Long intergenic non-coding RNAs (lincRNAs) are capable of regulating several tumours, while competitive endogenous RNA (ceRNA) networks are of great significance in revealing the biological mechanism of tumours. Here, we aimed to study the ceRNA network of lincRNA in glioblastoma (GBM).We obtained GBM and normal brain tissue samples from TCGA, GTEx, and GEO databases, and performed weighted gene co-expression network analysis and differential expression analysis on all lincRNA and mRNA data. Subsequently, we predicted the interaction between lincRNAs, miRNAs, and target mRNAs. Univariate and multivariate Cox regression analyses were performed on the mRNAs using CGGA data, and a Cox proportional hazards regression model was constructed. The ceRNA network was further screened by the DEmiRNA and mRNA of Cox model.A prognostic prediction model was constructed for patients with GBM. We assembled a ceRNA network consisting of 18 lincRNAs, 6 miRNAs, and 8 mRNAs. Gene Set Enrichment Analysis was carried out on four lincRNAs with obvious differential expressions and relatively few studies in GBM.We identified four lincRNAs that have research value for GBM and obtained the ceRNA network. Our research is expected to facilitate in-depth understanding and study of the molecular mechanism of GBM, and provide new insights into targeted therapy and prognosis of the tumour. Glioblastoma (GBM) is the most aggressive and destructive primary malignant central nervous system tumour . At presNEAT1 is a p53-regulated lincRNA, which plays a key role in cancer occurrence [Although approximately 90% of genes in the human genome can be transcribed, only approximately 2% of the genes are protein coding, and non-coding RNAs account for most of the remainder . Long nocurrence . LincRNAcurrence . In geneThe COX proportional hazard regression model can analyze the impact of several factors on survival simultaneously, and is the most commonly used multi-factor analysis method for survival analysis. Long et al. found that a prognostic model composed of 4 mRNAs is a reliable tool for predicting the survival of liver cancer patients . A new tA competitive endogenous RNA (ceRNA) network is the interaction between lncRNAs, miRNAs, and mRNAs that constitutes a complex regulatory network system, which has extensive functions in the human genome and plays a significant role in cancer . The ceRhttps://xena.ucsc.edu/). Further, the mRNA-seq and clinical data of 693 glioma samples were retrieved from CGGA (http://cgga.org.cn/), from which 248 GBM samples were selected for this study. Among the selected samples, 236 patients had survival information, while 198 had all clinical information. Additionally, we retrieved GSE50161 , GSE134783 , and GSE90603  from the GEO database (https://www.ncbi.nlm.nih.gov/geo/).Figure https://www.gencodegenes.org/human/). The RNA-seq data fetched from TCGA and GTEx were collated and merged, and the expression matrices of mRNA and lincRNA were obtained. We also combined the GEO data and obtained the mRNA and miRNA expression matrix. The unit of RNA-seq data is FPKM. The R package oligo [The IDs of all samples were transformed into gene symbols based on GENCODE .Differential expression analysis was performed on the mRNA data in the TCGA and GTEx combined data and GEO combined data, and the two selected differential genes were combined to obtain differentially expressed mRNA (DEmRNA). Additionally, differential analysis was performed on GSE90603 to obtain DEmiRNA. (http://www.mircode.org/) was used to predict miRNA, and the miRNA target mRNA was predicted using miRDB (http://www.mirdb.org/), miRTarBase (http://mirtarbase.mbc.nctu.edu.tw/), and TargetScan (http://www.targetscan.org/). The predicted mRNA, WGCNA key module mRNA , and DEmRNA were analysed to obtain the overlapping mRNA. Additionally, Cytoscape 3.7.2 software was used to visualise the ceRNA network.The most relevant modules to GBM were selected and analysed with DElincRNA to screen for overlapping lincRNA. Further, miRcode (p < 0.05).We used the clusterProfiler R package  for Gene0(t)*exp.(β1x1 + β2x2+ … + βnxn). The correlation between the prognostic characteristics of patients and overall survival rate was then calculated through univariate and multivariate Cox regression analyses of clinical factors related to overall survival. Finally, the R package survivalROC was used to draw the receiver operating characteristic curve and calculate the area under the curve (AUC).Univariate COX regression analysis was performed on the CGGA data, using the Survival R software package, to evaluate the effect of mRNA expression on the survival time of GBM patients. Additionally, we performed multivariate COX regression analysis, constructed a multivariate Cox regression model, and identified the corresponding coefficients of GBM prognostic features. Further, we calculated the risk score to predict survival time, dividing the samples into high-risk and low-risk groups, with the median as the critical value. The “predict ” function was used to calculate the risk score: risk score = hBased on the mRNA obtained through multivariate Cox regression analysis and DEmiRNA, we screened and reconstructed the ceRNA network.p < 0.05. A set of genes “c2. cp. kegg. v7.2. symbols. gmt” was retrieved from the Molecular Signature Database  and selected as the reference gene set.GSEA software was used to analyse the RNA-seq data of lincRNA retrieved from the CGGA database. According to the median of lincRNA expression, the samples were divided into high and low expression groups. Statistical significance was set at We combined GTEx and TCGA data to construct a co-expression network for all lincRNAs, using the R package ‘WGCNA’, and confirmed that the β value in the network was 4 Fig. a. FurtheWe also constructed the co-expression networks of all mRNAs for GTEx and TCGA combined data and GEO combined data, comprising 20,270 and 14,807 mRNAs, respectively. In the mRNA co-expression networks of the two sets of data, β values were 8 and 4 Fig. c. The blWe identified DElincRNAs and DEmRNAs from the GTEx and TCGA combined data, including 163 up-regulated lincRNAs, 176 down-regulated lincRNAs, 2953 up-regulated mRNAs, and 2932 down-regulated mRNAs. From the GEO data, 2434 up-regulated and 1995 down-regulated mRNAs were identified. The two groups of mRNAs were then comprehensively analysed to obtain overlapping 1040 down-regulated mRNAs and 1358 up-regulated mRNAs. Finally, 339 DElincRNAs and 2398 DEmRNAs were identified. Through the GSE90603 dataset, 155 down-regulated and 134 up-regulated miRNAs were identified.Through WGCNA analysis of lincRNAs, we obtained 2206 lincRNAs in the turquoise module and then integrated the analysis with 339 DElincRNAs to obtain 251 overlapping lincRNAs. Further, using the miRcode online tool, 251 lincRNAs were used to predict miRNAs. Additionally, using the miRDB, TargetScan, and miRTarBase datasets, the predicted miRNAs were used to obtain the corresponding mRNAs. By analysing the predicted mRNAs, WGCNA key module mRNA , and DEmRNA, we obtained 111 mRNAs (24 down-regulated and 87 up-regulated mRNAs). The predicted miRNAs and 251 lincRNAs were analysed using the 111 mRNAs, and 25 lincRNAs and 30 miRNAs were finally obtained. Subsequently, we used Cytoscape version 3.7.2 software to build a lincRNA-miRNA-mRNA ceRNA network   , isocitrate dehydrogenase (IDH) mutation (p = 0.009), 1p19q codeletion (p = 0.014), and risk score (p = 0.001) were predictors  and risk score (p < 0.001) were independent risk factors (Fig. Among the CGGA GBM samples, complete survival information was available for 236 samples, and all clinical information was available for 198 samples. Univariate Cox regression analysis was performed on the 111 mRNAs previously obtained, and 27 mRNAs related to survival time were obtained (Using the 13 mRNAs obtained from the previous multivariate COX regression analysis conducted to screen the ceRNA network, a total of 14 miRNAs and 23 lincRNAs that interacted were found. We combined the previously selected 14 miRNAs and DEmiRNAs, screened out 6 overlapping miRNAs, and then screened the ceRNA network again. Finally, a ceRNA network containing 8 mRNAs, 6 miRNAs, and 18 lincRNAs was constructed (Fig. MALAT1, MEG3, NEAT1, MIR7-3HG, FAM95B1, EPB41L4A-AS1, and AC125494.1. Among them, extensive research has been conducted on MALAT1 and NEAT1. Thus, we selected MEG3, MIR7-3HG, FAM95B1, and EPB41L4A-AS1 in 248 CGGA samples for GSEA. As shown in Fig. MEG3 showed potential effects on prostate cancer, colorectal cancer, and cancer pathways (Fig. MIR7-3HG demonstrated potential role in small cell lung cancer, prostate cancer, and cancer pathways (Fig. FAM95B1 was closely related to endometrial cancer, non-small cell lung cancer, renal cell carcinoma, and MTOR signalling pathway (Fig. EPB41L4A-AS1 was associated with small cell lung cancer, prostate cancer, and JAK/STAT signalling pathway (Fig. Using | log2FC | ≥2 as a condition to screen 18 kinds of lincRNA, we obtained seven of them: GBM, which is the most common type of glioma, accounts for about 15% of all brain tumours. It has been reported that there are about three GBM patients per 100,000 people, and the average survival time of patients is only approximately 12–18 months. The 5-year and 10-year survival rates are approximately 5.5 and 2.9%, respectively , 29. TheCDK6, FAM84B, FBLIM1, FJX1, GNB5, HOXA3, MLEC, PDXK, SOX11, SPRY4, TBPL1, TRIB2, and WEE1. We calculated the risk score of patients, and verified that the prognostic model is highly