A new instrument to describe indicators of well-being in old-old patients with severe dementia – The Vienna List

Background In patients with very severe dementia self-rating of quality of life usually is not possible and appropriate instruments for proxy-ratings are not available. The aim of this project is to develop an instrument of clinical proxy-ratings for this population.
Background
In industrial societies the proportions of old people and of people suffering from dementia are steadily increasing. Consequently, the number of people depending on different types of institutional care is growing. The care is provided in general hospitals, geriatric hospitals, nursing homes, private homes, and senior residences and within the families. Obviously, there are large differences in costs and efficacy of these different types of care. In general, quality of life data have been increasingly appreciated as the key outcome measure for the assessment of therapeutic interventions and for the usefulness of various treatment facilities. Quality of life is defined by WHO as "an individual's perception of their position in life, in the context of the culture and value systems in which they live, and in relation to their goals, expectations, standards, and concerns. It is a broad ranging concept, effected in a complex way by the person's physical health, psychological state, level of independence, social relationships and their relationships to salient features of their environment" [1]. As concerns people with dementia, many of the instruments in use represent brief global measures, which cannot be applied to patients with severe dementia.
The most important reason for this slow growth of empirical data in this area probably relates to the obvious methodological problems of obtaining reliable subjective accounts of individuals with severe dementia who have compromised cognitive abilities, frequently with concurrent impaired communicative skills.
Due to these impairments in this group of patients most of the available instruments are not applicable for assessing quality of life related issues by means of traditional measures such as questionnaires or interviews which require a highly complex procedure of introspection and evaluation, involving several components of cognition including implicit and explicit memory [2].
Facing these problems, attempts have been made to develop direct observational methods [3] measuring positive affects such as pleasure, interest, and contentment as well as negative affects such as sadness, anxiety/fear, and anger according to operationalized criteria during a series of 10-minutes direct observation. An alternative approach was chosen by the Bradford Research Group in the UK [4] with the Dementia Care Mapping (DCM) based on the psychosocial model of "person-centred care" which provides detailed observational ratings covering aspects of articulation, feeding, social withdrawal, passive engagement, walking and a number of indicators of well-being.
Literature searches (-2003) of Medline, Embase, Psyclit, Cinahl using the keywords dementia and well-being and quality of life were conducted. Lawton et al. [5] developed the Minimum Data Set (MDS) comprising cognition, activities of daily living, time use, depression, and problem behaviours. He proposed to include observations of demented patients' emotional behaviours [6]. Most of the measurements are derived from existing instruments and are confined to few of the following dimensions: affect, activity, enjoyment, self-esteem, and social interactions [7]. Ready and Ott concluded from their review [7] that the psychometric properties of most of the available instruments have to be regarded as preliminary.
As mentioned above, there were no quality of life assessment tools for patients with very severe dementia as representative of our population. Unfortunately, the instruments developed for demented patients were not found applicable to our patients who were in much more advanced stages of dementia (e.g. the Mini Mental State Examination could not be completed by any of our patients). For this reason a project was launched in 1998 by the department of Palliative Geriatrics (Geriatriezentrum am Wienerwald), to develop a new instrument based on observations made by the staff (physicians, nurses and physiotherapists) completely independent on the patients' cooperation.
Methods
Patients
In the present study 217 consecutive patients (44 males, 173 females) were included. The average age was 84 years (range 61–105 years). The majority of patients suffered from severe dementia according to ICD-10 (34,5 % F00 – Dementia in Alzheimer's Disease; 61,5 % F01 – Vascular Dementia; 4,0 & F02 – other). It appeared that more women (38 %) had been diagnosed as suffering from Alzheimer's disease compared with men (23 % – χ2 (df = 2) = 6,05; p = 0,049). As concerns age, the patients with Alzheimer's disease (87,3 ± 5,7 years) and vascular dementia (86,3 ± 7,0 years) were significantly older than those with other diagnoses (78,5 ± 15,6 years – t = 3,27; p = 0,002 and t = 2,74; p = 0,007 respectively).
Development of the item-pool
Thirteen staff members comprising doctor, nurses, and therapists from the department of Palliative Geriatrics at the Geriatriezentrum am Wienerwald in Vienna had observed severely demented patients during a one year period (May 1998 to April 1999). The patient's behaviour was documented at one of the wards (32 beds). Based on this documentation, 65 items for the description of behaviour in demented inpatients were derived and allocated to categories, supposedly reflecting relevant aspects of their well-being such as voice, language, mood, eye contact, acceptance of body contact, gait, muscular tension, hand movement, sleep, activities, communication, independence concerning food intake. This approach is different from prevailing approaches, which mainly are based on the use of items from existing instruments measuring specific aspects.
Subsequently, by means of this item-list, 771 assessments of 217 in-patients in various situations such as eating, dressing, grooming had been obtained between June 1999 and September 2000 by physicians and nurses. Each of the original 65 items was scored on a 5-point Likert scale from 0 = never to 4 = always.
Further assessments
Patients were diagnosed according to ICD-10. They were rated by means of the Brief Cognitive Rating Scale (BCRS) [8], the Global Deterioration Scale (GDS) [9], the Barthel-Index [10], and the Spitzer Index [11].
The BCRS describes the severity of cognitive impairment providing five main axes (concentration, short term memory, long term memory, orientation, and self-care ability) and five co-axes (language, psychomotoric, mood and behaviour, drawing skills, calculating skills) each rated on a 7-step scale. The GDS is a proxy rating scale to assess the severity of dementia in elderly people on a seven point Likert-type scale (1 = no impairment; 7 = most severe impairment).
The Barthel-Index was used to assess the activity of daily living in 10 areas (feeding, transfers bed to chair and back, grooming, toilet use, bathing, mobility, climbing stairs, dressing, stool control, bladder control).
The Spitzer Index is a global quality of life measure covering five areas (activity, daily life, health, social relations, future) with a maximum score of 10 points.
The BCRS, GDS, and Spitzer Index had been rated by physicians and the Barthel index by nurses. All ratings including the 65 items list were made on the same occasion. Data collection was carried out using electronic questionnaires implemented through the Quality-of-Life-Recorder technology [12].
Statistical analysis
Descriptive statistics were generated for demographic data and diagnostic categories and for the BCRS and GDS scales, for the Barthel and Spitzer Indices, as well as for the newly developed instrument.
A factor analysis (principle component analysis, oblimin with Kaiser normalisation as rotation method), based on these 771 assessments was performed. The number of interpretable factors was determined by interpretation of a Scree plot. The consistency of the factors was tested by Cronbach's alpha coefficients. To improve the consistency of the scales, items have been deleted based on the criteria of changes in magnitude of the Cronbach's alpha coefficients and on the fit of the item with the content of the core items of the factors. To test the stability of the factor structure, we conducted separate analyses for doctors and nurses. For testing construct validity, we used the two external criteria, Brief Cognitive Rating Scale and the Barthel-Index.
To test for inter-rater reliability Spearman rank correlation coefficients were calculated. We included only data in which the electronic recording confirmed that it was obtained at exactly the same time.
Results
Most patients suffered from severe dementia as indicated by the results of the BCRS (mean ± SD: 57 ± 8.8) and GDS (mean ± SD: 5.7 ± 1.1) and the low level of activity of daily living (mean ± SD of Barthel Index: 26.8 ± 29.7) in the present sample also reflected by the distribution of diagnoses according to ICD-10.
Of the 771 assessments 386 had been performed by nurses and 385 assessments by physicians. By means of the electronic recordings we identified 22 pairs of assessments made at the same time by a nurse and a physician. A planned feasibility analysis after 120 assessments resulted in the exclusion of the Spitzer Index because of a general floor effect (mean score <3).
The factor analysis suggested five factors based on the interpretation of the Scree plot. The results of the five-factor solution are given in Table 1 with high Eigenvalues and an explained variance of more than 60 per cent.
Table 1
Eigenvalues and explained amounts of variance for the 5-factor solution
Eigenvalues
% of Variance
Cumulative %
Factor
nurse
physician
nurse
physician
nurse
physician
1
10.0
10.7
24.4
26.0
24.4
26.0
2
5.7
6.1
13.8
15.0
38.2
41.0
3
4.9
4.8
11.9
11.8
50.0
52.1
4
2.5
2.1
6.1
5.0
56.1
58.0
5
2.0
2.5
4.9
6.1
61.0
63.9
Extraction Method: Principal Component Analysis.
To improve the consistency of the five factors, 18 of the original 65 items were deleted based on the criteria of changes in magnitude of the Cronbach alpha coefficients and on the fit of the item with the content of the core items of the factors. As a next step, eight further items were excluded due to different factor-loadings between raters and of content considerations resulting in the following five factors: communication (15 items), negative affect (10 items), bodily contact (5 items), aggression (4 items) and mobility (6 items). The factor structure matrix including the single items for physicians and nurses is shown in table 2 and demonstrates a high congruency of the factor structure between both groups of raters on the item level.
