We recruited 10 multiplex SLE families, each with two or three individuals diagnosed with biopsy confirmed lupus nephritis, classified according to the SLICC criteria (Petri et al., 2012) for whom we obtained genomic DNA (blood) in the Louise Coote Lupus Clinic at Guy’s and St Thomas’ Hospitals, London. We collected peripheral blood from a total of 22 patients and 17 relatives. Thirty five percent (%) of patients were male, and the same proportion were diagnosed before the age of 18 years. Genomic DNA was submitted to whole exome sequencing. Polymorphisms with a minor allele frequency (MAF) >0.01 in the publicly available database gnomAD (120,000 individuals), 1000 genome Project (2504 individuals) and our in-house database (>10,000 individuals) were excluded from analysis. We analyzed each kindred independently, under X-linked recessive, autosomal dominant, or autosomal recessive models of inheritance, and this analysis identified candidate genes in two kindreds.

In Family 1, we identified compound heterozygous missense variants in the non-receptor tyrosine kinase (NRTK) ACK1, which were confirmed by Sanger sequencing (Figure 1A, Figure 1—figure supplement 1). The two patients were males and developed a class IV lupus nephritis between age 10 and 15. The K161Q allele was inherited from one parent and the A156T allele from the other (Figure 1A). Principal components analysis (PCA) based on the whole-exome sequencing to analyze population structure, parental inbreeding, and familial linkage (Belkadi et al., 2016) reveals South Asian ancestry, the closest 1,000 Genomes Project individuals being those from North India (India, Bangladesh, and Pakistan, see Materials and methods). The ACK1 mutant alleles, A156T and K161Q (transcript ENST00000333602, Figure 1—figure supplement 1), have not been reported in South Asian (31,442 alleles) or other populations from public database (gnomAD, 1000 genomes project) or our own in-house databases of >10,000 exomes of patients with infectious phenotypes. Finally, these variants were not found in DNA from 100 individuals from the small southeastern island from which they originated (see Materials and methods). Altogether, ACK1 mutant alleles, A156T and K161Q are private to this family and their segregation is compatible with an autosomal recessive trait with complete penetrance.

In Family 2, we identified compound heterozygous missense variants in another NRTK, BRK in three siblings (Figure 1B, Figure 1—figure supplement 1). Two patients developed lupus nephritis, and the third patient developed a severe panniculitis, between the ages of 20 and 30 years. Two patients were female and one male. One parent, who was not genotyped, probably transmitted the G321R to their children, and the 2 other parents (who are also siblings) transmitted the G257A allele (only one parent was available for genotyping; Figure 1B, Figure 1—figure supplement 2). An unaffected sibling (2.III.2) carries the G321R allele but not the G257A allele (Figure 1B, Figure 1—figure supplement 2). PCA analysis showed that individuals from family 2 have Sub-Saharan African ancestry, with the closest populations of 1000 genomes project being African Caribbean in Barbados and African Ancestry in Southwest US and Luhya in Webuye in Kenya (see Materials and methods). These 2 BRK mutant alleles, G257A and G321R are reported with a maximum MAF of ~8×10^–5 and 5×10^–3 respectively in Sub-Saharan African subpopulations (Figure 1—figure supplement 1), predicting a homozygosity frequency of ~10^–8 and 10^–6, respectively. These alleles are extremely rare outside Africa in the non-African gnomAD populations. These results are fully consistent under a recessive model with the overall prevalence of SLE (40–200 per 100,000 individuals). Genes that may cause SLE with high or low penetrance in accordance with their inheritance mode (Figure 1—figure supplement 3) were not candidates in these kindreds.

The ACK1 K161Q A156T and BRK G257A and G321R variants are all localized in the evolutionarily conserved kinase domains of the two proteins (Figure 1C), within the N-terminal (ATP-binding) lobes of the kinase catalytic domains (Lougheed et al., 2004; Thakur et al., 2017; Figure 1D), and near the positions of other mutations that decrease kinase activity (Prieto-Echagüe et al., 2010b; Tsui and Miller, 2015; Figure 1C). The variants are all predicted to be deleterious based on combined annotation dependent depletion (CADD) corrected with mutation significance cutoffs (MSC; Kircher et al., 2014; Itan et al., 2016; Figure 1—figure supplement 1). To examine the functional effects of the mutations, we first expressed mutant forms of ACK1 and BRK in HEK293T cells. In vitro kinase assays indicated that ACK1 A156T and BRK G321R are kinase-dead mutants, and that ACK1 K161Q and BRK G257A are severe hypomorphs with an ~20% residual kinase activity in vitro (Figure 2A). ACK1 A156T and K161Q and BRK G321R variants also lacked auto-phosphorylation activity (Figure 2B), phenocopying the effect of the specific ACK1 and BRK kinase inhibitors AIM100 (Mahajan et al., 2012) and Cpd4f (Oelze et al., 2015) respectively (Figure 2C), while the BRK G257A allele has a small residual activity (Figure 2C). To examine the effects of the mutations in the patient’s cells, we generated iPSCs from unrelated WT donors, patient 1-III-1 (ACK1 mutant), his heterozygous parent 1-II-3, patient 2-III-3 (BRK mutant) and her heterozygous sibling 2-III-2 (Figure 2—figure supplement 1A–D), and differentiated them into iPSC-derived macrophages (Lachmann et al., 2015: Figure 2D; Figure 2—figure supplement 1E). hiPSC-derived macrophages from unrelated donors, familial controls, and patients presented with a normal morphology, survival, and phenotype and expressed comparable amount of ACK1 and BRK at the transcript and protein level (Figure 2D and E, Figure 2—figure supplement 1G), but an in vitro kinase assay indicated a loss of ACK1 kinase activity in ACK1^K161Q/A156T macrophages in comparison to controls (Figure 2E). The peptide substrate specificity of BRK overlaps with those of Src family kinases (Qiu and Miller, 2002) present in the anti-BRK IPs. We therefore studied BRK kinase activity by IP-WB, which showed reduced BRK Tyr342 phosphorylation in BRK^G257A/G321R macrophages in comparison to controls (Figure 2E). These data altogether indicate that the patients’ ACK1 A156T and BRK G321R variants are kinase-dead alleles and K161Q and BRK G257A are severe hypomorphs.