accurate. In addition, the nomogram based on the risk score has the highest AUC value. We combined DEmiRNA and 13 mRNAs to screen the ceRNA network. Finally, 8 mRNAs, 6 miRNAs, and 18 lincRNAs were used to construct a ceRNA network. We selected four lincRNAs, including MEG3, MIR7-3HG, FAM95B1, and EPB41L4A-AS1, and predicted their functions using the GSEA software. Our previous studies have shown that there are few studies on these four lincRNAs and GBM. These lincRNAs were shown to be closely related to a variety of cancers. Among them, MEG3 and MIR7-3HG are related to cancer pathways, while these two and EPB41L4A-AS1 all regulate the JAK/STAT signalling pathway.In this study, we used WGCNA to find key modules related to GBM pathogenicity and combined differentially expressed genes for analysis. Based on the lincRNA-miRNA-mRNA interaction, a ceRNA network of GBM patients was preliminarily constructed, and 111 mRNAs in the network were functionally enriched. The results showed that these mRNAs have potential roles in cell cycle, various cancers, miRNA in cancer, DNA-binding transcriptional activator activity, TGF-β signalling pathway, transcription factor complex, RNA polymerase II specificity, etc. Further, univariate Cox regression and multivariate Cox regression analyses were used to construct a Cox proportional hazards regression model with 13 key genes: MALAT1 and NEAT1 have been widely studied non-coding RNAs, and some studies have demonstrated that they have clear correlations with various cancers such as hepatocellular carcinoma and lung cancer [MALAT1 can be used as ceRNA of miR-199a to promote the expression of ZHX1, which in turn can regulate the proliferation of GBM cells [NEAT1 and miR-194-5p is related to the angiogenesis of glioma [g cancer –33. It hBM cells . The ceRf glioma .MEG3, also known as gene trap locus 2, is an imprinted gene located at 14q32 [MEG3 is under-expressed in gliomas, and its over-expression has a significant inhibitory effect on the proliferation and migration of glioma cells, while promoting its apoptosis and autophagy, and inhibiting the PI3K/AKT/mTOR signalling pathway [MEG3 can act as a ceRNA for miR-19a, thereby exerting an inhibitory effect on glioma [MEG3 is potentially valuable in multiple tumours and cancer pathways.at 14q32 . It is aat 14q32 , 38. Stu pathway , 40. MEGn glioma . The preMIR7-3HG is associated with tumour progression and is highly expressed in endometrial cancer. Bioinformatics analysis by Wang et al. showed that its high expression in breast cancer is significantly related to the survival time of patients [PEG10 by sponged miR-27a-3p, and thus plays a role in retinoblastoma [EPB41L4A-AS1, located in the 5q22.2 region of the human genome, is an induced gene of p53 and pGC-1α, can regulate glycolysis and glutamine metabolism, and plays an important role in cancer metabolic reprogramming [FAM95B1 has been shown to be associated with thyroid cancer [According to previous reports, patients , 43. It blastoma . EPB41L4gramming . It is hgramming . Additiod cancer . LimitatMIR7-3HG, FAM95B1, and EPB41L4A-AS1, are novel potential therapeutic targets for GBM, as there are no previous related studies. Further, we created a prognostic model containing 13 genes, which may serve as a reliable prognostic indicator of GBM. Limitations of our study include the lack of cell or animal experiments and its retrospective nature. Subsequently, we hope to verify our results by conducting cell-based experiments. However, the present study is expected to facilitate in-depth understanding and study the molecular mechanism of GBM, and provide new insights into targeted therapy and prognosis of tumours.Conclusively, in this study, we constructed the lincRNA-miRNA-mRNA ceRNA network of GBM, which may be involved in its molecular regulation, and identified four lincRNAs with potential roles in the tumour. To our knowledge, three of these lincRNAs, namely


Amelogenesis imperfecta (AI) is a highly heterogeneous group of hereditary developmental abnormalities which mainly affects the dental enamel during tooth development in terms of its thickness, structure, and composition. It appears both in syndromic as well as non-syndromic forms. In the affected individuals, the enamel is usually thin, soft, rough, brittle, pitted, chipped, and abraded, having reduced functional ability and aesthetics. It leads to severe complications in the patient, like early tooth loss, severe discomfort, pain, dental caries, chewing difficulties, and discoloration of teeth from yellow to yellowish-brown or creamy type. The study aimed to identify the disease-causing variant in a consanguineous family.We recruited a consanguineous Pashtun family of Pakistani origin. Exome sequencing analysis was followed by Sanger sequencing to identify the pathogenic variant in this family.SLC24A4 by using exome sequencing. Later, its co-segregation within the family was confirmed by Sanger sequencing. The human gene mutation database  has a record of five pathogenic variants in SLC24A4, causing AI phenotype.Clinical analysis revealed hypomaturation AI having generalized yellow-brown or creamy type of discoloration in affected members. We identified a novel nonsense sequence variant c.1192C > T (p.Gln398*) in exon-12 of SLC24A4, which extends its mutation spectrum and confirms the role of this gene in the morphogenesis of human tooth enamel. The identified variant highlights the critical role of SLC24A4 in causing a rare AI type in humans.This nonsense sequence variant c.1192C > T (p.Gln398*) is the sixth disease-causing variant in  Mature enamel is a thin outer protective layer and covers the crown of the tooth in the form of a shell . NaturalVarious accounts of both syndromic and non-syndromic/isolated cases of AI have been published in the literature. Depending upon the amount, structure, and composition of the dental enamel, the phenotypes of non-syndromic AI are highly variable and may be divided into hypoplastic, hypocalcified, and hypomaturation forms , 4.AMELX , a candidate gene for X-linked dominant hypoplastic AI (OMIM: 301200)  fashion , 12. LAMpe of AI . Howeverpe of AI . MMP20 , Laryngo-Onycho-Cutaneous  syndrome , Jalili syndrome  , Amelogenesis Imperfecta and Nephrocalcinosis (OMIM 204690) , Kohlschutter-Tonz Syndrome  , Amelo-Onycho-Hypohidrotic Syndrome (OMIM 104570), and Heimler Syndrome-1,2  .Occasionally, AI has been reported as a part of a syndrome. The most common of them include Tricho-Dento-Osseous  syndrome  in exon-12 of The recommendations of the Declarations of Helsinki were strictly followed for the approval of the study from the Research and Ethical Committee of Kohat University of Science and Technology (KUST), Khyber Pakhtunkhwa, Pakistan. Informed written consent was obtained from the affected and unaffected participants. A five generational pedigree diagram was constructed after a thorough interview of the unaffected mother (III-4). The pedigree showed an autosomal recessive mode of inheritance Fig. A. Venoushttps://varbank.ccg.uni-koeln.de/) was used for exome data analysis. The mean coverage of the data was 77%, while at 20X and 10X, the coverage of the targeted bases was 92.6 and 96.6%, respectively. Genome Aggregation Database  was consulted to establish the minor allele frequency  of the variants. As controls, an in-house database of 511 exomes, and a dataset of 65 exomes from the Pakistani population, including 44 exomes from Punjabi, Sindhi, and Balochi patients, and 21 exomes of ethnically matched Pakhtoon patients were also consulted. The rare variants in PSPH, CHCHD2, BNC2, and SLC24A4 were selected from the exome data and were considered for the co-segregation analysis. The online prediction tools like MutationTaster, PROVEAN, SIFT, and PolyPhen2.0 were used to predict the pathogenicity of the missense variants  were obtained from the University of California Santa Cruz (UCSC) genome database browser (http://genome.ucsc.edu/cgi-bin/hgGateway). Primer3Plus software (http://www.bioinformatics.nl/cgi-bin/primer3plus/primer3plus.cgi) was used for designing the primers for the amplification of the regions of interest. A nucleotide sequence of 600 bp up-and-downstream from the position of the rare variants was searched to find out a suitable primer pair (Table https://www.thermofisher.com) was used for purifying the PCR products. The DNA sequencing was performed on the ABI3730 genetic analyzer with BigDye chemistry v3.1. The sequence alignment against the reference sequence was carried out by a sequence alignment tool, BioEdit version 6.0.7 (http://www.mbio.ncsu.edu/BioEdit/bioedit.html).DNA (70 ng/μl) of an affected member (IV-4) was subjected to exome sequencing. The enrichment of DNA for the intron-exon boundaries was carried out with the SeqCap EZ human exome library v2.0 kit. The Illumina HiSeq 4000 sequencing machine via a paired-end 100-bp protocol  was usedFor clinical and radiological investigations, a 35-years old patient (IV-4) was referred to Khyber Medical College of Dentistry, Peshawar, Khyber Pakhtunkhwa, Pakistan. His major complaints were yellow-brown staining, eating, and chewing difficulties of all the teeth , CHCHD2 , BNC2 , and SLC24A4 . These variants lie in three regions of homozygosity (ROH) on chromosome 7 (11.6 MB), 9 (3.8 MB), and 14 (4.7 MB). The variants in CHCHD2 and SLC24A4 are neither reported in gnomAD nor HGMD . Both variants in PSPH and BNC2 are tremendously rare in gnomAD, where c.398A > G; p.Arg133Ser appears in 20 alleles out of 282,490 alleles (none homozygous) and c.2860G > A; p.A954T is found in 4 alleles (one is homozygous) out of 246,026 alleles. These variants are not identified in the in-house database of 511 exomes and 65 exomes of Pakistani patients with diverse phenotypes other than AI. The pathogenicity predictions of the variants in PSPH, CHCHD2, and BNC2 by four online prediction algorithms are described in Table Exome sequencing revealed rare homozygous variants in four genes: PSPH, CHCHD2, BNC2 did not segregate within the family while the homozygous nonsense variant  in SLC24A4 revealed its co-segregation in the family  accession number (VCV000689492.1) for this variant has been allocated.Sanger sequencing was used to check the segregation of these variants with the disease. The homozygous missense variants in AMELX, ENAM, AMBN, LAMB3, LAMA3, COL17A1, ITGB6, ACPT, FAM83H, WDR72, MMP20, KLK4, GPR68, RELT, DLX3, CNNM4, ROGDI, PEX1, and PEX6) reported so far, to cause syndromic and non-syndromic AI.Exome data did not expose any rare variant in other genes (+-dependent Na+/Ca+ 2 exchangers (NCKX1–5) have been characterized in humans [+ 2 and a K+ ion in exchange for four extracellular Na+ ions [+ 2 ions from ameloblasts into the enamel matrix. Genetic alterations in SLC24A4 in the human genome and its knockout mice Slc24a4−/− lead to the development of indisposed calcified enamel [Slc24a4−/− mice signify the essential role of this protein in enamel development [Five functionally different types of Kn humans , 33. NCKNa+ ions . Each NCNa+ ions . During d enamel . Clinicaelopment .SLC24A4 (OMIM 609840) encodes a protein of 622 amino acids, called solute carrier family 24 member 4 (SLC24A4), which is one of the members of K+-dependent Na+/Ca2+ exchanger family (SLC24A), comprising a total of five members. It has been mapped to the chromosome 14q32 [SLC24A4 has various transcripts  resulting from alternative splicing and the longest isoform (NM_153646) contains 17 coding exons. SLC24A4 is highly expressed in many types of tissues, such as aorta, brain, lungs, and thymus gland [SLC24A4 is expressed in ameloblasts, and it borders to the membrane in contact with the developing enamel [+/Ca2+ exchanger domains are composed of these transmembrane pockets. Each domain contains a hydrophobic and highly conserved region of 30–40 residues called alpha-1 (139–179 amino acids), and alpha-2 (495–526 amino acids) repeats, respectively, which form ion-binding regions after undergoing highly intricate interactions with each other [me 14q32 , 36. SLCus gland . In the g enamel . The prech other , 38.SLC24A4 by using exome sequencing. This unusual genetic alteration is expected to lead to the loss of function of SLC24A4 protein either by nonsense-mediated decay (NMD) or by the production of a truncated protein lacking the C-terminus. Since this nonsense variant introduces a premature stop codon at the position 398 in the cytoplasmic loop between the alpha-1 and alpha-2 repeats; hence the loss of remaining 225 amino acids  is predicted. The two Na+/Ca2+ exchanger domains  are crucial for the smooth transport of ions, which verifies the exceptional role of SLC24A4 during amelogenesis. The absence of one of the two Na+/Ca2+ exchanger domains, in this case, alpha-2-repeat only will ultimately render the protein nonfunctional and causes amelogenesis imperfecta, hypomaturation type AI2A5 (OMIM: 615887) phenotype [We have identified a novel nonsense variant  in exon-12 of SLC24A4, including three missense variants, one nonsense variant, and a gross deletion , and a nonsense variant c.1015C > T (p.Arg339*) in two consanguineous families. They performed Sanger sequencing of 37 AI patients of different ethnicities and suggested that pathogenic sequence variants in SLC24A4 are a rare cause of AI in general, but might be a frequent cause of AI in Pakistani population [SLC24A4 variants in patients reported so far in the literature, we have concluded that clinical manifestation of AI is moderately to severely variable among the cases (Table To date, a total of five pathogenic variants causing AI have been identified in the pulation . Researcpulation , 39, 40.SLC24A4 (Fig. The present study aimed to perform a clinical and molecular evaluation of an autosomal recessive Pakistani family. We have identified the sixth disease-causing variant in


Cells have developed response systems to constantly monitor environmental changes and accordingly adjust growth, differentiation, and cellular stress programs. The evolutionarily conserved, nutrient-responsive, mechanistic target of rapamycin signaling (mTOR) pathway coordinates basic anabolic and catabolic cellular processes such as gene transcription, protein translation, autophagy, and metabolism, and is directly implicated in cellular and organismal aging as well as age-related diseases. mTOR mediates these processes in response to a broad range of inputs such as oxygen, amino acids, hormones, and energy levels, as well as stresses, including DNA damage. Here, we briefly summarize data relating to the interplays of the mTOR pathway with DNA damage response pathways in fission yeast, a favorite model in cell biology, and how these interactions shape cell decisions, growth, and cell-cycle progression. We, especially, comment on the roles of caffeine-mediated DNA-damage override. Understanding the biology of nutrient response, DNA damage and related pharmacological treatments can lead to the design of interventions towards improved cellular and organismal fitness, health, and survival. Schizosaccharomyces pombe has been widely used as a model to study eukaryotic molecular mechanisms, including the conserved mTOR pathway. S. pombe contain homologues for Rheb, a GTP-binding protein, and an upstream activator of mTOR [The mechanistic target of rapamycin (mTOR) is a phosphatidylinositol 3-kinase-related serine/threonine kinase central to a signaling network that regulates cellular metabolism in eukaryotic cells in response to various extracellular and intracellular stimuli, including nutrient and hormone signals, stress, and growth factors ,2. The k of mTOR , as well of mTOR ,12,13.S. pombe contains two mTOR homologues, Tor1 and Tor2, and two TORC complexes, TORC1 and TORC2  Mec1 proteins [Cells continuously suffer various forms of DNA damage from both endogenous and exogenous sources. To avoid the “fixing” of mutations and/or chromosome mis-segregation during mitosis, cells must arrest cell cycle progression and delay mitosis (DNA damage checkpoints) until damaged DNA has been repaired . While vK family ,42,43,44The timing of mitosis is regulated by the opposing actions of the Cdc25 phosphatase and Wee1 kinase on Cdc2 tyrosine 15 phosphorylation, and hence activity ,46. DNA S. pombe following cell cycle arrest may thus facilitate the rapid resumption of cell cycle progression following the completion of DNA repair [During the S-phase, deleterious events such as nucleotide depletion stalled replication forks and DNA strand breaks result in the Rad3-dependent hyper-phosphorylation and activation of Cds1. Cds1 in turn directly inhibits Cdc25 by phosphorylating inhibitory serine/threonine residues, while inducing the accumulation of the Wee1-related kinase Mik1. The S-phase specific Mik1 kinase regulates Cdc2 tyrosine 15 phosphorylation in a Rad3-dependent manner ,54,55. AA repair ,63. Wee1A repair ,64.S. pombe cells also transiently arrest cell cycle progression and then rapidly progress through mitosis in response to nitrogen deprivation and various environmental stresses [S. pombe mutants lacking ssp1 become elongated and arrest cell cycle progression in G2, following exposure to heat stress, osmotic stress, and glucose deprivation [ssp1Δ mutants and the deletion of rad24 or srk1 is sufficient to restore cell cycle progression under stress conditions [stresses ,66,67. Tstresses ,69,70. Sstresses . Interesrivation ,72,73,74nditions   and Rad3 DNA damage response (DDR) regulated pathways, it has been proposed that these pathways may also interact with TORC1 and TORC2 signaling ,77,78,79Pab1 delays the timing of mitosis by negatively regulating Cdc25 and positively regulating Wee1 activity [Pab1 signaling, resulting in Cdc2 activation [Pab1 activity [Under nitrogen-rich conditions, active TORC1 inhibits the Greatwall kinase homologue Ppk18, which prevents its activation of the endosulphine Igo1 ,81. Follactivity ,84. TORCtivation ,83,84. Ftivation . TORC1 ttivation . As Rad3activity ,78  have identified TORC1 (Tor1) mutants that exhibit increased tolerance to caffeine. These studies suggested that, unlike rapamycin and other analogues, caffeine directly binds to Tor1 and acts as a competitive inhibitor of ATP [S. pombe . This results in constitutive Chk1 activation and enhanced inhibition of Cdc25 and Wee1 [wat1/pop3 mutants are unable to downregulate Chk1 activation in a manner like tor1 mutants. These findings provide further evidence for a TORC2-dependent role in maintaining genomic stability [More recent findings report Chk1 activation in and Wee1 ,100. Wattability ,99,100.S. pombe and mammalian cell lines [Caffeine initially aroused much interest owing to its ability to sensitize cancer cells to the lethal effects of genotoxic agents . Clinicall lines . These findings suggest that TORC1 may serve as a therapeutic target for enhancing the chemo- and radiosensitivity of cancer cells. Further studies on caffeine-mediated DNA checkpoint override in the context of TORC1 and mTOR inhibition are thus highly desirable. Importantly, it remains unclear if caffeine functions as a direct inhibitor of TORC1 or partially mediates its effects via the activation of the AMPK pathway ,75. Such


