Abstract

Background

   For many drugs, mechanisms of action with regard to desired effects
   and/or unwanted side effects are only incompletely understood. To
   investigate possible pleiotropic effects and respective molecular
   mechanisms, we describe here a catalogue of commonly used drugs and
   their impact on the blood transcriptome.

Methods and results

   From a population-based cohort in Germany (LIFE-Adult), we collected
   genome-wide gene-expression data in whole blood using in Illumina
   HT12v4 micro-arrays (n = 3,378; 19,974 gene expression probes per
   individual). Expression profiles were correlated with the intake of
   active substances as assessed by participants’ medication. This
   resulted in a catalogue of fourteen substances that were identified as
   associated with differential gene expression for a total of 534 genes.
   As an independent replication cohort, an observational study of
   patients with suspected or confirmed stable coronary artery disease
   (CAD) or myocardial infarction (LIFE-Heart, n = 3,008, 19,966 gene
   expression probes per individual) was employed. Notably, we were able
   to replicate differential gene expression for three active substances
   affecting 80 genes in peripheral blood mononuclear cells (carvedilol:
   25; prednisolone: 17; timolol: 38). Additionally, using gene ontology
   enrichment analysis, we demonstrated for timolol a significant
   enrichment in 23 pathways, 19 of them including either GPER1 or PDE4B.
   In the case of carvedilol, we showed that, beside genes with
   well-established association with hypertension (GPER1, PDE4B and
   TNFAIP3), the drug also affects genes that are only indirectly linked
   to hypertension due to their effects on artery walls or their role in
   lipid biosynthesis.

Conclusions

   Our developed catalogue of blood gene expressions profiles affected by
   medication can be used to support both, drug repurposing and the
   identification of possible off-target effects.

Introduction

   Over the last years, blood-based gene-expression (GE) analyses have
   been broadly used to identify biomarkers, to detect potential molecular
   drivers for diseases and to assess molecular phenotypes. This also
   provided new insights into disease processes, (sub-clinical) disease
   states, and response to therapy [[46]1].

   However, GE is affected by a plethora of factors including the genetic
   background of a person, the considered tissue, life-style,
   environmental and disease-related factors [[47]2, [48]3]. In this
   regard, the effect of drugs on single GE and defined pathways is still
   understudied. For pharmacologists the association between drugs and GE
   is highly relevant as drug based transcriptome analysis could provide
   new insights into the mechanisms of action of specific drugs. These
   insights can be used for drug repurposing [[49]4, [50]5] but also help
   to identify underlying reasons for off-target effects [[51]6].

   Beta-blockers are widely prescribed drugs that cover a wide spectrum of
   cardiovascular indications. As beta-blockers are inferior to
   calcium-channel blockers and renin-angiotensin system inhibitors they
   are considered as second line antihypertensive treatment [[52]7]. They
   are however effective in long term secondary prevention after
   myocardial infarction [[53]8] and are also used in the treatment of
   specific cardiovascular diseases [[54]9]. Beta-blockers are competitive
   antagonists that block the receptor sites for the endogenous
   epinephrine and norepinephrine on beta-adrenoceptors that are found on
   cells of the heart muscles, smooth muscles, arteries, kidneys, airways
   and other tissues which are part of the sympathetic nervous system.
   While non-selective beta-blockers block the activation of all types of
   beta-adrenoceptors, selective beta-blockers only act on designated
   receptor subtypes (β1 to β3). In this study, we analysed the impact of
   83 active substances on whole blood transcriptome. Results were
   replicated in an independent cohort. To gain deeper insight into the
   effects of beta blocking agents and their mechanisms of action, we
   analysed associated genes and pathways in more detail and compared the
   results with our current knowledge of the drug’s mechanisms.

Material and methods

Cohort description

   The LIFE-Adult study is a population-based cohort study of 10,000
   participants from Leipzig, a city in Germany [[55]10]. Most of the
   participants are aged between 40 and 79, with a small subgroup of 400
   participants being between 18 and 39. The study population is of
   central European descent and the main study goal is to investigate
   prevalence, genetic predisposition and the role of lifestyle-related
   factors (such as smoking habits, alcohol consumption, dietary patterns
   and physical activity) on major civilization diseases including
   subclinical signs. Initial data collection was performed between 2011
   and 2014.

