Molecular Vision 2024; 30:379-389 <http://www.molvis.org/molvis/v30/379>
Received 30 January 2024 | Accepted 08 November 2024 | Published 10 November 2024

Global mapping of BMAL1 protein-DNA interactions in human retinal Müller cells

Qianyi Luo,1 Neel Sangani,2 Surabhi Abhyankar,1 Sahiti Somalraju,2 Sarath Chandra Janga,2,3,4 Ashay D. Bhatwadekar1

The first two authors contributed equally to this manuscript.

1Department of Ophthalmology, Indiana University School of Medicine, Eugene and Marilyn Glick Eye Institute, Indianapolis, IN; 2Department of Biomedical Engineering and Informatics, Luddy School of Informatics, Computing & Engineering, Indiana University Indianapolis (IUI), Indianapolis, IN.; 3Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN.; 4Centre for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN

Correspondence to: Ashay Bhatwadekar, Department of Ophthalmology, Eugene and Marilyn Glick Eye Institute, 1160 W. Michigan Street GK-305P, Indianapolis, IN 46202;
Phone: (317) 278-5075; FAX: (317) 274-2277; email: abhatwad@iupui.edu

Abstract

The circadian clock, a conserved biologic timekeeping mechanism, is pivotal in orchestrating rhythmic physiologic processes. While extensively studied in the central clock, the involvement of BMAL1 in peripheral clocks, particularly in human Müller cells, remains underexplored. Müller cells, critical for retinal homeostasis, may unveil novel insights into circadian regulation. Employing ChIP-sequencing, we comprehensively mapped BMAL1 binding sites in human Müller cells. The analysis identified 275 reproducible peaks, with predominant distribution across promoters (26.6%), intronic (26.3%), and intergenic (22.1%) regions, with 80% of these confident peaks linked to protein-coding genes. Differential peak analysis revealed 89 unique genes significantly enriched with BMAL1 sites in their promoters, while functional enrichment of the associated genes indicated key biologic processes such as circadian regulation of gene expression, photoperiodism, and glucocorticoid receptor signaling pathway regulation. Motif analysis revealed a highly conserved 6-nucleotide motif, CACGTG, appearing in 89.09% of the peaks. Analysis of the binding sites across genomic regions highlighted the robust BMAL1 binding, further confirmed by qPCR validation of circadian targets such as G6PC3, CIART, PER1, and TXNIP, which are critical for Müller cell health, along with SHMT2 and MALAT1, which have emerged as novel genes that may have implications for Müller cell health. Our findings unveil the regulatory landscape of BMAL1 in Müller cells, contributing to a broader understanding of the clock-mediated mechanism in ocular tissues. These insights hold therapeutic potential for circadian-related retinal diseases, presenting avenues for chronotherapeutic interventions.

Introduction

The circadian clock, an evolutionarily conserved biologic timekeeping mechanism, orchestrates rhythmic physiologic processes in response to environmental cues such as light and temperature [1]. At the molecular level, the circadian clock is regulated by a network of genes, among which BMAL1 (Brain and Muscle ARNT-Like 1) stands as a key transcription factor [2]. BMAL1 plays a pivotal role in driving the expression of circadian clock genes, forming heterodimers with CLOCK (Circadian Locomotor Output Cycles Kaput), and orchestrating the transcription of downstream targets [3]. Bmal plays an integral role in retinal health, and previous studies demonstrate that Bmal is responsible for the rhythmicity of over 1000 genes in the retina [4]. Additionally, loss of Bmal results in degeneration of cone photoreceptors, shunting of bipolar cell dendrites [5], and distribution of opsins [6], including accelerated degeneration of retinal capillaries [7].

Müller cells, the principal glial cells of the retina, contribute to the maintenance of retinal homeostasis and play a crucial role in supporting retinal neurons [8]. Müller cells span the entire retina and provide structural support, helping facilitate nutrient transport and waste removal [8]. Additionally, Müller cells play an important role in potassium and water balance via inwardly rectifying potassium channels. We previously reported that Kir4.1 channels in the retina exhibit a diurnal rhythm and that Bmal has a potential role in regulating Kir4.1 channel expression by regulating insulin receptor substrate 1 (IRS-1) mediated phosphorylation of Akt [9]. Additionally, we observed that Bmal corrects Müller cell Kir4.1 channel downregulation in diabetes by working through adenosine monophosphate-activated protein kinase (AMPK) [10]. When Bmal was conditionally removed from endothelial cells, there was an accelerated response to both macrovascular and microvascular injury due to an increase in nitrotyrosine [7]. Thus, BMAL1, as a transcription factor, regulates many downstream targets in the retina; however, its involvement in human Müller cells remains less understood. Therefore, investigating the regulatory functions of BMAL1 in Müller cells can offer valuable insights into retinal physiology and its potential implications for vision and overall retinal health.