ACK1 and BRK sequences are highly conserved in human populations, when compared with other species (Figure 1C). Apart from the alleles we report here, only 90 and 35 predicted loss-of-function (LOF) alleles are reported in gnomAD for ACK1 or BRK respectively, with MAFs ≤10^–4, and none of them reported in homozygosity. Conversely, we examined the in vitro kinase activity of ACK1 mutants with a MAF ≥0.005 and reported as homozygous in gnomAD (n=4). In vitro kinase activity of these variants was normal (Figure 2—figure supplement 2A–C). There was no BRK mutant with MAF ≥0.005 reported as homozygous in gnomAD. Thus, autosomal recessive deficiency for either protein, whether complete or partial, is expected to be well below 1/100,000 in the general population, which is compatible with autosomal recessive ACK1 and BRK deficiencies underlying SLE in patients from these two kindreds. To investigate whether ACK1/TNK2 or BRK/PTK6 were subject to selection, we gathered data using different metrics quantifying negative selection in the human genome. Analysis of f parameter from SnIPRE (Eilertson et al., 2012), lofTool (Fadista et al., 2017), evoTol (Rackham et al., 2015), and CoNeS (Rapaport et al., 2021) metrics, as well as intraspecies metrics from RVIS (Petrovski et al., 2013), LOEUF (Karczewski et al., 2020), and pLI / pRec (Lek et al., 2016) suggested that the genes are not under strong negative selection, consistent with the deficiency being recessive (Figure 2—figure supplement 2D).

ACK1- and BRK-deficient T cells are characterized by increased proliferation and activation (Kasprzycka et al., 2006; Sridaran et al., 2022). However, ACK1- and BRK-deficient mice do not develop Lupus-like disease on a C57BL/6 background (Sridaran et al., 2022; Haegebarth et al., 2006). We therefore investigated the consequences of inhibiting ACK1 and BRK kinase activity in wild-type (WT) BALB/cByJ female mice which are more susceptible to developing autoimmunity. For this purpose, groups of BALB/cByJ mice were either left untreated of received a single intraperitoneal injection of pristane to induce a lupus-like disease (Satoh and Reeves, 1994) and a weekly injection of ACK1 or BRK inhibitors AIM100 (Mahajan et al., 2012) and Cpd4f (Oelze et al., 2015), or DMSO vehicle alone for 12 weeks. In the absence of pristane, mice that received ACK1 or BRK inhibitors developed a large array of circulating anti-nuclear IgG antibodies, including but not limited to autoantibodies associated with SLE such as anti-histones, anti-chromatin, anti U1-snRNP, anti-SSA, and anti-Ku (Figure 3A). These data suggested that ACK1 and BRK inhibition are sufficient to promote autoimmunity in mice. We did not observe glomerular deposit of IgG at 12 weeks in these mice (Figure 3B and C, Figure 3—figure supplement 1). In contrast, BALB/cByJ mice which received pristane treatment (Satoh and Reeves, 1994) in addition to either ACK1 or BRK inhibitors had increased kidney glomerular deposits of IgG as well as increased serum autoantibody levels including anti-Ku (p70/p80), LA/SSB, Ro-SSA, Histone H4 and H2B, in comparison to DMSO vehicle controls (Figure 3A–C, Figure 3—figure supplement 1). Therefore, inhibition of ACK1 or BRK increase serum autoantibody titers and worsen pristane-induced Lupus in WT BALB/cByJ mice. Together with the above genetic analysis, these findings support the hypothesis that autosomal recessive ACK1 and BRK kinase deficiency may underlie or contribute to the development of SLE in children and young adults depending on genetic and environmental context.

