A. sativa (AsGRs) were selected to explore their molecular characterization, phylogenetic relationship, and RNA expression changes during seed imbibition under abiotic stress. Seven AsGR genes were identified and mapped on six chromosomes of A, C, and D subgenomes. Phylogenetic analysis and subcellular localization of AsGR proteins divided them into two sub-families, AsGR1 and AsGR2, which were predicted to be mainly located in cytoplasm, mitochondrion, and chloroplast. Cis-elements relevant to stress and hormone responses are distributed in promoter regions of AsGRs. Tissue-specific expression profiling showed that AsGR1 genes were highly expressed in roots, leaves, and seeds, while AsGR2 genes were highly expressed in leaves and seeds. Both AsGR1 and AsGR2 genes showed a decreasing-increasing expression trend during seed germination under non-stress conditions. In addition, their responses to drought, salt, cold, copper, H2O2, and ageing treatments were quite different during seed imbibition. Among the seven AsGR genes, AsGR1-A, AsGR1-C, AsGR2-A, and AsGR2-D responded more significantly, especially under drought, ageing, and H2O2 stress. This study has laid the ground for the functional characterization of GR and the improvement of oat stress tolerance and seed vigor.Abiotic stress disturbs plant cellular redox homeostasis, inhibiting seed germination and plant growth. This is a crucial limitation to crop yield. Glutathione reductase (GR) is an important component of the ascorbate-glutathione (AsA-GSH) cycle which is involved in multiple plant metabolic processes. In the present study, GRs in  Reactive oxygen species (ROS) are key regulators of plant growth and development throughout the whole plant life cycle ,2. WithoGR genes can be significantly induced by unfavorable conditions. The deletion or down-regulation of GR genes would lead to the decrease of seed vigor DG[T/S]K)  .The three-dimensional structure of seven AsGR family members were modelled using Phyre2 server. Predicted models were based on the reported templates to heuristically maximize the identity and confidence score for the tested sequences. In AsGR proteins, the mainly predicted secondary structures were α-helix (28–33% in each AsGR) and β-strands (23–30% in each AsGR). All AsGR sequences showed high confidence of 100% with the template. In addition, about 46–48% identity was demonstrated between AsGR proteins and the template, suggesting that the predicted AsGRs models had extremely high accuracy .cis-acting element CAAT-box and the core promoter element TATA-box were found to be widely distributed in the promoter region of the AsGR genes (data not show). The other cis-acting elements are mainly related to hormone responsiveness, anaerobic induction, defense and stress responsiveness and light responsiveness , zein metabolism regulation , MYB binding site involved in drought-inducibility , meristem expression , and circadian regulation (AsGR2-C1) (The common siveness . HormonesGR2-C1) .AsGR1 and AsGR2 genes varied considerably in different tissues . The expression of AsGR1-C in aged seeds was significantly down-regulated in dry seeds (p < 0.05), which may cause the destruction of redox balance in the early imbibition of A. sativa seeds. AsGR1-A expression was up-regulated at most imbibition time under stress compared with the control. After 6 h of imbibition, the expression of AsGR1-A significantly increased in cold, PEG, salt, CuSO4 and H2O2 treatments. Under PEG and salt treatments, AsGR1-A expression increased significantly after 24–72 h imbibition. Under H2O2 treatment, the expression of AsGR1-A was the highest in dry seeds and seeds treated for 36 and 72 h. The expression of AsGR1-A in aged seeds was lower compared with other treatments at 24 h imbibition . After 72 h imbibition, the expression of AsGR1-C was also significantly induced under PEG stress (p < 0.05) and in aged seeds (p < 0.01) . The highest expression level was present at 36 h under H2O2 treatment. Cold treatment also could induce the expression of AsGR1-D at 6–36 h. However, the expression of AsGR1-D was lower at 72 h imbibition under cold, PEG, salt and CuSO4 treatments than control  B. AsGR1- control C. In add changed A,C.AsGR2 genes to different stress treatments is also different, and most of them have strong response in the early imbibition stage, except for AsGR2-C2 (AsGR2-C2 and AsGR2-D were significantly down-regulated compared with control in non-imbibition seeds (p < 0.05)  under stress treatments, especially PEG and D30 treatments. After 12 h imbibition, AsGR2-C1 abundance in aged seeds was still in a higher level  . AsGR2-A 24–72 h A. AsGR2-er level B. During at 72 h C. AsGR2-ly lower D.Arabidopsis, rice, wheat, B. distachyon, sorghum, Chinese cabbage and other species, and GRs were divided into cytoplasmic isoform and chloroplast/mitochondrial double-localization isoform. Loss of the two types of GR could decrease plant resistance to adversity [GR gene expression and increase GR activity [A. sativa have not been identified and analyzed systematically. In this study, we identified A. sativa GR family members from the whole genome of oat and analyzed their expression patterns during germination under stress. These gene candidates are useful for the following researches on oat stress resistance breeding and seed vigor improvement.Abiotic stresses, such as drought, salt, cold, heavy metals, and seed ageing, severely affect seed emergence rate and seedling uniformity after sowing. Under adverse conditions, seed germination and seedling growth are inhibited due to the oxidative damage of biomolecules and organelle structures ,28,29,30dversity ,35,36, aactivity ,17,37. HA. sativa, which is the same number as that in bread wheat and more than those in other species. It suggested that the GR family members are closely related to their genome duplication events and genome size [B. distachyon, and sorghum, but does not appear in A. sativa and bread wheat [GR genes in monocots is complicated. Additionally, AtGR1 and AtGR2 of Arabidopsis have several homologous in A. sativa, wheat and rice, which suggests that the evolutionary divergence of GR genes occurs after the split of monocotyledon and dicotyledon [A total of seven GR proteins were identified in ome size . GR protad wheat ,37. It sotyledon .AsGR genes showed that chloroplastic GR genes consisted of fewer exons as compared to cytosolic GR genes, which was highly consistent with the those of T. aestivum, Ae. tauschii, B. distachyon, S. bicolor, B. juncea, B. rapa, etc. [AsGR genes were more than those in most of the other species, which might be due to exon gain, insertion, and exonization during plant evolution [GR genes might be associated with its expression level [GR genes in oat play an important role in its subcellular expression and transcriptional regulation to cope with adverse environment. A total of 15 motifs were discovered in 7 AsGR proteins, which was consistent with bread wheat and B. distachyon [Brassica rapa, and Brassica napus [Arabidopsis (27.9%). Additionally, motif 12 and motif 13 are specific to AsGR2 and AsGR1, respectively. Motif 14 is at the N- and C- terminals in AsGR1 and AsGR2, respectively. This divergence might be associated with their biochemical activity and functional diversity, as cytosolic, chloroplastic, and mitochondrial GR isoforms showed great difference in catalytic activity [To understand the structure consistence and divergence of GR proteins, the gene structures and functional motifs of AsGR members were analyzed. The exon-intron patterns of pa, etc. ,24. Howevolution . Previouon level . Intronson level . Moreoveca napus ,17. In mca napus . The seractivity ,42.AsGR1-A and AsGR1-C were highly expressed in non-imbibed seeds, roots, and leaves, and might have a pivotal role in oxidative stress in these tissues. Similarly, the cytosolically localized BcGR1.1 in Chinese cabbage was highly expressed in the roots, and BcGR1.1-overexpressing Arabidopsis showed less damage to the root system under copper stress [TaGR2-A, TaGR2-B1, TaGR2-B2 and TaGR2-D), rice (OsGR2) and Arabidopsis (AtGR1) were also highly expressed in root [AsGR2 isoforms were expressed strongly in leaves, lemmas, and dry seeds. Chloroplastic GRs has been confirmed to be highly expressed in leaves and have the function of maintaining leaf function and coping with senescence [TaGR1-B and TaGR1-A were found upregulated at this stage, which would be conducive to alleviating the excessive accumulation of ROS in seeds. High expression levels of AsGR2 genes were detected in dry A. sativa seeds, which might be beneficial for maintaining seed longevity.Expression profiling is important for understanding gene functions and screening of candidate genes. Genes specifically expressed in different tissues often have specific functions as well. r stress . The cyt in root ,24. On tnescence . As lemmnescence . In plannescence . TaGR1-BGR genes in coping with stress has been widely reported. The deletion and low expression of GR genes are often associated with poor stress resistance, while overexpression of GR improves the capacity to withstand oxidative damage caused by abiotic stresses [AsGR genes were significantly induced during seed germination under drought stress, especially in the early stage of imbibition, and AsGR2 genes were more remarkable. This may be considerable for the special biological process of seed germination, since highly reduced GSH pools are needed in the early stage of seed imbibition, so as to alleviate the accumulation of ROS under strong respiration and display the reduction modification of proteins required for germination [AsGR genes showed diverse responses to salt treatment during germination. The functions of GR genes under salt stress have been well characterized in wheat, tomato, rice, Stipa purpurea, and other species [A. sativa, wheat, and rice, while cytoplasmic GR isoforms are highly expressed in roots, which may have a significant protective effect on radicle elongation [2O2 treatment, AsGR1-A, AsGR1-D, AsGR2-A, and AsGR2-D expressions were strongly induced during seed imbibition, indicating that they have more significant effects on protection against oxidative damage, and stress specific induction may exist for AsGR genes. Seed ageing is an inevitable and complex process, and its mechanisms have not been well revealed so far. The deletion of chloroplast specific GR2 leads to the embryo lethality, while lack of mitochondrial GR2 leads to a partially oxidized glutathione pool and decreased seed ageing resistance of Arabidopsis [GR1 in aged A. sativa seed embryos (aged from 24 d to 42 d) decreased by 50%–70% compared with the control after imbibition for 24 h and that GR2 levels decreased significantly in minorly and moderately aged seeds, but were raised significantly in severely aged seeds [AsGR2-C2 and AsGR2-D decreased significantly in dry seeds after ageing treatment, but they increased rapidly in the early stage of imbibition. Ageing may cause damage to the mitochondrial structural and functional systems [AsGR2-A, AsGR2-C1, and AsGR2-D was highly expressed in the early stage of seed imbibition, which could alleviate the damaged redox homeostasis in mitochondria and ensure the energy supply for seed germination. It is suggested that AsGR2 might