   LIFE-Heart is an observational study of patients collected at the Heart
   Center of Leipzig, Germany. A total of 6,994 patients were recruited
   with suspected or confirmed stable coronary artery disease (CAD) or
   myocardial infarction. The study design and a detailed description of
   patients can be found elsewhere [[56]11]. Initial data collection was
   performed between 2006 and 2014. For the present analysis, we excluded
   patients with acute myocardial infarction since the acute situation may
   have a profound impact on gene expression profiles.

   Baseline characteristics for the cohorts are provided in [57]Table 1.

Table 1. Study characteristics of LIFE-Adult and LIFE-Heart.

   Study characteristics
   Parameter LIFE-Adult (n = 3,378) LIFE-Heart (n = 2,978)
   With medication Without medication With medication Without medication
   Men / Women 1323 / 1419 422 / 214 1849 / 1003 99 / 27
   Age (years) 60.7 ± 12.1 51.1 ± 11.6 63.2 ± 10.8 53.9 ± 10.4
   Non Smoker / Smoker 2061 / 485 432 / 183 2344 / 508 90 / 36
   BMI (kg/m^2) 27.9 ± 4.8 26.1 ± 3.8 29.8 ± 5.0 27.3 ± 3.8
   Lymphocytes in % 29.8 ± 7.9 32.2 ± 7.6 25.4 ± 7.7 28.1 ± 7.3
   Monocytes in % 8.2 ± 2.1 8.3 ± 2.0 8.7 ± 2.3 8.6 ± 2.6
   Average number of active substances per individual 4.1 (median = 3, IQR
   = 2 to 6) 0 5.9 (median = 5, IQR = 3 to 8) 0
   [58]Open in a new tab

   For the continuous parameters, the arithmetic mean and SD is given.
   Additionally, average numbers of substances are given as median and
   interquartile range (IQR).

   Both studies meet the ethical standards of the Declaration of Helsinki
   and were approved by the Ethics Committee of the Medical Faculty of the
   University Leipzig, Germany (LIFE-Adult: Reg. No 263-2009-14122009;
   LIFE-Heart: Reg. No. 276–2005). Written informed consent including
   agreement with molecular-genetic analyses was obtained from all
   participants.

Gene expression analysis

   RNA was available from whole blood of n = 3,526 LIFE-Adult
   participants. Raw gene-expression data were measured by Illumina
   HumanHT-12 v4 Expression BeadChip. A total of 47,231 expression probes
   were successfully measured in all samples using Illumina GenomeStudio.
   We further processed these data within R 2.13.1 / Bioconductor.
   Transcripts not sufficiently expressed according to Illumina’s internal
   cut-off as implemented in Bioconductor package ‘lumi’ (detection
   p-value≤ 0.05) in at least 5% of all samples were not further
   considered in the analysis. Expression values were quantile normalised
   and log2-transformed [[59]12]. Furthermore, we defined for each
   individual a combined quantitative measure combining quality control
   features available for HT-12 v4 (i.e. perfect-match and miss-match
   control probes, control probes present at different concentrations,
   mean of negative control probes, mean of house-keeping genes, Euclidean
   distances of expression values, number of expressed genes, mean signal
   strength of biotin-control-probes, [60]S1 Fig). We calculated the
   Mahalanobis-distance between all individuals and an artificial
   individual showing average values for these quality control features
   ([61]S2 Fig). Samples had to be within 4 x interquartile range (IQR)
   from the median [[62]13] of this distance. Transcript levels were
   adjusted for the known batch Sentrix barcode (i.e. expression chip-ID)
   using an empirical Bayes method as described [[63]14]. The empirical
   Bayes method required that at least two individuals for each batch are
   provided. This excluded two individuals. Success of adjustment was
   checked using ANOVA for both, the Sentrix barcode as well as the
   processing batch (in a processing batch, several expression chips were
   jointly processed, in consequence, within a processing-batch, several
   Sentrix barcodes are nested, [64]S3 Fig). Finally, we controlled for
   the Euclidean distance between all samples and an artificial sample
   defined as the average of samples (after removing 10% samples farthest
   away from the average of all samples). We found no individual with a
   distance larger than median + 4 x IQR. The final sample size was n =
   3,378. As previously described [[65]15], we filtered gene expression
   probes for sufficiently good mapping leaving a final number of 19,974
   valid gene expression probes corresponding to 13,693 unique genes. The
   described pre-processing pipeline has been published as R-package
   HT12ProcessoR on GitHub [[66]16]. The pre-processing method corresponds
   to the method named “noBg_log_quantile” and was found to be one of the
   two best-performing methods regarding optimal bias and variance
   performance [[67]12] ([68]S4 Fig)o,

