Gut microbiome-brain crosstalk in the early life of chicken reveals the circadian regulation of key metabolic and immune signaling processes

doi:10.21203/rs.3.rs-4438538/v1

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Research Article

Gut microbiome-brain crosstalk in the early life of chicken reveals the circadian regulation of key metabolic and immune signaling processes

https://doi.org/10.21203/rs.3.rs-4438538/v1

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Background

Circadian rhythms are innate biological systems that control everyday behavior and physiology. Furthermore, bilateral interaction between the host's circadian rhythm and the gut microbes influences a variety of health ramifications, including metabolic diseases, obesity, and mental health. Several studies have revealed how neurological components interact with GALT physiology and the microbiome population in the host. Keeping these factors in regard, we are studying the correlation between differential gene expression in the chicken brain and microbiota abundance during circadian rhythms. To study rhythmic oscillations in immune genes, we raised freshly hatched chicks under two photoperiod treatments: normal photoperiod (NP = 12/12 LD) and extended photoperiod (EP 23/1 LD). The chicks were randomly assigned to one of two treatments. After 21 days of circadian entrainment, the chicks were euthanized at nine-time points spaced six hours apart over 48 hours to characterize the brain transcriptomes. Each sample's RNA was extracted, and 36 mRNA libraries were created and sequenced using Illumina technology, followed by data processing, count data generation, and differential gene expression analysis.

Results

We generated an average of 17.5 million reads per library for 237933593.3 reads. When aligned to the Galgal6 reference genome, 11,867 genes had detectable expression levels, with a common dispersion value of 0.105. To identify the genes that follow 24-hr rhythms counts per million data were performed in DiscoRhythm. We discovered 577 genes with Cosinor and 417 with the JTK cycle algorithm that exhibit substantial rhythms. We used weighted gene co-expression network analysis (WGCNA) to analyze the correlation between differentially expressed genes and microbiota abundance. The most enriched pathways included aldosterone-regulated sodium reabsorption, endocrine and other factor-regulated calcium reabsorption, GABAergic synapse, oxidative phosphorylation, serotonergic synapse, dopaminergic synapse, and circadian entrainment.

Conclusions

Photoperiods and robust 24-hr rhythms differentially influenced key neurochemical and mitochondrial pathways during early life. Furthermore, the interaction between gut microbiota and host gene expression highlights the role that specific microbial taxa (like Lactobacillus) may play in the early-life regulation of homeostasis and provides promising avenues for application.

Circadian rhythms

chicken

RNA- Sequencing

microbiome

WGCNA

Circadian rhythms (from Latin ‘‘circa’’ = around and ‘‘diem’’ = day) are autonomous 24-hour cycles that allow organisms to synchronize their internal biological clock and the oscillations with the external environment ^1–3. These oscillations driven by the circadian clock are observed in organisms, from single-celled to vertebrates ⁴. These clocks internally regulate the numerous physiological processes of an organism as they are transcription-translation factors functioning as feedback loops ⁵. The circadian pacemakers in birds are the suprachiasmatic nucleus (SCN), pineal gland, and retina (Fig. 1), in contrast to mammals, where the SCN is the sole pacemaker⁶. SCN is a master clock that constitutes about 20,000 neurons in the hypothalamus. It is driven by photoreception by the retinal cells. The molecular clock comprising BMAL1/2, CLOCK, CRY1/2, and PER1/2/3 governs the transcription and rhythmic molecular and functional activity patterns⁷. Hence, disruption in circadian rhythms directly affects the body’s functioning at the cellular and molecular levels, which are implicated in various metabolic, immune, and neurodegenerative disorders ^8–11.

Circadian signaling impacts gut physiology through the vagus nerve and modulates immune cells within the Gut Associated Lymphoid Tissue (GALT) ¹². The circadian-gut interaction is a complex and bidirectional relationship maintaining homeostasis, including diurnal gut microbiome composition and function ¹³. This synchronicity regulates metabolic responses to diet, lipid metabolism, and energy homeostasis^{12,14,15 16,17}. Conversely, the gut microbiota influences the circadian clock, regulating the expression of clock genes and impacting the daily rhythmicity of the host ^18,19. One feature of gut dysbiosis is circadian asynchrony( ^20,21).

