Annex 1: Additional Results from the BSE-Like/Non BSE-Like Test

SE0241 Annexes to SID5

Annex 1: Additional Results from the BSE-like/non BSE-like Test

This annex contains further results from the BSE test, in addition to those given in Section 3 of the main text.

A1.1 IAH Data - analyses of single mouse lines

The distribution of sources by their incubation period and the first principal component of their relative lesion profile (73% of total variation) in RIII mice is shown in Figure A1.1. While one of the vCJD sources lies outside the 99% BSE-like prediction region, there is a degree of discrimination between the BSE-like (maximum LRS = 13.7) and non-BSE-like sources (minimum LRS = 22.1). Adding the second and third principal components from the relative lesion profile increased somewhat the discrimination between BSE-like (maximum LRS = 22.2, 4 d.f.) and non-BSE-like sources (minimum LRS = 63.5, 4 d.f.). An analysis based on relative lesion profiles alone, even when including the first 6 principal components (99% of total variation) failed to provide any discrimination between BSE-like (maximum LRS = 48.1, 6 d.f.) and non-BSE-like sources (minimum LRS = 10.9, 6 d.f.). An analysis of C57 mice alone produced a broadly similar degree of discrimination to that obtained with RIII mice (data not shown). Analyses of VM mice alone and of C57xVM mice alone provided poorer discrimination (data not shown).

Analysis of R0 mice only (IAH data) Relative lesion profile vs incubation period 0 2

) % 3 7 (

e 0 l i f 1 o r p

n o i s e l 0

e v i t a l e R 0 1 - 200 400 600 800 1000 1200 Incubation period (days)

Natural BSE Experimental BSE vCJD Scrapie FSE Kudu Nyala Mink

Contours indicate 90, 95 and 99% 'prediction' regions

Figure A1.1 Analysis of RIII Mice Only

A1.2 IAH Data – analysis of RIII and C57 mouse lines

Further analyses were performed to determine the extent to which combining data on RIII and C57 mice enhances our ability to discriminate between BSE-like and non-BSE-like sources. An analysis based on incubation periods alone provided some discrimination between BSE-like (maximum LRS= 18.1, 2 d.f.) and non-BSE-like sources (minimum LRS = 44.1), although one of the vCJD sources lay outside the 99% BSE prediction region (Figure A1.2). Including the first principal component of the relative lesion profile (Figure A1.3) also provided discrimination with all BSE-like sources lying within the 99% prediction region (maximum LRS = 8.1, 2 d.f., P = 0.02) while all non-BSE-like sources lie

of 58 SE0241 Annexes to SID5 well outside the region (minimum LRS = 16.6, 2 d.f., P = 0.0002). Including additional components of the relative lesion profile did not lead to any substantial improvement in discrimination.

Analysis of RIII + C57 mice (IAH data) Incubation periods 0 0 0 ) 1 s y a d 0 (

0 d 8 o i r e p 0

0 n 6 o i t a b u 0 c 0 n i 4

7 5 C 0 0 2 200 400 600 800 1000 1200 RIII incubation period (days)

Natural BSE Experimental BSE vCJD Scrapie FSE Kudu Nyala

Figure A1.2 Analysis of RIII and C57 Mice – Incubation Period

Analysis of RIII + C57 mice (IAH data)

% Incubation periods versus relative lesion profiles 4 6 ( 0

. 2 c . p

, 0 s 1 e l i f o r p

n o i 0 s e l

0 d 1 n - a

I I I .8 1 1.2 1.4 1.6 1.8 R C57:RIII incubation period ratio

Natural BSE Experimental BSE vCJD Scrapie FSE Kudu Nyala

Figure A1.3 Analysis of RIII and C57 Mice – IP Ratio and LP 1st Principal Component

of 58 SE0241 Annexes to SID5

A1.3 Analysis of VLA Data

Data were available on 419 transmissions into RIII, C57Bl or VM mice. In these analyses we focus on the transmissions into RIII and C57Bl mice and comparing the results with those from IAH. (Transmissions from natural BSE sources to VM mice were not performed.)

Type of source Number of transmission to: RIII mice C57Bl mice RIII and C57BL mice Natural BSE in cattle 52 36 27 Natural TSE in sheep 256 166 147 Experimental BSE in sheep 10 1 1 Experimental BSE in pigs 0 6 0 Mixed BSE/scrapie 4 4 4 Subpassaged source 37 37 36 Total 359 250 215

There were notable differences between the incubation periods observed in the VLA experiments and those observed at IAH for BSE sources. The median incubation period in RIII mice across the 52 natural BSE sources transmitted at VLA was 522 days. The typical incubation period in RIII mice at IAH is around 320 days. Similarly, the median incubation period for VLA natural BSE sources in C57Bl mice was 695 days compared with a figure of around 420 days at IAH.

Application of the results of IAH analyses to VLA data

Unsurprisingly, given the above, application of the IAH principal components analysis based on RIII mice alone to the VLA data resulted in serious misclassification of sources as BSE-like or not-BSE- like (Figure A1.4). The shift towards longer incubation periods for known BSE sources is evident, with most VLA BSE sources lying outside the IAH defined BSE-like region. At the same time a small number of natural sheep TSE sources lie inside the BSE-like region as do some sources undergoing subpassage.

Application of the IAH principal components analysis based on both RIII and C57 mice to the VLA data performs somewhat better (Figure A1.5), as the analysis is based on the ratio of incubation periods in C57 and RIII mice rather than the absolute length of the incubation periods. The majority, but by no means all, of the natural BSE sources lie within the IAH defined BSE-like area. However, several BSE sources lie well outside the area, while a number of natural sheep TSE sources lie inside or very close to the BSE-like area. The BSE sources lying outside the BSE-like area were from Experiment 1901, sources 641 (cervical spinal cord), 682 (thoracic DRG), 776 (frontal cortex), 864 (frontal cortex), 870 (trigeminal ganglia). Natural sheep sources lying within the BSE-like 99% prediction region were experiment number 1929 source 533 (spinal cord); experiment number 1945 sources 012, 014, 015, 016, 017, 023 (all brain), 0.29, 0.30 (rostral medulla); experiment number 1919 source 059 (brain); experiment number 1938, sources 068 and 073 (both brain). Increasing the number of principal components of the lesion profiles included in the analysis to as many as 5 does not resolve the problem with natural sheep sources still well inside the BSE-like region while BSE sources lie well outside.

Analysis of RIII mice (VLA data)

) Incubation period versus relative lesion profiles % 0 3 4 7 (

. c . 0 p

3 t s 1

, e 0 l i 2 f o r p

0 n 1 o i s e l

e 0 v i t a l e 0 r

1 I - I I

R 200 400 600 800 1000 RIII incubation period

Bovine BSE Sheep BSE-sheep Mixed Sub-passage

Figure A1.4 VLA Data with IAH PCs – RIII Data

Analysis of R0 + C57 mice (VLA data)

% Incubation periods versus relative lesion profiles 4 0 6 4 (

. c . p 0

t 3 s 1

, s 0 e l 2 i f o r p

0 n 1 o i s e l

0 7 5 C

0 d 1 n - a

R .8 1 1.2 1.4 1.6 1.8 C57:R0 incubation period ratio

Figure A1.5 VLA Data with IAH PCs – RIII and C57 Data

Internal analysis of VLA data

An internal analysis of the VLA data was performed using a similar approach to that applied to the IAH data. For this analysis, data from Experiment SE1901 (transmissions involving natural, cattle BSE sources) were used to define BSE-like areas. Data from other experiments were then tested against

of 58 SE0241 Annexes to SID5 these areas. The results of an analysis based on RIII mice only are shown in Figure A1.6. As expected the BSE sources used to define the BSE-like prediction region lie mostly within the region. One natural BSE source 1901/776, with data from only one mouse, lies outside the region. All nine of the experimental BSE in sheep sources with the required data lie within the BSE-like region but so too do many natural sheep TSE sources. Increasing the number of components of the lesion profile included in the analysis did not enable discrimination between BSE and natural sheep TSE sources.

Analysis of RIII mice (VLA data)

. c . p

t 0 s 2 1

, e l i f o 0 r 1 p

n o i s e l 0

e v i t a l e 0 r

1 I - I I

R 0 200 400 600 800 1000 RIII incubation period

Figure A1.6 VLA Data with VLA PCs – RIII Data

A similar analysis was performed based on C57BL mice alone. The results are shown in Figure A1.7. As expected the natural BSE sources lie inside the BSE-like prediction region as does the one experimental BSE in sheep source. However, many natural sheep TSE sources also lie within the BSE-like region. Increasing the number of lesion profile components used in the analysis up to 5 improves the situation but does not resolve this problem.

Analysis of C57 mice (VLA data)

. c . p

t 0 s 1 1

, e l i f o 5 r p

n o i s 0 e l

e v i t a 5 l - e r

C 0 500 1000 1500 C57 incubation period

Bovine BSE Sheep BSE-sheep Mixed Pig Sub-passage

Figure A1.7 VLA Data with VLA PCs – C57 Data

A similar analysis was performed based on both RIII and C57Bl mice. The results are shown in Figure A1.8. Again, as expected, the natural BSE sources lie inside the BSE-like prediction region with the exception of 1901/776, as does the one experimental BSE in sheep source. However, many natural sheep TSE sources also lie within the BSE-like region. Increasing the number of lesion profile components used in the analysis up to 5 not resolve the problem.

% Analysis of RIII & C57 mice (VLA data) 4 3

( Relative incubation period versus relative lesion profiles

. c . 0 p 3

, e l 0 i f 2 o r p

n o 0 i 1 s e l

e v i t 0 a l e r

7 5 0 C

1 - d n a

.5 1 1.5 2 2.5 I I

I C57:RIII incubation period ratio R Bovine BSE Sheep BSE-sheep Mixed Sub-passage

Figure A1.8 VLA Data with VLA PCs – RIII and C57 Data

Finally, an analysis was performed using only lesion profile data from RIII and C57Bl mice (Figure A1.9). Excluding the incubation period data does not resolve the problem of distinguishing BSE sources from presumed non-BSE sources.

Analysis of RIII & C57 mice (VLA data)

) Relative lesion profiles % 5 5 1 5 (

. c . p 0

t 1 s 1

, e l i 5 f o r p

n 0 o i s e l

e 5 - v i t a l e 0 r

1 7 - 5

C -20 -10 0 10 20 30 RIII relative lesion profile, 1st p.c. (62%)

Figure A1.9 Figure A1.8 VLA Data with VLA PCs – Lesion Profiles Only

Annex 2 Testing for Gaps between Clusters

As discussed in the main body of the text (Section 4.2.4) we have chosen to define clusters such that two distinct clusters must have a clear gap between them. This definition has to be backed up with an objective criterion for the existence of a gap. Here we describe the criterion adopted, based on an analysis of the proximity matrix.

For any two items, A and B, in a data set on which we are doing cluster analysis, we have a distance d(A, B) between them. Another name for these distances is “proximities” (i.e. “how close are they together” rather than “how far are they apart”). Suppose then we have two putative clusters 1 and 2. Let us start by looking at cluster 1, and take all the pairwise proximities between its own members. This will define a distribution of intra-1 proximities, ranging from those between nearest neighbours out to those at opposite ends of the clusters. If there are N1 members of cluster 1, the number of intra- 1 proximities will be N1(N1 – 1)/2 . The idea then is:

for there to be a gap between the clusters, the nearest 2-member to the 1-cluster must be significantly further away from the 1-cluster than the typical distance between near neighbours in the 1-cluster.

To turn this into a mathematical test, we have to make the terms precise:

nearest 2-member to the 1-cluster – to define this we look at all the proximities between 2- members and 1-members and find the smallest value; the 2-member with this value is defined to be the nearest to the 1-cluster;

distance between the nearest 2-member and the 1-cluster – this distance, which we call D21, we take to be the smallest value identified in the previous step, i.e. the distance between the nearest 2-member and the 1-member that is closest to it;

typical distance between near neighbours in the 1-cluster – to get a measure of this we take the first N1 proximities in the intra-1 distribution and express this as a percentile of the distribution (this value is 2/(N1 – 1) ).

We then express the distance D21 as a percentile of the intra-1 distribution. The larger this is compared to the percentile calculated in the third step above, the more significant is the gap between the clusters.

It is always possible that this nearest 2-member to the 1-cluster is something of an outlier from the 2- cluster that creates a “bridge” between the clusters. To look for that we eliminate this 2-member and repeat the process to find the second nearest 2-member, and then repeat this to find the third nearest. (This can be iterated further, but in the spreadsheet that automates this method we stop at the third nearest.) If they are all as close to their nearest 1-neighbour as 1-members are to their neighbours, we cannot say that there is a significant gap.

We then turn the test around and compare the distances of the three nearest 1-members to the 2- cluster with the intra-2 distribution. It is possible that a gap can seem large on one of these tests and not on the other. This can happen when one of the clusters (say cluster 1 is more diffuse than the other. The distance between the two nearest inter-cluster neighbours may be large compared with the distances between intra-2 neighbours, but may be comparable to the distances between intra-1 neighbours.

We now illustrate this method using the examples referred to in the main text.

A2.1 V Cluster Substructure

The first example of this analysis is for the supposed V1 and V2 subclusters in the data displayed on Figure 4 in the main text.

closest V2 members from V1 closest V1 members from V2

1st 2nd 3rd 1st 2nd 3rd item 1919/008 1919/027 1919/058 item 1919/012 1919/039 1919/073 distance 0.920 1.222 1.314 distance 0.920 1.222 1.269 %ile of V1 intras 3.0% 11.7% 14.7% %ile of V2 intras 4.4% 17.8% 20.0%

compare with 1st n1 9.5% compare with 1st n2 22.2% Table A2.1 Proximity Analysis for V1 and V2

The left hand side of the table identifies the three V2 members closest to the V1 clusters, and compares the distances with the distance distribution within V1. The shortest distance across the divide is considerably smaller than typical distances within V1. The second and third are somewhat larger than the first n1 intra-V1 distances, but not substantially so. All of the three distances from the nearest V1 members to V2 are within the first n2 distances within V2. In other words, distances across the supposed gap are typical of the shorter distances within V2. From this we can conclude that there is no objective gap separating V1 and V2, and that therefore they should not be considered as distinct clusters.

