Uterine Leiomyoma gene expression function
Janis Corona
7/2/2020 - 7/12/2020
files located at: https://github.com/JanJanJan2018/Uterine-Fibroid-Beadchip-Genotypes-Analysis
These files are from https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE593 and https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GPL96 Note that there are only 5 samples of each class of uterine leiomyoma (UL) or nonUL all the gene related information was obtained from genecards.org
Gene expression in a cell sample of tissue can mean the cell is creating more proteins needed in the body that are needed to maintain its living functions or are being enhanced or reduced or modified due to external factors such as environment, chemical, radiation, health disturbances like a viral infections.
“Several steps in the gene expression process may be modulated, including the transcription, RNA splicing, translation, and post-translational modification of a protein. Gene regulation gives the cell control over structure and function, and is the basis for cellular differentiation, morphogenesis and the versatility and adaptability of any organism.” {Gene expression - Wikipedia, en.wikipedia.org/wiki/Gene_expression}
This study is done as a quick look into genes expressed by microarray sheets that have 1 or more array cells for the same gene when collected. Each gene can be measured in each sample depending on how many times it is seen in that microarray design in the lab. For more information on how these genes were collected and measured for the study obtained at the link above to visit the National Center for Bioinformatics Information (NCBI) for the GSE593 study in the Gene Expression Omnibus (GEO).
library(ggplot2)
library(DT)## Warning: package 'DT' was built under R version 3.6.3Lets build our tables by reading them in for the ULs and nonULs.
ul <- read.delim('UL_GSE593_GPL96.csv', sep=',', header=TRUE, comment.char='#',
na.strings=c('',' ','NA'), stringsAsFactors = TRUE)
non_ul <- read.delim('nonUL_GSE593_GPL96.csv', sep=',', header=TRUE, comment.char='#',
na.strings=c('',' ','NA'), stringsAsFactors = TRUE)head(ul)## X ID GB_ACC Representative.Public.ID
## 1 1 1007_s_at U48705 U48705
## 2 2 1053_at M87338 M87338
## 3 3 117_at X51757 X51757
## 4 4 121_at X69699 X69699
## 5 5 1255_g_at L36861 L36861
## 6 6 1294_at L13852 L13852
## Gene.Title
## 1 discoidin domain receptor tyrosine kinase 1 /// microRNA 4640
## 2 replication factor C (activator 1) 2, 40kDa
## 3 heat shock 70kDa protein 6 (HSP70B')
## 4 paired box 8
## 5 guanylate cyclase activator 1A (retina)
## 6 microRNA 5193 /// ubiquitin-like modifier activating enzyme 7
## Gene.Symbol ENTREZ_GENE_ID GSM9093 GSM9094 GSM9095 GSM9096 GSM9097
## 1 DDR1 /// MIR4640 780 /// 100616237 212.9 289.7 242.4 230.7 235.5
## 2 RFC2 5982 52.6 15.0 57.9 20.4 43.6
## 3 HSPA6 3310 30.4 32.3 43.7 35.7 66.2
## 4 PAX8 7849 226.7 376.9 285.5 313.1 325.3
## 5 GUCA1A 2978 23.6 30.4 2.6 29.7 29.1
## 6 MIR5193 /// UBA7 7318 /// 100847079 90.3 157.1 139.6 144.1 140.9colnames(ul)## [1] "X" "ID"
## [3] "GB_ACC" "Representative.Public.ID"
## [5] "Gene.Title" "Gene.Symbol"
## [7] "ENTREZ_GENE_ID" "GSM9093"
## [9] "GSM9094" "GSM9095"
## [11] "GSM9096" "GSM9097"Lets select only the samples and the gene symbol columns.
UL <- ul[,c(6,8:12)]
nonUL <- non_ul[,c(6,8:12)]Next, we will split the gene symbol column that has multiple entries into the first three entries as lists to add to our tables and pull from
ulList <- as.character(UL$Gene.Symbol)
list <- strsplit(ulList,split=' /// ')
first <- lapply(list, '[',1)
second <- lapply(list, '[',2)
third <- lapply(list, '[',3)
UL$first <- as.character(first)
UL$second <- as.character(second)
UL$third <- as.character(third)
nonulList <- as.character(nonUL$Gene.Symbol)
list2 <- strsplit(nonulList,split=' /// ')
first <- lapply(list2, '[',1)
second <- lapply(list2, '[',2)
third <- lapply(list2, '[',3)
nonUL$first <- as.character(first)
nonUL$second <- as.character(second)
nonUL$third <- as.character(third)Next, we will build the function to grab the gene by its gene symbol and return the fold change of the UL to nonUL ratio from the means and medians of our total genes across all five samples. This function is modified to write the information to a table for the UL and nonUL information. Make sure the files aren’t in this folder or write to a separate folder.
if (dir.exists('./UL and nonUL foldchange tables')){
unlink('./UL and nonUL foldchange tables', recursive=TRUE)
dir.create('./UL and nonUL foldchange tables')
} else {
dir.create('./UL and nonUL foldchange tables')
}getMeanMedian <- function(gene){
gene <- as.character(paste(gene))
gene0_ul <- UL[grep(gene,UL$Gene.Symbol),]
gene0_nonul <- nonUL[grep(gene,UL$Gene.Symbol),]
sub_ul <- subset(gene0_ul, gene0_ul$Gene.Symbol==gene |
gene0_ul$first==gene |
gene0_ul$third==gene |
gene0_ul$second==gene)
sub_nonul <- subset(gene0_nonul, gene0_nonul$Gene.Symbol==gene|
gene0_nonul$first==gene |
gene0_nonul$third==gene |
gene0_nonul$second==gene)
gene1_UL <- sub_ul[,2:6]
gene1_nonUL <- sub_nonul[,2:6]
gene1_UL$mean <- apply(gene1_UL,1,mean)
gene1_UL$median <- apply(gene1_UL,1,median)
gene1_nonUL$mean <- apply(gene1_nonUL,1,mean)
gene1_nonUL$median <- apply(gene1_nonUL,1,median)
gene1_UL$FoldChange_mean <- gene1_UL$mean/gene1_nonUL$mean
gene1_UL$FoldChange_median <- gene1_UL$median/gene1_nonUL$median
geneMeans <- gene1_UL$FoldChange_mean
geneMedians <- gene1_UL$FoldChange_median
print('The foldchage of UL means to nonUL means is:')
print(geneMeans)
print('The foldchage of UL medians to nonUL medians is:')
print(geneMedians)
colnames(gene1_UL) <- paste(colnames(gene1_UL), '_UL')
colnames(gene1_nonUL) <- paste(colnames(gene1_nonUL), '_nonUL')
setwd('./UL and nonUL foldchange tables')
write.table(gene1_UL[2:length(gene1_UL$median),], "allGenesUL.csv", append=TRUE,
col.names=FALSE, sep=",",
row.names=TRUE)
UL_names <- colnames(gene1_UL)
write.csv(UL_names,'header_UL_names.csv',row.names=FALSE)
write.table(gene1_nonUL[2:length(gene1_nonUL$median),], "allGenesNonUL.csv", append=TRUE,
col.names=FALSE, sep=",",
row.names=TRUE)
nonUL_names <- colnames(gene1_nonUL)
write.csv(nonUL_names,'header_nonUL_names.csv', row.names=FALSE)
setwd('../')
return(list(gene1_UL,gene1_nonUL))
}Lets look at the iron gene expression of transferrin in UL compared to nonUL
getMeanMedian("TF")## [1] "The foldchage of UL means to nonUL means is:"
## [1] 3.075665 1.394843 1.205266 8.888889
## [1] "The foldchage of UL medians to nonUL medians is:"
## [1] 2.813590 1.451360 1.300586 11.600000## [[1]]
## GSM9093 _UL GSM9094 _UL GSM9095 _UL GSM9096 _UL GSM9097 _UL mean _UL
## 2927 5.5 33.7 31.2 26.9 53.1 30.08
## 13442 14.1 35.8 40.8 27.3 55.1 34.62
## 13443 8.6 50.3 47.4 64.8 53.2 44.86
## 19473 8.7 0.9 0.7 6.9 6.8 4.80
## median _UL FoldChange_mean _UL FoldChange_median _UL
## 2927 30.64 3.075665 2.813590
## 13442 35.21 1.394843 1.451360
## 13443 48.85 1.205266 1.300586
## 19473 5.80 8.888889 11.600000
##
## [[2]]
## GSM9098 _nonUL GSM9099 _nonUL GSM9100 _nonUL GSM9101 _nonUL
## 2927 18.8 0.9 13.0 4.2
## 13442 37.1 23.7 12.7 43.1
## 13443 34.9 37.9 10.9 61.9
## 19473 0.4 1.0 0.5 0.5
## GSM9102 _nonUL mean _nonUL median _nonUL
## 2927 12.0 9.78 10.89
## 13442 7.5 24.82 24.26
## 13443 40.5 37.22 37.56
## 19473 0.3 0.54 0.50The above shows the TF or transferrin for iron transport is significantly higher in samples of UL than samples on nonUL.
Lets examine estrogen by looking at two receptors ESR1 and ESR2, where these form estrogen binding sites after transcription: genecards.org: Estrogen controls many cellular processes including growth, differentiation and function of the reproductive system. Estrogen is also responsible for the growth and maintenance of the skeleton and the normal function of the cardiovascular and nervous systems.
getMeanMedian("ESR1")## [1] "The foldchage of UL means to nonUL means is:"
## [1] 1.9681488 1.2787565 1.6072961 1.1439968 1.2810945 1.0840378 0.9803993
## [8] 0.9281508 0.9646910
## [1] "The foldchage of UL medians to nonUL medians is:"
## [1] 1.9514678 1.3590340 1.9984146 1.1091596 1.2892562 1.2271259 1.1440559
## [8] 1.0224913 0.8253852## [[1]]
## GSM9093 _UL GSM9094 _UL GSM9095 _UL GSM9096 _UL GSM9097 _UL mean _UL
## 4752 1373.2 1909.6 2691.5 1219.6 1123.3 1663.44
## 10671 61.3 106.1 78.8 77.9 46.1 74.04
## 10672 18.0 74.9 66.7 55.9 9.2 44.94
## 10673 38.8 79.7 87.9 60.1 20.3 57.36
## 11032 5.1 5.3 1.3 15.4 24.4 10.30
## 14924 22.3 60.6 56.3 47.6 31.2 43.60
## 14925 75.9 77.3 60.5 28.1 28.3 54.02
## 16530 25.4 19.7 3.1 26.4 4.2 15.76
## 16557 39.6 8.4 14.1 6.1 8.3 15.30
## median _UL FoldChange_mean _UL FoldChange_median _UL
## 4752 1518.32 1.9681488 1.9514678
## 10671 75.97 1.2787565 1.3590340
## 10672 50.42 1.6072961 1.9984146
## 10673 58.73 1.1439968 1.1091596
## 11032 7.80 1.2810945 1.2892562
## 14924 45.60 1.0840378 1.2271259
## 14925 57.26 0.9803993 1.1440559
## 16530 17.73 0.9281508 1.0224913
## 16557 11.25 0.9646910 0.8253852
##
## [[2]]
## GSM9098 _nonUL GSM9099 _nonUL GSM9100 _nonUL GSM9101 _nonUL
## 4752 1282.2 1092.0 710.9 687.0
## 10671 52.7 53.9 84.0 58.5
## 10672 55.1 22.5 12.4 15.0
## 10673 60.6 54.9 22.5 51.0
## 11032 4.4 3.6 3.7 20.8
## 14924 68.3 33.7 19.1 45.9
## 14925 85.3 43.1 44.1 45.0
## 16530 17.7 11.9 11.3 24.8
## 16557 11.4 25.6 32.1 5.2
## GSM9102 _nonUL mean _nonUL median _nonUL
## 4752 453.8 845.18 778.04
## 10671 40.4 57.90 55.90
## 10672 34.8 27.96 25.23
## 10673 61.7 50.14 52.95
## 11032 7.7 8.04 6.05
## 14924 34.1 40.22 37.16
## 14925 58.0 55.10 50.05
## 16530 19.2 16.98 17.34
## 16557 5.0 15.86 13.63getMeanMedian("ESR2")## [1] "The foldchage of UL means to nonUL means is:"
## [1] 1.0218978 0.6509146 1.1465517 1.6351351 1.5177936
## [1] "The foldchage of UL medians to nonUL medians is:"
## [1] 1.0570956 0.5543196 1.1132438 1.4090909 0.7074974## [[1]]
## GSM9093 _UL GSM9094 _UL GSM9095 _UL GSM9096 _UL GSM9097 _UL mean _UL
## 10240 21.2 54.6 36.2 57.9 68.1 47.60
## 10561 13.7 9.1 9.1 16.2 37.3 17.08
## 10562 23.1 20.2 6.3 46.8 23.3 23.94
## 10563 0.9 2.2 0.7 0.6 7.7 2.42
## 10564 5.5 6.9 5.6 6.5 60.8 17.06
## median _UL FoldChange_mean _UL FoldChange_median _UL
## 10240 51.10 1.0218978 1.0570956
## 10561 14.95 0.6509146 0.5543196
## 10562 23.20 1.1465517 1.1132438
## 10563 1.55 1.6351351 1.4090909
## 10564 6.70 1.5177936 0.7074974
##
## [[2]]
## GSM9098 _nonUL GSM9099 _nonUL GSM9100 _nonUL GSM9101 _nonUL
## 10240 67.5 13.1 50.1 63.6
## 10561 27.7 6.1 30.9 47.9
## 10562 44.9 5.5 28.8 20.8
## 10563 1.2 3.8 0.8 1.0
## 10564 7.7 3.0 19.0 20.7
## GSM9102 _nonUL mean _nonUL median _nonUL
## 10240 38.6 46.58 48.34
## 10561 18.6 26.24 26.97
## 10562 4.4 20.88 20.84
## 10563 0.6 1.48 1.10
## 10564 5.8 11.24 9.47From the above the ESR2 gene is expressed more in UL means 4/5 times than nonUL means and the UL medians are expressed 3/5 more than nonUL medians. A close comparison, and not many samples to say definitely a relationship with the ESR2 receptor than with the ESR1 receptor.
