Abstract
Upon activation, naive B cells exit their quiescent state and enter
germinal center (GC) responses, a transition accompanied by increased
protein synthesis. How protein translation efficiency is adequately
adjusted to meet the increased demand requires further investigation.
Here, we identify the methyltransferase METTL1 as a translational
checkpoint during GC responses. Conditional knockout of Mettl1 in mouse
B cells blocks GC entry and impairs GC formation, whereas conditional
knock-in of Mettl1 promotes GC responses. Mechanistically, METTL1
catalyzes m^7G modification in a specific subset of tRNAs to
preferentially translate BCR signaling-related proteins, ensuring
mitochondrial electron transporter chain activity and sufficient
bioenergetics in B cells. Pathologically, METTL1-mediated tRNA m^7G
modification controls B-cell autoreactivity in SLE patients or
lupus-prone mice, and deletion of Mettl1 alleviates dysregulated B-cell
responses during autoimmune induction. Thus, these results support the
function of METTL1 in orchestrating an effective B-cell response and
reveal that aberrant METTL1-mediated tRNA m^7G modification promotes
autoreactive B cells in systemic autoimmunity.
Subject terms: B cells, Autoimmunity, Methylation
__________________________________________________________________
An effective B cell response requires a rapid increase in protein
synthesis. Using multi-omics approaches, here the authors show that the
methyltransferase METTL1 drives B cell activation via the tRNA m^7G
modification-dependent translation of BCR signaling and that aberrant
METTL1 causes B cell autoreactivity in humans and mice, thus serving as
a therapeutic target in systemic autoimmunity.
Introduction
B cells are at the center of the adaptive immune system, which is
responsible for humoral immunity^[52]1. Upon antigen recognition, naive
B cells undergo profound metabolic and transcriptional changes to exit
their quiescent state^[53]2–[54]4. Following interaction with CD4^+ T
cells to receive costimulatory signals, antigen-encountered B cells
then enter germinal centers (GC). GC B cells give rise to long-lived
memory B cells (MBC) and plasma cells (PC) that provide immune memory
and protection against recurrent infections. Within GCs, GC B cells
undergo clonal expansion, somatic mutation, and affinity maturation,
where the demands of energy and protein synthesis are
increased^[55]5–[56]8. In addition to increasing mRNA transcription to
synthesize more proteins, one of the major mechanisms is to increase
protein translation efficiency (TE) to meet rapidly increasing
demands^[57]9. However, how the regulation of TEs is involved in GC
B-cell responses remains unclear. An aberrant GC B-cell response can
lead to the generation of autoreactive B cells and the development of
systemic autoimmunity^[58]10. It has not been yet reported whether
dysregulated protein translation is involved in aberrant GC B-cell
responses and the subsequent induction of autoimmunity.
The translation of messenger RNA (mRNA) into proteins in ribosomes is
an important step in gene expression. Protein translation can be
divided into initiation, extension, and termination^[59]11. Transfer
RNAs (tRNA) are key adaptor molecules that decipher the genetic code,
which is fundamentally important for mRNA translation.
Posttranscriptional modifications strongly affect tRNA stability, tRNA
structure, and mRNA translation^[60]12,[61]13. tRNA-modifying enzymes
are highly dysregulated in human cancers^[62]14, and aberrant
modification of tRNAs is a novel regulator and initiator of human
diseases and cancers^[63]15,[64]16. In T cells, tRNA-m^1A modification
enhances TEs in T cells and enables T cells to synthesize essential
amounts of protein for T-cell activation^[65]17. The function of tRNA
modifications in B-cell responses is not known.
N7-methylguanosine (m^7G) is one of the most prevalent tRNA
modifications, and it is highly conserved in prokaryotes, eukaryotes,
and some archaea^[66]18. In humans, m^7G modification is catalyzed by
the METTL1/WD repeat domain 4 (WDR4) complex. METTL1 is responsible for
m^7G catalysis, whereas WDR4 helps stabilize the methyltransferase
complex. Mutation in WDR4 causes disorders in neurologic
development^[67]19,[68]20. In addition, METTL1/WDR4-mediated tRNA m^7G
modification is crucial for mRNA translation and embryonic stem cell
self-renewal^[69]21. The upregulation of METTL1/WDR4-mediated tRNA m^7G
modification has been linked to increased tumorigenesis^[70]22–[71]24.
Recently, data showed that METTL1/WDR4-mediated tRNA m^7G modification
in cancer cells reshaped the tumor microenvironment by promoting the
accumulation of polymorphonuclear myeloid-derived suppressor cells in
the tumor microenvironment^[72]25. However, the function of METTL1 in
immunity has not yet been reported.
Here, our data reveal the important role of METTL1-mediated m^7G
modification in B-cell activation. Specifically, METTL1 controls GC
entry and GC formation, and deletion of Mettl1 in mouse B cells impairs
GC responses significantly. We further identify that METTL1-mediated
m^7G preferentially controls the translation of B-cell receptor (BCR)
signaling-related proteins to ensure mitochondrial respiration and
bioenergetics generation. Importantly, autoreactive B-cell response is
blunt when METTL1-mediated m^7G is abolished in SLE patients or
lupus-prone mice. Thus, this study provides evidence for the important
function of METTL1-mediated m^7G modification in B-cell-mediated immune
responses. These data also suggest the aberrant METTL1-mediated m^7G
modification in the pathogenesis of B-cell-mediated systemic autoimmune
diseases.
Results
METTL1-associated translation is enhanced in human B cells in response to
vaccine immunization
Upon activation, immune cells undergo fundamental changes in the
translatomic profile and protein synthesis to cope with proliferation
and differentiation^[73]26. To gain insight into the translational
status of B cells after vaccination, we first accessed publicly
available scRNA-seq data and performed bioinformatic analysis. The data
revealed that the percentage of B cells was greater on d 7 than on d 0
(Fig. [74]1a, b; Supplementary Fig. [75]1a–c) after influenza
vaccination. Gene Ontology (GO) enrichment analysis revealed that B
cells from d 7 were enriched in translation and ribosome biogenesis
compared with other immune cell compartments (Fig. [76]1c), suggesting
that greater translation demands are needed to meet B-cell activation
requirements during vaccination. tRNAs are key molecules that decode
mRNA codons during translation in protein synthesis^[77]12.
Bioinformatic analysis revealed that B cells present the highest score
for tRNA modifications among subsets of immune cells in the blood,
indicating high tRNA modification activity in B cells (Fig. [78]1d).
METTL1 is the dominant tRNA-modifying enzyme that converts guanosine to
m^7G. Recent data show that METTL1/WDR4-mediated m^7G tRNA modification
is required for normal mRNA translation^[79]21,[80]27.
Methyltransferase genes involved in tRNA modification, including
Mettl1, Mettl8, Trmt1, and Nsun2, were enriched in B cells
(Fig. [81]1e). Although the average expression level and percentage
expression of Mettl1 and Trmt1 were both greater in B cells from d 7
than in those from d 0, a significant change was observed only in
Mettl1. In contrast, the change in Trmt1 did not reach statistical
significance. In contrast, the expression of Mettl8 and Nsun2 was
reduced on d 7 after immunization (Fig. [82]1f). These data suggest
that METTL1 could represent the major methyltransferase for tRNA
modification in B cells, indicating the important role of METTL1 in the
B-cell response to immunization. Further analysis revealed that most of
the upregulated differential expressed genes (DEG) in d 7 were enriched
in translation and ribosome biogenesis when compared to d 0 B cells
(Fig. [83]1g), suggesting the important role of translation control in
B-cell responses. To further study the association between METTL1 and
B-cell responses to immunization, individuals who received influenza
vaccination were recruited, and blood samples were harvested
(Fig. [84]1h). Western blot analysis revealed that the expression of
both METTL1 and WDR4 was upregulated in B cells from d 7 after
immunization (Fig. [85]1i). The upregulation of METTL1/WDR4 in B cells
was accompanied by the expansion of antibody-secreting cells (ASC), as
measured by flow cytometry on d 28 (Fig. [86]1j, k). Notably, the
geometric titers of antibodies against the H1N1, H3N2, and Victoria
influenza strains increased on d 28 after immunization (Fig. [87]1l).
Fig. 1. METTL1-associated translation is enhanced in B cells following
vaccination.
[88]Fig. 1
[89]Open in a new tab
a–g Single-cell RNA sequencing (scRNA-seq) analysis of PBMCs from
donors before (d 0) and after (d 7) influenza vaccination
([90]GSE211560). a t-SNE displaying cell clusters. b Bar graph
displaying cell subset percentages before and after vaccination. c The
top 20 terms upregulated in B cells compared with other clusters
(adjusted p value via “clusterProfiler” GO analysis). d tRNA
modification score (GO: 0006400) among cell clusters (4023 B cells,
29435 CD4 T cells, 7354 CD8 T cells, 4546 Monocytes, 6402 NK cells,
4613 NKT cells). The upper whisker is 75th percentile plus 1.5*
interquartile range (IQR), and the lower is 25th percentile minus
1.5*IQR. The upper and lower bounds of box are 75th and 25th
percentile, respectively, and the center is median. e Dot plot
displaying differentially expressed genes (DEG) associated with common
tRNA-modified enzymes. f Dot plot displaying Mettl1, Mettl8, Trmt1 and
Nsun2 expression in B cells. g The top 10 GO-BP terms upregulated via
GO analysis using upregulated DEGs on d 7 compared to d 0 B cells. h
Experimental scheme of influenza vaccination. i METTL1 and WDR4
expression in B cells measured by western blotting. j, k The frequency
of CD27^hiCD38^hi antibody-secreting cells (ASC) in PBMCs from donors
receiving influenza vaccination (d 0 and d 28) measured by flow
cytometry (n = 12). l H1N1, H3N2, and Victoria antibody titers in the
sera of donors receiving influenza vaccination (d 0 and d 28), and
n = 12. m–o scRNA-seq analysis of PBMCs from donors on the first day
and 7 d after SARS-CoV-2 vaccination ([91]GSE201534). m t-SNE
displaying cell clusters from PBMCs. n Bar graph displaying cell subset
percentages. o The top 5 terms upregulated in B cells compared with
other clusters via GO analysis. p Experimental scheme of healthy donors
receiving SARS-CoV-2 vaccination. q METTL1 and WDR4 expression in B
cells measured by western blotting. r Representative flow cytometry
plots of spike-specific B cells from the PBMCs of donors that received
SARS-CoV-2 vaccination (d 0 and d 28). s, t Representative ELISpot and
summary data showing spike- and receptor-binding domain (RBD)-specific
IgG-secreting B cells from the PBMCs of donors who received SARS-CoV-2
vaccination (d 0 and d 28, n = 9). Biological independent samples for
k, l, t. The data are presented as the mean ± SEM. A two-tailed
unpaired Student’s t test in k, t, and Kruskal–Wallis test in l, d.
Source data are provided as a Source Data file. h and p were created
with BioRender.com.
To investigate the role of METTL1 in B-cell responses to immunization,
scRNA-seq data from the SARS-CoV-2 vaccination cohort were acquired,
and bioinformatic analysis was performed. We found that B cells were
also expanded from individuals who received SARS-CoV-2 vaccination
(Fig. [92]1m, n; Supplementary Fig. [93]1d, e). In line with influenza
vaccination, cytoplasmic translation was notably enriched in B cells
from individuals immunized with SARS-CoV-2 (Fig. [94]1o). Consistently,
the expression of METTL1 and WDR4 was upregulated in B cells
postimmunization, as determined by western blotting (Fig. [95]1p, q).
The upregulation of METTL1/WDR4 was followed by the induction of
spike-specific B cells, as measured by flow cytometry and ELISpot
(Fig. [96]1r–t). Together, these data highlight the importance of
METTL1-associated translation during B-cell responses to vaccination.
METTL1 is involved in GC responses
Secondary lymphoid organs (SLO) are sites where B cells recognize
antigens captured by follicular dendritic cells^[97]28. Upon antigen
recognition, naive B cells are activated, followed by the initiation of
the GC response. The GC represents the epicenter for the formation of
high-affinity antibodies through a somatic maturation mechanism^[98]29.
Given the rapid and massive changes in response to antigen recognition
by B cells during GC responses, we postulated the involvement of METTL1
in the early phase of B-cell activation in SLOs. To test this
hypothesis, we first accessed the scRNA-seq data of the mouse spleen to
obtain a broader picture of B-cell activation in response to
immunization in SLOs. scRNA-seq analysis revealed that B cells from the
spleens of NP-KLH-immunized mice could be divided into 7 clusters:
erarlyGC-1, earlyGC-2, earlyGC-3, MBC, naive, PB and preGC
(Supplementary Fig. [99]2a). Ribosome biogenesis scores among these
clusters revealed that the earlyGC-3 cluster presents the highest
activity of ribosome biogenesis (Supplementary Fig. [100]2b), which was
further verified by GO analysis of DEGs upregulated in earlyGC-3
(Supplementary Fig. [101]2c). Moreover, the tRNA modification score
indicated that the tRNA modification pathway was enriched in earlyGC-3
(Supplementary Fig. [102]2d). In line with our hypothesis, Mettl1 and
Wdr4 expression were mostly upregulated in preGC and early GC B cells
(Supplementary Fig. [103]2e, f), suggesting a role of METTL1/WDR4 in
the early stage of B-cell activation and GC formation. To determine the
involvement of METTL1 in B-cell responses, normal mice were immunized
with NP-OVA, and the spleens were harvested for staining, and FACS
analysis (Supplementary Fig. [104]2g). Immunofluorescence staining
revealed that METTL1 was expressed in the mouse spleen, with the
highest expression in PNA^+ GC B cells (Supplementary Fig. [105]2h).