A2.2 BSE Substructure

Next we examine the apparent gap between the two BSE subclusters in Figure 5 in the main text, BSE1 (mostly brainstem or whole brain) and BSE2 (other brain, spinal cord, ganglia). The results of the analysis of proximities are as follows

closest BSE2 members from BSE1 closest BSE1 members from BSE2

1st 2nd 3rd 1st 2nd 3rd item 1901/734 1901/868 1901/887 item 1901/184 1901/188 1901/187 distance 0.474 0.643 0.731 distance 0.474 0.588 0.635 %ile of BSE1 intras 8.3% 23.3% 35.0% %ile of BSE2 intras 3.6% 7.5% 12.3%

compare with 1st n1 13.3% compare with 1st n2 9.1% Table A2.2 Proximity Analysis for BSE1 and BSE2

Only one of the BSE2 members has a distance from BSE1 comparable with the typical intra-BSE1 distances. This is the inoculum 1901/734, already identified by visual inspection as a potential “bridging point”. Two of the BSE1 members are closer to BSE2 than the typical intra-BSE2 distance, but in both cases, these are distances to 1901/734. This suggests that when this one point is omitted, a well-defined gap should remain. This is borne out on Table A2.3.

closest BSE2 members from BSE1 closest BSE1 members from BSE2

1st 2nd 3rd 1st 2nd 3rd item 1901/868 1901/887 1901/736 item 1901/188 1901/187 1901/190 distance 0.643 0.731 0.846 distance 0.643 0.790 0.796 %ile of BSE1 intras 22.7% 37.9% 51.5% %ile of BSE2 intras 13.9% 35.9% 36.8%

compare with 1st n1 18.2% compare with 1st n2 9.5% Table A2.3 Proximity Analysis for BSE1 and BSE2, Omitting 1901/734

Annex 3 The Absence of A-Cluster Members in SE1938

As noted in the main body of the text (end of Section 4.2.3), there are no SE1938 inocula with strain typing signals near the A-cluster. This is demonstrated on Figures A3.1 and A3.2 below. In these principal components analyses, the reference clusters were put together with the SE1938 results, for C57 and RIII mice respectively. The SE1938 points are distinguished according to whether the donor was an x/V (i.e. either A/V or V/V) sheep or an A/A sheep. To get as full a coverage as possible, even inocula with only one “full mouse” in the panel have been included.

2 V vla V iah 1 A vla 2

c A iah p BSE iah 0 1938 x/V 1938 A/A -1

-2 0 1 2 3 4 5 6 7 8 pc1

Figure A3.1 Reference Clusters and SE1938 Scrapies – in C57 Mice

3 V vla 2 V iah A vla

2 A iah c 1 p BSE vla BSE iah 0 1938 x/V 1938 A/A -1

-2 0 1 2 3 4 5 6 7 8 9 pc1

Figure A3.2 Reference Clusters and SE1938 Scrapies – in RIII Mice

The SE1938 points lie on or around the V cluster, and none are unequivocally members of the A cluster. There is more scatter in the SE1938 points than in those from the reference V clusters, but that is only to be expected, given that we are including here inocula with attack numbers as low as one mouse. An analysis of the RIII results shows that, indeed, the points furthest from the V cluster all correspond to attack numbers of four mice or less.

Since A-cluster signals were an important feature of the SE1919 results, and were also found in the IAH data, the question is: why are there no A-cluster members in the extensive body of natural scrapie data provided by experiment SE1938? Based on the earlier data we would have expected around half the A/A inocula (of which there are 9 in C57 and 11 in RIII for SE1938) to be close to the A-cluster, but in fact none are seen.

There were three differences in experimental design between SE1919 and SE1938, which might be relevant to this result.

 The SE1919 sheep were collected between 1996 and 1999, whereas the SE1938 sheep were collected between 1998 and 2003.

 In SE1919 the sheep were mostly collected individually from separate farms, whereas SE1938 had most sheep in same-flock groups.

 In SE1919, the inocula were prepared from a section of brain stem rostral to the severed medulla, comprising the rostral medulla oblongata and pons (tissue code 7). In SE1938, eight of the 143 inocula were mixtures of tissue from five different brain areas (each from the same sheep), namely areas coded 1, 2, 4, 5 and 6. Only one was from a single brain area (area 5), and the rest were mixtures of three areas (2, 4 and 5).

One possibility is that the A-cluster scrapie had died out by the time SE1938 began. To test this, the SE1919 inocula were ordered as a time-series by means of the sheep PG numbers. The result was that the A-cluster cases are distributed across the time-range, except for the earliest times. There were four A-cluster cases in 1998 and 1999, when SE1938 was collecting sheep, including A/A sheep. A date effect therefore is implausible. A strain would not disappear in such a short period of time.

Because the SE1938 sample was made up of same-flock groups, the suggestion was made that the A/A sheep might have been in contact with x/V sheep in the same flock. This might then have predisposed them to have scrapie that gives rise to the V cluster, because they were more likely to have caught the disease from their x/V neighbours. When the data on the genotype and ownership of the sheep was examined, the same-flock groups were found to be uniform with respect to genotype. Five of the 14 groups had only A/A sheep, and the remaining eleven groups had only x/V sheep. No group had A/A and x/V mixed together. This does not of course preclude the possibility that the samples came from mixed flocks.

Attack rates in the SE1938 inoculations tended to be low. Moreover the A/A inocula gave disproportionately low attack rates. This is shown in Figure A3.3 below, which gives the distributions of the sum of the C57 and RIII attack numbers (defined as the number of “full mice” in the sub-panel).

8 AA . o 6 AV N VV 4

0 0 2 4 6 8 10 12 14 16 18 20 22 24 C57 + RIII Attack No.

Figure A3.3 Attack Number Distributions for Different Donor Genotypes

The fact that all but one of the SE1938 inocula were mixtures of different brain parts might explain the lower attack rates seen in the experiment. If the other brain parts had lower levels of infectivity than the brain stem, mixing them together may have effectively diluted the inocula. But it is less easy to see how the mixing could have prevented the manifestation of the A-cluster. These inocula were characterised in SE1919 by lower incubation periods and higher attack rates, so it might be thought that the A-cluster would be less affected by dilution rather than more. Any idea that for some reason mixtures might act against the appearance of A-cluster behaviour is at odds with the fact that the scrapie brain pool/BSE mixtures all gave results close to the A-cluster. We have to conclude that at present the absence of A-cluster items from the SE1938 data set remains unexplained.

Annex 4: Membership of the Reference Clusters

The members of the reference clusters for C57 and RIII mice are listed below, in Tables A4.1 and A4.2. Inoculum references in bold on both tables are those for which there is data for both C57 and RIII. These inocula are therefore members of the reference clusters for the joint C57/RIII test.

As pointed out in Section 4.3.1, because the division of the scrapie results into A and V is based purely on cluster analysis on strain typing data, there is here a danger that then using cluster methods for the membership tests results in a circular argument. This is a generic problem with cluster analysis. One can have cases where the answer to the question “is x a member of cluster A?” is “it depends on whether x is a member of cluster A or not”. Let us call the cluster without x “A” and the cluster with x included “A+”. Then for a given membership test, there are three outcomes.

(i) x passes the membership test for A (and therefore for A+ as well) – even if we initially exclude it, it manages to get in;

(ii) x passes the membership test for A+ but fails for A – it gets in if and only if we have included it in the first place;

(iii) x fails the membership test even for A+ – even if we try to let it in, it still fails the test, and A+ is not a well-formed cluster, as defined by the test.

The possibility of outcomes (i) and (iii) implies that circularity is not inevitable. This still leaves a problem with what to do with the “circular” case. What is really happening here is that there are two well-defined clusters, A, of which x is not a member, and A+, of which it is a member. One then has to make a choice which is the most meaningful entity, or indeed work with both, testing a new candidate against both A and A+ separately, with the possible outcome that it is not a member of the “inner circle”, A, but is a member of the A+ cluster, presumably because it is rather like x.

BSE A-Cluster Scrapie V-Cluster Scrapie IAH 200A-1A VLA 1919/019 VLA 1919/005 200B-1A 1919/031 1919/008 200C-1A 1919/042 1919/011 200D-1A 1919/053 1919/012 304A-1A 1919/055 1919/014 305A-1A 1919/063 1919/015 334A-1A 1919/069 1919/016 606A-1A 1919/072 1919/018 607A-1A 1919/082 1919/020 608A-1A 1919/089 1919/021 646A-1A 1919/090 1919/022 IAH 261A-1A 1919/027 275A-1A 1919/035 276A-1A 1919/039 1919/043 1919/048 1919/049 1919/050 1919/051 1919/057 1919/058 1919/067 1919/073 1919/075 1919/077 1919/079 1919/081 1919/084 1919/086 1919/087 IAH 201A-1A 214A-1M 220A-1A 221A-1A 223A-1A 231A-1A 241A-1A 242A-1A 257A-1A 258A-1A 259A-1A 265A-1A 266A-1A 267A-1A 268A-1A 269A-1A 270A-1A 271A-1A 273A-1A Table A4.1 Members of the C57 Reference Clusters

BSE A-Cluster Scrapie V-Cluster Scrapie VLA 1901/119 VLA 1919/019 VLA 1919/005 1901/184 1919/031 1919/008 1901/185 1919/042 1919/011 1901/186 1919/053 1919/012 1901/187 1919/055 1919/014 1901/188 1919/063 1919/020 1901/189 1919/069 1919/022 1901/190 1919/072 1919/027 1901/215 1919/082 1919/035 1901/279 1919/089 1919/038 1901/280 1919/090 1919/039 1901/281 IAH 261A-1A 1919/043 1901/304 275A-1A 1919/048 1901/368 276A-1A 1919/049 1901/391 1919/050 1901/964 1919/051 IAH 200A-1A 1919/057 200B-1A 1919/058 200C-1A 1919/067 200D-1A 1919/073 304A-1A 1919/075 305A-1A 1919/077 334A-1A 1919/079 606A-1A 1919/081 607A-1A 1919/083 608A-1A 1919/084 646A-1A 1919/086 1919/087 IAH 201A-1A 214A-1M 220A-1A 221A-1A 223A-1A 241A-1A 242A-1A 257A-1A 258A-1A 259A-1A 265A-1A 266A-1A 267A-1A 268A-1A 269A-1A 270A-1A 271A-1A 273A-1A 274A-1A 277A-1A Table A4.2 Members of the RIII Reference Clusters

Annex 5: Calculation of Shift Vectors

The first decision to be made concerning the derivation of a shift vector that aligns the clusters in the data from one laboratory with those from the other is whether the shift should be applied to the incubation periods (IPs) as well as the lesion scores. To be valid, the shift should be the same across all inocula. There are differences between the BSE IPs for the two laboratories (see Error: Reference source not found in the main text), but there is no single shift relating one distribution to the other. Instead the VLA distribution is not just shifted to higher values, it is also much wider. This could be due to variability in the titre of the individual inocula, rather than a systematic effect influencing all inocula from the laboratory.

By contrast when the IP distributions for the scrapie results are plotted – see Figure A5.1 below – we see there is no evidence of a systematic difference between the laboratories. The absence of a significant difference is confirmed by t-tests of the distribution means. Moreover in the VLA mouse subpassage results, the IPs found for the ME7 strain reproduce accurately those found in the work on strains done at the IAH. This re-inforces the conclusion that there is no systematic difference between the IPs as measured in the two laboratories. For this reason we confine the inter-laboratory shift to the lesion scores.

Scrapie IP distributions

0.20 vla A n o i

t iah A

c 0.15 a

r vla V f 0.10 iah V

Figure A5.1 Scrapie IP Distributions by Cluster and by Laboratory

We then want a shift vector for the lesion scores that simultaneously optimises the alignment of the three clusters, so that the data from the two laboratories can be combined in a single test of cluster membership. We do not want to arrive at the best possible alignment for one cluster and be left with bad alignments for the other two. With this in mind we chose the following procedure, applied separately to the C57 and RIII data sets.

1. The centroid of each cluster for each laboratory (that is, the multi-dimensional mean) is calculated by averaging over all the mouse results contributing to the cluster. This is done in preference to averaging over the inoculum means, because this latter would give undue weight to the inocula with low attack rates.

2. An overall centroid for each laboratory is then calculated by averaging over the centroids of the three clusters for that laboratory. This is done to give equal weight to each cluster, regardless of how many inocula or mice it contains. If the overall centroid were instead calculated using all the mice or all the inocula means, then the clusters with the most members (the scrapie V-cluster)

would be most heavily represented and the resulting shift vector would be best for it and worse for the other clusters. Taking the mean of cluster centroids meets the objective of simultaneous optimisation.

3. The shift vector is then the difference between the VLA and IAH centroid vectors.

In practice for the C57 data set we have to deviate from this procedure because there is no VLA BSE cluster (because all the natural bovine BSE results belonged to the BSE2 subcluster and therefore were excluded from the standard cluster). The shift vector is instead the difference between the centroid of the two scrapie cluster centroids.

Table A5.1 below shows the resulting shift vectors. They are compared with the vectors that would be used to align the cluster centroids separately. This comparison shows how well the hypothesis of a single shift across all the data works.

LS1 LS2 LS3 LS4 LS5 LS6 LS7 LS8 LS9 RIII overall shift 0.69 0.86 0.77 1.09 0.60 -0.16 0.55 -0.07 0.15

BSE1 0.13 0.81 1.25 0.80 0.58 -0.09 0.42 0.05 0.03 A 1.21 0.99 0.66 1.65 0.96 -0.36 0.63 -0.27 0.18 V 0.72 0.78 0.39 0.81 0.27 -0.04 0.61 0.01 0.23

C57 overall shift 0.72 0.62 0.53 0.57 0.33 -0.29 -0.12 -0.28 0.12

A 0.89 0.98 0.67 0.84 0.63 -0.27 -0.19 -0.22 0.30 V 0.55 0.26 0.39 0.31 0.02 -0.31 -0.05 -0.34 -0.06 Table A5.1 Shift Vectors

These overall shift vectors are added to the lesion scores of all the IAH data to produce unified C57 and RIII data sets. The pc1/pc2 scatterplots of the resulting reference clusters are shown in Figures A5.2 and A5.3 below.

