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A combined proteomics and Mendelian randomization approach to investigate the effects of aspirin-targeted proteins on colorectal cancer

Aayah Nounu1, Aayah Nounu2, Alexander Greenhough3, Alexander Greenhough1, Kate J. Heesom1, Rebecca C Richmond2, Jie Zheng2, Stephanie J. Weinstein4, Demetrius Albanes4, John A. Baron5, John L. Hopper6, John L. Hopper7, Jane C. Figueiredo8, Jane C. Figueiredo9, Polly A. Newcomb10, Polly A. Newcomb11, Noralane M. Lindor12, Graham Casey13, Elizabeth A. Platz14, Loic Le Marchand15, Cornelia M. Ulrich16, Christopher I. Li11, Fränzel J.B. van Duijnhoven17, Andrea Gsur, Peter T. Campbell18, Victor Moreno, Pavel Vodicka19, Pavel Vodicka20, Pavel Vodicka21, Ludmila Vodickova21, Ludmila Vodickova20, Ludmila Vodickova19, Hermann Brenner22, Jenny Chang-Claude23, Jenny Chang-Claude22, Michael Hoffmeister22, Lori C. Sakoda11, Lori C. Sakoda24, Martha L. Slattery25, Robert E. Schoen26, Marc J. Gunter27, Sergi Castellví-Bel28, Hyeong Rok Kim29, Sun-Seog Kweon29, Andrew T. Chan, Li Li13, Wei Zheng30, D. Timothy Bishop31, Daniel D. Buchanan32, Daniel D. Buchanan6, Graham G. Giles33, Graham G. Giles34, Stephen B. Gruber9, Gad Rennert35, Zsofia K. Stadler36, Tabitha A. Harrison11, Yi Lin11, Temitope O. Keku5, Michael O. Woods37, Clemens Schafmayer, Bethany Van Guelpen38, Steven Gallinger39, Heather Hampel40, Sonja I. Berndt4, Paul D.P. Pharoah41, Annika Lindblom42, Annika Lindblom43, Alicja Wolk43, Alicja Wolk44, Anna H. Wu9, Emily White10, Emily White11, Ulrike Peters10, Ulrike Peters11, David A. Drew45, Dominique Scherer46, Justo Lorenzo Bermejo46, Ann C. Williams1, Caroline L Relton2 
14 Aug 2020-bioRxiv (Cold Spring Harbor Laboratory)-

TL;DR: MCM6 and RRM2 are involved in DNA repair whereby reduced expression may lead to increased DNA aberrations and ultimately cancer cell death, whereas ARFIP2 is involved in actin cytoskeletal regulation indicating a possible role in aspirin’s reduction of metastasis.

AbstractBackground: Evidence for aspirin9s chemopreventative properties on colorectal cancer (CRC) is substantial, but its mechanism of action is not well-understood. We combined a proteomic approach with Mendelian randomization (MR) to identify possible new aspirin targets that decrease CRC risk. Methods: Human colorectal adenoma cells (RG/C2) were treated with aspirin (24 hours) and a stable isotope labelling with amino acids in cell culture (SILAC) based proteomics approach identified altered protein expression. Protein quantitative trait loci (pQTLs) from INTERVAL (N=3,301) and expression QTLs (eQTLs) from the eQTLGen Consortium (N=31,684) were used as genetic proxies for protein and mRNA expression levels. Two-sample MR of mRNA/protein expression on CRC risk was performed using eQTL/pQTL data combined with CRC genetic summary data from the Colon Cancer Family Registry (CCFR), Colorectal Transdisciplinary (CORECT), Genetics and Epidemiology of Colorectal Cancer (GECCO) consortia and UK Biobank (55,168 cases and 65,160 controls). Results: Altered expression was detected for 125/5886 proteins. Of these, aspirin decreased MCM6, RRM2 and ARFIP2 expression and MR analysis showed that a standard deviation increase in mRNA/protein expression was associated with increased CRC risk (OR:1.08, 95% CI:1.03-1.13, OR:3.33, 95% CI:2.46-4.50 and OR:1.15, 95% CI:1.02-1.29, respectively). Conclusion: MCM6 and RRM2 are involved in DNA repair whereby reduced expression may lead to increased DNA aberrations and ultimately cancer cell death, whereas ARFIP2 is involved in actin cytoskeletal regulation indicating a possible role in aspirin9s reduction of metastasis. Impact: Our approach has shown how laboratory experiments and population-based approaches can combine to identify aspirin-targeted proteins possibly affecting CRC risk.

Summary (3 min read)

1. Introduction

  • Several reports indicate that females have a stronger immune response, partly due to differences in hormonal profile [1, 2].
  • Recently, TTV has received attention as a possible endogenous biomarker for immune function, with immunocompetent individuals carrying lower levels of TTV in serum than immunocompromised, indicating a suppressing role of the immune system on the viral load [3].
  • The high turnover-rate of virions indicates that changes in immune status can be followed in a short time frame.
  • The differences in female and male immunity towards pathogens have implications for treatment and prevention of infectious diseases and may ultimately lead to a different approach depending on the sex of the patient.

2.1. Subjects

  • 27 healthy individuals were included according to a protocol approved by the Central Ethical Review Board (Swedish Research Council, Stockholm, Dnr: €O 24–2009) and consisted of 17 premenopausal women, 6 men and 4 postmenopausal women (Table 1).
  • The subjects were included and sampled during 6 months (between March and September, 2010).
  • Informed consent was obtained from the participants.
  • Premenopausal women aged 20–40 years with regular menstrual cycles, without hormonal contraceptives or other hormonal, anti-inflammatory (including ASA, systemic cortisone and NSAIDs) or any morphine treatment since>3 months, and parturition no later than 12 months before inclusion, also known as The inclusion criteria were.
  • Men (aged 20–70) and postmenopausal women (no menstrual bleeding since >12 months) without the above stated treatment during the last 3 months.