Spleen samples were further analyzed by flow cytometry to measure
METTL1 expression in B-cell subsets, and gating strategy was shown
(Supplementary Fig. [106]2i). In line with the immunofluorescence
staining, GC B cells presented the highest level of METTL1 in the
spleen (Supplementary Fig. [107]2j, k). Moreover, METTL1 expression in
B cells was notably upregulated after NP-OVA immunization, as
determined by flow cytometry (Supplementary Fig. [108]2l, m). To
further dissect the involvement of METTL1 in human B cells, surgically
removed spleen samples were harvested and analyzed by flow cytometry
(Supplementary Fig. [109]3a, b). Compared with other subsets, human GC
B cells presented the highest level of METTL1, consistent with data
from mice. Similar results were obtained for WDR4 expression in human B
cells (Supplementary Fig. [110]3c, d). These data indicate the
important role of METTL1 in GC responses.
METTL1 controls GC entry
To directly link the immune function of METTL1 to B-cell responses in
vivo, we first generated conditional knockout (cKO) mice by crossing
Cd19^Cre mice with Mettl1^flox/flox mice to obtain
Cd19^CreMettl1^flox/flox cKO mice (Supplementary Fig. [111]4a). The
deletion of Mettl1 was confirmed by western blotting (Fig. [112]2a).
Concomitantly, the expression of WDR4 was dramatically downregulated in
B cells when Mettl1 was deleted (Fig. [113]2a). We found that the
composition of B cells across different developmental stages in the
bone marrow was similar between cKO and WT mice (Supplementary
Fig. [114]4b–d), suggesting that Mettl1 deletion does not disrupt bone
marrow B-cell development. To evaluate the role of METTL1 in GC
responses to immunization, we performed scRNA-seq to delineate the
transcriptomic landscape to evaluate the impact of Mettl1 deletion on
B-cell responses to vaccination at the single-cell level. Spleens were
collected at d 14 after NP-OVA immunization, and B cells were processed
for scRNA-seq (Fig. [115]2b, c). Spleen B cells could be clustered into
14 cell clusters, and cluster 13 was removed for fewer than 50 cells
(Fig. [116]2d). Based on the signature gene expression profiles, these
13 clusters were defined into 7 cell subsets: naive, activated
precursor (AP, similar to the GC precursor in Yazicioglu, Y.F.’s
report^[117]30), early GC (highly similar to earlyGC-3 in Supplementary
Fig. [118]3a), GC, GC (IFN), memory and PB (Fig. [119]2d; Supplementary
Fig. [120]5a–h). By analyzing B-cell subsets in WT and cKO mice, we
detected an accumulation of APs in the spleens of cKO mice. In
contrast, early GCs, GCs and GCs (IFNs) were notably reduced in cKO
mice (Fig. [121]2e). To determine the cell state of APs, we compared
APs to other cell subsets, and DEGs were identified (Fig. [122]2f).
Further GO analysis of the DEGs revealed an activation state of APs
(Fig. [123]2g). It has been suggested that APs represent activated B
cells and are doomed to enter GCs^[124]30, therefore we compared the
gene signatures of GC B cells (upregulated genes compared with naive B
cells in [125]GSE4142) with those of naive, APs and early GC B-cell
clusters via gene set enrichment analysis (GSEA). The GC B-cell
signature was greater in APs than in naive GCs but lower in APs than in
early GCs, suggesting a transitional state of APs from naive to GC B
cells (Fig. [126]2h, i). Moreover, we performed Monocle pseudotime
analysis on these cell subsets to further confirm the AP status after
antigen recognition. Notably, naive B cells were found at the beginning
of the trajectory, and early GC B cells were found at one terminal end,
whereas APs were aligned along the trajectory between naive and early
GCs (Fig. [127]2j). Therefore, we identified a differentiation
trajectory from naive to APs and then early GC B cells. Additionally,
we observed an increase in Mettl1 expression along the trajectory from
APs to early GC (Fig. [128]2k, Supplementary Fig. [129]5i). A
comparison of the trajectories of WT and cKO mice revealed retention in
the AP state in cKO mice (Fig. [130]2l). Consistently, expansion of APs
(IgD^+GL-7^+ B cells) was detected in cKO mice by flow cytometry
(Fig. [131]2m, n). Given the accumulation of APs and the reduction in
early GC B cells in cKO mice, we proposed that METTL1 is required for
APs to enter GCs. To explore how Mettl1 deletion led to the
accumulation of APs, we identified DEGs between WT and KO AP cells
(Fig. [132]2o). Among these DEGs, Cd55 (also known as
decay-accelerating factor), whose expression is repressed during GC
formation^[133]31, was highly expressed in Mettl1-deleted AP. In
contrast, the expression of Cd83, an important costimulatory molecule
for B-cell activation and GC function^[134]32,[135]33, was reduced in
the Mettl1-deleted APs (Fig. [136]2o). Moreover, we detected higher
levels of transcription factors crucial for GC formation in WT APs than
in Mettl1-deleted APs (Fig. [137]2p). The expression of Foxp1, which is
downregulated during GC formation, was found to be increased in
Mettl1-deleted APs (Fig. [138]2p). GSEA revealed that compared with WT
APs, Mettl1-deleted APs was less activated from the naive state to the
GC B or early GC B state (Supplementary Fig. [139]5j, k). Taken
together, these findings indicate that GC entry is halted when Mettl1
is deleted in B cells, suggesting that METTL1 controls GC entry during
immunization.
Fig. 2. METTL1 controls germinal center (GC) B-cell entry.
[140]Fig. 2
[141]Open in a new tab
a B cells were isolated from conditional knockout (cKO) mice or
wild-type (WT) mice. Knockout of METTL1 in B cells was confirmed by
western blotting. b–p Spleen B cells from NP-OVA-immunized mice were
collected for scRNA-seq. b, c Experimental schemes were created with
BioRender.com. d UMAP displaying B-cell clusters and cell cluster
definitions. e Bar plots displaying the percentages of B-cell subsets
in WT and cKO mice. f Volcano plot displaying DEGs in the activated
precursor (AP) cell cluster compared with the other cell clusters
(adjusted p value via “limma” R package). g The top 10 GO-BP terms
enriched in the AP cell cluster compared with the other cell clusters
(adjusted p value via “clusterProfiler” GO analysis). h Gene set
enrichment analysis (GSEA) plot of the upregulated signature of
germinal center (GC) B cells vs naive B cells (GO-C7 term:
GSE4142_Naive_vs_GC_Bcell_DN) between the AP cell cluster and naive
B-cell cluster (p value via “clusterProfiler” GSEA analysis). i GSEA
plot of the upregulated signature in GC B cells vs naive B cells (GO-C7
term: [142]GSE4142 naive vs GC B-cell DN) between the AP cell cluster
and the GC B-cell cluster (p value via “clusterProfiler” GSEA
analysis). j–l Pseudotime analysis of naive, AP and early GC cell
clusters via Monocle2 analysis. j Pseudotime trajectory of naive, AP,
and early GC cell clusters. k Mettl1 expression trajectory. l Cell
trajectory in WT and cKO mice. m, n The frequencies of APs
(IgD^+GL7^+B220^+) in the spleens of cKO (n = 5 biological independent
samples) or WT (n = 5 biological independent samples) mice immunized
with NP-OVA were confirmed by flow cytometry, and representative plots
are shown. A p value was calculated via a two-tailed unpaired Student’s
t test. o Volcano plot displaying DEGs in the WT AP cell cluster
compared with the cKO AP cell cluster (p value via “limma” R package).
p Dot plot displaying the expression of important transcription factors
involved in GC formation. The data are presented as the mean ± SEM.
Source data are provided as a Source Data file.
METTL1 is required for GC responses
In the resting state, the size of the spleen and total number of
splenocytes were similar between cKO and WT mice (Supplementary
Fig. [143]6a). The percentages and numbers of total B220^+ B cells were
comparable between cKO and WT mice (Supplementary Fig. [144]6b, c).
Furthermore, the percentages and numbers of B-cell subsets were also
similar, except that the percentage and number of follicular B cells
(FoB) were slightly reduced, and the percentage and number of marginal
zone B cells (MZB) were increased in the spleens of cKO mice
(Supplementary Fig. [145]6d–q). No significant changes were observed in
the HE-stained spleen tissue (Supplementary Fig. [146]6r).
After NP-OVA immunization, the spleens and sera were harvested on d 14
to analyze B-cell responses and antibody affinity maturation
(Fig. [147]3a). Immunofluorescence staining of spleen sections revealed
a strong GC response after NP-OVA immunization in WT mice. However, GC
staining was absent in the cKO mice (Fig. [148]3b). Furthermore, the
percentage of GC B cells in cKO mice was notably lower than that in WT
mice, as measured by flow cytometry (Fig. [149]3c, d). Moreover, class
switching was impaired in cKO mice, and the number of IgG1-producing GC
B cells was lower in cKO mice (Fig. [150]3e, f). To further assess the
impact of Mettl1 deletion on B-cell responses to NP-OVA immunization,
the cells were stained with fluorescence-labeled NPs and measured by
flow cytometry. Our data revealed that the percentage of NP-specific B
cells was reduced in the spleens of cKO mice (Fig. [151]3g, h).
Surprisingly, NP-specific IgG1^+ GC B cells were almost undetectable in
the spleens of cKO mice (Fig. [152]3i, j). The percentage of
NP-specific CD138^+ cells was notably lower in the spleens of cKO mice
than in those of WT mice (Fig. [153]3k, l). NP2-BSA and NP25-BSA were
used to measure high-affinity IgG and total low-affinity IgG to
evaluate affinity maturation by ELISA. Our data revealed that the level
of IgG NP25 was significantly lower in the sera of cKO mice
(Fig. [154]3m). In addition to impaired IgG production, METTL1
deficiency led to reduced affinity maturation efficiency, as
demonstrated by the notably lower level of NP2 IgG in cKO mice
(Fig. [155]3n). For the FoB/MZB subsets, we observed a greater
proportion of MZB cells and a lower proportion of FoB cells in the cKO
mice (Supplementary Fig. [156]7a, b). Of note, our data revealed that
the percentages of T-cell subsets and the frequency of T[FH] cells were
not changed in cKO mice (Supplementary Fig. [157]7c–f), which was
consistent with the recent study^[158]34.
Fig. 3. METTL1 is required for GC B-cell responses.
[159]Fig. 3
[160]Open in a new tab
a Experimental scheme, which was created with BioRender.com. b
Representative images of immunofluorescence staining of spleen
sections. B220 (red), CD3 (blue), and PNA (white). c, d Representative
flow cytometry plots and frequencies of GL7^+CD38^− GC B cells in the
spleens of WT and cKO mice. e, f Representative flow plots and
frequencies of IgG1^+ GC B cells among the GC B cells of the WT and cKO
groups. g, h Representative flow cytometry plots and frequencies of
NP^+ B cells in WT and cKO mice. i, j Representative flow cytometry
plots and frequencies of NP^+IgG1^+ GC B cells in WT and cKO mice. k, l
Representative flow cytometry plots, and frequencies of NP^+
plasmablasts (PB) in WT and cKO mice. m, n Anti-NP25 and anti-NP2 IgG
antibodies in the serum were measured via ELISA. o–q Representative
flow cytometry plots and frequencies of total memory B cells (p) and
NP^+ memory B cells (q) in WT and cKO mice. WT = 5 biological
independent samples, cKO = 4 biological independent samples. The data
are presented as the mean ± SEM. Two-tailed unpaired Student’s t test
was used in d, f, h, j, l–n, p, q. Source data are provided as a Source
Data file.
In addition to the generation of PCs, the GC response results in the
production of MBCs^[161]6,[162]35. Although the percentage of total
MBCs was similar, the percentage of NP-specific MBCs was significantly
lower in cKO mice (Fig. [163]3o–q). To further explore whether METTL1
plays a role in the formation of MBCs, we immunized mice with OVA
repetitively after primary immunization and analyzed isotype-switched
OVA-specific B cells 2 weeks later (Supplementary Fig. [164]8a).
Notably, GC responses and the formation of PCs were reduced in cKO mice
(Supplementary Fig. [165]8b–f). The number of anti-OVA IgG^+ B cells in
the spleen was reduced in cKO mice, as determined by ELISpot
(Supplementary Fig. [166]8g, h). In line with the observed MBC
responses, the production of OVA-specific IgG in the serum was lower in
the cKO mice than in the control mice (Supplementary Fig. [167]8i).
Despite the presence of MBCs in the spleen, repetitive immunization did
not elicit a response as strong as the WT control did (Supplementary
Fig. [168]8), suggesting that METTL1 is required for the formation of
the MBCs and the recall response. Taken together, these data suggest
that METTL1 is essential for B-cell responses.
METTL1 regulates extrafollicular B-cell responses
To test whether METTL1 plays a role in extrafollicular
T-cell-independent (TI) responses, mice were immunized with NP-Ficoll
(Supplementary Fig. [169]9a). Consistently, deletion of METTL1 led to a
reduction in FoB cells and an increase in MZB cells (Supplementary
Fig. [170]9b, c). Immunofluorescence staining revealed that NP-Ficoll
failed to induce GC responses (Supplementary Fig. [171]9d). Although
the frequency of PBs did not change, the percentage of PCs was lower in
cKO mice than in WT mice, as measured by flow cytometry (Supplementary
Fig. [172]9e, f). Additionally, we did not observe differences in
NP-specific B cells or NP-specific PBs between the cKO and WT controls
(Supplementary Fig. [173]9g–[174]j). NP-specific PCs were reduced in
cKO mice (Supplementary Fig. [175]9k, l). Extrafollicular TI B-cell
responses are dominated by IgM and IgG3 antibodies^[176]36,[177]37.
