The Diversity and Molecular Evolution of B-Cell Receptorsduring Infection

Kenneth B Hoehndagger1 Anna Fowlerdagger2 Gerton Lunter2 and Oliver G Pybus1

1Department of Zoology University of Oxford Oxford United Kingdom2Wellcome Trust Centre for Human Genetics University of Oxford Oxford United KingdomdaggerThese authors contributed equally to this work

Corresponding author E-mail oliverpybuszoooxacuk

Associate editor Daniel Falush

Abstract

B-cell receptors (BCRs) are membrane-bound immunoglobulins that recognize and bind foreign proteins (antigens)BCRs are formed through random somatic changes of germline DNA creating a vast repertoire of unique sequences thatenable individuals to recognize a diverse range of antigens After encountering antigen for the first time BCRs undergo aprocess of affinity maturation whereby cycles of rapid somatic mutation and selection lead to improved antigen bindingThis constitutes an accelerated evolutionary process that takes place over days or weeks Next-generation sequencing ofthe gene regions that determine BCR binding has begun to reveal the diversity and dynamics of BCR repertoires inunprecedented detail Although this new type of sequence data has the potential to revolutionize our understanding ofinfection dynamics quantitative analysis is complicated by the unique biology and high diversity of BCR sequencesModels and concepts from molecular evolution and phylogenetics that have been applied successfully to rapidly evolvingpathogen populations are increasingly being adopted to study BCR diversity and divergence within individuals HoweverBCR dynamics may violate key assumptions of many standard evolutionary methods as they do not descend from asingle ancestor and experience biased mutation Here we review the application of evolutionary models to BCR rep-ertoires and discuss the issues we believe need be addressed for this interdisciplinary field to flourish

Key words molecular evolution B-cell receptor diversity immunoglobulin infection

IntroductionThe adaptive immune system ensures the survival of humansand other vertebrates in the face of rapidly evolving and ge-netically diverse infectious diseases B lymphocytes are anessential component of this system and express receptorson their cell surface (B-cell receptors BCRs) capable of specif-ically binding foreign antigens BCRs are membrane-boundimmunoglobulins composed of two large heavy chain mole-cules and two smaller light chain molecules encoded in hu-mans by the genes IGH and IGL (or IGK) respectively Thediversity of BCRs expressed by an individualrsquos B cells is vastand comprises both naive receptors that are randomly gen-erated from the germline during development as well as re-ceptors that are retained after successfully binding antigenduring previous infections Populations of BCRs can rapidlyimprove antigen binding during infection through an evolu-tionary process of mutation and selection known as affinitymaturation (Liu et al 1991) Because BCR sequences are di-verse and diverge rapidly concepts from molecular evolutionshould be beneficial in understanding the dynamics of theadaptive immune system within individuals T-cell receptors(TCRs) are a second class of immune receptors that can bindforeign antigen Although diverse TCRs are also generatedrandomly from germline gene sequences and their compar-ison with BCRs can be illuminating TCRs do not undergo

affinity maturation nor exhibit rapid evolution during infec-tion Therefore in this review we focus solely on BCR biology

Apart from molecular assays that characterize sequencelength polymorphisms (eg TCR immunoscope assays seeBercovici et al 2000) there was until recently a paucity ofdata on within-individual BCR sequence diversity for re-searchers to explore That situation has now changed withthe application of next-generation sequencing to BCRs (Boydet al 2009 Six et al 2013) With this technique researcherscan directly observe the somatic genetic changes that gener-ate the diversity of the BCR repertoire providing an unprec-edented picture of the adaptive immune system as anevolving population of cells Analyses of these data from anevolutionary perspective have led to insights into the aging ofthe B-cell repertoire (Wang Liu Xu et al 2014) and into theprocess of affinity maturation (Elhanati et al 2015 Yaari et al2015) and have many applications across a broad range ofdiseases (see table 1)

Despite these advances there are important challenges inapplying models and methods from molecular evolution toBCR sequences which stem both from the complex biologyof B cells and the nature of available data Unlike mostnatural populations naıve BCR sequences do not descendfrom a single common ancestor through a process of pointmutation but are instead generated from a diverse set of

Review

FastT

rack

The Author 2016 Published by Oxford University Press on behalf of the Society for Molecular Biology and EvolutionThis is an Open Access article distributed under the terms of the Creative Commons Attribution License (httpcreativecommonsorglicensesby40) which permits unrestricted reuse distribution and reproduction in any medium provided the original work isproperly cited Open AccessMol Biol Evol 33(5)1147ndash1157 doi101093molbevmsw015 Advance Access publication January 22 2016 1147

germline gene segments through a process of somatic re-combination (see below) In addition the mutation processduring affinity maturation is strongly dependent on the se-quence context of flanking nucleotides (Yaari et al 2013)and selection on the resulting amino acid sequence is com-plex and site-specific driven by the need to avoid dangerousself-reactivity while concurrently enhancing pathogen bind-ing Finally BCRs are a complex of heavy and light chainimmunoglobulin molecules and information from both isnecessary for a complete understanding of BCR evolutionand function Here we review the current literature on thesetopics and explore how molecular evolution and phyloge-netics may contribute to future BCR research

B-Cell DevelopmentThe initial diversity of the BCR repertoire is the result of asomatic recombination process called V(D)J recombinationThis process brings together one each of the variable (V)diversity (D) and joining (J) segments of the IGH locus onchromosome 14 to form an exon in the heavy chain immu-noglobulin gene and one each of the V and J segments of theIGL (or IGK) locus to form the light chain Not all gene seg-ments are utilized Of the 123ndash129 IGHV gene segments 44contain open reading frames (ORFs) further 25 of the 27 Dsegments and 6 of the 9 J segments have been shown to beused for somatic recombination in the heavy chain (LefrancM-P and Lefranc G 2001 Li 2004) During this process addi-tional sequence diversity is generated by random deletion orinsertion of nucleotides at segment junctions This processcombines highly variable sequence regions that determine

antigen binding (the complementarity determining regionsCDRs) with more conserved framework regions (FWRs) thatprovide structural support Thus each naıve B cell has its ownBCR sequence and the number of possible BCR sequences ishuge with models predicting at least 1018 (Elhanati et al2015) far greater than the number of B cells in the bodyThe process may generate nonproductive (eg out-of-frame)coding sequences when this happens the B cell may recom-bine its second copy of the IGH gene If this too fails to pro-duce a viable recombinant sequence then the cell undergoesapoptosis which further modulates the background geneticdiversity of receptors (fig 1) The surviving naıve B cells thenundergo an initial round of selection for lack of self-reactivitybefore they are released from the bone marrow into periph-eral blood (Murphy et al 2008)