Table 2
Structure Matrix
Factor
1
2
3
4
5
Nurse
Physician
Nurse
Physician
Nurse
Physician
Nurse
Physician
Nurse
Physician
Communication
ITEM 59
0.82
0.83
-0.19
-0.14
0.04
-0.32
0.29
0.05
0.06
0.25
ITEM 62
0.81
0.64
0.02
0.16
0.08
-0.39
0.28
0.22
-0.09
0.34
ITEM 6
0.79
0.78
-0.26
0.13
-0.07
-0.29
0.18
0.08
0.10
0.17
ITEM 61
0.79
0.67
-0.23
-0.13
0.10
-0.56
0.22
0.09
0.08
0.25
ITEM 8
0.78
0.81
-0.20
0.08
-0.16
-0.25
0.14
0.12
0.12
0.18
ITEM 65
0.73
0.77
-0.27
-0.13
-0.05
-0.05
0.52
0.10
0.01
0.48
ITEM 56
0.70
0.69
-0.08
0.08
-0.15
0.07
0.07
-0.06
0.05
0.10
ITEM 14
0.70
0.85
-0.11
-0.11
0.02
-0.10
0.44
-0.06
0.15
0.26
ITEM 32
0.66
0.64
-0.14
-0.06
0.35
-0.48
0.03
-0.11
0.15
0.22
ITEM 31
0.65
0.65
-0.12
0.01
0.32
-0.54
0.08
0.00
0.15
0.19
ITEM 13
0.64
0.76
-0.24
-0.04
0.06
-0.42
0.11
0.13
0.12
0.20
ITEM 50
0.64
0.77
-0.40
-0.19
-0.04
-0.17
0.46
-0.03
0.19
0.35
ITEM 64
0.62
0.61
0.05
0.13
0.06
-0.17
0.49
-0.21
0.03
0.40
ITEM 60
0.59
0.69
0.12
0.17
-0.27
0.03
0.01
0.28
-0.11
0.07
ITEM 15
0.45
0.73
0.03
-0.13
-0.08
0.19
0.34
-0.19
0.12
0.06
Negative Affect
ITEM 18
-0.08
0.04
0.83
0.87
-0.03
-0.14
-0.08
0.11
-0.28
-0.02
ITEM 22
-0.04
0.09
0.80
0.74
0.11
-0.34
-0.10
0.08
-0.20
0.00
ITEM 3
-0.07
0.02
0.75
0.82
0.16
-0.18
-0.13
0.17
-0.11
-0.06
ITEM 24
-0.09
-0.01
0.74
0.76
-0.05
-0.18
-0.05
0.12
-0.37
0.08
ITEM 27
0.04
0.02
0.66
0.77
-0.13
-0.10
-0.17
0.45
-0.45
-0.05
ITEM 17
-0.28
-0.17
0.66
0.71
-0.08
0.14
-0.16
0.38
-0.58
-0.12
ITEM 25
-0.30
0.17
0.65
0.63
0.03
-0.02
-0.29
-0.02
-0.08
-0.07
ITEM 54
-0.20
-0.20
0.62
0.58
-0.09
0.10
0.19
0.28
-0.34
0.05
ITEM 53
-0.10
-0.01
0.60
0.50
-0.07
0.23
0.14
0.17
-0.28
0.01
ITEM 47
-0.28
-0.26
0.53
0.61
0.00
0.11
-0.26
0.37
-0.45
-0.11
Bodily contact
ITEM 35
0.00
0.17
0.02
-0.03
0.93
-0.76
-0.08
-0.13
0.11
-0.11
ITEM 34
0.01
0.27
0.00
0.07
0.93
-0.81
-0.09
-0.12
0.12
-0.09
ITEM 33
0.03
0.37
0.00
0.14
0.90
-0.77
-0.09
-0.06
0.12
0.00
ITEM 36
-0.06
-0.21
0.01
0.10
0.89
-0.81
-0.13
-0.15
0.16
-0.08
ITEM 37
-0.06
-0.27
0.01
0.15
0.86
-0.76
-0.12
-0.16
0.17
-0.06
Aggression
ITEM 4
-0.14
0.01
0.25
0.27
-0.19
0.08
0.13
0.91
-0.88
0.08
ITEM 19
-0.24
-0.05
0.31
0.18
-0.22
0.16
0.13
0.84
-0.83
0.09
ITEM 1
0.05
0.20
0.28
0.20
-0.07
0.12
0.01
0.71
-0.78
0.08
ITEM 29
0.22
0.11
0.20
0.21
-0.10
0.05
0.03
0.84
-0.77
0.15
Mobility
ITEM 40
0.42
0.56
-0.31
-0.23
-0.20
0.08
0.79
-0.14
0.22
0.80
ITEM 41
0.49
0.64
-0.28
-0.27
-0.23
0.07
0.71
-0.19
0.23
0.63
ITEM 57
0.01
0.01
0.01
0.08
-0.05
0.00
0.68
0.14
-0.18
0.70
ITEM 42
0.04
0.07
0.07
0.06
-0.02
0.05
0.68
0.20
-0.18
0.80
ITEM 43
0.43
0.63
-0.43
-0.15
-0.08
-0.09
0.66
0.00
0.22
0.64
ITEM 55
0.41
0.46
-0.14
-0.07
-0.10
0.04
0.42
-0.23
0.03
0.34
Extraction Method: Principal Component Analysis. Rotation Method: Oblimin with Kaiser Normalization.
In addition, it appeared that the factors were generally unrelated to each other except for significant correlations between the factors 'communication' and 'bodily contact' (r = 0.25; p < 0.001 each) and of 'mobility' with 'negative affect' (physicians: r = 0.22; p = 0.001; nurses: r = -0.33; p < 0.01). The Cronbach alpha coefficients as a measure of internal consistency were high for both nurses and physicians (table 3). The congruence of nurses' and physician's ratings is further demonstrated by similar item severity (relative ratings) and selectivity of the single factors (table 4).
Table 3
Cronbach alpha coefficients
Factor
Number of Items
Nurse
Physician
1 – Communication
15
0.93
0.94
2 – Negative Affect
10
0.88
0.89
3 – Bodily Contact
5
0.90
0.90
4 – Aggression
4
0.86
0.87
5 – Mobility
6
0.81
0.82
Table 4
Item severity and Item selectivity
Item severity
Item selectivity
Communication
Nurse
Physician
Nurse
Physician
59 Responding to distant calls
0.57
0.63
0.82
0.85
62 Looking for contact
0.53
0.56
0.81
0.71
06 Speaks comprehensibly
0.64
0.69
0.79
0.81
61 Contact possible
0.83
0.90
0.79
0.74
08 Speaks meaningful groups of words
0.59
0.68
0.77
0.82
65 Eats and drinks by him-/herself
0.66
0.68
0.79
0.78
56 Reads newspaper
0.24
0.19
0.69
0.64
14 Carries out simple orders
0.39
0.64
0.73
0.84
32 Maintains visual contact
0.72
0.74
0.65
0.71
31 Visual contact possible
0.80
0.85
0.63
0.72
13 Comprehends single words
0.89
0.88
0.63
0.81
50 Uses both hands intentionally
0.62
0.67
0.70
0.79
64 Worries about others
0.25
0.23
0.65
0.63
60 Rings the bell
0.29
0.31
0.58
0.67
15 Carries out complicated orders
0.08
0.24
0.47
0.64
Negative Affect
18 Full of despair
0.40
0.43
0.82
0.86
22 Sad/crying
0.32
0.36
0.77
0.75
03 Whining voice
0.33
0.32
0.72
0.83
24 Nervous/anxious
0.37
0.40
0.76
0.75
27 Wailing
0.30
0.31
0.69
0.80
17 Tensed
0.36
0.43
0.73
0.72
25 Resignated
0.27
0.38
0.65
0.63
54 Restless/confused
0.25
0.31
0.62
0.59
53 Problems to fall asleep
0.25
0.36
0.59
0.49
47 Muscular tension
0.37
0.43
0.60
0.62
Bodily Contact
35 Bodily contact possible at shoulders
0.77
0.87
0.92
0.78
34 Bodily contact possible at arms
0.81
0.91
0.90
0.80
33 Bodily contact possible at hands
0.82
0.94
0.88
0.73
36 Bodily contact possible at the head
0.63
0.63
0.91
0.88
37 Bodily contact possible in the face
0.56
0.55
0.88
0.84
Aggression
04 Aggressive voice
0.27
0.27
0.89
0.90
19 Aggressive acts
0.21
0.19
0.85
0.84
01 Loud voice
0.32
0.33
0.80
0.80
29 Insulting others
0.23
0.22
0.81
0.88
Mobility
40 Walking upright
0.37
0.40
0.92
0.95
41 Walking straightup to
0.33
0.36
0.87
0.87
57 Departs from ward
0.07
0.05
0.53
0.46
42 Wanders around
0.16
0.17
0.53
0.62
43 Sitting upright
0.57
0.57
0.80
0.81
55 Ready to help on ward
0.13
0.09
0.55
0.54
For testing construct validity, we used two external criteria, the Brief Cognitive Rating Scale (BCRS) used by physicians and the Barthel-Index used by nurses (table 5). The correlation coefficients between the various areas of the BCRS and the two relevant scales of the new instrument (communication and mobility) point to a satisfactory validity. Concerning the second criteria, the Barthel-Index (a measure of activities of daily living), it was significantly correlated with the scales "communication" and "mobility" of our instrument. Furthermore, the latter was correlated with the scale "negative affect" and "acceptance of body contact" in the expected direction.
Table 5
Correlations with BCRS scores and Barthel Index
Communication
Negative Affect
Bodily contact
Aggression
Mobility
Physicians
BCRS 1 – concentration
-0.71*
0.05
0.07
-0.02
-0.45*
BCRS 2 – short time memory
-0.67*
0.02
0.11
-0.01
-0.42*
BCRS 3 – long time memory
-0.68*
0.14
0.09
0.08
-0.46*
BCRS 4 – orientation
-0.65*
0.12
0.12
0.11
-0.40*
BCRS 5 – everyday life competency
-0.47*
-0.04
-0.04
-0.10
-0.44*
BCRS 6 – language
-0.71*
-0.02
0.02
-0.09
-0.37*
BCRS 7 – psychomotorics
-0.41*
-0.01
0.06
-0.10
-0.59*
BCRS 8 – mood and behaviour
-0.60*
0.10
-0.02
0.03
-0.34*
BCRS 9 – constructive skills
-0.55*
0.03
0.02
-0.06
-0.34*
BCRS 10 – calculation skills
-0.59*
0.17*
0.09
0.09
-0.35*
Main axis
-0.73*
0.07
0.09
0.02
-0.49*
Co-axis
-0.71*
0.07
0.04
-0.03
-0.49*
BCRS total score
-0.74*
0.07
0.06
-0.01
-0.50*
Nurses
Barthel Item 1 – feeding
0.70*
-0.21*
-0.10
0.02
0.63*
Barthel Item 2 – transfer
0.46*
-0.27*
-0.17*
-0.08
0.83*
Barthel Item 3 – personal care
0.41*
-0.17*
-0.02
-0.12
0.36*
Barthel Item 4 – toilet use
0.47*
-0.25*
-0.20*
-0.12
0.67*
Barthel Item 5 – bathing
0.08
-0.07
0.04
-0.06
0.09
Barthel Item 6 – moving
0.43*
-0.28*
-0.18*
-0.09
0.83*
Barthel Item 7 – stairs
0.32*
-0.23*
-0.14
-0.03
0.72*
Barthel Item 8 – dressing
0.51*
-0.23*
-0.22*
-0.09
0.67*
Barthel Item 9 – bowel
0.48*
-0.20*
-0.16*
-0.15*
0.57*
Barthel Item 10 – bladder control
0.44*
-0.20*
-0.22*
0.14
0.56*
Barthel Index
0.56*
-0.28*
-0.20*
-0.10
0.83*
* p < 0.001
When testing for gender differences concerning the factors, we found significant differences for all but one factor (table 6).
Table 6
Factor Scores of Observations by Gender of the Patients
Factor
Observations of Males (x ± SD) N = 123
Observations of Females (x ± SD) N = 648
t-score
p value
1 – Communication
34.8 ± 13.2
33.8 ± 11.8
0.82
0.415
2 – Negative Affect
9.5 ± 6.7
14.8 ± 6.7
-8.37
<0.001
3 – Bodily Contact
14.9 ± 4.3
16.7 ± 4.9
-4.18
<0.001
4 – Aggression
3.1 ± 3.0
4.3 ± 3.5
-3.21
0.001
5 – Mobility
8.1 ± 5.5
6.3 ± 5.5
3.38
0.001
The interrater-reliability between sub-samples of physicians and nurses proved to be satisfactory (table 7).
Table 7
Paired sample test and Spearman Rank correlation coefficients between nurses and physicians related to the same patient at the same day (22 pairs)
Factor
Nurse (x ± SD)
Physician (x ± SD)
t-score/p
R/p
1 – Communication
25.8 ± 10.5
26.4 ± 8.9
-0.35/0.727
0.71/<0.001
2 – Negative Affect
11.9 ± 7.6
8.6 ± 5.0
2.46/0.023
0.57/0.006
3 – Bodily contact
15.9 ± 5.7
18.4 ± 2.9
-2.22/0.038
0.53/0.011
4 – Aggression
4.3 ± 3.0
2.2 ± 2.1
3.69/0.001
0.35/0.112
5 – Mobility
6.5 ± 5.7
5.1 ± 5.8
2.03/0.056
0.81/<0.001
Discussion
The special problem in the assessment of well-being in patients with severe dementia is their lack of competence which is compromising the reliability of their reports. Consequently, observer ratings are the only alternative for such self-ratings. However, observer ratings inherit the potential risk of overrating the well-being of patients if the provider and rater of health care services are identical. We have controlled for this risk by semi-quantitatively describing the frequency of distinct behaviour patterns in demented patients.
The results of this study demonstrate that the behaviour of old-old patients with severe dementia can be described by five factors of the Vienna List. By explaining more than 60% of the total variance these five factors obviously cover a considerable part of the possible spectrum of behaviour in these patients.
