The atypical RhoGTPase RhoE/Rnd3 is a key molecule to acquire a neuroprotective phenotype in microglia

Female C57Bl/6 mice (7–8  weeks old) were purchased from Charles River. They were used for obtaining P0 to P2 newborns to isolate primary microglia and for obtaining fat tissue to isolate ASCs. Mice were housed in a controlled-temperature/humidity environment (22 ± 1 °C, 60–70% relative humidity) in individual cages (10 mice per cage, with wood shaving bedding and nesting material), with a 12-h light/dark cycle (lights on at 7:00 a.m.) and fed with rodent chow (GlobalDiet 2018, Harlan) and tap water ad libitum. The experimental protocols of this study conform to EU Directive 2010/63 and followed the ethical guidelines for investigations with experimental animals approved by the Ethics Review Committee for Animal Experimentation of Spanish Council of Scientific Research. Animal studies are reported in compliance with the ARRIVE guidelines [14].

Primary microglia were prepared from P0 to P2 newborn C57Bl/6 mice, as described previously [7]. Briefly, dissected brains were discarded of olfactory bulb, cerebellum, hindbrain, and meninges. Then, they were homogenized in microglia growth medium consisting in DMEM (Invitrogen), supplemented with 10% fetal bovine serum (FBS, Gibco), 10% horse serum, and 1% penicillin/streptomycin (Gibco), using a Pasteur pipette and a 23-G syringe. Brain homogenates were centrifuged. The resulting cells were plated in poly-d-lysine-coated flasks and incubated with microglia growth medium for 10–12 days at 37 °C and 5% CO₂. Microglia were harvested on a shaker for 2 h at 200 rpm and plated on poly-d-lysine-coated cover slips at a density of 12,000 cells/cm² or tissue culture 6-well plates in microglia growth medium at a density of 37,000 cells/cm². After 24 h, microglia growth medium was replaced by fresh microglia growth medium.

ASCs were isolated from adipose tissue of adult C57Bl/6 mice as previously described [15]. These cells showed a fibroblast-like morphology and differentiation capacity to the adipocytic and osteocytic lineages and expressed the phenotype MHC⁻II⁻CD14⁻CD18⁻CD31⁻CD34⁻CD45⁻CD80⁻CD117⁻CD144⁻CD13⁺CD44⁺CD29⁺CD54⁺CD73⁺CD90⁺CD105⁺CD106⁺CD166⁺. CM was collected from passage 2 until passage 6 of ASC cultures, which were plated at a cell density of 15,000 cells/cm², grown for 2 days before collecting their supernatant and then stored at − 20 °C. Before use, CM was quickly thawed and passed through a 0.2-μm filter.

A custom-made siRNA library against 157 target genes (with three individual siRNAs against each gene) was ordered from Life Technologies. Primary microglia were plated for 48 h as described above. Then, the growth medium was replaced and cells grown on cover slips in 48-well plates were transfected with 12.5 pmol siRNA and 0.375 μl of Lipofectamine 3000 (Invitrogen) according to the manufacturer’s instructions. For 24-, 12-, or 6-well plate cultures, reagents were proportionally up-scaled. Four days after the siRNA transfection, cells were treated with CM for 4 h and fixed to determine their cell shape. Alternatively, after 4 days, cells were harvested for Western blot or RT-qPCR analysis. Not transfected cells or cells that were transfected with a control Scrambled siRNA were used as reference in all studies.