Once a naıve B cell is activated by binding a foreign anti-gen it undergoes cell division (clonal expansion) and initiatesprocesses that somatically alter the BCR sequence and diver-sify the clonal population In parallel a mechanism called classswitching alters the constant region of the heavy chainchanging the type and function of the BCR and its interactionwith other molecules although this does not affect itsantigen-binding properties it does leave a molecular markthat can be used to separate naıve BCRs from those thathave undergone affinity maturation Affinity maturationmodifies antigen binding through a process of random andrapid sequence change termed somatic hypermutation(SHM) and by selection SHM involves greatly increased mu-tation rates of approximately 103 changes per nucleotideper cell division corresponding to approximately one muta-tion per cell division in the relevant locus (Teng and

Table 1 Applications of BCR Repertoire Sequencing

Application DiseasesInfections Significance Challenges

Identification of broadlyneutralizingantibodies (BNAbs)

Infection with rapidly evolvingpathogens such as HIV HepatitisC virus influenza viruses

bull Potential for use as vaccinetargets (Haynes et al 2012)

bull Provide model system for un-derstanding affinity maturationcoevolution and immunedevelopment (eg Wuet al 2015)

bull Lineages are often large and di-verse often with high levels ofhypermutation long CDR3s andpoly reactivity (Haynes et al2012)

Study of vaccineresponses

Any disease for whichvaccines are used orbeing developed eg Influenzatyphoid or Ebola

bull A model system forimmune response with a knownstimulus (Galson et al 2014)

bull Identifying correlates of immuneprotection following vaccination

bull Distinguishing vaccine-specificchanges to the BCR repertoirefrom healthy repertoire diversity

bull Variable immuneresponses among individuals tothe same stimulus (Ademokunet al 2011 Jiang et al 2013)

Tracking B-cell migrationand developmentwithin the body

Autoimmune diseases such asmultiplesclerosis cancers

bull Identifies migration of B cellsbetween tissue compartments(von Budingen et al 2012)

bull Can identify the sites at which Bcells mature (Stern et al 2014)

bull Accurate B-cell lineageassignment

bull Modeling potentially complexmigration patterns

bull Differential samplingbetween tissues

Disease diagnosis Autoimmune diseases such as mul-tiple sclerosis and rheumatoidarthritis cancers in particularB-cell lymphoma

bull A direct and potentially cheapdiagnosis tool

bull Improved understanding ofdisease

bull Provide clinical markers ofdisease progression (Robinsonet al 2013)

bull Complex and multiple diseaseepitopes induce complexresponses

bull Low level presence of B cells as-sociated with disease

Hoehn et al doi101093molbevmsw015 MBE

1148

IGHV5minus51

IGHV4minus59IG

HV

3minus30

IGH

V3minus

23

IGHV3minus11

IGHJ6

IGH

J5

IGH

J4

IGHJ3

IGHJ2

IGHJ1

IGHV5minus51

IGHV4minus59

IGH

V3minus

30

IGH

V3minus2

3

IGHV3minus11

IGH

J6

IGH

J5

IGH

J4

IGHJ3

IGHJ2

IGHJ1

(a)

(b)

FIG 1 Chord diagrams showing the pairing of V and J segments within (a) productive and (b) nonproductive IgM sequences from a single healthyindividual Chord widths represent the proportion of sequences with a given V (colored) and J (gray) segment pairing The five most common Vsegments in productive rearrangements (and all J segments) are labelled Note that IGHV3-23IGHJ4 was significantly more common in productiveversus nonproductive rearrangements which may indicate functional bias of that pairing The figure was generated from data in Elhanati et al(2015) which was aligned to the IMGT reference (Lefranc et al 2009) using IgBLAST (Ye et al 2013) Productive rearrangements were subsampledto the same read depth as nonproductive rearrangements (2 105 reads) the values displayed in (a) are means of 100 subsampling repetitions

Diversity and Molecular Evolution of BCRs doi101093molbevmsw015 MBE

1149

Papavasiliou 2007 Victora and Nussenzweig 2012)Mechanistically these mutations are induced by the enzymeactivation-induced cytodine deanimase (AID) which deami-nates cytosine to uracil during transcription (Muramatsuet al 2000 Teng and Papavasiliou 2007 Peled et al 2008)Importantly for evolutionary analysis SHM is a random andstrongly nonuniform process and clearly distinct from theprocesses of germline mutation and evolution In particularSHM is context-dependent such that the probability of mu-tation at a site is strongly influenced by neighboring nucleo-tides (Shapiro et al 2003 Yaari et al 2013 Elhanati 2015) Theresulting mutations are further shaped by a round of selec-tion in which B cells compete for survival and replicationsignals by competitively binding to antigens (Peled et al2008) The combination of these processes shapes both thetype and rate of observed mutations across the IGH and IGLloci

Sequencing the BCR RepertoireThe extraordinary variability of BCR sequences poses chal-lenges for targeted sequencing We provide here only a briefsummary of current sequencing approaches in particular asthey relate to the analysis of BCR diversity Rearranged VDJsegments are flanked by introns so targeting germline DNArequires a cocktail of polymerase chain reaction (PCR) pri-mers (Larimore et al 2012) A challenge for this approach is tocontrol for PCR bias which could skew the frequency of se-quenced variants and obscure the signal of clonal expansionAn alternative approach that can significantly reduce theproblem of PCR bias is to target expressed mRNA in whichcase the constant regions flanking the VDJ segments in ma-ture mRNA can be used for PCR priming (Galson et al 2015)In addition different classes of B cells can be distinguished bytargeting different constant regions The challenges for mRNAsequencing are to 1) disentangle variation in sequence fre-quency that is due to differential expression which can beextensive from that due to clonal expansion and 2) ensurethat sequencing error and subsequent bioinformatic process-ing do not introduce systematic biases into subsequent evo-lutionary analyses For a more detailed discussion of BCRrepertoire sequencing see the reviews by Benichou et al(2012) and Robins (2013)

Sequencing of the somatically altered heavy chain has thepotential to reveal the clonal structure and dynamics of theB-cell population through time and this review focuses on theanalysis of bulk sequence data from this region However al-though the majority of variation in BCR sequences is concen-trated in the heavy chain and in particular the CDRs (Xu andDavis 2000 Georgiou et al 2014) the light chain also containsmutations that may affect antigen binding If onersquos goal is tocharacterize entire antibodies or to understand the bindingproperties of a given heavy chain sequence then knowledge ofpaired heavy and light chain sequences is requiredComputational approaches have previously sought to inferhow heavy and light chain sequences are paired from indepen-dently sequenced sets of sequences by using relative frequen-cies (Reddy et al 2010) or the shapes of phylogenetic trees