In this study, we employed ChIP-seq (Chromatin Immunoprecipitation followed by Next-Generation Sequencing) to comprehensively analyze the genome-wide binding activity of BMAL1 in human Müller cells. ChIP-seq enabled high-resolution mapping of BMAL1 binding sites, providing a detailed understanding of its genomic targets. By characterizing the regulatory landscape of BMAL1 in Müller cells, our genomic data analysis uncovered key clock-controlled genes, elucidating novel pathways influenced by BMAL1, thereby contributing to the broader understanding of circadian regulation in ocular tissues.

Methods

Tissue culture of human Müller cells

The human Müller cells were obtained from donor eyes from Saving Sight Eye Bank (Kansas City, MO), provided by Dr. Weiming Mao, Department of Ophthalmology, Indiana University. Our group previously characterized these cells [9]. For this study, we used cells from passages 3 to 6; all the cells were harvested at the same time of the day between 1 and 2 PM.

Sample preparation for ChIP-Seq

Five X 106 cells (80%–85% viability) were fixed in 1/10 volume of freshly prepared formaldehyde solution containing (11% formaldehyde, 0.1M NaCl, 1 mM EDTA, pH 8.0, and 50 mM HEPES, pH 7.9 in the presence of media. The fixation was stopped by treatment of 1/20 volume of Glycine solution (2.5M) for 5 min. After the glycine incubation, if the cells were still adherent, they were scraped thoroughly from the culture surface. The cell suspension was kept on the ice and centrifuged at 800 ×g for 10 min to pellet the cells. The supernatant was removed, and the cells were re-suspended in 10 ml chilled Igepal CA-640 (0.5% in PBS, pH 7.4), followed by centrifugation and re-pelleting of cells and re-suspension in Igepal CA-640 (Sigma). Finally, 100 μl of PMSF (1 mM) was added to each tube, the cells were pelleted, and the supernatant was removed carefully. The cell pellets were snap-frozen and stored at −80C until processed for ChIP-Seq at Active Motif (Carlsbad, CA).

ChIP reactions, generation and sequencing of libraries, and basic data analysis

In brief, the chromatin was isolated by adding lysis buffer, followed by disruption with a Dounce homogenizer. Lysates were sonicated, and the DNA sheared to an average length of 300–500 bp with Active Motif’s EpiShear probe sonicator (53051) and cooled sonication platform (53080). Genomic DNA (Input) was prepared by treating aliquots of Chromatin with RNase, proteinase K, and heat for de-crosslinking, followed by SPRl beads clean up. Eluted DNA was quantified by Clariostar. Extrapolation to the original chromatin volume allowed quantitation of the total chromatin yield. An aliquot of chromatin (40 ug) was precleared with protein G agarose beads (Invitrogen). Genomic DNA regions of interest were isolated using 10 ul of BMAL1 (Abcam cat# ab3350, lot# GR3285166–10). Complexes were washed, eluted from the beads with SDS buffer, and subjected to RNase and proteinase K treatment. Crosslinks were reversed by incubation overnight at 65 C, and ChIP DNA was purified by phenol-chloroform extraction and ethanol precipitation.

ChIP-seq analysis

Illumina sequencing libraries were prepared from the ChIP and Input DNAs using the standard consecutive enzymatic steps of end-polishing, dA-addition, and adaptor ligation using Active Motif’s custom liquid handling robotics pipeline. After the final PCR amplification step, the resulting DNA libraries were quantified and sequenced on Illumina NovaSeq 6000. Sequences. The computational workflow for the ChIP-seq experiment is highlighted in Figure 1, summarized below, and described in detail in this study [11].

