Supporting Information

Last Updated on 2025-01-31

This repository contains the supporting information for: Comparative analysis of proteomic expression between oesophageal adenocarcinoma and normal adjacent tissue.

This comprises Tables S1-S4 as csv files, and the prediction of the effect of the G95R mutation on NOP58 using DDGun (1) below.

The column names and contents of the csv files in the tables folder are described below.

Patient Information

S1 Table contains the patient information for the 7 male donors in this study.

                  File                                  
                ────────────────────────────────────────
                  S1-Table-OAC-Patient-Information.csv  
                ────────────────────────────────────────

Column names: File

Column name	Description
anonymised_donor_id	Anonymised Donor identifier
age_at_diagnosis	Age of patient at diagnosis
sex	Sex of patient
location_of_tumour	Location of tumour
treatment_modality	Treatment modality

Patient information

Peaks normalised Top 3 peptide intensities

Label free quantification using the Peaks Q module of Peaks Studio (2,3) yielding matrices of protein identifications as quantified by their normalised top 3 peptide intensities.

S2 Table contain normalised top 3 peptide intensities.

       File                                                        
     ──────────────────────────────────────────────────────────────
       S2-Table-OAC-Peaks-Normalised-Top3-Peptide-Intensities.csv  
     ──────────────────────────────────────────────────────────────

Column names: File

Column name	Description
protein	protein short name
gene	HGNC gene symbol
sample_id the donor id or donor id suffixed with T for tumour or N for NAT samples	Normalised top 3 peptide intensity from Peaks

Peaks normalised top 3 peptide intensities Table information

Differential protein expression with DEqMS

The normalised top 3 peptide intensities were filtered to remove any proteins for which there were more than two missing values across the samples. Differential protein expression (DEP) was then calculated with DEqMS using the default steps (4).

S3 Table contain the output of DEqMS. A negative logFC indicates higher expresssion in OAC tissue and a postive logFC indicates higher expression in NAT.

                     File                            
                   ──────────────────────────────────
                     S3-Table-OAC-DEqMS-Results.csv  
                   ──────────────────────────────────

Column names: File

Column name	Description
logFC	log2 fold change between two groups
AveExpr	the mean of the log2 ratios across all samples
t	Limma t-values
P.Value	Limma p-values
adj.P.Val	BH method adjusted Limma p-values
B	Limma B values
gene	HGNC gene symbol
count	peptide count values
sca.t	DEqMS t-statistics
sca.P.Value	DEqMS p-values
sca.adj.pval	BH method adjusted DEqMS p-values
protein	protein short name

DEqMS Table information

Functional analysis with g:Profiler

Functional enrichment analysis used g:Profiler (5) using default settings for homo sapiens modified to exclude GO electronic annotations. Protein identifiers were used as inputs for DEPs.

S4 Table contain the g:Profiler outputs.

                   File                                
                 ──────────────────────────────────────
                   S4-Table-OAC-gProfiler-Results.csv  
                 ──────────────────────────────────────

Column names: File

g:Profiler Table information

Column name	Description
query	the name of the input query
significant	indicator for statistically significant results
p_value	hypergeometric p-value after correction for multiple testing
term_size	number of genes that are annotated to the term
query_size	number of genes that were included in the query
intersection_size	the number of genes in the input query that are annotated to the corresponding term
precision	the proportion of genes in the input list that are annotated to the function (defined as intersection_size/query_size)
recall	the proportion of functionally annotated genes that the query recovers (defined as intersection_size/term_size)
term_id	unique term identifier
source term_name	the abbreviation of the data source for the term (e.g. GO:BP)
effective_domain_size	the total number of genes “in the universe” used for the hypergeometric test
source_order	numeric order for the term within its data source
parents	list of term IDs that are hierarchically directly above the term. For non-hierarchical data sources this points to an artificial root node.
evidence_codes	a lists of all evidence codes for the intersecting genes between input and the term. The evidences are separated by comma for each gene.
intersection	a comma separated list of genes from the query that are annotated to the corresponding term

NOP58 G95R mutation DDGun protein stability prediction

The effect of the G95R mutation on Nucleolar protein 58 (NOP58) was predicted using DDGun (1).

The output of the prediction using the amino acid sequence is showing how G95R decreases the stability of NOP58 is shown below:

PROTEIN: Protein name.
VARIANT: Protein mutation.
CONSERVATION: Frequencies of the wild-type and mutant residues in the mutated positions.
S_KD: Kyte-Doolittle substitution scores (AAINDEX1:KYTJ820101).
S_BL: BLOSUM62 substitution score (AAINDEX2:ENS920102).
S_DDG[SEQ]: Prediction of the DDG for the single amino acid substitutions.
T_DDG[SEQ]: Global DDG for multiple amino acid substitutions.
STABILITY[SEQ]: Variation of protein stability

PROTEIN	VARIANT	CONSERVATION	S_KD	S_BL	S_PROF	S_DDG[SEQ]	T_DDG[SEQ]	STABILITY[SEQ]
Nucleolar protein 58	G95R	30.1	0.3	0.107	-3.440	-1.222	-0.5	Decrease

References

1. Montanucci L, Capriotti E, Frank Y, Ben-Tal N, Fariselli P. DDGun: an untrained method for the prediction of protein stability changes upon single and multiple point variations. BMC Bioinformatics [Internet]. 2019 Jul;20(S14). Available from: http://dx.doi.org/10.1186/s12859-019-2923-1

2. Zhang J, Xin L, Shan B, Chen W, Xie M, Yuen D, et al. PEAKS DB: De novo sequencing assisted database search for sensitive and accurate peptide identification. Molecular & Cellular Proteomics. 2012;11(4):M111010587.

3. Lin H, He L, Ma B. A combinatorial approach to the peptide feature matching problem for label-free quantification. Bioinformatics [Internet]. 2013 May 10;29(14):1768–75. Available from: http://dx.doi.org/10.1093/bioinformatics/btt274

4. DEqMS [Internet]. Available from: http://bioconductor.org/packages/DEqMS/

5. Kolberg L, Raudvere U, Kuzmin I, Vilo J, Peterson H. gprofiler2 – an R package for gene list functional enrichment analysis and namespace conversion toolset g:Profiler. F1000Research [Internet]. 2020 Nov 17;9:709. Available from: http://dx.doi.org/10.12688/f1000research.24956.2