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Open Access

Peer-reviewed

Research Article

Glucocerebrosidase deficiency leads to neuropathology via cellular immune activation

  • Evelyn S. Vincow ,

    Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

    ‡ These authors share first authorship on this work.

    Affiliation Department of Genome Sciences, University of Washington, Seattle, Washington, United States of America

    ⨯
  • Ruth E. Thomas ,

    Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

    ‡ These authors share first authorship on this work.

    Affiliation Department of Genome Sciences, University of Washington, Seattle, Washington, United States of America

    ⨯
  • Gillian Milstein,

    Roles Data curation, Formal analysis, Investigation, Validation, Visualization

    Affiliation Department of Genome Sciences, University of Washington, Seattle, Washington, United States of America

    ⨯
  • Gautam Pareek,

    Roles Investigation, Methodology, Validation

    Current address: Department of Cell and Molecular Biology, St. Jude Children’s Research Hospital, Memphis, Tennessee, United States of America

    Affiliation Department of Genome Sciences, University of Washington, Seattle, Washington, United States of America

    ⨯
  • Theo K. Bammler,

    Roles Data curation, Formal analysis

    Affiliation Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, Washington, United States of America

    ⨯
  • James MacDonald,

    Roles Data curation, Formal analysis, Writing – review & editing

    Affiliation Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, Washington, United States of America

    ⨯
  • Leo J. Pallanck

    Roles Conceptualization, Funding acquisition, Project administration, Supervision, Writing – original draft, Writing – review & editing

    pallanck@uw.edu

    Affiliation Department of Genome Sciences, University of Washington, Seattle, Washington, United States of America

    ⨯

Abstract

Mutations in GBA (glucosylceramidase beta), which encodes the lysosomal enzyme glucocerebrosidase (GCase), are the strongest genetic risk factor for the neurodegenerative disorders Parkinson’s disease (PD) and Lewy body dementia. Recent work has suggested that neuroinflammation may be an important factor in the risk conferred by GBA mutations. We therefore systematically tested the contributions of immune-related genes to neuropathology in a Drosophila model of GCase deficiency. We identified target immune factors via RNA-Seq and proteomics on heads from GCase-deficient flies, which revealed both increased abundance of humoral factors and increased macrophage activation. We then manipulated the identified immune factors and measured their effect on head protein aggregates, a hallmark of neurodegenerative disease. Genetic ablation of humoral (secreted) immune factors did not suppress the development of protein aggregation. By contrast, re-expressing Gba1b in activated macrophages suppressed head protein aggregation in Gba1b mutants and rescued their lifespan and behavioral deficits. Moreover, reducing the GCase substrate glucosylceramide in activated macrophages also ameliorated Gba1b mutant phenotypes. Taken together, our findings show that glucosylceramide accumulation due to GCase deficiency leads to macrophage activation, which in turn promotes the development of neuropathology.

Author summary

Mutations in the gene GBA are the largest risk factor for developing Parkinson’s disease and Lewy body dementia, diseases in which important brain cells die. We know that the immune system can be involved in these diseases, and that GBA mutations cause immune changes. We did experiments to learn how the immune system changes could make brain cells more likely to die. Using a fruit fly that was missing the fly version of GBA, we found out that inappropriately activated immune cells, but not secreted immune proteins, were important in the development of brain problems. We also learned that the abnormal activation was triggered by the lack of GBA function in the immune cells, not by signals from the brain or other parts of the body. We would like to find out next whether the immune cells get inside the brain or cause harm from a distance. What we learned matters because it could help us prevent or cure brain diseases associated with GBA mutations. Treating the abnormal activation of immune cells in people with these mutations might help prevent damage to the brain.

Citation: Vincow ES, Thomas RE, Milstein G, Pareek G, Bammler TK, MacDonald J, et al. (2024) Glucocerebrosidase deficiency leads to neuropathology via cellular immune activation. PLoS Genet 20(11): e1011105. https://doi.org/10.1371/journal.pgen.1011105

Editor: Daniel Babcock, Lehigh University, UNITED STATES OF AMERICA

Received: December 12, 2023; Accepted: October 21, 2024; Published: November 11, 2024

Copyright: © 2024 Vincow et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All data are in the manuscript and/or in supporting information files.

Funding: This work was supported by two grants to LJP from the National Institute of Neurological Disorders and Stroke (National Institutes of Health): R01AG075100 and R21AG068356. https://www.ninds.nih.gov/ The funding agency played no role in the study design, data collection and analysis, decision to publish, or manuscript preparation.

Competing interests: The authors have declared that no competing interests exist.

Introduction

The single strongest genetic risk factor for both Parkinson’s disease (PD) and the related disorder Lewy body dementia is mutations in GBA [1–3], which encodes the lysosomal enzyme glucocerebrosidase (glucosylceramidase; GCase). Heterozygous GBA mutations increase PD risk, while homozygous mutations cause Gaucher disease (GD) [1,2], which is marked by varying degrees of neuropathology [4]. Exactly how GBA mutations increase the risk of neuropathology and neurodegeneration is not clear. In recent years, however, increasing evidence has indicated a connection between GBA mutations and immune system abnormalities [5–7]. Neuroinflammation has been implicated more generally in the pathogenesis of Parkinson’s disease [8,9], Lewy body dementia [10], and other neurodegenerative disorders [11]. Studies of GCase-deficient humans and animals have revealed increases in cytokine and chemokine abundance, both within and outside the central nervous system [12]. Also, abnormalities of peripheral immune cells such as macrophages are a hallmark of GD, including the classic morphological defects attributed to accumulation of glucosylceramide (GlcCer), the substrate of GCase [13,14]. Recent reports analyzing peripheral immune cells from humans with GBA mutations have described multiple defects including excessive activation, impaired phagocytosis, and excessive response to pathogen-associated molecular patterns [15–18]. Both homozygous and heterozygous mutations in GBA have been shown to cause large-scale transcriptional alterations, including increases in proinflammatory transcripts [7]. These transcriptomic studies provide a rich source of data, but do not indicate whether the immune abnormalities are pathogenic.

To investigate whether immune alterations are involved in the development of neurological phenotypes in GCase-deficient organisms, we used our previously created Drosophila model of GBA deficiency, the Gba1b mutant [19]. This mutant recapitulated key neurodegenerative phenotypes including neuronal death, the accumulation of insoluble ubiquitinated proteins, reduced lifespan, and impaired locomotion [19,20]. The major humoral immune pathways in mammals, including Toll, NF-κB/Imd, and JAK/STAT, are conserved in Drosophila [21,22], and Drosophila blood cells (hemocytes) are a well-validated model for macrophages and other human myeloid immune cells [23,24]. In addition, Drosophila studies have shown the same association between GCase deficiency and immune system activation that is seen in mammals [25,26]. We therefore used the Gba1b mutant to investigate which components of the immune system contribute to the neuropathology associated with GCase deficiency.

We began our investigation with RNA-Seq on heads from 11-day-old male Gba1b mutants and controls. We found large, significant increases in immune-related transcripts, including increased abundance of humoral immune factors and activated macrophage markers. When we genetically suppressed the expression of humoral immune factors in Gba1b mutants, there was no decrease in the accumulation of insoluble ubiquitinated protein. Manipulation of cellular immunity, by contrast, did decrease the development of Gba1b mutant pathology. Restoring Gba1b expression or reducing GlcCer production in activated macrophages ameliorated the mutants’ biochemical and behavioral phenotypes. Together, our findings implicate the accumulation of GlcCer in the activation of peripheral immune cells, which in turn promotes the neuropathology associated with GCase deficiency.