I. Sequencing and data processing-- ChIP-seq data for BMAL1 was obtained from two human Müller cell replicates. Single-end reads of 75bp in length were processed by trimming Illumina adaptor sequences using trim galore and underwent quality assessment with FastQC v0.11.5 [12].

II. Read mapping and format conversion-- Processed reads were aligned to the GRCh38 genome assembly using HISAT2 v2.1.0. Further, SAMtools-1.5 and BEDTools v2.26.0 were used for format conversion to BED and sorting before peak calling [13-15].

III. Peak calling and consensus analyses-- High-throughput transcription factor-DNA binding sites were identified using MACS2 with the narrow peaks’ parameter [16]. The significance for differential enriched sites was set at a p value of 0.05 for both replicates. Consensus peaks between replicates were determined using BEDTools' overlap feature (intersectBed) with parameters -f 0.05 -r to ensure that a peak from one replicate overlaps with at least 5% of the length of a peak from the second replicate to be considered a consensus peak. This method ensures robust reproducibility by identifying consistently present peaks across independent experiments.

IV. Genomic annotation and pathway analysis-- Genomic features and gene ontology of consensus peaks were annotated using HOMER v4.11 (annotatePeaks.pl) [17]. Proportions of genomic features and gene type distributions were computed and compared across samples. Functional enrichment analysis employed the ClueGO plugin within Cytoscape for biologic pathways [18]. Peak-called bed files from each replicate were converted to bedgraph format for visualization of enrichment sites on the UCSC browser custom tracks [19].

V. Motif analysis-- Before conducting motif discovery analysis, the nucleotide sequences corresponding to the consensus peak coordinates were retrieved from the GRCh38 genome using BEDtool's getfasta feature [15]. These sequences were then used as input for the motif discovery analysis performed with MEME v5.1.0 [20] using the –maxw 6 parameter to identify motifs consisting of six-nucleotides. The motif coordinates identified by MEME were used to extract the associated genes for functional enrichment analysis.

ChIP-qPCR

We performed the BMAL1 ChIP-qPCR assay using 40 μg of human Müller cell chromatin and 10 μg of antibody (Abcam, cat # ab3350). The VAMP2 primer was used as a positive control, and a primer pair that amplifies a region in a gene desert on chromosome 12 (Untr12) was used as a negative control. Quantitative PCR (qPCR) reactions were performed in triplicate for specific genomic regions using SYBR Green Supermix (Bio-Rad, Cat # 170–8882) on a CFX Connect™ Real-Time PCR system. The resulting signals were normalized for primer efficiency by carrying out qPCR for each primer pair using input DNA from each cell type. A negative control primer set (Active Motif, catalog number 71,001) was also used. Listed below in Table 1 are the set of primers used in the study.

MALAT1- metastasis-associated lung adenocarcinoma transcript 1, SHMT2- Serine Hydroxymethyltransferase 2, PER1-Period 1, TXNIP- thioredoxin interacting protein, G6PC3- glucose-6-phosphatase catalytic subunit 3, CIART-circadian-associated repressor of transcription

Results

Peak identification and genomic distribution

Two independent replicates with 38.9 million and 42.1 million ChIP-seq reads, respectively, were generated and underwent preprocessing, alignment, and peak calling to identify 3178 and 1807 enriched peaks. Consensus analysis, with at least a 5% overlap between peaks across the two replicates, yielded 275 highly enriched and reproducible peaks. The distribution of these peaks across transcript regions revealed that the majority mapped to promoter (26.6%), intron (26.3%), and intergenic (22.1%) regions (Figure 2A). Further examination of the fraction of gene types exhibiting enriched peaks showed that most were in the protein-coding areas (80%), followed by non-coding RNA regions (17%), as shown in Figure 2B.

Identification and functional analysis of promoter-bound peaks

Given the pivotal role of BMAL1 in binding promoter regions, our focus turned to genes associated with peaks in these regions. Differential analysis identified 89 unique genes that showed significant BMAL1 enrichment in promoter regions. Subsequent functional enrichment analysis underscored the involvement of these genes in pertinent biologic processes. This includes the circadian regulation of gene expression, the entrainment of the circadian clock through photoperiodic cues, the regulation of the glucocorticoid receptors signaling pathway, l-glutamate transmembrane transport, gluconeogenesis, tetrahydrofolate metabolic process and positive regulation of mTOR signaling (Figure 2C). Table 2 provides a detailed list of GO terms and their associated genes. The statistical significance of these associations was established at a threshold of both p value and false discovery rate (FDR) equal to 0.05. The genes implicated in these functions included well established clock genes: CRY1, CRY2, PER1, PER2, and PER3. For a detailed list of the identified promoter genes, see Appendix 1.