2.2. Blood sampling and hormonal analyses

  • From all individuals blood was drawn at four timepoints, and for the pre-MP women Ovustick® was used to identify the LH-peak.
  • Simultaneously, at one or more timepoints PBMC was also sampled.
  • The buffycoat was transferred to new tubes and slowly frozen in 20% dimethylsulphoxide (DMSO)-albumin, using isopropanol-loaded Mr. Frosty® freezing-container overnight, before long-term storage at -80 C.
  • Analyses were made of WBC, differential count (including B-monocytes, Blymphocytes, B-neutrophils, B-eosinophils, B-basophils), S-TSH (thyroid stimulating hormone), S-T4, S-SHBG (sex hormone binding globulin), Sestradiol, S-testosterone, S-progesterone, S-FSH, S-LH and S-prolactin.
  • The participants were assessed for hypo- or hyperthyroidism, and preMP women also whether they had a normal ovulation.

2.3. TTV DNA isolation and analysis

  • This was performed according to QIAamp® DNA Mini and Blood Mini Handbook to increase DNA yield.
  • This contained pre-prepared solutions of 5, 50, 500 and 5000 copies plasmid TTV DNA per μL, as well as a sensitivity control containing 1 copy/μL.
  • The sample wells were run in triplicates using 10 μL of concentrated DNA solution.
  • According to the standard curve obtained, this corresponded to CT of 37.09, 42.09 and 39.14 respectively on the included three TTV qPCR plates.
  • A sample was considered positive if 2 of 3 triplicate samples were above the detection limit (i.e. below the CT-threshold mentioned above).

2.4. Statistical analyses

  • The average hormone levels of TSH, estradiol, LH and testosterone were calculated for each of the 17 pre-MP women.
  • As a logistic model with logit-link, the following was used: TTV ~ log (Mean_TSH) þ log (Mean_estradiol) þ log (Mean_LH) þ log (Mean_testosterone).
  • The explanatory variables are treated as covariates.

3.2. TTV prevalence and TTV levels

  • The detected levels of TTVwere highest among the TTV positive pre-MPwomen and lower in the post-MP women and in the men, both in terms of detected TT viral copies/mL and when adjusting for total amount of DNA in the sample (Table 2).
  • The differences in TTV prevalence between pre-MP and postMP women as well as between pre-MP women and men were not statistically significant (Fisher's exact test, p > 0.999 and p ¼ 0.2786) .
  • The raw data suggested higher prevalence in men than in preMP women, but significance testing could not rule out a chance yroid status, sex hormone levels.
  • For pre-MP women, day of the menstrual cycle, rone and LH, is indicated.

3.3. Hormonal status in TTV-positive pre-MP women

  • To determine whether sex hormones influence the risk of being TTVpositive (TTVþ) the authors compared the average sex hormone levels in TTVþ (n ¼ 3) and TTV (n¼ 14) individuals using a binomial regression including S-estradiol, S-testosterone, S-LH and S-TSH.
  • When comparing average TSH from TTVþ and TTV individuals in a binominal regression, there was no significant difference (p-value¼ 0.337, Table 3).
  • Samples from themid-luteal phase revealed (using Welch's t-test) significantly lower progesterone levels (p ¼ 0.002) in TTVþ compared to TTV pre-MP women .
  • Dispersion parameter for binominal family eviance: 5.5798 on 13 Df. Akaike information criterion (AIC): 15.58.

3.4. Hormonal status in men and post-MP women

  • All three TTV men, and two out of three TTV post-MP women hade traces of TTV, but below cut-off (Table 4B).
  • All men and post-MP women (TTVþ and TTV ) had normal levels of TSH.
  • Due to the low number of participants, it was not possible to use regression models for analyzing hormone levels in relation to TTV status in men and post-MP women.
  • TTV status, age and range of hormone levels are shown in Table 5.

4. Discussion

  • The detection of TTV in 17.6% pre-MP females, 25.0% post-MP females, and 50.0%males suggests that TTV presence in the PBMC fraction of peripheral blood may be associated with sex.
  • TTVþ samples from pre-MP women in their cohort were mostly found during the first half of the menstrual cycle, and pre-MP women who were positive for TTV had hormonal aberrances being either anovulatory, hypothyroid or both.
  • Interactions of sex hormones with the immune system are established on multiple levels (reviewed in e.g. [10, 15, 16]).
  • In a study by Maggi et al. (2001), TTV-levels were considerably lower in PBMC than in plasma from the same individuals [31].
  • Only 2.4% of the plasma samples in the study from Fern andez-Ruíz et al. [45], were below lower limit of detection, whereas in their study on PBMC, the majority of individuals (74.1%) were below cut-off.

Author contribution statement

  • Conceived and designed the experiments; Performed the experiments;, also known as P. Brundin.
  • Analyzed and interpreted the data; Wrote the paper.
  • B-M. Landgren, P. Fj€allstr€om and A. Johansson: Analyzed and interpreted the data.
  • Conceived and designed the experiments;, also known as I. Nalvarte.
  • Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data.