Tumor suppressor p53 plays a key role in tumor suppression. In addition to tumor suppression, p53 is also involved in many other biological and pathological processes, such as immune response, maternal reproduction, tissue ischemia/reperfusion injuries and neurodegenerative diseases. While it has been widely accepted that the role of p53 in regulation of cell cycle arrest, senescence and apoptosis contributes greatly to the function of p53 in tumor suppression, emerging evidence has implicated that p53 also exerts its tumor suppressive function through regulation of many other cellular processes, such as metabolism, anti-oxidant defense and ferroptosis. Ferroptosis is a unique iron-dependent form of programmed cell death driven by lipid peroxidation in cells. Ferroptosis has been reported to be involved in cancer, tissue ischemia/reperfusion injuries and neurodegenerative diseases. Recent studies have shown that ferroptosis can be regulated by p53 and its signaling pathway as well as tumor-associated mutant p53. Interestingly, the regulation of ferroptosis by p53 appears to be highly context-dependent. In this review, we summarize recent advances in the regulation of ferroptosis by p53 and its signaling pathway. Further elucidation of the role and molecular mechanism of p53 in ferroptosis regulation will yield new therapeutic strategies for cancer and other diseases, including neurodegenerative diseases and tissue ischemia/reperfusion injuries. Tumor suppressor p53 plays a key role in tumor suppression ,2,3,4. pRecent studies have revealed a novel function of p53 in regulation of ferroptosis, a unique iron-dependent form of cell death driven by the accumulation of lipid-based reactive oxygen species (ROS) in cells ,16,17,18To ensure the proper function of p53 in regulating many fundamental cellular processes, p53 protein levels and activity are tightly regulated in cells. While p53 protein is usually maintained at low levels in normal cells under non-stressed conditions, the half-life of p53 protein is dramatically increased, leading to p53 protein accumulation in cells in response to a wide variety of intracellular and extracellular stress signals, such as DNA damage, hypoxia, nutrition depletion, activation of oncogenes and so forth. Once activated, p53 binds to the p53-responsive elements in its target genes to transcriptionally regulate their expression ,2,3,4. TWhile p53 is regulated by many different mechanisms, the post-translational modifications, especially the ubiquitination modification, are the most critical and efficient mechanism to regulate the level and activity of p53 ,34,35,36Interestingly, over 80% of p53 mutations in human cancers are missense mutations, leading to the production of full-length mutp53 proteins ,12,13. FGrowing evidence have shown that ferroptosis is involved in different physiological and pathological processes, including tissue ischemic injury  ,42,43,44Ferroptosis is different from apoptosis and other non-apoptotic cell death, such as autophagy and necrosis, at the morphological, biochemical and genetic levels. Morphologically, unlike apoptosis or necrosis, ferroptosis does not form apoptotic bodies or display chromatin condensation, cell shrinkage or rupture of the cell membrane ,16,17,18c-), which is constituted by a substrate-specific subunit SLC7A11 and a regulatory subunit SLC3A2, has been shown to be critical for ferroptosis. System xc- mediates the uptake of cystine, which is reduced to cysteine by GSH and/or thioredoxin reductase 1 (TXNRD1) and used for GSH biosynthesis [c- -mediated cystine import, which can decrease the bioavailability of GSH to trigger ferroptosis [c-, enzymes that are involved in glutamate and glutamine metabolism (such as glutaminase GLS2) have been shown to be important regulators of ferroptosis [2+ to Fe3+), as well as heme oxygenase-1  have been reported to regulate ferroptosis [Genetically, although the precise molecular mechanism of ferroptosis has not been fully understood, many different genes and pathways involved in regulation of iron metabolism, lipid synthesis and oxidative stress have been shown to regulate ferroptosis . For insynthesis . A grouproptosis ,16,17,69roptosis ,70. Addiroptosis ,16,17. Mroptosis ,62,71,72roptosis ,16,17,73roptosis ,75,76  in the DNA-binding domain of p53, fails to induce cell cycle arrest, senescence and apoptosis but can effectively repress SLC7A11 expression and induce ferroptosis in response to ROS-induced stress [3KR in xenograft tumor models [4KR98, an acetylation-defective p53 mutant with an additional lysine 98 (K98) mutation in p533KR, fails to repress SLC711A expression or induce ferroptosis. Notably, p534KR98 results in loss of tumor suppressive function of p53; compared with p533KR mice, the p534KR98 mice are prone to tumor development [p53 was first reported to sensitize cells to ferroptosis through transcriptionally repressing expression of SLC7A11 by Wei Gu’s group . SLC7A11d stress . Furtherd stress . SLC7A11d stress ,78,79. Nr models . The samelopment . These relopment . H2Bub1 elopment .3KR mice [The low-molecular-weight polyamines, including putrescine, spermidine and spermine, are amino acid-derived polycationic alkylamines essential for cellular growth, proliferation and survival . Their l3KR mice . Interes3KR mice . Additio3KR mice . ALOX12 3KR mice . These rFerroptosis is also modulated by glutamine metabolism. Upon the deprivation of amino acids, glutamine induces ferroptosis in a serum-dependent manner . In glut3KR/3KRMdm2−/− embryos but not in p53−/−Mdm2−/− embryos [Cyclooxygenase-2 encoded by the PTGS2 gene is an enzyme that acts both as a peroxidase and a dioxygenase . Cycloox embryos . Current embryos .As mentioned above, iron metabolism is important for ferroptosis. It was reported that p53 regulates iron metabolism through ferredoxin reductase (FDXR), an enzyme that transfers electron from NADPH (nicotinamide adenine dinucleotide phosphate) to cytochrome P450 via ferredoxin in mitochondria . FDXR isIn addition, recent studies have also reported that p53 induces ferroptosis through regulating noncoding RNAs. p53 has been reported to regulate expression of many noncoding RNAs, including microRNAs (miRNAs) and long noncoding RNAs (LncRNAs) and meanwhile, p53 is also regulated by many noncoding RNAs ,103,104.While many studies using different cultured cell systems and mouse models have shown the promoting effect of p53 on ferroptosis as summarized above, interestingly, some studies also reported that p53 can inhibit ferroptosis. p21  was reported to mediate p53 function to delay cystine deprivation-induced ferroptosis in human fibrosarcoma HT-1080 cells . In HT-1c- inhibitors  but not ferroptosis induced by GPX4 inhibitors  [Recently, p53 was also reported to inhibit ferroptosis in colorectal cancer HCT116 and SW48 cells through binding to DPP4 protein , a multiple functional protease that plays an important role in mediating cell death . The binr FIN56) , suggestr FIN56) . Interesr FIN56) . These fMitochondria have been reported to be involved in different programmed cell death processes, including apoptosis and mitophagy ,114. RecIn addition to wild-type p53, missense mutp53 has also been reported to be involved in the regulation of ferroptosis. It has been known that NRF2 induces the expression of SLC7A11 to protect cancer cells from ferroptosis , whereasGLS2 and SCO2, suggesting that the defect in metabolic regulation may contribute to the reduced ferroptosis and the tumor-prone phenotype observed in S47 mice [Some common single nucleotide polymorphisms (SNPs) in the p53 gene have been known to affect the p53 transcriptional activity and functions in tumor suppression, metabolism, maternal reproduction and aging ,125,126.S47 mice . NotablyS47 mice . S47 macS47 mice . These rS47 mice .p63 and p73 are two p53 family members, which are homologous to p53 and function as transcription factors like p53 ,130,131.Although MDM2 and MDM4 are critical negative regulators of p53, many studies have shown that MDM2 and MDM4 also exert oncogenic effects independently of their activities to inhibit p53 function ,38. InteAs a stress sensor, p53 responds to many different types of stress signals and initiate its transcription program to regulate different stress responses ,2,3,4. WAs a new form of programmed cell death, although remarkable progress has been made on the research on ferroptosis since its discovery in 2012, our current understanding of the molecular mechanism of ferroptosis, the role of ferroptosis in biological and pathological processes, as well as its application in treatments of cancer and other diseases is still limited. As summarized above, p53 has been shown to play an important role in regulation of ferroptosis. Notably, the original concept that ferroptosis can function as a tumor suppressive mechanism came from the studies showing the promoting effect of p53 on ferroptosis in mouse models ,80. Howe


