- Research article
- Open Access
Diurnal and circadian rhythmicity of the human blood transcriptome overlaps with organ- and tissue-specific expression of a non-human primate
BMC Biology volume 20, Article number: 63 (2022)
Twenty-four-hour rhythmicity in mammalian tissues and organs is driven by local circadian oscillators, systemic factors, the central circadian pacemaker and light-dark cycles. At the physiological level, the neural and endocrine systems synchronise gene expression in peripheral tissues and organs to the 24-h-day cycle, and disruption of such regulation has been shown to lead to pathological conditions. Thus, monitoring rhythmicity in tissues/organs holds promise for circadian medicine; however, most tissues and organs are not easily accessible in humans and alternative approaches to quantify circadian rhythmicity are needed. We investigated the overlap between rhythmic transcripts in human blood and transcripts shown to be rhythmic in 64 tissues/organs of the baboon, how these rhythms are aligned with light-dark cycles and each other, and whether timing of tissue-specific rhythmicity can be predicted from a blood sample.
We compared rhythmicity in transcriptomic time series collected from humans and baboons using set logic, circular cross-correlation, circular clustering, functional enrichment analyses, and least squares regression. Of the 759 orthologous genes that were rhythmic in human blood, 652 (86%) were also rhythmic in at least one baboon tissue and most of these genes were associated with basic processes such as transcription and protein homeostasis. In total, 109 (17%) of the 652 overlapping rhythmic genes were reported as rhythmic in only one baboon tissue or organ and several of these genes have tissue/organ-specific functions. The timing of human and baboon rhythmic transcripts displayed prominent ‘night’ and ‘day’ clusters, with genes in the dark cluster associated with translation. Alignment between baboon rhythmic transcriptomes and the overlapping human blood transcriptome was significantly closer when light onset, rather than midpoint of light, or end of light period, was used as phase reference point. The timing of overlapping human and baboon rhythmic transcriptomes was significantly correlated in 25 tissue/organs with an average earlier timing of 3.21 h (SD 2.47 h) in human blood.
The human blood transcriptome contains sets of rhythmic genes that overlap with rhythmic genes of tissues/organs in baboon. The rhythmic sets vary across tissues/organs, but the timing of most rhythmic genes is similar in human blood and baboon tissues/organs. These results have implications for development of blood transcriptome-based biomarkers for circadian rhythmicity in tissues and organs.
Twenty-four-hour rhythmicity is a prominent characteristic of behaviour, physiology and molecular processes in single- and complex multi-cellular eukaryotic organisms including humans and other primates [1, 2]. When this rhythmicity is observed in the presence of 24-h environmental cycles, it is referred to as diurnal rhythmicity. Near 24-h rhythmicity that persists in the absence of environmental cycles qualifies as circadian rhythmicity . Circadian rhythmicity is generated by a molecular ‘clock’ comprising a transcription/translation feedback loop consisting of core clock genes such as Arntl, Clock, Cry1, Cry2, Per1, Per2, Per3. This core clock drives circadian rhythmicity in gene/protein expression present in nearly all organs, tissues and cell types .
In mammals, rhythmicity in peripheral tissues and organs is synchronised to the 24-h day by neural and endocrine rhythms, such as rhythmicity in the autonomic nervous system and the cortisol rhythm driven by the suprachiasmatic nulcei (SCN), which in turn is synchronised to the 24-h light-dark cycle via neural connections from the retina and its photoreceptors . Tissue- and organ-specific temporal programmes are, however, also affected by behavioural and associated systemic factors. For example, in mice, rhythmicity in the liver transcriptome is driven by ‘clock genes’ as well as feeding . Likewise, in the mouse brain, rhythmicity is largely driven by the sleep-wake cycle and to a lesser extent by the circadian molecular clock .
In humans, twenty-four-hour rhythmicity in the brain is disrupted in conditions such as bipolar disorder, schizophrenia and dementia [7, 8]. In humans, twenty-four-hour rhythms in the blood transcriptome and physiological variables are also disrupted when the sleep-wake cycle and associated feeding-fasting cycles are desynchronised from the SCN such as occurs during shiftwork and jet lag, or when participants carry a sleep debt [9,10,11]. In both humans and rodents, this disruption can vary across different organs and tissues [12, 13] and is likely to mediate some of the negative health consequences of, for example, shift work . The disruption of rhythmicity in tissues and organs may be limited to rhythmicity downstream from the core circadian molecular clock, or encompass disruption to the core molecular clock itself.
Quantifying tissue- and organ-specific rhythmicity in humans and other species will provide new insights into the temporal organisation of gene expression and holds promise for circadian medicine [15, 16]. Quantifying rhythmicity in tissues and organs requires time series of samples, but for humans time series samples of most tissues and organs cannot easily be obtained. One tissue that is easily accessible in living humans is blood. It has been shown that the human blood transcriptome contains information on altered gene expression profiles in for example the brain  and lung . Recently, machine learning approaches demonstrated that the human blood transcriptome can be used to partially ‘predict’ the transcriptome in 16 out of 26 analysed tissues . These studies used single time point samples and could not assess whether rhythmic aspects of the human blood transcriptome are concordant with rhythmicity of gene expression profiles in tissues and organs. Analyses of human blood transcriptome time series have already demonstrated that rhythmicity in sets of blood transcripts reflect the phase of the human SCN, as indexed by the melatonin rhythm [11, 20,21,22]. However, to what extent rhythmicity in the human blood transcriptome overlaps with rhythmicity in other brain areas, tissues and organs remains unknown. Time series of tissue-specific transcriptomes for a large number of human tissues and organs are not available and comparison of the human blood transcriptome with available transcriptome time series of tissues and organs in rodents [23, 24] may be less informative and relevant, in part because rodents are nocturnal and are, from an evolutionary perspective, quite distant from humans. Time series of transcriptomes of sixty-four tissues and organs of a diurnal non-human primate, the baboon, have recently become accessible . In this species, across all tissues/organs, 81.7% of protein-coding genes were rhythmic and highly tissue/organ-specific, such that no single gene was rhythmic in all tissues/organs, and a total of 3199 baboon genes were rhythmic in only one tissue/organ. Most of the rhythmic genes were universally expressed genes, i.e. genes expressed (but not necessarily rhythmic) in all tissues/organs, supporting basic processes like transcription, RNA processing, DNA repair, protein homeostasis and cellular metabolism. Analysis of the timing of rhythmicity in the baboon tissues and organs revealed, similar to the human blood transcriptome [26, 27], a striking bimodality with peaks in the early morning and late afternoon. Analysis of the timing of core clock genes indicated that in the baboon their timing was rather similar across tissues and organs but differed from their timing in the nocturnal mouse.
Here, we compared the similarities in expression and timing of human blood and baboon tissue/organ-specific transcriptomes to determine: (1) the overlap between rhythmic transcripts in human blood and transcripts shown to be rhythmic in 64 tissues/organs of the baboon, (2) how these rhythms are aligned with light dark cycles and (3) whether timing of rhythmic transcripts in baboon tissues and organs is similar to their timing in human blood. A flow diagram illustrating the various analyses and comparisons we have conducted can be found in Fig. 1.
Overlap of the rhythmic transcriptomes of human blood and baboon tissues/organs
In total, 652 (~5.7%) of the 11,351 orthologous genes targeted by the human microarray platform and reported by  to be rhythmic in at least one of the sixty-four tissues/organs of the baboon, were also rhythmic in human blood samples collected in the presence of a sleep-wake cycle (Fig. 2, Additional file 1: Table S1). These 652 genes represented 86% of the orthologous genes (759) that were rhythmic in human blood (Fig. 2). In total, 438 (~67%) of these 652 genes were considered universally expressed genes (UEGs) in the baboon (Supp. Table S4 in ). The 652 human genes that were rhythmic in both baboon tissues/organs and human blood also included clock-associated genes (e.g. Arntl, Nr1d2, Per2, Per3, Nfil3, Npas2, Cry2 and Tef) and these will be discussed in detail below.