The bidirectional communication between the host circadian rhythm and the gut microbiota impacts various health outcomes, including metabolic disorders, obesity, and mental health ²². The absence or disruption of gut microbiota profoundly affects brain function, behavior, and various molecular factors such as neuropeptide Y (NPY) and PYY ²³. In some studies, germ-free mice have displayed various neurochemical and functional changes compared to conventionally colonized mice. For example, germ-free mice exhibit decreased expression of the NMDA receptor subunit 2A (NR2A) in the cortex and hippocampus ²⁴ and reduced expression of the NR2B subunit and the 5-HT receptor 1A (5HT1A) in the central amygdala and the hippocampus ²⁵. However, brain-derived neurotrophic factor (BDNF), a crucial neurotrophin involved in neuronal growth and survival, suggests that BDNF levels are reduced in the hippocampus, amygdala, and cortex of germ-free mice ^24,26. These studies reflect the key interactions between neural factors, the gut microbiome, and GALT physiology. In avian species, there is emerging evidence of the gut-brain crosstalk ^27–29 suggesting similar mechanisms as in mammalian models. Despite these studies there are relatively few reports on the correlation between CLOCK-regulated expression and the gut microbiota, especially in early life, and recent studies have only begun to explore the implications of these interactions for metabolic and immune health in avian species.

Here we show the crucial interactions and signaling pathways that are involved in the gut-brain crosstalk in the context of early-life circadian rhythms in chicken. We integrate brain transcriptome with gut microbiota patterns from a circadian experiment to reveal the different outcomes between functioning and dysfunctional circadian rhythms in early life. Furthermore, we relate co-expression and pathway activation data to highlight the inverse relationship between microbial abundance and key homeostatic signaling patterns.

Animal ethics statement

The research was conducted in compliance with international and national standards for animal care. The Institutional Animal Care and Use Committee (IACUC) at Texas A&M University authorized and regulated the animal studies (Assurance Number 2016-0064).

Animals and Experimental design

All the experimental details were previously reported in our paper Hieke et al (2019)³⁰, but are briefly repeated here for clarity. Eighty hatch-day female chicks of the Hy-Line Brown Layer breed (Gallus gallus domesticus) were procured from a local hatchery and transported to the Texas A&M Poultry Research and Education Center in College Station, Texas. In each treatment, 40 chicks were randomly assigned and transferred to two different rooms with independent lighting controls. Within each room, 20 chicks were raised in one of two brooder cages. Each room was configured for a certain photoperiod treatment - normal photoperiod (NP) of 12 h of light and 12 h of darkness (12/12 LD), with lights-on at 06:00 h, and Extended treatment (EP) of 23 h L and 1 h D (23/1 LD), with lights-off from 05:00 to 06:00 h. Zeitgeber Time 0 (ZT0) was established as the time of lights-on (0600 h). The experimental birds were raised in identical conditions except for photoperiod exposure, and they had unlimited access to food and water. A pullet diet containing 17% crude protein and 2,800 kcal of metabolizable energy per kg was used to rear the chicks. The experimental rooms were temperature controlled, starting at 32°C for a week and then gradually dropping by around 2–3°C every week until reaching 23°C, following standard chick-rearing protocols.

Sample collection

To evaluate circadian oscillations, two birds were selected randomly and euthanized by exposure to 5 min of CO2 followed by cervical dislocation at 6-hour intervals after the entrainment period (21 days). Two birds from each photoperiod treatment were sampled every 6-h (two individuals/treatment/time point) over a 48-hour period, starting at ZT0 (a total of nine time points, a total of 18 per treatment). For the sampling from dark period (NP) treatment, to prevent exposure to light, birds were kept in the dark with just an infrared bulb and transferred to a dark container for euthanasia. Chicks were euthanized by C0₂ exposure, followed by cervical dislocation. Brain samples were collected within 30 min of euthanasia and were placed into RNALater (Qiagen, Hilden, Germany) in a 1:5 ratio. Since birds from both treatments had to be sampled at precisely the exact times, four individuals concurrently carried out the same procedures, from bird euthanasia to brain tissue collection within 30 minutes post-mortem. The sample tubes were kept at 4°C for at least 24-h to ensure maximum absorption of RNALater. The samples were then pulled off of RNALater and stored at -80°C. Unlike our prior study, here we generated the whole transcriptome dataset and analyzed its interactions with the microbiota data.

RNA Isolation and Quantification

From each individual, we collected 3mm³ of brain tissue in the vicinity of the pineal gland, which was homogenized in Trizol reagent (Invitrogen, Carlsbad, CA, United States) using a Mini-Beadbeater-96 and 15–30 mg of 1.0 mm ZIRCONIA beads (cat. no. 11079124zx) (BioSpec, OK, United States). We isolated total RNA and quantified RNA samples, and quality was assessed by evaluating protein contamination (260/280 ratio) and other organic contamination (230/260 ratio) using a NanoDropTM 1000 spectrophotometer Thermo Fisher Scientific, MA, United States). The RNA integrity number (RIN) and suitability for library construction of the samples were evaluated using an Agilent RNA 6000 Nano kit (No:5067 − 1511) on a Bioanalyzer 2100 (Agilent Technologies, Inc., DE, United States) chip reader. Using a QubitTM RNA BR assay, 20–1,000 ng/l (Catalog number: Q10211), as well as a QubitTM dsDNA BR assay, 100 pg/l–1,000 ng/l, we screened total RNA isolates with RIN 8.5 or above for genomic DNA contamination (Catalog number: Q32853). For library construction, total RNA samples that passed these quality criteria were normalized by diluting at 400 ng/l using nuclease-free water (NF water).

