enoriega/keyword_pubmed · Datasets at Hugging Face

sentence stringlengths 1 5.95k	pmcid stringlengths 9 10	keyword_rank int32 -1 169k	keyword_indices sequence
Sequence processing ..	PMC4049103	-1	[]
An in-house Python script ( available at http://enve-omics.gatech.edu ) was used for quality trimming of raw Illumina reads as described previously ( 36 ) .	PMC4049103	-1	[]
In brief , this script trims from both the 5 ′ and the 3 ′ end of a sequence using an average Phred score threshold of 20 in 3-bp-long windows and discards resulting sequences shorter than 50 bp ( Table S5 ) .	PMC4049103	-1	[]
The same trimming strategy was applied to publicly available metagenomes for consistency .	PMC4049103	-1	[]
All BLAST + ( 37 ) and HMMER ( 38 ) analyses were based on both single reads , when the corresponding sister read was not available or discarded after the trimming step , and pair-end reads .	PMC4049103	-1	[]
In silico libraries and cutoff calculation ..	PMC4049103	-1	[]
An in-house Python script was used to generate in silico libraries from available complete genomes in the NCBI database as of April 2013 ( 2,355 sequenced genomes ) as described previously ( 36 ) .	PMC4049103	-1	[]
Briefly , this script simulates an Illumina run by generating 100-bp paired-end reads with a sequencing error of 0.5 % , an insert size of 500 bp , and a user-defined coverage of 3-fold .	PMC4049103	-1	[]
The script also reports the coordinates of the genome from which the reads were generated , so that gene encoding information for each read is available ( based on NCBI protein table files or ptt files ) .	PMC4049103	-1	[]
The first in silico-generated data set ( library I ) was built based on seven bacterial plasmids and 115 chromosomes previously determined to bear a nosZ gene ( 12 ) .	PMC4049103	-1	[]
In silico library II was constructed using the 122 DNA sequences from library I and an additional 959 sequenced chromosomes that did not encode NosZ ( confirmed independently by BLASTp analysis ) .	PMC4049103	-1	[]
Libraries I and II had a total of 7,460 NosZ-encoding reads and were ~ 14 and ~ 136 million reads in size , respectively.BLAST analyses were performed using the BLAST + 2.2.7 release with the following settings : a word size of 7 , a penalty of − 2 , no dust , an E value cutoff of 0.001 , and an x-drop gap of 150 .	PMC4049103	-1	[]
BLASTx settings were no SEG program , an E value cutoff of 0.001 , and a word size of 3 .	PMC4049103	-1	[]
Previously described nosZ nucleotide or protein references from complete genomes were clustered at 95 % sequence identity , and the longest representative sequence from each cluster was used to construct a reference database , consisting of 54 typical , 47 atypical , and 4 halophilic archaeal representative reference sequences .	PMC4049103	-1	[]
All reads originating from a nosZ gene , whether located on a chromosome or a plasmid , that matched a nucleotide or protein sequence reference were classified as true positives .	PMC4049103	-1	[]
Reads not originating from a nosZ reference sequence that matched a reference sequence were classified as false positives .	PMC4049103	-1	[]
The numbers of true and false positives obtained from each algorithm ( performance ) were evaluated by the receiver operating characteristic ( ROC ) curve using the R pROC library ( 39 ) .	PMC4049103	-1	[]
The bit score cutoff that maximized performance was calculated as the line that maximized the distance to the identity line ( i.e ., the nondiscriminatory diagonal line where sensitivity and specificity are equal ) according to the Youden method for a partial area under the curve ( pAUC ) between 90 % and 100 % of specificity ( 39 ) ( see Fig. S1 for a flow chart of the approach ) .	PMC4049103	-1	[]
A hidden Markov model ( HMM ) based on the sequences of six functionally characterized NosZ proteins ( Bradyrhizobium japonicum USDA 110 27375426 , Wolinella succinogenes 46934822 , Paracoccus denitrificans 2833444 , Achromobacter cycloclastes 37538302 , and Anaeromyxobacter dehalogenans 2CP-C 86158824 ) was built with HMMER 3.0 and used to query translated reads from libraries I and II for nosZ matches based on the hmmsearch algorithm ( 38 ) ( Table 1 ) .	PMC4049103	28,139	[ 33, 37 ]
ROC analyses were not performed for HMM searches due to the high sensitivity and specificity obtained after each search .	PMC4049103	-1	[]
Detecting nosZ reads in metagenomes ..	PMC4049103	-1	[]
Publicly available metagenomes from Alaskan permafrost ( 25 ) , soil biomes ( 26 ) , and soils exposed to a decade of warming ( 40 ) were downloaded from the DOE Joint Genome Institute ( http://www.jgi.doe.gov ) , MG-RAST ( http://metagenomics.anl.gov ) , and NCBI Sequence Read Archive Web servers , respectively .	PMC4049103	-1	[]
