Human gut metagenome
Ontology highlight
ABSTRACT:
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| DRR055301.fastq.gz | Fastqsanger.gz | |||
| DRR055302.fastq.gz | Fastqsanger.gz | |||
| DRR055303.fastq.gz | Fastqsanger.gz | |||
| DRR055304.fastq.gz | Fastqsanger.gz | |||
| DRR055305.fastq.gz | Fastqsanger.gz |
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Project description:The objectives of the study: 1. Does the phase of the menstrual cycle alter microRNA (miRNA) plasma profiles in healthy women of reproductive age and in women with endometriosis? 2. Does this alter prospects for development of a miRNA-based diagnostic test for endometriosis? Prospectively recruited asymptomatic control women and women with surgically diagnosed endometriosis (n = 8 in each group) were included. Each patient provided blood samples in the early proliferative, late proliferative and mid luteal phases of the menstrual cycle (n = 47 total plasma samples). The cycle phase was verified by hormonal profile. RNA was extracted from each sample and expression of microRNAs was assessed using TaqMan Low Density Human miRNA arrays.
2013-05-09 | E-GEOD-46735 | biostudies-arrayexpress
Project description:The objectives of the study: 1. Does the phase of the menstrual cycle alter microRNA (miRNA) plasma profiles in healthy women of reproductive age and in women with endometriosis? 2. Does this alter prospects for development of a miRNA-based diagnostic test for endometriosis?
2013-05-09 | GSE46735 | GEO
Project description:Introduction: Structural integrity of the cervical epithelial barrier is crucial for defending the female reproductive tract against sexually transmitted infections, including HIV. Female sex hormones, estradiol and progesterone, play an important role in modulating this epithelial barrier. However, the influence of fluctuating estradiol and progesterone on ectocervical tissue gene and protein expression in naturally cycling women at high risk for sexually transmitted infections is not well understood. Methods: Ectocervical biopsies, cervicovaginal lavage fluid, and venous blood samples were collected from naturally cycling Kenyan female sex workers at two time points, separated by 2 weeks. The first time point represented the luteal phase of the menstrual cycle and the second time point represented the follicular phase. Plasma estradiol and progesterone levels were measured at each time point. Ectocervical tissues were analyzed by RNA-sequencing and in situ immunofluorescence staining. Cervicovaginal lavage samples were evaluated using antibody-based protein profiling. Results: Employing a systems biology approach, we demonstrated that high plasma estradiol levels enhanced ectocervical epithelial integrity. These findings were more pronounced in samples from the follicular phase (when progesterone and estradiol levels were significantly lower) and included increased expression, and more intact distribution, of the desmosomal cadherin DSG1. The effects of progesterone on gene and protein levels, as well as on intact tissue (as visualized by in situ staining), were modest throughout the menstrual cycle. Both estradiol and progesterone levels had limited influence on mucosal immune factors. Conclusion: Estradiol levels were associated with modulation of cervical epithelial barrier integrity, including the expression and distribution of DSG1, during the follicular phase of natural menstrual cycles in Kenyan sex workers.
2025-01-17 | GSE273823 | GEO
Project description:While exercise effects on the immune system received increasing attention in recent years, it remains unclear to what extent gender and fluctuations in sex hormones during menstrual cycle influence immunological responses to exercise. Using a previously published microarray data set, we investigated mRNA changes induced through exhaustive exercise (half-marathon; pre-exercise and post-exercise [30 min, 3h, 24h], whole blood culture ± LPS (1h)) with a specific focus on sex differences (women in luteal phase vs men). Inflammation related pathways, TLRs, cytosolic DNA sensing and RIG-I like receptors were differentially activated between sexes in LPS-stimulated cultures. Genes differentially regulated between sexes included TNIP-1, TNIP-3, IL-6, HIVEP1, CXCL3, CCR3, IL-8 and CD69, revealing a bias towards less anti-inflammatory gene regulation in women compared to men. In addition, several genes relevant to brain function (OLIG2, TMEM106B, DDIT4, and KMO) showed differential activation between sexes. Some of these genes (e.g., KMO in women, DDIT4 in both sexes) potentially constitute neuroprotective mechanisms. These data reveal that the exercise-induced change in gene expression might be gender and menstrual cycle phase dependent. Factorial design comparing LPS and control treated blood samples of male and female athletes before and after a half-marathon at different timepoints (half-marathon; pre-exercise and post-exercise [30 min, 3h, 24h])
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Project description:Roblitz2013 - Menstrual Cycle following GnRH analogue administration
The model describes the menstrual cycle feedback mechanisms. GnRH, FSH, LH, E2, P4, inbibins A and B, and follicular development are modelled. The model predicts hormonal changes following GnRH analogue administration. Simulation results agree with measurements of hormone blood concentrations. The model gives insight into mechanisms underlying gonadotropin supression.