Identification of conserved binding site motifs among the high-confidence ChIP-seq regions

The exploration of DNA sequence motifs linked to the binding of the BMAL1 transcription factor through motif discovery analysis revealed a 6-nucleotide motif, CACGTG, as shown in Figure 3A. This motif, distinguished by an E-score of 5.8e-129, was identified in 245 out of 275 peaks (89.09%). The substantial prevalence of this motif underscores its high conservation across the analyzed genomic regions and indicates the ability of BMAL1 to bind to these regions. In other previous studies, this motif has been identified as a recognition pattern for BMAL1, suggesting its prominent role in binding and regulating via this consensus motif [21-23]. With 145 genes identified containing the BMAL1 binding motif, subsequent functional enrichment reveals various biologic processes, including glycogen biosynthetic process, regulation of protein maturation, nuclear receptor activity, regulation of transcription initiation of NRA polymerase II promoter, endoplasmic reticulum organization, replicated senescence, and maintenance of protein location in the cell (Figure 3B).

Visualization of differentially bound promoter regions and validation of the binding activity using ChIP-qPCR

Next, we selected six targets based on the focus on circadian rhythms, glucose metabolism, and technical limitations (e.g., primer design) to validate ChIP-Seq findings using qRT-PCR. Quantifying the ChIP-enriched DNA regions using quantitative PCR revealed amplification of 9 to 25-fold at SHMT2, MALAT1, GCPC3, CIART, PER1, and TXNIP over background signal (Figure 3C). To glean insights into the differential enrichment of BMAL1 in Müller cells, we visually represented promoter regions of genes exhibiting significant binding. This visualization highlights the robust binding activity of BMAL1 across the promoters, as demonstrated in Figure 3D in both replicates.

Discussion

We implemented a ChIP-seq computational pipeline to map BMAL1 binding sites in Human Müller cells comprehensively. Validation through ChIP-PCR identified targets associated with well known circadian rhythms and glucose metabolism relevant to understanding Müller cell health during health and conditions such as diabetes.

Identifying differentially enriched genes corresponding to promoter regions with ChIP-seq signals is pivotal in unraveling the functional implications of circadian regulation. Notably, key clock genes, including CRY1, CRY2, PER1, PER2, and PER3, emerged as central players in the orchestration of circadian clocks, influencing both photoperiodic cues and glucocorticoid receptor signaling pathways. Photoperiodic cues, defined as environmental signals modulating the duration of light within 24 h, are involved in modulating physiologic and behavioral responses in organisms [24]. This establishes a link between external light variations and internal circadian rhythms. Furthermore, the involvement of the clock genes in the regulation of glucocorticoid receptor signaling pathways adds a layer of complexity to the regulatory network [25-27]. Glucocorticoids, recognized as steroid hormones, are commonly associated with anti-inflammatory responses, and their receptor agonists find widespread use in treating inflammatory diseases of the eye [25].

In the biologic model governing clock regulation, the interaction of CLOCK-BMAL1 and NPAS-2 dimers involves binding to hexanucleotide E-Box motifs, characterized by the canonical sequence CANNTG, where N represents any nucleotide [28]. Thus, during motif analysis, we specifically targeted a six-nucleotide motif. Our findings revealed a highly conserved motif, CACGTG, present in 89.09% of peaks, with an E-value of 5.8e-129. This observation is particularly significant given a previous report indicating the tissue-specific nature of BMAL1 interaction, with CACGTG emerging as the most strongly associated motif with BMAL1 binding. Additionally, BMAL1 has also been shown to bind E-box-like sequences such as CACGTT in the promoter of the murine Per2 gene [21] and TGASTCA motif, a binding site of the AP-1 transcription factor [29]. Indeed, we did observe TGASTCA motifs in 22% of peaks in analysis; however, the difference was statistically insignificant.

