Abstract
Intermittent fasting and fasting mimetic diets ameliorate inflammation.
Similarly, serum extracted from fasted healthy and asthmatic subjects’
blunt inflammation in vitro, implicating serum components in this
immunomodulation. To identify the proteins orchestrating these effects,
SOMAScan technology was employed to evaluate serum protein levels in
healthy subjects following an overnight, 24-h fast and 3 h after
refeeding. Partial least square discriminant analysis identified
several serum proteins as potential candidates to confer feeding status
immunomodulation. The characterization of recombinant IGFBP1 (elevated
following 24 h of fasting) and PYY (elevated following refeeding) in
primary human CD4^+ T cells found that they blunted and induced immune
activation, respectively. Furthermore, integrated univariate serum
protein analysis compared to RNA-seq analysis from peripheral blood
mononuclear cells identified the induction of IL1RL1 and MFGE8 levels
in refeeding compared to the 24-h fasting in the same study. Subsequent
quantitation of these candidate proteins in lean versus obese
individuals identified an inverse regulation of serum levels in the
fasted subjects compared to the obese subjects. In parallel, IL1RL1 and
MFGE8 supplementation promoted increased CD4^+ T responsiveness to T
cell receptor activation. Together, these data show that caloric
load-linked conditions evoke serological protein changes, which in turn
confer biological effects on circulating CD4^+ T cell immune
responsiveness.
Keywords: fasting, refeeding, CD4^+ T cell activation, SOMAscan,
IGFBP1, PYY, IL1RL1, MFGE8, integrative bioinformatics
1. Introduction
Caloric restriction, intermittent fasting and time-restricted feeding
in animal models [[48]1,[49]2,[50]3,[51]4], in healthy volunteers
[[52]5,[53]6,[54]7,[55]8] and in overweight individuals [[56]9] have
been found to confer anti-inflammatory effects. Furthermore, caloric
restriction, mimetic diets and time-controlled fasting reduce
inflammatory markers in human disease [[57]10,[58]11,[59]12]. The
complexity of establishing the mechanisms orchestrating these
immunomodulatory effects include the difficulty in distinguishing the
relative contribution of adipose tissue remodeling with concomitant
weight loss [[60]9,[61]13], the role of changes in the microbiome
[[62]14,[63]15,[64]16] and/or how and which immune cells
[[65]17,[66]18] or organs [[67]19,[68]20] contribute to this
regulation.
The mechanisms implicated by these immunomodulatory effects include
pathways modified: by ketogenesis [[69]7,[70]16,[71]21], via gut biome
short chain fatty acids, via reduced insulin-like growth factor 1
(IGF-1) and protein kinase A (PKA) signaling [[72]7,[73]9,[74]22], by
the induction of corticosteroid-signaling [[75]1,[76]16], via
regulation of heme oxygenase-1 (HO-1) [[77]23] and through the
upregulation of autophagy [[78]13]. Fewer studies have explored the
mechanisms underpinning immunomodulatory effects of fasting
interventions in humans [[79]6,[80]7,[81]8,[82]9].
Additionally, a pertinent question is whether the beneficial effects of
restricting calories is purely due to the reduction of calories or due
to the dietary composition [[83]24,[84]25]. Initial investigations
suggest that the dietary content may be less impactful than duration of
caloric deprivation [[85]24,[86]26]. Hence time-controlled limitation
in calories, per se, may evoke the process of hormesis, which, in part,
is the concept where a mild, sublethal stress can protect against
subsequent excessive stressors [[87]27,[88]28]. This biological
phenomenon has been shown where caloric restriction increases
resistance to subsequent thermal or oxidative stress injury (reviewed
[[89]29]). Interestingly, data is emerging that macrophage polarization
may be modulated by hormetic triggers [[90]13,[91]30].
Given the advances in high-throughput ‘omic’ studies [[92]31], a
potential avenue to explore mechanisms involved in caloric
load-dependent immunomodulation could be via the study of serum
proteomics from individuals’ serum extracted under different caloric
load conditions. The potential utility of this approach is supported by
the finding that serum extracted after time-controlled fasting and
refeeding in healthy volunteers and in asthma subjects differentially
regulated the NLRP3 inflammasome response when used as the exclusive
incubation serum in transformed THP-1 monocytic/macrophage cells
[[93]6,[94]12]. A recent study identified that time-controlled fasting
similarly blunted CD4^+ T helper cell immune responsiveness [[95]32].
In that study, RNA-seq and flow-cytometric analysis was performed on
peripheral blood mononuclear cells from healthy volunteers in response
to a baseline overnight fast, a 24-h fast and 3 h of refeeding. The
findings included that the greater duration of the fast was linked to
greater effects on blunting immune responsiveness and this fasting
effect was very robust in blunting CD4^+ T helper cell activation
[[96]32].
To evaluate the effects of changes in serum proteins in response to
fasting and refeeding, serum from the same subjects in response to
overnight fasting, 24-h fasting and refeeding [[97]32] were analyzed
using SOMAscan proteomics, and multivariate analysis was employed to
identify caloric load-dependent candidate proteins. Additional
candidates were identified using integrative bioinformatics by
combining the SOMAscan results with the previously published peripheral
blood mononuclear cell (PBMC) RNA-seq data (GEO database link:
[98]https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE165149,
accessed on 20 January 2021). The immunomodulatory role of candidate
proteins was then functionally validated in primary CD4^+ T cells
extracted from a group of healthy volunteers and from a validation
cohort of lean and obese subjects. In this study, we identified
multiple novel nutrient load-dependent circulating proteins that either
promote or blunt CD4^+ T cell responsiveness. Furthermore, integrative
bioinformatic analysis can be used to find common circulating proteins
with parallel pro-inflammatory effects in serum from subjects after
fasting, and in obese versus lean subjects. These findings support that
acute or chronic nutrient load conditions can generate parallel
circulating signaling molecules to drive CD4^+ T helper cell
responsiveness and identify that these factors confer paracrine effects
on immune cell regulatory programs.
