Abstract
Ampk is an energy gatekeeper that responds to decreases in ATP by
inhibiting energy-consuming anabolic processes and promoting
energy-generating catabolic processes. Recently, we showed that Lkb1,
an understudied kinase in B lymphocytes and a major upstream kinase for
Ampk, had critical and unexpected roles in activating naïve B cells and
in germinal center formation. Therefore, we examined whether Lkb1
activities during B cell activation depend on Ampk and report
surprising Ampk activation with in vitro B cell stimulation in the
absence of energy stress, coupled to rapid biomass accumulation.
Despite Ampk activation and a controlling role for Lkb1 in B cell
activation, Ampk knockout did not significantly affect B cell
activation, differentiation, nutrient dynamics, gene expression, or
humoral immune responses. Instead, Ampk loss specifically repressed the
transcriptional expression of IgD and its regulator, Zfp318. Results
also reveal that early activation of Ampk by phenformin treatment
impairs germinal center formation but does not significantly alter
antibody responses. Combined, the data show an unexpectedly specific
role for Ampk in the regulation of IgD expression during B cell
activation.
Subject terms: Metabolomics, Metabolomics, B cells, B cells
Introduction
B lymphocyte activation is an early step in a humoral immune response,
whereby a naïve B cell with a unique antigen receptor recognizes its
cognate antigen to trigger growth, division, and differentiation.
Following activation, selected B cells can develop into long-lived
plasma cells that secrete antigen-specific antibodies to fight
infections^[42]1. During receptor-mediated activation, B cells undergo
class switch recombination (CSR), also called immunoglobulin isotype
switching, to modify the type of B cell antigen receptor (BCR)
expressed by the B cell, such as IgM or IgG1 isotypes^[43]2. Many of
the early signaling events linked to engagement of the BCR are well
studied^[44]3. Recently, we reported a role for the tumor suppressor
Lkb1 in B cell activation: Lkb1 knockout (KO) caused spontaneous B cell
activation in vivo without specific added antigenic stimulation,
resulting in a robust T cell-dependent germinal center (GC)
reaction^[45]4,[46]5. This result was interesting because Lkb1
signaling had not been previously implicated in B cell activation and
few models of spontaneous GC formation exist^[47]6. We therefore sought
to determine the mechanism(s) whereby Lkb1 controls B cell activation.
Lkb1 phosphorylates 14 different related kinase family member proteins
to control many cellular functions including protein synthesis and cell
growth, cell polarity, and metabolism^[48]7. We elected to examine one
of these 14 major downstream Lkb1 targets, 5′ AMP-activated protein
kinase (Ampk). Ampk is an energy sensor that couples metabolism with
nutrient availability during periods of energetic stress, as might
occur during rapid B cell expansion and differentiation^[49]8. Ampk
does this by sensing increasing levels of ADP or AMP with reducing
levels of ATP in a cell, which triggers the phosphorylation of well
characterized substrate proteins including Tsc2, Acc1/2, and Tbc1d1 to
inhibit protein synthesis, promote fatty acid oxidation, upregulate
glycolysis, and restore overall cell energy balance^[50]9. While Lkb1
is the major upstream kinase for Ampk, other upstream kinases also
phosphorylate Ampk including CamKK2 and Tak1^[51]10–[52]12. In T cells,
CD3 ligation results in rapid Ampk activation in a calcium- and
CamKK2-dependent manner^[53]13, and Ampk activation declines in
proliferating normal T cells^[54]14; however, the Ampk activation
pattern in B cells is unknown.
Studies of Lkb1 and Ampk have shown overlapping but also unique
functions in hematopoiesis. For example, Lkb1 maintains hematopoietic
stem cell quiescence by regulating metabolism and the cell cycle using
Ampk-dependent and -independent mechanisms^[55]15–[56]17. In T cells
and thymocytes, Lkb1 deletion reduced peripheral T cells and decreased
T cell proliferation when stimulated in vitro. However, while loss of
Ampk in T cells also led to metabolic and activation defects, it did
not fully recapitulate the loss of Lkb1^[57]18–[58]20. There is only
one reported study of Ampk in B cells, which showed that a whole mouse
knockout of Ampk left isolated B and T cells unable to survive in vitro
under oxidative stress when exposed to the ATP synthase inhibitor,
oligomycin^[59]21. Given the unexpected role for Lkb1 loss in B cells
in triggering a GC reaction, we sought to determine role(s) for Ampk
during B cell activation.
Results
Ampk activation during B cell stimulation
Initially, we investigated whether Ampk, a major downstream target of
Lkb1, was required for B cell activation^[60]4,[61]5. Previous studies
in T cells showed Ampk activation after T cell receptor
stimulation^[62]13. We examined the phosphorylation of Ampk at T172, a
marker residue for Ampk activation^[63]22 and determined that Ampk
activation occurs between 18–24 hours post-stimulation of B cells with
anti-CD40 antibody plus interleukin (IL)-4 that persists at least
through 72 hours (Fig. [64]1A). Activation of Ampk should initiate
cellular processes that halt the accumulation of biomass required for
cell division^[65]9. Instead, anti-CD40 plus IL-4 stimulated B cells to
divide rapidly between 48–72 hours (Fig. [66]1B). Ampk activation with
energy stress has been reported many times and occurs by sensing
decreasing amounts of ATP linked to increasing ratios of AMP:ATP and
ADP:ATP^[67]23. Therefore, we examined a previously published dataset
of nucleotide metabolite levels at 24 hours post-stimulation. UHPLC-MS
metabolomics data of ^13C[6]-glucose nutrient labeling during initial B
cell activation showed unexpected AMP:ATP and ADP:ATP ratios declining
at 24 hours with ATP steady-state levels significantly increasing
(Fig. [68]1C)^[69]24. Additional measurements of extracellular
nutrients shows maintenance of high levels of both glucose and
glutamine in the culture medium (Fig. [70]1D), indicating that Ampk
activation occurs in stimulated B cells during energy replete
conditions.
