A novel data mining system points out hidden relationships between immunological markers in multiple sclerosis.

0.34 MB·English

by  Gironi, Maira

Gironietal.Immunity&Ageing2013,10:1
IMMUNITY & AGEING
http://www.immunityageing.com/content/10/1/1
RESEARCH Open Access
A novel data mining system points out hidden
relationships between immunological markers in
multiple sclerosis
Maira Gironi1,4, Marina Saresella2, Marco Rovaris2, Matilde Vaghi4, Raffaello Nemni2, Mario Clerici2 and Enzo Grossi3*
Abstract
Background: Multiple Sclerosis (MS) is a multi-factorial disease, where a single biomarker unlikely can provide
comprehensive information. Moreover, due to thenon-linearity of biomarkers, traditionalstatistic is both unsuitable
and underpoweredto dissect theirrelationship. Patients affected with primary (PP=14), secondary (SP=33), benign
(BB=26), relapsing-remitting (RR=30) MS, and 42 sex and age matched healthy controls were studied.We performed
a depth immune-phenotypic and functional analysis of peripheral blood mononuclear cell (PBMCs)by flow-
cytometry. Semantic connectivity maps (AutoCM) were applied to find the natural associations among
immunological markers. AutoCM is a special kind of Artificial Neural Network able to find consistent trends and
associations among variables. The matrix of connections, visualized through minimum spanning tree, keeps non
linear associations among variablesand captures connectionschemesamong clusters.
Results: Compleximmunologicalrelationshipswereshowntoberelatedtodifferentdiseasecourses.LowCD4IL25+
cellslevelwasstronglyrelated(linkstrength,ls=0.81)toSPMS.ThisphenotypewasalsoassociatedtohighCD4ROR+
cellslevels(ls=0.56).BBMSwasrelatedtohighCD4+IL13celllevels(ls=0.90),aswellastohighCD14+IL6cells
percentage(ls=0.80).RRMSwasstrongly(ls=0.87)relatedtoCD4+IL25highcelllevels,aswellindirectlytohigh
percentagesofCD4+IL13cells.Inthislatterstrong(ls=0.92)associationcouldbeconfirmedtheinductionactivityof
theformercells(CD4+IL25)onthelatter(CD4+IL13).AnotherinterestingtopographicdatawastheisolationofTh9
cells(CD4IL9)fromthemainpartoftheimmunologicalnetworkrelatedtoMS,suggestingapossiblesecondaryroleof
thisnewdescribedcellphenotypeinMSdisease.
Conclusions:Thisnovelapplicationofnon-linearmathematicaltechniquessuggestspeculiarimmunological
signaturesfordifferentMSphenotypes.Notably,theimmune-networkdisplayedbythisnewmethod,ratherthana
singlemarker,mightbeviewedastherighttargetofimmunotherapy.Furthermore,thisnewstatisticaltechnique
couldbealsoemployedtoincreasetheknowledgeofotherage-relatedmultifactorialdiseaseinwhichcomplex
immunologicalnetworksplayasubstantialrole.
Keywords:Multiplesclerosis,Immunologicalnetwork,AutoCMmethod,Biomarkersofinflammationand
neurodegeneration
*Correspondence:[email protected]
3SemeionResearchCenter,ViaSersale117,Rome00128,Italy
Fulllistofauthorinformationisavailableattheendofthearticle
©2013Gironietal.;licenseeBioMedCentralLtd.ThisisanOpenAccessarticledistributedunderthetermsoftheCreative
CommonsAttributionLicense(http://creativecommons.org/licenses/by/2.0),whichpermitsunrestricteduse,distribution,and
reproductioninanymedium,providedtheoriginalworkisproperlycited.