Since nurses and physicians have different intensity of contact and corresponding different perspectives, it was surprising that their assessments were highly correlated in three of the five factors. The two factors, aggression and mobility, yielded higher scores among the nurses as compared to doctors.
As concerns aggression, there are mainly two explanations for this difference. Firstly, nurses spend more time and have closer contact with the patients and consequently have a higher risk to induce aggressive behaviour in the patients. In addition, the extended period of contact increases the chance to experience an episode of aggressive behaviour. Secondly, patients normally behave differently towards nurses and doctors due to differences in role expectation and familiarity related to the frequency of contact. However, we consider this later explanation as unlikely in these patients due to their cognitive impairment.
Regarding mobility it is plausible that the doctors report lower scores for mobility of the patients as the doctor mainly sees the patient under certain circumstances, i.e. during the rounds where the ward routines limit the mobility of the patient.
Since these five factors encompass most of the behavioural repertoire of demented old-old patients we assume that these factors can be regarded as a useful approach to describe the well-being in these patients.

well-being
old-old
severe dementia
quality of life


Detection of amyloid beta aggregates in the brain of BALB/c mice after Chlamydia pneumoniae infection

Neuroinflammation, initiated by cerebral infection, is increasingly postulated as an aetiological factor in neurodegenerative diseases such as Alzheimer’s disease (AD). We investigated whether Chlamydia pneumoniae (Cpn) infection results in extracellular aggregation of amyloid beta (Aβ) in BALB/c mice. At 1 week post intranasal infection (p.i.), Cpn DNA was detected predominantly in the olfactory bulbs by PCR, whereas brains at 1 and 3 months p.i. were Cpn negative. At 1 and 3 months p.i., extracellular Aβ immunoreactivity was detected in the brain of Cpn-infected mice but also in the brain of mock-infected mice and mice that were neither Cpn infected nor mock infected. However, these extracellular Aβ aggregates showed morphological differences compared to extracellular Aβ aggregates detected in the brain of transgenic APP751SL/PS1M146L mice. These data do not unequivocally support the hypothesis that Cpn infection induces the formation of AD-like Aβ plaques in the brain of BALB/c mice, as suggested before. However, future studies are required to resolve these differences and to investigate whether Cpn is indeed an etiological factor in AD pathogenesis.
Introduction
The most common cause of dementia is Alzheimer’s disease (AD), which manifests in a familial and a sporadic form. The neuropathology in both conditions is characterized by neuritic or senile plaques, consisting mainly of amyloid beta protein (Aβ) and neurofibrillary tangles with hyperphosphorylated tau protein as its key components. Mutations in the amyloid precursor protein (APP), presenilin-1 (PS-1) or -2 (PS-2) genes have been associated with the familial form of AD [27]. However, the most common type of this disease, sporadic AD, is not due to these mutations, and although risk factors have been identified, the initiating trigger is still unknown [18]. The potential importance of infections in the development of sporadic AD has been recently recognized [1, 21, 31]. Specifically, not only various viruses like the human immunodeficiency virus, herpes simplex virus and cytomegalovirus but also bacteria like Borrelia burgdorferi, have been linked to the process of neurodegeneration [6, 12, 13, 17, 20]. Some data also suggest that Chlamydia pneumoniae (Cpn) should be added to the list of pathogens possibly involved in the pathogenesis of AD. Cpn is a widespread, obligatory intracellular pathogen, often causing acute respiratory infections including pneumonia, sinusitis and bronchitis [10, 11, 15]. This microorganism has also been implicated in a variety of severe, chronic diseases including some disorders of the central nervous system [7, 14, 30, 32]. In an initial study, Balin et al. demonstrated the presence of Cpn in brains of 17 out of 19 patients with sporadic AD, using a variety of techniques, including PCR, electron microscopy and immunohistochemistry, while the pathogen could only be demonstrated in 1 out of 19 controls [2]. The presence of Cpn was demonstrated in various cell types like microglia, astrocytes, perivascular macrophages and monocytes, and predominantly in brain areas linked with AD pathology. Recently, these observations were confirmed [8]. On the contrary, others were unable to detect Cpn in AD patient tissues [9, 22, 23, 28]. The only animal study so far supporting a role for this pathogen in AD demonstrated an AD-like pathology in the brain of BALB/c mice post Cpn infection [19]. Three-month-old mice were inoculated intranasally with Cpn and the authors claimed that this resulted in the deposition of Aβ in the brain up to 3 months post infection with increasing pathology as the infection progressed. Based on these results, it was concluded that in naïve BALB/c mice, AD-like pathology could be induced by Cpn infection. In spite of these results, doubts remain concerning the amyloid-like plaques as being only unrelated elevations in amyloid [24]. Because of the possible implications of this study and the need for confirmation, it was our aim to repeat these experiments under quite corresponding conditions. Moreover, we compared our findings with the pathology found in a transgenic mouse model of AD.
Materials and methods
Chlamydia pneumoniae
The Cpn strain, TWAR 2043 (ATCC) was cultured and continuously propagated on Hep2 cells as previously described [25]. Bacterial titres were determined by titration in Hep2 cells [7] and staining with a monoclonal Chlamydia LPS-specific antibody followed by a FITC-conjugated secondary antibody (RR-402; DAKO, Glostrup, Denmark). Titres were expressed as the number of inclusion forming units (IFU) per ml.
Animals and infection protocol
A total of 43 female, specific pathogen-free inbred BALB/c mice were obtained from Charles River (Someren, The Netherlands). Animals were kept under standard housing and care conditions at the Central Animal Facilities of Maastricht University (Maastricht, The Netherlands). All experimental procedures were approved by the Ethical Committee for the Use of Experimental Animals of Maastricht University.
At 3 months of age n = 24 mice were inoculated intranasally with 107 IFU Cpn in 10 μl (5 μl per nostril), and were analyzed at 1 week, 1 month or 3 months p.i., respectively, as shown in Table 1. Pilot experiments revealed that this dosage ensures both survival of the mice and optimal dissemination throughout the body. Another n = 15 mice were mock infected with a sucrose-phosphate-glucose solution (SPG, solution for storage of Cpn) at 3 months of age and were also analyzed at 1 week, 1 month or 3 months after mock infection, as shown in Table 1. In addition, two mice, which did not receive any treatment, were investigated at 4 months of age, as well as two mice at 6 months of age (Table 1).
Table 1Numbers of animals per group investigated in the present studyMouse strain; analysisInfection with CpnMock infectionNo treatment1w1m3m1w1m3mM4M6M17BALB/c; PCR444111–––BALB/c; IHC–66–6622–APP751SL/PS1M146L––––––––1CpnChlamydia pneumoniae, IHC immunohistochemistry, 1w/1m/3m, analysis 1 week (or 1 month or 3 months, respectively) after Cpn infection or mock infection at 3 months of age. M4/M6/M17, 4, 6 or 17 months of age, respectively
In addition, a 17-month-old transgenic mouse expressing human mutant APP751 (carrying the Swedish and London mutations KM670/671NL and V717I, under the Thy1 promoter) and human mutant presenilin-1 (PS-1 M146L, under the HMG promoter) was used (APP751SL/PS1M146L mouse; for a detailed description see refs [4] and [24]) (Table 1). This APP751SL/PS1M146L mouse was bred on a CBA (12.5%) × C57Bl6 (87.5%) background.
Detection of Cpn DNA in brain tissue with real-time PCR
As shown in Table 1, 15 mice were used to detect Cpn DNA in brain tissue with real-time PCR at different time points after infection with Cpn or mock infection. To this end the animals received a weight-adjusted dose of pentobarbital (Nembutal; Sanofi Sante B.V., Maassluis, The Netherlands) and were then perfused with sterile phosphate buffered saline. Brains were removed quickly and dissected into five different regions, i.e., olfactory bulbs, cerebellum, hippocampus, subventricular zone and neocortex. The isolated regions were snap-frozen in liquid nitrogen and stored at −80°C until further processing.
DNA was isolated from brain material using methods previously described [5]. The Nanodrop® ND-1000, a full-spectrum spectrophotometer (220–750 nm) was used to calculate the DNA content of each sample. Finally, the Cpn DNA copy number/μg DNA in the collected brains was determined by real-time PCR (ABI Prism 7000; Applied Biosystems, Foster City, CA, USA) using the following primers and probe; forward (5′TCCGCATTGCTCAGCC3′), reverse (5′AAACAATTTGCATGAAGTCTGAGAA3′) and probe (5′-FAM-TAAACTTAACTGCATGGAACCCTTCTTTACTAGG3′-TAMRA) as described previously [5, 29]. All PCR runs were performed in duplo. Mean Cpn DNA copy numbers/μg brain DNA of three independent experiments (±SEM) was calculated. Then, comparisons between all groups of mice, analyzed for the presence of Cpn DNA in brain tissue, were performed with three-way ANOVA with Treatment (Cpn infection or mock infection), Time-point of analysis (1 week, 1 month or 3 months p.i.) and Brain region (olfactory bulbs, cerebellum, hippocampus, subventricular zone or neocortex) as fixed factors. P values smaller than 0.05 were considered statistically significant. Calculations were performed with SPSS v. 12.0.1 for Windows (SPSS, Chicago, IL, USA).
Detection of Aß aggregates in brain tissue with immunohistochemistry
As also shown in Table 1, n = 29 mice were used to detect Aβ aggregates in brain tissue with immunohistochemistry at different time points after infection with Cpn, mock infection or without any treatment, respectively. To this end, the animals received a weight-adjusted dose of pentobarbital (Nembutal; Sanofi Sante B.V., Maassluis, The Netherlands) and were then subjected to intracardial perfusion fixation, as previously described [26]. In case of the APP751SL/PS1M146L mouse, anaesthesia was performed with chloral hydrate (10% aqueous solution, 0.005 ml/g body weight, i.p.). Brains were removed rapidly and post-fixed for 2 h at 4°C in the same fixative, omitting the glutaraldehyde. In case of the APP751SL/PS1M146L mouse, the brains were halved in the midsagittal line. Brain tissues were then cryoprotected by immersion in 30% sucrose in Tris-buffered saline at 4°C overnight. Afterwards, brains (or brain halves, respectively) were quickly frozen and stored at −80°C until further processing.
For immunohistochemical analysis, the entire brains (or the right brain half in case of the APP751SL/PS1M146L mouse) were cut into entire series of 30-μm-thick frontal sections on a cryostat (Leica CM 3050 S; Leica, Nussloch, Germany). After cutting, sections were again stored at −80°C until further processing.
Immunohistochemical detection of Aβ (as well as of glial fibrillary acidic protein) was performed with standard immunofluorescence procedures using a first layer, consisting of a combination of mouse anti-human amyloid 17–24 (4G8 clone; 1:1,200 in TBS-T; Sigma-Aldrich, St Louis, USA) [3, 16, 33] and polyclonal rabbit anti-mouse GFAP (1:1,600 in TBS-T; DAKO, Glostrup, Denmark). The corresponding secondary antibodies were donkey anti-mouse Alexa 488 (1:100 in TBS-T; Molecular Probes) and donkey anti-rabbit Alexa 594 (1:100 in TBS-T; Molecular Probes). Finally, counterstaining was performed with Hoechst (1:500 in TBS; Sigma, St Louis, MO, USA) and sections were mounted on gelatine-coated glasses. As a negative control, sections were only incubated with the secondary antibodies.