be more active on the maintenance of redox homeostasis during germination, especially under PEG, ageing, and H2O2 stress.The positive role of stresses ,20,23,45mination ,14. AsGR species ,36,37,46ongation ,48. Undebidopsis . In addied seeds . In pres systems ,30,49. ICis-regulatory elements in the promoters control development and physiology by regulating gene expression. Hormone and stress responsive elements are the main functional factors detected in the promoters of AsGR genes. This study analyzed the expression of AsGR genes under stress during seed germination, but the response of AsGR genes to hormone should also be concerned. The crosstalk of hormonal and redox signals has been demonstrated to play a pivotal role in the regulation of plant resistance, leaf senescence, seed germination, seed development, and seed longevity [cis-elements, and only a small set of responsive genes can be regulated by a single cis-element [cis-elements seem to be species-specific and subcellular localization-specific. For instance, ABA-responsive elements were not found in the promoter of pea GR genes [AsGR2 genes and not in the promoter of AsGR1 genes in A. sativa. ABA is a crucial factor of seed vigor, and it has been found to play a regulatory role in mitochondrial ROS production and energy metabolism in recent years [AsGR2 genes and hormone signaling should be focused on in the future.ongevity ,51,52. P-element . GA, ABAGR genes , but onlnt years ,56. It iA. sativa and its possible ancestors A. longiglumis and A. insularis were downloaded from the GrainGenes database . The rice GR proteins  and Arabidopsis GR proteins (AT3G24170 and AT3G54660) were used as query sequences to obtain possible GR proteins in three genomes by BlastP search with a cutoff e-value of 1 × e−5. Further, the predicted GR protein sequences were reconfirmed by Simple Modular Architecture Research Tool , Pfam , and NCBI conserved domain database  [AsGR genes were named according to wheat GR genes, which referenced the international rule for wheat gene symbolization  and presented the chromosomal information of GR members.The genome information of ry 2022) . ChromosA. sativa GR proteins were analyzed by using the online program ExPaSy-ProtParam , including amino acids (AA) number, molecular weight (MW), theoretical isoelectric points (pI), etc. Protein subcellular localization prediction was performed using the online tools WoLF PSORT , CELLO v.2.5 , and Cell-PLoc 2.0 .The physical and chemical properties of Arabidopsis, rice and bread wheat by ClustalX2 software with the default parameters [The identified GR proteins were used to conduct multiple sequence alignment with GR members derived from rameters . Phylogerameters . The boohttp://meme-suite.org/, accessed on 19 February 2022). TBtools ver 1.0986 were used to edit the motif pattern and gene structure diagrams [GR protein sequences were used to predict the occurrence of functional motifs in the MEME website , was performed using ClustalX2 software and visualized using GeneDoc software [http://www.sbg.bio.ic.ac.uk/phyre2/html/, accessed on 2 April 2022) [Sinorhizobium meliloti 1021 (PDB: 4DNA) chain A was used as a template.Multiple sequence alignment of AsGRs with already known GRs from software . The consoftware ,23. All il 2022) . The topAsGR genes to stress treatments, we retrieved 2 kb of the deduced promoter region from A. sativa genome database to identify the cis-regulatory elements using PlantCARE [In order to determine the responses of lantCARE .A. sativa tissue-specific expression profiling of AsGRs, twelve samples including seeds, roots, leaves, stems, florets and lemmas were used. In detail, seed samples were collected in imbibition phases  and development phases . Young leaves, lemmas and florets were collected at flowering stage, and old leaves were from plants at 30 DAF. The roots were obtained from 10-day-old plants. To understand the expression profiling of AsGRs during seed germination under salt, drought and heavy metal stresses, seeds were subjected to 150 mM NaCl, 20% PEG 6000 and 200 mg L−1 CuSO4·5H2O solution for imbibition, respectively [2O2, since the germination percentage decreased from almost 100% to 45% after treatment. For control, cold treatment and ageing treatment, seeds were imbibed in distilled water. Seed ageing treatment were according to Xia et al. [Oat  seeds with germination percentage of 100% were used in the present study. For ectively ,64,65,66a et al. , and seea et al. . Cold trEasyScript® All-in-One First-Strand cDNA Synthesis SuperMix for qPCR Kit . qRT-PCR analysis was performed on a CFX96 Real-Time System  using 2×RealStar Green Fast Mixture  with AsEIF4A gene as reference gene [AsGR genes [AsGR genes during seed imbibition under stress were visualized using TBtools ver 1.0986. The significant expression change in comparison to normal conditions was calculated using Student’s t test. The details of the primers used in qRT-PCR assay are listed in Total RNA was extracted using Quick RNA isolation Kit . First-strand cDNA was synthesized from 1 μg RNA using nce gene . The theGR genes . The comAsGR genes were identified and mapped on six chromosomes of A, C and D subgenomes. Phylogenetic analysis and subcellular localization of AsGR proteins suggested they were divided in two sub-families, AsGR1 and AsGR2, which showed predicted subcellular location of cytoplasm and chloroplast/mitochondrion, respectively. Structure analysis suggested AsGR genes were highly conserved and that AsGR1 contained higher number of exon/introns. Both AsGR1 and AsGR2 genes have relatively high expression in leaves and dry seeds, suggesting they may play a role in maintaining leaf function and seed vigor. Their responses to drought, salt, cold, copper, H2O2, and ageing treatments were quite different during seed imbibition. Among the seven AsGR genes, AsGR1-A, AsGR1-C, AsGR2-A, and AsGR2-D responded more significantly, especially under drought, ageing, and H2O2 stress. It suggested that they might be more active on the maintenance of redox homeostasis during seed imbibition under unfavorable conditions. The present study will lay the foundation for future studies on verifications of the precise regulation of GR genes in A. sativa. Finally, stress-responsive AsGR genes can be used for genetic improvement of stress resistance and seed vigor in oat.In summary, seven


K or A valley-Hall-like photonic insulator based on  Photonic topological insulators support some of the most fascinating properties for signal transport and show excellent potential applications in modern optical devices ,4,5,6,7,The key to topological phase transition lies in opening an energy gap in the band structure at certain degenerate points by breaking either the time-reversal symmetry (TRS) or inversion symmetry, which can be realized in classical wave systems mimicking the quantum Hall effect (QHE) ,12,13,14lar rods ,24,34, slar rods , the newlar rods ,37,38.So far, varieties of topological insulators have been demonstrated. However, most of the current studies focus on the robust wave propagation along the topological interfaces ,39,40,41K or In this work, a new kind of valley-Hall-like photonic insulator based on two-dimensional (2D) a, z axis) for The 2D unit cell of K point can be approximately regarded as a two-band model, and its effective Hamiltonian has the following form through the K point, In SRPI, the Dirac cone near the n method ,49,50:(1iltonian :(2)Ω(δk)K valley:The valley Chern number can be obtained by the numerical integration of Berry curvature around the m (Equation  shows thK (or K (or K point (denoted as p and q) for p and q states flips, indicating a typical band inversion and topological phase transition. According to Equation  point . For oure phases .K point for V (V)) exist. In regions m (V (V), opposite signs of Dirac mass m  interface is demonstrated in K will be matched with the equifrequency curve of Si to determine the propagation direction of the radiated beam. The simulated distribution of fields at frequency K or k of background Si can be determined by f represents the incident frequency, c is light speed in air, and The K or A valley-Hall-like photonic insulator based on


Coronavirus disease 2019 (COVID-19) is an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Infected individuals display a wide spectrum of disease severity, as defined by the World Health Organization (WHO). One of the main factors underlying this heterogeneity is the host immune response, with severe COVID-19 often associated with a hyperinflammatory state.Our current study aimed to pinpoint the specific genes and pathways underlying differences in the disease spectrum and outcomes observed, through in-depth analyses of whole blood transcriptomics in a large cohort of COVID-19 participants.All WHO severity levels were well represented and mild and severe disease displaying distinct gene expression profiles. WHO severity levels 1-4 were grouped as mild disease, and signatures from these participants were different from those with WHO severity levels 6-9 classified as severe disease. Severity level 5 (moderate cases) presented a unique transitional gene signature between severity levels 2-4 (mild/moderate) and 6-9 (severe) and hence might represent the turning point for better or worse disease outcome. Gene expression changes are very distinct when comparing mild/moderate or severe cases to healthy controls. In particular, we demonstrated the hallmark down-regulation of adaptive immune response pathways and activation of neutrophil pathways in severe compared to mild/moderate cases, as well as activation of blood coagulation pathways.Our data revealed discrete gene signatures associated with mild, moderate, and severe COVID-19 identifying valuable candidates for future biomarker discovery. Infected patients have various disease manifestations and trajectories, which poses a challenge to patient management and resource planning. Initial findings from 44,672 confirmed COVID-19 cases published by the Chinese Center for Disease Control and Prevention in early 2020, identified 3 groups of patients: 1) 81% patients had only mild symptoms  with no or mild pneumonia; 2) 14% patients had severe symptoms with dyspnea and hypoxia (blood oxygen saturation ≤ 93%); 3) 5% patients had critical symptoms with respiratory failure, septic shock, and/or multiple organ dysfunction  are downregulated in the critical patients , neutrophil elastase (ELANE), olfactomedin 4 (OLFM4), myeloperoxidase (MPO), and alarmins   and tumor necrosis factor alpha (TNF)), C-reactive protein (CRP), and higher neutrophil/lymphocyte ratio –5. Furthpatients . Neutroppatients , 12. Tra and 12) . Lymphoprcentage , 16, 17. T cells , which c T cells ; 2) over T cells .These studies provide important evidence about the association of host immune responses with disease severity and outcome. However, how each severity level within the category differs from the others at transcriptomic level is not well studied. Here, we analyzed the whole blood transcriptomics by RNA sequencing (RNASeq), from COVID-19 patients with disease severity levels ranging from 0-9. In-depth DEG, deconvolution, correlation and WGCNA analyses of the RNASeq data were performed to compare between individual severity level and between severity