Separate testing protocols for fuel cell startup (SU) and shutdown (SD) are developed to distinguish between the effects of SU and SD on performance degradation. The internal currents during SU and ...SD operation are measured in a segmented cell to evaluate the charge exchanged between the active (H2/Air) and passive (Air/Air) portions of the cell. Cells with different membrane-electrode assemblies (MEAs) are subjected to SU or SD sequences to evaluate the time evolution of spatially resolved decrease of performance and electrochemical active surface area (ECSA). We examine the influence of the cathode and anode Pt loading, and the type of carbon for cathode catalyst support.
Both the CO2 emissions and the charges exchanged increase with the common residence time of air and hydrogen in the anode compartment. However, the evolved CO2 accounts for less than 25% of the total exchanged charge. Startups are consistently more damaging than the shutdowns, evidenced by more evolved CO2 and charge exchanged, severe ECSA decrease, and higher performance losses.
•To distinguish between the effects of startup and shutdown on performance degradation.•The internal currents during SU and SD operation are measured in a segmented cell.•Cells with different membrane-electrode assemblies are subjected to SU or SD sequences.•Influence of the cathode and anode Pt loading, and the type of carbon for cathode catalyst support.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
BACKGROUND The development competence of human oocytes declines with increasing age. The objective of this study was to investigate the effect of age on gene expression profile in mature human ...oocytes. METHODS mRNA was isolated for whole genome gene expression microarray analysis from metaphase II (MII) oocytes donated by IVF or ICSI patients 10 women aged <36 years (younger) and five women aged 37–39 years (both inclusive) (older) undergoing controlled ovarian stimulation. The oocytes were donated and prepared immediately after recovery from the follicle. RT–PCR on additional four younger and two older oocytes confirmed the array analysis. RESULTS On the basis of 15 independent replicates of single MII oocytes, 7470 genes (10 428 transcripts) were identified as present in the MII oocytes. Of these, 342 genes showed a significantly different expression level between the two age groups; notably, genes annotated to be involved in cell cycle regulation, chromosome alignment (e.g. MAD2L1 binding protein), sister chromatid separation (e.g. separase), oxidative stress and ubiquitination. The top signaling network affected by age was ‘cell cycle and organism development’ (e.g. SMAD2 and activin B1 receptor). CONCLUSION There is a substantial difference between younger and older oocytes in the transcriptional level of genes involved in central biological functions of the oocytes, thus providing information on processes that may be associated with the ageing phenomenon and possibly contributing to decreased fertility.
STUDY QUESTION
Which genes and molecular mechanisms are involved in the human ovulatory cascade and final oocyte maturation?
SUMMARY ANSWER
Up-regulated genes in granulosa cells (GC) represented ...inflammation, angiogenesis, extracellular matrix, growth factors and genes previously associated with ovarian cancer, while down-regulated genes mainly represented cell cycle and proliferation.
WHAT IS KNOWN ALREADY
Radical changes occur in the follicle during final follicle maturation after the ovulatory trigger: these range from ensuring an optimal milieu for the oocyte in meiotic arrest to the release of a mature oocyte and remodeling into a corpus luteum. A wide range of mediators of final follicle maturation has been identified in rodents, non-human primates and cows.
STUDY DESIGN, SIZE, DURATION
Prospective cohort study including 24 women undergoing ovarian stimulation with the long gonadotrophin-releasing hormone agonist protocol during 2010–2012 at Holbæk Fertility Clinic. Nine paired samples of GC and 24 paired samples of follicular fluid (FF) were obtained before and after recombinant human chorionic gonadotrophin (rhCG) administration.
PARTICIPANTS/MATERIALS, SETTING, METHODS
Nine paired (nine arrays before rhCG and nine arrays after rhCG) samples of GC mRNA were amplified and hybridized to Affymetrix Human Gene 1.0 ST GeneChip arrays, compared and bioinformatically analyzed. Eleven selected genes were validated by quantitative reverse transcriptase PCR. FF hormones were analyzed by enzyme-linked immunosorbent assay.
