Collapsing glomerulopathy (CG) is a severe form of FSGS, characterized by the collapse of the glomerular capillary tuft due to podocyte dedifferentiation, apoptosis, and glomerular epithelial cell proliferation,1 combined with pronounced tubulointerstitial inflammation and microcystic tubular dilation.234–5 CG usually presents with nephrotic range proteinuria and can progress rapidly to end-stage kidney failure. The pathogenesis of CG is not well understood, however, viral infections, such as CMV, Parvovirus B19, Dengue, and HIV-1 infections, have been recognized as infectious triggers.67891011121314151617–18 HIV-1–associated nephropathy (HIVAN),19 the classic viral form of CG, was first recognized as a late complication of HIV-1 infection before the availability of antiretroviral therapy, and still occurs in the modern era among patients who are treatment naïve or noncompliant HIV infected.78–9 More recently, severe acute respiratory syndrome coronavirus 2 infection has been shown to be associated to acute kidney injury due to CG,202122–23 further underscoring the importance of viral infections in disease pathogenesis.
Genetic susceptibility is a major contributor to CG pathogenesis. CG is more frequent in populations of African ancestry, largely attributable to two common risk variants in Apolipoprotein L1 (APOL1). Two APOL1 alleles, G1 and G2, which alter the sequence of the N terminal domain, have risen to high allelic frequency in individuals of African ancestry (35% and 14%, respectively) because heterozygosity confers resistance to infection from the parasite Trypanosoma brucei.9,24 Individuals with two copies of these variants have increased risk of CG, FSGS, and other nondiabetic kidney disease.25262728–29 However, kidney disease develops in only 10%–15% of individuals with high-risk APOL1 genotypes, and conversely, not all patients with CG or FSGS carry the APOL1 high risk genotype.30 These data support the contribution of other genetic, cellular, or environmental factors (such as coinfections, the microbiome, nephrotoxins, and nutrition) to CG and/or FSGS susceptibility.6
APOL1 does not have a mouse ortholog, requiring heterologous expression of risk variants in rodent models to study CG and FSGS pathogenesis in vivo.31,32 As an alternative, we and others have extensively studied an HIV-1 transgenic mouse model that closely recapitulates the clinical, pathologic, and molecular features of HIVAN and CG in humans.333435–36 We have previously reported that the severity of HIVAN in HIV-1 transgenic mice varied on the basis of genetic background. F1 hybrids between HIV-1 transgenic FVB/NJ (TgFVB) and other inbred strains showed that C57BL/6J, CAST/EiJ, and BALB/cJ backgrounds conferred resistance compared with CBA/J, DBA/2J, and C3H/HeJ backgrounds.373839–40 Using traditional mapping crosses between strains with contrasting susceptibility, we previously localized four quantitative trait loci (QTLs) for nephropathy in this mouse model.37383940–41 However, classic mapping crosses between two inbred strains have very low resolution, complicating identification of underlying susceptibility genes.
An alternative approach is to perform genome-wide association studies (GWAS) in inbred mouse strains. Classic laboratory inbred strains, or newly developed resources such as the collaborative cross,42,43 represent structured populations with a limited set of ancestors. Because they are separated from their founders by many more generations than traditional mapping crosses, GWAS in these mouse populations can achieve higher resolution with a relatively small sample size. Moreover, because the genome sequence of the strains is known, GWAS can be conducted after phenotyping the mice, without the need for genotyping. In this study, we leveraged the wide interstrain variability in severity of CG among HIV-1 transgenic mice to conduct a GWAS for CG/FSGS in the mouse.
Mouse Strains and Genotypes
The study was carried out with the recommendations and accordance with the Guide of the Care and Use of Laboratory animals of the National Institutes of Health, and the protocol was approved by the Institutional Animal Care and Use Committee at Columbia University Irving Medical Center. Mice were housed in a pathogen-free facility with a 12-hour light cycle, and were fed regular chow ad libitum. The following mouse strains were purchased from the Jackson Laboratory: 129S1/SvlmJ, A/J, BALB/cJ, C3H/HeJ, C57BL/10J, C57BL/6J, C57BL/6NJ, C57L/J, C58/J, CAST/EiJ, CBA/J, DBA/1J, DBA/2J, FVB/NJ, KK/HIJ, LP/J, NOD/ShiLtJ, NZB/BINJ, NZO/HILtJ, and WSB/EiJ. These mice were crossed with the HIV-1 transgenic mouse line TgN(pNL43d14) Lom26 (TgFVB) maintained on the inbred FVB/NJ background.39 Animals were euthanized at 8 weeks of age. A final urine collection was completed, serum was collected via cardiac puncture, and kidneys were collected for phenotypic studies and for comparisons between the different strains.
