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Velthut-Meikas, A.

Publications and source records attributed to Velthut-Meikas, A..

5 recordsLinked to original sources

Acute high-dose irradiation of human primary ovarian cells reveals a shift in transcriptomics profile and impairment in cell-cell adhesion

STUDY QUESTIONHow do human cortical (cPOCs) and medullary (mPOCs) primary ovarian cells respond to acute X-ray exposure? SUMMARY ANSWERAcute high-dose X-ray exposure causes a shift in cPOCs and mPOCs transcriptomic profiles and impairs significantly their cell-cell adhesion ability. WHAT IS KNOWN ALREADYRadiotherapy is a leading cancer treatment, due to its effectiveness in targeting malignant cells. However, it can also affect healthy cells, potentially causing organ dysfunction, among which ovaries. When targeted radiotherapy is not feasible, fertility preservation is recommended to avoid premature ovarian insufficiency. In addition, the effects of irradiation on ovarian somatic cells remain poorly understood. STUDY DESIGN, SIZE, DURATIONOvarian tissue was obtained from patients undergoing gender-affirming surgery at Karolinska University Hospital Huddinge, Sweden. The ovarian tissue was separated into cortex and medulla, then individually dissociated into single-cell suspensions using mechanical and enzymatic methods. Monolayer cultures from cPOCs and mPOCs were exposed to a single dose of 10 Gy X-ray irradiation or left unexposed as paired controls. Following irradiation, the cells were cultured at various time-points for further molecular and morphological evaluation. PARTICIPANTS/MATERIALS, SETTING, METHODSOvarian tissue from 8 patients (age 23-36 years) was used. Dissociated cPOCs and mPOCs were cultured to 80% confluence and irradiated with 10 Gy (1.33 Gy/min), with non-irradiated controls. Cellular ATP and mitochondrial activity were assessed, followed by immunofluorescence staining for canonical irradiation-induced effects in cells: DNA damage, apoptosis and cell cycle progression. Bulk RNA-sequencing was performed on controls and irradiated samples. Libraries were prepared using the Illumina Stranded mRNA Prep Ligation protocol and sequenced on Illumina NovaSeq6000 platform. Genes were considered to be differentially expressed under the cut-off of false discovery rate (FDR) < 0.05. Subsequently, affected biological pathway was predicted using all expressed genes ranked by log2 fold change again hallmark gene sets. To further investigate the potential upstream regulators, transcription factor enrichment analysis were performed based on DEGs. To assess changes at protein level, we mapped the proteomic profile using liquid chromatography-tandem mass spectrometry. Peptides were considered to be differentially expressed (DEPs) under the cut-off of p-value < 0.01. Finally, we measured the ability of cPOCs and mPOCs to form 3D aggregates after seeding irradiated and non-irradiated cells on Biosilk scaffolds. MAIN RESULTS AND THE ROLE OF CHANCEFollowing irradiation, ATP levels and mitochondrial activity in cPOCs and mPOCs were comparable to controls, indicating minimal irradiation impact on cell viability and proliferation. Immunofluorescence analysis confirmed the modulation of canonical pathways, such as DNA damage, apoptosis and cell cycle in both cPOCs and mPOCs. Transcriptomic analysis showed that cPOCs and mPOCs at 1 h post-irradiation clustered together with the related 1 h control. However, a shift in transcriptomic profile was observed after 4 h and even more after 24 h post-irradiation in both cPOCs and mPOCs. Gene set enrichment analysis (GSEA) indicated upregulation of the p53 pathway at 4 h and 24 h post-irradiation, alongside downregulation of MYC targets, E2F targets, the G2/M checkpoint and mTORC1 pathway. Gene pattern analysis showed irradiation-dependent trends related to extracellular matrix (ECM) organisation, p53-mediated apoptotic mechanisms and chromosome segregation during the 24 h period following irradiation. Additionally, transcription factor enrichment analysis based on DEGs suggested p53 and MYC as potential upstream regulators. On a proteomic level, DEPs associated with ECM organisation and cytoskeleton formation were detected at 4 and 24 h post-irradiation. Finally, X-ray exposure hindered the cell-cell adhesion ability of both cPOCs and mPOCs, leading to impaired formation of Silk-Ovarioids. LARGE SCALE DATAThe RNA sequencing count matrix is deposited in Gene Expression Omnibus (GEO) with accession number GSE291604. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD061796. The code used for the analysis can be found at https://github.com/tialiv/X-Ovary. LIMITATIONS, REASONS FOR CAUTIONThe ovarian tissue was obtained from gender-affirming surgery patients who received androgen treatment before removal. Even though unlikely, this hormonal treatment might influence ovarian environment and alter cellular response to irradiation. Additionally, in this study the impact of X-ray exposure was assessed on a monolayer cell model, thus limiting the extrapolation power of our results to ovary in vivo. Lastly, the impact of irradiation was focused on the somatic cell populations that are essential for ovarian function. Further studies are needed to investigate the effects of X-ray exposure on ovarian follicles and their function. WIDER IMPLICATIONS OF THE FINDINGSUnderstanding the roles of MYC, p53 and cell adhesion factors in response to irradiation could guide the development of future ovarian protective strategies. These findings lay the foundation for further studies on ovarian tissue protection and fertility preservation in cancer patients. STUDY FUNDING/COMPETING INTEREST(S)This work was funded by the European Unions HORIZON 2020 research and innovation programme (MATER) under the Marie Skodowska-Curie Actions (grant agreement No: 813707), the Estonian Research Council (grants PRG1076 and PSG608), the Orion Research Foundation sr personal grant, the Research grant from the Center for Innovative Medicine (CIMED) and the Karolinska Institutet Consolidator Grant.