2. Material and Methods
2.1. Study Design and Subjects
This fasting and refeeding pilot study was registered in
ClinicalTrials.gov with the registration number [99]NCT02719899 and
approved by the National Heart and Lung Institute IRB. Subjects were
screened in the ambulatory clinic and signed consent for the protocol
prior to undertaking the study (Visit 1). Subjects initiated the study
after an overnight fast with blood drawn for the baseline immune
response (Baseline—Visit 2). After overnight fasting, they consumed a
fixed 500 calorie meal before 8am in the morning and fasted for 24 h
except for unrestricted water intake. Following a 24-h fasting blood
draw (Fasting—Visit 3), the subjects ate another 500 calorie meal with
post-prandial blood draws 3 h later (Refed—Visit 3). The schematic of
the blood draw protocol is shown in [100]Supplemental Figure S1A. The
subject group consisted of 10 females and 10 males with an age range
from 22 to 29 years (means ± SD: 24.35 ± 1.98), 22.0–28.7 BMI range
(means ± SD: 24.56 ± 2.03) and had the following race distribution
(White/Non-Hispanic, n = 8; Asian, n = 8; African American, n = 3;
multiple, n = 1). All these subjects had no history of any acute or
chronic disease. Subjects had a choice between two isocaloric
breakfasts: option (1) vegetable omelet, toast with butter and jelly
and orange juice; option (2) oatmeal with walnuts, brown sugar, dried
cranberries and milk. Lean and obese African American females with an
age range from 24 to 78 years (lean: 53.73 ± 18.49 and obese: 53.00 ±
12.45) and BMI’s of 24.17 ± 2.17 (lean subjects, n = 15) and 40.29 ±
8.06 (obese subjects n = 15), respectively, were enrolled on the
Disease Discovery Natural History protocol ([101]NCT01143454). The
blood from healthy volunteers for functional validation of target
proteins was obtained from on the same Disease Discovery Natural
History protocol ([102]NCT01143454) and from the NIH clinical center
blood bank ([103]NCT00001846).
2.2. Blood Preparation and Bioassay
For the serum bioassay, blood was allowed to coagulate at room
temperature in serum tubes for 1 h and serum was collected by
centrifuge for 10 min and stored at −80 °C. Each serum sample was
analyzed with each assay kit of IGFBP1 (RayBiotech), Peptide tyrosine
tyrosine (PYY) (RayBiotech Peachtree Corners, GA, USA), ApoE (Thermo
Scientific, Waltham, MA, USA) and PCSK9, Leptin, MFG-E8 and ST2/IL1RL1
(R&D Systems, Minneapolis, MN, USA) according to the manufacturer’s
instructions. The absorbance at each assay was read at a wavelength of
405 nm with a plate reader (BioTek, Winooski, VT, USA). The mean value
of each subject represents the average of duplicate experiments (n = 18
biological distinct subjects).
2.3. PBMC RNAseq Analysis
RNA Sequencing and Bioinformatics Analysis
Libraries were prepared using RNA extracted from PBMCs of obese and
lean subjects (n = 4, in each group) and were sequenced on Illumina
Novaseq for paired-end 100 bps using standard Illumina sequencing
primers. RNAseq fasta file quality was checked using the FastQC
([104]http://www.bioinformatics.babraham.ac.uk/projects/fastqc,
accessed on 12 June 2020). The adapters were trimmed using Trimmomatic
[[105]33]. The RNA sequence data was aligned to the human genome
(GRCh38) using Spliced Transcripts Alignment to a Reference (STAR)
[[106]34]. FeatureCounts was used for gene expression quantification
and Limma-voom [[107]35,[108]36] was used to perform differential
expression analysis. Genes with p value < 0.05 were considered
differentially expressed (DE).
2.4. Cell Culture and Transfection
Primary PBMCs were isolated from human blood by density centrifugation
using a lymphocyte separation medium (MP Biomedicals, Santa Ana, CA,
USA). CD4^+ T cells were negatively selected from PBMCs using the CD4^+
T cell isolation kit (Miltenyi Biotec, Gaithersburg, MD, USA). Cell
purity of more than 95% was obtained after CD4^+ T cell isolation. For
knockdown experiments, Accell control siRNA and SMARTpool Accell siRNAs
targeted IGFBP1 or PYY (Dharmacon, Lafayette, CO, USA) and were
transfected with Accell siRNA delivery media or T cell nucleofector kit
according to manufacturer’s instructions (Lonza, Bend, OR, USA). To
increase cell viability, CD4^+ T cells were transfected with T cell
nucleofector kit (Lonza) and maintained in media supplemented with 50
ng/mL IL-2 (Peprotech, Cranbury, NJ, USA) for the first 24 h. The IL-2
media was then replaced with regular RPMI media before the T cell
receptor-mediated activation. THP1 human monocytes and H9 human T
lymphocytes (derivative of HuT78) obtained from ATCC were maitained in
RPMI 1640 media supplemented with 25 mM HEPES, 10% heat-inactivated FBS
and penicillin/streptomycin.