Sawant et al. [6] previously performed ChIP-seq for BMAL1 using whole mouse retina; their studies demonstrated the binding of BMAL1 to Dio2 promoter; however, in our studies, we did not find a similar binding of BMAL1 in human Müller cells, this suggests that BMAL1 binds differentially in retinal cell types. This is further reinforced by another BMAL1 ChIP-seq study in mice kidney and liver tissues, where the authors showed that most BMAL1 DNA binding is tissue-specific and is determined by the accessibility of chromatin and the co-binding of tissue-specific transcription factors [21,30].

Our ChIP qPCR studies validated TXNIP and G6PC3, which are known to play an important role in glucose metabolism and retinal health. The G6PC3 gene encodes the catalytic subunit of glucose-6-phosphatase (G6Pase), located in the endoplasmic reticulum [31], and is expressed in a variety of tissues with the highest levels in muscle and intermediate levels in the placenta, brain, heart, spleen, kidney, small intestine, colon, pancreas, and thymus [32]. Unlike liver G6Pase, G6PC3 does not regulate hepatic glucose production; however, it is responsible for producing non-hepatic glucose. G6PC3 is responsible for the hydrolysis of glucose-6-phosphate to glucose and phosphate in the last step of gluconeogenic and glycogenolytic pathways [31]. G6PC3 deficiency in humans exhibits impaired glucose uptake and translocation of transporter 1 (GLUT1) and a reduction in G6P, ATP, and lactate [32]. GLUT1 is a vital glucose transporter of the retina that facilitates transepithelial glucose transport into the outer and inner retina [33]. Our studies suggest that BMAL could indirectly affect glucose uptake by regulating GLUT1 via G6PC3; however, further studies would be required in this direction to establish a causal relationship.

TXNIP is an early response gene known to be induced by diabetes and hyperglycemia. The TXNIP levels are known to increase in the retina during diabetic retinopathy (DR), the most common complication of diabetes resulting in an increase in pro-inflammatory cytokines such as Co×-2, VEGF, ICAM1, and RAGE; consequently, TXNIP knockdown prevents early abnormalities of DR such as inflammation, fibrosis, gliosis and apoptosis [34]. In Müller cells, high glucose treatment upregulates TXNIP, resulting in ROS generation, ATP depletion, inflammation, and ER stress [34-36]. TXNIP positively regulates autophagy by inhibiting Müller cells' PI3K/AKT/mTOR pathway [37].

Additionally, our ChIP-qPCR demonstrates that BMAL1 binds to Per1 and CIART, which are known to play an important role in circadian rhythms, which has been reported previously [30], further reinforcing our study’s robustness. We also report that BMAL1 binds to MALAT1 (metastasis-associated lung adenocarcinoma transcript 1) and SHMT2 (Serine Hydroxymethyltransferase 2), both of which have unknown role in the context of circadian rhythms or Müller cells as such. However, strong binding of BMAL1 to these targets paves the way for future studies to explore these targets further.

Müller cells play a critical role in retinal homeostasis, and in conditions such as diabetes, the Müller cells are swollen, gliotic, and dysfunctional due to the underlying pathology [38]. Overall, there are limited studies focused on circadian rhythms in Müller cells; we previously reported that IRS-1 and Kir4.1 channels both exhibit a diurnal rhythm in retinal Müller cells [9], in particular, clock genes Bmal and Per2 [39] help regulate Kir4.1 channels in the Müller cells. While the present study further provides detailed information on the genome-wide binding of BMAL1, the non-synchronous nature of cell cultures presents a challenge in examining the circadian rhythm aspect of BMAL1. Cell synchronization using horse serum or dexamethasone could address this issue; however, such treatments may alter BMAL1 and downstream targets, complicating the analysis of BMAL1’s baseline effects in Müller cells. Additionally, previous studies demonstrate that Müller cells require Per1 to express circadian oscillations [40]; Per1 promoter activation was not expressed with a circadian rhythm in mouse cells. Interestingly, in human Müller cells, the circadian rhythm is not dependent on Per1 [40]; however, Bmal1 and Per2 play an important role. Future studies will be required to ascertain whether BMAL1 promoter activation follows circadian rhythms in human Müller cells similar to the above studies in the Müller cells of mouse origin. Our study certainly paves the road for future studies in this direction. Thus, this study offers valuable insights into the molecular pathways influenced by BMAL1 in Müller cells, setting the stage for potential chronotherapeutic interventions in circadian-related retinal diseases.