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Research article
Blood hormones and torque teno virus in peripheral blood
mononuclear cells
Peik M.A. Brundin
a
,
b
,
c
,
*
, Britt-Marie Landgren
d
, Peter Fj
allstr
om
a
, Anders F. Johansson
a
,
Ivan Nalvarte
b
a
Department of Clinical Microbiology, Infection and Immunology, The Laboratory for Molecular Infection Medicine Sweden, Umeå University, 901 87, Umeå, Sweden
b
Department of Biosciences and Nutrition, Karolinska Institutet, 141 57, Huddinge, Sweden
c
S:t G
orans Hospital, Dept of Medicine, Unit of Infectious Diseases, 112 81, Stockholm, Sweden
d
Kvinnoh
alsan, Karolinska University Hospital, 141 86, Huddinge, Sweden
ARTICLE INFO
Keywords:
Infectious disease
Immunology
Hematology
Immune response
Immunodeciency
Viruses
Reproductive hormone
Steroid hormones
Aging
Menstrual cycle
Estrogen
Anovulatory
Hypothyroidism
Infection
Immunity
Sex difference
Microbiome
Commensal viruses
Sex hormones
ABSTRACT
Men and women respond differently to infectious diseases. Women show less morbidity and mortality, partially
due to the differences in sex hormone levels which can inuence the immune response. Torque teno virus (TTV) is
non-pathogenic and ubiquitously present in serum from a large proportion (up to 90%) of adult humans with virus
levels correlating with the status of the host immune response. The source of TTV replication is unknown, but T-
lymphocytes have been proposed. In this study we investigated the presence and levels of TTV in peripheral blood
mononuclear cells (PBMCs) in premenopausal (pre-MP) women, post-menopausal (post-MP) women, and men,
and determined their serum sex hormone levels. Of the examined subjects (n ¼ 27), we found presence of TTV in
PMBC from 17.6% pre-MP (n ¼ 17), 25.0% post-MP (n ¼ 4) and 50.0% men (n ¼ 6). The levels of TTV/
μ
g DNA
were lower among TTV-positive men and post-MP women compared to pre-MP women. All the positive pre-MP
women were either anovulatory, hypothyroid, or both. In addition, the TTV-positive pre-MP women had
signicantly lower progesterone levels compared to TTV-negative pre-MP women. Although our study was per-
formed on a limited number of subjects, the data suggests that TTV in PBMC is associated with an anovulatory
menstrual cycle with low progesterone levels, and possibly with male sex.
1. Introduction
Several reports indicate that females have a stronger immune
response, partly due to differences in hormonal prole [1, 2]. In this
paper we have investigated the role of hormones on TTV (torque teno
virus), a group of commensal viruses that may be used as a secondary
marker for immunity [3, 4].
There are numerous examples of animals, including humans, where
females cope better than males when exposed to bacteria, virus, parasites
and fungi [1, 2, 5, 6, 7, 8, 9]. In part, this may be related to the hormonal
milieu, with sex hormones interacting with the immune system at mul-
tiple levels [10]. Sex hormone receptors (SHR) have been reported in
various immune cells [11, 12, 13], and both the serum levels of sex
hormones and the expression of SHR will determine the cellular
response. The female sex hormone 17-β Estradiol (E2), the dominating
form of circulating estrogen, generally acts immunostimulatory by
affecting gene expression in neutrophils, macrophages, dendritic cells,
CD4
þ
T-cells, CD8
þ
T-cells and B-cells, but the effect varies depending on
the immune measure used [1, 14, 15]. Androgens (including testosterone
and dihydrotestosterone), on the other hand, in general suppress immune
cell activity with e.g. decreased expression of toll-like receptor 4 (TLR4)
on macrophages, and increased expression of anti-inammatory IL-10 [1,
16]. Thus, sex hormones (androgens, estrogens and progesterone) have
distinct and overlapping effects on immune cell numbers, activity and
* Corresponding author.
E-mail address: peik.brundin@umu.se (P.M.A. Brundin).
Contents lists available at ScienceDirect
Heliyon
journal homepage: www.cell.com/heliyon
https://doi.org/10.1016/j.heliyon.2020.e05535
Received 16 March 2020; Received in revised form 4 June 2020; Accepted 13 November 2020
2405-8440/© 2020 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Heliyon 6 (2020) e05535