categories. Our results corroborate findings from previous studies as above. Most importantly, we discovered a transitional level between severity levels 2-4 (mild/moderate) and 6-9 (severe). Further dissection of this transitional group might unravel the mechanism underlying disease progression of COVID-19 from moderate to severe.In this study, participants were recruited from multiple centers from Sydney, Melbourne, and Perth in Australia and a single center in Czech Republic between February 2020 and February 2021. Eligibility criteria included (1) age equal or greater than 18 years, (2) World Health Organization definition of influenza-like illness , and (3) Participants with SARS-CoV-2 infection confirmed by virological testing- respiratory samples  collected from participants and tested for SARS-CoV-2 virus. All eligible participants were assessed by an admitting physician for likelihood of infection. Participants with a high likelihood of infection, based on history and clinical features, were also enrolled into the study. Seventy-one healthy volunteer’s samples included in this study were all collected prior 2018. Study data were collected and managed using REDCap electronic data capture tools  hosted aTwo and half millilitres of blood was collected into PAXgene Blood RNA tubes (Qiagen) from participants according to the manufacturer’s supplied protocol, resulting in a total of 203 samples (multiple samples were taken from some participants). Collected samples were invert 8–10 times gently, immediately after blood collection, kept for ~2h at room temperature, followed by incubation at -20°C for 24h. Thereafter tubes were transferred to -80°C prior to processing. Total RNA was isolated from whole blood samples stored and stabilized in PAXgene RNA tubes according to the manufacturer’s guidelines . The quality and quantity of extracted RNA was evaluated by visualization of 28S and 18S band integrity on a Tapestation 4200 system (Agilent) and stored at -80°C.Libraries were prepared with 300ng of total RNA per sample using the Illumina Stranded Total RNA Prep with Ribo-Zero Plus (RZP) as per manufacturer instructions . Final libraries were cleaned using beads , quantified, and normalised with qPCR using NEBNext Library Quant Kit for Illumina. All libraries were pooled with 32 samples per lane and sequenced with 150 bp paired-end (PE) reads using an Illumina NovaSeq 6000 with v1.5 chemistry and S4-300 flow cell. A minimum sequencing depth of 48.3 million (M) read pairs were generated from each library. Base calling and FASTQ conversion were complete with NovaSeq Control Software (NCS) v1.7.5, Real Time Analysis (RTA) v3.4.4 and Illumina DRAGEN BCL Convert 07.021.624.3.10.8. FASTQ files were uploaded into Partek Flow software , and primary QC was performed.https://github.com/Sydney-Informatics-Hub/RNASeq-DE (v1.0.0)  . Default(v1.0.0) . An aver(v1.0.0)  was used(v1.0.0)  to obtai(v1.0.0)  with -s (v1.0.0) , using f2 transformed using function rlogTransformation from the DESeq2 package [version 1.16.1 . VENN diagrams were generated with the function vennPlot (http://faculty.ucr.edu/~tgirke/Documents/R_BioCond/My_R_Scripts/overLapper.R). Deconvolution analysis was performed with the package immunedeconv [version 2.0.4 . Entries with no gene symbol were deleted. Then raw counts were normalized and logn 1.16.1 ]. An incn 1.16.1 , 31] wasn 1.16.1 . Pathwayn 1.16.1 ]. For ben 1.16.1 . was useon 2.0.4 ] and theon 2.0.4 . We usedon 2.0.4  includinAnalysis and visualization of expression data was performed using the R software package (version 3.4.0) . For ideDemographic and clinical characteristics of 88 COVID-19 participants are summarized in First, we examined the overall variance in sample transcriptomes by principal component analysis (PCA). The PCA showed an excellent separation between healthy controls and infected participants (We then compared the responses at the different severity levels (levels 2-9) to healthy controls (level 0). Only severity levels 0, 4, 5, 7, 9 had reasonably high numbers of samples to perform a contrast to healthy controls . For each severity level against healthy controls, a large number of differentially expressed genes (DEGs) could be identified  and correlated them to severity levels using a linear regression model. Applying a threshold of abs (> 0.5) for the correlation coefficient and a multiple testing adjusted p-value of < 0.05, 1885 genes were significantly correlated with these severity levels. Next, we performed WGCNA which applies an unsupervised clustering method to group genes into modules of co-regulated genes. This analysis revealed 21 modules, which were grouped into two main branches  and each category is subdivided into 2-3 severity levels based on location and supportive measures, e.g., mild = levels 1-3, moderate = levels 4-5, and severe = 6-9 , 18, yetPKMYT1, HIST1H2B0, FOXM1, HJURP are the top four genes among those that positively correlated with severity levels. Our data showed that for the participants with either progressive or regressive severity over time, expression profile of these four genes fit well with the changes of disease severity levels. However, in the two participants with unchanged severity at WHO 5, expression profile of these four genes presented a rather concerning disease progression which however had not been reflected in the clinical manifestation. Though we cannot not draw any general conclusion based on just ten participants, our data did demonstrate the need of future prospective studies to validate these genes in predicting disease progression. A particular focus on the host response in the moderate group (WHO 5) could lead to potential therapeutic targets forcing a regressive rather than a progressive course.Here, we analyzed the transcriptomic signature associated with severity levels 0, 2-7, and 9 and observed 4 distinct groups based on similarity in their transcriptomes: 1) level 0; 2) levels 2-4; 3) level 5; 4) levels 6/7/9. Overall, our grouping based on transcriptomics had a very good match with WHO categories for the uninfected (level 0) and the severe  categoriCurrently, host response biomarkers used for predicting COVID-19 severity include proinflammatory cytokines , inflammatory markers , neutrophil/lymphocyte ratio, and lymphopenia . These bvia chromatin remodeling , the major regulators of mammalian cell cycle –43. Our modeling . It has modeling –47. Our modeling –13 and omodeling . It has modeling , 50. OveCD177, MMP8, ELANE, OLFM4, and MPO, which have also been identified in another study  were recruited from a single center in Australia, whereas the COVID-19 participants were recruited from multiple sites across Australia and Czech Republic. However, our PCA did not indicate any site-specific differences. Furthermore, multiple samples were collected from some participants whereas for most participants, only a single sample was collected. These differences in collection sites and number of data points per patient could potentially create confounding bias in our findings. The fact that our data reproduces many findings from previous studies gives us confidence to assume that there is no strong confounder related to either the collection sites or number of data points per patient. The second limitation of the current study is that we could not perform correlation analyses between the transcriptome of each WHO severity level with clinical inflammatory biomarkers  or clinical severity scores . This was due to limited availability of the clinical data. Future prospective studies would be beneficial to assess the correlation between the clinical inflammatory biomarkers or clinical severity scores with the transcriptomic markers as identified in this study. The third limitation is that we were not able to keep the sampling time constant due to the different availability of the staff and the participants, which might have some unforeseen impact on the number and function of the blood cells. The fourth limitation is that few participants of our study cohort were undergoing immunosuppressive therapy at the time of sample collection, however most of our participants were not. Due to our study design, we were not able to assess the impact of the immunosuppressive agents on the blood transcriptomes. A future prospective study would be necessary to investigate this impact. Finally, we do not have molecular identification of the SARS-CoV2 variants infecting the individual participant. Since most samples were collected before 2021, we assume that most were derived from the pre-Delta period.Overall, our data demonstrated the presence of a transitional level between the mild/moderate and the severe groups. Genes and pathways associated with disease severity, which could potentially be used as prognostic biomarkers or therapeutic targets, were identified.https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE217948). Details can be found in Raw FASTQ data discussed in this publication have been deposited in NCBI's Sequence Read Archive under BioProject accession PRJNA901461. Count data were deposited to NCBI’s Gene Expression Omnibus  and are The studies involving human participants were reviewed and approved by Nepean Blue Mountain Local Health District Research Ethics Committee; Western Sydney Local Health District Human Research Ethics Committee. The patients/participants provided their written informed consent to participate in this study.Study concept and design: BT and MS, ethics/governance application: BT, MS, AM, YW, and ST, recruitment of participants, sample collection/processing: MS, ST, KK, TP, TK, and BK, clinical data collection and REDCap database: BT, AM, TP, ST, TK, BK, and AP, RNASeq data pre-processing and QC: TC; data analyses: KS, data interpretation and discussion: KS, YW, TC, MS, BT, AM, KS, JI, IT, JA, SM, and JB, manuscript writing and generating figures: KS and YW, manuscript revision: KS, YW, TC, and MS, funding acquisition and project supervision: BT, MS, KS, and AM. All authors contributed to the article and approved the submitted version. PREDICT-19 Consortium contributed to many aspects of this study including study concept and design, applications of material transfer agreements, recruitment of participants, sample collections, clinical data collection, setup of REDCap database, data interpretation and discussions. Members of PREDICT-19 consortium are listed below in alphabetic order according to their first names:Alberto Ballestrero, Allan Cripps, Amanda Cox, Amy L Phu, Andrea De Maria, Anthony McLean, Arutha Kulasinghe, Ben Marais, Benjamin Tang, Carl Feng, Damien Chaussabel, Darawan Rinchai, Davide Bedognetti, Gabriele Zoppoli, Gunawan Gunawan, Irani Thevarajan, Jennifer Audsley, John-Sebastian Eden, Jonathan Iredell, Karan Kim, Kirsty Renfree Short, Klaus Schughart, Mandira Chakraborty, Marcela Kralovcova, Marek Nalos, Marko Radic, Martin Matejovic, Maryam Shojaei, Meagan Carney, Michele Bedognetti, Miroslav Prucha, Mohammed Toufiq, Nandan Deshpande, Narasaraju Teluguakula, Nicholas West, Paolo Cremonesi, Philip Britton, Ricardo Garcia Branco, Rodolphe Thiebaut, Rostyslav Bilyy, Sally Teoh, Stephen MacDonald, Tania Sorrell, Thomas Karvunidis, Tiana Maria Pelaia, Tim Kwan, Tracy Chew, Tri Giang Phan, Velma Herwanto, Win Sen Kuan, Ya Wang, and Yoann Zerbib.