MAIN RESULTS AND THE ROLE OF CHANCE
Eleven hundred and eighty-six genes were differentially expressed (>2-fold, P<0.0001, false discovery rate <0.0012) when comparing GC isolated before and 36 h after hCG, among those were genes known to be expressed at ovulation, i.e. ADAMTS1 and HAS2. Many new ovulation-related genes were revealed, such as CD24, ANKRD22, CLDN11 and FBXO32. FF estrogen, androstenedione and anti-Müllerian hormone decreased significantly while progesterone increased, accompanied by radical changes in the expression of steroidogenic genes (CYP17A, CYP19A, HSD11B1 and HSD11B2, StAR). Genes related to inflammation, angiogenesis, extracellular matrix formation, growth factors and cancer were up-regulated while cell cycle genes were massively down-regulated. Seventy-two genes previously described in connection with ovarian cancer were among the highly regulated genes. In silico analysis for top upstream regulators of the ovulatory trigger suggested—besides LH—TNF, IGF1, PGR, AR, EGR1 (early growth response 1), ERK1/2 (extracellular signal regulated kinase 1/2) and CDKN1A (cyclin-dependent kinase inhibitor 1A) as potential mediators of the LH/hCG response.
LIMITATIONS, REASONS FOR CAUTION
The present dataset was generated from women under hormonal stimulation. However, comparison with a macaque natural cycle whole follicle ovulation dataset revealed major overlap, supporting the idea that the ovulation-related genes found in this study are relevant in the human natural cycle.
WIDER IMPLICATIONS OF THE FINDINGS
These data will serve as a research resource for genes involved in human ovulation and final oocyte maturation. Ovulation-related genes might be good candidate biomarkers of follicle and oocyte health. Further, some of the ovulation-related genes may serve as future ovarian cancer biomarkers.
STUDY FUNDING/COMPETING INTEREST(S)
Grants from the Research Fund of Region Sjælland are gratefully acknowledged. None of the authors declared any conflict of interest.
TRIAL REGISTRATION NUMBER
Not applicable.
MicroRNAs (miRNAs) are small non-coding RNAs, which regulate mRNA translation/decay, and may serve as biomarkers. We characterised the expression of miRNAs in clinically sampled oral and pharyngeal ...squamous cell carcinoma (OSCC and PSCC) and described the influence of human papilloma virus (HPV).
Biopsies obtained from 51 patients with OSCC/PSCC and 40 control patients were used for microarray analysis. The results were correlated to clinical data and HPV status. Supervised learning by support vector machines was employed to generate a diagnostic miRNA signature.
One hundred and fourteen miRNAs were differentially expressed between OSCC and normal oral epithelium, with the downregulation of miR-375 and upregulation of miR-31 as the most significant aberrations. Pharyngeal squamous cell carcinoma exhibited 38 differentially expressed miRNAs compared with normal pharyngeal epithelium. Differences in the miRNA expression pattern of both normal epithelium and SCC were observed between the oral cavity compared with the pharynx. Human papilloma virus infection revealed perturbations of 21 miRNAs, most significantly in miR-127-3p and miR363. A molecular classifier including 61 miRNAs was generated for OSCC with an accuracy of 93%.
MicroRNAs may serve as useful biomarkers in OSCC and PSCC. The influence of HPV on miRNA may provide a mechanism for the distinct clinical behaviour of HPV-infected tumours.
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DOBA, EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, IZUM, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, SIK, UILJ, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Abstract
STUDY QUESTION
Does women’s age affect the DNA methylation (DNAm) profile differently in mural granulosa cells (MGCs) from other somatic cells?
SUMMARY ANSWER
Accumulation of epimutations by ...age and a higher number of age-related differentially methylated regions (DMR) in MGCs were found compared to leukocytes from the same woman, suggesting that the MGCs have a distinctive epigenetic profile.