2.5. Cell Stimulation and Cytokine Assays
CD4^+ T cells from subjects were activated with plate-coated 5 μg/mL
CD3 and 10 μg/mL CD28 (BioLegend, Dedham, MA, USA) for 3 days in the
presence of 10% autologous fasted or refed serum. To see the effect of
heat-inactivated serum (HI) in CD4^+ T cells, the serum was incubated
at 56 °C for 30 min and the CD4^+ T cells from subjects after overnight
fasting (Baseline) were incubated for 3 days with 10% HI and non-HI
serum, respectively. THP-1 cells were differentiated into macrophages
by incubation with 5 ng/mL PMA (Sigma-Aldrich, St. Louis, MO, USA) for
48 h in media supplemented with 10% fasted or refed serum from the
subjects. THP-1 macrophages were incubated at 5 × 10^5 cells per well
in a 96-well plate with 10 ng/mL LPS for 4 h (Ultrapure Salmonella
minnesota R595; Enzo Life Sciences) and with 10 μM Nigericin
(Sigma-Aldrich) for the last 30 min to induce IL-1β secretion. H9 cells
were incubated for 24 h in media supplemented with 10% fasted or refed
serum, and then activated at 1 × 10^6 cells per well in 96-well plate
with plate-coated CD3 and CD28 for 24 h to induce IL-2 secretion. CD4^+
T cells (4 × 10^5/well in 96-well plate) from healthy volunteers were
activated with plate-coated CD3 and CD28 for 3 days with
supplementation with each recombinant protein (100 ng/mL IGFBP1,
ST2/IL1RL1 and MFGE8 (R&D Systems)), or 20 nM PYY[3-36] (Peprotech) for
the last 24 h of incubation. Supernatants were collected, centrifuged
to remove cells and debris and stored at −80 °C. The levels of
cytokines, including IL-1β, IL-2, IFNγ, IL-4, IL-5 and IL-17, were
measured by ELISA (R&D Systems). Results were normalized to cell number
using the CyQuant cell proliferation assay (Invitrogen, Waltham, MA,
USA).
2.6. SOMAscan Assay and Data Analysis
Proteomic profiles were characterized using the 1.3 k SOMAscan assay
(SomaLogic, Inc., Boulder, CO, USA). The basis of SOMAscan is built on
the use of a new generation of protein-capture Slow Offrate Modified
Aptamer (SOMAmer) reagents [[109]37]. Using these reagents, the
SOMAscan assay is able to comparatively evaluate protein abundance in
50 μL of serum. Generated by a technique referred to as the selected
evolution of ligands by exponential enrichment (SELEX), the 1.3 k assay
consists of 1305 SOMAmer reagents selected against a variety of human
proteins (47% secreted proteins, 28% extracellular domains, 25%
intracellular proteins) that belong to broad biological subgroups
including receptors, kinases, cytokines, proteases, growth factors,
protease inhibitors, hormones and structural proteins. SOMAmer reagents
are binned into three separate groups according to the expected
endogenous abundance of each SOMAmer’s cognate protein in typical human
samples. Each SOMAmer reagent exists in only one of the three
groupings. Serum samples (including controls) are then diluted into
three concentrations (0.005%, 1% and 40%) in order to create separate
groups for high-, medium- and low-abundance proteins, respectively.
Through this separation, the SOMAscan assay is able to quantify
proteins across a dynamic range spanning more than 8 orders of
magnitude. The diluted samples are then incubated with the
dilution-specific SOMAmers. Runs in the 1.3 k assay were performed
semi-automatically with a Tecan Freedom Evo 200 High Throughput System
(HTS), which utilizes 96-well plates. The SOMAscan plate design
included buffer wells (no sample added), quality control and calibrator
samples provided by SomaLogic. Quality control and calibrators were
pooled samples composed of the same matrix as the biological samples
being measured in the plate. Following standard data normalization
procedures [[110]38], raw data were first transformed by hybridization
control normalization, which utilizes 12 spike-in SOMAmer controls to
remove well-to-well variance in the hybridization process, followed by
median signal normalization to remove intraplate variance due to
sample-to-sample differences in loading volume, leaks, washing
conditions, etc. It should be noted that this was a single-plate study,
so the standard inter-plate calibration normalization step was not
necessary. An interactive Shiny web tool [[111]39] was used during the
quality control process at every step of the data normalization
process. The schematic of the SOMAscan assay and data analysis is shown
in [112]Supplementary Figure S1B. SOMAmer candidates with >50% missing
values were excluded from the analysis. The missing values were imputed
with the half of the minimum values measured for the respective SOMAmer
proteins. The dataset was analyzed by the partial least square
discriminant analysis (PLS-DA) from mixOmics library [[113]40] in R.
The variables with variable importance in prediction (VIP) score >1
were reported. The heatmaps were generated in R using ggplot2. Pathway
enrichment analysis was done using the blood transcription module
[[114]41] and clusterProfiler [[115]42]. Additionally, disease gene
enrichment analysis was performed using the DisGenNet R package
[[116]43].
2.7. Quantitative PCR Analysis
Total RNA was isolated using the Nucleospin RNA kit (Macherey-Nagel,
Bethlehem, PA, USA) and cDNA produced using a first-strand synthesis
kit (Invitrogen, Waltham, MA, USA). Quantitative real-time PCR was
performed using SYBR green PCR master mix (Roche, Indianaplis, IN, USA)
and run on Lightcycler 96 systems (Roche). Transcript levels of IGFBP1,
IL1RL1, MFGE8, PKIG and AMIGO1 were measured using validated
gene-specific primers (Qiagen, Germantown, MD, USA). The primers of PYY
were made by Integrated DNA Technologies (forward:
5′-CGGACACGCTTCTTTCCAAAACG-3′; reverse: 5′-TGGTTGGCAGATCTCCCAGGAG-3′).
Relative gene expression was quantified by normalizing Ct values with
18S using the 2^−∆Ct cycle threshold method.