Figure 1.
[71]Figure 1
[72]Open in a new tab
Activation of Ampk upon stimulation of B cells is independent of energy
stress and does not result in lowered biomass accumulation. (A)
Representative time course western blot for phosphorylated Ampkα
(T172), Ampkα, and β-tubulin during anti-CD40 plus IL-4 stimulation of
B cells. Image was cropped for clarity, full-length blots/gels are
presented in Supplementary Fig. [73]1. (B) Representative flow
cytometry of B220+ B cells at 0, 24, 48 and 72 hours post anti-CD40
plus IL-4 stimulation stained with Cell Trace Violet. (C) Relative fold
change in previously published UHPLC- MS metabolomics dataset^[74]24
for adenine nucleotides from 24 hours post stimulation with anti-CD40
plus IL-4 relative to naïve B cells (n = 3). (D) Measurement of
extracellular glucose and glutamine at 24 hours post stimulation with
anti-CD40 plus IL-4 (n = 3). (E) Live cell interferometry (LCI) images
of anti-CD40 plus IL-4 activated B cells 1, 24, and 48 hours post
stimulation showing significant growth, as confirmed by (F) a
significant increase in average cell mass over time, binned into 3 h
increments. (G) Average specific growth rate, computed as the
instantaneous slope of mass over time for each cell normalized by cell
mass, shows a peak at 24 hours, coincident with Ampk inactivation
(n = 5 independent experiments of at least 200 cells). Data represent
mean ± SD (C,D) or SEM (F,G). P values determined by 2-way ANOVA with
Bonferroni correction for multiple comparisons (C) or an unpaired
two-tailed Student’s t-test (D). **P ≤ 0.01.
Since Ampk activation typically inhibits protein, lipid, and additional
biosynthetic processes, we turned to live cell interferometry, a form
of quantitative phase microscopy, as a high precision and reproducible
approach to quantify changes in biomass accumulation during early B
cell activation^[75]25 (Fig. [76]1E). We discovered that isolated naïve
mouse B cells steadily increase cell biomass over the first 48 hours of
anti-CD40 plus IL-4 stimulated activation (Fig. [77]1F). Interestingly,
the specific growth rate calculated as a percentage of biomass change
per hour accelerates for the first 18 hours and then plateaus
(Fig. [78]1G). This growth rate peak coincides with the timing of Ampk
activation (Fig. [79]1A), although despite Ampk activity B cells
continue to acquire biomass at the peak (plateau) rate. This growth
rate profile suggests that Ampk activation may damp further growth rate
acceleration, perhaps as a rate-limiting step, but does not impede the
overall growth of B cells.
Ampk is not required for B cell activation or differentiation
It was a surprise that Ampk was activated in stimulated naïve mouse B
cells because a major role for Ampk in inhibiting lipid and protein
synthesis seems incompatible with the high growth rate and cell
division of activated B cells. Thus, we sought to determine whether
Ampk was required for B cell activation and differentiation by
generating a mouse model with B cell specific deletion of Ampk. Because
Ampkα1, encoded by Prkaa1, is the only α subunit expressed in trimeric
Ampk proteins in mature B cells^[80]26, deletion of this catalytic
subunit abolishes all Ampk activity. We generated a B cell specific
Ampkα1 KO mouse line by crossing Prkaa1^fl/fl mice with CD19-Cre
recombinase driver mice to delete Ampk activity in post-pro/pre B
cells. To monitor deletion efficiency, we crossed mice with a Rosa26
lox-STOP-lox YFP reporter allele (Fig. [81]2A). Deletion efficiency
measured by YFP+ B220+ B cells was >80% in both WT
(Prkaa1^+/+ × CD19-Cre) and Ampk KO (Prkaa^fl/fl × CD19-Cre) mice
(Fig. [82]2B), suggesting that there is no B cell survival disadvantage
with loss of Ampk, in contrast to a major B cell survival disadvantage
with Lkb1 loss^[83]4. Analysis of lysates from naïve and anti-CD40 plus
IL-4 stimulated WT and Ampk KO B cells confirmed loss of Ampkα1 protein
and abolishment of Ampk activity in Ampk KO B cells (Fig. [84]2C). Use
of a pan-Ampk alpha (α1 and α2) antibody also allows us to conclude
that there was no compensatory expression of the Ampkα2 subunit upon
deletion of Ampkα1.
Figure 2.
[85]Figure 2
[86]Open in a new tab
Ampk deletion does not significantly impair B cell function. (A)
Strategy for generating B-cell lineage specific knockout of Prkaa1 by
crossing CD19-Cre recombinase (JAX: 006785) mice with Prkaa1^fl/fl
(JAX: 014141) mice and Rosa26 lox-STOP-lox YFP (JAX: 006148) mouse
lines, yielding mice where CD19+ B cells lack the catalytic Ampkα
subunit. (B) Quantification of Cre-recombinase efficiency by
measurement of percentage of B220+ B cells that have YFP expression by
flow cytometry in WT and Ampk KO B cells (n = 3). (C) Representative
Western blot for P-Ampkα (T172), Ampkα and β-tubulin in WT and Ampk KO
mice. Two exposures are shown for Ampkα to illustrate some remaining
Ampk expression. Image was cropped for clarity, full-length blots/gels
are presented in Supplementary Fig. [87]2. (D) Flow cytometry of WT and
Ampk KO B cells during activation with anti-CD40L plus IL-4, including
activation markers (MHCII, CD86, CD69) at 24 hours, GC differentiation
(GC, %B220+ Fas+ GL7+) and CSR (%B220+ IgG1+) at day 3, and plasmablast
differentiation (%B220^lo CD138+) at day 5 (n = 5 for MHCII, CD86,
CD69, and PB, n = 6 for GC, and n = 7 for CSR). (E) Flow cytometry of
total splenocytes after immunization. Representative plots and
quantification of GC differentiation (GC B Cells, B220^+ Fas^+ GL7^+)
and CSR (B220^+ IgG1^+) 14 days post-immunization with NP-(28)-CGG
(n = 4). (F) Total IgM, total IgG1, NP8 IgG1, NP20 IgG1, and NP8/NP20
IgG1 ratio in serum 14 days after immunization of WT and Ampk KO mice
with NP-(28)-CGG (n = 4). (G) Representative flow cytometry for 2NBDG
glucose uptake at day 1, 3, and 5 post stimulation with anti-CD40 plus
IL-4, and quantification at day 0 through 5 in WT and Ampk KO B cells
(n = 3). Data represent mean ± SD (B,D–G). P values determined by
Student’s t-test (B,D–G), *P ≤ 0.05.