   In LIFE-Heart, RNA from peripheral blood mononuclear cells (PBMC) was
   used (n = 3,008). Gene expression data was processed and filtered as
   described above resulting in final sample-size n = 2,978 and 19,966
   valid gene expression probes mapping to 13,687 unique genes.

Drug assessment

   Participants of LIFE-Adult were asked to provide all packages of
   medicaments taken in the last 7 days to the study centre. Packages were
   recorded electronically. In LIFE-Heart, patient medical records were
   evaluated to determine the current medication. For both cohorts no
   information about previous medication is available.

   Results were classified based on the German anatomic therapeutic
   chemical (ATC) classification [[69]17], which is the German translation
   of the ATC/DDD Index published by the WHO Collaborating Centre for Drug
   Statistics Methodology, Oslo. The ATC-Classification is a five level
   system dividing substances into different groups according to the organ
   or organ system that they affect and their pharmacological and
   therapeutic properties. The active substance is named in the lowest
   level (level five). Based on the ATC codes provided for each study
   participant and the level 5 information available, we compiled a list
   of active substances for each individual. As shown in [70]Table 1 we
   had in both cohorts participants taking no medication, which were used
   as control group.

Statistical analysis

   Statistical analysis was performed using the statistical software
   package R 3.6.0. Gene expression analysis was performed using the
   R-packages lumi 2.3.8 [[71]18] and limma 3.42.2 [[72]19].

Polymedication

   Dose of medication was not available; therefore, we considered
   medication of active substances as binary traits. To account for the
   effects of polymedication in our analysis, we aimed to adjust for the
   drugs with the largest impact on gene-expression in our models. To
   define these, we first performed multivariate linear regression
   analysis of gene-expressions estimating the impact of fifteen
   substances that were used by more than five percent of our LIFE-Adult
   subjects. In this analysis, we adjusted for sex, age, lymphocytes,
   monocytes, smoking status, and log transformed Body-Mass-Index (BMI)
   and tested one of the fifteen active substances at a time. For our gene
   expression adjustment model we selected active substances that caused
   significantly differential gene expression (5% FDR per substance).

   We tested collinearity between the substances in the adjustment model
   using variance inflation factor (VIF), confirming that there is no
   multi-collinearity problem between the resulting variables used for
   adjustment.

Multivariate differential gene expression analysis

   Next, we used the adjustment model defined above to analyse the effects
   of the single active substances on gene expression. We first tested for
   an extreme pairwise odds ratio (OR < 0.125 or OR > 8) between the
   active substance considered and the drugs in the adjustment model. If
   such an extreme pairwise OR was detected, the respective drug of the
   adjustment model was dropped to avoid collinearity issues. A total of
   83 active substances were analysed (each of them used by at least 20
   LIFE-Adult participants) ([73]S1 Table: Active substances analysed).
   P-values for each active substance were estimated based on moderated
   t-statistics [[74]20] and adjusted according to Benjamini and
   Hochberg’s method to control the false discovery rate (FDR) [[75]21].
   Significant probes identified were mapped to the respective genes. A
   gene is considered significantly differentially expressed, when at
   least one probe mapped to this gene was significantly differentially
   expressed (q ≤ 0.05).

   We aimed at replicating the identified associations in the LIFE-Heart
   cohort using the same adjustment model for all probes that were
   significant in LIFE-Adult. For replication, we applied hierarchical
   multiple testing correction. First, we adjusted the p-values for each
   substance by Benjamini and Hochberg’s method [[76]21] resulting in
   q-values per substance. Then, for each substance we selected the lowest
   q-value for further calculation. In the next step, these lowest
   q-values per substance were taken and further adjusted for multiple
   testing of substances according to Benjamini and Bogomolov’s method for
   multiple testing in families of hypotheses [[77]22]. The result for a
   gene was considered replicated when at least one probe that mapped to a
   gene was significantly differentially expressed in LIFE-Heart and
   showed the same effect direction for the same drug. In addition we
   performed a sensitivity analysis, by comparing the results with those
   obtained from a sign-test and nominal significance ([78]Fig 1).