Functional enrichment analysis for over-represented non-redundant GO biological processes and molecular functions in this set of 652 genes identified significantly enriched (FDR < 0.05) terms not related to blood-specific processes and functions but terms related to translation (e.g. ribonucleoprotein complex biogenesis, tRNA metabolic process, translation initiation, translation elongation). The most significant GO term was ‘structural constituent of ribosome’ (enrichment ratio 4.73, P = 4.273e−12, FDR = 4.837e−9) with an overlap between human and baboon of 28 genes for ribosomal subunits, including 9 genes for mitochondrial ribosomal subunits.
Distribution of all overlapping rhythmic genes across tissues/organs
The overlap of rhythmic genes in tissues/organs with other organs/tissues in the baboon and with rhythmic genes in human blood is illustrated in Fig. 3. Across tissues/organs, an average of 6.02% ( SD 1.73%) of each individual tissue/organ rhythmic transcriptome was shared with the human blood rhythmic transcriptome and the percentage of overlap was on average 5.4% ( SD 2.15%). This compares to an overlap of rhythmic transcriptomes of on average 8.83% ( SD 8.05%) between dyads of tissues/organs in the baboon (Fig. 3b). The percentage of genes that were rhythmic in human blood and in baboon samples were not distributed evenly across the tissues/organs (Fig. 3, Additional file 2: Table S2). For each of the 64 tissues/organs, at least one rhythmic gene was also identified as rhythmic in human blood. The gene identified as rhythmic in the greatest number of baboon tissues was Arntl (46 baboon tissues). This was followed by Nr1d2 (38 tissues), Per2 (38 tissues), Per3 (36 tissues), Nfil3 (33 tissues), Npas2 (24 tissues), Cry2 (24 tissues) and Tef (24 tissues). Although these 8 genes are all associated with the core molecular clock and were all rhythmic in human blood, they were all rhythmic in only six baboon tissues/organs (cornea, pancreas, skin, white adipose tissue, thyroid and retinal pigment epithelium).
Detecting genes that are rhythmic in only one baboon tissue/organ in the rhythmic transcriptome in human blood
For 46 of the 64 tissues/organs, at least one tissue/organ-specific rhythmic gene was identified as rhythmic in human blood (see Additional file 1: Table S1). Of the in total 1788 genes reported to be rhythmic in only one baboon tissue/organ, 109 (~6%) were also identified as rhythmic in human blood collected in the presence of a sleep-wake cycle. Only 42 (~39%) of these 109 genes were considered UEGs related to basic biological functions, e.g. translation (Rpl36a, Mrpl47, Mrps33), lipid metabolism (Abhd5, Fabp5, Acsm3), antioxidant activity (Alox5ap, Gpx7), small molecule metabolism (Clybl, Dnajc15, Pask).
Examples of the time course and alignment of tissue/organ-specific rhythmic genes are plotted in Fig. 4. Examples were selected for genes that were robustly rhythmic and mostly coded for proteins with functions relevant for the tissue/organ. Of these example genes, only one, Aoc3, is classified as a UEG in the baboon. Some of these tissue/organ-specific genes are associated with functions that are relevant to those tissues/organs. Shisa7, which in the baboon was only classified as rhythmic in the mammillary bodies, is also rhythmically expressed in human blood both in the presence of a sleep-wake cycle and in the absence of a sleep-wake cycle with a phase difference of only − 2 h (Fig. 4a). Shisa7 is implicated in synaptic potentiation. Mobp (Fig. 4c), classified as rhythmic only in the globus pallidus, where its expression is closely aligned with its expression in human blood with a phase difference of 4 h, is implicated in myelin sheath regulation. Another brain tissue-specific rhythmic gene is Tmem145, which was classified as rhythmic only in the paraventricular nucleus. It has no known function but its time course in the paraventricular nucleus is closely aligned (lag = 0 h) with the human blood profiles (Fig. 4b). Tissue/organ-specific rhythmic genes were also observed in peripheral tissues and organs. Rnf207, a gene implicated in cardiac K channel regulation, was classified as rhythmic only in the baboon heart but was also rhythmic in human blood with a lag of 0 h (Fig. 4d). Umod is a gene that is important for renal function and was only rhythmic in the kidney cortex and in human blood with a phase lag of 0 h (Fig. 4e). Otx1 was rhythmic only in the cornea of the baboon as well as in human blood with a lag of − 4 h (Fig. 4g). This gene plays a role in sensory organ development. Aoc3 was specifically rhythmic in axillary lymph nodes and was also rhythmic in human blood with a lag of 2 h and its function is related to lymph node trafficking (Fig. 4h). As for brain tissues, some peripheral tissue/organ-specific rhythmic genes were not associated with a known function (e.g. Tmem116 in the bladder Fig. 4f). All tissue/organ-specific rhythmic genes that were also rhythmic in human blood are listed together with their functional annotation, where known, in Fig. 5. It should be noted that none of the tissue/organ-specific rhythmic genes has any known direct connection with circadian function.
Diurnal vs circadian overlapping rhythmic genes
Of the 652 overlapping genes that were rhythmic in the presence of a sleep-wake cycle, only 152 genes (23.3%) were also rhythmic in the absence of a sleep-wake cycle. These truly circadian, rather than just diurnal genes, were observed in all tissues/organs apart from bone marrow and the supraoptic nucleus (Additional file 1: Table S1). The highest number of truly circadian human overlapping genes was found in the thyroid (76 probes targeting 60 genes). GO enrichment analysis of the 152 truly circadian genes identified non-blood-specific processes such as ribonucleoprotein complex biogenesis and cellular carbohydrate metabolic process, but unlike the larger 652 gene set, also blood-related processes such as negative regulation of cytokine production, type 2 immune and T cell activation, although it should be noted that for all these GO terms the FDR was greater than 0.05 (see Table 1).
Diurnal vs circadian for tissue-specific overlapping rhythmic genes
Of the 109 overlapping tissue/organ-specific genes that were rhythmic in the presence of a sleep-wake cycle, only 25 (22.93%) were also rhythmic in the absence of a sleep-wake cycle. These truly circadian, rather than just diurnal tissue-specific genes were associated with 14 tissues. Tissues with the highest number of circadian genes were thyroid (Clybl, Pask, Creb5, Lats2), prefrontal cortex (Hpcal4, Krt32, Cntnap3, Spocd1), oesophagus (Aqp3, Trat1), cornea (Otx1, Tmem40) and antrum (Phf21a, Rsad2) (Additional file 1: Table S1). Some of these tissue-specific circadian genes have functions related to the tissue, e.g. Otx1 (orthodenticle homeobox 1) expressed in the cornea regulates brain/sensory organ development, and Cntnap3 (contactin associated protein family member 3) expressed in the prefrontal cortex and mediates neuron-glia interaction.
Alignment of human blood and baboon rhythmic genes
Alignment of rhythmic expression profiles to the light-dark cycle
To optimally compare the timing of rhythmic expression profiles in baboon tissues/organs to human blood, we first identified which phase reference point yielded the closest alignment between the baboon and human transcriptomes. For Per3 in the bladder, the lag between human and baboon was 0 h when lights are on (Fig. 6a), − 2 h for the midpoint of light (Fig. 6b) and − 4 h (Fig. 6c) when end of light was taken as a phase reference point. When alignment of the rhythmic transcriptome in each of the 64 tissues/organs was considered, this superiority of lights on as a phase reference point was confirmed (paired Wilcoxon rank sum test; lights on < midpoint of light lags P = 1.06e−06, lights on < end of light lags P = 2.787e−11) (Fig. 6d, e). In the vast majority of tissues/organs (n = 51 out of 64), start of light period produced the smallest lags. Exceptions included white adipose retroperitoneal tissue, paraventricular nucleus and the pons (Fig. 6d). Similar results were obtained when the analyses were applied to individual rhythmic gene profiles of all tissues and organs combined (Fig. 7). All data and phase relations are therefore presented relative to lights on.
