A response to Snyder LA, Saunders NJ: The majority of genes in the pathogenic Neisseria species are present in non-pathogenic Neisseria lactamica, including those designated as virulence genes. BMC Genomics 2006, 7:128

Hinds, Jason

Stabler, Richard

English

PubMed

LSHTM Research Online

Stabler, R; Hinds, J (2006) The majority of genes in the pathogenicNeisseria species are present in non-pathogenic Neisseria lactamica,including those designated as virulence genes: response. Bmc Ge-nomics, 7. p. 129. ISSN 1471-2164 DOI: 10.1186/1471-2164-7-129Downloaded from: http://researchonline.lshtm.ac.uk/11430/DOI: 10.1186/1471-2164-7-129Usage GuidelinesPlease refer to usage guidelines at http://researchonline.lshtm.ac.uk/policies.html or alterna-tively contact researchonline@lshtm.ac.uk.Available under license: http://creativecommons.org/licenses/by/2.5/BioMed CentralPage 1 of 3(page number not for citation purposes)BMC GenomicsOpen AccessCorrespondenceThe majority of genes in the pathogenic Neisseria species are present in non-pathogenic Neisseria lactamica, including those designated as virulence genes: responseRichard Stabler*1 and Jason Hinds2Address: 1Department of Infectious & Tropical Diseases, London, School of Hygiene and Tropical Medicine, London WC1E 7HT, UK and 2Bacterial Microarray Group, Division of Cellular & Molecular Medicine, St George's, University of London, London SW17 0RE, UKEmail: Richard Stabler* - Richard.Stabler@lshtm.ac.uk; Jason Hinds - jhinds@sgul.ac.uk* Corresponding author    AbstractA response to Snyder LA, Saunders NJ: The majority of genes in the pathogenic Neisseriaspecies are present in non-pathogenic Neisseria lactamica, including those designatedas virulence genes. BMC Genomics 2006, 7:128.BackgroundThe publication of a microarray analysis of selected Neis-seria lactamica strains by Snyder and Saunders [1] hasraised some interesting questions regarding the distribu-tion of genes between pathogenic Neisseria species and thenon-pathogenic N. lactamica, especially with regard topreviously reported "virulence genes." This paper has alsohighlighted the difficulties in comparing microarray datafrom complementary yet distinct investigations andplaces particular emphasis on discrepancies with one ofour own publications [2]. This comparison has raised gen-eral issues for discussion and a number of points for clar-ification that form the basis of this response.Initially, the two papers may appear to take similar micro-array-based approaches to address the general subject ofspecific gene distribution between pathogenic and non-pathogenic Neisseria species. Indeed, there are a numberof similarities in the practical methods used, as would beexpected for any microarray-based analysis, which makesthe reported discrepancies all the more surprising. How-ever, there are also a number of important distinctions tobe made between the two studies, primarily based onexperimental design and data analysis, which we believegreatly influence the interpretation of the results.Experimental designSnyder and Saunders determined gene presence in a col-lection of 13 N. lactamica strains, with a particular focuson genes previously described as virulence genes, and alsoreported the presence of a subset of genes in a range ofcommensal Neisseria species. The microarray analysis wasperformed using a pan-Neisseria microarray with standardlabelling and hybridisation protocols. Each N. lactamicastrain was directly compared to two other strains in a loopdesign that incorporated a dye-swap although data wasanalysed in a channel-independent intensity-based man-ner.Stabler et al. determined gene presence or absence in a col-lection of 38 Neisseria strains, including both pathogenicspecies and non-pathogenic commensal species, to estab-lish the phylogenetic relationship of strains that showedfavourable correlation to other typing methods. Genespresent in all 18 N. meningitidis serotype B strains orpresent in 7 commensal strains were identified to com-Published: 30 May 2006BMC Genomics 2006, 7:129 doi:10.1186/1471-2164-7-129Received: 18 May 2006Accepted: 30 May 2006This article is available from: http://www.biomedcentral.com/1471-2164/7/129© 2006 Stabler and Hinds; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.BMC Genomics 2006, 7:129 http://www.biomedcentral.com/1471-2164/7/129Page 2 of 3(page number not for citation purposes)pare the distribution of these genes in pathogenic andnon-pathogenic strains. It is important to note that thisdiffers to the goal of this study suggested by Snyder andSaunders. The microarray analysis was performed using apan-Neisseria microarray with standard labelling andhybridisation protocols. Each Neisseria strain was directlycompared to N. meningitidis MC58 in a common referencedesign and data was analysed in a channel-associatedratio-based manner.In terms of the microarray platforms used, there are insuf-ficient differences to explain the reported discrepancies.Clearly, the microarrays utilised by the two