(Zhu et al 2013) of heavy and light chain sequences Recentlysingle-cell technologies have enabled natively paired heavy andlight chains to be sequenced by attaching unique barcodes tocDNA from individual cells (Busse et al 2014 Lu et al 2014Tan Blum et al 2014 Tan Kongpachith et al 2014)Alternatively oligo-dT beads that link heavy and light chainsfrom a single cell have been used (DeKosky et al 2013 2015)

Measuring BCR DiversityOnce BCR sequences are generated statistical and computa-tional approaches are necessary to explore and summarizetheir diversity in order to reveal associations with immuneresponses or disease status or to identify BCR sequences ofspecific interest The exceptional diversity of the BCR reper-toire and its dynamic nature makes comparative studywithin and among individuals challenging

Several different measures have been proposed and can bedistinguished into those that characterize raw sequence var-iability versus those that depend on the frequency of BCRlineages clones or clusters (ie groups of identical or similarsequences see next section) In the context of viral infectionboth the number of somatic mutations (Chen et al 2012Wang Liu Xu et al 2014 Galson et al 2015) and V J geneusage (Zhou et al 2013) have proved useful CDR3 sequencelength also has been used to distinguish repertoires afterpneumococcal vaccination (Ademokun et al 2011 Chenet al 2012 Galson et al 2015) Diversity statistics such asthe Gini index or mean clone size are also used to investigateBCR diversity (Bashford-Rogers et al 2013 Galson et al 2015Hoehn et al 2015) Figure 2 provides a graphical representa-tion of BCR diversity under different conditions

Other approaches seek to characterize BCR diversity usingstatistical models Mora et al (2010) introduced a maximumentropy model that characterizes the repertoire as a statisticaldistribution whereas Elhanati et al (2015) used probabilisticinference to quantify the process of VDJ recombination andSHM Greiff et al (2015) proposed employing entropy mea-sures developed in ecology research which unify a range ofdiversity measures into a single profile

A common assumption of these approaches is that clonalexpansions observed in infected individuals correspond toB-cell responses against the pathogen under study This maynot always be true especially in instances of coinfection withmultiple pathogens Further some infections may manipulatehost immune responses through so-called superantigens (egstaphylococcal protein A) which trigger large nonspecific clo-nal expansions that disrupt antigen-specific affinity maturation(Thammavongsa et al 2015) Such phenomena do not in gen-eral prevent clonal expansions from being useful indicators ofimmune dynamics but require them to be carefully inter-preted in the context of the particular hostndashpathogen system

Comparison of BCR populations among individuals is ofinterest because repertoires may become similar if individualsare exposed to the same pathogen giving rise to a sharedldquopublicrdquo repertoire Differences in BCR repertoires betweenindividuals are likely generated by many factors including age(Wang Liu Xu et al 2014) germline genetics (Wang Liu

Hoehn et al doi101093molbevmsw015 MBE

1150

FIG 2 Network diagrams that visualize the diversity and clonal structure of BCR sequences BCR sequences were obtained from (a) two healthypeople (b) two individuals infected with HIV-1 sampled during early infection and (c) two patients with chronic lymphocytic leukemia a B-cellcancer Each pointcircle represents a unique BCR sequence the size of which is proportional to how common that sequence is Edges are drawnbetween pairs of sequences that differ by exactly one nucleotide change Note that samples do differ by read depth (approximately 34 104 and36 104 for part [a] 92 104 and 36 105 for part [b] 51 104 and 26 104 for part [c]) Parts (a) and (c) are reproduced with permissionfrom Bashford-Rogers et al (2013) and part (b) from Hoehn et al (2015)

Diversity and Molecular Evolution of BCRs doi101093molbevmsw015 MBE

1151

Cavanagh et al 2014) and infection history (Sasaki et al 2008Wang Liu Xu et al 2014) Infectious diseases typically presentmany epitopes and even when different B cells target thesame epitope it is possible for the different BCR sequences tobind equally effectively Despite these complexities BCR con-vergence following identical stimuli has been observed andhas enabled the identification of antibodies reactive againstinfluenza vaccines (Jackson et al 2014 Martins and Tsang2014 Truck et al 2015) and dengue virus vaccines(Parameswaran et al 2013) In addition antibodies againstHIV that exhibit the same broadly neutralizing phenotypeand which share some common sequence elements haveevolved independently in different patients (Scheid et al2011 Zhou et al 2013) It is currently an open questionwhether convergent molecular evolution of BCR sequencesis a common or an exceptional phenomenon (Yaari andKleinstein 2015) Some tests of convergence have been devel-oped in other contexts such as Zhang and Kumarrsquos (1997)convergent evolution hypothesis test which directly compa-res substitution models of convergent versus independentevolution along preselected lineages This and other methodsdesigned to detect convergence (eg Parker et al 2013) mayimprove our understanding

Clonal Lineage Assignment and ClusteringMolecular phylogenetics is an undeniably powerful tool foranalysis of sequence diversity However its application to BCRrepertoires is impeded by the V(D)J recombination processthe existence of which means that not all BCR sequence dif-ferences are due to point mutation through descent from acommon ancestor Consequently sequences must begrouped by lineage each representing sequences that de-scend from a single ancestral B cell before they can beanalyzed phylogenetically (Hershberg and Luning Prak 2015)

A key step in this process is the alignment of BCR se-quences to reference data sets of V D and J gene segmentsin order to determine their germline origin Several such align-ment methods are available IMGTHigh-V-Quest (Alamyaret al 2012) is popular and provides a well-curated referencedata set IgBLAST (Ye et al 2013) can be run with a user-specified reference data set and IgSCUEAL uses phylogeneticrelationships between germline genes to increase the accu-racy of assignment (Frost et al 2015) Other tools includeiHMM-Align (Gaeta et al 2007) that implements a hiddenMarkov model and VBASE2 (Retter et al 2005) that uses areference data set provided by Ensembl Other techniquesusing methods adopted from phylogenetic ancestral statereconstruction assign V(D)J segments while also quantifyinguncertainty in assignment (Kepler 2013) However germlineV D and J segments vary considerably among individuals andnew alleles are still being discovered so the reference data setmay be inaccurate (Gadala-Maria et al 2015) Segment sim-ilarity junctional diversity SHM and sequencing errors allfurther increase the difficulty of unambiguously assigningBCR sequences to specific germline segments This is partic-ularly true for D segments due to their short length (11ndash37nucleotides Lefranc M-P and Lefranc G 2001 Giudicelli et al