Microglia plated on cover slips were fixed for 4 min in ice-cold methanol and stained with Isolectin B4, labeled with AlexaFluor 568 (Invitrogen) for 30 min at room temperature and washed two times with PBS. Images were acquired with a × 10 objective on an Olympus IX 81 fluorescence microscope. To determine the circularity or “form factor” [7, 16], photos of three fields of view were taken per siRNA and analyzed by a Fiji/ImageJ macro to determine the circularity of each individual cell. Briefly, each image was processed by the median filter at a radius of 8 pixels, then a black and white threshold image was generated, cell surroundings were drawn, and the circularity within shape descriptions was determined as 4π*area/(perimeter)². Cells touching the borders of the image were excluded from the quantification procedure. In total, the siRNA screen was performed three times so that the effect of each target gene on circularity was calculated from three independent experiments. Subsequently, the circularity values were normalized within each experiment and compared to microglia transfected with Scrambled siRNA. The fold change difference between the circularity value for each gene and the Scrambled siRNA was called the differential circularity, since it indicates the difference between the Scrambled siRNA and gene siRNA, and is given as logarithm (log₂).

Migration of microglia was determined using a scratch assay. Cells were plated at 18,000 cells/cm² and transfected as described above. Growth medium was replaced 3 days after transfection to CM, and the cover slip was scratched with a yellow pipette tip. Bright field images were taken 1 and 24 h after the scratch. The scratch width was analyzed with the Fiji/ImageJ macro Wound healing tool (MRI_Wound_Healing_Tool.ijm). The scratch width at 24 h was expressed as a percentage of the original scratch width at 1 h. For the normalization, the scratch width of the Scrambled siRNA-transfected cells was set to 1.0 in each experiment, and the scratch width of the siRNA-transfected cells were set to values proportional to 1.0.

Microglia were transfected as described above and then harvested with cold lysis buffer consisting in 10 mM Tris-HCl pH 8.0, 150 mM NaCl, 1% Nonidet-P40, 1 mM EDTA, 10 mMNaF, 1 mM Na₃VO₄, and a cocktail of commercially available protease inhibitors (Sigma, containing 104 mM AEBSF, 80 μM Aprotinin, 4 mM Bestatin, 1.4 mM E-64, 2 mM Leupeptin, 1.5 mM Pepstatin A). After centrifugation for 15 min at 14,000 rpm, the protein concentration of the supernatants was determined by Bradford assay (Bio-Rad), and samples were prepared for SDS-PAGE with Laemmli SDS sample buffer. After semi-dry blotting or Western blotting in a wet chamber for Tiam1, PVDF membranes were blocked with 5% milk in TBS-Tween (0.1%) and incubated 8 h at 4 °C with the primary antibodies, rabbit anti-Ahrgef4, (Antibodies-Online), rabbit anti-IκBα (Cell Signaling), mouse anti-Rac1 (BD Transduction laboratories), rabbit anti-GAPDH (Sigma), mouse anti-Tiam1, and mouse anti-Map3k2 (both from Santa Cruz) diluted in 2% BSA/TBS-Tween (0.1%) or incubated 1 h at RT with the primary antibodies mouse anti-Creb1, rabbit anti-RhoE, rabbit anti-GM-CSFR, and rabbit anti-Mapk11 (all from Antibodies-Online) diluted in the blocking solution. Horseradish peroxidase-conjugated secondary antibodies (DakoCytomation) and ECL (GE Biotech) were used for detection. If necessary, membranes were stripped with stripping buffer (100 mM β-mercaptoethanol, 2% SDS, 62.5 mMTris pH 6.8) for 30 min at 55 °C.

Total RNA was extracted using Tripure (Roche) from microglia plated in 6-well plates. After DNase I treatment (Sigma), RNA (1 μg/sample) was reverse transcribed using RevertAid First Strand cDNA Synthesis kit (Fermentas) and random hexamer primers. The cDNA was analyzed by qPCR in triplicates on a Cfx96-Cycler (Bio-Rad) with the SensiFAST™ SYBR® No-ROX Kit (Bioline) and 2.5 pmol of the following primers: mouse Rac1 forward, CCC AAT ACT CCT ATC ATC CTC G; mouse Rac1 reverse, CAG CAG GCA TTT TCT CTT CC; mouse BDNF forward, CCC TCC CCC TTT TAA CTG AA; mouse BDNF reverse, GCC TTC ATG CAA CCG AAG TA with a primer efficiency of 100.7% and GAPDH forward and reverse primers from the RevertAid First Strand cDNA Synthesis kit (Fermentas), with a primer efficiency of 86.3%. After 42 cycles, the Ct values were determined. To normalize the samples, ΔCt between the gene of interest and GAPDH Ct values as reference gene was calculated. The x-fold difference in expression between the different treatments was then determined by subtraction of the ΔCt values and called ΔΔCt. Finally, the total change was calculated as 2^−ΔΔct and the relative amount compared to Scrambled siRNA-transfected cells was deducted.