cytokine production, which make their interaction with the immune
response complex.
Incidence and severity of numerous infectious diseases show sex bias,
with men having higher disease severity or pathogen load [10], and
higher mortality from infectious or parasitic diseases [17]. These sex
differences decline after menopause, suggesting a connection to sex
hormones [17]. Sex hormone levels are also partly attributed to the risk
of developing autoimmune diseases. Here, women have a higher risk of
developing for example multiple sclerosis (MS), rheumatoid arthritis
(RA) and systemic lupus erythematosus (SLE) [18, 19].
Consequently, differences in immune activity throughout the men-
strual cycle have been reported [20]. Indeed, several autoimmune dis-
eases (e.g. MS, RA and SLE) show uctuations in activity during the
phases of the menstrual cycle [20]. The menstrual cycle, 2532 days
long, is divided in a follicular phase, a mid-cycle ovulatory phase, and a
luteal phase. Increasing and decreasing levels of 17-β estradiol and pro-
gesterone, a peak of follicle-stimulating hormone (FSH) around day 3,
and a midcycle peak of luteinizing hormone (LH) characterize the
different phases. Although there is limited knowledge on the impact of
the menstrual cycle on immune response towards infectious diseases, it
has been shown that the cytokine prole during the menstrual cycle shifts
between Th1-associated and Th2-associated responses [2]. This is prob-
ably due to a biphasic effect of estrogen, where low levels of estrogen
stimulate a Th1 response (cell-mediated immunity) and high levels of
estrogen stimulate a Th2 response (humoral immunity) [21]. Further-
more, the number of regulatory T-cells (Treg-cells), which are important
for development of autoimmunity and immune tolerance, also vary
during the menstrual cycle. The number of Treg-cells are positively
correlated to the serum estrogen levels [22].
Torque teno viruses (TT viruses or TTVs) are a group of highly vari-
able single stranded DNA-viruses (family Anelloviridae, genus Alpha-
torquetenovirus) that consist of 29 species (TTV1-29) [3,23]. So far, there
is no associated pathology to TTV infection, and it may be regarded as a
commensal virus [3, 24]. Most healthy humans (up to 90%) carry several
species of TTV in their blood, and the levels normally range between 2-8
log
10
copies/mL [3, 25, 26]. Of the >3.8 10
10
virions produced per
day, approximately 90% are daily replaced [27, 28, 29]. Recently, TTV
has received attention as a possible endogenous biomarker for immune
function, with immunocompetent individuals carrying lower levels of
TTV in serum than immunocompromised, indicating a suppressing role
of the immune system on the viral load [3]. The high turnover-rate of
virions indicates that changes in immune status can be followed in a short
time frame. As with many other infectious diseases, TTV loads are also
higher in men compared to women and increase with age [30].
Previous studies on TTV-levels have been performed on both plasma
and PBMC, but to the best of our knowledge, none have correlated TTV-
levels to hormones or the menstrual cycle [31, 32, 33]. PBMC, containing
T- and B-lymphocytes, NK-cells, monocytes and a small fraction of den-
dritic cells, are widely used in diagnostics as sentinel markers for disease.
The aim of the present study is to investigate if TTV, as a potential marker
of immune function, can be detected in PBMC from healthy men and
women, and whether TTV load is associated with thyroid status, sex
hormone levels, and the different phases of the menstrual cycle. The
differences in female and male immunity towards pathogens have im-
plications for treatment and prevention of infectious diseases and may
ultimately lead to a different approach depending on the sex of the
patient.
2. Material and methods
2.1. Subjects
27 healthy individuals were included according to a protocol
approved by the Central Ethical Review Board (Swedish Research
Council, Stockholm, Dnr:
O242009) and consisted of 17 pre-
menopausal women, 6 men and 4 postmenopausal women (Table 1).
The subjects were included and sampled during 6 months (between
March and September, 2010). Informed consent was obtained from the
participants.
The inclusion criteria were: Premenopausal women aged 2040 years
with regular menstrual cycles, without hormonal contraceptives or other
hormonal, anti-inammatory (including ASA, systemic cortisone and
NSAIDs) or any morphine treatment since >3 months, and parturition no
later than 12 months before inclusion. Men (aged 2070) and post-
menopausal women (no menstrual bleeding since >12 months) without
the above stated treatment during the last 3 months.
The exclusion criteria were: Perimenopausal women (i.e. close to
menopause), medication according to the above stated criteria, and
pregnancy or irregular menstrual bleedings.
2.2. Blood sampling and hormonal analyses
From all individuals blood was drawn at four timepoints, and for the
pre-MP women Ovustick® was used to identify the LH-peak. Ovulation
was then conrmed by progesterone >20 nmol/mL, 57 days past LH-
peak. Simultaneously, at one or more timepoints PBMC was also
sampled. In Pre-MP women, blood samples were drawn at the following
four time-points: 1
st
sample at Day 13 (early follicular phase), 2
nd
sample at day 810, (mid follicular phase), 3
rd
sample at day 1214
(ovulatory phase) and 4
th
57 days past positive result on Ovustick®
(indicating mid-luteal phase or implantation window). In post-MP
women and men four samples were taken with 1-week intervals. Blood
was drawn at Kvinnoh
alsan (Karolinska University Hospital, Huddinge)
and analyzed at the Karolinska University Laboratory (KUL, Huddinge,
Sweden). All samples were drawn between 8-11 a.m. PBMC fractions
were prepared by centrifugation of whole blood using Vacutainer®
CPT mononuclear cell preparation tubes (Becton Dickinson, art no.
362780) according to the manufacturer's recommendation. The buffy-
coat was transferred to new tubes and slowly frozen in 20% dime-
thylsulphoxide (DMSO)-albumin, using isopropanol-loaded Mr. Frosty®
freezing-container overnight, before long-term storage at -80
C. Ana-
lyses were made of WBC, differential count (including B-monocytes, B-
lymphocytes, B-neutrophils, B-eosinophils, B-basophils), S-TSH (thyroid
stimulating hormone), S-T4, S-SHBG (sex hormone binding globulin), S-
estradiol, S-testosterone, S-progesterone, S-FSH, S-LH and S-prolactin.
Separate serum samples were taken and stored in -20
C before analysis
of Dihydrotestosterone (DHT) using a liquid chromatography tandem
mass spectrometry (LC-MS/MS) method at Helsinki University Hospital
Laboratory (HUSLAB), Helsinki, Finland. Reference values for DHT were
adopted from Swerdloff et al. [34] and Rothman et al. [35].
The participants were assessed for hypo- or hyperthyroidism, and pre-
MP women also whether they had a normal ovulation. A participant was
considered hypothyroid if TSH >3.5 mU/L (Ref 0.43.5 mU/L) and
anovulatory if LH was <18 nmol/L during the mid-cycle (mid follicular
or ovulatory) phases and progesterone <17 nmol/L during mid luteal
phase.
2.3. TTV DNA isolation and analysis
Frozen PBMC were gently thawed, lyzed and lter-concentrated in
7250 G (4 h, 4
C) to a volume of ca 200
μ
L using micro concentrators
(Amicon® Ultra 2mL Ultracel®-100K, Merck Millipore, Ireland). This
was performed according to QIAamp® DNA Mini and Blood Mini
Handbook (Qiagen) to increase DNA yield. DNA concentration was
measured using Nano-drop. DNA yield varied between 3.25-323 ng/
μ
L,
mean 58.8 ng/
μ
L. For TTV amplication, Argene TTV R-gene® (bio-
M
erieux S.A., Marcy lEtoile, France) kit (described in detail by [25]) was
used on an Applied Biosystems 7500 Real-time PCR system. The ther-
mocycler was programmed according to the TTV R-gene® protocol (95
C, 15 min followed by 45 cycles of 95
C, 10s and 60
C, 40s). An in-
ternal quantication standard was included in the TTV R-gene® kit. This
contained pre-prepared solutions of 5, 50, 500 and 5000 copies plasmid
P.M.A. Brundin et al. Heliyon 6 (2020) e05535
2