RNA Library Preparation and Transcriptome Profile Generation

We prepared the libraries from 200 ng of total RNA using the QuantSeq 3′ mRNA-Seq Library Prep Kit FWD for Illumina kit's instructions (Lexogen, Vienna, Austria). We reverse-transcribed mature (poly-A-tailed) mRNA using oligo (dT) primers and Illumina-specific Read-2 linker sequences to create complementary first-strand DNA. Next, we synthesized the second strand of DNA using a random primer containing the Illumina-specific Read-1 linker sequence while the DNA polymerase enzyme was present. To eliminate contaminants that may hinder the library enrichment and indexing processes, we purified libraries utilizing a magnetic bead-based purification step. Cleaned libraries were normalized to 4 nM and evaluated using the TapeStation 2200 equipment and the D1000 ScreenTape assay from Agilent Technologies, Inc. At the Texas A&M Institute for Genome Sciences and Society (TIGSS, College Station, TX, United States), twenty-four libraries (N = 4/treatment) were pooled in equimolar concentrations and sequenced using an Illumina NextSeq (Illumina, San Diego, CA, United States) platform. Libraries yielded an average of 8.8 million reads in 75-bp single-end mode.

Transcriptome Data Analysis

We carried out all bioinformatics analysis with open-source tools using a well-established RNAseq analysis pipeline for read counts-based analyses. After removing adapter contamination and Lexogen indices, the single-end raw reads in FASTQ format were validated for quality with FastQC (Babraham Institute, Cambridge, United Kingdom) version 0.11.9 and MultiQC version 1.9 ³¹, ³². Adapter removal and quality filtering (Phred Q > 30), as well as the removal of reads under 35 bp, were performed using Trim_Galore version 0.4.5 ³³. Reads that passed quality filters were mapped to the Gallus gallus genome, Galgal6 (v6, Ensembl Release 99 GRCg6a, January 2020) ³⁴ using the de novo splice mapper STAR (version STAR 2.5.3a modified). Using HTSeq-count (version 0.9.1) ³⁵, we scored the single-end reads mapped to exon features. The obtained counts were normalized and analyzed to study differential gene expression in the package EdgeR using a two-factor model (version 3.26.8) on the R statistical platform (version 4.0.0) ³⁶. Low-expressed genes with expression < 1 CPM were excluded from the data for further analysis. To determine the oscillating genes, the CPM values were used to run DiscoRhythm analysis, a web-based application (https://mcarlucci.shinyapps.io/discorhythm/) ³⁷. The genes from Discorhythm with p-value < 0.05 cut-off were pulled out from both algorithms, i.e., JTK and Cosinor. The genes from the Discorhythm were analyzed for the normal and extended photoperiods. These genes from both normal and extended photoperiods were analyzed using DiscoRhythm. Genes showing significant 24-hour rhythms were further analyzed in ingenuity pathway analysis (IPA) ^38,39, pathview Web ⁴⁰ and cluster profiler ⁴¹ to determine which pathway genes are activated in the rhythms.

Microbiota analysis

We used the OTUs obtained from the microbiota data from our previous study ³⁰.

Microbiota abundance - Gene Co-expression Quantification

To study the correlation between the differentially expressed genes and microbiome abundance, we used Weighted gene co-expression network analysis (WGCNA) ⁴², to identify the genes and potential OTUs that are correlated in. We further processed the data for dimensionality reduction using PCA and cluster analysis. The samples were clustered using the hierarchical cluster (hclust) function and Dynamic Tree Cut, and the network was constructed by selecting a soft thresholding of the correlation coefficient. We assessed the co-expression of circadian-regulated genes and microbiota abundance using blockwiseModules. The differentially expressed genes were assigned to different modules based on cluster analysis with the help of blockwiseModules and further analyzed their relationship with the individual OTUs.