BDNF protein levels in culture supernatants of siRNA-transfected primary microglia were determined using the Quantikine® ELISA Kit (R+D Systems®), a quantitative sandwich enzyme immunoassay, according to the manufacture’s recommendations.

All data are expressed as the mean ± SEM. The data and statistical analysis comply with the recommendations on experimental design and analysis in pharmacology (Curtis et al., 2018). Statistical analysis was carried out with two-way ANOVA followed by Student’s t test. We assumed significance at p < 0.05. For the statistical analysis of the siRNA screen, cell HTS2 package version 2.38.0 [17] from R version 3.3.2 was applied to calculate and normalize all circularity values within each experiment, as it is the case in the normalization by experiments. Finally, these normalized values were compared for each gene to a Scrambled siRNA used as negative control sample using limma software version 3.30.11 [18]. Achieved results are presented as a “fold change” in the circularity, given as logarithm (log₂) value. The threshold value between significant and not significant genes was determined by an adjusted P value: the Fold Discovery Rate (FDR). We applied the standardized FDR, as previously described [19].

First of all, we established the experimental conditions to evaluate the efficiency of the siRNA knockdown in primary mouse microglia. Since a dominant-negative mutant of the small RhoGTPase Rac1 inhibited the CM-induced conversion to an elongated neuroprotective microglia [7], we expected the same phenotype for its siRNA knock down. Therefore, we used Rac1 as test protein. Indeed, Rac1 protein and mRNA expression were efficiently downregulated (66.1 ± 9.0% protein and 59.7 ± 5.1% mRNA at 72 h) after transfection with Silencer-Select Rac1 siRNAs in comparison to scrambled siRNA (Additional file 1). Our initial assumption was confirmed as CM-induced cell elongation was inhibited in Rac1 siRNA-transfected microglia (Fig. 1a, b). The quantification shows that CM-treated microglia that were previously transfected with three individual siRNAs targeting Rac1 or a pool of them have a higher value for circularity, corresponding to a rounder cell shape, compared to control CM-treated microglia that were not transfected or transfected with scrambled siRNAs (Fig. 1c). Thus, these experiments show that our assay is functional and that Rac1 siRNA could be used as specific positive control in this siRNA screen.

Using this experimental strategy, we next evaluated the effect on CM-induced microglia elongation of a custom-made Silencer-Select siRNA library against 157 mouse proteins including 100 cytoskeletal proteins and 57 regulators of microglia activation/inflammatory pathways. We selected 22 small RhoGTPases [20] and 78 of their activators, Rho guanine nucleotide exchange factors RhoGEFs [21], because they are involved in regulation of cytoskeletal architecture and changes in the cell morphology are intrinsically related to the cytoskeleton. On the other hand, we selected major regulators of inflammatory pathways, including various members of the mitogen-activated protein kinases (MAPK), phosphatidylinositol-4,5-bisphosphate 3-kinase (PI3K) and nuclear factor kappa B subunit 1 (NF-κB) signaling routes, because these proteins are likely to interfere with the CM-induced anti-inflammatory phenotype of microglia and hence have a higher probability to influence the inflammatory state of microglia. The supplementary Tables S1 and S2 in Additional files 2 and 3, respectively, show a list of all genes that were targeted by an initial screening using our siRNA library (a pool of three individual siRNAs) including their effects on the differential circularity. A positive differential circularity (Additional file 2) signifies that the cells were rounder than cells transfected with Scrambled siRNA, and negative values of circularity (Additional file 3) imply that microglia transfected with this gene showed an enhanced ramification pattern. Since we were interested in genes that inhibited the neuroprotective ramified phenotype of microglia, the statistically significant positive hits (genes marked in bold in Additional file 2) were compared to publicly available datasets of PD-associated gene expression [22] and pathways [23]. We also compared them to differential transcriptome expression data from post mortem tissue of the substantia nigra between healthy and PD patients [24]. Overlapping genes between our siRNA screen and those databases as well as the seven hits with the highest differential circularities were subjected to a literature search to collect information about their potential physiological role in microglia. From there, the selected hits Asef (Arhgef4), Tiam1, GM-CSFR (Csf2rA), IκBα (Nfκbia), RhoE (Rnd3), p38β (Mapk11), Creb1, MEK3 (Map2k3), Map3k2, and JNK2 (Mapk9) were then validated in the following step.