AP2, MYB, WRKY, PsbP, bZIP, and NAM, heat shock proteins , as well as genes related to photosynthesis (PsaD & PsaN), antioxidation (APX and CAT) and polyamines (Spd and Spm). KEGG pathways analyses showed that the metabolic overview pathway and secondary metabolites biosynthesis pathway, with the involvement of 264 and 146 genes, respectively, were highly enriched in response to heat stress. Notably, the expression changes of the most common HS-responsive genes were typically much more significant in CML 25, which might explain why CML 25 is more heat tolerant. Seven DEGs were common in leaf, pollen, and ovule; and involved in the polyamines biosynthesis pathway. Their exact role in maize heat stress response would warrant further studies. These results enhanced our understanding to heat stress responses in maize. Heat stress (HS) is one of the alarming issues today due to global warming and is the foremost detrimental to crop production. Maize is one of the versatile crops grown over different agro-climatic conditions. However, it is significantly sensitive to heat stress, especially during the reproductive phase. The heat stress tolerance mechanism is yet to be elucidated at the reproductive stage. Thus, the present study focused on identifying transcriptional changes in two inbreds, LM 11 (sensitive to HS) and CML 25 (tolerant to HS), under intense heat stress at 42°C during the reproductive stage from three tissues viz. flag leaf, tassel, and ovule. Samples from each inbred were collected after 5 days of pollinations for RNA isolation. Six cDNA libraries were constructed from three separate tissues of LM 11 and CML 25 and sequenced using an Illumina HiSeq2500 platform. A total of 2,164 (1127 up-regulated and 1037 down-regulated) differentially expressed genes (DEGs) were identified with 1151, 451, and 562 DEGs in comparisons of LM 11 and CML 25, corresponding to a leaf, pollen, and ovule, respectively. Functional annotated DEGs associated with transcription factors (TFs)  Diverse environmental challenges pose a severe threat and renewed concern to the world’s food security for the burgeoning human population . ClimateTo gain a comprehensive understanding of the molecular mechanisms involved in heat stress (HS) tolerance, next-generation sequencing (NGS) approaches like transcriptomics or RNA-sequencing (RNA-seq) is a powerful tool for whole-genome gene expression profiling and is especially useful for studying complex gene regulatory networks . TranscrZea mays L.), the ‘Queen of Cereals’, is the third most important cereal crop globally , heat shock proteins (HSPs) response pathways, response to reactive oxygen species (ROS), increasing production of antioxidant and osmoprotectants, and network of hormones participate in plant heat tolerance . The recPresently, little is known about the molecular mechanism of heat resilience in maize . The traviz. LM 11, heat stress susceptible (HS), and CML 25, heat stress-tolerant (HT). The seedlings of two inbreds were raised during the second week of March in glasshouse conditions at 28°C/23°C and 16 h light (Day)/8 h dark (Night) photoperiod at the School of Agricultural Biotechnology, Punjab Agricultural University, India, during spring 2016. The plants were grown till they reached the reproductive stage  for each inbred to reduce biological sampling error. A total of six samples (3 tissues x 2 inbreds) were immediately frozen in liquid nitrogen and stored at -80°C until processed for RNA isolations.The experimental material comprised two parental maize inbred lines ve stage Figure 1ve stage . Three dTotal RNA was isolated from each tissue sample  of LM 11 and CML 25 under heat stress conditions using the NucleoSpin RNA Plant kit  according to the manufacturer’s protocol. The RNA integrity number (RIN) and concentration were checked using an Agilent 2100 Bioanalyzer . The RNA (1µg) having RIN values >6 was used for further analysis. 5 μg of total RNA was used for mRNA enrichment, and complementary DNA (cDNA) library was constructed using the manufacturer’s protocol of Illumina HiSeq 2500 RNA library preparation kit . The quality and quantity of cDNA libraries were checked using the Qubit 2.0 Fluorometer (Thermo Scientific) and Agilent 2100 Bioanalyzer . The libraries were then sequenced using HiSeq Illumina 2500 sequencing platform  outsourced from the Nucleome Informatics Pvt. Ltd, Hyderabad, India. High throughput sequencing of transcriptome libraries generated an aggregate of 2.93 billion raw reads accounting for 45.19 Gb of data.via Trimmomatic 0.36 (The raw reads were processed with FastQC  to checktic 0.36 . The tritic 0.36 . The trahttp://bioinfogp.cnb.csic.es/tools/venny/)  . VolcanoDEGs and consensus sequences of isoforms were mapped to GO classifications using Blast2GO . Gene Onhttps://www.genscript.com/tools/real-time-pcr-tagman-primer-design-tool).An aliquot of total RNA isolated from heat-stressed leaf samples of LM 11 (HS) and CML 25 (HT) was used for cDNA synthesis by PrimeScript™ first strand cDNA synthesis kit  as per the manufacturer’s instructions. To validate the reliability of gene expression obtained by RNA seq, a set of six DEGs in the leaf were randomly selected for qRT-PCR. Gene-specific primers were designed using GenScript Real-time PCR (TaqMan) Primer Design tool  from maize was used as an endogenous  control for normalization in all the qRT-PCR analyses. Three biological replicates were performed for each sample, and data were indicated as mean ± SE (n = 3). Cycle threshold (CT) difference between the reference 18S gene and the target gene product was used to calculate the relative expression levels of the genes using the 2-ΔΔct method  and run-on Light Cycler 96 Real-Time PCR system . Each reaction contains 5 μl of SYBR Green Master, 0.8 μl of template cDNA, 0.4 μl of each of the primers (10 μM), and 3.4 μl of nuclease-free water with a total volume of 10 μl. The qRT-PCR profile was as follows: 2 minutes at 95°C followed by 40 cycles of 10 seconds at 95°C, 30 seconds at 60°C with fluorescent amplification signal detection, and 30 seconds at 72°C. The melting curve was obtained by PCR following the last cycle: 15 seconds at 95°C followed by constant heating between 65°C for 15 seconds and 95°C for 2 seconds. The reference gene t method .A total of 141.03 and 152. 86 million raw reads and 125.86 and 135.61 clean reads were obtained from the cDNA sample of LM 11 and CML 25, respectively. LM 11 leaf, pollen, and ovule had 56.88, 38.71, and 45.43 million raw reads, while 52.92, 44.85, and 55.09 million raw reads from leaf, pollen, and ovule, respectively, were obtained from CML 25. The reads with adapter contamination and low base quality (≤ Q20) were removed, and high-quality (HQ) clean reads were retained. The maximum number of raw and clean base reads were obtained in the LM 11 leaf, followed by the CML 25 ovule, CML 25 Leaf, LM 11 ovule, and other samples Table 1.A total of 2,164 transcripts were differentially expressed between LM 11 and CML 25. 1151, 451, and 562 DEGs were specific in LM 11 versus CML 25 leaf, pollen, and ovule, respectively. A total of 1,095 (52.5%), 419 (20.1%), and 503 (24.1%) DEGs were uniquely expressed in LM 11 leaf versus CML 25 leaf, LM 11 pollen versus CML 25 pollen and LM 11 ovule versus CML 25 ovule, respectively  and q-value (0.001)  and “cellular process” (GO:0009987) were commonly highly enriched components. The top two enriched terms under CC were “cell” (GO:0005623) and “cell part” (GO:0044464). Catalytic (GO:0003824) and binding activity (GO:0005488) were highly heightened under the MF category. Collectively, molecular function regulator (GO:0098772), signal transducer activity (GO:0004871), and metabolic process (GO:0008152) were highly enriched in leaf, pollen, and ovule. Exclusively, the metabolic process (GO:0008152) was prevalent in leaf, pollen, and ovule , whereas, Asp, SapB_2, bZIP_1, and WRKY were down-regulated (fold changes varying from -3.31 to -5.44)  were up-regulated in the leaf, and among all, one transcript was highly expressed with fold change of 5.22 (up-regulated) followed by WRKY (4.74) , abscisic acid (ABA), oxidative stress, photosynthesis (photosystem I and chloroplast thylakoid membrane), and cytochrome-c peroxidase activity  belongs to lipoxygenase involved in the seed germination processes. Also, corresponding four DEGs A total of fourteen, seven, and four DEGs related to HSPs were identified in leaf, pollen, and ovule, respectively. Six DEGs were highly expressed in the leaf, of which only one was up-regulated (HSP20), and five were down-regulated. In pollen, three HSP genes were up-regulated, two encoding for HSP20 and the other encoding 101 kDa heat shock protein (Hsp101/ClpB). One down-regulated DEG in pollen related to Rieske Iron-Sulfur Protein was associated with HSP70 , regulation of jasmonic acid (JA) mediated signaling pathway and response to auxin. In addition, several other up-regulated genes belong to DNA binding, transcription factor activity, polyprenol biosynthetic process, terpene synthase activity, and response to water deprivation  due to an increase in global mean temperature as a result of climate change can remarkably suppress plant growth and development. It has been reported as one of the most critical causes of yield reduction and dry matter production in many crops, including maize . It is n per ear . Thus, t per ear .In the present study, comparative transcriptomics was performed for leaf, pollen, and an ovule of heat-tolerant inbred CML 25, and susceptible inbred LM 11 was analyzed under prolonged heat stress at 42°C. A total of 1127 up-regulated and 1037 down-regulated DEGs were identified in response to heat stress. The higher number of up-regulated genes indicates the cumulative activation of defense responses upon heat stress. Similarly, in heat transcriptomic studies conducted in wheat, rice, and maize, the number of induced genes was 3-6 times more than the number of repressed genes . In the In our study, the most enriched GO are biological processes comprising metabolic processes, cellular processes, and regulation. Among the molecular function category, catalytic activity and binding were mainly dominant. Also, Transcription factors play fundamental roles in biotic and abiotic stresses and are considered frontline defenders. Other studies suggested that most of the identified DEGs under heat stress in crop plants encode members of the ERF, MYB, bZIP, bHLH, WRKY, NAC, and MYB-related TF families   is dismuted by superoxide dismutase (SOD) into H2O2 and further scavenged by catalase (CAT) and peroxidases (such as POD) through converting into H2O (Heat stress prompts the formation of reactive nitrogen species (RNS like NO) and reactive oxygen species (ROS), such as OH−, Hinto H2O . Variousinto H2O . In the Saccharum officinarum) were confirmed by Heat stress affects plant photosynthesis negatively by inactivating photosystems, PSII, and PSI . PhotochArabidopsis, rice, chickpea, and grape  generated during heat stress could enhance the ABA-dependent expression of HSP70 and sHSPs to tolerate heat stress  is a key enzyme controlling the rate of polyamines formation that plays a pivotal role in plant growth, development, and adaptation to abiotic stresses . In the Brassica napus stated that heat treatment specifically affects the pathways, including ribosome, biosynthesis of amino acids, starch and sucrose metabolism, and protein processing in the endoplasmic reticulum and carbon metabolism  gave insights into tissue-specific stress-related genes involved in biological pathways like metabolic processes, secondary metabolite synthesis, starch, and sucrose metabolism, carbon metabolism, protein synthesis, etc. Most DEGs were transcription factors, heat shock proteins, antioxidants, hormone biosynthesis, and polyamine biosynthesis-related genes. Seven DEGs were common in leaf, pollen, and ovule; and involved in the polyamines biosynthesis pathway that could be further explored in understanding heat tolerance mechanism. The up-regulated genes identified in heat-tolerant inbred CML 25 would be the potential candidate genes that could be utilized for the development of heat-resilient maize using marker-assisted backcross breeding.The original contributions presented in the study are publicly available. This data can be found here: NCBI, PRJNA656908.Conceptualization: YV and GJ; Project administration and resources: YV; Methodology: AJ, YV, and IY; Bioinformatics analysis: AJ and IY; Methodology: IY and AJ; Validation: NK and AS; Original draft: AJ, UP, and YV; Finalized the manuscript: AJ, YV, IY, and GJ. All authors contributed to the article and approved the submitted version.