In conclusion, our ChIP-seq analysis unveils BMAL1's pivotal role in regulating gene expression in human Müller cells, emphasizing its significance in the clock-medaited mechanism in retinal physiology. Identifying reproducible peaks, a highly conserved DNA motif, and enrichment in key promoter genes highlight the specificity and functional relevance of BMAL1 binding. Our study contributes to the broader understanding of clock-mediated mechanisms in ocular tissues, opening new avenues for future research.

Appendix 1. Supplementary table 1.

Acknowledgments

This work is supported by a funding support from NIH-NEI grants R01EY027779, R01EY027779-S1, and R01EY032080 to AB, NIGMS grant R01GM123314 to SJ, Challenge grant from RPB to the Department of Ophthalmology, Indiana University School of Medicine. Author Contributions QL and SA performed cell culture of human Müller cells and processed them for ChIP-Seq. NS wrote the first draft of the manuscript and performed data analysis. SS helped with computational analysis, manuscript preparation, and writing. SJ supervised NS and SS and helped with the study's design and writing. AB conceived the idea, wrote the manuscript, supervised QL and SA, and supervised the overall project. All authors discussed the results and commented on the manuscript. Data Availability The data supporting the findings of this study are available on request from the corresponding author, AB Competing Interests AB is an ad hoc District Support Pharmacist at CVS/Health Aetna. The content of this manuscript does not reflect that of the CVS/Health Aetna. Other authors do not have any competing interests.