Since siRNA screens in mammalian cells might be influenced by off-target effects [25], we confirmed selected positive hits by transfecting individually the three single siRNAs of the original pool. To exclude these off-target effects, at least two siRNA oligonucleotides targeted against the same gene should exhibit the same phenotypic effect on CM-treated microglia (assayed by changes in circularity values) and efficiency to knock down its gene target (assayed by Western blot analysis). As shown in Table 1, not all positive hits of the previous siRNA screening were verified in this step. None of the three siRNAs against MEK3, and the two siRNAs against JNK2, were able to individually change circularity values in CM-treated microglia (Table 1), and thus, the genes MEK3 and JNK2 were marked as false positives. In addition, the protein levels of all tested genes, except Arhgef4, were significantly downregulated by at least two single siRNAs (Fig. 2). Since none of the individual siRNAs targeting Arhgef4 were able to downregulate the protein Arhgef4, this hit had to be considered as false positive.

To pinpoint the functional consequences of the ramification impairment, we ran secondary screens with the seven validated hits (Tiam1, GM-CSFR, IκBα, RhoE, p38β, Creb1, and Map3k2) by investigating their effects in two processes that are related to microglia activation or with mechanisms through which microglia might exert their neuroprotective functions, namely cell migration capacity and production of neurotrophic factors. Since CM-induced BDNF production in primary microglia [7], we transfected primary microglia with the two individual siRNAs of the seven positive hits that efficiently downregulated their target protein (see Fig. 2), and measured the expression of BDNF mRNA 4 days later. siRNAs against Creb1, RhoE, and p38β were able to downregulate basal BDNF mRNA expression by microglia (Fig. 3a), suggesting that they could achieve their neuroprotective function via BDNF production. However, siRNAs against Tiam1, GM-CSFR, IκBα, and Map3k2 failed to regulate BDNF mRNA expression (data not shown). To confirm the downregulation of BDNF mRNA at the protein level, we performed an ELISA to measure the total amount of BDNF protein in the culture supernatants of Creb1, RhoE, and p38β siRNA-transfected cells. Indeed, downregulation of these three proteins also resulted in a significant reduction of BDNF protein (Fig. 3b).

On the other hand, since the amoeboid cell shape in microglia is associated with cell migration [26, 27], we used a scratch assay to test the effect of our seven validated hits on microglia migration. We transfected primary microglia with two individual siRNAs of our positive hits, and 3 days later, we added CM and scratched the cover slip. We observed that microglia transfected with siRNAs against RhoE, Tiam1, or IκBα siRNAs closed their scratch prior to Scrambled siRNA (Fig. 4), indicating that these three genes are negative regulators for microglia migration. In contrast, siRNAs against GM-CSFR, p38β, Creb1, and Map3k2 did not affect their migratory capacity (data not shown). The downregulation of RhoE, Tiam1, or IκBα by siRNA transfection did not affect MTT reduction in microglia cultures (data not shown), suggesting that their effects measured by the scratch assay are not due to an action on cell viability/proliferation. Table 2 summarizes the results of both secondary screens.