Results

Gba1b mutants have increased abundance of innate immune factors

To identify immune-related genes that might contribute to Gba1b mutant phenotypes, we performed RNA-Seq on samples from the heads of Gba1b mutants and controls. We detected 10,903 transcripts, of which 379 (3.5%) were significantly increased in abundance in Gba1b mutants, while 175 (1.6%) were decreased in abundance (S1 Dataset). The transcripts most strikingly increased in abundance in Gba1b mutants included multiple members of the Turandot family, a group of genes transcribed in response to immune threats and stressors [27,28], and the Tep (thioester-containing protein) family (Fig 1A), which is orthologous to the mammalian complement system [29,30]. Enrichment analysis with PANGEA [31] revealed that the transcripts increased in abundance in Gba1b mutants were strongly enriched for immune- and defense-related functions (Fig 1B–1C).

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Fig 1. RNA-Seq and proteomics demonstrate immune activation in Gba1b mutants.

(A) Volcano plot of RNA-Seq fold change data in heads from Gba1b mutants vs. controls. Dashed lines mark log2FC of 2 and negative log10 p value of 4. (B-C) Plots of GO Biological Process (B) and KEGG Pathways terms (C) from PANGEA enrichment analysis of Gba1b RNA-Seq data. (D-E) Comparison of transcripts (D) and proteins (E) with increased abundance in Gba1b mutants to transcripts with increased abundance in immune challenge studies (see Materials and Methods for details). ***p < 0.005 by Fisher exact test, indicating a significantly greater correspondence between Gba1b mutant transcriptional changes and immune challenge transcriptional changes than would be expected by chance.

https://doi.org/10.1371/journal.pgen.1011105.g001

To characterize the Gba1b mutant immune response in more detail, we investigated whether the transcriptional changes in Gba1b mutants resembled responses to any specific type of pathogen. In particular, as GlcCer is a known fungal virulence factor [32], we were interested in whether the immune response resembled the response to fungal infection. To do this, we compared our findings to data mined from previously published Drosophila RNA-Seq and microarray studies of immune response. Our data sources included sets of transcriptional changes detected in response to fungi, bacteria, viruses, and parasitoids [33–38]. We also compared our RNA-Seq data to a set of 62 core genes responsive to immune challenge, derived from a meta-analysis of 12 pathogen challenge studies [28]. See Methods and S1 Fig for details of our analyses. We found that transcripts from all the immune challenge lists were overrepresented among the transcripts increased in abundance in Gba1b mutants (~7-fold to 18-fold enrichment, p < 0.005 by Fisher exact test; Fig 1D). The strongest enrichment was for gene expression associated with parasitoid attack. We then compared the RNA-Seq findings to the results from our previous proteomic analyses of Gba1b mutant heads [39], to see whether a similar immune response enrichment could be detected at the protein level. To do this, we converted the previously mentioned lists of transcripts from immune response studies to lists of the proteins they encoded, and compared the converted lists to our head proteomics data. As in the RNA-Seq, proteins from three of the immune challenge lists were more likely to be increased in abundance in Gba1b mutants than would be expected by chance (Fig 1E). Gba1b mutants thus demonstrated a generalized immune response at the translational as well as the transcriptional level.

Genetic manipulation of humoral immune factors does not rescue Gba1b mutant pathology

Given the many humoral factors increased in abundance in our RNA-Seq and proteomic data, we hypothesized that suppression of humoral immune pathways would ameliorate Gba1b mutant phenotypes. As in our previous work, our primary outcome measure was accumulation of insoluble ubiquitinated proteins in the head, which is a hallmark of neurodegenerative diseases [40–42]. For each manipulation, we measured insoluble ubiquitinated protein in the following four groups of flies:

Gba1b mutants with GAL4 driver and gene of interest RNAi transgene

 (tests the influence of decreasing expression of the gene of interest on Gba1b mutants)

Gba1b mutants with GAL4 driver and control RNAi transgene (e.g., mCherry RNAi)

 (shows the phenotype of Gba1b mutants without intervention)

Gba1b wild type controls with GAL4 driver and gene of interest RNAi transgene

 (shows any effect of the genetic manipulation in a wild-type background)

Gba1b wild type controls with GAL4 driver and control RNAi transgene

 (shows the phenotype of healthy flies; controls for possible effects of genetic background)

In most cases, the genetic manipulations showed no effect on either group of wild type control flies; we therefore report the results for Gba1b mutants with and without the intervention (see S2 Dataset for full list of genotypes).

Recent reports have emphasized the importance of Gba1b in glia [43,44], which are the primary immune-reactive cells of the nervous system [45], and we therefore first tested knockdown of humoral immune function in glia. We used the UAS/GAL4 tissue-specific expression system (pan-glial driver repo-GAL4) to knock down expression of transcription factors from the major humoral innate immune pathways [46–49]: Rel (Imd), Dif and dl (Toll), Stat92E (JAK/STAT), kay (JNK), and Atf-2 (p38/MAPK). None of these manipulations had a significant effect on ubiquitinated protein accumulation in Gba1b mutants (Fig 2A). We therefore examined the effects of immune factor knockdown in fat body, the primary peripheral source of humoral immune effectors [50]. Knockdown of Relish using the adult fat body driver ppl-GAL4 reduced ubiquitinated protein aggregates in the heads of both Gba1b mutants and controls (Figs 2B and S2A–S2B), consistent with the previously reported influence of Relish on neurodegeneration [51,52]. Further perturbations of the Imd pathway (a null mutant for PGRP-LC and the hypomorph imd1), however, showed no genetic interaction with Gba1b and did not alter insoluble Ub accumulation (S2C–S2D Fig). As the effect of Rel knockdown in fat body was independent of Gba1b genotype, we did not consider Relish to have a specific effect on Gba1b neuropathology. Fat body knockdown of the other immune-related transcription factors had no significant effect on Ub accumulation (Fig 2B).

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Fig 2. Suppression of humoral immune factors does not reduce accumulation of insoluble ubiquitinated proteins in Gba1b mutants.

All panels represent quantification of anti-Ub (anti-ubiquitin) immunoblots using Triton-insoluble head samples from 10-day-old flies. Each graph compares Gba1b mutants with a genetic intervention to Gba1b mutants without the intervention. (A) RNAi to the transcription factor genes indicated, driven in glia with repo-GAL4. (B) RNAi to the transcription factor genes indicated, driven in fat body with ppl-GAL4. (C) Gba1b mutants bearing mutations in immune genes. The first and last panels in this section represent immunoblots of whole bodies and the remainder represent immunoblots of heads. Each experiment was performed using at least three biological replicates. See S2 Dataset for full genotypes.

https://doi.org/10.1371/journal.pgen.1011105.g002

As interfering with humoral immune expression in glia or fat body did not ameliorate Ub accumulation, we attempted to influence this phenotype using whole-organism gene ablation of immune effector groups. The alleles used were as follows (Fig 2C): a deletion of Upd2 and Upd3 [53], a deficiency removing 10 of 12 Bomanin genes [54], multiple deletions removing all known antimicrobial peptide genes on the second chromosome (AMP Δ8 deletes Def, AttA, AttB, AttC, Dro, Mtk, DptA, and DptB)[55], and a deletion of four thioester-containing proteins (TepqΔ)[29]. None of these four interventions rescued the accumulation of ubiquitinated protein in Gba1b mutants. In addition, the AMP Δ8 and TepqΔ deletions did not change the mortality of the Gba1b mutants (S2E–S2F Fig). Finally, because we found prominent upregulation of Turandot family proteins in the RNA-Seq and proteomics, we tested for involvement of that family in the Gba1b mutant phenotype. Heterozygous ablation of TotA, TotB, TotC, and TotZ using a large deficiency produced no effect on ubiquitin accumulation in the mutants (Fig 2C). We thus found no evidence to implicate canonical humoral immune pathways in the development of Gba1b mutant neuropathology.

