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float64 0
1
|
---|---|---|---|---|---|---|---|---|---|---|
M Step: Calculate r { τ +1} using the equation,
|
15274749_p22
|
15274749
|
A general framework
| 1.782028 |
biomedical
|
Other
|
[
0.6478082537651062,
0.0033333844039589167,
0.3488583564758301
] |
[
0.06472310423851013,
0.9325968623161316,
0.0017248805379495025,
0.000955255061853677
] |
en
| 0.999997 |
The E step and M step among Eqs. (4) – (7) are repeated until r converges to a value with satisfied precision. The converged values are regarded as the MLEs of Θ .
|
15274749_p23
|
15274749
|
A general framework
| 1.843001 |
other
|
Other
|
[
0.42422908544540405,
0.001593484659679234,
0.5741775035858154
] |
[
0.38210102915763855,
0.6141165494918823,
0.0023639535065740347,
0.0014185113832354546
] |
en
| 0.999997 |
Unlike an inbred line cross, a full-sib family may have many different marker segregation types. We symbolize observed marker alleles in a full-sib family by A 1 , A 2 , A 3 and A 4 , which are codominant to each other but dominant to the null allele, symbolized by O . Wu et al. listed a total of 28 segregation types, which are classified into 7 groups based on the amount of information for linkage analysis:
|
15274749_p24
|
15274749
|
Model for partially informative markers
| 3.638983 |
biomedical
|
Study
|
[
0.9919514656066895,
0.000196155728190206,
0.007852371782064438
] |
[
0.870555579662323,
0.12815992534160614,
0.0010224799625575542,
0.00026204620371572673
] |
en
| 0.999998 |
A. Loci that are heterozygous in both parents and segregate in a 1:1:1:1 ratio, involving either four alleles A 1 A 2 × A 3 A 4 , three non-null alleles A 1 A 2 × A 1 A 3 , three non-null alleles and a null allele A 1 A 2 × A 3 O , or two null alleles and two non-null alleles A 1 O × A 2 O ;
|
15274749_p25
|
15274749
|
Model for partially informative markers
| 3.875948 |
biomedical
|
Study
|
[
0.9962905645370483,
0.0004706262261606753,
0.0032388060353696346
] |
[
0.5678867101669312,
0.430139422416687,
0.0013443466741591692,
0.0006295522325672209
] |
en
| 0.999997 |
B. Loci that are heterozygous in both parents and segregate in a 1:2:1 ratio, which include three groups:
|
15274749_p26
|
15274749
|
Model for partially informative markers
| 2.519088 |
biomedical
|
Other
|
[
0.9822648763656616,
0.0012174827279523015,
0.016517704352736473
] |
[
0.33033695816993713,
0.666420578956604,
0.001954933162778616,
0.0012875156244263053
] |
en
| 0.999998 |
B 1 . One parent has two different dominant alleles and the other has one dominant allele and one null allele, e.g., A 1 A 2 × A 1 O ;
|
15274749_p27
|
15274749
|
Model for partially informative markers
| 2.288597 |
biomedical
|
Other
|
[
0.9366403222084045,
0.001453620265237987,
0.06190597638487816
] |
[
0.1636282205581665,
0.8338069319725037,
0.0016181111568585038,
0.0009467467316426337
] |
en
| 0.999997 |
B 2 . The reciprocal of B 1 ;
|
15274749_p28
|
15274749
|
Model for partially informative markers
| 1.660859 |
biomedical
|
Other
|
[
0.7372786402702332,
0.0031088029500097036,
0.25961264967918396
] |
[
0.1755307912826538,
0.8179870843887329,
0.0047257826663553715,
0.0017563796136528254
] |
en
| 0.999998 |
B 3 . Both parents have the same genotype of two codominant alleles, i.e., A 1 A 2 × A 1 A 2 ;
|
15274749_p29
|
15274749
|
Model for partially informative markers
| 2.523355 |
biomedical
|
Other
|
[
0.9714279770851135,
0.0009802225977182388,
0.02759171463549137
] |
[
0.4607242941856384,
0.5364750623703003,
0.001640789327211678,
0.0011598380515351892
] |
en
| 0.999996 |
C. Loci that are heterozygous in both parents and segregate in a 3:1 ratio, i.e., A 1 O × A 1 O ;
|
15274749_p30
|
15274749
|
Model for partially informative markers
| 2.65802 |
biomedical
|
Other
|
[
0.9723560214042664,
0.0011081643169745803,
0.02653585933148861
] |
[
0.2674424648284912,
0.7300360798835754,
0.0016217129305005074,
0.0008998113335110247
] |
en
| 0.999996 |
D. Loci that are in the testcross configuration between the parents and segregate in a 1:1 ratio, which include two groups:
|
15274749_p31
|
15274749
|
Model for partially informative markers
| 2.252887 |
biomedical
|
Other
|
[
0.9271968007087708,
0.0015915378462523222,
0.07121169567108154
] |
[
0.2342308610677719,
0.7634746432304382,
0.0013446036027744412,
0.000949866371229291
] |
en
| 0.999996 |
D 1 . Heterozygous in one parent and homozygous in the other, including three alleles A 1 A 2 × A 3 A 3 , two alleles A 1 A 2 × A 1 A 1 , A 1 A 2 × OO and A 2 O × A 1 A 1 , and one allele (with three null alleles) A 1 O × OO ;
|
15274749_p32
|
15274749
|
Model for partially informative markers
| 3.310266 |
biomedical
|
Other
|
[
0.9937830567359924,
0.000731924781575799,
0.005485114175826311
] |
[
0.3789137005805969,
0.6188371777534485,
0.0008759534102864563,
0.0013730921782553196
] |
en
| 0.999998 |
D 2 . The reciprocals of D 1 .
|
15274749_p33
|
15274749
|
Model for partially informative markers
| 1.525176 |
biomedical
|
Other
|
[
0.643204391002655,
0.002961317077279091,
0.35383427143096924
] |
[
0.15115447342395782,
0.8434606790542603,
0.0037034046836197376,
0.0016814457485452294
] |
en
| 0.999999 |
The marker group A is regarded as containing fully informative markers because of the complete distinction of the four progeny genotypes. The other six groups all contain the partially informative markers since some progeny genotype cannot be phenotypically separated from other genotypes. This incomplete distinction leads to the segregation ratios 1:2:1 (B), 3:1 (C) and 1:1 (D). Note that marker group D can be viewed as fully informative if we are only interested in the heterozygous parent.
|
15274749_p34
|
15274749
|
Model for partially informative markers
| 3.13583 |
biomedical
|
Study
|
[
0.9643869400024414,
0.0005834618350490928,
0.0350295789539814
] |
[
0.7822626233100891,
0.2165650874376297,
0.0007613434572704136,
0.00041095729102380574
] |
en
| 0.999996 |
In the preceding section, we defined a (4 × 4)-matrix H for joint genotype frequencies between two fully informative markers. But for partially informative markers, only the joint phenotypes can be observed and, thus, the joint genotype frequencies, as shown in H , will be collapsed according to the same phenotype. Wu et al. designed specific incidence matrices ( I ) relating the genotype frequencies to the phenotype frequencies for different types of markers. Here, we use the notation for a ( b 1 × b 2 ) matrix of the phenotype frequencies between two partially informative markers, where b 1 and b 2 are the numbers of distinguishable phenotypes for markers and , respectively. Correspondingly, we have . The EM algorithm can then be developed to estimate the recombination fraction between any two partial informative markers.
|
15274749_p35
|
15274749
|
Model for partially informative markers
| 4.123783 |
biomedical
|
Study
|
[
0.9993583559989929,
0.00015334610361605883,
0.0004883017390966415
] |
[
0.9988728165626526,
0.0007361774332821369,
0.00034257571678608656,
0.00004839417670154944
] |
en
| 0.999996 |
E Step: At step τ , based on the matrix ( DH )' derived from the current estimate r { τ } , calculate the expected number of recombination events between the two markers for a given progeny genotype and :
|
15274749_p36
|
15274749
|
Model for partially informative markers
| 3.609768 |
biomedical
|
Study
|
[
0.9907641410827637,
0.0008184061152860522,
0.008417411707341671
] |
[
0.5281897187232971,
0.47022542357444763,
0.0009833283256739378,
0.0006015108665451407
] |
en
| 0.999996 |
where , , and is the ( j 1 j 2 )th element of matrices ( DH )', H ', P ' and Q ', respectively.
|
15274749_p37
|
15274749
|
Model for partially informative markers
| 1.819869 |
other
|
Other
|
[
0.40448108315467834,
0.0018655897583812475,
0.5936532616615295
] |
[
0.19415882229804993,
0.8026576638221741,
0.0020529101602733135,
0.0011306172236800194
] |
en
| 0.999998 |
M Step : Calculate r { τ +1} using the equation,
|
15274749_p38
|
15274749
|
Model for partially informative markers
| 1.782028 |
biomedical
|
Other
|
[
0.6478082537651062,
0.0033333844039589167,
0.3488583564758301
] |
[
0.06472310423851013,
0.9325968623161316,
0.0017248805379495025,
0.000955255061853677
] |
en
| 0.999997 |
The E and M steps between Eqs. (8) – (11) are repeated until the estimate converges to a stable value.
|
15274749_p39
|
15274749
|
Model for partially informative markers
| 1.630053 |
other
|
Other
|
[
0.35699522495269775,
0.0024295318871736526,
0.6405752301216125
] |
[
0.3097001314163208,
0.6837183833122253,
0.004537767730653286,
0.002043685410171747
] |
en
| 0.999997 |
Consider three markers in a linkage group that have three possible orders , and . Let o 1 , o 2 and o 3 be the corresponding probabilities of occurrence of these orders in the parental genome. Without loss of generality, for a given order, the allelic arrangement of the first marker between the two homologous chromosomes can be fixed for a parent. Thus, the change of the allelic arrangements at the other two markers will lead to 2 × 2 = 4 parental diplotypes. The three-marker genotype of parent P (12/12/12) may have four possible diplotypes, , , and . Relative to the fixed allelic arrangement 1|2| of the first marker on the two homologous chromosomes 1 and 2 , the probabilities of allelic arragments 1|2| and 2|1| are denoted as p 1 and 1 - p 1 for the second marker and as p 2 and 1 - p 2 for the third marker, respectively. Assuming that allelic arrangements are independent between the second and third marker, the probabilities of these four three-marker diplotypes can be described by p 1 p 2 , p 1 (1 - p 2 ), (1 - p 1 ) p 2 and (1 - p 1 ) (1 - p 2 ), respectively. The four diplotypes of parent Q can also be constructed, whose probabilities are defined as q 1 q 2 , q 1 (1 - q 2 ), (1 - q 1 ) q 2 and (1 - q 1 ) (1 - q 2 ) respectively. Thus, there are 4 × 4 = 16 possible diplotype combinations (whose probabilities are the product of the corresponding diplotype probabilities) when parents P and Q are crossed.
