The gut microbiome modulates the transformation of microglial subtypes

doi:10.21203/rs.3.rs-1730767/v1

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The gut microbiome modulates the transformation of microglial subtypes

https://doi.org/10.21203/rs.3.rs-1730767/v1

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Clinical and animal studies have shown that gut microbiome disturbances can affect neural function and behaviors via the microbiota-gut-brain (MGB) axis, and may be implicated in the pathogenesis of several brain diseases. However, exactly how the gut microbiome modulates nervous system activity remains obscure. Here, using a single-cell nucleus sequencing (snRNA-seq) approach, we sought to characterize the cell type-specific transcriptomic changes in the prefrontal cortex (PFC) and hippocampus (Hip) derived from Germ-Free (GF), Specific Pathogen Free (SPF), and Colonized-GF (CGF) mice. We found that the absence of gut microbiota resulted in cell-specific transcriptomic changes. Furthermore, microglia transcriptomes were preferentially influenced, which could be effectively reversed by microbial colonization. Significantly, the gut microbiome modulated the mutual transformation of microglial subpopulations in the two regions. Cross-species analysis showed that the transcriptome changes of these microglial subpopulations were mainly associated with Alzheimer’s disease (AD) and major depressive disorder (MDD), which were further supported by animal behavioral tests. Our findings demonstrate that gut microbiota mainly modulate the mutual transformation of microglial subtypes, which may lead to new insights into the pathogenesis of AD and MDD.

Gut microbiome

Microbiota-gut-brain axis

Germ-free mice

Microglia

Microbial colonization

Alzheimer’s disease

Major depressive disorder

Substantial basic research has demonstrated that the gut microbiome can modulate neurochemistry and various behaviors through the microbiota-gut-brain (MGB) axis[1–3]. Clinical studies have found that major neuropsychiatric diseases such as Alzheimer's disease[4] (AD), Parkinson's disease[5], autism[6], and major depressive disorder[7, 8] (MDD) were accompanied by disturbances of the gut microbiome. Significantly, investigations further showed that certain microbial species were associated with pathogenesis of representative neuropsychiatric diseases[9–13]. However, exactly how gut microbiota modulate molecular changes in the brain remains largely unknown. Uncovering this information is critical to understanding whether the gut microbiome might contribute directly to the pathology or treatment of brain diseases.

Germ-free mice, which are entirely devoid of gut microbiota, are an important model for studying the roles of the gut microbiome and the MGB axis[6, 10, 14–16]. Previously, several studies showed that gut microbiome absence altered hippocampus, prefrontal lobe, and amygdala transcriptomes, which were related to anxiety, depression, and cognitive impairment[17–20]. However, given the heterogeneity of neural cell types and their roles, bulk-tissue-level investigations such as these may mask the complexity of changes across cells, and cannot interrogate the complex interplay between different cells[21]. In addition, previous studies highlighted that specific neural cell subpopulations were preferentially linked with the pathogenesis of neuropsychiatric diseases[22–25]. For example, Keren-Shaul and colleagues identified, the DAM subtype (Disease-associated microglia), which was linked with AD[22]. Single-cell nucleus sequencing (snRNA-seq) approaches enable characterization of cell type-specific transcriptome changes for tens of thousands of individual cells[26, 27]. snRNA-seq studies have identified new potential pathophysiological mechanisms and intervention targets for neuropsychiatric diseases [28, 29], which highlight new avenues of investigation for how the gut microbiome affects the brain through the MGB axis.

To gain insight into cell type–specific transcriptomic changes, we performed unbiased snRNA-seq of the prefrontal lobe cortex (PFC) and hippocampus (Hip) derived from Germ-Free (GF), Specific Pathogen Free (SPF), and Colonized-GF (CGF) mice. CGF mice were generated by co-housing juvenile (4-week-old) GF and SPF mice in flexible film gnotobiotic isolators for four weeks. We chose to investigate the PFC and Hip brain regions because: (i) The PFC and Hip are known principal targets of gut microbiome action on the brain via the MGB axis[15, 29, 30], and (ii) The PFC and Hip are closely linked with development of neuropsychiatric disorders[31–33]. We found that the transcriptomes of microglia and their subpopulations were substantially modulated by the gut microbiome, which may have implications for the pathogenesis and treatment of AD and MDD.

Ethics statement

The Ethical Committee of Chongqing Medical University (ECCMU, Chongqing, China; 2017013) reviewed the animal protocols and experiments. Kun Ming mice were used in all experiments. GF mice were obtained by sterile treatment on SPF mice in the NHC Key Laboratory of Diagnosis and Treatment on Brain Functional Diseases. The sterilization verification on GF mice was tested by sterile experiment on feces and skin, in accordance with Chinese Laboratory Animal-Microbiological Standards and Monitoring (GB 14922.2-2011). We followed the ‘Reduce, Reuse, Recycle’ Waste Hierarchy, using the minimum sample size of 3 samples/group (n=3/GF, n=3/SPF, n=3/CGF). This sample size is also commonly used in the single-cell analysis of murine brain tissues in previous studies[22, 28, 29].