Alignment of ‘core clock genes under diurnal conditions’
The 652 human genes that were rhythmic in both baboon tissues/organs and human blood included clock genes. Timing of Per2, Nr1d2, Per3, Cry2 and Npas2 in human blood was close to their timing in baboon tissues/organs with average timing in blood being 2 h and 0.55 h later for Per2 and Npas2, respectively, and 4.81 h, 4.63 h, 0.15 h earlier for Nr1d2, Per3 and Cry2, respectively. Nfil3 and Tef peaked on average 6.66 h and 8.182 h, respectively, earlier in human blood than in baboon tissues/organs. Timing of Arntl profiles was on average 10 h phase advanced in human blood compared to baboon tissues/organs (lag range across tissues: − 10 to + 12 h) (Fig. 8, and Additional file 3: Table S3). For Per2, Nr1d2, Per3 and Cry2 the phase relationships between baboon tissues/organs and human blood were not systematically different when rhythms in the baboon brain were compared to rhythms outside the brain. Since these 8 clock genes are rhythmic in several tissues, their timing in human blood can only provide limited information about differences in timing of rhythmicity across individual tissues and organs.
Timing of rhythmic genes in the baboon and human blood
When the timing of all overlapping rhythmic genes was considered, a clear bimodality in the timing of acrophases was observed. For example, in the thyroid, bimodality was observed in both human blood and the baboon when all overlapping rhythmic genes were considered (Fig. 9a, b). In human blood, the two peaks were located 2–4 h after lights on and 2–4 h after the onset of darkness. In the baboon, these two peaks were located at 6–10 h after lights on and in the middle of the dark phase (18–22 h). When all individual acrophases of rhythmic genes in baboon thyroid were plotted against the corresponding acrophases in human blood, a significant association emerged (Fig. 9c). Similar patterns can be observed in the mammillary bodies (Fig. 9d, e, f) and antrum (Fig. 9g, h, i).
To further assess the extent the human blood transcriptome mirrors the baboon tissue/organ transcriptome, we used circular K-means clustering analysis to characterise the observed bimodal acrophase distribution of rhythmic genes. We clustered all rhythmic genes into two groups based on both their acrophase in human blood and their acrophase in baboon tissue/organs (see Fig. 10a). This allowed the classification of the rhythmic genes within the two peaks of the acrophase bimodal distributions (see Fig. 10 and Additional file 4: Table S4).
The two cluster analysis defined genes with peak acrophases that corresponded to the human morning peak (2–4 h after lights on), the human early night peak (16–18 h after lights on), the baboon afternoon peak (6–10 h after lights on) and the baboon late night peak (18–22 h after lights on). Bimodality was also observed for the tissue/organ-specific rhythmic genes (see Fig. 11).
Genes in the night cluster for each species were strongly enriched for biological processes and molecular functions related to translation and protein synthesis (see Table 2). Although the number of unique genes analysed in the night and day clusters were similar (286 night vs. 275 day), the greater range of processes and functions associated with day-peaking genes led to reduced overall enrichment compared with the night peak. In fact, enrichment of genes for the day peak for each species did not survive the FDR < 0.05 threshold. When ranked by nominal statistical significance, these gene sets appeared enriched for a wider range of processes and functions and generalised cellular activity (e.g. extracellular structure organisation, P = 0.0002; glutamate receptor signalling pathway, P = 0.0005; organ growth, P = 0.0006; regulation of cell morphogenesis, P = 0.0029). Of interest, the day cluster contains the core clock genes Arntl, Per2, Cry2, Csnk1e, Rara and Nfil3, while the night cluster contains Per3, Nr1d2, Npas2 and Tef. The night cluster also contained Rbm3, which is a temperature-sensitive RNA-binding protein known to regulate the translation of circadian proteins . It is also of interest that genes in the human blood day cluster were not associated with typical blood-specific functions such as inflammatory responses and immune function .
We next quantified the extent to which rhythmic profiles of genes in baboon tissues/organs were synchronised with the orthologous rhythmic profile in human blood by circular correlations of acrophases for overlapping rhythmic transcripts, separately for each tissue/organ. A significant correlation (BH-corrected p value < 0.05) was observed in 25 out of 64 tissues/organs (See Fig. 12a, c). These 25 tissues/organs included endocrine organs such as the thyroid, brain structures such as the mammilary bodies, paraventricular nucleus and medial globus pallidus and the intestine (cecum and duodenum) and the immune system (axillary lymph nodes).
When the differences in acrophases of the overlapping rhythmic genes in human blood and baboon tissues were computed, it emerged that across the 64 tissues/organs vs the human blood transcriptome human genes were timed 3.21 h (SD 2.47 h) earlier than baboon genes. When a similar comparison was made for the timing of the overlapping transcriptomes for all baboon tissue/organ dyads, the average acrophase difference was 0.00 h (SD 3.43 h) (see Fig. 12b, d). Similar results were obtained when we computed the circular cross-correlation of the temporal profiles in baboon tissues/organs and human blood (see Fig. 13). This correlation was substantial (mean > 0.7) for most tissues/organs, with the exception of the supraoptic nucleus (Fig. 13b). Based on the circular cross-correlation of the profiles, human rhythms were advanced on average by 2.69 h (SD 2.21 h), which is less than the 3.21 h (SD 2.47 h) estimated based on acrophase values, but it should be noted that the standard deviation of the lag was considerable for most tissues/organs (Fig. 13d).
A blood-based multivariate single blood sample predictor for tissue-specific rhythmicity?
The similarity in the timing of tissue/organ-specific rhythmicity in tissues and organs in the baboon and rhythmicity in human blood implies that the blood transcriptome may be used to assess the circadian alignment of tissues/organs. This has potential implications for the development of blood-based biomarkers for rhythmicity in tissues and organs. These biomarkers may either be univariate and would require a time series of blood samples, or multivariate, i.e. based on a set of transcriptomic features which potentially would require only one or two samples.
We explored whether it was in principle possible to create a multivariate biomarker which would allow to predict the phase of tissue/organ rhythmicity from one or two blood samples. To provide proof-of-principle evidence, we selected the antrum of the stomach (ANT) and the paraventricular nucleus (PVN) (see ‘Methods’ for selection procedure). The time of tissue-specific rhythmicity assigned to each blood sample was based on one marker gene per tissue (see Fig. 14). For the antrum, we selected Slc44a2, which is a choline transporter active in the intestine, and for the PVN, we selected Lrfn3, which is a synaptic adhesion-like molecule. The assumption of our analysis is that the phase of the selected blood marker gene is a good proxy for the phase of these genes in the specific tissue.
The overlap of the rhythmic transcriptome in these two tissues with the rhythmic human blood transcriptome was 81 genes and 118 genes for ANT and PVN respectively, of which only 10 genes overlapped.
To develop predictive models, we eliminated all features (targeting probes) associated with classical clock genes, because their timing is non-tissue-specific. We also excluded the selected marker gene for each tissue. The number of features selected by the predictive model were 74 genes and 113 genes for ANT and PVN respectively, of which only 8 genes overlapped (see Additional file 5: Table S5). From these sets of the blood-ANT and blood-PVT transcripts, PLSR applied to the training samples, identified 45 probes (targeting 41 genes) and 55 probes (targeting 49 genes) for the ANT and PVN biomarker, respectively. When these predictors were applied to the blood samples in the validation set, they predicted the ANT and PVN phase with an R2 of 0.63 and 0.67, and a mean absolute error of 2.94 and 2.81 h for ANT and PVN, respectively (Fig. 15 and Additional file 6: Table S6).