The differential expression analysis of Brain Tissue

To analyze the role of photoperiods and circadian rhythms on gene expression patterns in the brain, we focused on samples collected at ZT0 (0600 hours local time) (Fig. 2A). We analyzed 12 RNAseq libraries from brain tissue, with six samples from each photoperiod treatment (N = 6). We generated an average of 17.5M reads per library, totaling 237933593.3 reads (Table 1). When aligned against the Galgal6 reference genome [ Ensembl 99 release version],11,867 genes showed detectable expression levels, and the dataset showed a common dispersion estimate of 0.105. Of the expressed genes, 607 genes were differentially expressed between the normal and extended photoperiod treatments at the ZT0 timepoint (6 a.m.). Among these differentially expressed genes (DEGs), 451 genes were downregulated, and 156 genes were upregulated in the normal photoperiods (Fig. 2B). The IPA analysis showed that Dopamine degradation, S100g family signaling pathway, Mitochondrial dysfunction, G-protein coupled receptor signaling, and Serotonin degradation were downregulated in extended photoperiods. PathfindR results showed the most enriched pathways such as Aldosterone-regulated sodium reabsorption, Endocrine and other factor-regulated calcium reabsorption, GABAergic synapse, Oxidative phosphorylation, Serotonergic synapse, Dopaminergic synapse and Circadian entrainment etc. with the Fold enrichment from range 1.5–6.6 (Fig. 3). The cluster enrichment analysis done with hierarchical clustering method grouped the pathways based on molecular function (Fig. 4). The master circadian clock is governed by the pituitary gland in birds and regulates the host’s physiological functions ⁴³. The recent study reveals that the enzyme monoamine oxidase A (MAOa) plays a crucial role in dopamine metabolism, is regulated by the circadian clock, and impacts mood-related behaviors such as depression and addiction ⁴⁴. In addition to synthesis and metabolism, dopamine release contributes significantly to circadian rhythms in behavior and physiology. For example, melatonin release from the pineal gland exhibits robust rhythmicity and is commonly used as a circadian marker in human studies. Melatonin release depends on the heteromerization of adrenergic receptors with dopamine D4 receptors, underscoring the pivotal role of dopamine in regulating pineal function ⁴⁵. Also, dopamine neuroendocrine neurons express circadian clock genes, contributing to the circadian regulation in the host organism. This propounds that dopamine plays a dual role as a noteworthy input to the daily rhythms of circadian clock gene expression in specific parts of the brain ⁴⁶.

Additionally, mitochondrial functioning regulated under circadian activity is a dynamic process crucial for maintaining cellular homeostasis and overall health ⁴⁷. Mitochondria plays a role in regulating cellular redox balance, and circadian disruption impacts mitochondrial respiration, which may induce hypoxia regulated by HIF-1 signaling pathway ⁴⁸. The perturbations in circadian rhythms can trade-off mitochondrial integrity and function, exacerbating oxidative stress and impairing cellular bioenergetic, which may drive a surge of reactive oxygen species (ROS) ⁴⁹. These interactions underscore the interconnectedness of circadian-regulated processes, including mitochondrial function, and ROS signaling in orchestrating cellular physiology.

Table 1

provides a Quality control summary of RNASeq Reads obtained from samples of both normal photoperiod (NP) and extended photoperiods (EP).
QC	NP_ZT0	NP_ZT24	NP_ZT48	EP_ZT0	EP_ZT24	EP_ZT48
Total M Seqs	21.48	16.43	15.31	16.25	14.66	17.84
Length	73	73	73	73	73	73
M Aligned	18.81	14.8	13.24	14.32	12.78	14.55
% Aligned	87%	90%	86%	88%	87%	84%
M Assigned	10.54	8.29	6.93	6.96	6.34	6.99
% Assigned	47%	50%	44%	43%	42%	41%

The genes following circadian rhythm

We analyzed expression intensities (CPM) of the samples of normal and extended photoperiod in DiscoRhythm to identify genes following diurnal oscillations(Fig. 5B). We found 577 oscillating genes with Cosinor and 417 with the JTK-cycle algorithm showing significant 24-hour rhythms (p < 0.05) (Fig. 5A). These two sets accounted for 723 unique genes, of which 269 were shared between the two algorithms (Supplementary file 1 & 2).

Further analyzing the 723 genes, these were found to be enriched for molecular function (MF), biological process (BP), and cellular component (CC). “The organic substance metabolic process”, “primary metabolic process,” and “cellular metabolic process” are the top three enriched biological processes with the highest gene counts involved. The top three enriched cellular components with the highest gene counts included “intracellular anatomical structure”, “organelle,” and “cytoplasm.”. The top enriched molecular functions were “heterocyclic compound binding," “organic cyclic compound binding," and “ protein binding”(Fig. 5C). To investigate differences between the NP and EP treatments, we pulled out the data for ZT0, as this time point represents the initiation of the light phase. These genes were predicted to be enriched in the canonical pathways FAK signaling, Phagosome formation, and Caveolar-mediated endocytosis signaling (activation Z-score > 2). The primary clock genes such as CLOCK, CRY1/2, BMAL1, ROR and TIPIN and four GPCR receptors FGFR, SUCNR1, HTR5, CXCL14, TACR3 showed robust 24-hr rhythms. Additionally, these receptor genes mediate G-protein coupled receptor signaling, which includes further tachykinin signaling, hydroxytryptamine receptor signaling, cytokine signaling and phagosome formation, which also showed 24-hr rhythms. These are essential genes for the overall immune health of the host ^50–53. Activation of these genes shows that 24-hr rhythms are key for normal immune signaling. The activation of SUCNR1 suggests the invocation of succinate, a versatile compound that functions like hormones and cytokines⁵⁴. SUCNR1 regulates metabolism by modulating circadian signaling and leptin expression, and SUCNR1 deficient adipocytes disrupts the body's ability to respond to leptin after feeding⁵⁵. Succinate, a significant component within the tricarboxylic acid cycle also triggers the the production of inflammatory cytokines such as IL-1β, IL-6, IL-8, and TNF-α ⁵⁶. Secondly, HTR signaling is responsible for serotonin release, and its expression is clock- dependent⁵⁷. Serotonin released by the gut comprises about 95% of total serotonin and is linked with the microbiota composition ⁵⁸. Serotonin also immunomodulates immune cells by secreting cytokines. Serotonin suppresses pro-inflammatory cytokines such as TNF-A and IL-1b ⁵⁹. Another chemokine under clock control is CXCL14, which is secreted by a broad range of cells (immune and non-immune) and modulates inflammatory responses through GPCRs ⁵². Our findings of these interactions in the early life a chick, and during the window of microbiome assembly implies a close coordination of microbiota-mediated processes with circadian-regulated genes.