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Background Thisnew artificial adaptive system is able to define the
Multiple Sclerosis (MS) is an autoreactive Tcell–driven strength of the associations of each variable with all the
CNSdiseaserepresentingthemostcommoncauseofnon- others andtovisuallyshowthemapofthemainconnec-
traumatic disability for the young adult. Different clinical tions of the variables and the basic semantic of their en-
phenotypes can be recognized in this disease: the most semble. The matrix of connections, visualized through
common clinical phenotype of MS is the relapsing- minimum spanning tree, by AutoCM, keeps non linear
remitting (RR) form, which is characterized by an acute associations among variables and captures connection
onset of symptoms and signs suggestive of neurological schemes among clusters. These artificial adaptive sys-
dysfunction, followed by complete or partial recovery. The tems were previously applied to clinical dataset of Alz-
long-term prognosis of RR is usually unfavorable, since heimer patients [6] where they successfully disclosed
patients enter the so-called secondary progressive (SP) previously unknown connections among the large body
phase of the disease and accumulate irreversible neuro- offactors related tothis multifactorialdisease.
logical disability. Approximately 50% of the patients with In this pilot study we investigated immunological mar-
RR will have a transition to SP by 15 years after disease kers' network, using AutoCM in 103 MS patients and 42
onset in the absence of any treatment [1]. A different dis- healthy controls (HC). The same clinical and immuno-
ease pattern, primary progressive (PP) MS, is seen in logical dataset was previously studied with traditional
patients showing a progressive course from the onset; an statisticalanalyses[7].
even rarer form of disease is benign (BB) MS. In this case The aim of this study was to verify these data with a
absent or minimal neurological impairment are present completelydifferentstatisticaltechnique,andtoinvestigate
manyyearsaftertheonsetmanifestation.Notably,whereas whether this revolutionary mathematical approach can in-
RR MS appears to be largely driven by inflammatory pro- creasetheintelligibilityoftheimmunologicalconnections.
cesses, neurodegeneration plays an important role in the
chronic brain and spinal cord injury in patients with PP Results
MSandSPMS.Unfortunately,inspiteofthenewimaging The main relationships between immunological markers
biomarkers discovered, the state and course of the disease and MS clinical phenotypes emerging from the AutoCM
remains rather unpredictable. Since long time ago several analyses are shown in Figure 1. Results clearly indicated
studies [2,3] tried to finger out whether peculiar immuno- that the progressive forms (PP and SP MS) of the disease
logicalsignaturescouldbeassociatedwithdifferentcourses can be visually separated from the other clinical pheno-
of MS. Literature data indeed suggest that certain im- types (RR and BB). Thus, the progressive forms of MS are
munological relevant markers could identify specific dis- associatedwithdifferentimmunologicalmarkerscompared
easestateanddiseaseactivityinMSpatients[4].Moreover toRRMSandBBMS.BymeansofAutoCM,itispossible
findings in humans are corroborated by results from ex- to obtain not only the direction of the association as pro-
perimental autoimmune encephalomyelitis (EAE), a well- vided by standard statistical analyses, but importantly also
established animal model of MS, where a specific pattern the strength of this association. A reduced percentage of
of lymphocyte infiltration and cytokine secretion along CD4/IL25+ cells was strongly (ls=0.81) related to second-
withthedifferentphasesofthediseasehasbeendescribed ary progressive MS, this phenotype being also (ls=0.56)
[5].Carefulanalysisofthealterationsinimmuneprocesses associated to increased amounts of CD4/ROR+cells. On
should further advance knowledge of the pathogenic theoppositepartofthegraph,benignphenotypes(BBMS)
mechanisms of this disease, and might have predictive wereclearlyrelatedtohigherquantitiesofCD4+/IL13and
valuetowarddiseaseevolution. CD14+/IL6cells(ls=0.80).Thisrelationshipdepictsacom-
Unfortunately, the overall immunological results of lit- parable impact of anti-inflammatory (CD4+IL13 cells) fac-
eraturearesometimesconflictingandofteninsufficientto tors and of inflammatory markers (CD14+IL6 cells) in the
disclosetheeffectiverelationshipamongstudiedvariables. shaping of this favorable clinical course. Interestingly, the
Althoughdifferencesbetweentrialdesigns,patients’popu- RR MS form was directly associated with CD4+/IL25 cells
lation, immunological markers, and technical methodolo- (ls=0.87), whereas an indirect association with CD4+/IL13
gies can explain the most of this inconsistency, statistical cells was detected. In turn, CD4+IL25 cells are strongly
analyses might be an important factor to be considered. (ls=0.92) associated to CD4+IL13 cells, strengthening the
Thus, traditionalstatistical algorithmsare both unsuitable well known induction activity of the former (CD4+/IL25)
and underpowered to dissect the relationship between onthelatter(CD4+/IL13)Tlymphocytepopulations[8,9].
