Is obesity a risk factor for low back pain? An example of using the evidence to answer a clinical question

Background Obesity as a causal factor for low back pain has been controversial with no definitive answer to this date. The objective of this study was to determine whether obesity is associated with low back pain. In addition this paper aims to provide a step-by-step guide for chiropractors and osteopaths on how to ask and answer a clinical question using the literature.
Introduction
Obesity is a problem of epidemic proportion [1,2]. Despite record rates of non-physician supervised dieting and the availability of numerous weight loss programs, the problem is not abating [3]. Complicating this, is that most primary care physicians do not treat obesity, citing a lack of time, resources, insurance reimbursement, and knowledge of effective interventions as significant barriers [4]. Musculoskeletal disorders including low back pain (LBP) represent a considerable public health problem and a common diagnosis creating absenteeism and the need for disability pensions [5]. It is estimated that about 80% of the United States and Canadian population will experience LBP during adulthood [6]. Most low back pain is self-limiting and will ultimately resolve in two weeks (50% of those affected) to six weeks (90% of those affected), however it remains an intriguing clinical problem [6].
It is widely noted that the economic cost of obesity and its related disorders are staggering, with lifestyle related conditions such as diabetes mellitus and coronary heart disease placing a large economic burden on the health care system [1-4]. However, low back pain also has a significant socioeconomic impact. Cost estimates range from US$20 billion to $50 billion annually, with 10% of the patients accounting for 85 to 90% of the costs [6]. In Australia, Walker et al estimated the cost of low back pain in 2001 alone to be AUD$9.17 billion [7].
One question, which arises from the discussion concerning obesity, is whether obesity is a risk factor for low back pain. "Buckwalter et al contended that a number of medical conditions including obesity, along with diabetes and hypertension, may influence the pathophysiology of diseases of the tendons and ligaments during the process of aging thus potentially leading to low back pain [8]. Along with low back pain, the conventional wisdom is that overweight persons are at risk of osteoarthritis in weight-bearing joints such as the knee, the hips, and feet [9].
To date, literature reviews have given conflicting views based on the available data and method of data retrieval. The purpose of this review is to establish, from recent research, if there is a causal link between obesity and the affliction of low back pain. A secondary purpose of this review is to present the concepts of evidence-based practice to aid the chiropractor or osteopath in looking for health-related evidence for their patients who present with obesity.
Methods
A MEDLINE search, from the National Library of Medicine, was used to ascertain pertinent articles between the years 1990 and 2004. The use of keywords "obesity", "body mass index", "BMI" and "low back pain" was used to obtain relevant studies. The references of papers retrieved were also reviewed, as were key texts and references.
This section is devoted to presenting the concepts of evidence-based practice (EBP) to demonstrate to the reader the discovery process for finding a possible link between obesity and low back pain. The EBP method used is shown in Table 1.
Table 1
Steps to asking the answerable question using EBP principles
Step 1: Asking an answerable question
Step 2: Selecting an evidence resource
Step 3: Executing the search strategy
Step 4: Examining the evidence summary
Step 5: Application of the evidence
The first step in this process is "asking an answerable question." In this paper we assume a patient has asked whether being overweight can cause low back pain. Construction of an appropriate answerable question would possibly be "Does an increased BMI cause low back pain?" "Does being overweight create osteoarthritis?" In this way questions can be constructed to allow the practitioner to effectively answer a clinical concern.
Once the answerable question has been constructed the next task is to find an adequate resource. Internet access to the U.S. National Library of Medicine's MEDLINE or PUBMED, these database systems are considered by many experts to be the most up to date data source on medically related topics. The next step is to determine keywords to place in the search engine. From the answerable question(s) it can be appreciated that the initial keywords will be "low back pain", "BMI" and "osteoarthritis." This search constitutes the third step. In initiating the search, one should look for the search engines "limits" area. In this area can one designate an age group (ex: 45 to 60 years), date span of the literature search, (ex: 1998 to 2003), and to select either English language or articles in foreign languages.
Once the search has been completed, the articles, which may answer the question, are isolated and can be read. Step four involves collating the evidence to answer the question. In searches one may find answers that were not known to exist, and information that may challenge an already preconceived notion. The evidence summary should list the main author and year the paper was published. This is in order to retrieve the data should anyone wish to examine the source. As an example of an evidence summary see Table 2.
Table 2
Recent evidence: Obesity and low back pain (chronological order)
Author, Year (Ref)
N
BMI
LBP
Association
Melissas, 2003 [14]
50
>40
58%
direct
Bener, 2003 [10]
802
(26.4 males/ 27.8 females)
56.1% males
73.8% females
moderate
Tsuritani, 2002 [16]
709
--
40.3%
none
Bowerman, 2001 [4]
252
--
29.2%
none
Kostova, 2001 [12]
898
--
--
increased risk
Bayramoglu, 2001 [15]
25
--
--
direct
Mortimer, 2001 [13]
475
30 (43.6%)
31–40 (28.8%)
40+ (1.3%)
--
increased risk
Han, 1997 [11] 7018 women
5887 men NR
--
females increased risk
N = number; BMI = body mass index; LBP = low back pain; NR = not reported
It is at this time that the clinician is ready for the final step of applying the evidence. In our example clinical data from the experimental literature may or may not indicate that there is a link between overweight and low back pain.
Results
The literature search into obesity and joint pain revealed several studies pertinent to the debate. Table 2 reveals an overview of the studies selected. Several studies [4,10-13] had large populations to draw from yet the data from these studies were not in agreement as to a cause or association. In fact, only two studies [14,15] found a direct association for obesity as a risk factor while two [4,16] studies found no association. Several of the studies reviewed were unable to clarify BMI to the satisfaction of the authors.
Discussion
Interest in the association between obesity and low back pain has piqued researchers interest for many years. Intuitively, a burgeoning waistline and an increased lordotic lumbar spine led researchers to conclude that overweight people would be more prone to low back pain. Historically, Kellgren and Lawrence (1958) found that that the prevalence of disk degeneration with obesity was not significant [17]. However, it was not until the mid-1970's when several studies observed a possible association. Obesity was found to increase the prevalence of disk degeneration significantly in a study by Magora and Schwartz in 1976 [18]. Barton et al (1976), in a review of 144 cases, found that 70% of those who complained of low back pain had been classified as being overweight [19]. This basic research appeared to conclude what was already intuitively thought about low back pain and increased weight.