Next lets look at the genes that catabolize alcohol, the genecards.org summary of ADH1B: ADH1B (Alcohol Dehydrogenase 1B (Class I), Beta Polypeptide) is a Protein Coding gene. Diseases associated with ADH1B include Alcohol Dependence and Alcoholic Liver Cirrhosis. Among its related pathways are Drug metabolism - cytochrome P450 and Cytochrome P450 - arranged by substrate type. Gene Ontology (GO) annotations related to this gene include oxidoreductase activity and alcohol dehydrogenase activity, zinc-dependent. An important paralog of this gene is ADH1C.
ADH1A:GeneCards Summary for ADH1A Gene ADH1A (Alcohol Dehydrogenase 1A (Class I), Alpha Polypeptide) is a Protein Coding gene. Diseases associated with ADH1A include Substance Dependence and Fetal Alcohol Syndrome. Among its related pathways are Drug metabolism - cytochrome P450 and Signaling by Retinoic Acid. Gene Ontology (GO) annotations related to this gene include oxidoreductase activity and alcohol dehydrogenase (NAD) activity. An important paralog of this gene is ADH1B.
ADH1C: GeneCards Summary for ADH1C Gene ADH1C (Alcohol Dehydrogenase 1C (Class I), Gamma Polypeptide) is a Protein Coding gene. Diseases associated with ADH1C include Alcohol Dependence and Parkinson Disease, Late-Onset. Among its related pathways are Drug metabolism - cytochrome P450 and Signaling by Retinoic Acid. Gene Ontology (GO) annotations related to this gene include oxidoreductase activity and alcohol dehydrogenase (NAD) activity. An important paralog of this gene is ADH1B.
Lets get ADH1A, then ADH1B, then ADH1C and compare the UL to nonUL foldchange means and median values to see if a relationship exists between alcohol use in people with UL noting that in this sample the ULs were tissue samples taken out of the same uterus as the nonUL samples.What we find will just be interesting because it will show if the alcohol catabolism gene is in the UL samples more or less than the nonUL samples. But this depends on if none of these women drank alcohol or they all did, or only some.
getMeanMedian("ADH1A")## [1] "The foldchage of UL means to nonUL means is:"
## [1] 0.6986864
## [1] "The foldchage of UL medians to nonUL medians is:"
## [1] 0.6843122## [[1]]
## GSM9093 _UL GSM9094 _UL GSM9095 _UL GSM9096 _UL GSM9097 _UL mean _UL
## 7339 18.4 26.8 20.5 15.8 3.6 17.02
## median _UL FoldChange_mean _UL FoldChange_median _UL
## 7339 17.71 0.6986864 0.6843122
##
## [[2]]
## GSM9098 _nonUL GSM9099 _nonUL GSM9100 _nonUL GSM9101 _nonUL GSM9102 _nonUL
## 7339 28.5 27.4 16.3 21.1 28.5
## mean _nonUL median _nonUL
## 7339 24.36 25.88getMeanMedian("ADH1B")## [1] "The foldchage of UL means to nonUL means is:"
## [1] 0.5111040 0.4703303 1.1760939
## [1] "The foldchage of UL medians to nonUL medians is:"
## [1] 0.2882656 0.2628052 1.6809672## [[1]]
## GSM9093 _UL GSM9094 _UL GSM9095 _UL GSM9096 _UL GSM9097 _UL mean _UL
## 9102 7.2 441.4 609.8 45.3 7.9 222.32
## 9103 25.5 296.7 359.2 24.9 1.5 141.56
## 9104 11.8 22.0 37.4 17.9 21.1 22.04
## median _UL FoldChange_mean _UL FoldChange_median _UL
## 9102 133.81 0.5111040 0.2882656
## 9103 83.53 0.4703303 0.2628052
## 9104 21.55 1.1760939 1.6809672
##
## [[2]]
## GSM9098 _nonUL GSM9099 _nonUL GSM9100 _nonUL GSM9101 _nonUL GSM9102 _nonUL
## 9102 493.4 275.5 292.4 565.5 548.1
## 9103 334.7 59.5 91.6 462.2 556.9
## 9104 6.9 6.2 18.8 5.9 55.9
## mean _nonUL median _nonUL
## 9102 434.98 464.19
## 9103 300.98 317.84
## 9104 18.74 12.82getMeanMedian("ADH1C")## [1] "The foldchage of UL means to nonUL means is:"
## [1] 0.4103535
## [1] "The foldchage of UL medians to nonUL medians is:"
## [1] 0.3070175## [[1]]
## GSM9093 _UL GSM9094 _UL GSM9095 _UL GSM9096 _UL GSM9097 _UL mean _UL
## 5788 4.2 18 2.3 3.1 4.9 6.5
## median _UL FoldChange_mean _UL FoldChange_median _UL
## 5788 4.55 0.4103535 0.3070175
##
## [[2]]
## GSM9098 _nonUL GSM9099 _nonUL GSM9100 _nonUL GSM9101 _nonUL GSM9102 _nonUL
## 5788 24.7 8 23 9.7 13.8
## mean _nonUL median _nonUL
## 5788 15.84 14.82From the above alcohol catabolysm genes looked at above, UL gene expression is almost halved when being expressed less than the amounts in nonUL samples.
So far we looked at transferrin (higher in ULs), estrogen receptor genes (higher in ULs), and alcohol catabolism (lower in ULs) genes in UL and nonUL samples. Lets now see if we see any relationships with the Vitamin D absorption, calcium, and glucose genes.
Entrez Gene Summary for VDR Gene This gene encodes vitamin D3 receptor, which is a member of the nuclear hormone receptor superfamily of ligand-inducible transcription factors. This receptor also functions as a receptor for the secondary bile acid, lithocholic acid. Downstream targets of vitamin D3 receptor are principally involved in mineral metabolism, though this receptor regulates a variety of other metabolic pathways, such as those involved in immune response and cancer. Mutations in this gene are associated with type II vitamin D-resistant rickets. A single nucleotide polymorphism in the initiation codon results in an alternate translation start site three codons downstream. Alternatively spliced transcript variants encoding different isoforms have been described for this gene. A recent study provided evidence for translational readthrough in this gene, and expression of an additional C-terminally extended isoform via the use of an alternative in-frame translation termination codon. [provided by RefSeq, Jun 2018]
Entrez Gene Summary for CASR Gene The protein encoded by this gene is a plasma membrane G protein-coupled receptor that senses small changes in circulating calcium concentration. The encoded protein couples this information to intracellular signaling pathways that modify parathyroid hormone secretion or renal cation handling, and thus this protein plays an essential role in maintaining mineral ion homeostasis. Mutations in this gene are a cause of familial hypocalciuric hypercalcemia, neonatal severe hyperparathyroidism, and autosomal dominant hypocalcemia. [provided by RefSeq, Aug 2017]
Entrez Gene Summary for INS Gene This gene encodes insulin, a peptide hormone that plays a vital role in the regulation of carbohydrate and lipid metabolism. After removal of the precursor signal peptide, proinsulin is post-translationally cleaved into three peptides: the B chain and A chain peptides, which are covalently linked via two disulfide bonds to form insulin, and C-peptide. Binding of insulin to the insulin receptor (INSR) stimulates glucose uptake. A multitude of mutant alleles with phenotypic effects have been identified. There is a read-through gene, INS-IGF2, which overlaps with this gene at the 5’ region and with the IGF2 gene at the 3’ region. Alternative splicing results in multiple transcript variants. [provided by RefSeq, Jan 2019]
Our genes to look at are VDR (vit D), CASR (calcium), and INS (glucose uptake by insulin)
Lets also add in tendon/ligament protein and muscle protein: Entrez Gene Summary for TTN Gene This gene encodes a large abundant protein of striated muscle. The product of this gene is divided into two regions, a N-terminal I-band and a C-terminal A-band. The I-band, which is the elastic part of the molecule, contains two regions of tandem immunoglobulin domains on either side of a PEVK region that is rich in proline, glutamate, valine and lysine. The A-band, which is thought to act as a protein-ruler, contains a mixture of immunoglobulin and fibronectin repeats, and possesses kinase activity. An N-terminal Z-disc region and a C-terminal M-line region bind to the Z-line and M-line of the sarcomere, respectively, so that a single titin molecule spans half the length of a sarcomere. Titin also contains binding sites for muscle associated proteins so it serves as an adhesion template for the assembly of contractile machinery in muscle cells. It has also been identified as a structural protein for chromosomes. Alternative splicing of this gene results in multiple transcript variants. Considerable variability exists in the I-band, the M-line and the Z-disc regions of titin. Variability in the I-band region contributes to the differences in elasticity of different titin isoforms and, therefore, to the differences in elasticity of different muscle types. Mutations in this gene are associated with familial hypertrophic cardiomyopathy 9, and autoantibodies to titin are produced in patients with the autoimmune disease scleroderma. [provided by RefSeq, Feb 2012]
UniProtKB/Swiss-Prot Summary for BMP2 Gene Induces cartilage and bone formation (PubMed:3201241). Stimulates the differentiation of myoblasts into osteoblasts via the EIF2AK3-EIF2A- ATF4 pathway. BMP2 activation of EIF2AK3 stimulates phosphorylation of EIF2A which leads to increased expression of ATF4 which plays a central role in osteoblast differentiation. In addition stimulates TMEM119, which upregulates the expression of ATF4 (PubMed:24362451). BMP2_HUMAN,P12643
Entrez Gene Summary for COL1A1 Gene This gene encodes the pro-alpha1 chains of type I collagen whose triple helix comprises two alpha1 chains and one alpha2 chain. Type I is a fibril-forming collagen found in most connective tissues and is abundant in bone, cornea, dermis and tendon. Mutations in this gene are associated with osteogenesis imperfecta types I-IV, Ehlers-Danlos syndrome type VIIA, Ehlers-Danlos syndrome Classical type, Caffey Disease and idiopathic osteoporosis. Reciprocal translocations between chromosomes 17 and 22, where this gene and the gene for platelet-derived growth factor beta are located, are associated with a particular type of skin tumor called dermatofibrosarcoma protuberans, resulting from unregulated expression of the growth factor. Two transcripts, resulting from the use of alternate polyadenylation signals, have been identified for this gene. [provided by R. Dalgleish, Feb 2008]
The muscle tissue gene (TTN), the cartilage or ligament (BMP2), and the collagen or tendon (COL1A1) genes will be added to our list of other genes in our exploratory data analysis to see how UL tissue compares to nonUL tissue in gene expression of those genes.
Vitamin D gene expression:
getMeanMedian("VDR")## [1] "The foldchage of UL means to nonUL means is:"
## [1] 1.787879 1.939938 1.288538 2.741403
## [1] "The foldchage of UL medians to nonUL medians is:"
## [1] 2.665976 1.784193 1.577308 4.228090## [[1]]
## GSM9093 _UL GSM9094 _UL GSM9095 _UL GSM9096 _UL GSM9097 _UL mean _UL
## 3780 71.2 24.6 35.7 53.4 62.9 49.56
## 3781 82.2 37.4 11.4 33.5 84.2 49.74
## 3782 62.1 11.4 49.2 30.0 42.9 39.12
## 13072 7.5 49.8 35.4 76.3 30.3 39.86
## median _UL FoldChange_mean _UL FoldChange_median _UL
## 3780 51.48 1.787879 2.665976
## 3781 43.57 1.939938 1.784193
## 3782 41.01 1.288538 1.577308
## 13072 37.63 2.741403 4.228090
##
## [[2]]
## GSM9098 _nonUL GSM9099 _nonUL GSM9100 _nonUL GSM9101 _nonUL
## 3780 10.9 48.4 62.2 9.9
## 3781 23.2 19.6 20.8 35.9
## 3782 57.5 26.6 22.0 25.4
## 13072 6.3 8.4 5.0 43.6
## GSM9102 _nonUL mean _nonUL median _nonUL
## 3780 7.2 27.72 19.31
## 3781 28.7 25.64 24.42
## 3782 20.3 30.36 26.00
## 13072 9.4 14.54 8.90Vitamin D is significantly more expressed in ULs than nonULs.
Calcium gene expression:
getMeanMedian("CASR")## [1] "The foldchage of UL means to nonUL means is:"
## [1] 1.021035 1.573770
## [1] "The foldchage of UL medians to nonUL medians is:"
## [1] 0.9006821 1.5753968## [[1]]
## GSM9093 _UL GSM9094 _UL GSM9095 _UL GSM9096 _UL GSM9097 _UL mean _UL
## 10044 30.1 61.8 16.5 19.8 51.4 35.92
## 10800 4.3 2.8 1.9 4.1 6.1 3.84
## median _UL FoldChange_mean _UL FoldChange_median _UL
## 10044 33.01 1.021035 0.9006821
## 10800 3.97 1.573770 1.5753968
##
## [[2]]
## GSM9098 _nonUL GSM9099 _nonUL GSM9100 _nonUL GSM9101 _nonUL
## 10044 53.6 35.6 39.9 9.1
## 10800 2.6 3.7 1.1 2.8
## GSM9102 _nonUL mean _nonUL median _nonUL
## 10044 37.7 35.18 36.65
## 10800 2.0 2.44 2.52The calcium gene expression was found to be more expressed in UL than nonULs from the above data.
Glucose uptake gene expression:
getMeanMedian("INS")## [1] "The foldchage of UL means to nonUL means is:"
## [1] 1.098485
## [1] "The foldchage of UL medians to nonUL medians is:"
## [1] 1.266667## [[1]]
## GSM9093 _UL GSM9094 _UL GSM9095 _UL GSM9096 _UL GSM9097 _UL mean _UL
## 6124 3.8 2.6 2.9 2.4 2.8 2.9
## median _UL FoldChange_mean _UL FoldChange_median _UL
## 6124 2.85 1.098485 1.266667
##
## [[2]]
## GSM9098 _nonUL GSM9099 _nonUL GSM9100 _nonUL GSM9101 _nonUL GSM9102 _nonUL
## 6124 2.5 5.9 1 2 1.8
## mean _nonUL median _nonUL
## 6124 2.64 2.25Insulin or glucose uptake gene expression is found more expressed in UL than nonULs.
Cartilage and Ligament gene expression:
getMeanMedian("BMP2")## [1] "The foldchage of UL means to nonUL means is:"
## [1] 0.3889748 0.4469767
## [1] "The foldchage of UL medians to nonUL medians is:"
## [1] 0.4533133 0.4335535## [[1]]
## GSM9093 _UL GSM9094 _UL GSM9095 _UL GSM9096 _UL GSM9097 _UL mean _UL
## 4816 16.9 26.6 15.0 9.4 7.6 15.10
## 4817 8.6 7.3 33.3 33.3 13.6 19.22
## median _UL FoldChange_mean _UL FoldChange_median _UL
## 4816 15.05 0.3889748 0.4533133
## 4817 16.41 0.4469767 0.4335535
##
## [[2]]
## GSM9098 _nonUL GSM9099 _nonUL GSM9100 _nonUL GSM9101 _nonUL GSM9102 _nonUL
## 4816 66.7 29.5 33.0 33.4 31.5
## 4817 59.3 74.1 20.5 28.4 32.7
## mean _nonUL median _nonUL
## 4816 38.82 33.20
## 4817 43.00 37.85Cartilage or ligamentous gene expression was found almost halved, with less expression in ULs than nonULs.