X-ray crystallography has proven to be an invaluable tool in understanding the structural changes of SERCA, and more than 70 SERCA crystal structures representing major biochemical states (defined by bound ligand) have been deposited in the Protein Data Bank. Consequently, SERCA is one of the best characterized components of the calcium transport machinery in the cell. Emerging approaches in the field, including spectroscopy and molecular simulation, now help integrate and interpret this rich structural information to understand the conformational transitions of SERCA that occur during activation, inhibition, and regulation. In this review, we provide an overview of the crystal structures of SERCA, focusing on identifying metrics that facilitate structure-based categorization of major steps along the catalytic cycle. We examine the integration of crystallographic data with different biophysical approaches and computational methods to link biochemical and structural states of SERCA that are populated in the cell. Finally, we discuss the challenges and new opportunities in the field, including structural elucidation of functionally important and novel regulatory complexes of SERCA, understanding the structural basis of functional divergence among homologous SERCA regulators, and bridging the gap between basic and translational research directed toward therapeutic modulation of SERCA.Sarcoendoplasmic reticulum calcium ATPase (SERCA), a member of the P-type ATPase family of ion and lipid pumps, is responsible for the active transport of Ca This st-P state ,16,17,18-P state ,20 requi-P state ,21,22. F-P state ,23,24, t2+ affinity, while SLN also decreases maxV (activity at high Ca2+). Nonetheless, the effect of SLN on the maxV of SERCA remains controversial because independent studies have proposed that this protein either stimulates or has no effect on the maxV of the pump . T. TC4 andransport . In addigbya sp. , as wellgbya sp. , thus demaxV and Ca2+ affinity [The emerging view that lipids allosterically modulate protein activity, and protein−ligand and protein−protein interactions of membrane proteins ,121,122,affinity ,127. Theaffinity . More reaffinity ,129 fromaffinity . These saffinity , (2) theaffinity , and (3)affinity . A more affinity .PLB phosphorylation regulates SERCA function by inducing an order-to-disorder structural transition of the cytosolic domain of PLB, destabilizing an inhibitory T state in favor of the disordered, non-inhibitory R state . The strIn addition to PLB and SLN, newly discovered micropeptide regulators have been reported in the literature, including transmembrane proteins dwarf open reading frame (DWORF) , myoregu2+ affinity [2+ binding to SERCA by inducing unique changes in the energy-transduction domain of the pump. Testing these hypotheses will ultimately answer the most critical question: Why are PLB and SLN so similar, yet so different in their functional outcomes?Structural mechanisms behind differential regulation of SERCA by PLB and SLN is not well characterized, so this remains one of the greatest gaps in the field. Experiments and molecular simulation studies have shown that the N-terminal domain of SLN plays a primary role in SERCA uncoupling, while the TM domain is necessary for binding and stabilizing the regulatory complex, as well as for inhibiting Caaffinity ,23. SLN affinity , and memaffinity . More reaffinity . These aaffinity ,29, now 2+ overload that induces changes in Ca2+ handling, alterations in the mitochondrial swelling, oxidative stress, and ultimately necrosis. Reactivation of Ca2+ transport has been proposed as a therapeutic approach to treat these devastating diseases, and modulation of SERCA is amenable by small molecules [Abnormal SERCA activity and regulation are implicated in several conditions affecting human health, including heart failure ,147,148,olecules ,164, thurepair_model module of WHAT IF server [2o9j, 2oa0, 3ba6, 3tlm, 4h1w, 4kyt, 4y3u, 4ycm, 4ycn, 5a3r, and 5ncq were modeled using MODELLER v9.22 [Oryctolagus cuniculus SERCA1a homology models (UniProt: P04191-2 [5mpm and 6hxb) and SERCA2b (5ztf and 6jju) isoforms. SERCA1a contains only 10 TMs, therefore the fragment of TM11 (residues 1018 to 1030) solved in 5ztf was not considered in this analysis. The produced homology models were used to carry out all the comparative analyses described below  using ted below .2c9m as reference. The matrix was subsequently used to generate the clusters employing the linkage method with a cut-off value of 0.23 nm.Backbone RMSD matrix was computed with GROMACS 5.1 software  using th2c9m TM7 (residues 831–855), TM8 (residues 895–915), TM9 (residues 933–948), and TM10 (residues 966–994). Translation distances and rotation angles were computed for the three cytosolic domains using 2c9m as reference with the draw_rotation_axis.py python script developed by Pablo Guardado Calvo for the PyMOL software (http://pymolwiki.org/index.php/RotationAxis). A domain (residues 1–40 and 115–241), P domain (residue 300–356 and 603–738), and N domain (residues 357–602) were isolated from the pre-aligned structures for the calculation. The distance between the center of geometry (COG) of the domains was computed using PyMOL.Due to its high conformational conservation, all SERCA structures were aligned to the Cα atoms of dssp module [helanal_trajectory library of MDAnalysis software [anglebetweenhelices.py python script developed by Thomas Holder for the PyMOL software (https://pymolwiki.org/index.php/AngleBetweenHelices). Crystal structures 5zmw and 5ncq were not included in this analysis.TM domain helicity was defined using the p module  implemensoftware ,174 withsoftware . FinallyFigures were generated with PyMOL v1.7  and Gnup


Background: The development of 3D cerebral organoid technology using human-induced pluripotent stem cells (iPSCs) provides a promising platform to study how brain diseases are appropriately modeled and treated. So far, understanding of the characteristics of organoids is still in its infancy. The current study profiled, for the first time, the electrophysiological properties of organoids at molecular and cellular levels and dissected the potential age equivalency of 2-month-old organoids to human ones by a comparison of gene expression profiles among cerebral organoids, human fetal and adult brains. Results: Cerebral organoids exhibit heterogeneous gene and protein markers of various brain cells, such as neurons, astrocytes, and vascular cells  at 2 months, and increases in neural, glial, vascular, and channel-related gene expression over a 2-month differentiation course. Two-month organoids exhibited action potentials, multiple channel activities, and functional electrophysiological responses to the anesthetic agent propofol. A bioinformatics analysis of 20,723 gene expression profiles showed the similar distance of gene profiles in cerebral organoids to fetal and adult brain tissues. The subsequent Ingenuity Pathway Analysis (IPA) of select canonical pathways related to neural development, network formation, and electrophysiological signaling, revealed that only calcium signaling, cyclic adenosine monophosphate (cAMP) response element-binding protein (CREB) signaling in neurons, glutamate receptor signaling, and synaptogenesis signaling were predicted to be downregulated in cerebral organoids relative to fetal samples. Nearly all cerebral organoid and fetal pathway phenotypes were predicted to be downregulated compared with adult tissue. Conclusions: This novel study highlights dynamic development, cellular heterogeneity and electrophysiological activity. In particular, for the first time, electrophysiological drug response recapitulates what occurs in vivo, and neural characteristics are predicted to be highly similar to the human brain, further supporting the promising application of the cerebral organoid system for the modeling of the human brain in health and disease. Additionally, the studies from these characterizations of cerebral organoids in multiple levels and the findings from gene comparisons between cerebral organoids and humans (fetuses and adults) help us better understand this cerebral organoid-based cutting-edge platform and its wide uses in modeling human brain in terms of health and disease, development, and testing drug efficacy and toxicity. A host of diseases affect the human brain, including congenital, traumatic, and neurodegenerative conditions. Clinical approaches to garner information on such conditions mostly revolve around imaging, biomarkers, and cognitive assessment, which are often of poor resolution and only are possible after significant disease progression ,2. The cTwo-dimensional (2D) monolayer iPSC-derived neural cell models have been widespread for studying neurodegenerative disorders , pharmaceutical testing, and personalized medicine ,5,6. HowTo combat these limitations regarding homogeneity and functionality, Lancaster et al. were the first to publish a detailed protocol of how to use iPSCs to generate three-dimensional (3D) cerebral organoids . These cThus, the purpose of the current study was to expand upon the previous development of the iPSC-derived cerebral organoid model by the characterization of electrophysiological properties at both molecular and cellular levels, and dynamic development throughout the 2-month differentiation process, dissecting the approximate age of cerebral organoids relative to the fetal and adult human brains by a comparison of genome-wide expression profiles among organoids, fetal and human brain tissues. This is the first time in the field that the dynamic emergence of spontaneous electrical activity, electrochemical responses, drug action, heterogeneous tissue components, and comparisons of gene profiles among organoids, fetal and human brains have been reported. In addition, pathway analyses of gene expression profiles shed light on the equivalency between cerebral organoids and the fetal brain versus the adult brain. 