RNA-Seq and proteomics suggest prominent cellular immune activation in Gba1b mutants

Because the findings above indicated that humoral immune activation did not contribute significantly to Gba1b mutant neurological phenotypes, we considered the possibility that cellular rather than humoral immunity was involved. While Drosophila immune cells, or macrophages, were previously divided into three categories, recent single-cell RNA-Seq studies have revealed that they exist in a wide spectrum of states, from quiescent or proliferative states to states reflecting immune activation [56–61]. Using the classifications of Tattikota et al. and Cattenoz et al. [56,57], we compiled a list of markers associated with immune-activated macrophage subtypes in scRNA-seq studies. We also drew markers from a microarray study of mutants with excess lamellocytes, another activated immune cell type [62]. Although lamellocytes were previously considered to exist only in wasp-infested larvae [61,63,64], recent work has made clear that cells bearing lamellocyte biochemical markers can be found in adults [61], and we found that these markers were increased in abundance in Gba1b mutants. Markers of activated macrophages and lamellocytes were strikingly overrepresented among the transcripts increased in abundance in Gba1b mutants (Fig 3A), and the corresponding proteins were overrepresented as well (Fig 3B). We found the same increased enrichment when we compared our data to a proteomic analysis of lamellocytes (Fig 3B) [65]. The data as a whole strongly suggested increased activation of immune cells in Gba1b mutants.

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Fig 3. Analysis of RNA-Seq and proteomic data reveals increased abundance of markers associated with activated macrophage subtypes.

(A-B) Enrichment of macrophage cell type markers in Gba1b mutant RNA-Seq (A) and proteomic data (B), calculated as in Fig 1. ***p < 0.005 by Fisher exact test, indicating a significantly greater correspondence between Gba1b mutant transcriptional changes and activated immune cell marker lists than would be expected by chance. (C) Analysis of Gba1b RNA-Seq data using the Drosophila RNAi Screening Center (DRscDB) enrichment function. The top 20 terms by fold enrichment are shown. Terms indicating macrophages or macrophage subtypes (17 out of 20 cell types) are highlighted in red.

https://doi.org/10.1371/journal.pgen.1011105.g003

We further tested this theory using a single-cell RNA-Seq data resource from the Drosophila RNAi Screening Center [66]. The Enrichment function allows the user to enter a list of genes and compares the list to cell type markers from many single-cell RNA-Seq datasets. The user receives information on which cell types most closely match the set of genes submitted. When we entered the genes significantly increased in abundance in Gba1b mutants, the cell types with which they were associated were overwhelmingly macrophages (here described with the invertebrate-specific term “hemocytes”), particularly activated macrophage subtypes (LM, PM7, PL-Rel; Fig 3C) [56–58]. This alternative analysis supported the idea that Gba1b mutant macrophages were abnormally activated.

Fluorescent cell type markers show macrophage activation in Gba1b mutants

Having found evidence of macrophage activation in Gba1b mutants, we tested whether the activation could be detected on direct visualization of affected cells. We first tested whether there was a general proliferation of blood cells using the engineered fluorescent marker SrpHemo-mCherry [67], which is recognized as common to 99% of fly immune cells and maintains expression well into adulthood (S3 Fig) [61]. Immunoblotting revealed no significant difference in marker abundance between Gba1b mutants and controls, and microscopy also revealed no obvious difference in total cell number (Fig 4A–4C).

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Fig 4. In vivo fluorescent cell type markers confirm an increase in activated macrophages in Gba1b mutants.

(A) srpHemo-mCherry fluorescence in heads from Gba1b mutants and control flies on day 1 of adult life. (B) srpHemo-mCherry fluorescence in heads at day 10. (C) Immunoblot and quantification of mCherry on day 10 heads from Gba1b mutants and controls expressing srpHemo-mCherry. (D) HmlΔ-GAL4 > GFP fluorescence in heads from Gba1b mutants and control flies on day 1 of adult life. (E) HmlΔ-GAL4 > GFP fluorescence in heads at day 10. (F) Immunoblot and quantification of GFP on day 10 heads from Gba1b mutants and controls expressing HmlΔ-GAL4 > GFP. (G) zfh1-GAL4 > tdTomato-HA fluorescence in heads from Gba1b mutants and control flies on day 1 of adult life. H) zfh1-GAL4 > tdTomato-HA fluorescence in heads at day 10. I) Anti-HA immunoblot and quantification on day 10 heads from Gba1b mutants and controls expressing zfh1-GAL4 > tdTomato-HA. All protein samples were extracted using RIPA buffer as described in Materials and Methods. Heads were imaged caudal side down, with the ventral aspect to the left. Significance was tested using Student’s t test, ** = p < 0.01, *** = p < 0.005. Each experiment described in this figure was performed using at least three biological replicates.

https://doi.org/10.1371/journal.pgen.1011105.g004

Given that there was no gross change in macrophage number, we hypothesized that Gba1b mutants had a shift in the relative abundance of macrophage subpopulations, specifically an increase in activated macrophages at the expense of inactive macrophages. We decided to examine macrophage subpopulations in Gba1b mutants using several GAL4 drivers and markers. Because a number of macrophage GAL4 drivers stop driving expression early in adult life [68], we screened available reagents for GAL4 drivers and markers that continued to drive expression in macrophages 10 days after the fly reached adulthood. We found two relevant GAL4 drivers: HmlΔ-GAL4 and GMR35H09-GAL4 (S3 Fig). GMR35H09-GAL4 contains DNA sequences derived from the zfh1 gene [69], which is highly expressed in activated macrophage subtypes [56,57], and we therefore refer to it henceforth as zfh1-GAL4. The other driver HmlΔ-GAL4, when expressing GFP, has decreased expression in activated macrophages (HmlΔ-GAL4 > UAS-GFP, henceforth HmlΔ-GFP) [67,70].

Using these tools, we tested whether the evidence of immune cell activation seen on RNA-Seq could be detected via expression of fluorescent markers. We predicted that a marker expressed in activated cell types would be increased in Gba1b mutants relative to controls, while a marker for other macrophage types would be decreased. We tested one marker, HmlΔ-GFP, known to decrease in activated macrophages [67,70] and one associated with activated macrophages (zfh1-GAL4 driving tdTomato-HA) [56,57]. In one-day-old flies, mutants and controls showed no difference in fluorescent signal from these markers, consistent with the lack of obvious phenotypes in Gba1b mutants at that age (Fig 4D and 4G) [19]. However, at 10 days of age, immunoblotting and microscopy showed that HmlΔ-GFP signal was severely depleted (Fig 4E and 4F); the same result was found with HmlΔ-dsRed (S4A Fig), while tdTom-HA driven by zfh1-G4 was increased in abundance in Gba1b mutants vs. controls (Fig 4H and 4I). These findings indicated that the macrophage population in Gba1b mutants is shifted toward activated cell types. We further tested this hypothesis using VT17559-GAL4, whose promoter comes from the activated macrophage marker Lis-1 [68]. When we drove tdTom-HA with this driver, as with zfh1-GAL4, signal was increased in Gba1b mutant macrophages at day 10 (S4B Fig). Together, these findings demonstrate age-dependent cellular immune activation in Gba1b mutants.