2.8. Statistical Analysis
Statistical analyses were performed using PRISM (GraphPad Software, San
Diego, CA, USA) and R (URL [117]https://www.R-project.org/, accessed on
17 September 2020). For in vivo and in vitro studies, n represents the
number of biological replicates per group and is reported in the figure
legends. For histograms, the means ± SEM for the indicated number of
observations are reported. The box plots show the median and
upper/lower quartile of the observation and show the mean as ‘+’. The
whiskers show Tukey distribution and the outlier levels are shown as
individual points. Statistical significance between the two groups was
determined using a two-tailed Student’s t-test when analyzing the
response between groups. A p value < 0.05 was considered statistically
significant.
3. Results
3.1. Validation of Paracrine Effects of Fasting and Refeeding Serum on Immune
Cell Responsiveness
As previously described, a clinical study was performed on peripheral
blood mononuclear cells (PBMCs) to compare cellular gene expression
profiles in response to an overnight fast (Baseline), to a 24-h fast
(Fasting) and in response to 3 h of refeeding (Refed;
[118]Supplementary Figure S1A). In this study, the 24-h fast was shown
to have more robust gene regulatory responses versus refeeding when
compared to the overnight fast, supporting the concept that the
hormetic stress of the longer fast initiates more robust
immunomodulatory effects. The unbiased bioinformatic analysis of the
data in the PBMCs supported the results that fasting had a robust
effect on CD4^+ T cells, although the study could not distinguish
between cell intrinsic regulatory effects versus paracrine effects on
the cells from the 24-h fast [[119]32]. Prior studies showed that serum
extracted from fasting versus refed subjects and used for the
incubation of THP-1 macrophages showed differential modulation of the
NLRP3 inflammasome [[120]6,[121]12], although what factors in serum
orchestrated this immunomodulation has not been determined. To address
this question, study serum from these 21 healthy volunteer subjects was
used here to assay whether the serum conferred or contributed to these
immunomodulatory effects, and to perform proteomics analysis to
identify and validate putative regulatory proteins ([122]Supplementary
Figure S1B). To first validate whether these fasting serum effects were
operational in THP-1 cells, we assayed the IL-1β released in this
macrophage cell line in response to LPS and nigericin, and confirmed
that the fasting serum blunted the IL-1β release compared to cells
incubated in serum extracted from the same refed study subjects
([123]Figure 1a). We then evaluated whether this serum effect could be
extended to T cells by assessing the response of incubation of fasted
and refed serum in the human transformed H9 T cell line. Here too,
incubation with the fasted serum blunted the release of IL-2, a
canonical CD4^+ T cell-associated cytokine, in response to T cell
receptor activation ([124]Figure 1b). To evaluate the effect of this
serum on primary human immune cells, CD4^+ T cells were acquired from
study subjects following an overnight fast (Baseline), and then
incubated with naïve or heat-inactivated serum extracted from these
same subjects. Here too, the exogenous naïve or heat-inactivated serum
showed the same pattern, where the 24-h fasting-extracted serum
conferred a reduction in both IFNγ and IL-5 release compared to the
refed, supplemented cells ([125]Figure 1c).
Figure 1.
[126]Figure 1
[127]Open in a new tab
Evaluating whether serum from fasted versus fed subjects has an effect
on in-vitro inflammation in cultured cells. (a) THP1 cells with 10%
serum for preincubation for 1 day and then stimulated with 10 ng/mL LPS
for 4 h and 10 μM Nigericin for 30 min. The values represent the
average of duplicate experiments (n = 8). Paired t-test; * p value <
0.05. (b) H9 cells with 10% serum for preincubation for 1 day and then
activated by aCD3/aCD28 for 1 day with 10% serum. The values represent
the average of duplicate experiments (n = 8). Paired t-test; * p value
< 0.05. (c) CD4^+ T cells isolated from subjects after ON fasting
(Baseline) were incubated for 3 days with 10% heat-inactivated (HI)
serum, respectively (n = 9). HI, 56 °C for 30 min; Paired student
t-test; * p value < 0.05; ** p < 0.01.
3.2. SomaLogic Serum Proteomic Analysis Identified Candidate Differentially
Expressed Peptides from the Three Nutritional States
Serum protein levels were quantified and then subjected to SOMAscan
analysis. The data revealed a large number of differentially expressed
(DE) proteins in the paired comparisons between the three groups
(Wilcoxin rank sum test <0.05, [128]Figure 2a and [129]Supplementary
Table S1). DE proteins obtained from the three nutrient conditions are
depicted in a Venn diagram ([130]Figure 2b). The relative fluorescent
intensity of 19 common proteins regulated in three nutrient conditions
are shown in a heatmap ([131]Figure 2c). Pathway enrichment analysis
using blood transcription modules (BTM) [[132]41] showed that DE
proteins from fasting and refed states were enriched in T cell
activation (p < 0.05, [133]Supplementary Figure S2A and Supplementary
Table S2) using the ClusterProfiler pathway (p < 0.05,
[134]Supplementary Figure S2B and Supplementary Table S3). We performed
a partial least square discriminant analysis (PLS-DA) with all three
groups to identify proteins that had the highest variable of importance
in prediction scores (VIP score) that could discriminate between the
three groups. The PLS-DA plot shows a robust distinction in the serum
protein levels in the three nutritional load states ([135]Figure 2d).
Overall, 14 proteins had highly robust variables of importance in
prediction (VIP) scores (>5), that can discriminate differences in
protein levels between the three nutritional groups ([136]Figure 2e).
Additionally, disease gene enrichment analysis of the VIPs with score
>1 revealed a significant enrichment in heart disease and obesity
([137]Figure 2f).
Figure 2.