Correlation of gene expression and gut microbiota

To further investigate the specific interactions between circadian-regulated gene expression and the microbiota, we performed co-expression analysis using the tool WGCNA integrated the RNAseq data generated here and microbiota data from our previous study ³⁰ .The hierarchical clustering (using ‘hclust’) showed the normal and photoperiod treatments formed two distinct clusters (Fig. 6A). The pickSoftThreshold was used for network topology analysis and set as power 5 (Fig. 6B ). Based on this power, the gene expression modules for each sample were clustered into four modules such as MEbrown, MEturquoise, MEblue, and MEgrey (Fig. 6C, and Fig. 7). Each module defines the correlation between each OTU and signifies the module-microbiome relationship (Fig. 6D) (Supplementary table 3 )

The relative abundance of specific OTUs was correlated with the Turquoise module, enriched for Cholesterol biosynthesis and Mitochondrial dysfunction. The relative abundance of OTU069 (Lactobacillus_72.9%. P = 0.007), OTU144 (Lachnospiraceae_NC2004, 71.9%, P = 0.008), Otu0134 (Defluviitaleaceae_UCG-011, 67.3%, P = 0.016), Otu0102 (Lachnospiraceae_FCS020_group, 62.1%, P = 0.03), Otu0113 (Ruminococcaceae_UCG-002, 61.9%, P = 0.03), Otu0054 (Clostridiales_vadinBB60_group, 60.4%, P = 0.037) was negatively correlated with the turquoise module. On the other hand MEturquoise is positively correlated with relative abundance of Otu0055 (Gastranaerophilales_unclassified, 58.6%, P = 0.04), OTU0012 (Alistipes, 76.4%, P = 0.003), Otu0105 (Ruminococcaceae_UCG-014, 58.6%, P = 0.04), Otu0152 (Ruminiclostridium_9, 60.8%, P = 0.03), Otu0063 (Ruminococcaceae_UCG-014, 68.1%, P = 0.014).

The correlations between the expressed genes and taxa in the Turquoise module highlight the intricate relationship between microbiome composition, cholesterol biosynthesis, and mitochondrial function. The implications of these correlations can be understood through the importance of cholesterol biogenesis for normal function. Altered cholesterol is associated with mitochondrial dysfunction and subsequent ATP deficiency, impacting neuronal function⁶⁰. Additionally, mitochondrial fusion and ERK activity are crucial in cholesterol transport, affecting steroidogenesis ⁶¹ .

Mitochondrial dysfunction is also sensitive to cholesterol levels, as studies have shown that external cholesterol levels impact mitochondrial function and inflammatory responses ⁶². Furthermore, mitochondrial membrane transporter deficiencies lead to iron imbalance, affecting cholesterol biosynthesis and metabolic pathways ⁶³. The circadian regulation of this crucial bidirectional interaction between cholesterol metabolism and mitochondrial function points to the important interactions indicated by this module. The correlation of these functions with specific microbial taxa in the turquoise module further suggests the circadian-regulated microbiota-homeostatic signaling crosstalk. In our previous studies ³⁰, Rikenellaceae (Alistipes), Lachnospiraceae, and Ruminococcaceae were dominant in EP. Alistipes aids in producing saturated fatty acid ⁶⁴ and the turquoise module's most enriched pathway was cholesterol biosynthesis and Mitochondrial dysfunction⁶⁵. Lactobacillus abundance was correlated with the expression levels of NPC1L1, CYP7A1, and ABCG5, which regulate cholesterol in humans. These results suggest that the abundance of Lactobacillus might play a role in Cholesterol biosynthesis and Mitochondrial dysfunction pathways during microbiome assembly in chicken. The differential enrichment of Lactobacillus and its correlation with cholesterol biosynthesis can be interpreted through the lens of probiotic effects on lipid metabolism. Certain Lactobacillus strains can influence lipid metabolism and cholesterol levels. For example, abundant probiotic Lactobacillus strains can impact cholesterol assimilation and bile acid levels ^66,67. Additionally, Lactobacillus strains lower total bile acids in serum, indicating a potential role in cholesterol metabolism ⁶⁸. Furthermore, Lactobacillus strains like Lactobacillus fermentum and Lactobacillus plantarum have probiotic effects on hypercholesterolemia, affecting cholesterol levels in animal models ^{69 70}. These strains have been shown to lower cholesterol levels in vitro and in vivo, suggesting underlying mechanisms for these taxa to modulate cholesterol metabolism ⁷⁰.