studies were ofindependent design and construction so the gene repre-sentation, reporter element sequences and PCR productamplicons will differ. However, given the rigorousapproaches to design and construction of both arrays,these important but subtle differences are unlikely toaccount for the level of discordance reported. Even thestrikingly similar hybridisation and washing conditionsshould produce a comparable level of stringency, whichwould be one obvious methodological reason for suchwidespread disagreement in the presence/absence callingof genes.The major difference in the experimental design of thetwo studies comes from the comparisons made in eachstudy and how these comparisons were executed. By theirvery nature, microarray comparative genomics studies arealways limited by the gene content represented on thearrays and by the number and representation of strainsanalysed. In both cases, the arrays were essentially limitedin content to the sequenced pathogenic Neisseria andtherefore any novel genes present only in N. lactamica orother commensal species were not surveyed. Likewise, thestrains selected in each study were considered by theauthors to be representative and suitable for the intendedinvestigation. It is evident that the analysis of additionalstrains may affect conclusions; Snyder and Saundersnoted this with the inclusion of another N. lactamicastrain that was not part of their initial MLST set andaccounted for 72% of the strain specific genes theyreported. However, the distinct selection of strains for thetwo studies still fails to fully explain the scale of discrep-ancies reported by Snyder and Saunders. One point tonote would be that no pathogenic strains were included inthe comparison by Snyder and Saunders whereas Stableret al. compared all strains directly to a pathogen, namelythe N. meningitidis MC58 common reference. This isimportant to note with regard to the execution of the com-parisons for the data analysis approaches discussed in thefollowing section.Data analysisWhen considering the above points, in balance with otherreasonable explanations, it would appear that data analy-sis and subsequent interpretation represents the key areain which the two groups adopted fundamentally differentapproaches that may help to explain the discrepanciesobserved. Snyder and Saunders employed an intensity-based method that analysed the two channels of a two-colour microarray independently whereas Stabler et al.used a ratio-based method that analysed the two channelsof a two-colour microarray in combination.Snyder and Saunders used a metric (pON) that reflects theprobability of there being a hybridisation signal for eachspot in each channel independently. Whilst this seems areasonable approach it takes no account of any relativeintensity levels; a gene may be called present in two strainsif the pON threshold is exceeded in both yet the relativeintensity level may be significantly higher in one strainthan the other. For example, for a pathogen gene that washighly divergent in N. lactamica there may still be a lowbut sufficient signal intensity above background to callthe gene as present in N. lactamica using the pON metric,yet this level of hybridisation would be insignificant whencompared to the signal intensity achieved with a patho-gen; therefore a more accurate call for this gene should beabsent or highly divergent rather than present. This alsoraises the issue that the PMT gain settings during scanningmay have an impact on the number of genes passing thepON threshold and thus being assigned as present. If thiswere true then a normalisation strategy would need to beemployed to account for this to ensure the definition ofpresence is consistent across different arrays and strainsand is not dependent on scanning. Furthermore, the com-parison of absolute intensities for different genes can beaffected by factors such as the concentration, length, andTm of reporter elements. The authors have essentiallyadopted a one-colour approach to the analysis of two-col-our data, although generally some form of normalisationand an attempt to address the issue of relative intensitywould be included. However, in their study relative inten-sities would not have been informative as no pathogenswere included in the comparison.Stabler et al. approached data analysis in a more commonapproach for two-colour data by first removing unreliablelow intensity data based on image analysis QC flags, nor-malisation of ratio data to remove any systematic effectsand applying ratio cutoffs to determine genes present orabsent in comparison to the control strain hybridised toeach array as a common reference. The GACK softwareused to set dynamic ratio cutoffs and make the 'present','divergent' or 'absent/highly divergent' calls was set at itsmost conservative, classified as 'present' if the estimatedprobability of presence (EPP) = 100% and 'absent/highlyBMC Genomics 2006, 7:129 http://www.biomedcentral.com/1471-2164/7/129Page 3 of 3(page number not for citation