TTV DNA per
μ
L, as well as a sensitivity control containing 1 copy/
μ
L.
The sample wells were run in triplicates using 10
μ
L of concentrated DNA
solution.
The detection limit was set to 1 viral particle in the sample reagent
(10
μ
L). According to the standard curve obtained, this corresponded to
CT of 37.09, 42.09 and 39.14 respectively on the included three TTV
qPCR plates. A sample was considered positive if 2 of 3 triplicate samples
were above the detection limit (i.e. below the CT-threshold mentioned
above).
2.4. Statistical analyses
The average hormone levels of TSH, estradiol, LH and testosterone
were calculated for each of the 17 pre-MP women. The average hormone
levels were used in binomial regression to explain the variance of TTV
þ
/
TTV
. Given the difference in variance in progesterone levels, LH levels
and sample sizes, Welch's t-tests were used to test the null hypothesis of
equality among pre-MP women at the 4
th
time point (progesterone) and
3
rd
time point (LH).
As a logistic model with logit-link, the following was used: TTV ~ log
(Mean_TSH) þ log (Mean_estradiol) þ log (Mean_LH) þ log (Mean_-
testosterone). In the model TTV is a dependent variable and log mean
TSH, estradiol, LH and testosterone are explanatory (independent) vari-
ables. The explanatory variables are treated as covariates. No interactions
were investigated. The regression model was analyzed using R 3.6.0 and
RStudio 1.2.1335. dplyr 1.0.2 was used for data processing.
3. Results
3.1. Clinical data
Clinical information on age, BMI, parity, menstrual cycle length and
years since last menses for all individuals are included in Table 1.
3.2. TTV prevalence and TTV levels
Of 27 included individuals (6 men, 17 pre-MP women and 4 post-MP
women), in total 7 were positive for TTV in PBMC; 3 men (50.0%), 3 pre-
MP women (17.6%), and 1 post-MP woman (25.0%). The detected levels
of TTV were highest among the TTV positive pre-MP women and lower in
the post-MP women and in the men, both in terms of detected TT viral
copies/mL and when adjusting for total amount of DNA in the sample
(Table 2). The differences in TTV prevalence between pre-MP and post-
MP women as well as between pre-MP women and men were not sta-
tistically signicant (Fisher's exact test, p > 0.999 and p ¼ 0.2786)
(Figure 1). The raw data suggested higher prevalence in men than in pre-
MP women, but signicance testing could not rule out a chance
Table 1. Clinical information on included individuals, range (median).
Age Parity Menstrual cycle length in days Years since last menses BMI
Pre-MP 2537 (31) 02 (0) 2531 (28) - 17.927.5 (22.1)
Post-MP 5862 (61.5) 04 (2) - 613 (10) 21.234.1 (29)
Males 2861 (51) - - - 20.930.0 (24.5)
Table 2. Clinical and hormonal data on TTV
þ
individuals, including TT virus load, thyroid status, sex hormone levels. For pre-MP women, day of the menstrual cycle,
whether or not ovulation was present, range of estradiol, and peak levels of progesterone and LH, is indicated.
Subject # 12 25A
1
25B
1
37 24 28 31 32
Category Pre-MP Pre-MP Pre-MP Pre-MP Post-MP Male Male Male
Day of menstrual cycle 27 3 12 9 - - - -
Age 29 29 29 37 58 32 53 61
BMI 21.6 33 33 27.5 27.9 20.1 25.6 30
TTV/
μ
g DNA 9.036 4857 32.60 229.8 4.743 3.554 0.9358 3.314
Log
10
TTV copies/mL 2.56 5.11 3,36 3.23 2.30 2.25 2.21 2.53
LH (nmol/L) 5.4 11 20 8.1 19 3.8 3.1 2.9
Max LH 8.9 21 21 14 - - - -
Progesterone (nmol/L) 4 2.1 2.2 3.2 <1.0 <1.0 1 1.8
Max Progesterone 11 9.8 9.8 20 - - - -
Testosterone (nmol/L) 1.2 1.5 1.8 0.5 <0.4 18 13 11
DHT (nmol/L) 0.2 0.2 0.5 0.5 0 1.3 1.8 1.2
Estradiol (pmol/L) 353 164 <150 392 27 46 105 36
Range Estradiol 189353 <150301 <150301 <1501030 - - - -
FSH (U/L) 3.6 5.5 6 23 52 3.6 2.8 5.6
Range FSH 25.1 4.16 4.16 5.823----
Thyroid status
2
Euthyroid Hypothyroid Hypothyroid Hypothyroid Euthyroid Euthyroid Euthyroid Euthyroid
Ovulation
3
No No No Yes ----
Abbreviations: Luteinizing hormone (LH). Dihydrotestosterone (DHT). Follicle-stimulating hormone (FSH).
Reference values:
Pre-MP females: S-17β-estradiol (follicular phase) 100200 pmol/L; (ovulatory phase) 5001500; (luteal phase) 200800. S-FSH (follicular phase) 2.510 U/L;
(ovulatory phase) 4.014; (luteal phase) 0.78.5. S-LH (follicular phase) 1.812 nmol/L, (ovulatory phase) 1890, (luteal phase) 0.615. S-progesterone, (follicular
phase) < 4.8 nmol/L; (luteal phase) > 17. S-Testosterone <2.7 nmol/L. DHT ~0.3 nmol/L.
Post-MP females: S-17β-estradiol <50 pmol/L; S-FSH 25150 U/L; S-LH 1878 nmol/L; S-Progesterone <3.0 nmol/L; S-Testosterone <2.7 nmol/L. DHT ~0.1 nmol/L.
Males: S-17β-estradiol 50150 pmol/L; S-FSH: 1.012.5 U/L; S-LH 1.29.6 nmol/L; S-Progesterone <3.0 nmol/L; S-Testosterone 1030 nmol/L. DHT 0.383.27 nmol/L.
1
25A and 25B represents samples of one individual at two different timepoints.
2
Hypothyroidism is dened as S-thyroid-stimulating hormone (TSH) > 3.5 mU/L.
3
Anovulation is dened as LH < 18 nmol/L in ovulatory phase and progesterone <17 nmol/L in the luteal phase.
P.M.A. Brundin et al. Heliyon 6 (2020) e05535
3