2) at 37 °C. Upon reaching 80% confluency, iPSCs were passaged with Versene .All experiments utilizing human iPSCs were approved by the Medical College of Wisconsin Institutional Review Board. The detailed iPSC culture procedure and medium were described previously ,18. Brie2) at 37 °C. Briefly, embryoid bodies formed in the ultra-low attachment plates for 6 days, and mTeSR1 was added every other day. On day 6, embryoid bodies were transferred to 24-well low attachment plates, and cells were cultured in neuroepithelial media Dulbecco’s Modified Eagle Medium  and 1 μg/mL Heparin 50  for 5 days. On day 11, neuroepithelial tissues were embedded in matrigel droplets, and plated on 100 mm plates in 12 mL cerebral organoid differentiation media , 0.025% Insulin (Sigma), and 0.035% mercaptoethanol (Sigma) for 5 days. Plates were transferred to a spinner on day 16, and cultured long-term in 12 mL cerebral organoid differentiation media supplemented with vitamin A  0.025% Insulin, and 0.035% mercaptoethanol (Sigma). Cerebral organoids were cultured up until 2 months, with a subset of iPSCs and 1-month-old cerebral organoids collected for imaging and gene expression studies (see below). iPSC colonies, embryoid bodies, neuroepithelial tissues, and cerebral organoids were imaged under bright field by an EVOS FL Auto microscope (Thermo Fisher Scientific).All experiments below were from the 0-, 1-, or/and 2-month-old organoids. The protocol we used for generation of cerebral organoids was developed by Lancaster et al. . During For iPSC colony staining, iPSCs plated on glass coverslips were fixed in 4% paraformaldehyde, washed with phosphate buffered saline (PBS) (Life Technologies), 10% donkey serum was used as a blocker. Anti-rabbit octamer binding transcription factor 4  and anti-mouse microtubule-associated protein 2  primary antibodies were used, with the Alexa Fluor 594 (Thermo Fisher Scientific) secondary antibody. Hoechst 33,342 (Thermo Fisher Scientific) was used to stain cell nuclei. The coverslips were placed on glass slides with mounting medium (Thermo Fisher Scientific) and sealed with nail polish at the conclusion of immunostaining. Samples were imaged by laser scanning confocal microscopy .Cerebral organoids were fixed in 10% zinc formalin (Sigma), embedded in paraffin, and slices of 4 µm thickness were attached to glass slides. Tissues were deparaffinized by sequential washing in xylenes, hydrated in graded ethanol, and incubated in antigen retrieval solution  at 95 °C. Following PBS washing, 10% donkey serum was used as a blocker, and the following primary antibodies were used: Paired box 6 , MAP2 , S100 calcium binding protein B , synapsin I , smooth muscle actin , and cluster of differentiation 31 . The corresponding secondary antibody for all anti-rabbit primary antibodies was Alexa Fluor-488 nanometer conjugated immunoglobulin G (IgG), and Alexa Fluor–594 (Thermo Fisher Scientific) conjugated IgG for all anti-mouse antibodies. Hoechst 33,342 (Thermo Fisher Scientificwas used to stain cell nuclei. Coverslips and mounting medium (Thermo Fisher Scientific) were placed over the tissues and sealed with nail polish at the conclusion of immunostaining. Slides were imaged by laser scanning confocal microscopy .Cerebral organoids were fixed with 2% glutaraldehyde at 4 °C in 0.1 M sodium cacodylate buffer, and post-fixed for 1 h with 1% osmium tetroxide on ice. Cerebral organoids were rinsed with water and dehydrated by acetonitrile and graded methanol. Cerebral organoids were then embedded in epoxy resin (Electron Microscopy Sciences) and polymerized overnight at 70 °C. Sections 60 nm in size were cut and stained with lead citrate and uranyl acetate. A H600 Electron Microscope (Hitachi) was used for imaging.The isolation of total RNA was performed using the phenol–chloroform method described previously ,19. BrieThe RevertAid First Strand cDNA synthesis kit (Thermo Fisher Scientific) was used for cDNA synthesis following the manufacturer’s instructions. Briefly, 2 µg template RNA was mixed with water and the primer. Following incubation at 65 °C for 5 min, 5× Reaction Buffer, Ribolock RNase Inhibitor, deoxynucleoside triphosphate (dNTP) Mix, and Revertaid were added to the RNA mixture with the total volume of 20 µL following the manufacturer’s instructions. cDNA synthesis included 5 min at 25 °C, 60 min at 42 °C, and 5 min at 70 °C.A total of 5 ng cDNA, Power up SYBR Green Master Mix (Thermo Fisher Scientific), primers, and water (Qiagen) were loaded in triplicate in 384-well plates (10 μL/well) and PCR was performed using QuantStudio™ 6 Real-Time PCR detection system (Thermo Fisher Scientific). Primers were designed for MAP2, OCT4, S100B, SCN1A, SCN2A, SCN3A, SCN8A, SCN9A (SCN gene family members encode for respective Nav 1 family of sodium channels) ,21, and 2PO4, 0.5 CaCl2, 7 MgSO4, 23 NaHCO3, 25 glucose, 11.6 sodium ascorbate, and 3.1 sodium pyruvate. Then, slices were allowed to recover for at least 1 h in the artificial cerebrospinal fluid (ACSF) containing (in mM): 119 NaCl, 2.5 KCl, 2 CaCl2, 1.3 MgCl2, 1.25 NaH2PO4, 23 NaHCO3, and 10 glucose. All solutions were saturated with 95% O2 and 5% CO2. Cerebral organoids were embedded in 3% low-melting-point agarose (Thermo Fisher Scientific) and slices were cut (300 μm-thick) using a vibrating slicer . Slices were prepared in a choline-based solution containing (in mM): 110 choline chloride, 2.5 KCl, 1.25 NaH+ and K+ currents and action potentials, glass pipettes (3–5 MΩ) were filled with the internal solution containing the following (in mM): 140 K-gluconate, 5 KCl, 10 HEPES, 0.2 ethylene glycol-bis (β-aminoethyl ether)-N,N,N′,N′-tetraacetic acid (EGTA), 2 MgCl2, 4 Mg-ATP, 0.3 Na2GTP and 10 Na2-phosphocreatine (pH 7.3 with KOH). For recording Na+ and K+ currents, 500 ms-long steps (10 mV) from a voltage of −60 mV to +50 mV were given with the voltage clamp. For current clamp recordings, 500 ms-long steps  were applied.Whole-cell patch clamp recordings were made using patch clamp amplifiers (Multiclamp 700B). Data were acquired with DigiData 1440A digitizers and analysis software pClamp 10.6 . Cells were visualized under infrared differential interference contrast optics (Nikon Eclipse FN1 and Olympus BX51WI) and a 40× water immersion lens. Series resistance (10–20 MΩ) was monitored throughout the recordings and data were discarded if the resistance changed by more than 20%. Signals were filtered at 2 kHz and sampled at 10 kHz. For recording Na+-based solution as described above. For recordings of N-methyl-d-aspartate (NMDA) currents, the internal solution contained Cs+-based solution (in mM): 100 Cs-methanesulfonate, 10 CsCl, 10 HEPES, 1.1 EGTA, 2 MgCl2, 2.5 Mg-ATP, 0.3 Na2GTP and 10 Na2-phosphocreatine (pH 7.3 with CsOH) were used. Cells were voltage-clamped at +40 mV, and glycine (1 µM) was present in the ACSF. Patch pipettes were filled with ACSF containing glutamate (0.5 mM) or N-methyl-d-aspartate  and were positioned near recorded cells (20 µm). Glutamate or NMDA was applied by a brief pressure . For recordings of gamma aminobutyric acid (GABA) or propofol currents, the internal solution contained (in mM): 100 K-gluconate, 50 KCl, 10 HEPES, 1 EGTA, 0.1 MgCl2, 4 Mg-ATP, 0.3 Na2GTP and 10 Na2-phosphocreatine (pH 7.3 with KOH) were used. Gamma-aminobutyric acid  or propofol (10 µM) was applied by a pressure ejection similar to the glutamate or NMDA pressure injection. For studies of the effects of propofol (10 μM) on the amplitude and the decay time of GABA currents, GABA currents were evoked and recorded before and after the bath and the application of propofol. For recordings of glutamate currents, cells were voltage-clamped at −70 mV; the internal solution contained Kn = 3) was obtained from Cell Applications  and Takarabio . Adult human brain tissue (n = 3) was obtained from Biochain  and Takarabio . Before performing the microarray assay, the RNA samples underwent quality control analysis for RNA integrity, quantity, purity, and genomic DNA contamination. The RNA was reverse transcribed to cDNA, from which the Cy-3 labeled cRNA was synthesized. The cRNA was hybridized to microarray probes for fluorescence intensity scanning. The p-values were calculated using an unpaired t-test. The fold change was calculated by comparing the normalized intensities between two conditions. Differentially expressed mRNAs were designated by expressing above ± 2.0 fold change and p < 0.05 between cerebral organoid, fetal, and adult brain samples, and were shown in volcano plots. Volcano plots are useful tools for visualizing differential expression between two different conditions. They are constructed using fold change values and p-values, and thus allow us to visualize the relationship between fold change (magnitude of change) and statistical significance. The volcano plot is constructed by plotting the negative log10 of the p value on the y axis. The x axis is the log2 of the fold change between the two conditions. The red data points denote significantly upregulated expression and the green points denote downregulated genes.Microarray and IPA analyses were performed in a similar manner to previous publications ,19. The The heatmap shows the entire gene profile for all samples. The heatmap was generated in R software. The log2-transformed fragments per kilobase of exon model per million reads mapped (FPKM) gene expression values were hierarchically bi-clustered for the gene expression and the samples using the Euclidean distance metric and the average linkage method. The closeness of the samples was displayed on the top dendrogram. The samples were clustered together, unsupervised