For both tissues, the predictors were related to cell cycle/cell growth/apoptosis, ubiquitination/protein degradation/autophagy and transcription/translation. Only one gene in the ANT was related to circadian rhythmicity which was Rai1, a transcription factor that interacts with the circadian transcription factor CLOCK.
We generated 50 negative control biomarkers for ANT and PVN by using N randomly selected probes from the set of orthologous genes assessed in Archer et al.  and Mure et al. , where N is the number of candidate biomarker genes (input to PLSR model) in the real model. The negative control biomarkers predicted the ANT and PVN phase with a significantly lower R2 (p = 02).
We also investigated the performance of a two-sample-based model. From the sets of the blood-ANT and blood-PVT transcripts in the training samples, the two-sample PLSR identified 20 probes (targeting 18 genes) and 25 probes (targeting 22 genes) for the ANT and PVN biomarkers, respectively. When these predictors were applied to the blood samples in the validation set, they predicted the ANT and PVN phase with an R2 of 0.71 and 0.87, and a mean absolute error of 1.74 and 1.19 h for ANT and PVN, respectively.
Summary of results
Rhythmic genes in human blood overlap with rhythmic genes in baboon tissues/organs and the timing of most rhythmic genes is similar across tissues/organs and human blood.
Extent and characteristics of overlapping rhythmicity in human blood and baboon tissues/organs
The sets of orthologous genes
The analyses were necessarily restricted to orthologous genes that were present in both data sets. Overall, these sets corresponded to 47% of all baboon genes as defined by Ensembl on 30/7/2020 and only 23% of all human genes as defined by Ensembl on 30/7/2020. However, here we were interested in genes that were rhythmic in both baboon and humans and this set amounted to 46.7% (652/1396) of all human blood rhythmic genes and 86% of the orthologous genes (759) in . Thus, even though the overall set of orthologous genes represents only a small portion of all human genes, the set of orthologous genes that are rhythmic represents a substantial portion of the total number of human blood rhythmic genes. Improved annotation of the baboon and human genomes may allow for a more extensive comparison of the rhythmic transcriptome across human and baboon.
Extent and characteristics of overlapping rhythmic genes
The overlap between human blood rhythmic genes and genes that are rhythmic in one or more of the 64 tissues/organs was somewhat lower than the overlap in rhythmic gene sets across all dyads of baboon tissues/organs, but there was quite a range of overlap both across baboon tissues/organs and human blood and across baboon tissues/organs. Thus, from this perspective, human blood-tissue/organ ‘dyads’ behaved similarly to any of the within baboon dyads.
The majority of rhythmic genes in the baboon and human-baboon overlapping rhythmic genes were considered UEGs in the baboon (see Additional file 1: Table S1). The set of overlapping rhythmic genes was primarily enriched for basic processes predominantly associated with mRNA translation, and not for specific blood functions (e.g. immune function). This indicates that the overlapping rhythmic transcriptome across blood and baboon tissue/organs is representative of rhythmicity in organs and tissues. The observation that only 39% of the set of genes that are rhythmic in human blood and in only one tissue/organ belong to the list of UEGs, and the functional annotation of this set, imply that tissue-specific rhythmic genes serve tissue-specific functions.
Origin of overlapping rhythmic transcripts in human blood
Human blood contains many cell types and diurnal and circadian rhythmicity in blood has been described in the number of the various cell types  and each of these cell types may contribute to rhythmic transcriptome in whole blood. The baboon tissues/organs were perfused prior to sampling and so were not likely to contain significant numbers of blood cells. Some tissues/organs may have contained resident immune cells, such as Kupffer cells, that cannot be removed by perfusion, but their overall contribution to the rhythmic tissue/organ transcriptome will, in general, be minimal. Therefore, the lack of, or under-representation of immunity-related GO terms in the human/baboon rhythmic transcript overlap is presumably due to the overall absence of blood cells in the baboon tissues/organs. We performed over/under-representation analysis of known blood immune cell type gene markers of the overlapping rhythmic genes (see Additional file 7: Table S7). We found significantly higher than expected enrichment for neutrophil cell markers in some tissues, including thyroid, mammilary bodies, ileum, antrum and bladder. Furthermore, this neutrophil enrichment was only present in the day-peaking transcripts in these tissues. These observations point to a potential albeit small contribution of this cell type to the overlapping rhythmic transcriptome in those tissues/organs. Neutrophils are the most common type of white blood cell (~60%) and the first line of defence in the innate immune system. They are produced in large numbers during the day and then cleared by multiple tissue sites at night. In our data the neutrophil enrichment was only present in the day-peaking transcripts in these tissues and this is consistent with the rhythmicity in their abundance level which peaks during the day . Some of the baboon tissue/blood overlapping gene sets that showed enrichment for neutrophil cell markers are involved with innate immune function. For example, thyroid hormones increase neutrophil function, the ileum contains Peyer’s patches which are lymphoid tissues that perform immune surveillance and signal an immune response to infection, and the antrum produces lymphoid follicles during an immune response to infection.
Timing of rhythmicity of rhythmic transcripts in human blood and baboon tissue/organs
Alignment with the light-dark cycle
Baboon and humans are both diurnal species but baboons and humans living in industrialised societies are exposed to different light-dark cycles. Here, we present a first comparison of the timing of transcriptomes in these two diurnal primates. Our analyses of the alignment across tissues/organs and human blood indicates that the start of the light period, which is the activity and feeding period, is the best phase reference point for most rhythmic genes and tissues/organs, at least in the comparison of human blood and baboon tissues/organs. Previous studies in which comparisons across species or conditions were made have used other phase reference points (e.g. sunrise ), and this may account for some of the discrepancies across these studies. Overall, these data highlight the need for the use of appropriate phase reference points for a comparison of the temporal organisation of gene expression across species and conditions.
Alignment of ‘clock genes’
As previously reported [21, 27], most of the known core clock genes were rhythmically expressed in human blood. In the comparison with the baboon, the most noticeable exception is Chrono (Ciart, Circadian Associated Repressor of Transcription) which was reported to be the most rhythmic gene by , but its human homologue, C1orf51 (targeted by two probes in the Agilent arrays), was lowly expressed and not rhythmic in human blood. This may reflect a specific functional difference in the human blood circadian clock, or it may be that the two targeting probes do not perform well. Further molecular analyses could address this.
The timing of the majority of core clock genes was similar in human blood and baboon tissues and organs. Exceptions were Arntl, Nfil3 and Tef, which were all phase advanced in human blood compare to baboon tissues/organs. The different timing of ARNTL in human blood has been noted before . We note that there were 36 probes targeting ARNTL but only one of these probes was rhythmic, which may indicate that the estimate of the acrophase for ARNTL is not very reliable.
Alignment of all overlapping rhythmic transcripts
There are two aspects of timing that can be considered: distribution of peak times across the 24-h cycle, and the correlation of acrophases. Distribution of peak times of rhythmic genes has been reported to be bimodal in human blood , skin , mouse liver  and baboon tissues  including rhythmic splice isoforms of the same gene . The exception to this is the distribution of peak times in human organs as reconstructed from samples that were not labelled with clock time . In the current study, this bimodality was similarly observed in the subset of rhythmic baboon genes that were also expressed rhythmically in human blood. This indicates that the timing of these rhythmic genes in human blood is similar to the timing in baboon tissues/organs. Furthermore, for 25 out of 64 tissues/organs, the timing of the baboon genes, i.e. their acrophases, correlated significantly. Recently, several reports have discussed the associations of gene expression across tissues [25, 34, 35] and machine learning approaches have demonstrated correlations between gene expression levels in blood and a range of other tissues/organs . Tissue-specific gene expression is not driven by a set of tissue-specific transcription factors but rather combinations of non-tissue-specific transcription factors functioning in tissue-specific regulatory pathways . Thus, it is possible that blood cells share regulatory pathways that are specific to other tissues/organs.