high number of markers due to the non-linearity and Another interesting topographic data is the isolation of
complexityoftheimmunologicalnetwork;afuzzycluster- Th9 cells (CD4IL9) from the main part of the immuno-
ing approach based on evolutionary programming (PST) logical network related to MS, suggesting a possible
and Semantic connectivity map (AutoCM) could find the secondary role of these recently discovered immune cells
naturalassociationsamongimmunologicalmarkers. inMS.
Gironietal.Immunity&Ageing2013,10:1 Page3of7
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Figure1SemanticconnectivitymapofthepathwaylinkingimmunologicalmarkerstodifferentphenotypesofMS.Semantic
connectivitymapofvariablesonstudy:valuesrangingfrom0(noassociation)to1(thestrongestassociation)expressthestrengthofassociation.
Discussion the immunological support of the better prognosis and
Immunological markers are objective measures with less destructive mechanisms associated to RR and BB
clinical and scientific relevance. They can be useful in comparedtoSPandPP.
diagnostic definition/exclusion (e.g., anti acquaporin anti- In particular, AutoCM found a strong relationship be-
bodies to differentiate MS from Neuromyelitis Ottica), to tween RR and high percentages of IL25-expressing CD4
assesstreatmentresponse(e.g.,antiIFNneutralizinganti- cells. These lymphocytes are central in down-regulating
bodies in non-responder patients), but also to dissect the inflammatory potential of TH17 cells, directly or via
pathogenetic mechanisms of disease [10-12]. Due to the IL-13- secreting CD4 cells [8,14]. Thus, it has been sug-
complexityandredundancyofimmunesystem,investigat- gested that the disease can enter temporary quiescence
ing the pattern of correlations among immune markers is phase (remission) by these anti-inflammatory mechan-
a challenging task for traditional statistic. The latter can isms. Interestingly the AutoCM caught the sense of this
provide binary correlation, and show immunological pat- relationship, showing the direct link of RR phenotype to
tern best associated to a clinical phenotype but it is un- IL-25 CD4 cells and indirect with IL-13- secreting CD4
suitable to finger out the non-linear interconnections cells, confirming preliminary data from literature but
among variables. In order to find the natural associations even adding new information. Our previous study
amongimmunologicalmarkersweappliedafuzzycluster- showed that IL-6-expressing CD14+ cells were signifi-
ing approach based on evolutionary programming (PST) cantly augmented in all the different MS phenotypes.