Collagen gene expression:
getMeanMedian("COL1A1")## [1] "The foldchage of UL means to nonUL means is:"
## [1] 1.791564 2.998301 1.470046 1.460881
## [1] "The foldchage of UL medians to nonUL medians is:"
## [1] 2.036462 3.032546 1.501486 1.857368## [[1]]
## GSM9093 _UL GSM9094 _UL GSM9095 _UL GSM9096 _UL GSM9097 _UL mean _UL
## 1838 12251.2 7626.1 3906.3 9909.3 11523.3 9043.24
## 1839 1308.2 702.3 382.6 878.5 964.1 847.14
## 1840 46.3 47.8 22.8 43.2 63.2 44.66
## 16795 5471.3 1139.4 532.2 1961.5 2741.3 2369.14
## median _UL FoldChange_mean _UL FoldChange_median _UL
## 1838 9476.27 1.791564 2.036462
## 1839 862.82 2.998301 3.032546
## 1840 45.48 1.470046 1.501486
## 16795 2165.32 1.460881 1.857368
##
## [[2]]
## GSM9098 _nonUL GSM9099 _nonUL GSM9100 _nonUL GSM9101 _nonUL
## 1838 2579.1 4711.2 10021.6 4595.4
## 1839 171.6 119.3 420.4 414.9
## 1840 32.2 8.9 30.2 51.7
## 16795 614.2 1197.4 4146.7 1134.2
## GSM9102 _nonUL mean _nonUL median _nonUL
## 1838 3331.1 5047.68 4653.30
## 1839 286.5 282.54 284.52
## 1840 28.9 30.38 30.29
## 16795 1016.1 1621.72 1165.80Collagen is found significantly more expressed in ULs than in nonULs from the above data.
Striated muscle:
getMeanMedian("TTN")## [1] "The foldchage of UL means to nonUL means is:"
## [1] 0.4470272
## [1] "The foldchage of UL medians to nonUL medians is:"
## [1] 0.5227602## [[1]]
## GSM9093 _UL GSM9094 _UL GSM9095 _UL GSM9096 _UL GSM9097 _UL mean _UL
## 7701 2.4 33.3 30.2 85.1 31.7 36.54
## median _UL FoldChange_mean _UL FoldChange_median _UL
## 7701 32.5 0.4470272 0.5227602
##
## [[2]]
## GSM9098 _nonUL GSM9099 _nonUL GSM9100 _nonUL GSM9101 _nonUL GSM9102 _nonUL
## 7701 42.6 133 158.3 38.6 36.2
## mean _nonUL median _nonUL
## 7701 81.74 62.17Striated muscle gene expression is almost halved in being less expressed in ULs than in nonULs.
Lets recap what we have discovered about ULs when compared to nonULs in the tissue extracted directly from both. We looked at cartilage (lower in ULS), collagen (higher in ULs), striated muscle (lower in ULs), vitamin D (higher in ULs), calcium (higher in ULs), glucose uptake by way of insulin (higher in ULs), transferrin (higher in ULs), estrogen receptor genes (higher in ULs), and alcohol catabolism (lower in ULs) genes in UL and nonUL samples.
Lets put this together with what I have personally been told and read about ULs from doctors of gynecology and by articles published on peer reviewed and NIH studies. Alcohol increases UL risk, estrogen as well, and that ULs are a muscle that is a benign tumor in the uterus and also that vitamin D is lower in UL patients than in nonUL patients. What we have shown is that in the samples extracted directly from UL and nonUL tissue from the same uterus extractions of female donors, is that there is less alcohol catabolism in ULs than in nonULs, also striated muscle is lower in ULs than nonULs, and so is cartilage that is lower in ULs than nonULs. This could mean ULs do not use alcohol to grow or be facilitated by alcohol to grow and that a UL is not a muscle of striated muscle that can be broken down, nor made of cartilage that scars and hardens when injured and can’t be broken down. Also, Since there are higher calcium and vitamin D gene regulating genes in UL samples, either the ULs need calcium and vitamin D to grow and take up more of it than healthy tissue when circulating in the blood stream or it has those genes searching for Vitamin D and calcium to destroy the UL and keep it from growing thereby expressing more of those genes that process Vitamin D and calcium. Those would involve a deeper knowledge about those genes and the networks involved. The estrogen receptor genes and iron uptake genes as well as glucose uptake genes are expressed more in ULs too, so this shows that estrogen being expressed more in a UL indicates that it wants more estrogen to grow and expand, it needs more glucose to feed it by expressing insulin more which triggers more glucose uptake, and the iron transport gene called transferrin is also expressed more so that more blood cells can deliver those nutrients to the UL where it is making more of those genes searching for those nutrients. But also it could mean it is a protection mechanism to regulate the blood of the donor having a UL who might have low iron in the blood and use the uterus to regulate the need for more iron or that there is not much iron in the blood because the UL is using most of it. These genes are being requested by the UL to the body and the body responds in a positive feedback system. Some tests could be done that would require invitro testing to show some possible scenarios of what this means exactly. Put more iron in same amounts of blood that are from a donor with regular levels, lower levels, and much higher than normal levels of iron, and see if the UL responds by expressing more transferrin or less transferrin, then assume whichever the response, then the UL tissue is going to request more or stop requesting or slow down requests by way of how much gene expression the UL shows. Also, for glucose and estrogen to apply the same procedure to see the results, and measure the weight of the UL mass to see if it grows more over the study with each different sample test of lower, same, or higher amounts of each TF, INS, and ESR1 and ESR2.
Another couple of genes that would be interesting to discover if they have a relationship with ULs would be the genes that trigger more adrenaline and dopamine for when a donor is fearful or in fight or flight state as in working out intensely with heavy weight lifting or martial arts type fighting that releases more adrenaline and also with the euphoric feelings after working out from endorphins in the blood stream that act as dopamine in the brain.
Epinephrine or adrenaline: Entrez Gene Summary for PNMT Gene The product of this gene catalyzes the last step of the catecholamine biosynthesis pathway, which methylates norepinephrine to form epinephrine (adrenaline). The enzyme also has beta-carboline 2N-methyltransferase activity. This gene is thought to play a key step in regulating epinephrine production. Alternatively spliced transcript variants have been found for this gene. [provided by RefSeq, Nov 2012]
Entrez Gene Summary for DBH Gene The protein encoded by this gene is an oxidoreductase belonging to the copper type II, ascorbate-dependent monooxygenase family. The encoded protein, expressed in neuroscretory vesicles and chromaffin granules of the adrenal medulla, catalyzes the conversion of dopamine to norepinephrine, which functions as both a hormone and as the main neurotransmitter of the sympathetic nervous system. The enzyme encoded by this gene exists exists in both soluble and membrane-bound forms, depending on the absence or presence, respectively, of a signal peptide. Mutations in this gene cause dopamine beta-hydroxylate deficiency in human patients, characterized by deficits in autonomic and cardiovascular function, including hypotension and ptosis. Polymorphisms in this gene may play a role in a variety of psychiatric disorders. [provided by RefSeq, Aug 2017]
Entrez Gene Summary for ADRA2A Gene Alpha-2-adrenergic receptors are members of the G protein-coupled receptor superfamily. The alpha-2-adrenergic receptors are a type of adrenergic receptors (for adrenaline or epinephrine), which inhibit adenylate cyclase. These receptors include 3 highly homologous subtypes: alpha2A, alpha2B, and alpha2C. They are involved in regulating the release of neurotransmitter molecules from sympathetic nerves and from adrenergic neurons in the central nervous system. The sympathetic nervous system regulates cardiovascular function by activating adrenergic receptors in the heart, blood vessels and kidney. Studies in mouse revealed that both the alpha2A and alpha2C receptor subtypes were required for presynaptic transmitter release from the sympathetic nervous system in the heart and from central noradrenergic neurons. The alpha-2-adrenergic receptors are also involved in catecholamine signaling by extracellular regulated protein kinase 1 and 2 (ERK1/2) pathways. A clear association between the alpha-2-adrenergic receptor and disease has not been yet established. [provided by RefSeq, Sep 2019]
Entrez Gene Summary for ADRB2 Gene This gene encodes beta-2-adrenergic receptor which is a member of the G protein-coupled receptor superfamily. This receptor is directly associated with one of its ultimate effectors, the class C L-type calcium channel Ca(V)1.2. This receptor-channel complex also contains a G protein, an adenylyl cyclase, cAMP-dependent kinase, and the counterbalancing phosphatase, PP2A. The assembly of the signaling complex provides a mechanism that ensures specific and rapid signaling by this G protein-coupled receptor. This receptor is also a transcription regulator of the alpha-synuclein gene, and together, both genes are believed to be associated with risk of Parkinson’s Disease. This gene is intronless. Different polymorphic forms, point mutations, and/or downregulation of this gene are associated with nocturnal asthma, obesity, type 2 diabetes and cardiovascular disease. [provided by RefSeq, Oct 2019]
endorphines Entrez Gene Summary for OPRM1 Gene This gene encodes one of at least three opioid receptors in humans; the mu opioid receptor (MOR). The MOR is the principal target of endogenous opioid peptides and opioid analgesic agents such as beta-endorphin and enkephalins. The MOR also has an important role in dependence to other drugs of abuse, such as nicotine, cocaine, and alcohol via its modulation of the dopamine system. The NM_001008503.2:c.118A>G allele has been associated with opioid and alcohol addiction and variations in pain sensitivity but evidence for it having a causal role is conflicting. Multiple transcript variants encoding different isoforms have been found for this gene. Though the canonical MOR belongs to the superfamily of 7-transmembrane-spanning G-protein-coupled receptors some isoforms of this gene have only 6 transmembrane domains. [provided by RefSeq, Oct 2013]
Entrez Gene Summary for POMC Gene This gene encodes a preproprotein that undergoes extensive, tissue-specific, post-translational processing via cleavage by subtilisin-like enzymes known as prohormone convertases. There are eight potential cleavage sites within the preproprotein and, depending on tissue type and the available convertases, processing may yield as many as ten biologically active peptides involved in diverse cellular functions. The encoded protein is synthesized mainly in corticotroph cells of the anterior pituitary where four cleavage sites are used; adrenocorticotrophin, essential for normal steroidogenesis and the maintenance of normal adrenal weight, and lipotropin beta are the major end products. In other tissues, including the hypothalamus, placenta, and epithelium, all cleavage sites may be used, giving rise to peptides with roles in pain and energy homeostasis, melanocyte stimulation, and immune modulation. These include several distinct melanotropins, lipotropins, and endorphins that are contained within the adrenocorticotrophin and beta-lipotropin peptides. The antimicrobial melanotropin alpha peptide exhibits antibacterial and antifungal activity. Mutations in this gene have been associated with early onset obesity, adrenal insufficiency, and red hair pigmentation. Alternatively spliced transcript variants encoding the same protein have been described. [provided by RefSeq, Jan 2016]
Entrez Gene Summary for CRH Gene This gene encodes a member of the corticotropin-releasing factor family. The encoded preproprotein is proteolytically processed to generate the mature neuropeptide hormone. In response to stress, this hormone is secreted by the paraventricular nucleus (PVN) of the hypothalamus, binds to corticotropin releasing hormone receptors and stimulates the release of adrenocorticotropic hormone from the pituitary gland. Marked reduction in this protein has been observed in association with Alzheimer’s disease. Autosomal recessive hypothalamic corticotropin deficiency has multiple and potentially fatal metabolic consequences including hypoglycemia and hepatitis. In addition to production in the hypothalamus, this protein is also synthesized in peripheral tissues, such as T lymphocytes, and is highly expressed in the placenta. In the placenta it is a marker that determines the length of gestation and the timing of parturition and delivery. A rapid increase in circulating levels of the hormone occurs at the onset of parturition, suggesting that, in addition to its metabolic functions, this protein may act as a trigger for parturition. [provided by RefSeq, Nov 2015]
Entrez Gene Summary for GNRH1 Gene This gene encodes a preproprotein that is proteolytically processed to generate a peptide that is a member of the gonadotropin-releasing hormone (GnRH) family of peptides. Alternative splicing results in multiple transcript variants, at least one of which is secreted and then cleaved to generate gonadoliberin-1 and GnRH-associated peptide 1. Gonadoliberin-1 stimulates the release of luteinizing and follicle stimulating hormones, which are important for reproduction. Mutations in this gene are associated with hypogonadotropic hypogonadism. [provided by RefSeq, Nov 2015]
Entrez Gene Summary for OXT Gene This gene encodes a precursor protein that is processed to produce oxytocin and neurophysin I. Oxytocin is a posterior pituitary hormone which is synthesized as an inactive precursor in the hypothalamus along with its carrier protein neurophysin I. Together with neurophysin, it is packaged into neurosecretory vesicles and transported axonally to the nerve endings in the neurohypophysis, where it is either stored or secreted into the bloodstream. The precursor seems to be activated while it is being transported along the axon to the posterior pituitary. This hormone contracts smooth muscle during parturition and lactation. It is also involved in cognition, tolerance, adaptation and complex sexual and maternal behaviour, as well as in the regulation of water excretion and cardiovascular functions. [provided by RefSeq, Dec 2013]
dopamine GeneCards Summary for DRD2 Gene DRD2 (Dopamine Receptor D2) is a Protein Coding gene. Diseases associated with DRD2 include Cocaine Dependence and Substance Dependence. Among its related pathways are Monoamine GPCRs and Signaling by GPCR. Gene Ontology (GO) annotations related to this gene include G protein-coupled receptor activity and identical protein binding. An important paralog of this gene is ADRA2A.