WHAT IS KNOWN ALREADY
The mechanisms underlying the decline in women’s fertility from the mid-30s remain to be fully elucidated. The DNAm age of many healthy tissues changes predictably with and follows chronological age, but DNAm age in some reproductive tissues has been shown to depart from chronological age (older: endometrium; younger: cumulus cells, spermatozoa).
STUDY DESIGN, SIZE, DURATION
This study is a multicenter cohort study based on retrospective analysis of prospectively collected data and material derived from healthy women undergoing IVF or ICSI treatment following ovarian stimulation with antagonist protocol. One hundred and nineteen women were included from September 2016 to June 2018 from four clinics in Denmark and Sweden.
PARTICIPANTS/MATERIALS, SETTING, METHODS
Blood samples were obtained from 118 healthy women with varying ovarian reserve status. MGCs were collected from 63 of the 119 women by isolation from pooled follicles immediately after oocyte retrieval. DNA from leukocytes and MGCs was extracted and analysed with a genome-wide methylation array. Data from the methylation array were processed using the ENmix package. Subsequently, DNAm age was calculated using established and tailored age predictors and DMRs were analysed with the DMRcate package.
MAIN RESULTS AND ROLE OF CHANCE
Using established age predictors, DNAm age in MGCs was found to be considerable younger and constant (average: 2.7 years) compared to chronological age (average: 33.9 years). A Granulosa Cell clock able to predict the age of both MGCs (average: 32.4 years) and leukocytes (average: 38.8 years) was successfully developed. MGCs differed from leukocytes in having a higher number of epimutations (P = 0.003) but predicted telomere lengths unaffected by age (Pearson’s correlation coefficient = −0.1, P = 0.47). DMRs associated with age (age-DMRs) were identified in MGCs (n = 335) and in leukocytes (n = 1) with a significant enrichment in MGCs for genes involved in RNA processing (45 genes, P = 3.96 × 10−08) and gene expression (152 genes, P = 2.3 × 10−06). The top age-DMRs included the metastable epiallele VTRNA2-1, the DNAm regulator ZFP57 and the anti-Müllerian hormone (AMH) gene. The apparent discordance between different epigenetic measures of age in MGCs suggests that they reflect difference stages in the MGC life cycle.
LARGE SCALE DATA
N/A.
LIMITATIONS, REASONS FOR CAUTION
No gene expression data were available to associate with the epigenetic findings. The MGCs are collected during ovarian stimulation, which may influence DNAm; however, no correlation between FSH dose and number of epimutations was found.
WIDER IMPLICATIONS OF THE FINDINGS
Our findings underline that the somatic compartment of the follicle follows a different methylation trajectory with age than other somatic cells. The higher number of epimutations and age-DMRs in MGCs suggest that their function is affected by age.
STUDY FUNDING/COMPETING INTEREST(S)
This project is part of ReproUnion collaborative study, co-financed by the European Union, Interreg V ÖKS, the Danish National Research Foundation and the European Research Council. The authors declare no conflict of interest.
Purpose
To investigate if rare gene variants in women with severe ovarian hyperstimulation syndrome (OHSS) provide clues to the mechanisms involved in the syndrome.
Methods
Among participants in a ...prospective randomized study (Toftager et al. 2016), six women with predicted low and six women with predicted high risk of OHSS developing severe OHSS (grades 4 and 5, Golan classification) were selected. In the same cohort, six plus six matched controls developing no signs of OHSS (Golan grade 0) were selected. Whole-exome sequencing was performed. Analysis using a predefined in silico OHSS gene panel, variant filtering, and pathway analyses was done.
Results
We found no convincing monogenetic association with the development of OHSS using the in silico gene panel. Pathway analysis of OHSS variant lists showed substantial overlap in highly enriched top pathways (
p
value range
p
< 0.0001 and
p
> 9.8E-17) between the low- and high-risk group developing severe OHSS, i.e., “the integrin-linked kinase (ILK) signaling pathway” and the “axonal guidance signaling pathway,” both being connected to vasoactive endothelial growth factor (VEGF) and endothelial function.
