A multilevel analysis identifies the different relationships between amino acids and the competence of oocytes matured individually or in groups

Kowsar, Rasoul; Mansouri, Alireza; Sadeghi, Nima; Abadi, Mohammad Heidaran Ali; Ghoreishi, S.M.; Sadeghi, Khaled; Miyamoto, Akio

doi:10.1038/s41598-020-73225-7

Cited by 8 publications

(10 citation statements)

References 72 publications

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“…Following the LH receptor activation, the LH signal was transmitted from the periphery of the follicle to the COC and downregulates the level of 3’5’-cyclic guanine monophosphate while simultaneously providing a meiotic-inducing signal. The EGF system also plays a role in the regulation of amino acid metabolism, and this regulatory pathway may participate in the regulation of competence of COCs and fertility in bovines ( 69 ). On the other hand, local angiogenic signals are necessary to provide blood flow to the corpus luteum, thereby allowing it to develop the proper structure and acquire the steroidogenic capacity ( 70 ).…”

Section: Discussionmentioning

confidence: 99%

Oocyte-specific disruption of adrenomedullin 2 gene enhances ovarian follicle growth after superovulation

Chang¹,

Lo²,

Lee³

et al. 2022

Front. Endocrinol.

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BackgroundAdrenomedullin 2 (ADM2), adrenomedullin (ADM), and calcitonin gene-related peptides (α- and β-CGRPs) signal through heterodimeric calcitonin receptor-like receptor/receptor activity-modifying protein 1, 2 and 3 (CLR/RAMP1, 2 and 3) complexes. These peptides are important regulators of neurotransmission, vasotone, cardiovascular development, and metabolic homeostasis. In rodents, ADM is essential for regulating embryo implantation, fetal–placental development, and hemodynamic adaptation during pregnancy. On the other hand, ADM2 was shown to affect vascular lumen enlargement, and cumulus cell-oocyte complex (COC) communication in rodent and bovine ovarian follicles. To investigate whether oocyte-derived ADM2 plays a physiological role in regulating ovarian folliculogenesis, we generated mice with oocyte-specific disruption of the Adm2 gene using a LoxP-flanked Adm2 transgene (Adm2 loxP/loxP) and crossed them with Zp3-Cre mice which carry a zona pellucida 3 (Zp3) promoter-Cre recombinase transgene.ResultsWhile heterozygous Adm2 +/-/Zp3-Cre and homozygous Adm2 -/-/Zp3-Cre mice were fertile, Adm2 disruption in oocytes significantly increased the number of ovulated oocytes following a superovulation treatment. Oocyte-specific Adm2 disruption also significantly impaired the developmental capacity of fertilized eggs and decreased the size of the corpus luteum following superovulation, perhaps due to a reduction of ovarian cyclin D2-associated signaling.ConclusionsThe disruption of intrafollicular ADM2 signaling leads to follicular dysfunction. These data suggested that oocyte-derived ADM2 plays a facilitative role in the regulation of hormonal response and follicle growth independent of the closely related ADM and CGRP peptides, albeit in a subtle manner.

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Section: Discussionmentioning

confidence: 99%

Oocyte-specific disruption of adrenomedullin 2 gene enhances ovarian follicle growth after superovulation

Chang¹,

Lo²,

Lee³

et al. 2022

Front. Endocrinol.

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“…The Pearson correlation (Bonferroni correction) was used in the bivariate study to determine the relationship between blood biochemical parameters, age, BMI, and diabetes incidence. To evaluate the validity of the relationships in the correlation matrix, the Bonferroni correction was used 33 .…”

Section: Methodsmentioning

confidence: 99%

“…The Heatmapper ( http://www1.heatmapper.ca/expression/ ) was used to produce patterns of blood biochemical parameters, age, and BMI in relation to diabetes incidence 33 . The heatmap was created using the Pearson distance measure and complete linkage as the clustering method.…”

Section: Methodsmentioning

confidence: 99%

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Multi-level analysis reveals the association between diabetes, body mass index, and HbA1c in an Iraqi population