Fig 1. Replication of LIFE-Adult results in LIFE-Heart.

   [79]Fig 1
   [80]Open in a new tab

   Top graph shows results of hierarchical multiple testing correction,
   which we selected as replication criteria. For information purposes, we
   include results for nominal significance (graph in the middle) and
   sign-test (lower graph). Results show strong transferability of results
   for whole blood to PBMC for carvedilol, timolol and prednisolone.

   For the replicated substances and genes, we performed a pathway
   enrichment analysis considering all analysed genes as background. Here,
   we used ontologies KEGG, GO, DOSE and Reactome [[81]23–[82]26] and
   considered an FDR value per substance of 0.05 as cut-off. The complete
   analysis workflow is shown in [83]S5 Fig.

Results

Polymedication

   The majority of study participants took more than one active substance
   ([84]Fig 2). Most participants hereby took medication affecting the
   cardiovascular system. In total, we identified 745 drugs with 587
   different active substances in our LIFE-Adult discovery cohort
   (LIFE-Heart: 568 drugs; 512 active substances). From the 587 active
   substances taken by LIFE-Adult participants, we considered 15
   substances as potential covariates to adjust for polymedication. Among
   these, eight substances in LIFE-Adult caused significantly (FDR ≤ 0.05)
   differentially expressed gene expression probes ([85]Table 2). Thus, we
   adjusted for a total of eight substances in our final model for gene
   expression analyses in conjunction with sex, age, lymphocytes,
   monocytes, smoking status and log transformed Body-Mass-Index (BMI).

Fig 2. Polymedication of LIFE-Adult and LIFE-Heart participants and most
common substances.

   [86]Fig 2
   [87]Open in a new tab

   Top: Polymedication of LIFE-Adult and LIFE-Heart participants, shown by
   number of active substances consumed. Participants taking no medication
   were used as control group. Bottom: Most common substances used in both
   cohorts.

Table 2. Active substances showing significant effects on gene expression
levels in LIFE-Adult and hence adjusted for in gene expression analysis.

   LIFE Adult LIFE Heart
   # (%) of study participants taking substance # of significant probes
   (fdr≤0.05) # (%) of study participants taking substance # of
   significant probes (fdr≤0.05)
   Acetylsalicylic acid 436 (15.9%) 9 1,681 (58.9%) 1
   Allopurinol 176 (6.4%) 387 330 (11.6%) 6
   Bisoprolol 439 (16.0%) 1,529 805 (28.3%) 0
   Hydrochlorothiazide 179 (6.5%) 5 716 (25.1%) 11
   Metformin 274 (8.1%) 210 396 (13.2%) 14
   Metroprolol 297 (8.8%) 3,597 740 (25.9%) 1
   Simvastatin 454 (13.4%) 558 1022 (35.9%) 5
   Valsartan 211 (6.2%) 8 294 (10.3%) 0
   [88]Open in a new tab

   These substances were used to account for polymedication in the
   multivariate analysis models for both studies. In LIFE-Adult n = 636
   (LIFE-Heart n = 126) participants did not take medication.

Differential gene expression analysis

   Using the polymedication based adjustment model, we identified fourteen
   (of initially analysed 83 substances taken by 20 or more LIFE-Adult
   participants) active substances that significantly (q ≤ 0.05) affected
   the expression of a total of 544 probes matching 534 genes in
   LIFE-Adult ([89]S2 Table: Summary of significant substances). The ratio
   between up and down regulation was balanced (down regulation: n = 248;
   up regulation n = 286). Effect sizes varied between β = -0.75, q =
   2.36x10^-2 (effect of Propranolol on GZMB) and β = 0.45, q = 7.84x10^-6
   (effect of Carvedilol on ADRB2). The number of genes affected by an
   active substance varied between one gene for phenprocoumon and 265
   genes being affected by metoprolol. The fourteen substances included
   two non-selective (carvedilol, propranolol) and two selective
   (bisoprolol, metoprolol) beta-blockers which are primarily used to
   treat hypertension and cardiovascular diseases. A complete list of the
   analysed active substances and affected genes is provided in [90]S3
   Table (statistics for each significant probe / substance combination).
   With respect to the number of genes affected by a single active
   substance our study shows that beta blockers affect the highest number
   of genes (376 of a total of 534 genes shown to be affected by drugs
   from this class in our study), followed by bronchodilators (59 genes),
   estrogen (46 genes) and corticosteroids (33 genes).