Entrez Gene Summary for ADRA2A Gene Alpha-2-adrenergic receptors are members of the G protein-coupled receptor superfamily. The alpha-2-adrenergic receptors are a type of adrenergic receptors (for adrenaline or epinephrine), which inhibit adenylate cyclase. These receptors include 3 highly homologous subtypes: alpha2A, alpha2B, and alpha2C. They are involved in regulating the release of neurotransmitter molecules from sympathetic nerves and from adrenergic neurons in the central nervous system. The sympathetic nervous system regulates cardiovascular function by activating adrenergic receptors in the heart, blood vessels and kidney. Studies in mouse revealed that both the alpha2A and alpha2C receptor subtypes were required for presynaptic transmitter release from the sympathetic nervous system in the heart and from central noradrenergic neurons. The alpha-2-adrenergic receptors are also involved in catecholamine signaling by extracellular regulated protein kinase 1 and 2 (ERK1/2) pathways. A clear association between the alpha-2-adrenergic receptor and disease has not been yet established. [provided by RefSeq, Sep 2019]
Dopamine and adrenaline both use ADRA2A (Alpha-2-adrenergic receptors) gene, but the other gene selected for dopamine is DRD2 (dopamine receptor D2), and for adrenaline the genes are PNMT, DBH, ADRA2B, and ADRA2C. For endorphines the genes are OPRM1, POMC, CRH (cortico tropin hormone releasing factor), OXT (oxytocin encoder), and GNRH1 (gonadotropin releasing hormone).
Lets look at the adrenaline genes first: ADRA2A, PNMT, DBH, ADRA2B, and ADRA2C. then we’ll look at the Dopamine genes of ADRA2A and DRD2 finally, we will look at the endorphine related genes: OPRM1, POMC, CRH, OXT, and GNRH1
Adrenaline genes:ADRA2A, PNMT, DBH, ADRA2B, and ADRA2C
getMeanMedian("ADRA2A")## [1] "The foldchage of UL means to nonUL means is:"
## [1] 0.8215006
## [1] "The foldchage of UL medians to nonUL medians is:"
## [1] 0.8575723## [[1]]
## GSM9093 _UL GSM9094 _UL GSM9095 _UL GSM9096 _UL GSM9097 _UL mean _UL
## 9355 64.6 86.6 65 75.6 75 73.36
## median _UL FoldChange_mean _UL FoldChange_median _UL
## 9355 74.18 0.8215006 0.8575723
##
## [[2]]
## GSM9098 _nonUL GSM9099 _nonUL GSM9100 _nonUL GSM9101 _nonUL GSM9102 _nonUL
## 9355 97.2 51.9 56.1 83.7 157.6
## mean _nonUL median _nonUL
## 9355 89.3 86.5From the above 1 out of 5 adrenaline genes is less expressed in ULs.
getMeanMedian("PNMT")## [1] "The foldchage of UL means to nonUL means is:"
## [1] 1.829907
## [1] "The foldchage of UL medians to nonUL medians is:"
## [1] 1.7125## [[1]]
## GSM9093 _UL GSM9094 _UL GSM9095 _UL GSM9096 _UL GSM9097 _UL mean _UL
## 6319 7.4 12.7 27.3 13.3 37.2 19.58
## median _UL FoldChange_mean _UL FoldChange_median _UL
## 6319 16.44 1.829907 1.7125
##
## [[2]]
## GSM9098 _nonUL GSM9099 _nonUL GSM9100 _nonUL GSM9101 _nonUL GSM9102 _nonUL
## 6319 15.5 5.5 8.2 15.8 8.5
## mean _nonUL median _nonUL
## 6319 10.7 9.6From the above 1 out of 5 adrenaline genes is significantly more expressed in ULs.
getMeanMedian("DBH")## [1] "The foldchage of UL means to nonUL means is:"
## [1] 1.211429
## [1] "The foldchage of UL medians to nonUL medians is:"
## [1] 1.252308## [[1]]
## GSM9093 _UL GSM9094 _UL GSM9095 _UL GSM9096 _UL GSM9097 _UL mean _UL
## 5976 3.9 5.9 2.3 2.3 6.8 4.24
## median _UL FoldChange_mean _UL FoldChange_median _UL
## 5976 4.07 1.211429 1.252308
##
## [[2]]
## GSM9098 _nonUL GSM9099 _nonUL GSM9100 _nonUL GSM9101 _nonUL GSM9102 _nonUL
## 5976 2.7 4.2 3 5.6 2
## mean _nonUL median _nonUL
## 5976 3.5 3.25From the above we can add another significantly expressed adrenaline gene in ULs making it 2 out of 5.
getMeanMedian("ADRA2B")## [1] "The foldchage of UL means to nonUL means is:"
## [1] 0.7598456
## [1] "The foldchage of UL medians to nonUL medians is:"
## [1] 0.8115385## [[1]]
## GSM9093 _UL GSM9094 _UL GSM9095 _UL GSM9096 _UL GSM9097 _UL mean _UL
## 8042 18.3 7.5 13.6 27.4 31.6 19.68
## median _UL FoldChange_mean _UL FoldChange_median _UL
## 8042 18.99 0.7598456 0.8115385
##
## [[2]]
## GSM9098 _nonUL GSM9099 _nonUL GSM9100 _nonUL GSM9101 _nonUL GSM9102 _nonUL
## 8042 20.9 10.9 19.9 50.3 27.5
## mean _nonUL median _nonUL
## 8042 25.9 23.4From the above we can add another adrenaline gene less expressed in ULs making it 2 out of 5 less expressed adrenaline genes in ULs.
getMeanMedian("ADRA2C")## [1] "The foldchage of UL means to nonUL means is:"
## [1] 0.7610536
## [1] "The foldchage of UL medians to nonUL medians is:"
## [1] 0.889433## [[1]]
## GSM9093 _UL GSM9094 _UL GSM9095 _UL GSM9096 _UL GSM9097 _UL mean _UL
## 5654 148.5 80.6 37 162.8 137.4 113.26
## median _UL FoldChange_mean _UL FoldChange_median _UL
## 5654 125.33 0.7610536 0.889433
##
## [[2]]
## GSM9098 _nonUL GSM9099 _nonUL GSM9100 _nonUL GSM9101 _nonUL GSM9102 _nonUL
## 5654 4.9 355.9 166.4 133 83.9
## mean _nonUL median _nonUL
## 5654 148.82 140.91The above gene is also expressed less as an adrenaline gene in ULs and this makes 3 out of 5 adrenaline genes expressed less in ULs than nonULs, but the genes that were expressed more were significantly more in ULS.
Dopamine genes are ADRA2A and DRD2, we already saw ADRA2A in the adrenaline genes.
getMeanMedian("DRD2")## [1] "The foldchage of UL means to nonUL means is:"
## [1] 2.795054 3.868666 1.509873 4.593879
## [1] "The foldchage of UL medians to nonUL medians is:"
## [1] 2.568739 4.197736 1.499874 4.248605## [[1]]
## GSM9093 _UL GSM9094 _UL GSM9095 _UL GSM9096 _UL GSM9097 _UL mean _UL
## 6116 79.0 485.8 223.3 191.5 161.9 228.30
## 11029 35.0 160.4 64.6 52.6 61.5 74.82
## 16294 40.5 93.8 73.3 59.6 31.0 59.64
## 16308 125.6 451.9 185.5 210.3 137.5 222.16
## median _UL FoldChange_mean _UL FoldChange_median _UL
## 6116 207.40 2.795054 2.568739
## 11029 63.05 3.868666 4.197736
## 16294 59.62 1.509873 1.499874
## 16308 197.90 4.593879 4.248605
##
## [[2]]
## GSM9098 _nonUL GSM9099 _nonUL GSM9100 _nonUL GSM9101 _nonUL
## 6116 102.4 44.6 47.6 134.0
## 11029 9.0 5.3 40.3 31.4
## 16294 63.0 40.0 24.2 41.9
## 16308 65.7 34.0 57.4 44.8
## GSM9102 _nonUL mean _nonUL median _nonUL
## 6116 79.8 81.68 80.74
## 11029 10.7 19.34 15.02
## 16294 28.4 39.50 39.75
## 16308 39.9 48.36 46.58The above dopamine gene is significantly expressed more in ULs, but the first one we saw in the adrenaline genes was expressed less in ULs.
Endorphine genes: OPRM1, POMC, CRH, OXT, and GNRHR
getMeanMedian("OPRM1")## [1] "The foldchage of UL means to nonUL means is:"
## [1] 0.7662338 0.3619403 1.4927368
## [1] "The foldchage of UL medians to nonUL medians is:"
## [1] 0.6525705 1.0971429 1.5484439## [[1]]
## GSM9093 _UL GSM9094 _UL GSM9095 _UL GSM9096 _UL GSM9097 _UL mean _UL
## 7500 15.5 6.3 18.8 3.5 3.1 9.44
## 7505 1.0 1.9 1.1 3.0 2.7 1.94
## 10775 23.9 66.4 54.1 65.8 46.7 51.38
## median _UL FoldChange_mean _UL FoldChange_median _UL
## 7500 7.87 0.7662338 0.6525705
## 7505 1.92 0.3619403 1.0971429
## 10775 52.74 1.4927368 1.5484439
##
## [[2]]
## GSM9098 _nonUL GSM9099 _nonUL GSM9100 _nonUL GSM9101 _nonUL
## 7500 23.9 2.0 3.8 20.1
## 7505 2.1 20.8 1.1 1.4
## 10775 52.7 19.2 33.7 44.2
## GSM9102 _nonUL mean _nonUL median _nonUL
## 7500 11.8 12.32 12.06
## 7505 1.4 5.36 1.75
## 10775 22.3 34.42 34.06The above endorphine gene is expressed significantly less in ULs for 1 out of 5 genes.
getMeanMedian("POMC")## [1] "The foldchage of UL means to nonUL means is:"
## [1] 0.899654
## [1] "The foldchage of UL medians to nonUL medians is:"
## [1] 1.010417## [[1]]
## GSM9093 _UL GSM9094 _UL GSM9095 _UL GSM9096 _UL GSM9097 _UL mean _UL
## 5247 4.5 4.4 3.3 8.1 5.7 5.2
## median _UL FoldChange_mean _UL FoldChange_median _UL
## 5247 4.85 0.899654 1.010417
##
## [[2]]
## GSM9098 _nonUL GSM9099 _nonUL GSM9100 _nonUL GSM9101 _nonUL GSM9102 _nonUL
## 5247 4.7 4.9 10.9 4.7 3.7
## mean _nonUL median _nonUL
## 5247 5.78 4.8The above endorphine gene is the 2nd gene expressed less in ULs, making it 2 out of 5 endorphine genes expressed less in ULs.
getMeanMedian("CRH")## [1] "The foldchage of UL means to nonUL means is:"
## [1] 1.1057692 0.2853535
## [1] "The foldchage of UL medians to nonUL medians is:"
## [1] 0.9456265 0.1639028## [[1]]
## GSM9093 _UL GSM9094 _UL GSM9095 _UL GSM9096 _UL GSM9097 _UL mean _UL
## 5156 5.8 9.5 3.4 2.1 2.2 4.60
## 5157 1.6 5.5 3.0 22.1 1.7 6.78
## median _UL FoldChange_mean _UL FoldChange_median _UL
## 5156 4.00 1.1057692 0.9456265
## 5157 4.25 0.2853535 0.1639028
##
## [[2]]
## GSM9098 _nonUL GSM9099 _nonUL GSM9100 _nonUL GSM9101 _nonUL GSM9102 _nonUL
## 5156 4.3 3.4 5.7 4.4 3.0
## 5157 30.2 28.1 13.9 41.4 5.2
## mean _nonUL median _nonUL
## 5156 4.16 4.23
## 5157 23.76 25.93The above gene shows more occurences of less gene expression in ULs, making it 3 out of 5 genes less expressed in UL for endorphine genes.
getMeanMedian("OXT")## [1] "The foldchage of UL means to nonUL means is:"
## [1] 1.072254
## [1] "The foldchage of UL medians to nonUL medians is:"
## [1] 1.032868## [[1]]
## GSM9093 _UL GSM9094 _UL GSM9095 _UL GSM9096 _UL GSM9097 _UL mean _UL
## 7097 8.4 10.3 21.9 9.7 23.9 14.84
## median _UL FoldChange_mean _UL FoldChange_median _UL
## 7097 12.57 1.072254 1.032868
##
## [[2]]
## GSM9098 _nonUL GSM9099 _nonUL GSM9100 _nonUL GSM9101 _nonUL GSM9102 _nonUL
## 7097 10.5 34 3.6 15.8 5.3
## mean _nonUL median _nonUL
## 7097 13.84 12.17The above gene is slightly more expressed in ULs for endorphine genes making it 1 out of 5 of the endorphine genes expressed more in ULs.
getMeanMedian("GNRHR")## [1] "The foldchage of UL means to nonUL means is:"
## [1] 0.7298475 1.0680851 1.1158771
## [1] "The foldchage of UL medians to nonUL medians is:"
## [1] 0.630137 1.303268 1.263314## [[1]]
## GSM9093 _UL GSM9094 _UL GSM9095 _UL GSM9096 _UL GSM9097 _UL mean _UL
## 10928 1.8 12.3 2.4 14.5 2.5 6.70
## 10929 16.3 3.5 13.3 7.2 9.9 10.04
## 15712 8.7 32.4 42.0 43.0 48.2 34.86
## median _UL FoldChange_mean _UL FoldChange_median _UL
## 10928 4.60 0.7298475 0.630137
## 10929 9.97 1.0680851 1.303268
## 15712 38.43 1.1158771 1.263314
##
## [[2]]
## GSM9098 _nonUL GSM9099 _nonUL GSM9100 _nonUL GSM9101 _nonUL
## 10928 2.2 7.4 7.2 25.3
## 10929 4.2 23.6 2.9 5.9
## 15712 54.6 34.6 12.7 29.6
## GSM9102 _nonUL mean _nonUL median _nonUL
## 10928 3.8 9.18 7.30
## 10929 10.4 9.40 7.65
## 15712 24.7 31.24 30.42The above gene is expressed more times in ULs than non ULs, making it 2 out of 5 genes expressed more in ULs for endorphine genes.