Detection of extracellular Aß aggregates in brain tissue with thioflavin-S
For the detection of extracellular, fibrillary Aβ, a standard thioflavin-S (Sigma) staining was performed. Finally, the sections were counterstained with Hoechst and mounted as described above.
Microscopy and photography
Microscopic inspection of sections was performed with a MBF Bioscience Stereo Investigator Confocal Spinning Disk (SI-SD) system (MBF Bioscience; Williston, VT, USA), consisting of a modified Olympus BX51 fluorescence microscope (Olympus, Tokyo, Japan) with UPlanSApo objectives 10 × (N.A. = 0.4), 40 × (N.A = 0.9) and 100 × (oil; N.A = 1.4); Olympus, customized spinning disk unit (DSU; Olympus), computer-controlled excitation and emission filter wheels (Olympus), three-axis high-accuracy computer-controlled stepping motor specimen stage (4 × 4 Grid Encoded Stage; Ludl Electronic Products, Hawthorne, NY, USA), linear z-axis position encoder (Ludl), ultra-high sensitivity monochrome electron multiplier CCD camera (1,000 × 1,000 pixels, C9100-02; Hamamatsu Photonics, Hamamatsu City, Japan) and controlling software (MBF Bioscience). In case of sections processed for the detection of Aß and GFAP, digital RGB image stacks showing the same microscopic field in 50 consecutive focal planes with a distance of 0.5 μm between the focal planes were generated with the 40× objective. In the case of sections stained with thioflavin-S, digital image stacks showing the same microscopic field at 150 consecutive RGB focal planes with a distance of 0.2 μm between the focal planes were generated with the 100× objective. From these image stacks three-dimensional reconstructions (maximum intensity projections) were produced with Imaris software (Version 4.0; Bitplane, Zurich, Switzerland). In addition, low-power photomicrographs (10×) were taken from sections stained with thioflavin-S. Final figures were constructed using Corel Photo-Paint v.11 and Corel Draw v.11 (Corel, Ottawa, Canada). Only minor adjustments of contrast and brightness were made, without altering the appearance of the original materials. No deconvolution was performed on images obtained by confocal microscopy.
Results
Presence of Cpn in the brain of BALB/c mice infected with Cpn
At 1 week p.i., the mean Cpn DNA copy number per μg DNA was 310.97 ± 61.6 (mean ± SEM) in the olfactory bulbs, 2.6 ± 0.8 in the cerebellum and 7.9 ± 0.8 in the hippocampus, while no Cpn DNA was detected in the subventricular zone and the neocortex (Table 2). No Cpn DNA was detected in any brain region of the Cpn infected mice at 1 and 3 months p.i.. Furthermore, Cpn DNA was not detected in the brain of mock-infected BALB/c mice at any time point. Statistical analysis revealed significant differences among the groups of mice with respect to Treatment (T) (P = 0.024), Time point of analysis (Tpoa) (P = 0.008) and Brain region (Br) (P = 0.002), as well as with respect to the combinations T–Tpoa (P = 0.008), T–Br (P = 0.002), Tpoa–Br (P < 0.001) and T–Tpoa–Br (P < 0.001).
Table 2Detection of Cpn DNA in various brain regions (olfactory bulbs, cerebellum, hippocampus, subventricular zone and neocortex) with real-time PCR at 1 week, 1 month and 3 months post infection1 week p.i.1 month p.i.3 months p.i.Olfactory bulbs310.97 ± 61.600Cerebellum2.6 ± 0.800Hippocampus7.9 ± 0.800Subventricular zone000Neocortex000Data were expressed as mean values of three independent experiments ±SEM
Detection of Aβ and GFAP in the brain of BALB/c and APP751SL/PS1M146L mice
Amyloid beta immunoreactivity was found in the brains of all mice, i.e. in Cpn-infected BALB/c mice at both 1 and 3 months p.i. (Fig. 1a), mock-infected BALB/c mice at both 1 and 3 months after mock infection (Fig. 1b), not inoculated BALB/c mice (Fig. 1c) and in APP751SL/PS1M146L mice (Fig. 1d). However, Aβ-immunopositive aggregates were found only occasionally in the brains of the BALB/c mice (on average one to two aggregates per section without a preference for a certain brain region), whereas they were found abundantly in hippocampus and neocortex of APP751SL/PS1M146L mice (see also Fig. 2H in [24]). Furthermore, there were several qualitative differences between the APP751SL/PS1M146L and the BALB/c mice. First, in the brain of APP751SL/PS1M146L mice, the materials immunopositive for Aβ formed a dense core, devoid of Hoechst-positive structures (i.e. cell nuclei). In contrast, the materials immunopositive for Aβ in the brain of BALB/c mice did not present with a dense core, and Hoechst-positive structures were scattered within these materials. Second, the astrocytic reaction in the brain of APP751SL/PS1M146L mice, surrounding the Aβ-immunopositive materials, was much stronger than in the brain of BALB/c mice (arrowheads in Fig. 1a–d). Third, in the brain of APP751SL/PS1M146L mice, cells with intracellular deposits of Aβ were found (asterisk in Fig. 1d), which was not observed in the brain of BALB/c mice, irrespective of treatment.
Fig. 1Immunohistochemical detection of Aβ (green) and GFAP (red) in 30 μm-thick cryostat sections counterstained with Hoechst (blue) from the brain of a 6-month-old Cpn-infected BALB/c mouse 3 months post infection (a), a 6-month-old mock infected BALB/c mouse 3 months after mock infection (b), a 6-month-old BALB/c mouse neither infected with Cpn nor mock infected (c) and a 17-month-old APP751SL/PS1M146L transgenic mouse (d). Note that material immunopositive for Aβ was found in the brain of all mice (arrows). However, there were differences between the APP751SL/PS1M146L mouse and the BALB/c mice: (1) in the brain of the APP751SL/PS1M146L mouse the material immunopositive for Aβ formed a dense core devoid of Hoechst-positive structures (i.e. cell nuclei). In contrast, the material immunopositive for Aβ in the brain of the BALB/c mice did not present with a dense core, and Hoechst-positive structures were scattered within it. (2) The astrocytic reaction in the brain of the APP751SL/PS1M146L mouse surrounding the material immunopositive for Aβ was much stronger than in the brain of the BALB/c mice (arrowheads). (3) In the brain of the APP751SL/PS1M146L mouse cells with intracellular deposits of material immunopositive for Aß were found (asterisk in d). This was not observed in the brain of the BALB/c mice. Furthermore, extracellular thioflavin-S-positive structures in the brain of the 17-month-old APP751SL/PS1M146L mouse are shown [e (arrowheads) and f, resembling fibrillary Aβ deposits. Scale bar = 25 μm in a–d, 100 μm in e and 10 μm in f
Detection of thioflavin-S-positive structures in the brain of APP751SL/PS1M146L but not in the brain of BALB/c mice
In the brain of APP751SL/PS1M146L mice, extracellular thioflavin-S-positive structures were found (Fig. 1e, f) as also previously reported [4, 26], resembling fibrillary Aβ deposits. Such extracellular thioflavin-S-positive structures were not found in the brain of BALB/c mice.
Discussion
The role of inflammatory factors, predominantly caused by infections, in the initiation or acceleration of neurodegenerative diseases is increasingly recognized. Next to several viruses [6, 12, 13, 17], the obligatory intracellular bacterium Cpn has also been linked to a variety of neurodegenerative diseases such as AD. In 1998, the presence of this pathogen was described in postmortem brains from patients with AD [2]. That Cpn may contribute to neurodegenerative processes was recently supported by data from our laboratory, as we demonstrated that Cpn is able to infect neurons, ultimately resulting in neuronal cell death in vitro [5]. However, to the best of our knowledge only one experimental in vivo study has been published so far supporting a role for Cpn in AD [19]. In this study, naïve BALB/c mice were inoculated intranasally with Cpn at an age of 3 months. Brains were recovered at 1–3 months p.i., revealing the presence of amyloid-like plaques and astrocyte reactivity. Due to the possible impact of this study, it was our goal to confirm these data by repeating these experiments. Focusing on our results of Cpn-inoculated mice at 3 months p.i., material immunopositive for Aβ was indeed detected in the brain. Accordingly, at first glance, our results are in line with the study of Little et al. [19]. Comparing the morphological appearance of this material immunopositive for Aβ with corresponding material in the brain of APP751SL/PS1M146L mice, however, some significant and important differences were observed. First, one characteristic of neuritic (AD) plaques is the presence of filamentous, star-shaped aggregates of Aβ fibrils with a thioflavin-S-positive dense core. However, neither such star-shaped aggregates of Aβ fibrils nor extracellular thioflavin-S-positive structures were found in any of the brains of Cpn infected or mock infected BALB/c mice, whereas both lesions were abundant in the brain of APP751SL/PS1M146L mice. Second, no damage was found in the tissue surrounding the material immunopositive for Aβ. Rather Hoechst-positive structures (i.e. cell nuclei) could be detected within the material immunopositive for Aβ, a feature that was not seen in the brain of APP751SL/PS1M146L mice. Finally, although astrocytes were found in the vicinity of the material immunopositive for Aβ in the brain of Cpn infected or mock infected BALB/c mice, they were not intimately associated with the Aβ immunoreactivity, as seen in the brain of APP751SL/PS1M146L mice. In summary, except that Aβ plaques were detected in most brains, our data suggest that at 3 months after Cpn infection most characteristic features of AD pathology could not be detected in 6-month-old naïve, non-transgenic BALB/c mice, whereas these were prominent in the brain of the “control” APP751SL/PS1M146L mice. It may still be argued that the material immunopositive for Aβ in the brain of the Cpn-infected mice represented early precursors of neuritic plaques. To our surprise, however, nearly identical patterns of Aβ immunoreactivity were detected in mock-infected BALB/c mice as well as in BALB/c mice, neither Cpn infected nor mock infected. This suggests that the observed immunoreactivity for Aβ in the brain of BALB/c mice does not directly result from Cpn infection but might represent unrelated elevations in amyloid. Positive staining for Aβ in the brain of mock-infected mice was also observed by Little et al. [19], who found that the amyloid deposition load and size were significantly enhanced in Cpn-infected mice compared to mock infected ones. As outlined above, this was not found in the present study. Furthermore, Little et al. also demonstrated the presence of a small subset of thioflavin-S positive plaques, which were not found in our mice. These discrepancies between the study by Little et al. [19] and the present study could be due to the fact that Little et al. [19] used a Cpn strain (C. pneumoniae, 96-41, isolated from the postmortem brain of an AD patient) that was rather different from the Cpn strain used in the present study (TWAR 2043, ATCC). Notably, Little et al. [19], were able to detect Cpn in olfactory tissues of infected mice up to 3 months p.i., by both light and electron microscopy. In the present study, however, we were only able to detect Cpn DNA in the olfactory bulb of infected BALB/c mice at 1 week p.i. but not at 1 or 3 months p.i., despite the use of a more sensitive detection method (real-time PCR). As it is well established that the clinical isolates of pathogens are often more infectious than their laboratory counterparts, this could partially explain the discrepancy between both studies. Furthermore, it cannot be excluded that differences in solutions used for mock infection (Hanks balanced salt solution in the study by Little et al. [19] vs. SPG solution in the present study) may have affected the final outcome. Yet, in the present study, the immunosignal in mice that were neither Cpn infected nor mock infected appeared somewhat weaker than in the brain of the BALB/c mice that were either Cpn- or mock-infected (a detailed quantitative analysis was not performed). This suggests that intranasal application of Cpn or other infectious agents may trigger the production and deposition of Aβ in the brain of BALB/c mice. For the time being, however, the impact of this remains unclear.