|
15274749_p40
|
15274749
|
A general framework
| 4.369823 |
biomedical
|
Study
|
[
0.9978173971176147,
0.0003181480278726667,
0.0018644287483766675
] |
[
0.9973556995391846,
0.002098559169098735,
0.00045874156057834625,
0.00008704965148353949
] |
en
| 0.999998 |
Let r 12 denote the recombination fraction between markers and , with r 23 and r 13 defined similarly. These recombination fractions are associated with the probabilities with which a crossover occurs between markers and and between markers and . The event that a crossover or no crossover occurs in each interval is denoted by D 11 and D 00 , respectively, whereas the events that a crossover occurs only in the first interval or in the second interval is denoted by D 10 and D 01 , respectively. The probabilities of these events are denoted by d 00 , d 01 , d 10 and d 11 , respectively, whose sum equals 1. According to the definition of recombination fraction as the probability of a crossover between a pair of loci, we have r 12 = d 10 + d 11 , r 23 = d 01 + d 11 and r 13 = d 01 + d 10 . These relationships have been used by Haldane to derive the map function that converts the recombination fraction to the corresponsding genetic distance.
|
15274749_p41
|
15274749
|
A general framework
| 4.248227 |
biomedical
|
Study
|
[
0.9977073669433594,
0.00018817877571564168,
0.0021044316235929728
] |
[
0.9944972395896912,
0.0044209337793290615,
0.0010119666112586856,
0.00006986239168327302
] |
en
| 0.999998 |
For a three-point analysis, there are a total of 16 (16 × 4)-matrices for genotype frequencies under a given marker order ( ), each corresponding to a diplotype combination, denoted by , where for 1|2| or 2 for 2|1| denote the two alternative allelic arrangements of the second and third marker, respectively, for parent P, and for 1|2| or 2 for 2|1| denote the two alternative allelic arrangements of the second and third marker, respectively, for parent Q. According to Ridout et al. and Wu et al. , elements in are expressed in terms of d 00 , d 01 , d 10 and d 11 .
|
15274749_p42
|
15274749
|
A general framework
| 4.099314 |
biomedical
|
Study
|
[
0.9989602565765381,
0.00016756450349930674,
0.0008721590857021511
] |
[
0.9985824823379517,
0.001110444194637239,
0.0002547135518398136,
0.00005238965241005644
] |
en
| 0.999998 |
Similarly, there are 16 (16 × 4)-matrices for the expected numbers of crossover that have occurred for D 00 , D 01 , D 10 and D 11 for a given marker order, denoted by , , and respectively. In their Table 2 , Wu et al. gave the three-locus genotype frequencies and the number of crossovers on different marker intervals under marker order .
|
15274749_p43
|
15274749
|
A general framework
| 3.476697 |
biomedical
|
Study
|
[
0.9936514496803284,
0.00022206729045137763,
0.00612643314525485
] |
[
0.9908748865127563,
0.008650343865156174,
0.0003632048610597849,
0.00011160195572301745
] |
en
| 0.999995 |
The joint genotype frequencies of the three markers can be viewed as a mixture of 16 diplotype combinations and three orders, weighted by their occurring probabilities, and is expressed as
|
15274749_p44
|
15274749
|
A general framework
| 3.785331 |
biomedical
|
Study
|
[
0.9984910488128662,
0.00022471939155366272,
0.0012841797433793545
] |
[
0.9180355668067932,
0.08081767708063126,
0.0007779331062920392,
0.0003688151191454381
] |
en
| 0.999997 |
Similarly, the expected number of recombination events contained within a progeny genotype is the mixture of the different diplotype and order combinations, expressed as:
|
15274749_p45
|
15274749
|
A general framework
| 3.223032 |
biomedical
|
Study
|
[
0.9919620156288147,
0.0005221643950790167,
0.0075158593244850636
] |
[
0.6859400272369385,
0.31209656596183777,
0.0012330356985330582,
0.0007303794845938683
] |
en
| 0.999998 |
Also define
|
15274749_p46
|
15274749
|
A general framework
| 1.015277 |
other
|
Other
|
[
0.45678821206092834,
0.007938975468277931,
0.5352728366851807
] |
[
0.14377163350582123,
0.8326998949050903,
0.019459182396531105,
0.004069310147315264
] |
it
| 0.571425 |
The occurring probabilities of the three marker orders are the mixture of all diplotype combinations, expressed, in matrix notation, as
|
15274749_p47
|
15274749
|
A general framework
| 2.911111 |
biomedical
|
Study
|
[
0.9837386608123779,
0.0006641378276981413,
0.015597302466630936
] |
[
0.6715763807296753,
0.32603564858436584,
0.001499517122283578,
0.0008884837152436376
] |
en
| 0.999996 |
We implement the EM algorithm to estimate the MLEs of the recombination fractions between the three markers. The general equations formulating the iteration of the { τ + 1}th EM step are given as follows:
|
15274749_p48
|
15274749
|
A general framework
| 3.072365 |
biomedical
|
Study
|
[
0.9794095754623413,
0.0006134022842161357,
0.01997697539627552
] |
[
0.6699672341346741,
0.3282093107700348,
0.001100241206586361,
0.0007232158095575869
] |
en
| 0.999998 |
E Step: As step τ , calculate the expected number of recombination events associated with D 00 ( α ), D 01 ( β ), D 10 ( γ ), D 11 ( δ ) for the ( j 1 j 2 j 3 )th progeny genotype (where j 1 , j 2 and j 3 denote the progeny genotypes of the three individual markers, respectively):
|
15274749_p49
|
15274749
|
A general framework
| 3.906655 |
biomedical
|
Study
|
[
0.9955797791481018,
0.0004409408138599247,
0.003979346249252558
] |
[
0.8811928629875183,
0.11787616461515427,
0.0006207257392816246,
0.00031028917874209583
] |
en
| 0.999998 |
Calculate , , , and , ( k = 1,2,3) using
|
15274749_p50
|
15274749
|
A general framework
| 1.591451 |
biomedical
|
Other
|
[
0.6059655547142029,
0.002740735886618495,
0.3912937045097351
] |
[
0.1398737132549286,
0.8564279079437256,
0.0025128391571342945,
0.0011854725889861584
] |
en
| 0.999995 |
where n j 1 j 2 j 3 denote the number of progeny with a particular three-marker genotype, h j 1 j 2 j 3 , , , , , p 1( j 1 j 2 j 3) , p 2( j 1 j 2 j 3) , q 1( j 1 j 2 j 3) and q 2( j 1 j 2 j 3) are the ( j 1 j 2 j 3 )th element of matrices H , D 00 , D 01 , D 10 , D 11 , P 1 , P 2 , Q 1 and Q 2 , respectively.
|
15274749_p51
|
15274749
|
A general framework
| 3.767357 |
biomedical
|
Study
|
[
0.9873276352882385,
0.00032202451257035136,
0.012350308708846569
] |
[
0.9630761742591858,
0.036219652742147446,
0.0004868637479376048,
0.00021728475985582918
] |
en
| 0.714285 |
M Step: Calculate , , and using the equations,
|
15274749_p52
|
15274749
|
A general framework
| 1.57573 |
biomedical
|
Other
|
[
0.802297055721283,
0.0226536113768816,
0.1750493198633194
] |
[
0.015123022720217705,
0.9803061485290527,
0.002994963899254799,
0.001575889065861702
] |
en
| 0.999996 |
The E and M steps are repeated among Eqs. (19) – (32) until d 00 , d 01 , d 10 and d 11 converge to values with satisfied precision. From the MLEs of the g's , the MLEs of recombination fractions r 12 , r 13 and r 23 can be obtained according to the invariance property of the MLEs.
|
15274749_p53
|
15274749
|
A general framework
| 2.789132 |
biomedical
|
Study
|
[
0.8494277000427246,
0.0009132137056440115,
0.14965908229351044
] |
[
0.8903380036354065,
0.10804858058691025,
0.0010886911768466234,
0.0005248179077170789
] |
en
| 0.999997 |
Consider three partially informative markers with the numbers of distinguishable pheno-types denoted by b 1 , b 2 and b 3 , respectively. Define is a ( b 1 b 2 × b 3 ) matrix of genotype frequencies for three partially informative markers. Similarly, we define , and .
|
15274749_p54
|
15274749
|
Model for partial informative markers
| 3.005344 |
biomedical
|
Study
|
[
0.9751099944114685,
0.0005272716516628861,
0.02436278760433197
] |
[
0.7675170302391052,
0.23055103421211243,
0.0013402984477579594,
0.0005916254594922066
] |
en
| 0.999996 |
Using the procedure described in Section (2.2), we implement the EM algorithm to estimate the MLEs of the recombination fractions among the three partially informative markers.
|
15274749_p55
|
15274749
|
Model for partial informative markers
| 3.478916 |
biomedical
|
Study
|
[
0.9928739070892334,
0.0003805421001743525,
0.006745578721165657
] |
[
0.9812132716178894,
0.018192149698734283,
0.0004073492018505931,
0.00018713614554144442
] |
en
| 0.999995 |
Three-point analysis considering the dependence of recombination events among different marker intervals can be extended to perform the linkage analysis of an arbitrary number of markers. Suppose there are m ordered markers on a linkage group. The joint genotype probabilities of the m markers form a (4 m -1 × 4)-dimensional matrix. There are 2 m -1 × 2 m -1 such probability matrices each corresponding to a different parental diplotype combination. The reasonable estimates of the recombination fractions rely upon the characterization of a most likely parental diplotype combination based on the multilocus likelihood values calculated.
|
15274749_p56
|
15274749
|
m-point analysis
| 4.086855 |
biomedical
|
Study
|
[
0.9987467527389526,
0.0001862608187366277,
0.0010670134797692299
] |
[
0.9863982796669006,
0.01285719033330679,
0.0006067192880436778,
0.00013780883455183357
] |
en
| 0.999996 |
The m -marker joint genotype probabilities can be expressed as a function of the probability of whether or not there is a crossover occurring between two adjacent markers, where l 1 , l 2 , ..., l m -1 are the indicator variables denoting the crossover event between markers and , markers and , ..., and markers and , respectively. An indicator is defined as 1 if there is a crossover and 0 otherwise. Because each indicator can be taken as one or zero, there are a total of 2 m -1 D's.
|
15274749_p57
|
15274749
|
m-point analysis
| 3.956264 |
biomedical
|
Study
|
[
0.9974536299705505,
0.00030768857686780393,
0.0022386377677321434
] |
[
0.9138498306274414,
0.08501031994819641,
0.0008427619468420744,
0.00029711355455219746
] |
en
| 0.999997 |
The occurring probability of interval-specific crossover can be estimated using the EM algorithm. In the E step, the expected number of interval specific crossovers is calculated (see Eqs. (19) – (22) for three-point analysis). In the M step, an explicit equation is used to estimate the probability . The MLEs of are further used to estimate m ( m - 1)/2 recombination fractions between all possible marker pairs. In m -point analysis, parental diplotypes and gene orders can be incorporated in the model.