purposes)divergent' if EPP = 0% with 'divergent' genes betweenthese two extremes. Snyder and Saunders wrongly indi-cate that the calls of absence by Stabler et al. were basedpurely on a lack of hybridisation; if this were the case thenwe would agree with the other possible technical reasonsfor this that they describe in detail. In actual fact, the callsof absence by Stabler et al. are based on the ratio of inten-sities for the test strain channel and the common referencestrain control channel. Therefore, the call of absencedepends on direct comparison to the positive signal inten-sity for the same spot which serves as an internal control.This presents one of the great strengths of two-colouranalysis that helps to circumvent the highlighted prob-lems of missing or poorly performing reporter elements asthe same reporter is being used in each channel of thecomparison.These fundamental differences in data analysis present themost likely source of discrepancies between the two stud-ies as all subsequent interpretations are based on the issueof gene presence in different strains. Snyder and Saunderswere likely to overestimate gene presence and unable todiscriminate conserved genes from highly divergent genesas relative intensities are not taken into account nor anydirect comparison to pathogens. In contrast, Stabler et al.are likely to underestimate gene presence due to the per-haps overly strict criteria applied to gene selection, that is,the need for a 100% certainty of gene presence in everystrain within a group, that would exclude any gene justmissing these thresholds in a single strain. Given thesetwo likely extremes it is not surprising that so many dis-crepancies were reported.Data interpretationThe final interpretation of data is clearly dependent on themethods used to determine gene presence as outlinedabove. Many of the discrepancies reported by Snyder andSaunders were related to comparison with the microarraydata of Stabler et al. but also extended to other studiesidentifying "virulence genes" or pathogen-specific genes.Whilst we want to avoid an extended reanalysis and inter-rogation of both datasets, Snyder and Saunders make crit-ical misrepresentations of Stabler et al. data that need tobe addressed in more general terms.One section discussed in detail by Snyder and Saundersrelates to the genes presented by Stabler et al. in Table 3.This list of genes was produced by identifying genespresent in all N. meningitidis serotype B strains and exclud-ing genes classified as absent in all commensal strainsanalysed. The extremely stringent method employed waspurposefully chosen due to the relatively low numbers ofstrains compared in this study and the desire to avoid anyfalse positives. Whilst there is agreement with Snyder andSaunders on the known strain-specific genes, reflectingconfirmed gene absence, there is less agreement on a sig-nificant number of genes that will most likely differ in thedegree of sequence divergence and thus relate to the meth-ods used to determine gene presence. Confidence in ourown data comes from the fact that the relative intensitiesof these genes are in the order of 500 times lower in thecommensals than the pathogens when compared to thecommon reference, suggesting these genes are highlydivergent and thus likely to encode functionally distinctproteins.Table 3 in Stabler et al. is also discussed in relation to thepathogen-specific genes reported by Snyder and Saundersin Table 3 as 5/6 genes were not reported by Stabler et al.However, it is incorrect and misleading to conclude thatlack of genes in this list equates to presence in commen-sals as suggested. The reason for these genes not appearingin Stabler et al. Table 3 was due to them not meeting thestrict criteria for inclusion rather than indicating presencein commensals. The data for these genes also demon-strates excellent differential distribution between patho-gens and commensals and so is in agreement with thefindings of Snyder and Saunders. This does highlight thatperhaps the analysis stringency of Stabler et al. should bereduced to ensure that potential genes are not missedalthough this would increase the risk of more false posi-tives.SummaryThis response has hopefully highlighted some of theissues that influence the interpretation of microarray datafrom different groups. To a non-specialist reader this mayseem bewildering but it is important to appreciate that theapplication of apparently similar experimentalapproaches can lead to very different conclusions and theneed to understand the basis of this.References1. Snyder LA, Saunders NJ: The majority of genes in the patho-genic Neisseria species are present in non-pathogenic Neis-seria lactamica, including those designated as virulencegenes.  BMC Genomics 2006, 7:128.2. Stabler RA, Marsden GL, Witney AA, Li Y, Bentley SD, Tang CM,Hinds J: Identification of pathogen-specific genes throughmicroarray analysis of pathogenic and commensal Neisseriaspecies.  Microbiology 2005, 151(9):2907-22.