Despite the increased frequency of MZ B cells, NP-specific IgM and
NP-specific IgG3 antibodies were notably lower in cKO mice than in WT
control mice (Supplementary Fig. [178]9m, n), suggesting a role for
METTL1 in extrafollicular B-cell responses.
METTL1 controls GC responses via BCR signaling
The METTL1/WDR4 complex catalyzes the m^7G modification of tRNAs in
cancer cells and stem cells^[179]21,[180]22. We thus performed a
northwestern blot assay to assess the m^7G modification level in
Mettl1-deleted B cells and WT control cells. Notably, m^7G modification
was reduced in Mettl1-deleted B cells (Fig. [181]4a, Supplementary
Fig. [182]10a). We employed TRAC-seq to profile the global m^7G
modifications in tRNAs in B cells (Fig. [183]4b). Using TRAC-seq, we
identified 22 tRNAs that contain m^7G modifications at the “AGGTC”
motif sequence (Fig. [184]4c, d). Knockout of METTL1 significantly
reduced cleavage scores (Fig. [185]4e, f). m^7G signals in the
identified tRNAs were significantly reduced in Mettl1-deleted B cells
(Fig. [186]4g, h). To validate the METTL1-regulated tRNA components, we
performed METTL1-RIP sequencing in 293 T cells overexpressing Mettl1.
We found that the tRNAs identified by METTL1-RIP sequencing were highly
consistent with tRNAs regulated by m^7G modification identified by
TRAC-seq (Fig. [187]4i–k, Supplementary Fig. [188]10b). Given the
important function of m^7G modification in mRNA translation, we
verified translation impairment in Mettl1-deleted B cells via a
puromycin uptake assay (Fig. [189]4l, m; Supplementary Fig. [190]10c,
d). Taken together, these data reveal the importance of METTL1 in the
regulation of tRNA m^7G modification and mRNA translation in B cells.
Fig. 4. METTL1 regulates m^7G tRNA modification in B cells.
[191]Fig. 4
[192]Open in a new tab
a Northwestern blot showing m^7G modification in B cells from WT and
cKO mice. Representative bands are shown. b Experimental scheme of
TRAC-seq for calculating cleavage scores. c List of m^7G-modified tRNAs
identified by TRAC-seq in spleen B cells from WT and cKO mice. d The
motif sequence “AGGTC” at the m^7G site was identified by TRAC-seq in B
cells. e Representative image showing the different cleavage scores at
the motif sequence. f Quantitative comparison of cleavage scores
between B cells from WT and cKO mice (n = 1 mouse for each group). The
upper whisker is the maxima, and the lower whisker is the minima. The
upper and lower bounds of the box are the 75th percentile and 25th
percentile, respectively. The center of the box is the median. g, h
Expression profile of the 22 m^7G-modified tRNAs based on TRAC-seq. The
relative expression of each tRNA type was calculated from the combined
expression of all tRNA genes belonging to the same tRNA type. The
expression of the indicated tRNA type was then normalized by its
overall average level in both groups and transformed by log2 (n = 1
mouse for each group). The upper whisker is the maxima, and the lower
whisker is the minima. The upper and lower bounds of the box are the
75th percentile and 25th percentile, respectively. The center of the
box is the median. i–k METTL1-IP tRNA experiments. i Western blots
showing METTL1 overexpression in 293 T cells. Representative bands were
shown. j Scatter plot displaying METTL1-IP tRNAs recognized by the tRNA
library-seq. k Venn diagram displaying overlap of METTL1-RIP tRNA-seq
and TRAC-seq data. l, m A puromycin intake assay was performed via flow
cytometry, and representative histograms of B cells from WT (n = 5
biological independent samples) and cKO (n = 4 biological independent
samples) mice are shown. The data are presented as the mean ± SEM. A
two-tailed Mann–Whitney test was used in f, h, and a two-tailed
unpaired Student’s t test was used in m. Source data are provided as a
Source Data file.
We combined RNA-seq and proteomics to screen for affected genes and
pathways in Mettl1-deleted B cells. The RNA-seq data revealed 563
downregulated and 381 upregulated DEGs (Fig. [193]5a). Specifically,
Aicda, Fas, Xbp1 and Jchain, which are activation markers of GC B cells
and PCs, were downregulated in Mettl1-deleted B cells compared with WT
B cells (Fig. [194]5a). Compared with those in Mettl1-deleted B cells,
mitochondrion-, ribosome- and immunoglobulin-related pathways in WT B
cells were enriched according to KEGG and GO analyses (Fig. [195]5b;
Supplementary Fig. [196]11a, b). Proteomic data suggested enrichment of
the BCR pathway, and ribosome- and mitochondrion-related pathways in WT
B cells compared with Mettl1-deleted B cells (Fig. [197]5c,
Supplementary Fig. [198]11c). To link the differences found by RNA-seq
and proteomics, we further performed Ribo-seq to determine the precise
mRNAs read by the ribosome and the transcripts associated with the
translation machinery. By calculating the TEs of genes, 902 genes with
downregulated TEs and 1331 genes with upregulated TEs were found in
Mettl1-deleted B cells (Fig. [199]5d). Codon usage analysis revealed
that mRNAs with decreased TEs (TE-down) had significantly greater usage
of codons decoded by m^7G-modified tRNAs (Fig. [200]5e). To identify
the enriched codons regulated by METTL1, we compared codon frequency in
TE-down genes in WT samples and found that AAG and GTG, which were
decoded by LysCTT and ValCAC respectively, were the top 2 enriched
codons regulated by METTL1 (Fig. [201]5f). Overall, these data suggest
that decreased tRNA m^7G modification selectively impaired the
translation of mRNAs with a relatively high frequency of
m^7G-tRNA-decoded codons in B cells. To perform pathway enrichment
analysis, we chose TE-downregulated (TE-down) mRNAs for which the fpkm
of the WT sample was greater than 5. GO analysis revealed that most of
these genes were involved in the BCR signaling pathway and B-cell
responses (Fig. [202]5g).
Fig. 5. METTL1 controls the GC B-cell responses via BCR signaling.
[203]Fig. 5
[204]Open in a new tab
a Volcano plot displaying DEGs between cKO B cells and WT control B
cells through RNA-seq (p value via “limma” R package). b Top 10 terms
enriched with downregulated mRNA in Mettl1-deleted B cells (adjusted p
value via “clusterProfiler” GO-BP analysis). c Top 10 terms enriched
with downregulated proteins in Mettl1-deleted B cells via Reactome
pathway analysis (adjusted p value via “clusterProfiler” Reactome
analysis). d–g Ribo-seq analysis of WT and cKO spleen B cells. d
Scatterplots displaying the translation efficiency (TE) of genes in B
cells from WT and cKO mice. e Frequency of m^7G tRNA-decoded codon
usage in genes with increased TEs (TE-up) and decreased TEs (TE-down)
in Mettl1-deleted B cells (n = 1 mouse for each group). The upper
whisker is 75th percentile plus 1.5*IQR, and the lower whisker is 25th
percentile minus 1.5*IQR. The upper and lower bounds of box are 75th
and 25th percentile, respectively, the center is median, and points
show outliers. f Codon frequency of m^7G tRNA-decoded codons in TE-down
genes. g GO terms enriched with downregulated TE genes in
Mettl1-deleted B cells (p value via “clusterProfiler” R package). h
Volcano plot displaying nonsignificant changes in Cd19 expression via
RNA-seq and significant downregulation of CD19 in Mettl1-deleted B
cells via proteomic data (p value via “limma” R package). i, j
Representative flow plot and summarized data showing CD19 expression
levels in WT or Mettl1-deleted B cells (n = 4). k Bar plots displaying
the relative TE changes after the transfection of WT-Cd19 or MUT-Cd19
with base mutations in specific codon plasmids into Mettl1-knockdown
293 T cells and control 293 T cells (n = 3). l Representative flow plot
and summary data showing surface IgM (sIgM) expression levels in WT or
Mettl1-deleted B cells (WT = 5 and cKO = 4). m The expression of p-SYK,
p-BTK, p-PI3K, and p-AKT in WT or Mettl1-deleted B cells was measured
via western blot and representative bands. n, o GC B cells or PCs were
induced in vitro (n = 4). Representative plots are shown. Biological
independent samples for j–l, o. The data are presented as the
mean ± SEM. A two-tailed Mann–Whitney test was used in e, and a
two-tailed unpaired Student’s t-test was used in j–l, o. Source data
are provided as a Source Data file.
CD19 is a B-cell-specific transmembrane protein that functions as a
coreceptor of BCR. Following BCR crosslinking and CD19 phosphorylation,
B-cell activation is initiated by the Src family, which then activates
Igα/Igβ^[205]38. CD19 can amplify the function of Src family kinases
and recruit PI3K, thereby promoting BTK and AKT phosphorylation.
Activated BTK could induce calcium signaling for B-cell activation
(Supplementary Fig. [206]12a)^[207]39,[208]40. RNA-seq revealed that
the transcript level of Cd19 was comparable between Mettl1-deleted and
WT B cells (Fig. [209]5h). Intriguingly, proteomic data suggested a
significantly lower level of CD19 in Mettl1-deleted B cells than in WT
B cells (Fig. [210]5h). To validate these findings, we measured the
expression of CD19 at the protein level by flow cytometry. Notably,
deletion of Mettl1 led to reduced CD19 expression on the B-cell surface
(Fig. [211]5i, j), further supporting the notion that METTL1 controls
BCR signaling-related proteins at the posttranscriptional level. We
then conducted ribosome nascent-chain complex-bound mRNA (RNC) analysis
and reported decreased TE of Cd19 mRNA in Mettl1-deleted B cells
(Supplementary Fig. [212]12b). To validate, we performed synonymous
base mutations in TTC, GTG, GCT, ACT, and CCT codons in Cd19 mRNA
expression plasmid, which was transfected into Mettl1-knockdown 293 T
cells (Supplementary Fig. [213]12c). These five codons were selected as
the related tRNAs exhibited relatively large expression differences
between the control and cKO groups and had a higher frequency within
the Cd19 mRNA compared to other codons. Moreover, synonymous mutations
of these codons can be achieved because they have synonymous non-m^7G
codons. As we expected, after base mutations, the TE of Cd19 was
rescued (Fig. [214]5k), suggesting that TTC, GTG, GCT, ACT, and CCT,
which are encoded by 5 m^7G-modified tRNAs (PheGAA, ValCAC, AlaAGC,
ThrAGT and ProAGG), were specific codons regulated by METTL1. Moreover,
surface IgM (sIgM) was reduced in Mettl1-deleted B cells, indicating
impaired BCR aggregation (Fig. [215]5l). Both the RNA-seq and proteomic
data revealed significantly lower levels of IgM in Mettl1-deleted B
cells than in control B cells (Supplementary Fig. [216]12d). To compare
the mRNA and protein abundance of BCR signaling pathway-related genes,
we performed heatmap analysis using RNA-seq and proteomic data in
Mettl1-deleted B cells and control B cells. The data revealed that mRNA
of BCR signaling pathway-related genes were comparable between the two
groups, while the protein level of these genes was mostly lower in
Mettl1-deleted B cells (Supplementary Fig. [217]12e). SYK, an important
member of the Src family and a key molecule in the BCR signaling
pathway^[218]39,[219]40, whose phosphorylation level was reduced when
Mettl1 was deleted in B cells, as measured by western blotting, and
further confirmed by flow cytometry (Fig. [220]5m; Supplementary
Fig. [221]12f, g), resulting in decreased levels of phosphorylated BTK
in Mettl1-deleted B cells (Fig. [222]5m). CD19 is the key coreceptor in
BCR signaling and is essential for PI3K-AKT signaling pathway
activation^[223]39. Notably, the phosphorylation of PI3K was lower in
Mettl1-deleted B cells than in WT control B cells, as measured by
western blotting (Fig. [224]5m). Downstream PI3K signaling was further
assessed, revealing that the level of phosphorylated AKT was reduced in
Mettl1-deleted B cells (Fig. [225]5m, Supplementary Fig. [226]12h, i).
BCR (sIgM) signaling capacity was further determined by measuring Ca^2+
influx following stimulation with soluble goat F(ab’)2 anti-mouse IgM.
Notably, calcium influx was lower in the Mettl1-deleted B cells than in
the WT control cells (Supplementary Fig. [227]12j). Finally, we linked
METTL1/WDR4-mediated TEs involved in BCR signaling to B-cell responses.
Our data revealed that the B-cell proliferation and differentiation of
GC B cells and PC cells were profoundly suppressed in Mettl1-deleted B
cells (Fig. [228]5n, o; Supplementary Fig. [229]12k, l). Together,
these data suggest that METTL1 promotes B-cell responses by enhancing
BCR signaling through translation.
To explore the function of METTL1 in human B cells, we knocked down
Mettl1 in human B cells sorted from healthy donors via a lentivirus. In
vitro experiments revealed that Mettl1-knockdown B cells presented
impaired proliferation and were likelier to remain in the G0 and G1
phases (Supplementary Fig. [230]13a–d). However, apoptosis was not
affected by the knocking down of Mettl1 (Supplementary Fig. [231]13e,
f). Moreover, impaired ASC differentiation and a class switch to IgG
were found in Mettl1-knockdown B cells (Supplementary Fig. [232]13g–j).