[138]Figure 2
[139]Open in a new tab
Differentially expressed proteins identified in the indicated
comparisons. (a) Table showing the number of DE (differentially
expressed) proteins identified in the indicated comparisons (Wilcoxin
rank sum test <0.05, n = 20). (b) Venn diagram of DE proteins from
three comparisons (Wilcoxin rank sum test <0.05, n = 20). (c) Heatmap
of 19 overlapped proteins identified by SomaLogic analysis in three
comparisons (log transformed value of the mean relative fluorescent
intensity). (d) PLS-DA (artial least square-discriminant analysis)
plots from three comparisons (n = 20, blue, Baseline; orange, Fasting;
gray, Refed). (e) Bar graphs of 14 VIP (variables of importance in
prediction) proteins from three comparisons (VIP score >5, left panel).
VIP scores rank the serum proteins as the most important for
differentiating overall serum protein profiles between the groups
indicated in the key. All VIP proteins indicated also differed in post
hoc analyses (p < 0.05). Heatmap of 14 VIP proteins in three
comparisons (log value of the relative fluorescent intensity, right
panel). (f) Disease association analysis with VIP proteins (p < 0.05).
3.3. Enzyme-Linked Immunoassay Analysis Validation of Identified SOMAscan
Candidate Peptides
To determine if the levels of the identified VIP score proteins were
differentially expressed in the serum from the different nutrient
conditions, serum was assayed by ELISA’s targeting six of these
proteins. The highest ranked VIP proteins were insulin-like growth
factor-binding protein 1 (IGFBP1) and proprotein convertase
subtilisin/kexin 9 (PCSK9). Interestingly, the ELISA assays showed the
IGFBP1 levels were significantly elevated in the 24-h fasting serum,
and the PCSK9 distinguished the baseline compared to the fasting and
refeeding states ([140]Figure 3a,b). Interestingly, the leptin levels
paralleled PCSK9 with the distinct group being at baseline ([141]Figure
3c). Apolipoprotein E (ApoE) mirrored IGFBP1, with the highest levels
in the fasted state, and PYY was most highly expressed in the refed
state ([142]Figure 3d,e). The relative fluorescence intensity graph and
heatmap measured from the three nutrient conditions is shown in
[143]Supplementary Figure S3A–E.
Figure 3.
[144]Figure 3
[145]Open in a new tab
Verify the VIP SOMAscan results by ELISA. (a) IGFBP1, (b) PCSK9, (c)
Leptin, (d) ApoE, and (e) PYY level were measured by ELISA using serum
collected from three metabolic conditions (n = 18). Paired t test; ** p
< 0.01. VIP, Variables of importance in prediction; IGFBP1,
Insulin-like growth factor-binding protein 1; PCSK9, Proprotein
convertase subtilisin/kexin 9; ApoE, Apolipoprotein E; PYY, Peptide
tyrosine-tyrosine.
3.4. Characterization of Immunomodulatory Effects of IGFBP1 and PYY
As IGFBP1 and peptide tyrosine-tyrosine (PYY) had high VIP scores,
IGFBP1 levels were the most highly differentiated in the fasting group
compared to the baseline and refed groups and PYY showed the greatest
difference in the refed state compared to the other two nutritional
load states, we characterized the effects of their recombinant proteins
on T cell activation. Interestingly, PYY was also identified as a
highly regulated transcript level in the ClusterProfiler pathway
analysis comparing refeeding to both the fasting conditions
([146]Supplementary Figure S2B). Primary human CD4^+ T cells were
studied in response to T cell receptor (TCR) activation and showed that
IGFBP1 significantly blunted the secretion of IFNγ, IL-4 and IL-17, and
PYY significantly and conversely increased secretion of the same Th1,
Th2 and Th17 cytokine levels ([147]Figure 4a and [148]Supplementary
S4A). Interestingly, interrogation of publicly available CD4^+ T cell
libraries show that both IGFBP1 and PYY are expressed in activated T
cells [[149]44] and we found that these transcripts encoding for these
proteins are expressed in CD4^+ T cells and regulated by TCR
activation. Interestingly, RNA expression of IGFBP1 did not show the
same pattern as its secreted protein, suggesting post-transcriptional
control of IGFBP1 by the nutritional load ([150]Supplementary Figure
S4A). To assess the effects of these two proteins, we then employed a
siRNA targeting control, IGFBP1 and PYY transcripts in primary CD4^+ T
cells. The transcript levels of IGFBP1 were reduced by
[MATH: ≈ :MATH]
55% and PYY by
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45% ([151]Supplementary Figure S4B). Following the subsequent TCR
activation, IGFBP1-depleted cells showed excess secretion of IFNγ, IL-4
and IL-17 levels, whereas knockdown of PYY had the opposite effect
([152]Figure 4b). Together these data support that IGBP1, which is
elevated in the fasting state, blunts CD4^+ T cell responsiveness, and
that PYY augments this activation capacity.
Figure 4.
[153]Figure 4
[154]Open in a new tab
Characterization of immunomodulatory effects of IGFBP1 and PYY. (a)
Cytokine release after treatment of recombinant proteins (100 ng/mL
IGFBP1 or 20 nM PYY[3-36]) for the last 24 h of incubation in CD4^+ T
cells followed TCR activation for 3 days (n = 6). (b) Cytokine release
following KD in CD4^+ T cells by TCR activation for 3 days (n = 6–8).
Unpaired student T test; * p value < 0.05; ** p < 0.01. IGFBP1,
Insulin-like growth factor-binding protein 1; PYY, Peptide
tyrosine-tyrosine; TCR, T cell receptor.
