In matteodelucchi/TRAL-Result-Analysis: Analysis of the output of TRAL

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.width=6
  )
options(tibble.print_min = 4L, tibble.print_max = 4L)
set.seed(123)

library(TRALResultAnalysis)

Introduction

[TODO: Extend and reformulate]

All the molecular pathways cross-talk with each other and are regulated by one another. Interestingly, concerted regulation exists between NF-κB, Wnt and other adhesion proteins such as E-cadherin. E-cadherin directly binds to β-catenin and NF-κB sequestering them at the plasma membrane level and subtracting them from the nucleus. When the transcription of WNT5A is activated by NF-κB, WNT5A binds its specific Wnt receptor inducing transcription of target genes such as snail, CD44 and YAP1. Snail upregulation, in turn, represses E-cadherin expression releasing β-catenin and NF-κB, available to translocate into the nucleus (43,44).

GSK-3β is a protein that has a pivotal role in cross-talk between Wnt and NF-κB (45). In colon and pancreatic cancer cells, GSK-3β positively regulates NF-κB activity and its activation confers a selective growth advantage to these cells, therefore acting as a tumour promoter (46). The molecular mechanisms underlying GSK-3β/NF-κB interaction remain to be further investigated. More than 100 proteins, involved in a wide spectrum of cellular processes, are substrates of GSK-3β, of which β-catenin and NF-κB inhibitor iκb are the most well known. Genes upregulated by β-catenin/TCF/LEF and/or NF-κB include proto-oncogenes, such as c-Myc and cyclin-D1, and genes regulating cell invasion/migration, such as Snail, CD44 and MMP-7 (47). [TODO][Rosa2015]

[Rosa2015][https://doi.org/10.3892/or.2015.4108]

Methods

Same as the others. See Generaloverview.rmd.

base_path <- "/home/delt/ZHAW/TRALResultAnalysis/"

library(TRALResultAnalysis)
tr_crcfavorable_path <- paste0(base_path, "inst/extdata/TRs_favorable_proteins_CRC_sp_l1000.tsv")
tr_crcunfavorable_path <- paste0(base_path, "inst/extdata/TRs_unfavorable_proteins_CRC_sp_l1000.tsv")
tr_gsk3b_path <- paste0(base_path, "inst/extdata/TRs_gsk3beta_proteins_CRC_sp_l1000.tsv")
dest_file_sp <- paste0(base_path, "data/swissprot_human.tsv")
dest_file_kin <- paste0(base_path, "data/swissprot_human_kinome.tsv")

tr_fav <- load_tr_annotations(tr_crcfavorable_path)
tr_unfav <- load_tr_annotations(tr_crcunfavorable_path)
tr_gsk3b <- load_tr_annotations(tr_gsk3b_path)

To get deeper insights about the proteins containing TRs, more information is added from Swiss-Prot by a left join

sp_url <- "https://www.uniprot.org/uniprot/?query=organism:%22Homo%20sapiens%20(Human)%20[9606]%22%20reviewed:yes&format=tab&columns=id,entry%20name,reviewed,protein%20names,genes,organism,length,virus%20hosts,encodedon,database(Pfam),interactor,comment(ABSORPTION),feature(ACTIVE%20SITE),comment(ACTIVITY%20REGULATION),feature(BINDING%20SITE),feature(CALCIUM%20BIND),comment(CATALYTIC%20ACTIVITY),comment(COFACTOR),feature(DNA%20BINDING),ec,comment(FUNCTION),comment(KINETICS),feature(METAL%20BINDING),feature(NP%20BIND),comment(PATHWAY),comment(PH%20DEPENDENCE),comment(REDOX%20POTENTIAL),rhea-id,feature(SITE),comment(TEMPERATURE%20DEPENDENCE)&sort=organism"

# Download the swissprot file only if it doesn't already exist.
# Uncomment this, if you want to use the most recent available data!
if(!file.exists(dest_file_sp)){
    download.file(sp_url, destfile = dest_file_sp)
}

sp_all_fav <- load_swissprot(dest_file_sp, tr_fav)
sp_all_unfav <- load_swissprot(dest_file_sp, tr_unfav)
sp_all_gsk3b <- load_swissprot(dest_file_sp, tr_gsk3b)

tr_unfav_sp <- merge(x = tr_unfav, y = sp_all_unfav, by = "ID", all.x = TRUE)
tr_fav_sp <- merge(x = tr_fav, y = sp_all_fav, by = "ID", all.x = TRUE)
tr_gsk3b_sp <- merge(x = tr_gsk3b, y = sp_all_gsk3b, by = "ID", all.x = TRUE)

Results \& Discussion

109 proteins were associated in Swiss-Prot with the GSK-3$\beta$-pathway, where we could de novo detect 218 TRs of which 42 TRs remained after filtering and clustering.

More TRs Were Detected in Proteins Associated With GSK-3$\beta$ Pathway

# CRC favorable
length(unique(tr_fav_sp$ID))/403 # through unique() we ensure to count those proteins with >1 TR only once.

# CRC unfavorable
length(unique(tr_unfav_sp$ID))/286 

# Wnt pathway
length(unique(tr_gsk3b_sp$ID))
length(unique(tr_gsk3b_sp$ID))/109

Of all proteins expressed by genes associated for beeing favorable or unfavorable for CRC, 10% and 23% contain at least one TR respectively. Analogously, we could detect TRs in 42 (27%) of the 109 proteins involved in the Human GSK-3$\beta$-pathway.

AA bias in TRs

[NO AA COMPOSITION ANALYSIS BECAUSE OF LITTLE AMAOUNT OF SEQUENCES]

TR length distribution in the NF-$\kappa$-B Pathway

plot_TRunitnoTRunitlength(tr_sp = tr_gsk3b_sp, ngeq = 10, lgeq = 3)

Interesting TRs

Summarising uniprot functions with a focus on CRC.

DIAP1

[TODO]

tr_gsk3b_sp[which(tr_gsk3b_sp$prot_name == 'DIAP1_HUMAN'),c(1,31)]

DAB2P

H15

MACF1

GSK3A

GSK-3$\beta$ in CRC associated proteins

(tr_fav_sp[which(tr_fav_sp$ID %in% tr_gsk3b_sp$ID),])

nrow(tr_unfav[which(tr_unfav$ID %in% tr_gsk3b_sp$ID),])

matteodelucchi/TRAL-Result-Analysis documentation built on Dec. 2, 2019, 11:42 p.m.