   Matching the significant probes with available LIFE-Heart probes
   resulted in 473 probes and 465 genes eligible for replication. This
   excludes 71 probes were the active substance was ethinylestradiol,
   levonorgestrel, propranolol, thiazide or vildagliptin which were not
   recorded in LIFE-Heart. Thus, a total of 473 probes associated with
   nine active substances were tested in LIFE-Heart. The aggregated
   replication results are provided in [91]Table 3.

Table 3. Substances that cause differential gene expression in LIFE-Adult and
their replication in LIFE-Heart.

   Substance number sign. probes LIFE-Adult thereof: Minimum q-value
   number probes available in LIFE-Heart number sign. probes LIFE-Heart
   thereof: Minimum q-value
   up-regulated down-regulated up-regulated down-regulated
   Bisoprolol 22 6 16 5.03x10^-04 22 0 0 0 2.56x10^-01
   Carvedilol 34 30 4 1.57x10^-10 34 25 22 3 1.50x10^-12
   Ethinyl estradiol 47 33 14 2.30x10^-03 0 n/a n/a n/a n/a
   Insulin aspart 4 2 2 4.80x10^-02 4 0 0 0 4.41x10^-01
   Levonorgestrel 8 7 1 2.75x10^-02 0 n/a n/a n/a n/a
   Metoprolol 265 107 158 2.05x10^-04 265 0 0 0 4.90x10^-01
   Phenprocoumon 1 1 0 1.88x10^-02 1 0 0 0 9.14x10^-01
   Prednisolone 34 25 9 1.64x10^-05 34 17 14 3 3.33x10^-07
   Propranolol 11 6 5 7.14x10^-04 0 n/a n/a n/a n/a
   Salbutamol 61 41 20 1.54x10^-02 61 0 0 0 8.91x10^-01
   Thiazide 1 1 0 2.74x10^-02 0 n/a n/a n/a n/a
   Timolol 49 27 22 9.14x10^-09 49 38 20 18 1.73x10^-04
   Torasemide 3 1 2 1.87x10^-02 3 0 0 0 3.06x10^-01
   Vildagliptin 4 4 0 1.24x10^-02 0 n/a n/a n/a n/a
   [92]Open in a new tab

   Not all probes significant in LIFE-Adult were available in LIFE-Heart.
   Minimum q-value refers to the probe associated with a substance with
   lowest q-value.

   For three of the nine active substances (carvedilol, prednisolone and
   timolol), we were able to also show differential gene expression in
   PBMC in LIFE-Heart ([93]Fig 3). More specifically, we could replicate
   25 of the originally identified 34 probes for carvedilol, 17 of the
   originally identified 33 probes for prednisolone and 38 of originally
   49 probes in the case of timolol. For all three substances the
   differential gene expression of all replicated genes had the same
   direction in both cohorts. Between carvedilol and timolol there was an
   overlap of 15 genes ([94]Fig 4), causing an upregulation, among others,
   of GPER1, PDE4B and TNFAIP3, which are genes directly associated with
   hypertension [[95]27–[96]29]. The full list of replicated genes and
   their effect strength and -direction is provided in [97]S4 Table. For
   all three substances we also identified a total of 120 significantly
   enriched pathways of replicated genes. The majority of the enriched
   pathways were caused by the beta blockers timolol (69 enriched
   pathways) and carvedilol (48 enriched pathways). For Prednisolone we
   identified three enriched pathways. Between timolol and carvedilol
   there was an overlap of 20 enriched pathways ([98]Fig 5, [99]S6 Fig,
   [100]S5 Table). Of the 20 pathways, 15 included either GPER1 or
   TNFAIP3.

Fig 3. Differential gene expression caused by carvedilol, prednisolone and
Timolol.

   [101]Fig 3
   [102]Open in a new tab

   Original results as obtained from LIFE-Adult and successfully
   replicated in LIFE-Heart. Genes may be captured on multiple probes and
   are then shown multiple times. All replicated genes show the same
   effect direction.

Fig 4. Genes overexpressed by more than one substance.