3.5. Integration of SomaLogic and RNAseq Data to Identify Putative
Interacting Pathways in the Modulation of T Cell Responsiveness
In the initial study on this cohort of subjects comparing RNAseq
analysis and flow cytometry, it was found that the immunomodulatory
effects of the 24-h fast were more robust than the overnight fast when
both groups were compared to refeeding [[155]32]. Hence, an additional
integrated bioinformatics approach was employed to evaluate if we could
uncover additional differences in circulating protein levels that could
correlate with PBMC gene expression level changes in response to 24 h
of fasting or refeeding. Here, the differentially expressed genes from
the 24-h fasting vs. refeeding PBMC RNAseq data (GEO database link:
[156]https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE165149,
accessed on 20 January 2021) were correlated with the differentially
expressed SOMAmer candidates from the same individuals. This analysis
could potentially identify serum protein candidates that may indirectly
impact gene expression in immune cells. While correlation is not
causation, the serum protein-gene pairs could then be further evaluated
in a cell culture to explore mechanisms of immunomodulation.
Significantly differentially expressed serum protein candidates
identified by univariate analysis were further assessed for correlation
with the DE genes from the published RNAseq dataset. Five candidate
proteins showed significant correlations, including KLK7 (negative
correlation with a subset of 24-h fasting genes), IL1RL1 (positive
correlation with a subset of fasting genes), ANGPT1 (negative
correlation with a subset of refed genes), BMP10 (negative correlation
a subset of refed genes) and MFGE8 (negative correlation with a subset
of refed genes). IL1RL1 and MFGE8 were further studied because both of
these proteins were linked to immune modulatory effects
[[157]45,[158]46], were both significantly induced in the refed
compared to the 24-h fasting samples by SomaLogic analysis
([159]Supplementary Figure S5A,B) and both were confirmed in serum at
the protein levels ([160]Figure 5a) and at the transcript level in
CD4^+ T cells ([161]Figure 5b). The functional validation of the
effects of these two candidates were validated where recombinant IL1RL1
and MFGE8 induced the secretion of the Th1 and Th17 cytokines IFNγ and
IL-17 ([162]Figure 5c).
Figure 5.
[163]Figure 5
[164]Open in a new tab
Integrating SomaLogic and RNAseq data. (a) Verify the SOMAscan results
of IL1RL1 and MFGE8 by ELISA using serum collected from three metabolic
conditions (n = 18). Paired t-test; ** p < 0.01. (b) RNA expression
level in CD4^+ T cells (n = 4–6). Unpaired t-test; ** p < 0.01. (c)
Cytokine release after treatment of recombinant proteins (24 h) in
CD4^+ T cells followed by aCD3/aCD28 activation for 3 days (n = 4–6).
Unpaired t-test; * p value < 0.05; ** p < 0.01. IL1RL1, Interleukin 1
receptor like-1; MFGE8, Milk fat globule EGF/factor VIII.
3.6. Evaluation of Identified Serum Protein Candidates in a Lean Versus Obese
Subject Cohort
Given the significant enrichment of the VIP serum proteins in obesity,
we further evaluated whether any of these serum protein candidates are
operational in other nutritional load conditions. We assayed their
levels in serum from overnight fasted lean and obese subjects. While
the levels of IGFBP1 and PYY were not different between these two
groups (data not shown), both IL1RL1 and MFGE8 were significantly
elevated in serum and at the transcript levels in CD4^+ T cells,
respectively, in the obese compared to lean serum group ([165]Figure
6a,b). Body mass index from lean and obese subjects is described in
Materials and Methods. Although obesity is linked to inflammation
through multiple mechanisms [[166]47], in this study exploring CD4^+ T
helper cell responsiveness, the extent of TCR-mediated cytokine
production was restricted to Th1 and Th17 cells, as evident by the
significantly higher levels of IFNγ and IL-17 in the obese versus lean
subject cells ([167]Figure 6c).
Figure 6.
[168]Figure 6
[169]Open in a new tab
Evaluate the level of fasting serum targets comparing lean to obese
subjects. (a) IL1RL1 and MFGE8 levels in the serum of lean (n = 11) and
obese subjects (n = 15). Data point of each subject is shown as dot and
quantitative data are presented as means ± SEM. Unpaired student
t-test, ** p < 0.01. (b) RNA expression in CD4^+ T cells from lean and
obese subjects (n = 10). Data point of each subject is shown as dot and
quantitative data are presented as means ± SEM. Unpaired student
t-test, * p < 0.05; *** p < 0.001. (c) Cytokine release in CD4^+ T
cells from lean and obese subjects (lean, n = 6–8; obese, n = 8–10).
Ratio paired Student t-test, ** p < 0.01. (d) IL1RL1- and
MFGE8-correlated genes (IL1RL1, 4 genes; MFGE8, 62 genes) from the
fasting-refed RNAseq dataset were used as the discovery cohort. The
expression levels of these genes were found in the lean-obese RNAseq
dataset (IL1RL1, 2 genes; MFGE8, 11 genes), * p < 0.05. (e,f)
Integration plot with IL1RL1 or MFGE8 serum protein and correlated
genes (left panel) and transcript level correlated with IL1RL1 or MFGE8
in CD4^+ T cells (right panel, n = 8), * p < 0.05; ** p < 0.01. (g) RNA
expression in CD4^+ T cells from lean and obese subjects (lean, n = 8;
obese, n = 10). Unpaired Student t-test, * p < 0.05.
To identify potential gene regulation linked to the elevated IL1RL1 and
MFGE8, respectively, we intersected the fasting-refed SOMAmer-gene
correlation pairs with that of the DE genes from the lean versus obese
RNAseq dataset ([170]Supplementary Table S4). Specifically, the IL1RL1-
and MFGE8-correlated genes from the fasting-refed datasets were used as
the discovery cohort. From the refed expression dataset, 4 genes
correlated with IL1RL1 and 62 correlated genes with MFGE8. To explore
their potential link with obesity, the expression levels of the genes
were further analyzed in the lean and obese RNAseq dataset. Notably,
the expression levels of 2 genes correlated with IL1RL1, and 11
correlated with MFGE8, which were also differentially expressed in the
obese versus lean subjects ([171]Figure 6d and [172]Supplementary
Tables S4 and S5). Specifically, the transcript levels of the gene
encoding the cAMP-dependent protein kinase inhibitor (PKIG), which
correlated with IL1RL1 levels, and of the gene encoding the adhesion
molecule with Ig-like domain-1 (AMIGO1), which correlated with MFGE8
levels, showed the same relationship in the lean versus obese subjects
([173]Figure 6e,f). Consistently, the transcript levels of PKIG and
AMIGO1 were significantly induced in obese compared to lean subjects
([174]Figure 6g).
