Gene expression analysis of neuropeptides in oral mucosa during periodontal disease in non‐human primates

Ferrin, John; Kirakodu, Sreenatha; Jensen, David; Al‐Attar, Ahmad; Peyyala, Rebecca; Novak, M. John; Dawson, Dolphus R.; Al‐Sabbagh, Mohanad; Stromberg, Arnold J.; Orraca, Luis; González‐Martínez, Janis; Burgos, Armando; Ebersole, Jeffrey L.; González, Octavio A.

doi:10.1002/jper.17-0521

Cited by 9 publications

(6 citation statements)

References 37 publications

(67 reference statements)

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“…Individual buccal gingival samples from healthy or periodontitisaffected premolar/molar maxillary region tissues of each animal were taken and maintained frozen in RNAlater for microarray analysis (GeneChip Rhesus Gene 1.0 ST Array; Affymetrix; Gonzalez et al 2011). Normalization of values across the chips was accomplished through Affymetrix RMA and the MAS 5 algorithms (Ferrin et al 2018). For each gene, differences in expression were assessed across the groups through analysis of variance (SAS 9.3; SAS Institute).…”

Section: Tissue Sampling and Gene Expression Microarray Analysismentioning

confidence: 99%

Oral Microbiome and Gingival Transcriptome Profiles of Ligature-Induced Periodontitis

et al. 2020

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This investigation evaluated the relationship of the oral microbiome and gingival transcriptome in health and periodontitis in nonhuman primates ( Macaca mulatta). Subgingival plaque samples and gingival biopsies were collected from healthy sites and at sites undergoing ligature-induced periodontitis. Microbial samples were analyzed with 16S amplicon sequencing to identify bacterial profiles in young (3 to 7 y) and adult (12 to 23 y) animals. The gingival transcriptome was determined with a microarray analysis and focused on the expression level of 452 genes that are associated with the development of inflammation and innate and adaptive immune responses. Of the 396 total operational taxonomic units (OTUs) identified across the samples, 81.8% were detected in the young group and 99.5% in the adult group. Nevertheless, 58 of the OTUs composed 88% of the signal in adults, and 49 OTUs covered 91% of the OTU readouts in the young group. Correlation analyses between the microbiome members and specific gingival genes showed a high number of significant bacteria-gene correlations in the young healthy tissues, which decreased by 75% in diseased tissues. In contrast, these correlations increased by 2.5-fold in diseased versus healthy tissues of adult animals. Complexes of bacteria were delineated that related to specific sets of immune genes, differing in health and disease and in the young versus adult animals. The correlated gene profiles demonstrated selected pathway overrepresentation related to particular bacterial complexes. These results provide novel insights into microbiome changes with disease and the relationship of these changes to specific gene profiles and likely biologic activities occurring in healthy and diseased gingival tissues in this human-like periodontitis model.

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Section: Tissue Sampling and Gene Expression Microarray Analysismentioning

confidence: 99%

Oral Microbiome and Gingival Transcriptome Profiles of Ligature-Induced Periodontitis

et al. 2020

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“…Total RNA from each capsule (n=36, 3 samples/polymer) for weeks 1, 4, and 8 was isolated using The RNeasy ® Plus Mini Kit. cDNA was synthesized using The Transcriptor First Strand cDNA Synthesis Kit from Roche following the manufacturer's protocol [17]. The primers for each gene analyzed include: glyceraldehyde 3-phosphate dehydrogenase (GAPDH): Forward: ACTCCCATTCTTCCACCTTTG, Reverse: CCCTGTTGCTGTAGCCATATT, bone morphogenetic protein 2 (BMP-2): Forward: TGTGAGGATTAGCAGGTCTTTG, Reverse: TTGTGGAGTGGATGTCCTTTAC, Runt-related transcription factor 2 (Runx2): Forward: CGCATTCCTCATCCCAGTAT, Reverse: CTGACTCTGTCCTTGTGGATTA, alkaline phosphatase (ALP): Forward: AACAACCTGACTGACCCTTC, Reverse: ATCCTGCCTCCTTCCACTA, vascular endothelial growth factor (VEGF): Forward: GATGACGACGACGAAGAGATG, Reverse: TCTTCGATGATGCTGACCAC, interleukin 6 (IL-6): Forward: GAAGTTAGAGTCACAGAAGGAGTG, Reverse: GTTTGCCGAGTAGACCTCATAG, nuclear factor kappa-light-chain-enhancer of activated B cells (NF-ĸB): Forward: AGACATCCTTCCGCAAACTC, Reverse: TAGGTCCATCCTGCCCATAA.…”