In conclusion, our data do not unequivocally support the hypothesis that Cpn infection induces the formation of AD like Aβ plaques in the brain of BALB/c mice, as suggested by Little et al. [19]. Accordingly, future studies are required to investigate whether Cpn infections may aggravate AD pathology by stimulating the formation of Aβ plaques as has been suggested in atherosclerosis, a disease that is also characterized by pronounced local inflammation.

chlamydia pneumoniae
neuroinflammation
amyloid-beta


Liver Transplantation Across Rh Blood Group Barriers Increases the Risk of Biliary Complications

Background Cold ischemia time and the presence of postoperative hepatic arterial thrombosis have been associated with biliary complications (BC) after liver transplantation. An ABO-incompatible blood group has also been suggested as a factor for predisposal towards BC. However, the influence of Rh nonidentity has not been studied previously.
Introduction
Biliary complications (BC) after liver transplantation (LT) are still a significant cause of morbidity and mortality.1,2 Hepatic arterial thrombosis, long ischemia time, inadequate exposure of the biliary epithelium to the preservation solution, and chronic rejection have been associated with BC.3–5 However, complications may also occur in the absence of these pathogenic factors. The expression of donor ABH antigen in the vascular and biliary epithelium of hepatic allografts has been linked to higher risk of biliary and arterial complications, especially in ABO-incompatible grafts.6 However, the influence of Rh nonidentity in LT has not been analyzed until now.
The aim of this study is to analyze the incidence of BC after LT, to define their etiological risk factors, and to study the influence of Rh nonidentity on presentation of BC after LT.
Materials
Three hundred and fifty-six (n = 356) liver transplants were performed at our hospital between January 1995 and November 2000. Eleven were excluded from the study because important data were missing (n = 345). The study was closed on May 2001 with a 6-month-minimum graft follow-up. The procurement procedure was based on the rapid flush technique.7 After reperfusion of the allograft, cholecystectomy was performed, the donor bile duct being divided just above the cystic duct junction in most cases. Biliary anastomosis was performed with interrupted stitches of 6-0 Polydioxanone suture Ethicon® (Johnson & Johnson, Brussels, Belgium). The biliary reconstruction technique was end-to-end anastomosis in the majority of LTs (315 patients, 91%). T-tubes were used in four patients due to diameter discrepancy between the biliary ducts of the donor and the recipient. If the primary disease affected the biliary tract, or if technical factors made end-to-end anastomosis difficult, Roux-en-Y-hepaticojejunostomy was performed (25 out of 345, 7%). The outcome was assessed in terms of biliary and arterial complications and patient status (alive, retransplanted, or dead). BCs were studied routinely with abdominal ultrasonography at day 1 post LT, weekly before discharge from hospital and monthly thereafter or when there was clinical or biochemical suspicion of BC, and this was confirmed with magnetic resonance cholangiography, percutaneous transhepatic cholangiography, or endoscopic retrograde cholangio-pancreatography when necessary. The immunosuppressive regimen was based on quadruple sequential therapy with antilymphocyte globulin, Sandimmune Neoral® (Novartis, Basel, Switzerland), steroids, and azathioprine in most patients, as reported elsewhere.8 Cellular rejection was diagnosed according to histological criteria.9
Statistical analysis Chi-squared analysis was used to compare dichotomous variables and the presence of BC. Variables that were statistically significant in the univariate analysis were introduced in a multivariate logistic regression analysis. Other known risk factors for developing BC were introduced into the model as covariates: donor age, acute rejection, and chronic rejection. Variables with P > 0.05 were excluded from the final equation. Kaplan–Meier estimates of the rate of BC for both groups and the results were compared with a log-rank test.
Results
Perioperative data and surgical details The preservation solutions used were the University of Wisconsin solution in 332 (97%), Celsior solution in 5 (1%), and histidine-tryptophan-ketoglutarate solution in 8 (2%). Twenty grafts (6%) were shipped by another team. The study of donor risk factors demonstrated that there was no hemodynamic instability (systolic pressure lower than 60 mmHg for more than 1 h) in 265 cases (77%), and that the main cause of death was traumatic head injury (129/36%). Donor Rh blood group was positive in 301 (87%) and negative in 44 cases (13%). Recipient Rh blood group was positive in 284 (82%) patients, and negative in 61 (18%). Rh blood groups of donor and recipient were positive to positive in 255 cases (74%), positive to negative in 46 (13%), negative to positive in 30 (9%), and negative to negative in 14 (4%). Donors and recipients had identical Rh in 269 cases (78%) and nonidentical in 76 (22%). The donor ABO blood group was A in 157 (46%), B in 29 (8%), AB in 19 (5%), and O in 140 (41%), while recipients were A in 161 cases (46%), B in 34 (10%), AB in 20 (6%) and O in 130 (38%). The ABO groups of transplanted grafts and hosts were identical in 97% of cases (335 patients) and compatible in 10 cases. There were no cases of incompatible grafts.During postoperative development, 25 LTs presented arterial thrombosis (7%). Eight grafts presented primary nonfunction and were retransplantated (2.2%), and 43 presented initial poor function that recovered spontaneously (12%).3 Biopsy-proven acute rejection was diagnosed in 66 grafts (19%) after transplantation, with development to chronic ductopenic rejection in 8 (2%).
BCs and Rh mismatch BCs appeared in 70 of the 345 patients (20%). Cross-tabs were built to analyze differences between the grafts that suffered BCs and the rest. Both groups were similar in terms of donor preoperative evaluation and support, recipient descriptive data, and surgical terms. The incidence of BC in nonidentical Rh graft–host cases (23/76; 30%) was significantly higher than in cases of identical Rh graft–host (47/269; 17%, P = 0.01). Cases of arterial thrombosis after LTs and ischemia time longer than 430 min were also associated with a higher incidence of BC (Table 1).
Table 1Demographics and Major Complications Occurring in Both Groups Studied, Chi-square Test BCs (n = 70)No BCs (n = 275)Chi-squareDonor dataABO blood group0.2 A25/157 (16%)132/157 (84%) B6/29 (21%)23/29(79%) AB6/19 (32%)13/19(68%) O33/140 (24%)107/140 (76%)Rh blood group0.4 Positive59/301 (19%)242/301 (81%) Negative11/44 (25%)33/44 (75%)Sex0.4 Male51/233 (22%)182/233 (75%) Female19/110 (17%)91/110 (83%)Donor age0.4 ≤70 years66/320 (20%)254/320 (80%) >70 years4/25 (16%)21/25 (84%)Recipient dataRh blood group0.2 Positive54/284 (19%)230/284 (81%) Negative16/61 (26%)45/61 (74%)Rh D-R crossing0.1 Positive–positive45/255 (18%)210/255 (82%) Positive–negative14/46 (30%)32/46 (70%) Negative–positive9/30 (30%)21/30 (70%) Negative–negative2/14 (14%)12/14 (86%)Rh D-R identity0.01 Identical47/269 (17%)222/269 (83%) Nonidentical23/76 (30%)53/76 (70%)ABO blood group0.4 A27/161 (17%)134/161 (83%) B7/34 (20%)27/34(80%) O30/130 (23%)100/130(77%) AB6/20 (30%)14/20 (70%)ABO D-R identity0.4 Identical67/335 (20%)268/335 (80%) Nonidentical3/10 (30%)7/10 (70%)Sex of recipient0.4 Male48/214(22%)166/214 (78%) Female22/130(17%)108/130(83%)Recipient age0.2 <60 years41/221 (18%)180/221 (82%) ≥60 years29/124 (24%)95/124 (76%)Diagnosis0.3 Choleostasis2/14(15%)12/14(85%) Cirrhosis32/162 (20%)130/162(80%) Hepatocarcinoma27/103(26%)76/103(74%) Other etiology1/18(5%)17/18(95%) Re-OLT7/39(18%)32/39 (89%) Other tumors1/9(11%)8/9 (89%)Surgical dataCold ischemic time0.01 ≤430 min26/178 (15%)148/178 (85%) >430 min44/171 (26%)127/171 (74%)Type of anastomosis0.6 Termino-terminal64/315(20%)251/315(80%)Graft evolution dataArterial thrombosis0.03 Yes9/25 (36%)16/25(64%) No60/319 (19%)259/319(81%)Initial poor function0.6 Yes10/43 (23%)33/43 (77%) No60/302 (20%)242/302 (80%)Primary nonfunction0.1 Yes08/8 (100%) No70/336 (20%)266/336 (80%)Acute rejection0.4 Yes11/66(17%)55/66(83%) No59/279(21%)220/279(79%)Chronic rejection0.5 Yes1/8(12%)7/8(88%) No69/337 (20%)268/337(80%)OLT orthotopic liver transplantation
Multivariate Analysis of Risk Factors for BCs
In multivariate analysis, arterial thrombosis presented an adjusted relative risk (aRR) = 2.6, CI 95% = (1.1–6.4) (P = 0.02), cold ischemia time aRR = 1.8 (CI 95%  = 1–3.2) (P = 0.02), and Rh graft–host nonidentity aRR = 2 (CI 95% = 1.1–3) (P = 0.02) were confirmed as independent risk factors for BC. Other variables included in the initial analysis were nonsignificant (Table 2). Kaplan–Meier estimator and log-rank test confirmed these findings (P = 0.01, Fig. 1). To discard the possible association between the Rh match and the two other risk factors, we demonstrated that arterial thrombosis had similar incidence in Rh-nonidentical grafts (3/75, 4%) and in Rh-identical grafts (22/269, 8%; P = 0.1). Moreover, grafts with long ischemia times (>430 min) had similar incidence in the Rh-nonidentical (42/76, 55%) group and in Rh-identical patients (129/269, 48%; P = 0.1).
Table 2Biliary Complications Univariate logistic regressionMultivariate logistic regressionDonor age >70 years0.50.7Rh D-R identity identical nonidentical0.01; 2(1.1–3.6)0.02; 2 (1.1–3.6)Cold ischemic time >430 min0.01; 1.9(1.1–3.3)0.02; 1.8(1–3.2)Arterial thrombosis (yes)0.04; 2.4(1–5.7)0.02; 2.6 (1.1–6.4)Acute rejection (yes)0.40.4Chronic rejection (yes)0.50.5Univariate and multivariate logistic regression.Figure 1Kaplan–Meier estimates for the onset of BCs for nonidentical Rh graft–host (P = 0.01).
Type and Management of BCs
BCs were diagnosed in 70 patients. Biliary duct anastomosis stricture was the main complication, presented clinically, with (7/10%) or without postoperative leak (21/30%). Solitary leaks (17/24%), ischemic-type BCs (ITBC) with arterial thrombosis (6/9%), ITBC without arterial thrombosis (10/14%), and lithiasis (9/13%) were also related complications. The therapeutic approach was surgical in 23 patients (33%), endoscopic in 20 (28%), retransplantation in 11 (16%), and conservative treatment in 16 (23%).
Chi-square test was performed to analyze differences between the type of BC and the three risk factors found. As would be expected, arterial thrombosis was identified in all the cases of ITBC with thrombosis, in a higher percentage than the other BCs (P < 0.001). However, the different types of BCs were not associated with long ischemia time (P = 0.2) or Rh-identity (P = 0.4).