cell biology↗

Small and large extracellular vesicles from human preovulatory follicular fluid display distinct ncRNA cargo profile and differential effect on granulosa cell line KGN

Follicular fluid extracellular vesicles (FF EVs) facilitate communication between oocytes and somatic cells within the ovarian follicle, playing a pivotal role in follicular development. This study highlights the molecular and functional distinctions between small (SEV) and large (LEV) FF EV subpopulations, revealing their specialized regulatory roles in granulosa cell (GC) biology and their consequential impact on ovarian function. Single-EV profiling uncovered distinct tetraspanin distributions, with LEVs containing a lower proportion of CD9/CD63/CD81-positive particles compared to SEVs. Functionally, SEVs reduced estradiol secretion by GCs, whereas LEVs enhanced progesterone production, demonstrating their differential effects on steroidogenesis. Transcriptomic analysis revealed extensive SEV-induced changes in GC gene expression, affecting pathways involved in transcription, TGF-{beta} signaling, extracellular matrix (ECM) remodeling, and cell cycle regulation. In contrast, LEVs elicited minimal transcriptional changes, primarily modulating genes associated with immune regulation and oxidative stress defense. Small RNA sequencing further revealed distinct non-coding RNA (ncRNA) profiles, with SEVs enriched in miRNAs targeting pathways critical for GC differentiation, while LEVs carried higher levels of piRNAs implicated in maintaining genomic stability. These findings advance our understanding of FF EV-mediated intercellular communication and underscore the importance of investigating EV subpopulations independently.

cell biology↗

MicroRNAs expressed from FSHR and aromatase genes target important ovarian functions

MicroRNAs (miRNAs) have known roles in the post-transcriptional regulation of various biological processes including ovarian follicle development. We have previously identified miRNAs from human pre-ovulatory granulosa cells that are expressed from the intronic regions of two key genes in normal follicular development: FSH receptor (FSHR) and CYP19A1, the latter encoding the aromatase enzyme. In the present study, we aim to identify the targets regulated by those two miRNAs: hsa-miR-548ba and hsa-miR-7973, respectively. The miRNAs of interest were endogenously expressed in KGN cell-line, gene expression changes were analyzed by Affymetrix microarray and confirmed by RT-qPCR. Potential miRNA-regulated sequences were further filtered from the obtained results by bioinformatic target prediction algorithms and validated for direct miRNA:mRNA binding by luciferase reporter assay. Our results verified Leukemia inhibitory factor receptor (LIFR), Phosphatase and tensin homolog (PTEN), Neogenin 1 (NEO1) and SP110 nuclear body protein (SP110) as target genes for hsa-miR-548ba. Hsa-miR-7973 target genes ADAM metallopeptidase domain 19 (ADAM19), Peroxidasin (PXDN) and Formin like 3 (FMNL3) also passed all verification steps. In conclusion we propose that hsa-miR-548ba may be involved in the regulation of follicle growth and activation via LIFR and PTEN. Hsa-miR-7973 may be implicated in the modulation of extracellular matrix and cell-cell interactions. Taken together, our results suggest that those two miRNAs of interest have important regulatory roles in granulosa cells and in follicle development in general.\n\nSummary sentenceConfirmed targets of miRNAs hsa-miR-548ba and hsa-miR-7973 are involved in follicle recruitment, apoptosis, intercellular interactions and extracellular matrix remodeling pathways in KGN cells.