Section: Quantitative Real Time Polymerase Chain Reaction (Qrt-pcr) Analysismentioning

confidence: 99%

Biodegradable polymerized simvastatin stimulates bone formation

et al. 2019

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Previous research from our labs demonstrated the synthesis of polymerized simvastatin by ringopening polymerization and slow degradation with controlled release of simvastatin in vitro. The objective of the present study was to evaluate the degradation and intramembranous bone-forming potential of simvastatin-containing polyprodrugs in vivo using a rat calvarial onlay model.Poly(ethylene glycol)-block-poly(simvastatin) and poly(ethylene glycol)-block-poly(simvastatin)ran-poly(glycolide) were compared with simvastatin conventionally encapsulated in poly(lacticco-glycolic acid) (PLGA) and pure PLGA. The rate of degradation was higher for PLGA with and without simvastatin relative to the simvastatin polyprodrugs. Significant new bone growth at the circumference of poly(ethylene glycol)-block-poly(simvastatin) disks was observed beginning at 4 weeks, whereas severe bone resorption (4 weeks) and bone loss (8 weeks) were observed for PLGA loaded with simvastatin. No significant systemic effects were observed for serum total cholesterol and body weight. Increased expression of osteogenic (BMP-2, Runx2, and ALP), angiogenic (VEGF), and inflammatory cytokines (IL-6 and NF-ĸB) genes was seen with all polymers at the end of 8 weeks. Poly(ethylene glycol)-block-poly(simvastatin), with slow degradation and drug release, controlled inflammation, and significant osteogenic effect, is a candidate for use in bone regeneration applications.

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“…Frequently a qPCR technique is used as a validation step for microarray outcomes. While we did not incorporate this approach into the analysis of this large array of AMF genes, we have previously published the use of qPCR to validate 28 genes from the larger microarray dataset including inflammatory cytokines and chemokines, apoptosis genes, and neuropeptides and receptors, and various transcription factors that demonstrated consistent directional changes, albeit, the magnitude of change was frequently somewhat greater with the qPCR [40][41][42][43].…”

Section: Discussionmentioning

confidence: 99%

Gingival Transcriptome of Innate Antimicrobial Factors and the Oral Microbiome With Aging and Periodontitis

Ebersole

Kirakodu

Linh-Trung

et al. 2022

Front. Oral. Health

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The epithelial barrier at mucosal sites comprises an important mechanical protective feature of innate immunity, and is intimately involved in communicating signals of infection/tissue damage to inflammatory and immune cells in these local environments. A wide array of antimicrobial factors (AMF) exist at mucosal sites and in secretions that contribute to this innate immunity. A non-human primate model of ligature-induced periodontitis was used to explore characteristics of the antimicrobial factor transcriptome (n = 114 genes) of gingival biopsies in health, initiation and progression of periodontal lesions, and in samples with clinical resolution. Age effects and relationship of AMF to the dominant members of the oral microbiome were also evaluated. AMF could be stratified into 4 groups with high (n = 22), intermediate (n = 29), low (n = 18) and very low (n = 45) expression in healthy adult tissues. A subset of AMF were altered in healthy young, adolescent and aged samples compared with adults (e.g., APP, CCL28, DEFB113, DEFB126, FLG2, PRH1) and were affected across multiple age groups. With disease, a greater number of the AMF genes were affected in the adult and aged samples with skewing toward decreased expression, for example WDC12, PGLYRP3, FLG2, DEFB128, and DEF4A/B, with multiple age groups. Few of the AMF genes showed a >2-fold increase with disease in any age group. Selected AMF exhibited significant positive correlations across the array of AMF that varied in health and disease. In contrast, a rather limited number of the AMF significantly correlated with members of the microbiome; most prominent in healthy samples. These correlated microbes were different in younger and older samples and differed in health, disease and resolution samples. The findings supported effects of age on the expression of AMF genes in healthy gingival tissues showing a relationship to members of the oral microbiome. Furthermore, a dynamic expression of AMF genes was related to the disease process and showed similarities across the age groups, except for low/very low expressed genes that were unaffected in young samples. Targeted assessment of AMF members from this large array may provide insight into differences in disease risk and biomolecules that provide some discernment of early transition to disease.

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Gene expression analysis of neuropeptides in oral mucosa during periodontal disease in non‐human primates

Cited by 9 publications

References 37 publications

Oral Microbiome and Gingival Transcriptome Profiles of Ligature-Induced Periodontitis

Oral Microbiome and Gingival Transcriptome Profiles of Ligature-Induced Periodontitis

Biodegradable polymerized simvastatin stimulates bone formation

Gingival Transcriptome of Innate Antimicrobial Factors and the Oral Microbiome With Aging and Periodontitis

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