Co-housing method

CGF mice were 4-week-old GF mice reared with SPF mice for 4 weeks in one germ free isolator (4 CGF with 1 SPF /cage), see Sup Fig 1a. GF and CGF mice were kept in a flexible film gnotobiotic isolators until experiment and behavioral tests. All mice were housed on a 12-h light/dark cycle and had food and water ad libitum, with controlled temperature (22-24°C) and (50-60%) humidity conditions. All mice were harvested at 8 weeks old.

Extraction of single-cell nuclei

The PFC and Hip were dissected, snap frozen in liquid nitrogen, then homogenized in 250 mM Sucrose, 10 mM Tris-HCl, 3 mM MgAc2, 0.1% Triton X-100 (SigmaAldrich, USA), 0.1 mM EDTA, 0.2U/μl RNase Inhibitor (Takara, Japan) (Fig 1a), as previously described[34]. Nuclei were purified by sucrose gradient, and used at about 1000 nuclei/μl for snRNA-Seq.

10x library construction

The 10X Genomics Chromium Controller Instrument and Chromium Single Cell 3'V3.1 Reagent Kits (10X Genomics, Pleasanton, USA) were used to establish the 10x library. Nuclear suspensions (1000 nuclei/ul) were added to each channel to generate single-cell gel bead-in-emulsions (GEMs). This procedure breaks up GEMs, and purifies and amplifies barcoded-cDNA, after RT (Reverse transcription). The cDNA was then cut to make it fragmented, the A-end was loaded at the tail, the adapters joined, then index PCR amplification was performed. The final library was quantified using High Sensitivity DNA chip on a Bioanalyzer 2200 (Agilent, USA), and sequenced using a Novaseq 6000 (Illumina, USA) on a 150 bp paired-end run.

Single-cell nucleus RNA statistical analysis

After applying the default filtering parameters (to exclude the adapted sequences and low-quality sequences) by using Fastp (v0.18.0), clean and high-quality sequence data were obtained. CellRanger v3.1.0 was used to get feature-barcode matrices through match reads to the mouse genome (mm10_Ensembl_Ensembl92). The aggregated matrix was obtained by down sample analysis of mapped barcoded reads per cell of each sample. Cells expressing fewer than 200 genes and cells with a mitochondrial UMI (Unique molecular identifiers) rate higher than 10% were excluded. Based on the above expression matrix, the Seurat package (version: 3.1.4) achieved the scaled data after cell normalization and regression[35, 36]. According to the scaled data, the top2000 high variable genes and top10 principals were used to construct a PCA (Principal Component Analysis) for UMAP (Uniform Manifold Approximation and Projection for Dimension Reduction). The unsupervised cell cluster was obtained based on the above PCA analysis and graph-based cluster method, and marker genes were calculated by FindAllMarkers (Wilcoxon) with 1. |Average log2FoldChange|> 0.25; 2. FDR <0.05; 3. min.pct > 0.1. as the restriction conditions, in line with single-cell approaches. Simultaneously, we used re-UMAP analysis, graph-based clustering, and marker analysis in the same cell type to find new cell subtypes.

Differentially expressed genes (DEGs) burden analysis on downsampling data

Firstly, we identified differentially expressed genes among samples, the function FindMarkers (Wilcoxon) was used under following criteria:1. |log2FoldChange| > 0.25; 2. FDR < 0.05; 3. min.pct > 0.1.

Next, we used a weighting algorithm that balanced the difference in the number of DEGs caused by unequal cell counts to explore preferentially influenced cell types in GF mice, as previously described[37]. We then calculated the number of DEGs by randomly sampling 1992 cells ten times (Student’s T-test, two-tailed).

Gene ontology and canonical pathway analyses

Gene ontology analysis and Canonical Pathways analysis were conducted by GO (geneontology.org) and Ingenuity Pathway Analysis (IPA) software (Qiagen, Shanghai, China), cutoff as |-logFDR|＞1.31 and |-logP|＞1.31.

Pseudo-time analysis

After selecting marker genes by the Seurat clustering result, Monocle2 was used to analyze Single-Cell Trajectories using DDR-tree with default parameters[38]. Finally, branch expression analysis modeling (BEAM Analysis) was applied for branch fate determined gene analysis.

QuSAGE Analysis (gene enrichment analysis)

A customized 41-signature gene set including immune-, cytokine-, and neurobiology-related terms was collected from the CellphoneDB database[39, 40], the neurotransmitters receptor gene of Genebank (https://www.ncbi.nlm.nih.gov/genbank/), and the immune scoring gene set as previously described[41]. QuSAGE analysis was used to quantify the relative activation of a given gene set，using QuSAGE 82 (version 2.16.1). Full probability density function (PDF) was used to show gene set differential expression levels, and significant enrichment pathway (p value＜0.05) analysis was used to visualize data.