To determine whether Ampk deletion affects B cell activation, we
stimulated isolated WT and Ampk KO B cells with anti-CD40 plus IL-4 and
assessed activation and differentiation of YFP+ B cells. Surprisingly,
despite Ampk activation at 24 hours, no changes in the levels of
expression of activation markers CD69, CD86 or MHCII occur for the Ampk
KO B cells compared to WT B cells. At later time points, no changes in
GC-like B cell differentiation at day 3 and a minor reduction in CSR to
IgG1 occurred in Ampk KO compared to WT B cells. By day 5, there was a
small trending increase in plasmablast differentiation in Ampk KO
versus WT B cells (Fig. [88]2D). These data strongly contrast with
deletion of Lkb1 in B cells, which causes opposing results including
increased activation marker expression and GC-like B cell
differentiation, an increase in CSR, and a decrease in plasmablast
differentiation^[89]4. Together, the results suggest that Ampk is not
required for normal B cell function and is not required for
Lkb1-dependent B cell phenotypes.
Ampk does not control a humoral immune response in vivo
We next examined whether Ampk deletion from B cells affects antibody
responses in vivo. A prior study showed that whole mouse KO of Ampk did
not affect IgG responses to Ars-KLH antigen in vivo^[90]21; however,
other potential effects on the GC reaction, CSR, or antibody
specificity were not examined. We inoculated Ampk KO and WT mice with
the T cell-dependent antigen, NP-(28)-CGG, and analyzed B cells on day
14 post-immunization. We observed no differences in GC formation or CSR
(Fig. [91]2E), and serum IgG1 levels were similar between WT and Ampk
KO mice, with a slight but statistically insignificant difference in
total IgM (Fig. [92]2F). Multivalent NP antigen enables detection of
highly specific antibodies by probing for binding to specific NP molar
ratios, with fewer NP molecules revealing higher specificity responses.
Our data show that both broad spectrum and highly specific IgG1 against
NP antigen were similar in WT and Ampk KO mice (Fig. [93]2F). These
results show that Ampk is dispensable for a T cell-dependent humoral
immune response, although Ampk may be critical in other contexts, such
as T cell-independent, mucosal, or antiviral immune responses.
Ampk is dispensable for activation-induced glucose uptake
While we did not detect differences in differentiation patterns of WT
and Ampk KO B cells, B cell activation in response to a range of
triggering stimuli causes increased glucose import^[94]24,[95]27,[96]28
and the deletion of Ampk in T effector cells reduces glucose uptake in
vivo^[97]29. Therefore, we hypothesized that Ampk KO B cells might have
defects in glucose uptake. To test this postulate, we utilized a
fluorescent glucose analog, 2-NBDG, to measure glucose import in B
cells from WT and Ampk KO mice that lack the Rosa26 lox-STOP-lox YFP
tracer, because YFP and 2-NBDG fluorophore emission spectra overlap
(527 nm and 540 nm, respectively). Unexpectedly, there was no
difference between Ampk KO and WT B cells in glucose uptake during 5
days of B cell activation (Fig. [98]2G). Thus, Ampk activation does not
regulate glucose import into activated B cells, suggesting an
alternative glucose import mechanism.
Ampk does not affect glucose or glutamine B cell nutrients
While we did not detect differences in glucose uptake in Ampk KO B
cells, Ampk also regulates other metabolic pathways to regulate energy
stress, and Ampk activation at 24 hours of stimulation may impact
nutrient choice or routing without affecting B cell differentiation or
antibody responses. Therefore, we performed metabolomics profiling with
^13C[6]-glucose and ^13C[5]-glutamine in resting and anti-CD40 plus
IL-4 stimulated WT and Ampk KO B cells (Table [99]S1). Principal
component analysis (PCA) of total intracellular metabolites revealed
that the major segmentation was from differences between stimulation
time points, and not from differences between genotypes within each
time point (Fig. [100]3A). We generated correlation circle plots by
fitting each of the metabolites to 4 vectors based on the observed
clustering (Fig. [101]3B), which indicated metabolites that are known
to be changed upon activation, including increases in ATP with
activation and decreases in the AMP/ATP ratio, consistent with prior
results (Fig. [102]1C)^[103]24. With no significant differences in
total metabolites between WT and Ampk KO B cells at rest or for any
stimulated time point, we next analyzed specific ^13C-isotopomer
labeling. PCA of molecular IDs for isotopomers (Fig. [104]3C, top)
derived from the glucose label (left) or glutamine label (right) showed
similar segmentation to the total metabolites, with the largest
differences from stimulation time point rather than from genotype. We
then assessed whether any biologically relevant metabolites were
different between WT and Ampk KO at each time point. As a discovery
tool, we plotted the non-corrected P-values for naïve, resting (middle
row) and anti-CD40 plus IL-4 stimulated (bottom row) B cells labeled
with glucose (left) or glutamine (right) (Fig. [105]3C). The data
failed to identify any biologically relevant differentially produced
isotopomers (DPIs) linked to known functions of Ampk. These labeling
patterns occurred despite >90% uptake of labeled glucose or glutamine
in each respective experiment for all conditions examined
(Fig. [106]3D). To further investigate whether there were any DPIs
between WT and Ampk KO B cells irrespective of time point, we
calculated P-values and could not identify metabolites below the
corrected false discovery rate of 0.05 (Fig. [107]3E). We considered
that some broad metabolic differences may not be discernible at the
individual DPI level, and small changes may accumulate and reveal
deficiencies or surpluses in whole pathways. We therefore performed
Metabolite Set Variation Analysis (MSVA) utilizing curated KEGG
metabolic pathways and again identified significant changes occurred
only between naïve and stimulated B cells, with no separation by
genotype (Fig. [108]4F). Together, these results show that Ampk is not
modulating metabolism during B cell activation, despite known metabolic
roles related to glucose metabolism.