4. Discussion
In this study, we undertook a serum proteomics analysis using SomaLogic
to identify and characterize nutrient-responsive circulating proteins
that directly modulate CD4^+ T cell responsiveness. When exploring the
SOMAscan data in isolation and comparing the levels between the three
nutritional load states (Baseline; Fasting; Refed), the PLS-DA
identified IGFBP1 as an important variable whose levels were mostly
highly elevated and differentially regulated by the 24-h fast compared
to the other two groups, and conversely, that PYY was most distinctly
induced by refeeding. The functional validation studies supported that
fasting mediated the blunting of CD4^+ T cell activation, which was
conferred by elevated IGFBP1, and that the increased immune
responsiveness of refeeding correlated with increased CD4^+ T cell
activation by PYY. In parallel, univariate analysis of the serum
proteins from the fasting versus refed groups identified several
proteins that were significantly differentially expressed. Since serum
proteins can impart wide-ranging paracrine effects in the body,
including on immune cells, we utilized the previously published RNAseq
gene expression datasets from the PBMCs of these subjects [[175]32] to
specifically explore the correlation in expression between serum
proteins and genes. This integration of the RNAseq data with that of
the proteomics data delineated several significantly correlated SOMAmer
protein-gene pairs. Notably, the strong positive correlations of IL1RL1
and MFGE8 with genes from the refed RNAseq expression dataset further
expanded the candidate genes that likely play a role in nutrient
load-dependent immunomodulation in coordination with the serum
proteins. Consistently, several significantly correlated SOMAmer
protein-gene pairs were confirmed by qPCR after supplementing the cell
culture media of the CD4^+ cells with the respective SOMAmer candidate.
Additionally, several serum proteins were also significantly enriched
in heart disorders and obesity, suggesting their broader role in
nutrient load-dependent signaling. Consistent with this, IL1RL1 and
MFGE8, which were increased by refeeding, were also elevated in obese
compared to lean subjects, and functional validation studies showed
that these two proteins specifically augmented Th1 and Th17 cytokines
levels in healthy subjects. Taken together, this study found that
numerous circulating proteins under different nutritional states
contribute to the modulation of the CD4^+ T cell immune responsiveness
by directly or indirectly modulating the gene expression levels, and
thus, this study supports the emerging concept of the paracrine role of
serum proteins in immunomodulation. Additionally, the concordant
upregulation of a subset of these serum proteins and transcripts in
both the refed subjects and obese subjects potentially substantiates
their broader roles in immunomodulation and metabolism.
Insulin-like growth factor (IGF)-binding proteins (IGFBPs) comprise a
family of regulatory proteins that can stimulate or inhibit IGF
activity through high-affinity binding [[176]48]. IGFBP1 is a 30-kDa
circulating protein that is predominantly expressed in the liver and is
regulated by nutrient cues [[177]49,[178]50]. Additionally, its levels
inversely reflect the risk of cardiometabolic complications such as
atherosclerosis, hypertension and insulin resistance [[179]51]. Its
direct role in immunomodulation is less well characterized, although
its cognate substrate IGF-1 via AKT-mTOR promotes Th17 cell
differentiation [[180]52], and the increased ratio of IGF-1/IGFBP1 is
linked to increased activity of monocytes, B, T and NK cells [[181]53].
Interestingly, and consistent with our findings, IGFBP1 levels are
known to be induced during fasting [[182]49]. Consistent with the
fasting and feeding effect on CD4^+ T cell responsiveness [[183]32],
this study showed that IGFBP1 blunts Th1, Th2 and Th17 cell
responsiveness and its genetic depletion in CD4^+ T cells has the
inverse effect. Interestingly, the knockdown effects show that IGFBP1
has cell-intrinsic effects on CD4^+ T cell immunomodulation. In
contrast to the effects with fasting and refeeding, the lack of change
in IGFBP1 levels in the lean and obese cohort may reflect the
combination of the role of ethnicity in determining IGFBP1 levels
[[184]54,[185]55] and the different racial compositions of the
fasting/refeeding and the obese/lean cohorts.
Peptide tyrosine-tyrosine (PYY) is a 36-amino acid peptide which
belongs to the neuropeptide Y (NPY) family of biologically active
peptides, which also includes NPY itself and the pancreatic polypeptide
(PP). PYY is produced by the enteroendocrine L cells in the gut and
predominantly signals through the G-protein-coupled Y[2] receptor,
although it can also signal through the Y[1] receptor [[186]56]. The
two endogenous forms PYY[1-36] and PYY[3-36] are low in the fasted
state and released post-prandially [[187]57]. Interestingly, enteral
TLR agonists and the butyrate, which are produced by gut bacterial
fermentation, increase L cell PYY expression through NF-B-dependent
signaling [[188]58]. PYY[3-36] can also be generated by the cleavage of
the amino terminal Tyr-Pro amino terminal residues by the enzyme
dipeptidyl peptidase IV (DP-IV). Although the major role of PYY is
transduced via binding to Y[2] receptors in the hypothalamic arcuate
nucleus as an anorexigenic input, a limited amount of data implicates
its role in immunomodulation. This has been mostly assessed in the
myeloid system, where PYY acting via Y[1] receptors decreased rat
peritoneal macrophages’ adhesion capacity and suppressed phagocytosis
and NO production in the resident macrophages [[189]59]. Furthermore,
an increased inflammatory macrophage response was evidence in the
activated NPY PYY double-knockout macrophages [[190]60]. Additionally,
the PPY cleavage ectoenzyme DP-IV is present on numerous leucocytes as
the surface antigen CD26, and the antigen is expressed on resting T
cells and induced during T cell activation. Its role in T cell biology
is further supported in that DP-IV inhibitors suppress clonal CD4^+ T
cell responsiveness [[191]61]. The data from this study extend these
findings to show that it can also promote CD4^+ effector T cell
responsiveness and that, similarly to IGFBP1, it has cell-intrinsic
effects. Interestingly, levels of PYY are blunted with fasting and in
response to feeding in African Americans compared to Caucasians
[[192]57], and this race distinction may explain the unchanged PYY
levels in the lean versus obese African American cohort.