Restoring GCase activity to activated macrophages rescues Gba1b mutant phenotypes

To identify the contribution of immune cell activation to pathogenesis in Gba1b mutants, we tested whether driving expression of Gba1b in activated macrophages would ameliorate the mutants’ phenotypes, including macrophage activation, insoluble Ub accumulation, short lifespan, and impaired locomotor performance. We hypothesized that driving Gba1b in the macrophages showing aberrant activation in the mutants would rescue their phenotypes, while driving Gba1b in other macrophage subtypes would not. To test this, we used three of the GAL4 drivers described above: Srp-GAL4, which drives in most macrophage subtypes; zfh1-GAL4, which drives in the aberrantly activated macrophages; and eater-GAL4, which drives in a different subset of macrophages [57]. We predicted that Srp-GAL4 and zfh1-GAL4 would rescue Gba1b mutant phenotypes and that eater-GAL4 would not. When we examined the accumulation of insoluble ubiquitinated protein in Gba1b mutants, our predictions were validated (Fig 5A–5C).

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Fig 5. Expression of Gba1b in activated macrophages rescues insoluble ubiquitin accumulation in Gba1b mutants.

(A-C) Immunoblot and quantification of insoluble ubiquitinated head proteins from Gba1b mutants and controls with the following genetic manipulations: (A) UAS-Gba1b under the control of the pan-macrophage driver Srp-GAL4. (B) UAS-Gba1b under the control of the driver eater-GAL4, which drives in a subset of macrophages. (C) UAS-Gba1b under the control of the driver zfh1-GAL4, which drives in a subset of activated macrophages. (D) Immunoblot and quantification of insoluble ubiquitinated head proteins from flies with endogenous Gba1b gene expression restored in macrophages. This was achieved by excising the transposon causing Gba1b loss of function from the Gba1bCRIMIC allele (expressing FLP recombinase under the control of SrpHemo-QF2). (E) Immunoblot and quantification of insoluble ubiquitinated head proteins from flies expressing GAL80 under the control of pan-neuronal driver elav-QF2 and pan-glial driver repo-QF2, as well as Gba1b under the control of the driver zfh1-GAL4. Significance was tested using Student’s t test, * = p < 0.05, *** = p < 0.005. “Sibs” refers to siblings of the flies that bear both a UAS construct and a GAL4 driver. Siblings may have the GAL4 or the UAS, but not both. Each experiment described in this figure was performed using at least three biological replicates.

https://doi.org/10.1371/journal.pgen.1011105.g005

We tested alternate explanations for our findings. First, we addressed the possibility that rescue of insoluble ubiquitinated protein levels by Gba1b expression in macrophages was the result of supraphysiological gene expression produced by the UAS-GAL4 system. To do this, we used flies bearing the Gba1bCRIMIC allele, which disrupts Gba1b function [43], and removed the transposon to restore function of the endogenous gene. Because no zfh1-QF2 driver was available, we used the pan-macrophage driver SrpHemo-QF2 driving FLP recombinase to target transposon removal to macrophages. This manipulation robustly rescued the accumulation of insoluble ubiquitinated proteins in Gba1b mutant heads, indicating that our findings were not an artifact of high expression levels (Fig 5D). Second, we used the GAL4 repressor GAL80 to address the possibility that our findings with zfh1-GAL4 were the result of Gba1b expression in neurons or glia. Specifically, we created a strain expressing the GAL4 repressor GAL80 under the control of both elav-QF2 (pan-neuronal) and repo-QF2 (pan-glial), drivers that do not interact with the UAS-GAL4 system [71]. In these flies, we drove Gba1b using zfh1-GAL4 as above. We found that rescue of ubiquitin accumulation in these animals was comparable to the rescue in flies without GAL80 inhibition, indicating that misexpression in neurons or glia does not explain our results (Fig 5E).

We next tested whether Gba1b expression under control of zfh1-GAL4 also ameliorated other phenotypes of Gba1b mutants, including macrophage activation, reduced lifespan, locomotor deficits, and autonomic nervous system abnormalities. To test macrophage activation, we expressed tdTomato under the control of zfh1-lexA as a measure of fluorescence that would not be affected by GAL4 drivers [72,73]. We examined this marker in Gba1b mutants and controls with and without zfh1-GAL4–driven restoration of GCase activity. Restoring GCase activity in Gba1b mutants caused a substantial decrease in fluorescent signal, indicating a normalization of the proportion of activated macrophages (Fig 6A). As macrophage activation in Gba1b mutants is also marked by loss of HmlΔ-dsRed (S4A Fig), we tested whether driving Gba1b with zfh1-GAL4 would reverse this phenotype as well, and we found that this was the case (S5 Fig). These findings show that macrophage activation in Gba1b mutants is dependent on loss of GCase activity in macrophages. We then tested whether Gba1b expression in activated macrophages would also rescue the mutants’ deficits in lifespan, locomotor performance, and enteric nervous system function. Expressing Gba1b in activated macrophages ameliorated the mutants’ shortened lifespan (S6A Fig) and restored their climbing ability as measured by the RING assay [74] (Fig 6B). We also assayed gut transit time, a measure of enteric nervous system function, using a procedure modified from Olsen and Feany (S7A Fig) [75]. Gba1b mutants, like some other neurodegeneration model flies, display markedly impaired clearance of food from the gut (S7B–S7C Fig). Restoring Gba1b to activated macrophages completely normalized gut transit (Fig 6C). These same phenotypes were also ameliorated by restoration of endogenous levels of Gba1b to macrophages using removal of the CRIMIC transposon via FLP expression under the control of SrpHemo-QF2 (Figs 6D–6F and S6B).

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Fig 6. Restoring GCase function in macrophages rescues the defects of Gba1b mutant in macrophage activation, climbing and gut transit.

(A) Visualization of macrophage activation (using zfh1-lexA > tdTom) in 10-day-old Gba1b mutants and controls with or without rescue expression of Gba1b under the control of zfh1-GAL4 (n = 6–8 animals per genotype). Graph depicts the mean fluorescence from male flies. (B) RING climbing assay in Gba1b mutants and controls with or without rescue expression of Gba1b under control of zfh1-GAL4. All flies were 13-day-old females. (C) Gut transit assay to measure enteric nervous system function. Fifteen-day-old female flies (Gba1b mutants and controls with or without zfh1-GAL4 driving UAS-Gba1b) were scored for the presence of blue dye in the abdomen 2.5 h after being transferred from dyed to plain food. (D) Visualization of macrophage activation (using zfh1-lexA > tdTom) in 10-day-old Gba1b mutants and controls with or without restored expression of Gba1b in macrophages under the control of SrpHemo-QF2 (n = 6–8 animals per genotype). Graph depicts the mean fluorescence from male flies. (B) RING climbing assay in Gba1b mutants and controls with or without restored expression of Gba1b in macrophages under control of SrpHemo-QF2. All flies were 20-day-old females. (C) Gut transit assay to measure enteric nervous system function in fifteen-day-old female flies (Gba1b mutants and controls with or without SrpHemo-QF2 driving QUAS-FLP to remove CRIMIC transposon) were scored for the presence of blue dye in the abdomen 4 h after being transferred from dyed to plain food. Significance was tested using one-way ANOVA with Dunnett’s T3 multiple comparisons test. * = p < 0.05, ** = p < 0.01, *** = p < 0.005.

https://doi.org/10.1371/journal.pgen.1011105.g006

Macrophage activation appears to be caused by cell-autonomous accumulation of GlcCer

Having shown that cellular immune activation is important to the development of Gba1b mutant phenotypes, we wanted to test the mechanism by which they are activated. Gba1b mutants have been shown to have compromised circadian rhythms due to lipid alterations in central clock neurons [44], and disruption of circadian rhythm can lead to abnormal macrophage activation [76,77]. We therefore considered the possibility that normalizing the activity of central clock neurons in Gba1b mutants might be sufficient to ameliorate both Ub accumulation and macrophage activation. We expressed Gba1b with Pdf-GAL4, which has been shown to rescue cyclic neurite remodeling in sLNv clock neurons [44]. We assessed macrophage activation as described above (zfh1-lexA driving tdTom and Pdf-GAL4 driving UAS-Gba1b). Restoring Gba1b function to Pdf-GAL4 neurons did not ameliorate macrophage activation (S8 Fig) or significantly reduce Ub accumulation (Fig 7A). These findings suggest that GlcCer accumulation in sLNv clock neurons is not a major cause of ubiquitinated protein accumulation in Gba1b mutant heads and is not responsible for macrophage activation.