Finally, chi-square test was also performed to analyze differences among the three BC risk factors and medical management. The therapeutic approach regarding Rh-mismatch was similar in both groups (P = 0.3). Retransplantation was a frequent approach in arterial thrombosis management (4/9, 40%; P = 0.04). Interestingly, when the BC was presented in grafts with short ischemic time (<430 min), management by surgery (10/26, 38%) or an endoscopic approach (10/26, 38%) were sufficient, and it was not necessary to retransplant. However, when the BC arose in a graft with a long ischemic time (>430 min) the management was more aggressive, with 29% of patients (13/44) needing surgery and 25% (11/44) needing retransplantation.
Discussion
Etiopathogenesis of BC
Currently, orthotopic liver transplants are performed with good results at several centers without taking the donor–recipient Rh relationship into account. In fact, no prior work has shown greater morbidity or mortality after the usage of Rh-incompatible liver grafts. However, it was in studying the causes of BC in our grafts in prior studies that we began to suspect the existence of a possible relationship between BC and Rh incompatibility. First, we observed a greater rate of BC in the presence of preservation lesions in postreperfusion biopsies.10 Thus, we found that the biliary epithelium is very sensitive to changes during preservation. Second, the description of lesions like ITBC,11 to which surgical technique does not contribute as a primary cause, led us to suspect possible immunological pathogenesis of the BC. Lastly, it is paradoxical to find that, while surgical technique is improving and satisfactory results are achieved in liver transplants, BCs are still a problem, leading us to suspect that there are unidentified factors that cause them. The arterial thrombosis and long cold ischemia time were independent risk factors for developing BC, as was expected and reported by others.11,12 In our study we have demonstrated that even though BC in grafts with short ischemia can be resolved with surgery or endoscopy, the prognosis of BC was worse in cases of long ischemic grafts requiring retransplantation.
Relationship Between ABO and Rh in Liver Transplants
As for the donor–recipient ABO relationship, the usage of ABO-incompatible grafts has been discouraged due to the high rate of BC and poor graft survival.6,13,14 In the Sanchez-Urdazpal study,6 82% of the 18 ABO-incompatible grafts presented BC in comparison with 6% of the control group, while Farges,14 published slightly better results several years later with BC of 54%. Therefore, these grafts are used in extremely urgent cases when there is no other possible alternative. Immunological phenomena, such as rejection,3 may also lead to biliary strictures. In the same way, the ABO system was shown to cause more BC and worse graft survival in LT.6,13–15 However, Rh nonidentity seems to have better tolerance and is not a cause of graft refusal when a donor appears. Surprisingly, our study demonstrated a higher incidence of BC in the Rh-nonidentical group.
Some authors16,17 reported low rates of alloimmunization in Rh-negative recipients of Rh-incompatible transfusion after LT. It was suspected that immunosuppressant drugs modified the immunosuppressive response.16 However, debate still exists as to whether the D barrier can be crossed in LT. Previous studies of ABO barrier and BC suggested the hypothesis of an immunological injury to the bile duct epithelium, and the expression of ABH antigens in the donor 150 days after transplantation.6 However, the D antigen is only expressed in erythrocytes.
The nonidentical Rh group has two mismatch possibilities: positive donor to a negative recipient or negative donor to positive recipient. In the first case (positive to negative) the immunologic mechanism is easy to understand because the humoral anti-D (Rh) response may be responsible for the graft injury.
The other subgroup (negative to positive) may have a different pathogenic explanation. Bryan et al.17 hypothesized that two mechanisms could be involved in the same process in kidney transplantation: other Rh antigenic loci (C and E) and histocompatibility antigenic crossings. A negative liver graft in a positive recipient with lymphatic cells and tissues predisposes to cellular response against it. Finally, the biliary tract can probably suffer immunological damage, and thus further BC. Therefore, while the results of the study lead to suspicion of an immunological pathogenesis, the mechanism is still unclear.
In conclusion, Rh-nonidentical LT involves a higher rate of BCs. Future studies should examine the influence of Rh donor and blood group on graft development. Finally, our results suggest that there is a summation effect of BC risk factors. In our opinion, Rh-nonidentical liver grafts should not undergo a very long ischemia time.

liver transplantation
biliary complications
rh nonidentity


Genomotyping of Pseudomonas putida strains using P. putida KT2440-based high-density DNA microarrays: implications for transcriptomics studies

Pseudomonas putida KT2440 is the only fully sequenced P. putida strain. Thus, for transcriptomics and proteomics studies with other P. putida strains, the P. putida KT2440 genomic database serves as standard reference. The utility of KT2440 whole-genome, high-density oligonucleotide microarrays for transcriptomics studies of other Pseudomonas strains was investigated. To this end, microarray hybridizations were performed with genomic DNAs of subcultures of P. putida KT2440 (DSM6125), the type strain (DSM291T), plasmid pWW0-containing KT2440-derivative strain mt-2 (DSM3931), the solvent-tolerant P. putida S12, and several other Pseudomonas strains. Depending on the strain tested, 22 to 99% of all genetic elements were identified in the genomic DNAs. The efficacy of these microarrays to study cellular function was determined for all strains included in the study. The vast majority of DSM6125 genes encoding proteins of primary metabolism and genes involved in the catabolism of aromatic compounds were identified in the genomic DNA of strain S12: a prerequisite for reliable transcriptomics analyses. The genomotypic comparisons between Pseudomonas strains were used to construct highly discriminative phylogenetic relationships. DSM6125 and DSM3931 were indistinguishable and clustered together with strain S12 in a separate group, distinct from DSM291T. Pseudomonas monteilii (DSM14164) clustered well with P. putida strains.
Introduction
The sequencing and annotation of the Pseudomonas putida KT2440 genome (Nelson et al. 2002) has greatly catalyzed research on this strain and other academically and biotechnologically relevant but non-sequenced P. putida strains. A number of key scientific advances (both pure and applied) have been made via proteome and transcriptome analysis of P. putida strains (Dominguez-Cuevas et al. 2006; Hallsworth et al. 2003; Morales et al. 2006; Reva et al. 2006; Santos et al. 2004; Segura et al. 2005; Volkers et al. 2006; Yuste et al. 2006). In our laboratories, we have studied both P. putida KT2440 (for recent publications: Hallsworth et al. 2003; Martins dos Santos et al. 2004; Nelson et al. 2002; Timmis 2002) and the solvent-tolerant P. putida S12 (De Bont 1998; Hartmans et al. 1990; Wery and De Bont 2004). Unlike most pseudomonads, solvent-tolerant strains possess an extraordinary tolerance to a broad variety of toxic solvents (for reviews: De Bont 1998; Kieboom and De Bont 2000; Ramos et al. 2002). A large number of high-added value bioconversions involve toxic, generally apolar, products (aromatics, aliphatic alcohols, epoxides, etc.). Especially for bioprocesses involving such products, the use of solvent-tolerant P. putida strains renders advantages in terms of productivity and the application of multiphase media for product recovery (Ramos-Gonzalez et al. 2003; Rojas et al. 2004; Wery and De Bont 2004; Wery et al. 2000; Wierckx et al. 2005). Despite their biotechnological potential, the genomes of S12 and other Pseudomonas strains have not been sequenced. So, recent studies of their metabolic potential have been limited to comparative proteomics (Segura et al. 2005; Volkers et al. 2006) or transcriptomics based on the database information for P. putida KT2440.
The genus Pseudomonas is very heterogeneous (Anzai et al. 2000); even strains of one species tend to vary greatly in terms of both phenotypic (Grimont et al. 1996) and genotypic characteristics (Brosch et al. 1996). Strains belonging to the species P. putida can be categorized into biovar A and B: the former grouping (A) is the more heterogeneous (Brosch et al. 1996) and the phylogenetic and metabolic diversity of biovar A strains has yet to be fully characterized and industrially exploited.
Comparative transcriptomics-based approaches have played a pivotal role in recent investigations of complex cellular responses of P. putida strains (Dominguez-Cuevas et al. 2006; Duque et al. 2007; Yuste et al. 2006). As transcriptome profiling is based on the highly-sensitive detection of DNA–cDNA hybridization, DNA sequence similarity determines the validity of these analyses. The inherent heterogeneity of the P. putida grouping provides additional cause for concern that microarray platforms derived from strain KT2440 may provide a valid framework for the study of nonsequenced P. putida strains.
We therefore carried out this study to assess the utility of KT2440-based high-density DNA microarrays for transcriptomics studies of DSM 6125 (subculture of KT2440), DSM 3931 (subculture of mt-2), DSM 291T (the P. putida type strain), the solvent-tolerant S12, and other nonsequenced Pseudomonas strains. In addition, the use of such microarrays to establish highly discriminative phylogenetic relationships between these strains was demonstrated.
Materials and methods
Strains, cultivation conditions, and DNA extraction
Single colonies from each Pseudomonas strain studied (see Table 1) grown on Pseudomonas Isolation Agar (Difco) were used to inoculate Luria–Bertani broth (LB) (Sambrook et al. 1982) in duplicate. After cultures were grown overnight at 30°C, genomic DNAs were prepared from 5 ml of culture (approximately 3 × 108 cells/ml) using the Genomic DNA 100/G Kit (Qiagen, Germany) according to the manufacturer’s instructions. DNA concentrations were determined at 260 nm using ND-1000 spectrophotometer (NanoDrop, Wilmington, DE, USA), and purity was confirmed by agarose (1% w/v) gel electrophoresis.
Table 1Source and designations of Pseudomonas strains used in this studySpecies and strain numberaSource or referencebOther designationscP. putida DSM 6125DSMZKT2440, ATCC 47054; NCIMB 11950P. putida DSM 3931DSMZmt-2, ATCC 23973; ATCC 33015; JCM 6156; NCIMB 12182P. putida S12Hartmans et al. 1990ATCC 700801P. putida DSM 291TDSMZDSM 50202T; ATCC 12633T; ICPB 2963T; NCTC 10936T; CCUG 12690T; LMG 2257TP. putida DSM 50198DSMZATCC 17453; ICPB 2563-77; JCM 6157; NCIMB 10007P. putida DSM 50208DSMZATCC 17485; ICPB 2789-111; JCM 6158; NCIMB 12092P. monteilii DSM 14164DSMZATCC 700476; CCUG 38736; LMG 21609; CIP 104883P. fluorescens DSM 50090TDSMZATCC 13525T; ICPB 3200T; NCIMB 9046T; NCTC 10038T; CCUG 1253T; LMG 1794TaStrain designations used in this studybDSMZ Deutsche Sammlung von Mikroorganismen und Zellkulturen, GermanycSubcultures of the used strains available in other culture collections. ATCC American Type Culture Collection, ICPB International Collection of Phytopathogenic Bacteria, USA, NCIMB National Collections of Industrial and Marine Bacteria, UK, NCTC National Collection of Type Cultures, UK, JCM Japan Collection of Microorganisms, Japan, CIP Collection bacteriènne de l’Institut Pasteur, France, CCUG Culture Collection University Göteborg, Sweden, LMG BCCM/LMG Bacteria Collection, Belgium.
Preparation of biotin-labelled fragmented genomic DNAs
Approximately 17 μg of heat-denatured genomic DNA was fragmented using 0.3 U DNaseI (Pharmacia)/μg DNA at 37°C for 20 min (Wolfgang et al. 2003). DNaseI was inactivated by immediate heat treatment at 95°C for 10 min, followed by cooling on ice. An aliquot of fragmented DNA was analyzed by agarose (4% w / v in TAE buffer) gel electrophoresis to confirm the purity and establish the mean size of DNA fragments (20–50 bp). Approximately 7 μg of fragmented DNA was labelled with biotin according to the Affymetrix “Expression Analysis Technical Manual for Prokaryotic Samples”.