   [103]Fig 4
   [104]Open in a new tab

   Analysis shows high overlap between timolol and carvedilol.

Fig 5. Pathways with significant enrichment in LIFE-Adult and LIFE-Heart (FDR
< 5%).

   [105]Fig 5
   [106]Open in a new tab

   If two pathways are enriched due to the identical set of replicated
   genes, only the pathway with the higher enrichment (i.e. higher Odds
   ratio) is shown here. All significantly enriched pathways are reported
   in [107]S5 Table. Differentially expressed genes per pathway are shown
   in [108]S6 Fig.

Discussion

   At the current state of research, the effects of drugs on defined gene
   expression profiles is understudied. We here performed the first
   population based transcriptome-wide association analysis of 83 drugs on
   gene expression in two independent cohorts. We discovered 534 genes
   affected by 14 substances in whole blood in our cohort of 3,378
   subjects of LIFE-Adult. Notably, we were able to replicate differential
   gene expression for three drugs affecting 80 genes in peripheral blood
   mononuclear cells (carvedilol: 25; prednisolone: 17; timolol: 38).

   Replication in LIFE-Heart shows the transferability of our results from
   whole blood to PBMC on gene as well as on pathway level. As PBMC
   represents only a subset of the cells available in whole blood the
   replication is even more notable. This was true for the two
   beta-blockers, carvedilol, timolol, as well as for prednisolone. Using
   more relaxed criteria for replication (nominal significance and
   sign-test) also led to results for bisoprolol that could be replicated.
   While timolol is used as long term medication in form of eye drops to
   treat glaucoma and decrease intraocular pressure, the results confirm
   gene expression due to systemic concentrations after local application
   [[109]30] as timolol avoids to a large extend the first pass metabolism
   and about 80% of the drug are systemically absorbed [[110]31]. This is
   in line with the large effect of timolol, which we observed at both,
   gene- and pathway level.

   We were not able to compare our results with other studies, because
   population based gene expression studies for substances have not been
   published yet. The only study that had analysed the effect of
   carvedilol on gene expression was for heart tissue in mice [[111]32].
   There was no overlap between the genes found in this study and the
   genes identified by us. To better understand if our results provide
   potential insights into possible off-target-effects, we therefore
   searched the literature for the genes that were affected in both
   cohorts by carvedilol, a commonly used beta- and alpha-blocker that
   causes vasodilation. Only three of the 23 unique genes replicated, have
   been directly associated with hypertension (GPER1, PDE4B and TNFAIP3)
   so far [[112]27–[113]29]. All three genes were also differentially
   expressed by timolol. Further eight genes are associated with having
   effects on artery walls (LMO2 [[114]33], ADRB2 [[115]34], SPRED1
   [[116]35], CX3CR1 [[117]36], ADAP2 [[118]37], PTPRO [[119]38], RAPH1
   [[120]39] and CEBPG [[121]40, [122]41]) and are therefore indirectly
   linked to blood pressure. Another six genes (S1PR3 [[123]42], MBOAT1
   [[124]43], FAM20C [[125]44], CAP1 [[126]45], SORT1 [[127]46] and
   SLC27A3 [[128]47]) are involved in lipid biosynthesis and can therefore
   affect blood pressure indirectly via influencing atherosclerosis. For
   the remaining six genes, no studies exist so far linking them to
   hypertension. These genes are CEBPA, CEBPA-DT, SLITRK4, TEX2, AVPI1 and
   PTRH2. As beta blockers affect the whole sympathetic nervous system,
   effects other than on the cardiovascular system are not surprising.
   Further research is needed to clarify if the differential expression
   observed for these genes may lead to unwanted side-effects of
   carvedilol in regards to the cardiovascular system or if the genes
   indeed are involved in cardiovascular pathomechanisms. Regarding the 17
   genes linked directly or indirectly to hypertension, we showed that
   carvedilol delivers cardioprotective effects on multiple levels. They
   either affect blood pressure directly, support the repair response to
   acute cardiac damage or decrease the risk of plaque formation. Also
   plausible pathway enrichments were found, e.g. negative regulation of
   interleukin-1 production and brown fat cell differentiation, which are
   linked to lower blood pressure [[129]48] and better cardio-metabolic
   health [[130]49].