The majority of genes in the pathogenic Neisseria species are present in non-pathogenic Neisseria lactamica, including those designated as virulence genes: response.

Abstract A response to Snyder LA, Saunders NJ: The majority of genes in the pathogenic Neisseria species are present in non-pathogenic Neisseria lactamica, including those designated as virulence genes. BMC Genomics 2006, 7:128.</p

Hinds Jason

Stabler Richard

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BMC Genomics

The majority of genes in the pathogenic <it>Neisseria </it>species are present in non-pathogenic <it>Neisseria lactamica</it>, including those designated as virulence genes: response

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BioMed CentralBMC Genomics
ssOpen AcceCorrespondence
The majority of genes in the pathogenic Neisseria species are 
present in non-pathogenic Neisseria lactamica, including those 
designated as virulence genes: response
Richard Stabler*1 and Jason Hinds2
Address: 1Department of Infectious & Tropical Diseases, London, School of Hygiene and Tropical Medicine, London WC1E 7HT, UK and 2Bacterial 
Microarray Group, Division of Cellular & Molecular Medicine, St George's, University of London, London SW17 0RE, UK
Email: Richard Stabler* - Richard.Stabler@lshtm.ac.uk; Jason Hinds - jhinds@sgul.ac.uk
* Corresponding author    
Abstract
A response to Snyder LA, Saunders NJ: The majority of genes in the pathogenic Neisseria
species are present in non-pathogenic Neisseria lactamica, including those designated
as virulence genes. BMC Genomics 2006, 7:128.
Background
The publication of a microarray analysis of selected Neis-
seria lactamica strains by Snyder and Saunders [1] has
raised some interesting questions regarding the distribu-
tion of genes between pathogenic Neisseria species and the
non-pathogenic N. lactamica, especially with regard to
previously reported "virulence genes." This paper has also
highlighted the difficulties in comparing microarray data
from complementary yet distinct investigations and
places particular emphasis on discrepancies with one of
our own publications [2]. This comparison has raised gen-
eral issues for discussion and a number of points for clar-
ification that form the basis of this response.
Initially, the two papers may appear to take similar micro-
array-based approaches to address the general subject of
specific gene distribution between pathogenic and non-
pathogenic Neisseria species. Indeed, there are a number
of similarities in the practical methods used, as would be
expected for any microarray-based analysis, which makes
the reported discrepancies all the more surprising. How-
ever, there are also a number of important distinctions to
be made between the two studies, primarily based on
experimental design and data analysis, which we believe
greatly influence the interpretation of the results.
Experimental design
Snyder and Saunders determined gene presence in a col-
lection of 13 N. lactamica strains, with a particular focus
on genes previously described as virulence genes, and also
reported the presence of a subset of genes in a range of
commensal Neisseria species. The microarray analysis was
performed using a pan-Neisseria microarray with standard
labelling and hybridisation protocols. Each N. lactamica
strain was directly compared to two other strains in a loop
design that incorporated a dye-swap although data was
analysed in a channel-independent intensity-based man-
ner.
Stabler et al. determined gene presence or absence in a col-
lection of 38 Neisseria strains, including both pathogenic
species and non-pathogenic commensal species, to estab-
lish the phylogenetic relationship of strains that showed
favourable correlation to other typing methods. Genes
present in all 18 N. meningitidis serotype B strains or
present in 7 commensal strains were identified to com-
Published: 30 May 2006
BMC Genomics 2006, 7:129 doi:10.1186/1471-2164-7-129
Received: 18 May 2006
Accepted: 30 May 2006
This article is available from: http://www.biomedcentral.com/1471-2164/7/129
© 2006 Stabler and Hinds; licensee BioMed Central Ltd.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), 
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.Page 1 of 3
(page number not for citation purposes)
BMC Genomics 2006, 7:129 http://www.biomedcentral.com/1471-2164/7/129pare the distribution of these genes in pathogenic and
non-pathogenic strains. It is important to note that this
differs to the goal of this study suggested by Snyder and
Saunders. The microarray analysis was performed using a
pan-Neisseria microarray with standard labelling and
hybridisation protocols. Each Neisseria strain was directly
compared to N. meningitidis MC58 in a common reference
design and data was analysed in a channel-associated
ratio-based manner.
In terms of the microarray platforms used, there are insuf-
ficient differences to explain the reported discrepancies.
Clearly, the microarrays utilised by the two studies were of
independent design and construction so the gene repre-
sentation, reporter element sequences and PCR product
amplicons will differ. However, given the rigorous
approaches to design and construction of both arrays,
these important but subtle differences are unlikely to
account for the level of discordance reported. Even the
strikingly similar hybridisation and washing conditions
should produce a comparable level of stringency, which
would be one obvious methodological reason for such
widespread disagreement in the presence/absence calling
of genes.
The major difference in the experimental design of the
two studies comes from the comparisons made in each
study and how these comparisons were executed. By their
very nature, microarray comparative genomics studies are
always limited by the gene content represented on the
arrays and by the number and representation of strains
analysed. In both cases, the arrays were essentially limited
in content to the sequenced pathogenic Neisseria and
therefore any novel genes present only in N. lactamica or
other commensal species were not surveyed. Likewise, the
strains selected in each study were considered by the
authors to be representative and suitable for the intended
investigation. It is evident that the analysis of additional
strains may affect conclusions; Snyder and Saunders
noted this with the inclusion of another N. lactamica
strain that was not part of their initial MLST set and
accounted for 72% of the strain specific genes they
reported. However, the distinct selection of strains for the
two studies still fails to fully explain the scale of discrep-
ancies reported by Snyder and Saunders. One point to
note would be that no pathogenic strains were included in
the comparison by Snyder and Saunders whereas Stabler
et al. compared all strains directly to a pathogen, namely
the N. meningitidis MC58 common reference. This is
important to note with regard to the execution of the com-
parisons for the data analysis approaches discussed in the
following section.
Data analysis
When considering the above points, in balance with other
reasonable explanations, it would appear that data analy-
sis and subsequent interpretation represents the key area
in which the two groups adopted fundamentally different
approaches that may help to explain the discrepancies
observed. Snyder and Saunders employed an intensity-
based method that analysed the two channels of a two-
colour microarray independently whereas Stabler et al.
used a ratio-based method that analysed the two channels
of a two-colour microarray in combination.