Diurnal vs circadian
Many of the overlapping rhythmic genes in human blood assessed under diurnal conditions were no longer rhythmic under circadian conditions. This implies that the rhythmicity of this set of transcripts is driven by external and behavioural rhythmicity rather than circadian mechanisms which persist under constant conditions. This is consistent with many recent findings in both humans and rodents (see [11, 16]). For most practical purposes, e.g. describing abnormal rhythmicity, the diurnal condition will be most relevant. When we are interested in mechanisms underlying abnormal rhythmicity, the comparison diurnal vs constant conditions becomes relevant.
Implications for development of blood transcriptome-based biomarkers for organ- and tissue-specific rhythmicity
Our proof-of-principle exercise demonstrates that sets of rhythmic genes (associated transcriptome features) in human blood can predict the phase of marker genes in the brain (PVN) and body (Antrum of the stomach) with reasonable accuracy (mean absolute error less than 3 h, based on one blood sample) which is comparable to the accuracy of predictors of, for example, rhythmicity in skin from a skin biopsy .
Functional annotation of the genes that predict the phase of the PVN and Antrum (see Additional file 8: Text S1) did not suggest an obvious mechanism by which blood could be synchronised with the target tissue/organ. This is in contrast to the functional annotation of blood transcriptome predictors of SCN phase, which identified glucocorticoid-dependent regulation of expression as the likely mechanism of synchronisation. Blood perfuses all tissues/organs and so cross-talk between blood and tissues/organs is inevitable. Thus, it is to be expected that other blood-borne signalling agents derived from either blood or tissues/organs could synchronise gene expression in both blood and tissues/organs . Thus, blood and tissues/organs throughout the body are intrinsically linked in biological communication by a variety of mechanisms [37,38,39], and it is not surprising that some features of their respective transcriptomes like expression levels  or timing (current data) are correlated and can be used as predictors.
To further test the validity and robustness of these blood-based predictors will require the availability of data on the rhythmic transcriptome of organs and tissues under conditions or in populations in which these rhythms are disrupted .
Our analyses already indicate that the overlapping rhythmic transcripts are affected by the presence or absence of sleep-wake and light-dark cycles, i.e. the differences between the diurnal and circadian transcripts. None of the 45 probes (targeting 41 genes) that were part of the predictor set of the ANT that were rhythmic when sleeping in-phase remained rhythmic when sleeping out of phase. Only two probes (targeting SLC22A4 and MPZL1) out the 55 probes (targeting 49 genes) that were part of the predictor set of the PVN that were rhythmic when sleeping in-phase remained rhythmic when sleeping out of phase. Not surprisingly, when the predictor sets were used to ‘predict’ ANT and PVN phase by using blood samples collected during sleep out of phase, their ‘performance’ deteriorated drastically compared to the sleeping in-phase condition (Fig. 16 and Additional file 6: Table S6). Whether this deterioration reflects reduction in true performance of the predictor or reduced rhythmicity in the PVN and ANT is obviously unknown because no data are available on rhythmicity in the PVN and ANT in the baboon sleeping out of phase.
Although the presented analyses are based on the best currently available data sets, these data sets have their limitations. The baboon data set contained no biological replicates and consisted of only 12 samples with a 2-h resolution. It may be argued that for the detection of rhythmicity, these limitations lead to false positives and false negatives although the high temporal resolution (2 h) is beneficial for the accurate estimate of phase. We conducted an analysis of how error prone the cycling detection in the baboon data is likely to be and found that the data set is sufficient to accurately estimate rhythmicity and hence our estimates of the overlap of baboon and human rhythmicity are likely to be accurate (see Additional file 8: Text S2).
Our comparison of baboon and human data is based on data collected in different photoperiods. Although we explored the impact of different approaches to quantify the photoperiod and its impact on entrainment, we recognise that this area needs further exploration.
Human transcriptome data were collected under laboratory conditions during one of which participants were sleep deprived, and baboon data were collected by sacrificing animals under diurnal conditions. The conditions under which the transcriptome data were collected may have led to changes in gene expression beyond the variations driven by circadian clocks.
The human data were obtained in healthy young participants. Thus, the extent to which the current results can be extrapolated to conditions in which rhythmicity in organs and tissues is compromised by adverse health conditions, such as tissue- and organ-specific perturbations of circadian rhythmicity as may be observed in shift work and jet lag, remains to be established.
Another limitation of the study is that we have had no access to the baboon blood transcriptome. This would have allowed for a comparative analysis of the extent to which tissue-specific rhythmicity overlaps in the whole-blood transcriptome of these two diurnal primates.
Furthermore, rhythmicity here is described at the level of the transcriptome and how this will relate to more functionally relevant rhythmicity in the proteome is an open question.
Characteristics of the temporal organisation of gene expression levels in human whole blood may provide information on rhythmicity in tissues and organs. This has potential for the monitoring of tissue- and organ-specific abnormalities and effects of tissue- and organ-specific intervention. For example, monitoring of Rnf207, which is implicated in Cardiac K channel regulation, may provide new insights in the diurnal variation of Long QT syndrome and syncope. Likewise, monitoring of a set of markers for a specific organ may provide information on the timing of organ-specific rhythmicity which then could be used to optimise chronotherapy for a specific organ’.
To further our insights into the validity of this approach requires a comparison of the blood transcriptome and time series of gene expression in human tissues and organs in patients with for example renal or cardiac or other abnormalities.
Rhythmic genes in human blood overlap with rhythmic genes in baboon tissues/organs. Although the sets of rhythmic genes vary across tissues and organs, the timing of most rhythmic genes is similar across tissues/organs and human blood.
These results have implications for our understanding of the regulation of rhythmicity across tissues/organs and species and development of blood transcriptome-based biomarkers for rhythmicity in tissues and organs.
Human and baboon data sources
Human blood transcriptome data collected in protocols approved by local ethics committees and as previously described [26, 27] were accessed via GEO [40, 41] (accession no. GSE48113  and GSE39445 , respectively). The first dataset represents the baseline diurnal condition in which 22 participants were scheduled to sleep during the night for 9 h and 20 min in phase with the circadian clock. The second dataset represents the sleeping out-of-phase with the circadian clock condition. The same 22 participants continued on a forced descinchrony protocol to achieve a sleep-wake cycle where the sleep period is 12 h out of phase with the melatonin rhythm. Seven 4-hourly blood transcriptome samples (spanning > 24 h) were collected in each condition, with sampling starting 4 h (for sleeping in-phase) and 16 h (for sleeping out-of-phase) before the end of the light phase . For this analysis, we extracted all baseline (in-phase) and mistimed (out-of-phase) samples for 19 participants (all participants excepting those labelled 104, 141 and 219, due to a lack of samples passing QC for time series analysis, as described in ), and performed quantile normalisation. The third dataset represents the human circadian condition; data from 26 participants were collected under constant routine conditions, i.e. during approximately 40 h of wakefulness in dim light, immediately following 1 week of exposure to a 14 h light 10 h dark cycle and sufficient sleep . Here, we extracted the data for the ten 3-hourly blood samples collected from 19 participants (all participants excepting those labelled 79, 91, 112, 182, 186, 214 and 267, due to a lack of samples passing QC for time series analysis, as described in ) and performed quantile normalisation. Rhythmic transcripts (probes) and their acrophase (i.e. the timing of the fitted maximum of the rhythm) in human blood were as reported [26, 27] and defined as having a prevalent (across participants) oscillatory component with a ∼24-h period.
Rhythmic genes, and their associated attributes (e.g. acrophase), in baboon were as provided by . Rhythmicity of a gene within a specific tissue/organ was assessed by applying Metacycle  to the single (no biological replicates) time series consisting of 12 samples taken 2 h apart. Circadian period was allowed to vary between 20 and 26 h. In a given tissue/organ, genes with a Metacycle P value < 0.05 were considered to be rhythmic. The processed baboon expression data were downloaded from GEO (accession no. GSE98965 ).