Cell communication analysis

The CellPhoneDB database[40] (cellphonedb V1.10 R package) analyzed cell-to-cell molecular communications in the Hip and PFC. Membrane, secreted, and peripheral proteins of the cluster of different time points were annotated. The mean counts of cell communication and its significance (p-value < 0.05) were calculated based on the interactions.

Behavioral tests

Prior to all behavioral tests, the experimental 8week-old GF and CGF were removed from the sterile chamber and placed in a quiet test room for at least one hour before test initiation, as previously described[7]. Tests were conducted during the 9:00 am-17:00 pm window, under the same conditions as the typical breeding environment. All tests were video recorded and Noldus and ANY-maze software were used for data analyses. Mice were tested in the open field test (OFT), forced swim test (FST), and Y-maze tested.

OFT[42]: Mice were individually placed in an uncovered cube of 45 cm x 45 cm length and 45cm height, with a black background. The mouse was placed in a corner of the box. After 5 min of adaptation, 5 min video was collected for analysis. The total distance traveled was used to evaluate the mouse’s locomotor activity.

FST[43]: Cylinder tanks of 30 cm height and 15 cm diameter, containing 15 cm of water level at 24°C were used. Mice were placed vertically, wetting all the hair, then video was collected for 6 min. Duration of immobility during the middle 5 min of the total 6 min was used to evaluate the behavior despair.

Y Maze Test[44]: The Y maze test instrument was composed of three open arms each 45 cm long, 10cm wide and 29cm height in a black background. Each mouse was placed in the same starting arm at the beginning of the experiment. The free exploration trajectory of the mouse was recorded for 8 min, and the order and times of entering the three open arms was used to calculate the spontaneous alternation percentage (%alternation) using the following equation = (the number of triads in turn)/ (the total number of arm entries -2) *100%.

Statistical methods and reproducibility

One-way ANOVA measure the proportion of cell types and numeric data of behavioral tests. Significance between two groups calculated by post-hoc analysis with the least significant difference test (LSD).

Single-cell nucleus RNA-seq profiling of Hip and PFC

A schematic of nuclei isolation and the snRNA-seq workflow from the Hip and PFC is shown in Fig 1a. Using the droplet-based single-nucleus method, we captured 72,226, and 67,698 nuclei from the Hip and PFC, respectively, in the 9 mice (3 per group). We obtained an average of 45,455 reads per nuclei in the Hip and 50,245 reads in PFC after stringent quality control (Supplement data S1).

After dimensionality reduction and graph-based clustering (UMAP), we identified 36 distinct clusters in the Hip (Sup Fig 1b) and 29 clusters in PFC (Sup Fig 1c). Then we annotated major cell types in the two regions based on the expression of well-established marker genes[45]. Here, excitatory neurons (n=43956, 41837 in the Hip and PFC, respectively; marked by Grin2a, Syt1, Grin1), interneurons (n=6036, 7702; Gad1, Gad2), oligodendrocyte (n=6021, 2984; Plp1, Mog, Mbp), OPC (Oligodendrocyte precursor cells; n=2089, 1994; Pdgfra, Vcan), microglia (n=2343, 2353; Csf1r, Ctss, C1qa), and astrocytes (n=6803, 5862; Aqp4, Gja1) were clearly identified (Sup Fig 1d and e; Fig 1b and c).

The absence of gut microbiota changed glial cells proportion in the Hip and PFC

Initially, we calculated the proportion of major cell types in two brain regions. In Hip, we found that the microglial proportion was significantly lower in GF compared to SPF (p=0.013), and microbial colonization failed to rescue this change (GF vs. CGF, p=0.662; Fig 1d). Meanwhile, hippocampal astrocyte, oligodendrocyte and OPC proportion were down-regulated in the GF group relative to the SPF group (GF vs SPF, p=0.005 for astrocyte, 0.0394 for oligodendrocyte and 0.015 for OPC), astrocyte and OPC were reversed by microbial colonization in the CGF group (SPF vs CGF, p=0.0003, 0.034), excluded oligodendrocyte. Furthermore, contrast to glial cells, excitatory neuron increased in GF (GF vs. SPF, p=0.0444), microbial colonization reversed this trend in CGF (GF vs. CGF, p=0.0342). In PFC, the proportion of microglia in the GF group relative to the SPF group trended upward (p=0.062), which could be reversed by microbial colonization (Fig 1e). We did not find any difference in the composition ratio of the remaining cell types between the three groups. These observations demonstrate that presence or absence of the gut microbiome primarily impacted the relative composition of glia in the Hip and PFC.

The absence of gut microbiota resulted in cell-specific transcriptomic changes.