P<0.001) and the yellow module  was found to be critical for follicle growth and development. A total of 2066 genes comprising the black and yellow modules was used for functional enrichment. The results reveal that these genes are mainly enriched in Cell cycle, PI3K-Akt signaling pathway, Oocyte meiosis, Apoptosis, and other important signaling pathways. We also identified seven hub genes  that may play a role in follicle development. Our study may provide several important new markers allowing in depth exploration of the genetic basis for multiparous reproduction in sheep.Sheep exhibit a distinct estrous cycle that includes four different phases: proestrus, estrus, late estrus, and luteal phase. As the estrous cycle repeats, follicular development regularly alternates. We thus investigated ovarian transcriptome data from each of the four phases using weighted gene co-expression network analysis (WGCNA) to identify modules, pathways, and genes essential to follicle growth and development. We clustered mRNA and long non-coding RNA (lncRNA) into different modules by WGCNA, and calculated correlation coefficients between genes and Stages of the estrous cycle. Co-expression of the black module (cor = 0.81,  FecB) gene associated with sheep reproduction lead to the inactivation of the BMPR1B protein and impair BMP signaling, thereby regulating ovarian granulosa cell differentiation, increasing ovulation rate and litter size (KLF5) (MYH15) (GDF9) (BMP15) genes  , myosin  (MYH15) , growth ) (GDF9) , and bon5) genes .Long non-coding RNAs (LncRNAs) regulate gene expression through multiple mechanisms and are involved in follicle maturation. For example, LncRNA affects promoter activity and regulates ovarian granulosa cell proliferation and follicle maturation , 13. LncVAV1, RUNX3, ZC3H12D, MYCL, IRF5, WEE2, and miR-3940-5P play critical roles in oocyte development and ovarian granulosa cell proliferation  has been applied by researchers in fields including medicine, microbiology, and zoology. The WGCNA algorithm uses the inter-molecular relationship to simplify complex data into different classes of modules , 18. Molferation –22. In roduction , 24.FecB, GDF9, and BMP15, are not significantly differentially expressed in Cele black sheep. Therefore, it is speculated that other genes act as potential regulators to alter the expression patterns of reproduction-related genes, thereby affecting the multiparity of Cele black sheep .Twelve Cele black ewes of similar age, between 3 and 4 years, were selected based on breeding records, age, and body size. The 12 ewes were synchronized to estrus by injecting human synthetic progesterone, and the ewes were tested daily for artificial and ram estrus. Then, after the third estrus, the two ovaries of the ewes were collected in groups: 7 days after the end of estrus, 14 days after the end of estrus, day 1 of estrus, and days 2–3 of estrus. The estrous cycle was divided into the luteal stage, pre-estrus, estrus, and late estrus, and three replicate samples were selected from each group and named sequentially . Tubes containing test samples were immediately frozen in liquid nitrogen and finally stored at −80°C for subsequent experiments.Total tissue RNA was extracted using TRIzol reagent  according to the manufacturer's manual, and the extracted RNA was tested for quality and integrity . Next, rRNA was removed using the Epicenter-Ribo-zero rRNA Removal Kit , and rRNA-free residues were removed by ethanol precipitation. Then, sequencing libraries were generated using NEBNext Ultra Directed RNA Library Preparation Kit for Illumina . The library was sequenced at Novogene  using the Illumina Hiseq 4000 base platform, and 150 bp paired-end reads were generated.http://ftp.ensembl.org/pub/release-103/fasta/ovis_aries1) comparison analysis. StringTie  assembled mapped reads for each sample.Poor-quality bases and adaptor sequences were removed from the raw data using FastQC software (v0.11.9) to obtain clean reads. The reference genome index was constructed using bowtie2 (v2.2.8), and paired-end clean reads were aligned to the reference genome using HISAT2 (v2.2.1), followed by the reference sheep genome  (The screening process involved the following steps:  Filter o< 0.001)  softwareP-adjust < 0.05 were assigned as differentially expressed.The Ballgown suite includes functions for interactive exploration of the transcriptome assembly, visualization of transcript structures and feature-specific abundances for each locus, and post-hoc annotation of assembled features to annotated features. Transcripts with an https://horvath.genetics.ucla.edu/html/CoexpressionNetwork/Rpackages/WGCNA/). First, the genes with the highest average FPKM values were selected from the analyzed samples after using the Pearson correlation matrix and the average linkage method, using the power function A_mn=|C_mn|∧β . β is a soft threshold parameter that can emphasize strong correlations between genes and penalize weak correlations. After selecting the power of the βvalue, the adjacency was transformed into a topological overlap matrix (TOM), which can measure the network connectivity of a gene, defined as the sum of its adjacencies with all other genes, which is used for network generation, and the corresponding dissimilarity (1-TOM) is calculated.We constructed a co-expression network using the WGCNA algorithm under the R package and RStudio (v 4.1.0) environment , we selected a cutting line for the module dendrogram, merging a few modules. We performed a correlation analysis between each module and each stage of the estrous cycle to unearth the relevant modules highly related to follicle development. Subsequently, intra-module analysis was performed using gene significance (GS) and module membership (MM). GS signifies the relationship between gene expression levels and the stages of the estrous cycle of sheep, whereas MM represents the association between the gene expression profile of a given module and ME. Modules containing genes with significant correlations between GS and MM were considered significant.http://kobas.cbi.pku.edu.cn/). Gene set enrichment analysis (GSEA) is a method to identify functional pathways. Using R package functions “ClusterProfiler”, “GSEABase”, and RStudio (v4.1.0) for GSEA, genes were ranked briefly based on the absolute value of logFC between each genome of sheep in the QD, QE, QM, and QP groups, and enrichment scores were calculated. Next, we fed the sorted list of genes into the GSEA algorithm to correlate gene expression with functional enrichment. The criteria for statistical significance were at p < 0.05 and FDR < 0.25.LncRNA target genes and differentially expressed mRNAs (DEmRNAs) were subjected to GO and KEGG enrichment analysis using the KOBAS online database (https://string-db.org/) for the construction of the protein-protein interaction (PPI) network. The network graph was visualized and analyzed using the MCODE plugin in Cytoscape (v3.7.1) for highly connected hub proteins throughout the network. Ultimately, overlapping genes among PPI hub proteins, intra-module genes, and differential genes were identified as key candidate genes for regulating follicle development.We searched through the Search Tool for Interacting Genes/Proteins (STRING) database at  database was used for transcription factor prediction. In addition, control options were set by Blast E-value (1.0E-5) and selection of Hummsacn E-value (1.0E-5).AnimalTFDB , QE , QM , QP  12 libraries. After removing redundant and low-quality reads, 1,330,939,226 clean single-ended reads were obtained. The mapping rate ranged from 97.15 to 97.85%, the Q30 ratio was above 93.23%, and the GC content was above 47.17% .Figure 3A, Gene expression levels were quantified using FPKM. Volcano plots were used to illustrate all DEmRNAs and DElncRNAs in the genome between QE and QM, QD and QE, QD and QM, QD and QP, QE and QP, and QM and QP. Based on gene differential expression levels, there were significant differences between QD and QP, QE and QP, and QM and QP, with 913, 921, and 511 detected DEmRNAs, respectively . A totalEighteen, 24, and 16 DElncRNAs were detected in QD-QP, QE-QP, and QM-QP comparisons, respectively . FourteeWe searched coding genes 100 k upstream and downstream of lncRNA and took the intersection with the genes that had significant co-expression with these lncRNAs. As a result, 56 DElncRNAs had to target regulatory relationships with 145 genes . In thisThe top 20 pathways are shown in . As a re2 = 0.86, the best β = 17 was selected in the gene expression matrix to construct an approximately scale-free topological overlap matrix  were positively correlated with the luteal phase, and genes in the yellow module  were positively correlated with estrus (P<0.002) and the genes in the salmon module  were significantly positively correlated with the pre-estrus and luteal phases, respectively |> 1. The intra-module hub genes in each module are listed in BUB1B, MAD2L1, and ASPM genes in the yellow module and the HSD3B1 gene in the black module , stimulates cell proliferation in ovarian granulosa cells, and promotes follicle growth and maturation . Our network analysis indicated  is that DEG mainly analyzes sample-to-sample differences, whereas WGCNA considers not only individual gene functions but also the relationships between genes.We used WGCNA to probe gene association patterns and assess potential interactions between expressed genes. Two modules  were determined to be highly correlated with the estrus cycle. KEGG analysis of both module genes found significant enrichment in the cell cycle, PI3K-Akt signaling pathway, and Oocyte meiosis. During reproduction, regulating cell cycle-related factors contributes to better follicle development and protects female fertility , 31. ActBUB1B, MAD2L1, ASPM, WDHD1, CENPA, MXI1, and HSD3B1). Steroid hormones are essential for follicular development, and HSD3B1 is a steroid hormone metabolism gene involved in progesterone biosynthesis , is localized as a marker of centromeric components (CENPA mediates an important role for MYBL2 in ovarian cancer cell proliferation (MAX interactor 1 (Mxi1), a member of the mitotic arrest defect (MAD) family, can be regulated at the transcriptional level .XZ conducted the experiments. JW and XZ designed the study, analyzed the data, and drafted the manuscript. HC conducted parts of the experiments and collected samples. All authors read and approved the final manuscript.