V vla 0 V iah 2

c A vla p A iah -1 BSE iah

-3 1 2 3 4 5 6 7 pc1

Figure A5.2 Reference Clusters for C57 Data (with shift)

4 V vla V iah

2 A vla c 3 p A iah BSE vla 2 BSE iah

0 1 2 3 4 5 6 7 pc1

Figure A5.3 Reference Clusters for RIII Data (with shift)

The principal components on these two figures are: pc1(C57) = 0.18×(IP/200 days) + 0.28×LS1 + 0.42×LS2 + 0.25×LS3 + 0.40×LS4 + 0.34×LS5 + 0.41×LS6 + 0.28×LS7 + 0.24×LS8 + 0.26×LS9 pc2(C57) = 0.33×(IP/200 days) + 0.44×LS1 + 0.19×LS2  0.08×LS3 + 0.42×LS4  0.01×LS5  0.33×LS6  0.42×LS7  0.30×LS8  0.33×LS9 pc1(RIII) = +0.24×(IP/200 days)  0.02×LS1 + 0.18×LS2 + 0.22×LS3 + 0.00×LS4 + 0.35×LS5 + 0.47×LS6 + 0.47×LS7 + 0.38×LS8 + 0.39×LS9 pc2(RIII) = 0.30×(IP/200 days) + 0.45×LS1 + 0.32×LS2  0.12×LS3 + 0.67×LS4 + 0.12×LS5  0.07×LS6 + 0.27×LS7  0.20×LS8  0.04×LS9

In the RIII case, where we are trying simultaneously to align three clusters with only one shift, we see that the alignment along the pc1 axis is good (as it is along the pc3 axis – not shown here). The pc2 alignment is not so good. The optimal alignment of the BSE clusters would have the IAH data shifted to lower pc2 values, while that for the A Cluster would have them shifted upwards.

The significance of these shift vectors – why there are these differences in the mean lesion profiles for the reference clusters – is not currently understood. Although the shifts are broadly similar for the two mouse strains, there are also some differences, particularly in grey matter areas 4 and 7. A constant shift across all areas is a bad approximation to the shifts. Typically the shifts are larger in areas 1 to 4 and smaller in areas 6 to 9. Without these shifts it is not possible to combine the data from the two laboratories into a single data set with a coherent cluster structure, against which diagnostic membership tests could be made.

Annex 6: Statistical Basis of the Tests against Reference Clusters

The cluster membership tests described in Section 4.4 in the main text are derived from the multivariate T-test method (Morrison, 1990). These are the multivariate generalisation of the t-tests used to investigate whether two single-variate sample means are consistent with coming from distributions with the same mean. This annex first describes the standard method, then the generalisation developed in the current project.

A6.1 The T-Test Method

Suppose we have two samples I, and J of items each characterised by a p-dimensional vector of variables. (In what follows, variables in lower-case bold will denote (p×1) vectors and in upper case bold (p×p) matrices.) Assuming the two samples have the same underlying covariance matrix , we want to test the null hypothesis that the underlying mean vectors I and J are the same. The test is the multi-dimensional analogue of the familiar one-dimensional t-test for the sameness of means of two samples, given sameness of variance.

Let the numbers of items in the two samples be NI and NJ respectively, and let the vectors associated with the items be:

xi for i = 1, … NI and xj for j = 1, …, NJ

The sample estimate of the mean vector for I is

1 N I  x I    xi N I i1

and similarly for sample J.

To estimate the covariance matrix, we first define for I the matrix

T A I  (xi   x I )((xi   x I ) iI and similarly for J. Here the superscript T denotes matrix transpose. Expressed in terms of the components of the matrix:

m, n = 1, … , p this form of the matrix product is

AI mn  (xi m   xI m )((xi n   xI n ) iI

The estimator for the covariance matrix based on the pooled samples is then

1 S  (A I  A J ) (N I  N J  2)

To test the null hypothesis that the means of the two populations from which the two samples have been taken are the same, we form the T2-statistic:

2 N I N J T 1 T  ( x I    x J ) S ( x I    x J ) (N I  N J )

If the distribution underlying the two samples is multinormal, and if the null hypothesis is true, then the statistic

N I  N J  p 1 2 FI J  T (N I  N J  2) p

will be distributed as the F-distribution with degrees of freedom p and NI + NJ – p – 1. The null hypothesis can be tested using a one-tail F-test on the value of F derived from the values from the two samples. In other words the P-value derived from the test is the area under the appropriate F- distribution for the range F > FIJ.

These formulae show what happens when the tests are applied to inocula with low attack rates.

Suppose I is the reference cluster and J is the test inoculum. The test inoculum attack rate is NJ, and as this decreases with all else being kept constant, we see that T2 decreases approximately linearly 2 with NJ. The effect of low attack rate is therefore to make the test inoculum “closer” (in the T sense) to the reference cluster, making it harder to rule out membership of it.

A6.2 Application of T-Tests to Strain Typing

Suppose it were the case that when comparing each reference cluster member with the cluster formed from all the other members of its own cluster using the T-test, the null hypothesis was not rejected. We could then say that the differences between individual member means and the cluster mean were not significant. In spite of all the potential differences between the inoculum donors, the inoculum mean dependent only on whatever it is about the infective agent that determines cluster membership.

Given the differences between donors it is surprising how many of the inocula pass this test. This is illustrated inTable A6.1 below, which shows, for the C57 reference clusters, the P-values of the T- tests of each inoculum against (the rest of) its own cluster. For example six of the eleven BSE cluster members pass the test.

If the test were perfect, then the T-test could be used as the test of cluster membership. Each test inoculum could be tested against a reference cluster, and if and only if the null hypothesis was not rejected, then the test inoculum could be accepted as a member. However the T-test results are not good enough for that. The T-test criterion is more strict than cluster membership. For example, inoculum 200A-1A is, according to the cluster analysis methods described earlier, a valid member of the BSE cluster, and yet the P-value from the T-test is 3 × 10-6. If a test inoculum had a P-value close to this, then we would not want to reject it from cluster membership. Rather we would want to accept it, on the grounds that there was a bona fide member of the cluster with a similar value. This motivates the development of membership tests based on the distributions of the T2-statistic derived from the values seen with cluster members. These are the subject of the following section.

BSE Cluster V Cluster A Cluster 200A-1A 2.78E-06 1919/005 0.352 1919/019 0.063 200B-1A 3.69E-05 1919/008 0.285 1919/031 2.36E-03 200C-1A 0.101 1919/011 0.382 1919/042 0.909 200D-1A 0.109 1919/012 0.047 1919/053 0.018 304A-1A 9.26E-04 1919/014 2.79E-06 1919/055 0.260 305A-1A 0.408 1919/015 0.235 1919/063 1.29E-09 334A-1A 2.03E-03 1919/016 0.384 1919/069 0.374 606A-1A 0.132 1919/018 0.349 1919/072 0.077 607A-1A 0.582 1919/020 0.229 1919/082 0.871 608A-1A 0.118 1919/021 1.69E-03 1919/089 0.016 646A-1A 7.49E-04 1919/022 0.066 1919/090 0.315 1919/027 0.120 261A-1A 8.45E-03 1919/035 0.114 275A-1A 0.026 1919/039 0.040 276A-1A 6.26E-05 1919/043 0.365 1919/048 2.03E-03 1919/049 0.224 1919/050 2.07E-03 1919/051 0.276 1919/057 0.335 1919/058 0.091 1919/067 0.156 1919/073 0.172 1919/075 0.038 1919/077 3.22E-03 1919/079 0.010 1919/081 0.156 1919/084 3.26E-07 1919/086 0.129 1919/087 0.054 201A-1A 7.20E-14 214A-1M 0.596 220A-1A 2.74E-03 221A-1A 0.731 223A-1A 0.721 231A-1A 0.572 241A-1A 0.263 242A-1A 0.825 257A-1A 0.225 258A-1A 0.106 259A-1A 0.054 265A-1A 0.016 266A-1A 0.027 267A-1A 0.399 268A-1A 0.332 269A-1A 0.575 270A-1A 0.052 271A-1A 0.492 273A-1A 0.020 Table A6.1 T-Tests of C57 Reference Cluster Members Against Their Own Clusters

A6.3 Tests Based on Actual T2 Distributions

Even if the conditions required for the strict application of the T-test may not hold, the T2 statistic can still be a good measure of the distance between a cluster and an inoculum panel, taking account of the covariance structure of the distributions involved. This is the basis for the tests described in the Section 4.4. The mathematical detail underlying these tests is described here.

Let the indices K and L range over the reference clusters – in our case B, V and A. Let the number of 2 reference inocula in each cluster be NK. For simplicity let us write the T statistic, that is, our distance measure, as x, and refer to it simply as the “distance”. Our T2 analysis of the reference clusters gives us the following set of distances:

xLK(k) = distance of standard inoculum k in K, from L .

The smoothed distribution of K inocula distances from L is then:

2 1 1 N K  (x  x (k))  p (x)  exp  LK LK   2  2  N K k 1  2 

The Gaussian smoothing kernel is characterised by its standard deviation, . Its value has to be large enough to smooth out the fluctuations caused by the discrete nature of the sample, but small enough to preserve structure present at x-scales larger than the typical distance between points. After numerical experiments changing the value of , a value of 20 x-units was judged to fit these criteria.

Suppose we have trial inocula, j = 1, …, Ntr . Each of these will then be characterised by a set of three distances from each of the reference clusters:

xL(j) for L = B, V and A .

The P-value for the classical test of j for L-membership is defined as the area under the distribution:

 . PL ( j)  dx pLL (x) x ( j) L

Using the Gaussian smoothed distribution, this becomes:

N 2 L  1 1  (x  xLL (ℓ))  PL ( j)  dx exp  x ( j)  2  2  N L ℓ1 L  2 

The partial integrals over the Gaussians can then be evaluated using the Excel function, NORMDIST.

The Bayesian probability of j being a member of L, given the assumption that it must be a member of one of the three clusters is:

p(xB ( j), xV ( j), xA ( j) | j  L) p( j  L) p( j  L | xB ( j), xV ( j), xA ( j))   p(xB ( j), xV ( j), xA ( j) | j  K) p( j  K) K B,V ,A where p( j  L) is the prior probability that j is a member of cluster L. If we make the additional assumption of equal priors for all trial inocula, this becomes simply

p(xB ( j), xV ( j), xA ( j) | j  L) p( j  L | xB ( j), xV ( j), xA ( j))   p(xB ( j), xV ( j), xA ( j) | j  K) K B,V ,A

We could approximate the distributions over the three distances by the product of those for each distance separately: p(xB ( j), xV ( j), xA ( j) | j  L)  p(xB ( j) | j  L) p(xV ( j) | j  L) p(xA ( j) | j  L) where, in the notation used above

p(xB ( j) | j  L)  pKL (xK ( j)) .

However the values of the distances are likely to be strongly correlated, so the product approximation may well be a bad one. Fortunately the full three-dimensional distribution can be calculated directly by a generalisation of the Gaussian smoothing technique, avoiding the need to make the approximation:

3  1  1 N L  1  p(x ( j), x ( j), x ( j) | j  L)    exp  {x ( j)  x (ℓ)}2 B V A     2  K KL   2   N L ℓ1  2 K B,V ,A  These are then used for calculating the Bayesian probability of membership of each of the clusters, given:

 the three distances of the test inoculum from each reference cluster,  the assumption that the inoculum must belong to one of the clusters, and  the assumption that, prior to the strain typing tests the inoculum has equal probability of belonging to each of the clusters.

This assumption of equal prior probabilities deserves some discussion. It could be argued that our previous experience with TSEs provides relevant information that should be taken into account in the priors. Since all of the natural TSEs found in cattle appear to have been BSE, the prior probabilities for natural bovine inocula should be weighted heavily towards BSE. Since none of the many sheep scrapie cases has been unequivocally determined to be BSE, the prior probabilities for natural ovine inocula should be weighted heavily towards the scrapie clusters.

Against this there are two arguments for retaining the equal priors. The aspiration for the tests based on strain typing is that they should be as far as possible objective and self-standing. If the previous judgements of what was BSE and what was scrapie were based on something other than strain typing, then the results of the other tests would be influencing the strain typing test outcomes. If the prior judgements were in fact based largely on strain typing results then we would be “double counting” these results if we allowed them to affect the priors.

The second argument is that past correlations between donor and TSE type may no longer hold for future donors – if, for example, there is a change in the donor selection regime, or even a change in the nature of the disease. The past information, embodied in the suggested unequal priors, may not be relevant to future diagnoses. We want the diagnosis to be based solely on the the results of current tests (strain typing and others) and their similarity to the existing results associated with what we have called BSE and scrapie, and not on past correlations between donor and disease. (This is similar to the legal principle that the jury should judge the case solely on the basis of facts relevant to this case, taking no account of the defendant’s previous record.) For these reasons, the Bayesian tests are based on equal (uninformative) priors.

Annex 7: Tables of Test Results

The results of cluster membership tests of inocula not in the standard clusters are summarised in Section 4.4.3 and 4.5. The tables of results (classical P-values and Bayesian probabilities) on which this discussion is based are collected in this Annex.