|
15274749_p58
|
15274749
|
m-point analysis
| 4.114858 |
biomedical
|
Study
|
[
0.9990823268890381,
0.0002251294645247981,
0.0006925930501893163
] |
[
0.9962627291679382,
0.0033178995363414288,
0.0003359867259860039,
0.00008333076402777806
] |
en
| 0.999998 |
Simulation studies are performed to investigate the statistical properties of our model for simultaneously estimating linkage, parental diplotype and gene order in a full-sib family derived from two outbred parents. Suppose there are five markers of a known order on a chromosome. These five markers are segregating differently in order, 1:1:1:1, 1:2:1, 3:1, 1:1 and 1:1:1:1. The diplotypes of the two parents for the five markers are given in Table 1 and using these two parents a segregating full-sib family is generated. In order to examine the effects of parameter space on the estimation of linkage, parental diplotype and gene order, the full-sib family is simulated with different degrees of linkage ( r = 0.05 vs. 0.20) and different sample sizes ( n = 100 vs. 200).
|
15274749_p59
|
15274749
|
Monte Carlo simulation
| 4.08465 |
biomedical
|
Study
|
[
0.9993672966957092,
0.0002675047144293785,
0.0003652257437352091
] |
[
0.9994290471076965,
0.00021335775090847164,
0.0002958824625238776,
0.00006168358231661841
] |
en
| 0.999997 |
As expected, the estimation precision of the recombination fraction depends on the marker type, the degree of linkage and sample size. More informative markers, more tightly linked markers and larger sample sizes display greater estimation precision of linkage than less informative markers, less tightly linked markers and smaller sample sizes (Tables 1 and 2 ). To save space, we do not give the results about the effects of sample size in the tables. Our model can provide an excellent estimation of parental linkage phases, i.e., parental diplotype, in two-point analysis. For example, the MLE of the probability ( p or q ) of parental diplotype is close to 1 or 0 (Table 1 ), suggesting that we can always accurately estimate parental diplotypes. But for two symmetrical markers (e.g., markers and in this example), two sets of MLEs, = 1, = 0 and = 0, = 1, give an identical likelihood ratio test statistic. Thus, two-point analysis cannot specify parental diplotypes for symmetrical markers even when the two parents have different diplotypes.
|
15274749_p60
|
15274749
|
Monte Carlo simulation
| 4.138612 |
biomedical
|
Study
|
[
0.99854975938797,
0.0002621993771754205,
0.0011879854137077928
] |
[
0.9994926452636719,
0.00024754408514127135,
0.00021797783847432584,
0.000041909337596734986
] |
en
| 0.999997 |
The estimation precision of linkage can be increased when a three-point analysis is performed (Table 2 ), but this depends on different marker types and different degrees of linkage. Advantage of three-point analysis over two-point analysis is more pronounced for partially than fully informative markers, and for less tightly than more tightly linked markers. For example, the sampling error of the MLE of the recombination fraction (assuming r = 0.20) between markers and from two-point analysis is 0.0848, whereas this value from a three-point analysis decreases to 0.0758 when combining fully informative marker but increases to 0.0939 when combining partially informative marker . The three-point analysis can clearly determine the diplotypes of different parents as long as one of the three markers is asymmetrical. In our example, using either asymmetrical marker or , the diplotypes of the two parents for two symmetrical markers ( and ) can be determined. Our model for three-point analysis can determine a most likely gene order. In the three-point analyses combining markers , markers and marker , the MLEs of the probabilities of gene order are all almost equal to 1, suggesting that the estimated gene order is consistent with the order hypothesized.
|
15274749_p61
|
15274749
|
Monte Carlo simulation
| 4.177852 |
biomedical
|
Study
|
[
0.9992870688438416,
0.0002757375477813184,
0.0004372271941974759
] |
[
0.999376118183136,
0.00027093073003925383,
0.00029736149008385837,
0.00005562879960052669
] |
en
| 0.999997 |
To demonstrate how our linkage analysis model is more advantageous over the existing models for a full-sib family population, we carry out a simulation study for linked dominant markers. In two-point analysis, two different parental diplotype combinations are assumed: (1) [ aa ] [ oo ] × [ aa ] [ oo ] ( cis × cis ) and (2) [ ao ] [ oa ] × [ ao ] [ oa ] ( trans × trans ). The MLE of the linkage under combination (2), in which two dominant alleles are in a repulsion phase, is not as precise as that under combination (1), in which two dominant non-alleles are in a coupling phase . For a given data set with unknown linkage phase, the traditional procedure for estimating the recombination fraction is to calculate the likelihood values under all possible linkage phase combinations (i.e., cis × cis , cis × trans , trans × cis and trans × trans ). The combinations, cis × cis and trans × trans , have the same likelihood value, with the MLE of one combination being equal to the subtraction of the MLE of the second combination from 1. The same relationship is true for cis × trans and trans × cis . A most likely phase combination is chosen corresponding to the largest likelihood and a legitimate MLE of the recombination fraction ( r ≤ 0.5) .
|
15274749_p62
|
15274749
|
Monte Carlo simulation
| 4.17144 |
biomedical
|
Study
|
[
0.9990972280502319,
0.0003222333325538784,
0.0005804713000543416
] |
[
0.9994528889656067,
0.0002530908677726984,
0.00023928798327688128,
0.00005483562927111052
] |
en
| 0.999997 |
For our data set simulated from [ aa ] [ oo ] × [ aa ] [ oo ], one can easily select cis × cis as the best estimation of phase combination because it corresponds to a larger likelihood and a smaller (Table 3 ). Our model incorporating the parental diplotypes can provide comparable estimation precision of the linkage for the data from [ aa ] [ oo ] × [ aa ] [ oo ] and precisely determine the parental diplotypes (see the MLEs of p and q ; Table 3 ). Our model has great advantage over the traditional model for the data derived from [ ao ] [ oa ] × [ ao ] [ oa ]. For this data set, the same likelihood was obtained under all possible four diplotype combinations (Table 3 ). In this case, one would select cis × trans or trans × cis because these two phase combinations are associated with a lower estimate of r . But this estimate of r is biased since it is far less than the value of 0.20 hypothesized. Our model gives the same estimation precision of the linkage for the data derived from [ ao ] [ oa ] × [ ao ] [ oa ] as obtained when the analysis is based on a correct diplotype combination (Table 3 ). Also, our model can precisely determine the parental diplotypes ( = = 0 ).
|
15274749_p63
|
15274749
|
Monte Carlo simulation
| 4.177096 |
biomedical
|
Study
|
[
0.9988217949867249,
0.00028961460338905454,
0.0008886534487828612
] |
[
0.9995088577270508,
0.00023210812651086599,
0.00021130924869794399,
0.00004777759750140831
] |
en
| 0.999998 |
In three-point analysis, we examine the advantage of implementing linkage analysis with gene orders. Three dominant markers are assumed to have two different parental diplotypes combinations: (1) [ aaa ] [ ooo ] × [ aaa ] [ ooo ] and (2) [ aao ] [ ooa ] × [ aao ] [ ooa ]. The traditional approach is to calculate the likelihood values under three possible gene orders and choose one of a maximum likelihood to estimate the linkage. Under combination (1), a most likely gene order can be well determined and, therefore, the recombination fractions between the three markers well estimated, because the likelihood value of the correct order is always larger than those of incorrect orders (Table 4 ). However, under combination (2), the estimates of linkage are not always precise because with a frequency of 20% gene orders are incorrectly determined. The estimates of r 's will largely deviate from their actual values based on a wrong gene order (Table 4 ). Our model incorporating gene order can provide the better estimation of linkage than the traditional approach, especially between those markers with dominant alleles being in a repulsion phase. Furthermore, a most likely gene order can be determined from our model at the same time when the linkage is estimated.
|
15274749_p64
|
15274749
|
Monte Carlo simulation
| 4.222179 |
biomedical
|
Study
|
[
0.9991980195045471,
0.00026050562155433,
0.0005414815968833864
] |
[
0.9994687438011169,
0.00020974678045604378,
0.0002757837646640837,
0.00004567529686028138
] |
en
| 0.999997 |
Our model is further used to perform joint analyses including more than three markers. When the number of markers increases, the number of parameters to be estimated will be exponentially increased. For four-point analysis, the speed of convergence was slow and the accuracy and precision of parameter estimation have been affected for a sample size of 200 (data not shown). According to our simulation experience, the improvement of more-than-three-point analysis can be made possible by increasing sample size or by using the estimates from two- or three-point analysis as initial values.
|
15274749_p65
|
15274749
|
Monte Carlo simulation
| 3.599655 |
biomedical
|
Study
|
[
0.9969826340675354,
0.0002473849162925035,
0.002770023187622428
] |
[
0.9969196319580078,
0.002786993281915784,
0.00021135905990377069,
0.00008210679516196251
] |
en
| 0.999998 |
We use an example from published literature to demonstrate our unifying model for simultaneous estimation of linkage, parental diplotype and gene order. A cross was made between two triple heterozygotes with genotype AaVvXx for markers , and . Because these three markers are dominant, the cross generates 8 distinguishable genotypes, with observations of 28 for A - / V - / X - , 4 for A - / V - / xx , 12 for A - / vv / X - , 3 for A - / vv / xx , 1 for aa / V - / X - , 8 for aa / V - / xx , 2 for aa / vv / X - and 2 for aa / vv / xx . We first use two-point analysis to estimate the recombination fractions and parental diplotypes between all possible pairs of the three markers. The recombination fraction between markers and is , whose the estimated parental diplotypes are [ Av ] [ aV ] × [ AV ] [ av ] or [ AV ] [ av ] × [ Av ] [ aV ]. The other two recombination fractions and the corresponding parental displotypes are estimated as , [ Vx ] [ vX ] × [ VX ] [ vx ] or [ VX ] [ vx ] × [ Vx ] [ vX ] and , [ AX ] [ ax ] × [ AX ] [ ax ], respectively. From the two-point analysis, one of the two parents have dominant alleles from markers and are repulsed with the dominant alleles from marker .
|
15274749_p66
|
15274749
|
A worked example
| 4.163313 |
biomedical
|
Study
|
[
0.9994064569473267,
0.0002197787252953276,
0.00037374391104094684
] |
[
0.99928879737854,
0.00041805097134783864,
0.0002345847460674122,
0.0000585722409596201
] |
en
| 0.999996 |
Our subsequent three-point analysis combines parental diplotypes and gene orders to estimate the linkage between these three dominant markers. The estimated gene order is . The MLEs of the recombination fractions are , and . The parental diplotype combination is [ XAV ] [ xav ] × [ XAv ] [ xaV ] or [ XAv ] [ xaV ] × [ XAV ] [ xav ]. The three-point analysis for these three markers by Ridout et al. led to the estimates of the three recombination fractions all equal to 0.20. But their estimates may not be optimal because the effect of gene order on was not considered.