References

  1. Dunlap JC. Molecular bases for circadian clocks. Cell. 1999; 96:271-90. [PMID: 9988221]
  2. Gekakis N, Staknis D, Nguyen HB, Davis FC, Wilsbacher LD, King DP, Takahashi JS, Weitz CJ. Role of the CLOCK protein in the mammalian circadian mechanism. Science. 1998; 280:1564-9. [PMID: 9616112]
  3. Kondratov RV, Chernov MV, Kondratova AA, Gorbacheva VY, Gudkov AV, Antoch MP. BMAL1-dependent circadian oscillation of nuclear CLOCK: posttranslational events induced by dimerization of transcriptional activators of the mammalian clock system. Genes Dev. 2003; 17:1921-32. [PMID: 12897057]
  4. Storch K-F, Paz C, Signorovitch J, Raviola E, Pawlyk B, Li T, Weitz CJ. Intrinsic circadian clock of the mammalian retina: importance for retinal processing of visual information. Cell. 2007; 130:730-41. [PMID: 17719549]
  5. Baba K, Piano I, Lyuboslavsky P, Chrenek MA, Sellers JT, Zhang S, Gargini C, He L, Tosini G, Iuvone PM. Removal of clock gene Bmal1 from the retina affects retinal development and accelerates cone photoreceptor degeneration during aging. Proc Natl Acad Sci U S A. 2018; 115:13099-104. [PMID: 30498030]
  6. Sawant OB, Horton AM, Zucaro OF, Chan R, Bonilha VL, Samuels IS, Rao S. The circadian clock gene Bmal1 controls thyroid hormone-mediated spectral identity and cone photoreceptor function. Cell Rep. 2017; 21:692-706. [PMID: 29045837]
  7. Bhatwadekar AD, Beli E, Diao Y, Chen J, Luo Q, Alex A, Caballero S, Dominguez JM, , 2nd Salazar TE, Busik JV, Segal MS, Grant MB. Conditional deletion of Bmal1 accentuates microvascular and macrovascular injury. Am J Pathol. 2017; 187:1426-35. [PMID: 28432873]
  8. Reichenbach A, Bringmann A. New functions of Müller cells. Glia. 2013; 61:651-78. [PMID: 23440929]
  9. Luo Q, Xiao Y, Alex A, Cummins TR, Bhatwadekar AD. The diurnal rhythm of insulin receptor substrate-1 (IRS-1) and Kir4. 1 in diabetes: implications for a clock gene Bmal1. Invest Ophthalmol Vis Sci. 2019; 60:1928-36. [PMID: 31042800]
  10. Alex A, Luo Q, Mathew D, Di R, Bhatwadekar AD. Metformin corrects abnormal circadian rhythm and Kir4. 1 channels in diabetes. Invest Ophthalmol Vis Sci. 2020; 61:46 [PMID: 32572457]
  11. Nakato R, Sakata T. Methods for ChIP-seq analysis: A practical workflow and advanced applications. Methods. 2021; 187:44-53. [PMID: 32240773]
  12. Wilson BC, Butler ÉM, Grigg CP, Derraik JGB, Chiavaroli V, Walker N, Thampi S, Creagh C, Reynolds AJ, Vatanen T, O’Sullivan JM, Cutfield WS. Oral administration of maternal vaginal microbes at birth to restore gut microbiome development in infants born by caesarean section: A pilot randomised placebo-controlled trial. EBioMedicine. 2021; 69103443 [PMID: 34186487]
  13. Kim D, Paggi JM, Park C, Bennett C, Salzberg SL. Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype. Nat Biotechnol. 2019; 37:907-15. [PMID: 31375807]
  14. Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R, Subgroup GPDP, 1000 Genome Project Data Processing Subgroup. The sequence alignment/map format and SAMtools. Bioinformatics. 2009; 25:2078-9. [PMID: 19505943]
  15. Quinlan AR, Hall IM. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics. 2010; 26:841-2. [PMID: 20110278]
  16. Zhang Y, Liu T, Meyer CA, Eeckhoute J, Johnson DS, Bernstein BE, Nusbaum C, Myers RM, Brown M, Li W, Liu XS. Model-based analysis of ChIP-Seq (MACS). Genome Biol. 2008; 9:R137 [PMID: 18798982]
  17. Heinz S, Benner C, Spann N, Bertolino E, Lin YC, Laslo P, Cheng JX, Murre C, Singh H, Glass CK. Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol Cell. 2010; 38:576-89. [PMID: 20513432]
  18. Bindea G, Mlecnik B, Hackl H, Charoentong P, Tosolini M, Kirilovsky A, Fridman W-H, Pagès F, Trajanoski Z, Galon J. ClueGO: a Cytoscape plug-in to decipher functionally grouped gene ontology and pathway annotation networks. Bioinformatics. 2009; 25:1091-3. [PMID: 19237447]
  19. Karolchik D, Baertsch R, Diekhans M, Furey TS, Hinrichs A, Lu YT, Roskin KM, Schwartz M, Sugnet CW, Thomas DJ, Weber RJ, Haussler D, Kent WJ, University of California Santa Cruz. The UCSC genome browser database. Nucleic Acids Res. 2003; 31:51-4. [PMID: 12519945]
  20. Bailey TL, Boden M, Buske FA, Frith M, Grant CE, Clementi L, Ren J, Li WW, Noble WS. MEME SUITE: tools for motif discovery and searching. Nucleic Acids Res. 2009; 37suppl_2W202-8 [PMID: 19458158]