NosZ-encoding reads ( or nosZ reads for simplicity ) in the above-named metagenomes were identified by BLAST searches against the NosZ reference sequences clustered by 95 % identity ( Table S1 ) and classified as typical or atypical NosZ sequences based on their best match .	PMC4049103	-1	[]
To account for differences in the numbers of sequences among the publicly available metagenomes , the presence or absence of each type of nosZ read was represented as a binomial distribution for each metagenome .	PMC4049103	-1	[]
Assuming independence in the presence or absence of each type of nosZ gene in each soil sample , the probability of finding either type was calculated from the frequency of nosZ reads detected in each metagenome ( i.e ., a probability closer to 1 implies a higher abundance for the corresponding type of nosZ gene in the metagenome ) .	PMC4049103	-1	[]
To account for differences in the numbers of reads for each metagenome , the standard deviation of the sample mean was calculated for each distribution .	PMC4049103	-1	[]
Fractions of genomes containing a nosZ gene ..	PMC4049103	-1	[]
To estimate the fractions of the microbial populations in the soil community with nosZ genes in their genomes , the following approach was used .	PMC4049103	-1	[]
Sequences of three single-copy housekeeping genes ( dnaK , recA , and rpoB ) were used as references to query each metagenome .	PMC4049103	-1	[]
The reference set for each housekeeping gene included sequences from 30 different bacterial species ( denoted by an asterisk in Table S1 ) that also contained a typical or an atypical nosZ gene ( i.e ., half of the species in the set harbored a typical nosZ and the other half an atypical gene ) .	PMC4049103	-1	[]
The total number of matches obtained in a BLASTn search ( settings were as follows : no dust , a word size of 7 , a penalty of − 2 , a maximum number of target sequences of 1 , an x-drop of 150 , and an E value of 0.001 ) for each set of housekeeping and nosZ genes was normalized by the average length of the query ( reference ) sequences .	PMC4049103	-1	[]
The fraction of the microbial community harboring nosZ genes was calculated as the ratio of the normalized number of nosZ reads to the number of reads assigned to each of the housekeeping genes ( assuming one nosZ gene copy per genome , which is the case for > 97 % of the analyzed genomes in Table S1 ; see also Table S3 ) .	PMC4049103	-1	[]
Both the Havana sand and Urbana silt loam metagenomes are available under accession numbers SRR1152189 and SRR1153387 in the Sequence Read Archive server .	PMC4049103	-1	[]
SUPPLEMENTAL MATERIAL .	PMC4049103	-1	[]
Text S1Supplemental results .	PMC4049103	-1	[]
Download Text S1 , DOCX file , 0.1 MB Text S2Supplemental materials and methods .	PMC4049103	-1	[]
Download Text S2 , DOCX file , 0.1 MB Figure S1Flow chart for calculating gene-specific bit score cutoffs .	PMC4049103	27,603	[ 10 ]
Download Figure S1 , DOCX file , 0.1 MB Figure S2Soil metagenomic library quality and domain-level composition based on taxonomic affiliation of protein-encoding reads .	PMC4049103	-1	[]
Download Figure S2 , DOCX file , 0.3 MB Figure S3Taxonomic characterization of the metagenomes based on the recovered 16S rRNA gene reads .	PMC4049103	-1	[]
Download Figure S3 , DOCX file , 0.2 MB Table S1Genomes used to generate in silico libraries I and II.Table S1 , DOCX file , 0.1 MB .	PMC4049103	-1	[]
Table S2List of NosZ reference sequences representing more than 0.1 % of total nosZ reads in soil metagenomes.Table S2 , DOCX file , 0.1 MB .	PMC4049103	-1	[]
Table S3Fraction of the soil microbial community encoding NosZ proteins.Table S3 , DOCX file , 0.1 MB .	PMC4049103	-1	[]
Table S4Physicochemical properties of the agricultural soils used in the study.Table S4 , DOCX file , 0.1 MB .	PMC4049103	-1	[]
Table S5Agricultural soil metagenomes and assembly statistics.Table S5 , DOCX file , 0.1 MB .	PMC4049103	-1	[]
Identification of endogenous control genes for normalisation of real-time quantitative PCR data in colorectal cancer .	PMC2825202	-1	[]
Background .	PMC2825202	-1	[]
Gene expression analysis has many applications in cancer diagnosis , prognosis and therapeutic care .	PMC2825202	-1	[]
Relative quantification is the most widely adopted approach whereby quantification of gene expression is normalised relative to an endogenously expressed control ( EC ) gene .	PMC2825202	-1	[]
Central to the reliable determination of gene expression is the choice of control gene .	PMC2825202	-1	[]
The purpose of this study was to evaluate a panel of candidate EC genes from which to identify the most stably expressed gene ( s ) to normalise RQ-PCR data derived from primary colorectal cancer tissue .	PMC2825202	-1	[]
Results .	PMC2825202	-1	[]