NRTKs, including ACK1 and BRK, regulate phosphorylation of downstream effectors/ adaptor proteins involved in cell activation, migration, and proliferation including RAC1, AKT, STATs, and ERK (Tsui and Miller, 2015; Kamalati et al., 2000; Zheng et al., 2010; Mahajan et al., 2010; Liu et al., 2006; Mahendrarajah et al., 2017; Mao and Finnemann, 2015; van der Horst et al., 2005). NRTK deficiency can result in defective regulation of immune cell activation and survival which can lead to autoimmunity (Kasprzycka et al., 2006; Sridaran et al., 2022; Yu et al., 2001; Chan et al., 1997; Hanke et al., 1996; Hibbs et al., 1995; Lamagna et al., 2014; Nishizumi et al., 1995). We found that, in contrast to the reference ACK1 and BRK alleles, the patient’s ACK1 and BRK variant alleles do not phosphorylate AKT and STAT3 (Figure 4A and B), and do not activate RAC1 to generate RAC-GTP (Figure 4C). NRTKs such as ACK1 (Mahajan et al., 2005) and PTK2/FAK (Wu et al., 2005) are also downstream targets of the TAM family receptor MERTK which is expressed on macrophages and controls the anti-inflammatory engulfment of apoptotic cells, a process known as efferocytosis (Scott et al., 2001; Henson and Bratton, 2013; Henson, 2017). Efferocytosis allows for the clearance of apoptotic cells before they undergo necrosis and release intracellular inflammatory molecules, and simultaneously leads to increased production of anti-inflammatory molecules (TGFβ, IL-10, and PGE2) and a decreased secretion of proinflammatory cytokines (TNF-alpha, IL-1β, IL-6; Scott et al., 2001; Henson and Bratton, 2013; Henson, 2017; deCathelineau and Henson, 2003; Nagata, 2018; Fadok et al., 1998; Voll et al., 1997; Rothlin et al., 2015; Cvetanovic and Ucker, 2004). In line with these findings, mice deficient in molecular components used by macrophages to efficiently perform efferocytosis, such as MFG-E8, MERTK, TIM4, and C1q, develop phenotypes associated with autoimmunity (Scott et al., 2001; Cohen et al., 2002; Henson and Bratton, 2013; Nagata, 2018; Fadok et al., 1998; Voll et al., 1997; Cvetanovic and Ucker, 2004; Hanayama et al., 2004; Miyanishi et al., 2012; Colonna et al., 2016; Nagata et al., 2010; Kimani et al., 2014; Hanayama et al., 2002; Kawano and Nagata, 2018; Watanabe-Fukunaga et al., 1992; Singer et al., 1994). Furthermore, defects in efferocytosis are also observed in patients with SLE and glomerulonephritis (Nagata, 2018; Herrmann, 1998; Baumann, 2002; Schrijvers et al., 2005; Morioka et al., 2019).

In IP kinase assays, MERTK activated the kinase activity of wild-type ACK1 (Mahajan et al., 2005) but not of the ACK1 A156T and ACK1 K161Q variant alleles (Figure 4D). In addition, MERTK also activated BRK kinase activity, but the BRK G321R and G257A alleles were kinase dead and hypomorph variants, respectively (Figure 4D). MERTK mediates recognition of PtdSer on apoptotic cells via GAS6 and Protein S (Scott et al., 2001; Seitz et al., 2007; Cohen et al., 2002) leading to their engulfment, which involves activation of RAC1 for actin reorganization and the formation of a phagocytic cup (Wu et al., 2005; Mao and Finnemann, 2015). PtdSer recognition also typically stimulates an anti-inflammatory process mediated in part via AKT (Sen et al., 2007) and STAT3 and their target genes such as SOCS3 (Yi et al., 2009; Vergadi et al., 2017; Byles et al., 2013; Liao et al., 2011; Roberts et al., 2017; Matsukawa et al., 2005; Sica and Mantovani, 2012) and results in the inhibition of LPS-mediated production of inflammatory mediators such as TNF and IL1β, and the production of cytokines such as IL-10, TGFβ (Henson and Bratton, 2013; Fadok et al., 1998; Voll et al., 1997; Rothlin et al., 2015; Cvetanovic and Ucker, 2004). Altogether, these data raised the hypothesis that one of the consequences of the defective activity of ACK1 and BRK kinase variants might be an impaired efferocytic response to PtdSer on apoptotic cells.

Efferocytosis can be carried out by multiple cell types; however, macrophages are the main contributors in this process (Roberts et al., 2017; Parnaik et al., 2000; Nishi et al., 2014; Lazarov et al., 2023). MERTK kinase activity mediates efferocytosis by human iPSC-derived macrophages (Wanke et al., 2021). We thus examined the transcriptional responses of controls, patients, and inhibitor-treated iPSC-derived macrophages to apoptotic thymocytes by RNA-seq (Figure 4—figure supplement 1). GSEA analysis of differentially expressed genes indicated that, in contrast to control, ACK1- and BRK-deficient macrophages, as well as WT macrophages treated with ACK1 or BRK inhibitors failed to upregulate gene sets associated with AKT signaling and the negative regulation of the inflammatory response (Figure 4E). Transcriptional repressors including the AKT targets ATF3, TGIF1, NFIL3, and KLF4, the STAT3 targets SOCS3 and DUSP5, as well as CEBPD and the inhibitor of E-BOX DNA Binding ID3 were among the top-ten genes which expression is induced by apoptotic cells in WT macrophages (Figure 4F), but this regulation was lost in mutant and inhibitor-treated macrophages (Figure 4F). ATF3, TGIF1, NFIL3, and KLF4 are involved in the negative regulation of inflammation in macrophages (Vergadi et al., 2017; Byles et al., 2013; Liao et al., 2011; Roberts et al., 2017), SOCS3 is an inhibitor of the macrophage inflammatory response and DUSP5 is a negative regulator of ERK activation (Matsukawa et al., 2005; Sica and Mantovani, 2012; Seo et al., 2017). These data suggest that the kinase domain of ACK1 and BRK contribute to the macrophage anti-inflammatory gene expression program driven by apoptotic cells.