Snyder and Saunders used a metric (pON) that reflects the
probability of there being a hybridisation signal for each
spot in each channel independently. Whilst this seems a
reasonable approach it takes no account of any relative
intensity levels; a gene may be called present in two strains
if the pON threshold is exceeded in both yet the relative
intensity level may be significantly higher in one strain
than the other. For example, for a pathogen gene that was
highly divergent in N. lactamica there may still be a low
but sufficient signal intensity above background to call
the gene as present in N. lactamica using the pON metric,
yet this level of hybridisation would be insignificant when
compared to the signal intensity achieved with a patho-
gen; therefore a more accurate call for this gene should be
absent or highly divergent rather than present. This also
raises the issue that the PMT gain settings during scanning
may have an impact on the number of genes passing the
pON threshold and thus being assigned as present. If this
were true then a normalisation strategy would need to be
employed to account for this to ensure the definition of
presence is consistent across different arrays and strains
and is not dependent on scanning. Furthermore, the com-
parison of absolute intensities for different genes can be
affected by factors such as the concentration, length, and
Tm of reporter elements. The authors have essentially
adopted a one-colour approach to the analysis of two-col-
our data, although generally some form of normalisation
and an attempt to address the issue of relative intensity
would be included. However, in their study relative inten-
sities would not have been informative as no pathogens
were included in the comparison.
Stabler et al. approached data analysis in a more common
approach for two-colour data by first removing unreliable
low intensity data based on image analysis QC flags, nor-
malisation of ratio data to remove any systematic effects
and applying ratio cutoffs to determine genes present or
absent in comparison to the control strain hybridised to
each array as a common reference. The GACK software
used to set dynamic ratio cutoffs and make the 'present',
'divergent' or 'absent/highly divergent' calls was set at its
most conservative, classified as 'present' if the estimated
probability of presence (EPP) = 100% and 'absent/highlyPage 2 of 3
(page number not for citation purposes)
BMC Genomics 2006, 7:129 http://www.biomedcentral.com/1471-2164/7/129divergent' if EPP = 0% with 'divergent' genes between
these two extremes. Snyder and Saunders wrongly indi-
cate that the calls of absence by Stabler et al. were based
purely on a lack of hybridisation; if this were the case then
we would agree with the other possible technical reasons
for this that they describe in detail. In actual fact, the calls
of absence by Stabler et al. are based on the ratio of inten-
sities for the test strain channel and the common reference
strain control channel. Therefore, the call of absence
depends on direct comparison to the positive signal inten-
sity for the same spot which serves as an internal control.
This presents one of the great strengths of two-colour
analysis that helps to circumvent the highlighted prob-
lems of missing or poorly performing reporter elements as
the same reporter is being used in each channel of the
comparison.
These fundamental differences in data analysis present the
most likely source of discrepancies between the two stud-
ies as all subsequent interpretations are based on the issue
of gene presence in different strains. Snyder and Saunders
were likely to overestimate gene presence and unable to
discriminate conserved genes from highly divergent genes
as relative intensities are not taken into account nor any
direct comparison to pathogens. In contrast, Stabler et al.
are likely to underestimate gene presence due to the per-
haps overly strict criteria applied to gene selection, that is,
the need for a 100% certainty of gene presence in every
strain within a group, that would exclude any gene just
missing these thresholds in a single strain. Given these
two likely extremes it is not surprising that so many dis-
crepancies were reported.
Data interpretation
The final interpretation of data is clearly dependent on the
methods used to determine gene presence as outlined
above. Many of the discrepancies reported by Snyder and
Saunders were related to comparison with the microarray
data of Stabler et al. but also extended to other studies
identifying "virulence genes" or pathogen-specific genes.
Whilst we want to avoid an extended reanalysis and inter-
rogation of both datasets, Snyder and Saunders make crit-
ical misrepresentations of Stabler et al. data that need to
be addressed in more general terms.
One section discussed in detail by Snyder and Saunders
relates to the genes presented by Stabler et al. in Table 3.
This list of genes was produced by identifying genes
present in all N. meningitidis serotype B strains and exclud-
ing genes classified as absent in all commensal strains
analysed. The extremely stringent method employed was
purposefully chosen due to the relatively low numbers of
strains compared in this study and the desire to avoid any
false positives. Whilst there is agreement with Snyder and
Saunders on the known strain-specific genes, reflecting
confirmed gene absence, there is less agreement on a sig-
nificant number of genes that will most likely differ in the
degree of sequence divergence and thus relate to the meth-
ods used to determine gene presence. Confidence in our
own data comes from the fact that the relative intensities
of these genes are in the order of 500 times lower in the
commensals than the pathogens when compared to the
common reference, suggesting these genes are highly
divergent and thus likely to encode functionally distinct
proteins.
Table 3 in Stabler et al. is also discussed in relation to the
pathogen-specific genes reported by Snyder and Saunders
in Table 3 as 5/6 genes were not reported by Stabler et al.
However, it is incorrect and misleading to conclude that
lack of genes in this list equates to presence in commen-
sals as suggested. The reason for these genes not appearing
in Stabler et al. Table 3 was due to them not meeting the
strict criteria for inclusion rather than indicating presence
in commensals. The data for these genes also demon-
strates excellent differential distribution between patho-
gens and commensals and so is in agreement with the
findings of Snyder and Saunders. This does highlight that
perhaps the analysis stringency of Stabler et al. should be
reduced to ensure that potential genes are not missed
although this would increase the risk of more false posi-
tives.
Summary
This response has hopefully highlighted some of the
issues that influence the interpretation of microarray data
from different groups. To a non-specialist reader this may
seem bewildering but it is important to appreciate that the
application of apparently similar experimental
approaches can lead to very different conclusions and the
need to understand the basis of this.
References
1. Snyder LA, Saunders NJ: The majority of genes in the patho-
genic Neisseria species are present in non-pathogenic Neis-
seria lactamica, including those designated as virulence
genes.  BMC Genomics 2006, 7:128.
2. Stabler RA, Marsden GL, Witney AA, Li Y, Bentley SD, Tang CM,
Hinds J: Identification of pathogen-specific genes through
microarray analysis of pathogenic and commensal Neisseria
species.  Microbiology 2005, 151(9):2907-22.Page 3 of 3
(page number not for citation purposes)