METTL1 regulates ETC activity in B cells
Consistent with the enrichment of mitochondrion-related pathways in WT
B cells compared with Mettl1-deleted B cells (Supplementary
Fig. [233]11), the scRNA-seq data revealed that a series of genes
related to the ETC were upregulated in WT B cells (Fig. [234]6a). KEGG
analysis and GO-CC analysis revealed significant enrichment of pathways
related to mitochondria (Fig. [235]6b, c). When the OXPHOS levels in
B-cell subsets were scored, the early GC-3 cluster presented the
highest OXPHOS score (Fig. [236]6d). After calculating the DEGs between
WT early GC B cells and Mettl1-deleted early GC B cells, we performed
GO analysis and detected enrichment of mitochondria-related pathways in
WT early GC B cells (Fig. [237]6e, Supplementary Fig. [238]14a, b). The
copy numbers of Ndufa1, Ndufa4, and Ndufa12 for the ETC complex (C) C
I, Sdhb, and Uqcrq for C II, Cyc1 for C III and Cox6c, and Cox7b for C
IV were notably reduced when Mettl1 was deleted in B cells, as
confirmed by qPCR (Fig. [239]6f). On the other hand, ribo-seq data
revealed that TEs of most of the key components of C I, II, III and IV
in the ETC were not affected by Mettl1 deletion (Fig. [240]6g),
suggesting that the expression of ETC proteins by METTL1 is regulated
at the transcriptional level. Sdhb in C I and Ndufa12 in C II were
among the most downregulated genes in Mettl1-deleted B cells. We
further confirmed the downregulation of SDHB and NDUFA12 at the protein
level by western blotting (Fig. [241]6h). Transmission electronic
microscopy studies of B cells and flow cytometry revealed that Mettl1
deletion led to reduced mitochondrial mass (Fig. [242]6i, Supplementary
Fig. [243]14c, d). To determine whether METTL1 is required for optimal
mitochondrial function, a Seahorse assay was used to assess
mitochondrial respiration in B cells. Notably, Mettl1 deletion led to
impaired mitochondrial respiration in B cells. The OCR was reduced at
the basal level. The maximal respiration capacity and ATP-linked
production were lower in Mettl1-deleted B cells. The spare respiratory
capacity, which represents the parameter for the mitochondrial reserve,
was also decreased in Mettl1-deleted B cells (Fig. [244]6j, k).
Fig. 6. METTL1 controls mitochondrial electron transport train (ETC) activity
in B cells.
[245]Fig. 6
[246]Open in a new tab
a–e scRNA-seq analysis of spleen B cells from NP-OVA-immunized mice. a
Volcano plot displaying DEGs between WT B cells and Mettl1-deleted B
cells (adjusted p value via “limma” R package). The purple dots
represent DEGs in the ETC. b The top 10 KEGG terms upregulated in WT B
cells (adjusted p value via “clusterProfiler” KEGG analysis). c Network
plot displaying the top 10 GO-CC terms upregulated in WT B cells. d Box
plot displaying the OXPHOS score among different B-cell clusters (n = 1
mouse in WT group and n = 2 mice in cKO group). The upper whisker is
75th percentile plus 1.5*IQR, and the lower whisker is 25th percentile
minus 1.5*IQR. The upper and lower bounds of box are 75th and 25th
percentile respectively. The center of box is the median. e The top 10
terms upregulated in the WT early GC B-cell cluster compared with the
Mettl1-deleted early GC B-cell cluster (adjusted p value via
“clusterProfiler” GO-BP analysis). f Heatmap displaying the qPCR
results for genes in the ETC (n = 4 biological independent samples). g
Scatterplot displaying the TEs of genes in the ETC via Ribo-seq data
analysis. h SDHB and NDUFA12 expression in spleen B cells from WT and
cKO mice measured by western blot. i Transmission electronic microscopy
studies displaying the mitochondria of B cells from WT and cKO mice and
representative images were shown. j, k Seahorse Mito Stress assay was
used to measure the mitochondrial respiration of WT and Mettl1-deleted
B cells. Curves for the oxygen consumption rate (OCR) are displayed in
j, and the data are summarized in k (n = 5 biological independent
samples). A two-tailed unpaired Student’s t-test was used in k. The
data (mean ± SEM) are shown. ns, not significant. Oligo oligomycin,
FCCP carbonyl cyanide-p-trifluoromethoxyphenylhydrazone, R + A:
rotenone + antimycin. Source data are provided as a Source Data file.
We further confirmed the role of METTL1 in the ETC in human B cells.
Mettl1 was knocked down in human B cells via shRNA. We found that
knockdown of Mettl1 also led to impaired mitochondrial respiration in
human B cells (Supplementary Fig. [247]14e, f). ETC couples oxidative
OXPHOS with ATP synthase to drive the generation of ATP in
mitochondria. Disruption of the ETC assembly leads to mitochondrial
dysfunction^[248]41. These data indicate that METTL1 is important for
mitochondrial ETC activity in B cells.
METTL1 controls mitochondrial ETC activity through BCR signaling
BCR stimulation is involved in increased mitochondrial OXPHOS in B
cells^[249]42,[250]43. Mettl1 deletion had no effect on the translation
of ETC proteins, suggesting that METTL1 might control mitochondrial
OXPHOS through BCR signaling. To study the regulation of the BCR
signaling-ETC axis in B-cell responses, we performed a gain-of-function
study to investigate the function of METTL1-mediated BCR signaling in
B-cell responses in vitro and in vivo. We first generated Mettl1
conditional knock-in (cKI) mice by crossing Cd19^Cre mice with
Rosa26-CAG-LSL-Mettl1 mice to obtain Cd19^CreMettl1^cKI mice
(Supplementary Fig. [251]15a). The overexpression of Mettl1 in B cells
was confirmed by western blotting (Fig. [252]7a). As expected,
overexpression of Mettl1 resulted in increased WDR4 expression in B
cells (Fig. [253]7a). No autoreactive B-cell responses were observed in
these Cd19^CreMettl1^cKI mice (Supplementary Fig. [254]15b–f). Our data
above revealed that METTL1 controls the TEs of BCR signaling-related
genes and ETC activities (Figs. [255]5 and [256]6). A previous study
showed that energy for early events in B cells following antigen
recognition by BCRs is provided primarily by OXPHOS^[257]44. We thus
hypothesized that METTL1 promotes B-cell responses by enhancing BCR
signaling-mediated mitochondrial ETC activity. We first assessed BCR
signaling in Mettl1-KI B cells to evaluate the role of METTL1 in BCR
signaling. Our data revealed that surface IgM-BCR levels were greater
in Mettl1-KI B cells than in WT control cells (Supplementary
Fig. [258]16a, b). Concomitantly, SYK phosphorylation was increased in
Mettl1-KI B cells stimulated with the anti-IgM antibody
(Fig. [259]7b–d). Notably, the phosphorylation level of BTK was
increased in Mettl1-KI B cells. Additionally, the phosphorylation of
both PI3K and AKT was increased in Mettl1-KI B cells, as measured by
western blotting (Fig. [260]7d). These data further confirmed the
function of METTL1 in controlling BCR signaling in B cells.
Consistently, overexpression of Mettl1 in B cells led to increased
mitochondrial ETC activity. Transmission electronic microscopy studies
revealed increased mitochondrial mass in Mettl1-KI B cells, which was
reversed by the SYK inhibitor R406 (Fig. [261]7e). The key components
of the ETC complex were upregulated in Mettl1-KI B cells, as measured
by qPCR, and were normalized to the control level by a SYK inhibitor
(Fig. [262]7f). In line with the qPCR data, the protein expression of
SDHB and NDUFA12 was notably increased in Mettl1-KI B cells, which
could also be normalized to the level of the WT control by inhibiting
BCR signaling via a SYK inhibitor (Fig. [263]7g). Furthermore, BCR
stimulation by anti-IgM led to increased mitochondrial mass in
Mettl1-KI B cells, as measured by flow cytometry, and this effect was
reversed by the SYK inhibitor (Supplementary Fig. [264]16c, d).
Functionally, overexpression of Mettl1 increased mitochondrial
respiration in B cells, which was consistently normalized to the level
in control cells by a SYK inhibitor (Supplementary Fig. [265]16e, f).
The induction of GC B-cell differentiation was increased in Mettl1-KI B
cells, which was counteracted by BCR inhibition (Supplementary
Fig. [266]16g, h).
Fig. 7. METTL1 regulates mitochondrial ETC activity through BCR signaling.
[267]Fig. 7
[268]Open in a new tab
a METTL1 and WDR4 expression in B cells from Mettl1-cKI mice or WT mice
was measured via western blotting. Representative bands are shown. b, c
Representative flow cytometry plots for p-SYK stimulated with
anti-mouse IgM. The data are summarized in c (n = 4 biological
independent samples). d The expression of p-PI3K, p-AKT, and p-BTK in B
cells was measured by western blotting. Representative bands are shown.
e Transmission electronic microscopy images showing the mitochondria of
WT B cells, cKI B cells, and cKI B cells treated with R406.
Representative images are shown. f The expression level of the ETC in
WT B cells, cKI B cells, or cKI B cells treated with the SYK inhibitor
R406 was measured by qPCR (n = 4 biological independent samples). g The
expression of SDHB and NDUFA12 in WT B cells, cKI B cells, or cKI B
cells treated with the SYK inhibitor R406 was measured via western
blotting. Representative bands are shown. h Experimental scheme of
NP-OVA immunization. i Frequencies of B cells in the spleens of WT or
cKI mice (n = 6 biological independent samples). j, k Representative
flow cytometry plots, and frequencies of GC B cells in WT and cKI mice
(n = 6 biological independent samples). l–o OVA^+ B cells (l, m) and
OVA^+ GC B cells (n, o) in WT and cKI mice were measured via flow
cytometry (n = 6 biological independent samples). p, q Representative
ELISpot wells and summary data for anti-NP25- and anti-NP2-specific B
cells in the splenocytes of WT and cKI mice (n = 4 biological
independent samples). r ELISA for anti-NP25 and anti-NP2 IgG antibodies
in the sera of WT and cKI mice (n = 4 biological independent samples).
The data are presented as the mean ± SEM. A two-tailed unpaired
Student’s t test was used in c, i, k, m, o, q, r. Source data are
provided as a Source Data file.
We then performed an in vivo study to further investigate the role of
METTL1 in B-cell responses to immunization. WT or cKI mice were
challenged with NP-OVA, and the mice were euthanized 14 d later
(Fig. [269]7h). In line with the findings in cKO mice, the number of B
cells in the spleen was not different between cKI mice and WT control
mice (Fig. [270]7i). The percentage of GC B cells in the spleen was
increased in cKI mice (Fig. [271]7j, k). We then analyzed
antigen-specific B cells in cKI or WT mice. Notably, the frequencies of
both OVA-specific total B cells and OVA-specific GC B cells were
increased in the spleens of cKI mice (Fig. [272]7l–o). GC B cells rely
on fatty acid oxidation to perform OXPHOS^[273]45, and elevated OXPHOS
activity promotes B-cell clonal expansion and positive
selection^[274]42. To investigate whether METTL1-controlled ETC
activities and OXPHOS are involved in GC responses and antibody
maturation, NP25-BSA and NP2-BSA were used to measure low-affinity IgG
NP25 and high-affinity IgG NP2, respectively, by ELISpot and ELISA.
ELISpot revealed that the numbers of both NP25-specific B cells and
NP2-specific B cells were increased in B cells from cKI mice
(Fig. [275]7p, q). Notably, the concentrations of both IgG NP25 and the
high-affinity antibody IgG NP2 were increased in the serum of cKI mice,
as measured by ELISA (Fig. [276]7r), suggesting that the affinity
maturation efficiency was increased in cKI mice. These data further
confirm that METTL1 controls GC B-cell responses through BCR
signaling-mediated mitochondrial ETC activities.
CD19 overexpression in Mettl1-deleted B cells rescues ETC activity during
B-cell differentiation
For further validation, we overexpressed CD19 in Mettl1-deleted B cells
(Fig. [277]8a). Similarly, total SYK expression was similar between WT
and Mettl1-deleted B cells. CD19 overexpression also had no effect on
total SYK expression in Mettl1-deleted B cells (Fig. [278]8b, c).
Strikingly, the reduction in phosphorylated SYK (p-SYK) in
Mettl1-deleted B cells stimulated with anti-IgM was reversed after CD19
was overexpressed (Fig. [279]8d, e). We then investigated whether the
rescue of BCR signaling is accompanied by improved ETC function. The
qPCR data revealed that the overexpression of CD19 in Mettl1-deleted B
cells increased the transcript levels of genes that encode the ETC
(Fig. [280]8f), which was confirmed by western blotting (Fig. [281]8g).
A Seahorse assay was used to assess the actual activities of the ETC in
Mettl1-deleted B cells after the overexpression of CD19. Consistently,
CD19 overexpression rescued impaired mitochondrial respiration in
Mettl1-deleted B cells (Fig. [282]8h, i). Importantly, the
differentiation of GC B cells from Mettl1-deleted B cells was notably
reversed when CD19 expression in Mettl1-deleted B cells was rescued
(Fig. [283]8j, k). These data further confirm that METTL1 specifically
controls the translation of CD19 in B cells.
Fig. 8. CD19 overexpression rescues ETC activity in Mettl1-deleted B cells.
[284]Fig. 8
[285]Open in a new tab
a Expression level of CD19 in WT B cells, Mettl1-deleted B cells, and
Mettl1-deleted B cells overexpressing Cd19 were measured by western
blot. Representative bands were shown. b, c Representative histograms
and MFI data of SYK expression were summarized (n = 3 biological
independent samples). d, e Representative histograms and MFI data of
p-SYK expression were summarized (n = 3 biological independent
samples). f Heatmap displaying qPCR data of ETC in WT B cells,
Mettl1-deleted B cells, and Mettl1-deleted B cells overexpressing Cd19
(n = 4 biological independent samples). g Western blots displaying
protein levels of ETC in WT B cells, Mettl1-deleted B cells and
Mettl1-deleted B cells overexpressing Cd19. Representative bands were
shown. h, i Seahorse assay measuring mitochondrial respiration of WT B
cells, Mettl1-deleted B cells, and Mettl1-deleted B cells
overexpressing Cd19 (n = 4 biological independent samples). j, k
Representative flow plots and summarized data displaying GC
differentiation of WT B cells, Mettl1-deleted B cells, and
Mettl1-deleted B cells overexpressing Cd19 (n = 4 biological
independent samples). Data are shown as mean ± SEM. One-way ANOVA was
used in c, e, i, k. Source data are provided as a Source Data file.