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Fig 7. GlcCer in activated macrophages is critical to the development of protein aggregation in Gba1b mutants.

(A-B) Immunoblot and quantification of insoluble ubiquitinated head proteins from flies expressing (A) Gba1b under the control of Pdf-GAL4, which drives in circadian rhythm neurons or (B) GlcT RNAi under the control of the activated hemocyte driver zfh1-GAL4. Each experiment was performed using at least three biological replicates. *p < 0.05, **p < 0.01, ***p < 0.005 by Student’s t test. C) Gut transit assay on Gba1b mutants with or without zfh1-GAL4 driving UAS-GlcT. Female flies, 20–22 days old, were tested as described above. * = p < 0.05, ** = p < 0.01, *** = p < 0.005 by one-way ANOVA with Dunnett’s T3 multiple comparisons test.

https://doi.org/10.1371/journal.pgen.1011105.g007

Given the above findings, we hypothesized that GlcCer accumulation in the macrophages themselves was responsible for the aberrant activation in Gba1b mutants. To test this hypothesis, we knocked down of the rate limiting synthetic enzyme for GlcCer, GlcT [78], under control of zfh1-GAL4. This manipulation reduced the accumulation of insoluble Ub in Gba1b mutant heads (Fig 7B). We also found that GlcT knockdown improved autonomous nervous system function (Fig 7C). Effects on climbing could not be assessed because zfh1-GAL4 knockdown of GlcT impaired locomotion even in control flies (S9 Fig). We conclude that excess GlcCer in Gba1b macrophages promotes aberrant activation in a cell-autonomous manner, and that these activated macrophages promote the development of behavioral and biochemical abnormalities associated with GCase deficiency.

Discussion

In this work, we have shown that the development of central nervous system phenotypes in GCase-deficient flies requires activation of peripheral immune cells. This activation appears to be a direct, cell-autonomous consequence of membrane lipid alterations in macrophages due to excess GlcCer.

Humoral immunity was also clearly activated in Gba1b mutants, but a large set of genetic manipulations found no evidence that the humoral immune system was involved in the development of neurological phenotypes. The most likely explanation for this is that the increased production of humoral factors is a consequence of macrophage activation [79–81]. Some previously published models of neurodegenerative disease have shown that excess production of humoral immune factors is sufficient to cause neurodegeneration [51,52,82,83] and in Drosophila, the detrimental effects of humoral activation were blocked by interfering with the upstream immune transcription factor Relish [51,52]. Other work, by contrast, has shown that increased abundance of antimicrobial peptides promoted survival and recovery after head injury [84]. In our model, modifying humoral immune function appeared neither helpful nor harmful. Our results underscore the fact that, while neuroinflammation is a common finding in neurodegenerative states [85], it is necessary to test the functional significance of immune changes before concluding that they are pathogenic [84,86].

Inappropriate macrophage activation and even central nervous system invasion has been described in various neurodegenerative diseases [87–90], but reports conflict as to whether this phenomenon promotes or opposes neurodegeneration [91–96]. Similarly, while macrophage alterations are a hallmark finding in severe GCase deficiency [13,14], their role in neurological abnormalities has not been established. Earlier reports emphasized the importance of lipid abnormalities in neurons [97], although these have not been universally found. Our findings show that restoring Gba1b function specifically in macrophages rescues neuronally mediated disease phenotypes such as impaired climbing and slowed gut transit, indicating that the aberrant activation of macrophages in Gba1b mutants is harmful to the nervous system.

Two recent papers indicated that Gba1b acts primarily in glia, which receive and degrade the GlcCer produced in neurons [43,44]. These findings fit with data in vertebrates where loss of GCase function in microglia, an immune-specific glial subtype, has been implicated in neuronal damage [98]. In Drosophila, the functions associated with vertebrate microglia are distributed among multiple types of glia [99]. While restoring GCase function in glia did rescue Gba1b mutant phenotypes [43,44], we found that expressing Gba1b in activated macrophages rescued ubiquitin-protein accumulation even when GAL4-driven Gba1b expression was blocked in both neurons and glia and we found that restoring endogenous levels of Gba1b to macrophages only produced the same rescue. These findings indicate that our results are not the result of glial misexpression; rather, it appears that defects in both macrophages and glia are required to produce Gba1b phenotypes, and that restoring expression in either cell type is therefore sufficient to produce rescue.

GCase deficiency phenotypes have been attributed to the accumulation of GlcCer. Our findings are consistent with this idea; specifically, they suggest that GlcCer accumulation within macrophages in Gba1b mutants causes cell-autonomous activation by mimicking the changes in lipid balance that normally occur during macrophage activation. First, the process of macrophage activation involves synthesis of sphingolipids and a large increase in cellular sphingolipid content [100], and other factors that change lipid balance (e.g., high-fat diet) have been shown to cause pro-inflammatory macrophage phenotypes [101,102]. Second, reducing GlcCer synthesis within activated macrophages was sufficient both to reduce ubiquitin-protein aggregation and to ameliorate gut transit abnormalities. Third, humoral factors are normally involved in the signaling that leads to macrophage activation, but reduction of humoral factors had no effect on Gba1b mutant phenotypes [103]. Alternatively, GlcCer accumulation in macrophages could cause hyperreactivity to normal immune stimuli, as previously reported in macrophages from PD patients with GBA mutations [15]. Atilano et al. [26] partially addressed this possibility by raising GCase-deficient flies in germ-free conditions. The improvements in lifespan and climbing after this intervention, however, were modest compared to those seen when we restored GCase activity in activated macrophages. Together, these findings suggest that macrophage activation is more a primary result of lipid changes than a sensitization to external immune challenges.

Another essential question is how macrophage activation contributes to the development of central nervous system abnormalities. Fig 8 depicts possible mechanisms warranting further investigation. One possibility could be macrophages enter the brain and act directly on central nervous system cells. Invasion of the CNS by peripheral immune cells has been reported in multiple mammalian models of neurodegenerative disease [91,104–106], and more recently in immune-challenged Drosophila [90]. In the Drosophila study, the invading macrophages caused direct destruction of neuronal processes through phagocytosis [90]. Invading macrophages can also cause harm through mechanisms such as signaling to microglia [107], oxidative stress [108], or glutamate excitotoxicity [109,110]. However, not all models of neuropathology in GCase deficiency show blood-derived macrophages in the brain [111–113], and macrophages also influence brain function from beyond the blood-brain barrier [107]. Given our previous findings of altered extracellular vesicle biology in Gba1b mutants [39], long-distance EV signaling from macrophages to brain is a possible mechanism [107]. In a Drosophila Alzheimer’s disease model, macrophages clustering outside the brain promoted neurodegeneration via TNF-JNK signaling [95]. In addition, phagocytosis by macrophages can impact the central nervous system through its effect on the abundance of aggregation-prone proteins. Significant quantities of these proteins (including alpha-synuclein, tau, and amyloid beta) [114] are constantly released from the brain; in the case of amyloid beta, about half of the protein released from the brain is normally eliminated in the periphery [115]. Moreover, interfering with macrophage clearance of amyloid beta is reported to lead to increased levels of the protein in the brain [116]. One possible mechanism of harm, therefore, is inadequate peripheral clearance of aggregation-prone proteins by macrophages in Gba1b mutants, leading to accumulation of ubiquitinated protein in brain and periphery alike. As noted above, abnormalities in phagocytosis have been described in macrophages from GCase-deficient humans [18,98]. Further investigation will be required to determine the relative contributions of these or other mechanisms of pathogenesis. Our findings do suggest, however, that targeting both peripheral and central immune cells may be necessary to prevent neurodegeneration caused by GCase deficiency.