Decreased TNF gene expression by macrophages in response to apoptotic cells was prevented by the ACK1 inhibitor (Figure 4G), but the production of TNF at the protein level is not detectable by ELISA in this model (see Figure 4H). MERTK-deficient mice are susceptible to LPS-induced endotoxic shock (Camenisch et al., 1999), and MERTK-dependent anti-inflammatory program elicited by apoptotic cells on macrophages is best evidenced by the reduction of LPS-mediated production of inflammatory mediators such as TNF or IL1β (Fadok et al., 1998; Voll et al., 1997; Cvetanovic and Ucker, 2004; Sen et al., 2007; Camenisch et al., 1999). We thus tested the decrease of LPS-induced production of TNF and IL1β by apoptotic cells. For this purpose, we generated isogenic variants and control hiPSCs and hiPSCs-derived macrophages from the same donor (Figure 4—figure supplement 2). Both isogenic variants and control macrophages produced similar amounts of TNF in response to LPS (Figure 4H). However, exposure to apoptotic cells inhibited TNF production in control macrophages by 50% but did not inhibit TNF production in ACK1 and BRK-deficient macrophages (Figure 4H). Similarly, isogenic variants and control macrophages produced similar amounts of IL1β in response to LPS (Figure 4I), but apoptotic cells only decreased IL1β production by isogenic macrophages and not by mutant macrophages (Figure 4I). These data altogether indicate that ACK1 and BRK kinase activities contribute to the macrophage anti-inflammatory response during efferocytosis and are required for the decrease of TNF and IL1β production induced by LPS in response to apoptotic cells, a hallmark of their anti-inflammatory effect on macrophages (Henson and Bratton, 2013; Fadok et al., 1998; Voll et al., 1997; Cvetanovic and Ucker, 2004).

MERTK-dependent signaling for anti-inflammatory response and for cargo engulfment driven by recognition of PtdSer on apoptotic cells are distinct and separable molecular events (Tibrewal et al., 2008; Elliott and Ravichandran, 2010), however because RAC1, which controls engulfment of apoptotic cells (Albert et al., 2000), is a target of ACK1 and BRK (see Figure 4C) as well as PTK2/FAK (Wu et al., 2005), we investigated the engulfment of apoptotic cells by ACK1- and BRK-deficient macrophages. We assessed actin-ring formation by total internal reflection fluorescence (TIRF) microscopy in frustrated engulfment assays on PtdSer-coated glass slides. Wild-type iPSC-derived macrophages formed a typical actin-ring; however, isogenic ACK1 and BRK mutant macrophages presented with an altered actin-ring (Figure 5A), and a reduced actin clearance factor (Figure 5B), indicating an impairment of actin remodeling following binding to PtdSer. The actin-ring was also altered and actin clearance factor was decreased in WT macrophages treated with ACK1 and BRK inhibitors in comparison to controls (Figure 5C and D). These data suggest the kinase activity of ACK1 and BRK contributes to link PtdSer receptors to cytoskeleton rearrangement at the phagocytic synapse.

We therefore investigated whether this defect translated to a reduced uptake of apoptotic cells by macrophages. We found that engulfment of apoptotic thymocytes labeled with the pH-sensitive probe pHrodo (Nishi et al., 2014; Miksa et al., 2009; Shiraishi et al., 2004) was only moderately reduced, by 10–20% in ACK1 and BRK isogenic mutant macrophages (Figure 5E). This phenotype, although modest, was reproducible by a 30-min exposure of WT macrophages to two different ACK1 inhibitors, AIM100 (2 µM) and R-9b (Lawrence et al., 2015; 4µM), and to the BRK inhibitor Cpd4f (0.5 μM; Figure 5F). This reduced uptake of apoptotic cells was not attributable to a global engulfment defect, because ACK1 and BRK genetic kinase deficiency or inhibitors did not prevent Fc-dependent phagocytosis of a large cargo such as opsonized red blood cells (Figure 5G), or the uptake of polystyrene beads (Figure 5—figure supplement 1A) or microorganisms such as bacteria and fungi (Figure 5—figure supplement 1B, C). Altogether, these data show that the kinase activity of ACK1 and BRK participate to the formation of phagocytic synapse but are largely dispensable for engulfment of apoptotic cells and are not required for phagocytosis of microbes and opsonized cargo.

The study was approved by the Institutional Review Board of St Thomas’ Hospital; Guy’s hospital; the King’s College London University and the Memorial Sloan Kettering Cancer Center. All subject samples were obtained after written informed consent from patients and their families according to the Helsinki convention (Ethics approval: 11/LO/1433). Ten multiplex families with lupus have been enrolled from Guy’s and St Thomas’ NHS Foundation Trust and UCL Hospital in London, UK from July 2010 to January 2012, following stringent criteria: a severe phenotype (lupus nephritis for at least one patient in each family), and a familial disease (≥2 family members affected in first degree). A total of 24 patients and 17 healthy controls from different ethnic origins (5 African ancestry, 4 Asian ancestry and 1 European ancestry) were selected. The patients each met the Systemic Lupus International Collaborating Clinics (SLICC) classification criteria for SLE (Petri et al., 2012). Lupus nephritis was confirmed by a kidney biopsy classified per the 2004 ISN/RPS (International Society of Nephrology/Renal Pathology Society) classification and verified independently by two renal histopathologists.

One hundred Mauritian participants were enrolled under the Ethical Clearance provided by the University of Mauritius Research Ethics Committee. Written consent with due signatures was recorded from all participants prior to partaking in the study. Consent was documented on a confidential form in duplicate, with one copy given to the participants for their records. The University of Mauritius Research Ethics Committee approved, sanctioned and fully endorsed this mode of consent recording. The ethnic backgrounds of the 100 Mauritian participants consisted of 26 Creole, 16 Franco-Mauritian, 21 Indo-Mauritian, 2 Sino-Mauritian, 24 other or undisclosed Mauritians.