within the organoid, fetal brain, and adult brain groups. The color key on the top left represents the log2 (FPKM) value. d between any two dots in 3D can be calculated using the following formula:Principal component analysis (PCA) was performed to determine the relative expressional distances between cerebral organoids, fetal, and adult brains in 3D coordinate space. The original log2-transformed normalized intensities were used for PCA in R. The data points on the PCA plot represent the samples, such that the expressional distances between them were maximized for visualization on the 3D plots. The Euclidean distance p < 0.05) between groups were inputted into the IPA software. To more closely focus on signaling pathways related to functional neural networks, canonical pathways were screened based on statistically significant z scores (p < 0.05) generated by IPA, and phenotypic relevance was determined by literature searches. To propose and compare/contrast phenotypes and pathways relevant to neurodevelopment, maturation, and neuro-electrophysiology between cerebral organoids, fetal brain tissue, and adult brain tissue, mRNA abundance profiles from each group underwent bioinformatics assessment using IPA software (Qiagen Bioinformatics). IPA generates a causal network analysis based on previously reported upstream regulators, downstream effects, and a respective gene’s involvement in established pathways based on the literature and databases. The analysis provides gene clusters in disease mechanisms, toxicity functions, and canonical signaling pathways Using IPA predictions, a proposed phenotypic overlap between groups was reported. From the current dataset of raw gene abundance values for 20,723 mRNAs across the three groups, only mRNAs with a ± 2.0-fold change difference . For qRT-PCR data comparing iPSC, 1-month-old cerebral organoid, and 2-month-old cerebral organoid groups, a one-way ANOVA was used to detect significant differences between groups, with a Holm–Sidak post-hoc test when applicable. Student’s unpaired Over the 2-month differentiation process, singularized iPSCs transitioned to embryoid bodies, to neuroepithelial tissue, then to cerebral organoids. This transition was accompanied by an increase in tissue size A. NormalAt 1 month, immunofluorescence staining images indicate that cerebral organoids were primarily composed of PAX6-positive neuroepithelial cells, relative to MAP2-positive neurons. There was a qualitative increase in neurons at two months, and a decrease in the neural stem cell signal C. Due toS100B (a marker of astrocytes) was expressed in 2-month-old cerebral organoids throughout the tissue. Like the developing brain, most cells were neurons and astrocytes and comprised roughly 8% of the heterogeneous neural tissue within the cerebral organoids. Neurons remained the most prominent cell type in the continuous cerebral organoid tissue. CD31- and SMA-positive blood vessel-like structures were located more sporadically throughout the tissue in 2-month-old cerebral organoids D. These p < 0.001)but an increase in MAP2 gene abundance after 1 month (p = 0.02). At 2 months after the initiation of differentiation, there was an even greater increase in MAP2 abundance relative to what was observed at the iPSC (p < 0.001) and 1-month stage. There was a significant, sustained increase in S100B (both p < 0.05) in cerebral organoids at 1 month and 2 months old, relative to respective gene abundance in iPSCs. There was a sustained increase in SMA and gene abundance in 1-month and 2-month-old cerebral organoids relative to iPSCs (both p < 0.05). Additionally, CD31 gene abundance was increased at 1 month (p = 0.016) and 2 months old (p = 0.018) relative to iPSCs (Quantitative reverse transcription polymerase chain reaction (qRT-PCR) analysis further confirms that, as cerebral organoids were differentiated from iPSCs, there was a reduction in pluripotency, as indicated by a significant decrease in OCT4 gene abundance both one month and 2 months into the differentiation process (both to iPSCs .p < 0.05). A significant increase in iPSCs was observed at 1 month in SCB1A, SCN3A and SCN9A gene abundance relative to iPSCs, with a further increase at 2 months , and Gamma aminobutyric acid (GABA) from a patch pipette, their respective currents were detected in cerebral organoids (A) receptors. All current values are quantified in A receptor agonist, potentiated the GABAergic current, quantitatively shown as an increased current amplitude and prolonged current decay time when propofol was perfused, relative to the control GABA response (n = 5). In developing neurons, the GABAergic current was potentiated by propofol. Current quantification is shown in p < 0.05).Upon pressure ejection  of glutamate, response E. Pressup < 0.05). In the heatmap of all genes assessed by the microarray, clustering/expression values showed gene profile similarity between adult and fetal samples (Separate comparisons of gene profiles between 1) cerebral organoid versus fetal brain tissue, 2) cerebral organoid versus adult brain tissue, and 3) adult brain tissue versus fetal brain tissue were performed following the microarray of 20,723 genes. The volcano plot illustrated the differentially abundant messenger ribonucleic acids (mRNAs) in the different tissues A. Among  samples B. PCA ex samples C. The PCIngenuity Pathway Analysis (IPA) analysis nominated various canonical pathways related to neural development, network formation, and electrophysiological signaling to be dysregulated between cerebral organoids, fetal brain tissue, and adult brain tissue. Based on the Z score, an indication of the degree to which a treatment group deviates from the mean, cerebral organoids and fetal tissue were predicted to show the most similar pathway activation. Of the selected canonical pathways, only calcium signaling, cyclic adenosine monophosphate (cAMP) response element-binding protein (CREB) signaling in neurons, glutamate receptor signaling, and the synaptogenesis signaling were predicted to be downregulated in cerebral organoids relative to fetal samples. Nearly all cerebral organoid and fetal pathway phenotypes  were predicted to be consistently downregulated compared with adult brain tissue .In the current study, we characterized cerebral organoids as models of the human brain in the aspects of development, electrophysiological properties, gene profiles, and their potential to be utilized in physiological studies of human brain structure and function. We found that as cerebral organoids develop from iPSCs in the culture over time, they exhibit (1) a heterogeneous gene and protein markers of various brain cells, such as neuron, astrocytes, and vascular cells including endothelial cells and smooth muscle cells, (2) an increased gene expression of brain cell-specific markers over time, and (3) functional electrophysiological properties as evidenced by the neurons with action potential and synapse-like structure, functional response of ion channels to the drug stimulation. Additionally, a bioinformatics analysis of 20,723 gene expression profiles showed a similar distance between gene profiles in cerebral organoids compared to fetal and adult brain tissues. The subsequent IPA analysis of select canonical pathways related to neural development, network formation, and electrophysiological signaling, revealed that only calcium signaling, CREB signaling in neurons, glutamate receptor signaling, and synaptogenesis signaling were predicted to be downregulated in cerebral organoids relative to fetal brain samples. Nearly all cerebral organoid and fetal brain pathway phenotypes were predicted to be downregulated compared with adult brain tissues. These novel findings bring scientists one step closer to understanding how to better utilize cerebral organoids for their wide application in modeling human brains in health and disease, and testing drugs for their efficacy and toxicity. Moving forward from the iPSC stage, we noted a downregulation in the OCT4 pluripotency marker and Pax6 neural stem cell marker, and an upregulation in the markers of neural cell types , and adult human brain tissue (21–66 years) A. It hasFirst, when looking at expression of all 20,723 genes obtained via the microarray, fetal and adult brain tissues showed the greatest similarities based on gene clustering B. A bioiCerebral organoids are currently used as models of the developing brain, while future approaches may be geared toward obtaining more of an adult-like maturational status by targeting transcriptomic signaling pathways. For instance, activating transcription factor 4 (ATF4) is associated with early synaptogenesis , and accOverall, for the first time, this study reported neurotransmitter currents and the functional response of channel activities of cerebral organoids to the drug propofol. These observed characteristics of diverse cell components, functional channel activities, and progressive development in culture further suggest that this model has tremendous promise in being the more clinically relevant manner by which scientists can dissect detailed cellular, molecular, and functional mechanisms of brain development, physiology and disorders. Additionally, the findings from the comparisons of human iPSC-derived cerebral organoids to both human fetal brain samples and the adult human brain help us better understand this cerebral organoid-based cutting-edge platform and its wide uses in modeling the human brain in health and disease, development, and testing drug efficacy and toxicity.