andSemanticconnectivitymap(AutoCM)tothedataofa The differentiation processes of these cells occurring
wide immunological study, dissecting new pathways of when they get the target tissue, mainly depends on in-
immunologicalmechanisms[7].AutoCMisaspecialkind flammatory or anti-inflammatory milieu they find. Ac-
ofArtificialNeuralNetworkabletofindconsistenttrends cordingly, most of these cells find in the inflammmated
and associations among variables. The matrix of connec- CNS the ideal milieu priming their differentiation into
tions, visualized through minimum spanning tree, keeps functional myeloid dendritic cells able to support the
non linear associations among variables and captures proliferation and expansion of TH17 cells [15]. In turn
connection schemes among clusters. The use of this these latter cells orchestrate the effective inflammatory
mathematicalapproach,asshownbyliterature[13]should damage to myelin and cells. Interestingly, the new find-
disclosethe complexrelationshipsamong markers, other- ing provided with this analyses suggest that IL-6-
wise impossible to be fingered out by traditional analyses. expressing CD14+ cells play a role also in benign course,
A global view can suggest how AutoCM can visually sep- but arestrictly associated (in the visualmap) with IL-13-
arate the progressive forms of disease (PP and SP) from expressing CD4 cells. These lymphocytes are endowed
RR and BB phenotypes, connecting the last to a network with anti-inflammatory properties [16] and therefore
ofdifferentimmunologicalmarkers.Thisfindingcouldbe could turn off the potential detrimental effect of IL-6-
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secreting CD14+ cells as well as that of TH17 cells. The Conclusion
clarity and strength of the relationship between IL-6- In conclusion AutoCM clearly show PP and SP strongly
secreting CD14+ cells and IL-13- secreting CD4 cells associated to “lowCD4IL25”, conversely BB appear
definitely comes out better by AutoCM analyses. Con- related to both “high CD14 IL13” and “high CD4IL6”
sidering the upper part of the graph, the most striking immunological signature. Being MS a multifactorial dis-
element is the central hub represented by low percent- order, several other biological variables (genetic, epigen-
age of IL-25/CD4 cells. These cells, as aforementioned, etic, transcriptomics...) rely on the clinical differences
represent one of the most important ways to regulate an of these opposite phenotypes. Notwithstanding the need
inflammatoryresponse[8].LowlevelsofIL-25CD4cells of other reliable biological markers, our findings shed
could be associated to a predisposing condition to in- further light on the meaning of the immunological mar-
flammation that would be further enhanced by the co- kers associatedtodifferentdisease courses”
presence of inflammatory cells (e.g., CD4+ROR+cells). The superiority of AutoCM method compared to those
The co-existence of low IL-25 CD4 and high CD4+ obtained with Euclidean or other linear-correlation-dis-
ROR+cells is shown by AutoCM as the signature of SP tance-matrix-basedanalysesisevidencedbytheglobalin-
phenotype. These data suggest that the defect of a formation it provides. Not only the reciprocal binary
counter-regulative (anti-inflammatory) mechanism could connection among biological and clinical variables, but
be a possible explanation for the particularly aggressive the totality of these connections and the strength of the
course of SP MS. Figure 1 shows how strong is also the entire immunological network can be disclosed by this
link between low IL-25 CD4 and PP as well as between new revolutionary method. These data sound to suggest
IL-25CD4 and HC . However it’s worth to note that by that future studies based on this new mathematical ap-
AutoCm analyses PP is not shown to be associated with proach could better define also the relationship between
high percentages of CD4+/ROR+ cells as SP is; it is the inflammatory component and neurodegeneration fac-
tempting to speculate that this difference might play a torsinvolvedinMS.Moreover,AutoCMcouldbeauseful
role in determining the clinical course of these different tool to finger out complex networks underpinning age-
phenotypes.Ourdataareinlinewithliteraturethathigh- relatedmultifactorialdiseases.
lights the paucity and controversial role of inflammation Increasing knowledge of so far unveiled relationships
presentinPPcomparingwithotherMSphenotypes[17]. of factors involved in these diseases could pave the way
Notably, low levels of IL-25/CD4 cells were also asso- formorespecificdrugs.
ciated to healthy subjects, but again inthis group the as-
sociation with high inflammatory subsets (CD4+ROR Methods
+cells) lacks. This data could suggest that a defective We performed a depth immune-phenotypic and func-
anti-inflammatory response could not be per se a suffi- tional analysis of peripheral blood mononuclear cell
cient condition leading to multiple sclerosis.The carrier- (PBMCs) by flow cytometry on 103 MS patients and
ship of other immunological risk factors (beyond the 42 HC. Studied population consisted of 103 MS
focus of our investigation) and/or non-immunogical patients with relapsing remitting (RR, n= 30), benign
(genetic, epigenetic, and environmental) variables can (BB, n=26), primary (PP, n=14), or secondary progres-
make this anti-inflammatory impairment the trigger for sive (SP, n=33) MS diagnosed according international
the disease. Interestingly, CD4+IL9 secreting cells (Th9) consensus criteria [21].