association (Fisher's exact test, p ¼ 0.2786), possibly due to the limited
number of study subjects.
3.3. Hormonal status in TTV-positive pre-MP women
To determine whether sex hormones inuence the risk of being TTV-
positive (TTV
þ
) we compared the average sex hormone levels in TTV
þ
(n
¼ 3) and TTV
(n ¼ 14) individuals using a binomial regression including
S-estradiol, S-testosterone, S-LH and S-TSH. The results showed no sig-
nicant relationship between hormone levels and TTV-status (Table 3).
We noted that out of three TTV
þ
pre-MP women, two (# 12 and 25)
were aberrant in their hormonal status and did not ovulate. Two (#25
and 37) also had laboratory signs of hypothyroidism, of which one (#25)
had an exceptionally high viral load (Table 2).
None of the TTV
pre-MP women had signs of hypothyroidism, i.e.
normal TSH-levels (range 0.43.2, average 1.27 mU/L, Ref 0.43.5 mU/
L). The boxplot of TSH comparing TTV
þ
and TTV
-individuals (Figure 2)
indicates a distinction between the two groups. However, when
comparing average TSH from TTV
þ
and TTV
individuals in a binominal
regression, there was no signicant difference ( p-value ¼ 0.337, Table 3).
To establish if this result could be due to a power problem, a bootstrap
power analysis was performed and showed that a binomial regression
with given group sizes; standard deviation and average difference in
TSH-levels, had 11.7% probability only to detect this mean difference.
Additional binominal regression analysis on serum hormones showed
that it was not meaningful to further analyze average values.
The levels of progesterone and LH during the menstrual cycle are
important indicators for ovulation. Progesterone levels are normally ex-
pected to rise during mid-luteal phase. Samples from the mid-luteal phase
revealed (using Welch's t-test) signicantly lower progesterone levels (p
¼ 0.002) in TTV
þ
compared to TTV
pre-MP women (Figure 3A).
Notably, three out of four TTV
þ
samples were from the early phases of
Pre-MP vs Post-MP
Pre-MP vs males
Post-MP vs males
Effect
Value
95% CI
Value
95% CI
95% CI
RR
1.098
0.7053 to 2.789
1.647
0.9006 to 4.467
0.5037 to 4.335
OR
1.556
0.09184 to 14.06
4.667
0.7269 to 26.94
0.2321 to 50.28
A
p > 0.999
p = 0.2786
p = 0.5714
B
C
Figure 1. Distribution of TTV
and TTV
þ
post-MP women (A) and men (B) relative to pre-MP women, and post-MP relative to men (C). Numbers in graphs indicate n
values. Statistics tables below each graph show the respective relative risks (RR) and odds ratios (OR) following Fisher's exact test. No signicance could be detected.
Table 3. Binominal regression analysis of thyroid stimulating hormone (TSH), prolactin, and luteinizing hormone (LH). Dispersion parameter for binominal family
taken to be 1. Null deviance: 1.6220 10
1
on 17 degrees of freedom (Df). Residual deviance: 5.5798 on 13 Df. Akaike information criterion (AIC): 15.58. Number of
Fisher Scoring iterations: 10.
Deviance residuals
Min 1Q Median 3Q Max
-1.356 -0.1560 -0.00543 -0.00032 -1.622
Coefcients: Estimate Standard error Z value Pr (>|z|)
Intercept -6.611 29.67 -0.223 0.824
Log Average TSH 16.291 16.97 0.960 0.337
Log Average Estradiol 0.1415 5.239 0.027 0.978
Log Average LH -2.651 3.599 -0.737 0.461
Log Average Testosterone -7.593 14.05 -0.541 0.589
Figure 2. TSH-levels in TTV
(Neg, n = 14) and TTV
þ
(Pos, n = 3) pre-MP
women, divided by phase of the menstrual cycle. Of the three TTV
þ
, one was
anovulatory, one hypothyroid with normal ovulation and one both anovulatory
and hypothyroid. None of the TTV
were hypothyroid. Hypothyroidism was
dened by TSH >3.5 mU/L at one or more occasions.
P.M.A. Brundin et al. Heliyon 6 (2020) e05535
4

the menstrual cycle, when progesterone is also low (Table 2). LH that is
expected to peak at ovulatory phase was low, but not signicantly
different in TTV
þ
as compared to TTV
(Figure 3B). Low LH together
with low progesterone indicates anovulation.
Altogether, all 3 TTV
þ
pre-MP women showed hormonal aberrance.
One was anovulatory, one hypothyroid with normal ovulation, and one
both hypothyroid and anovulatory (Table 2). None of the TTV
in-
dividuals had signs of anovulation or irregularities in thyroid hormone
levels. Although this result hindered us from determining if menstrual
cycle phases could be linked to TTV load in PBMCs, we noted that of 4
TTV
þ
samples, 3 were obtained from the follicular phase (day 3, 9 and
12) and 1 from the luteal phase (day 27) (Table 2 and Table 4A).
3.4. Hormonal status in men and post-MP women
All three TTV
men, and two out of three TTV
post-MP women hade
traces of TTV, but below cut-off (Table 4B). All men and post-MP women
(TTV
þ
and TTV
) had normal (euthyroid) levels of TSH. Due to the low
number of participants, it was not possible to use regression models for
analyzing hormone levels in relation to TTV status in men and post-MP
women. TTV status, age and range of hormone levels are shown in
Table 5.
4. Discussion
The detection of TTV in 17.6% pre-MP females, 25.0% post-MP fe-
males, and 50.0% males suggests that TTV presence in the PBMC fraction
of peripheral blood may be associated with sex. This observation sup-
ports ndings from previous studies [30]. Our observations also suggest
Figure 3. Levels of progesterone (mid-luteal phase) and LH (ovulatory phase) in
TTV
þ
(n = 3) and TTV
(n = 14) pre-MP women. Standard deviation is shown in
error bars. The average S-progesterone levels (A) were signicantly different in a
two-tailed Student's t-test, using Welch's correction (p =0.002,t =3.989,Df=
12.53). Difference in average S-LH were not statistically signicant (p =0,156,
t = 1.5937, Df = 6.874).
Table 4. Amount of TTV (TTV particles/
μ
g template DNA) in investigated subjects using qPCR. TTV status, (þ) or (-), was determined according to whether ampli-
cation of TTV-DNA was present in the respective samples and whether the qPCR was above cut-off (1.0 viral particle per sample, i.e. qPCR reaction well (10
μ
L)). 4A.
Pre-MP women (sampled four times through the menstrual cycle for TTV). 4B Post-MP women and men (sampled once for TTV). Not detected indicates no detection
signal on the qPCR assay.
4A
Subject # Early follicular Mid follicular Ovulatory Mid luteal TTV status Comment
Pre-MP
(sampled 4 times)
4 N A N A Not detected Not detected - Not detected
12 B C B C 9.036 B C þþ
13 Not detected Not detected Not detected Not detected - Not detected
15 N A Not detected Not detected Not detected - Not detected
18 N A N A Not detected Not detected - Not detected
21 Not detected Not detected Not detected Not detected - Not detected
22 N A Not detected Not detected Not detected - Not detected
25 4857 32.60 BC NA þþ
30 Not detected Not detected Not detected Not detected - Not detected
35 Not detected Not detected Not detected Not detected - Not detected
36 Not detected Not detected Not detected Not detected - Not detected
37 B C 229.8 BC NA þþ
38 BC NA NA NA - BC
39 N A Not detected B C N A - B C
41 N A Not detected Not detected Not detected - Not detected
42 N A Not detected Not detected Not detected - Not detected
43 BC BC BC BC - BC
4B
Subject # TTV status Comment
Post-MP
(Sampled once)
3BC -BC
14 Not detected - Not detected
16 B C -BC
24 4.743 þþ
Men
(Sampled once)
23 B C - BC
27 B C - BC
28 3.554 þþ
31 0.9358 þþ
32 3.314 þþ
40 B C -BC
Abbreviations: Not available (N A). Below cut-off (B C).
P.M.A. Brundin et al. Heliyon 6 (2020) e05535
5