Lets also look at testosterone genes:
Entrez Gene Summary for HSD17B3 Gene This isoform of 17 beta-hydroxysteroid dehydrogenase is expressed predominantly in the testis and catalyzes the conversion of androstenedione to testosterone. It preferentially uses NADP as cofactor. Deficiency can result in male pseudohermaphroditism with gynecomastia. [provided by RefSeq, Jul 2008]
Entrez Gene Summary for GNRH1 Gene This gene encodes a preproprotein that is proteolytically processed to generate a peptide that is a member of the gonadotropin-releasing hormone (GnRH) family of peptides. Alternative splicing results in multiple transcript variants, at least one of which is secreted and then cleaved to generate gonadoliberin-1 and GnRH-associated peptide 1. Gonadoliberin-1 stimulates the release of luteinizing and follicle stimulating hormones, which are important for reproduction. Mutations in this gene are associated with hypogonadotropic hypogonadism. [provided by RefSeq, Nov 2015]
Entrez Gene Summary for SHBG Gene This gene encodes a steroid binding protein that was first described as a plasma protein secreted by the liver but is now thought to participate in the regulation of steroid responses. The encoded protein transports androgens and estrogens in the blood, binding each steroid molecule as a dimer formed from identical or nearly identical monomers. Polymorphisms in this gene have been associated with polycystic ovary syndrome and type 2 diabetes mellitus. Alternative splicing results in multiple transcript variants. [provided by RefSeq, Jan 2014]
Entrez Gene Summary for CYP19A1 Gene This gene encodes a member of the cytochrome P450 superfamily of enzymes. The cytochrome P450 proteins are monooxygenases which catalyze many reactions involved in drug metabolism and synthesis of cholesterol, steroids and other lipids. This protein localizes to the endoplasmic reticulum and catalyzes the last steps of estrogen biosynthesis. Mutations in this gene can result in either increased or decreased aromatase activity; the associated phenotypes suggest that estrogen functions both as a sex steroid hormone and in growth or differentiation. Alternative promoter use and alternative splicing results in multiple transcript variants that have different tissue specificities. [provided by RefSeq, Dec 2016]
Entrez Gene Summary for AR Gene The androgen receptor gene is more than 90 kb long and codes for a protein that has 3 major functional domains: the N-terminal domain, DNA-binding domain, and androgen-binding domain. The protein functions as a steroid-hormone activated transcription factor. Upon binding the hormone ligand, the receptor dissociates from accessory proteins, translocates into the nucleus, dimerizes, and then stimulates transcription of androgen responsive genes. This gene contains 2 polymorphic trinucleotide repeat segments that encode polyglutamine and polyglycine tracts in the N-terminal transactivation domain of its protein. Expansion of the polyglutamine tract from the normal 9-34 repeats to the pathogenic 38-62 repeats causes spinal bulbar muscular atrophy (SBMA, also known as Kennedy’s disease). Mutations in this gene are also associated with complete androgen insensitivity (CAIS). Alternative splicing results in multiple transcript variants encoding different isoforms. [provided by RefSeq, Jan 2017]
Entrez Gene Summary for LEP Gene This gene encodes a protein that is secreted by white adipocytes into the circulation and plays a major role in the regulation of energy homeostasis. Circulating leptin binds to the leptin receptor in the brain, which activates downstream signaling pathways that inhibit feeding and promote energy expenditure. This protein also has several endocrine functions, and is involved in the regulation of immune and inflammatory responses, hematopoiesis, angiogenesis, reproduction, bone formation and wound healing. Mutations in this gene and its regulatory regions cause severe obesity and morbid obesity with hypogonadism in human patients. A mutation in this gene has also been linked to type 2 diabetes mellitus development. [provided by RefSeq, Aug 2017]
Testosterone genes: LEP, AR, CYP19A1, SHBG, HSD17B3
getMeanMedian("LEP")## [1] "The foldchage of UL means to nonUL means is:"
## [1] 0.8532934
## [1] "The foldchage of UL medians to nonUL medians is:"
## [1] 1## [[1]]
## GSM9093 _UL GSM9094 _UL GSM9095 _UL GSM9096 _UL GSM9097 _UL mean _UL
## 6618 3.1 6 3.2 9.2 7 5.7
## median _UL FoldChange_mean _UL FoldChange_median _UL
## 6618 5.85 0.8532934 1
##
## [[2]]
## GSM9098 _nonUL GSM9099 _nonUL GSM9100 _nonUL GSM9101 _nonUL GSM9102 _nonUL
## 6618 13.1 6.5 4.6 5.2 4
## mean _nonUL median _nonUL
## 6618 6.68 5.85The above gene is expressed less in ULs for 1 out of 5 testosterone genes expressed less in ULs.
getMeanMedian("AR")## [1] "The foldchage of UL means to nonUL means is:"
## [1] 1.393361 1.411132
## [1] "The foldchage of UL medians to nonUL medians is:"
## [1] 1.326411 1.515377## [[1]]
## GSM9093 _UL GSM9094 _UL GSM9095 _UL GSM9096 _UL GSM9097 _UL mean _UL
## 10554 77.6 261.0 245.0 124.4 160.9 173.78
## 11026 11.2 106.7 99.8 78.5 84.1 76.06
## median _UL FoldChange_mean _UL FoldChange_median _UL
## 10554 167.34 1.393361 1.326411
## 11026 81.30 1.411132 1.515377
##
## [[2]]
## GSM9098 _nonUL GSM9099 _nonUL GSM9100 _nonUL GSM9101 _nonUL
## 10554 154.1 93.6 127.6 105.5
## 11026 90.2 53.4 29.3 40.4
## GSM9102 _nonUL mean _nonUL median _nonUL
## 10554 142.8 124.72 126.16
## 11026 56.2 53.90 53.65The above testosterone gene is significantly more expressed in ULs, for 2 out of 5 genes expressed more in ULs out of these 5 testosterone genes.
getMeanMedian("CYP19A1")## [1] "The foldchage of UL means to nonUL means is:"
## [1] 1.588477
## [1] "The foldchage of UL medians to nonUL medians is:"
## [1] 1.67226## [[1]]
## GSM9093 _UL GSM9094 _UL GSM9095 _UL GSM9096 _UL GSM9097 _UL mean _UL
## 3002 11.8 10.6 32.9 49.5 49.6 30.88
## median _UL FoldChange_mean _UL FoldChange_median _UL
## 3002 31.89 1.588477 1.67226
##
## [[2]]
## GSM9098 _nonUL GSM9099 _nonUL GSM9100 _nonUL GSM9101 _nonUL GSM9102 _nonUL
## 3002 14.6 32.8 23.1 8 18.7
## mean _nonUL median _nonUL
## 3002 19.44 19.07The above gene is significantly more expressed in ULs for 2 out of 5 testosterone genes more expressed in ULs.
getMeanMedian("SHBG")## [1] "The foldchage of UL means to nonUL means is:"
## [1] 1.171053
## [1] "The foldchage of UL medians to nonUL medians is:"
## [1] 1.193995## [[1]]
## GSM9093 _UL GSM9094 _UL GSM9095 _UL GSM9096 _UL GSM9097 _UL mean _UL
## 15062 4 6.4 5 4.8 6.5 5.34
## median _UL FoldChange_mean _UL FoldChange_median _UL
## 15062 5.17 1.171053 1.193995
##
## [[2]]
## GSM9098 _nonUL GSM9099 _nonUL GSM9100 _nonUL GSM9101 _nonUL
## 15062 6.4 3.6 2.1 6.6
## GSM9102 _nonUL mean _nonUL median _nonUL
## 15062 4.1 4.56 4.33This is the third testosterone gene more expressed and significantly in ULs as seen in above data.
getMeanMedian("HSD17B3")## [1] "The foldchage of UL means to nonUL means is:"
## [1] 0.7835478
## [1] "The foldchage of UL medians to nonUL medians is:"
## [1] 0.8089967## [[1]]
## GSM9093 _UL GSM9094 _UL GSM9095 _UL GSM9096 _UL GSM9097 _UL mean _UL
## 6511 36.8 15.3 34.6 28.9 54.9 34.1
## median _UL FoldChange_mean _UL FoldChange_median _UL
## 6511 34.35 0.7835478 0.8089967
##
## [[2]]
## GSM9098 _nonUL GSM9099 _nonUL GSM9100 _nonUL GSM9101 _nonUL GSM9102 _nonUL
## 6511 62.1 28.3 41.4 48.9 36.9
## mean _nonUL median _nonUL
## 6511 43.52 42.46The above gene is the 2nd out of 5 genes expressed less in ULs. Overall the testosterone is expressed more in ULs than in nonULs.
That is a lot of information to process at this point, so lets recap. We looked at genes from a list of genes provided on genecards.org for each of the following: - testosterone (3 out of 5 genes expressed more in ULs) - adrenaline (3 out of 5 genes expressed more in ULs and 2 out of 5 less in ULs) - endorphines (2 out of 5 less, 2 out of 5 more, and 1 out of 5 either of mean or median) - dopamine (1 out of 2 genes is expressed less in ULs and the other is more in ULs) - vitamin D (1 out of 1 gene is expressed more in UL) - calcium (1 out of 1 gene is expressed more in UL) - estrogen (2 out of 2 genes is more expressed in ULs) - insulin (1 out of 1 gene is expressed more in UL) - iron (1 out of 1 gene is more expressed in ULs) - alcohol catabolism (3 out of 3 genes are expressed less in ULs) - muscle tissue (1 out of 1 gene is expressed less in UL) - cartilage or ligament tissue (1 out of 1 gene is expressed less in UL) - collagen or tendon tissue (1 out of 1 gene is expressed more in UL)
So, we can see that for adrenaline, endorphines, and dopamine the gene expression varies as either more or less, by vote adrenaline is expressed more, but a tie by vote for endorphines and dopamine.
The alcohol catabolism, striated muscle, and cartilage or ligament genes are less expressed in ULs.
The vitamin D, calcium, estrogen, insulin, iron, and collagen genes are expressed more in ULs. But also, testosterone, is produced more in ULs by a vote of 3 out of 5 genes.
This implies that the fight or flight hormones aren’t determinant on ULs because they can be either more or less expressed by sampling location in the array that extracted these gene expression measures from 5 UL and 5 nonUL samples. So, we can assume being in fear or angered/threatened has no effect on having a UL or not.
Now, for the genes expressed less in ULs involving alcohol catabolism, striated muscle, and cartilage or ligaments, this is tricky. Because studies have shown and it is common knowledge that alcohol consumption from surveyed UL patients has shown that alcohol increases risk of UL based on those surveys where many with ULs had a history of alcohol. Then we have questions to answer this implication of interpreting these findings of less gene expression in ULs: Does the UL tissue not express more of these genes or less than the same amount as nonUL because it has less need or use for these tissue types because the body is supplying enough of those items, such as alcohol? Does that imply these genes would shrink the UL if they had more, so why express more of those genes? Lets recall how much change there was of less expression of these three genes in ULs: each gene was expressed approximately half as much in UL compared to nonULs. That is enough evidence to say some type of relationship exists in these genes when finding causal relationships for UL pathogenesis. So, if having less of these gene expressed means the UL doesn’t need to make more because enough is in the bloodstream, then this could mean that the patient with a UL is not working out and building muscle, not drinking alcohol, and not stretching or exerting her tendons in sports activities.
Lets look at tumor necrosis factor, TNF, to see if we can answer the above questions on why UL tissue expresses less of those genes in alchol catabolism, muscle, and ligaments.
getMeanMedian('TNF')## [1] "The foldchage of UL means to nonUL means is:"
## [1] 0.9961014
## [1] "The foldchage of UL medians to nonUL medians is:"
## [1] 1.010152## [[1]]
## GSM9093 _UL GSM9094 _UL GSM9095 _UL GSM9096 _UL GSM9097 _UL mean _UL
## 6639 43.4 35.9 42.7 33.4 49 40.88
## median _UL FoldChange_mean _UL FoldChange_median _UL
## 6639 41.79 0.9961014 1.010152
##
## [[2]]
## GSM9098 _nonUL GSM9099 _nonUL GSM9100 _nonUL GSM9101 _nonUL GSM9102 _nonUL
## 6639 47.2 41.7 28 61.5 26.8
## mean _nonUL median _nonUL
## 6639 41.04 41.37The tumor necrosis factor results are mixed and also close to no change in ULs compared to nonULs.
Now, for the genes that the ULs have more gene expression than nonULs for mostly minerals the body uses: vitamin D, calcium, estrogen, insulin, iron, and collagen genes. The UL is making more of the genes that process these minerals and collagen, so it would appear that the UL is in control and as far as UL pathogenesis is concerned, it needs more of those genes to grow.