Next, we performed a DEGs analysis between GF and SPF (Sup Fig 2a, b). We identified 4999 and 6122 DEGs across the major six cell types in the Hip and PFC, respectively (Sup Fig 2c). In these two brain regions, glial cells had more DEGs than neurons (Sup Fig 2d and e). The top DEGs in the two regions were mainly involved in mitochondrial dysfunction and the RNA translation process even across cell types (Sup Fig 2f and g).

To further uncover the cell-specific transcriptomic changes modulated by gut microbiota，we identified 846 and 1333 cell-specific DEGs across the six major cell types in the Hip and PFC (Fig 2a-b), respectively. This observation highlights that the single-cell-level resolution is vital to uncover how the gut microbiome modulates transcriptional changes in the brain. The function enrichment pathways of these cell-specific genes were also significantly different. For example, the altered microglial cell-specific DEGs were enriched for neuroinflammatory and complement system signaling pathways in the Hip (Sup Fig 3a), such as alterations of chemokine receptor-Cx3cr1, interferon gamma receptor-Ifngr1, interleukin receptor-Il6ra, and complement family-C1qa, C1qb, and C1qc, respectively. In contrast, in the PFC, microglial cell-specific DEGs were enriched for RhoGDI and IL-8 signaling pathways (Sup Fig 3b), for example, Map2k1, Nfatc3, and Rhot1. Together, our DEGs analysis showed that the absence of gut microbiota resulted in cell-specific transcriptomic changes.

Microglial transcriptomes were preferentially influenced

Here we explored which cell types were preferentially modulated by the gut microbiome. Disregarding different cellular counts, oligodendrocytes, astrocytes, and microglia mainly contributed to DEGs detected in the Hip (Sup Fig 2d), while the majority of DEGs in PFC were derived from excitatory neurons, interneurons, and microglia (Sup Fig 2e). To rule out the inherent confounding effects of unequal captured cell ratios (neuron: glia ratio = 2.89:1 in PFC and 3.2:1 in Hip), the DEGs burden analysis[37] was carried out by comparing the same number of nuclei across all cell types for ten times by down-sampling the data. Accordingly, we found that microglia had the largest number of DEGs in both the Hip and PFC (Fig 2c-d), suggesting that microglia were preferentially impacted among the six major cell types in the two regions. These findings aligned with the disparate microglial ratios also found in the two brain regions.

We determined whether microglial DEGs were brain-specific. Venn diagram analysis showed that there were 370 genes shared in two regions, while 563 genes only changed in the Hip, and 694 in PFC (Sup Fig 4a). Functionally, we found that the GF mice were enriched for mitochondrial dysfunction, oxidative phosphorylation, inflammasome pathway, and NRF2-mediated oxidative stress response, and depleted for synaptogenesis, synaptic long-term potentiation, and synaptic long-term depression signaling in the Hip. In the PFC, some pathways such as NRF2-mediated oxidative stress response were consistently enriched in GF relative to SPF mice. However, synaptic related pathways such as the synaptogenesis signaling and long-term synaptic potentiation showed opposite changes in the PFC relative to Hip (Sup Fig 4b). Our results suggest that microglial transcriptional changes caused by the gut microbiome vary in a brain region-specific manner.

The gut microbiome mainly modulated microglia-astrocyte communication

We conducted CellPhoneDB database[40] (cellphonedb V1.10 R package) analysis to uncover potential ligand-receptor pairs between cells to understand how gut microbiome absence influenced communication between microglia and other major cell types. Detailed data are shown in supplemental data S2. Microglia communicated mostly with astrocytes, followed by oligodendrocytes, in the Hip of the SPF group. Similar cell-to-cell communications were also found in the GF group, but the lack of a gut microbiome in that group resulted in decreased interaction intensities. Microbial colonization slightly increased the microglia-astrocyte communication (Sup Fig 5a). For example, we found diminished the communication between microglial Entpd1 to astrocytic Adora1 in the GF group, which was restored in the CGF group. The CD39 (Entpd1) and Adora1 pair can regulate neuronal activity via its participation in adenosine metabolism [46]. In the SPF group, cellular communication between microglia and other cells was weaker in the PFC than in Hip. Interestingly, lack of a gut microbiome led to significantly increased microglia-astrocyte communication, ranking first in the communication between microglia and other cells. Furthermore, microbial colonization failed to modulate the communication between microglia and other cells (Sup Fig 5a). In summary, the gut microbiome mainly influenced microglia-astrocyte communication in the Hip and PFC of GF and CGF mice.