Conclusion
Rare variants in OHSS cases with two distinct risk profiles enrich the same signaling pathways linked to VEGF and endothelial function. Clarification of the mechanism as well as potentially defining genetic predisposition of the high vascular permeability is important for future targeted treatment and prevention of OHSS; the potential roles of ILK signaling and the axonal guidance signaling need to be validated by functional studies.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OBVAL, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
During folliculogenesis, the granulosa cells differentiate into two cell types: cumulus cells (CCs) and mural granulosa cells (MGCs). The objective of the study was to generate and compare the ...transcriptomes of MGCs and CCs from the pre-ovulatory follicle to characterize the detailed profile of the two cell populations shortly before ovulation. Twenty-one IVF/ICSI patients undergoing controlled ovarian stimulation (COS) donated CCs and MGCs from individual follicles containing metaphase II oocytes. Cells were prepared immediately after recovery and mRNA was isolated for whole-genome gene expression analysis and reverse transcriptase-polymerase chain reactions. Paired (within the individual follicle) comparisons between the CC and MGC expression profiles were performed and corrected for multiple comparisons. A total of 1562 genes were differentially expressed by >2-fold (P < 0.01) in the two cell types. Of these, 156 genes were >8-fold changed and represented specialized cellular functional categories such as inflammatory response, extracellular matrix and cell-cell communication, whereas the 1406 genes were 2-8-fold changed and represented functional categories such as proliferation and lipid metabolism. Transcripts not previously linked to the follicle were found to be differentially expressed between CCs and MGCs, suggesting specialized function in these compartments, e.g. pepsinogen A was selectively expressed in MGCs, whereas ryanodine receptor-2 (RYR2) was selectively expressed in CCs. Positive correlations were present between expression levels of RYR2 and the amphiregulin and gap-junction proteins. In conclusion, the transcriptomes of corresponding CCs and MGCs from individual pre-ovulatory follicles clearly revealed two distinct cell types. New as well as known genes representing specific cell functions close to ovulation were highlighted.
Background:
Previous studies of multiple sclerosis (MS) have indicated differences in the pathogenesis in relapsing–remitting (RRMS), secondary progressive (SPMS) and primary progressive (PPMS) ...disease.
Objective:
We hypothesized that different MS subtypes would show differences in gene expression that could be traced to specific subsets of peripheral blood mononuclear cells (PBMCs).
Methods:
Gene expression in RRMS, SPMS, PPMS and healthy control (HC) PBMCs was analyzed on Affymetrix arrays. In addition, we studied gene expression in pools of purified PBMC subsets.
Results:
We found 380 genes that were differentially expressed in RRMS, PPMS, SPMS and HCs (false discovery rate < 5%). There were no major differences between the subtypes of MS. The genes showing most prominent expression changes in RRMS were associated with adaptive immune pathways, while genes in PPMS were associated with innate immune system pathways. SPMS patients shared pathways with RRMS and PPMS patients. Gene expression changes were most prominent in B cells, CD8+ T cells and monocytes.
Conclusion:
Differences in gene expression, which could be traced to B cells, CD8+ T cells and monocytes, were found between MS patients and HCs but only minor differences were observed between MS subgroups.
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NUK, OILJ, SAZU, UKNU, UL, UM, UPUK
An important step in achieving fundamental understanding of fuel cell failure mechanisms and development of technology to mitigate these failures is accomplished by analysis of directed lifetime and ...failure test results. Several lifetime, accelerated stress, and drive cycle test protocols have been developed and carried out. The two major ASTs that have been developed to evaluate membrane degradation are 1) Open circuit voltage tests, which are designed to accelerate chemical degradation, and 2) Relative humidity cycling tests, which are designed to accelerate mechanical degradation. The results from these tests have been compared to field tests. The ultimate goal is to use the laboratory tests to predict data in the field. An overall predictive decay model is being developed through a combination of specific modeling and tests.
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