So, if I were to suggest to a person with UL to keep from having her UL grow, a UL patient should not drink alcohol, workout, use sports related activities to workout that require extending and flexing the ligaments and muscles, stay out of the sun to avoid creating vitamin D or producing more calcium, avoid high glucose foods like carbohydrates, find other ways to minimize estrogen production, don’t eat high iron foods, and stay away from products that claim to build collagen production in your body. This way the UL would starve itself and shrink. There would need to be better information or an examination of patients with UL doing those specific requests and then seeing if they notice by belly bloat or size, if their bodies have smaller abdomens where their ULs are located.
It is unlikely advice, but at the moment, with this data analysis and assumptions made, either the assumptions need to be corrected, or current literature on UL pathogenesis is incorrect. Because based on blood samples of UL patients and surveys on health history that include alcohol weekly consumption. Iron levels are low and attributed to women having heavy mensa, vitamin D is low, and the women with UL have stated they drink regularly, and also women with UL are heavy or obese if even slightly, and have familial history of ULs.
Any volunteers on who would want to try this out? The regimen is: not to drink alcohol, workout, participate in sports related or competitive type athletic conditioning like kickboxing, wrestling, or even yoga that you consider fun and stop once you start to feel defensive or competitive or like you have to prove yourself so as to not create testosterone which could feel like fun based on a reward system so stop when fun just keep it simple to work out your tendons, muscles, and ligaments, so that there are more of those genes being navigated through your bloodstream but only until you feel aggressive and stop once you feel you have to prove yourself or stress your body out beyond normal so as to avoid releasing or creating testosterone as ULs express more testosterone genes than nonULs, stay out of the sun and away from dairy foods to avoid producing too much vitamin D and calcium, don’t eat any dark leafy greens to avoid high iron levels, and eat a lot of foods that compete with estrogen like the lentils, beans, and nuts for phytoestrogens to keep your body from producing the estrogen the UL is asking for more of by expressing more of it. And avoid vampire facials that promote collagen production, or any creams and hair additives that claim to have collagen so as to avoid providing your UL with collagen.
Lets see if lipoprotein has an effective relationship in UL compared to nonUL tissue. Entrez Gene Summary for LPL Gene LPL encodes lipoprotein lipase, which is expressed in heart, muscle, and adipose tissue. LPL functions as a homodimer, and has the dual functions of triglyceride hydrolase and ligand/bridging factor for receptor-mediated lipoprotein uptake. Severe mutations that cause LPL deficiency result in type I hyperlipoproteinemia, while less extreme mutations in LPL are linked to many disorders of lipoprotein metabolism. [provided by RefSeq, Jul 2008]
getMeanMedian("LPL")## [1] "The foldchage of UL means to nonUL means is:"
## [1] 0.6087676 0.8615715
## [1] "The foldchage of UL medians to nonUL medians is:"
## [1] 0.4627342 0.8917845## [[1]]
## GSM9093 _UL GSM9094 _UL GSM9095 _UL GSM9096 _UL GSM9097 _UL mean _UL
## 3075 37.3 8.5 2.9 8.1 16.8 14.72
## 3076 44.6 57.3 50.9 71.8 56.1 56.14
## median _UL FoldChange_mean _UL FoldChange_median _UL
## 3075 11.61 0.6087676 0.4627342
## 3076 56.12 0.8615715 0.8917845
##
## [[2]]
## GSM9098 _nonUL GSM9099 _nonUL GSM9100 _nonUL GSM9101 _nonUL GSM9102 _nonUL
## 3075 37.6 14.4 9.6 26 33.3
## 3076 81.3 60.7 48.4 77 58.4
## mean _nonUL median _nonUL
## 3075 24.18 25.09
## 3076 65.16 62.93Interestingly, the lipoprotein expressed in heart, muscle, and adipose tissue or fat tissue is less expressed in ULs than nonULs by 13-53% less. I would also add that fat production in a UL is reduced the same as with musle and ligament gene expression and alcohol catabolism. If you look at pictures of UL online they look like white hardened balls in muscle tissue of the uterus from images hard to take in without immediately wanting to turn the page to something else. They actually look like onions from images alone or as an onion of muscle fascia that lumped together into a knot of bone ligament tissue and muscle layer wrapping called fascia. They also look like fennel bulbs in a way. I looked up fennel bulbs and found they are rich in manganese. The functions getMeanMedian on SLC30A10 which supports manganese from a search of it on genecards.org showed that uterine fibroids are underexpressed in manganese, and that maybe incorporating fennel into the diet would help shrink a uterine fibroid or keep it from growing. See below.
getMeanMedian('SLC30A10')## [1] "The foldchage of UL means to nonUL means is:"
## [1] 0.5960729
## [1] "The foldchage of UL medians to nonUL medians is:"
## [1] 0.6418011## [[1]]
## GSM9093 _UL GSM9094 _UL GSM9095 _UL GSM9096 _UL GSM9097 _UL mean _UL
## 19799 0.7 14.7 10.6 11.2 5.3 8.5
## median _UL FoldChange_mean _UL FoldChange_median _UL
## 19799 9.55 0.5960729 0.6418011
##
## [[2]]
## GSM9098 _nonUL GSM9099 _nonUL GSM9100 _nonUL GSM9101 _nonUL
## 19799 23.8 15.5 9.2 17.8
## GSM9102 _nonUL mean _nonUL median _nonUL
## 19799 5 14.26 14.88We can see that the manganese is almost halved from the results above, suggesting ULs do not want any more manganese. Melatonin and cortisol genes were looked at as well. The melatonin protein used the genecards.org gene, MTNR1A, and for cortisol the gene, HSD11B2. Lets look at those results.
getMeanMedian('MTNR1A')## [1] "The foldchage of UL means to nonUL means is:"
## [1] 0.5846774
## [1] "The foldchage of UL medians to nonUL medians is:"
## [1] 0.3490328## [[1]]
## GSM9093 _UL GSM9094 _UL GSM9095 _UL GSM9096 _UL GSM9097 _UL mean _UL
## 20732 3.5 29.7 2 4.4 3.9 8.7
## median _UL FoldChange_mean _UL FoldChange_median _UL
## 20732 4.15 0.5846774 0.3490328
##
## [[2]]
## GSM9098 _nonUL GSM9099 _nonUL GSM9100 _nonUL GSM9101 _nonUL
## 20732 32.7 4.8 2.1 25.9
## GSM9102 _nonUL mean _nonUL median _nonUL
## 20732 8.9 14.88 11.89We can see that melatonin is halved and so this suggests ULs don’t want more melatonin that the body produces when in the sun and sleeping at night. This is reason to go in the sun, that contradicts staying out of the sun because the UL wants more vitamin D and calcium. However, getting enough sleep seems plausible.
Next, cortisol.
getMeanMedian('HSD11B2')## [1] "The foldchage of UL means to nonUL means is:"
## [1] 0.732475
## [1] "The foldchage of UL medians to nonUL medians is:"
## [1] 0.8800727## [[1]]
## GSM9093 _UL GSM9094 _UL GSM9095 _UL GSM9096 _UL GSM9097 _UL mean _UL
## 3657 22.1 27.4 51.1 7.2 45.8 30.72
## median _UL FoldChange_mean _UL FoldChange_median _UL
## 3657 29.06 0.732475 0.8800727
##
## [[2]]
## GSM9098 _nonUL GSM9099 _nonUL GSM9100 _nonUL GSM9101 _nonUL GSM9102 _nonUL
## 3657 9.3 96.3 24.1 60.4 19.6
## mean _nonUL median _nonUL
## 3657 41.94 33.02Cortisol is related to stress, and the UL produces 10-20% less of the gene responsible for synthesizing cortisol. This can be increased by coffee on an empty stomach or driving in traffic. So I would say the UL doesn’t want more stress and produces less of this hormone to reduce the feeling of being stressed.
If we follow this deductive reasoning and see that many females who drink don’t have bloated guts or pregnant looking bellies for the most part that are indicative of ULs. While doctors who follow known research say alcohol is a risk factor of ULs, then why do women who don’t drink get them and have them continue to grow? So which is it, alcohol, or not, or in moderation? Maybe, adding in a routin of working out but not to exertion, and drinking a glass of red wine a day every day, and incorporating less carbohydrates to reduce glucose, not consuming calcium rich foods, eating more nuts and lentils and soy with phyto-estrogen, staying out of the sun, and also consuming a daily diet of some amount of fennel will help shrink a UL by starving it of its needed or craved nutrients and depressing it. Only a study would help prove that this diet and wellness plan would work.
Read in the table from above.
allGenesUL <- read.delim('./UL and nonUL foldchange tables/allGenesUL.csv', sep=",", header=FALSE,na.strings = c('',' ','NA'))ULheader <- read.csv('./UL and nonUL foldchange tables/header_UL_names.csv', sep=',')ULnames <- as.character(ULheader$x)colnames(allGenesUL)[2:10] <- ULnames
allGenesUL <- allGenesUL[complete.cases(allGenesUL),]
genes <- as.data.frame(allGenesUL$V1)
colnames(genes) <- 'rowID'symbol <- as.data.frame(UL$Gene.Symbol)
colnames(symbol) <- 'symbol'
symbol$rowID <- row.names(symbol)
ULgenes_tested <- merge(genes,symbol, by.x='rowID',by.y='rowID')
ULgenes_tested## rowID symbol
## 1 1839 COL1A1
## 2 1840 COL1A1
## 3 3002 CYP19A1
## 4 3076 LPL
## 5 3657 HSD11B2
## 6 3781 VDR
## 7 3782 VDR
## 8 4817 BMP2
## 9 5157 CRH
## 10 5247 POMC
## 11 5654 ADRA2C
## 12 5788 ADH1C
## 13 5976 DBH
## 14 6124 INS
## 15 6319 PNMT
## 16 6511 HSD17B3
## 17 6618 LEP
## 18 6639 TNF
## 19 7097 OXT
## 20 7339 ADH1A
## 21 7505 OPRM1
## 22 7701 TTN
## 23 8042 ADRA2B
## 24 9103 ADH1B
## 25 9104 ADH1B
## 26 9355 ADRA2A
## 27 10561 ESR2
## 28 10562 ESR2
## 29 10563 ESR2
## 30 10564 ESR2
## 31 10671 ESR1
## 32 10672 ESR1
## 33 10673 ESR1
## 34 10775 OPRM1
## 35 10800 CASR
## 36 10929 GNRHR
## 37 11026 AR
## 38 11029 DRD2
## 39 11032 ESR1
## 40 13072 VDR
## 41 13442 TF
## 42 13443 TF
## 43 14924 ESR1
## 44 14925 ESR1
## 45 15062 SHBG
## 46 15712 GNRHR
## 47 16294 DRD2
## 48 16308 DRD2
## 49 16530 ESR1
## 50 16557 ESR1
## 51 16795 COL1A1
## 52 19473 TF
## 53 19799 SLC30A10
## 54 20732 MTNR1AULgenes <- merge(ULgenes_tested,allGenesUL, by.x='rowID', by.y='V1')
ULgenes <- ULgenes[order(ULgenes$symbol),]