Microbial colonization rescued microglial gene alterations

Here, we identified 933 and 1064 microglial DEGs by comparing the GF and SPF groups. Interestingly, we found that most of DEGs from these two groups (74.91% and 78.76%) could be rescued by microbial colonization. These rescued genes were associated with chemical synaptic transmission, and cell-cell adhesion in the Hip (Supplement data S3; Sup Fig 5b). For example, we observed 6 rescued cadherin family genes (e.g., Cdh8, Cdh9, Cdh11 and Cdh12), mainly involving in cell adhesion, and 7 rescued Gamma-Aminobutyric Acid (GABA) receptor genes (e.g., Gabarap, Gabarapl2, Gabra2, Gabrb1). Additionally, genes enriched in fundamental molecular processes like protein binding and transport, were rescued in the PFC by microbial colonization, such as 6 reversed genes (e.g., Eif1, Eif1b, Eif2s3y, Eif4e, Eif4h, and Eif5a) belonging to eukaryotic initiation factor (Sup Fig 5c). Our findings suggest that microbial colonization effectively reversed microglial transcriptomic changes in the Hip and PFC.

Gut microbiota modulated mutual transformation of microglial subpopulations

Having demonstrated that gut microbiome absence mainly affects microglia, we wanted to further clarify if or how microglial subpopulations changed. Therefore, we performed a microglial re-clustering analysis, that yield 10 and 6 subpopulations in the Hip and PFC, respectively. In the Hip, two microglial subpopulations (Hip_M1, M4), with a composition ratio of 83.98%, were significantly enriched in GF compared to SPF (0.53%), and microbial colonization could effectively reverse these changes in the CGF group (0.44%; p= 5.2523E-8, one-way ANOVA; Fig 3a and b). For Hip_M0, a contrasting pattern was observed between the three groups (0.27% in GF, 34.99% in SPF, 45.18% in CGF; p=0.014, one-way ANOVA；Fig 3a and b). QuSAGE was used to identify functional gene sets of each subpopulation. This analysis showed that the anti-inflammatory and regulatory T cells (Treg) gene sets were most activated in Hip_M1 and Hip_M4 (sup Fig 6a-c), such as enrichment of Entpd1, Mif and Tgfb1 (sup Fig 7a-d), which were inhibited in Hip_M0 (sup Fig 6a and 6d).

In the PFC, the composition ratio of PFC_M2 (37.36%) was enriched in GF relative to SPF (10.23%), and could be effectively rescued in the CGF group (11.53%; p=0.011 between the three groups, one-way ANOVA; Fig 3d and e). Meanwhile, the composition ratio of PFC_M0 was significantly reduced in GF (13.10%) compared to SPF (46.85%) and CGF (51.19%), and enriched in SPF and CGF (46.85% in SPF, 51.19% in CGF; p=0.015, one-way ANOVA; Fig 3d and e). QuSAGE analysis showed that the anti-inflammatory and Treg gene sets, such as Entpd1, Mif, Vegfa and Tgfb1 (sup Fig 7e-f), were most activated in PFC_M2 (sup Fig 6e and 6g), and inhibited in PFC_M0 (sup Fig 6e-f). Together, these findings demonstrated that the gut microbiome modulated the mutual transformation of microglial subpopulations in the two regions.

Next, Pseudotime analysis was conducted to further explore these mutual transformational relationships. We found that microglia started from state1 to state2, or to state3 (Sup Fig 8a and b). We further divided the distribution of cells by experimental groups (Sup Fig 8c and d), and by each microglial subcluster (Sup Fig 8e and f). We found that Hip_M1 and Hip_M4 were distributed at state 1. Microbial colonization made the two microglial subpopulations develop to the middle stage as Hip_M0 (Sup Fig 8e). The marker gene of Hip_M1&4, Mafb[47], is a principal transcription factor that regulates adult microglial homeostasis. Moreover, PFC_M2 was initially mostly distributed peripherally, but after microbial colonization, it returned to the middle stage as PFC_M0 (Sup Fig 8f). We also observed that Runx1[48] and Selplg[49], which were marker genes of PFC_M2, involved in maintaining microglial homeostasis, could be the inducer of PFC_M2. These findings suggest that gut microbiota modulate transformational control between different microglial subpopulations, displaying the shift from Hip_M1&4 to Hip_M0, and PFC_M2 to PFC_M0 (Fig 3c and f).

The microglial genes rescued by microbial colonization are linked with AD and MDD

To explore potential associations between the rescued genes and representative neuropsychiatric disorders, the DisGeNET database[50] was used for Disease Enrichment analysis. In both the Hip and PFC, the microglial genes rescued by microbial colonization were linked to neuropsychiatric diseases such as AD (n=173 in Hip, 174 in PFC), MDD (n=98, 61), and autism (n=116, 52；Fig 4a and 4b).