2005) and the frequent occurrence of deletions during V(D)Jrecombination which may remove part or all of a D segment(Elhanati et al 2015)

Several studies sidestep the problem of germline assign-ment and instead use clustering approaches to group similarBCR sequences by using either the entire V(D)J region se-quence (fig 2 Bashford-Rogers et al 2013 Hoehn et al2015) or the CDR3 region (Jiang et al 2013 Sok et al 2013Laserson et al 2014) A threshold number of differences (edit-distance) is often used to determine whether sequences be-long to the same or different clusters One difficulty with thisapproach is the choice of threshold Some studies address thisby exploring multiple thresholds edit-distances of three tofive differences have been chosen by looking at how clusternumbers and sizes change as the threshold is increased (Yaariet al 2013 Laserson et al 2014) However a more principledapproach is clearly needed to test how closely these clusteringtechniques reconstruct the true clonal structure of the B-cellpopulation At present they appear well suited for the de-tailed analysis of recently diverged clones (Laserson et al2014) and for quantifying the diversity of the BCR repertoirein general (Bashford-Rogers et al 2013 Hoehn et al 2015Truck et al 2015) However it is unlikely that clusteringapproaches based on edit-distances will be effective in accu-rately identifying large diverse lineages For example broadlyneutralizing HIV lineages often show high levels of geneticdiversity (fig 3 Wu et al 2015) and the intermediate (ieancestral) sequences necessary for accurate clustering maynot be available in many cases because affinity maturationoccurs in the germinal centers rather than in peripheral blood(Parham 2009) Studies that have successfully isolated largeand diverse B-cell lineages from HIV-infected patients havegenerally done so using by combining sequence analysis withdetailed experimental work (Zhou et al 2013)

Untangling Mutation and SelectionThe enzyme-driven nature of SHM poses a challenge for study-ing the molecular evolution of BCRs Standard nucleotide sub-stitution models typically assume that sites (either nucleotidesor codons) evolve independently (Felsenstein 1981) HoweverSHM is strongly context dependent to the extent that ob-served mutation rates vary more than 10-fold across sites(see fig 4) (Elhanati et al 2015) Consequently traditionalmethods for identifying positive and negative selection thatrely on uniform-rate independent-site models can generatefalse positives when applied to BCR sequences for examplewithin nonproductive (out-of-frame) sequences that are notsubject to selection (Dunn-Walters and Spencer 1998)

Models of SHM based on empirical data have been devel-oped and include di- tri- penta- and hepta-nucleotide models(Smith et al 1996 Shapiro et al 2002 2003 Yaari et al 2013Elhanati et al 2015) and have been used to investigate selectionon the naıve B-cell repertoire (Elhanati et al 2015) Selectionhas been explored using the ldquofocusedrdquo binomial test whichdetermines whether the observed number of replacement mu-tations is significantly different from that expected under a nullmodel of biased mutation but no selection (Hershberg et al

Hoehn et al doi101093molbevmsw015 MBE

1152

02

1995

2001-2002

2006-2007

2008-2009

Year sampled

0202

FIG 3 Maximum-likelihood phylogenetic tree of the VRC01 BNAb lineage sampled at ten time points over 15 years of diversification within asingle individual infected with HIV-1 Each tip represents a BCR heavy chain sequence terminal branches are colored by time point of sampling (seekey) The red circle at the root represents the germline sequence (IGHV1-202 and IGHJ101 D region left unassigned) Note the general but notcomplete trend of increasing genetic divergence from the root with sampling time Late-sampled sequences near the root indicate very high rateheterogeneity among lineages these sequences might represent inactive memory B cells BNAb sequences were obtained through cell sorting (Wuet al 2010) followed by high-throughput sequencing data to identify related BCRs (Zhou et al 2013) See Wu et al (2015) for full experimentaldetails Sequences for this tree were obtained from GenBank (Wu et al 2015) and aligned using MUSCLE (Edgar 2004) A maximum-likelihoodphylogeny was estimated using the GTRGAMMA substitution model in RAxML (Stamatakis 2014) and rerooted to position the germlinesequence at the root with a divergence of zero Scale bar represents genetic distance (expected changes per nucleotide site)

000

005

010

015

020

175 200 225 250 275

V gene nucleotide position

Obs

erve

d m

utat

ion

freq

uenc

y

FWR3FWR2 CDR2

FIG 4 Observed mutation (sequence difference from germline) frequency among productive heavy chain immunoglobulin sequences across theV-gene sequence (horizontal axis IMGT unique numbering) The distribution of mutations across the region is strongly nonuniform withmutations more likely to occur at certain positions The CDR2 region (middle shaded box) has a high rate of observed mutations and is thoughtto be more important in antigen binding than the surrounding framework regions (FWR2 and FWR3) This figure was generated from the samedata set as figure 1

Diversity and Molecular Evolution of BCRs doi101093molbevmsw015 MBE

1153

2008) This framework has subsequently been extended usingBayesian inference (BASELINe Yaari et al 2012) In commonwith other components of the acquired immune system (egclass I and II MHC glycoproteins Yang and Swanson 2002Furlong and Yang 2008) analyses indicate that BCR sequencesare a mosaic of regions under a mixture of positive and puri-fying selection (ie CDRs) and structural regions whose evolu-tion is highly constrained by purifying selection (ie FWRs)(Yaari et al 2012 2015 McCoy et al 2015)

It should also be possible to detect the action of antigen-driven selection from the shape of BCR lineage phylogenieswhich represent the common ancestry of a sample of se-quences from a lineage of clonally related B cells (Dunn-Walters et al 2002) Computer simulation of lineage treesgenerated by affinity maturation under a variety of scenariosfound seven measures of tree shape that correlatedstrongly with immunological parameters (Shahaf et al2008) However recent analyses using these measures con-cluded that they are affected by experimental factors that aredifficult to control such as the number of sequences sampledfrom a lineage and the number of cell divisions since initialVDJ rearrangement (Uduman et al 2014) Utilizing lineageinformation such as excluding terminal branch mutationshas been shown to increase the sensitivity of methods basedon the expected number of replacement mutations(Uduman et al 2014) It is interesting to note that very similarapproaches were developed independently in viral phyloge-netics specifically in studies of HIV-1 and influenza popula-tions under strong positive selection (eg Bush et al 1999Lemey et al 2007)