Capsicum spp.) fruits accumulates large amounts of carotenoids. Although the carotenoid biosynthesis pathway in the Capsicum genus has been widely studied from different perspectives, the transcriptional regulation of genes encoding carotenoid biosynthetic enzymes has not been elucidated in this fruit. We analyzed RNA-Seq transcriptomic data from the fruits of 12 accessions of Capsicum annuum during the growth, development, and ripening processes using the R package named Salsa. We performed coexpression analyses between the standardized expression of genes encoding carotenoid biosynthetic enzymes (target genes (TGs)) and the genes of all expressed transcription factors (TFs). Additionally, we analyzed the promoter region of each biosynthetic gene to identify putative binding sequences for each selected TF candidate. We selected 83 TFs as putative regulators of the carotenogenic structural genes. From them, putative binding sites in the promoters of the carotenoid-biosynthesis-related structural genes were found for only 54 TFs. These results could guide the search for transcription factors involved in the regulation of the carotenogenic pathway in chili pepper fruits and might facilitate the collection of corresponding experimental evidence to corroborate their participation in the regulation of this biosynthetic pathway in Capsicum spp.During the ripening process, the pericarp of chili pepper ( Capsicum spp.) is a globally cultivated horticultural crop. Chili pepper fruits are popular because of their great variety of forms, sizes, flavors, and aromas, especially for their unique and characteristic pungency. Chili pepper pods produce a series of compounds, such as anthocyanins, carotenoids, vitamin C, flavonoids, which are powerful antioxidants, and hot capsaicinoid analogs, among others. Chili pepper represents an important model for studying the carotenogenesis process as well as investigating and establishing the carotenoid biosynthetic pathway  ARABIDOPSIS PSEUDO RESPONSE REGULATOR2-LIKE gene (APRR2-Like), was upregulated at the breaker stage in wild-type tomato fruits and, when overexpressed in transgenic lines, increased the plastid number, area, and pigment content, enhancing the levels of chlorophyll in immature unripe fruits and carotenoids in red ripe fruits. Additionally, a putative ortholog of the tomato APPR2-Like gene in sweet pepper (Capsicum annuum) was associated with pigment accumulation in fruit tissues . Car. CarCaps tissues . Song etr fruits . In a gephytoene desaturase (PDS) gene in Arabidopsis [PSY1 gene, which was enriched following a chromatin immunoprecipitation assay (ChIP); moreover, the PSY1 gene showed an increase in expression at the onset of ripening in wild-type but not in rin mutant fruits [Carica papaya), Fu et al. (2017) indicated that CpEIN3a regulated the carotenoid-biosynthesis-related genes CpPDS4 and CpBCH during fruit ripening, as demonstrated by electrophoretic mobility shift assays (EMSA) and transient expression analyses. Moreover, CpEIN3a physically interacted with the transcription factor CpNAC2, which acted as a transcriptional activator of CpPDS2/4, CpZDS, CpLCYE, and CpBCH by directly binding to their promoters [lycopene β-cyclase genes LCYB1 and LCYB2 were used as bait in a yeast one-hybrid screen cDNA library for promoter-binding proteins from sweet orange (Citrus sinensis) to identify TFs that might regulate carotenoid metabolism, and they isolated the transcription factor gene CsMADS6, which was demonstrated to modulate carotenoid metabolism since its overexpression increased the expression of the PSY, PDS, carotenoid cleavagedioxygenase 1 (CCD1), and LCYB1 genes in transformed overexpressing transgenic citrus calli and tomato (Solanum lycopersicum) plants. Furthermore, CsMADS6 upregulated the expression of the PSY, PDS, and CCD1 genes by directly binding to their promoters [MYB7 gene, whose protein product was found to activate the promoter of the kiwifruit AdLCYb gene [PSY1 than those from nontransformed plants, and the expression of the negative regulator of tomato fruit ripening, SlMADS1, was repressed [Citrus clementina through differential transcriptomic analyses  associated with fruit color changes in two mutants (39B3 and 39E7) differing in delayed color break (carotenoid accumulation and chlorophyll degradation), and they observed that CcGCC1 expression was strongly induced at the onset of color change in the flavedo and that the expression of CcGCC1 was associated with peel ripening [CsMYB1 transcription factor gene from Crocus stigmas (saffron) by comparing the expression patterns during development in C. sativus (from yellow to the dark red stage) and two species with branched stigmas and yellow color (C. cancellatus and C. pulchellus), and they observed that the CsMYB1 transcription factor gene was highly expressed in C. sativus stigmas in the dark red stage [Citrus lanatus) fruits by Illumina RNA sequencing, and RNA-Seq expression profiling yielded a set of transcription factor genes showing a positive correlation with ripening and carotenoid accumulation. Among them, 19 were homologous to tomato carotenoid-related genes ; 5 CNR (COLORLESS NON RIPENING squamosa promoter binding-like protein); 1 NAC-NOR (NAC domain protein); 3 SIAP2a (AP2 transcription factor); 2 SlERF6 (ERF6 mRNA), 1 DET1 (deetiolated1 homolog), 1 DDB1 (UV-damaged DNA binding protein 1); 1 CUL4 (cullin 4); and 1 HB-1 (homeodomain leucine zipper protein)), all homologous to S. lycopersicum. Among these, six  were differentially expressed in the flesh during fruit development and ripening [MADS-box gene and 4 CCT domain transcription factors, showed positive coexpression with carotenoid-biosynthesis-related genes. Other transcription factors, such as the AUX/IAA, bHLH, MYB, SBP-box, transcription factor B3, and zinc fingerprotein genes, also exhibited a high correlation with carotenoid metabolism, suggesting a complex underlying regulatory network [Citrus maxima) ‘GXMY’ and its mutants ‘HRMY’ and ‘HJMY’ in three fruit developmental stages and found 12 differentially expressed genes related to carotenoid biosynthesis; a coexpression analysis revealed that the expression patterns of several transcription factors, such as bHLH, MYB, ERF, NAC, and WRKY, were highly correlated with differentially expressed genes involved in carotenoid biosynthesis [CmMYB305, CmMYB29, CmRAD3, CmbZIP61, CmAGL24, and CmNAC1) in Chrysanthemum X morifolium ), using a weighted gene coexpression correlation network analysis (WGCNA) combined with a correlative analysis to determine whether they played an important role in carotenoid accumulation by regulating structural genes related to the carotenoid metabolism pathway and plastid development [However, in addition to these findings in chili pepper fruits, transcriptional regulation of carotenoid biosynthesis seems to be a very complex process since diverse transcription factors have been proposed as regulators of this pathway in different plant species using different experimental approaches; for example, Welsch et al. 2007) demonstrated that AtRAP2.2, a member of the APETALA2 (AP2)/ethylene-responsive element-binding protein transcription family, showed positive binding to the ATCTA cis-element present in the promoter of the bidopsis . Similart fruits . Accordit fruits . In paparomoters . Accordiromoters . AmpomahCYb gene . Recentlepressed . Anotherripening . Gómez-Get al. 20 indicateripening . Ye et a07 demonselopment .In the present work, we applied a coexpression analysis method developed by our research team  to identCapsicum annuum: 4 wild, 6 domesticated, and 2 reciprocal crosses between a domesticated and a wild-type accession   for coexdatabase . Neverthreported . The expCapsicum annuum, instead of individual SEPs, are presented in PSY1, PDS, and ZDS2 showed transcript accumulation in the flower (0 DAA), although the transcripts of the PSY and PDS genes decreased during the growth of chili pepper fruit, while the transcripts of ZDS2 exhibited a slight increase at 20 DAA and then decreased. At close to 50 DAA, a notable increase in the expression levels was observed for these three genes. In addition, the BCH1 and CCS genes showed little or no accumulation of transcripts in the flower and growing fruit (10–40 DAA), although the expression increased at 50 and 60 DAA. PSY1, BCH1, and CCS reached their highest levels of expression at 60 DAA, whereas the transcript levels of PDS and ZDS2 at 60 DAA were only slightly higher than at 50 DAA , with an abrupt decrease in the accumulation of transcripts at 10 DAA  selected by the researcher, and a minimum number of accessions (n.min.acc) = 12. For all analyzed TGs, the list of TF “winners” and the statistical data of coexpression analyses are compiled in The prediction of transcription factor (TF) gene candidates as possible participants in the regulation of carotenoid-biosynthesis-related genes was carried out by a coexpression analysis using the function g2g.TFcandidates, which is available in the R package sa v 1.0 . HypotheCapsicum reference genome, the prediction of consensus sequences of the promoter region , and downstream promoter element (DPE)), and the putative transcription start site (TSS) are shown in Arabidopsis locus names and S. lycopersicum orthologs are included in As a second criterion for the selection of TF candidates, we searched for putative binding sites for TF “winners” in the regulatory regions of TGs. The localization of TGs in the Capsicum spp. After the coexpression analysis, we selected 83 TFs as possible regulators of the different carotenogenic structural genes , we obtained six good candidates for the transcriptional regulation of the PSY1 gene (PSY gene showed eight putative motif union sites just for PREDICTED NAC domain-containing protein 72-like (NAC72) and one putative binding site for Homeobox-leucine zipper protein ATHB-12-like (HDZIP12), , .PDS, a coexpression analysis using stringent parameters did not identify appropriate TFs for its regulation; therefore, additional stringent parameters were tested . The algorithm displayed four TF candidate genes with similar expression profiles as PDS: NAC domain-containing protein 83 (NAC83), a putative WRKY transcription factor 31 (WRKY31), NAC