 Chloroform Sigma Merck Cat# 1.02445 CAS:67-66-3 2-Propanol Panreac Cat# 131090 CAS: 67-63-0 Ethanol VWR Cat# 100983.2500; CAS: 64-17-5 Actinomycin D Sigma Cat# A9415-5MG Nuclease-Free Water Ambion Cat# AM9937 Lipofectamine RNAiMAX Transfection Reagent ThermoFisher Cat# 13778150 Dichloroacetic acid Merck Cat# 347795-10G Renal Epithelial Cell Growth Medium Lonza Cat# CC-3190 Intercept (TBS) Blocking Buffer LICOR Cat# 927-60001 Critical commercial assays BCA Protein Assay Kit ThermoFisher Cat# 23225 cDNA Reverse Transcription Kit Applied Biosystems Cat# 4368813 Seahorse XFe96 FluxPak mini Agilent Technologies Cat# 102601-100 Chromium Single Cell 3’ Library & Gel Bead Kit V3 10X Genomics (USA) Cat# PN-1000075 NSQ 500/550 Hi Output KT v2.5 (75 CYS) Illumina (San Diego, Cat# 20024906 CA 92122 USA) Streptavidin/Biotin blocking kit Vector laboratories Cat# SP-2002 (Continued on next page) Cell Metabolism 34, 857–873.e1–e9, June 7, 2022 e2 ll OPEN ACCESS Article Continued REAGENT or RESOURCE SOURCE IDENTIFIER Deposited data scRNA seq data kidney organoids This study GEO: GSE181002 Data S1 Source Data This study https://doi.org/10.17632/3m6gs9hfsd.1 CRISPResso2parser.py open source https://doi.org/10.5281/zenodo.6457752; https://github.com/amarcog/ CRISPResso2parser.py Experimental models: Cell lines ES[4] Human Embryonic Stem Cell line The National Bank of Stem https://www.isciii.es/ Cells (ISCIII,Madrid) CBiPS1sv-4F-40 The National Bank of Stem https://www.isciii.es/ Cells (ISCIII,Madrid) Vero E6 ATCC CRL 1586 N/A Oligonucleotides crRNA sequences This paper N/A ACE2ko Cr1 (5’-3’) TAGACTACAATGAGAGGCTC N/A N/A ACE2ko Cr2 (5’-3’) GCCATTATATGAAGAGTATG N/A N/A BSGko Cr1 (5’-3’) CTTGGAGCCAAGGTCTTCTA N/A N/A BSGko Cr2 (5’-3’) TTCACTACCGTAGAAGACCT N/A N/A NRP1ko_Cr1 (5’-3’) GGAATTTGAAAGCTTTGACC N/A N/A NRP1ko_Cr2 (5’-3’) GGGGACTTTATCACTCCACT N/A N/A MiSeq oligos This paper N/A MiSeq_ACE2ko_F (5’-3’) ACACTCTTTCCCTACACGA N/A N/A CGCTCTTCCGATCTTGTGTGCTTTGGGATAACAGGT MiSeq_ACE2ko_R (5’-3’) GACTGGAGTTCAGACGTG N/A N/A TGCTCTTCCGATCTGCCACACAGAGAGCTTCAGG MiSeq_BSGko_F (5’-3’) ACACTCTTTCCCTACACGA N/A N/A CGCTCTTCCGATCTGGGGAGGAGCCGCAGGTTC MiSeq_BSGko_R (5’-3’) GACTGGAGTTCAGACGTG N/A N/A TGCTCTTCCGATCTCGTCCTCCTTCAGCACCACG MiSeq_NRP1ko_F (5’-3’) TGGGGAAGTTGTT N/A N/A TAAGTGGGA MiSeq_NRP1ko_R (5’-3’) ATCCATCCCAGAT N/A N/A TTCTAGCCG qPCR oligos This paper N/A SARS-CoV-2 (oligo1)_Forward GCCTCTTCTC Eurofins N/A GTTCCTCATCAC SARS-CoV-2 (oligo1)_Reverse AGCAGCATCA Eurofins N/A CCGCCATTG SARS-CoV-2 (oligo2)_Forward AGCCTCTTCT Eurofins N/A CGTTCCTCATCAC SARS-CoV-2 (oligo2)_Reverse CCGCCATTG Eurofins N/A CCAGCCATTC TMPRSS2_Forward GTCCCCACTGTCTACGAGGT Thermo Fisher N/A TMPRSS2_Reverse CAGACGACGGGGTTGGAAG Thermo Fisher N/A NRP1_Forward GGCGCTTTTCGCAACGATAAA Thermo Fisher N/A NRP1_Reverse TCGCATTTTTCACTTGGGTGAT Thermo Fisher N/A BSG_Forward CCGCAACCACCTTACTCG Thermo Fisher N/A BSG_Reverse GGACAGAGGTTTGGATGGTG Thermo Fisher N/A ACE2_Forward CGAAGCCGAAGACCTGTTCTA Thermo Fisher N/A ACE2_Reverse GGGCAAGTGTGGACTGTTCC Thermo Fisher N/A HK2_Forward AGCCCTTTCTCCATCTCCTT Thermo Fisher N/A HK2_Reverse AACCATGACCAAGTGCAGAA Thermo Fisher N/A (Continued on next page) e3 Cell Metabolism 34, 857–873.e1–e9, June 7, 2022 ll Article OPEN ACCESS Continued REAGENT or RESOURCE SOURCE IDENTIFIER LDHA_Forward GGAGATCCATCATCTCTCCC Thermo Fisher N/A LDHA_Reverse GGCCTGTGCCATCAGTATCT Thermo Fisher N/A PGC1a _Forward CTGCTAGCAAGTTTGCCTCA Thermo Fisher N/A PGC1a _Reverse AGTGGTGCAGTGACCAATCA Thermo Fisher N/A COL3A1_Forward AGGACTGACCAAGATGGGAA Thermo Fisher N/A COL3A1_Reverse AGGGGAGCTGGCTACTTCTC Thermo Fisher N/A COL4A1_Forward CCTTTTGTCCCTTCACTCCA Thermo Fisher N/A COL4A1_Reverse CTCCACGAGGAGCACAGC Thermo Fisher N/A RPLP0 _Forward CCATTCTATCATCAACGGGTACAA Thermo Fisher N/A RPLP0 _Reverse AGCAAGTGGGAAGGTGTAATCC