Genotyping of Mice
DNA was isolated from mouse tail using a genomic DNA isolation kit (LAMDA Biotech) according to the manufacturer’s instructions. DNA was quantified using a nanodrop instrument. Genomic DNA was amplified using PCR for Env, Vif, and Vpr genes to identify the mice with the HIV-1 transgene.
Mouse Phenotyping and Analysis
The kidneys of 365 transgenic mice (Supplemental Table 1) and 137 nontransgenic control mice were bisected, formalin fixed, paraffin embedded, sectioned at 4 μm, and stained with Periodic Acid–Schiff. Kidney histology was scored by a renal pathologist (VDD) who was blinded to the genotype on the basis of analysis of ≥200 glomeruli per mouse in whole kidney cross-sections of cortex and medulla. The percentage of glomeruli with segmental and global sclerosis, the percentage of tubules with casts and cystic dilation, the percentage of the cortical area with tubular atrophy/interstitial fibrosis, and the percentage of the cortical area with interstitial inflammation were quantified as previously described.3738–39,44 Proteinuria and hematuria in the urine were determined using the Chemstrip 10 UA (Roche) according to manufacturer’s instructions and were scored by a single investigator (ND). The BUN was assessed in the plasma of transgenic and nontransgenic mice using the BUN Colorimetric Detection Kit (Invitrogen) according to the manufacturer’s instructions. Plasma concentrations were calculated using the standards provided in the kit. The urinary concentration of neutrophil gelatinase-associated lipocalin (NGAL) was determined using an ELISA (Abcam), according to the manufacturer’s instructions. Urinary NGAL was assessed at the time proteinuria was first detected. BUN, NGAL, and proteinuria levels were compared between the mice strains, statistical differences were determined using a Wilcoxon rank test, and P<0.01 was considered significant. Plasma BUN and urinary NGAL were correlated with pathology using a Kendall correlation.
Genome-wide Association Study
The whole genome sequencing data in the VCF format for all of the strains was downloaded from Wellcome Trust Mouse Genome project.4546–47 Biallelic, homozygous, and polymorphic single nucleotide polymorphisms (SNPs) were extracted for selected strains (20 strains) using bcftools with parameters “view–threads 24 -m2 -M2 -v snps -g hom -q 0.05:minor -S Strains_list.”48 SNPs within high linkage disequilibrium were excluded using PLINK1.9 with parameters “–indep-pairwise 5000 500 0.99” as described previously.49 Afterward, all of the SNPs missing in FVB/NJ strains were removed. An in-house perl script was used to create genotype for F1 crosses between FVB/NJ and other strains. In total, 194,097 variants were used to perform downstream analysis. We used the median values for the phenotypes for each strain and each sex (male and female) for the GWAS. Afterward, all of the phenotypes were evaluated to follow normal distribution using Shapiro–Wilk test and by manually inspecting histograms plots using R. Because none of the phenotypes followed a normal distribution, we used bestNormalize R package to standardize our phenotype. Within the bestNormalize R package,50 Ordered Quantile normalization yielded the best results, and was therefore used to normalize and transform the raw phenotype data. However, dipstick proteinuria was too skewed to be adequately transformed for GWAS and was therefore not used in the mapping. QTL mapping was performed by GEMMA software (linear mixed model)51 using a python-based in-house–created wrapper to implement the Leave One Chromosome Out method.52 As described previously,53 to avoid any sex differences the standardized phenotypes were regressed against sex and residuals were used for mapping the QTL. Previously, it has been shown that linear mixed model reduces type 1 error (false positive), whereas the Leave One Chromosome Out