Body mass index
Before an in-depth discussion of low back pain and obesity can ensue, the concept of Body Mass Index (BMI) needs to be discussed. BMI is a measure of fatness and is calculated by dividing the patient's weight in kilograms by height in metres squared kg/M2 [20]. It is widely accepted, easily measured, and predicts morbidity and mortality in many populations [15]. Obesity is generally defined as a BMI of 30 kg/m2 and higher [20,21]. Overweight is defined as a BMI between 25 and 30 kg/m2 [19,20]. Overweight tends to be more common in men with obesity being more prevalent among women [21]. When body weight is increased 20% above average, mortality rises to 20% for men and 10% for women [22]. (Table 3) Overweight individuals demand more from their cardio-respiratory and musculoskeletal systems [22]. It is known that more than 50% of adult Americans have a BMI equal to or greater than 25 [23]. Although there are certain limitations to BMI i.e. large muscular athletes who are in good cardiovascular shape, the rationale behind these numbers is that, across large population groups, there is an increased prevalence of certain diseases in people with a BMI over 25, and a much greater risk of disease and death in those with a BMI over 30 [4]. Being overweight or obese substantially raises the risk of developing hypertension, coronary heart disease, type 2 diabetes, stroke, gallbladder disease, sleep apnea and other respiratory problems, prostate and colon cancers [4,23]. Yet, the evidence to date linking it to low back pain is not as clear cut as it is with the previously stated pathologies.
Table 3
Clinically relevant differentiation between obesity and overweight
Overweight
Obesity
BMI of 25.0 to 29.9 kg/m2
BMI greater than 30 kg/m2
BMI calculation without benefit of BMI charts
Body Mass Index (BMI) charts and hand held scales are available for individual clinician use. It is, however, unknown to what degree chiropractors or osteopaths use such tools. The following section is designed to aid the clinician with calculating BMI without benefit of chart or hand held scales.
As noted earlier, BMI is calculated as weight in kilograms divided by height in square metres [20,24]. This method is often too difficult to calculate for most people. A simpler method for those using the imperial system of measures is to take body weight in pounds × 703/height in inches squared.
For example, a person weighing 150 pounds at 6 foot tall would correspond to a BMI of 20.3. TABLE 4
Table 4
Calculation of BMI
150 × 703 = 105450 divided by 72 inches (6 foot) squared.
105450 divided by 5184 (72 × 72) = 20.3 BMI.
Additional research findings
Leboeuf-Yde concluded from a review of the literature that due to lack of evidence, body weight should be considered a possible weak risk indicator and suggested that there is insufficient data to assess if it is a true cause of LBP [25]. Kostova found that in men over 40, overweight, obesity and number of pack years of smoking, estimated by duration of smoking and daily cigarette consumption (more than 20 years and more than 20 cigarettes per day), increased the risk of developing back disorders [12].
Despite these two studies, Garzillo et al and Leboeuf-Yde et al have given conflicting opinions [26,27]. Garzillo's review of the data revealed a possible association between obesity and low back pain only in the upper quintile of obesity, and no evidence of a temporal relationship between weight change and changes in low back pain [26]. Leboeuf-Yde concluded from a twin study that obesity is modestly positively associated with low back pain, in particular with chronic or recurrent low back pain [27].
What appears to be a main concern in linking obesity as a causal factor for low back pain is the numerous variables encountered in these subjects. For example, it is hypothesized that overweight adult females may have negative self-concepts and body images compounded by chronic low back pain and obesity, these may be confounding factors [28]. Other variables such as less activity and/or muscular weakness leading to obesity are also possible considerations.
Obesity and low back pain-related conditions
Not only is there controversy in obesity and low back pain, but there exists conflicting views of obesity and low back pain-related conditions such as spondylosis, decreased physical activities and discal herniation. The studies demonstrating a positive association are many. O'Neil et al noted that increasing BMI is associated with more frequent findings of osteophytes (bone spurs) at both the thoracic and lumbar spines [29]. The correlation of osteophytes and increased BMI is highest at the thoracic level [29]. Biering-Strenson et al noted absolute weight and BMI are significantly higher in persons 60 years of age with spondylosis [30]. Both men and women with BMI of 30 kg/m2 or higher were twice as likely to have difficulties in performing a range of basic daily physical activities [30]. Compared with women with BMI lower than 25 kg/m2, those with BMI of 30 kg/m2 or higher were 1.5 times more likely to have symptoms of intervertebral disk herniation [31].
Conversely, Luoma et al concluded that disc degeneration is not related to body height, overweight, smoking, or the frequency of physical activity [32]. In addition, studies by Riihimaki, Symmons, and Kang have shown no association between BMI and low back related problems [33-35].
Confounding the data is that the mechanism by which excess body weight causes osteoarthritis is poorly understood [9]. It is believed that contributions from both local increased force across the joint and systemic factors play a role [9]. A discriminating factor between fit and unfit patients with back pain may be the fact that fit persons more frequently are still employed, and as such may be involved more in physical activity [36]. Table 5 indicates where the research currently exists for the link between low back pain and obesity along with obesity and osteoarthritis.
Table 5
BMI-related risk of osteoarthritis and low back pain
If your BMI is
then your risk based solely on BMI
<25
minimal
25 – <27
minimal
27 – <30
minimal
30 – <35
moderate
35 – <40
moderate
>40
moderate to high
We conclude, based on the available evidence to date, that those individuals with a BMI of under 30 are at a minimal risk of developing low back pain while those persons whose BMI increases to over 30 are a moderate risk of developing low back pain. We also suggest, based on the findings of the Melissas study [14] of those patients who relieved their low back pain symptoms after obesity surgery, that patients with a BMI of greater than 40 are at a high risk of developing low back pain. Albeit controversial, Table 5 may lead to a further refinement of risk of osteoarthritis and low back pain based solely on BMI.
Limitations of obesity as a risk factor for low back pain
A significant difficulty in ascertaining cause and effect between obesity and low back pain is undoubtedly the term "low back pain" itself. Low back pain is a symptom not a diagnosis. A specific diagnosis, instead of the generalized form of "low back pain" may help separate out the association between LBP and obesity.