The interleukin 1 receptor like-1 (IL1RL1) encodes the membrane-bound
ST2 receptor for IL-33 [[193]62] and is expressed on a subset of T
cells and on numerous myeloid cells [[194]63]. In CD4^+ T cells, the
IL-33/ST2 signaling pathway plays an important role in Th2 activation,
and interestingly, IL1RL1 variants are linked to increased risk of
IL-33-driven type 2 inflammation in asthma [[195]45]. Although the role
of the IL-33/ST2 pathway under different nutrient conditions does not
appear to have been studied, data show that the Th2 pathway is induced
in asthmatic subjects in response to refeeding after a 24-h fast
[[196]12].
Milk fat globule EGF/factor VIII (MFGE8), also called lactadhedrin, has
two domains including an Arg-Gly-Asp sequence that binds to dendritic
cells, macrophage integrins and a phosphatidylserine (PS)-binding
sequence, though which it associates with phosphatidyl serine
(PS)-containing membranes including plasma membranes and derived
exososomes. Interestingly, MFG-E8 is linked to dendritic cell
exosome-mediated antigen presentation [[197]64]. At the same time, the
genetic depletion of MFG-E8 leads to murine autoimmunity linked to the
impaired clearance of germinal center B cells due to the role of MFGE8
in binding to PS on apoptotic B cells to facilitate their engulfment by
macrophages [[198]46]. A role of MFGE8 in the modulation of CD4^+ T
cell function does not appear to have been shown previously, and here
we showed that it is both nutrient-level dependent and plays a role in
the activation of Th1 and Th17 CD4^+ T cells.
Numerous limitations are present in this study that should be
highlighted. Firstly, in the time-controlled fasting and refeeding
study, the subjects had different meal options, and whether the meal
composition played a role in modulating the refeeding serum protein
levels was not determinable because of the small study size when the
groups were analyzed separately. Secondly, the fasting/refeeding and
the lean/obese cohorts were not matched for age or racial ethnicity.
Although this is a study limitation, it also underscores that the
IL1RL1 and MFGE8 immunomodulatory effects are operational across
different ages, BMIs and ethnic groups under different nutritional
conditions. Finally, in the fasting and refed study, the time of the
blood draws for fasting and refeeding were not the same, which could
introduce a circadian rhythm effect. This concept would need to be
explored in a follow up study, when blood could be drawn at the same
time on different days to exclude circadian effects on time-controlled
fasting and refeeding serum protein levels.
5. Conclusions
The recognition that restricting calories by numerous interventions,
such as intermittent fasting or time-restricted feeding, have
ameliorative effects on inflammation is bringing these interventions to
the forefront as a potential therapeutic strategy [[199]65]. Numerous
immune cell-intrinsic signaling pathways [[200]65] and effects on
immune cell localization [[201]20] linked to nutrient load
immunomodulation have been defined, and recent evidence also shows that
this regulation can be modulated at the transcriptional level in CD4^+
T cells [[202]32]. Furthermore, numerous studies have also shown that
serum metabolites, in part derived from gut bacteria such as short
chain fatty acids, and ketones derived from the liver or from dietary
interventions, also regulate immunomodulation
[[203]21,[204]66,[205]67]. In this study, we expanded the understanding
of the regulatory control nodes in nutrient load-dependent
immunomodulation by identifying that circulating proteins, including
IGFBP1, PYY, IL1RL1 and MFGE8, can similarly affect nutrient
load-dependent CD4^+ T cell regulation. Of these serum proteins, IGFBP1
and PYY were exclusively differentially expressed in the fasted and
refed states, suggesting that many such proteins may impart
fasting-mediated immunomodulation irrespective of the BMI of the
subjects. However, future studies delineating differential
immunomodulation in fasted lean and obese subjects will be required to
fully understand the nutrient load-dependent signaling immunomodulatory
processes that are impacted by subject BMI. Furthermore, we undertook
an approach of integrating serum proteomics expression data with the
PBMC gene expression data to decipher the potential effects of the
serum proteins on immune cell gene expression. Previous work has
revealed that broad gene expression changes in PBMCs, contributing to
the fasting-mediated immunomodulation [[206]32]. This study helped to
delineate the interplay between the changes in serum protein levels and
their potential impact on genes expression in the CD4^+ subpopulation
of PBMCs as additional drivers of nutrient load-dependent immune
modulation. The expanding complexity of this control further supports
that the ability to regulate immune responsiveness by caloric
restriction interventions is controlled at cell autonomous, paracrine
and probably systemic levels. This in turn may explain why fasting
mimetic therapeutic compounds may not necessarily be able to
recapitulate the comprehensive ameliorative effects of caloric
restriction interventions on inflammation and/or on other systemic
effects [[207]65].
Acknowledgments