This model is described in the article:
A mathematical model of the human menstrual cycle for the administration of GnRH analogues.
Röblitz S, Stötzel C, Deuflhard P, Jones HM, Azulay DO, van der Graaf PH, Martin SW.
J. Theor. Biol. 2013 Mar; 321: 8-27
Abstract:
The paper presents a differential equation model for the feedback mechanisms between gonadotropin-releasing hormone (GnRH), follicle-stimulating hormone (FSH), luteinizing hormone (LH), development of follicles and corpus luteum, and the production of estradiol (E2), progesterone (P4), inhibin A (IhA), and inhibin B (IhB) during the female menstrual cycle. Compared to earlier human cycle models, there are three important differences: The model presented here (a) does not involve any delay equations, (b) is based on a deterministic modeling of the GnRH pulse pattern, and (c) contains less differential equations and less parameters. These differences allow for a faster simulation and parameter identification. The focus is on modeling GnRH-receptor binding, in particular, by inclusion of a pharmacokinetic/pharmacodynamic (PK/PD) model for a GnRH agonist, Nafarelin, and a GnRH antagonist, Cetrorelix, into the menstrual cycle model. The final mathematical model describes the hormone profiles (LH, FSH, P4, E2) throughout the menstrual cycle of 12 healthy women. It correctly predicts hormonal changes following single and multiple dose administration of Nafarelin or Cetrorelix at different stages in the cycle.
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2024-09-02 | BIOMD0000000494 | BioModels
Project description:It is well recognized that men and women differ in circulating lipid profiles and consequently coronary artery disease (CAD). While sex hormones like estrogens are thought to protect women from CAD risk by promoting protective lipid profiles, hormone replacement therapy in women paradoxically increases CAD risk. Biological sex is determined by both sex chromosomes and sex hormones. We used mouse models to separate effects of sex chromosomes and hormones on atherosclerosis, circulating lipids and intestinal fat metabolism. We found that an XX sex chromosome complement increases food intake, body weight, fat absorption, serum lipid concentrations and atherosclerosis in gonadal male and female mice, indicating a primary effect of sex chromosome complement. Small intestine expression of enzymes involved in lipid absorption and chylomicron assembly were increased in XX male and female mice with elevated intestinal lipids. These results reveal that an XX sex chromosome complement promotes the absorption and bioavailability of dietary fat to accelerate the development of atherosclerosis.
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Project description:Model with functions depending on Age, Male, BP (Blood Pressure).
There are 3 disease states: Healthy, Sick, and Dead, where the Dead state is terminal. The yearly transition probabilities are: Healthy to Dead: Age/1000; Healthy to Sick: According to function F1 depending on Age and Male and BP; Sick to Healthy: 0.1; Sick to Dead: according to function F2 depending on Age and Male.
Pre-Transition Rules: Age increased by 1 and BP by Age/10 each simulation cycle. Post-Transition Rules: Treatment = BP>140 , becomes 1 when BP crosses 140 threshold; BP =BP-Treatment*10 , meaning a drop of 10 once treatment is applied; CostThisYear = Age + \Treatment*10 , cost depends on age and if treatment was taken; Cost= Cost + CostThisYear , it accumulates cost over time.
Initial conditions: Healthy = (50 Male, 50 Female with Age =1,2,...,50 for each individual), BP =120, Sick = (0,0) and Dead = (0,0).
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