The Agency for Health Care Policy and Research (AHCPR) in their 1995 Acute Low Back Problems in Adults noted common diagnoses used to explain back problems [37] (Table 6). Given these possible diagnoses one can readily appreciate the dilemma in attempting to link obesity with its specificity in measurement to a broad symptom such as low back pain.
Table 6
Common diagnoses used to explain back symptoms
Annular tear
Adult spondylolysis
Myofascitis
Fibromyalgia
Disc syndrome
Strain
Spondylosis
Lumbar disc disease
Facet syndrome
Degenerative joint disease
Sprain
Spinal OA
Disc derangement/disruption
Dislocation
*Other potential causes of low back pain symptomology
Failed Back Surgery Syndrome*
Osteoporosis*
Urinary tract infection*
Compression fracture*
Somato-visceral mimicry syndrome*
Organic pathology (tumor, rheumatoid, endometriosis, arthritic disorders)*
Leg length inequity*
Sacro-iliac dysfunction*
Hip disorder*
**Disagreement in research as cause of low back symptomology
Morbid obesity?**
OA = osteoarthritis
Another problem is the hypothesis that a person who suffers with continuing bouts of low back pain may be predisposed, due to inactivity or inability to exercise, to gain weight thus increasing their BMI. This hypothesis to our knowledge, has yet to be fully discussed and investigated.
Conclusion
The data for a link between obesity and low back pain appears to be controversial. Yet, this does not adequately address the appropriate therapeutic approach to the obese patient with low back pain. The studies chosen for this review fail to document a definitive causal link between obesity and low back pain. Further research and epidemiologic data is needed to continue the search for a definitive answer.
Competing interests
The author(s) declare that they have no competing interests.

obesity
risk factor
low back pain
association
evidence-based practice


A Review of Yoga Programs for Four Leading Risk Factors of Chronic Diseases

Yoga, a form of physical activity, is rapidly gaining in popularity and has many health benefits. Yet healthcare providers have been slow to recognize yoga for its ability to improve health conditions, and few interventions have been developed that take full advantage of its benefits. The purpose of this article is to review published studies using yoga programs and to determine the effect of yoga interventions on common risk factors of chronic diseases (overweight, hypertension, high glucose level and high cholesterol). A systematic search yielded 32 articles published between 1980 and April 2007. The studies found that yoga interventions are generally effective in reducing body weight, blood pressure, glucose level and high cholesterol, but only a few studies examined long-term adherence. Additionally, not enough studies included diverse populations at high risk for diabetes and its related common health problems.
Chronic diseases such as heart disease, stroke and diabetes are leading causes of death in the US (1). Common conditions for these chronic diseases are overweight, high blood pressure (BP), high glucose and high cholesterol. These conditions are commonly associated with each other. According to a prospective cohort study focusing on men (2), there were positive relationships between body mass index (BMI) and hypertension incidence. Men with a higher BMI have a higher likelihood to have diabetes and high cholesterol. Another study with men and women showed that overweight is linked to type 2 diabetes (3). Even among patients newly diagnosed with type 2 diabetes, the Hypertension in Diabetes Study found that ∼40% had hypertension, and that hypertension considerably increased mortality in people with type 2 diabetes (4). Another recent study found a 79% rate of hypertension in patients who had received outpatient care for type 2 diabetes for at least 2 years (5). Individuals with impaired glucose tolerance also are more likely to have risk factors for cardiovascular disease such as elevated levels of triglycerides and low-density lipoprotein (LDL) and low levels of high-density lipoprotein (HDL)(6).
Promising in this regard is appropriate physical activity because it can reduce body weight, BP, glucose level and cholesterol (7,8). Yoga, a form of physical activity consisting of various postures (Asana) and breathing and meditation techniques (Pranayama) (9), has been shown to have therapeutic benefits for individuals with a wide range of health conditions, including hypertension (10) and diabetes (11). Yoga also appears to be effective in reducing stress (12) and improving exercise tolerance as it is related to cardiovascular response (13).
The number of people practicing yoga in the US increased significantly between 1997 and 2002 (14). According to the 2002 National Health Interview Survey (14), 5% of American adults practiced yoga in the month previous to the study. Yoga also is well received as a therapeutic intervention; for example, participants in a yoga intervention for insomnia found that it was easily learned and performed (15). Despite its popularity and positive physiologic effects, however, yoga has not been widely recognized in efforts to prevent and treat major chronic health conditions. The purpose of this article is to review published studies using yoga practice to determine the effects of yoga on common health problems, such as overweight, hypertension, high glucose level and high cholesterol.
Methods
Articles were retrieved from electronic databases (CINAHL, Ovid MEDLINE and PsychInfo) using yoga as a keyword. This initial retrieval, performed in April 2007, yielded 2349 articles, of which 861 have been published since 1980. A search within those 861 articles, using the keywords overweight, blood pressure, glucose and cholesterol, identified 56 research articles. After the author had read the full text of the 56 articles and identified those that met the purpose of the study, 24 articles were excluded, mainly because they described outcome variables and characteristics of yoga that were irrelevant. Poor quality was not a reason for exclusion, but studies were excluded if they focused only on meditation or relaxation (Pranayama) or if they were case studies. This process resulted in a final total of 32 articles to be reviewed.
The review comprised studies involving yoga interventions and using an experimental or quasi-experimental design. In addition, observational studies were included because they often supply important information beyond the results of clinical trials (16). Studies that used yoga as a control, not as an intervention, were included as long as they provided evidence of the effectiveness of yoga on variables of interest (overweight, BP, glucose and cholesterol).
Findings
Of the 32 articles reviewed, 12 described experimental studies, 18 described quasi-experimental studies and two described observational studies. Only 2 of the 12 true experimental studies (17,18) described the randomization method. Even though risk factors for chronic health conditions were used as keywords in the search strategy, many of the studies used healthy adult samples; only half of the 32 studies actually focused on subjects with diabetes (19–23), hypertension (10,11,18,24–27), or cardiovascular disease (28–31). Seven of the studies were conducted in the US; the others, in India and other countries.
Content of Yoga Programs
Other than duration and frequency, most articles did not describe the yoga sessions in detail. Only a few articles (17,20,22,31,32) provided details of the yoga sequences used. Some listed the names of postures and breathing techniques. Of the few articles that identified the type of yoga studied, the most common was Hatha yoga, a popular form of yoga in the Western world.