domain-containing protein 1 (NAC1), and WRKY transcription factor 71 (WRKY71)  . Putativ(WRKY71) ; accordicessions .Z-ISO: min.fdr = 0.1, min.r2 = 0.6, and n.min.acc = 6. The transcriptional profile of three TF genes showed a positive correlation (r.Med < 0.87) with the Z-ISO expression profile in seven accessions of Capsicum annuum (Z-ISO promoter showed 12 and 11 putative binding motifs for WRKY transcription factor 7 (WRKY7) and WRKY71, respectively   with that of ZDS1 were predicted by g2gTF.candidates (ZDS promoter showed binding sites only for a putative WRKY transcription factor 29 (WRKY29) and a transcription factor MYBS3 (MYBS3)  with ZDS2: WRKY transcription factor 22 (WRKY22) and Dof zinc finger protein DOF3.1 (DOF3.1)  a; thus, ndidates . Among t (MYBS3) ; therefo (MYBS3) . By usin(DOF3.1) . For theectively . Both TFm annuum .CRTISO were identified in Salsa (CRTISO1 and CRTISO2)  and the number of putative binding sites in the CRTISO1 promoter are summarized in CRTISO2 using less strict parameters  showed seven candidates with r.Med ≥ 0.85 , auxin response factor 3 (ARF3), transcription factor MYB1R1 (MYB1R1), transcription factor TGA7 (bZIP_TGA7), and a putative WRKY transcription factor 20 isoform X1 (WRKY20)  c,d. Theyd ≥ 0.85 . The CRT(WRKY20) . In the LCYE rendered results in five accessions  using less strict parameters: min.fdr = 0.1, min.r2 = 0.6, and n.min.acc = 5. For these accessions, the g2g.TFcandidates algorithm identified 11 options with a positive correlation (r.Med > 0.9) between TG and TF SEPs , Dof zinc finger protein DOF2.5-like isoform X2 (DOF2.5), zinc finger protein NUTCRACKER (C2H2), homeobox-leucine zipper protein ATHB-6 (HDZIP6), PREDICTED homeobox-leucine zipper protein HAT5 (HDZIP_HAT5.2), Dof zinc finger protein DOF2.1 (DOF2.1), Dof zinc finger protein DOF1.4 (DOF1.4.1), and Dof zinc finger protein DOF3.6 (DOF3.6)  . Figure LCYB (LCYB1 and LCYB2)  in only six accessions  with LCYB2 SEPs (LCYB2 in nine accessions of Capsicum annuum.Two SEPs with different expression patterns were identified for d LCYB2) b,c. Ther.acc = 6 i,j. For cessions . MoreoveYB2 SEPs . Among tYB2 SEPs ; therefo2 = 0.6, and n.min.acc= 12, four TF “winners” were suggested as candidates for CYP97A regulation by a coexpression analysis with a correlation coefficient of r.Med < 0.9  and 13 putative binding motifs for trihelix transcription factor GTL1-like (THLX1)  .2 = 0.7, and n.min.acc = 12) were applied to the coexpression analysis between BCH1 and the TF gene candidates. Eight TF “winners” were displayed by the algorithm, and remarkably, at least six TFs presented a positive correlation of r.Med > 0.95 (BCH promoter region for six TFs: MADS-box protein 04g005320 (MADS04g), NAC domain-containing protein 2 (NAC2), trihelix transcription factor DF1 (THLX_DF1), PREDICTED NAC domain-containing protein 72-like (NAC72), TGACG-sequence-specific DNA-binding protein TGA-2.1 (bZIP_2.1), and homeobox-leucine zipper protein HAT5 (HDZIP_HAT5.3)  . AlthougZEP was identified based on a coexpression analysis using very strict parameters  , PREDICTED transcription factor bHLH122-like (bHLH122), Dof zinc finger protein DOF1.4 (DOF1.4.1), MYB-like transcription factor 4 (MYB4), homeobox protein 13-like isoform X2 (HDZIP13), PREDICTED transcription factor HBI1 (bHLH_HBI1), Dof zinc finger protein DOF2.4-like (DOF2.4), transcription factor TCP4 (TCP4), and PREDICTED transcription factor DIVARICATA-like isoform X1 (MYB_DIV) (A positive correlation (r.Med ≥ 0.92) between the SEPs of 13 TFs and cc = 12) . An analMYB_DIV) , and theMYB_DIV) m.2 = 0.6, and n.min.acc = 8) were employed to search for TF candidates for VDE. Only two TFs with low correlation coefficients (r.Med > 0.8) were displayed by the Salsa algorithm: an ethylene-responsive transcription factor-like protein, At4g13040, and a chloroplastic zinc finger protein, VAR3 , and it showed at least six TFs with a positive correlation (r.Med ≥ 0.94) , ethylene-responsive transcription factor ERF113 (ERF113), trihelix transcription factor DF1 (THLX_DF1), NAC domain-containing protein 2-like (NAC2), and PREDICTED TGACG-sequence-specific DNA-binding protein TGA-2.1 (bZIP_2.1)  . The seaZIP_2.1) . The expPSY is regulated by abiotic factors such as temperature, photoperiod, salt, drought, high light, and ABA [Carotenoids are important pigments in plants because they are involved in essential processes, such as photosynthesis, photoprotection, and photomorphogenesis, and they are also precursors for the biosynthesis of the growth regulators abscisic acid (ABA) and strigolactones . The bio and ABA . Finally and ABA .Capsicum spp., the content of carotenoids in fruits is variable and changes from green or yellow in immature stages to red, orange, and yellow in mature fruits [PSY, LCYB, BCH, and CCS genes are necessary for the synthesis of capsanthin, and variations in their expression patterns result in the variety of colors of chili pepper fruits [In e fruits . It has r fruits . In thisr fruits .Capsicum spp. fruits. After the coexpression analyses, we obtained a list of 83 TF “winners”. After the analysis of the binding sites of these TFs in the promoter regions of the carotenoid-biosynthesis-related structural genes, it was possible to compile a list of 54 TF candidates susceptible to experimental assessment. A correlation analysis was previously used with a similar purpose in Capsicum spp. A positive correlation between the expression patterns of CaERF82, CaERF97, CaERF66, CaERF107, and CaERF101 and the accumulation patterns of carotene, zeaxanthin, and capsorubin suggested their possible participation in the transcriptional regulation of carotenoid biosynthesis [CabHLH009, CabHLH032, CabHLH048, CabHLH095, and CabHLH100 genes [Capsicum spp. A positive correlation between the expression pattern of the CCS gene and the TF genes CaDIV1 and CaMYB3R-5 was previously reported [CCS in Capsicum [The transcriptional regulation of the carotenogenic pathway in chili pepper fruits is poorly understood, as mentioned previously. Few transcription factors have been identified as regulators of the expression of the genes encoding the carotenoid biosynthetic enzymes. In our present study, we identified TF candidates that might be involved in the transcriptional regulation of these carotenogenic enzymes by using coexpression analysis and searching for putative binding sites for each TF candidate in the promoter of structural genes of the carotenoid biosynthesis pathway. We consider that this approach represents a good option for selecting TF candidates for subsequent studies of function and characterization through different experimental approaches. In the present work, an analysis of transcriptomic data identified 1859 TFs that are expressed during the growth, development, and ripening of 00 genes . A coexpBCH and CCS. Interestingly, 9 and up to 56 putative binding sites for this TF were identified in the promoters of BCH and CCS, respectively  as a candidate to regulate CCS only in the ST accession, which corresponds to MYB_SRM1 (XP_016551180.1), which was suggested as a regulator of LCYB1 in the present study  were demonstrated to be ubiquitously expressed in buds, flowers, leaves, and fruits [Two members of the Trihelix TF family (THLX1 and THLX_DF1) were identified as candidates for d fruits . Interesd fruits ,44.CaWRKYs have been detected during fruit ripening [Ralstonia solanacearum. This process is regulated by a signaling pathway mediated by jasmonic acid, salicylic acid, and ethylene and involves a network of WRKY TFs [At least six TFs belonging to the WRKY TF family  were identified as candidates to regulate some points in the first steps of the carotenogenic pathway in this work. These TFs have been typically associated with environmental stress responses and defense, although important levels of the transcripts of several ripening . For exaripening . Among tWRKY TFs .Capsicum spp. have associated them with biotic and abiotic stress; for instance, CaNAC1 was found to be induced during incompatible interactions between chili pepper and pathogens (viruses and bacteria) [S. lycopersicum have been grouped phylogenetically in the same cluster (Va) and associated with stress responses [SlNAC1 and SlNAC4 (ortholog of NAC2 selected as candidates in this work) [PSY, LCYB, and LCYE structural genes of the carotenoid biosynthesis pathway [Regarding the NAC TF family, we propose four candidates as regulators of carotenoid biosynthesis: NAC1, NAC2, NAC72, and NAC83. Previous reports of NAC TFs in acteria) , CaNAC2 acteria) , CaNAC06acteria) , and CaNacteria) . In the esponses . Howeveris work) ,54. Moreis work) . In part pathway .Additionally, seven different transcription factors of the DOF family were identified as TF candidates in the present study , and theC. annuum. HDZIP (CaCBF) was reported as a cold-inducible TF [R. solanacearum [CRTISO2 and CCS, respectively. In chili pepper, bZIPs have been involved in the modulation of ABA signaling and drought tolerance [Six candidates belonging to the HD-ZIP TF family were selected as candidates to regulate the biosynthesis of carotenoids in cible TF , and CaHnacearum . In tomanacearum . In the olerance  and in eolerance , while Colerance ,64.Capsicum spp., these could only be performed for PSY, BCH, CYP97A, CCS, and ZEP in the 12 accessions, and they were not affected by the late ripening time of AS, CW, and JE, which mature at 80, 70, and 70 DAA, respectively [PDS, Z-ISO, ZDS, CRTISO, LCYE, LCYB, and VDE in a minimum of five and a maximum of nine accessions; in these cases, the selected TFs predicted to be good candidates by the correlation analysis only apply for those accessions that were highly correlated.Regarding the coexpression analyses in the accessions of ectively . A correCapsicum annuum: 4 wild, 6 domesticated, and 2 reciprocal crosses between domesticated and wild-type accessions .Transcriptomic data were obtained from 12 accessions of cessions . Fruits Capsicum annuum, based on ternary models in scale-free measurements [Data from all RNA-seq libraries were analyzed as standardized expression profiles (SEPs). SEPs are seven dimensional numeric vectors in which each of one of the numbers summarizes the standardized mean expression at the corresponding time point  