Thermo Fisher N/A SLC16A1_Forward GGCTGTCATGTATGGTGGAG Thermo Fisher N/A SLC16A1_Reverse GACAAGCAGCCACCAACAATC Thermo Fisher N/A PODXL_Forward GATAAGTGCGGCATACGGCT Thermo Fisher N/A PODXL_Reverse GCTCGTACACATCCTTGGCA Thermo Fisher N/A WT1_Forward GCCAGGATGTTTCCTAACGC Thermo Fisher N/A WT1_Reverse CGAAGGTGACCGTGCTGTAA Thermo Fisher N/A NPHS1_Forward GGCTCCCAGCAGAAACTCTT Thermo Fisher N/A NPHS1_Reverse CACAGACCAGCAACTGCCTA Thermo Fisher N/A SLC3A1_Forward CACCAATGCAGTGGGACAAT Thermo Fisher N/A SLC3A1_Reverse CTGGGCTGAGTCTTTTGGAC Thermo Fisher N/A MAFB_Forward GACGCAGCTCATTCAGCAG Thermo Fisher N/A MAFB_Reverse CTCGCACTTGACCTTGTAGGC Thermo Fisher N/A Endoglin_Forward CCTACGTGTCCTGGCTCATC Thermo Fisher N/A Endoglin_Reverse GGTGTGTCTGGGAGCTTGAA Thermo Fisher N/A Meis1_Forward GGGCATGGATGGAGTAGGC Thermo Fisher N/A Meis1_Reverse GGGTACTGATGCGAGTGCAG Thermo Fisher N/A Vimentin_Forward GTTGACAATGCGTCTCTGG Thermo Fisher N/A Vimentin_Reverse TGTTCCTGAATCTGAGCCTG Thermo Fisher N/A VEGFR_Forward CACATTGGCCACCATCTGAAC Thermo Fisher N/A VEGFR_Reverse CCATCAGAGGCCCTCCTTG Thermo Fisher N/A PDFGRa_Forward GAGCGCTGACAGTGGCTACAT Thermo Fisher N/A PDFGRa_Reverse TCGTCCTCTCTCTTGATGAAGGT Thermo Fisher N/A PDK1_Forward GGATTGCCCATATCACGTCTTT Thermo Fisher N/A PDK1_Reverse TCCCGTAACCCTCTAGGGAATA Thermo Fisher N/A PDK2_Forward ATGAAAGAG ATCAACCTGCTTCC Thermo Fisher N/A PDK2_Reverse GGCTCTGGACATACCAGCTC Thermo Fisher N/A Plasmids psPAX2 Addgene Cat#12260; https://www.addgene.org/12260/ pMD2.G Addgene Cat#12259; https://www.addgene.org/12259/ pLENTI_hACE2_PURO Addgene Cat#155295; https://www.addgene.org/155295/ Software and algorithms Fiji ImageJ2 version 2.3.0 NIH https://imagej.net/software/fiji/ Prism 5 Graphpad Software https://www.graphpad.com/ scientific-software/prism Image Studio Lite Version 5.2 software LICOR https://www.licor.com/bio/ image-studio-lite/ FlowJo Software FlowJo N/A Cell Ranger 4.0.0 10x Genomics https://support.10xgenomics. com/single-cell-gene-expression/ software/downloads/latest (Continued on next page) Cell Metabolism 34, 857–873.e1–e9, June 7, 2022 e4 ll OPEN ACCESS Article Continued REAGENT or RESOURCE SOURCE IDENTIFIER Seurat R package 3.2.1 open source https://satijalab.org/seurat/ fgsea R package 1.16.0 open source http://bioconductor.org/packages/ release/bioc/html/fgsea.html enrichR R package v3.0 open source https://CRAN.R-project.org/ package=enrichR RESOURCE AVAILABILITY Lead contact Further information and requests for resources and reagents should be directed to and will be fulfilled by the Lead Contact, Nuria Montserrat (nmontserrat@ibecbarcelona.eu). Materials availability All unique organoids generated in this study are available from the Lead Contact with a completed Materials Transfer Agreement. Data and code availability d Raw sequencing data for the single cell kidney organoid reported in this paper were deposited in Gene Expression Omnibus (GEO) under the accession number GEO: GSE181002. d Original code has been deposited at Zenodo and is publicly available at https://doi.org/10.5281/zenodo.6457752. d All values used to generate the graphs of the paper, original western blot images and additional microscopy images can be found in the file Data S1 that is also available from Mendeley Data at https://doi.org/10.17632/3m6gs9hfsd.1. Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request. EXPERIMENTAL MODEL AND SUBJECT DETAILS Cells hPSC lines were obtained after the approval of the Ethics Committee from the Clinical Translational Program for Regenerative Med- icine in Catalonia (P-CMR [C]) and the Comisión de Seguimiento y Control de la Donación de Células y Tejidos Humanos del Instituto de Salud Carlos III (project numbers: 0336E/1517/2015; 0336E/4166/2020; 0336E/4168/2020; 0336E/1124/2021/ 0336E/2723/ 2021). hESC ES[4] line and CBiPS1sv-4F-40 iPSC line were obtained from The National Bank of Stem Cells (ISCIII, Madrid). Both hPSC lines were maintained and grown in Essential 8 medium (A1517001, ThermoFisher) in cell culture plates coated with 5 mg/mL vitronectin (A14700, ThermoFisher) with 5% CO2 at 37  C. Cells were passaged every 4–6 days by disaggregating hPSC colonies into small cell clusters using 0.5 mM EDTA (15575-038, Thermofisher). Vero E6 (ATCC CRL 1586) were maintained in Dul- becco’s modified Eagle’s medium (DMEM) (Gibco) supplemented with 10% fetal bovine serum (FBS; 10270-106, Gibco) and 1% penicillin/streptomycin (15140122, ThermoFisher). Primary renal proximal tubular epithelial cells were isolated as previously described (Baer and Geiger, 2008; Baer et al., 1997) upon ethics committee of Hospital Clinic de Barcelona approved the procedure (project number: HCB/2021/0119). Renal proximal tubular epithelial cells were isolated from healthy renal tissues after nephrectomy of renal cell carcinomas. Briefly, prior to cell isolation, the fibrous capsule and the inner medulla were removed and the pieces of tissue (1-mm2) were digested for 1 h with agitation at 37  C in Iscove’s Modified Dulbecco’s Medium containing 1% collagenase IV (Invitrogen). The digested tissue frag- ments were filtered in 300-, 100-, and 70-mm cell strainers (BD Biosciences), and cell suspensions were overlaid on a pre-cooled Percoll density gradient solution (starting density 1.07 g/ml, Amersham Biosciences) and centrifuged for 40 min at 4  C at 16,000 rpm. This procedure established a gradient with densities between 1.019 and 1.139 g/ml. The fraction between 1.05 and 1.076 g/ml was collected and washed three times in three volumes of cold Hanks’ buffered saline solution (Invitrogen). Finally, renal proximal tubular epithelial cells were plated on plastic plates in proximal tubular cell medium consisting of DMEM without glucose (11966025, Thermofisher) supplemented with 5% FBS (Gibco), 5 mM glucose (Merck), penicillin/streptomycin (15140122, ThermoFisher), ITS 1X (I3146, Merck), and 40 ng/mL human EGF (236-EG-01M, R&D systems) for expansion. Cells were also cultured in REGM medium (CC-3190, Lonza) when indicated. SARS-CoV-2 isolate SARS-CoV-2 were isolated on Vero-E6 cells from nasopharyngeal sample of patient diagnosed with COVID-19 in Sweden as described before in Monteil et al. (2020). Alternatively, SARS-CoV-2 (strain BavPat1) was obtained via the European Virology Archive. Virus were amplified in Vero E6 cells and tittered using a plaque assay as previously described (Becker et al., 2008) with fixation of cells 72 hours post infection. Virus was used at a passage 3. e5 Cell Metabolism 34, 857–873.e1–e9, June 7, 2022 ll Article OPEN ACCESS METHOD DETAILS Generation of genome edited hPSC lines ACE2 and BSG knockout lines were generated using engineered ES[4] and CB[40] PSC lines, in which a doxycycline-inducible Cas9 cassette was introduced at the AAVS1 safe harbor locus (Marco, 2019, 2020). Two crRNAs per gene were designed using the Alt-R Custom Cas9 crRNA Design Tool (IDT) (key resources table). IDT crRNA/tracrRNA duplexes were annealed following manufacturer’s guidelines and transfected using Lipofectamine RNAiMAX Transfection Reagent (13778150, Thermofisher) at 10 nM final concentra- tion in 0.25 million doxycycline-treated ES[4] or CB[40] PSCs/mL. Cells were grown 3-5 days after transfection and single-cell seeded at 10, 20, 50, 100 cells/cm2. Leftover cells were used for DNA purification and further pool editing analysis. Clonal lines were estab- lished by manual colony picking (48 clones/crRNA) and further replica-plated. One replica was stored in nitrogen, while the other was used for DNA purification and MiSeq analysis of a locus-specific pooled PCR library