   Limitations: Both analysed studies are cross-sectional, i.e. we could
   not compare gene-expression prior and after start of medication. We
   cannot exclude that effects of active substances on blood
   gene-expressions are at least in parts caused by the underlying disease
   conditions. Replication of the LIFE-Adult results in LIFE-Heart may be
   restricted by the different tissues analysed (whole blood vs. PBMC).
   Differences between LIFE-Adult and LIFE-Heart in the medication
   anamnesis as well as missing temporal aspects for medication history
   may have affected the results of differential gene-expression. In
   LIFE-Adult the medicaments taken in the last 7 days were reported,
   while in LIFE-Heart only the current medication (based on medical
   records) was available. No information about previous medication or
   recent changes in medication was available in both studies. As we
   analysed the effect on the level of active substances, the medication
   specific resorption may have affected the results. We also have to
   acknowledge that case numbers differ largely between different types of
   medication resulting in largely different power to detect associations.
   Thus, number of identified genes is not a measure of the total impact
   of the respective drug on gene-expression.

   In conclusion, this is the first study that provides insights on how
   active substances may affect blood gene-expression. Several novel
   associations contribute to the understanding of pleiotropic effects and
   mechanisms of actions of the investigated substances.

Supporting information

   S1 Table. Active substances analysed for differential gene expression
   in in LIFE Adult.

   Number of users in LIFE Adult and LIFE Heart.

   (XLSX)
   [131]Click here for additional data file.^ (12.2KB, xlsx)
   S2 Table. Active substances in LIFE-Adult causing differential gene
   expression.

   Active substances causing significant changes in genes expression after
   adjustment by multivariate model in LIFE Adult. In some cases, multiple
   probe sets map to the same gene. The total number of unique genes with
   differential expression is 442. Some genes were effected by more than
   one active substance.

   (XLSX)
   [132]Click here for additional data file.^ (10KB, xlsx)
   S3 Table. Active substances and differentially expressed genes.

   Not all probes identified as significant in LIFE-Adulte were available
   in LIFE-Heart. Some probes were not linked to a gene.

   (XLSX)
   [133]Click here for additional data file.^ (102.9KB, xlsx)
   S4 Table. Genes with differential expression caused by Carvedilol,
   Prednisolone and Timolol.

   We present results of LIFE-Adult and their replication in LIFE-Heart.
   Genes captured on multiple probes and are shown for each probe. Data
   sorted by LIFE-Adult q-value.

   (XLSX)
   [134]Click here for additional data file.^ (15.6KB, xlsx)
   S5 Table. Enrichment analysis for genes that were significant in
   LIFE-Adult and LIFE-Heart.

   For enrichment analysis we used a p-value of 0.05 as cut of point. For
   all three substances significant pathways were identified. Timolol and
   Carvedilol show overlap of 20 pathways.

   (XLSX)
   [135]Click here for additional data file.^ (28.5KB, xlsx)
   S1 Fig. Distribution of attributes for filtering technically failed
   chips.

   Distribution of attributes for filtering technically failed chips for
   LIFE-Heart.

   (GIF)
   [136]Click here for additional data file.^ (476.6KB, GIF)
   S2 Fig. Distribution Mahalanobis-distance.

   Mahalanobis distance for LIFE-Heart.

   (GIF)
   [137]Click here for additional data file.^ (106.9KB, GIF)
   S3 Fig. ANOVA test results.

   ANOVA test results for Sentrix and fileset-id before and after Combat
   for LIFE-Heart.

   (GIF)
   [138]Click here for additional data file.^ (85.5KB, GIF)
   S4 Fig. Preprocessed expression values (nomralized and transformed).

   Exemplarily shown (for the top-probes of Carvedilol, Prednisolone und
   Timolol), distribution of pre-processed, i.e. normalized and
   transformed data. Dashed-line represents normal distribution. Coloured
   area shows actual data after transformation and normalization.

   (GIF)
   [139]Click here for additional data file.^ (58KB, gif)
   S5 Fig. Study design schematic for discovery and validation of genes.

   Despriction of processing steps performed.

   (GIF)
   [140]Click here for additional data file.^ (75.4KB, GIF)
   S6 Fig. Differentially expressed genes per pathway.

   Differentially expressed genes and associated pathways.

   (GIF)
   [141]Click here for additional data file.^ (233.6KB, GIF)

Acknowledgments