The majority of genes in the pathogenic  species are present in non-pathogenic , including those designated as virulence genes: response

A response to Snyder LA, Saunders NJ: The majority of genes in the pathogenic Neisseriaspecies are present in non-pathogenic Neisseria lactamica, including those designated

as virulence genes. BMC Genomics 2006, 7:128

Stabler, R

Hinds, J

St George's Online Research Archive

BioMed CentralBMC GenomicsssOpen AcceCorrespondenceThe majority of genes in the pathogenic Neisseria species are present in non-pathogenic Neisseria lactamica, including those designated as virulence genes: responseRichard Stabler*1 and Jason Hinds2Address: 1Department of Infectious & Tropical Diseases, London, School of Hygiene and Tropical Medicine, London WC1E 7HT, UK and 2Bacterial Microarray Group, Division of Cellular & Molecular Medicine, St George's, University of London, London SW17 0RE, UKEmail: Richard Stabler* - Richard.Stabler@lshtm.ac.uk; Jason Hinds - jhinds@sgul.ac.uk* Corresponding author    AbstractA response to Snyder LA, Saunders NJ: The majority of genes in the pathogenic Neisseriaspecies are present in non-pathogenic Neisseria lactamica, including those designatedas virulence genes. BMC Genomics 2006, 7:128.BackgroundThe publication of a microarray analysis of selected Neis-seria lactamica strains by Snyder and Saunders [1] hasraised some interesting questions regarding the distribu-tion of genes between pathogenic Neisseria species and thenon-pathogenic N. lactamica, especially with regard topreviously reported "virulence genes." This paper has alsohighlighted the difficulties in comparing microarray datafrom complementary yet distinct investigations andplaces particular emphasis on discrepancies with one ofour own publications [2]. This comparison has raised gen-eral issues for discussion and a number of points for clar-ification that form the basis of this response.Initially, the two papers may appear to take similar micro-array-based approaches to address the general subject ofspecific gene distribution between pathogenic and non-pathogenic Neisseria species. Indeed, there are a numberof similarities in the practical methods used, as would beexpected for any microarray-based analysis, which makesthe reported discrepancies all the more surprising. How-ever, there are also a number of important distinctions tobe made between the two studies, primarily based onexperimental design and data analysis, which we believegreatly influence the interpretation of the results.Experimental designSnyder and Saunders determined gene presence in a col-lection of 13 N. lactamica strains, with a particular focuson genes previously described as virulence genes, and alsoreported the presence of a subset of genes in a range ofcommensal Neisseria species. The microarray analysis wasperformed using a pan-Neisseria microarray with standardlabelling and hybridisation protocols. Each N. lactamicastrain was directly compared to two other strains in a loopdesign that incorporated a dye-swap although data wasanalysed in a channel-independent intensity-based man-ner.Stabler et al. determined gene presence or absence in a col-lection of 38 Neisseria strains, including both pathogenicspecies and non-pathogenic commensal species, to estab-lish the phylogenetic relationship of strains that showedfavourable correlation to other typing methods. Genespresent in all 18 N. meningitidis serotype B strains orpresent in 7 commensal strains were identified to com-Published: 30 May 2006BMC Genomics 2006, 7:129 doi:10.1186/1471-2164-7-129Received: 18 May 2006Accepted: 30 May 2006This article is available from: http://www.biomedcentral.com/1471-2164/7/129© 2006 Stabler and Hinds; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.Page 1 of 3(page number not for citation purposes)BMC Genomics 2006, 7:129 http://www.biomedcentral.com/1471-2164/7/129pare the distribution of these genes in pathogenic andnon-pathogenic strains. It is important to note that thisdiffers to the goal of this study suggested by Snyder andSaunders. The microarray analysis was performed using apan-Neisseria microarray with standard labelling andhybridisation protocols. Each Neisseria strain was directlycompared to N. meningitidis MC58 in a common referencedesign and data was analysed in a channel-associatedratio-based manner.In terms of the microarray platforms used, there are insuf-ficient differences to explain the reported discrepancies.Clearly, the microarrays utilised by the two studies were ofindependent design and construction so the gene repre-sentation, reporter element sequences and PCR productamplicons will differ. However, given the rigorousapproaches to design and construction of both arrays,these important but subtle differences are unlikely toaccount for the level of discordance reported. Even thestrikingly similar hybridisation and washing conditionsshould produce a comparable level of stringency, whichwould be one obvious methodological reason for suchwidespread disagreement in the presence/absence callingof genes.The major difference in the