A7.1 1938 Scrapies

Inoculum Classical P-values Bayesian probabilities PB PV PA pB pV pA 1938/036 0.00E+00 0.505 0.00E+00 4.49E-94 1.000 3.06E-86 1938/041 0.00E+00 0.049 6.63E-08 1.47E-160 1.000 4.32E-16 1938/061 0.00E+00 0.505 0.00E+00 4.49E-167 1.000 2.08E-137 1938/032 0.00E+00 0.472 3.49E-09 8.99E-34 1.000 1.45E-14 1938/055 0.00E+00 0.345 0.00E+00 3.91E-70 1.000 1.91E-40 1938/084 0.00E+00 0.397 4.25E-10 8.67E-52 1.000 5.90E-19 1938/038 1.85E-10 0.492 6.67E-09 2.88E-17 1.000 1.46E-16 1938/092 0.00E+00 0.613 2.04E-14 3.15E-47 1.000 6.16E-23 1938/141 2.10E-08 0.586 1.27E-03 1.94E-13 1.000 8.36E-11 1938/042 0.00E+00 0.395 2.60E-12 3.24E-28 1.000 8.77E-18 1938/077 0.00E+00 0.059 0.00E+00 1.25E-79 1.000 1.87E-31 1938/078 3.41E-09 0.663 5.04E-10 2.60E-17 1.000 1.64E-19 1938/089 0.00E+00 0.586 0.00E+00 1.76E-49 1.000 1.05E-33 1938/010 1.71E-12 0.502 5.02E-03 5.37E-17 1.000 1.71E-06 1938/026 1.22E-09 0.687 0.030 1.63E-15 1.000 6.31E-08 1938/030 6.86E-13 0.654 7.37E-08 2.42E-20 1.000 7.12E-15 1938/064 5.67E-14 0.641 8.02E-12 1.43E-23 1.000 2.38E-19 1938/066 1.82E-05 0.631 5.04E-04 2.26E-10 1.000 1.46E-10 1938/068 6.66E-16 0.575 1.79E-10 1.45E-24 1.000 6.49E-17 1938/074 0.00E+00 0.445 1.18E-13 8.81E-35 1.000 8.62E-20 1938/079 0.00E+00 0.134 1.22E-15 1.95E-45 1.000 2.62E-22 1938/085 1.59E-11 0.593 1.50E-08 7.84E-19 1.000 4.78E-16 1938/093 4.36E-09 0.704 1.89E-08 1.21E-16 1.000 7.43E-18 1938/116 2.42E-11 0.642 7.33E-05 3.63E-17 1.000 3.62E-11 1938/125 0.020 0.659 0.045 1.61E-05 1.000 1.59E-04 Table A7.1 Results for 1938 Scrapies in C57

Inoculum Classical P-values Bayesian probabilities PB PV PA pB pV pA 1938/002 0.00E+00 0.439 0.00E+00 1.15E-45 1.000 7.63E-40 1938/010 0.00E+00 0.624 0.00E+00 9.61E-40 1.000 9.35E-27 1938/021 0.00E+00 0.459 0.00E+00 3.81E-94 1.000 8.10E-45 1938/026 3.41E-13 0.546 7.02E-05 4.27E-14 1.000 2.30E-19 1938/030 0.00E+00 0.512 0.00E+00 2.99E-42 1.000 1.08E-35 1938/038 1.02E-03 0.489 0.381 5.65E-04 0.999 4.80E-07 1938/041 0.00E+00 0.163 9.76E-04 6.48E-89 1.000 2.23E-04 1938/042 0.00E+00 0.502 4.22E-15 5.41E-59 1.000 2.35E-14 1938/043 5.28E-13 0.551 2.39E-10 1.01E-17 1.000 2.95E-25 1938/054 5.59E-13 0.350 0.068 1.77E-10 1.000 3.64E-07 1938/055 6.58E-06 0.619 0.104 4.15E-05 1.000 2.86E-08 1938/061 9.22E-07 0.416 0.064 4.73E-05 1.000 1.89E-08 1938/064 8.98E-03 0.673 0.105 0.014 0.986 2.71E-11 1938/065 0.00E+00 0.357 2.62E-04 1.49E-41 0.999 6.78E-04 1938/066 2.89E-03 0.648 0.062 0.036 0.964 7.96E-12 1938/068 0.00E+00 0.274 0.014 4.29E-23 1.000 2.23E-08 1938/071 5.23E-10 0.486 0.039 6.12E-08 1.000 3.11E-10 1938/074 1.12E-08 0.373 0.059 1.22E-06 1.000 3.27E-08 1938/075 0.00E+00 0.454 1.88E-12 9.31E-41 1.000 5.66E-15 1938/077 7.90E-08 0.674 0.054 1.84E-06 1.000 1.30E-10 1938/078 1.33E-15 0.564 4.80E-13 1.10E-22 1.000 1.82E-24 1938/079 0.00E+00 0.379 0.00E+00 2.09E-36 1.000 3.07E-24 1938/080 3.70E-10 0.655 3.10E-05 2.20E-11 1.000 1.16E-20 1938/083 0.00E+00 0.459 0.00E+00 6.67E-112 1.000 1.33E-35 1938/084 0.00E+00 0.558 6.53E-06 1.13E-64 1.000 8.11E-06 1938/085 1.90E-10 0.605 9.83E-03 3.80E-09 1.000 9.32E-14 1938/086 0.00E+00 0.501 8.78E-05 4.19E-24 1.000 1.99E-12 1938/087 4.30E-07 0.685 1.10E-04 5.77E-08 1.000 1.07E-19 1938/091 7.00E-03 0.622 0.113 0.013 0.987 1.33E-10 1938/092 0.00E+00 0.519 5.23E-06 5.52E-42 1.000 4.20E-06 1938/102 2.90E-14 0.429 0.112 4.23E-12 1.000 7.15E-06 1938/103 0.00E+00 0.300 0.00E+00 8.66E-54 1.000 4.16E-40 1938/104 6.22E-08 0.541 2.85E-03 7.07E-07 1.000 7.74E-15 1938/107 3.33E-15 0.455 0.00E+00 9.37E-33 1.000 2.34E-34 1938/112 0.00E+00 0.571 4.15E-11 1.54E-32 1.000 3.33E-17 1938/113 0.00E+00 0.441 0.00E+00 1.37E-56 1.000 3.54E-45 1938/115 4.62E-10 0.559 1.20E-12 9.11E-16 1.000 3.41E-30 1938/121 0.00E+00 0.555 0.00E+00 6.37E-60 1.000 8.83E-26 1938/122 7.17E-13 0.511 0.00E+00 1.93E-30 1.000 3.30E-37 1938/126 1.12E-04 0.666 0.031 1.74E-03 0.998 6.58E-13 1938/137 2.55E-03 0.623 0.025 0.049 0.951 5.82E-14 1938/141 6.78E-11 0.328 3.33E-16 6.38E-18 1.000 2.00E-29 Table A7.2 Results for 1938 Scrapies in RIII

Inoculum Classical P-values equal-prior Bayesian estimates test sample PB PV PA pB pV pA 1938/010 0.00E+00 0.541 0.00E+00 0.00E+00 1.000 8.88E-39 1938/026 0.00E+00 0.682 1.01E-08 0.00E+00 1.000 3.48E-58 1938/030 0.00E+00 0.550 0.00E+00 0.00E+00 1.000 2.44E-73 1938/038 0.00E+00 0.462 6.02E-05 7.01E-182 1.000 8.31E-58 1938/041 0.00E+00 0.033 0.00E+00 0.00E+00 1.000 4.78E-18 1938/042 0.00E+00 0.368 0.00E+00 0.00E+00 1.000 1.76E-66 1938/055 0.00E+00 0.448 0.00E+00 1.13E-246 1.000 1.22E-74 1938/061 0.00E+00 0.400 0.00E+00 0.00E+00 1.000 1.47E-177 1938/064 0.00E+00 0.742 1.10E-10 3.55E-210 1.000 2.80E-64 1938/066 0.00E+00 0.715 4.12E-05 3.70E-230 1.000 9.06E-47 1938/068 0.00E+00 0.280 0.00E+00 3.19E-290 1.000 6.59E-54 1938/074 0.00E+00 0.339 1.11E-16 6.56E-206 1.000 2.34E-69 1938/077 0.00E+00 0.138 0.00E+00 0.00E+00 1.000 2.22E-85 1938/078 0.00E+00 0.646 0.00E+00 0.00E+00 1.000 2.52E-57 1938/079 0.00E+00 0.094 0.00E+00 0.00E+00 1.000 2.96E-76 1938/084 0.00E+00 0.461 0.00E+00 0.00E+00 1.000 8.56E-47 1938/085 0.00E+00 0.644 1.11E-16 0.00E+00 1.000 4.19E-56 1938/092 0.00E+00 0.615 0.00E+00 0.00E+00 1.000 3.87E-85 1938/141 0.00E+00 0.367 0.00E+00 0.00E+00 1.000 4.50E-98 Table A7.3 Results for 1938 Scrapies – Combined Analysis

A7.2 Bovine BSE from Tissues other than Brainstem

Inoculum Classical P-values Bayesian probabilities PB PV PA pB pV pA 1901/640 0.00E+00 0.141 0.00E+00 1.59E-46 1.000 3.76E-31 1901/641 0.00E+00 0.025 0.00E+00 2.31E-267 1.000 5.68E-238 1901/642 0.00E+00 0.097 0.00E+00 1.40E-56 1.000 1.39E-50 1901/643 0.00E+00 0.081 0.00E+00 2.45E-75 1.000 5.07E-67 1901/733 0.00E+00 0.175 0.00E+00 1.96E-52 1.000 9.59E-41 1901/734 4.55E-14 0.456 1.51E-11 1.58E-22 1.000 3.97E-18 1901/735 0.00E+00 0.020 0.00E+00 1.19E-113 1.000 8.50E-92 1901/736 0.00E+00 0.013 0.00E+00 4.76E-41 1.000 1.67E-31 1901/737 1.27E-04 0.447 3.76E-05 2.24E-08 1.000 2.61E-11 1901/738 0.269 0.440 0.433 0.019 0.938 0.042 1901/775 0.00E+00 0.146 0.00E+00 1.32E-37 1.000 4.37E-36 1901/776 4.16E-03 0.306 6.88E-04 1.55E-05 1.000 8.21E-08 1901/777 0.00E+00 0.119 8.50E-13 1.29E-24 1.000 1.07E-16 1901/778 0.00E+00 0.130 0.00E+00 1.65E-36 1.000 3.90E-32 1901/779 0.00E+00 0.103 0.00E+00 8.41E-37 1.000 6.15E-33 1901/780 0.00E+00 2.25E-03 0.00E+00 2.90E-20 1.000 1.33E-15 1901/782 2.31E-03 0.307 0.014 3.76E-06 1.000 3.95E-05 1901/799 0.00E+00 0.142 0.00E+00 2.57E-70 1.000 1.42E-62 1901/864 0.00E+00 0.143 0.00E+00 2.62E-103 1.000 2.55E-87 1901/865 0.00E+00 0.035 0.00E+00 1.35E-26 1.000 6.67E-26 1901/866 0.00E+00 0.010 0.00E+00 2.11E-76 1.000 5.71E-49 1901/867 0.00E+00 3.58E-03 0.00E+00 5.56E-34 1.000 3.10E-28 1901/868 2.96E-13 0.055 1.58E-08 2.28E-16 1.000 1.61E-11 1901/887 9.70E-09 0.046 1.61E-07 1.72E-10 1.000 2.69E-12 1901/907 2.29E-10 0.395 1.42E-12 1.44E-18 1.000 1.55E-20 1901/908 0.00E+00 0.583 0.00E+00 2.36E-42 1.000 1.72E-38 1901/909 0.00E+00 0.015 0.00E+00 3.97E-73 1.000 1.50E-60 1901/910 0.00E+00 0.024 0.00E+00 1.78E-58 1.000 3.23E-44 1901/911 0.00E+00 0.023 0.00E+00 8.43E-49 1.000 1.52E-49 1901/912 0.00E+00 1.88E-04 0.00E+00 1.33E-34 1.000 2.19E-23 1901/913 0.00E+00 0.021 0.00E+00 3.71E-59 1.000 3.97E-44 1919/111 1.82E-10 0.038 3.57E-07 5.66E-12 1.000 1.88E-10 Table A7.4 Results for 1901 BSEs not from Brainstem or Whole Brain, in C57

Inoculum Classical P-values Bayesian probabilities PB PV PA pB pV pA 1901/640 0.038 0.418 0.071 0.364 0.636 3.13E-12 1901/643 0.125 0.369 0.149 0.395 0.605 7.71E-13 1901/682 0.344 0.533 0.533 0.270 0.730 3.77E-16 1901/733 0.013 0.286 0.071 0.371 0.629 5.11E-10 1901/734 0.466 0.577 0.335 0.403 0.597 4.73E-18 1901/735 1.54E-05 0.061 0.012 0.022 0.978 4.19E-08 1901/736 0.112 0.261 0.123 0.586 0.414 4.22E-12 1901/737 0.020 0.427 0.091 0.122 0.878 9.21E-11 1901/775 0.034 0.177 0.111 0.439 0.561 3.96E-09 1901/777 0.041 0.041 0.267 0.816 0.184 6.94E-06 1901/778 0.017 0.388 0.095 0.120 0.880 3.38E-10 1901/779 8.48E-04 0.322 3.70E-03 0.046 0.954 4.30E-14 1901/780 1.87E-03 0.163 0.039 0.298 0.702 1.27E-09 1901/782 0.120 0.294 0.394 0.240 0.760 2.41E-11 1901/799 0.377 0.624 0.376 0.299 0.701 3.03E-17 1901/864 0.124 0.539 0.145 0.195 0.805 7.12E-14 1901/865 0.066 0.227 0.324 0.212 0.788 1.28E-09 1901/866 2.65E-03 0.020 0.103 0.771 0.222 7.66E-03 1901/867 0.019 3.36E-03 0.128 0.999 7.73E-04 4.21E-04 1901/868 0.022 0.026 0.074 0.978 0.022 1.04E-06 1901/870 0.477 0.616 0.419 0.332 0.668 2.84E-18 1901/887 2.21E-03 0.111 0.019 0.476 0.524 1.94E-10 1901/907 0.204 0.590 0.223 0.232 0.768 3.93E-15 1901/990 0.093 0.611 0.076 0.231 0.769 1.02E-14 Table A7.5 Results for 1901 BSEs not from Brainstem or Whole Brain, in RIII