ULgenes## rowID symbol GSM9093 _UL GSM9094 _UL GSM9095 _UL GSM9096 _UL GSM9097 _UL
## 20 7339 ADH1A 18.4 26.8 20.5 15.8 3.6
## 24 9103 ADH1B 25.5 296.7 359.2 24.9 1.5
## 25 9104 ADH1B 11.8 22.0 37.4 17.9 21.1
## 12 5788 ADH1C 4.2 18.0 2.3 3.1 4.9
## 26 9355 ADRA2A 64.6 86.6 65.0 75.6 75.0
## 23 8042 ADRA2B 18.3 7.5 13.6 27.4 31.6
## 11 5654 ADRA2C 148.5 80.6 37.0 162.8 137.4
## 37 11026 AR 11.2 106.7 99.8 78.5 84.1
## 8 4817 BMP2 8.6 7.3 33.3 33.3 13.6
## 35 10800 CASR 4.3 2.8 1.9 4.1 6.1
## 1 1839 COL1A1 1308.2 702.3 382.6 878.5 964.1
## 2 1840 COL1A1 46.3 47.8 22.8 43.2 63.2
## 51 16795 COL1A1 5471.3 1139.4 532.2 1961.5 2741.3
## 9 5157 CRH 1.6 5.5 3.0 22.1 1.7
## 3 3002 CYP19A1 11.8 10.6 32.9 49.5 49.6
## 13 5976 DBH 3.9 5.9 2.3 2.3 6.8
## 38 11029 DRD2 35.0 160.4 64.6 52.6 61.5
## 47 16294 DRD2 40.5 93.8 73.3 59.6 31.0
## 48 16308 DRD2 125.6 451.9 185.5 210.3 137.5
## 31 10671 ESR1 61.3 106.1 78.8 77.9 46.1
## 32 10672 ESR1 18.0 74.9 66.7 55.9 9.2
## 33 10673 ESR1 38.8 79.7 87.9 60.1 20.3
## 39 11032 ESR1 5.1 5.3 1.3 15.4 24.4
## 43 14924 ESR1 22.3 60.6 56.3 47.6 31.2
## 44 14925 ESR1 75.9 77.3 60.5 28.1 28.3
## 49 16530 ESR1 25.4 19.7 3.1 26.4 4.2
## 50 16557 ESR1 39.6 8.4 14.1 6.1 8.3
## 27 10561 ESR2 13.7 9.1 9.1 16.2 37.3
## 28 10562 ESR2 23.1 20.2 6.3 46.8 23.3
## 29 10563 ESR2 0.9 2.2 0.7 0.6 7.7
## 30 10564 ESR2 5.5 6.9 5.6 6.5 60.8
## 36 10929 GNRHR 16.3 3.5 13.3 7.2 9.9
## 46 15712 GNRHR 8.7 32.4 42.0 43.0 48.2
## 5 3657 HSD11B2 22.1 27.4 51.1 7.2 45.8
## 16 6511 HSD17B3 36.8 15.3 34.6 28.9 54.9
## 14 6124 INS 3.8 2.6 2.9 2.4 2.8
## 17 6618 LEP 3.1 6.0 3.2 9.2 7.0
## 4 3076 LPL 44.6 57.3 50.9 71.8 56.1
## 54 20732 MTNR1A 3.5 29.7 2.0 4.4 3.9
## 21 7505 OPRM1 1.0 1.9 1.1 3.0 2.7
## 34 10775 OPRM1 23.9 66.4 54.1 65.8 46.7
## 19 7097 OXT 8.4 10.3 21.9 9.7 23.9
## 15 6319 PNMT 7.4 12.7 27.3 13.3 37.2
## 10 5247 POMC 4.5 4.4 3.3 8.1 5.7
## 45 15062 SHBG 4.0 6.4 5.0 4.8 6.5
## 53 19799 SLC30A10 0.7 14.7 10.6 11.2 5.3
## 41 13442 TF 14.1 35.8 40.8 27.3 55.1
## 42 13443 TF 8.6 50.3 47.4 64.8 53.2
## 52 19473 TF 8.7 0.9 0.7 6.9 6.8
## 18 6639 TNF 43.4 35.9 42.7 33.4 49.0
## 22 7701 TTN 2.4 33.3 30.2 85.1 31.7
## 6 3781 VDR 82.2 37.4 11.4 33.5 84.2
## 7 3782 VDR 62.1 11.4 49.2 30.0 42.9
## 40 13072 VDR 7.5 49.8 35.4 76.3 30.3
## mean _UL median _UL FoldChange_mean _UL FoldChange_median _UL
## 20 17.02 17.71 0.6986864 0.6843122
## 24 141.56 83.53 0.4703303 0.2628052
## 25 22.04 21.55 1.1760939 1.6809672
## 12 6.50 4.55 0.4103535 0.3070175
## 26 73.36 74.18 0.8215006 0.8575723
## 23 19.68 18.99 0.7598456 0.8115385
## 11 113.26 125.33 0.7610536 0.8894330
## 37 76.06 81.30 1.4111317 1.5153774
## 8 19.22 16.41 0.4469767 0.4335535
## 35 3.84 3.97 1.5737705 1.5753968
## 1 847.14 862.82 2.9983011 3.0325460
## 2 44.66 45.48 1.4700461 1.5014856
## 51 2369.14 2165.32 1.4608810 1.8573683
## 9 6.78 4.25 0.2853535 0.1639028
## 3 30.88 31.89 1.5884774 1.6722601
## 13 4.24 4.07 1.2114286 1.2523077
## 38 74.82 63.05 3.8686660 4.1977364
## 47 59.64 59.62 1.5098734 1.4998742
## 48 222.16 197.90 4.5938792 4.2486046
## 31 74.04 75.97 1.2787565 1.3590340
## 32 44.94 50.42 1.6072961 1.9984146
## 33 57.36 58.73 1.1439968 1.1091596
## 39 10.30 7.80 1.2810945 1.2892562
## 43 43.60 45.60 1.0840378 1.2271259
## 44 54.02 57.26 0.9803993 1.1440559
## 49 15.76 17.73 0.9281508 1.0224913
## 50 15.30 11.25 0.9646910 0.8253852
## 27 17.08 14.95 0.6509146 0.5543196
## 28 23.94 23.20 1.1465517 1.1132438
## 29 2.42 1.55 1.6351351 1.4090909
## 30 17.06 6.70 1.5177936 0.7074974
## 36 10.04 9.97 1.0680851 1.3032680
## 46 34.86 38.43 1.1158771 1.2633136
## 5 30.72 29.06 0.7324750 0.8800727
## 16 34.10 34.35 0.7835478 0.8089967
## 14 2.90 2.85 1.0984848 1.2666667
## 17 5.70 5.85 0.8532934 1.0000000
## 4 56.14 56.12 0.8615715 0.8917845
## 54 8.70 4.15 0.5846774 0.3490328
## 21 1.94 1.92 0.3619403 1.0971429
## 34 51.38 52.74 1.4927368 1.5484439
## 19 14.84 12.57 1.0722543 1.0328677
## 15 19.58 16.44 1.8299065 1.7125000
## 10 5.20 4.85 0.8996540 1.0104167
## 45 5.34 5.17 1.1710526 1.1939954
## 53 8.50 9.55 0.5960729 0.6418011
## 41 34.62 35.21 1.3948429 1.4513603
## 42 44.86 48.85 1.2052660 1.3005857
## 52 4.80 5.80 8.8888889 11.6000000
## 18 40.88 41.79 0.9961014 1.0101523
## 22 36.54 32.50 0.4470272 0.5227602
## 6 49.74 43.57 1.9399376 1.7841933
## 7 39.12 41.01 1.2885375 1.5773077
## 40 39.86 37.63 2.7414030 4.2280899allGenesNonUL <- read.delim('./UL and nonUL foldchange tables/allGenesNonUL.csv', sep=",", header=FALSE)nonULheader <- read.csv('./UL and nonUL foldchange tables/header_nonUL_names.csv', sep=',')nonULnames <- as.character(nonULheader$x)colnames(allGenesNonUL) <- c('rowID',nonULnames)
head(allGenesNonUL)## rowID GSM9098 _nonUL GSM9099 _nonUL GSM9100 _nonUL GSM9101 _nonUL
## 1 13442 37.1 23.7 12.7 43.1
## 2 13443 34.9 37.9 10.9 61.9
## 3 19473 0.4 1.0 0.5 0.5
## 4 10671 52.7 53.9 84.0 58.5
## 5 10672 55.1 22.5 12.4 15.0
## 6 10673 60.6 54.9 22.5 51.0
## GSM9102 _nonUL mean _nonUL median _nonUL
## 1 7.5 24.82 24.26
## 2 40.5 37.22 37.56
## 3 0.3 0.54 0.50
## 4 40.4 57.90 55.90
## 5 34.8 27.96 25.23
## 6 61.7 50.14 52.95allGenesNonUL <- allGenesNonUL[complete.cases(allGenesNonUL),]genes2 <- as.data.frame(allGenesNonUL$rowID)
colnames(genes2) <- 'rowID'
symbol2 <- as.data.frame(nonUL$Gene.Symbol)
colnames(symbol2) <- 'symbol'
symbol2$rowID <- row.names(symbol2)
nonULgenes_tested <- merge(genes2,symbol2, by.x='rowID',by.y='rowID')
nonULgenes_tested## rowID symbol
## 1 1839 COL1A1
## 2 1840 COL1A1
## 3 3002 CYP19A1
## 4 3076 LPL
## 5 3657 HSD11B2
## 6 3781 VDR
## 7 3782 VDR
## 8 4817 BMP2
## 9 5157 CRH
## 10 5247 POMC
## 11 5654 ADRA2C
## 12 5788 ADH1C
## 13 5976 DBH
## 14 6124 INS
## 15 6319 PNMT
## 16 6511 HSD17B3
## 17 6618 LEP
## 18 6639 TNF
## 19 7097 OXT
## 20 7339 ADH1A
## 21 7505 OPRM1
## 22 7701 TTN
## 23 8042 ADRA2B
## 24 9103 ADH1B
## 25 9104 ADH1B
## 26 9355 ADRA2A
## 27 10561 ESR2
## 28 10562 ESR2
## 29 10563 ESR2
## 30 10564 ESR2
## 31 10671 ESR1
## 32 10672 ESR1
## 33 10673 ESR1
## 34 10775 OPRM1
## 35 10800 CASR
## 36 10929 GNRHR
## 37 11026 AR
## 38 11029 DRD2
## 39 11032 ESR1
## 40 13072 VDR
## 41 13442 TF
## 42 13443 TF
## 43 14924 ESR1
## 44 14925 ESR1
## 45 15062 SHBG
## 46 15712 GNRHR
## 47 16294 DRD2
## 48 16308 DRD2
## 49 16530 ESR1
## 50 16557 ESR1
## 51 16795 COL1A1
## 52 19473 TF
## 53 19799 SLC30A10
## 54 20732 MTNR1AnonULgenes <- merge(nonULgenes_tested,allGenesNonUL, by.x='rowID', by.y='rowID')
nonULgenes <- nonULgenes[order(nonULgenes$symbol),]
nonULgenes## rowID symbol GSM9098 _nonUL GSM9099 _nonUL GSM9100 _nonUL GSM9101 _nonUL
## 20 7339 ADH1A 28.5 27.4 16.3 21.1
## 24 9103 ADH1B 334.7 59.5 91.6 462.2
## 25 9104 ADH1B 6.9 6.2 18.8 5.9
## 12 5788 ADH1C 24.7 8.0 23.0 9.7
## 26 9355 ADRA2A 97.2 51.9 56.1 83.7
## 23 8042 ADRA2B 20.9 10.9 19.9 50.3
## 11 5654 ADRA2C 4.9 355.9 166.4 133.0
## 37 11026 AR 90.2 53.4 29.3 40.4
## 8 4817 BMP2 59.3 74.1 20.5 28.4
## 35 10800 CASR 2.6 3.7 1.1 2.8
## 1 1839 COL1A1 171.6 119.3 420.4 414.9
## 2 1840 COL1A1 32.2 8.9 30.2 51.7
## 51 16795 COL1A1 614.2 1197.4 4146.7 1134.2
## 9 5157 CRH 30.2 28.1 13.9 41.4
## 3 3002 CYP19A1 14.6 32.8 23.1 8.0
## 13 5976 DBH 2.7 4.2 3.0 5.6
## 38 11029 DRD2 9.0 5.3 40.3 31.4
## 47 16294 DRD2 63.0 40.0 24.2 41.9
## 48 16308 DRD2 65.7 34.0 57.4 44.8
## 31 10671 ESR1 52.7 53.9 84.0 58.5
## 32 10672 ESR1 55.1 22.5 12.4 15.0
## 33 10673 ESR1 60.6 54.9 22.5 51.0
## 39 11032 ESR1 4.4 3.6 3.7 20.8
## 43 14924 ESR1 68.3 33.7 19.1 45.9
## 44 14925 ESR1 85.3 43.1 44.1 45.0
## 49 16530 ESR1 17.7 11.9 11.3 24.8
## 50 16557 ESR1 11.4 25.6 32.1 5.2
## 27 10561 ESR2 27.7 6.1 30.9 47.9
## 28 10562 ESR2 44.9 5.5 28.8 20.8
## 29 10563 ESR2 1.2 3.8 0.8 1.0
## 30 10564 ESR2 7.7 3.0 19.0 20.7
## 36 10929 GNRHR 4.2 23.6 2.9 5.9
## 46 15712 GNRHR 54.6 34.6 12.7 29.6
## 5 3657 HSD11B2 9.3 96.3 24.1 60.4
## 16 6511 HSD17B3 62.1 28.3 41.4 48.9
## 14 6124 INS 2.5 5.9 1.0 2.0
## 17 6618 LEP 13.1 6.5 4.6 5.2
## 4 3076 LPL 81.3 60.7 48.4 77.0
## 54 20732 MTNR1A 32.7 4.8 2.1 25.9
## 21 7505 OPRM1 2.1 20.8 1.1 1.4
## 34 10775 OPRM1 52.7 19.2 33.7 44.2
## 19 7097 OXT 10.5 34.0 3.6 15.8
## 15 6319 PNMT 15.5 5.5 8.2 15.8
## 10 5247 POMC 4.7 4.9 10.9 4.7
## 45 15062 SHBG 6.4 3.6 2.1 6.6
## 53 19799 SLC30A10 23.8 15.5 9.2 17.8
## 41 13442 TF 37.1 23.7 12.7 43.1
## 42 13443 TF 34.9 37.9 10.9 61.9
## 52 19473 TF 0.4 1.0 0.5 0.5
## 18 6639 TNF 47.2 41.7 28.0 61.5
## 22 7701 TTN 42.6 133.0 158.3 38.6
## 6 3781 VDR 23.2 19.6 20.8 35.9
## 7 3782 VDR 57.5 26.6 22.0 25.4
## 40 13072 VDR 6.3 8.4 5.0 43.6
## GSM9102 _nonUL mean _nonUL median _nonUL
## 20 28.5 24.36 25.88
## 24 556.9 300.98 317.84
## 25 55.9 18.74 12.82
## 12 13.8 15.84 14.82
## 26 157.6 89.30 86.50
## 23 27.5 25.90 23.40
## 11 83.9 148.82 140.91
## 37 56.2 53.90 53.65
## 8 32.7 43.00 37.85
## 35 2.0 2.44 2.52
## 1 286.5 282.54 284.52
## 2 28.9 30.38 30.29
## 51 1016.1 1621.72 1165.80
## 9 5.2 23.76 25.93
## 3 18.7 19.44 19.07
## 13 2.0 3.50 3.25
## 38 10.7 19.34 15.02
## 47 28.4 39.50 39.75
## 48 39.9 48.36 46.58
## 31 40.4 57.90 55.90
## 32 34.8 27.96 25.23
## 33 61.7 50.14 52.95
## 39 7.7 8.04 6.05
## 43 34.1 40.22 37.16
## 44 58.0 55.10 50.05
## 49 19.2 16.98 17.34
## 50 5.0 15.86 13.63
## 27 18.6 26.24 26.97
## 28 4.4 20.88 20.84
## 29 0.6 1.48 1.10
## 30 5.8 11.24 9.47
## 36 10.4 9.40 7.65
## 46 24.7 31.24 30.42
## 5 19.6 41.94 33.02
## 16 36.9 43.52 42.46
## 14 1.8 2.64 2.25
## 17 4.0 6.68 5.85
## 4 58.4 65.16 62.93
## 54 8.9 14.88 11.89
## 21 1.4 5.36 1.75
## 34 22.3 34.42 34.06
## 19 5.3 13.84 12.17
## 15 8.5 10.70 9.60
## 10 3.7 5.78 4.80
## 45 4.1 4.56 4.33
## 53 5.0 14.26 14.88
## 41 7.5 24.82 24.26
## 42 40.5 37.22 37.56
## 52 0.3 0.54 0.50
## 18 26.8 41.04 41.37
## 22 36.2 81.74 62.17
## 6 28.7 25.64 24.42
## 7 20.3 30.36 26.00
## 40 9.4 14.54 8.90We now have our tables, but will be using the ULgenes and not the nonULgenes table, as the ULgenes table is the table with the mean gene expression fold change values for the number of gene variants that the data doesn’t include.