|
15274749_p67
|
15274749
|
A worked example
| 4.050046 |
biomedical
|
Study
|
[
0.998917818069458,
0.00019822361355181783,
0.0008839271613396704
] |
[
0.999320387840271,
0.00046190276043489575,
0.0001661942369537428,
0.000051482609705999494
] |
en
| 0.999996 |
Several statistical methods and software packages have been developed for linkage analysis and map construction in experimental crosses and well-structured pedigrees , but these methods need unambiguous linkage phases over a set of markers in a linkage group. For outcrossing species, such as forest trees, it is not possible to know exact linkage phases for any of two parents that are crossed to generate a full-sib family prior to linkage analysis. This uncertainty about linkage phases makes linkage mapping in outcrossing populations much more difficult than that in phase-known pedigrees .
|
15274749_p68
|
15274749
|
Discussion
| 3.970963 |
biomedical
|
Study
|
[
0.9977465271949768,
0.00015687984705436975,
0.002096564508974552
] |
[
0.9533678889274597,
0.04008256644010544,
0.0063846115954220295,
0.00016484566731378436
] |
en
| 0.999995 |
In this article we present a unifying model for simultaneously estimating the linkage, parental diplotype and gene order in a full-sib family derived from two outbred parents. As demonstrated by simulation studies, our model is robust to different parameter space. Compared to the traditional approaches that calculate the likelihood values separately under all possible linkage phases or orders , our approach is more advantageous in three aspects. First, it provides a one-step analysis of estimating the linkage, parental diplotype and gene order, thus facilitating the implementation of a general method for analyzing any segregating type of markers for outcrossing populations in a package of computer program. For some short-generation-interval outcrossing species, we can obtain marker information from grandparents, parents and progeny. The model presented here allow for the use of marker genotypes of the grandparents to derive the diplotype of the parents. Second, our model for the first time incorporates gene ordering into a unified linkage analysis framework, whereas most earlier studies only emphasized on the characterization of linkage phases through a multilocus likelihood analysis . Instead of a comparative analysis of different orders, we proposed to determine a most likely gene order by estimating the order probabilities.
|
15274749_p69
|
15274749
|
Discussion
| 4.260465 |
biomedical
|
Study
|
[
0.9992745518684387,
0.00037803215673193336,
0.0003474292461760342
] |
[
0.9989916682243347,
0.00031805678736418486,
0.0006102172192186117,
0.00008001503010746092
] |
en
| 0.999997 |
Third, and most importantly, our unifying approach can significantly improve the estimation precision of the linkage for dominant markers whose alleles are in repulsion phase. Previous analyses have indicated that the estimate of the linkage between dominant markers in a repulsion phase is biased and imprecise, especially when the linkage is not strong and when sample size is small . There are two reasons for this: (1) the linkage phase cannot be correctly determined, and/or (2) there is a fairly high possibility (20%) of detecting a wrong gene order. Our approach provides more precise estimates of the recombination fraction because correct parental diplotypes and a correct gene order can be determined.
|
15274749_p70
|
15274749
|
Discussion
| 4.147145 |
biomedical
|
Study
|
[
0.9994638562202454,
0.00019667520246002823,
0.00033940470893867314
] |
[
0.9987254738807678,
0.0007207505987025797,
0.0004921100335195661,
0.00006163786019897088
] |
en
| 0.999996 |
Our approach will be broadly useful in genetic mapping of outcrossing species. In practice, a two-point analysis can first be performed to obtain the pairwise estimates of the recombination fractions and using this pairwise information markers are grouped based on the criteria of a maximum recombination fraction and minimum likelihood ratio test statistic . The parental diplotypes of markers in individual groups are constructed using a three-point analysis. With a limited sample size available in practice, we do not recommend more-than-three-point analysis because this would bring too many more unknown parameters to be precisely estimated. If such an analysis is desirable, however, one may use the results from these lower-point analyses as initial values to improve the convergence rate and possibly the precision of parameter estimation.
|
15274749_p71
|
15274749
|
Discussion
| 4.056203 |
biomedical
|
Study
|
[
0.9987026453018188,
0.00022500334307551384,
0.0010722881415858865
] |
[
0.9855924248695374,
0.013293854892253876,
0.000969881541095674,
0.00014375336468219757
] |
en
| 0.999996 |
In any case, our two- and three-point analysis has built a key stepping stone for map construction through two approaches. One is the least-squares method, as originally developed by Stam , that can integrate the pairwise recombination fractions into reconstruction of multilocus linkage map. The second is to use the hidden Markov chain (HMC) model, first proposed by Lander and Green , to construct genetic linkage maps by treating map construction as a combinatorial optimization problem. The simulated annealing algorithm for searching for optima of the multilocus likelihood function need to be implemented for the HMC model. A user-friendly package of software that is being written by the senior author will implement two- and three-point analyses as well as the algorithm for map construction based on the estimates of pairwise recombination fractions. This software will be online available to the public.
|
15274749_p72
|
15274749
|
Discussion
| 4.083495 |
biomedical
|
Study
|
[
0.9986586570739746,
0.00019548943964764476,
0.0011458267690613866
] |
[
0.9691382646560669,
0.029636062681674957,
0.0010507027618587017,
0.0001748864451656118
] |
en
| 0.999997 |
Our maximum likelihood-based approach is implemented with the EM algorithm. We also incorporate the Gibbs sampler into the estimation procedure of the mixture model for the linkage characterizing different parental diplotypes and gene orders of different markers. The results from the Gibbs sampler are broadly consistent with those from the EM algorithm, but the Gibbs sampler is computationally more efficient for a complicated problem than the EM algorithm. Therefore, the Gibbs sampler may be particularly useful when our model is extended to consider multiple full-sib families in which the parents may be selected from a natural population. For such a multi-family design, some population genetic parameters describing the genetic structure of the original population, such as allele frequencies and linkage disequilibrium, should be incorporated and estimated in the model for linkage analysis. It can be anticipated that the Gibbs sampler will play an important role in estimating these parameters simultaneously along with the linkage, linkage phases, and gene order.
|
15274749_p73
|
15274749
|
Discussion
| 4.158381 |
biomedical
|
Study
|
[
0.9992390871047974,
0.00020741866319440305,
0.0005534913507290184
] |
[
0.9985700845718384,
0.0008775762398727238,
0.0004901514621451497,
0.0000620588252786547
] |
en
| 0.999997 |
QL derived the genetic and statistical models and wrote computer programs. YHC participated in the derivations of models and statistical analyses. RLW conceived of ideas and algorithms, and wrote the draft. All authors read and approved the final manuscript.
|
15274749_p74
|
15274749
|
Authors' contributions
| 0.802998 |
other
|
Other
|
[
0.3661973774433136,
0.004076710436493158,
0.6297259330749512
] |
[
0.010951543226838112,
0.9861857295036316,
0.00185599725227803,
0.0010067749535664916
] |
en
| 0.999997 |
Histones H3 and H4 are among the most evolutionarily conserved proteins (>90% identity from yeast→humans) . Octamers composed of one histone H3/H4 tetramer and two histone H2A/H2B dimers package 146 bp of DNA into the basic repeating subunit of chromatin, the nucleosome . Hence, as fundamental components of chromatin, these proteins are an integral part of all cellular processes involving chromosomal DNA.
|
15274751_p0
|
15274751
|
Background
| 4.107669 |
biomedical
|
Other
|
[
0.9986893534660339,
0.0006440853467211127,
0.0006665717228315771
] |
[
0.1849854439496994,
0.7468984723091125,
0.06627673655748367,
0.0018393703503534198
] |
en
| 0.999997 |
The physical characteristics of the histones are precisely regulated in the cell by an elaborate network of post-translational modifications that include acetylation, methylation, phosphorylation, ubiquitination and ADP-ribosylation . These modifications are found primarily on the NH 2 -terminal tails of the histones. These domains, which protrude from the core of the nucleosome, are free to interact with, and be acted upon by, the nuclear environment. The past several years has seen the identification of numerous enzymes that are capable of modifying the histones. These enzymes are generally found in large, multi-subunit complexes and have activities that are not only specific for a given histone but are specific for particular amino acid residues within the histone .
|
15274751_p1
|
15274751
|
Background
| 4.194579 |
biomedical
|
Study
|
[
0.9990905523300171,
0.00031528164981864393,
0.0005941413110122085
] |
[
0.5522546768188477,
0.02823742851614952,
0.41867220401763916,
0.0008356450125575066
] |
en
| 0.999995 |
The most well characterized histone modifying enzymes are the histone acetyltransferases (HATs). HATs catalyze the transfer of an acetyl moiety from acetyl-coenzyme A to the ε-amino group of lysine residues in the histone NH 2 -terminal tails. Historically, these enzymes have been classified as either type A or type B, based upon substrate specificity and cellular localization . Found in the nucleus, type A HATs utilize nucleosomal histones as substrates. A number of Type A HATs have been identified in yeast. These include Gcn5p (SAGA, ADA, SLIK, SALSA and HAT-A2 complexes), Sas2p (SAS complex), Sas3p (NuA3 complex), Esa1p (NuA4 and picNuA4 complexes) and Elp3 (Elongator complex) . These enzymes have been characterized primarily in the context of transcriptional activation but are likely to be involved in other chromatin mediated events as well .
|
15274751_p2
|
15274751
|
Background
| 4.439783 |
biomedical
|
Study
|
[
0.9991289973258972,
0.00035039676004089415,
0.0005205558845773339
] |
[
0.7464038729667664,
0.004298037849366665,
0.2487640529870987,
0.0005339771742001176
] |
en
| 0.999998 |
Type B HATs were initially described as cytoplasmic enzymes that acetylate free histones in conjunction with chromatin assembly . The de novo assembly of chromatin is a complex, multi-step process that occurs most prominently during DNA replication (but also accompanies other cellular processes involving DNA synthesis) . Following induction of histone mRNA synthesis, histone proteins are translated in the cytoplasm. For histones H3 and H4, synthesis is rapidly followed by the acetylation of specific lysine residues in their NH 2 -terminal tail domains . For newly synthesized histone H4, this acetylation occurs on lysine residues at positions 5 and 12 in all eukaryotic organisms examined to date . For newly synthesized histone H3, acetylation appears to occur in distinct patterns that can differ from organism to organism . The acetylated H3 and H4 form tetramers that are translocated into the nucleus and loaded onto DNA . Following completion of the histone octamer by histone H2A/H2B addition, mature chromatin is formed following the deacetylation of histones H3 and H4 .