The expression of thirteen candidate EC genes : B2M , HPRT , GAPDH , ACTB , PPIA , HCRT , SLC25A23 , DTX3 , APOC4 , RTDR1 , KRTAP12-3 , CHRNB4 and MRPL19 were analysed in a cohort of 64 colorectal tumours and tumour associated normal specimens .	PMC2825202	1,498	[ 24, 32 ]
CXCL12 , FABP1 , MUC2 and PDCD4 genes were chosen as target genes against which a comparison of the effect of each EC gene on gene expression could be determined .	PMC2825202	-1	[]
Data analysis using descriptive statistics , geNorm , NormFinder and qBasePlus indicated significant difference in variances between candidate EC genes .	PMC2825202	16,786	[ 10 ]
We determined that two genes were required for optimal normalisation and identified B2M and PPIA as the most stably expressed and reliable EC genes .	PMC2825202	-1	[]
Conclusion .	PMC2825202	-1	[]
This study identified that the combination of two EC genes ( B2M and PPIA ) more accurately normalised RQ-PCR data in colorectal tissue .	PMC2825202	-1	[]
Although these control genes might not be optimal for use in other cancer studies , the approach described herein could serve as a template for the identification of valid ECs in other cancer types .	PMC2825202	-1	[]
Background .	PMC2825202	-1	[]
Colorectal cancer ( CRC ) is one of the most common causes of cancer worldwide affecting almost a million people annually and resulting in approximately 500,000 deaths [ 1 ] .	PMC2825202	-1	[]
Approximately 5 % of individuals born today will be diagnosed with colorectal cancer during their lives , representing a lifetime risk of 1 in 19 .	PMC2825202	-1	[]
CRC remains a serious threat to life with approximately 20 % of patients presenting with late stage metastatic disease .	PMC2825202	-1	[]
Although 5 year survival rates are favourable at 80-90 % for early stage disease , this drops significantly to less than 10 % with the presence of distal metastasis.The majority of colorectal tumours originate from adenomatous precursor lesions and develop along a well-defined adenoma-carcinoma sequence .	PMC2825202	-1	[]
According to this model the culmination of mutational events including activation of oncogenes and loss of function of tumour suppressor genes results in the emergence of carcinomas [ 2 ] .	PMC2825202	-1	[]
Molecular profiling across the spectrum of normal-adenoma-tumour tissue types has yielded many candidate genes in the search for novel molecular diagnostic and prognostic markers and treatment strategies [ 3-5 ] .	PMC2825202	-1	[]
In latter years real-time quantitative ( RQ - ) PCR has become established as the gold standard for accurate , sensitive and rapid quantification of gene expression [ 6,7 ] .	PMC2825202	-1	[]
In comparison to alternative methods such as Northern blotting and Ribonuclease Protection Assays ( RPA ) , RQ-PCR has been universally adopted as the transcriptomic method of choice due to its superiority with regard to speed , sensitivity , reproducibility and the wide range of instrumentation and reagents commercially available.To accurately quantify an mRNA target by RQ-PCR , samples are assayed during the exponential phase of the PCR reaction during which the amount of target is assumed to double with each cycle of PCR without bias due to limiting reagents .	PMC2825202	-1	[]
Analysis of cycle threshold ( C ) , the cycle number at which signals are detected above background , can be used to estimate gene expression levels by relating C values either to a standard curve ( absolute quantification ) or to a control gene ( relative quantification ) .	PMC2825202	-1	[]
The latter method requires the generation of standard curves of known copy number for each target and so is limited due to logistical issues associated with the generation of standards in studies of multiple gene targets .	PMC2825202	-1	[]
Relative quantification is the most widely adopted approach and as the name suggests , quantification of gene expression is based on the analysis of a target gene whose expression is normalised relative to the expression of a control gene .	PMC2825202	-1	[]
Central to the reliable determination of gene expression is the choice of control gene with which to normalise real-time data from target genes .	PMC2825202	-1	[]
Normalisation can be achieved using endogenous or exogenous controls ; however the use of endogenous control ( EC ) genes is the most widely adopted approach as it excludes variation associated with differences in amounts of template RNA .	PMC2825202	-1	[]
Vandestompele et al 2002 described a normalisation method whereby geometrical averaging of multiple EC genes improved accuracy [ 8] .	PMC2825202	-1	[]
This approach has been adopted to reliably measure levels of gene expression in many studies in different tissue types including breast [ 9-11 ] , lung [ 12 ] , kidney [ 13 ] , brain [ 14 ] and liver [ 15 ] .	PMC2825202	-1	[]