method reduces type 2 error (false negatives).52P values from the likelihood ratio test were obtained from GEMMA and converted to log of odds ratios scores.54 The empirical significance threshold (genome wide P<0.05) for QTL was calculated on the basis of 1000 permutations for each phenotype on genotype. A QTL was considered suggestive if it had a genome-wide P<0.10 or a chromosome-wide P<0.05, on the basis of 1000 permutations. We also performed 10,000 permutations for the glomerulosclerosis phenotype, which verified the robustness of these statistical thresholds. Confidence intervals for a QTL were defined by log of odd ratios -1.5 support intervals.54 Integrative Genomics Viewer (version 2.8.7) was used to create Manhattan plots. Fine mapping of QTL on chromosome 13 was done by first obtaining all SNPs within 5 Mb region of the peak SNP (obtained using whole-genome sequencing) and performing association test using GEMMA. Phenotypic variance and heritability estimates were obtained using Bayesian Sparse Linear Mixed models within GEMMA software.55
All of the plotting was performed using R packages. Boxplots and Violin plots were generated using ggplot2 R package. Phylogenetic trees were constructed using LD-pruned 194,097 SNPs or 463 significant and suggestive SNPs on chromosome 13. Briefly, PLINK-formatted BED file consisting of these SNPs was converted to GDS format using SNPRelate R package.56 The functions “snpgdsDiss,” “snpgdsHCluster,” and “snpgdsCutTree” within the same package were used to calculate dissimilarity matrix, perform hierarchical clustering, and create dendrogram. ggtree R package57 was used for plotting the dendrogram and associated phenotypes, where the median for every phenotype was calculated for each strain and then scaled between 0–1 across different strains.
Single-cell RNA Sequencing
One FVB/NJ mouse kidney and one TgFVB mouse kidney were disassociated using cold active protease and DNAse at 4°C, as previously described.58 The disassociated kidney was initially passed through a 100 μm filter, followed by a 40 μm filter, and live cells were selected using a dead cell removal kit (Miltinyi Biotech), before single-cell RNA sequencing. There was a minimum of 75% viability of cells before progressing to single-cell RNA sequencing. Single-cell sequencing was completed at The Columbia Single Cell Analysis Core as part of the Sulzberger Columbia Genome Center using the 10× Genomics Chromium microfluidic platform for high-throughput single cell RNA sequencing of 5000–8000 cells per sample. Cell Ranger software generated the differential gene expression for each of the clusters. Heat maps were generated using the R package pheatmap. Clusters were identified using the gene expression patterns for kidney cells described by Park et al.59
SSBP2 Staining in Mouse Kidneys
Immunohistochemistry for SSBP2 was performed on 19 FVB mouse kidneys (including eight Tg positive and 11 Tg negative) procured at the ages of 2 and 12 weeks. FFPE kidney sections, 3 µm thick, were pretreated with CC1 heat antigen retrieval for 36 minutes followed by incubation for 36 minutes with SSBP2 rabbit polyclonal antibody (LSBio; aa 131–180, Biotin; LS-C426868) diluted 1:300, followed by Ventana Ultraview Universal DAB detection kit (Tucson, AZ) on a Benchmark Ultra automated platform.
Quantitative RT-PCR of Genes in the Locus Identified from the GWAS
Kidneys isolated from mice were stored in RNA later (ThermoFisher Scientific) at 4°C before storage at -80°C. RNA was extracted using TRIzol reagent (Invitrogen) and stored at -80°C. Next, 2 μg of total RNA was reverse transcribed into cDNA (Applied Biosystems) and 10 ng was used in each RT-PCR reaction (SYBR select master mix, ThermoFisher Scientific) and run on a LightCycler 96 (Roche). Primers (IDT) for each of the genes identified in the locus were designed using primer blast (Supplemental Table 2).