METTL1 promotes autoreactive B-cell responses
The data above revealed the essential role of METTL1/WDR4-mediated m^7G
modification in early B-cell activation and the response of GCs to
immunization. We further investigated the role of METTL1 under disease
conditions. In NZM2328 lupus model mice, METTL1 expression was
significantly increased in B cells from pre-diseased mice compared with
those from disease-free mice. METTL1 expression was further increased
in B cells from diseased mice (Fig. [286]9a–c). Furthermore, the
expression of METTL1 was greater in naive B cells and GC B cells from
pre-diseased and diseased mice than in those from diseased free mice
and was not detected in the compartments of the MBC and PC
(Fig. [287]9d, e; Supplementary Fig. [288]17a–c). To rule out the
effect of aging on METTL1 expression, we also measured METTL1
expression in normal B6 mice at comparable ages. We did not observe
increased METTL1 expression in aged mice (Supplementary
Fig. [289]17d–f). The upregulation of METTL1 in naive B cells in
pre-diseased NZM2328 mice points to an important role of METTL1 in
initiating early B-cell responses and disease development. Importantly,
METTL1 expression was closely correlated with the number of
anti-dsDNA^+ B cells in the spleen, suggesting a role for METTL1 in
promoting the autoreactive B-cell response in NZM2328 mice
(Fig. [290]9f, g).
Fig. 9. METTL1 promotes autoreactive B-cell responses.
[291]Fig. 9
[292]Open in a new tab
a–g Experiments on 3-month (3 m), 6 m and 9 m NZM238 mice (n = 4
biological independent samples). a Experimental scheme. b, c METTL1
expression in B cells from 3-, 6- and 9-month-old NZM2328 mice was
measured via flow cytometry. Representative histograms and MFI data
summarized in c. d, e Representative flow cytometry plots and MFIs of
METTL1 expression in naive and GC B cells from 3 m, 6 m, and 9 m
NZM2328 mice. f Representative ELISpot wells of anti-dsDNA
IgG-secreting B cells in the splenocytes of 3 m, 6 m, and 9 m NZM2328
mice. g Linear correlation analysis between METTL1 MFI and anti-dsDNA
IgG-secreting B cells in the spleens of NZM2328 mice (a simple linear
regression analysis was used). h–s Autoimmune mouse models induced by
apoptotic thymocytes were generated in WT and cKO mice (n = 6
biological independent samples). h Experimental scheme of the
autoimmune mouse model induced by apoptotic thymocytes. i
Immunofluorescence staining of spleen GCs in WT and cKO mice: B220
(red), CD3 (blue), and PNA (white). j, k Representative flow cytometry
plots, and frequencies of GL7^+CD38^− GC B cells in the spleens of WT
and cKO mice. l, m Representative flow cytometry plots and frequencies
of PBs and PCs in the spleens of WT and cKO mice. n Representative
ELISpot wells and summarized data showing anti-dsDNA IgG-secreting B
cells in splenocytes from WT and cKO mice. o ELISA of anti-dsDNA IgG
antibodies in the serum. p–r Representative anti-mouse IgG and C3
staining, and PAS staining of kidney sections and summary scores. s
Proteinuria score curve of mice. The data are presented as the
mean ± SEM. A two-tailed unpaired Student’s t test was used in k, m–o,
q–s, and one-way ANOVA was used in c, e, f. Source data are provided as
a Source Data file. a and h were created with BioRender.com.
To further study the function of METTL1 in the development of
autoimmune diseases, autoimmunity was induced in cKO or WT control mice
via apoptotic autologous thymocytes as previously described^[293]46
(Fig. [294]9h). Strikingly, immunofluorescence staining revealed that
the GC response was almost absent in the spleens of cKO mice
(Fig. [295]9i). Moreover, the percentages of GC B cells and CD138^+
PC/PB cells were reduced in the spleens of cKO mice (Fig. [296]9j–m).
ELISpot assays revealed that the number of anti-dsDNA^+ B cells was
decreased in cKO mice (Fig. [297]9n). Additionally, the concentration
of anti-dsDNA antibodies was significantly lower in the serum of cKO
control mice than in that of WT control mice (Fig. [298]9o). Notably,
the immune complex deposition, cellular proliferation and basement
membrane thickening in the glomeruli were reduced when Mettl1 was
deleted in B cells (Fig. [299]9p–r). Importantly, the development of
proteinuria was notably inhibited in Mettl1-KO mice (Fig. [300]9s). The
percentage of T[FH] cells was not affected by Mettl1 deletion
(Supplementary Fig. [301]17g). These data suggest that METTL1 promotes
an autoreactive GC B-cell response to drive the development of systemic
autoimmunity.
METTL1 promotes B-cell responses in systemic autoimmunity
SLE represents a prototypical autoimmune disease featuring the
expansion of autoreactive B cells and the production of
autoantibodies^[302]47. BCR signaling is enhanced in B cells from
patients with SEL^[303]48,[304]49. To link METTL1 to altered BCR
signaling in patients with systemic autoimmunity, we first accessed a
public database of scRNA-seq data from patients with SLE
(Fig. [305]10a). scRNA-seq analysis revealed that the transcripts of
both Mettl1 and Wdr4 were upregulated in B cells from patients with SLE
compared with those from HCs (Fig. [306]10b). Among the B-cell subsets,
ASC cells expressed the highest levels of Mettl1 and Wdr4 in patients
with SLE (Fig. [307]10c), suggesting a role for METTL1/WDR4 in systemic
autoimmunity. To confirm the involvement of METTL1/WDR4 in B-cell
dysregulation in patients with SLE, blood samples were collected from
SLE patients or HCs, and the expression of METTL1/WDR4 in B cells was
measured by flow cytometry or western blotting. In line with the
scRNA-seq data, the flow cytometry data revealed that ASC cells
presented the highest level of METTL1 expression (Fig. [308]10d–f).
Notably, METTL1 expression was upregulated in B cells from patients
with SLE and correlated with disease activity in SLE patients
(Fig. [309]10g, h). The expression of METTL1 and WDR4 in B cells from
patients with SLE was further measured by western blotting. Similarly,
both METTL1 and WDR4 were notably upregulated in B cells from patients
with SLE (Fig. [310]10i). As a result, the level of m^7G modification
was also increased in B cells from patients with SLE (Fig. [311]10j,
Supplementary Fig. [312]18). In accordance with the data from the mice,
increased METTL1/WDR4-mediated m^7G modification led to enhanced BCR
signaling in B cells from patients with SLE, which could be inhibited
and normalized to the control level when Mettl1 was knocked down in B
cells by lentivirus (Fig. [313]10k). Similarly, compared with HCs, SLE
B cells presented increased ETC activity. The expression of ETC
components decreased when Mettl1 expression was knocked down in SLE B
cells (Fig. [314]10l, m). To gain more insight into mitochondrial ETC
activity in SLE B cells, mitochondrial respiration was measured by the
Seahorse assay. The data revealed that SLE B cells presented elevated
mitochondrial respiration and that knockdown of Mettl1 normalized
mitochondrial respiration in SLE B cells (Fig. [315]10n, o).
Importantly, the expansion of ASC cells in patients with SLE was
counteracted by knocking down Mettl1 in SLE B cells (Fig. [316]10p–r).
These data suggest that METTL1 promotes the dysregulation of
autoreactive B cells through BCR signaling in human systemic
autoimmunity.
Fig. 10. METTL1 promotes B-cell responses in systemic autoimmunity.
[317]Fig. 10
[318]Open in a new tab
a–c scRNA-seq data analysis of B-cell subclusters from systemic lupus
erythematosus (SLE) patients and HCs. a UMAP displaying B-cell
clustering. b Dot plot displaying Mettl1 and Wdr4 expression in SLE
patients and HCs. c Dot plot displaying Mettl1 and Wdr4 expression in
B-cell clusters. d Gating strategy for B-cell subsets in PBMCs. e, f
Representative flow cytometry plots and MFIs of METTL1 expression among
SLE B-cell subsets gated on d (n = 12 biological independent samples).
g MFIs of METTL1 expression in PBMC B cells (SLE = 12 biological
independent samples, HCs = 10 biological independent samples). h
Correlation between MFI of METTL1 expression in PBMC B cells and
disease activity (SLEDAI) in SLE patients (simple linear regression
analysis was used). i METTL1 and WDR4 expression in B cells from HCs
and SLE patients was measured by western blotting. Representative bands
were shown. j m^7G levels in B cells from HCs and SLE patients were
quantified by northern blotting. Representative bands were shown. k–r
SLE B cells were transfected with shMettl1 or vector. k The
phosphorylation of PI3K and AKT in HC B cells, SLE B cells, and SLE B
cells treated with shMettl1 was measured by western blotting.
Representative bands were shown. l Heatmap displaying the results of
qPCR analysis of the ETC genes in HC B cells, SLE B cells, and SLE B
cells treated with shMettl1 (n = 3 biological independent samples). m
SDHB and NDUFA12 expression in HC B cells, SLE B cells, and SLE B cells
treated with shMettl1 were measured by western blotting. Representative
bands were shown. n, o Seahorse assay measuring mitochondrial
respiration in HC B cells, SLE B cells, and SLE B cells treated with
shMettl1 (n = 4 biological independent samples). The OCRs are displayed
in n and summarized in o. p Representative flow cytometry plots of
CD27^hiCD38^hi ASCs in HCs and SLE patients. q, r Representative flow
cytometry plots and frequencies of ASC differentiation from SLE B cells
(n = 8 biological independent samples). The data are presented as the
mean ± SEM. A two-tailed unpaired Student’s t test in g and a
two-tailed paired Student’s t test in r, paired one-way ANOVA in f, and
one-way ANOVA in o. Source data are provided as a Source Data file.
Discussion
The GC response is essentially important for the generation of
high-affinity antibodies. The formation of GCs is driven by activated
naive B cells, which exit their naive state upon antigen
recognition^[319]8,[320]29. This complicated process involves early
B-cell activation and GC B-cell responses, which require efficient
protein translation and bioenergy. In the present work, we show that
METTL1 is essential for fulfilling the rapid requirements of protein
synthesis for effective B-cell responses. At the epigenetic translation
level, we identify METTL1 as an important checkpoint in controlling
early B-cell activation and GC responses, allowing selection and
antigen-specific GC B-cell expansion. Pathologically,
METTL1/WDR4-mediated tRNA m^7G modification promotes autoreactive
B-cell responses and drives the development of systemic autoimmunity in
both mice and humans. Specifically, codon-dependent translation control
by METTL1/WDR4-mediated tRNA m^7G modification enables B cells to
increase the translation of proteins involved in BCR signaling.
Overall, this work highlights the importance of translational control
in early B-cell responses and that targeting METTL1 could normalize
autoreactive B-cell responses in systemic autoimmunity.
B-cell activation involves antigen recognition and T-cell
costimulation^[321]2. BCR stimulation leads to upregulation of the
translational machinery^[322]50. Here, we show that translation and
ribosome biogenesis are notably upregulated in human and mouse B cells
in response to immunization. m^7G modification represents the most
prevalent type of tRNA modification and actively participates in
biological and pathological functions by controlling cellular TEs.
METTL1/WDR4 functions as the dominant enzyme of tRNA m^7G
modification^[323]12. In line with previous studies in stem cells and
cancers^[324]21,[325]24, our data suggest that METTL1 is required for
m^7G modification in B cells. Moreover, METTL1/WDR4-mediated tRNA m^7G
modification is essential for maintaining TEs for highly demanding
proteins during B-cell responses. Early B-cell activation is initiated
by the ligation of BCRs with antigens captured by FDCs in the follicle.
Upon activation, FO B cells undergo serial critical and complicated
changes. First, antigen-experienced FO B cells migrate to the T:B
border to receive costimulatory signals from T[FH] cells and then enter
GCs to initiate GC responses^[326]2,[327]29. Recently, data have shown
that tRNA modification enables sufficient protein synthesis for early
T-cell activation^[328]17. In accordance with data from T cells, our
scRNA-seq data reveals that Mettl1 deletion leads to impaired formation
of GCs, leading to the accumulation of GC progenitors in
Mettl1-deficient mice. In addition, both the formation and
high-affinity IgG production are reduced by deleting Mettl1,
highlighting the importance of METTL1 in GC responses.
CD19 is a B-cell–specific coreceptor that is essential for normal
antibody responses^[329]51. Altered CD19 signaling results in early GC
B-cell differentiation^[330]52. CD19^-/- mice are deficient in GC
formation and antibody production, with no significant effect on the
number of B-cell precursors in the bone marrow^[331]53. In humans,
mutation of the Cd19 gene causes a defective response to antigenic
stimulation of mature B cells and hypogammaglobinemia^[332]54. Here, we
reveal that CD19 expression in B cells is controlled by tRNA m^7G
modification at the translational level. The protein level of CD19 is
decreased by Mettl1 deletion without affecting the Cd19 mRNA level,
indicating epigenetic translational control of CD19 by METTL1. CD19
functions as a coreceptor to enhance BCR signaling through the PI3K-AKT
pathway^[333]39,[334]51. Accordingly, decreased CD19 expression caused
by Mettl1 deletion impairs BCR signaling. In addition, our Ribo-seq
data reveals a broader regulatory effect of METTL1 on B cells in which
the TEs of the BCR signaling pathway are downregulated in
Mettl1-deleted B cells. It has been shown that increased BCR signaling
strength reduces the number of MZ B cells^[335]55,[336]56. Consistent
with these reports, we reveal that Mettl1 deletion impairs BCR
signaling and favors MZ B-cell development. Together,
METTL1/WDR4-mediated tRNA m^7G modification causes biased preferential
translation of BCR signaling, which is required for early B-cell
activation and GC B-cell responses.