Recently two further approaches to analyzing B-cell selec-tion have been developed Kepler et al (2014) used a statis-tical model of selection and an empirical model of sequencemutability to study their interplay along the BCR sequences ofan antibody lineage Alternatively one can adjust and controlfor the motif-targeted nature of SHM by studying and com-paring productive and nonproductive BCR rearrangementswithin a given data set (see ldquoB-Cell Developmentrdquo Larimoreet al 2012 Elhanati et al 2015 McCoy et al 2015) McCoyet al (2015) combined this information with a statisticalmodel of trait evolution (Lemey et al 2012) in order to derivea per-residue map of natural selection along the BCR

Although antigen-driven positive selection is of great in-terest of equal importance to the evolution of antigen-specific BCR sequences is the influence of purifying selectionwhich results from the removal of self-reactive and nonpro-ductive receptors and which partly precedes the affinity mat-uration stage (see B-Cell Development) This initial selectioncan be studied by comparing the mutation profiles ofnonproductive and productive BCR sequences the latter of-ten have shorter CDR3 sequences postselection and exhibitcomplex and position-dependent selection for and againstparticular amino acids (Elhanati et al 2015)

BCR PhylogeneticsThe process of affinity maturation generates rapid sequenceevolution so it is unsurprising that phylogenetic approaches

are now routinely used to visualize how B-cell lineages un-dergo diversification and divergence in response to an antigenPhylogenies have been used to address important problemssuch as reconstructing ancestral BCR sequences within a lin-eage (Kepler 2013 Sok et al 2013) detecting and measuringselection on B-cell populations (Uduman et al 2014) andstudying how broadly neutralizing antibodies sometimesevolve in response to HIV infection (Wu et al 2015) Furtherintegration of phylogenetic concepts including those fromfields such as viral phylodynamics (Grenfell et al 2004 Volzet al 2013) may improve our understanding of affinity mat-uration dynamics during infection For example the rate ofSHM evolution in a lineage over time (and its variabilityamong lineages) could in theory be revealed by using molec-ular clock models to analyze BCR sequences sampled at dif-ferent times Further asymmetric tree shapes might help toidentify the action of strong positive selection on serially sam-pled antibody lineages (fig 3) analogous to phylogeneticfootprint left by recurrent selection on some influenza viruslineages (Grenfell et al 2004) However as noted in the previ-ous section BCR lineage tree shapes may be subject tobiases that are not yet fully understood (Uduman et al2014) so for the time being they should be interpreted withcaution

Although many phylogenetic analyses focus exclusively onBCR heavy chain sequences the light chain may also be in-cluded for example by concatenating the two gene se-quences together (Wu et al 2015) As both chains areinherited together during B-cell replication they should sharethe same phylogenetic topology By adding more sites to thealignment concatenation may improve the accuracy of phy-logeny estimation (Huelsenbeck et al 1996 Gadagkar et al2005) However if the mode or tempo of molecular evolutiondiffers between heavy and light chains then it may be advis-able to divide the concatenated sequences into separate par-titions each with its own molecular clock and nucleotidesubstitution model (eg Nylander et al 2004)

However current phylogenetic models may not representadequately the particular processes of growth and mutationthat generate BCR lineages and therefore they should be ap-plied with caution For example Wu et al (2015) recently useda relaxed molecular clock model to analyze the evolution of abroadly neutralizing antibody lineage (VRC01) sampled over15 years of HIV-1 infection The estimated date of the com-mon ancestor of the lineage was implausibly old which ledthe authors to conclude that the molecular clock model usedwas unrealistic Specifically they concluded that the meanrate of BCR evolution of VRC01 and other lineages (Liaoet al 2013 Doria-Rose et al 2014) slowed over the courseof lineage development This work poses interesting avenuesfor future research as it should be possible to test the slow-down hypothesis directly using a time-dependent molecularclock model Alternatively the apparent slowdown could becaused by the AID motif-driven nature of BCR mutation inwhich case fundamental assumptions of the nucleotide sub-stitution model (eg independence among site and time-reversibility) may be inappropriate It is likely that currentevolutionary models will need to be substantially modified

Hoehn et al doi101093molbevmsw015 MBE

1154

or carefully selected before we can be confident in evolution-ary inferences from BCR sequence data

ConclusionBCR sequence data contain a wealth of novel immunologicalinformation and have the potential to improve our observa-tion and understanding of the mechanisms of autoimmunedisease and acquired immunity (table 1) However the dy-namic processes that determine the response of B-cell pop-ulations to diverse antigens differ from other forms ofbiological evolution in key ways some of which are currentlypoorly understood We conclude by outlining four importantchallenges facing the molecular evolutionary analysis of BCRsequences

(1) Distinguishing between biological signal and exper-imental error or bias Many aspects of experimentalprotocol may have an effect on observed sequencediversity including read depth and length PCR condi-tions and primers and cell sorting Close collaborationbetween experimentalists and analysts is needed toensure that experimental choices are appropriate forsubsequent evolutionary analyses

(2) Identifying clonally related cellssequences For evo-lutionary methods to be maximally informative it isnecessary to distinguish within-individual BCR se-quence differences caused by SHM from those derivedfrom V(D)J recombination Advances here might in-clude improvements in sequencing or experimentalprotocols development of methods to probabilisticallycluster into clonal lineages and the creation of a ldquogoldstandardrdquo test data set allowing evaluation of methodsfor determining clonal lineages

(3) Detecting convergent evolution among B cells re-sponding to the same stimulus The prevalence andimportance of this process and its utility for under-standing the underlying biology of BCRs is currentlyunder debate Improved understanding of the fre-quency distribution of naıve BCR sequences shouldhelp to estimate the fraction of the public shared rep-ertoire that occurs by random chance In addition itmay be possible to adapt methods from molecularevolution and phylogenetics to make progress in thisarea

(4) Models to describe the process of BCR affinity mat-uration Although descriptive summary statistics haveproven useful for the visualization and qualitative anal-ysis of BCR repertoires further understanding will begained by developing stochastic process models thatembody the known mechanisms of SHM and B-cellproliferation and by the application of such models toempirical data Finally it is important to understandthe potential biases arising from applying standardphylogenetic and molecular evolutionary models toBCR sequences These could be investigated by analy-zing artificial BCR data sets simulated under complexand biologically realistic models of sequence evolution

AcknowledgmentsThis work was funded by the European Research Councilunder the European Unionrsquos Seventh FrameworkProgramme (FP72007-2013)ERC grant agreement no614725-PATHPHYLODYN (to OGP) Marshall scholarship(to KBH) and Wellcome Trust grant 090532Z09Z (toGL) The authors thank Rachael Bashford-Rogers for provid-ing network diagrams and Aleksandra Walczak and YuvalElhanati for their help with data used to generate figures 1and 4