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Fig 8. Possible mechanisms of pathogenesis due to aberrant macrophage activation in Gba1b mutants.

Model: Excess GlcCer in macrophages causes activation via mimicry of the lipid changes associated with normal activation, with several possible detrimental outcomes that are not mutually exclusive. 1) Failed peripheral aggregate clearance: Activated macrophages may have decreased capacity for phagocytosis, leading to inadequate clearance of aggregation-prone proteins from the periphery and their consequent accumulation in the brain. 2) Signaling: Activated macrophages may release harmful extracellular vesicles (EVs) or other signals that cause long-distance damage to the brain. 3) Invasion: Activated macrophages may migrate into the brain or just outside the blood-brain barrier. They may cause damage by short-range mechanisms such as phagocytosis of neurons, release of ROS, or excitotoxicity.

https://doi.org/10.1371/journal.pgen.1011105.g008

Materials and methods

Drosophila strains and culture

Fly stocks were maintained on standard cornmeal-molasses food at 25°C on a 12:12 light/dark cycle. The Gba1b (Gba1bΔTT), Gba1brv, and Gba1bMB03039 (Gba1b Minos) alleles have been previously described [19,39]. “Gba1b mutants” refers to any combination of Gba1bΔTT homozygotes, Gba1bΔTT/Gba1bMB03039 transheterozygotes, and Gba1bMB03039 homozygotes. These alleles have identical effects on the ubiquitinated protein accumulation phenotype [39]. Unless otherwise specified, controls for the genetic manipulation are siblings generated from the same cross and bear a balancer chromosome. In some cases, flies bearing a control transgene were used (UAS-mCherry RNAi for third chromosome transgenes, UAS-lexA RNAi for second chromosome transgenes). All third chromosome transgenes or alleles tested for modifying effects on Gba1b mutant phenotypes were recombined with the Gba1b Minos allele.

    Endogenous Gba1b expression was restored using the following genotypes:

    Control groups:        CyO/+ or QUAS-FLP/+; Gba1brv/Gba1bCRIMIC

                            CyO/+ or QUAS-FLP/+; Gba1bΔTT/Gba1bCRIMIC

                            QUAS-FLP/SrpHemo-QF2; Gba1brv/Gba1bCRIMIC

    Experimental group: QUAS-FLP/SrpHemo-QF2; Gba1bΔTT/Gba1bCRIMIC

Other strains and alleles are as described in Table 1. Full genotypes for each experiment are listed in S2 Dataset.

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Table 1. Fly stock information.

https://doi.org/10.1371/journal.pgen.1011105.t001

RNA-Seq Analyses

RNA extraction.

Heads from 11-day-old male Gba1b mutant and control flies were cut off using razor blades, on ice, and homogenized in TRIzol (500 μL per 50 heads). The homogenate was flash-frozen and stored at −80°C. RNA was extracted using a Direct-zol kit (Zymo Research R2050) according to the manufacturer’s instructions, using DNase treatment on column, and was stored at −80°C.

RNA sequencing.

RNA sequencing was performed at the University of Washington Northwest Genomics Center (NWGC). RNA concentration was measured using the Quant-iT RNA assay (Invitrogen), and RNA integrity was tested using a fragment analyzer (Advanced Analytical). Total RNA was

Total RNA is normalized to 12.5 ng/μL in a total volume of 47 μL on the Perkin Elmer Janus Workstation (Perkin Elmer, Janus II). Poly-A selection and cDNA synthesis were performed using the TruSeq Stranded mRNA kit as outlined by the manufacturer (Illumina RS-122-2103). All steps were automated on the Perkin Elmer Sciclone NGSx Workstation. Final

RNA-Seq libraries were quantified using the Quant-it dsDNA High Sensitivity assay, and library insert size was checked using a fragment analyzer (Advanced Analytical; kit ID DNF474). Samples in which adapter dimers constituted more than 3% of the electropherogram area were failed prior to sequencing. Technical controls (K562,Thermo Fisher Scientific AM7832) were compared to expected results to ensure that batch-to-batch variability was minimized. Successful libraries were normalized to 10 nM for submission to sequencing. Barcoded libraries were pooled using liquid handling robotics prior to loading. Massively parallel sequencing-by-synthesis with fluorescently labeled, reversibly terminating nucleotides was carried out on the MiSeq sequencer. Base calls were generated in real time on the MiSeq instrument, and demultiplexed, unaligned fastq files were generated by Samtools bcl2fastq.

Gene classification.

Genes with increased transcript abundance in Gba1b mutants were classified to identify immune factors. If a gene met any one of four criteria, it was marked “immune,” meaning that it was either an active part of the immune response or a marker for immune cells. The four criteria were as follows: 1) Inclusion in the Lemaitre Lab’s list of immune factors (https://www.epfl.ch/labs/lemaitrelab/excel-lists-of-drosophila/, 2013 version). 2) One or more associated GO terms referring to immune function. 3) Significant increase in transcript abundance in one or more of the immune challenge studies described in Table 2. 4) Identified as a marker of activated macrophages or lamellocytes in a study from Table 3. Immune genes were subclassified as primarily humoral, primarily cellular, humoral/cellular, or unclear. This was done by manual curation based on localization, function, marker status, and interaction.

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Table 2. Details of immune challenge comparison datasets.

https://doi.org/10.1371/journal.pgen.1011105.t002

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Table 3. Details of activated immune cell comparison datasets.

https://doi.org/10.1371/journal.pgen.1011105.t003

GO Biological Process and KEGG pathway analyses were performed on RNA-Seq data using the PANGEA Gene Ontology tool [31] with Benjamini & Yekutieli correction for multiple testing. The cutoff for inclusion of terms or pathways was an adjusted p value < 0.05.

Cell type marker analysis using DRscDB.

We used DRscDB, a manually curated single-cell RNA-Seq database [66], to identify the Drosophila cell types with markers most closely resembling the transcriptional changes in Gba1b mutants vs. controls. We performed this analysis using the Single Gene List Enrichment feature. All 379 transcripts increased in abundance were included. From the 102 cell marker terms with significant enrichment (Benjamini & Yekutieli correction), we selected the 20 cell types with the greatest fold enrichment.

Proteomics.

Proteomic studies of fly heads were performed as previously described [39].

Comparisons of transcriptomic and proteomic data to published data.

Our aim in these analyses was to characterize our Gba1b mutant findings by evaluating their resemblance to other sets of findings associated with immune activation. These included acute responses to immune stimuli and markers representative of activated immune cell types.

Creating marker lists.

For each comparison dataset, we compiled a “marker list” of all transcripts with significantly increased abundance. For the immune challenge studies, this represented all transcripts increased in abundance after the relevant stimulus; for the immune cell type studies, the list was all transcripts enriched in the target cell type compared to other cell types studied. In some cases, the marker list was generated from two closely related datasets (e.g., transcripts increased in abundance in both of two parasitoid infestation studies). In one case, we used a proteomic dataset by converting the proteins into gene/transcript equivalents [65]. Each marker list was finalized by eliminating any transcripts not also detected in the Gba1b RNA-Seq dataset. Details of the studies, criteria used, and numbers of transcripts included are given in Table 2 for immune challenge studies and in Table 3 for cell type marker studies.

Comparing marker lists to Gba1b RNA-Seq data.