were clearly “topographically excluded”. This newly Clinical history and drug assumption were recorded
described functional immunological phenotype has been and neurological examination was performed, including
suggested to be involved in the pathogenesis of auto- the Expanded Disability Status Scale (EDSS) rating.
immune diseases [18,19]. Moreover these cells seem to Patients were considered affected by BB MS when EDSS
be the trigger of the inflammatory response driven by score was ≤ 3.0 and disease duration equal to or longer
Th17 during MS [20]. However our previous study [7] than 15 years [22]. Median disease duration was 7 years
failed to detect a clear difference in IL-9-secreting cells (range: 1–29 years); the median Expanded Disability Sta-
between any clinical MS phenotype and HC. AutoCM tus Scale (EDSS) score was 1.5 (range: 1–6). Demo-
strengthenthisfindingwithadifferentanalysisindicating graphic and clinical data are reported in Table 1.
the contemporary association of Th9 cells to the 3 main PatientswererecruitedatMultipleSclerosisCenterFon-
immune CD4+ T lymphocytes phenotypes: Th2 dazione Don Carlo Gnocchi (Milan) and CAM, Polidiag-
(CD4GATA+),Th1(CD4TBET)andTh17(CD4IL22).In nosticCenter (Monza).
conclusion, we presented a new mathematical algorithm All subjects had to be free of relapse or of a confirmed
to dissect a complex network of variables. The AutoCM disease progression in the last 30 days and stopped any
system reshapes the distances among immunological immunosuppressive drugs at least 12 months before en-
variableselsewherestudiedbystandardapproach. rollment; symptomatic drugs (SSRI, Baclofen, Oxibutine)
Gironietal.Immunity&Ageing2013,10:1 Page5of7
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Table1Demographicandclinicaldataofstudiedpatientsandhealthycontrols
RR BB SP PP HC
N 30 26 33 14 42
Age(range) 40(20–59) 45(36–61) 49(32–67) 50(37–64) 48(32–62)
F:M 19:11 18:8 21:12 6:8 28:14
Diseaseduration 7 21 20 12 –
EDSS 1.5 2 6.5 6 –
Relapsingremitting(RR);benign(BB);secondaryprogressive(SP);andprimaryprogressive(PP)multiplesclerosispatientswereselected.Agroupof42sexand
agematchedhealthycontrols(HC)wereenrolledinthestudyaswell.F:female,M:male.Averageandrangeofagearereported,EDSS:Expandeddisabilitystatus
scale.AgevaluesarenotsignificantlydifferentamongMSsubgroups;EDSS,asexpected,ishigherinPPandSPsubgroupsincomparisonwithRRandBB.
were accepted. Subjects with a clinically significant or washed, and suspended in 100μl of permeabilization
unstable medical condition (cardiovascular, pulmonary, medium with mAbs against the following proteins: anti-
hepatic, gastrointestinal, renal, metabolic diseases or ma- RORC/RORγτ, anti-T-bet, anti- GATA3, anti- NFkB,
lignancies) were excluded from the study. Following ac- anti-NFATc1, or with IFN-γ IL-4, IL-6, IL-9, IL-10, IL-
quisitionofinformed consent,fastingblood sampleswere 12, IL-13, IL-17, IL-21, IL-22, IL-23, IL-25 and TGF-β-
collectedinthemorningandimmediatelyanalyzed. specific mAb FITC or PE-conjugated for 30 minutes at
4°Cinthedark.