generated through two rounds of PCR introducing Illumina adapters and specific indexes labeling each clone (key resources table). Indel frequency in edited pools and allelic composition of clones was obtained by analyzing trimmed Fastq reads using CRIPResso2 webtool (Clement et al., 2019). Analyzed clones were automatically genotyped based on CRISPResso2 results by our in-house CRISPResso2parser.py scripts (available at https://doi.org/10.5281/zenodo.6457752). For each crRNA edit, at least one non-edited clone (+/+), one heterozygous clone (+/frameshift, or +/fs), and one homozygous or trans-heterozygous clone (fs/fs) were amplified, stocked, and used for further experiments. Kidney organoid differentiation Prior differentiation (day -1), undifferentiated hPSCs colonies were dissociated into cell clumps using 0.5 mM EDTA (15575- 038Thermofisher) at 37 C for 3 minutes. To obtain single cell suspensions samples were then incubated in Accumax (07921, Stem Cell Technologies) at 37 C for 3 additional minutes. Cells were counted using the Countess Automated Cell Counter (Invitrogen) and seeded at 100,000 cells/well on 24 multi-well plates coated with 5 mg/mL vitronectin in the presence of Essential 8 medium (A1517001, Life Technologies) at 37 C overnight. The next day (day 0), the differentiation was initiated by treating mono- layer cultures with 8 mM CHIR99021 (CHIR; SML1046, Merck) in Advanced RPMI medium consisting of Advanced RPMI 1640 basal medium (12633020, ThermoFisher) supplemented with 1% Penicillin-Streptomycin and 1% of GlutaMAX (35050061, ThermoFisher) for 3 days with daily medium changes. On day 3, monolayer cultures were treated with 200 ng/mL FGF9 (100- 23, Peprotech), 1 mg/mL heparin (H3149, Merck) and 10 ng/mL activin A (Act A) (338-AC-050, Vitro) in Advanced RPMI 1640 basal medium supplemented with 1% Penicillin-Streptomycin and 1% of GlutaMAX for 1 day. On day 4, cell monolayers were treated with 5 mM CHIR, 200ng/mL FGF9 and 1 mg/mL Heparin Advanced RPMI 1640 basal medium (12633020, ThermoFisher) supple- mented with 1% Penicillin-Streptomycin and 1% of GlutaMAX for 1 hour. After this 1 hour-treatment cell monolayers were disso- ciated using TrypLE Express Enzyme (1260402, ThermoFisher) for 1 min collected, and counted. Next, 100,000 cells/well were dispensed on a V-shape 96 multi-well plate (249935, ThermoFisher) and centrifuged at 300g for 5min. Cell spheroids were cultured in Advanced RPMI 1640 basal medium supplemented with 1% Penicillin-Streptomycin and 1% of GlutaMAX, 200ng/mL FGF9 and 1 mg/mL heparin for 7 days with medium changes every other day. From day 11, organoids were maintained in Advanced RPMI 1640 basal medium supplemented with 1% Penicillin-Streptomycin and 1% of GlutaMAX. From that stage medium was changed every other day. Glucose challenge in kidney organoids Normoglycemic conditions were emulated exposing kidney organoids to DMEM glucose-free (11966025, ThermoFisher) supple- mented with 5 mM D-Glucose (G7021, Merck) changing medium every day until day 23. High oscillatory glucose conditions involved the exposure of day 16 kidney organoids to DMEM glucose-free (11966025, ThermoFisher) supplemented with 5mM D-Glucose (5 mM glucose medium) on even days or with 25mM of D-Glucose (25 mM glucose medium) on odd days until day 23 (5-25mM oscil- latory glucose regime). 