Upon antigen ligation, B cells upregulate the phosphorylation of the
downstream protein BTK through SYK^[337]57. Here, we show that tRNA
m^7G modification leads to enhanced BCR signaling in B cells and SYK,
BTK phosphorylation. A previous study revealed that the overexpression
of BTK in B cells causes spontaneous formation of GCs and increased PC
numbers, leading to antinuclear autoantibody production and systemic
lupus SLE-like autoimmune pathology^[338]58. Consistently, our data
reveals that deletion of Mettl1 in B cells abolishes the induction of
autoimmunity. The formation of autoreactive B cells and the production
of autoantibodies are notably reduced. In addition, our data show that
BCR signaling is important for mitochondrial ETC activity and that
overexpression of Mettl1 enhances mitochondrial ETC activity. These
data lead us to propose that METTL1/WDR4-mediated tRNA modification
promotes autoreactive GC B-cell responses by enhancing BCR
signaling-controlled mitochondrial ETC activities, which is in line
with our previous finding that autoreactive B cells in SLE exhibit
increased fatty acid oxidation and mitochondrial respiration in GC B
cells^[339]46. Recently, data have shown that mitochondrial translation
in B cells is important for the entry of activated GC precursors into
the GC reaction^[340]30. Our Ribo-seq data reveals that the translation
of the ETC in mitochondria is not affected by Mettl1 deletion, implying
that the ETC is downstream of BCR signaling rather than the direct
target of METTL1/WDR4-mediated tRNA m^7G modification.
Our study reveals that METTL1 is required for effective B-cell
responses. Furthermore, this study reveals a novel mechanism by which
tRNA m^7G modification by METTL1 allows essential protein translation
via the BCR signaling pathway to promote early B-cell activation and GC
entry (Supplementary Fig. [341]19). Systemic autoimmunity is
characterized by overactivation of autoreactive B cells and the
production of autoantibodies. B-cell depletion therapy has shown
promising potential in the treatment of lupus. Rituximab, a monoclonal
antibody targeting CD20, has been extensively studied and has
demonstrated significant efficacy in reducing disease activity in lupus
patients, particularly in those with refractory or severe
disease^[342]59,[343]60. Furthermore, more recent agents like
belimumab, a monoclonal antibody that inhibits B-cell activating
factor, show sustained improvements in disease outcomes and have been
approved for the treatment of SLE^[344]61. Ongoing research into novel
B-cell-targeted therapies such as new molecules and pathways, continues
to advance the field, offering hope for more effective and personalized
treatment options for lupus patients in the near future. Given the
importance of translational control in B-cell activation, our study
identified a key molecule in controlling autoreactive B-cell responses.
METTL1 could serve as a therapeutic target to treat patients with
systemic autoimmunity.
Methods
Mice
Mettl1^flox/flox mice were generated as previously described^[345]23
and were crossed with Cd19^Cre mice (#T003785, GemPharmatech, Nanjing,
China) to generate Mettl1^cKO mice. CD19 conditional knock-in
(Cd19^CreMettl1^cKI) mice were generated by crossing Cd19^Cre mice with
Rosa26-CAG-LSL-Mettl1 mice^[346]23. Co-housed Cd19^CreMettl1^+/+ mice
served as controls. All experimental mice were bred and maintained in
specific pathogen-free conditions. The mice were housed under a 12-hour
(h) light/12-h dark cycle at ~18–23 °C with 40–60% air humidity. Eight
to 10-week-old mice of both sexes were used for the experiments.
9-month-old Cd19^CreMettl1^cKI and control mice were used for
autoreactive response analysis. Female NZM2328 mice were generously
provided by Professor Shu Man Fu, University of Virginia^[347]46.
Wild-type (WT) C57BL/6 mice were obtained from the Model Organisms
Center (#SM-001, Shanghai, China). All animal experiments were approved
by the Ethics Committee of Sun Yat-sen University.
Human samples
For experiments on vaccination, peripheral blood samples were collected
from healthy individuals before and after vaccination (influenza
vaccine or inactivated SARS-CoV-2 vaccine). The baseline
characteristics are listed in Supplementary Table [348]1. In addition,
peripheral blood samples were collected from patients who fulfilled the
American College of Rheumatology 1997 criteria for SLE^[349]62 at the
First Affiliated Hospital, Sun Yat-sen University. The exclusion
criteria were as follows: acute and chronic infections; malignancy; and
pregnancy or lactation in females. Age- and sex-matched healthy
controls (HC) were recruited accordingly. Patient demographics are
summarized in Supplementary Table [350]2. Human spleen samples were
collected as we described previously^[351]46. Informed consent and
written consent forms were obtained prior to inclusion from all
participants, with approval from the Institutional Ethical Committee of
the First Affiliated Hospital, Sun Yat-sen University.
Cell lines
293 T cells were purchased from the American Type Culture Collection
(ATCC). 293 T cells were cultured in DMEM containing 10% fetal bovine
serum (FBS), 1% Penicillin, and 1% streptomycin in a water-saturated
atmosphere under 5% CO[2] at 37 °C in an incubator (Thermo Scientific,
USA).
Mouse immunization
Age- and sex-matched Cd19^CreMettl1^flox/flox and Cd19^CreMettl1^+/+
mice aged 8-10 weeks were immunized with NP-OVA (100 µg/mouse, LGC,
Cat#: N-5051-10), OVA (100 µg/mouse, Sigma-Aldrich, Cat#: A5503) or
NP-Ficoll (100 µg/mouse, Biosearch, Cat#: F-1420-10) mixed with an
equal volume of alum adjuvant (Sigma-Aldrich, Cat#:77161)
intraperitoneally. Age- and sex-matched 8–10-week-old
Cd19^CreMettl1^cKI and Cd19^CreMettl1^+/+ mice were immunized with
NP-OVA (100 µg/mouse, LGC, Cat#: N-5051-10). Blood samples were
collected on day (d) 7 after immunization, and the B-cell response to
immunization in the spleen was analyzed 2 weeks after immunization. To
measure the recall of MBs, the mice were immunized with OVA
repetitively every 2 weeks 3 times, and B-cell responses in the spleen
were analyzed. Mice were euthanatized using excessive 1% pentobarbital
sodium anesthesia at the endpoint.
Autoimmune mouse model
An autoimmune mouse model was induced as described previously^[352]46.
Briefly, thymocytes isolated from WT C57BL/6 J mice were irradiated
with
[MATH: γ :MATH]
-irradiation (600 rads) for 1 h and cultured in a complete medium at
37 °C with 5% CO[2] for 3 h. The apoptosis rate was confirmed by
Annexin V and 7-aminoactinomycin D (7-AAD) staining (>95%). Age- and
sex-matched Cd19^CreMettl1^flox/flox and Cd19^CreMettl1^+/+ female
8-10-week-old mice were injected with apoptotic thymocytes (1
[MATH: × :MATH]
10^7 cells/mouse/week, for 4 weeks in total) intravenously. Mice were
euthanatized using excessive 1% pentobarbital sodium anesthesia at the
endpoint.
B-cell isolation
For mouse B-cell isolation, mouse spleens were first smashed into
single-cell suspensions, and red blood cells were removed by red blood
cell lysis buffer, after which B cells were isolated with an EasySep
Mouse B-Cell Isolation Kit (STEMCELL Technologies, Cat#: 19854) from
splenocytes. For mouse naive B-cell isolation, purified B cells were
subsequently stained with FITC-conjugated anti-mouse CD23 and isolated
with an EasySep mouse FITC-Positive Selection Kit (STEMCELL
Technologies, Cat#: 17668). For human B-cell isolation, peripheral
blood mononuclear cells (PBMC) were freshly isolated by density
gradient centrifugation and then isolated with an EasySep Human B-Cell
Isolation Kit (STEMCELL Technologies, Cat#: 19054).
B-cell culture
For mouse B-cell activation, the cells were cultured in RPMI 1640
containing 10% FBS and 1% penicillin‒streptomycin (complete medium) in
the presence of anti-mouse CD40 antibodies (5 µg/ml, BioLegend, Cat#:
102802), anti-mouse IgM (10 µg/ml, Jackson ImmunoRsearch, Cat#:
115-001-020) and IL-2 (20 ng/ml, Sino Biological, Cat#: 510661-MNAE)
for 48 h. GC B-cell differentiation was induced as described
previously^[353]34,[354]63. The cells were cultured in a complete
medium in the presence of anti-mouse CD40 antibodies (5 µg/ml),
anti-mouse IgM (10 µg/ml), IL-4 (20 ng/ml, PeproTech, Cat#: 214-14-5),
and IL-2 (20 ng/ml) for 4 d. For PC differentiation, the cells were
cultured in a complete medium supplemented with anti-mouse CD40
antibodies (5 µg/ml), anti-mouse IgM (10 µg/ml), IL-21 (50 ng/ml, Sino
Biological, Cat#: 50137-MNAE) and IL-2 (20 ng/ml) for 7 d. SYK
inhibitors (R406, Selleck, Cat#: S2194) were added for specific
experiments as indicated. The cells were cultured in a humidified
atmosphere at 37 °C with 5% CO[2]. For human B-cell differentiation,
the cells were cultured in complete medium supplemented with CD40L
(5 µg/ml, Sino Biological, Cat#: 10239-H01H), anti-human IgM (5 µg/ml,
Sigma-Aldrich, Cat#: I0759), IL-21 (50 ng/ml, Sino Biological, Cat#:
GMP-10584-HNAE-20) and IL-2 (20 ng/ml, PeproTech, Cat#: 200-02-10) for
7 d.
Public single-cell RNA sequencing (scRNA-seq) data acquisition
Public scRNA-seq data ([355]GSE211560, [356]GSE201534, [357]GSE189819,
and [358]GSE163121) were acquired from the GEO database
([359]http://www.ncbi.nlm.nih.gov/geo/).
Tissue dissociation and preparation of single-cell suspensions and sample tag
labeling for scRNA-seq
The spleens were cut into small pieces and smashed gently to obtain
single-cell suspensions. After B-cell isolation, the single-cell
suspensions were subjected to centrifugation for 5 min (4 °C, 500 × g).
The supernatant was discarded, and 200 µL of cold sample buffer (BD
Biosciences) was added to the cell mixture. After careful and gentle
pipetting, the cell suspension for single-cell experiments was
prepared. The samples were labeled with sample tags (BD Biosciences,
Cat#: 633793).
Cell capture, scRNA library preparation, and sequencing
For cell capture and library preparation for scRNA-seq, a BD Rhapsody
system (BD Biosciences) was used on the basis of the manufacturer’s
protocols. In brief, 1 µL of calcein AM (2 mM; Thermo Fisher, Cat#:
C1430) and 1 µL of DRAQ7 (0.3 mM; Thermo Fisher, Cat#: 564904) were
added to a 200 µL cell suspension (at a 1:200 dilution). Then, the
mixture was pipetted gently and incubated at 37 °C in the dark for
5 min. Subsequently, the cell viability and concentration of the
suspension were measured with a hemocytometer (BD Bioscience, Cat#:
633703). The single-cell suspension was then loaded onto a BD Rhapsody
cartridge (BD Bioscience, Cat#: 400000847). Thereafter, the cell
capture beads were loaded into the microwells and thoroughly washed to
ensure that a single magnetic bead bonded with only one cell in each
microwell. The lysis mixture was subsequently added and incubated at
room temperature (RT) for 2 min. The cell capture beads were
subsequently retrieved for subsequent steps, including complementary
DNA synthesis, exonuclease I digestion, and multiplex PCR-based library
construction. The sequencing libraries were prepared via
whole-transcriptome analysis index PCR, and the PCR products were
purified to enrich the 3’ ends of the transcripts, which were linked
with the cell label and molecular indices. Finally, quality checks of
the indexed libraries were performed with a Qubit fluorometer by the
Qubit dsDNA HS Assay. The sequencing of the libraries was conducted on
an Illumina NovaSeq 6000 system.
scRNA-seq data preprocessing
The raw sequencing reads were trimmed to keep the first 75 bases. The
trimmed reads were then quality filtered via fastp. The BD Rhapsody
whole-transcriptome official analysis pipeline was applied under the
default settings to obtain a cell–gene expression matrix and a quality
control report for each sample. The mouse reference genome was used for
read alignment.
scRNA-seq data analysis
The R package “Seurat” (version: 4.4.0) was used to convert the
scRNA-seq data to Seurat objects. First, we performed quality control
of the scRNA-seq data by removing cells with fewer than 200 genes, more
than 5000 genes, or more than 20% mitochondrial genes. After data
filtering, the data were normalized, and cells expressing high levels
of Cd3e, Cd4, and Cst3 were excluded from the matrix. The top 2,000
highly variable genes across cells were subjected to principal
component analysis (PCA). For cell clustering, in the scRNA-seq data of
the mice that received NP-OVA, the top 35 significant principal
components (PC) were used for t-SNE clustering, and clusters containing
fewer than 50 cells were removed. In the public scRNA-seq data, the top
20 significant PCs were used for t-SNE clustering. DEGs among different
clusters were defined by a fold change >0.25 and adjusted P < 0.05.