obtained from RNA-Seq in the 12 accessions of urements ,23.Salsa v 1.0. [2) between the expression profile of the TG and the expression profiles of each one on the TF genes annotated in the Capsicum reference genome  in turn with each of the accessions that we wanted to consider preferentially with the 12 accessions of Capsicumannuum. By evaluating pairwise correlations between the TG and each TF gene profile in each accession at all stages of growth, development, and ripening, the algorithm filtered the “winners” . The first search of TF gene candidates was performed using the minimum r2 for the correlations, which was set to min.r2 = 0.7, implying that all relations should have r2 > 0.7 to be considered “winners” and should appear in the output. Note that this threshold was equivalent to asking that ˆr > √0.7 = 0.84. Robustness was evaluated by the number of times that a correlation between the expression profiles of the TG and TF genes was independently detected in different accessions at the fixed false discovery rate (FDR) selected by the researcher. Thus, the stringent parameters employed for predicting TF gene candidates were min.fdr = 0.05, min.r2 = 0.7, and n.min.acc = 12. If the algorithm did not display options using these strict parameters, less stringent values were tested . Finally, the number of accessions in which we wanted to identify TF candidates could be modified and tested once more. The statistical analyses were complemented by the maximum of the absolute differences (m.a) between the SEPs of the TG and TF genes. Values of m.a.Max > 1.5 were considered to contain extreme outliers. An m.a.Max < 0.5 was acceptable. Good TF gene candidates should have a “large” value of r2 and a “small” value of m.a. Complete statistical values of each of the TF “winners” are compiled in The prediction of transcription factor (TF) gene candidates as possible participants in the regulation of the carotenoid-biosynthesis-related genes was carried out by a coexpression analysis using the function g2g.TFcandidates, which is available in the R package a v 1.0. . Hypothehttp://www.bioinformatics.org/yapp/cgi-bin/yapp.cgi, accessed on 24 May 2022), and the results of the promoter region location of each gene are summarized in Arabidopsis thaliana as references [Capsicum spp. in A. thaliana [For the list of candidates displayed by g2g.TFcandidates, we analyzed the promoter region of each biosynthetic target gene to identify putative binding sequences for each TF candidate. Consensus DNA-binding sequences, including the TATA box, initiator element (INR), downstream promoter element (DPE), and putative transcriptional starting site (TSS) of each promoter region, were located using the YAPP Eukaryotic Core Promoter program  are necessary to confirm or discard the participation of them in the transcriptional regulation of carotenoid biosynthesis. Due to the limited information about the transcriptional regulation of the carotenoid biosynthesis pathway in chili pepper and other nonclimacteric fruits, we propose a list of putative candidates that could guide future experimental approaches.Some TF candidates, such as MADS04g and NAC2, have previously been reported as regulators of the ripening process in chili pepper and tomato, respectively. Thus, our RNA-Seq coexpression analysis represents a valuable tool for the analysis and selection of novel candidates to develop further functional TF gene studies and gene characterization. Gene function analysis using virus-induced gene silencing and the yeast one-hybrid assay of some of the selected candidates  are currently underway.It is important to mention that the TF “winners” listed in Although most TFs of plants have been associated with the regulation of different processes other than the biosynthesis of carotenoids, there are several cases (addressed in the discussion section) where different members of families of TFs have been found to act as regulators of this biosynthetic pathway.


Ribonucleotide reductase (RNR) small subunit M2 (RRM2) levels are known to regulate the activity of RNR, a rate-limiting enzyme in the synthesis of deoxyribonucleotide triphosphates (dNTPs) and essential for both DNA replication and repair. The high expression of RRM2 enhances the proliferation of cancer cells, thereby implicating its role as an anti-cancer agent. However, little research has been performed on its role in the prognosis of different types of cancers. This pan-cancer study aimed to evaluate the effect of high expression of RRM2 the tumor prognosis based on clinical information collected from The Cancer Genome Atlas (TCGA) and The Genotype-Tissue Expression (GTEx) databases. We found RRM2 gene was highly expressed in 30 types of cancers. And we performed a pan-cancer analysis of the genetic alteration status and methylation of RRM2. Results indicated that RRM2 existed hypermethylation, associated with m6A, m1A, and m5C related genes. Subsequently, we explored the microRNAs (miRNA), long non-coding RNAs (lncRNA), and the transcription factors responsible for the high expression of RRM2 in cancer cells. Results indicated that has-miR-125b-5p and has-miR-30a-5p regulated the expression of RRM2 along with transcription factors, such as CBFB, E2F1, and FOXM. Besides, we established the competing endogenous RNA (ceRNA) diagram of lncRNAs-miRNAs-circular RNAs (circRNA) involved in the regulation of RRM2 expression. Meanwhile, our study demonstrated that high-RRM2 levels correlated with patients’ worse prognosis survival and immunotherapy effects through the consensus clustering and risk scores analysis. Finally, we found RRM2 regulated the resistance of immune checkpoint inhibitors through the PI3K-AKT single pathways. Collectively, our findings elucidated that high expression of RRM2 correlates with prognosis and tumor immunotherapy in pan-cancer. Moreover, these findings may provide insights for further investigation of the RRM2 gene as a biomarker in predicting immunotherapy’s response and therapeutic target. Ribonucleotide reductase (RNR) is responsible for the de novo synthesis of deoxyribonucleotide triphosphate (dNTP), thereby playing an important role in DNA synthesis and DNA repair. However, abnormal dNTP levels lead to inaccurate DNA replication, causing genomic instability . The actExpression levels of RNR subunits have been studied in various types of cancers, leading to the findings of over-expression of RRM2 in cancer cells . The higThe public database The Cancer Genome Atlas (TCGA) project contains functional genomics datasets, thereby providing us with high-throughput RRM2 expression data and clinical information of cancer patients. Therefore, in this study, we utilized the TCGA data to establish a comprehensive bioinformatics-based pan-cancer analysis of RRM2. Besides, we performed analyses to explore the potential mechanism responsible for the pathogenesis. The overall study flowchart is shown in Analysis of the data obtained from TCGA and GTEx revealed that the expression level of RRM2 in the tumor tissues of ACC, BLCA, BRCA, CESC, CHOL, COAD, DLBC, ESCA, GBM, HNSC, KIRP, KIRC, LAML, LGG, LUAD, LIHC, LUSC, OV, PAAD, PRAD, READ, STAD, SKCM, THCA, TGCT, UCEC, THYM, UCS (P < 0.0001), SARC, and PCPG (P < 0.05) was higher than that of the control tissues . HoweverRRM2 genetic alterations was 1.5%  appeared in patients with endometrial carcinoma , as a quantifiable biomarker, reflected the number of mutations in cancer. Significant correlations were observed between RRM2 expression and TMB in ACC, BLCA, BRCA, CESC, CHOL, COAD, KICH, KIRC, LGG, LIHC, LUAD, LUSC, MESO, OV, PAAD, PRAD, SARC, SKCM, STAD, THYM, and UCEC . In this study, the TIDE score, dysfunction, exclusion, MDSC, TAM-M2, and CAF were generated from the TIDE system. Results indicated that the risk score was positively correlated with the TIDE score, whereas dysfunction was negatively correlated with TAM-M2 and CAF . We searched RRM2 expression and retrieved data regarding overall survival (OS), progression-free interval (PFI), and disease-free interval (DFI) through the UCSCXenaShiny website ), were obtained. Also, the top 100 RRM2-correlated genes were obtained from the Gene Expression Profiling Interactive Analysis (GEPIA2) website. An intersection analysis was performed for the above two groups of RRM2-related genes. Correlation analysis between RRM2-related genes was performed by R-package “corrplot.” Next, the consensus cluster analysis, to classify the tumor samples into separate clusters built-in the expression profiles of RRM2-related genes in the KIRC TCGA datasets, was performed using the R package “ConsensusClusterPlus” [Based on the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) website (terPlus” . The oveterPlus” . Based ohttp://tide.dfci.harvard.edu/) was utilized for exploring the effect of RRM2 expression on immune dysfunction and immune exclusion [http://www.sangerbox.com/tool). Based on the TCGA clinical data, the expression levels of the RRM2 gene were analyzed by the treatment outcome of different cancer patients. Correlation between The Genomics of Drug Sensitivity in Cancer (GDSC) and RRM2-related genes was performed through the GSCALite database. The co-expression of RRM2 and the gene of resistance of immunotherapy-related signal pathway was performed based on the R package “Limma.” The heatmap was constructed based on the R package “reshape2” and “RColorBrewer.” Then, the liver hepatocellular carcinoma (LIHC) samples were subjected to “pathview” R package  for the construction of pathway maps for PI3K-AKT signal pathway (hsa04151).The differentially expressed genes (DEGs) were screened from different cluster subgroups using the R package “Limma”, and the prognostic DEGs were then selected for PCA. The following formula was utilized to establish the risk score = Σ(PC1i + PC2i), where “i” was the expression levels of the prognostic DEGs . Kaplan-xclusion . Also, tIn this study, the statistical operation and visualization were performed by utilizing the R 4.1.1 software. Based on the recommended methods, the statistical analysis for various data was performed based on different packages. Last but not the least, all tests were bilateral, and statistical significance was defined as P<0.05.Supplementary Figure 1