Susceptibility to HIVAN Disease Severity Varies across 20 Inbred Strains
We crossed 20 inbred strains with the TgFVB mice to generate Tg F1 hybrids. At 8 weeks of age, the severity of nephropathy was evaluated by renal histology and measurement of BUN, proteinuria, hematuria, and urinary NGAL. No kidney pathology (Figure 1, A, C, and E) or abnormalities of blood or urine chemistries were identified in any of the 20 nontransgenic F1 hybrid strains. In contrast, there was a striking variation in the severity of renal pathology between the 20 HIV-1 transgenic F1 strains (Figure 1, B, D, and F). In the analysis of glomerulosclerosis and tubular injury (Figures 1, G and H), F1 hybrids with six strains displayed severe pathology (A/J, C3H/HeJ, DBA/1J, KK/HiJ, WSB/EiJ, and LP/J; average glomerulosclerosis ≥51%). Nine strains were resistant, resulting in absent or very limited pathology (129S1/SvImJ, BALB/cJ, C57BL/6J, C57BL/6NJ, C57BL/10J, C57BL/J, C58/J, CAST/EiJ and NZB/BINJ; average glomerulosclerosis ≤10%); and four strains had intermediate disease (CBA/J, DBA/2J, NOD/ShiLtJ, NZO/HlLtJ; average glomerulosclerosis 11%–50%). The Tg-FVB mice were comparable with the intermediate pathology strains and had a statistically significant difference in disease severity, compared with strains with low and high pathology (Supplemental Tables 3–10). Consistent with these findings, BUN and urinary NGAL were elevated in the F1 transgenic mice that displayed pathology (Figures 1, I and J) and correlated positively with severity of all histopathologic scores (Kendell correlation tau range 0.38–0.61, Supplemental Figures 1, 2, and 3). In concordance with the literature elevated urinary NGAL correlated with tubulointerstitial damage and significantly correlated with tubular atrophy and interstitial fibrosis (Supplemental Figure 3).60 There were no differences in the histopathological scores or biochemical values between male and female mice (Supplemental Figure 1).
Using 194,097 genome-wide SNPs, we next constructed a phylogenetic tree of the inbred strains to compare genetic relatedness with renal pathology scores, serum BUN, and urinary NGAL concentrations (Figure 1K). There was no consistent relationship between phylogenetic branches and the renal phenotypes observed. Several phylogenetically distant strains were phenotypically similar (e.g., A/J and WSB/EiJ), whereas many closely related strains showed contrasting phenotypes (e.g., 129S1/SvImJ and LP/J). These data suggest specific loci, rather than overall similarity in genetic background, may influence disease susceptibility. To estimate the heritability of the kidney traits (Figure 1), we used Bayesian Sparse Linear Mixed models, as implemented in GEMMA, to estimate the proportion of phenotypic variance explained by typed genotypes (chip heritability estimation) and phenotype prediction.55 We obtained significant estimated heritability for all phenotypes (h2 range of 0.78–83 for histologic traits and 0.49 for BUN, Supplemental Table 11).
A GWAS in HIV Transgenic F1 Hybrids Maps a Susceptibility Locus for Glomerulosclerosis to the Sspb2 Locus
To map loci predisposing to kidney disease, we performed a GWAS with 194,097 LD-pruned SNPs. A GWAS for glomerulosclerosis identified a genome-wide significant signal (rs48893293) on chromosome 13 (Table 1, Figure 2, A and B, Supplemental Figure 4). The same SNP (rs48893293) was the top signal for tubular casts, tubular atrophy, and interstitial inflammation (Supplemental Figures 5–7). We performed 10,000 additional permutations for the glomerulosclerosis trait and verified that this did not affect the genome-wide significant thresholds. In addition to the significant signal to Chr 13, we identified five suggestive signals (chromosome-wide significant). These loci contain genes that are highly expressed in podocytes, such as Kirrel (Chr 3), and represent potential candidates that require follow-up in larger crosses (Supplemental Table 12 and Supplemental Figure 8).
Table 1. -
HIV-1 transgenic mice kidney pathology scores and their association to the chromosome 13 locus.
||Peak Pos (Mb)
||95% CI (Mb)
||87.02 to 90.81
||87.02 to 90.81
||87.02 to 90.81
||87.02 to 90.81
PVE = var(x) × (beta^2+se^2)/var(y) where x = genotype vector and y = phenotype. Chr, chromosome; Mb, mega bases; 95% CI, 95% confidence interval; LOD, log of odd ratios; PVE, proportion of variance explained; TA/IF, tubular atrophy/interstitial fibrosis: Int/infl, interstitial inflammation.