An ideal EC gene ( or genes ) should be stably expressed and unaffected by parameters such as disease status and in the case of CRC , should remain unaffected by whether a tissue was derived from normal , adenoma or carcinoma lesions .	PMC2825202	-1	[]
Traditionally GAPDH ( glyceraldehyde phosphate dehydrogenase ) has been widely used to normalise RQ-PCR data .	PMC2825202	-1	[]
A common feature of earlier studies was that the stability of reference gene expression between different sample types was assumed with little consideration paid to validation of these EC genes as suitable normalisers .	PMC2825202	-1	[]
More recent studies have brought into question the stability of commonly used EC genes such as GAPDH on the basis that gene expression levels have been found to vary in response to treatment or as a result of physiological , pathological or experimental changes .	PMC2825202	-1	[]
For example , alteration in oxygen tension and hypoxia were found to be associated with wide variation in GAPDH , B-ACTIN and CYCLOPHILIN expression [ 16 ] .	PMC2825202	-1	[]
In addition , GAPDH expression was found to be strongly unregulated in diabetic patients and down-regulated in response to the administration of bisphosphonate compounds in the treatment of metastatic breast cancer [ 17 ] .	PMC2825202	-1	[]
Other evidence indicates that neoplastic growth can affect EC expression levels [ 18 ] .	PMC2825202	-1	[]
Goidin et al [ 19 ] found differences in the expression of GAPDH and B-ACTIN in two sub-populations of melanoma cells derived from a tumour in a single patient .	PMC2825202	-1	[]
Treatment agents such as dexamethasone , deprenyl and isatin also affect EC gene expression [ 20,21 ] .	PMC2825202	-1	[]
Schmittgen et al [ 22 ] reported increased expression of GAPDH , B2M , 18S rRNA and β - ACTIN in fibroblasts after the addition of serum : evidence of the effect of experimental conditions on EC expression .	PMC2825202	-1	[]
These findings were further supported by Wu et al [ 23 ] in their investigation of the effect of different skin irritants on GAPDH and PolyA + RNA expression .	PMC2825202	-1	[]
GAPDH was found to be involved in age-induced apoptosis in mature cerebellar cells [ 24 ] and also as a tRNA binding protein present in the nuclei of HeLa cells [ 25 ] .	PMC2825202	-1	[]
As the use of unreliable ECs can result in inaccurate results , the identification of the most reliable gene or set of genes at the outset of an investigation is critical .	PMC2825202	-1	[]
Thus far , a pervasive stably expressed gene ( or genes ) has yet to be identified across all tissue types [ 26,27 ] .	PMC2825202	-1	[]
This would indicate that the identification of robust ECs at the outset of transcriptomic analysis would yield more reliable and meaningful RQ-PCR data.The aim of this study was to evaluate a panel of thirteen candidate EC genes from which to identify the most stably expressed gene ( or genes ) to normalise RQ-PCR data derived from primary colorectal tumour and tumour associated normal ( TAN ) tissue .	PMC2825202	-1	[]
Six of the candidate EC genes were selected from the literature and represent the most frequently studied reference genes in cancer including , but not limited to , colorectal cancer .	PMC2825202	-1	[]
Each gene was previously reported as being constitutively expressed in various tissues .	PMC2825202	-1	[]
These EC genes included B2M ( beta-2-microglogulin ) [ 5 ] , HPRT ( hypoxanthine guanine phosphoribosyl transferase 1 ) [ 3,28 ] , GAPDH [ 29 ] , ACTB ( beta-actin ) [ 30 ] , PPIA ( peptidyl-prolyl isomerise A ) [ 9 ] and MRPL19 ( mitochondrial ribosomal protein L19 ) [ 9 ] .	PMC2825202	14,380	[ 47 ]
The remaining seven genes included HCRT , SLC25A23 , DTX3 , APOC4 , RTDR1 , KRTAP12-3 , and CHRNB4 .	PMC2825202	1,498	[ 11 ]
The latter candidates were selected from an unpublished whole genome microarray dataset of 20 human tumour specimens and represented the most stably expressed probes with a fold-change of 1.0-1.2 , ( p < 0.05 ) .	PMC2825202	-1	[]
Expression of CXCL12 [ 31 ] , FABP1 [ 32 ] , MUC2 [ 33 ] and PDCD4 genes were chosen as targets against which to measure the effects of candidate EC expression on the basis of their previously identified roles in tumourigenesis .	PMC2825202	-1	[]
In addition to its tumour suppressor properties , PDCD4 [ 34 ] also has diagnostic and prognostic utility and represents a promising target for anti-cancer therapy .	PMC2825202	-1	[]
Results .	PMC2825202	-1	[]
Range of Expression of Candidate EC Genes .	PMC2825202	-1	[]
A range of C values was observed across the candidate EC genes in tumour and TAN tissue from CRC patients as indicated in table 1 .	PMC2825202	-1	[]
Only samples with a standard deviation < 0.3 from the mean C of the triplicates were included for further analysis .	PMC2825202	-1	[]