ReferencesAdemokun A Wu Y-C Martin V Mitra R Sack U Baxendale H Kipling

D Dunn-Walters DK 2011 Vaccination-induced changes in humanB-cell repertoire and pneumococcal IgM and IgA antibody at differ-ent ages Aging Cell 10922ndash930

Alamyar E Duroux P Lefranc M-P Giudicelli V 2012 IMGTVR tools forthe nucleotide analysis of immunoglobulin (IG) and T cell receptor(TR) V-(D)-J repertoires polymorphisms and IG mutations IMGTV-QUEST and IMGTHighV-QUEST for NGS Methods Mol Biol882569ndash604

Bashford-Rogers RJM Palser AL Huntly BJ Rance R Vassiliou GS FollowsGA Kellam P 2013 Network properties derived from deep sequenc-ing of human B-cell receptor repertoires delineate B-cell populationsGenome Res 231874ndash1884

Benichou J Ben-Hamo R Louzoun Y Efroni S 2012 Rep-Seq uncoveringthe immunological repertoire through next-generation sequencingImmunology 135183ndash191

Bercovici N Duffour M-T Agrawal S Salcedo M Abastado J-P 2000 Newmethods for assessing T-cell responses Clin Diagn Lab Immunol7859ndash864

Boyd SD Marshall EL Merker JD Maniar JM Zhang LN Sahaf B JonesCD Simen BB Hanczaruk B Nguyen KD et al 2009 Measurementand clinical monitoring of human lymphocyte clonality by massivelyparallel V-D-J pyrosequencing Sci Transl Med 1(12)12ra23

Bush RM Bender CA Subbaro K Cox NJ Fitch WM 1999 Predicting theevolution of human influenza A Science 2861921ndash1925

Busse CE Czogiel I Braun P Arndt PF Wardemann H 2014 Single-cellbased high-throughput sequencing of full-length immunoglobulinheavy and light chain genes Eur J Immunol 44597ndash603

Chen W Prabakaran P Zhu Z Feng Y Streaker ED Dimitrov DS 2012Characterization of human IgG repertoires in an acute HIV-1 infec-tion Exp Mol Pathol 93399ndash407

DeKosky BJ Ippolito GC Deschner RP Lavinder JJ Wine Y Rawlings BMVaradarajan N Giesecke C Dorner T Andrews SF et al 2013 High-throughput sequencing of the paired human immunoglobulinheavy and light chain repertoire Nat Biotechnol 31166ndash169

DeKosky BJ Kojima T Rodin A Charab W Ippolito GC Ellington ADGeorgiou G 2015 In-depth determination and analysis of the hu-man paired heavy- and light-chain antibody repertoire Nat Med2186ndash91

Doria-Rose NA Schramm CA Gorman J Moore PL Bhiman JN DeKoskyBJ Ernandes MJ Georgiev IS Kim HJ Pancera M et al 2014Developmental pathway for potent V1V2-directed HIV-neutralizingantibodies Nature 50955ndash62

Dunn-Walters DK Belelovsky A Edelman H Banerjee M Mehr R 2002The dynamics of germinal centre selection as measured by graph-theoretical analysis of mutational lineage trees Dev Immunol9233ndash243

Dunn-Walters DK Spencer J 1998 Strong intrinsic biases towards mu-tation and conservation of bases in human IgVH genes during so-matic hypermutation prevent statistical analysis of antigen selectionImmunology 95339

Edgar RC 2004 MUSCLE multiple sequence alignment with high accu-racy and high throughput Nucleic Acids Res 321792ndash1797

Diversity and Molecular Evolution of BCRs doi101093molbevmsw015 MBE

1155

Elhanati Y Sethna Z Marcou Q Callan CG Mora T Walczak AM 2015Inferring processes underlying B-cell repertoire diversity Philos TransR Soc Lond B Biol Sci 37020140243

Felsenstein J 1981 Evolutionary trees from DNA sequences a maximumlikelihood approach J Mol Evol 17368ndash376

Frost SDW Murrell B Hossain ASMM Silverman GJ Pond SLK 2015Assigning and visualizing germline genes in antibody repertoiresPhilos Trans R Soc Lond B Biol Sci 37020140240

Furlong RF Yang Z 2008 Diversifying and purifying selection in the peptidebinding region of DRB in mammals J Mol Evol 66384ndash394

Gadagkar SR Rosenberg MS Kumar S 2005 Inferring species phyloge-nies from multiple genes concatenated sequence tree versus con-sensus gene tree J Exp Zool B Mol Dev Evol 304B64ndash74

Gadala-Maria D Yaari G Uduman M Kleinstein SH 2015 Automatedanalysis of high-throughput B-cell sequencing data reveals a highfrequency of novel immunoglobulin V gene segment alleles ProcNatl Acad Sci U S A 112E862ndashE870

Gaeta BA Malming HR Jackson KJL Bain ME Wilson P Collins AM2007 iHMMune-align hidden Markov model-based alignment andidentification of germline genes in rearranged immunoglobulin genesequences Bioinformatics 231580ndash1587

Galson JD Clutterbuck EA Truck J Ramasamy MN Munz M Fowler ACerundolo V Pollard AJ Lunter G Kelly DF 2015 BCR repertoiresequencing different patterns of B-cell activation after two menin-gococcal vaccines Immunol Cell Biol 93885ndash895

Galson JD Pollard AJ Truck J Kelly DF 2014 Studying the antibodyrepertoire after vaccination practical applications Trends Immunol35319ndash331

Georgiou G Ippolito GC Beausang J Busse CE Wardemann H QuakeSR 2014 The promise and challenge of high-throughput sequencingof the antibody repertoire Nat Biotechnol 32158ndash168

Giudicelli V Chaume D Lefranc M-P 2005 IMGTGENE-DB a compre-hensive database for human and mouse immunoglobulin and T cellreceptor genes Nucleic Acids Res 33D256ndashD261

Greiff V Bhat P Cook SC Menzel U Kang W Reddy ST 2015 Abioinformatic framework for immune repertoire diversityprofiling enables detection of immunological status Genome Med749

Grenfell BT Pybus OG Gog JR Wood JLN Daly JM Mumford JA HolmesEC 2004 Unifying the epidemiological and evolutionary dynamics ofpathogens Science 303327ndash332

Haynes BF Kelsoe G Harrison SC Kepler TB 2012 B-cell-lineage immu-nogen design in vaccine development with HIV-1 as a case studyNat Biotechnol 30423ndash433

Hershberg U Luning Prak ET 2015 The analysis of clonal expansions innormal and autoimmune B cell repertoires Philos Trans R Soc Lond BBiol Sci 37020140239

Hershberg U Uduman M Shlomchik MJ Kleinstein SH 2008 Improvedmethods for detecting selection by mutation analysis of Ig V regionsequences Int Immunol 20683ndash694