5 mM glucose medium was additionally supplemented with 20 mM D-Mannitol (M9647, Merck) to obtain the same molarity as in the 25 mM glucose medium. Isolation of proximal tubular-like cells from kidney organoids Kidney organoids were stained with fluorescein-conjugated LTL (FL-1321, Vector Laboratories) as described elsewhere (Garreta et al., 2019). Kidney organoids were then dissociated to single cells using Accumax (07921, Stem Cell Technologies) for 15 min fol- lowed by 0.25% (wt/vol) trypsin (25300–054, Life Technologies) for 15 min at 37  C. SA3800 software version 2.0.4 (SONY) was used to acquire flow cytometry samples in the Sony SA3800 spectral cell analyzer (SONY). FACSDiva software version 8.0.1 (BD Biosci- ences) was used in the FACS Aria Fusion instrument (BD Biosciences) for cell sorting experiments. FlowJo software version 10 was used to analyze the data. ACE2 mRNA half-life in kidney organoids exposed to 5mM and 5-25 mM glucose conditions Kidney organoids were treated with 50 mg/ml actinomycin D (Sigma) to inhibit transcription. At the indicated times (0, 4, and 8 hours) total RNA was isolated from the kidney organoids (two pools of 12 kidney organoids per time point) and relative ACE2 mRNA levels were determined by RT-qPCR. Cell Metabolism 34, 857–873.e1–e9, June 7, 2022 e6 ll OPEN ACCESS Article ACE2 lentiviral production and transduction of kidney organoids Lentiviruses containing full length human ACE2 were produced by polyethylenimine (PEI) transfection of HEK 293T cells (4mg pMDG2, 4mg sPAX, and 8ug lentivirus of interest per well). Corresponding plasmids were purchased from Addgene (see key resources table). Three days post-transfection the lentivirus containing supernatants were collected, filtered with 0.45mm filters and pelleted at 20,000 rpm for 3 hours. The virus pellet was resuspended in 200mL of Optimem, aliquoted and stored at -80  C. 10^6 lentivirus par- ticles were added to each kidney organoid 3 day prior to infection. Lentiviruses were removed at the time of infection and media con- taining SARS-CoV-2 or fresh media was added to organoids for 1 hour. Following virus infection, media was removed, organoids were washed 2x and fresh media was added. 24h post-infection organoids were harvested for RNA, protein or fixed in 4% PFA for immunofluorescence. SARS-CoV-2 infections of kidney organoids and proximal tubular kidney cells Kidney organoids were infected with 106 SARS-CoV-2 infectious particles (as determined in Vero cells) in Advanced RPMI medium (Thermofisher) in a volume of 50ml per well of a 96-well ultra-low attachment plate for 1 hour. Proximal tubular cells were seeded in 24-well plates in REGM medium. One day post-seeding, cells were infected with 106 MOI 1 (as determined in Vero cells) SARS-CoV-2 infectious particles in a final volume of 200ml per well in proximal tubular cell medium at 37 C for 1 hour followed by washing with PBS and adding 500ml of new proximal tubular cell medium. At the different days post-infection, samples (organoids or cells) were washed 3 times with PBS and then lysed using Trizol (Thermofisher), RIPA buffer (Thermofisher) or 4% paraformaldehyde (153799, Aname) for ulterior analysis. SARS-CoV-2 infections of proximal tubular kidney cells exposed to DCA Proximal tubular cells were seeded in 24-well plates in proximal tubular cell REGM medium. One day post-seeding cells were exposed to 10 mM DCA for 16 hours. After treatments cells were washed with PBS and subsequently infected with SARS-CoV-2 infectious particles (MOI 1) in a final volume of 200ml of REGM medium at 37 C for 1 hour. Cells were washed with PBS and 500ml of new REGM medium was added. At 1 day post-infection, cells were washed 3 times with PBS and then lysed using Trizol (Thermo- fisher) for further analysis. Single-cell RNA sequencing Single cell RNA sequencing was performed as described in our previous study (Monteil et al., 2020). Briefly, kidney organoids were collected and washed twice with PBS, and further dissociated into a single cell suspension by treating them with Accumax (07921, Stem Cell Technologies) for 15 min at 37 C followed by Trypsin-EDTA 0.25% (wt/vol) trypsin (25300-054, Life Technologies) for addi- tional 15 min at 37 C. The reaction was