Whole Exome Sequencing (WES) of the 10 multiplex families (patients and familial healthy controls) was performed at the New York Genomics Centre on an Illumina HiSeq 2000 sequencing machine. Genomic DNA extracted from the patients and familial healthy control’s peripheral blood cells were sheared with a Covaris S2 Ultrasonicator. An adapter-ligated library was prepared with the Paired-End Sample Prep kit V1 (Illumina). Exome capture was performed with the SureSelect Human All Exon kit (Agilent Technologies). Paired-end sequencing was performed on a HiSeq 2000, generating 100-base reads. For sequence alignment, variant calling and annotation, we used BWA aligner (Li and Durbin, 2009) to align sequences with the human genome reference sequence (hg19 build). Downstream processing was performed with the Genome analysis toolkit (McKenna et al., 2010), SAMtools (Li et al., 2009), and Picard Tools. Substitution and indel calls were identified with a GATK Unified Genotyper and a GATK Indel GenotyperV2, respectively. All calls with a read coverage ≤2 x and a Phred-scaled SNP quality of ≤20 were filtered out. All the variants were annotated with the GATK Genomic Annotator. Variants were annotated following their minor allele frequency (MAF) in Exome Variant Server, 1000 Genomes Project, and Genome Aggregation Database (gnomAD), including the MAF in each ethnic subpopulation from gnomAD.

First, the variants have been prioritized at the gene level. We used the gene damage index (GDI) which provides the accumulated mutational damage of each human gene in healthy human population, based on the 1000 Genomes Project database (Phase 3) gene variations of healthy individuals and of the CADD score for calculating impact (Itan et al., 2015). GDI is very effective to filter out variants harbored in highly damaged (high GDI) genes that are unlikely to be disease-causing. We used a cut-off of 13.84, the recommended GDI value above which a gene is unlikely to be disease-causing which is the 95% CI upper boundary value, removing 5% of the genes in our analysis.

Secondly, we prioritized variants at the allele level. We excluded variants that were too frequent in our in-house database to explain the disease (Maffucci et al., 2019). We then filtered out variants based on the predicted damaging impacts. The deleteriousness of each variant was assessed using in silico algorithms: CADD (http://cadd.gs.washington.edu/score) and Mutation Significance Cutoff browser (MSC, http://pec630.rockefeller.edu/MSC/). MSC scores were generated using 99% confidence interval based on the CADD 1.3 scores of all disease causing-mutations in Human Gene Mutation Database for any given gene (Kircher et al., 2014; Itan et al., 2016). We kept only the variants with a CADD/MSC higher than 1.

We thus analyzed our WES with the remaining variants by keeping all non-synonymous coding variants and essential splice site variants with a MAF according to the genetic model: (1) heterozygous variations with MAF <10^–4 in all ethnic subpopulations under an autosomal dominant (AD) model; (2) homozygous or compound heterozygous variants with MAF <10^–2 in all ethnic subpopulations under an autosomal recessive (AR) model; and (3) hemizygous (male) or homozygous (female) variations with MAF <10^–4 in all ethnic subpopulations under X-linked genetic models.

PCA was performed with 2504 individuals of the 1000 Genomes database using WES high-quality variants as described in Belkadi et al. We searched for the closest neighbors of the patients in terms of ethnic origin using an Euclidian distance computed from the 10 first PCs (Belkadi et al., 2016; Mullaert et al., 2021).

C57BL/6 J mice were used for preparation of apoptotic thymocytes and BALB/cByJ female mice were used for in vivo inhibitor treatment experiments (see below). Mice were purchased from The Jackson Laboratory. All mouse studies were performed in adherence with Institutional Review Board (IACUC 15-04-006) from MSKCC.

The following antibodies were used for human iPSCs-derived macrophages phenotyping: PE/Cy7 anti-human MERTK Ab (Biolegend; 367609); PE/Cy7 anti-human CD11b Ab (BioLegend; 301321); PE anti-human Intα5β3 Ab (R&D; FAB3050P); APC anti-human TIM4 Ab (BioLegend; 354007); AF647 anti-human TIM4 Ab (BioLegend; 354007); APC/Cy7 anti-human CD36 Ab (Biolegend; 336213); BV786 anti-human CD115 Ab (CSF-1R) (BD Biosciences; 743145); PE-Cy5 anti-human CD11c Ab (BD Biosciences; 561692); Alexa Fluor 700 anti-human HLA-DR Ab (BD Biosciences; 560743); APC/Cy7 anti-human CD45 Ab (BioLegend; 304014); BV650 anti-human CD14 Ab (BioLegend; 301836); Alexa Fluor 647 anti-human CD369 (Clec7A) Ab (BD Biosciences; 564855); Alexa Fluor 488 anti-human CD206 (MRC1) Ab (ThermoFisher Scientific; 564855).

iPSCs-derived macrophages were detached using trypsin (TrypLE Express, ThermoFisher Scientific; 12605–010), pelleted at 400 × g for 5 min and resuspended in fluorescence-activated cell sorting (FACS) buffer (PBS +0.5% bovine serum albumin (BSA) +1 mM EDTA). After blocking Fc receptors (Miltenyi; 130-059-901) at 1/10 dilution for 10 min, the cells were washed with FACS buffer, pelleted at 400 G for 5 min, and immunostained in FACS buffer +antibody (1:50 to 1:200 dilution) for 30 min at 4 °C. Data were acquired on an ARIA III BD flow cytometer or a FACS Fortessa SORP instrument and analyzed with FlowJo. Dead cells and debris were excluded from the analysis using DAPI (1 µg/ml), side (SSC-A) and forward scatter (FSC-A) gating, and doublet exclusion using forward scatter width (FSC-W) against FSC-A. At least 5000 cells were acquired for each condition.