Hoehn KB Gall A Bashford-Rogers R Fidler SJ Kaye S Weber JNMcClure MO SPARTAC Trial Investigators Kellam P Pybus OG2015 Dynamics of immunoglobulin sequence diversity in HIV-1 in-fected individuals Philos Trans R Soc Lond B Biol Sci 37020140241

Huelsenbeck JP Bull JJ Cunningham CW 1996 Combining data in phy-logenetic analysis Trends Ecol Evol 11152ndash158

Jackson KJL Liu Y Roskin KM Glanville J Hoh RA Seo K Marshall ELGurley TC Moody MA Haynes BF et al 2014 Human responsesto influenza vaccination show seroconversion signatures and con-vergent antibody rearrangements Cell Host Microbe 16105ndash114

Jiang N He J Weinstein JA Penland L Sasaki S He X-S Dekker CL ZhengN-Y Huang M Sullivan M et al 2013 Lineage structure of thehuman antibody repertoire in response to influenza vaccinationSci Transl Med 5(171)171ra19

Kepler TB 2013 Reconstructing a B-cell clonal lineage I Statistical in-ference of unobserved ancestors F1000Res [Internet] Availablefrom httpf1000researchcomarticles2-103v1

Kepler TB Munshaw S Wiehe K Zhang R Yu J-S Woods CW DennyTN Tomaras GD Alam SM Moody MA et al 2014 Reconstructing

a B-cell clonal lineage II Mutation selection and affinity maturationB Cell Biol 5170

Larimore K McCormick MW Robins HS Greenberg PD 2012 Shapingof human germline IgH repertoires revealed by deep sequencingJ Immunol 1893221ndash3230

Laserson U Vigneault F Gadala-Maria D Yaari G Uduman M HeidenJAV Kelton W Jung ST Liu Y Laserson J et al 2014 High-resolutionantibody dynamics of vaccine-induced immune responses Proc NatlAcad Sci U S A 1114928ndash4933

Lefranc M-P Giudicelli V Ginestoux C Jabado-Michaloud J Folch GBellahcene F Wu Y Gemrot E Brochet X Lane J et al 2009IMGT(R) the international ImMunoGeneTics informationsystem(R) Nucleic Acids Res 37D1006ndashD1012

Lefranc M-P Lefranc G 2001 The Immunoglobulin FactsBook London(United Kingdom) Academic Press

Lemey P Kosakovsky Pond SL Drummond AJ Pybus OG Shapiro BBarroso H Taveira N Rambaut A 2007 Synonymous substitutionrates predict HIV disease progression as a result of underlying rep-lication dynamics PLoS Comput Biol 3e29

Lemey P Minin VN Bielejec F Pond SLK Suchard MA 2012 A countingrenaissance combining stochastic mapping and empirical Bayes toquickly detect amino acid sites under positive selectionBioinformatics 283248ndash3256

Li A 2004 Utilization of Ig heavy chain variable diversity and joininggene segments in children with B-lineage acute lymphoblastic leu-kemia implications for the mechanisms of VDJ recombination andfor pathogenesis Blood 1034602ndash4609

Liao H-X Lynch R Zhou T Gao F Alam SM Boyd SD Fire AZ RoskinKM Schramm CA Zhang Z et al 2013 Co-evolution of abroadly neutralizing HIV-1 antibody and founder virus Nature496469ndash476

Liu Y-J Zhang J Lane PJL Chan EY-T Maclennan ICM 1991 Sites ofspecific B cell activation in primary and secondary responses to Tcell-dependent and T cell-independent antigens Eur J Immunol212951ndash2962

Lu DR Tan Y-C Kongpachith S Cai X Stein EA Lindstrom TM SokoloveJ Robinson WH 2014 Identifying functional anti-Staphylococcusaureus antibodies by sequencing antibody repertoires of patientplasmablasts Clin Immunol 15277ndash89

Martins AJ Tsang JS 2014 Random yet deterministic convergentimmunoglobulin responses to influenza Trends Microbiol22488ndash489

McCoy CO Bedford T Minin VN Bradley P Robins H Matsen FA 2015Quantifying evolutionary constraints on B-cell affinity maturationPhilos Trans R Soc Lond B Biol Sci 37020140244

Mora T Walczak AM Bialek W Callan CG 2010 Maximum entropymodels for antibody diversity Proc Natl Acad Sci U S A1075405ndash5410

Muramatsu M Kinoshita K Fagarasan S Yamada S Shinkai Y Honjo T2000 Class switch recombination and hypermutation require acti-vation-induced cytidine deaminase (AID) a potential RNA editingenzyme Cell 102553ndash563

Murphy K Trivers P Walport M 2008 Janewayrsquos immunobiology 7th edNew York Garland Science

Nylander JAA Ronquist F Huelsenbeck JP Nieves-Aldrey J 2004Bayesian phylogenetic analysis of combined data Syst Biol 5347ndash67

Parameswaran P Liu Y Roskin KM Jackson KK Dixit VP Lee J-YArtiles K Zompi S Vargas MJ Simen BB et al 2013Convergent antibody signatures in human dengue Cell HostMicrobe 13691ndash700

Parham P 2009 The immune system London Garland ScienceParker J Tsagkogeorga G Cotton JA Liu Y Provero P Stupka E Rossiter

SJ 2013 Genome-wide signatures of convergent evolution in echo-locating mammals Nature 502228ndash231

Peled JU Kuang FL Iglesias-Ussel MD Roa S Kalis SL Goodman MFScharff MD 2008 The biochemistry of somatic hypermutationAnnu Rev Immunol 26481ndash511

Reddy ST Ge X Miklos AE Hughes RA Kang SH Hoi KH ChrysostomouC Hunicke-Smith SP Iverson BL Tucker PW et al 2010 Monoclonal

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antibodies isolated without screening by analyzing the variable-generepertoire of plasma cells Nat Biotechnol 28965ndash969

Retter I Althaus HH Munch R Muller W 2005 VBASE2 an integrative Vgene database Nucleic Acids Res 33D671ndashD674

Robins H 2013 Immunosequencing applications of immune repertoiredeep sequencing Curr Opin Immunol 25646ndash652

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Scheid JF Mouquet H Ueberheide B Diskin R Klein F Oliveira TYKPietzsch J Fenyo D Abadir A Velinzon K et al 2011 Sequence andstructural convergence of broad and potent HIV antibodies thatmimic CD4 binding Science 3331633ndash1637

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Shapiro GS Aviszus K Murphy J Wysocki LJ 2002 Evolution of Ig DNAsequence to target specific base positions within codons for somatichypermutation J Immunol 1682302ndash2306