|
15274751_p3
|
15274751
|
Background
| 4.671258 |
biomedical
|
Study
|
[
0.9987183809280396,
0.0006523248157463968,
0.0006293691112659872
] |
[
0.6892087459564209,
0.01630493625998497,
0.2926796078681946,
0.0018066588090732694
] |
en
| 0.999997 |
In contrast to the type A HATs, only one type B HAT has been characterized to date, Hat1p. Hat1p is an evolutionarily conserved enzyme that specifically acetylates free histone H4 . Consistent with its identification as a type B HAT, recombinant yeast Hat1p, as well the Xenopus and Human Hat1p homologs, acetylates both lysine 5 and lysine 12 . Hat1p was originally purified from yeast cytoplasmic extracts in a complex with Hat2p, a yeast homolog of the mammalian Rbap46/48 proteins . Subsequent studies have shown that yeast Hat1p, as well as its higher eukaryotic counterparts, can also localize to the nucleus . These results suggest that, while specificity for free histones is a bona fide characteristic, cytoplasmic localization may not be a strict criterion for classification as a type B HAT.
|
15274751_p4
|
15274751
|
Background
| 4.390687 |
biomedical
|
Study
|
[
0.9995651841163635,
0.00019506397075019777,
0.00023972877534106374
] |
[
0.9955897331237793,
0.0006394619704224169,
0.003654862055554986,
0.00011601012374740094
] |
en
| 0.999998 |
Evidence has accumulated indicating that the acetylation of newly synthesized histones H3 and H4 play over-lapping roles in chromatin assembly. While yeast strains carrying a deletion of either the H3 or H4 NH 2 -terminal tail are viable, concomitant deletion of both NH 2 -termini (or combining tail deletions with alterations in specific sites of acetylation) results in a defect in nucleosome assembly and cell death . In addition, while deletion of the HAT1 gene produces no observable phenotype, combining a deletion of HAT1 with specific lys→arg mutations in the NH 2 -terminus of histone H3 generates defects in both telomeric silencing and DNA damage repair . However, despite the importance of the acetylation of newly synthesized histone H3 in chromatin assembly, there have been no type B histone acetyltransferases described that specifically target histone H3.
|
15274751_p5
|
15274751
|
Background
| 4.39775 |
biomedical
|
Study
|
[
0.9995166063308716,
0.0002552841615397483,
0.0002280570479342714
] |
[
0.9965329170227051,
0.00046663216198794544,
0.002894333330914378,
0.00010606984869809821
] |
en
| 0.999997 |
To identify potential histone H3-specific type B HATs, we have systematically surveyed yeast extracts for candidate activities. Here we detail one such activity, termed HatB3.1. We provide evidence that this is a novel complex that utilizes Gcn5p as its catalytic subunit. Intriguingly, unlike previously identified Gcn5p-containing HAT complexes, HatB3.1 contains Ada3p, but not Ada2p.
|
15274751_p6
|
15274751
|
Background
| 4.185548 |
biomedical
|
Study
|
[
0.999445378780365,
0.00023853499442338943,
0.00031603191746398807
] |
[
0.9991629123687744,
0.0004987504798918962,
0.0002655833959579468,
0.0000727633960195817
] |
en
| 0.999994 |
The highly selective activity of the native Hat1p/Hat2p complex for free versus nucleosomal histone H4 is the primary characteristic that distinguishes this enzyme from the type A histone acetyltransferases . Therefore, to identify putative histone H3-specific type B HAT complexes, we systematically surveyed yeast extracts for activities that acetylated free histone H3 but not histone H3 packaged into chromatin. Extracts were prepared from cell cultures grown to mid-log phase to enrich for actively dividing cells, as the most robust period of chromatin assembly occurs during DNA replication. Yeast cell walls were digested with zymolyase and cytosolic extracts were produced by the lysis of the cells in low salt buffer followed by centrifugation to remove nuclei and large cell debris. Hence, this extract contained soluble cytoplasmic proteins as well as proteins loosely associated with the nucleus. The nuclear extract was obtained by incubating the nuclear pellet in buffer containing 1.0 M NaCl to extract proteins that are more tightly associated with the nucleus.
|
15274751_p7
|
15274751
|
Identification of histone H3-specific type B histone acetyltransferase activities in yeast
| 4.212768 |
biomedical
|
Study
|
[
0.9995197057723999,
0.00025727442698553205,
0.0002231002872576937
] |
[
0.9992371797561646,
0.00027481259894557297,
0.0004259099659975618,
0.00006202333315741271
] |
en
| 0.999995 |
It is difficult to reliably detect histone acetyltransferase activities in the relatively crude cytosolic and nuclear extracts. Therefore, to evaluate the intrinsic HAT activities present in each of the extracts, they were fractionated by anion and cation exchange chromatography. Fractions were assayed for HAT activity using 3 H-acetyl Coenzyme A and equivalent amounts of either free histones or chromatin as substrate. Histones were then resolved by SDS-PAGE and acetylated species visualized by fluorography.
|
15274751_p8
|
15274751
|
Identification of histone H3-specific type B histone acetyltransferase activities in yeast
| 4.100116 |
biomedical
|
Study
|
[
0.999602735042572,
0.00020468227739911526,
0.00019260072440374643
] |
[
0.9991623163223267,
0.0003832507645711303,
0.0003886688209604472,
0.0000657676937407814
] |
en
| 0.999998 |
Fractionation of the cytosolic extract on a DEAE column is shown Figure 1A . As expected, the predominant type B activity present in these preparations was attributable to Hat1p, as indicated by robust, free histone H4 acetylation . The identity of the Hat1p/Hat2p complex was confirmed by western blot analysis using polyclonal antibodies against both Hat1p and Hat2p (data not shown).
|
15274751_p9
|
15274751
|
Identification of histone H3-specific type B histone acetyltransferase activities in yeast
| 4.111884 |
biomedical
|
Study
|
[
0.9995591044425964,
0.00022248781169764698,
0.0002183745673391968
] |
[
0.9992775321006775,
0.00039865871076472104,
0.00025840208400040865,
0.00006541170296259224
] |
en
| 0.999997 |
The cytosolic extract also contained at least two additional HAT activities. The first showed a clear peak that was centered on fraction 14 and acetylated free histones H3, H2B and H4. The activity of this HAT on chromatin was more difficult to determine as the H3 and H4 labeling seen in these fractions does not show a marked peak in fraction 14 and may be due to the leading edge of a HAT activity eluting at higher salt. Therefore, this activity may be a candidate type B HAT. There was also a distinct peak of HAT activity at fractions 18–20. With free histone substrates, this activity primarily acetylated histone H3. However, there was also a coincident peak of chromatin H3 and H4 acetylating activity in these fractions suggesting this activity is likely to be a type A HAT.
|
15274751_p10
|
15274751
|
Identification of histone H3-specific type B histone acetyltransferase activities in yeast
| 4.214958 |
biomedical
|
Study
|
[
0.9995478987693787,
0.00020073629275429994,
0.00025125895626842976
] |
[
0.9991350769996643,
0.0004958618083037436,
0.00030035298550501466,
0.00006869469507364556
] |
en
| 0.999997 |
DEAE fractionation of the nuclear extract also revealed several distinct HAT activities . There were two H4-specific type A HAT activities that peaked at fractions 14 and 18, as indicated by activity on both free and nucleosomal histones. There was also a significant peak of activity that acetylated free histone H3 that was coincident with a minor nucleosomal H3 HAT activity . This activity also partly overlapped the nucleosomal H4 activities. This peak of activity was rather broad and most probably results from the partial overlap of at least two distinct activities. In fact, the separation of these activities was readily apparent in Figures 3 and 4 . The strong overall preference of these activities for free histone H3 makes them good candidates for H3-specific type B HATs. As these are chromatographically distinct activities we have termed them HatB3.1 and HatB3.2 as indicated .
|
15274751_p11
|
15274751
|
Identification of histone H3-specific type B histone acetyltransferase activities in yeast
| 4.211559 |
biomedical
|
Study
|
[
0.9995469450950623,
0.00021532390383072197,
0.00023773638531565666
] |
[
0.9991901516914368,
0.0003938866138923913,
0.00034996031899936497,
0.0000659899233141914
] |
en
| 0.999997 |
Unbound material from the initial DEAE fractionation of the cytosolic and nuclear extracts was analyzed by cation exchange chromatography (carboxymethyl sepharose (CM)). While this fraction from the cytosolic extract appeared inactive, there were several additional HAT activities resolved from the nuclear extract . The presence of these activities in the DEAE flowthrough fraction is not simply due to column overloading as recycling the flowthrough fraction over the DEAE column a second time did not result in significant protein retention. Hence, these activities are chromatographically distinct from those that bind the DEAE resin. Two activities, centered on fractions 22 and 30, acetylated primarily histone H4. These appeared to be typical type A HAT's as they were active on both free histones and chromatin. A broad peak of histone H3-specific activity eluted from the CM column from fraction 10 through fraction 24 (with activity trailing through the remainder of the gradient). Comparison of the free histone and chromatin activities in these fractions suggested that this region of the gradient actually contained overlapping type A and type B activities. There was a distinct peak of free histone H3 acetylating activity centered on fractions 12 – 14 while acetylation of chromatin associated H3 peaked in fraction 16. Hence, the activity in fractions 12–14 is another candidate H3-specific type B HAT (labeled HatB3.3).
|
15274751_p12
|
15274751
|
Identification of histone H3-specific type B histone acetyltransferase activities in yeast
| 4.395944 |
biomedical
|
Study
|
[
0.999433696269989,
0.0003246850974392146,
0.00024166614457499236
] |
[
0.9990227222442627,
0.0003749237221200019,
0.000500773370731622,
0.00010161454702029005
] |
en
| 0.999998 |
The fractions from the DEAE column that contained the activity that we have termed HatB3.1 modified not only free histone H3 but also free H4. In addition, a low level of nucleosomal H3 activity could also be seen in these fractions. To determine whether these activities were the result of a single enzyme complex or were due to multiple, overlapping complexes, these fractions were pooled, dialyzed and fractionated over a Mono-Q column . Inspection of the HAT activity profile of the fractions eluting from the Mono-Q column clearly demonstrated that multiple HAT activities overlapped with HatB3.1 during the initial fractionation of the nuclear extract. The HatB3.1 activity eluted from the Mono-Q column very early in the gradient and appeared to be highly specific for free histone H3. The second activity to elute from the Mono-Q column was specific for chromatin-associated histone H4. The third activity acetylated both free and nucleosomal histones H3, H2B and H4. These results indicated that the acetylation of multiple histones in the DEAE elution profile was the result of at least three overlapping activities and confirmed that HatB3.1 is a chromatographically distinct free histone H3-specific activity. Therefore, HatB3.1 was a good candidate for further characterization.
|
15274751_p13
|
15274751
|
HatB3.1 is specific for free histone H3
| 4.342707 |
biomedical
|
Study
|
[
0.9994982481002808,
0.00030963908648118377,
0.0001921232760651037
] |
[
0.9990608096122742,
0.00045860515092499554,
0.0003614682937040925,
0.00011916584480786696
] |
en
| 0.999997 |
To gain insight into the identity of the catalytic subunit of HatB3.1, we constructed null mutants for each of the yeast HAT's that have demonstrated histone H3 activity as well as the known type B HAT, HAT1 . Isogenic deletion strains (Δ gcn5 , Δ sas2 , Δ sas3 and Δ hat1 ) were grown and protein extracts prepared exactly as for the wild type strain. Nuclear extracts were again fractionated via DEAE column chromatography and fractions of equivalent conductivity assayed for HAT activity as described above.