Fine Mapping and Annotation of the Chr 13 Locus Nominates Ssbp2 as the Lead Candidate Gene
To fine-map the chromosome 13 interval, we performed association analysis with a total of 55,345 unpruned SNPs located within 5 Mb of the top signal.45 This analysis revealed that the rs48893293 signal is supported by 463 additional suggestive or significant SNPs (Figure 2B). We did not identify any coding variants or indels in linkage disequilibrium with the top signal, suggesting the causal variant(s) resides in a noncoding region. This locus accounted for 6.6% of the variance in the kidney pathologic traits, and 3.8% and 4.9% for BUN and NGAL, respectively. The rs48893293-G allele was associated with significantly worse pathology scores (Figure 2, C and D), BUN (Figure 2E), and urinary NGAL levels (Figure 2F). We next constructed a local phylogenetic tree of the strains using only the 463 suggestive or significant SNPs within the Chr 13 locus (Figure 2G). As expected, this local tree divided the strains into two distinct branches with strongly contrasting phenotypes, confirming the large effect imparted by the Chr 13 locus. Interestingly, a few strains such as DBA2/J harboring the Chr 13 risk alleles did not show severe disease, pointing to nonpenetrance or potential protective factors that will yet need to be identified.
The 95% confidence interval for the Chr 13 locus spanned 3.79 Mb (from 87.02 Mb to 90.81 Mb), containing six high-priority candidate genes: Xrcc4, Tmem167, Atp6ap1l, Atg10, Ssbp2, and Acot12 (Supplemental Table 13). Xrcc4 is involved in the repair of DNA double-strand breaks and works in conjunction with DNA ligase IV.61Tmem167 encodes a transmembrane protein implicated in the vesicle transport system.62Atp6ap1l encodes an ATPase H+ Transporting Accessory Protein 1 Like. Atg10 is an E2-like enzyme involved in two ubiquitin-like modifications necessary for the formation of the autophagosome.63Ssbp2 encodes an evolutionarily conserved member of LDB1-LHX containing multiprotein transcriptional complex.6465–66Acot12 encodes an enzyme that catalyzes the hydrolysis of acyl-CoAs.67
Because FSGS and CG are attributable to injury or dysregulation of podocytes, we first annotated these genes by examining recently published single-cell transcriptomic data,59,68 which indicated Ssbp2 is highly expressed in podocytes, whereas the other five positional candidates showed lower expression (Supplemental Figure 9B and Supplemental Figure 10). We next confirmed these findings by performing single-cell sequencing of kidney derived from TgFVB mice and wildtype (FVB/NJ) littermates. The analysis, on the basis of a training set of genes unique for the kidney resident cells, divided kidney cells from the wildtype mice into 13 unique populations (Figure 3A).59 The podocyte population (cluster 7 in Figure 3A) had increased expression of known podocyte-specific genes such as Nphs1, Nphs2, Synpo, and Lmx1b (Figure 3B). Among the six high-priority positional candidates, only Ssbp2 was significantly enriched in the podocyte cluster compared with the other clusters, whereas Acot12 was depleted (supported by additional available datasets, Supplemental Figure 9, B and C). None of the remaining candidate genes displayed significant differences in gene expression between the kidney cell clusters. In the TgFVB mice, we did not identify any clusters expressing known podocyte-specific genes, probably attributable to disease-mediated podocyte depletion and/or dedifferentiation, as expected with HIVAN pathophysiology (Supplemental Figure 11). We sought confirmation of the single-cell data by interrogating the Human Protein Atlas,69,70 which documented strong SSBP2 expression by human glomerular podocytes, and some weaker tubular staining (Supplemental Figure 10A). Consultation of protein interactions databases revealed an interaction between SSBP2 and its transcriptional cofactors LDB1 with the known FSGS-associated LMX1B in HEK293T and HCT116 cells71,72 (Figure 3C). Interrogation of the molecular signature database indicated that SSBP2 expression is reduced in glomeruli derived from patients with diabetic nephropathy (type 2 diabetes mellitus) and in mice with inactivation of Tcf21, a transcription factor critical for podocyte differentiation and homeostasis.73,74 We did not identify strong glomerular expression or an interaction with podocyte genes for any of the other positional candidates. Finally, SSBP2 expression was analyzed in the kidney of FVB and TgFVB mice at 2 and 12 weeks of age. We observed a decrease in the staining of SSBP2 in the glomeruli of TgFVB mice at 12 weeks compared with the FVB mice (Figure 3D and E). Analysis of Ssbp2 gene expression in the kidney cortex also demonstrated a trend of reduced gene expression in the TgFVB mice compared with the FVB mice (Figure 3F and Supplemental Figure 12A), whereas the expression of the other genes in the locus were unaffected (Supplemental Figure 12, B–E). Altogether, these data support Ssbp2 as the most plausible candidate responsible for interstrain susceptibility to glomerulosclerosis at the chromosome 13 locus.