Shapiro GS Ellison MC Wysocki LJ 2003 Sequence-specific targeting oftwo bases on both DNA strands by the somatic hypermutationmechanism Mol Immunol 40287ndash295

Six A Mariotti-Ferrandiz E Chaara W Magadan S Pham H-P Lefranc M-P Mora T Thomas-Vaslin V Walczak AM Boudinot P 2013The past present and future of immune repertoire biologymdashtherise of next-generation repertoire analysis Front Immunol 4413

Smith DS Creadon G Jena PK Portanova JP Kotzin BL Wysocki LJ 1996Di- and trinucleotide target preferences of somatic mutagenesis innormal and autoreactive B cells J Immunol 1562642ndash2652

Sok D Laserson U Laserson J Liu Y Vigneault F Julien J-P Briney BRamos A Saye KF Le K et al 2013 The effects of somatic hyper-mutation on neutralization and binding in the PGT121 family ofbroadly neutralizing HIV antibodies PLoS Pathog 9e1003754

Stamatakis A 2014 RAxML version 8 a tool for phylogeneticanalysis and post-analysis of large phylogenies Bioinformatics301312ndash1313

Stern JNH Yaari G Heiden JAV Church G Donahue WF Hintzen RQHuttner AJ Laman JD Nagra RM Nylander A et al 2014 B cellspopulating the multiple sclerosis brain mature in the draining cer-vical lymph nodes Sci Transl Med 6(248)248ra107

Tan Y-C Blum LK Kongpachith S Ju C-H Cai X Lindstrom TMSokolove J Robinson WH 2014 High-throughput sequencing ofnatively paired antibody chains provides evidence for original anti-genic sin shaping the antibody response to influenza vaccinationClin Immunol 15155ndash65

Tan Y-C Kongpachith S Blum LK Ju C-H Lahey LJ Lu DR Cai X WagnerCA Lindstrom TM Sokolove J et al 2014 Barcode-enabled sequenc-ing of plasmablast antibody repertoires in rheumatoid arthritisArthritis Rheumatol 662706ndash2715

Teng G Papavasiliou FN 2007 Immunoglobulin somatic hypermuta-tion Annu Rev Genet 41107ndash120

Thammavongsa V Kim HK Missiakas D Schneewind O 2015Staphylococcal manipulation of host immune responses Nat RevMicrobiol 13529ndash543

Truck J Ramasamy MN Galson JD Rance R Parkhill J Lunter GPollard AJ Kelly DF 2015 Identification of antigen-specific BCell receptor sequences using public repertoire analysisJ Immunol 194252ndash261

Uduman M Shlomchik MJ Vigneault F Church GM Kleinstein SH2014 Integrating B cell lineage information into statistical tests fordetecting selection in Ig sequences J Immunol 192867ndash874

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Volz EM Koelle K Bedford T 2013 Viral phylodynamics PLoS ComputBiol 9e1002947

von Budingen H-C Kuo TC Sirota M van Belle CJ Apeltsin L Glanville JCree BA Gourraud P-A Schwartzburg A Huerta G et al 2012 B cellexchange across the blood-brain barrier in multiple sclerosis J ClinInvest 1224533ndash4543

Wang C Liu Y Cavanagh MM Saux SL Qi Q Roskin KM Looney TJ LeeJ-Y Dixit V Dekker CL et al 2014 B-cell repertoire responses tovaricella-zoster vaccination in human identical twins Proc Natl AcadSci U S A 112(2)500ndash505

Wang C Liu Y Xu LT Jackson KJL Roskin KM Pham TD Laserson JMarshall EL Seo K Lee J-Y et al 2014 Effects of aging cytomegalo-virus infection and EBV infection on human B cell repertoiresJ Immunol 192603ndash611

Wu X Yang Z-Y Li Y Hogerkorp C-M Schief WR Seaman MS Zhou TSchmidt SD Wu L Xu L et al 2010 Rational design of envelopeidentifies broadly neutralizing human monoclonal antibodies toHIV-1 Science 329856ndash861

Wu X Zhang Z Schramm CA Joyce MG Do Kwon Y Zhou T Sheng ZZhang B OrsquoDell S McKee K et al 2015 Maturation and diversity ofthe VRC01-antibody lineage over 15 years of chronic HIV-1 infectionCell 161470ndash485

Xu JL Davis MM 2000 Diversity in the CDR3 region of VH is sufficientfor most antibody specificities Immunity 1337ndash45

Yaari G Benichou JIC Vander Heiden JA Kleinstein SH Louzoun Y 2015The mutation patterns in B-cell immunoglobulin receptors reflectthe influence of selection acting at multiple time-scales Philos TransR Soc Lond B Biol Sci 37020140242

Yaari G Kleinstein SH 2015 Practical guidelines for B-cell receptor rep-ertoire sequencing analysis Genome Med 7 Article 121

Yaari G Uduman M Kleinstein SH 2012 Quantifying selection in high-throughput immunoglobulin sequencing data sets Nucleic AcidsRes 40(17)e134

Yaari G Vander Heiden JA Uduman M Gadala-Maria D Gupta NStern JNH OrsquoConnor KC Hafler DA Laserson U Vigneault F et al2013 Models of somatic hypermutation targeting and substitutionbased on synonymous mutations from high-throughput immuno-globulin sequencing data Front Immunol 4 Article 358

Yang Z Swanson WJ 2002 Codon-substitution models to detect adap-tive evolution that account for heterogeneous selective pressuresamong site classes Mol Biol Evol 1949ndash57

Ye J Ma N Madden TL Ostell JM 2013 IgBLAST an immunoglobulinvariable domain sequence analysis tool Nucleic Acids Res41W34ndashW40

Zhang J Kumar S 1997 Detection of convergent and parallelevolution at the amino acid sequence level Mol Biol Evol14527ndash536

Zhou T Zhu J Wu X Moquin S Zhang B Acharya P Georgiev IS Altae-Tran HR Chuang G-Y Joyce MG et al 2013 Multidonor analysisreveals structural elements genetic determinants and maturationpathway for HIV-1 neutralization by VRC01-Class antibodiesImmunity 39245ndash258

Zhu J Ofek G Yang Y Zhang B Louder MK Lu G McKee K Pancera MSkinner J Zhang Z et al 2013 Mining the antibodyome for HIV-1-neutralizing antibodies with next-generation sequencing and phylo-genetic pairing of heavylight chains Proc Natl Acad Sci U S A1106470ndash6475

Diversity and Molecular Evolution of BCRs doi101093molbevmsw015 MBE

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