The blue module was negatively correlated with Otu0116 (Ruminococcaceae_UCG-004, 67.5%, P = 0.016). However, it is positively correlated with Otu0046 (Ruminococcaceae_UCG-014, 58.1%, P = 0.047), Otu0142 (Lachnospiraceae, 59.01%, P = 0.043), Otu0033 (Flavonifractor, 63.5%, P = 0.03), OTU0039 (Clostridiales vadinBB60, 86%, P = 5.68E-09) and OTU0 049 (Eubacterium coprostanoligenes, 98%, P = 0.0002).

The most enriched canonical pathways in blue modules were Dopamine receptor signaling, Melatonin degradation, Myelination signaling pathway, and WNT/𝛃-catenin signaling pathway analyzed in IPA (Fig. 6E).

The enrichment of canonical pathways such as Dopamine receptor signaling, Melatonin degradation, Myelination signaling pathway, and WNT/𝛃-catenin signaling pathway in the blue module points to the functional implications of these microbiota members (Fig. 6E). These pathways play crucial roles in various physiological processes, including neurotransmission, circadian rhythm regulation, myelin formation, and cell signaling. The negative correlation with Otu0116 (Ruminococcaceae_UCG-004) may suggest a potential regulatory role of this taxon in modulating the pathways associated with Dopamine receptor signaling, Melatonin degradation, Myelination signaling pathway, and WNT/𝛃-catenin signaling pathways. Conversely, the positive correlations with Otu0046, Otu0142, Otu0033, Otu0039, and Otu0049 indicate the potential contribution of these microbial taxa to the activation or enhancement of these pathways. The presence of Ruminococcaceae and Lachnospiraceae taxa in the positive correlations is noteworthy, as these bacterial families are known for their roles in promoting gut health and metabolism. Flavonifractor, Clostridiales vadinBB60, and Eubacterium coprostanoligenes are also important gut microbiota members with potential implications for host physiology and metabolism. In chickens, there is also evidence for the the influence of these taxa on behavior: cecal microbiota transplantation with Lachnospiraceae and Ruminococcaceae UCG-005 in chickens at an early promoted aggressive behavior in recipient chickens by controlling the functions of the catecholaminergic and serotonergic systems in the brain⁷¹.

The grey module was negatively correlated Otu0181 (Ruminococcaceae, 66.%2, P = 0.0188), Otu0080 (Oscillibacter, 64.8%, P = 0.022), Otu0068 (Lachnospiraceae, 60.9%, P = 0.03) significant P < 0.05. The grey module is positively correlated with Otu0029 (Ruminococcaceae_UCG-014, 58.2%, P = 0.04), Otu0151 (Christensenellaceae_R-7_group, 60.4%, P = 0.03), Otu0100 (Lachnospiraceae, 66.2%, P = 0.01) significant. The enriched pathways in this module are the Visual Cycle, ABRA signaling pathway, Apelin cardiomyocyte signaling pathway, Regulation of actin-based motility by Rho (Fig. 6E).

The negative correlations with Otu0181 (Ruminococcaceae), Otu0080 (Oscillibacter), and Otu0068 (Lachnospiraceae) suggest a potential regulatory role of these taxa in modulating the pathways associated with the Visual Cycle, ABRA signaling pathway, Apelin cardiomyocyte signaling pathway, and Regulation of actin-based motility by Rho within the grey module. On the other hand, the positive correlations with Otu0029 (Ruminococcaceae_UCG-014), Otu0151 (Christensenellaceae_R-7_group), and Otu0100 (Lachnospiraceae) indicate a potential contribution of these microbial taxa to the activation or enhancement of these pathways. The apelin-APJ pathway can directly antagonize vascular disease-related Ang II actions ⁷², providing insights into the regulatory mechanisms of the apelin signaling pathway in cardiovascular health. The microbiota associations suggest intriguing hypotheses about how early-life microbiome interactions with host homeostatic pathways can affect multiple major functions.