Figure 3.
[109]Figure 3
[110]Open in a new tab
Ampk does not control metabolism during B cell stimulation. (A) PCA
from UHPLC-MS metabolomics analysis of total metabolites isolated from
naïve and stimulated B cells from WT and Ampk KO mice. (B) K-means
clustering analysis of variable loading plots, indicating the
contribution of metabolites to variation across principal components in
four groups (k = 4). Projection arrow tips indicate Pearson correlation
of the listed metabolite. Transparency denotes cosine^[111]2 value or
the strength of metabolite representation. Shown are the top 4
metabolites contributing to each cluster. (C) Principal component
analysis and volcano plots of UHPLC-MS metabolomics of ^13C[6]-glucose
labeled or ^13C[5]-glutamine labeled isotopomers (MID) in naïve and
stimulated B cells from WT and Ampk KO mice. Plotted are non-corrected
P values for KO/WT amounts of specific isotopomers (n = 3). (D)
Isotopomer distribution of labeled glucose or labeled glutamine in
naïve or stimulated WT and Ampk KO B cells shows uptake of labeled
respective carbon source. (E) Quantification of Differentially Produced
Isotopomers (DPIs) analyzed both by day and by genotype shows no DPIs
between genotypes, only between time points for uncorrected or
Benjamini-Hochberg corrected P values < 0.05. (F) Heatmap of
significant (Benjamini-Hochberg FDR adjusted P value < 0.05) metabolite
set variation analysis (MSVA) pathway activity scores across WT and
Ampk KO naïve and stimulated B cells for curated KEGG metabolic
pathways. Data represent the mean ± SD (D) of relative metabolite
amounts from n = 3 independent experiments. Data from separate glucose
and glutamine labeling experiments were pooled, and relative amounts
analyzed for (A,B). P values determined by Student’s t test (C), 2-way
ANOVA with Bonferroni correction for multiple comparisons (D), or
empirical Bayes with Benjamini-Hochberg correction for multiple
comparisons (F).
Figure 4.
[112]Figure 4
[113]Open in a new tab
B cell activation and differentiation expression signatures are
maintained despite loss of Ampk. (A) PCA from bulk RNA sequencing
analysis of total RNA isolated from day 0 through day 5 stimulated B
cells from WT and Ampk KO mice. PC1 and PC2 are shown. (B) Kinetic time
course expression plot of Prkaa1 during stimulation. (C) Heat map of KO
signature gene expression across averaged samples. KO signature was
generated as the intersection of all KO/WT DEGs at each time point from
day 1 to day 5 of stimulation (adjusted P value < 0.05, abs(log[2]FC)
>0.5 at each time point comparison). (D) Heat maps for B cell
activation, germinal center, and ASC gene signatures
from^[114]32,[115]33,[116]55. Genes shown were selected as the top 55
differentially expressed from the Shi 2015 signatures between WT day 0
and day 2 (activation), WT day 0 and day 3 (GC), and WT day 0 and day 5
(ASC), with Prdm1 included post hoc. Pathway scores represent total
GSVA enrichment of the respective Shi 2015 signatures for each
comparison (adjusted P value < 0.05). Heat map values represent row
z-score (n = 3 each WT and KO for each time point). Adjusted P values
determined by Wald test (B). ****P ≤ 0.0001.
Ampk regulates IgD levels but not transcripts controlling B cell fate
Additional canonical Ampk substrates include Hdac4 and Hdac5, proteins
that deacetylate histones and thereby regulate gene expression^[117]30.
To assess potential differences in steady-state RNA expression, and to
investigate potential alternative processes regulated by Ampk, we
performed RNA-Seq in WT and Ampk KO B cells on culture days 0 through 5
using anti-CD40 plus IL-4 stimulation (Table [118]S2). At day 0, there
are no PCA plotted statistical differences between WT and Ampk KO naïve
B cells. However, as B cells differentiate from day 1 through day 5,
Ampk KO B cells cluster separately from their WT counterparts, but
follow a similar trajectory towards differentiation (Fig. [119]4A). We
confirmed reduced Prkaa1 expression in Ampk KO B cells, as expected
(Fig. [120]4B). To determine whether loss of Ampk conveyed a specific
gene signature during 5 days of differentiation, we analyzed
differentially expressed genes (DEGs) for days 0 through 5 of
stimulation and identified 168 genes that were either consistently
increased or decreased each day in Ampk KO compared to WT B cells
(Fig. [121]4C). Pathway enrichment analysis of these 168 genes did not
reveal any pathways related to B cell activation or differentiation,
with the exception of 5 genes linked to apoptotic signaling in response
to endoplasmic reticulum stress (Grina, Atf4, Trib3, Lrrk2, Chac1)
(Table [122]S2). Endoplasmic reticulum stress has an important role in
plasma cell differentiation and antibody secretion^[123]31. Therefore,
we analyzed activation, GC B cell, and antibody-secreting cell (ASC)
gene expression signatures^[124]32,[125]33 as previously reported in
our data set (Fig. [126]2D). Similar to our earlier data, Ampk KO and
WT B cells activate similarly and increase GC-signature transcripts
equally. Expression of ASC signature genes were similar between WT and
Ampk KO samples (Fig. [127]4D), although Ampk KO B cells showed
increases in some ASC transcripts, including Prdm1 at days 4 and 5
(Fig. [128]4D). This finding is consistent with a trend towards an
increase in plasmablasts at day 5 (Fig. [129]2D), and a divergence in
the PCA plot of days 4 and 5 WT versus Ampk KO B cells (Fig. [130]4A).