Human and baboon orthology
Similarity between the rhythmic transcriptome in human blood and the rhythmic transcriptome of baboon tissues/organs was assessed on the set of orthologous genes. Orthology between human and baboon genes was as defined in Ensembl  using R  with package BioMart  (v2.40, accessed 23/1/2019). In total, there were 13,838 unique human genes (Ensembl IDs) and 13,960 unique baboon genes assessed by both Archer et al.  and Mure et al.  that we considered (Fig. 2). Of the 1502 unique microarray probes identified as targeting rhythmic genes in the blood transcriptome of human participants on a normal sleep-wake cycle, 843 probes, targeting 759 unique human genes (Ensembl IDs), could be mapped to 786 orthologous baboon genes assessed by Mure et al. (Fig. 2). For the overlapping rhythmic genes between human blood and across baboon tissues/organs, the overlap percentage between two sets was defined as 2 × (c) × 100/(n+m), where c is the number of common elements, n is the size of first set and m is the size of the second set.
Gene expression levels of human blood and baboon tissues/organs were derived using different platforms
RNA-seq has a wider dynamic range and allows for sequencing of the whole transcriptome while microarrays only profile pre-determined transcripts. However, both platforms have their own biases. The difference in the platforms used to estimate mRNA abundance did not hinder the analyses conducted because all comparisons were performed using either z-scored gene expression temporal profiles or gene expression peak times. The z-scored gene expression temporal profiles represent the deviation of expression level relative to the mean value in standard deviation units, while the acrophase value represents the time at which the maximum expression value is estimated. Both objects represent time-dependent relative changes in expression within baboon or within human, and as such, are robust across platforms. Microarray models can directly predict RNA-seq-profiled samples if the gene expression data were z-scored before modelling and prediction .
Circular cross-correlation of expression profiles
Cross-correlation is a technique that can be used to compare two time series. The procedure identifies the displacement of one time series relative to the other that provides the best match, measured by correlation, between the two time series. The basic process involves:
Calculate a correlation coefficient between the two series.
Shift one series creating a lag and repeat the calculation of the correlation coefficient.
Repeat 1 and 2 for all time points.
Identify the lag with the highest correlation coefficient.
In the circular cross-correlation, the data is shifted circularly. To achieve this, we translated the Matlab implementation of the circular cross-correlation  to R. A comparison of two hypothetical time series with a 4-h lag using circular cross-correlation is shown in Fig. 17.
Alignment of human and baboon data to the light-dark cycle
During baseline diurnal conditions, the timing of the light-dark cycles to which human and baboon were exposed was different; human 18 h:40 min light: 9 h:20 min darkness; baboon 12 h light:12 h darkness. When the duration of the photoperiod is different across conditions, the choice of the reference external phase marker (e.g. onset of light period, onset of dark period, midpoint of light period, clock time) has an impact on the observed alignment of circadian rhythmicity across these conditions . External phase markers that have been used are the midpoint of the light period , sunrise, e.g. , the onset of waketime (i.e. the onset of the light period in humans) or habitual bedtime (i.e. the end of the light period in humans) .
To identify the appropriate phase reference point, we compared the alignment of human and baboon molecular rhythms using circular cross-correlation on three reference points: onset of light, the midpoint of light and the end of light. Human blood gene expression profiles of each participant were z-scored and aligned to either start, midpoint or end of light and interpolated to the baboon sampling time points with the same light alignment. For each probe targeting a human/baboon orthologous gene, the mean and standard deviation across participants per interpolated time point were calculated. The mean values were used to calculate the circular cross-correlation with the z-scored baboon gene expression profiles.
Circular cross-correlation between the z-scored expression profiles referenced to either lights on, midpoint of light or end of light were computed. The lag at which the maximum circular cross-correlation coefficient was obtained was taken to represent the level of alignment. A lag of zero indicates no difference in 2-h binned peak times, a negative lag x indicates the transcript peaks x h in human blood before the baboon tissue, and a positive lag y indicates the transcript peaks y h in the baboon tissue before the human blood.
The lags identified using the circular cross-correlation based on alignment with start, midpoint and end of light were ranked for each probe such that the alignment with the smallest lag was given the top rank, i.e. rank 1. Ranks of lags of all rhythmic probes were averaged within each baboon tissue/organ or across all tissues/organs. The resulting averages were used for the comparison of alignment strategies.
Comparison of human and baboon acrophases
We obtained the human blood acrophases from Archer et al.  and the baboon acrophases from Mure et al. . All acrophases were expressed relative to the selected phase marker (i.e. aligned with start, midpoint or end of light). All calculations involving acrophases were performed using circular statistics.
Circular correlation from the R circular package  [v 0.4-93] was used to compare the acrophases (i.e. the timing of the maximum relative to the phase reference point) of rhythmic transcripts in human blood and rhythmic transcripts in baboon tissues and organs. Figure 18 describes the comparison of human and baboon acrophases using circular correlation. The human blood acrophases were represented by the average value across all participants. Benjamini-Hochberg (BH) correction  was applied to the P values of the circular correlations of the tissues/organs.
Circular clustering analysis
K-means clustering  was used to cluster rhythmic genes based on their acrophases in human blood and baboon tissues/organs. We implemented a circular version of the clustering method by using the Cartesian coordinates (x, y) of the acrophases as input to the clustering function, i.e. x = cos(θ) and y = sin(θ), where θ is the acrophase value in radians.
Over-representation of Gene Ontology (GO) biological process and molecular function terms within a given list was performed using the functional enrichment analysis tool Webgestalt R accessed in August 2020 (v0.4.3) . Default settings were used except that the background set was the Agilent 4x44k v2 as used in Archer et al. , and the false discovery-adjusted P value threshold was set to < 0.05.
Over- and under-representation of blood immune cells within a given set of rhythmic genes was assessed using a two-sided Fisher exact test. Immune cell markers were as defined previously . All calculations were adjusted to include only orthologous human/baboon genes.
Identifying blood-based biomarkers for timing of tissue/organ-specific rhythmicity
We conducted a proof-of-principle exercise to explore whether the phase of rhythmicity in tissues/organs could potentially be ‘predicted’ by a selected set of transcripts from one or two blood samples. To develop and validate such a multivariate whole-blood mRNA-based putative predictor of tissue/organ-specific rhythmicity, which does not require a time series of samples, we proceeded as follows. We used a single gene marker as a tissue phase marker (See Fig. 14). This gene was selected based on the circular cross-correlation analysis between human blood and the selected baboon tissue/organ per participant. The marker was required to have a high average circular cross-correlation and a small standard deviation of circular cross-correlation lag (< 2 h) across participants. The marker gene was selected from the top candidates according to these criteria and applying a tissue/organ specificity criterion, i.e. the gene marker should be rhythmic in only a low number of baboon tissues/organs. From the remaining (non-marker, non-clock gene) transcriptome features, we generated a predictive model for the timing of tissue-specific rhythmicity. We performed this exercise for two tissues: paraventricular nucleus and the antrum of the stomach. In these tissues, the correlation of the acrophases of the overlapping rhythmic genes in blood and baboon tissue was significant (BH-corrected P < 0.05). The tissues were also characterised by a relatively large number of rhythmic genes shared between the baboon tissue and human blood, and the overlap of these rhythmic genes between the two baboon tissues was relatively small. A final selection criterion was that the two tissues represented different physiological systems in the baboon. The model’s target was the human blood sample phase relative to the tissue phase given by the gene marker, i.e. the model predicts the phase of rhythmicity in the tissue at which a blood sample is taken (See Fig. 14). We used a Partial Least Squares Regression (PLSR) model as described previously . This model has been shown to perform equally well as other machine learning approaches (e.g. ). Input to the model was the set of probes (transcriptome features) targeting orthologous genes rhythmic in both human blood and the selected baboon tissues/organs, excluding canonical clock genes. Samples (N = 129) were pre-processed and split into a training set (sleeping in-phase dataset, N = 61 samples, 9 participants) to develop the models, and two independent validation sets (set 1: sleeping in-phase dataset, N = 68 samples, 10 participants; set 2: sleeping out-of-phase, N = 68 samples, 10 participants) to test the predictive performance of the models as described in . As a control, we also tested the predictive performance of random sets of transcriptome features targeting orthologous genes, where the size of each set was the same as the PLSR-selected set.