Cells were collected into FBS and centrifuged (800 rpm, 8 min, low acceleration) onto Superfrost slides (Thermo Fisher Scientific) using a Cytospin 3 (Thermo Shandon). Slides were air-dried for at least 30 min, and fixed for 5 min in methanol, stained in 50% May-Grunwald solution (Sigma-Aldrich, MG500) for 15 min, 5% Giemsa (Sigma-Aldrich, 48900) for 15 min, washed with Sorensons buffered distilled water (pH 6.8) for 5 min and rinsed with Sorensons buffered distilled water (pH 6.8). Slides were air-dried and mounted with Entellan New (Merck) and representative pictures were taken using an Axio Lab.A1 microscope (Zeiss) under a N-Achroplan 100 x/01.25 objective.

WT iPSCs-derived macrophages were plated at a density of 35,000 cells per well in 24-well plates and maintained in 0.5 ml of RPMI containing 10% FBS. 24 hr after plating, the macrophages were pretreated for 30 min with AIM100 (Tocris, 4946; 2 μM) and/or Cpd4f (Thermo Fisher Scientific, 53-100-00001; 0.5 μM) in RPMI without FBS prior to incubation with beads, E. coli or C. albicans.

Red fluorescent 2 μM Beads (Microparticles Invitrogen, F8826) were resuspended in RPMI (5x10⁵ beads/ml), sonicated for 5 min, and part of the suspension was warmed to 37 °C or cooled to 4 °C. 400 μl of the suspension was added per well (2x10⁵ beads/well) to pretreated iPSC-macrophages for each condition, and the plates were incubated for 1 hr at 37 °C or 4 °C.

pHrodo E. coli BioParticles (Invitrogen, P35361) were resuspended in RPMI (0.5 mg/ml), sonicated for 5 min, and part of the suspension was warmed to 37 °C or cooled to 4 °C. 400 μl of the suspension was added per well (0.2 mg/well) to pretreated iPSC-macrophages for each condition, and the plates were incubated for 1 hr at 37 °C or 4 °C.

Nonfluorescent and tdTomato positive C. albicans (clinical isolate SC5314) were grown overnight in YPD media at 30 °C with 225 rpm shaking. The cells were washed with PBS, spun at 1000 × g for 5 min, and resuspended in RPMI to a density of 740,000 cells/ml. The suspensions were then warmed to 37 °C or cooled to 4 °C. 500 µl of the suspension was added per well (370,000 C. albicans cells) of iPSC-macrophages for each condition, and the plates were incubated for 1 hr at 37 °C or 4 °C.

After the incubation with beads, E. coli or C. albicans, the media was removed and the wells were washed with PBS. The cells were then detached with a 3 min, 37 °C incubation with trypsin, and collected by centrifugation at 400 × g for 5 min. The iPSC-macrophages incubated with beads or E. coli were resuspended in 200 μl of FACS buffer. The cells incubated with C. albicans were resuspended in PBS containing 20 μg/ml Calcofluor White (CFW) stain (Sigma), and were incubated for 15 min at RT to label unengulfed yeast. The cells were then washed with FACS buffer and pelleted at 400 G for 5 min at 4 °C, before being resuspended in 200 μl of FACS buffer.

The samples were analyzed using a FACS Fortessa SORP instrument. pHrodo Red and tdTomato fluorescence was detected through a 586/15 bandpass optical filter, on a 561 nm laser excitation. CFW was excited by a 405 nm laser, and detected through a 495LP, 525/50 bandpass optical filters. At least two replicates were done for each condition.

Engulfment of apoptotic cells was assayed with pHrodo-labeled mouse apoptotic thymocytes as previously described (Miksa et al., 2009; Toda et al., 2012). Briefly, thymocytes from 4- to 8-week-old C57BL/6 J mice were treated with Human leucine-zipper-tagged Fas ligand (FIZ-shFasL see below for preparation and concentration) in RPMI1640 containing 10% FBS for 2 hr at 37 °C to induce apoptosis, washed with PBS, and incubated with 0.1 μg/ml pHrodo (Invitrogen, P36600) for 30 min at RT. After the reaction was stopped with 1 ml FBS, the cells were washed with PBS containing 10% FBS and were used as prey.

Sheep red blood cells (MP Biomedicals, 0855876) were washed two times in PBS at 600 × g for 5 min, at RT. The cells were resuspended in PBS, counted and further diluted to a concentration of 1x10⁸ cells/ml. To opsonize the RBCs, 1/500 dilution of Rabbit Anti-Sheep RBC IgG (Cell Biolabs; 122001; CBA- 220) was added and the cells were incubated at 37 °C for 30 min. The cells were then washed twice with PBS at 600 × g for 5 min, and stained in 1 ml of PBS with 10 µg/ml pHrodo under a 45-min incubation at RT. 1 ml of FBS was added to block the staining, and the cells were washed with PBS containing 10% FBS before being used in engulfment.

WT or mutant iPSCs-derived macrophages were plated at a density of 60,000 cells per well in 12-well plates and maintained in 0.75 ml of RPMI containing 10% FBS. 24 hr after plating, for select experiments (as indicted in figures) the macrophages were pretreated for 30 min with AIM100 (Tocris, 4946; 2 μM) and/or Cpd4f (Thermo Fisher Scientific, 53-100-00001; 0.5 μM), and/or R-9b (Sigma, SML2073-25MG; 4 µM) in RPMI without FBS prior to incubation with apoptotic cells or opsonized RBCs. 0.5×10⁶ pHrodo-labeled apoptotic cells, or opsonized RBCs were added to 60,000 iPSCs-derived macrophages in 0.75 ml of RPMI containing 10% FBS in a 12-well plate, and incubated at 37 °C for 90 min. The cells were washed with PBS and detached with trypsin (TrypLE Express, Thermo Fisher Scientific; 12605–010). The cells were collected by centrifugation at 400 × g for 5 min, suspended in 300 μl of CHES (N-cyclohexyl-2-aminoethane- sulfonic acid)–fluorescence-activated cell sorter (FACS) buffer (20 mM CHES buffer [pH 9.0] containing 150 mM NaCl and 2% FBS) and analyzed by flow cytometry with a FACS Fortessa SORP instrument. At least 5000 cells were acquired for each condition. pHrodo was excited with the yellow green laser 561 nm and detected with 610/620 nm bandpass filter.