Background: The outbreak of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has posed great threat to human health. T cells play a critical role in antiviral immunity but their numbers and functional state in COVID-19 patients remain largely unclear.Methods: We retrospectively reviewed the counts of T cells and serum cytokine concentration from data of 522 patients with laboratory-confirmed COVID-19 and 40 healthy controls. In addition, the expression of T cell exhaustion markers were measured in 14 COVID-19 cases.Results: The number of total T cells, CD4+ and CD8+ T cells were dramatically reduced in COVID-19 patients, especially in patients requiring Intensive Care Unit (ICU) care. Counts of total T cells, CD8+ T cells or CD4+ T cells lower than 800, 300, or 400/μL, respectively, were negatively correlated with patient survival. T cell numbers were negatively correlated to serum IL-6, IL-10, and TNF-α concentration, with patients in the disease resolution period showing reduced IL-6, IL-10, and TNF-α concentrations and restored T cell counts. T cells from COVID-19 patients had significantly higher levels of the exhausted marker PD-1. Increasing PD-1 and Tim-3 expression on T cells was seen as patients progressed from prodromal to overtly symptomatic stages.Conclusions: T cell counts are reduced significantly in COVID-19 patients, and the surviving T cells appear functionally exhausted. Non-ICU patients with total T cells counts lower than 800/μL may still require urgent intervention, even in the immediate absence of more severe symptoms due to a high risk for further deterioration in condition. In December 2019, a series of acute respiratory illnesses were reported in Wuhan, Hubei Province, China . This di+ and CD8+ T cells from COVID-19 patients and healthy controls. Our results thus provide a preliminary demonstration of T cell exhaustion during COVID-19 infection and suggest that more urgent, early intervention may be required in patients with low T lymphocyte counts.Since an effective immune response against viral infections depends on the activation of cytotoxic T cells that can clear infection by killing virus-infected cells , boostinMedical records from 522 patients (aged from 5 days to 97 years) with confirmed COVID-19 and admitted into the General Hospital of Central Theater Command or Hanyang Hospital in Wuhan from December 2019 to January 2020, and 40 healthy people (aged from 2 to 62 years), who came to the hospitals for routine physical examination, were collected and retrospectively analyzed. Diagnosis of COVID-19 was based on the New Coronavirus Pneumonia Prevention and Control Program (5th edition) published by the National Health Commission of China (2) over inspiratory oxygen fraction (FIO2) of <300 mm Hg or arterial oxygen saturation of 93% or lower. According to the staging of infectious disease  published by the National Health Commission of China . Within  disease , the proWe reviewed clinical records, nursing records, laboratory findings, and chest X-rays or CT scans for all the patients and physical examination records of the 40 healthy people. All information was obtained and curated with a customized data collection form. Three investigators  independently reviewed the data collection forms to verify data accuracy.2) and PBMCs were harvested by density gradient centrifugation. Isolated PBMCs were stained with a BD multitest IMK Kit  to analyze the frequency and cell number of total, CD4+ and CD8+ T cells, as well as B and NK cells in healthy controls and patients. The exhaustion of T cells was detected using human CD4-PerCP , CD8-APC , CD8-PE , PD-1-PE , and TIM-3-FITC  antibodies. After being stained, the cells were measured by flow cytometry on an LSR Fortessa Cell Analyzer (BD Biosciences) and data analyzed using the FlowJo software (TreeStar). All experimental procedures were completed under biosafety level II plus condition.Peripheral blood samples from 14 patients and 3 healthy volunteers were simultaneously processed in the Central Lab of General Hospital of Central Theater Command to isolate peripheral blood mononuclear cells (PBMCs) for further testing. The peripheral blood was supplemented with anticoagulants . Continuous variables were directly expressed as a range. Categorical variables were expressed as numbers/NUMBERS (%). Data in  from χ2 , non-pai , 75.95% (379/499), and 71.54% (357/499) patients had remarkably low total T cell counts, CD4CU group . The couCU cases . All theCU cases , suggest+ and CD8+ T cells in the severe and critical disease groups as well as the perished group were significantly lower than in the mild/moderate disease group. Most importantly, the numbers of total T cells, CD8+ T cells and CD4+ T cells in severe and perished groups were lower than 800, 300, or 400/μL, respectively . The Non-ICU patients were further divided into four groups based on clinical outcomes. Among these patients, 151, 40 and 13 cases had mild/moderate, severe and critical disease, respectively, while 8 patient deaths occurred, in the perished group. Statistical analysis showed that T cell numbers including total T cells, CD4ectively . This reThe expression of angiotensin converting enzyme 2 (ACE2), the predicted receptor of SARS-CoV-2 viruses, is absent on T cells , suggest+ counts, and CD8+ counts, respectively  capable of secreting an array of molecules such as perforin, granzymes, and IFN-γ to eradicate viruses from the host (+ helper T cells (Th) can assist cytotoxic T cells and B cells and enhance their ability to clear pathogen  patients showed a decrease in both total T cells and CD4+ T cells, and most importantly, all of the patients displayed decreases in CD8+ T cells. We also analyzed Non-ICU patients in greater detail, and found that urgent intervention may be necessary to preempt the development of severe symptoms in patients with low T cell counts.T cells play a vital role in viral clearance, with CD8the host . At the pathogen . Howeverpathogen , 16. Earpathogen , 6. In t+ T and CD8+ T cells are negatively correlated to levels of TNF-α, IL-6, and IL-10, respectively  and multiple organ dysfunction syndrome (MODS) , 18. It ectively , suggestvia interacting with its receptor, TNFR1, which expression is increased in aged T cells  and Hanyang Hospital (20200217). Written informed consent to participate in this study was provided by the participants' legal guardian/next of kin. Written informed consent was obtained from the individual(s), and minor(s)' legal guardian/next of kin, for the publication of any potentially identifiable images or data included in this article.YW and YC were involved in the final development of the project and manuscript preparation. XC and YZ wrote the manuscript draft. ZY, CW, and ZF analyzed the data. BD, YL, YT, LN, LC, ML, YL, and GW performed most of experiments.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.