Sequence processing ..

PMC4049103

-1

[]

An in-house Python script ( available at http://enve-omics.gatech.edu ) was used for quality trimming of raw Illumina reads as described previously ( 36 ) .

PMC4049103

-1

[]

In brief , this script trims from both the 5 ′ and the 3 ′ end of a sequence using an average Phred score threshold of 20 in 3-bp-long windows and discards resulting sequences shorter than 50 bp ( Table S5 ) .

PMC4049103

-1

[]

The same trimming strategy was applied to publicly available metagenomes for consistency .

PMC4049103

-1

[]

All BLAST + ( 37 ) and HMMER ( 38 ) analyses were based on both single reads , when the corresponding sister read was not available or discarded after the trimming step , and pair-end reads .

PMC4049103

-1

[]

In silico libraries and cutoff calculation ..

PMC4049103

-1

[]

An in-house Python script was used to generate in silico libraries from available complete genomes in the NCBI database as of April 2013 ( 2,355 sequenced genomes ) as described previously ( 36 ) .

PMC4049103

-1

[]

Briefly , this script simulates an Illumina run by generating 100-bp paired-end reads with a sequencing error of 0.5 % , an insert size of 500 bp , and a user-defined coverage of 3-fold .

PMC4049103

-1

[]

The script also reports the coordinates of the genome from which the reads were generated , so that gene encoding information for each read is available ( based on NCBI protein table files or ptt files ) .

PMC4049103

-1

[]

The first in silico-generated data set ( library I ) was built based on seven bacterial plasmids and 115 chromosomes previously determined to bear a nosZ gene ( 12 ) .

PMC4049103

-1

[]

In silico library II was constructed using the 122 DNA sequences from library I and an additional 959 sequenced chromosomes that did not encode NosZ ( confirmed independently by BLASTp analysis ) .

PMC4049103

-1

[]

Libraries I and II had a total of 7,460 NosZ-encoding reads and were ~ 14 and ~ 136 million reads in size , respectively.BLAST analyses were performed using the BLAST + 2.2.7 release with the following settings : a word size of 7 , a penalty of − 2 , no dust , an E value cutoff of 0.001 , and an x-drop gap of 150 .

PMC4049103

-1

[]

BLASTx settings were no SEG program , an E value cutoff of 0.001 , and a word size of 3 .

PMC4049103

-1

[]

Previously described nosZ nucleotide or protein references from complete genomes were clustered at 95 % sequence identity , and the longest representative sequence from each cluster was used to construct a reference database , consisting of 54 typical , 47 atypical , and 4 halophilic archaeal representative reference sequences .

PMC4049103

-1

[]

All reads originating from a nosZ gene , whether located on a chromosome or a plasmid , that matched a nucleotide or protein sequence reference were classified as true positives .

PMC4049103

-1

[]

Reads not originating from a nosZ reference sequence that matched a reference sequence were classified as false positives .

PMC4049103

-1

[]

The numbers of true and false positives obtained from each algorithm ( performance ) were evaluated by the receiver operating characteristic ( ROC ) curve using the R pROC library ( 39 ) .

PMC4049103

-1

[]

The bit score cutoff that maximized performance was calculated as the line that maximized the distance to the identity line ( i.e ., the nondiscriminatory diagonal line where sensitivity and specificity are equal ) according to the Youden method for a partial area under the curve ( pAUC ) between 90 % and 100 % of specificity ( 39 ) ( see Fig. S1 for a flow chart of the approach ) .

PMC4049103

-1

[]

A hidden Markov model ( HMM ) based on the sequences of six functionally characterized NosZ proteins ( Bradyrhizobium japonicum USDA 110 27375426 , Wolinella succinogenes 46934822 , Paracoccus denitrificans 2833444 , Achromobacter cycloclastes 37538302 , and Anaeromyxobacter dehalogenans 2CP-C 86158824 ) was built with HMMER 3.0 and used to query translated reads from libraries I and II for nosZ matches based on the hmmsearch algorithm ( 38 ) ( Table 1 ) .