deactivated by adding 10% FBS. The cell suspension was then passed through a 40mm cell strainer. After centrifugation at 1,000 RPM for 5 minutes cell numbers and viability were analyzed using Countess Automated Cell Counter (Invitrogen). Then cell suspensions were loaded onto a well of a 10x Chromium Single Cell instrument (10x Genomics). Bar- coding and cDNA synthesis were performed according to the manufacturer’s instructions. Qualitative analysis was performed using the Agilent Bioanalyzer High Sensitivity assay. The cDNA libraries were constructed using the 10x Chromium Single cell 3’ Library Kit v3.1 according to the manufacturer’s original protocol. Libraries were sequenced on either Illumina NovaSeq 6000 or NextSeq 500 2x150 paired-end kits using the following read length: 28bp Read1 for cell barcode and UMI, 8bp I7 index for sample index and either 94bp Read2 in the 5 mM, 11 mM and 5-25 mM oscillatory glucose samples (diabetic samples). For ACE2 WT and ACE 2KO samples (ACE2 samples) 55bp read length was used for Read2. Libraries were pre-processed using Cell Ranger (4.0.0) from 10X Genomics (http://10xgenomics.com). Reads from the ACE2 samples were aligned to the reference human genome (GRch38), whilst diabetic samples were aligned to a custom reference built for this work including the SARS-CoV-2 genome (isolate Wuhan-Hu-1, NC_045512.2). Genome annotation corresponded to Ensembl v93, adding a single gene with a single exon spanning the entire SARS-CoV-2 sequence. The median number of unique molecular identifiers (UMIs) per cell was between 2,300 and 4,600, with a median of 1,300-2,200 genes detected per condition. Protein extraction and western blot analysis in kidney organoids Protein was extracted from kidney organoids cultured in 5 mM glucose, 5-25 mM oscillatory glucose or Advance RPMI with or w/o infection using RIPA buffer (ThermoFisher) supplemented with complete protease inhibitor cocktail (ThermoFisher). Samples were centrifuged at 13,000 g for 15 mins at 4 C. Supernatant were collected and protein concentration was measured using a bicincho- ninic acid (BCA) protein quantification kit (Thermo Scientific). For western blot analyses, 25 mg of protein were separated in 4-15% Mini-PROTEAN TGX precast gel (BioRad) and blotted onto nitrocellulose membranes. Membranes were blocked at room tempera- ture for 1 hour with TBS 1X-5% BSA. Membranes were then incubated in primary antibody (see key resources table) overnight at 4 C. The membranes were then washed with PBST (PBS 1X + 0.05% Tween20; Merck) for three times 5 minutes and incubated with the secondary antibody (see key resources table). After washing with PBST for 5 minutes twice and with PBS for 5 minutes once, mem- brane-bound antibodies were detected by fluorescence with the Odyssey Fc Imaging System. Beta-actin (1:5000, Sigma) or Alpha- tubulin (1:5000; Abcam) was used as a loading control for normalization and quantification. Images were analyzed with Image Studio Lite Version 5.2 software. e7 Cell Metabolism 34, 857–873.e1–e9, June 7, 2022 ll Article OPEN ACCESS Histology and Immunocytochemistry Specimens were fixed at the indicated time points with 4% paraformaldehyde (153799, Aname) overnight at 4 C. Samples were then washed twice with PBS, embedded in paraffin and sectioned into 5 to 10 mm samples. Sections were stained for Hematoxylin and Eosin, Masson’s trichrome and Periodic acid–Schiff. Images were captured using the AF7000 Leica microscope. Immunofluorescence samples were fixed with 4% paraformaldehyde (153799, Aname) for 20 minutes at room temperature, washed trice with PBS and blocked using Tris-buffered saline (TBS) containing 6% donkey serum (S30, Millipore) and 1% Triton X-100 (T8787, Sigma) for 1h at room temperature. Samples were treated overnight at 4 C with the