The Compendium of Physical Activities, (33) a coding scheme that classifies physical activity based on energy expenditure, does not define energy expenditure while performing various types of yoga. However, Hatha yoga is classified as a conditioning exercise, in the same category as stretching, which has a metabolic equivalent (MET) of 2.5 (1 MET equals the amount of energy used for resting). A recent study (34) found that when young adult women (19 to 40-years old) performed a 30-min session of Hatha yoga, the MET was 2.17. However, if the session was made more active using Sun Salutations, a flowing series of physical postures, the MET increased to 3.74 (SD = 0.70).
Frequency and Duration of Yoga Sessions
The total dose of yoga training, which depends on both the duration and frequency of yoga sessions, also needs to be considered in evaluating and comparing yoga studies. The most common duration and frequency of yoga sessions in the studies were 30–60 min per session and sessions meeting daily for 4–10 weeks, but many studies used sessions meeting 2–3 times per week for 8–12 weeks. Some yoga programs met more frequently than others but for a shorter time span. For example, in a study by Damodaran et al. (24), persons with essential hypertension received 1 h of yoga training daily for 3 months, which is counted as 84 h of training, whereas Ray et al. (32) studied healthy young adults who received 1 h of yoga training three times a week for 10 months, for a total of 120 h.
Some findings can be useful in trying to determine effective durations of yoga sessions. For example, in one study, 1 h daily yoga practice was associated with significant reductions in body weight and cholesterol levels after 4 weeks, and those significant effects lasted for 14 weeks (28). In a study of 20 patients with essential hypertension, daily 30-min sessions of yoga led to a decrease in BP by the fifth day (27).
Adherence to the Yoga Program
Any persistent benefits from yoga would rely on long-term adherence, which was examined in only a few of the reviewed studies. In some studies, after yoga training, subjects were asked to continue their programs with daily home practice (28,35). One of these (35) compared adherence to yoga practice after a 10-week yoga intervention between white and black American adults (mean age: 69 years for whites and 70 years for blacks). Although the dropout rate did not differ significantly (16% for whites, 22% for blacks), the black participants did not engage in yoga at home as frequently as whites. A different study (36) found greater compliance with subsequent home practice among participants in a yoga class that met three times per week than in those who attended a weekly yoga class (86% versus 65%, P < 0.05). However, found no difference in compliance with home practice between groups engaged in yoga or aerobic exercise (36).
Weight Decreased
According to a retrospective observational study of 15,550 adults aged 53–57 years (37), regular yoga practice for 4 or more years was significantly associated with weight loss by overweight participants. Several intervention studies (10,26,29,31,38–41) also showed that yoga practice was effective in reducing body weight. After 4-day residential yoga practice followed by 14 weeks of 1 h daily home practice, one study (28) found a significant loss in mean body weight from 72.26 to 70.48 kg among subjects with risk factors for coronary artery disease (CAD). Other studies found that yoga was associated with significant weight loss by subjects with CAD (29–31) and subjects without CAD (30). Manchanda et al. (29) showed a 7% loss of body weight among adult men with CAD after 1 year of yoga practice, and in a study by Schmidt and colleagues, healthy adults lost an average of 5.7 kg after 3 months of yoga practice (39). All overweight adults studied by Yogendra et al. reached a normal weight within 1 year after initiating yoga-based lifestyle modifications (31). However, this article did not show the effect size of this change.
Blood Glucose Level Lowered
Of the six studies that examined blood glucose, all found that yoga was effective in reducing blood glucose level (11,19–22,31). For example, after 4 months of yoga practice, fasting glucose fell significantly (from 144 to 119 mg dl−1, P < 0.005) in a group of adults with type 2 diabetes, (19). Another sample of 24 adults with type 2 diabetes had significantly decreased fasting glucose (from 190.1 to 141.5 mg dl−1, P < 0.001) after 40 consecutive days of yoga practice, (20). Adults with normal blood glucose levels (11) also had significantly lower glucose levels after 3–4 h of yoga practice for 8 days (P < 0.001).
BP Decreased
This review found ample evidence that yoga was effective in reducing BP. In a group of low-income elderly people, effects on systolic BP did not differ between a yoga class and an aerobic exercise class, both held three times a week for 10 weeks (36). However, eight other studies found that yoga practice was effective in lowering BP in healthy samples, regardless of the type of yoga (13,32,38–44). Yoga practice also significantly improved BP among people with hypertension (10,11,18,24–27), cardiovascular disease (28–31) or type 2 diabetes (19–23). For example, in 13 patients, aged 41–60 years, with essential hypertension (25), BP dropped significantly during the third week of a 4-week yoga program (1 h per day, 6 days per week), and it fell further after the program. For example, systolic BP dropped from 141.7 to 127.9 mmHg by the third week and to 120.7 mmHg by the fourth week.
Cholesterol Level Improved
The practice of yoga was associated with significant decreases in cholesterol among subjects with cardiovascular disease (28,29), hypertension (11) or type 2 diabetes (19). One study (28) examined a regimen involving 4 days of a yoga program at a residential course, followed by 1 year of yoga practice at home. In both men with angina and asymptomatic participants with CAD risk factors, all lipid variables except HDL decreased beginning the fourth week of yoga practice (e.g. total cholesterol fell from 206.6 to 193.6 mg dl−1), and the level of total cholesterol continued falling to 176.06 mg dl−1 at 14 weeks. A study of subjects at risk for cardiovascular disease and diabetes (11) found significant improvements (P < 0.01) in total cholesterol, triglycerides, LDL, HDL and very-LDL (VLDL, defined as total cholesterol minus LDL minus HDL) after short-term intensive yoga practice (3–4 h per day for 8 days). Notably, for subjects whose baseline total cholesterol was 200 mg dl−1 or higher, the reduction in triglycerides (from 151.5 ± 48.9 to 132.7 ± 50.5 mg dl−1, P < 0.001) and VLDL (from 36.7 ± 13.8 to 30.2 ± 14.6 mg dl−1, P < 0.001) was significantly greater than in subjects with lower baseline total cholesterol (triglycerides falling from 113.6 ± 46.5 to 110.5 ± 38.1 mg dl−1, P > 0.05; VLDL from 23.7 ± 12.8 to 23.2 ± 12.5 mg dl−1, P > 0.05). Finally, a study of healthy adults over 40 years old found that 5 years of yoga practice reduced age-related deterioration in cardiovascular functions (45). Although the article describing this observational study did not detail the type of yoga performed nor the frequency or intensity of the yoga sessions, the data showed a long-term change indicating the effectiveness of yoga on cardiovascular functioning.