Because of the potential for increased ASCs in Ampk KO mice, despite
similar amounts of secreted IgM and IgG1 from in vivo immunizations
(Fig. [131]2F), we evaluated the levels of immunoglobulin transcripts
in WT and Ampk KO B cells. We determined that heavy chain variable
(Ighv) region expression levels were similar for WT and Ampk KO B
cells, although there was an increase in some transcripts including
Ighv6-3 and Ighv14-3 in Ampk KO B cells (Fig. [132]5A). From in vivo
immunization data, we also detected a slight but insignificant increase
in secreted IgM, so we analyzed transcripts for immunoglobulin constant
regions to identify potential isotype biases. Whereas Ighm transcripts
matched our observations in vivo with a minor but insignificant
increase at day 5 for Ampk KO versus WT B cells, we were surprised to
discover that Ighd expression was severely repressed in Ampk KO B cells
from day 1 stimulation onwards (corrected P-value = 9.5 × 10^−30 at day
1) (Fig. [133]5B). IgD is normally co-expressed with IgM on the surface
of all mature, naïve B cells, and is a relatively understudied antibody
class that has potential implications in mucosal immune
responses^[134]34. The expression of IgD is not impacted by
activation-induced cytidine deaminase (AID) like most isotype switched
immunoglobulins but is instead regulated by Zfp318^[135]35,[136]36.
Interestingly, the most robust DEGs at day 3 are Ighd and Zfp318, each
of which are greater than an order of magnitude more repressed than the
next most differentially expressed genes (Fig. [137]5C). In fact,
Zfp318 expression is repressed throughout the stimulated time course in
Ampk KO B cells (Fig. [138]5D), coinciding with the drop in Ighd
expression (Fig. [139]5B). Further supporting the specificity of Ampk
loss on Zfp318 regulation of Ighd levels, expression of Aicda, encoding
AID which regulates CSR, is the same in WT and Ampk KO B cells from
days 0–4 and increased on day 5 of B cell activation (Fig. [140]5D). To
confirm that the loss of Ighd transcripts affects IgD protein levels,
we analyzed surface expression over 5 days of differentiation by flow
cytometry, which shows that Ampk KO B cells have decreased IgD
expression from days 2 through 5, particularly evident for the loss of
IgD-high B cells (Fig. [141]5E). Given the limited number of DEGs, it
appears that Ampk exerts highly specific control of IgD during B cell
activation, likely through regulation of Zfp318 expression.
Figure 5.
[142]Figure 5
[143]Open in a new tab
Ampk specifically regulates expression of IgD via downregulation of
Zfp318 during activation. (A) Heat maps for immunoglobulin heavy-chain
variable region expression (Ighv) across naïve and day 4/day 5
stimulated B cells. Genes were selected based on significant
differential expression between WT naïve and day 5 stimulation
(adjusted P value < 0.05). Values represent row z-score. (B) Kinetic
time course expression plots for immunoglobulin heavy-chain constant
region (Igh) expression across genotype and stimulation. (C) Volcano
plot of differentially expressed genes between Ampk KO and WT at day 3
of stimulation. Adjusted P value cutoff represents values < 0.05
calculated using the Wald test following DESeq2 normalization. (D)
Kinetic time course expression plot of Zfp318 and Aicda across genotype
and stimulation. (E) Representative flow cytometry plot of IgD at day 0
through day 5 of stimulation in WT and Ampk KO B cells and
quantification by MFI (top) and % IgD (bottom) (n = 3 each WT and Ampk
KO). P values were determined by Student’s t-test (E), adjusted P
values were determined by Wald test (B,D) *P ≤ 0.05, **P ≤ 0.01,
**P ≤ 0.001, ***P ≤ 0.0001.
Pharmacological activation of Ampk
The limited scope of impact for Ampk loss on B cell physiology seems
surprising, so we examined whether the timing of Ampk activation could
regulate B cell functions. We utilized two pharmacological activators
of Ampk, phenformin and A-769662, to alter the timing of Ampk
activation. Phenformin is a mitochondrial electron transport chain
complex I inhibitor that activates Ampk by inhibiting ATP production,
thereby increasing AMP/ATP and ADP/ATP ratios, and is an analogue of
the diabetes drug metformin, whereas A-769662 is a direct and specific
activator of Ampk^[144]23. We examined early activation of Ampk by
treating B cells with each activator at the time of anti-CD40 plus IL-4
stimulation in culture and observed that both drugs decreased CD86
activation biomarker expression, but only phenformin reduced CD69
expression (Fig. [145]6A). Phenformin had a drastic effect on B cell
differentiation by day 3 with greatly decreased CSR to IgG1 and
inhibited differentiation into GC-like B cells (Fig. [146]6B). By
contrast, A-769662 had little effect on GC-like B cell differentiation
and only a slight defect in CSR (Fig. [147]6B). These results show that
electron transport chain activity and ATP production per se, and not
accelerated Ampk activation, are critical for B cell activation,
differentiation, and CSR, in agreement with an effect mainly targeting
markedly reduced Zfp318 and Ighd expression levels in stimulated Ampk
KO B cells (Fig. [148]5).
Figure 6.