Single cell studies of AD and MDD were selected to further confirm these findings. AD had the highest numbers of rescued microglial genes in the Hip and PFC, and we had a long-term interest in MDD. We found that, although the cell types associated with distinctive diseases were different, a large number of disease risk genes aligned with the microglial genes rescued by microbial colonization (Fig 4a). In particular, for AD, 19 microglial genes overlapped between PIGs[51] (plague-induced genes), DAM[22] (disease associated microglia), and reversed genes, including Apoe, Fcer1g, C1qa, Frcls, C1qb, Itm2b, C1qc, Man2b1, Cd9, Olfml3, Cst3, Trem2, Ctsl, Ctsb, Ctss, Gusb, Ctsz, Hexa, and Cx3cr1 (Fig 4d). As for MDD, we used our single-cell analysis of dorsolateral PFC from a non-human primate depression model (unpublished data, not shown), and matched them against MDD risk genes in the DisGeNet database. We found 10 overlapped genes, including FKBP5, AUTS2, ERBB4, NEGR1, NRG3, RABGAP1L, SLC1A3, ANK3, CTTNBP2, and ITGB5 (Fig 4e). These findings demonstrated that microglial genes reversed by microbial colonization were mainly linked with AD and MDD, suggesting that microbial modulation of these key microglial genes via the gut-brain axis may be a potential therapeutic strategy for AD and MDD.

Cross-species analysis showed that microglia subpopulations regulated by gut microbiota were associated with AD and MDD.

Next, we further verified whether the transcriptomic changes of these 5 microglial subpopulations were linked with AD and MDD. We performed cross-species analysis of the association between these 5 microglial subpopulations and these two disorders by using animal and human sc/snRNA-seq data from 5 publications[22-24, 52], and snRNA-seq analysis from our non-human primate depression model (unpublished data, not shown). The marker genes of 5 microglia subpopulations were compared with the microglial marker genes or enriched DEGs associated with these diseases. We found that transcriptomic changes of these microglial subpopulations were highly associated with AD and MDD across human, mouse, and macaque (Fig 5a). Furthermore, the noted DAM was highly similar with Hip_M1 (FDR= 3.52E-30, odd ratio=7.379; Supplement data S4) and Hip_M4 (FDR= 3.32E-08, odd ratio=3.896). In addition, we found that only PFC_M2 was significant relative to MDD associated microglia in Macaca (FDR=0.001, odd ratio=2.605). This cross-species analysis provided evidence that microglia subpopulations’ transcriptomic changes modulated by gut microbiota were highly linked with AD and MDD.

Behavioral tests support the association between gut microbiota, AD, and MDD

We used an animal behavioral test panel related to AD and MDD including OFT, Y maze and FST to confirm the above findings. There was no difference in locomotion activity between the three groups (p=0.109; Fig 5b). However, the percent immobility time was significantly decreased in the FST of GF compared to SPF mice (p=6.9879E-11), suggesting impacts on behavior despair. Microbial colonization increased the percent immobility time in CGF mice, although it was not completely restored to the same level as SPF mice (Fig 5c). In the Y maze test, the spontaneous alternation rate was significantly decreased in GF relative to SPF (p=0.023), and this change could be completely reversed by microbial colonization (CGF) (p=0.013; Fig 5d). This behavioral test suggested a close association between the gut microbiome and short-term memory changes. Together, our behavioral studies support the single-cell observations that transcriptomic changes of microglial subpopulations were highly linked with AD and MDD.

Our study showed that the absence of a gut microbiome significantly affected transcriptional changes at the single-cell level, especially in microglia and their subtypes. Microbial colonization could effectively reverse these transcriptional changes. Significantly, we found that the gut microbiome modulated mutual transformation of microglial subtypes. Cross-species analysis showed that transcriptomic changes of microglial subpopulations were linked with AD and MDD, and with animal behavior changes relevant to these diseases. Our findings provide new evidence for how the gut microbiome may regulate brain function through the MGB at single-cell molecular resolution. Furthermore, these findings may pave the way for a better understanding of the pathophysiological mechanisms of AD and MDD.

In recent years, important progress has been made in understanding how the gut microbiome affect the brain[1, 2], but its transcriptional changes at the single-cell level have not been uncovered. Here, we found that the lack of a gut microbiome can lead to systematic changes in the transcription of neuronal and glial cells. Among them, microglia were most vulnerable to the influence of the gut microbiome. In line with our findings, Erny et al. observed significant differences in microglial structure and function between SPF and GF mice[53], and Thion M.S et al. found that signals from the maternal gut microbiome may shape fetal microglia developmental trajectories near birth[54]. We found that most microglial DEGs derived from microglia could be rescued by microbial colonization. For example, changes in a panel of GABA DEGs (eg., Gabarap, Gabarapl2, Gabra2, Gabrb1) were reversed by microbial recolonization, suggesting the gut microbiome may influence GABA-related signaling pathways. Our findings are consistent with several publications reporting GABA-related signaling pathways modulation through the MGB axis[15, 55, 56], although the specific cell types impacted in those studies were not delineated. Olson et al. have shown that a ketogenic diet could reduce seizures through gut-brain GABA related pathways[15]. Cryan et al. have reported that Lactobacillus strain ingestion regulates emotional behaviors and central GABA receptor expression in mice[55]. We have also reported that the gut microbiome may be linked with schizophrenia through the gut-brain glutamate-glutamine-GABA cycle[56]. Since microglial gene transcription was both affected by the lack of a gut microbiome, and could be rescued by microbial colonization, targeted regulation of microglial transcription through the MGB axis may represent a promising new therapeutic avenue for brain disease. In addition, we found that gut microbiome absence significantly increased microglia-astrocyte communication, which could not be reversed by microbial colonization. This result suggests that the roles of microglia-astrocyte communication in MGB-axis related diseases need to be further explored in the future.