The brown module is negatively correlated significantly (P < 0.05) with Otu0039 (Clostridiales vadinBB60_group, 60.3%, P = 0.03), Otu0037 (Clostridiales_vadinBB60_group, 58.9%, P = 0.04), Otu0074 (Ruminococcaceae_UCG-014, 58.07%, P = 0.04. However is positively correlated with Otu0118 (Anaerotruncus, 57.7%, P = 0.03), Otu0183 (Ruminiclostridium_9, 61.26%, P = 0.03), Otu0066 ([Eubacterium]_hallii_group, 64.8%, P = 0.022). Top canonical pathways in this module are the Virus Entry via Endocytic Pathways, GABA Receptor Signaling, Adipogenesis pathway, Semaphorin Neuronal Repulsive Signaling Pathway, HMGB1 Signaling (Fig. 6E).

The interactions observed within the brown module, as indicated by the significant correlations with specific OTUs, suggest a complex interplay. The negative correlations with OTUs such as Otu0039 (Clostridiales vadinBB60_group), Otu0037 (Clostridiales_vadinBB60_group), and Otu0074 (Ruminococcaceae_UCG-014) may indicate a potential regulatory role of these taxa in modulating pathways associated with the Virus Entry via Endocytic Pathways, GABA Receptor Signaling, Adipogenesis pathway, Semaphorin Neuronal Repulsive Signaling Pathway, and HMGB1 Signaling within the brown module. These interactions highlight links between gut dysbiosis, neurotransmitter disturbance, and inflammatory responses, shedding light on the potential impact of microbial taxa on behavior and neurotransmitter metabolism. Altogether, these modules indicate intriguing correlations between the microbiota and gene expression data in the context of circadian rhythms, and point to the foundational role these interactions may have on later life metabolic and immune health.

KEGG pathway analysis

The most enriched pathways found in our IPA and WGCNA analysis were GPCR signaling, Dopamine signaling, Serotonin signaling, Melatonin degradation, Myelination signaling, tachykinin signaling, and Mitochondrial dysfunction. We used the KEGG terms to understand the key molecules in these pathways to identify the potential mechanisms of importance in the microbiota-host crosstalk. Across these pathways, most enriched genes were linked with calcium signaling, MAPK Signaling, circadian entrainment and chemokine signaling (Fig. 8). The clustering in Fig. 3 shows that Enhanced functional processes in GPCR signaling, Dopamine signaling, Serotonin signaling, Melatonin degradation, Myelination signaling, tachykinin signaling, and Mitochondrial dysfunction collectively invoke a complex and intricate interplay of various signaling pathways. While many pathways could be impacted, calcium signaling is one of the key pathways. Calcium signaling is fundamental in various cellular responses to environmental stimuli, including plant defense systems ⁷³. It also regulates cell cycle progression in response to abiotic stress ⁷⁴. Furthermore, reactive oxygen species (ROS) in mitochondria trigger monoamine-induced calcium signals, influencing physiological and pathophysiological responses to dopamine ⁷⁵. Calcium signaling is versatile in numerous cellular functions and crucial for translocating nuclear proteins like PKC-γ ⁷⁶. The calcium signaling is a critically important phenomenon that regulates synaptic activity in the nervous system. Also, synaptic activity is the key feature of circadian oscillations, and changes in circadian rhythms alter synapse numbers. Calcium is the most important signaling molecule that plays a role in nervous excitability and regulation of biological clock ⁷⁷. Calcium signaling is intricately linked to circadian rhythms, playing a significant role in regulating the molecular clock and coordinating various physiological processes throughout the day. Circadian rhythms in the SCN neurons are synchronized with calcium rhythms, indicating a causal relationship between intracellular calcium dynamics and the generation of circadian rhythms ⁷⁸. Furthermore, regulating voltage-dependent calcium channels (VDCCs) by circadian mechanisms is crucial for maintaining rhythmic clock gene expression in the SCN, highlighting the importance of calcium signaling in the molecular machinery of circadian clocks ⁷⁹. Additionally, mitochondrial calcium signaling has been implicated in mediating rhythmic extracellular ATP accumulation in SCN astrocytes, further emphasizing the role of calcium in coordinating circadian processes at the cellular level ⁸⁰. These enriched pathways point towards key homeostatic processes. These pathways play a role in intracellular calcium signaling, which is crucial for activating lymphocytes and neurological functions (KEGG hsa04020). The gene regulatory network of differentially expressed genes involved in these pathways are shown in Fig. 9. The genes in the maroon-colored PPI network (Fig. 9) were found to be upregulated and blue-colored were downregulated in extended photoperiods.