Design of high-density oligonucleotide microarrays and hybridization with genomic DNAs
High-density oligonucleotide microarrays based on the annotated genome of P. putida KT2440 (NC 002947.3) were constructed according to Affymetrix specifications (http://www.affymetrix.com/support/technical/other/custom_design manual. pdf ) with the pair-wise configuration of 13 perfect match (PM) and mismatch (MM) 25-mer oligonucleotides per gene (probe set). The microarrays included 7,781 probe sets: 5,338 representing 5,350 annotated genes or open reading frames (orfs), and 2,443 targeting intergenic regions. Hybridization and washing of the microarrays were performed on a GeneChip® Node system (Hybridization temperature 49°C; Hybridization oven 640, Fluidics station 450; Affymetrix, Santa Clara, CA, USA) following the supplier’s instructions. Scanning was carried out by ServiceXS (Leiden, The Netherlands) on a high resolution Gene Chip® Scanner 3000 7G system with autoloader (Affymetrix, Santa Clara, CA, USA) using the default analysis settings of the manufacturer (filter: 570 nm; pixel size: 2.5 μm). Hybridization intensity data were extracted from the scanned array images, and the designations for presence or absence were calculated with Affymetrix Microarray Suite (MAS) 5.0 software. A one-sided Wilcoxon’s Signed Rank was used to calculate p-values reflecting the statistical significance of differences between PM and MM of a probe set. The significance levels for detection calls of probe sets were p-value < 0.05 for present and p-value ≥ 0.05 for absent. Differences between PM and MM were considered insignificant and removed from further consideration by comparisons of the discrimination score [(PM−MM)/(PM+MM)] with the defined discrimination threshold τ (discrimination score<0.015). Comparisons of present/absent designations for genes in the different strains were made with GeneSpring version 7.2 (Agilent). Replicate assays of biological duplicates were performed for P. putida DSM 6125 and S12. The deviation caused by present/absent designations of genes in only one of both replicates was 0.8% for DSM 6,125 and 4.2% for S12 regarding all probe sets for putative genes and intergenic regions (Table 2).
Table 2Similarity indices based on designations of presence or absence (stated in brackets) from hybridization signals of genomic DNA fragments from different Pseudomonas members on P. putida KT2440-based microarraysaStrainPresent (absent) in percent (%)Putative genes and intergenic regions (7,781 probe sets)Putative genes (5,338 probe sets)Putative genes with assigned function (3,670 probe sets)P. putida DSM 6125b97.6 (1.4)99.6 (0.0)99.8 (0.0)P. putida DSM 393198.6 (1.4)99.9 (0.1)100.0 (0.0)P. putida S12b78.0 (16.9)81.8 (13.9)86.9 (9.7)P. putida DSM 291T60.5 (39.5)64.2 (35.8)69.3 (30.7)P. putida DSM 5019856.2 (43.8)60.7 (39.3)66.5 (33.5)P. putida DSM 5020858.8 (41.2)62.6 (37.4)67.8 (32.2)P. monteilii DSM 1416457.7 (42.3)61.7 (38.3)66.8 (33.2)P. fluorescens DSM 50090T22.0 (78.0)27.2 (72.8)30.7 (69.3)aPresent/absent designations (present: p < 0.05; absent: p ≥ 0.05; τ = 0.015) derived from a decision matrix in Affymetrix MAS 5.0bReplicate assays were performed for P. putida DSM 6125 and S12 and the values listed include only genes that were designated present or absent in both replicates.
Calculation of dendrograms
Simple matching similarity matrix based on the present/absent detection calls in microarray analysis of different pseudomonads (SM = m/n; m, number of matching probe sets; n, total number probe sets), and the microarray genotyping dendrogram were calculated using UPGMA parameters (unweighted pair group method, arithmetic average) and agglomerative hierarchical clustering with XLSTAT version 7.5.3 (Addinsoft, Paris, France).
AFLP analysis
In this paper, we used amplified fragment length polymorphism (AFLP) (Janssen et al. 1996; Savelkoul et al. 1999; Vos et al. 1995) to analyze different strains of P. putida a P. monteilii and a Pseudomonas fluorescens. AFLP is based on selective amplification of restriction fragments from totally digested genomic DNA. AFLP fingerprints were performed by KeyGene (Wageningen, The Netherlands). Genomic DNA of the different pseudomonads was digested using restriction enzyme combination NlaIII (Westburg, Leusden, The Netherlands) and Csp6I (Fermentas, St. Leon-Rot, Germany) according to manufacturers instructions. Each restriction enzyme was combined with the ligation of specific linker oligonucleotide pairs (NlaIII: 5-GACGATGAGTCCTGAG-3/5-TGTACGCAGTCTAC-3; Csp6I: 5-GACGAT GAGTCCTGAG-3/5-TACTCAGGACTCAT-3). For each of these linker combinations, AFLP was performed using nine N/C AFLP primer combinations, which were selected using KT2440 genome sequence as reference and software package REcomb (Keygene) for prediction analysis. These primers were extended with a 3′ terminal dinucleotide (+ 2) and the extensions were CA/AC, CA/CA, CA/CC, CA/GG, CA/TT, CT/TT, CT/CA, CT/CT, and CT/TC. For further detail, please refer to Van den Braak et al. (2004). PCRs were performed in the presence of radioactive nucleotides, and the amplimers obtained were separated on 50 × 20 cm polyacrylamide slabgels. Using phosphor-imaging, the individual presence/absence in a total of 757 markers (DNA bands) per strain was analyzed.
The total marker score data table (presence/absence of individual DNA bands) was subjected to genetic distance analysis using simple matching similarity matrix (SM = m/n; m, the number of matched scores; n the total sample size), consisting of similarity indices for all combinations of AFLP-banding patterns. Simple matching coefficients were calculated using NTSYSpc-software version 2.2 (Exeter Software, Setauket, NY, USA). To visualize the relationship between the strains, a dendrogram was generated using Sequential Agglomerative Hierarchical Nested (SAHN) cluster analysis with the use of UPGMA parameters.
Standard PCR and sequencing
Diagnostic PCR of selected putative orfs (PP1265, PP5224) that were called absent in microarray analyses of P. putida DSM 3931 genomic DNA was performed using proofreading enzyme mixture High Fidelity PCR master (Roche Diagnostics, The Netherlands) and specific primers (PP1265: forward: 5′-CTGCTGCACCAGGCCTAT-3′, reversed: 5′-TTGGTCACATAGCCGTCAAC-3′; PP5224: forward: 5′-CAACGGCTAAACCTTTGCAT -3′, reversed: 5′-AGGATCGAGACCTTGCCTTC-3′). Yielded PCR-amplicons of expected sizes, 1,108 and 1,062 bp, respectively, were sent to Baseclear (Leiden, The Netherlands) for sequencing according to Sanger et al. (1977) using nested sequencing primers (PP1265: 5′-CCAGGCAATCCGTGTCAT-3′; PP5224: 5′-GGTGTCCTGACCGTCAAGTT-3′), and the resulting sequences were used for sequence alignments.
Biological function-derived phylogenetic analysis
The concept of Clusters of Orthologous Genes (COG; see Tatusov et al. 1997, 2003) was used during analysis of the genomic content of nonsequenced Pseudomonas strains by linkage to primary biological function (Table 3). A COG consists of individual proteins or groups of paralogs from at least three lineages and thus corresponds to an ancient conserved domain (Tatusov et al. 1997, 2003). In the NCBI-COG database, 4,497 proteins of 5,350 putative orfs in P. putida KT2440 were identified as COGs and were arranged in functional categories (see http://www.ncbi.nlm.nih.gov/sutils/coxik.cgi?gi=266 ). Because of the limited number of COGs in the functional categories (A) RNA processing and modification, (B) chromatin structure/dynamics, and (W) extracellular structures (1, 2, and 3, respectively), these COGs were collectively grouped together with general function prediction COGs (Category R) under the designation R′(see Table 3).
Table 3Primary functional designationa of genes identified in P. putida S12 genomic DNACodebDescriptionCOGs in KT2440cCOGs KT2440 in S12FNucleotide transport and metabolism9387 (93.5%)JTranslation194178 (91.8%)HCoenzyme transport and metabolism183166 (90.7%)TSignal transduction mechanisms427387 (90.6%)ILipid transport and metabolism194174 (89.7%)NCell motility130116 (89.2%)GCarbohydrate transport and metabolism264232 (87.9%)PInorganic ion transport and metabolism368322 (87.5%)UIntracellular trafficking and secretion119104 (87.4%)EAmino acid transport and metabolism630550 (87.3%)OPosttranslational modification, protein turnover, chaperones194169 (87.1%)CEnergy production and conversion321276 (86.0%)QSecondary metabolites biosynthesis, transport and catabolism161138 (85.7%)KTranscription499426 (85.4%)VDefense mechanisms6656 (84.8%)R′General function prediction only733620 (84.6%)SFunction unknown442365 (82.6%)MCell membrane biogenesis288227 (78.8%)DCell cycle control5641 (73.2%)–Not in COGs853544 (63.8%)LReplication, recombination and repair269158 (58.7%)aBased on the Clusters of Orthologous Groups (COG) system, also see Materials and methods section)bCodes of functional categories of COGcProteins, 4,497 of 5,350 putative orfs in P. putida KT2440 can be found in COG database (http://www.ncbi.nlm.nih.gov/ COG/)
Results
Genomic DNA hybridizations with P. putida KT2440-based high-density DNA microarrays
Total genomic DNA from P. putida strains, and other nonsequenced Pseudomonas members (see Table 1), was hybridized to custom KT2440-based high-density oligonucleotide microarrays. Presence or absence designations for each probe set (designed for specific genes and intergenic regions) were calculated by the Affymetrix MAS 5.0 algorithm from the significant difference (see Materials and methods section) in hybridization intensities between the corresponding perfect match and mismatch oligonucleotides (Table 2). Absence designation is a synonym for the absence of significant signal values and stands for divergent DNA still coding a similar biological function or for the complete absence of the specific DNA. Replicate array hybridizations were performed for DSM 6125 DNA and S12 DNA only. For these DNAs the values given in Table 2 represent only probe sets designated present or absent in both replicates. As expected, the DSM 6125 DNA yielded an almost perfect score: 97.6% for probe sets corresponding to all genetic elements (including the intergenic regions) and 99.8% for probe sets designed for genes with an assigned function. Strain DSM 3931 (subculture of P. putida mt-2: Teruko, 2002) is a TOL plasmid (pWW0)-containing variant of DSM 6125 and was used as an additional control for the accuracy of the microarray experiments. As expected, both strains were virtually indistinguishable in the array hybridization study (Table 2). Nonetheless four orfs were indicated absent in DSM 3931. These orfs were found to be called absent in only one of the DSM 6125 replicates. We therefore used diagnostic PCR to investigate the presence of two of them (PP1265, PP5224) in DSM 3931; the other two appeared less important due to their limited size (~90 bp). In both cases, PCR products of the expected sizes (1,108 and 1,062 bp) were obtained that, after sequencing, proved to be identical to the KT2440 homologs.
Apart from DSM 3931, the solvent-tolerant S12 showed the highest genomic similarity to KT2440. Nearly 3,188 of 3,670 (86.9%) genes with assigned functions in the KT2440 genome were identified in the genomic DNA of S12 (Table 2). Approximately 71% of 1,668 (putative) genes without an assigned function, and 70% of the intergenic regions were found to be present in the S12 (data not shown).