DEGs between WT and KO were defined by a fold change >0.10 and
P < 0.05. GO and KEGG analyses were performed via the R package
“clusterProfiler” (version: 4.0.2). GSEA was performed to assess
related pathways and molecular mechanisms between the 2 groups via
“GSEA” function in “clusterProfiler”. The pseudotime trajectories were
generated with the “Monocle2” package (version: 2.20.0).
Western blot
B cells from PBMCs or splenocytes were lysed with RIPA lysis buffer,
and the concentrations were detected with a BCA protein assay kit
(Thermo Fisher, Cat#: 23227). Proteins were loaded on sodium dodecyl
sulfate-polyacrylamide gel electrophoresis (SDS‒PAGE) gels and then
transferred to PVDF membranes. The membranes were blocked with 5%
bovine serum albumin (BSA) in TBST buffer for 1 h and incubated with
primary antibodies against METTL1 (1:1000, Abcam, Cat#: ab271063), WDR4
(1:1000, Novus Biological, Cat#: 15902), SDHB (1:1000, Abcam, Cat#:
ab175225), NDUFA12 (1:1000, Abcam, Cat#: ab192617), SYK (1:1000, Cell
Signaling Technology, Cat#: 2717), p-AKT (1:1000, Cell Signaling
Technology, Cat#: 4060), AKT (1:1000, Cell Signaling Technology, Cat#:
4691 T), p-PI3K (1:3000, Cell Signaling Technology, Cat#: 17366 or
Immunoway, Cat#: YP0224), PI3K (1:1000, Cell Signaling Technology,
Cat#: 4257 T), p-BTK (1:3000, Cell Signaling Technology, Cat#: 87141),
BTK (1:1000, Cell Signaling Technology, Cat#: 8547 T), and CD19
(1:1000, Cell Signaling Technology, Cat#: 3574S) at 4 °C overnight.
Anti-β-actin (1:5000, Servicebio, Cat#: GB15003) or anti-GAPDH (1:5000,
Cell Signaling Technology, Cat#: 2118) primary antibodies were used as
internal controls. The membranes were incubated with horseradish
peroxidase (HRP)-conjugated anti-rabbit (1:2000, Cell Signaling
Technology, Cat#: 7074) or anti-mouse (1:2000, Cell Signaling
Technology, Cat#: 7076) secondary antibodies. Protein expression was
detected by enhanced chemiluminescence (Cytiva, Cat#: AI800).
Flow cytometry
For cell surface staining, the cells were stained with the following
antibodies at RT for 25 min: APC-conjugated anti-mouse B220 (1:100,
Cat#103212, BioLegend), FITC-conjugated anti-mouse CD19 (1:100,
Cat#152404, BioLegend), Pacific Blue-conjugated anti-mouse CD38 (1:100,
Cat#102720, BioLegend), PE-Cy7-conjugated anti-mouse GL7 (1:100,
Cat#144620, BioLegend), Percp-conjugated anti-mouse IgD (1:100,
Cat#405736, BioLegend), APC-Cy7-conjugated anti-mouse CD138 (1:100,
Cat#142530, BioLegend), Pacific Blue-conjugated anti-mouse CD21 (1:100,
Cat#123414, BioLegend), APC-Cy7-conjugated anti-mouse CD23 (1:100,
Cat#101630, BioLegend), AF647-conjugated anti-mouse IgM (1:100,
Cat#406526, BioLegend), AF488-conjugated IgG1 (1:100, Cat#406626,
BioLegend), APC-conjugated anti-mouse CXCR5 (1:100, Cat#145506,
BioLegend), Percp/cy5.5-conjugated anti-mouse PD-1 (1:100, Cat#135208,
BioLegend), PE-conjugated anti-mouse CD3 (1:100, Cat#100205,
BioLegend), and PB-conjugated anti-mouse CD8 (1:100, Cat#100725,
BioLegend). For antigen-specific mouse B-cell staining, the cells were
stained with AF555-conjugated OVA (1:2000, Thermo Fisher, Cat#:
[360]O34782), AF647-conjugated OVA (1:2000, Thermo Fisher, Cat#:
[361]O34785) or NP-PE (1:100, Biosearch, Cat#: N-5070-1) at 4 °C for
25 min.
For surface staining of human cells, the cells were stained with the
following antibodies at RT for 25 min: Zombie Red, PE-conjugated
anti-human CD19 (1:100, Cat#363004, BioLegend), BV421-conjugated
anti-human CD138 (1:100, Cat#356516, BioLegend), FITC-conjugated
anti-human IgD (1:100, Cat#348206, BioLegend), AF700-conjugated
anti-human CD27 (1:100, Cat#356416, BioLegend), BV650-conjugated
anti-human CD38 (1:100, Cat#356620, BioLegend), PE/Dazzle594-conjugated
anti-human CD24 (1:100, Cat#311134, BioLegend), and BV605-conjugated
anti-human IgM (1:100, Cat#314524, BioLegend). For spike-specific
B-cell staining, the biotinylated spike protein was tetramerized by the
addition of either BV510-conjugated streptavidin (Cat#405233,
BioLegend) or BV785-conjugated streptavidin (Cat#405249, BioLegend) at
a protein:streptavidin molar ratio of 6:1 at 4 °C for 1 h to make 2
tetramers: [spike][4]-BV510 and [spike][4]-BV785. The cells were
stained with tetramers at 4 °C for 30 min^[362]64.
For intracellular staining, the cells were permeabilized and fixed with
the eBioscience™ Foxp3/Transcription Factor Staining Buffer Set
(Invitrogen, Cat#: 00-5523-00) at 4 °C for 30 min, followed by
incubation with a primary antibody against METTL1 (1:500, Abcam, Cat#:
ab271063) at 4 °C for 30 min and incubation with AF488-conjugated
anti-rabbit secondary antibodies (1:500, Thermo Fisher, Cat#: A-11008)
at 4 °C for 30 min. For BCR signaling pathway staining, the cells were
first stimulated with anti-mouse IgM (10 µg/ml) for the indicated time
points. After surface staining, the cells were permeabilized and fixed
with a Cytofix/Cytoperm Fixation and Permeabilization Kit (BD
Biosciences, Cat#: 554722) and stained with primary antibodies against
SYK (1:400, Cell Signaling Technology, Cat#: 13198), p-SYK (1:400, Cell
Signaling Technology, Cat#: 2717), and p-AKT (1:400, Cell Signaling
Technology, Cat#: 4060), followed by an AF488-conjugated anti-rabbit
secondary antibody (1:500). All the antibodies used for flow cytometry
are listed in Supplementary Data [363]1.
Serum H1N1, H3N2, and Victoria antibody titer measurements
The antibody titers against H1N1, H3N2, or Victoria was measured by a
hemagglutination inhibition (HAI) assay as described
previously^[364]65.
Ex vivo ELISpot
Mouse IgG secretion ELISpot was performed as we described
previously^[365]46. ELISpot plates (Mabtech, Cat#: 3654-TP-10) were
precoated with calf thymus DNA (100 µg/ml, MCE, Cat#: HY-109517), OVA
(10 µg/ml, Sigma‒Aldrich, Cat#: A5503), NP25-BSA (5 µg/ml, Biosearch,
Cat#: N-5050XL-10) or NP2-BSA (5 µg/ml, Biosearch, Cat#: N-5050H-10) at
4 °C overnight. After being blocked with RPMI 1640 containing 10% FBS
at RT for 1 h, 5
[MATH: × :MATH]
10^5 splenocytes/well were plated on ELISpot plates in the presence of
R848 (1 µg/ml, Tocris, Cat#: 4536/10) and IL-2 (20 ng/ml, Sino
Biological, Cat#: 51061-MNAE). The plates were incubated at 37 °C with
5% CO[2] for 20 h, followed by incubation with anti-mouse IgG-HRP
(1:2000, Huabio) at RT for 2 h. For human spike/RBD-specific
IgG-secreting B cells, ELISpot plates were precoated with recombinant
protein spike (50 µg/ml, Sino Biological, Cat#: 40589-V08B1) or spike
RBD (50 µg/ml, Sino Biological, Cat#: 40592-VNAH) at 4 °C overnight.
After blockage, 5 × 10^5 PBMCs (prestimulated with R848 and IL-2 for
72 h) were plated on ELISpot plates in the presence of R848 (1 µg/ml,
Tocris, Cat#: 4536/10) or IL-2 (20 ng/ml, PeproTech, Cat#: 200-02-10).
The plates were incubated at 37 °C with 5% CO[2] for 20 h, followed by
incubation with biotin anti-human IgG (1:1000, MabTech, Cat#:
3850-6-250) at RT for 2 h and HRP-avidin (1:1000, vector, Cat#:
A-2004-5) at RT for 1 h. An AEC coloring system (DAKEWE, Cat#: 2030613)
was used to detect antigen-specific IgG-secreting B cells. Spots were
captured and counted by an ImmunoSpot Analyzer.
Immunofluorescence staining and imaging
Spleen paraffin blocks were cut into 4 μm sections. The slides were
deparaffinized in xylene and then rehydrated with a graded alcohol
series. Antigen retrieval was performed by a microwave with
citrate-antigen retrieval buffer for 15 min. Slides were then washed
with distilled water for 1 min and TBST for 2 min, followed by blocking
with 10% normal goat serum at RT for 30 min and then incubation with
the following primary antibodies: anti-mouse B220 (1:200, Santa Cruz,
Cat#: sc-19597), PNA (1:500, Vector, Cat#: B-1075-5) and anti-mouse CD3
(1:100, Abcam, Cat#: ab5690) or anti-mouse METTL1 (1:1000) at 4 °C
overnight. After 5 washes with TBST, the slides were incubated with the
following secondary antibodies: AF488-conjugated anti-rabbit secondary
antibody (1:500, Thermo Fisher, Cat#: A-11008), CY5-conjugated anti-rat
secondary antibody (1:200, Jackson ImmunoResearch, Cat#: 712-175-153)
and DyLight 594-conjugated streptavidin (1:200, Vector, Cat#:
SA-5594-1). The stained slides were scanned by a fluorescence
microscope (Olympus).
Kidney tissues were embedded in optimal cutting temperature (OCT)
compound (SAKURA, Cat#: 4583) and frozen in liquid nitrogen. The tissue
blocks were sectioned into 5 μm slides, followed by fixation in
precooled acetone for 10 min. After 10 min of washing with PBST and 3
washes with PBS, the slides were incubated with 10% normal goat serum
at RT for 1 h, followed by staining with AF488-conjugated anti-mouse
IgG (1:500, Thermo Fisher, Cat#: A10680) or a FITC-conjugated
anti-mouse complement component 3 (C3) antibody (1:150, Thermo Fisher,
Cat#: PA1-29718) at 4 °C overnight. The slides were washed and stained
with DAPI. The fluorescence signals were examined by a fluorescence
microscope (Olympus), and the mean intensity of fluorescence was scored
from 0-4 in a blinded manner as we described previously^[366]66.
RNA isolation and quantitative real-time PCR (qPCR)
RNA was extracted from cells by an RNA Quick Purification Kit
(ESscience) or TRIzol. cDNA was generated by Evo M-MLV RT Master Mix
(AG), and the amplification of target genes was accomplished by SYBR
Green Pro Taq HS (AG) kits. The amplification protocol was as follows:
denaturation for 30 s at 95 °C, followed by 40 cycles of denaturation
for 5 s at 95 °C and annealing/extension for 30 s at 60 °C. The data
are expressed as the expression relative to that of GAPDH. The
sequences of primers used in this study are listed in Supplementary
Table [367]3.
Northwestern blot
Northwestern blotting was conducted as previously described^[368]21.
Briefly, 2 µg of total RNA was heat denatured at 70 °C for 5 min and
loaded on a 15% polyacrylamide Tris-borate-EDTA (TBE) urea gel to
separate the RNA. The RNAs were subsequently transferred onto a
positively charged nylon membrane and crosslinked with a UVP
crosslinker (Analytik Jena, USA) for 3 min on each side. RNAs with m^7G
modification were detected by western blotting with an anti-m^7G
antibody (MBL International, Cat#: RN017M).
TAE agarose gel electrophoresis
Two micrograms of total RNA were mixed with 6× DNA loading dye
(Biosharp) and loaded onto a 2% agarose gel prepared with TAE buffer.
Electrophoresis was performed at 140 V for 35 minutes to separate the
RNA molecules on the basis of their size. After the run, the RNA bands
were visualized under UV light.
Ribosomal-nascent chain (RNC) extraction
RNC extraction was performed as previously described^[369]67. Sorted B
cells were cultured with anti-CD40 (5 µg/ml), anti-mouse IgG (10 µg/ml)
and IL-2 (20 ng/ml) for 48 h. After pretreatment with 100 µg/ml
cycloheximide (MedChemExpress, Cat#: HY-12320) for 15 min, the cells
were washed with prechilled PBS and treated with 2 ml of cell lysis
buffer (1% Triton X-100 in ribosome buffer (RB buffer) [20 mM HEPES-KOH
(pH 7.4), 15 mM MgCl[2], 200 mM KCl, 100 µg/ml cycloheximide and 2 mM
dithiothreitol]). After 30 min of lysis on ice, the cell lysates were
transferred into prechilled Eppendorf tubes for centrifugation at
16,200 × g at 4 °C for 10 min. Then, 10% of the supernatants were
separated as input samples for subsequent qPCR, and the remaining
samples were transferred to the surface of 30% sucrose buffer (30%
sucrose in RB buffer). After 5 h of ultracentrifugation at 18,250 × g
and 4 °C, the RNCs were extracted for subsequent qPCR.