[149]Figure 6
[150]Open in a new tab
Early pharmacological activation of Ampk modifies B cell function both
in vitro and in vivo. (A,B) Representative flow cytometry plots and
quantification of activation markers (CD86, CD69) at 24 hours (A), and
germinal center differentiation (GC B Cells, %B220^+ Fas^+ GL7^+) and
class switch recombination (CSR, %B220^+ IgG1^+) at day 3 (B) of cells
during in vitro activation with anti-CD40 plus IL-4 and phenformin
(100 µM) or A-769662 (50 µM) (n = 3). (C) Strategy for in vivo
assessment of B cells responses with Ampk activation. Prior to
immunization, mice were given phenformin or vehicle (sucralose) in
their water. Water was changed 2×/week, and samples collected 14 days
post immunization with NP-(28)-CGG. (D) Flow cytometry of total
splenocytes after immunization. Representative plots and quantification
of GC differentiation (GC B Cells, B220^+ Fas^+ GL7^+) and CSR (B220^+
IgG1^+) 14 days post-immunization with NP-(28)-CGG (n = 4). (E)
Anti-NP8 and anti-NP32 IgG1 serum response and NP8/NP32 IgG1 ratio by
ELISA 14 days after immunization with NP-(28)-CGG (n = 4). Data
represent mean ± SD. P values determined by 2-way ANOVA with Bonferroni
correction for multiple comparisons (A,B), or Student’s t-test (D,E),
*P ≤ 0.05, **P ≤ 0.01, ***P ≤ 0.001, ****P ≤ 0.0001.
Because of recent interest in using metformin and phenformin clinically
to treat B cell malignancies^[151]37, we further evaluated the impact
of phenformin on B cell function. To determine whether the effects of
phenformin in vitro replicate in vivo, we immunized mice with
NP-(28)-CGG to induce a T cell-dependent humoral immune response while
delivering phenformin or sucralose vehicle in the drinking water
(Fig. [152]6C). In vivo results show that 14-day treatment with
phenformin substantially decreased the percentage of GC B cells in the
spleen and reduced the percentage of IgG1+ isotype switched B cells
(Fig. [153]6D). Interestingly, however, mice on phenformin had similar
amounts of total IgM and IgG1 in their serum (Fig. [154]6E). There was
a minor but non-significant defect in the generation of NP8-specific
high affinity IgG1 antibody in phenformin treated mice, but no effect
on broader NP20 IgG1 antibodies (Fig. [155]6F). Overall, these findings
suggest that phenformin reduces GC formation, but still allows for
generation of antigen-specific antibody production.
Discussion
Our recent results showing that loss of Lkb1 kinase signaling triggers
the B cell GC reaction^[156]4,[157]5 prompted studies of Ampk as a main
Lkb1 target kinase during B cell activation. We found Ampk activation
24 hours after stimulation of naïve B cells with anti-CD40 antibody
plus IL-4 (Fig. [158]1A). A key role for activated Ampk in mammalian
cells is to block anabolic processes that consume energy by target
protein phosphorylation in response to energy stress. To our surprise,
B cell activation with rapid biomass accumulation and cell
proliferation coincides with sustained Ampk activation in the absence
of energy stress (Fig. [159]1A,B). We anticipated the opposite result,
that Ampk activity would prevent biomass accumulation. While unexpected
given the canonical targets and activities of Ampk, including
inhibition of major anabolic targets Acc1, Tsc2, and Raptor, these
findings also make sense in the biological context of B cell
activation. For example, the Ampk target Raptor is essential for Bcl6
expression and recruitment of activated B cells into GCs^[160]38, and
inhibition of Raptor by Ampk at this time would prevent GC formation.
Similarly, Ampk-driven inhibition of protein and lipid synthesis
through Acc1 and Tsc2 would antagonize the need for biomass
accumulation while B cells prepare for rapid division in the
GC^[161]24,[162]39,[163]40. While activation of Ampk during B cell
activation was unexpected due to the role of Ampk in biomass
accumulation, there are precedents for Ampk activation in other immune
contexts. For example, in T cells, Ampk is transiently and immediately
activated after CD3 or calcium stimulation^[164]13, and Ampk
phosphorylation declines in proliferating T effector cells^[165]14.
This contrasts with B cells, where Ampk activation after 24 hours of
stimulation persists during proliferation (Fig. [166]1A). In T cells,
Ampk activation by nutrient limitation, metformin treatment, or AICAR,
results in decreased IFN gene transcription and reduced T cell effector
function^[167]29. Here, activation of Ampk by phenformin prevents GC
differentiation and CSR, but still allows for generation of
high-specificity antibodies (Fig. [168]6A–E).
To study Ampk during B cell activation in vivo, we made a B cell
specific KO of the catalytic Ampk alpha subunit, Prkaa1 that achieved
>80% deletion efficiency, but did not detect a phenotype similar to B
cell specific Lkb1 KO mice^[169]4. In fact, Lkb1 loss increases CSR and
decreases plasmablast differentiation, whereas Ampk loss instead
decreases CSR and slightly increases plasmablast differentiation
(Figs [170]2D, [171]4D). These results suggest that Lkb1 acts through
substrates other than Ampk to regulate B cell activation and GC
formation, or that other related Lkb1 targets may compensate for the
loss of Ampk. For example, Lkb1 phosphorylates 13 other Ampk family
member proteins including Mark, Brsk, and Nuak proteins^[172]7. Sik2
and Mark2 are Lkb1 targets that phosphorylate Crtc2^[173]41, a
transcriptional co-activator of CREB required for GC exit^[174]42, and
their redundant activity might compensate for the loss of Ampk to
rescue plasma cell differentiation.
We were further surprised to find no evidence for Ampk in regulating
metabolism in B cells. Nutrient uptake and routing was identical in WT
and Ampk KO B cells (Figs [175]2G, [176]3). One possibility is that
Ampk is dispensable for homeostatic nutrient handling but required for
metabolic adaptations under stressful conditions not examined here.