|
15274751_p14
|
15274751
|
HatB3.1 activity is dependent on GCN5
| 4.079089 |
biomedical
|
Study
|
[
0.9994679093360901,
0.00022772450756747276,
0.00030442315619438887
] |
[
0.9993841648101807,
0.00028362867305986583,
0.0002790043654385954,
0.000053148309234529734
] |
en
| 0.999999 |
Parallel comparison of the HAT activity profiles from each strain provided biochemical evidence for the dependency of specific histone acetyltransferase activities on the presence of a particular HAT catalytic subunit . While subtle variations in observed specificity and intensity of HAT activity were seen throughout the profiles of the Δ sas2 and Δ sas3 strains, the robust H3 acetylation attributed to the HatB3.1 activity appeared unaffected by deletion of these enzymes . Conversely, HatB3.1 activity was abolished in a Δ gcn5 strain . In addition, the HatB3.2 activity also appeared to be absent in extracts from a gcn5 strain indicating that both of these putative type B HAT activities are dependent on Gcn5p. Additionally, the integrity, in a Δ gcn5 strain, of the overlapping free histone and chromatin (data not shown) activities in this region of the gradient confirmed that HatB3.1 was a chromatographically distinct HAT activity exhibiting specificity for free histone H3.
|
15274751_p15
|
15274751
|
HatB3.1 activity is dependent on GCN5
| 4.307489 |
biomedical
|
Study
|
[
0.999494194984436,
0.0002952993381768465,
0.00021054016542620957
] |
[
0.9991974234580994,
0.0003001220466103405,
0.0004154443449806422,
0.00008702147897565737
] |
en
| 0.999997 |
Analysis of the activity profile from nuclear extracts derived from a Δ hat1 strain identified a broad peak of Hat1p dependent activity that spanned fractions ~22–34. Western blot analysis using antibodies against Hat1p and Hat2p confirmed the presence of these proteins in fractions from this region of the gradient from the wild type extract (data not shown). As with the Hat1p-dependent activity in cytosolic extracts, this activity also appeared to be specific for free histone H4. This result confirmed previous observations indicating that Hat1p is localized to both the cytoplasm and the nucleus . In addition, the presence of an authentic type B HAT activity in our nuclear extracts validated our use of these extracts for the identification of putative histone H3-specific type B HAT activities.
|
15274751_p16
|
15274751
|
HatB3.1 activity is dependent on GCN5
| 4.192709 |
biomedical
|
Study
|
[
0.9995496869087219,
0.0002383758983341977,
0.0002119142300216481
] |
[
0.99940025806427,
0.0002810654987115413,
0.00025108439149335027,
0.00006758824019925669
] |
en
| 0.999997 |
There are two proteins, Ada2p and Ada3p, that are components of all known Gcn5p-containing HAT complexes and that are required for the activity of these complexes . To determine whether the HatB3.1 activity was also dependent on these proteins, nuclear extracts were prepared from isogenic Δ ada2 and Δ ada3 strains and the status of the HatB3.1 activity determined by DEAE chromatography. As shown in Figure 4 , the loss of ADA2 did not affect either the HatB3.1 or HatB3.2 activity but did cause a substantial increase in the free histone H4 specific activity that eluted late in the DEAE gradient. However, the HAT activity profile of the Δada3 extracts was strikingly similar to that seen for the gcn5 extracts with both the HatB3.1 and HatB3.2 activities absent. These results indicated that the HatB3.1 activity was dependent on ADA3 and that Ada2p is either not a component of the HatB3.1 activity or is not required for its stability.
|
15274751_p17
|
15274751
|
HatB3.1 activity is dependent on ADA3 but not ADA2
| 4.179677 |
biomedical
|
Study
|
[
0.9994507431983948,
0.0002414891787339002,
0.00030777876963838935
] |
[
0.9995135068893433,
0.0002390338049735874,
0.00019029287796001881,
0.00005715687802876346
] |
en
| 0.999997 |
To further characterize HatB3.1, this activity was purified through several chromatographic steps. The purification scheme is diagramed in Figure 5A . HatB3.1 containing fractions from the DEAE column were pooled, dialyzed to a conductivity similar to that of the loading buffer (DN(50)) and the dialysate applied to a cation exchange column (CM sepharose). HAT activity assays indicated that the HatB3.1 activity flowed through the CM sepharose column while bound proteins, resolved by a linear salt gradient, contained co-purifying HAT activities that acetylated both free and nucleosomal, H3 and H4 (data not shown). The presence of HatB3.1 in the CM sepharose flow through also confirmed that HatB3.1 and HatB3.3 were distinct activities.
|
15274751_p18
|
15274751
|
Partial purification of HatB3.1
| 4.129881 |
biomedical
|
Study
|
[
0.9995492100715637,
0.00022305030142888427,
0.0002276529121445492
] |
[
0.9994739890098572,
0.0002695505681913346,
0.0002000741515075788,
0.00005644060729537159
] |
en
| 0.999998 |
The proteins that flowed through the CM sepharose column were applied to a Mono-Q column and then eluted with a linear salt gradient. Fractions containing free histone H3 activity were pooled and concentrated by precipitation with 75% ammonium sulfate. The sample was then fractionated by size exclusion chromatography using a Superose 6 column. As seen in Figure 5B , the HatB3.1 activity peaked at fractions 48–50, indicating that a high molecular weight complex of ~500 kDa was responsible for this activity. The size of HatB3.1 remained stable throughout the course of purification as Superose 6 fractionation of the pooled HatB3.1 activity from the initial DEAE column displayed an identical mass (data not shown). The highly purified HatB3.1 retained its high degree of specificity for free histone versus chromatin substrates. There were also two peaks of free histone H4 specific activity seen in the Superose 6 elution profile. Western blot analysis indicated that Hat1p co-eluted with the low molecular weight species. The second peak of H4 activity co-purified with HatB3.1. Whether this acetylation of histone H4 was the result of a weak specificity of HatB3.1 for H4 or due to a second, co-eluting, HAT activity has not been resolved.
|
15274751_p19
|
15274751
|
Partial purification of HatB3.1
| 4.241871 |
biomedical
|
Study
|
[
0.9994753003120422,
0.00032298918813467026,
0.0002017428632825613
] |
[
0.9992789626121521,
0.0003487080684863031,
0.0002719883050303906,
0.00010044124064734206
] |
en
| 0.999996 |
The absence of HatB3.1 activity in extracts from a Δgcn5 strain indicated that HatB3.1 was dependent on Gcn5p, either indirectly via Gcn5p-mediated transcriptional regulation, or directly, as its catalytic subunit. While the HatB3.1 activity was highly purified relative to the initial nuclear extract, the peak Superose 6 fractions were still too complex to allow the definitive identification of specific bands that co-purified with the activity . Extensive efforts to purify HatB3.1 to homogeneity have been unsuccessful. To determine whether Gcn5p was likely to be functioning as the catalytic subunit of HatB3.1, fractions across the peak of HatB3.1 activity from the Superose 6 column were probed with anti-Gcn5p antibodies. As seen in Figure 5C , Gcn5p was present in the fractions containing the peak of HatB3.1 activity from the Superose 6 column. This result is consistent with direct association of Gcn5p with the HatB3.1 complex.
|
15274751_p20
|
15274751
|
Gcn5p and Ada3p, but not Ada2p, co-purified with the HatB3.1 activity
| 4.213004 |
biomedical
|
Study
|
[
0.9993751645088196,
0.00026823204825632274,
0.0003565774532034993
] |
[
0.9995404481887817,
0.00022759794956073165,
0.00017410643340554088,
0.00005791000512544997
] |
en
| 0.999997 |
Duplicate blots were probed with anti-Ada2p and anti-Ada3p antibodies to determine whether these proteins also co-fractionated with the HatB3.1 complex. As expected, both Ada2p and Ada3p are present in the nuclear extracts . However, while Ada3p precisely co-purified with Gcn5p and the peak of HatB3.1 activity, Ada2p did not appear to be associated with this complex. The absence of the Ada2p from the peak of HatB3.1 activity is consistent with the observation that HatB3.1 activity is independent of the ADA2 gene and suggests that Ada2p is not a component of the HatB3.1 complex. The absence of an Ada2p signal on the Western blot was not due to problems with sensitivity as comparison of the relative signals of Gcn5p, Ada2p and Ada3p in the nuclear extracts and Superose 6 fractions demonstrated that the presence of Ada2p in the Superose 6 fractions would have been readily apparent. While the HatB3.1 activity is enriched in the Superose 6 peak fractions relative to the original nuclear extract, the amount of Gcn5p and Ada3p present in these fractions is not enriched relative to the nuclear extract due to the fact that these proteins are components of at least five other histone acetyltransferase complexes. Hence, only a fraction of the Gcn5p and Ada3p present in the cell extracts was associated with HatB3.1.
|
15274751_p21
|
15274751
|
Gcn5p and Ada3p, but not Ada2p, co-purified with the HatB3.1 activity
| 4.223769 |
biomedical
|
Study
|
[
0.9993917942047119,
0.0002910339389927685,
0.00031718891113996506
] |
[
0.9993988275527954,
0.0002892311313189566,
0.00024879814009182155,
0.00006318988016573712
] |
en
| 0.999995 |
Considerable genetic and biochemical evidence indicates that, in most organisms, newly synthesized histone H3 is acetylated and that this acetylation plays a role in the de novo assembly of chromatin . However, the enzymes responsible for this modification have remained elusive. In the present study we have comprehensively surveyed yeast extracts for putative, histone H3-specific, type B histone acetyltransferase activities. At least three candidate activities were identified, HatB3.1, HatB3.2 and HatB3.3. Further characterization of HatB3.1 indicated that this activity is a novel ~500 kDa HAT complex. In addition, our results suggest that Gcn5p and Ada3p are components of this complex but that, contrary to all previously isolated Gcn5p complexes, HatB3.1 is not associated with Ada2p. It does not appear that the HatB3.1 complex is merely an unstable form of one of the previously characterized Gcn5p-containing complexes as the apparent molecular weight of HatB3.1 did not vary during the course of its purification.