RNA sequencing and data processing
Total RNA from the sorted B cells was isolated and purified via the
TRIzol reagent. The RNA amount and purity were quantified, and the RNA
integrity was assessed via a Bioanalyzer 2100 (Agilent) with an RNA
integrity number (RIN) > 7.0 and confirmed by electrophoresis on a
denaturing agarose gel. The RNA fragments were reverse transcribed into
cDNA with SuperScript™ II Reverse Transcriptase (Invitrogen, cat.
#1896649). After library construction, the average insert size for the
final cDNA library was 300 ± 50 bp. Finally, we performed 2×150 bp
paired-end sequencing (PE150) on an Illumina NovaSeq™ 6000 (LC-Bio
Technology Co., Ltd., Hangzhou, China) following the vendor’s
recommended protocol. DEGs were analyzed by Limma package (v3.54.2) and
defined as p < 0.05 and |log2Foldchange | >0.25. Pathway analysis was
performed using ClusterProfiler package (v4.6.2).
Proteomics
The sorted B cells were centrifuged at 500 × g for 5 min. After that,
50 µl lysis buffer (8 M urea, 1% protease inhibitor cocktail) was added
to the cell sediment, followed by sonication three times on ice using a
high-intensity ultrasonic processor (Scientz). The remaining debris was
removed by centrifugation at 12,000 × g at 4 °C for 10 min. Finally,
the supernatant was collected, and the protein concentration was
determined with BCA kit according to the manufacturer’s instructions.
For digestion, the protein solution was reduced with 5 mM
dithiothreitol for 30 min at 56 °C and alkylated with 11 mM
iodoacetamide for 15 min at RT in darkness. The protein sample was then
diluted by adding 100 mM TEAB to urea concentration less than 2 M.
Finally, trypsin was added at 1:50 trypsin-to-protein mass ratio for
the first digestion overnight and 1:100 trypsin-to-protein mass ratio
for a second 4 h-digestion. Finally, the peptides were desalted by C18
SPE column. The tryptic peptides were dissolved in solvent A (0.1%
formic acid, 2% acetonitrile/in water) and loaded to Mass Spectrometer
(ThermoFisher Orbitrap MS) for MS analysis after drying. The resulting
MS/MS data were processed using ProteinDiscovery search engine (v2.5).
Differential expressed proteins (DEP) were analyzed by Limma package
(v3.54.2) and defined as p < 0.01 and |log2Foldchange|>0.25. Pathway
analysis was performed using ClusterProfiler package (v4.6.2).
Ribosome profiling sequencing (Ribo-seq)
The ribosomal profiling technique was carried out as previously
reported^[370]68 with minor modifications, as described below. Isolated
B cells were incubated with a cell culture medium containing
cycloheximide (100 µg/ml) for 2 min to halt translation. The cell
extracts were treated with RNase H and DNase I and subsequently
subjected to selection via size exclusion columns (Illustra MicroSpin
S-400 HR Columns, GE Healthcare, Cat#: 27-5140-01). An RNA Clean and
Concentrator-25 kit (ZYMO Research, Cat#: R1017) was used for ribosome
footprint fragment isolation, and magnetic beads (Vazyme) were used for
purification. Next, Ribo-seq libraries were constructed via the NEBNext
Multiple Small RNA Library Prep Set for Illumina (New England Biolabs,
Cat#: E7300L), followed by reverse transcription and PCR amplification.
The 140–160 bp PCR products were enriched to generate a cDNA library
and sequenced via the Illumina Nova6000 platform.
Ribo-seq data analysis
Read filtering, reference genome alignment and quantification of gene
abundance were performed on the ribo-seq data. After differentially
translated gene analysis and comparisons of differences between
translation and transcription, GO and KEGG enrichment analyses were
performed.
Transmission electron microscopy (TEM)
B cells were centrifuged and fixed in 2.5% glutaraldehyde and 2%
paraformaldehyde (wt/vol) in 0.1 M phosphate buffer (pH 7.4). The cell
sediment was then processed for electron microscopy and examined with a
Tecnai G^2 Spirt Twin electron microscopy as we described
before^[371]69.
Seahorse assay
B-cell mitochondrial respiration was detected with an XF Mitochondria
Stress Test Kit (Agilent, Cat#: 103015-100) following the
manufacturers’ protocols. Mouse or human B cells were harvested,
counted and replated in a Cell-tak (22.4 µg/ml, Corning) precoated XF96
microplate with 5
[MATH: × :MATH]
10^5 B cells/well. To accelerate the attachment of the cells to the
plate, the plate was centrifuged at 200 × g for 1 min. The plate was
then incubated at 37 °C without CO[2] for 45-60 min before analysis by
a Seahorse XF96 analyzer. The data were analyzed by Agilent Seahorse
Analytics software.
ELISA
To measure the level of anti-OVA/NP25/NP2/dsDNA antibodies in mouse
sera, OVA (10 µg/ml), NP25 (5 µg/ml), NP2 (5 µg/ml) or calf thymus DNA
(10 µg/ml) was precoated on 96-well plates at 4 °C overnight. The wells
were blocked with PBS containing 3% BSA at 37 °C for 2 h, followed by
incubation with diluted mouse serum in PBS (1:3200) at 37 °C for 2 h.
The plates were washed three times with PBST and then incubated with
goat anti-mouse IgG-HRP (1:5000) at 37 °C for 1 h. For anti-mouse IgM
or anti-mouse IgG3 antibody measurement, the plates were incubated with
biotin anti-mouse IgM (1:500, BioLegend, Cat#: 406503) or biotin
anti-mouse IgG3 (1:1000, BioLegend, Cat#: 406803) followed by
HRP-avidin (1:1000, Vector, Cat#: A-2004-5). A TMB coloring system
(Proteintech, Cat#: B200051) was used to detect the O.D. value at
450 nm.
Calcium flux assay
Fresh splenocytes were incubated with Fluo-3 AM (4 µM, Sigma‒Aldrich,
Cat#: 39294) in Ca^2+-free buffer for 30 min at 37 °C in the dark.
After 3 washes, the cells were stained with APC-conjugated anti-mouse
B220 and suspended in a buffer containing Ca^2+ to detect Ca^2+ influx
by flow cytometry. During the process, the basal level of Ca^2+ was
detected for 90 s, and anti-mouse IgM (10 µg/ml) was added to stimulate
Ca^2+ influx. The level of Ca^2+ influx was recorded by flow cytometry
for 5 min.
Hematoxylin-eosin (HE) staining
Tissue samples embedded in paraffin were cut into 5 µm thick sections,
which were subsequently deparaffinized with xylene, rehydrated with a
descending gradient of ethanol, stained with hematoxylin and eosin,
dehydrated with an elevated gradient of ethanol and made transparent
with xylene. Finally, the sections were scanned under a microscope to
observe the spleen morphology.
Periodic acid-schiff (PAS) staining
Mouse Kidneys were fixed in 10% neutral formalin, dehydrated, and
embedded with paraffin. Tissue blocks were cut into 4 µm thick sections
and followed by PAS staining. Histopathological evaluation was
performed on a scale of 0–3 according to cell proliferation and cell
infiltration by two observers blind to the protocol as previously
described^[372]66.
Puromycin intake assay
Cells were incubated with puromycin (10 µg/ml, MedChemExpress, Cat#:
HY-K1057-1) at 37 °C for 30 min, permeabilized and fixed for
intracellular staining with AF488-conjugated anti-puromycin (1:100,
BioLegend, Cat#: 381505). Puromycin intake levels were detected by flow
cytometry.
tRNA reduction and cleavage (TRAC-seq)
TRAC-seq was conducted as previously described^[373]70. First, total
RNA was extracted from mouse B cells with or without METTL1 deletion
via TRIzol reagent and then subjected to small RNA purification via the
miRNA Isolation Kit (mirVana, Cat#: AM1561). The isolated small RNAs
were demethylated with recombinant wild-type AlkB and D135S AlkB
proteins. The demethylated small RNAs were treated with 0.2 M NaBH[4]
on ice for 30 min in the dark and then purified via the Oligo Clean &
Concentrator Kit (Zymo Research, USA, Cat#: D4060). The NaBH[4]-treated
small RNAs were further incubated with 100 mL of cleavage buffer
(H[2]O: glacial acid: aniline=7:3:1) at RT in the dark for 2 h. After
purification via the Oligo Clean & Concentrator Kit, the small RNAs
were subjected to cDNA library construction via the NEBNext Multiplex
Small RNA Library Prep Set for Illumina Kit. High-throughput sequencing
was performed on these libraries, and the obtained results were
analyzed as previously described^[374]70.
METTL1 knockdown in B cells
Lentiviral vectors expressing pLKO.1 shRNA targeting GFP (shGFP) or
Mettl1 (shM1) was purchased from Horizon Discovery. For lentivirus
production, the lentiviral vectors, packaging vector pCMV-ΔR8.9, and
enveloped vector pCMV-VSVG were cotransfected into 293 T cells with
Lipofectamine 3000 reagent (Invitrogen). The packaged viruses were
collected 48 h after transfection and used for infection of B cells
with 10 µg/ml polybrene (Yeasen). The cells were centrifuged at 800 × g
for 1 h. After infection, the supernatant was discarded, and the medium
was replaced with a fresh, complete medium. Puromycin (2.5 µg/ml;
MedChemExpress, Cat# HY-K1057) was used to select the infected cells
for 48 h.
Plasmid construction
The Cd19 overexpression plasmid was constructed by cloning the mouse
Cd19 gene into pcDNA3.1(+). Briefly, mRNA was isolated from mouse
splenocytes to generate cDNA, and Cd19 was then cloned with forward
primer (5’- cttggtaccgagctcggatccGCCACCATGCCATCTCCTCTCCCTGTCTC-3’) and
reverse primer (5’- aacgggccctctagactcgagTCACGTGGTTCCCCAAGTCC-3’),
using 2× Phanta Flash Master Mix (P510-01, Vazyme Biotech, China).
After digested by BamHI and XhoI restriction endonucleases (TransGen
Biotech, China), the Cd19 overexpression plasmid pcDNA3.1(+)-Cd19 was
constructed using a MultiS One Step Cloning kit (C113-01, Vazyme
Biotech, China) according to the manufacturer’s instructions. After
linearization, plasmid vectors and insert fragments were recombined by
recombinase Exnase MultiS, then pcDNA3.1-Cd19 was transformed into DH5α
and selected by ampicillin. The recombinant plasmids were extracted
using an Endofree Maxi Plasmid Kit (TIANGEN DP117, China) and confirmed
by sequencing (BGI, China). Isolated mouse B cells were transfected
with pcDNA3.1-Cd19 plasmid using a Nucleofector Kit (Lonza) according
to the manufacturer’s protocols. Cells were then let to rest overnight
to recover from the electroporation. Efficiency was confirmed by
Western blot.
Cd19 luciferase reporter construct
The coding sequences of wild-type Cd19 (WT-Cd19) and a mutant Cd19
(MUT-Cd19) featuring synonymous mutations were cloned and inserted into
the pmirGLO plasmid vector between the ATG start codon and the coding
sequence of the firefly luciferase (F-luc) reporter gene. Specifically,
the following synonymous mutations in the coding sequence were
introduced: TTC codons were replaced with TTT (15 sites), GTG codons
were replaced with GTC (13 sites), GCT codons were replaced with GCC (7
sites), ACT codons were replaced with ACG (6 sites), and CCT codons
were replaced with CCC (16 sites). All mutations do not alter the
encoded amino acids. The plasmid constructs were then transfected into
293 T cells. The relative TEs of the WT- and MUT-Cd19 sequences were
determined by quantifying the F-luc luciferase activity and normalizing
it to the activity of the Renilla luciferase (R-luc) reporter.
METTL1-RIP-sequencing
293 T cells were transfected with either an exogenous METTL1
overexpression plasmid or an empty vector control plasmid. At 48 h
post-transfection, the cells were subjected to UV crosslinking at
4000 × 100 µJ/cm^2. The cells were then lysed, and anti-FLAG magnetic
beads were used for immunoprecipitation of crosslinked RNA‒protein
complexes at 4 °C for 4 h with rotation. RNA was subsequently extracted
from the immunoprecipitated material by TRIzol reagent following the
manufacturer’s instructions. Recombinant WT AlkB and D135S AlkB MUT
proteins were employed to remove dominant methylations from the
isolated RNA samples, facilitating efficient reverse transcription of
tRNAs. The demethylated RNA samples were then subjected to cDNA library
construction via the Multiplex Small RNA Library Prep Set for Illumina
(New England Biolabs, USA) according to the manufacturer’s instructions
for high-throughput sequencing. The cDNA libraries were sequenced with
an Illumina NextSeq 500.
Statistical analysis and reproducibility
The data are presented as the means ± SEMs. Statistical analysis was
performed by Prism 9.0. Comparisons were assessed by two-tailed
unpaired Student’s t test, paired Student’s t test, or one-way ANOVA
with repeated measurements followed by multiple comparison post-test.
Statistical methods are indicated in the figure legends for each panel.
For blots and microscopy image studies, three times, each experiment
was repeated independently, and representative images were shown. In
western/northwestern blot studies, the samples were derived from the
same experiment, and blots were processed in parallel.
Reporting summary
Further information on research design is available in the [375]Nature
Portfolio Reporting Summary linked to this article.
Supplementary information
[376]Supplementary Information^ (3.3MB, pdf)
[377]Transparent Peer Review file^ (526KB, pdf)
[378]Reporting Summary^ (206.2KB, pdf)
[379]41467_2024_54941_MOESM4_ESM.pdf^ (67.2KB, pdf)
Description of Additional Supplementary Files
[380]Supplementary Data 1^ (17.1KB, docx)
Source data
[381]Source data file^ (5.9MB, xlsx)
Acknowledgements