from BSE1 from BSE2 from V from A BSE1 200A-1A 2.78E-06 7.59E-20 7.71E-27 1.54E-24 200B-1A 3.69E-05 5.33E-31 1.92E-40 1.33E-27 200C-1A 0.101 8.01E-06 3.17E-11 2.42E-06 200D-1A 0.109 1.57E-19 1.12E-27 7.56E-17 304A-1A 9.26E-04 5.64E-21 2.39E-22 4.49E-19 305A-1A 0.408 4.04E-13 7.16E-16 1.11E-12 334A-1A 2.03E-03 2.30E-18 8.99E-15 1.74E-15 606A-1A 0.132 1.71E-23 2.68E-28 1.78E-14 607A-1A 0.582 3.64E-25 8.81E-31 1.39E-17 608A-1A 0.118 2.02E-24 3.13E-30 7.10E-15 646A-1A 7.49E-04 9.03E-13 3.18E-22 2.57E-10 BSE2 1901/640 2.45E-28 4.42E-03 2.02E-05 2.46E-29 1901/641 1.08E-42 4.19E-03 1.88E-10 3.78E-54 1901/642 4.72E-29 0.055 2.43E-06 1.38E-35 1901/643 2.13E-31 0.132 8.07E-07 4.78E-39 1901/733 9.13E-29 0.677 7.21E-05 2.86E-32 1901/734 6.76E-21 0.604 0.040 1.81E-23 1901/735 2.96E-36 0.751 2.70E-11 5.68E-43 1901/736 1.09E-28 0.577 2.37E-13 2.31E-32 1901/737 2.45E-13 0.583 0.034 1.95E-17 1901/738 1.23E-04 0.416 0.030 9.86E-03 1901/775 2.21E-25 0.778 2.43E-05 1.48E-31 1901/776 8.74E-11 0.046 2.33E-03 2.38E-15 1901/777 4.95E-23 0.146 7.58E-06 1.26E-24 1901/778 1.35E-25 0.044 1.27E-05 1.84E-30 1901/779 1.44E-25 0.697 3.44E-06 1.47E-30 1901/780 1.12E-25 0.278 1.79E-17 2.50E-28 1901/782 2.69E-11 0.495 2.39E-03 7.94E-12 1901/799 2.02E-30 0.270 2.11E-05 1.04E-37 1901/864 8.10E-34 0.125 2.24E-05 2.65E-41 1901/865 2.35E-24 0.553 3.21E-09 2.37E-29 1901/866 1.33E-34 0.609 3.96E-14 1.03E-36 1901/867 8.71E-28 0.240 1.21E-16 2.16E-32 1901/868 1.21E-20 0.785 7.89E-08 2.78E-21 1901/887 1.07E-17 0.868 2.43E-08 2.43E-20 1901/907 1.14E-18 0.404 0.014 4.34E-24 1901/908 4.44E-25 0.024 0.257 1.38E-31 1901/909 1.18E-32 0.859 1.60E-12 3.27E-39 1901/910 1.98E-31 5.70E-03 1.21E-10 1.09E-35 1901/911 2.02E-28 0.229 8.64E-11 6.44E-37 1901/912 1.54E-29 0.044 1.19E-20 1.70E-32 1901/913 1.88E-31 0.377 3.25E-11 1.22E-35 1919/111 5.29E-19 6.40E-03 6.80E-09 4.99E-20 Table A7.6 P-Values for 4-Cluster T-Tests, Including BSE1 and BSE2, in C57 (part 1)

from BSE1 from BSE2 from V from A V 1919/005 3.25E-17 2.05E-05 0.352 1.49E-17 1919/008 3.06E-23 8.56E-15 0.285 5.00E-19 1919/011 1.69E-16 4.58E-04 0.382 1.50E-13 1919/012 9.57E-34 1.12E-25 0.047 2.65E-29 1919/014 3.60E-33 1.06E-36 2.79E-06 7.38E-17 1919/015 8.61E-17 8.87E-03 0.235 1.55E-21 1919/016 1.61E-12 9.33E-03 0.384 2.54E-14 1919/018 2.96E-10 1.44E-04 0.349 3.43E-13 1919/020 3.14E-22 8.60E-08 0.229 2.74E-29 1919/021 6.40E-17 4.47E-08 1.69E-03 1.09E-17 1919/022 4.99E-22 1.56E-15 0.066 4.71E-23 1919/027 1.49E-25 9.14E-11 0.120 8.85E-26 1919/035 6.88E-12 9.38E-03 0.114 4.17E-21 1919/039 3.09E-19 1.32E-09 0.040 3.94E-22 1919/043 9.63E-16 2.18E-03 0.365 3.18E-18 1919/048 2.31E-26 3.17E-08 2.03E-03 3.86E-35 1919/049 1.65E-16 2.93E-16 0.224 1.04E-09 1919/050 6.81E-29 1.15E-21 2.07E-03 2.08E-30 1919/051 3.43E-15 8.28E-09 0.276 9.29E-12 1919/057 1.27E-09 0.212 0.335 3.74E-11 1919/058 8.85E-16 7.39E-06 0.091 1.45E-20 1919/067 1.20E-10 7.56E-05 0.156 4.76E-12 1919/073 2.90E-19 2.55E-19 0.172 1.46E-19 1919/075 6.14E-30 2.62E-15 0.038 1.80E-24 1919/077 8.81E-26 7.10E-22 3.22E-03 1.54E-22 1919/079 1.40E-25 1.15E-17 0.010 1.67E-15 1919/081 7.47E-25 3.18E-16 0.156 7.74E-19 1919/084 7.52E-29 9.35E-13 3.26E-07 4.75E-31 1919/086 3.66E-13 2.00E-15 0.129 5.57E-07 1919/087 5.61E-15 2.51E-07 0.054 1.28E-10 201A-1A 4.55E-36 2.34E-50 7.20E-14 2.66E-29 214A-1M 2.10E-10 4.04E-03 0.596 2.58E-14 220A-1A 1.69E-25 1.68E-06 2.74E-03 3.09E-35 221A-1A 6.98E-19 0.036 0.731 1.19E-24 223A-1A 2.98E-12 0.380 0.721 4.99E-13 231A-1A 2.93E-14 4.63E-06 0.572 2.55E-16 241A-1A 1.56E-26 4.40E-07 0.263 2.76E-30 242A-1A 2.87E-06 2.12E-05 0.825 8.35E-05 257A-1A 3.51E-31 3.58E-29 0.225 2.52E-18 258A-1A 2.42E-24 2.33E-13 0.106 1.18E-13 259A-1A 5.17E-21 4.94E-07 0.054 8.19E-20 265A-1A 8.99E-23 0.105 0.016 2.28E-29 266A-1A 4.05E-33 1.27E-12 0.027 1.16E-38 267A-1A 9.63E-28 5.39E-18 0.399 6.21E-19 268A-1A 1.02E-39 3.73E-11 0.332 2.10E-47 269A-1A 1.32E-25 2.94E-14 0.575 4.47E-19 270A-1A 1.39E-42 3.86E-11 0.052 1.30E-47 271A-1A 1.02E-30 3.20E-21 0.492 4.94E-23 273A-1A 1.49E-37 1.29E-08 0.020 3.24E-41 Table A7.6 P-Values for 4-Cluster T-Tests, Including BSE1 and BSE2, in C57 (part 2)

from BSE1 from BSE2 from V from A A 1919/019 1.26E-23 1.50E-21 7.22E-28 0.063 1919/031 8.38E-39 2.69E-32 4.69E-34 2.36E-03 1919/042 3.77E-24 3.41E-22 1.28E-22 0.909 1919/053 6.61E-32 7.48E-31 1.54E-32 0.018 1919/055 2.06E-25 1.54E-27 2.67E-30 0.260 1919/063 2.56E-29 5.95E-23 3.80E-26 1.29E-09 1919/069 9.53E-32 5.57E-33 4.97E-29 0.374 1919/072 3.15E-09 1.32E-13 4.69E-10 0.077 1919/082 6.89E-21 1.62E-21 1.04E-18 0.871 1919/089 1.38E-22 3.10E-19 5.01E-16 0.016 1919/090 5.61E-18 1.50E-15 3.73E-21 0.315 261A-1A 5.64E-25 1.95E-24 1.66E-25 8.45E-03 275A-1A 8.93E-31 1.85E-34 1.17E-29 0.026 276A-1A 1.47E-24 3.32E-19 4.55E-13 6.26E-05 Table A7.6 P-Values for 4-Cluster T-Tests, Including BSE1 and BSE2, in C57 (part 3)

from BSE1 from BSE2 from V from A BSE1 1901/119 2.08E-11 0.017 2.25E-06 2.57E-13 1901/184 0.022 4.17E-11 1.32E-11 2.24E-12 1901/185 0.053 5.15E-06 2.65E-05 1.62E-04 1901/186 0.029 2.56E-20 6.22E-17 4.79E-17 1901/187 0.035 5.01E-07 3.34E-11 3.83E-08 1901/188 0.337 3.71E-13 8.78E-16 7.16E-13 1901/189 0.058 1.40E-20 8.75E-22 8.11E-23 1901/190 0.028 1.63E-15 8.86E-20 8.40E-16 1901/215 4.04E-07 1.68E-04 6.42E-07 3.06E-08 1901/279 0.063 7.98E-20 6.21E-26 1.60E-19 1901/280 0.045 2.44E-13 6.53E-14 1.29E-10 1901/281 7.95E-06 1.95E-19 5.83E-21 3.53E-19 1901/304 1.23E-03 3.67E-11 1.78E-11 2.32E-08 1901/368 1.48E-03 1.07E-12 1.02E-16 9.29E-10 1901/391 1.72E-09 1.29E-06 8.95E-20 7.90E-13 1901/964 0.471 3.06E-08 1.49E-13 2.98E-09 200A-1A 3.49E-05 5.39E-37 7.63E-65 7.27E-51 200B-1A 3.33E-06 4.81E-38 9.80E-67 1.37E-50 200C-1A 0.041 3.23E-32 8.20E-46 3.20E-45 200D-1A 3.03E-10 1.24E-41 1.04E-63 1.11E-58 304A-1A 0.295 1.37E-14 6.15E-20 3.88E-20 305A-1A 4.09E-03 3.40E-20 8.87E-24 8.65E-24 334A-1A 0.268 4.03E-25 2.69E-32 4.29E-32 606A-1A 0.519 1.94E-21 1.64E-30 1.52E-26 607A-1A 1.33E-04 1.27E-22 5.36E-28 1.72E-29 608A-1A 0.239 1.18E-19 5.89E-21 8.72E-24 646A-1A 0.012 1.15E-22 2.44E-31 2.86E-28 Table A7.7 P-Values for 4-Cluster T-Tests, Including BSE1 and BSE2, in RIII (part 1)

from BSE1 from BSE2 from V from A BSE2 1901/640 4.15E-12 0.152 7.96E-03 3.95E-11 1901/643 1.14E-08 0.425 2.92E-03 1.35E-07 1901/682 8.16E-05 0.817 0.065 5.98E-03 1901/733 2.22E-14 0.966 4.02E-04 3.41E-11 1901/734 1.77E-03 0.834 0.131 7.90E-05 1901/735 1.07E-21 0.513 5.17E-09 4.67E-17 1901/736 4.69E-09 0.999 2.08E-04 2.50E-08 1901/737 1.68E-13 0.455 9.60E-03 1.32E-09 1901/775 1.97E-12 0.990 1.25E-05 9.65E-09 1901/777 5.78E-12 0.411 2.83E-10 1.17E-05 1901/778 6.85E-14 0.168 4.32E-03 1.96E-09 1901/779 4.51E-18 0.077 1.00E-03 1.66E-18 1901/780 3.61E-17 0.981 6.95E-06 2.30E-14 1901/782 7.95E-09 0.370 5.00E-04 3.23E-04 1901/799 1.99E-04 0.904 0.260 2.14E-04 1901/864 1.08E-08 0.940 0.071 1.10E-07 1901/865 1.08E-10 0.492 7.56E-05 5.87E-05 1901/866 9.53E-17 0.117 2.36E-12 4.35E-09 1901/867 1.10E-13 2.33E-03 1.07E-15 3.19E-08 1901/868 2.30E-13 0.491 1.20E-11 5.96E-11 1901/870 2.25E-03 0.928 0.232 5.73E-04 1901/887 5.63E-17 0.763 4.36E-07 2.90E-16 1901/907 6.82E-07 0.533 0.160 3.02E-06 1901/990 1.14E-09 0.384 0.217 1.22E-10 Table A7.7 P-Values for 4-Cluster T-Tests, Including BSE1 and BSE2, in RIII (part 2)

from BSE1 from BSE2 from V from A V 1919/005 6.65E-30 2.32E-08 0.078 5.65E-20 1919/008 9.21E-14 3.65E-12 0.401 8.31E-08 1919/011 5.34E-34 2.42E-21 0.015 1.28E-16 1919/012 3.60E-12 1.00E-12 0.059 0.012 1919/014 1.49E-33 1.11E-15 7.44E-03 1.16E-11 1919/020 7.04E-23 5.29E-09 0.468 1.72E-21 1919/022 3.72E-12 8.82E-10 0.023 7.33E-03 1919/027 2.69E-36 8.39E-14 0.113 6.89E-21 1919/035 3.75E-33 2.11E-07 0.022 1.88E-21 1919/038 1.29E-22 8.62E-13 1.98E-03 2.12E-09 1919/039 1.29E-32 4.02E-17 0.022 1.01E-15 1919/043 8.94E-41 6.29E-22 0.278 7.99E-23 1919/048 5.68E-38 6.85E-21 0.037 2.22E-24 1919/049 8.01E-65 4.31E-23 0.015 2.24E-40 1919/050 1.77E-25 9.21E-21 3.25E-03 1.74E-10 1919/051 8.03E-30 2.31E-12 0.269 8.27E-18 1919/057 1.41E-51 2.50E-13 0.043 2.41E-26 1919/058 3.49E-50 1.97E-15 2.58E-04 2.10E-32 1919/067 2.11E-20 2.15E-12 0.363 1.92E-09 1919/073 1.69E-26 2.76E-13 0.084 5.66E-11 1919/075 9.37E-53 3.89E-22 0.012 1.80E-33 1919/077 3.96E-40 4.56E-38 7.62E-03 2.39E-15 1919/079 5.70E-53 5.63E-27 8.01E-04 1.04E-34 1919/081 5.36E-35 1.66E-17 0.118 1.95E-19 1919/083 5.15E-40 1.54E-07 0.039 1.07E-25 1919/084 2.36E-42 6.32E-16 9.06E-04 9.68E-31 1919/086 2.19E-34 2.06E-14 6.70E-03 1.25E-16 1919/087 9.11E-13 9.09E-06 0.090 2.99E-08 201A-1A 1.11E-52 6.43E-40 5.81E-11 1.28E-25 214A-1M 1.34E-13 1.26E-06 0.700 1.47E-07 220A-1A 1.78E-19 5.15E-11 0.665 3.82E-14 221A-1A 6.99E-04 1.31E-06 1.53E-03 7.09E-04 223A-1A 2.05E-19 9.03E-13 0.600 1.17E-06 241A-1A 0.021 0.062 0.768 0.448 242A-1A 2.82E-10 4.63E-05 0.961 4.59E-06 257A-1A 2.40E-49 7.81E-48 1.85E-07 2.03E-19 258A-1A 1.65E-45 3.42E-30 0.224 4.22E-16 259A-1A 5.00E-19 5.85E-12 6.59E-03 3.66E-13 265A-1A 1.25E-23 8.44E-16 0.342 1.62E-12 266A-1A 1.69E-41 8.21E-20 0.549 1.95E-32 267A-1A 4.19E-63 3.77E-35 0.676 2.49E-26 268A-1A 3.24E-69 2.54E-31 0.042 1.31E-42 269A-1A 4.96E-32 1.07E-21 0.483 7.42E-21 270A-1A 5.90E-37 2.37E-21 0.381 1.26E-15 271A-1A 2.80E-68 2.95E-42 0.194 3.58E-30 273A-1A 8.18E-25 2.66E-16 0.564 8.12E-15 274A-1A 5.97E-54 8.40E-29 3.02E-11 1.02E-70 277A-1A 3.84E-59 1.16E-30 4.71E-09 5.85E-53 Table A7.7 P-Values for 4-Cluster T-Tests, Including BSE1 and BSE2, in RIII (part 3)