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01 Jan 2019
Abstract: To further dissect the genetic architecture of colorectal cancer (CRC), we performed whole-genome sequencing of 1,439 cases and 720 controls, imputed discovered sequence variants and Haplotype Reference Consortium panel variants into genome-wide association study data, and tested for association in 34,869 cases and 29,051 controls. Findings were followed up in an additional 23,262 cases and 38,296 controls. We discovered a strongly protective 0.3% frequency variant signal at CHD1. In a combined meta-analysis of 125,478 individuals, we identified 40 new independent signals at P < 5 × 10−8, bringing the number of known independent signals for CRC to ~100. New signals implicate lower-frequency variants, Krüppel-like factors, Hedgehog signaling, Hippo-YAP signaling, long noncoding RNAs and somatic drivers, and support a role for immune function. Heritability analyses suggest that CRC risk is highly polygenic, and larger, more comprehensive studies enabling rare variant analysis will improve understanding of biology underlying this risk and influence personalized screening strategies and drug development.Genome-wide association analyses based on whole-genome sequencing and imputation identify 40 new risk variants for colorectal cancer, including a strongly protective low-frequency variant at CHD1 and loci implicating signaling and immune function in disease etiology.

14 citations



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TL;DR: A status report on the global burden of cancer worldwide using the GLOBOCAN 2018 estimates of cancer incidence and mortality produced by the International Agency for Research on Cancer, with a focus on geographic variability across 20 world regions.
Abstract: This article provides a status report on the global burden of cancer worldwide using the GLOBOCAN 2018 estimates of cancer incidence and mortality produced by the International Agency for Research on Cancer, with a focus on geographic variability across 20 world regions There will be an estimated 181 million new cancer cases (170 million excluding nonmelanoma skin cancer) and 96 million cancer deaths (95 million excluding nonmelanoma skin cancer) in 2018 In both sexes combined, lung cancer is the most commonly diagnosed cancer (116% of the total cases) and the leading cause of cancer death (184% of the total cancer deaths), closely followed by female breast cancer (116%), prostate cancer (71%), and colorectal cancer (61%) for incidence and colorectal cancer (92%), stomach cancer (82%), and liver cancer (82%) for mortality Lung cancer is the most frequent cancer and the leading cause of cancer death among males, followed by prostate and colorectal cancer (for incidence) and liver and stomach cancer (for mortality) Among females, breast cancer is the most commonly diagnosed cancer and the leading cause of cancer death, followed by colorectal and lung cancer (for incidence), and vice versa (for mortality); cervical cancer ranks fourth for both incidence and mortality The most frequently diagnosed cancer and the leading cause of cancer death, however, substantially vary across countries and within each country depending on the degree of economic development and associated social and life style factors It is noteworthy that high-quality cancer registry data, the basis for planning and implementing evidence-based cancer control programs, are not available in most low- and middle-income countries The Global Initiative for Cancer Registry Development is an international partnership that supports better estimation, as well as the collection and use of local data, to prioritize and evaluate national cancer control efforts CA: A Cancer Journal for Clinicians 2018;0:1-31 © 2018 American Cancer Society

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"A combined proteomics and Mendelian..." refers background in this paper

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TL;DR: It is found that local genetic variation affects gene expression levels for the majority of genes, and inter-chromosomal genetic effects for 93 genes and 112 loci are identified, enabling a mechanistic interpretation of gene regulation and the genetic basis of disease.
Abstract: Characterization of the molecular function of the human genome and its variation across individuals is essential for identifying the cellular mechanisms that underlie human genetic traits and diseases. The Genotype-Tissue Expression (GTEx) project aims to characterize variation in gene expression levels across individuals and diverse tissues of the human body, many of which are not easily accessible. Here we describe genetic effects on gene expression levels across 44 human tissues. We find that local genetic variation affects gene expression levels for the majority of genes, and we further identify inter-chromosomal genetic effects for 93 genes and 112 loci. On the basis of the identified genetic effects, we characterize patterns of tissue specificity, compare local and distal effects, and evaluate the functional properties of the genetic effects. We also demonstrate that multi-tissue, multi-individual data can be used to identify genes and pathways affected by human disease-associated variation, enabling a mechanistic interpretation of gene regulation and the genetic basis of disease.