Bloodsamplecollectionandcellseparation
Whole blood was collected in vacutainer tubes containing MonoclonalAbs
ethylenediaminetetraaceticacid(EDTA)(BectonDickinson ThefollowingmAbswereusedinthisstudy:Phycoerythrin-
& Co., Rutherford, NJ, USA). Peripheral blood mono- Cyanin-7 (PC7)- labeled anti-CD4 (clone SFCI12T4D11)
nuclearcells(PBMC)wereseparatedonlymphocytesepar- (mouse IgG1), Fluorescein Isothiocyanate (FITC)-or
ation medium (Organon Teknika Corp., Durham, NC, Phycoerythrin-Cyanin-5 (PC5) labeled anti-CD14 (clone
USA) and washed twice in PBS. Viable leukocytes were 116), (Beckman-Coulter, Fullerton, CA). The intracellular
determined by ScepterTM Handheld Automated Cell molecule detection mAbs used were: anti-human Phyco-
Counter(Millipore,MA,USA). erythrin(PE)-coupledIL-10(cloneJES9D7;mouseIgG1iso-
type; Caltag Laboratories), anti-human IL-4 FITC (clone
SynthesisoftheMBPpeptides MP4-25D2, rat IgG1 isotope, eBioscience Cornerstone
k
Thirty-one HLA I restricted and 7 HLA II restricted Court West, San Diego, CA), anti-human IFNγ-PE, anti-
promiscuous peptides partially overlapping and spanning humanIL-6-FITC(clone1936,mouseIgG isotope,R&D
2B
the whole Myelin Basic Protein (MBP) were synthesized Systems Inc., Minneapolis), anti-human IL-9- PE (clone
using Fmoc chemistry. Peptides purity, as assayed by MH9A4,mouseIgG isotype,Biolegend,SanDiego,CA),
2B,k
HPLC, was > 70%, and their composition was verified by anti-human IL-12- FITC (clone27537, mouseIgG isotype,
1
mass spectrometry. Lyophilized peptides were dissolved R&D), anti-human IL-13 FITC (clone 32007, mouse IgG1i-
at 25 mg/ml in DMSO or sterile water to prepare pep- sotype, R&D) anti-human IL-17-PC5 (clone BL168, mouse
tidepools(10mg/mlfinal concentration). IgG isotype,Biolegend),anti-humanIL-21-PE(clone3A3-
1k
N2,mouseIgG isotype,eBioscience),anti-humanIL-22-PE
1
StimulationofPBMCforFACSanalysis (clone 142928, mouse IgG isotype R&D), anti-human IL-
1
1 x 106 PBMC were stimulated with non-immunogenic 23-PE (clone C11.5, mouse IgG isotype, Biolegend), anti-
1k
peptides or with a pool of the MBP peptides (10 mg/ml) + humanIL-25-PE(IL-17E,clone182203,mouseIgG1isotype,
anti-CD28mAb(clone37407.111;R&DSystems,Inc.,Min- R&D)anti-mouse/humanRORC/RORγτPE(cloneAFKJS-9,
neapolis,MN,USA)(1μg/ml)tofacilitateco-stimulation,at rat IgG isotype, eBioscience), anti-mouse/human T-bet-
2a
37°C in a humidified 5% CO2 atmosphere for 24 hours. PE (clone 39D, mouse IgG isotype, eBioscience), anti-
1
Fr cytokine analyses, 10 μg/ml Brefeldin A (Sigma-Aldrich, mouse/humanGATA3-PE(cloneTWAY,ratIgG isotype,
2B,k
St. Louis, MO,USA) was added to the cell cultures during eBioscience), anti- human NFkB-FITC (clone C-5, mouse
thelast6hofstimulationtoblockproteinsecretion. IgG isotype, Santa Cruz Biotechnology, Santa Cruz, CA),
2a
anti- human NFATc1-PE (clone H-10, mouse IgG isotype,
1
Immunofluorescentstaining SantaCruz-Biotechnology).
PBMC were stained with CD4, CD19 and CD14 mAbs
(Beckman Coulter, Brea, CA, USA), washed in PBS and Cytometricanalysis
treated with FIX and PERM (FIX & PERM Cell Analyses were performed using a Beckman-Coulter
Permeabilization kits; eBioscience San Diego, CA, USA), Cytomics FC-500 flow cytometer equipped with a single
then fixed for 10 min in fixation medium (100 μl), 15 mW argon ion laser operating at 488 nm and
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interfaced with CXP Software 2.1. Two-hundred- The strength of the link can be read as the probability
thousands events were acquired and gated on CD4 or oftransition from anystate-variable toanyoneelse.