molecular biology↗

Endometrial receptivity revisited: endometrial transcriptome adjusted for tissue cellular heterogeneity

STUDY QUESTIONDoes cellular composition of the endometrial biopsy affect the gene expression profile of endometrial whole-tissue samples?\n\nSUMMARY ANSWERThe differences in epithelial and stromal cell proportions in endome-trial biopsies modify whole-tissue gene expression profiles, and also affect the results of differential expression analysis.\n\nWHAT IS ALREADY KNOWNEach cell type has its unique gene expression profile. The proportions of epithelial and stromal cells vary in endometrial tissue during the menstrual cycle, along with individual and technical variation due to the way and tools used to obtain the tissue biopsy.\n\nSTUDY DESIGN, SIZE, DURATIONUsing cell-population specific transcriptome data and computational deconvolution approach, we estimated the epithelial and stromal cell proportions in whole-tissue biopsies taken during early secretory and mid-secretory phases. The estimated cellular proportions were used as covariates in whole-tissue differential gene expression analysis. Endometrial transcriptomes before and after deconvolution were compared and analysed in biological context.\n\nPARTICIPANTS/MATERIAL, SETTING, METHODSPaired early- and mid-secretory endometrial biopsies were obtained from thirty-five healthy, regularly cycling, fertile volunteers, aged 23 to 36 years, and analysed by RNA sequencing. Differential gene expression analysis was performed using two approaches. In one of them, computational deconvolution was applied as an intermediate step to adjust for epithelial and stromal cells proportions in endometrial biopsy. The results were then compared to conventional differential expression analysis.\n\nMAIN RESULTS AND THE ROLE OF CHANCEThe estimated average proportions of stromal and epithelial cells in early secretory phase were 65% and 35%, and during mid-secre-tory phase 46% and 54%, respectively, that correlated well with the results of histological evaluation (r=0.88, p=1.1x10-6). Endometrial tissue transcriptomic analysis showed that approximately 26% of transcripts (n=946) differentially expressed in receptive endometrium in cell-type unadjusted analysis also remain differentially expressed after adjustment for biopsy cellular composition. However, the other 74% (n=2,645) become statistically non-significant after adjustment for biopsy cellular composition, underlining the impact of tissue heterogeneity on differential expression analysis. The results suggest new mechanisms involved in endometrial maturation involving genes like LINC01320, SLC8A1 and GGTA1P, described for the first time in context of endometrial receptivity.\n\nLIMITATIONS, REASONS FOR CAUTIONOnly dominant endometrial cell types were considered in gene expression profile deconvolution; however, other less frequent endometrial cell types also contribute to the whole-tissue gene expression profile.\n\nWIDER IMPLICATIONS OF THE FINDINGSThe better understanding of molecular processes during transition from pre-receptive to receptive endometrium serves to improve the effectiveness and personalization of assisted reproduction protocols. Biopsy cellular composition should be taken into account in future endometrial omics studies, where tissue heterogeneity could potentially influence the results.\n\nTRIAL REGISTRATION NON/A

genomics↗

TAC-seq: targeted DNA and RNA sequencing for precise biomarker molecule counting

Targeted next-generation sequencing based biomarker detection methods have become essential for biomedical diagnostics. In addition to their sensitivity and high-throughput capacity, absolute molecule counting based on unique molecular identifier (UMI) has high potential to increase biomarker detection accuracy even further through the reduction of systematic technical biases. Here, we present TAC-seq, a simple and cost-effective targeted allele counting by sequencing method that uses UMIs to estimate the original molecule counts of different biomarker types like mRNAs, microRNAs and cell-free DNA. We applied TAC-seq in three different applications and compared the results with standard sequencing technologies. RNA samples extracted from human endometrial biopsies were analyzed using previously described 57 mRNA-based receptivity biomarkers and 49 selected microRNAs at different expression levels. Cell-free DNA aneuploidy testing was based on cell line (47,XX,+21) genomic DNA. TAC-seq mRNA biomarker profiling showed identical clustering results to full transcriptome RNA sequencing, and microRNA detection demonstrated significant reduction in amplification bias, allowing to determine minor expression changes between different samples that remained undetermined by standard sequencing. The mimicking experiment for cell-free DNA fetal aneuploidy analysis showed that TAC-seq can be applied to count highly fragmented DNA, detecting significant (p=4.8x10-11) excess of molecules in case of trisomy 21. Based on three proof-of-principle applications we show that TAC-seq is a highly accurate and universal method for targeted nucleic acid biomarker profiling.

genomics↗