PMC4049103

28,139

[ 33, 37 ]

ROC analyses were not performed for HMM searches due to the high sensitivity and specificity obtained after each search .

PMC4049103

-1

[]

Detecting nosZ reads in metagenomes ..

PMC4049103

-1

[]

Publicly available metagenomes from Alaskan permafrost ( 25 ) , soil biomes ( 26 ) , and soils exposed to a decade of warming ( 40 ) were downloaded from the DOE Joint Genome Institute ( http://www.jgi.doe.gov ) , MG-RAST ( http://metagenomics.anl.gov ) , and NCBI Sequence Read Archive Web servers , respectively .

PMC4049103

-1

[]

NosZ-encoding reads ( or nosZ reads for simplicity ) in the above-named metagenomes were identified by BLAST searches against the NosZ reference sequences clustered by 95 % identity ( Table S1 ) and classified as typical or atypical NosZ sequences based on their best match .

PMC4049103

-1

[]

To account for differences in the numbers of sequences among the publicly available metagenomes , the presence or absence of each type of nosZ read was represented as a binomial distribution for each metagenome .

PMC4049103

-1

[]

Assuming independence in the presence or absence of each type of nosZ gene in each soil sample , the probability of finding either type was calculated from the frequency of nosZ reads detected in each metagenome ( i.e ., a probability closer to 1 implies a higher abundance for the corresponding type of nosZ gene in the metagenome ) .

PMC4049103

-1

[]

To account for differences in the numbers of reads for each metagenome , the standard deviation of the sample mean was calculated for each distribution .

PMC4049103

-1

[]

Fractions of genomes containing a nosZ gene ..

PMC4049103

-1

[]

To estimate the fractions of the microbial populations in the soil community with nosZ genes in their genomes , the following approach was used .

PMC4049103

-1

[]

Sequences of three single-copy housekeeping genes ( dnaK , recA , and rpoB ) were used as references to query each metagenome .

PMC4049103

-1

[]

The reference set for each housekeeping gene included sequences from 30 different bacterial species ( denoted by an asterisk in Table S1 ) that also contained a typical or an atypical nosZ gene ( i.e ., half of the species in the set harbored a typical nosZ and the other half an atypical gene ) .

PMC4049103

-1

[]

The total number of matches obtained in a BLASTn search ( settings were as follows : no dust , a word size of 7 , a penalty of − 2 , a maximum number of target sequences of 1 , an x-drop of 150 , and an E value of 0.001 ) for each set of housekeeping and nosZ genes was normalized by the average length of the query ( reference ) sequences .

PMC4049103

-1

[]

The fraction of the microbial community harboring nosZ genes was calculated as the ratio of the normalized number of nosZ reads to the number of reads assigned to each of the housekeeping genes ( assuming one nosZ gene copy per genome , which is the case for > 97 % of the analyzed genomes in Table S1 ; see also Table S3 ) .

PMC4049103

-1

[]

Both the Havana sand and Urbana silt loam metagenomes are available under accession numbers SRR1152189 and SRR1153387 in the Sequence Read Archive server .

PMC4049103

-1

[]

SUPPLEMENTAL MATERIAL .

PMC4049103

-1

[]

Text S1Supplemental results .

PMC4049103

-1

[]

Download Text S1 , DOCX file , 0.1 MB Text S2Supplemental materials and methods .

PMC4049103

-1

[]

Download Text S2 , DOCX file , 0.1 MB Figure S1Flow chart for calculating gene-specific bit score cutoffs .

PMC4049103

27,603

[ 10 ]

Download Figure S1 , DOCX file , 0.1 MB Figure S2Soil metagenomic library quality and domain-level composition based on taxonomic affiliation of protein-encoding reads .

PMC4049103

-1

[]

Download Figure S2 , DOCX file , 0.3 MB Figure S3Taxonomic characterization of the metagenomes based on the recovered 16S rRNA gene reads .

PMC4049103

-1

[]

Download Figure S3 , DOCX file , 0.2 MB Table S1Genomes used to generate in silico libraries I and II.Table S1 , DOCX file , 0.1 MB .

PMC4049103

-1

[]

Table S2List of NosZ reference sequences representing more than 0.1 % of total nosZ reads in soil metagenomes.Table S2 , DOCX file , 0.1 MB .

PMC4049103

-1

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Table S3Fraction of the soil microbial community encoding NosZ proteins.Table S3 , DOCX file , 0.1 MB .

PMC4049103

-1

[]

Table S4Physicochemical properties of the agricultural soils used in the study.Table S4 , DOCX file , 0.1 MB .