from BSE1 from BSE2 from V from A A 1919/019 8.60E-35 2.69E-26 3.46E-20 0.082 1919/031 3.40E-30 1.17E-21 1.79E-19 0.433 1919/042 9.76E-30 1.31E-19 4.10E-21 0.211 1919/053 1.10E-44 1.05E-29 2.64E-27 4.69E-04 1919/055 5.37E-35 1.68E-23 1.34E-25 0.066 1919/063 2.92E-28 6.32E-20 3.04E-26 3.48E-05 1919/069 1.88E-66 1.66E-30 1.58E-36 7.92E-04 1919/072 1.24E-30 1.10E-17 9.19E-18 0.055 1919/082 9.04E-26 3.21E-16 8.64E-17 0.115 1919/089 2.79E-53 4.64E-33 2.40E-42 2.00E-06 1919/090 1.05E-58 6.11E-33 1.30E-39 2.84E-03 261A-1A 6.94E-35 1.81E-18 1.10E-16 0.062 275A-1A 3.34E-49 2.96E-30 1.52E-29 2.62E-05 276A-1A 1.03E-50 2.38E-25 9.93E-13 1.67E-14 Table A7.7 P-Values for 4-Cluster T-Tests, Including BSE1 and BSE2, in RIII (part 4)

A7.3 Exotic and Experimental BSE

Classical P-values equal-prior Bayesian estimates PB PV PA pB pV pA 1929/189 0.362 1.64E-08 8.28E-03 1.000 2.36E-18 4.77E-19 1929/367 0.231 1.11E-16 5.98E-05 1.000 1.63E-29 1.12E-21 1929/407 0.101 2.07E-11 7.43E-05 1.000 2.19E-21 9.75E-18 1929/528 0.105 0.00E+00 4.94E-09 1.000 1.13E-33 6.80E-27 1929/532 0.305 0.00E+00 1.22E-14 1.000 4.81E-49 6.11E-42 1929/533 0.420 0.00E+00 1.20E-13 1.000 3.53E-48 6.89E-42 1929/857 0.189 4.58E-11 3.98E-05 1.000 6.69E-23 6.31E-21 1929/368 0.183 1.47E-08 0.017 1.000 2.03E-16 9.19E-15 1929/870 0.103 6.99E-07 0.019 1.000 3.91E-13 4.09E-13 1929/865 0.290 3.20E-06 0.046 1.000 3.41E-12 3.14E-15 1929/529 0.433 3.81E-05 0.029 1.000 7.85E-12 3.31E-18 1929/350 0.386 2.46E-05 0.037 1.000 1.56E-11 5.14E-17 1929/518 0.304 1.92E-05 0.046 1.000 4.27E-11 2.57E-15 1929/550 0.084 4.00E-06 0.059 1.000 2.95E-11 1.86E-10 1929/387 0.047 2.38E-10 0.022 1.000 8.28E-16 3.08E-09 1929/307 0.027 2.72E-05 0.014 1.000 4.02E-09 1.01E-10 1929/308 0.023 1.35E-04 0.048 1.000 9.84E-08 2.03E-08 1929/877 0.094 8.64E-04 0.086 1.000 1.55E-06 1.19E-08 1929/653 0.167 1.14E-03 0.104 1.000 4.09E-06 6.33E-10 1929/408 8.49E-03 1.60E-04 0.073 1.000 9.30E-07 2.74E-05 1929/873 0.512 7.51E-03 0.090 1.000 3.38E-05 1.78E-15 1929/306 0.149 0.011 0.078 1.000 1.49E-04 1.43E-10 1929/298 2.39E-03 1.03E-03 0.035 1.000 2.31E-04 3.87E-06 1929/749 0.134 0.030 0.055 0.999 9.15E-04 4.68E-12 1929/652 0.218 0.016 0.133 0.999 1.05E-03 5.25E-11 1929/524 1.97E-03 1.78E-03 0.023 0.999 1.07E-03 1.11E-06 1929/654 3.34E-03 3.03E-03 0.019 0.998 1.96E-03 1.93E-07 1929/290 0.610 0.025 0.220 0.996 4.20E-03 1.60E-15 1929/551 0.025 0.019 0.056 0.993 6.99E-03 7.73E-08 1929/361 0.025 0.014 0.079 0.992 7.77E-03 3.62E-06 1929/546 0.051 0.055 0.045 0.991 8.93E-03 4.07E-11 1929/289 0.303 0.026 0.260 0.990 0.010 1.04E-11 1929/089 0.040 0.063 0.034 0.988 0.012 1.38E-11 1929/303 5.16E-03 0.019 0.064 0.941 0.059 2.55E-05 1929/541 5.58E-04 5.18E-03 0.021 0.941 0.059 1.25E-05 1929/547 0.015 0.036 0.077 0.938 0.062 2.01E-06 1929/186 0.040 0.034 0.158 0.928 0.072 2.95E-06 1929/190 0.016 0.080 0.069 0.865 0.135 3.06E-08 1929/876 0.351 0.126 0.238 0.847 0.153 4.16E-14 1929/544 0.040 0.170 0.070 0.783 0.217 1.19E-10 1929/513 0.015 0.071 0.095 0.743 0.257 9.84E-07 1929/388 0.086 0.191 0.095 0.717 0.283 2.30E-11 1929/398 8.27E-03 0.178 0.025 0.693 0.307 9.47E-12 1929/304 0.011 0.135 0.070 0.668 0.332 1.24E-08 1929/180 0.076 0.423 0.064 0.606 0.394 7.20E-14 1929/383 5.82E-03 0.201 0.023 0.550 0.450 8.73E-12 Table A7.7 SE1929 Ovine BSE in RIII (part 1, pB > 50%)

Classical P-values equal-prior Bayesian estimates PB PV PA pB pV pA 1929/860 0.015 0.066 0.193 0.491 0.509 2.16E-05 1929/188 0.394 0.256 0.604 0.481 0.519 5.23E-15 1929/185 0.039 0.153 0.166 0.397 0.603 1.64E-08 1929/875 0.082 0.327 0.171 0.283 0.717 1.96E-11 1929/363 1.41E-04 0.043 0.014 0.194 0.806 8.48E-08 1929/293 7.42E-04 0.145 0.020 0.169 0.831 2.73E-10 1929/404 7.75E-06 0.025 4.27E-03 0.034 0.966 2.10E-07 1929/358 2.15E-08 0.015 1.81E-03 7.87E-05 1.000 2.63E-07 1929/868 2.00E-08 0.078 1.34E-05 4.92E-08 1.000 9.01E-16 1929/364 3.34E-10 0.041 2.17E-11 3.31E-13 1.000 1.36E-22 Table A7.7 1929 Ovine BSE in RIII (part 2, pB < 50%)

from BSE1 from BSE2 from V 1929/290 20.1 38.4 82.5 1929/873 26.5 77.8 99.7 1929/529 31.9 107.9 141.6 1929/533 32.8 210.2 269.3 1929/350 35.3 74.1 144.1 1929/189 37.1 112.5 177.8 1929/876 38.0 44.5 50.6 1929/532 41.8 206.3 276.4 1929/518 41.8 84.9 145.5 1929/865 43.1 84.9 154.8 1929/367 48.9 146.7 234.3 1929/857 53.7 151.5 198.7 1929/368 54.5 84.8 178.2 1929/653 56.6 56.7 118.2 1929/749 61.6 72.7 79.1 1929/528 67.0 162.4 239.2 1929/870 67.3 96.3 162.0 1929/407 67.7 137.8 201.2 Table A7.8 SE1929 Ovine BSE in RIII – T2 Values from 4-Cluster Test (Part 1 – Closer to BSE1)

from BSE1 from BSE2 from V 1929/289 42.0 33.9 81.7 1929/652 50.3 42.9 89.6 1929/306 59.3 45.8 95.3 1929/877 69.2 59.1 120.5 1929/388 70.9 23.4 42.3 1929/550 71.4 69.5 153.7 1929/875 71.8 18.3 31.0 1929/180 73.5 8.14 24.7 1929/546 80.6 23.1 67.5 1929/387 82.0 64.6 193.2 1929/544 84.3 21.5 44.6 1929/089 84.4 25.2 64.8 1929/186 84.6 15.6 76.8 1929/185 85.0 35.2 46.7 1929/307 90.4 48.2 143.5 1929/361 91.2 38.0 91.2 1929/551 91.5 39.5 86.8 1929/308 92.5 55.9 133.8 1929/190 96.8 17.0 59.7 1929/860 97.7 47.8 63.7 1929/513 97.8 9.88 62.3 1929/547 98.2 20.9 75.8 1929/304 101.0 15.7 49.2 1929/408 104.2 62.4 132.7 1929/398 104.5 17.8 43.7 1929/383 108.0 18.2 41.2 1929/303 109.2 11.6 87.1 1929/654 113.2 49.3 109.4 1929/298 116.1 41.3 119.0 1929/524 117.7 38.6 114.4 1929/293 125.3 24.3 47.7 1929/541 127.3 47.7 103.9 1929/363 136.5 8.75 72.5 1929/404 152.7 32.9 82.3 1929/358 178.6 53.0 90.6 1929/868 178.9 34.9 60.1 1929/364 193.7 23.5 73.3 Table A7.8 SE1929 Ovine BSE in RIII – T2 Values from 4-Cluster Test (Part 2 – Closer to BSE2)

Classical P-values equal-prior Bayesian estimates PB PV PA pB pV pA 1802/001 1.18E-06 3.07E-04 1.11E-16 1.000 1.60E-05 1.11E-21 1802/006 5.88E-08 8.76E-09 1.66E-06 1.000 3.06E-10 5.10E-07 1802/011 0.014 0.304 0.082 7.95E-04 0.971 0.028 1802/012 0.054 0.342 0.017 5.29E-04 0.999 1.36E-04 1802/028 5.41E-05 6.39E-03 2.70E-09 0.168 0.832 1.20E-12 1802/032 3.68E-11 0.00E+00 0.00E+00 1.000 7.03E-21 2.85E-21 Table A7.9 SE1802 Porcine BSE in C57

from BSE 1 from BSE2 from V 1802/012 66.9 15.1 26.7 1802/011 81.9 17.7 29.2 1802/001 139.3 53.3 142.7 1802/006 151.8 64.9 197.8 1802/028 120.6 92.1 109.3 1802/032 177.7 100.0 259.2 Table A7.10 SE1802 Porcine BSE in C57 – T2 Values from 4-Cluster Test

Annex 8: Subpassage Analyses

A8.1 22A-22F Passage Lines

An extensive series of experiments carried out at the IAH addressed what happens when scrapie strains 22C and 22A stabilised in one mouse type are passaged into another mouse type. These strains were produced by the passage of the sheep scrapie brain pool material, SSBP/1 into C57 and VM mice respectively (Bruce et al., 1992). The raw data from two of the passage lines in this series was made available for analysis in the present project, namely those in which 22A was passaged and stabilised in C57, to produce a strain called 22F (which was distinct from the 22C originally stabilised in C57).

The two passage lines were called “S” and “L”. The 10 successive panels in S are here referred to as S01 to S10, and similarly the 12 successive panels in L are L01 to L12. The details of the donors for each of these panels is given here in Table A8.1 below. Note that not all the donors are from the parts of the panel for which data were supplied. The main difference between the two lines was that in the S-line the 22A was cloned (that is, passaged at limiting dilution) twice in VM before the passages are switched to C57, whereas in the L-line there was only one cloning step.

Panel Donor S01 VM mouse inoculated with 22A at 10-6 dilution (cloning step) S02 S01 VM mouse inoculated at 10-6 dilution ( 2nd cloning step) S03 S02 C57 mouse (IP = 398 days) S04 S03 C57 mouse (IP = 469 days) S05 S04 C57 mouse (IP = 307 days) S06 S05 C57 mouse (IP = 277 days) S07 S06 C57 mouse (IP = 286 days) S08 S07 C57 mouse (IP = 279 days) S09 S08 C57 mouse (IP = 265 days) S10 S09 C57 mouse (IP = 398 days) L01 MM mouse inoculated with 22A L02 L01 VM mouse inoculated at 10-6 dilution (cloning step) L03 L02 C57 mouse (IP = 427 days) L04 L03 C57 mouse (IP = 345 days) L05 L04 C57 mouse (IP = 279 days) L06 L05 C57 mouse (IP = 273 days) L07 L06 C57 mouse (IP = 284 days) L08 L07 C57 mouse (IP = 261 days) L09 L08 C57 mouse (IP = 291 days) L10 L09 C57 mouse (IP = 265 days) L11 L10 C57 mouse inoculated at 10-6 dilution (cloning step) L12 L11 C57 mouse (not in panel for which data were given) Table A8.1 Donor History in the Passage Lines

The statistical analysis methods were developed to study transitions along passage chains and examine:  the extent to which the changes in the lesion profiles parallel those in the IPs  whether statistically valid intermediate forms can be identified  whether all the mice in the sub-panels change together, or whether there is sub-panel splitting.

A8.1.1 IP Behaviour

Figures A8.1 and A8.2 show the IP behaviour of the S and L lines respectively. The figures show the IPs for the VM, C57 and C57xVM panels, both the sub-panel means and individual mouse numbers. Note that the lines joining the sub-panel means are there to help show trends in these values and do not indicate donor-host relations. The behaviour of the means was shown in the original paper of Bruce et al. (1992).