Microarray-based genomotyping
A dendrogram was constructed (Fig. 1a) based on genomic similarity of all 7,781 genetic elements of KT2440 in the tested genomic DNAs except for the control strain DSM 3931 (Table 2). DSM 6125, DSM 3931 (not shown), and S12 clustered in a group separate from the other P. putida strains (Fig. 1a). The nonsequenced P. fluorescens (DSM 50090T), that was included as an out-group, did not cluster with any other strain. By contrast, Pseudomonas monteilii DSM 3931 that was included as a non-putida member clustered with other P. putida strains suggesting a closer relationship with the P. putida taxonomic grouping (Fig. 1). To assess the validity of these microarray-derived phylogenetic relationships, AFLP DNA fingerprinting was used to obtain an independent phylogenetic classification of strains (Janssen et al. 1996; Savelkoul et al. 1999; Vos et al. 1995). The phylogenetic tree constructed following AFLP analyses showed an identical pattern in terms of strain grouping. (Fig. 1b).
Fig. 1Genome similarity between different Pseudomonas strains. a High-density microarray genotyping tree based on absent/present designations generated by Affymetrix MAS 5.0 algorithm (Table 2) using simple matching similarity coefficient, UPGMA, and agglomerative hierarchical clustering. b AFLP-genotyping. Tree is based on the score of 757 AFLP markers using the simple matching similarity coefficient and Sahn cluster analysis
An inventory was made of genes encoding proteins belonging to COGs (Tatusov et al. 1997, 2003) that were identified in the different genomic DNAs (Fig. 2). Of 5,350 orfs in the KT2440 genomic DNA sequence, 4,497 encode proteins that have been categorized into classes of primary biological function based on the COG system (http://www.ncbi.nlm.nih.gov/ COG/). It was found that, after DSM 3931, P. putida S12 showed the highest present score in all functional classes (Fig. 2). The “present” designations for S12 genes encoding COG members ranged from 58.7 to 93.5%, depending on their primary biological function (Table 3, Fig. 2). The unequal distribution of the present designations over the different functional classes was also characteristic of other Pseudomonas strains (Fig. 2). Strikingly, all pseudomonads other than DSM 6125, DSM 3931, S12 and the out-group DSM50090T showed a comparable distribution pattern of identified genes over the different COGs.
Fig. 2Distribution of genes encoding COG proteins over different functional categories as identified in Pseudomonas strains. Functional categories were adopted from the COG database for P. putida KT2440 (http://www.ncbi.nlm.nih.gov/sutils/coxik.cgi?gi=266 ). The categories of biological function corresponding to COG codes are given in Table 3.
P. putida strains are well-known for their broad metabolic potential regarding aromatic compounds (Jimenez et al. 2002; Wackett 2003, for reviews). Several pseudomonads, including P. putida KT2440, have been reported to degrade and/or transform a variety of aromatics. Among these are compounds of industrial importance, and there is an interest in studying these metabolic pathways on the level of gene expression and regulation thereof. The utility of KT2440-based microarrays in transcriptomics studies of aromatics metabolism of the pseudomonads under investigation was appraised. The presence of 70 genes of central and peripheral metabolic routes of aromatics were chosen based on reports by Jimenez et al. (2002) and Wackett (2003) (Table 4). All genes (100%) were detected in DSM 6125, S12 and in DSM 3931. Higher divergence was found for P. putida DSM 291T (82.9% detectable), DSM 50198 and DSM 50208 (both 75.7% detectable), and DSM 14164 (74.3% detectable). DSM 50090T again behaved as an out-group with only 25.7% of the 70 genes detectable. In the P. putida strains used in the present study and DSM 14164, almost all genes for degradation of benzoate (ben), homogentisate (hmg, mai), phenylalanine/tyrosine (phh, tyrB), and catAB were detected. In DSM 291, 50198, 50208 and 14164 putative regulatory genes of different pathways (e.g. catR, pcaQ, pobR, phaNM) and some isoenzymes (e.g., catA2) were not found (Table 4). Interestingly, in all these strains, the muconolactone isomerase (catC), and in the catabolism of phenylpropenoid compounds, vanillin dehydrogenase (vdh), putative conifer aldehyde dehydrogenase (calB; except DSM 50198), and feruloyl-CoA synthetase (fcs; except for DSM 50208) were not identified and can thus not be studied in KT2440-based microarray-based transcriptome analysis.
Table 4Comparison of the presence/absence designations of genes encoding metabolic pathways of aromatic compounds in different pseudomonads derived from Affymetrix decision matrix MAS 5.0StrainPresent (%)aGenes of aromatic pathways not identified in hybridizations with genomic DNAP. putida DSM 6125100.0P. putida DSM 3931100.0P. putida S12100.0P. putida DSM 291T82.9catC; fadAxB2x; fcs; pcaQ; phaDHMN; vdh; calB; catA2P. putida DSM 5019875.7benE-1; catCR; ech; fadAxB2x; fcs; pcaDQ; phaGHMN; pobAR; vdh; catA2P. putida DSM 5020875.7catCR; ech; fadAxB2x; pcaBCQ; phaDHJMN; phhR; pobR; vdh; calB; catA2P. monteilii DSM 1416474.3benE-1; catCR; ech; fadB2x; fcs; pcaBDQ; phaEGHMN; pobAR; vdh; calB; catA2P. fluorescens DSM 50090T25.7aat; acdA; benACDE-1E-2FKR; catABCR; fadAxBB1xB2xDx; fcs; hpd; pcaCDHIJKQT; pcm; phaAB-EG-ILMN; phhBR; pobAR; tyrB-2; vanAB; vdh; calB; ferR; catA2aSelected 70 genes (100%) involved in catabolism of aromatic compounds annotated in P. putida DSM 6125: protocatechuate (pcaBCDFGHIJKQRT), catechol branches β-ketoadipate pathway (catABCRA2), homogentisate pathway (hmgA, mai), central meta-cleavage (pcm), p-hydroxybenzoate (pobAR), benzoate (benABCDE-1E-2FKR), phenylacetate pathway (phaABCDEFGHIJKLMNJ1), n-phenylalkanoic acids (fadABDHAxB1xB2xD2Dx), phenylpropenoid compounds (vanAB, vdh, calB, ferR, fcs, ech, aat, acdA), and phenylalanine/tyrosine (phhABR, tyrB-1B-2, hpd, pcm).
Discussion
A major advantage of microarray-based comparisons of species is the ability to pinpoint differences in individual genes and intergenic regions. Through these comparisons, detailed insight was gained in the utility of P. putida KT2440-based microarrays in transcriptomics studies of different pseudomonads at the level of specific categories of biological function. It was found that genes involved in, e.g., “translation” and “nucleotide transport and metabolism” could be identified at a high frequency (>80%) in all P. putida strains tested, in contrast to other functional groups where the frequency of gene identification dropped below 60%. Whereas P. fluorescens DSM 50090T behaved as a typical out-group in these functional studies, it was clear that, depending on the functional category, up to 45% of the genes of this strain could still be identified.
There is a biotechnological requirement for P. putida biocatalysts that can function at high solvent concentrations, such as strain S12, and that can be swiftly optimized for different bio-based production processes. In our group, studies have focused on the construction of strains that are able to convert renewable substrates, such as sugars, into aromatics of interest via central metabolites (Nijkamp et al. 2005, 2006; Wierckx et al. 2005). These conversions take place via multiple metabolic pathways each consisting of several enzymatic steps with regulatory mechanisms that are being investigated using S12 as a model system. A comparative transcriptomics approach is invaluable to gain detailed insights into the complex cellular systems of the metabolically versatile pseudomonads. The employment of the high-density KT2440 microarrays would enable highly sensitive and reproducible transcriptome analyses that are compatible with those used for model species such as P. aeruginosa (Ochsner et al. 2002; Wagner et al. 2003; Whiteley et al. 2001) and Escherichia coli (Woo et al. 2004).
In the present study, we showed that the use of KT2440-based microarrays would enable reliable transcriptomics analysis of P. putida S12. Significantly, we found that of all pseudomonads tested, the genomic content of P. putida S12 showed the highest similarity to that of P. putida DSM 6125 (KT2440). The vast majority of KT2440 genes coding for proteins involved in primary metabolism, including biosynthesis of important intermediates such as amino acids, and the genes involved in the conversion of aromatic compounds were shown to be sufficiently similar to those of S12.
The high resolution achieved by comparative genomotyping enabled the identification of minute genotypic differences between tested strains, making a meaningful analysis of phylogenetic relationships feasible. For example, the genomic DNA of P. putida DSM 6125 was shown to be virtually identical to that of the control strain DSM 3931, and this is consistent with the origin of strain KT2440 as a derivative of strain mt-2 (Regenhardt et al. 2002).
The relationship between the P. putida DSM 291T and KT2440 has been an issue of controversy. Based on 16S rRNA gene comparisons (99% identity), both strains appeared closely related; however, a DNA–DNA hybridization experiment indicated only 50.5% genome relatedness between both strains (Regenhardt et al. 2002). The results lead to doubts about the classification of both strains as part of the same Pseudomonas species, given the widely accepted recommendation that strains of the same species shall have genome similarities higher than 70% (Stackebrandt and Goebel 1994). In the same study, an appreciable distance between DSM 291T and KT2440 was established by REP-PCR genomic fingerprint patterns and Biolog GN metabolic profiling. In our genomotyping approach, 69.3% of the genes with an assigned function, 60.5% of all genetic elements (including intergenic regions), and 52.2% of the intergenic regions (not shown) were identified in the genomic DNA of P. putida DSM 291T. These differences, which are supported by the AFLP analysis, indicate a considerable phylogenetic distance between DSM 291T and KT2440.
The diversity within the species P. putida was previously reflected in studies concerning genomic DNA ribotyping (Brosch et al. 1996), whole cell protein electrophoretic fingerprinting (Vacanneyt et al. 1996) and Biolog/Biotype-100 experiments (Grimont et al. 1996). In our genomotyping studies, strains of P. monteilii and P. fluorescens were included as out-groups. P. fluorescens DSM 50090T was shown to be distantly related to the other Pseudomonas members tested, which supports its classification as a separate species. However, P. monteilii DSM 14164 clustered well with P. putida DSM 50198 and to a lesser extent with DSM 291T and P. putida DSM 50208. AFLP analysis showed a comparable result and confirmed the close relation between P. putida and P. monteilii. The present study thus suggests that DSM 14164 should more accurately be classified as a P. putida.
Other studies based on classification of P. monteilii by classical, well established taxonomic methods do not support our findings. DNA–DNA hybridizations among P. monteilii CFML 90-60T, DSM 291T, and DSM 50208 generated relative bindings of genomic DNA of 40 and 48%, with ΔTm values of 9.2 and 7.9, respectively (Elomari et al. 1997). Pyoverdine typing (siderotyping) analyzing the excreted siderophores during iron starvation of P. monteilii CFML 90-60T and DSM 291T produced different patterns for both strains (Dabboussi et al. 2002). Phenotypically, however, P. monteilii was previously shown to be highly similar to P. putida and could only be differentiated by assimilation experiments of the substrates inositol, α-aminobutyrate, and o-/m-hydroxybenzoate (Dabboussi et al. 2002; Elomari et al. 1997).
In conclusion, the genomotyping of different pseudomonads using KT2440-based DNA microarrays yielded novel insights in their phylogenetic relationships and the underlying identification of genes and their distribution over different primary and secondary biological functions. This revealed the utility of KT2440-based microarrays in transcriptomics and classification studies of these strains.

genomotyping
pseudomonas putida
transcriptomics
solvent-tolerant bacteria