experimental design of thetwo studies comes from the comparisons made in eachstudy and how these comparisons were executed. By theirvery nature, microarray comparative genomics studies arealways limited by the gene content represented on thearrays and by the number and representation of strainsanalysed. In both cases, the arrays were essentially limitedin content to the sequenced pathogenic Neisseria andtherefore any novel genes present only in N. lactamica orother commensal species were not surveyed. Likewise, thestrains selected in each study were considered by theauthors to be representative and suitable for the intendedinvestigation. It is evident that the analysis of additionalstrains may affect conclusions; Snyder and Saundersnoted this with the inclusion of another N. lactamicastrain that was not part of their initial MLST set andaccounted for 72% of the strain specific genes theyreported. However, the distinct selection of strains for thetwo studies still fails to fully explain the scale of discrep-ancies reported by Snyder and Saunders. One point tonote would be that no pathogenic strains were included inthe comparison by Snyder and Saunders whereas Stableret al. compared all strains directly to a pathogen, namelythe N. meningitidis MC58 common reference. This isimportant to note with regard to the execution of the com-parisons for the data analysis approaches discussed in thefollowing section.Data analysisWhen considering the above points, in balance with otherreasonable explanations, it would appear that data analy-sis and subsequent interpretation represents the key areain which the two groups adopted fundamentally differentapproaches that may help to explain the discrepanciesobserved. Snyder and Saunders employed an intensity-based method that analysed the two channels of a two-colour microarray independently whereas Stabler et al.used a ratio-based method that analysed the two channelsof a two-colour microarray in combination.Snyder and Saunders used a metric (pON) that reflects theprobability of there being a hybridisation signal for eachspot in each channel independently. Whilst this seems areasonable approach it takes no account of any relativeintensity levels; a gene may be called present in two strainsif the pON threshold is exceeded in both yet the relativeintensity level may be significantly higher in one strainthan the other. For example, for a pathogen gene that washighly divergent in N. lactamica there may still be a lowbut sufficient signal intensity above background to callthe gene as present in N. lactamica using the pON metric,yet this level of hybridisation would be insignificant whencompared to the signal intensity achieved with a patho-gen; therefore a more accurate call for this gene should beabsent or highly divergent rather than present. This alsoraises the issue that the PMT gain settings during scanningmay have an impact on the number of genes passing thepON threshold and thus being assigned as present. If thiswere true then a normalisation strategy would need to beemployed to account for this to ensure the definition ofpresence is consistent across different arrays and strainsand is not dependent on scanning. Furthermore, the com-parison of absolute intensities for different genes can beaffected by factors such as the concentration, length, andTm of reporter elements. The authors have essentiallyadopted a one-colour approach to the analysis of two-col-our data, although generally some form of normalisationand an attempt to address the issue of relative intensitywould be included. However, in their study relative inten-sities would not have been informative as no pathogenswere included in the comparison.Stabler et al. approached data analysis in a more commonapproach for two-colour data by first removing unreliablelow intensity data based on image analysis QC flags, nor-malisation of ratio data to remove any systematic effectsand applying ratio cutoffs to determine genes present orabsent in comparison to the control strain hybridised toeach array as a common reference. The GACK softwareused to set dynamic ratio cutoffs and make the 'present','divergent' or 'absent/highly divergent' calls was set at itsmost conservative, classified as 'present' if the estimatedprobability of presence (EPP) = 100% and 'absent/highlyPage 2 of 3(page number not for citation purposes)BMC Genomics 2006, 7:129 http://www.biomedcentral.com/1471-2164/7/129divergent' if EPP = 0% with 'divergent' genes betweenthese two extremes. Snyder and Saunders wrongly indi-cate that the calls of absence by Stabler et al. were basedpurely on a lack of hybridisation; if this were the case thenwe would agree with the other possible technical reasonsfor this that they describe in detail. In actual fact, the callsof absence by Stabler et al. are based on the ratio of inten-sities for the test strain channel and the common referencestrain control channel. Therefore, the call of absencedepends on direct comparison to the positive signal inten-sity for the same spot which serves as an internal control.This presents one of the