FIZ-shFasL was produced using HEK293T as described previously (Shiraishi et al., 2004) and concentrated (around 50 fold) by ultrafiltration with Amicon Ultra-15 10 K column (Sigma; UFC901008). The concentration used from each batch was determined with Alexa Fluor 488 Annexin V/Dead Cell Apoptosis Kit (Invitrogen, V13241), as the concentration for which more than 80% of cells were annexin V positive and less than 10% propidium iodide positive.

Supported lipid bilayers containing 1,2-dioleoyl-sn-glycero-3-phospho-L-serine (DOPS; Avanti Polar Lipids, 840035) were prepared as previously described (Abeyweera et al., 2011). Cells were incubated on bilayers (20 min, 37 °C) and fixed by adding 3% paraformaldehyde (20 min). Fixed cells were permeabilized with 0.2% Triton X-100 (15 min) and blocked in 10% goat serum/PBS (1 hr). Then cells were incubated with 0.1 U/ml Alexa Fluor 594–labeled phalloidin (Thermo Fisher Scientific; A12381; 1 hr, RT).

TIRF images of fluorescently labeled cells in contact with bilayers were collected with a 60×objective lens (1.45 NA; Olympus) using 561 nm lasers (Melles Griot) for imaging of Phalloidin. Quantification of actin clearance ratio was performed with Matlab software (Mathworks) from the TIRF images of background-corrected mean fluorescence intensity (MFI) as previously described (Le Floc’h et al., 2013). Briefly, using two perpendicular linescans for each cell, the background-corrected MFI at the edges (positions F1 and F2) of the IS was compared with the background-corrected MFI of three equally spaced central positions (F3, F4, and F5) as follows: mean (F3 +F4+F5) / mean (F1 +F2). Clearance ratios derived from the two-perpendicular line- scans were averaged to yield a clearance ratio for the cell in question.

70,000 iPSCs-derived macrophages were plated per well in 12-well plates in RPMI1640/GlutaMax medium supplemented with 10% FBS, and 100 ng/ml human M-CSF. WT and mutants (ACK1A156T/K161Q and BRKG257A/G321R) iPSCs-derived macrophages were co-incubated or not with 450,000 mouse apoptotic thymocytes (as described above) in RPMI for 90 min. WT iPSC-derived macrophages were also pretreated for 30 min with AIM100 (2 μM) or cpd4f (0.5 μM) in RPMI and then incubated or not with mouse apoptotic thymocytes. Every condition was done in duplicate or triplicate. After 90 min incubation media was removed and macrophages were washed once with PBS. Then 1 ml TRIzol (Thermo Fisher Scientific; 15596018) was added per well and cells were harvested and stored at –80 °C. RNA from cells suspended in TRIzol was extracted with chloroform. Isopropanol and linear acrylamide were added, and the RNA was precipitated with 75% ethanol. Sample were resuspended in RNase-free water.

Total RNA was extracted from cells using a quick-RNA Microprep kit (Zymo research; R1050) as per manufacturer’s instructions. RNA was extracted from 150,000 iPSCs-derived macrophages in 6-well plates or 37500 iPSCs-derived macrophages in 24-well plates depending on the quantity of RNA needed. Lysis buffer was added directly in the well after 1 wash with PBS, then RNA was extracted directly or cells in lysis buffer were stored at –80 °C. cDNA preparation was performed with Quantitect Reverse transcription kit (QIAGENiagen; 205313) as per manufacturer instructions. qRT-PCR are done with 20 ng cDNA. qRT-PCR are performed on a Quant Studio 6 Flex using TaqMan Fast Advance Mastermix (ThermoFisher Scientific; 4444557), and TaqMan probes (ThermoFisher Scientific) for GAPDH (Hs02758991_m1), TIMD4 (Hs00293316_m1), MERTK (Hs01031973_m1), ITGB5 (Hs00174435_m1), ITGB1 (Hs01127536_m1), ITGB3 (Hs01001469_m1), TNK2 (Hs01006880_m1), PTK6 (Hs00966641_m1), TNF (Hs00174128_m1). Comparative threshold cycles (CT) was used to determine gene expression. For each sample, genes CT value were normalized with the formula ΔCT = CTgene − CTGAPDH. For relative expression, the mean ΔCT was determined, and relative gene expression was calculated with the formula 2^(−ΔCT).

hiPSC macrophages plated in 24-well tissue culture plates (density of 15,000 cells/cm²) were co-cultured with or without apoptotic thymocytes (1/8 mac./apop.t. ratio) for 90 min at 37 °C in complete media (RPMI, 10% FBS, 100 ng/ml M-CSF). The wells were then washed once with PBS at 37 °C to remove the majority of remaining apoptotic thymocytes. 300 µl of complete media with or without 1 ng/ml LPS was added in each well and the cells were incubated for 18 hr at 37 °C in a CO₂ incubator. The media was then collected and stored at –80 °C. ELISA was conducted according to manufacturer’s protocol (Human TNF-alpha DuoSet (R&D; DY210) and IL1β DuoSet (R&D, DY201) ELISA kit).