PMC4049103

-1

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Table S5Agricultural soil metagenomes and assembly statistics.Table S5 , DOCX file , 0.1 MB .

PMC4049103

-1

[]

Identification of endogenous control genes for normalisation of real-time quantitative PCR data in colorectal cancer .

PMC2825202

-1

[]

Background .

PMC2825202

-1

[]

Gene expression analysis has many applications in cancer diagnosis , prognosis and therapeutic care .

PMC2825202

-1

[]

Relative quantification is the most widely adopted approach whereby quantification of gene expression is normalised relative to an endogenously expressed control ( EC ) gene .

PMC2825202

-1

[]

Central to the reliable determination of gene expression is the choice of control gene .

PMC2825202

-1

[]

The purpose of this study was to evaluate a panel of candidate EC genes from which to identify the most stably expressed gene ( s ) to normalise RQ-PCR data derived from primary colorectal cancer tissue .

PMC2825202

-1

[]

Results .

PMC2825202

-1

[]

The expression of thirteen candidate EC genes : B2M , HPRT , GAPDH , ACTB , PPIA , HCRT , SLC25A23 , DTX3 , APOC4 , RTDR1 , KRTAP12-3 , CHRNB4 and MRPL19 were analysed in a cohort of 64 colorectal tumours and tumour associated normal specimens .

PMC2825202

1,498

[ 24, 32 ]

CXCL12 , FABP1 , MUC2 and PDCD4 genes were chosen as target genes against which a comparison of the effect of each EC gene on gene expression could be determined .

PMC2825202

-1

[]

Data analysis using descriptive statistics , geNorm , NormFinder and qBasePlus indicated significant difference in variances between candidate EC genes .

PMC2825202

16,786

[ 10 ]

We determined that two genes were required for optimal normalisation and identified B2M and PPIA as the most stably expressed and reliable EC genes .

PMC2825202

-1

[]

Conclusion .

PMC2825202

-1

[]

This study identified that the combination of two EC genes ( B2M and PPIA ) more accurately normalised RQ-PCR data in colorectal tissue .

PMC2825202

-1

[]

Although these control genes might not be optimal for use in other cancer studies , the approach described herein could serve as a template for the identification of valid ECs in other cancer types .

PMC2825202

-1

[]

Background .

PMC2825202

-1

[]

Colorectal cancer ( CRC ) is one of the most common causes of cancer worldwide affecting almost a million people annually and resulting in approximately 500,000 deaths [ 1 ] .

PMC2825202

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Approximately 5 % of individuals born today will be diagnosed with colorectal cancer during their lives , representing a lifetime risk of 1 in 19 .

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CRC remains a serious threat to life with approximately 20 % of patients presenting with late stage metastatic disease .

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Although 5 year survival rates are favourable at 80-90 % for early stage disease , this drops significantly to less than 10 % with the presence of distal metastasis.The majority of colorectal tumours originate from adenomatous precursor lesions and develop along a well-defined adenoma-carcinoma sequence .

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According to this model the culmination of mutational events including activation of oncogenes and loss of function of tumour suppressor genes results in the emergence of carcinomas [ 2 ] .

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Molecular profiling across the spectrum of normal-adenoma-tumour tissue types has yielded many candidate genes in the search for novel molecular diagnostic and prognostic markers and treatment strategies [ 3-5 ] .

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In latter years real-time quantitative ( RQ - ) PCR has become established as the gold standard for accurate , sensitive and rapid quantification of gene expression [ 6,7 ] .

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In comparison to alternative methods such as Northern blotting and Ribonuclease Protection Assays ( RPA ) , RQ-PCR has been universally adopted as the transcriptomic method of choice due to its superiority with regard to speed , sensitivity , reproducibility and the wide range of instrumentation and reagents commercially available.To accurately quantify an mRNA target by RQ-PCR , samples are assayed during the exponential phase of the PCR reaction during which the amount of target is assumed to double with each cycle of PCR without bias due to limiting reagents .

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Analysis of cycle threshold ( C ) , the cycle number at which signals are detected above background , can be used to estimate gene expression levels by relating C values either to a standard curve ( absolute quantification ) or to a control gene ( relative quantification ) .

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The latter method requires the generation of standard curves of known copy number for each target and so is limited due to logistical issues associated with the generation of standards in studies of multiple gene targets .

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Relative quantification is the most widely adopted approach and as the name suggests , quantification of gene expression is based on the analysis of a target gene whose expression is normalised relative to the expression of a control gene .

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Central to the reliable determination of gene expression is the choice of control gene with which to normalise real-time data from target genes .