While we observed that microglia were most vulnerable to gut microbiome absence, single-cell technology provides an advantage to further clarify the major altered microglial subtypes. Here, we found that the gut microbiome regulated mutual transformation of microglial subpopulations, which could be effectively reversed by microbial colonization. Functionally, these altered microglial subpopulations were enriched with anti-inflammatory and Treg gene sets, suggesting changes of inflammation related signals. In line with our findings, Shemer et al. found that peripheral inflammation induced by lipopolysaccharide (LPS) could result in microglial activation changes[57, 58]. Meanwhile, Erny et al. also observed increased LPS levels in GF mice relative to SPF mice[53]. In the future, it is necessary to further explore how the gut microbiome mediates transformation of microglial subtypes through inflammatory pathways.

In addition, using cross-species analysis, we found that transcriptome changes of microglial subpopulations regulated by gut microbiota were associated with AD and MDD. Previously, clinical and animal studies have shown that AD and MDD were accompanied by disturbances of the gut microbiome[4, 9, 13, 59]. Targeted intervention of the gut microbiota with drugs, prebiotics, and short chain fatty acids has potential therapeutic value for these two disorders. Significantly, recent single-cell investigations have shown that AD was associated with specific changes of microglial subtypes[22–24, 52], and intervention of microglia subtypes showed a potential therapeutic effect[22]. In agreement with these findings, we found that some altered classic genes in microglia, such as Apoe and Trem2, could be reversed by microbial colonization. Corresponding to these results, we found that absence of a gut microbiome resulted in altered short-term memory in the Y maze test, which was also reversed by microbial colonization. A similar phenomenon was found in depression as well, while comparing the transcriptome changes of microglial subpopulations across different species including human, monkey, and mouse. These studies suggest that the gut microbiome may be involved in the pathogenesis of AD and MDD by regulating transcriptional changes of microglial subtypes.

Nonetheless, the following caveats are worth noting: (i) Like most studies[6, 10, 15, 16], we used GF mice as the main model to study brain function is affected by the MGB axis. Thus, single-cell analysis of SPF mice with antibiotic treatment would further confirm our findings. In addition, (ii) Spatial transcriptomic data would help further clarify the spatial localization of these vital cell-specific genes related to AD and MDD. Finally, (iii) intervening cell-specific DEGs of microglia and their subtypes also needs to be explored to uncover their roles in the development of AD and MDD.

In conclusion, using a single-cell nucleus sequencing approach, we showed transcriptome changes of neural cell types modulated by the absence of gut microbiome. Microglia and their subpopulations were preferentially impacted in the Hip and PFC. The gut microbiome may regulate the mutual transformation of microglial subtypes. Furthermore, their transcriptomic changes were associated with AD and MDD. Our research provided single-cell transcriptional evidence for an in-depth understanding of how the gut microbiome affects the brain, which may help uncover the pathogenesis of AD and MDD.

Funding: This work was supported by the National Key R&D Program of China (2017YFA0505700), Projects of International Cooperation and Exchanges NSFC (81820108015), Non-profit Central Research Institute Fund of Chinese Academy of Medical Sciences (2019PT320002), the Natural Science Foundation Project of China (81971296, 82171523, and 82101596), Chongqing Science &Technology Commission (cstc 2019 jcyjjqX0009), China Postdoctoral Science Foundation(2020TQ0393, 2021TQ0396, 2021MD703928, 2021M693926), Chongqing Talents Plan for Young Talents (CQYC202105017) and institutional funds from the State University of New York (SUNY) Upstate Medical University. This paper is subject to the SUNY Open Access Policy.

Author Contributions: Designed the experiments: P.X. and P.Z. Operated experiments: Y.H., J.W., Y.F.L., L.W., H.P.Z., K.C., and H.Y.W. Performed the sc-RNA seq analysis: Y.H., J.W., Y.F.L., L.X.L., and J.C.P. Animal behaviors analysis: Y.H., J.W., Y.F.L., X.M.T., Y.Y.L. Drafted the manuscript: Y.H., and P.Z. Revised the manuscript for intellectual content: P.X., P.Z., S.W.P., MLW, J.L.

Competing interests: The authors have declared no conflict of interest in the submission of this manuscript.

Data and materials availability: All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Materials. The raw single-cell sequence data reported in this paper have been made available at the Gene Expression Omnibus (GEO) repository under the accession number GSE202704.