2,449 citations


"A combined proteomics and Mendelian..." refers methods in this paper

  • ...As found by the Genotype-Tissue Expression 658 (GTEx) study, cis eQTLs are either shared across tissues or are specific to a small number of tissues 659 (54)....

    [...]


Journal ArticleDOI
TL;DR: An adaption of Egger regression can detect some violations of the standard instrumental variable assumptions, and provide an effect estimate which is not subject to these violations, and provides a sensitivity analysis for the robustness of the findings from a Mendelian randomization investigation.
Abstract: Background: The number of Mendelian randomization analyses including large numbers of genetic variants is rapidly increasing. This is due to the proliferation of genome-wide association studies, and the desire to obtain more precise estimates of causal effects. However, some genetic variants may not be valid instrumental variables, in particular due to them having more than one proximal phenotypic correlate (pleiotropy). Methods: We view Mendelian randomization with multiple instruments as a meta-analysis, and show that bias caused by pleiotropy can be regarded as analogous to small study bias. Causal estimates using each instrument can be displayed visually by a funnel plot to assess potential asymmetry. Egger regression, a tool to detect small study bias in meta-analysis, can be adapted to test for bias from pleiotropy, and the slope coefficient from Egger regression provides an estimate of the causal effect. Under the assumption that the association of each genetic variant with the exposure is independent of the pleiotropic effect of the variant (not via the exposure), Egger’s test gives a valid test of the null causal hypothesis and a consistent causal effect estimate even when all the genetic variants are invalid instrumental variables. Results: We illustrate the use of this approach by re-analysing two published Mendelian randomization studies of the causal effect of height on lung function, and the causal effect of blood pressure on coronary artery disease risk. The conservative nature of this approach is illustrated with these examples. Conclusions: An adaption of Egger regression (which we call MR-Egger) can detect some violations of the standard instrumental variable assumptions, and provide an effect estimate which is not subject to these violations. The approach provides a sensitivity analysis for the robustness of the findings from a Mendelian randomization investigation.

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TL;DR: The use of germline genetic variants that proxy for environmentally modifiable exposures as instruments for these exposures is one form of IV analysis that can be implemented within observational epidemiological studies and can be considered as analogous to randomized controlled trials.
Abstract: Observational epidemiological studies suffer from many potential biases, from confounding and from reverse causation, and this limits their ability to robustly identify causal associations. Several high-profile situations exist in which randomized controlled trials of precisely the same intervention that has been examined in observational studies have produced markedly different findings. In other observational sciences, the use of instrumental variable (IV) approaches has been one approach to strengthening causal inferences in non-experimental situations. The use of germline genetic variants that proxy for environmentally modifiable exposures as instruments for these exposures is one form of IV analysis that can be implemented within observational epidemiological studies. The method has been referred to as 'Mendelian randomization', and can be considered as analogous to randomized controlled trials. This paper outlines Mendelian randomization, draws parallels with IV methods, provides examples of implementation of the approach and discusses limitations of the approach and some methods for dealing with these.

1,701 citations


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    [...]

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Journal ArticleDOI
TL;DR: A novel weighted median estimator for combining data on multiple genetic variants into a single causal estimate is presented, which is consistent even when up to 50% of the information comes from invalid instrumental variables.
Abstract: Developments in genome-wide association studies and the increasing availability of summary genetic association data have made application of Mendelian randomization relatively straightforward. However, obtaining reliable results from a Mendelian randomization investigation remains problematic, as the conventional inverse-variance weighted method only gives consistent estimates if all of the genetic variants in the analysis are valid instrumental variables. We present a novel weighted median estimator for combining data on multiple genetic variants into a single causal estimate. This estimator is consistent even when up to 50% of the information comes from invalid instrumental variables. In a simulation analysis, it is shown to have better finite-sample Type 1 error rates than the inverse-variance weighted method, and is complementary to the recently proposed MR-Egger (Mendelian randomization-Egger) regression method. In analyses of the causal effects of low-density lipoprotein cholesterol and high-density lipoprotein cholesterol on coronary artery disease risk, the inverse-variance weighted method suggests a causal effect of both lipid fractions, whereas the weighted median and MR-Egger regression methods suggest a null effect of high-density lipoprotein cholesterol that corresponds with the experimental evidence. Both median-based and MR-Egger regression methods should be considered as sensitivity analyses for Mendelian randomization investigations with multiple genetic variants.

1,489 citations


"A combined proteomics and Mendelian..." refers background or methods in this paper

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    [...]

  • ...The weighted median approach is useful as it allows a consistent estimate even if 50% of the 514 SNPs proxying protein/mRNA expression are invalid instruments (40) and the mode estimate also 515 provides a consistent causal effect estimate even if the majority of the instruments are invalid, as 516 the estimate depends on the largest number of similar instruments (39)....

    [...]


Related Papers (5)
Frequently Asked Questions (2)
Q1. What have the authors contributed in "Blood hormones and torque teno virus in peripheral blood mononuclear cells" ?

In this study the authors investigated the presence and levels of TTV in peripheral blood mononuclear cells ( PBMCs ) in premenopausal ( pre-MP ) women, post-menopausal ( post-MP ) women, and men, and determined their serum sex hormone levels. Of the examined subjects ( n 1⁄4 27 ), the authors found presence of TTV in PMBC from 17. Although their study was performed on a limited number of subjects, the data suggests that TTV in PBMC is associated with an anovulatory menstrual cycle with low progesterone levels, and possibly with male sex. 

Preferably, future studies including sex and hormonal status ( e. g. pre- or post-menopause, contraceptives, hormonal replacement therapies and ovulation, as well as signs of hypothyroidism ), should be performed to obtain more information on the impact of the menstrual cycle on TTV load and immune response.