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Normalisation can be achieved using endogenous or exogenous controls ; however the use of endogenous control ( EC ) genes is the most widely adopted approach as it excludes variation associated with differences in amounts of template RNA .

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Vandestompele et al 2002 described a normalisation method whereby geometrical averaging of multiple EC genes improved accuracy [ 8] .

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This approach has been adopted to reliably measure levels of gene expression in many studies in different tissue types including breast [ 9-11 ] , lung [ 12 ] , kidney [ 13 ] , brain [ 14 ] and liver [ 15 ] .

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An ideal EC gene ( or genes ) should be stably expressed and unaffected by parameters such as disease status and in the case of CRC , should remain unaffected by whether a tissue was derived from normal , adenoma or carcinoma lesions .

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Traditionally GAPDH ( glyceraldehyde phosphate dehydrogenase ) has been widely used to normalise RQ-PCR data .

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A common feature of earlier studies was that the stability of reference gene expression between different sample types was assumed with little consideration paid to validation of these EC genes as suitable normalisers .

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More recent studies have brought into question the stability of commonly used EC genes such as GAPDH on the basis that gene expression levels have been found to vary in response to treatment or as a result of physiological , pathological or experimental changes .

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For example , alteration in oxygen tension and hypoxia were found to be associated with wide variation in GAPDH , B-ACTIN and CYCLOPHILIN expression [ 16 ] .

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In addition , GAPDH expression was found to be strongly unregulated in diabetic patients and down-regulated in response to the administration of bisphosphonate compounds in the treatment of metastatic breast cancer [ 17 ] .

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Other evidence indicates that neoplastic growth can affect EC expression levels [ 18 ] .

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Goidin et al [ 19 ] found differences in the expression of GAPDH and B-ACTIN in two sub-populations of melanoma cells derived from a tumour in a single patient .

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Treatment agents such as dexamethasone , deprenyl and isatin also affect EC gene expression [ 20,21 ] .

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Schmittgen et al [ 22 ] reported increased expression of GAPDH , B2M , 18S rRNA and β - ACTIN in fibroblasts after the addition of serum : evidence of the effect of experimental conditions on EC expression .

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These findings were further supported by Wu et al [ 23 ] in their investigation of the effect of different skin irritants on GAPDH and PolyA + RNA expression .

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GAPDH was found to be involved in age-induced apoptosis in mature cerebellar cells [ 24 ] and also as a tRNA binding protein present in the nuclei of HeLa cells [ 25 ] .

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As the use of unreliable ECs can result in inaccurate results , the identification of the most reliable gene or set of genes at the outset of an investigation is critical .

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Thus far , a pervasive stably expressed gene ( or genes ) has yet to be identified across all tissue types [ 26,27 ] .

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This would indicate that the identification of robust ECs at the outset of transcriptomic analysis would yield more reliable and meaningful RQ-PCR data.The aim of this study was to evaluate a panel of thirteen candidate EC genes from which to identify the most stably expressed gene ( or genes ) to normalise RQ-PCR data derived from primary colorectal tumour and tumour associated normal ( TAN ) tissue .

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Six of the candidate EC genes were selected from the literature and represent the most frequently studied reference genes in cancer including , but not limited to , colorectal cancer .

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Each gene was previously reported as being constitutively expressed in various tissues .

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These EC genes included B2M ( beta-2-microglogulin ) [ 5 ] , HPRT ( hypoxanthine guanine phosphoribosyl transferase 1 ) [ 3,28 ] , GAPDH [ 29 ] , ACTB ( beta-actin ) [ 30 ] , PPIA ( peptidyl-prolyl isomerise A ) [ 9 ] and MRPL19 ( mitochondrial ribosomal protein L19 ) [ 9 ] .

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The remaining seven genes included HCRT , SLC25A23 , DTX3 , APOC4 , RTDR1 , KRTAP12-3 , and CHRNB4 .

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The latter candidates were selected from an unpublished whole genome microarray dataset of 20 human tumour specimens and represented the most stably expressed probes with a fold-change of 1.0-1.2 , ( p < 0.05 ) .

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Expression of CXCL12 [ 31 ] , FABP1 [ 32 ] , MUC2 [ 33 ] and PDCD4 genes were chosen as targets against which to measure the effects of candidate EC expression on the basis of their previously identified roles in tumourigenesis .

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In addition to its tumour suppressor properties , PDCD4 [ 34 ] also has diagnostic and prognostic utility and represents a promising target for anti-cancer therapy .

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Results .

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Range of Expression of Candidate EC Genes .

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A range of C values was observed across the candidate EC genes in tumour and TAN tissue from CRC patients as indicated in table 1 .

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Only samples with a standard deviation < 0.3 from the mean C of the triplicates were included for further analysis .

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