CD14 expression and side scatter properties. Data were TheAutoCM matrix of connections preserves non lin-
collected using linear amplifiers for forward and side ear associations among variables, while at the same time
scatter and logarithmic amplifiers for FL1, FL2, FL4 and capturing elusive connection schemes among clusters
FL5. Samples were first run using isotype control or sin- that are often overlooked by traditional cluster analyses,
gle fluorochrome-stained preparations for color com- and highlighting complex similarities among variables
pensation.Rainbow Calibration Particles (Spherotec, Inc. on various dimensions–role, connectivity, essentiality,
Lake Forest, IL) were used to standardize results in sam- and so on. The AutoCM algorithms used for all the
plesobtainedovertime. computations presented in this paper are implemented
only by a Semeion proprietary research software, which
Mathematicalmethods isexclusivelyavailablefor academic purposes.
The analysis performed on this database has the aim of The immunological findings obtained by FACS were
increasing our understanding of the complex pathway translated in absolute natural values, scaled from 1 to
linking immunological markers to different phenotypes zero,becomingsingleinputsfor AutoCM
of MS. This goal has been achieved through a new data We transformed the 11 immunological variables in 22
mining method, based on a particular artificial adaptive input variables constructing for each of the variable,
system,theAutoSemanticConnectivity Map(AutoCM), scaledfrom zeroto1,itscomplement(Table2).
that is able to compute the association strength of each Consider for example the variable CD14_IL6. Absolute
variable with all the others in any dataset (i.e. in terms natural values range from 0.1 to 60. According to the
of many-to-many rather than dyadic associations). The transformation, 60 (thehighestvalue) becomes 1and 0.1
architecture and mathematics of AutoCM is described (the lowest value) becomes 0. All other natural values
elsewhere[13,23]. are scaled in this new range. For instance: the value 25
In non-technical terms, AutoCM is a new data mining becomes 0.41; the value 11 becomes 0.18, etc. The pro-
tool based on an Artificial Neural Network developed at jectionofthevariableinthemapwillpointoutthefuzzy
Semeion Research Center [24] that is especially effective position ofCD14_IL6 accordingto itshighvalues. Inthe
at highlighting any kind of consistent patterns and/or complement transformation, by subtracting the scaled
systematic relationships and hidden trends and associa- valuefrom1(e.g.11becomesnow0.82)weallowthesys-
tions among variables. Quite uncommonly, the weights tem to project and point out the fuzzy position of
determined by AutoCM after the training phase, admit a CD14_IL6 according to its low values. This is important
direct interpretation. Specifically, they are proportional because in non linear systems, the position of high and
to the strength of many-to-many associations across all lowvaluesofagivenvariableisnotnecessarilysymmetric.
variables. This allows a further, useful processing: asso- In this way the projection of the original variables
ciation strengths may be easily visualized by transform- tended to show highvalues while the complement trans-
ing weights into physical distances. Such a 'translation' formationtendedtoshowlowvaluesoftheoriginalvari-
proceeds in an intuitive way: couples of variables whose ables. In the map we have named these two different
connection weights are higher get relatively nearer, and
vice versa. By applying a simple mathematical filter
Table2Variables'transformation
such as the minimum spanning tree to the matrix of
Originalvariables Variables’transformation
distances, a graph is generated, whose use has been
already tested in the medical field [6,25], and that is 1 CD4+RORC/γτ+ 1-CD4+RORC/γτ+
termed connectivity map as detailed by Buscema and 2 CD4+IL17A+ 1-CD4+IL17A+
colleagues [13,25]. This representation then allows a 3 CD4+IL22+ 1-CD4+IL22+
very intuitive visual mapping of the complex web of 4 CD4+TBET+ 1-CD4+TBET+
connection schemes among variables, and greatly eases 5 CD4+IL9+ 1-CD4+IL9+
the detection of the variables that play a key role in the
6 CD4+GATA+ 1-CD4+GATA+
schemes, i.e. that turn out to be “hubs” of the graph.