For each marker list, we determined the number of transcripts that were also increased in abundance in Gba1b mutants. We divided that value by the whole-dataset percentage of transcripts with increased abundance in Gba1b mutants, which was 3.48% for RNA-Seq and 8.58% for proteomics. The result was an enrichment score describing the degree of overlap between the marker list and the set of transcripts increased in abundance in Gba1b mutants. We determined whether that overlap was greater than would be predicted by chance alone using Fisher’s exact test.

Comparing marker lists to Gba1b proteomic data.

We compared our marker lists to proteomic data by converting the list of transcripts to a list of encoding genes. We then compared these to the genes encoding proteins in the proteomic dataset. Any protein product of the encoding gene was considered a match. We used protein abundance data from 12-day-old flies (the second stable isotope labeling time point). Statistical significance was tested as above. In the case of Wan et al. [65], we compared a proteomic dataset directly to our head proteomics, but we also matched any protein product of a given gene rather than matching specific protein isoforms.

Preparation of protein extracts for SDS-PAGE

RIPA buffer extraction.

Ten to twelve heads from 1- or 10-day-old flies (equal numbers of males and females) were homogenized in 2x RIPA buffer with Sigma protease inhibitor cocktail (10 μL/head). RIPA buffer consisted of 50 mM Tris·HCl (pH 8), 150 mM NaCl, 0.5% NaDOC, 1% NP40, and 0.1% SDS. Samples were centrifuged for 5 min at 13,000 x g. An equal volume of 2x Laemmli buffer with β-mercaptoethanol (1:50) was added to each supernatant, and the supernatants were then boiled for 10 min and stored at −80°C.

Preparation of Triton-soluble and insoluble fractions.

Samples consisted of 10 to 12 heads from 10-day-old flies or four whole flies (equal numbers of males and females). Samples were homogenized in 1x Triton lysis buffer (50 mM Tris-HCl [pH 7.4], 1% Triton X-100, 150 mM NaCl, 1 mM EDTA) with Sigma protease inhibitor cocktail (10 μL/head or 25 μL/fly). Homogenates were centrifuged at 15,000 x g for 20 min. The detergent-soluble supernatant was collected and mixed with an equal volume of 2x Laemmli buffer, and the same buffer was used to resuspend the detergent-insoluble pellet. All supernatant and pellet samples were boiled for 10 min. The detergent-insoluble protein extracts were centrifuged at 15,000 x g for 10 min, after which the detergent-insoluble supernatants were collected.

Immunoblotting

Proteins were separated by SDS-PAGE on 4%-20% MOPS-acrylamide gels (GenScript Express Plus, M42012) and electrophoretically transferred onto PVDF membranes. After gel transfer, we cut the membranes at 56 kDa. The bottom part of each membrane was incubated the anti-actin antibody and the top with the anti-Ub antibody. Immunodetection was performed using the iBind Flex Western Device (Thermo Fisher, SLF2000). Antibodies were used at the following concentrations: 1:25,000 mouse anti-Actin (Chemicon/Bioscience Research Reagents, MAB1501), 1:500 mouse anti-ubiquitin (Santa Cruz, sc-8017), 1:2000 mouse anti-HA (BioLegend), 1:1000 mouse anti-mCherry (Invitrogen), and 1:500 mouse anti-GFP (BioLegend).

HRP secondary antibodies were used as follows: 1:500 to 1:1000 anti-mouse (BioRad, 170–6516), 1:500 to 1:1000 anti-rabbit (BioRad, 172–1019). Signal was detected using Pierce ECL Western Blotting Substrate (Thermo Scientific, 32106). Densitometry measurements were performed blind to genotype and condition using Fiji software [49]. Signal was normalized to Actin [117, 118]. For comparisons involving a genetic manipulation, the value for the control genotype was then set at 1. Normalized immunoblot data were log2-transformed to stabilize variance, and means were compared using Student t test. Significant results were defined as increases of at least 1.25-fold or decreases of at least 0.8-fold with p < 0.05. Each experiment was performed using at least three independent biological replicates.

Fluorescent microscopy of Drosophila heads

One- or 10-day-old Drosophila (6–8 per condition, equal numbers of males and females) were anesthetized with CO2. Heads were removed and placed in Fluoromount aqueous mounting medium (Sigma F4680) on a glass slide with coverslip. Heads were then immediately visualized using a Leica MZFLIII microscope with Spot Insight Color Camera 3.2.0 and Spot Advanced software (exposure time 6–9 s). All heads within a comparison were imaged using the same magnification and exposure time. All cross-genotype comparisons were conducted on heads matched for sex. Red fluorescence was measured with Fiji software [49], using the mean value from the Color Histogram analysis function.

Drosophila behavior

All flies were housed in mixed-sex groups for at least 16 h to ensure mating.

Survival assay.

Flies were initially housed in groups of 10 to 24, with 5–10 vials per sex per genotype. At least 120 flies of a given sex were used for each genotype. The number of flies in each vial (10–24) was recorded at 20 d of age, and flies were transferred to fresh food three times a week until they reached 40 d of age. Flies were again counted, and the percentage surviving to 40 d was calculated.

RING assay.

Locomotor function was assessed using the Rapid Iterative Negative Geotaxis (RING) assay, modified from published protocols [74]. Flies were housed 8 to 15 per vial, separated by sex, and each genotype was represented by 6 to 12 vials. For the experiments involving expression of Gba1b in activated macrophages (zfh1-GAL4 > UAS-Gba1b), we used female flies, 13 d old; for the experiments restoring endogenous Gba1b to macrophages (SrpHemo-QF2 > QUAS-FLP with Gba1bCRIMIC) we used female flies, 15 to 17 d old; in the GlcT knockdown experiments, we used both male and female flies, 15 to 17 d old. Flies had a minimum of 24 h recovery time between carbon dioxide anesthesia and climbing. Six sets of flies were transferred to disposable plastic vials and loaded into a custom-built RING apparatus. Each set of six vials included both experimental and control vials. A Canon PowerShot ELPH 360 camera was used to record climbing height. The operator simultaneously allowed the RING apparatus to drop and pressed the camera button, which initiated a 3-second timer. Images were thus recorded ~3 s after the flies were forced to the bottom of the vial. After a pause of 60 s, the procedure was repeated. At least five trials were performed for each set of vials. Climbing was scored using the first four trials without technical problems, by an experimenter blind to genotype. The position of each fly in the vial was manually marked on the image using Fiji software, and the average height climbed per vial was calculated for each trial.

Gut transit assay.

Enteric nervous system function was tested using a procedure modified from Olsen and Feany [75]. Standard cornmeal-molasses food was dyed dark blue by adding two drops of blue food color (Safeway) per vial. The vials were then plugged and allowed to stand until dye was absorbed. Each genotype was represented by 4–5 vials. Flies were 15 d old in the UAS-Gba1b experiment and 20–22 d old in both the SrpHemo-QF2 rescue and the GlcT knockdown experiments. Groups of 18–26 female flies were transferred to dyed food and allowed to feed overnight. The next day, a set of flies was quickly anesthetized with CO2, and the amount of blue dye visible through the ventral abdominal wall was scored for each fly under a dissecting microscope. This baseline measurement was performed to rule out confounding differences in food intake, and no such differences were observed. The flies were then transferred to regular fly food, and blue dye in the abdomen was scored at 2.5 and 4 hours. Two time points were used because the overall rate of gut clearance across all flies varied from cohort to cohort. Data are reported from the first time point where significant gut clearance was seen. All scoring was done by an experimenter blind to genotype.

The assay was scored on a three-point scale (see S7 Fig). No visible blue in the abdomen was scored 0, faint to moderate blue was scored 1, and intense blue was scored 2. Color intensity was scored by eye using an “area under the curve” approach, evaluating the total amount of dye visible in the abdomen. Flies with a score of 2 were considered to have impaired gut transit.