Four studies used aerobic training as an intervention and yoga as a control (38,43,46,47). One of these, a study of healthy active people aged 65 years or older (38), found that aerobic exercise produced no significant reduction in weight or BP, whereas 4 weeks of yoga practice did lead to some reduction in weight or BP (for example, systolic BP decreased from 146 to 139 mmHg). DiPietro et al. (47) found no change in glucose and insulin responses in their yoga control group. However, the yoga control group was monitored to ensure that pulse rates did not exceed 90 beats per min during yoga practice (47). Therefore, this restriction should be considered in interpreting this result.
Discussion
The reviews showed that yoga had beneficial effects on body weight, BP, blood glucose level and cholesterol level (Fig. 1). Nonetheless, several shortcomings in research on this topic need to be addressed, in order for clinical programs to capitalize on these health benefits.
Figure 1.The possible effects of yoga on behavioral, psychological and physiological measures are illustrated. This article focuses on current evidences for physiological changes.
Of the articles describing interventions, only a few provided details regarding the yoga programs or the names and sequence of yoga postures. Asana and Pranayama provide different types of health benefits, and therapeutic yoga programs can involve various combinations of these two components. An optimal comparison of study results thus requires knowing the combination of Asana and Pranayama used. The sequence of yoga postures can be inferred from the list of posture names, but more straightforward information is essential to future studies seeking to replicate or generalize the results. A related issue that remains to be resolved is how to standardize yoga exercises for research purposes.
The optimal duration and intensity required to maximize the effectiveness of yoga need to be determined, as does the need for a booster to provide long-term effects. Because the MET of yoga is low, increasing the frequency may increase the benefits. However, these factors cannot be judged from the reviews studies; many articles did not clearly identify the dosage of the yoga program studied, and they focused on the short-term health benefits of yoga. Only a few studies included follow-up data beyond 6 months. It also remains to be determined whether more intensive training, that is, a greater dosage, improves the likelihood of adopting and maintaining active behavior in the long run.
The samples in the reviewed studies pose additional dilemmas. It was not a surprise that a large portion of the studies were conducted in India, where the philosophy and practice of yoga originated. However, this focus on one geographical region, where yoga is particularly ingrained in the culture, limits the generalizability of results. Few studies have addressed variables of interest specific to minorities in the US (Blacks, Hispanics and Asians), which are populations believed to be more vulnerable to type 2 diabetes (48,49) and physical inactivity (8).
Yoga has beneficial effects on various health conditions. A large portion of the reviewed studies analyzed the effects of yoga with healthy samples. There is evidence that yoga practice was more effective in lowering triglycerides among people with higher cholesterol than those with a lower cholesterol level (11). Therefore, it is important to consider participants’ health conditions because there are possible differences in the effects of yoga by their health severity.
Conclusions
Analyses of yoga intervention should be designed and conducted to identify programs best suited for diverse populations and for specific populations with high-risk factors for chronic health conditions. Such studies could guide the development of more practical and effective interventions.

yoga
risk factors
chronic disease
overweight
hypertension
glucose
cholesterol


Promiscuous, non-catalytic, tandem carbohydrate-binding modules modulate the cell-wall structure and development of transgenic tobacco (Nicotiana tabacum) plants

We have compared heterologous expression of two types of carbohydrate binding module (CBM) in tobacco cell walls. These are the promiscuous CBM29 modules (a tandem CBM29-1-2 and its single derivative CBM29-2), derived from a non-catalytic protein1, NCP1, of the Piromyces equi cellulase/hemicellulase complex, and the less promiscuous tandem CBM2b-1-2 from the Cellulomonas fimi xylanase 11A. CBM-labelling studies revealed that CBM29-1-2 binds indiscriminately to every tissue of the wild-type tobacco stem whereas binding of CBM2b-1-2 was restricted to vascular tissue. The promiscuous CBM29-1-2 had much more pronounced effects on transgenic tobacco plants than the less promiscuous CBM2b-1-2. Reduced stem elongation and prolonged juvenility, resulting in delayed flower development, were observed in transformants expressing CBM29-1-2 whereas such growth phenotypes were not observed for CBM2b-1-2 plants. Histological examination and electron microscopy revealed layers of collapsed cortical cells in the stems of CBM29-1-2 plants whereas cellular deformation in the stem cortical cells of CBM2b-1-2 transformants was less severe. Altered cell expansion was also observed in most parts of the CBM29-1-2 stem whereas for the CBM2b-1-2 stem this was observed in the xylem cells only. The cellulose content of the transgenic plants was not altered. These results support the hypothesis that CBMs can modify cell wall structure leading to modulation of wall loosening and plant growth.
Introduction
Plant cell expansion depends on the interplay between internal driving forces and the controlled yielding of the cell wall. The ability of the wall to extend under this internal pressure is largely determined by wall loosening processes that modify the interaction of cellulose microfibrils with one another, and/or with hemicelluloses (Darley et al. 2001). Likewise, organ growth is brought about by differential rates of wall loosening, which causes differential tissue growth and, consequently, tissue tension within the organ (Peters and Tomos 1996, 2000). Thus, progressive modification of the cellulose–hemicellulose (mostly cellulose–xyloglucan) networks is important in plant development.
In addition to their effect on plant growth, there are indications that interactions of hemicelluloses with cellulose can affect cellulose fibre properties. Using the Acetobacter model system, it was shown that cellulose–glucomannan and cellulose–galactomannan composites cause 57 and 32% loss of the crystallinity, respectively, of the native bacterial cellulose (Whitney et al. 1998). For cellulose–xyloglucan composites 29% loss of cellulose crystallinity was observed (Whitney et al. 1999). It was also shown that the composites were less stiff, leading to a dramatic reduction in mechanical strength, for example 80% reduction in composites with xyloglucan (Whitney et al. 1999, 2000). There are also reports which indicate that fibre properties depend on the side-chain substitution of the hemicelluloses. Studies on Arabidopsis thaliana mutants with mutations of the MUR2 and MUR3 genes, which encode xyloglucan-specific fucosyl and galactosyl transferases, respectively, revealed that the tensile strength of the fibre was enhanced by galactosylation of the xyloglucan (Pena et al. 2004; Ryden et al. 2003).