|
15274751_p22
|
15274751
|
Discussion
| 4.283124 |
biomedical
|
Study
|
[
0.9994590878486633,
0.0003312972839921713,
0.00020952786144334823
] |
[
0.9992284774780273,
0.0002271119737997651,
0.0004554094048216939,
0.00008905935828806832
] |
en
| 0.999994 |
There have been at least a dozen distinct HAT complexes identified in yeast . Conservative analysis of our systematic fractionation of yeast cytosolic and nuclear extracts resolved 12 chromatographically separable activities. However, many of these activities were represented by rather broad peaks, likely to be composed of partially overlapping activities that may differentiate upon further purification . While many of the activities identified here may correspond to previously characterized complexes, it is difficult to determine these relationships, as our initial purification steps differ from those typically used for the isolation of other yeast HAT complexes. In particular, the purification of the SAGA, ADA, SLIK, SALSA, NuA3 and NuA4 complexes start from Ni 2+ -NTA agarose fractionated whole cell extracts, as these enzymes fortuitously bind to this resin .
|
15274751_p23
|
15274751
|
Discussion
| 4.166619 |
biomedical
|
Study
|
[
0.9995290040969849,
0.00020712714467663318,
0.0002638883306644857
] |
[
0.9992050528526306,
0.0002495963126420975,
0.0004918953636661172,
0.00005348781996872276
] |
en
| 0.999996 |
Most histone acetyltransferases have substrate specificities that direct the acetylation of specific residues within one or more of the core histones . However, these substrate specificities are not fixed and can be altered by the association of the catalytic subunits with different protein complexes . The presence of numerous HAT complexes expands the repertoire of modification states that can be generated on the chromatin template. Therefore, as growing evidence indicates that specific cellular processes are associated with precise patterns of histone modification, the presence of multiple HAT complexes in cells is likely to be a reflection of the myriad events that must take place in the context of chromatin .
|
15274751_p24
|
15274751
|
Discussion
| 4.158737 |
biomedical
|
Study
|
[
0.9993525147438049,
0.00032221179571934044,
0.00032520503737032413
] |
[
0.8207417726516724,
0.03815960884094238,
0.14027732610702515,
0.0008213230175897479
] |
en
| 0.999998 |
Despite the importance of histone acetylation in regulating chromatin structure, with the exception of Esa1p, none of the yeast histone acetyltransferases are essential for viability . Also, the deletion of most HAT genes results in only relatively mild phenotypes . One explanation for this observation is that some HATs perform functionally redundant roles in the cell . Alternatively, examination of the HAT activity profiles of fractionated extracts derived from HAT deletion strains presented here suggests that there may be mechanisms that can compensate for the lack of one histone acetyltransferase by increasing the activity of other HAT complexes. For example, in a Δ sas2 strain, there is a dramatic increase in an activity present in nuclear extracts that acetylates free histone H4 and which elutes from a DEAE column at a salt concentration similar to that of the nuclear form of Hat1p . In addition, deletion of the HAT1 gene causes a large increase in an activity that is coincident with the HatB3.1 activity. These results suggest the possibility that cells may monitor levels of histone modification and adjust specific HAT activities accordingly.
|
15274751_p25
|
15274751
|
Discussion
| 4.403438 |
biomedical
|
Study
|
[
0.99943608045578,
0.00035215308889746666,
0.00021174612629693002
] |
[
0.9982094764709473,
0.0005040377145633101,
0.001159673323854804,
0.00012689930736087263
] |
en
| 0.999997 |
HatB3.1 is the third native HAT complex identified from yeast that is only capable of acetylating free histones . In addition to the histone H4 specific Hat1p/Hat2p complex, the SAS complex, composed of Sas2p, Sas4p and Sas5p, was recently shown to acetylate free histones H3 and H4. The potential classification of the SAS complex as a type B HAT is supported by the fact that the SAS complex has also been shown to be physically associated with the histone deposition proteins Cac1p and Asf1p . However, the specific target of SAS complex acetylation, histone H4 lysine 16, has not been found to be acetylated in the pool of newly synthesized histones in any organism . Therefore, it remains to be determined whether the SAS complex participates in the acetylation of newly synthesized histones H3 and H4 prior to histone deposition or whether it is involved in the post-assembly modification of histones.
|
15274751_p26
|
15274751
|
Discussion
| 4.42413 |
biomedical
|
Study
|
[
0.9995102882385254,
0.0002528578625060618,
0.0002368979767197743
] |
[
0.9968217611312866,
0.0007874720613472164,
0.0022746571339666843,
0.00011611492664087564
] |
en
| 0.999998 |
Gcn5p is the prototypical type A histone acetyltransferase. While rGcn5p is only capable of acetylating free histones under most experimental conditions, it has been identified as the catalytic subunit of five native HAT complexes that acetylate nucleosomal substrates (SAGA, ADA, A2, SLIK and SALSA) . The most straightforward interpretation of the dependence of the HatB3.1 activity on a functional GCN5 gene and the co-elution of Gcn5p with highly purified HatB3.1 is that Gcn5p is also the catalytic subunit of HatB3.1. In the context of the type A HAT complexes, the Ada2p, Ada3p and TAF II 68 proteins have been shown to be important for expanding the substrate specificity of Gcn5p to allow for the acetylation of nucleosomal histones . Hence, the ability of Gcn5p to acetylate histones in chromatin is a property that must be conferred upon it by association with other proteins. The identification of Gcn5p as a component of a type B histone acetyltransferase activity suggests that classification as either type A or type B may not be an inherent property of an enzyme but, rather, may be a function of the association of the enzyme with specific accessory factors.
|
15274751_p27
|
15274751
|
Discussion
| 4.792706 |
biomedical
|
Study
|
[
0.9990272521972656,
0.0005558878183364868,
0.0004168325394857675
] |
[
0.9863449335098267,
0.0019602857064455748,
0.011270107701420784,
0.0004247926117386669
] |
en
| 0.999998 |
Several properties of HatB3.1 indicate that it is distinct from previously identified Gcn5p-containing complexes. First, HatB3.1 is the only native Gcn5p-containing complex that does not have detectable activity on nucleosomal substrates. Second, the apparent molecular weight of HatB3.1 (~500 kDa), as determined by size exclusion chromatography, is much lower than that of the SAGA, ADA, SALSA and SLIK complexes but is similar to that reported for the A2 complex . However, unlike HatB3.1, the A2 complex is both dependent upon, and co-purifies with, Ada2p. These results clearly distinguish HatB3.1 as a novel Gcn5p-containing HAT complex .
|
15274751_p28
|
15274751
|
Discussion
| 4.293581 |
biomedical
|
Study
|
[
0.9994714856147766,
0.0002209168887929991,
0.0003076143912039697
] |
[
0.9990023970603943,
0.000535669329110533,
0.00038408287218771875,
0.00007791301322868094
] |
en
| 0.999997 |
Ada2p, Ada3p and Gcn5p form a module that provides the catalytic activity to their associated type A HAT complexes . In these complexes, there does not appear to be any direct physical interaction between Ada3p and Gcn5p but, rather, their association is mediated through Ada2p . The absence of Ada2p from the HatB3.1 activity suggests that Ada3p and Gcn5p can directly associate under certain circumstances or that another subunit(s) of the HatB3.1 complex can replace the function of Ada2p in bridging the interaction of Ada3p and Gcn5p. The identification of a Gcn5p-containing complex that is independent of Ada2p also suggests that there are cellular processes, such as histone deposition, that are influenced by Gcn5p (and Ada3p) but that do not require Ada2p. However, with the exception of the specific synthetic lethality seen with Δ gcn5 Δ sas3 mutants, deletions of the GCN5 , ADA2 and ADA3 genes have similar in vivo consequences . The absence of phenotypes unique to Δ gcn5 and Δ ada3 mutants may be the result of the complex functional redundancies observed in the assembly of chromatin. For example, Δ hat1 and Δ hat2 mutants only display phenotypes when combined with mutations in multiple lysine residues in the histone H3 NH 2 -terminal tail . Uncovering these redundancies and deciphering the potential role of Gcn5p in the acetylation of newly synthesized histones is likely to require the characterization of the complete set of complexes that display type B histone acetyltransferase activity.
|
15274751_p29
|
15274751
|
Discussion
| 4.628156 |
biomedical
|
Study
|
[
0.9990639090538025,
0.0004857909516431391,
0.00045033590868115425
] |
[
0.9966052770614624,
0.0008192473906092346,
0.00239370996132493,
0.00018179419566877186
] |
en
| 0.999997 |
In conclusion, we have fractionated yeast cytoplasmic and nuclear extracts and resolved several putative histone H3-specific type B histone acetyltransferase activities. One of these activities, HatB3.1, is highly specific for histone H3 that is free in solution. A combination of genetic and biochemical evidence indicates that HatB3.1 is a novel complex that depends on GCN5 and ADA3 but that is independent of ADA2 .
|
15274751_p30
|
15274751
|
Conclusions
| 4.223894 |
biomedical
|
Study
|
[
0.9995436072349548,
0.0002444491838105023,
0.0002118072734447196
] |
[
0.9990505576133728,
0.00046654214384034276,
0.0004009924305137247,
0.00008193369285436347
] |
en
| 0.999996 |
UCC1111 was used as the wild type yeast strain that serves as the genetic background for all deletion strains . Null mutants for GCN5 , SAS3 , SAS2 , ADA2 , ADA3 and HAT1 were constructed using PCR-mediated gene disruption with the HIS3 reporter gene .
|
15274751_p31
|
15274751
|
Yeast strains
| 3.952144 |
biomedical
|
Study
|
[
0.9992795586585999,
0.0002168990031350404,
0.0005035546491853893
] |
[
0.9945586323738098,
0.0049131219275295734,
0.0004221227718517184,
0.00010616281360853463
] |
en
| 0.999995 |
Cells were grown to mid-log phase in 1% yeast extract, 2% peptone, 2% glucose and 50 μg/mL ampicillin at 30°C. Cells were harvested at 4000 × g, 10', 4°C and total grams of cells recorded. All buffers contain 1.0 mM PMSF. Spheroplasts were prepared essentially as described previously using 0.25 mg of Zymolyase (U.S. Biologicals) per gram of cells for spheroplasting . Spheroplasts were burst in 0.5 mL/g cells Lysis Buffer (18% Ficoll 400, 10 mM HEPES [pH 6.0]) followed by dilution in 1.0 mL/g cells Buffer A (50 mM NaCl, 1.0 mM MgCl 2 , 10 mM HEPES [pH 6.0]). Supernatant from a 1500 × g, 15' spin at 4°C was retained as a cytosolic extract. Pelleted material was washed once with Buffer A then resuspended in DN (DN buffers contain 25 mM Tris [pH 7.5], 10% glycerol, 0.1 mM EDTA and mM [NaCl] listed in parentheses). Supernatant from another 1500 × g spin as above yielded the nuclear extract. This extract was dialyzed O/N at 4°C into DN(0) to a conductivity similar to that of the cytosolic extract. Extracts were cleared by high speed centrifugation (~30,000 × g) prior to their chromatographic fractionation.