Statistics

Statistics were calculated using Microsoft Excel and GraphPad Prism. For ubiquitin-protein immunoblotting, in which only two conditions were compared, significance was calculated using Student’s t tests. As noted above, a criterion of 1.25-fold or 0.8-fold change in abundance was also applied to exclude biologically irrelevant differences.

Significance for RING and gut transit assays, in which four genotypes were compared, was calculated using one-way analysis of variance. If there were no significant differences in standard deviation, we used ordinary ANOVA with Tukey post-tests; if SDs differed, we used Brown-Forsythe and Welch ANOVA with Dunnett post-tests. Significance was set at p < 0.05 after correction for multiple testing.

Error bars represent standard error of the mean in all graphs. All experiments were performed using at least 3 biological replicates.

Supporting information

S1 Fig. Schematic of analysis method comparing Gba1b mutant omics data to publicly available datasets.

The diagram illustrates our method of comparing Gba1b mutant RNA-Seq and proteomic data to available datasets on immune response and immune cell markers, showing two scenarios. See Materials and Methods for full details. The example is based on the marker list from the immune meta-analysis study [28], which includes 55 transcripts. (A) Chance-level overlap (no enrichment). Two transcripts appear both in the marker list and in the list of transcripts increased in abundance in Gba1b mutants. This is consistent with chance-level overlap, as 2/55 transcripts approximates the whole-dataset percentage of 3.5% transcripts with increased abundance. (B) Significant overlap. Twenty-eight transcripts appear both in the marker list and in the list of transcripts increased in abundance in Gba1b mutants. This is a statistically significant overlap, as 28/55 transcripts is 50.9%, more than 14 times the chance-level overlap of 3.5%.

https://doi.org/10.1371/journal.pgen.1011105.s001

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S2 Fig. Interfering with expression of Imd pathway members does not ameliorate Gba1b insoluble ubiquitinated protein accumulation and removal of humoral factors did not change viability of Gba1b mutants.

(A) Immunoblot of insoluble ubiquitinated proteins from heads of Gba1b mutants with and without Relish RNAi driven by ppl-GAL4. (B) Quantification of panel A for Gba1b revertant controls with vs. without Relish RNAi. The same comparison for Gba1b mutants is shown in Fig 2B. *p < 0.05 by Student’s t test. (C) Quantification of insoluble ubiquitinated proteins in heads from Gba1b mutants with and without loss of PGRP-LC function. (D) Quantification of insoluble ubiquitinated proteins from Gba1b mutants (whole flies) with and without the hypomorphic imd1 mutation. (D) Percentage of flies alive at day 40, Gba1b mutants and controls with or without AMPΔ8 mutation. n = 7 vials per group, 21 flies per vial, total of at least 145 flies per genotype. (E) Percentage of flies alive at day 40 Gba1b mutants and controls with or without TepqΔ mutation. n = 8–10 vials per group, 18–21 flies per vial, total of 170–200 flies per genotype. *** p < 0.005 by one-way ANOVA with Dunnett’s T3 multiple comparisons test.

https://doi.org/10.1371/journal.pgen.1011105.s002

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S3 Fig. Tests of macrophage markers and GAL4 drivers for expression through 10 days of adult life.

Heads were matched for sex and imaged at 1 or 10 d of age, then evaluated for fluorescent signal in a distribution consistent with macrophages. Heads were imaged caudal side down, with the ventral aspect to the left. (A) Markers and GAL4 drivers that maintain macrophage expression through 10 d of adult life. (B) Well-known macrophage GAL4 drivers that lack visible macrophage expression at 10 d of age. (C) Other macrophage GAL4 drivers with expression that lack visible macrophage expression at 10 d of age.

https://doi.org/10.1371/journal.pgen.1011105.s003

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S4 Fig. Confirmation of macrophage activation changes in 1- and 10-day-old Gba1b mutants using additional reagents.

(A) The HmlΔ promoter directly driving dsRed expression in control flies and Gba1b mutants gives the same result as HmlΔ-GAL4. (B) VT17559-GAL4 contains promoter sequence from the Lis-1 gene, an additional marker of macrophage activation. Like zfh1-GAL4, this marker is elevated in Gba1b mutants vs. controls at 10 d of age.

https://doi.org/10.1371/journal.pgen.1011105.s004

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S5 Fig. Confirmation of rescue of macrophage activation using HmlΔ-dsRed.

The experiment was performed similarly to the rescue experiment shown in Fig 6A with zfh1-GAL4 driving UAS-Gba1b. In this case, the HmlΔ-dsRed marker was substituted for zfh1-lexA > tdTom. Flies were 10 days old.

https://doi.org/10.1371/journal.pgen.1011105.s005

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S6 Fig. Restoring Gba1b expression in activated macrophages rescues lifespan in Gba1b mutants.

(A) Percentage of flies alive at day 40 in Gba1b mutants and controls with or without rescue expression of Gba1b under control of zfh1-GAL4. n = 4–6 vials per group, 10–24 flies per vial at 20 d, total 150–200 flies per genotype. (B) Percentage of flies alive at day 40 in Gba1b mutants and controls with or without restored expression of Gba1b in macrophages under control of SrpHemo-QF2. n = 6–7 vials per group, 10–24 flies per vial at 20 d, total 120–136 flies per genotype. ***p < 0.005 by one-way ANOVA with Dunnett’s T3 multiple comparisons test.

https://doi.org/10.1371/journal.pgen.1011105.s006

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S7 Fig. Gba1b mutants have abnormal enteric nervous system function.

(A) Photos of gut transit assay scoring categories. Flies with a score of 2 (blue dye strongly visible in abdomen) were considered to have impaired gut transit, while flies with a score of 1 (faint to moderate blue) or 0 (no blue) are considered to have unimpaired gut transit. (B) Examples of control and Gba1b flies 2.5 h after switching from dyed to regular food. (C) Percent of flies retaining dye after 2.5 h. The Gba1b mutants and control flies used in panel C also bear mCherry RNAi. ***p < 0.005 by Student t test.

https://doi.org/10.1371/journal.pgen.1011105.s007

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S8 Fig. Gba1b expression driven in sLNv clock neurons by Pdf-GAL4 did not ameliorate macrophage activation in Gba1b mutants.

Macrophage activation was visualized using zfh1-lexA driving lexAop-tdTom-HA (n = 6–8 heads per genotype). Flies were 10 d old. Graph is the mean fluorescence from male flies. Significance was tested using one-way ANOVA with Dunnett’s T3 multiple comparisons test and no significant difference was found between Gba1b mutants with or without Pdf-GAL4 driven Gba1b expression.

https://doi.org/10.1371/journal.pgen.1011105.s008

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S9 Fig. Activated macrophage knockdown of GlcT using zfh1-GAL4 caused impaired climbing in both control and Gba1b mutant flies.

Climbing was measured using the RING assay in male and female flies at 15–17 d of age. ***p < 0.005 by one-way ANOVA.

https://doi.org/10.1371/journal.pgen.1011105.s009

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S1 Dataset. RNAseq results from Gba1b mutants and gene lists from studies listed in Table 2.

https://doi.org/10.1371/journal.pgen.1011105.s010

(XLSX)

S2 Dataset. Complete genotypes for each experiment, listed by figure.

https://doi.org/10.1371/journal.pgen.1011105.s011

(XLSX)

Acknowledgments

We thank Utpal Banerjee, Bruno Lemaitre, Iwan Evans, and Steven Wasserman for fly stocks and Chris Frazar of the University of Washington Northwest Genomics Center for sequencing services. Amy Platenkamp contributed to immune knockdown experiments.

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