Supporting this idea, studies in T cells show that Ampk can regulate
glutamine metabolism during glucose deprivation^[177]29 and total body
knockout of Ampk makes B and T cells unable to survive ATP synthase
inhibition with oligomycin^[178]21. Recent studies show that GCs in
mice are hypoxic microenvironments^[179]43 and Ampk links to
inflammation in hypoxia^[180]44. Resting and activated Ampk KO B cells
express genes and gene profile signatures similar to WT B cells, except
for two repressed transcripts, Zfp318 and Ighd. This specificity is
remarkable and replicates the specificity of Zfp318 KO B cells, in
which there were only two differentially expressed genes, Ighd and Sva,
an antigen related to the Vav-Cre recombinase deletion
construct^[181]35. Loss of Ighd transcripts parallels a loss of surface
IgD protein expression in the Ampk KO B cells after stimulation. A
possible but less likely contributor to a difference in later time
point IgD expression between WT and Ampk KO B cells could be
differential increase in cell death in the in vitro culture system
between days 4 and 5 of stimulation. The role(s) of IgD in B cells
remains elusive, as IgD is present at very low levels in human and
rodent serum^[182]45, but has recently been suggested to modulate Th2
responses to soluble antigens through interactions with
basophils^[183]46. Because of co-expression of IgM and IgD in immature
B cells, IgD may sequester signaling molecules from IgM to inhibit BCR
signaling^[184]47, and IgD may play a similar role in mature B cells.
Regardless of the role of IgD, the specific regulation of Zfp318 and
Ighd by Ampk provides new insight into immunoglobulin gene regulation
as a non-canonical role for Ampk.
Materials and Methods
Mice
C57BL/6J, Prkaa1^fl/fl, CD19-Cre, and Rosa26 lox-STOP-lox EYFP mice
(JAX: 000664, 014141, 006785, and 006148) were housed in a specific
pathogen-free animal facility at UCLA. All studies were on mixed-sex
mice between 6 to 16 weeks of age with approval from the UCLA
Institutional Animal Research Committee (#1998-113-63C). All
experiments were performed according to the National Institutes of
Health and ARRIVE guidelines on the use of laboratory animals.
Figures [185]1, [186]6 used WT C57BL/6J mice, and Figs [187]2–[188]5
used B cell specific Ampk KO or Ampk WT littermate mice. Genotypes for
WT (Prkaa1^+/+) and Ampk KO (Prkaa1^−/−) mice were as follows:
Prkaa1^wt/wt or Prkaa1^fl/fl, respectively, with CD19-Cre^+/−, and
Rosa26-YFP^+/+ or ^+/− for experiments requiring YFP (Fig. [189]3B–D,F)
or Rosa26-YFP^−/− for experiments without YFP tracer (Figs [190]3E,F,
[191]4–[192]6).
Stimulation of isolated mouse B cells
Red blood cell-lysed mouse spleen cells were enriched for B cells using
CD43 negative magnetic selection (Miltenyi). Cells were grown in
RPMI1640 supplemented with 10% FBS and 50 μM β-mercaptoethanol. B cells
were stimulated with 1 μg/ml anti-CD40 mAb (BD Pharmingen) and 25 ng/ml
IL-4 (R&D Systems). At day 3, anti-CD40 was washed out and cells
replated in medium containing only IL-4 until day 5. For early Ampk
activation, phenformin (Sigma) was resuspended in 1x PBS, pH 7.4, and
used at 100 µM, and A-769662 (Abbott) was resuspended in DMSO and used
at 50 µM at the time of stimulation.
Immunoblotting
Cells were lysed in Lysis Buffer containing 50 mM Tris HCl pH 7.4,
100 mM NaCl, 1 mM EDTA and 1% Triton X-100 supplemented with Protease
Inhibitor and Phosphatase Inhibitor Cocktails 2 and 3 (Sigma). Extracts
were quantified and denatured by boiling with DTT and 10–30 µg protein
separated by SDS-PAGE and transferred to nitrocellulose before blocking
in 5% milk in TBST. Membranes were incubated overnight in the indicated
antibodies in 5% BSA in TBST. Membranes were then incubated in
fluorescent secondary antibody and imaged using the Odyssey Fc imaging
system (LI-COR). Complete antibody information can be found in the
Supplemental Methods.
Flow cytometry
Single cell suspensions were incubated with Fc Block (BD Pharmingen) at
1:500 for 15 min, then washed and stained for 20 min in 50 µl FACS
buffer (2% FBS in PBS) on ice in the dark. Data were obtained on a BD
LSRII or BD Fortessa (BD Biosciences) and analyzed with FlowJo software
(Treestar). Antibodies were used at a 1:200 dilution. Complete antibody
information can be found in the Supplemental Methods. Assessments of
glucose import used a 2-NBDG Glucose Uptake Assay Kit (Biovision)
according to the manufacturer’s instructions.
Extracellular metabolite analysis
Glutamine levels in full media and after 24 hours of stimulation were
measured by plating 10^6 cells/ml in 2 ml, centrifuging to remove cells
and debris, and analyzing using a BioProfile Basic Analyzer (NOVA
Biomedical).
Live cell interferometry
Live cell interferometry (LCI) was used to measure biomass accumulation
rate^[193]25. Cells were plated at 7.5 × 10^5 cells/ml on Poly-L-Lysine
(Sigma) coated μ-Slide 2-Well Ph+ glass bottom slides (Ibidi) and
imaged every 15 min for 72 h in a custom-built chamber as previously
described^[194]48. LCI was performed on a Zeiss Axio Observer A1 with
stage-top incubation system (Zeiss) using using a 20 × 0.4 NA
objective. LCI data were captured with a SID4Bio (Phasics) QWLSI
camera^[195]49. MATLAB (Mathworks) was used to analyze LCI data.
Quantitative phase microscopy data was processed using SID4Processing
(Phasics) to generate phase-shift images compatible with MATLAB. A
custom MATLAB script was used to track the mass of individual cells as
previously described^[196]50.
