Transcriptome dynamics and molecular cross-talk between bovine oocyte and its companion cumulus cells

<p>Abstract</p> <p>Background</p> <p>The bi-directional communication between the oocyte and its companion cumulus cells (CCs) is crucial for development and functions of both cell types. Transcripts that are exclusively expressed either in oocytes or CCs and molecular mechanisms affected due to removal of the communication axis between the two cell types is not investigated at a larger scale. The main objectives of this study were: 1. To identify transcripts exclusively expressed either in oocyte or CCs and 2. To identify those which are differentially expressed when the oocyte is cultured with or without its companion CCs and vice versa.</p> <p>Results</p> <p>We analyzed transcriptome profile of different oocyte and CC samples using Affymetrix GeneChip Bovine Genome array containing 23000 transcripts. Out of 13162 genes detected in germinal vesicle (GV) oocytes and their companion CCs, 1516 and 2727 are exclusively expressed in oocytes and CCs, respectively, while 8919 are expressed in both. Similarly, of 13602 genes detected in metaphase II (MII) oocytes and CCs, 1423 and 3100 are exclusively expressed in oocytes and CCs, respectively, while 9079 are expressed in both. A total of 265 transcripts are differentially expressed between oocytes cultured with (OO + CCs) and without (OO - CCs) CCs, of which 217 and 48 are over expressed in the former and the later groups, respectively. Similarly, 566 transcripts are differentially expressed when CCs mature with (CCs + OO) or without (CCs - OO) their enclosed oocytes. Of these, 320 and 246 are over expressed in CCs + OO and CCs - OO, respectively.</p> <p>While oocyte specific transcripts include those involved in transcription (<it>IRF6, POU5F1, MYF5, MED18</it>), translation (<it>EIF2AK1, EIF4ENIF1</it>) and CCs specific ones include those involved in carbohydrate metabolism (<it>HYAL1, PFKL, PYGL, MPI</it>), protein metabolic processes (<it>IHH, APOA1, PLOD1</it>), steroid biosynthetic process (<it>APOA1, CYP11A1, HSD3B1, HSD3B7</it>). Similarly, while transcripts over expressed in OO + CCs are involved in carbohydrate metabolism (<it>ACO1, 2</it>), molecular transport (<it>GAPDH, GFPT1</it>) and nucleic acid metabolism (<it>CBS, NOS2</it>), those over expressed in CCs + OO are involved in cellular growth and proliferation (<it>FOS, GADD45A</it>), cell cycle (<it>HAS2, VEGFA</it>), cellular development (<it>AMD1, AURKA, DPP4</it>) and gene expression (<it>FOSB, TGFB2</it>).</p> <p>Conclusion</p> <p>In conclusion, this study has generated large scale gene expression data from different oocyte and CCs samples that would provide insights into gene functions and interactions within and across different pathways that are involved in the maturation of bovine oocytes. Moreover, the presence or absence of oocyte and CC factors during bovine oocyte maturation can have a profound effect on transcript abundance of each cell types, thereby showing the prevailing molecular cross-talk between oocytes and their corresponding CCs.</p

Twelve-month observational study of children with cancer in 41 countries during the COVID-19 pandemic

Introduction Childhood cancer is a leading cause of death. It is unclear whether the COVID-19 pandemic has impacted childhood cancer mortality. In this study, we aimed to establish all-cause mortality rates for childhood cancers during the COVID-19 pandemic and determine the factors associated with mortality. Methods Prospective cohort study in 109 institutions in 41 countries. Inclusion criteria: children &lt;18 years who were newly diagnosed with or undergoing active treatment for acute lymphoblastic leukaemia, non-Hodgkin's lymphoma, Hodgkin lymphoma, retinoblastoma, Wilms tumour, glioma, osteosarcoma, Ewing sarcoma, rhabdomyosarcoma, medulloblastoma and neuroblastoma. Of 2327 cases, 2118 patients were included in the study. The primary outcome measure was all-cause mortality at 30 days, 90 days and 12 months. Results All-cause mortality was 3.4% (n=71/2084) at 30-day follow-up, 5.7% (n=113/1969) at 90-day follow-up and 13.0% (n=206/1581) at 12-month follow-up. The median time from diagnosis to multidisciplinary team (MDT) plan was longest in low-income countries (7 days, IQR 3-11). Multivariable analysis revealed several factors associated with 12-month mortality, including low-income (OR 6.99 (95% CI 2.49 to 19.68); p&lt;0.001), lower middle income (OR 3.32 (95% CI 1.96 to 5.61); p&lt;0.001) and upper middle income (OR 3.49 (95% CI 2.02 to 6.03); p&lt;0.001) country status and chemotherapy (OR 0.55 (95% CI 0.36 to 0.86); p=0.008) and immunotherapy (OR 0.27 (95% CI 0.08 to 0.91); p=0.035) within 30 days from MDT plan. Multivariable analysis revealed laboratory-confirmed SARS-CoV-2 infection (OR 5.33 (95% CI 1.19 to 23.84); p=0.029) was associated with 30-day mortality. Conclusions Children with cancer are more likely to die within 30 days if infected with SARS-CoV-2. However, timely treatment reduced odds of death. This report provides crucial information to balance the benefits of providing anticancer therapy against the risks of SARS-CoV-2 infection in children with cancer

Computational investigation of the valid valence states contribution in calculating the electronic stopping power of proton in bulk Al within the linear response approach

The electronic stopping power is a fundamental quantity to many technological fields that use ion irradiation. Here we investigate the validity of using a fully ab initio computational scheme based on linear response time-dependent density functional theory to predict the random electronic stopping power (RESP) of a proton in bulk aluminum. We verify the power of using the extrapolation scheme to overcome the expected convergence issue of the RESP calculations. We show that the calculated RESP of valence electrons compares well with experimental data for low proton velocity only when at full convergence and including the exchange-correlation effect. We demonstrate that the inclusion of valence states only is sufficient for calculating the electronic stopping power up to the stopping maximum.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author