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The authors have declared there is NO conflict of interest to disclose

SupplementaryTableS1.xlsx
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MicrobiomesnRNASuplementFiguresupload.pptx
Sup Figure 1. Overview of the experimental approach and snRNA seq dataset. (a) Experimental schedule showing that the CGF group were 4-week-old (4w) GF mice cohoused with the SPF group for 4 weeks. Tissues from 8-week-old (8w) SPF, GF and CGF mice (n=3 in per group) were harvested for snRNA analysis.(b,c) UMAP graph embedding plot depicted 72,226 nuclei divided into 36 unbiased initially identified clusters in Hip (b) and 67,698 nuclei in 29 unbiased initially identified initial clusters in PFC (c). (d,e) Cell-type annotation was performed on the expression of well-established marker genes. Marker genes were coded according to the cell types in which they should be detected. The dot plot depicted the expression of known marker genes in 36 clusters in Hip (d) and 29 clusters in PFC (e). The size of the dots represents the percentage of expressed marker genes (Pct. exp.), and the color intensity represents the scaled average expression level (Avg. exp. scale). Sup Figure 2. Gut microbiome absence influenced genes expressed in major cell types.(a,b) Volcano plots show DEGs (FDR< 0.05) expressed in microglia, astrocyte, oligodendrocyte, OPC, excitatory neuron and interneuron in Hip (a) and PFC (b). (c) A total of 4999 and 6122 DEGs (FDR< 0.05) were obtained across six major cell types in Hip and PFC, respectively. (d,e) Rose diagrams show that glial cells contributed more DEGs than neurons in Hip (d) and PFC (e). (f,g) Mitochondrial dysfunction and RNA translation process were disturbed in major cell types by the absence of microbiome in Hip (f) and PFC (g). Sup Figure 3. Cell-specific DEGs enrichment in distinguished pathways.(a,b) Heatmap of canonical pathways of interest enriched for cell-specific DEGs in six cell types by IPA.Sup Fig 4. Gut microbiota absence mainly affects the microglial transcriptome in a brain region-specific manner.(a) Venn diagram depicted 370 genes shared in two regions, and 563 genes only changed in Hip, 694 in PFC. (b) Heatmap of region-specific DEGs (GF vs SPF) enriched in top 20 canonical pathways. Opposite changes in synaptic function-related pathways showed a regional specificity in transcriptional changes caused by the lack of gut microbiome. Sup Figure 5. The gut microbiome mainly modulated microglia-astrocyte communication.(a) Heatmaps showed cell-cell ligand/receptor-receptor pair for microglia with other cell types based on CellPhoneDB. Counts of interactions in GF and CGF demonstrated gut microbiome mainly influenced the microglia-astrocyte communication in both Hip (top) and PFC (bottom). (b,c) Reversed genes mainly involved chemical synaptic transmission and cell adhesion in Hip (b) and protein binding and transport in PFC (c) by Gene Ontology enrichment analysis.Sup Fig 6. Microglial subpopulations modulated by microbial absence were activated in the anti-inflammatory gene set.(a) QuSAGE analysis showed the anti-inflammatory and regulatory T cells (Treg) gene sets were most activated in Hip_M1 and Hip_M4 and inhibited in Hip_M0. (b-d) The activity level of the anti-inflammatory and Treg gene sets in Hip_M0(b), Hip_M1(c) and Hip_M4(d). (e) QuSAGE analysis showed that the anti-inflammatory and Treg gene sets were mostly activated in PFC_M2 and inhibited in PFC_M0. most activated gene sets and least activated gene sets of PFC_M0, PFC_M2. (f,g) The activity level of the anti-inflammatory and Treg gene sets in PFC_M0(f) and PFC_M2(g).Sup Figure 7. Anti-inflammatory and Treg genes activation in Hip_M1&4 and PFC_M2.(a-f) The average activity level of genes enriched in the Anti-inflammatory gene set and the Treg gene set of Hip_M1(a,b), Hip_M4(c,d) and PFC_M2(e,f).Sup Fig 8. Pseudotime analysis confirmed a mutual transforming relationship between microglial subpopulations modulated by the gut microbiome.(a,b) In Hip(a) and PFC(b), microglial cells distribution on the pseudo-time trajectory depicted transforming direction, starting at state1 to state2 or state 3. (c,d) Microglial cells distribution of experimental groups in Hip(c) and PFC(d) described different development state between GF and SPF, CGF group.(e,f) Microglial cells distribution of each subcluster in Hip(e) and PFC(f) described a mutual transformation trend between Hip_M1&4 and Hip_M0, PFC_M2 and PFC_M0.

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The gut microbiome modulates the transformation of microglial subtypes

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Abstract

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Introduction

Method

Results

The absence of gut microbiota resulted in cell-specific transcriptomic changes.

Cross-species analysis showed that microglia subpopulations regulated by gut microbiota were associated with AD and MDD.

Behavioral tests support the association between gut microbiota, AD, and MDD

Discussion

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References

Additional Declarations

Supplementary Files

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Version 1