In both lines, although the C57 and C57xVM IPs change considerably, the ratio between them remains constant. Averaged over all the panel means (both S and L) for which there are C57xVM data, the mean ratio and its sem is (1.155 ± 0.008). In what follows we shall discuss only the C57 and VM IPs. In these the individual mouse IPs within the panel are unimodally distributed around the mean. There is no panel splitting, for example with some mice in the transition stage staying with 22A and others going to 22F.

600 C mean

s V mean y

a X mean

d 400 ( C mice P I 300 V mice X mice 200

100 1 2 3 4 5 6 7 8 9 10 passage

Figure A8.1 IP Behaviour in the S-line

C mean

s V mean y

a X mean d ( C mice P I 300 V mice X mice

200 1 2 3 4 5 6 7 8 9 10 11 12 passage

A8.2 IP Behaviour in the L-line

The panels L03 and S03 were the first to receive material from a C57 mouse. In L03 the C57 IP dropped to about half way between the initial and final values, whereas in S03 it remained at the 22A value, then dropped more precipitately at the fourth passage. In both cases the C57 value appears by visual inspection of the graphs to have stabilised at the fifth passage, whereas the VM values did not make the transition till the sixth passage. In the S line the fifth passage had a 22F-like C57 value while retaining a largely 22A-like VM value. (There is no fifth passage VM data for the L-line.)

To test these conclusions statistically we carried out pairwise t-tests comparing each C57 sub-panel IP distribution with all the other C57 sub-panel distributions, and similarly for the VM panels. The results can be displayed as a matrix of P-values. To guide the eye, values greater than 5% (where the null hypothesis that the distributions are the same is not rejected at the 5% level) are shaded white, values between 5% and 1% light blue and values less than 1% dark blue. Blocks where most of the inocula pairs pass the test are indicated by heavier borders. The internal S-line comparisons are shown on Tables A8.2 and A8.3 for C57 and VM respectively.

S01 S02 S03 S04 S05 S06 S07 S08 S09 S10 S01 0.014 0.713 1.60E-13 6.61E-12 4.84E-11 2.39E-07 3.88E-08 5.10E-12 4.69E-10 S02 0.014 9.28E-03 3.06E-05 2.64E-05 2.77E-05 4.39E-07 5.70E-07 2.58E-05 2.64E-05 S03 0.713 9.28E-03 3.94E-09 1.16E-07 1.12E-07 2.86E-09 1.34E-09 1.09E-07 8.69E-07 S04 1.60E-13 3.06E-05 3.94E-09 1.21E-05 1.73E-05 0.101 5.98E-03 5.61E-06 1.85E-05 S05 6.61E-12 2.64E-05 1.16E-07 1.21E-05 0.356 0.127 0.649 0.983 0.590 S06 4.84E-11 2.77E-05 1.12E-07 1.73E-05 0.356 0.203 0.940 0.324 0.628 S07 2.39E-07 4.39E-07 2.86E-09 0.101 0.127 0.203 0.286 0.136 0.168 S08 3.88E-08 5.70E-07 1.34E-09 5.98E-03 0.649 0.940 0.286 0.652 0.806 S09 5.10E-12 2.58E-05 1.09E-07 5.61E-06 0.983 0.324 0.136 0.652 0.574 S10 4.69E-10 2.64E-05 8.69E-07 1.85E-05 0.590 0.628 0.168 0.806 0.574 Table A8.2 Internal S-Line t-tests: C57 IPs

S01 S02 S03 S04 S05 S06 S07 S08 S09 S10 S01 0.049 7.57E-05 0.083 0.039 4.41E-10 1.34E-05 3.57E-11 1.46E-05 2.34E-08 S02 0.049 0.372 0.337 0.807 4.19E-04 4.06E-04 1.30E-03 0.041 1.45E-03 S03 7.57E-05 0.372 0.757 0.389 5.31E-09 4.17E-05 3.22E-10 1.89E-04 1.36E-07 S04 0.083 0.337 0.757 0.352 3.46E-07 3.30E-06 2.48E-05 2.50E-04 8.10E-06 S05 0.039 0.807 0.389 0.352 1.19E-04 8.33E-05 1.33E-03 7.07E-03 8.69E-04 S06 4.41E-10 4.19E-04 5.31E-09 3.46E-07 1.19E-04 0.824 0.185 7.48E-03 0.199 S07 1.34E-05 4.06E-04 4.17E-05 3.30E-06 8.33E-05 0.824 0.226 0.011 0.226 S08 3.57E-11 1.30E-03 3.22E-10 2.48E-05 1.33E-03 0.185 0.226 0.028 0.897 S09 1.46E-05 0.041 1.89E-04 2.50E-04 7.07E-03 7.48E-03 0.011 0.028 0.035 S10 2.34E-08 1.45E-03 1.36E-07 8.10E-06 8.69E-04 0.199 0.226 0.897 0.035 Table A8.3 Internal S-Line t-tests: VM IPs

These tables confirm the description of the behaviour seen on the graphs. In the C57 panels the first three are approximately the same, although with S02 being something of an outlier. The strong similarity between S01 and S03 shows that the strain stays as unmodified 22A in its first passage through C57. S04 is a transitional form, with virtually no similarity to earlier or later panels, and then S05 to S10 are all strikingly similar to each other. For the VM panels 22A behaviour persists up to S05 and then changes abruptly to 22F behaviour.

This pattern can be described symbolically as:

C57: S01  S02  S03, S04, S05  S06  S07  S08  S09  S10, S04  S01-03, S04  S05-10

VM: S01  S02  S03  S04  S05, S06  S07  S08  S09  S10

where ”” means “is consistent with being from the same distribution”, and “” is the negation of this relation. The analogous results for the L-line are

C57: L01  L02, L03, L04  L05  L06  L07  L08  L09  L10  L11, L03  L01-02, L03  L05-11, L12  everything else except L04 and L10

VM: L03  L04, L06  L07  L08  L09  L10  L11  L12

As seen on the graph, the transitional form in C57 comes at the third passage, where as in VM it at the sixth.

The main points of the comparisons between the S and L lines can be summarised as:

C57: L01  L02  S02, L01-02  S01, L01-02  S03, L04-11  S05-10, L01-02  S05-10, L04-11  S01-03

VM: L03-04  S01-05, L06-12  S06-10, L06-12  S01-05, L03-04  S06-10

In C57 the S02 version of 22A is the one closest to the 22A seen in the L-line; the stabilised 22F is identical between the two lines. In VM both the 22A and 22F are identical between the two lines.

A8.1.2 Lesion Profile Behaviour

We performed a similar analysis for the lesion profiles using multivariate T-tests. The difficulty here was that the multivariate tests need larger panels, so it is necessary to combine the existing panels, and the results vary according to how this is done. As an indication of the sort of results obtained, we show two matrices in Tables A8.4 and A8.5 below for the S-line panels, in C57 and VM, with one particular choice of grouping of panels.

S01_02_03 S04 S05_06 S07_08 S09_10 S01_02_03 0.040 5.92E-05 4.69E-03 6.26E-05 S04 0.040 0.850 0.454 0.423 S05_06 5.92E-05 0.850 0.325 0.572 S07_08 4.69E-03 0.454 0.325 0.858 S09_10 6.26E-05 0.423 0.572 0.858 Table 8.4 Internal S-Line T-tests: C57 Lesion Profiles

S01_02 S03 S04 S05 S06 S07 S08_10 S01_02 0.575 0.139 0.835 0.346 0.051 8.50E-03 S03 0.575 0.456 0.553 0.325 0.401 0.188 S04 0.139 0.456 0.553 3.16E-03 0.088 1.35E-03 S05 0.835 0.553 0.553 0.777 0.612 0.087 S06 0.346 0.325 3.16E-03 0.777 0.046 9.55E-03 S07 0.051 0.401 0.088 0.612 0.046 0.163 S08_10 8.50E-03 0.188 1.35E-03 0.087 9.55E-03 0.163 Table A8.5 Internal S-Line T-tests: VM Lesion Profiles

The C57 results show that the 22F lesion profiles are internally consistent and are different from the pooled 22A profiles (S01_02_03). The VM profiles show a high level of similarity across the whole line, showing that there is little or no difference between the strains in this respect.

In an attempt to visualise the behaviour of the C57 lesion profiles along the passage lines, we performed a principal component analysis on the 22 panel mean profiles, and then scatterplotted the first two principal components. The result is shown in Figure A8.3 below. The S-line points are diamonds and the L-line points are triangles. The passage number is indicated by the colour, with an approximate “rainbow order” from red to violet.

4 S01 S02 S03 S04 3 S05 S06 S07 S08 S09 2

c 2 S10 p L01 L02 L03 L04 1 L05 L06 L07 L08 L09 0 L10 1 2 3 4 5 L11 L12 pc1

Figure A8.3 C57 Lesion Profile Principal Components

The first three S-line panels make up a cluster in the lower right corner, corresponding to the 22A

pattern. The remainder make up a second cluster in the centre of the plot, with pc1 values between 2.7 and 3.7 and pc2 values in an even tighter range, between 1.67 and 1.94, corresponding to the 22F pattern. The L-line panel results show more variability.

A8.1.3 Conclusions of the 22A-22F Analysis

The statistical tests can be used to confirm stabilisation and to identify statistically valid intermediate forms that are unlike either the initial or the final stabilised strain. The transition behaviour of the LPs can be explored and compared with the behaviour of the IPs. In the 22A-22F case studied here, for example, the IP in C57 stabilises to its new value one passage before the VM IP changes. In C57 the lesion profile stabilises at the same point as the IP, whereas we can see little significant change in the VM profile.

The techniques can also be used to look for sub-panel spliting, we found no evidence here of sub- panel splitting. The sub-panels move as coherent entities, well characterised by their mean values.

In each passage material from only one donor was taken, so possible dependence of the results on donor characteristics cannot be tested.

Using these techniques on a dataset containing more examples of strain-to-strain transitions would enable us to build up a more representative picture of the phenomenology of these transitions, and the extent to which TSE strain variability can be induced by passage into mice with different prion protein genotypes. This in turn could be used to test hypotheses about the mechanisms underlying the transitions.

A8.2 VLA Subpassages

The VLA subpassage experiments are summarised on Table A8.6 below.

Primary Donor Cluster Subpassages Donor Mice Host Panels 1929/533 Ovine BSE 1938/144 VM VM 1919/059 Outlier 1919/479-482 VM VM 1919/042 A 1919/499-501, 503 VM VM 1919/063 A 1919/502 VM VM 1919/031 A 1919/508-511 VM VM 1919/055 A 1919/119-136, 157 C57, RIII C57, RIII, VM 1919/014 V 1919/137-145, 147-156 C57, RIII C57, RIII, VM Table A8.6 VLA Subpassage Experiments

A8.2.1 VM-to-VM Subpassages

The first six sets of experiments involve subpassaging material from VM mice into VM panels only. Four of the original donors are scrapies from the A-cluster, the fifth is the donor 1919/059 that in some contexts gave BSE-like results, and the sixth is the ovine BSE 1929/533.

The 1929/533 subpassages (1938/144) gave a mean IP of 130 days with a standard error of the mean (SEM) of 2.5 days. A very low IP in VM mice is strongly characteristic of VM-stabilised BSE, known as 301V. Incubation periods for VM-passaged BSE quoted in the literature are given on Table A8.7 below (where possible the original donors have been identified with the inoculum references in the database).

Reference Inoculum IP in days  SEM Bruce et al. (1994) 200A-1A at second passage 116  3 Moore et al. (1998) “301V” 119  2.1 Bruce et al. (2002) 200A-1A at third passage 112 200B-1A at third passage 117 200C-1A at third passage 123 Table A8.7 301V Incubation periods

The value of 130 days is somewhat high for 301V from bovine sources. For example, the second passage result from Bruce et al. (1994) is (116  3) days compared with the 1929/533 result of (130  2.5) days – the SEM values show that this difference is statistically significant. Nevertheless 130 days is much lower than anything seen in the scrapie VM to VM subpassages (the lowest mean IP being 303 days, in 1919/481).

In Figure A8.4 below the mean lesion profile from 1938/144 is compared with two profiles from the literature (the profiles labelled 301V_1 and 301V_2 are from Moore et al. (1998) and Bruce et al. (2002), 200A-1A respectively). For completeness the corresponding IPs are also included in the Figure.

120 3 100 301V_1IP 301V_2IP 80 1938/144IP S P

I 2 L 301V_1LP 60 301V_2LP 1938/144LP 40 1 20

0 0 0 1 2 3 4 5 6 7 8 9

Figure A8.4 VM Subpassage of Ovine BSE versus 301V

The shape of the profile from 1938/144 looks similar to those from the literature for 301V, but the values are mostly higher, especially in areas 1 to 6. The absolute difference between the profiles raises the question of whether this is another example of an inter-laboratory shift, in this case in the readings from VM mice. To investigate whether this could be a possible explanation, we took the ME7-like profiles in the subpassage into VM of material derived from 1919/014 (described below) and formed the difference between them and the literature profiles of ME7 (from the IAH). The shift vector is:

LS1 LS2 LS3 LS4 LS5 LS6 LS7 LS8 LS9 VM ME7 shift 0.69 0.86 0.77 1.09 0.60 -0.16 0.55 -0.07 0.15 Table A8.8 VLA – IAH Shift Vector for ME7 in VM Mice

The result of shifting the 1938/144 profile by this vector is shown on Figure A8.5 below. The agreement is improved in areas 1, 7, 8 and 9, but the shifted profile is too low in area 2 and still too high in areas 3, 4, 5 and 6. It remains possible that better agreement could be obtained if the shift were optimised over all the VM results from the VLA subpassages.

301V vs 1938/144 shifted

120 3 100 301V_1IP 80 2 301V_2IP 1938/144IP S P I L 301V_1LP 60 1 301V_2LP 144 shifted ME7 40 0 20

0 -1 0 1 2 3 4 5 6 7 8 9 10

Figure A8.5 VM Subpassage of Ovine BSE, Shifted by Inter-laboratory difference for ME7, versus 301V

The similarities between the 1938/144 results and 301V suggest that, even when the BSE has been passaged once through sheep, it retains its characteristic ability to produce something like 301V in VM mice. However one should note that while the SE1929 ovine BSEs were scattered across the BSE1 and BSE2 bovine clusters (see Figure 22 in the main text), the 1929/533 inoculum was among those that gave results much closer to BSE1 than BSE2. It would be useful to carry out VM to VM subpassages for material from the BSE2 cluster to test whether the 301V behaviour is also found there.