The interaction between circadian rhythms and the gut microbiome may include calcium signaling as a key mediator. Calcium signaling regulates neurotransmitter release and cellular functions essential for circadian rhythm generation ⁸¹. The influx of calcium ions into the presynaptic terminal triggers the release of neurotransmitters into the synaptic cleft, facilitating signal transmission between neurons ⁸². This process is tightly regulated and involves various proteins and signaling pathways. For example, synaptotagmins, calcium sensors on synaptic vesicles, are vital for vesicle fusion with the presynaptic membrane and subsequent neurotransmitter release ⁸³. Calcium signaling also modulates synaptic plasticity, where changes in calcium levels can impact the strength of synaptic connections and neuronal communication ⁸⁴. On the other hand, the gut microbiome can be influenced by factors such as diet, disease, and probiotics, impacting cognitive function and overall health⁸⁵. Given the bidirectional communication between the gut microbiome and the host, calcium signaling may serve as a signaling pathway through which circadian rhythms interact with the gut microbiome. Furthermore, the gut microbiome's role in modulating host metabolism and immune function suggests a potential link between calcium signaling, circadian rhythms, and gut microbiome-mediated effects on health and disease.

This study investigated the impact of photoperiods and circadian rhythms on gene expression in the brain, revealing significant alterations in gene expression patterns associated with normal and extended photoperiods. Our RNAseq analysis of chicken brain tissue collected at the ZT0 timepoint identified 607 DEGs, mostly downregulated under normal photoperiods. Pathway enrichment analyses highlighted the downregulation of pathways related to dopamine degradation, mitochondrial dysfunction, and G-protein coupled receptor signaling under extended photoperiods. These findings underscore the influence of light exposure on neurochemical pathways and mitochondrial function, which are critical for maintaining cellular homeostasis and overall health.

Our DiscoRhythm analysis revealed 723 unique genes with circadian oscillations, indicating a robust circadian regulation of metabolic processes, intracellular structures, and binding activities within the brain. Notably, key circadian clock genes and GPCR receptors were differentially regulated between normal and extended photoperiods, suggesting that light treatment modulates neuropeptides, neurotransmitters, and cytokine signaling. The pathways related to GPCR signaling, such as dopamine degradation, serotonin signaling, and mitochondrial dysfunction, were downregulated in extended photoperiods, signifying the impact of photoperiods on key physiological processes. Pathway enrichment analyses show that Aldosterone-regulated sodium reabsorption, GABAergic synapse, and oxidative phosphorylation correlate with circadian rhythms on cellular functions. This supports the dual role of dopamine in regulating circadian clock gene expression and the importance of mitochondrial integrity in circadian rhythm maintenance.

The co-expression analysis integrated with microbiota data revealed distinct gene expression modules correlated with specific microbial taxa. These modules, MEbrown, MEturquoise, MEblue, and MEgrey, were associated with various physiological pathways, such as cholesterol biosynthesis, mitochondrial dysfunction, dopamine receptor signaling, and melatonin degradation. The correlations between gene expression and gut microbiota composition highlight the complex interactions between the host circadian system and the microbiome, with implications for metabolic and immune health.

Our findings suggest that the circadian clock exerts a broad regulatory influence on gene expression in the brain, affecting pathways involved in neurotransmission, metabolism, and immune response. The photoperiod modulates these pathways, with extended light exposure leading to alterations in key neurochemical and mitochondrial pathways. Additionally, the interplay between gut microbiota and host gene expression underscores the potential of microbiota-targeted interventions to modulate circadian-regulated physiological processes.

Data Availability

The raw reads for the RNAseq experiment are available at NCBI SRA with accession number PRJNA1111828. The 16S rRNA amplicon sequence data is available on Figshare at https://figshare.com/articles/dataset/circadian_tar_gz/6938249/1

Acknowledgements

We thank C. Hieke, S. Hubert, H. Nguyen Phuc for assistance with the sampling. We thank A. Hillhouse of the Texas Institute for Genome Sciences and Society for assistance with sequencing.

Funding

This work was partly supported by funds from USDA NIFA award 2019-67015-29441 to GA, and funds from the Institute for Advancing Health Through Agriculture at Texas A&M University.

Author Contributions

Mridula Gupta and Giri Athrey: Study design, analysis, visualization, interpretation and writing and editing

Mustafa Cilkiz and Mohamed Magdy Ibrahim : For analysis and visualization and manuscript editing.

Ethics approval and consent to participate: The design of the animal study was reviewed and approved by the Texas A&M University’s IACUC (Assurance Number 2016-0064).

Consent for publication: This manuscript does not contain any personal data.

Competing Interests: The authors declare that they have no competing interests.

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Gut microbiome-brain crosstalk in the early life of chicken reveals the circadian regulation of key metabolic and immune signaling processes

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Abstract

Background

Results

Conclusions

Figures

Introduction

Material and Methods

Animal ethics statement

Animals and Experimental design

Sample collection

RNA Isolation and Quantification

RNA Library Preparation and Transcriptome Profile Generation

Transcriptome Data Analysis

Microbiota analysis

Microbiota abundance - Gene Co-expression Quantification

Results and Discussion

The differential expression analysis of Brain Tissue

The genes following circadian rhythm

Correlation of gene expression and gut microbiota

KEGG pathway analysis

Conclusion

Declarations

References

Additional Declarations

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