FSGS-like Pathology in Ssbp2 Null Mice
Ssbp2 null mice, generated on the 129/SvEvxC57/BL6 background, have been previously reported to develop cancer in multiple tissues, and a chronic glomerulopathy that was not fully characterized.75 We obtained kidney histopathological slides from these Ssbp2 null mice and wildtype littermates at 65 weeks of age. Whereas wildtype littermates had no obvious pathologic changes (Figure 3G), the kidneys from the Ssbp2 null mice displayed global glomerulosclerosis with variable glomerular hypercellularity, tubular casts, focal tubular atrophy, interstitial fibrosis, and inflammation (Figure 3, H and I), very similar to the pathologic changes observed in our susceptible HIV-1 transgenic mice (Figure 1B). These data further support Ssbp2 as the high-priority candidate gene producing susceptibility to nephropathy in the HIVAN mouse model.
Host factors play an important role in the susceptibility to many infectious agents including malaria,76 tuberculosis,77 and HIV.78 The pathogenesis of CG and FSGS due to viral infections is poorly understood, and the recent reports of CG associated with acute severe acute respiratory syndrome coronavirus 2 infection202122–23 motivate continued investigation into viral mechanisms of podocyte injury.
In this study, we used our established TgFVB mouse model of HIVAN to map loci influencing the severity of CG. We first generated F1 hybrid mice between the TgFVB and 20 inbred strains and demonstrated a striking strain-dependent susceptibility to nephropathy, varying from complete resistance to severe glomerulosclerosis, tubulointerstitial inflammation, and fibrosis. These data demonstrated that the traditionally studied FVB/NJ genetic background only conferred intermediate susceptibility, whereas some strains such as the TgFVBxA/J F1 or TgFVBxC3H/HeJ F1 mice were severely and consistently affected. Studies of the TgFVB mouse models have been complicated by the incomplete penetrance of nephropathy. Thus, the identification of several F1 hybrids that display severe disease with nearly complete penetrance may facilitate future research into the pathogenesis of HIVAN and other viral forms of CG. TgFVB mice also develop a range of other complications of HIV-1 infection such as leukemia/lymphoma,79 neurogenic deficits,80 or reduced cardiac contractility.81 Broader phenotypic analysis of different F1 hybrids can determine whether genetic background also modifies the severity of these non-renal phenotypes.
GWAS in inbred mouse strains has emerged as a powerful tool for mapping and identifying genes for many complex traits, as exemplified by studies of genetic susceptibility to infectious agents, such as SARS82 or Ebola viruses.83 Compared with studies in humans, GWAS in inbred mice typically require a relatively small sample size because of the structured nature of the laboratory mouse populations.84 Our GWAS led to the identification of a significant locus for glomerulosclerosis on chromosome 13 (peak SNP rs48893293), spanning 3.8 Mb and containing six candidate genes: Xrcc4, Tmem167, Atp6ap1l, Atg10, Ssbp2, and Acot12. This locus resides within a broader QTL interval previously mapped in an F2 intercross between TgFVB and C57BL/6J, increasing confidence in its validity.38,85 The higher resolution achieved by GWAS thus offered a significant advantage compared with traditional mapping crosses, which typically map loci spanning 20–60 Mb.