primary antibodies indicated in key resources table diluted in antibody dilution buffer (TBS solution with 6% donkey serum and 0.5% Triton X-100). Samples were then washed three times with antibody dilution buffer and further incubated during 4h at room temperature with fluorescent conjugated secondary antibodies indicated in key resources table. For detection of LTL positive cells (LTL+), samples were stained with biotinylated LTL (B-1325, Vector Labs) using a streptavidin/biotin blocking kit (SP-2002, Vector Labs). LTL+ cells were detected using Alexa Fluor 488 conjugated with streptavidin (SA5488, Vector Labs). Nuclei were stained using 4,6-diamidino-2- phenylindole (DAPI; 1:5000, D1306, Life Technologies) for 30min. Samples were mounted in Fluoromount-G (0100-01, Southern Biotech) and visu- alized using a Zeiss LSM780 or LSM 880-Airyscan Elyra confocal microscope. Electron microscopy Specimens were fixed with 2.5% glutaraldehyde containing 1% tannic acid in 0.1 M phosphate buffer (PB, pH 7.4). Then samples were post-fixed for 1 hour at 4 C with 1% OsO4 in 0.1 M PB. Epoxy resin embedding was performed after graded ethanol series. Then toluidine blue staining was performed in semithin sections using a light microscope. Finally, ultrathin sections were prepared using an EM UC7 ultramicrotome (Leica Microsystems, Mannheim, Germany) and collected on copper grids. Then 4% uranyl acetate and lead citrate were used for staining. Samples were subsequently analyzed with a JEM 1230 electron microscope (JEOL, UK, Ltd). qRT-PCR RNA was isolated from cells and kidney organoids using Trizol (Invitrogen). 2 mg RNA was reverse transcribed using the cDNA archival kit (Life Technology), and qRT-PCR was run in the ViiA 7 System (Life Technology) machine using SYBRGreen Master Mix (Applied Biosystem) and gene-specific primers. The data were normalized and analyzed using the DDCt method. The primers sequences used are shown in key resources table. Oxygen consumption rate (OCR) The measurement of OCR in kidney organoid-derived cells and proximal tubular cells was performed using an XFe24 extracellular flux analyzer (Seahorse Bioscience) as previously described (Dhillon et al., 2021; Garreta et al., 2019). Cells were plated at density of 20,000 cells/well in a Seahorse cell culture microplate in warm Seahorse XF Assay Medium supplemented with glucose 5mM, py- ruvate 0.5mM and glutamine 2mM (Seahorse Bioscience). After 1 h of incubation at 37  C, plates were loaded into an XF24 respi- rometry machine (Seahorse Bioscience). Uncoupled and maximum OCR were assayed with oligomycin (10 mM) and FCCP (2 mM). To inhibit complex I- and III-dependent respiration, rotenone (5 mM) and antimycin A (15 mM) were used, respectively. OCR represents the oxygen tension and acidification of the medium as a function of time (pmol min1). OCR was normalized to protein quantity in each well. QUANTIFICATION AND STATISTICAL ANALYSIS scRNA-Seq data analysis The computational analysis of the resulting UMI count matrices was performed using the R package Seurat (3.2.1) (Stuart et al., 2019). Poor quality cells were removed, such as those in the lower and upper 2.5% quantiles for the number of UMIs and detected genes per cell in diabetic samples and those with < 1,000 detected genes per cell in ACE2 samples. For the latter, the upper thresh- olds were obtained from visual inspection of the UMI and detected genes distribution. In all cases cells with more than 10% of UMIs assigned to mitochondrial genes were discarded and genes expressed in less than 3 cells were removed from the analyses. Each dataset was subjected to normalization, identification of highly variable features and scaling using the SCTransform function, regressing out for the number of detected genes, the % of mitochondrial