transcription; hypoxia; RNAseq; meta-analysis

  Formal Meta- Analysis of Hypoxic Gene Expression Biographies  Reveals a Universal Gene hand 

1. preface  Oxygen homeostasis is essential to sustain cellular metabolism in eukaryotes. Hypoxia   triggers multiple adaptive mechanisms, from metabolism reprogramming to towel restruc-  turing, aimed tore-balancing oxygen  force and demand( 1). In multicellular organisms   this response can be  veritably different, depending on cell type, extension and degree of the  oxygen  privation, or pathological state.  utmost of these responses are orchestrated at the transcriptional  position, with the Hypoxia  Inducible Factors( HIFs) being the main  motorists of the hypoxic gene expression pattern( 2).  The heterodimeric HIF recap factor consists on a β subunit( ARNT), constitutively  expressed, and an α subunit( HIF1A, EPAS1, HIF3A) which, in normoxic conditions, is   pronounced for  declination by the  combined action of a family of oxygen-dependent enzymes   Biomedicines 2022, 10, 2229. https//doi.org/10.3390/biomedicines10092229 https//www.mdpi.com/journal/biomedicinesBiomedicines 2022, 10, 2229 2 of 15   EGLN family) and the von Hippel- Lindau( VHL) ubiquitylation complex( 3 – 5). When  oxygen  attention decreases, the α subunits escape  declination due to the reduced   exertion of the EGLNs, translocate to the  nexus and bind to Hypoxia Response Elements  along the β subunit. Transcriptional  exertion of HIFs depends also on commerce with co-activators  similar as CREB- binding protein or p300, whose list is also regulated in an  oxygen-dependent manner(,7).  Given the  significance of the transcriptional response for towel oxygen homeostasis  and its  revision in  complaint, a large number of  workshop have  tried to identify the full  set of genes regulated by hypoxia through gene profiling  trials. Since these studies  were performed in a wide variety of experimental conditions( cell types, oxygen pressure,  exposure time) integrating their results could lead to identify a set of genes nowhere  regulated by hypoxia, as well as genes whose  revision is  confined to specific situations in  addition to hypoxia. still, little  trouble has been done in this regard and, to the stylish of  our knowledge, only two attempts to integrate all the hypoxic gene profiling  trials  have been done(,9). The first analysis of this type, grounded on the analysis of gene biographies  generated by means of DNA microarrays, produced the first list of genes widely   convinced by hypoxia and revealed that the set of genes  convinced by hypoxia were more  conserved than those repressed( 8). A alternate, more recent study, exploited the information    deduced from RNA- seq  trials producing a more comprehensive list of hypoxia-  regulated genes and characterized HIF- isoform common and specific targets( 9). In  malignancy of   their merit, none of these  workshop employed formal meta- analysis approach for their analysis  which, given the  miscellaneous nature of the data, is critical to draw statistically sound  conclusions( 10).  Among the  colorful meta- analysis  styles applicable to transcriptomic data( 11), we  employed a model that combines the effect sizes. rather of assuming a  fixed effect of hypoxia on any given gene across the different studies, we used a  arbitrary   goods model that considers that the true effect could vary from study to study to reflect,  for  illustration, the different response in distinct cell types. In this study we aim to define  core  factors of the transcriptional response to hypoxia taking advantage of the wider  public vacuity of coming generation sequencing data, RNA- seq in particular. Applying a   arbitrary  goods model to the expression data gathered we were  suitable to define a molecular   hand representing the early( ≤ 48 h) transcriptional response to hypoxia,  singly  of cell type.

2. Materials and Methods

2.1. RNA- seq Data Download and Processing Raw reads of the RNA- seq trials were downloaded from Sequence Read Library( 12). Pseudocounts for each gene were attained with salmon( 13) using RefSeq( 14) mRNA sequences for mortal genome assembly GRCh38/ hg38 as reference. Differential expression in individual subsets was calculated with the R package DE- Seq2( 15) using original dissipation fit and apeglm( 16) system for effect size loss. . Meta- Analysis The meta- analysis intended to identify the effect of sustained hypoxia on early gene expression in mortal cells compared to normoxic controls. To identify studies to be included in the meta- analysis Gene Expression Ommibus( GEO) depository was searched with the terms ‘ hypoxia( Description) AND. The hunt redounded in a aggregate of 394 studies. We only kept studies performed in mortal cells that determined steady- state RNA situations in total poly- A) RNA samples and barred analysis that didn't include replicates, employed treatments other than reduced oxygen pressure(e.g., chemical impediments or other hypoxia Biomedicines 2022, 10, 2229 3 of 15 mimetics) or those where gene expression was anatomized after 48 h. We also barred studies that used cycling/ intermittent hypoxia or that were performed innon-human cell lines. A aggregate of 46 studies( independent GSE entries) remained after operation of the addition/ rejection criteria and were used for the meta- analyses( Suplementary Table S1). A pooled estimate of the size effect of hypoxia on expression was determined for each gene using the R packages metafor( 17) and meta( 18) using as input thelog2-Fold change value and its associated standard error reckoned for each individual RNA- seq trial using the R package DESeq2( 15). Given that the individual estimates decide from an miscellaneous group of trials, including different cell types and experimental conditions, we assumed that these individual estimates decide from a distribution of true effect sizes rather than a single bone
and therefore applied a arbitrary- goods model for the meta- analysis. Since some of the named studies included several cell types and/ or experimental conditions( see results for details), we fitted a 3- position model( 19) that, in addition to slice error and between- study diversity, takes into account possible dependences between data subsets deduced from a single study. . Functional Enrichment Analysis Enrichment of Gene Ontology terms was performed with the Bioconductor’s cluster- Profiler package( 20) using a q cut-off value of0.05. The list of background genes included those expressed in at least 90 of the datasets and as focus list the subset of genes significantly regulated by hypoxia

3. Results

3.1. Hypoxia- Induced Transcriptional Biographies Show Limited Overlap In order to identify genes constantly regulated by hypoxia across a wide range of cell types and experimental conditions, we compared the results from 46 studies assaying the transcriptional response to hypoxia by means of RNA- seq( Supplementary Table S1). Since some studies included several cell types, oxygen pressures or times of exposure to hypoxia, we took subsets of the study’s data thus, our original data set included a total of 81 subsets of normoxia- hypoxia paired samples, each one comprising a single cell line, exposure time and oxygen pressure( Table 1). For each of these 81 subsets, we linked the genes significantly regulated

4. Discussion

The integration of multiple datasets representing the transcriptional response to a given encouragement, allows for the identification of harmonious changes in gene expression. still, transcriptional biographies are noisy, and the correlation between them is poor(,26). therefore, the number of common DEGs decreases fleetly with the number of studies taken into consideration( Figure 1). To identify genes generally regulated by hypoxia bone
can set a minimal number of studies where the gene needs to be set up as a DEG( 9). also again, there's no objective criteria to elect minimum thresholds and this approach results in a list of generally regulated genes which doesn't give information regarding the magnitude of their regulation. Fortunately, applying meta- analysis styles appears to be a good and practical result to reduce noise and increase signal across different studies( 10). Herein we describe the operation of a formal meta- analysis procedure to identify genes whose expression is significantly modulated across a number of different gene profil- ing studies.. also, by applying a arbitrary goods model, this strategy takes into account the wide variability in gene expression anticipated from the integration of transcriptomes deduced from different experimental con- Biomedicines 2022, 10, 2229 12 of 15 ditions. The operation of this approach to 70 paired normoxic/ hypoxic transcriptomes representing a aggregate of 430 samples redounded in the identification of 6242 genes, roughly of the sensible genes, as significantly