Five-week-old BALB/cByJ female mice received intra-peritoneal injection of DMSO (vehicle, 20 µl/mice), AIM100 (25 mg/kg in 20 µl), or Cpd4f (20 mg/kg in 20 µl) for 3 months. Injection were administered biweekly for the first 5 weeks, and weekly subsequently. The mice received a single intraperitoneal injection of PBS (500 µl) or pristane oil (Sigma, P9622; 500 µl) when 7 weeks old. Blood was collected by cardiac puncture. To collect serum, the blood was left to clot, undisturbed at RT for 30 min. The samples were then centrifuged at 1500 × g for 10 min at 4 °C and the supernatant/serum was collected and stored at –80 °C. The kidneys were removed, washed with PBS at 4 °C, and were fixed whole in 4% PFA overnight at 4 °C. The kidneys were then washed with PBS and place in 15% sucrose at 4 °C for 3.5 hr. The kidneys were transferred in 30% sucrose and incubated overnight at 4 °C. The kidneys were then dried with kimwipes and embedded in blocks in frozen section compound (Leica Ref:3801480). The blocks were placed in dry ice ethanol bath to freeze and were transferred to –80 °C for storage.

Frozen kidneys were sectioned longitudinally into 12 µm sections and transferred on glass slides. The sections were left to dry for 20–30 min at RT and were transferred for storage at –20 °C. To stain, the sections were thawed at RT for 15 min in a humidified box, and rehydrated by washing in PBS at RT three times. The sections were incubated for 1 hr at RT in PBS, 5% BSA to block. The sections were then washed two times with PBS at RT and stained overnight at 4 °C with IgG AF555 (Goat Anti-Mouse; Thermo Fisher A21424) (1/500 dilution in PBS, 0.5% BSA), and Podoplanin (Syrian Hamster Anti-Mouse; Biolegend 127401; 1/100 dilution in PBS, 0.5% BSA). The sections were washed with PBS at RT three times and were incubated for 1.5 hr at RT with Anti-Syrian Hamster AF647 (Jackson, 107-605-142; 1–500 in PBS, 0.5% BSA). The sections were washed with PBS at RT three times and stain with Hoechst (Thermo Fisher 62249; 20 µM final concentration in PBS) for 10 min at RT. The slides were washed with PBS 2 times, and mounted using 170 µl of ProLong Gold antifade reagent (Invitrogen P36930) and high precision microscope cover glass (24X50 mm; 170 µM; No. 1.5 H). Entire kidneys sections were imaged on confocal microscope with ×10 objective.

To analyze IgG mean fluorescence intensity (MFI) of glomeruli, podoplanin staining was used to algorithmically generate outlines around glomeruli in full kidney section images using ImageJ. The outlines were manually verified to remove incorrectly marked glomeruli and to outline the glomeruli the algorithm missed. Over 95% of all the glomeruli in a section (about 250 per whole longitudinal kidney section) were captured and analyzed for each mice (5 mice were analyzed for each condition [30 whole kidney sections in total]). The MFI of each outlined glomeruli was extracted with ImageJ.

Autoantibody profiling was conducted by the UT Southwestern Medical Center Genomics & Microarray Core Facility (https://microarray.swmed.edu/products/product/autoantigen-microarray-service-igg-igm/). For each sample, 10 µl of serum was treated with DNAse I, diluted 1:50, and incubated with autoantigen array (Autoantigen Microarray Panel I, https://microarray.swmed.edu/products/product/autoantigen-microarray-panel-i/). The autoantibodies binding to the antigens on the array were detected with Cy3 labeled anti-IgG. The arrays were scanned with GenePix 4400 A Microarray Scanner and the images were analyzed using GenePix 7.0 software to generate GPR files. The averaged net fluorescent intensity (NFI) of each autoantigen was normalized to internal (IgG) controls. Plotted values represent Ab Scores (Log₂ [antigen net fluorescence intensity (NFI) x signal-to-noise ratio (SNR) +1]). Heatmaps were plotted with Morpheus (https://software.broadinstitute.org/morpheus/). Heatmap rows were sorted top to bottom starting with most significantly increased Ab Score in Cpd4f and AIM100 in comparison to DMSO treated mice. Single row p value between DMSO and Cpd4f or AIM100 mice was calculated by paired t test. Full panel p-values were calculated using a paired t test. Hierarchical clustering was done by one minus Pearson correlation with complete linkage method. K Means clustering was done by Euclidean distance, with two clusters, with 10,000 maximum iterations.

Data are shown as mean with individual values or as bar plots with mean values and standard deviations. Statistical significance was analyzed with GraphPad Prism unless otherwise indicated (see below). Statistical significance was determined using a Student t test, Mann-Whitney, ordinary one-way ANOVA with Tukey’s multiple comparisons test, or Kruskal-Wallis multiple comparisons tests depending on the number of conditions being compared and whether the data was normally distributed as determined by a Shapiro-Wilk normality test. p<0.05 values were considered statistically significant. R software was used for statistical analysis of RNA-seq data. For differential gene expression approach significance was considered for FDR (q value)<0.05. For gene set enrichment analysis (GSEA) approach significance was considered for FDR (q value)<0.25 AND p-value <0.05.

Further information and request for reagents may be directed to and will be fulfilled by the corresponding author Frederic Geissmann: geissmaf@mskcc.org.