We identified Ssbp2 as the most likely candidate, on the basis of its high expression in human and mouse podocytes, a reduction in gene and protein expression in the TgFVB mice, and a previously described interaction with LMX1B, a transcription factor implicated in Nail Patella syndrome, an autosomal dominant disease featuring FSGS.86,87 Moreover, analysis of renal pathology showed that older Ssbp2 null mice develop an FSGS phenotype similar to that observed in our TgFVB HIVAN mice. In addition to kidney disease, both TgFVB and Ssbp2 null mice develop lymphoma with older age, further suggesting a common biologic link between HIV-1 infection and SSBP2 dysregulation. SSBP2 contributes to gene regulation by preventing ubiquitin-mediated degradation of the transcriptional adaptor protein Lim domain–binding protein 1 (LDB1). LBD1 is a known LMX1B cofactor, enabling transcription of LMX1B target genes, such as Nphs2 or Col4a3.88 Consequently, podocyte-specific inactivation of Ldb1 in mice results in kidney failure,88 mimicking the renal phenotype Lmx1b null mice. To our knowledge, there are no reported interactions between HIV-1 proteins and the SSBP2-LDB1-LMX1B complex, but SSBP2 has been implicated in the pathology of other viral infections including a direct interaction with adenoviral protein E1B55k,66,89 and downregulated expression levels in neuronal cells infected with Japanese encephalitis virus and West Nile virus.90 These data, together with the phenotypic overlap between HIV-1 transgenic and Ssbp2 null mice, suggest the possibility that the SSBP2-LDB1-LMX1B transcriptional activity, critical in podocyte differentiation, may be compromised in HIVAN and other forms of CG.
In humans, two risk alleles in APOL1 confer a significant risk of FSGS and CG, including HIVAN. Follow-up studies have shown that APOL1 is an innate immunity factor that protects from infection by trypanosomes and potentially other pathogens.28 The mechanisms implicated in cellular injury mediated by APOL1 risk alleles in cells and animal models include disruption of intracellular Na+ and K+ channels,91,92 impairment of endosomal trafficking and autophagic flux,31 and increased mitochondrial permeability or fission.93,94 As most individuals with high-risk APOL1 genotypes do not develop kidney disease, and many individuals with CG or FSGS do not carry high-risk APOL1 genotypes, additional genetic and/or environmental modifiers have been invoked. For example, recent studies have shown that the APOL1 haplotype background can influence disease severity.95 Genetic studies have also identified variation at the UBD, encoding a ubiquitin, as a potential modifier of APOL1 toxicity.96 It is not known whether the SSBP2 locus modifies the risk of CG, FSGS, or other kidney diseases in humans. Consultation of public databases such as gnomAD indicate that SSBP2 is highly mutation intolerant (pLi score =1). Analysis of exome data from 3150 patients with various forms of CKD also did not identify any rare variant signals in SSBP2.97 These data suggest deleterious mutations in SSBP2 are probably rare in humans and association analysis for nephropathy will require a very large sample size. Furthermore, quantitative differences in SSBP2 levels might perturb transcriptional activity of LDB1-LMX1b complexes.
The majority of GWAS signals in humans and in mice reside in noncoding regions, complicating identification of the underlying causal allele(s) and culprit gene(s). In fact, the causal genes for the vast majority of GWAS signals in humans have not been conclusively identified. Although our studies nominate Ssbp2 as the likely candidate gene at Chr 13 locus, formal proof of causality will require the generation of genetically engineered mice where the entire risk haplotype is introduced into a resistant strain background. Alternatively, systematic CRISPR/Cas mutagenesis of each positional candidate gene could determine whether a resistant strain can be sensitized to the HIV-1 transgene. Finally, we identified multiple suggestive signals, which include several podocyte-expressed genes such as Kirrel, a gene implicated in FSGS in humans and mice.98,99 Because the resolution and power of mouse GWAS increase with the number of strains studied, a GWAS using a larger number of inbred strains can validate and refine suggestive signals to identify new susceptibility loci for HIVAN, CG, and FSGS.
A.G. Gharavi reports having consultancy agreements with the AstraZeneca Center for genomics research and Goldfinch Bio; reports receiving research funding from the Renal Research Institute and Natera; reports receiving honoraria from Sanofi; and reports being a scientific advisor or member via Editorial Boards of JASN and Journal of Nephrology. S. Sanna-Cherchi reports receiving research funding from National Institutes of Health/National Institute of Diabetes and Digestive and Kidney Diseases, Department of Defense; reports being a scientific advisor or member via the Editorial Boards of, with no royalties received from, Acta Biomedica, Journal of Nephrology, Kidney International, and Negative Results. V. D'Agati reports being a scientific advisor or member via the Editorial Board for Kidney International. All remaining authors have nothing to disclose.
This work was supported by the Department of Defense Award W81XWH-16-1-0450 and Department of Defense Award W81XWH2110550.