There are indications that the interactions between mannan-based polysaccharides and cellulose can affect the structural properties of the cell wall (Carpita et al. 2001; Hosoo et al. 2002). An immuno-localisation study of A. thaliana revealed that mannans were present in all thickened cell walls of stems and leaves, including those of the xylem parenchyma and epidermis (Handford et al. 2003). Mannan transglycosylase was recently identified and characterized as cell wall enzyme acting on mannan-based plant polysaccharides in primary cell walls of higher plants (Schroder et al. 2004). This body of evidence thus supports an emerging idea that mannan-based polysaccharides in cell walls may have a role analogous to that of xyloglucans, introducing flexibility and forming a growth-restraining network with cellulose.
Proteins, for example expansins, have been shown to be involved in modifying cellulose–hemicellulose interaction, thereby loosening the cell wall and ultimately regulating growth and developmental processes in which rearrangements in the cell wall are thought to be important (Cho and Cosgrove 2000; Choi et al. 2003; Pien et al. 2001; Zenoni et al. 2004). There is evidence that (heterologous) carbohydrate-binding modules (CBMs) could also affect these interactions (Levy et al. 2002; Shpigel et al. 1998), leading to altered structural morphology in transgenic plants (Kilburn et al. 2000; Quentin 2003; Shoseyov et al. 2001) or plant growth (Safra-Dassa et al. 2006). CBMs are non-catalytic polysaccharide-recognizing modules, appended to glycoside hydrolases, that often degrade water-insoluble polysaccharides and concentrate the enzymes on their target polysaccharides. CBMs are grouped into families based on amino acid sequence similarities; there are currently 47 families of CBMs in the database (http://www.afmb.cnrs-mrs.fr/CAZY/). The biochemical characteristics of members of most of the CBM families have been established, including those that target non-cell wall polysaccharides such as starch and glycogen (reviewed by Boraston et al. 2004). Although most CBMs are linked to the catalytic modules of carbohydrate-active enzymes, some non-catalytic carbohydrate-binding proteins are not appended to any catalytic module. Examples of such proteins include putative CBMs of the A. thaliana former X8 family, now CBM43 (GenBank accession no. AL161503) (Henrissat et al., 2001), with olive pollen allergen Ole e 10 (GenBank accession no. AY082335) being the first characterized member of this family (Barral et al. 2005). Another example is the non-catalytic protein (NCP1) (CBM29; GenBank accession no. AY026754), which is a component of the anaerobic fungus Piromyces equi cellulase–hemicellulose complex (Freelove et al. 2001).
Because of the heterogeneous nature of the cell wall polysaccharides, several CBMs have evolved structures which enable them to recognize more than one wall polysaccharide (Boraston et al. 2004). Promiscuous recognition of carbohydrates by CBMs enables the associated enzyme (complex) to be more efficient in cell wall degradation. McCartney et al. (2006) showed that different xylan-specific CBM families can vary substantially in their binding to the cell wall. This indicates that the specificity of CBMs is so intricate that after expression in planta their effects on cell wall modification might be very local. To really affect wall architecture, it might be important to select CBMs that bind to different kinds of polysaccharide (promiscuous), so that the effect is not confined to one location or cell type. Most research activity relating to the potential use of CBMs for plant cell wall modification has been on cellulose-specific CBMs. In this investigation we have used different CBMs which not only recognise hemicellulose as opposed to CBM3 but are also to some extent promiscuous.
The CBM29 modules are an excellent model of promiscuity in protein–carbohydrate recognition. The modules bind soluble glucomannan, galactomannan, β-glucan, and hydroxyethylcellulose (HEC), and insoluble forms of cellulose and mannan (Charnock et al. 2002; Freelove et al. 2001). For all the ligands tested two appended copies of CBM29 (CBM29-1-2) had affinity substantially higher than the additive value of the individual modules. This indicates the two CBMs act in synergy to bind all their target ligands, with CBM29-2 having more affinity than CBM29-1. The tandem CBM29-1-2 was, additionally, able to interact with xyloglucan and different forms of xylans, whereas the single CBM had little affinity for these polysaccharides (Freelove et al. 2001; McCartney et al. 2004). An ex-situ labelling study has revealed that the tandem CBM29-1-2 modules bind strongly to the cell walls of the maize coleoptile sheath and the enclosed developing leaves (McCartney et al. 2004). This is indicative of the great abundance of their interacting ligands in the primary cell walls of grasses. It was therefore of interest to investigate the heterologous expression of these promiscuous CBMs in plants, and, more importantly, to compare their effects with those of less promiscuous CBMs, whose characteristics are described below.
Some ligand promiscuity is also observed for two consecutive family 2b CBMs (CBM2b-1 and CBM2b-2) from Cellulomonas fimi xylanase 11A. The tandem CBM2b-1-2 (note the underlining used throughout this report to distinguish it from CBM29-1-2) has very strong affinity for xylan and much lower affinity for insoluble acid-swollen cellulose (Bolam et al. 2001). It also has an 18 to 20-fold greater affinity for xylan than the individual modules.
We have expressed two CBM29 constructs, the tandem CBM29-1-2 and the single derivative CBM29-2, and the tandem CBM2b-1-2 construct in tobacco (all proteins targeted the cell wall) under the control of 35S cauliflower mosaic virus (CaMV) promoter. It is shown that cell wall structural alterations in transgenic plants expressing CBM29-1-2 are more severe than those in CBM2b-1-2 transformants. Our results indicate that promiscuous carbohydrate binding modules can modulate cell wall structure and the development of transgenic tobacco plants.