7 CD4+IL13+ 1-CD4+IL13+
The system provides also a quantification of the
8 CD4+IL25+ 1-CD4+IL25+
“strength” of links among variables (nodes of the graph)
9 CD14+IL6+ 1-CD14+IL6+
by a numerical coefficient (link strength, ls) ranging
10 CD19+IL-6+ 1-CD19+IL-6+
from zero to 1.
11 CD19+TGFβ 1-CD19+TGFβ
The value superimposed to the link is proportional to
Wetransformedthe11immunologicalvariablesin22inputvariablesandwe
thestrengthofthelink.Thestrengthofthelink(ls)ranges
madeforeachofthevariable,scaledfrom0to1,itscomplementasbetter
from0(minimumstrength)to1(maximalstrength). detailedinthetext.
Gironietal.Immunity&Ageing2013,10:1 Page7of7
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forms ashigh andlow.Thispre-processingscalingis ne- 9. FortMM,CheungJ,YenD,LiJ,ZurawskiSM,LoS,MenonS,CliffordT,
cessary to make possible a proportional comparison HunteB,LesleyR,MuchamelT,HurstSD,ZurawskiG,GormanDM,Rennick
DM:IL-25inducesIL-4,IL-5,andIL-13andTh2-associatedpathologies
amongallthe variablesandunderstandtheexistinglinks invivo.Immunity2001,15:985–995.
ofeachvariablewhen the values tendtobehigh orlow. 10. VillosladaP:Biomarkersformultiplesclerosis.DrugNewsPerspect2010,
23:585–595.
Abbreviations 11. HemmerB,StüveO,KieseierB,SchellekensH,HartungHP:Immune
MS:MultipleSclerosis;HC:HealthyControls;RR:RelapsingRemitting; responsetoimmunotherapy:theroleofneutralisingantibodiesto
SP:SecondaryProgressive;PP:PrimaryProgressive;BB:Benign; interferonbetainthetreatmentofmultiplesclerosis.LancetNeurol2005,
AutoCM:SemanticConnectivityMap;EDSS:ExpandedDisabilityScale; 4:403–412.
PBMC:PeripheralBloodMononuclearCell. 12. PaulF,JariusS,AktasO,BluthnerM,BauerO,AppelhansH,FranciottaD,
BergamaschiR,LittletonE,PalaceJ,SeeligHP,HohlfeldR,VincentA,ZippF:
Competinginterests Antibodytoaquaporin4inthediagnosisofneuromyelitisoptica.PLoS
Med2007,4:e133.
Theauthorhasdeclaredthatnocompetinginterestexists.
13. BuscemaM,HelgasonC,GrossiE:Autocontractivemaps,Hfunctionand
Authors’contributions maximallyregulargraph:theoryandapplications,specialsessionon“artificial
adaptivesystemsinmedicine:applicationsintherealworld.NewYork:
MGconceivedthestudy,itsdesignandcoordination,criticallyrevisedthe
NAFIPS2008(IEEE);2008.
clinical-immunologicalandstatisticalanalysesandwrotethemanuscript.MS
14. SaresellaM,MarventanoI,LonghiR,LissoniF,TrabattoniD,MendozziL,
carriedoutalltheimmune-phenotypicandfunctionalanalysisofPBMCsby
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flow-cytometry.MRenrolledthepatients,criticallycommentedthedataand
andstablerelapsing-remittingmultiplesclerosisandtheirmodulation
revisedthemanuscript.MVparticipatedinthecriticalreviewofthedataand
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1INSPE,SanRaffaeleHospital,Milan,Italy.2DonCarloGnocchiFoundation,
similaritiesanddifferencesfromotherformsofMS,diagnosticcriteria,
IRCCS,S.MariaNascente,Milan,Italy.3SemeionResearchCenter,ViaSersale prosandconsofprogressivediagnosis.MultScler2004,10(Suppl1):S2–S7.
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18. TownsendJM,FallonGP,MatthewsJD,SmithP,JolinEH,McKenzieNA:IL-
9-deficientmiceestablishfundamentalrolesforIL-9inpulmonary
Received:26October2012Accepted:30December2012
mastocytosisandgobletcellhyperplasiabutnotTcelldevelopment.
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