Immunization and ELISA
Mice were immunized with 50 µg NP-(28)-CGG (Biosearch Technologies) in
ImJect Alum (Thermo Scientific) via intra-peritoneal injection. Blood
was collected at 14 days and assayed by ELISA. Serum Ig concentrations
were determined using anti-mouse Ig as a capture antibody and developed
with isotype-specific goat anti-mouse antibodies conjugated to HRP
(Southern Biotech). Antigen-specific ELISA was performed by coating a
plate with NP-(8)- or NP-(20)-BSA and developed with an isotype
specific goat anti-mouse antibody conjugated to HRP. For immunizations
with phenformin, mice were given water containing either vehicle
(5 mg/ml sucralose, Sigma) or phenformin (1.8 mg/ml, Sigma) with
vehicle in amber bottles one day before immunization, and water was
changed twice per week.
Metabolomics
B cells were grown for 24 hours in media with glucose or glutamine free
RPMI (Gibco) supplemented with 2 g/L [U-^13C] glucose or 3 mM [U-^13C]
glutamine, respectively (Cambridge Isotope Laboratories) and
non-dialyzed FBS. Metabolites were extracted with cold 80% methanol and
measured using Ultra High Performance Liquid Chromatography Mass
Spectrometry (UHPLC-MS), as previously
described^[197]24,[198]51,[199]52. To extract intracellular
metabolites, cells were pelleted by centrifugation (1000 RPM, 4 °C) and
rinsed with cold 150 mM ammonium acetate (pH 7.3), pelleted again,
followed by addition of 1 ml cold 80% MeOH in water. To the cell
suspensions, 10 nmol D/L-norvaline were added and rigorously mixed
followed by centrifugation (1.3 × 10^4 rpm, 4 °C). The supernatant was
transferred into a glass vial and pellet was further extracted with
200 µl cold 80% MeOH in water. After centrifugation, supernatant was
combined, metabolites dried down under vacuum, and resuspended in 70%
acetonitrile. For the mass spectrometry-based analysis of the sample,
5 μl were injected onto a Luna NH2 (150 mm × 2 mm, Phenomenex) column.
The samples were analyzed with an UltiMate 3000RSLC (Thermo Scientific)
coupled to a Q Exactive mass spectrometer (Thermo Scientific). The Q
Exactive was run with polarity switching (+3.50 kV/−3.50 kV) in full
scan mode with an m/z range of 65–975. Separation was achieved using A)
5 mM NH4AcO (pH 9.9) and B) ACN. The gradient started at 15% A) going
to 90% A) over 18 min, followed by an isocratic step for 9 min and
reversal to the initial 15% A) for 7 min. Metabolites and isotopomers
thereof were quantified with TraceFinder 3.3 using accurate mass
measurements (≤3 ppm) and retention times. For isotopologue
distribution measurements, data was corrected for naturally occurring
^13C as described in^[200]53. Fractional contributions were calculated
using the formula
[MATH:
FC=∑0ni⁎m<
mrow>in∑
0nmi<
/mi> :MATH]
as described^[201]54, where m[i] denotes the intensity of the
isotopologue, and n marks the number of carbons in a given metabolite.
Data were normalized to cell counts. Metabolite relative amounts,
isotopomer distribution values, MSVA scores, and DPI lists are included
in a supplemental excel file (Table [202]S1).
RNA extraction
At least 10^7 WT and Ampk KO B cells were grown in biological
triplicates and RNA purified immediately after isolation, or 24 hours
after anti-CD40 plus IL-4 stimulation using the RNeasy Mini Kit
(Qiagen) and RNase-free DNase (Qiagen) following the manufacturer’s
protocols. All samples showed an A260/280 ratio >1.99. Prior to library
preparation, quality control of the RNA was performed using the
Advanced Analytical Technologies Fragment Analyzer (Advanced
Analytical, Inc.) and analyzed using PROSize 2.0.0.51 software. RNA
Quality Numbers (RQNs) were computed per sample between 8.7 and 10,
indicating intact total RNA per sample prior to library preparation.
RNA-seq library preparation
Strand-specific ribosomal RNA (rRNA) depleted RNA-Seq libraries were
prepared from 1 µg of total RNA using the KAPA Stranded RNA-Seq Kit
with Ribo-Erase (Kapa Biosystems, Roche). Briefly, rRNA was depleted
from total RNA samples, the remaining RNA was heat fragmented, and
strand-specific cDNA was synthesized using a first strand random
priming and second strand dUTP incorporation approach. Fragments were
then A-tailed, adapters were ligated, and libraries were amplified
using high-fidelity PCR. All libraries were prepared in technical
duplicates per sample and resulting raw sequencing reads merged for
downstream alignment and analysis. Libraries were paired-end sequenced
at 2 × 150 bp on an Illumina NovaSeq 6000.
Lists of transcript/gene-level expression values, KO signature ORA
results, and differentiation signature GSVA results are included in a
supplemental excel file (Table [203]S2).
Statistical analyses
All metabolomics and transcriptomics statistical analyses are described
in the above methods. Values represent mean ± S.D. or S.E.M. Data were
analyzed with Prism 6 (GraphPad) (Figs [204]1–[205]3, [206]6), MATLAB
(Mathworks) (Fig. [207]1E–G) or R (Figs [208]3–[209]5). Parametric data
were analyzed using unpaired two-tailed Student’s t-tests, or 2-way
ANOVA with Bonferroni correction for multiple comparisons.
Transcriptomic volcano and kinetic time course expression plots were
analyzed using DESeq2 Wald tests with Benjamini-Hochberg FDR correction
for multiple comparisons. For all data sets, P ≤ 0.05 was considered
significant. *P ≤ 0.05, **P ≤ 0.01, ***P ≤ 0.001, ****P ≤ 0.0001.
Supplementary information
[210]Supplementary Materials^ (184.2KB, docx)
[211]Table S1^ (267.6KB, xlsx)
[212]Table S2^ (14.8MB, xlsx)
Acknowledgements