great strengths of two-colouranalysis that helps to circumvent the highlighted prob-lems of missing or poorly performing reporter elements asthe same reporter is being used in each channel of thecomparison.These fundamental differences in data analysis present themost likely source of discrepancies between the two stud-ies as all subsequent interpretations are based on the issueof gene presence in different strains. Snyder and Saunderswere likely to overestimate gene presence and unable todiscriminate conserved genes from highly divergent genesas relative intensities are not taken into account nor anydirect comparison to pathogens. In contrast, Stabler et al.are likely to underestimate gene presence due to the per-haps overly strict criteria applied to gene selection, that is,the need for a 100% certainty of gene presence in everystrain within a group, that would exclude any gene justmissing these thresholds in a single strain. Given thesetwo likely extremes it is not surprising that so many dis-crepancies were reported.Data interpretationThe final interpretation of data is clearly dependent on themethods used to determine gene presence as outlinedabove. Many of the discrepancies reported by Snyder andSaunders were related to comparison with the microarraydata of Stabler et al. but also extended to other studiesidentifying "virulence genes" or pathogen-specific genes.Whilst we want to avoid an extended reanalysis and inter-rogation of both datasets, Snyder and Saunders make crit-ical misrepresentations of Stabler et al. data that need tobe addressed in more general terms.One section discussed in detail by Snyder and Saundersrelates to the genes presented by Stabler et al. in Table 3.This list of genes was produced by identifying genespresent in all N. meningitidis serotype B strains and exclud-ing genes classified as absent in all commensal strainsanalysed. The extremely stringent method employed waspurposefully chosen due to the relatively low numbers ofstrains compared in this study and the desire to avoid anyfalse positives. Whilst there is agreement with Snyder andSaunders on the known strain-specific genes, reflectingconfirmed gene absence, there is less agreement on a sig-nificant number of genes that will most likely differ in thedegree of sequence divergence and thus relate to the meth-ods used to determine gene presence. Confidence in ourown data comes from the fact that the relative intensitiesof these genes are in the order of 500 times lower in thecommensals than the pathogens when compared to thecommon reference, suggesting these genes are highlydivergent and thus likely to encode functionally distinctproteins.Table 3 in Stabler et al. is also discussed in relation to thepathogen-specific genes reported by Snyder and Saundersin Table 3 as 5/6 genes were not reported by Stabler et al.However, it is incorrect and misleading to conclude thatlack of genes in this list equates to presence in commen-sals as suggested. The reason for these genes not appearingin Stabler et al. Table 3 was due to them not meeting thestrict criteria for inclusion rather than indicating presencein commensals. The data for these genes also demon-strates excellent differential distribution between patho-gens and commensals and so is in agreement with thefindings of Snyder and Saunders. This does highlight thatperhaps the analysis stringency of Stabler et al. should bereduced to ensure that potential genes are not missedalthough this would increase the risk of more false posi-tives.SummaryThis response has hopefully highlighted some of theissues that influence the interpretation of microarray datafrom different groups. To a non-specialist reader this mayseem bewildering but it is important to appreciate that theapplication of apparently similar experimentalapproaches can lead to very different conclusions and theneed to understand the basis of this.References1. Snyder LA, Saunders NJ: The majority of genes in the patho-genic Neisseria species are present in non-pathogenic Neis-seria lactamica, including those designated as virulencegenes.  BMC Genomics 2006, 7:128.2. Stabler RA, Marsden GL, Witney AA, Li Y, Bentley SD, Tang CM,Hinds J: Identification of pathogen-specific genes throughmicroarray analysis of pathogenic and commensal Neisseriaspecies.  Microbiology 2005, 151(9):2907-22.Page 3 of 3(page number not for citation purposes)

Identification of pathogen-specific genes through microarray analysis of pathogenic and commensal Neisseria species.

NJ: The majority of genes in the pathogenic Neisseria species are present in non-pathogenic Neisseria lactamica, including those designated as virulence genes.

The majority of genes in the pathogenic Neisseria species are present in non-pathogenic Neisseria lactamica, including those designated as virulence genes: response

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The majority of genes in the pathogenic Neisseria species are present in non-pathogenic Neisseria lactamica, including those designated as virulence genes: response

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