Availability of data and materials
All data generated or analysed during this study are included in this published article, its supplementary information files and publicly available repositories. The datasets analysed during the current study are available in GEO accession numbers GSE48113 , GSE39445  and GSE98965 . R code used to generate the analyes presented in this published article are available in Zenodo: https://doi.org/10.5281/zenodo.6126137 .
Benjamini and Hochberg multiplicity correction
False discovery rate
Lateral Globus Pallidus
Medial Globus Pallidus
Optic nerve head
Partial Least Squares Regression
Retinal pigment epithelium
White adipose mesenteric
White adipose pericardial
White adipose perirenal
White adipose subcutaneous
White adipose tissue
Rijo-Ferreira F, Takahashi JS. Genomics of circadian rhythms in health and disease. Genome Med. 2019;11(1):82.
Kuhlman SJ, Craig LM, Duffy JF. Introduction to chronobiology. Cold Spring Harb Perspect Biol. 2018;10(9):a033613.
Takahashi JS. Molecular components of the circadian clock in mammals. Diabetes Obes Metab. 2015;17(S1):6–11.
Hastings MH, Maywood ES, Brancaccio M. The mammalian circadian timing system and the suprachiasmatic nucleus as its pacemaker. Biology. 2019;8(1):13.
Greenwell BJ, Trott AJ, Beytebiere JR, Pao S, Bosley A, Beach E, et al. Rhythmic food intake drives rhythmic gene expression more potently than the hepatic circadian clock in mice. Cell Rep. 2019;27(3):649–57.e5.
Hor CN, Yeung J, Jan M, Emmenegger Y, Hubbard J, Xenarios I, et al. Sleep–wake-driven and circadian contributions to daily rhythms in gene expression and chromatin accessibility in the murine cortex. Proc Natl Acad Sci. 2019;116(51):25773–83.
Walker WH, Walton JC, DeVries AC, Nelson RJ. Circadian rhythm disruption and mental health. Transl Psychiatry. 2020;10(1):28.
Logan RW, McClung CA. Rhythms of life: circadian disruption and brain disorders across the lifespan. Nat Rev Neurosci. 2019;20(1):49–65.
Qian J, Scheer FAJL. Circadian system and glucose metabolism: implications for physiology and disease. Trends Endocrinol Metab. 2016;27(5):282–93.
Dijk D-J, Duffy JF, Silva EJ, Shanahan TL, Boivin DB, Czeisler CA. Amplitude reduction and phase shifts of melatonin, cortisol and other circadian rhythms after a gradual advance of sleep and light exposure in humans. PLoS One. 2012;7(2):e30037.
Archer SN, Oster H. How sleep and wakefulness influence circadian rhythmicity: effects of insufficient and mistimed sleep on the animal and human transcriptome. J Sleep Res. 2015;24(5):476–93.
Wehrens SMT, Christou S, Isherwood C, Middleton B, Gibbs MA, Archer SN, et al. Meal timing regulates the human circadian system. Curr Biol. 2017;27(12):1768–75.e3.
Menaker M, Murphy ZC, Sellix MT. Central control of peripheral circadian oscillators. Curr Opin Neurobiol. 2013;23(5):741–6.
Laing EE, Johnston JD, Möller-Levet CS, Bucca G, Smith CP, Dijk DJ, et al. Exploiting human and mouse transcriptomic data: identification of circadian genes and pathways influencing health. Bioessays. 2015;37(5):544–56.
Cederroth CR, Albrecht U, Bass J, Brown SA, Dyhrfjeld-Johnsen J, Gachon F, et al. Medicine in the fourth dimension. Cell Metab. 2019;30(2):238–50.
Dijk D-J, Duffy JF. Novel approaches for assessing circadian rhythmicity in humans: a review. J Biol Rhythm. 2020;35(5):421–38.
Sullivan PF, Fan C, Perou CM. Evaluating the comparability of gene expression in blood and brain. Am J Med Genet B Neuropsychiatr Genet. 2006;141b(3):261–8.
Halloran JW, Zhu D, Qian DC, Byun J, Gorlova OY, Amos CI, et al. Prediction of the gene expression in normal lung tissue by the gene expression in blood. BMC Med Genet. 2015;8:77.
Xu W, Liu X, Leng F, Li W. Blood-based multi-tissue gene expression inference with Bayesian ridge regression. Bioinformatics. 2020;36(12):3788–94.
Wittenbrink N, Ananthasubramaniam B, Münch M, Koller B, Maier B, Weschke C, et al. High-accuracy determination of internal circadian time from a single blood sample. J Clin Invest. 2018;128(9):3826–39.
Laing EE, Möller-Levet CS, Poh N, Santhi N, Archer SN, Dijk DJ. Blood transcriptome based biomarkers for human circadian phase. Elife. 2017;6:e20214.
Kervezee L, Cuesta M, Cermakian N, Boivin DB. The phase-shifting effect of bright light exposure on circadian rhythmicity in the human transcriptome. J Biol Rhythm. 2019;34(1):84–97.
Zhang R, Lahens NF, Ballance HI, Hughes ME, Hogenesch JB. A circadian gene expression atlas in mammals: Implications for biology and medicine. Proc Natl Acad Sci. 2014;111(45):16219–24.
Hughey JJ, Butte AJ. Differential phasing between circadian clocks in the brain and peripheral organs in humans. J Biol Rhythm. 2016;31(6):588–97.
Mure LS, Le HD, Benegiamo G, Chang MW, Rios L, Jillani N, et al. Diurnal transcriptome atlas of a primate across major neural and peripheral tissues. Science. 2018;359(6381):eaao0318.
Archer SN, Laing EE, Möller-Levet CS, van der Veen DR, Bucca G, Lazar AS, et al. Mistimed sleep disrupts circadian regulation of the human transcriptome. Proc Natl Acad Sci. 2014;111(6):E682–E91.
Möller-Levet CS, Archer SN, Bucca G, Laing EE, Slak A, Kabiljo R, et al. Effects of insufficient sleep on circadian rhythmicity and expression amplitude of the human blood transcriptome. Proc Natl Acad Sci. 2013;110(12):E1132–E41.
Liu Y, Hu W, Murakawa Y, Yin J, Wang G, Landthaler M, et al. Cold-induced RNA-binding proteins regulate circadian gene expression by controlling alternative polyadenylation. Sci Rep. 2013;3(1):2054.
Ackermann K, Revell VL, Lao O, Rombouts EJ, Skene DJ, Kayser M. Diurnal rhythms in blood cell populations and the effect of acute sleep deprivation in healthy young men. Sleep. 2012;35(7):933–40.
Wu G, Ruben MD, Francey LJ, Smith DF, Sherrill JD, Oblong JE, et al. A population-based gene expression signature of molecular clock phase from a single epidermal sample. Genome Med. 2020;12(1):73.
Koike N, Yoo SH, Huang HC, Kumar V, Lee C, Kim TK, et al. Transcriptional architecture and chromatin landscape of the core circadian clock in mammals. Science. 2012;338(6105):349–54.