Materials and methods
Preparation of constructs
Three constructs were prepared for expression in tobacco plants, one for single CBM29-2, one for tandem CBM29-1-2, and one for tandem CBM2b-1-2. The CBM29 and CBM2b-1-2 constructs were prepared by amplifying their respective gene fragments from pET22b and pET28a, recombinant plasmid vectors used for cloning and expression in Escherichia coli (Bolam et al. 2001; Freelove et al. 2001). For the CBM29-1-2 fragment the polymerase chain reaction (PCR) was performed using primers that included BamHI and SmaI recognition sites (5′- cgggatccgttagtgctacttactctgttgtttat-3′ and 5′-tcccccgggccttttaatttattgggtcaacgaaa-3′; the BamHI and SmaI sites, respectively, are underlined). The three bases highlighted in bold type are the stop codon. The amplified 914-base-pair fragment of the tandem CBM29-1-2 was digested with BamHI and SmaI (Invitrogen, The Netherlands) and cloned into a similarly digested binary vector pGreen7k (Hellens et al. 2000). Similarly, amplification of the single CBM29-2 fragment were performed using primers with the same restriction sites as for the tandem CBM29-1-2 (5′-cgggatcccgtaatgtcagagccacttacactgt-3′ and 5′-tcccccgggccttttaatttattgggtcaacgaaa-3′; the BamHI and SmaI sites, respectively, are underlined). The amplified 559-base-pair fragment was digested with BamHI and SmaI (Invitrogen, The Netherlands) and cloned into pGreen7k binary vector. Similarly, amplification and cloning of the 635-base-pair CBM2b-1-2 fragment were performed using primers that included SmaI and EcoRI recognition sites at either end (5′-cccgggtgacacgggcggaggcggcggt-3′ and 5′-ggaattctcagcccgtggcgca-3′; the SmaI and EcoRI sites, respectively, are underlined). The cloning of the three fragments was in-frame with fusion peptides of two sequences, which were cloned upstream in the binary vector. The first sequence codes for a tobacco transit peptide for transporting a cellular glycoprotein NTP303 across the plasma membrane into the cell wall (Wittink et al. 2000) whereas the second sequence encodes a hexahistidine tag. The sequence of the transit peptide was obtained as a product of annealing two oligonucleotide primers, TP1 (5′- agcttatgggaagtggtaaagtaacatttgtggctttgctactttgcctctccgtaggggtgatagctt-3′) and TP2 (5′-ctagaagctatcacccctacggagaggcaaagtagcaaagccacaaatgttactttaccacttcccata-3′). The underlined nucleotides produced overhangs of HindIII and XbaI, respectively. This fragment was then cloned into the HindIII and XbaI cloning sites of the vector. Similarly, the sequence of the hexahistidine tag was obtained and ligated into XbaI and BamHI sites as annealing product of His1 (5′-ctagaagaggatcgcatcaccatcaccatcacg-3′) and His2 (5′-gatccgtgatggtgatggtgatgcgatcctctt-3′), with the underlined nucleotides producing overhangs of XbaI and BamHI, respectively. The purpose of using the hexahistidine tag was to facilitate affinity purification of the CBM29 proteins. The control construct did not contain any of the CBMs, the transit peptide, or the hexahistidine epitope tag. All constructs were sequenced to verify their integrity.
Immunofluorescence detection of CBM29-1-2 and CBM2b-1-2 binding and microscopy
Wild-type tobacco plants (Nicotiana tabacum L.) were grown at 24°C under 16 h light and 8 h dark for 6–7 weeks. Regions of stem were excised and immediately fixed in PEM buffer (50 mmol L−1 PIPES (piperazine-N,N′-bis[2-ethanesulfonic acid]), 5 mmol L−1 EGTA (ethylene glycol bis(β-aminoethyl ether)-N,N,N′N′-tetraacetic acid), and 5 mmol L−1 MgSO4, pH 6.9) containing 4% paraformaldehyde. Samples were then dehydrated in an ethanol series (30, 50, 70, 90, and 97%, each for 30 min at 4°C, ethanol and wax 1:1, 37°C, overnight) and embedded in Steedman’s wax. Sections were cut to a thickness of 12 μm and collected on polylysine-coated microscope slides (BDH Laboratory Supplies, Dorset, UK), de-waxed, and re-hydrated through a reverse ethanol series into phosphate-buffered saline (PBS) (97% for 3 × 10 min, 90%, 50% and water for 10 min each, and a final step of water 90 min). For CBM labelling, sections were incubated in milk protein/PBS (to reduce any non-specific binding of proteins) and 5 μg mL−1 CBM29-1-2 for 1.5 h. Immunofluorescence detection of binding of CBM29-1-2 and CBM2b-1-2 to sections and microscopy were performed as described elsewhere (McCartney et al. 2004).
Tobacco transformation and regeneration
In vitro leaf explants of N. tabacum cv. Samsun NN were used for Agrobacteriumtumefaciens-mediated transformation. Cloned binary vector pGreen7k was co-transformed with the helper plasmid pSoup (Hellens et al. 2000) into A.tumefaciens strain LBA4404 by electroporation. This was plated out on LB-agar plates containing kanamycin (100 μg mL−1) and rifampicin (30 μg mL−1), and incubated for three days at 28°C, to obtain single colonies. The integrity of the binary plasmids was tested by restriction analysis of plasmids isolated from A. tumefaciens cultures used for plant transformation. LB medium (20 mL) without selection was inoculated with a single colony and incubated overnight at 28°C. The grown culture (100 μL) was added to a Petri dish containing 10 mL Murashige–Skoog (MS30). Leaf explants, without major veins and edges, from young seedlings were transferred upside-down in to the MS30 medium. Agrobacterium infection was performed in the dark for 2 days at 24–25°C. Two controls were used for the procedure, untransformed wild-type control and empty pGreen7k control without an insert. Leaf explants were washed in three changes of liquid washing medium of MS30 with 250 mg L−1 carbenicillin. The washed explants were transferred upside-up to an MS30-phytagel plate containing 0.1 mg L−1 α-naphthalene acetic acid (NAA), 1 mg L−1 6-benzylaminopurine (BAP), 200 mg L−1 kanamycin and 250 mg L−1 carbenicillin, and incubated overnight in the dark at 28°C. Plants were transferred to a growth chamber (25°C), where they were gradually adapted to light and incubated for callus induction. After two weeks in culture, calli generated were transferred to shoot-inducing medium, MS20-phytagel, containing 0.2 mg L−1 NAA, 2 mg L−1 BAP, 200 mg L−1 kanamycin, and 250 mg L−1 carbenicillin. Well-formed shoots were harvested and transferred to root-inducing medium, MS15-phytagel plate containing 100 mg L−1 kanamycin, 250 mg L−1 carbenicillin, and 200 mg L−1 vancomycin. Transformed plantlets were transferred to the greenhouse to generate mature plants. Between fifteen to eighteen antibiotic-resistant tobacco transformants were generated for each of the three CBM transgenes.