Kowsar

Mansouri

2022

Sci Rep

Self Cite

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Type 2 diabetes (T2D) known as a complex metabolic disorder may cause health problems and changes in blood biochemical markers. A growing number of studies have looked into several biomarkers and their connections with T2D risk. However, few have explored the interconnection of these biomarkers, as well as the prospective alterations in the diabetes biomarker correlation network. We conducted a secondary analysis in order to introduce a multi-level approach to establish a relationship between diabetes, pre-diabetes, blood biochemical markers, age, and body mass index (BMI). The dataset was obtained from the Mendeley Data (available at https://data.mendeley.com/datasets/wj9rwkp9c2/1. In this study, three groups were established: non-diabetic (n = 103), pre-diabetic (n = 53), and diabetic (n = 844). According to the Heatmap analysis, non-diabetic and pre-diabetic individuals had the lowest BMI, age, and HbA1c. Diabetes and pre-diabetes were correlated with BMI (r = 0.58 and − 0.27, respectively), age (r = 0.47 and − 0.28, respectively), and HbA1c (r = 0.55 and − 0.21, respectively) using Pearson analysis. Using multivariate analysis, we found that diabetes, BMI, age, HbA1c, cholesterol, triglyceride, LDL, VLDL, and HDL were all associated. Network analysis revealed a connection between BMI and diabetes at the highest cut-off point. Moreover, receiver operating characteristic (ROC) analysis validated the network findings, revealing that BMI (area under the ROC curve, AUC = 0.95), HbA1c (AUC = 0.94), and age (AUC = 0.84) were the best predictors of diabetes. In conclusion, our multi-step study revealed that identifying significant T2D predictors, such as BMI and HbA1c, required a series of mathematical analyses.

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“…Using Pearson similarity indices and the Fruchterman-Reingold algorithm, a force-directed layout algorithm for network research was created. By using this method, a network is constructed according to the frequency of node connections [27,28]. The PAST program was utilized in this study to do network analysis and data visualization.…”

Section: Network Analysismentioning

confidence: 99%

Meta-Analysis and Network Analysis Differentially Detect Various Pro-Inflammatory Mediators and Risk Factors for Type 2 Diabetes in the Elderly

Gu,

Du,

Liang

2024

Horm Metab Res

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Type 2 diabetes (T2D) has a pathophysiological component that includes inflammation. Inflammation-sensitive marker measurement may be helpful in determining the risk of complications for both older T2D patients and the public. This study aimed to investigate the association between blood pro-inflammatory mediators and the characteristics of elderly patients with T2D using Meta and network analyses. The Web of Science, Scopus, PubMed, and Cochrane Library databases were selected as study methodology. The Quality in Prognosis Studies (QUIPS) tool in the meta-analysis assessed the studies' methodological quality. The selected studies were statistically analyzed using the META-MAR tool based on the standardized mean difference (SMD). The selected studies included nine examinations involving 6,399 old people [>55 years old, 65.9±4.09 (mean±SD)]. The meta-analysis showed that pro-inflammatory mediators (SMD 0.82) and patient-related variables [risk factors (SMD 0.71)] were significantly associated with T2D (p<0.05). Subgroup analysis revealed that tumor necrosis factor alpha (TNF-α; SMD 1.08), body mass index (SMD 0.64), high-density lipoprotein (HDL; SMD -0.61), body weight (SMD 0.50), and blood pressure (SMD 1.11) were factors significantly associated with T2D (p<0.05). Network analysis revealed that among patient characteristics, diastolic blood pressure and, among inflammatory mediators, leptin were the most closely associated factors with T2D in older adults. Moreover, network analysis showed that TNF-α and systolic blood pressure were most closely associated with leptin. Overall, alternate techniques, such as meta-analysis and network analysis, might identify different markers for T2D in older people. A therapeutic decision-making process needs to consider these differences in advance.

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A multilevel analysis identifies the different relationships between amino acids and the competence of oocytes matured individually or in groups

Cited by 8 publications

References 72 publications

Oocyte-specific disruption of adrenomedullin 2 gene enhances ovarian follicle growth after superovulation

Oocyte-specific disruption of adrenomedullin 2 gene enhances ovarian follicle growth after superovulation

Multi-level analysis reveals the association between diabetes, body mass index, and HbA1c in an Iraqi population

Meta-Analysis and Network Analysis Differentially Detect Various Pro-Inflammatory Mediators and Risk Factors for Type 2 Diabetes in the Elderly

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