  21. Marri D, Filipovic D, Kana O, Tischkau S, Bhattacharya S. Prediction of mammalian tissue-specific CLOCK-BMAL1 binding to E-box DNA motifs. Sci Rep. 2023; 13:7742 [PMID: 37173345]
  22. Gillessen M, Kwak PB, Tamayo A. A simple method to measure CLOCK-BMAL1 DNA binding activity in tissue and cell extracts. F1000Res. 2017; 6:1316 [PMID: 28928952]
  23. Hatanaka F, Matsubara C, Myung J, Yoritaka T, Kamimura N, Tsutsumi S, Kanai A, Suzuki Y, Sassone-Corsi P, Aburatani H, Sugano S, Takumi T. Genome-wide profiling of the core clock protein BMAL1 targets reveals a strict relationship with metabolism. Mol Cell Biol. 2010; 30:5636-48. [PMID: 20937769]
  24. Wood SH, Hindle MM, Mizoro Y, Cheng Y, Saer BRC, Miedzinska K, Christian HC, Begley N, McNeilly J, McNeilly AS, Meddle SL, Burt DW, Loudon ASI. Circadian clock mechanism driving mammalian photoperiodism. Nat Commun. 2020; 11:4291 [PMID: 32855407]
  25. Han D-H, Lee Y-J, Kim K, Kim C-J, Cho S. Modulation of glucocorticoid receptor induction properties by core circadian clock proteins. Mol Cell Endocrinol. 2014; 383:170-80. [PMID: 24378737]
  26. Nader N, Chrousos GP, Kino T. Circadian rhythm transcription factor CLOCK regulates the transcriptional activity of the glucocorticoid receptor by acetylating its hinge region lysine cluster: potential physiological implications. FASEB J. 2009; 23:1572-83. [PMID: 19141540]
  27. So AY-L, Bernal TU, Pillsbury ML, Yamamoto KR, Feldman BJ. Glucocorticoid regulation of the circadian clock modulates glucose homeostasis. Proc Natl Acad Sci U S A. 2009; 106:17582-7. [PMID: 19805059]
  28. Menet JS, Pescatore S, Rosbash M. CLOCK:BMAL1 is a pioneer-like transcription factor. Genes Dev. 2014; 28:8-13. [PMID: 24395244]
  29. Jachim SK, Zhong J, Ordog T, Lee J-H, Bhagwate AV, Nagaraj NK, Westendorf JJ, Passos JF, Matveyenko AV, LeBrasseur NK. BMAL1 modulates senescence programming via AP-1. Aging (Albany NY). 2023; 15:9984-10009. [PMID: 37819791]
  30. Beytebiere JR, Trott AJ, Greenwell BJ, Osborne CA, Vitet H, Spence J, Yoo S-H, Chen Z, Takahashi JS, Ghaffari N, Menet JS. Tissue-specific BMAL1 cistromes reveal that rhythmic transcription is associated with rhythmic enhancer-enhancer interactions. Genes Dev. 2019; 33:294-309. [PMID: 30804225]
  31. G6PC3 glucose-6-phosphatase catalytic subunit 3 [Homo sapiens (human)] - Gene - NCBI. 2024; Available from: https://www.ncbi.nlm.nih.gov/pubmed/
  32. Bennett KA, Forsyth L, Burchell A. Functional analysis of the 5′ flanking region of the human G6PC3 gene: regulation of promoter activity by glucose, pyruvate, AMP kinase and the pentose phosphate pathway. Mol Genet Metab. 2011; 103:254-61. [PMID: 21474354]
  33. Swarup A, Samuels IS, Bell BA, Han JYS, Du J, Massenzio E, Abel ED, Boesze-Battaglia K, Peachey NS, Philp NJ. Modulating GLUT1 expression in retinal pigment epithelium decreases glucose levels in the retina: impact on photoreceptors and Müller glial cells. Am J Physiol Cell Physiol. 2019; 316:C121-33. [PMID: 30462537]
  34. Devi TS, Lee I, Hüttemann M, Kumar A, Nantwi KD, Singh LP. TXNIP links innate host defense mechanisms to oxidative stress and inflammation in retinal Muller glia under chronic hyperglycemia: implications for diabetic retinopathy. Exp Diabetes Res. 2012; 2012438238 [PMID: 22474421]
  35. Mohamed IN, Hafez SS, Fairaq A, Ergul A, Imig JD, El-Remessy AB. Thioredoxin-interacting protein is required for endothelial NLRP3 inflammasome activation and cell death in a rat model of high-fat diet. Diabetologia. 2014; 57:413-23. [PMID: 24201577]
  36. Trueblood KE, Mohr S, Dubyak GR. Purinergic regulation of high-glucose-induced caspase-1 activation in the rat retinal Müller cell line rMC-1. Am J Physiol Cell Physiol. 2011; 301:C1213-23. [PMID: 21832250]
  37. Ao H, Li H, Zhao X, Liu B, Lu L. TXNIP positively regulates the autophagy and apoptosis in the rat müller cell of diabetic retinopathy. Life Sci. 2021; 267118988 [PMID: 33412212]
  38. Coughlin BA, Feenstra DJ, Mohr S. Müller cells and diabetic retinopathy. Vision Res. 2017; 139:93-100. [PMID: 28866025]
  39. Hassan I, Luo Q, Majumdar S, Dominguez JM, , 2nd Busik JV, Bhatwadekar AD. Tumor necrosis factor alpha (TNF-α) disrupts Kir4. 1 channel expression resulting in Müller cell dysfunction in the retina. Invest Ophthalmol Vis Sci. 2017; 58:2473-82. [PMID: 28460049]
  40. Xu L, Ruan G, Dai H, Liu AC, Penn J, McMahon DG. Mammalian retinal Müller cells have circadian clock function. Mol Vis. 2016; 22:275-83. [PMID: 27081298]