|
15274751_p32
|
15274751
|
Extract preparation
| 4.205559 |
biomedical
|
Study
|
[
0.9994283318519592,
0.0003416410181671381,
0.00022992298181634396
] |
[
0.9970405697822571,
0.002343492116779089,
0.000484388874610886,
0.00013159979425836354
] |
en
| 0.999997 |
All columns were equilibrated with and run using DN Buffers. HPLC (ÄKTA purifier – Pharmacia) was employed for all column runs.
|
15274751_p33
|
15274751
|
Extract fractionation
| 2.321194 |
biomedical
|
Study
|
[
0.9895830154418945,
0.00117387343198061,
0.00924304872751236
] |
[
0.5280660390853882,
0.4677458107471466,
0.0024027128238230944,
0.0017855028854683042
] |
en
| 0.999998 |
DEAE – Cleared extracts were loaded onto a HiPrep 16/10 DEAE FF column (Pharmacia). Following a 5 C.V. wash with DN(50), proteins were eluted with a linear, 20 C.V., salt gradient from 50 mM to 1.0 M NaCl. A flow rate of 1.0 mL/min. was used and 3.0 mL fractions were collected.
|
15274751_p34
|
15274751
|
Anion and cation exchange chromatography
| 4.011676 |
biomedical
|
Study
|
[
0.999062716960907,
0.0004113384347874671,
0.0005259819445200264
] |
[
0.9434030652046204,
0.05521786957979202,
0.0008752549765631557,
0.000503777468111366
] |
en
| 0.999997 |
CM – Either pooled peak fractions, dialyzed into DN(0) until at similar conductivity as DN(50) start buffer, or Flowthrough from the DEAE were loaded onto a HiPrep 16/10 CM FF column (Pharmacia). The column was washed and proteins eluted as described above.
|
15274751_p35
|
15274751
|
Anion and cation exchange chromatography
| 3.814014 |
biomedical
|
Study
|
[
0.9988208413124084,
0.00039782418753020465,
0.0007813393021933734
] |
[
0.8807613253593445,
0.11755254864692688,
0.0010844268836081028,
0.0006017544073984027
] |
en
| 0.999995 |
Mono Q – The flowthrough fraction from the CM column was loaded onto a Mono Q HR 5/5 column (Pharmacia). Following a 5 C.V. wash with DN(50), a 20 C.V., linear, salt gradient was employed as above and 0.5 mL fractions were collected.
|
15274751_p36
|
15274751
|
Anion and cation exchange chromatography
| 3.66135 |
biomedical
|
Study
|
[
0.9978812336921692,
0.0006719859666191041,
0.0014467370929196477
] |
[
0.7740129232406616,
0.22396139800548553,
0.0011447106953710318,
0.0008809976861812174
] |
en
| 0.999996 |
Peak fractions of HatB3.1 activity from the Mono Q column were pooled and brought to 75% (NH 4 ) 2 SO 4 (0.516 g/mL) over 30' at 4°C. Following an additional 30' equilibration period at 4°C, precipitated protein was pelleted (10,000 × g, 10', 4°C) and resuspended in 300 μL cold, DN(0).
|
15274751_p37
|
15274751
|
Ammonium sulfate precipitation
| 4.051032 |
biomedical
|
Study
|
[
0.999448835849762,
0.00024301746452692896,
0.00030806363793089986
] |
[
0.994708776473999,
0.004819158930331469,
0.00030655492446385324,
0.0001655860833125189
] |
en
| 0.999996 |
A 250 μL aliquot of resuspended ammonium sulfate precipitate was loaded onto a Superose 6 HR 10/30 column (Pharmacia). The column was equilibrated with and run in DN(350) at a flow rate of 0.3 mL/min. and 0.25 mL fractions were collected. Molecular weight standards were run using the same parameters and 24 μL aliquots of every other fraction run on a 10% SDS-polyacrylamide gel. The elution profile of the MW standards was determined by protein visualization via Coomassie blue staining.
|
15274751_p38
|
15274751
|
Gel filtration chromatography
| 4.098212 |
biomedical
|
Study
|
[
0.9994775652885437,
0.0002515126543585211,
0.00027093992684967816
] |
[
0.9966691136360168,
0.0029312216211110353,
0.0002956210810225457,
0.00010406741057522595
] |
en
| 0.999997 |
Chicken erythrocyte core histones and chromatin were isolated as previously described . Typically 10 μL aliquots of column fractions were incubated with 0.1 μM 3 H-Acetyl Coenzyme A (5.50 Ci/mmol, Pharmacia) and ~1.0 mg/mL core histones or chromatin in a final volume of 100 μL at 1X [DN(75)]. 50 μL of each reaction was analyzed for HAT activity via liquid scintillation counting. The remaining assay mixture was brought to 1X [SDS Load Dye] to stop the reaction. In general, aliquots (24 μL) of these remaining assay mixtures were run on 18% SDS-polyacrylamide gels to resolve the histones. Gels were incubated in Autofluor (National Diagnostics), dried down and acetylated histone species visualized via fluorography.
|
15274751_p39
|
15274751
|
Liquid HAT assays
| 4.148852 |
biomedical
|
Study
|
[
0.9995942711830139,
0.00021562137408182025,
0.00019019213505089283
] |
[
0.9984663724899292,
0.0009795567020773888,
0.00046881349408067763,
0.00008527279715053737
] |
en
| 0.999998 |
Superose 6 fractions exhibiting HAT B3 activity, as determined above, were run on 10% SDS-polyacrylamide gels and proteins were either visualized by silver staining or transferred to nitrocellulose using a semi-dry transfer apparatus (Biorad). Blots were processed following standard procedures. Goat, polyclonal antibodies against Gcn5p, Ada2p and Ada3p (Santa Cruz Biotechnology, Inc.) were used at 1:100 dilutions in 5% Milk/TBS-T. Donkey, HRP-labeled Anti-Goat IgG secondary antibody (Santa Cruz Biotechnology, Inc.) was used at 1:2500 dilution followed by detection with ECL+Plus (Pharmacia) and visualization via phosphoimager (STORM 860, Pharmacia).
|
15274751_p40
|
15274751
|
Western blot and gel analysis
| 4.099276 |
biomedical
|
Study
|
[
0.999530553817749,
0.000203973802854307,
0.00026543799322098494
] |
[
0.9946730732917786,
0.004768399056047201,
0.00043586172978393734,
0.00012272126332391053
] |
en
| 0.999997 |
A.R.S. performed all of the experiments presented here and drafted the manuscript. M.R.P. directed the project and edited the manuscript.
|
15274751_p41
|
15274751
|
Authors' contributions
| 0.761853 |
other
|
Other
|
[
0.08870390802621841,
0.0021914434619247913,
0.9091047048568726
] |
[
0.0038917423225939274,
0.994228720664978,
0.001238726545125246,
0.0006408860208466649
] |
en
| 0.999997 |
DNA base substitutions do not occur randomly . Instead, they may be clustered in hotspots, for example around methylated CG dinucleotides, or subject to more general biases such as the excess of transitions relative to transversions. In addition, local structural context may be important, with neighbouring bases interacting to favour some changes over others . However, many nonrandom patterns of sequence evolution remain unexplained. Here we explore how an abundant class of repetitive sequences, microsatellites, may influence the pattern of mutations in sequences that surround them.
|
15314644_p0
|
15314644
|
Introduction
| 4.148209 |
biomedical
|
Study
|
[
0.9994503855705261,
0.0002174914552597329,
0.00033221096964553
] |
[
0.9989302754402161,
0.0004567017895169556,
0.0005443515838123858,
0.0000686377243255265
] |
en
| 0.999997 |
Microsatellites are sequences of repeated 1–6-bp motifs that mutate primarily through the gain and loss of repeat units, in a process thought to depend on DNA replication slippage . Previous studies indicate that their flanking sequences evolve unusually and often contain mutated versions of microsatellites . Estimates of flanking sequence mutation rates vary greatly. Very slow evolution is suggested by sequence comparisons between distantly related species, where divergence rates may be as low as 0.016% to 0.1% per million years . Elsewhere, pedigree studies suggest much higher rates and even hypermutability . There is also disagreement about trends in mutation rate, some studies indicating an increase towards the microsatellite while others claim a more even distribution .
|
15314644_p1
|
15314644
|
Introduction
| 4.299973 |
biomedical
|
Study
|
[
0.9991057515144348,
0.0002788101264741272,
0.0006154346046969295
] |
[
0.8944002985954285,
0.0012740845559164882,
0.10406716912984848,
0.0002584717876743525
] |
en
| 0.999997 |
To our knowledge, no one has yet conducted a systematic study of mutational biases operating around microsatellites. The direct study of naturally occurring mutations in flanking sequences is virtually prohibited by their slow rate of accumulation, and inferences based on comparisons between homologous microsatellite loci rely on small numbers of sequences. However, an indirect approach is possible, based on comparisons among very large numbers of microsatellite flanking sequences from the finished human genome. If microsatellites have little or variable influence on their flanking regions, among-locus similarities will be minimal or absent. Conversely, if microsatellites generate similar local mutation biases, nonhomologous loci should betray evidence of convergent evolution. With the publication of large blocks of sequence from the chimpanzee genome, one can extend this approach to ask questions about rate of divergence between homologous flanking sequences.
|
15314644_p2
|
15314644
|
Introduction
| 4.194009 |
biomedical
|
Study
|
[
0.9994626641273499,
0.00017391002620570362,
0.00036338699283078313
] |
[
0.9982284903526306,
0.0004906143294647336,
0.0012226480757817626,
0.000058216093748342246
] |
en
| 0.999996 |
Here we use a combination of these indirect approaches to show that microsatellites appear to create regions around them in which both the rate and spectrum of mutations are modified.
|
15314644_p3
|
15314644
|
Introduction
| 3.628008 |
biomedical
|
Study
|
[
0.998313307762146,
0.0002848294097930193,
0.0014019542140886188
] |
[
0.997162401676178,
0.0023524751886725426,
0.000377317686798051,
0.0001078527930076234
] |
en
| 0.999999 |
We studied the most abundant class of human dinucleotide repeats, (AC) n , and for simplicity considered only ‘isolated’ repeats, defined as those at least 100 bp from the nearest AC repeat as small as two units in length. 47% of AC repeats on human Chromosome 1 match these criteria. From the human genomic sequence, maximum sample size was set at 5,000 randomly selected loci for length classes (AC) 2 to (AC) 5 . For longer microsatellites, of which fewer than 5,000 could be found, all sequences encountered were included. Figure 1 displays the length frequency distribution and sample sizes. Additionally, a control set of 5,000 randomly selected, non-microsatellite-associated sequences, each 50 bases long and containing no (AC) 2+ repeats, was generated from Chromosome 1.
|
15314644_p4
|
15314644
|
Results
| 4.108745 |
biomedical
|
Study
|
[
0.9994832277297974,
0.00021190578991081566,
0.00030473925289697945
] |
[
0.9995716214179993,
0.00016510886780451983,
0.0002169566141674295,
0.00004627950329449959
] |
en
| 0.999996 |
Subsets and Splits
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