Lets chart some of these values for those genes we looked at.
genesFoldChange <- ULgenes[,c(2,10,11)]
genesFoldChange## symbol FoldChange_mean _UL FoldChange_median _UL
## 20 ADH1A 0.6986864 0.6843122
## 24 ADH1B 0.4703303 0.2628052
## 25 ADH1B 1.1760939 1.6809672
## 12 ADH1C 0.4103535 0.3070175
## 26 ADRA2A 0.8215006 0.8575723
## 23 ADRA2B 0.7598456 0.8115385
## 11 ADRA2C 0.7610536 0.8894330
## 37 AR 1.4111317 1.5153774
## 8 BMP2 0.4469767 0.4335535
## 35 CASR 1.5737705 1.5753968
## 1 COL1A1 2.9983011 3.0325460
## 2 COL1A1 1.4700461 1.5014856
## 51 COL1A1 1.4608810 1.8573683
## 9 CRH 0.2853535 0.1639028
## 3 CYP19A1 1.5884774 1.6722601
## 13 DBH 1.2114286 1.2523077
## 38 DRD2 3.8686660 4.1977364
## 47 DRD2 1.5098734 1.4998742
## 48 DRD2 4.5938792 4.2486046
## 31 ESR1 1.2787565 1.3590340
## 32 ESR1 1.6072961 1.9984146
## 33 ESR1 1.1439968 1.1091596
## 39 ESR1 1.2810945 1.2892562
## 43 ESR1 1.0840378 1.2271259
## 44 ESR1 0.9803993 1.1440559
## 49 ESR1 0.9281508 1.0224913
## 50 ESR1 0.9646910 0.8253852
## 27 ESR2 0.6509146 0.5543196
## 28 ESR2 1.1465517 1.1132438
## 29 ESR2 1.6351351 1.4090909
## 30 ESR2 1.5177936 0.7074974
## 36 GNRHR 1.0680851 1.3032680
## 46 GNRHR 1.1158771 1.2633136
## 5 HSD11B2 0.7324750 0.8800727
## 16 HSD17B3 0.7835478 0.8089967
## 14 INS 1.0984848 1.2666667
## 17 LEP 0.8532934 1.0000000
## 4 LPL 0.8615715 0.8917845
## 54 MTNR1A 0.5846774 0.3490328
## 21 OPRM1 0.3619403 1.0971429
## 34 OPRM1 1.4927368 1.5484439
## 19 OXT 1.0722543 1.0328677
## 15 PNMT 1.8299065 1.7125000
## 10 POMC 0.8996540 1.0104167
## 45 SHBG 1.1710526 1.1939954
## 53 SLC30A10 0.5960729 0.6418011
## 41 TF 1.3948429 1.4513603
## 42 TF 1.2052660 1.3005857
## 52 TF 8.8888889 11.6000000
## 18 TNF 0.9961014 1.0101523
## 22 TTN 0.4470272 0.5227602
## 6 VDR 1.9399376 1.7841933
## 7 VDR 1.2885375 1.5773077
## 40 VDR 2.7414030 4.2280899Lets also add in the Gene.Title column that spells out the gene symbol from the original ul table read in at the beginning of this script.
title <- ul[,c(5:6)]
geneTitle <- merge(title,genesFoldChange,by.x='Gene.Symbol', by.y='symbol')
geneTitle <- geneTitle[!duplicated(geneTitle),]
geneTitle## Gene.Symbol Gene.Title
## 1 ADH1A alcohol dehydrogenase 1A (class I), alpha polypeptide
## 2 ADH1B alcohol dehydrogenase 1B (class I), beta polypeptide
## 3 ADH1B alcohol dehydrogenase 1B (class I), beta polypeptide
## 8 ADH1C alcohol dehydrogenase 1C (class I), gamma polypeptide
## 9 ADRA2A adrenoceptor alpha 2A
## 10 ADRA2B adrenoceptor alpha 2B
## 11 ADRA2C adrenoceptor alpha 2C
## 12 AR androgen receptor
## 14 BMP2 bone morphogenetic protein 2
## 16 CASR calcium-sensing receptor
## 18 COL1A1 collagen, type I, alpha 1
## 19 COL1A1 collagen, type I, alpha 1
## 20 COL1A1 collagen, type I, alpha 1
## 30 CRH corticotropin releasing hormone
## 32 CYP19A1 cytochrome P450, family 19, subfamily A, polypeptide 1
## 33 DBH dopamine beta-hydroxylase (dopamine beta-monooxygenase)
## 34 DRD2 dopamine receptor D2
## 35 DRD2 dopamine receptor D2
## 36 DRD2 dopamine receptor D2
## 46 ESR1 estrogen receptor 1
## 47 ESR1 estrogen receptor 1
## 48 ESR1 estrogen receptor 1
## 49 ESR1 estrogen receptor 1
## 50 ESR1 estrogen receptor 1
## 51 ESR1 estrogen receptor 1
## 52 ESR1 estrogen receptor 1
## 53 ESR1 estrogen receptor 1
## 118 ESR2 estrogen receptor 2 (ER beta)
## 119 ESR2 estrogen receptor 2 (ER beta)
## 120 ESR2 estrogen receptor 2 (ER beta)
## 121 ESR2 estrogen receptor 2 (ER beta)
## 138 GNRHR gonadotropin-releasing hormone receptor
## 139 GNRHR gonadotropin-releasing hormone receptor
## 144 HSD11B2 hydroxysteroid (11-beta) dehydrogenase 2
## 145 HSD17B3 hydroxysteroid (17-beta) dehydrogenase 3
## 146 INS insulin
## 147 LEP leptin
## 148 LPL lipoprotein lipase
## 150 MTNR1A melatonin receptor 1A
## 151 OPRM1 opioid receptor, mu 1
## 152 OPRM1 opioid receptor, mu 1
## 157 OXT oxytocin/neurophysin I prepropeptide
## 158 PNMT phenylethanolamine N-methyltransferase
## 159 POMC proopiomelanocortin
## 160 SHBG sex hormone-binding globulin
## 161 SLC30A10 solute carrier family 30, member 10
## 162 TF transferrin
## 163 TF transferrin
## 164 TF transferrin
## 174 TNF tumor necrosis factor
## 175 TTN titin
## 176 VDR vitamin D (1,25- dihydroxyvitamin D3) receptor
## 177 VDR vitamin D (1,25- dihydroxyvitamin D3) receptor
## 178 VDR vitamin D (1,25- dihydroxyvitamin D3) receptor
## FoldChange_mean _UL FoldChange_median _UL
## 1 0.6986864 0.6843122
## 2 1.1760939 1.6809672
## 3 0.4703303 0.2628052
## 8 0.4103535 0.3070175
## 9 0.8215006 0.8575723
## 10 0.7598456 0.8115385
## 11 0.7610536 0.8894330
## 12 1.4111317 1.5153774
## 14 0.4469767 0.4335535
## 16 1.5737705 1.5753968
## 18 1.4608810 1.8573683
## 19 2.9983011 3.0325460
## 20 1.4700461 1.5014856
## 30 0.2853535 0.1639028
## 32 1.5884774 1.6722601
## 33 1.2114286 1.2523077
## 34 3.8686660 4.1977364
## 35 4.5938792 4.2486046
## 36 1.5098734 1.4998742
## 46 1.2787565 1.3590340
## 47 1.0840378 1.2271259
## 48 0.9646910 0.8253852
## 49 1.2810945 1.2892562
## 50 1.1439968 1.1091596
## 51 0.9281508 1.0224913
## 52 1.6072961 1.9984146
## 53 0.9803993 1.1440559
## 118 1.5177936 0.7074974
## 119 1.1465517 1.1132438
## 120 1.6351351 1.4090909
## 121 0.6509146 0.5543196
## 138 1.0680851 1.3032680
## 139 1.1158771 1.2633136
## 144 0.7324750 0.8800727
## 145 0.7835478 0.8089967
## 146 1.0984848 1.2666667
## 147 0.8532934 1.0000000
## 148 0.8615715 0.8917845
## 150 0.5846774 0.3490328
## 151 0.3619403 1.0971429
## 152 1.4927368 1.5484439
## 157 1.0722543 1.0328677
## 158 1.8299065 1.7125000
## 159 0.8996540 1.0104167
## 160 1.1710526 1.1939954
## 161 0.5960729 0.6418011
## 162 8.8888889 11.6000000
## 163 1.2052660 1.3005857
## 164 1.3948429 1.4513603
## 174 0.9961014 1.0101523
## 175 0.4470272 0.5227602
## 176 2.7414030 4.2280899
## 177 1.9399376 1.7841933
## 178 1.2885375 1.5773077Lets separate this into those genes the UL are underexpressing, and those that the UL are overexpressing.
underexpressed <- subset(geneTitle, geneTitle$`FoldChange_mean _UL` < 1)
overexpressed <- subset(geneTitle, geneTitle$`FoldChange_median _UL` > 1)underexpressed_DT <- datatable(data=underexpressed, rownames=FALSE,
extensions=c('Buttons','Responsive'),
filter=list(position='top'),
options=list( dom='Bfrtip',scrollX = TRUE, scrollY=TRUE,
buttons=c('colvis','csv'),
language=list(sSearch='Filter:')
)
)
underexpressed_DT## Warning in instance$preRenderHook(instance): It seems your data is too big
## for client-side DataTables. You may consider server-side processing: https://
## rstudio.github.io/DT/server.htmloverexpressed_DT <- datatable(data=overexpressed, rownames=FALSE,
extensions=c('Buttons','Responsive'),
filter=list(position='top'),
options=list( dom='Bfrtip',scrollX = TRUE, scrollY=TRUE,
buttons=c('colvis','csv'),
language=list(sSearch='Filter:')
)
)
overexpressed_DT## Warning in instance$preRenderHook(instance): It seems your data is too big
## for client-side DataTables. You may consider server-side processing: https://
## rstudio.github.io/DT/server.htmlLets chart the overexpressed genes in UL compared to nonUL by more than 2.
doubled <- subset(overexpressed, overexpressed$`FoldChange_mean _UL`>2)
doubled## Gene.Symbol Gene.Title
## 19 COL1A1 collagen, type I, alpha 1
## 34 DRD2 dopamine receptor D2
## 35 DRD2 dopamine receptor D2
## 162 TF transferrin
## 176 VDR vitamin D (1,25- dihydroxyvitamin D3) receptor
## FoldChange_mean _UL FoldChange_median _UL
## 19 2.998301 3.032546
## 34 3.868666 4.197736
## 35 4.593879 4.248605
## 162 8.888889 11.600000
## 176 2.741403 4.228090doubleOrMore_DT <- datatable(data=doubled, rownames=FALSE,
extensions=c('Buttons','Responsive'),
filter=list(position='top'),
options=list( dom='Bfrtip',scrollX = TRUE, scrollY=TRUE,
buttons=c('colvis','csv'),
language=list(sSearch='Filter:')
)
)
doubleOrMore_DT## Warning in instance$preRenderHook(instance): It seems your data is too big
## for client-side DataTables. You may consider server-side processing: https://
## rstudio.github.io/DT/server.htmlggplot(data = doubled, aes(x=Gene.Symbol, y=`FoldChange_mean _UL`, fill=Gene.Symbol)) +
geom_bar(stat='identity', position=position_dodge())+
scale_fill_brewer(palette='Paired') + theme_minimal()+
ylab(label="Fold Change UL to nonUL") +
xlab("Genes Doubled in Gene Expression in UL to nonUL")
Lets also chart those genes that are underexpressed in UL compared to nonUL by at least half.
halved <- subset(underexpressed, underexpressed$`FoldChange_mean _UL` <= 0.5)halved## Gene.Symbol Gene.Title
## 3 ADH1B alcohol dehydrogenase 1B (class I), beta polypeptide
## 8 ADH1C alcohol dehydrogenase 1C (class I), gamma polypeptide
## 14 BMP2 bone morphogenetic protein 2
## 30 CRH corticotropin releasing hormone
## 151 OPRM1 opioid receptor, mu 1
## 175 TTN titin
## FoldChange_mean _UL FoldChange_median _UL
## 3 0.4703303 0.2628052
## 8 0.4103535 0.3070175
## 14 0.4469767 0.4335535
## 30 0.2853535 0.1639028
## 151 0.3619403 1.0971429
## 175 0.4470272 0.5227602underByHalf_DT <- datatable(data=halved, rownames=FALSE,
extensions=c('Buttons','Responsive'),
filter=list(position='top'),
options=list( dom='Bfrtip',scrollX = TRUE, scrollY=TRUE,
buttons=c('colvis','csv'),
language=list(sSearch='Filter:')
)
)
underByHalf_DT## Warning in instance$preRenderHook(instance): It seems your data is too big
## for client-side DataTables. You may consider server-side processing: https://
## rstudio.github.io/DT/server.htmlggplot(data = halved, aes(x=Gene.Symbol, y=`FoldChange_mean _UL`, fill=Gene.Symbol)) +
geom_bar(stat='identity', position=position_dodge())+
scale_fill_brewer(palette='Paired') + theme_minimal()+
ylab(label="Fold Change UL to nonUL") +
xlab("Genes Halved in Gene Expression in UL to nonUL")