N.J. Steers, conceived and performed most of the experiments, analyzed and interpreted the data, performed statistical analysis, and wrote the manuscript. Y. Gupta and T.Y. Lim. performed and interpreted the GWAS analysis, performed statistical analysis and QTL data. V.D. D’Agati, scored the histology slides, interpreted the data and wrote the manuscript. N. DeMaria, W. Lam, J. Liang, A. Mo, and K.O. Stevens performed the experiments. D. Ahram, analyzed and interpreted the data. M. Gagea generated the histology for the Ssbp2−/− mice. L. Nagarajan provided access to the Ssbp2−/− mice and insights into SSBP2 activity. S. Sanna-Cherchi analyzed and interpreted the data and wrote the manuscript. A.G. Gharavi conceived the experiments, analyzed, and interpreted the data, wrote the manuscript, and obtained the funding. Dr. G. Lozano, Department of Genetics MDACC, supported in accessing materials from null mice. Because A.G. Gharavi is an editor of JASN, he was not involved in the peer review process for this manuscript. A guest editor oversaw the peer review and decision-making process for this manuscript.
This article contains the following supplemental material online at http://jasn.asnjournals.org/lookup/suppl/doi:10.1681/ASN.2021040543/-/DCSupplemental.
Supplemental Figure 1. Histopathological analysis of 20 strains of male and female F1 transgenic mice.
Supplemental Figure 2. BUN of the transgenic female and male mice strains correlates with histopathology.
Supplemental Figure 3. NGAL measured in the urine of the transgenic female and male mice strains correlates with histopathology.
Supplemental Figure 4. Manhattan plots for the loci influencing the glomerulosclerosis phenotypes of the Tg-F1 mice.
Supplemental Figure 5. Manhattan plots for the loci influencing the casts phenotypes of the Tg-F1 mice.
Supplemental Figure 6. Manhattan plots for the loci influencing the tubular atrophy and interstitial fibrosis phenotypes of the Tg-F1 mice.
Supplemental Figure 7. Manhattan plots for the loci influencing the interstitial inflammation phenotypes of the Tg-F1 mice.
Supplemental Figure 8. Differential expression of the genes located in suggestive loci.
Supplemental Figure 9. Mouse kidney tissue mRNA expression by snRNA-seq of the genes located in the identified locus on chromosome 13.
Supplemental Figure 10. Immunostaining of kidney tissue of the genes identified in the QTL on chromosome 13.
Supplemental Figure 11. Single cell sequencing of TgFVB kidney.
Supplemental Figure 12. Quantitative RT-PCR analysis of genes identified within the chromosome 13 locus.
Supplemental Table 1. Numbers of Tg-F1 mice for each strain.
Supplemental Table 2. Primers for the RT-PCR.
Supplemental Table 3. Statistical analysis of the glomerulosclerosis score between the different F1 hybrids compared with Tg-C57BL/10J mice.
Supplemental Table 4. Statistical analysis of the glomerulosclerosis score between the different F1 hybrids compared with Tg-FVB/NJ mice.
Supplemental Table 5. Statistical analysis of the casts score between the different F1 hybrids compared with Tg-C57BL/10J mice.
Supplemental Table 6. Statistical analysis of the casts score between the different F1 hybrids compared with Tg-FVB/NJ mice.
Supplemental Table 7. Statistical analysis of the tubular atrophy and interstitial fibrosis score between the different F1 hybrids compared with Tg-C57BL/10J mice.
Supplemental Table 8. Statistical analysis of the tubular atrophy and interstitial fibrosis score between the different F1 hybrids compared with Tg-FVB/NJ mice.
Supplemental Table 9. Statistical analysis of the interstitial inflammation score between the different F1 hybrids compared with Tg-C57BL/10J mice.
Supplemental Table 10. Statistical analysis of the interstitial inflammation score between the different F1 hybrids compared with Tg-FVB/NJ mice.
Supplemental Table 11. Estimated heritability for the kidney pathology scores and BUN.
Supplemental Table 12. Suggestive signals (chromosome wide significant) of the glomerulosclerosis scores from the HIV-1 transgenic mice.
Supplemental Table 13. Selection of priority genes in the locus identified on chromosome 13.
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