Next we examine the results for the VM-to-VM subpassages of scrapie inocula. In Figure A8.6 below we show the distribution of inoculum mean IPs. As noted above, none of these scrapie subpassages gives an IP approaching that of 301V. Although 1919/059 shows some signs of looking like BSE at first passage, it behaves entirely like scrapie in VM-to-VM subpassage. The inocula exhibit a wide range of mean IPs (from 303 to 556 days). Two groups can be identified on the basis of IP: one group having mean IPs between 303 and 405 days, and another with mean IPs between 500 and 556 days. These groups are given the temporary names Y and Z respectively. Formally we can define Y and Z membership as having a mean IP of less than and greater than 450 days respectively; this definition is made meaningful because of the gap in the IP distribution.

IP distribution

3 r e b

m 2 u n

0 300 320 340 360 380 400 420 440 460 480 500 520 540 560 580 IP/day Figure A8.6 Distribution of mean IPs in VM to VM Subpassage of Scrapie

The lesion profiles for each of these groups (obtained by averaging the lesions scores for each of the grey matter areas over all the mice in the panels assigned to each group, are shown on Figure A8.7. They are compared with the lesion profile of the VM-stabilised strain 87V, because this has an IP around the lower end of the Y range – values of 303 days (Dickinson and Outram, 1979) and 289 days (Bruce, 1996) being reported. . This is similar to the approach used by Bruce and Dickinson (1987) in their study of the strains 87A and ME7. They looked at IPs at the individual mouse level, and averaged the lesion scores over mice according to their IP, whereas here we averaged over mice according to the mean IP of the panel to which they belonged.

2.00 87V Y 1.50 Z

0.00 1 2 3 4 5 6 7 8 9

Figure A8.7 VM Subpassage of Scrapie: Mean Lesion Scores versus 87V

The average Y profile has a similar shape to that of 87A, except that it is higher over all, except in area 5. By contrast, the Z profile is even higher and has a different shape.

The judgements of similarity of profile in Figures A8.4 and A8.7 have so far been based on subjective similarity of appearance. To put this on a more objective basis, we looked at the Euclidean distance between the profiles in question, defined by:

distance(1, 2) = [(LS1(1) – LS1(2))2 + … + (LS9(1) – LS9(2))2]1/2

In other words, this distance between profiles 1 and 2 is the square root of the sum over all nine grey matter areas of the square of the difference between the lesion scores of 1 and 2.

If this is done with the lesion scores themselves these distances are dominated by the absolute size of the scores. The VLA profiles come out as more similar to each other and distant from the IAH literature strain profiles as a second group. However if we compare the shapes of the profiles, not their overall magnitude by normalising each profile (so that in each case the sum of the squares of the scores equals one) and use the normalised values in the distance equation, the matrix of distances is as given in Table A8.9 below.

301V_1 301V_2 1938/144 87V Y Z 301V_1 0 0.26 0.28 0.53 0.43 0.47 301V_2 0.26 0 0.19 0.51 0.38 0.52 1938/144 0.28 0.19 0 0.42 0.24 0.37 87V 0.53 0.51 0.42 0 0.30 0.55 Y 0.43 0.38 0.24 0.30 0 0.30 Z 0.47 0.52 0.37 0.55 0.30 0 Table A8.9 Normalised Lesion Profile Distances

The distance between the two 301V profiles from the literature is 0.26; we can use this value as a approximate measure of the variability in profiles of the same strain in different experiments. The distances of 1938/144 from each of them are comparable with this, while its distance from 87V is 0.42. Conversely the distance of Y from 87V is 0.30, as compared to values around 0.4 from 301V. The distances from Z to both 301V profiles and from that of 87V are around 0.5. This provides objective evidence that 1938/144 has a profile like that of 301V and not like that of 87V, that the Y profile is like 87V and not like 301V, and that the Z profile is unlike that of either strain.

Examining the data at the individual mouse level we see similar behaviour, with the mice falling broadly into two groups on the basis of their IPs.

Distribution of individual mouse IPs

20 18 16 14

r 12 e b

m 10 u

n 8 6 4 2 0

IP (days)

Table A8.8 Normalised Lesion Profile Distances

This suggests we can follow the procedure used by Bruce and Dickinson (1987) and average the profiles over the individual mice, according to their IP, regardless of which inoculum they received. The resulting profiles are shown on Figure A8.9 below.

mouse groups vs 87V

2.50 87V 2.00 avg < 450 avg > 450 1.50

0.00 1 2 3 4 5 6 7 8 9

Figure A8.9 VM Subpassage of Scrapie: Mouse Mean Lesion Scores versus 87V

Table A8.11 below shows the distances between the normalised profiles. Comparing this with the lower right corner of Table A8.9, we see that the less than 450 day profile is slightly closer to 87V and the greater than 450 day profile is slightly further away.

87V avg < 450 avg > 450 87V 0 0.28 0.58 avg < 450 0.28 0 0.36 avg > 450 0.58 0.58 0 Table A8.11 Normalised Lesion Profile Distances for Mouse Groups

Because the sub-panel means split into distinct groups, and even different mice given the same inoculum show different IP and lesion behaviour (sub-panel splitting), we investigated if there were any correlations between sub-passage behaviour (membership of the Y or Z group) and characteristics of the donor mouse.

Table A8.10 below shows that the original sheep donor is not determining group membership; donor mice from the same first passage inoculum panel can give rise to results in different groups.

Sheep Inoculum Subpassage Group 031 1919/510 Y 031 1919/511 Y 031 1919/508 Z 031 1919/509 Z 042 1919/501 Y 042 1919/503 Y 042 1919/499 Z 042 1919/500 Z 059 1919/479 Y 059 1919/480 Y 059 1919/481 Y 059 1919/482 Z 063 1919/502 Z 069 1919/504 Y 069 1919/506 Y 069 1919/507 Y 069 1919/505 Z Table A8.10 Effect of Sheep Inoculum on Subpassage Group

In subpassage experiments, one has to choose which mouse (or mice) from the panel to use as the new donor. We looked at a number of features of the donor mouse to see if they were correlated with the group to which the new hosts belonged. We found that the sum over all the nine grey matter lesion scores in the donor mouse was correlated with the new host group. This is shown in Figure A8.10 below and Figure A8.11 below.

Figure A8.10 below shows the donor sum of scores plotted against the subpassage inoculum mean IPs. Membership of the Z and Y group is here determined by reference to these panel means. All the Z group come from donors with the sum of scores of 12 or below, and all the Y group from donors with the sum of scores of 10 or above.

Figure A8.11 below shows the same plot, except that the individual host mouse IPs are shown instead of the panel means. Again good correlation between group membership (based now on individual IPs) and the sum of lesion scores is obtained. The original assignment to the Y and Z group based on panel means is shown on the figure to illustrate the panel splitting. A number of the mice belonging to panels classified as Z on the basis of the panel mean, lie in the Y region, and similarly some Y mice lie in the Z region.

An explanation that has been suggested for the relationship between the sum of lesion scores in the donor and the IP in the host is that it arises from a correlation between the sum of lesion scores and the titre of infectivity in the host mouse and that the higher titres then give rise to the lower IPs in the

of 58 SE0241 Annexes to SID5 subpassage hosts. However this does not explain the discontinuity in the distribution of host IPs, nor the fact that the two populations have distinctively different lesion profiles. If there is an effect of choice of donor mouse on the outcome of a subpassage, then this is something that should be taken into account in the design of subpassage experiments, especially if material from only one donor mouse is being carried forward.

Z means P I

s 400 Y means o h

200 0 5 10 15 20 25 30 donor LS sum

Figure A8.10 Donor Lesion Score Sum versus Host Mean IP

600 Member of Z group based on individual mouse IPs 500

P 400 I

Y group t

s based on o

h 300 individual mouse IPs

0 0 5 10 15 20 25 30 35 donor LS sum

Member of Y group based on panel mean IPs Member of Z group based on panel mean IPs Figure A8.11 VM Subpassage of Scrapie: Sum of Donor Lesions versus Host IP

In conclusion, there is evidence here that the VM-to-VM passages of scrapie at the VLA are giving rise to the strain 87V. Interestingly, whereas earlier transmissions of natural scrapie at the IAH

(Dickinson, 1976), produced 87V from VM-to-VM transmission, this did not happen in a recent series of transmissions (Bruce et al., 2002).

A8.2.2 Subpassages From C57 and RIII

C57 and RIII mice from the panel inoculated with 1919/014 and from the panel inoculated with 1919/055 were subpassaged into C57, RIII and VM mice. In this exploratory study, we have looked mostly at the results from the subpassages into C57 mice to look for any evidence of the emergence of the strains reported over many years by the IAH workers

In a more detailed investigation this could be extended to the other mouse strains and also use the quantitative comparisons between lesion profiles exemplified above.

Figure A8.12 shows the distribution of sub-panel mean IPs for the 014 subpassages into C57. Most of these IPs are tightly clustered in the region between 160 and 190 days. We have grouped the inocula into three groups:  an X population with mean IP less than 300,  a Y population consisting of two inocula (1919/149 and 1919/150) with mean IPs of 411 and 447 days, and  an X group consisting of an inoculum (1919/153) with one mouse attacked only, with an IP of 522 days.

6 r e b

m 5 u n 4

IP/day

Figure A8.12 Distribution of IPs in C57 to C57 and RIII to C57 Subpassages of 1919/014

In Figure A8.13 below shows the sum of lesions plotted against the IP in the host, for both the inoculum means and the individual mice. This shows that the mice can be split into the three populations described above. The three non-X inocula were prepared from donors with zero lesions (for presentational purposes, the points for 1919/150 and 153 have been given a small offset to the right, but the actual values of the donor lesion sum are all zero.)

The X population IPs lie in a very narrow range with a mean and SEM of:  (183 ± 4) days in C57, and

 (352 ± 3) days in VM

Dickenson and Outram (1979) give ME7 strain IPs as 180 and 349 day in C57 and VM respectively, showing excellent agreement. Figures A8.14 and A8.15 show lesion profiles in C57 and VM for the X population compared with ME7 profiles given by Bruce et al. (1991). The X-profile shape is similar to ME7 in both strains, although the profile in C57 is higher in most areas, while the profile in VM is higher in areas 1 and 2.

X mice 400 X means

P 149 mice I

s 149 mean o

h 150 mice 300 150 mean 153 mouse

100 0 5 10 15 20 25 30 35 donor LS sum

Figure A8.12 C57 Subpassage of 014 Scrapie: Sum of Donor Lesions versus Host IP

014-X vs ME7 (in C57)

2.00 ME7 X mean 1.50

0.00 1 2 3 4 5 6 7 8 9

Figure A8.13 Subpassage of 014 Scrapie: X-Profiles in C57, versus ME7

014-X vs ME7 (in VM)

2.50 ME7 X Mean 2.00

0.00 1 2 3 4 5 6 7 8 9

Figure A8.14 Subpassage of 014 Scrapie: X-Profiles in VM, versus ME7

A similar analysis can be carried out for the 1919/055 subpassage in C57; the inoculum mean results are shown on Figure A8.15 below. The inocula have again been split into three groups:

 an X group consisting of a single inoculum 1919/134 with a very low IP – this has a lesion profile similar to that of ME7 (not shown)  a Y group with mean IPs between 300 and 450 days and  a Z group with IPs above 450 days.

P X mean I

s 400 Y means o

h Z means

200 0 5 10 15 20 25 30 donor LS sum

Figure A8.15 C57 Subpassage of 055 Scrapie: Sum of Donor Lesion versus Host IP

The Y-population inoculum mean IPs lie between 350 and 430 days. We tested the conjecture that these might show a similarity with the strain 87A, with an IP (Dickenson and Outram, 1979) of 353 days, as perhaps being a not fully stabilised version of 87A. Figure A8.16 below compares the lesion profiles in C57 of all mice with IPs between 300 and 450 days with that of 87A (Bruce and Fraser, 1991). (Note that a typographical error in the labelling of the lesion profiles in this paper has been taken into account, and these profiles are indeed those of 87A). This shows a similarity between the two profiles, with, as before, the VLA subpassage profile being somewhat higher. There is also some evidence that the 014-Y population is similar to 87A (data not show). These findings should however be subject to full statistical testing. For this access to individual mouse data for exemplars of the classical strains are required.

055-Y vs 87A (in C57)

2.00 87A Y-mean 1.50

0.00 1 2 3 4 5 6 7 8 9

Figure A8.16 Subpassage of 055 Scrapie: Y-Profiles in C57, versus 87A

Although more work needs to be done to test the affinities between the VLA results and the stabilised strains reported in the literature (for example testing against the 221C strain found more recently by Bruce et al. (2002)), the findings above, would if verified, suggest the following outline picture of the subpassage behaviour of 014 and 055.

014  (14/18) X-parent (lesions)  (14/14) 014-X ( ME7)

 (4/18) Y, and Z-parent (no lesions)  (3/4) 014-Y ( 87A)

055  (18/20) X, Y-parent (mostly high lesions)  (17/18) 055-Y ( 87A)

 (1/18) 055-X ( ME7)

 (2/20) Z-parent (low lesions)  (2/2) 055-Z

Although these experiments have looked at only one V-cluster scrapie (they were carried out before the A/V distinction was discovered), the pattern of results suggests the following conjecture, namely that 87A and 87V are the murine versions of the A-cluster scrapie, while ME7 is the murine version of V-cluster scrapie. A more extensive set of subpassage experiments, currently under way at the VLA, will be able to test this conjecture.

However, separate evidence in favour of the conjecture comes from observations regarding the formation of amyloid plaques. Far more plaques were seen with 87A and 87V than in ME7 (Bruce et al. 1989). Work at the VLA on primary-passage scrapie mice shows that plaques are far more common with inocula assigned to the A-cluster than to the V-cluster (Green et al, 2006).

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Bruce, M. E. and Dickinson, A. G. (1987), “Biological evidence that scrapie agent has an independent genome”, Journal of General Virology, 68, pp 79-89.

Bruce, M.E., McBride, P. A. and Farquhar, C. F. (1989), “Precise targeting of the pathology of the sialoglycoprotein, PrP, and vacuolar degeneration in mouse scrapie”, Neuroscience Letters, 102, pp 1-6.

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