El-Athman R, Knezevic D, Fuhr L, Relógio A. A computational analysis of alternative splicing across mammalian tissues reveals circadian and ultradian rhythms in splicing events. Int J Mol Sci. 2019;20(16):3977.
Ruben MD, Wu G, Smith DF, Schmidt RE, Francey LJ, Lee YY, et al. A database of tissue-specific rhythmically expressed human genes has potential applications in circadian medicine. Sci Transl Med. 2018;10(458):eaat8806.
Sonawane AR, Platig J, Fagny M, Chen C-Y, Paulson JN, Lopes-Ramos CM, et al. Understanding tissue-specific gene regulation. Cell Rep. 2017;21(4):1077–88.
Breschi A, Muñoz-Aguirre M, Wucher V, Davis CA, Garrido-Martín D, Djebali S, et al. A limited set of transcriptional programs define major cell types. Genome Res. 2020;30(7):1047–59.
Poher A-L, Tschöp MH, Müller TD. Ghrelin regulation of glucose metabolism. Peptides. 2018;100:236–42.
Orlova EG, Shirshev SV, Loginova OA. Mechanisms of leptin and ghrelin action on maturation and functions of dendritic cells. Biochem Mosc. 2019;84(1):1–10.
LaMarche NM, Kohlgruber AC, Brenner MB. Innate T cells govern adipose tissue biology. J Immunol. 2018;201(7):1827–34.
Aroca-Crevillén A, Adrover JM, Hidalgo A. Circadian features of neutrophil biology. Front Immunol. 2020;11:576.
Barrett T, Wilhite SE, Ledoux P, Evangelista C, Kim IF, Tomashevsky M, et al. NCBI GEO: archive for functional genomics data sets—update. Nucleic Acids Res. 2012;41(D1):D991–D5.
Edgar R, Domrachev M, Lash AE. Gene Expression Omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res. 2002;30(1):207–10.
Archer SN, Laing EE, Möller-Levet CS, Bucca G, Slak A, Kabiljo R, et al. Mistimed sleep disrupts circadian regulation of the human blood transcriptome: GEO; 2014. https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE48113
Möller-Levet CS, Archer SN, Bucca G, Laing EE, Slak A, Kabiljo R, et al. Effect of sleep restriction on the human transcrptome during extended wakefulness: GEO; 2013. https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE39445
Wu G, Anafi RC, Hughes ME, Kornacker K, Hogenesch JB. MetaCycle: an integrated R package to evaluate periodicity in large scale data. Bioinformatics. 2016;32(21):3351–3.
Mure LS, Le HD, Benegiamo G, Chang MW. Diurnal transciptome atlas of a primate across all major neural and peripherial tissues: GEO; 2016. https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE98965
Hunt SE, McLaren W, Gil L, Thormann A, Schuilenburg H, Sheppard D, et al. Ensembl variation resources. Database. 2018. https://doi.org/10.1093/database/bay119.
Team RC. R: a language and environment for statistical computing. Vienna: R Found Stat Comput; 2019.
Durinck S, Spellman PT, Birney E, Huber W. Mapping identifiers for the integration of genomic datasets with the R/Bioconductor package biomaRt. Nat Protoc. 2009;4(8):1184–91.
Su Z, Fang H, Hong H, Shi L, Zhang W, Zhang W, et al. An investigation of biomarkers derived from legacy microarray data for their utility in the RNA-seq era. Genome Biol. 2014;15(12):523.
Levin G. Circular cross correlation. 2020. https://www.mathworks.com/matlabcentral/fileexchange/4810-circular-cross-correlation.
Daan S, Merrow M. External time–internal time. J Biol Rhythm. 2002;17(2):107–9.
Wright KP, Gronfier C, Duffy JF, Czeisler CA. Intrinsic period and light intensity determine the phase relationship between melatonin and sleep in humans. J Biol Rhythm. 2005;20(2):168–77.
Agostinelli C, Lund U. R package 'circular': Circular Statistics (version 0.4-93). 2017. https://r-forge.r-project.org/projects/circular/.
Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B Methodol. 1995;57(1):289–300.
Macqueen J. Some methods for classification and analysis of multivariate observations. Barkeley: University of California Press; 1967.
Liao Y, Wang J, Jaehnig EJ, Shi Z, Zhang B. WebGestalt 2019: gene set analysis toolkit with revamped UIs and APIs. Nucleic Acids Res. 2019;47(W1):W199–w205.
Abbas AR, Baldwin D, Ma Y, Ouyang W, Gurney A, Martin F, et al. Immune response in silico (IRIS): immune-specific genes identified from a compendium of microarray expression data. Genes Immun. 2005;6(4):319–31.
Möller-Levet CS, Laing EE, Archer SN, Dijk DJ. Diurnal and circadian rhythmicity of the human blood transcriptome overlaps with organ and tissue specific expression of a non-human primate: Zenodo; 2022. https://doi.org/10.5281/zenodo.6126137.
The collection of the human data was funded by BBSRC grant BB/F022883/1 and US AFOSR grant FA9550-0080. The funders had no role in the design of the study, collection, analysis, interpretation of data and in writing the manuscript.
Ethics approval and consent to participate
The protocols in which the human data were collected received a favourable opinion (EC/2007/110/FHMS & EC/2008/61/FHMS) from the University of Surrey’s Ethics committee. All participants provided written informed consent. Baboon data were collected by Mure et al.  and, as reported, approved by the Kenya IACUC (Institute Animal Care and Use Committee, Approval 160811) and ISERC (Institution Scientific and Ethical Review Committee).
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Distribution of all overlapping rhythmic genes across tissues/organs. Overlap of rhythmic genes between human blood and baboon tissues/organs.
Circular cross-correlation lags of clock genes. Circular cross-correlation lags for the clock genes that were rhythmic in human blood and identified as rhythmic in the greatest number of baboon tissues/organs.
Clustering analysis results. Clustering results of circular clustering analysis of all overlapping rhythmic genes in human blood and baboon tissues/organs combined.
Biomarker candidate genes. Sets of probes targeting orthologous genes rhythmic in both human blood and the selected baboon tissues/organs. Column F marks (with 0) genes excluded from the sets. Column G indicates (with 1) genes used in the 1-sample PLSR model. Column H indicates (with 1) genes used in the 2-sample PLSR model.
Prediction performance of phase of rhythmicity. Performance of phase of rhythmicity prediction in the paraventricular nucleus (PVN) and the antrum of the stomach (ANT) based on a selected set of transcripts (number of predictor genes) from one (1-sample based) or two (2-sample based) blood samples using a Partial Least Squares Regression (PLSR) model. Performance is evaluated using R2, median absolute error, error mean and standard deviation (sd), and proportion of samples with error within 1, 2 or 3 hours. The PLSR models were trained using a training set from the sleeping in-phase dataset and performance was evaluated using validation sets from the sleeping in-phase or sleeping out-of-phase datasets.
Over and under representation of blood immune cells. Over and under representation of blood immune cells, as indicated by immune cell markers defined in , within the day or night cluster (see main text), of overlapping rhythmic genes identified in a given tissue/organ was assessed using a two-sided Fisher exact test. Here, overlapping genes within a given cluster, within a tissue/organ, were directly compared with each set of orthologous immune cell markers. The table contains the observed vs expected values, and associated p values, for each cluster, tissue and immune cell type combination.
Functional annotation of the genes that predict the phase of the PVN and Antrum. Text S2. Effect of Baboon’s sampling scheme on the identification of rhythmic genes and their overlap with human blood rhythmic genes.
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Möller-Levet, C.S., Laing, E.E., Archer, S.N. et al. Diurnal and circadian rhythmicity of the human blood transcriptome overlaps with organ- and tissue-specific expression of a non-human primate. BMC Biol 20, 63 (2022). https://doi.org/10.1186/s12915-022-01258-7
- Circadian rhythmicity
- Non-human primate
- Shift work