UMIs and cell cycle. All samples were integrated indepen- dently to remove batch effects among them and enable downstream comparisons between different conditions. The infection sam- ples at differentiating conditions (11mM) were processed and integrated in the same way as 5mM and high oscillatory glucose conditions. Principal component analysis was performed, and the top 20 components were kept for further analysis in the integrated ACE2 dataset. In the diabetic data set the top 40 component were kept for analysis. Clustering was performed by setting the resolution parameter to 0.6 in both cases Dimensional reduction for data visualization was done using the RunUMAP function. Cell markers in each cluster were identified using the FindConservedMarkers and FindAllMarkers functions in the non-integrated counts by using the Wilcoxon Rank Sum test. Genes with p value < 0.05 (adjusted by Bonferroni’s correction) were retained. Clusters were labelled by comparing the expression of the identified markers with publicly available databases located in KIT (Kidney Inter- active Transcriptomics) webpage [http://humphreyslab.com/SingleCell/] and with markers from previous publications (Lindström et al., 2018a, 2018b). Cell type annotations from the diabetic samples were transferred to the 11 mM analysis using the Cell Metabolism 34, 857–873.e1–e9, June 7, 2022 e8 ll OPEN ACCESS Article TransferData function in Seurat. Differential expression analysis to identify changes across conditions were also performed using the Wilcoxon test, keeping as up-regulated genes those present in a fraction of 0.1 of either population, with a log fold-change greater than 0.1 and with an adjusted p value < 0.05. To perform over-representation analysis the R package enrichR (Kuleshov et al., 2016) was used with the database MSigDB_Hallmark_2020’’. Gene set enrichment analysis (GSEA) was done with the fgsea R package (Korotkevich et al., 2021), considering the hallmark gene sets from MSigDB. Image analysis Quantification of collagen fibers in Masson’s trichrome staining of kidney organoids was performed as described elsewhere (Chen et al., 2017) using Fiji ImageJ2 version 2.3.0 software. Briefly, the color deconvolution plugin was used to generate monochromatic images from Masson’s trichrome staining images. The integrated intensity of the green component corresponding to the collagen fibers was then calculated using ImageJ. All raw confocal microscope images were processed with the Fiji ImageJ2 version 2.3.0 software. For optimal image visualization, Z-stacks were projected onto a tiff image where the background signal was removed, and the brightness contrast was enhanced. For fluorescent signal quantification, Z-stacks were projected as a sum of all image pixel grey values across the stack and then selection masks were created at each channel by the SetThreshold tool to measure area, intensity and integrated density of background-free signal. Colocalization was analyzed in Z-stacks using JACoP plugin under Fiji ImageJ2 version 2.3.0 software and calculated as changes of Manders’ overlap coefficient. Data representation and statistical analysis Student’s t test was used to analyze differences between two groups, and One-way or Two-way ANOVA was used to analyze inter- group differences (Tukey’s multiple comparisons test). p values less than 0.05 were considered statistically significant. The analysis was performed using GraphPad Prism 5 (GraphPad software). Densitometry results of Western Blots were quantified using the Image Studio Lite Version 5.2 software. All data are presented as mean ± SD and other details such as the number of replicates and the level of significance is mentioned in figure legends and supplementary tables. e9 Cell Metabolism 34, 857–873.e1–e9, June 7, 2022