RNA gel blot analysis of transgenic plants
Total RNA was isolated from 3 to 5 g transformed in-vitro shoots as described elsewhere (Kuipers et al. 1995). Aliquots of 20 μg per lane were separated on a 1% formaldehyde agarose gel and blotted onto a Hybond-N nylon membrane (Amersham) by vacuum transfer in 0.4 mol L−1 sodium hydroxide. The membranes were hybridized with probes consisting of 60 ng [α32P]dCTP-labelled restricted fragments of CBM29-1-2, CBM29-2, or CBM2b-1-2. The radioactive labelled blots were exposed to X-OMAT S and AR scientific imaging films (Kodak) at −80°C. The blots were re-hybridized, after stripping, with a 489 bp [α32P]dCTP-labelled fragment of a tobacco 18S ribosomal RNA gene (GenBank accession no. AJ236016), as a control. The ribosomal probe was amplified from tobacco genomic DNA using oligonucleotides 5′-gaaactgcgaatggctcatt-3′and 5′-attaccgcggctgctggc-3′ for PCR amplification.
Protein extraction
Protein extracts were prepared from transformed tobacco stems using 250 mmol L−1 sodium phosphate buffer (pH 6.0), 0.5 mol L−1 NaCl, 5 mmol L−1 EDTA, 0.1% (v/v) Tween 20, protease inhibitor (Complete, Roche), 37.5 mg mL−1 PVPP (adapted from Vincken et al. 1998). The protein samples were incubated for 2 h at 4°C with gentle shaking. After incubation the samples were centrifuged at 4,500g and 4°C for 20 min. The supernatant containing the soluble protein fraction was then collected. The protein samples were then concentrated with Centricon Plus-20 devices (Millipore) according to supplier’s procedure. Protein concentrations were determined with an ESL protein assay (Roche) using BSA as standard.
Incubation of RGS·(HIS)6-tagged size marker in protein extract of wild-type tobacco
Protein extracts were prepared from wild-type tobacco as described above, either lacking or supplemented with 5 mmol L−1 EDTA and protease inhibitor (Complete, Roche). RGS·(HIS)6-tagged size marker was incubated with the protein extracts as follows. A batch of RGS·(HIS)6-tagged size marker (Qiagen) was dissolved in 100 μL 50 mmol L−1 Tris, pH 7.5, such that the final concentration of the individual proteins varied from 50 to 75 ng μL−1. The epitope-tagged size marker (2 μL) was added to 18 μL tobacco extracts. The mixtures were incubated at room temperature for approximately 15 h. As a control, RGS·(HIS)6-tagged size marker was incubated in 50 mmol L−1 Tris, pH 7.5.
Western blot analysis
The protein samples were separated with SDS-PAGE on 12% SDS-polyacrylamide gels (Mini-Protean II apparatus, Bio-Rad). The separated proteins were electroblotted on to a nitrocellulose membrane in 192 mmol L−1 glycine, 25 mmol L−1 Tris base, 0.1% (w/v) SDS, 20% (v/v) ethanol, pH 8.3. Blocking against non-specific binding was performed for 2 h with 3% (w/v) BSA (Sigma) in TBS (10 mmol L−1 Tris, 150 mmol L−1 NaCl, pH 7.5). The membrane was then incubated with a 1:1000 dilution of the primary anti-RGS·HIS antibody (Qiagen) in TBS solution containing 3% (w/v) BSA for 2 h. The membrane was washed twice in TBS-TT buffer (20 mmol L−1 Tris base, 500 mmol L−1 NaCl, pH 7.5, 0.05% (v/v) Tween 20, 0.2% (v/v) Triton X-100), and once in TBS buffer, for 10 min each. The membrane was then washed with 1:2000 dilution horseradish peroxidase (HRP) conjugated sheep anti-mouse antibody (Amersham) in TBS buffer containing 10% (w/v) non-fat dried milk. The membrane was washed in TBS-TT buffer four times, 10 min each, and subsequently submerged in Supersignal ULTRA substrate working solution (Pierce), which is a mixture of equal parts of Ultra luminol/enhancer solution and Ultra stable peroxide solution. Finally, the membrane was exposed to X-OMAT S Scientific Imaging films (Kodak) at room temperature.
Monitoring the growth of the transformed plants
To monitor growth of the transformed plants with time, three replicates per line were grown in the greenhouse. The transformants were grown in two series, the CBM29s and CBM2b-1-2 series, with each series having its separate controls. Height measurements of the stems were taken weekly for nine weeks, starting from week two after transplant. The height measurements of six individual high expressers, each from transgenic lines containing CBM29-1-2 and CBM29-2 constructs, six transformed controls, and six untransformed controls from week 3 to week 9 were subjected to one-way analysis of variance (ANOVA), using an α of 0.05. To test whether the differential stem elongation was significant, a Fisher’s unprotected least significant difference test was used. The null hypothesis of no difference between plant heights was rejected at an error of 0.05.
Light microscopy
Three individual plants per transgenic tobacco line and three wild-type plants, as control, were used for microscopic examination. Three transgenic lines were used per construct (two high expressers and one low expresser). Stem samples were taken from the second internode from the top of the plant. Stem sections 1 mm-thick were fixed in 3% glutardialdehyde (Merck) and 3% paraformaldehyde (Merck) in 0.1 mol L−1 phosphate buffer containing 0.1% Triton X-100 for 2 h. The samples were then washed and dehydrated in an ethanol series. After dehydration they were embedded in Technovit 7100 resin (USA) (Kuroiwa et al. 1990). Sections 4 μm-thick were stained with 0.1% toluidine blue (Aldrich) and examined under a bright field microscope.
Cryo-scanning electron microscopy and quantification of cell size and cell wall diameter
One high expresser from each of the CBM29s and CBM2b-1-2 transgenic lines and one individual plant each from their respective controls were used for cryo-SEM examination. The plants that were analysed were chosen on the basis of the results obtained from the extensive light microscopic analysis that showed a consistent pattern of alterations in high-expressing transgenic plants. Stem sampling was the same as for the light microscopy. Stem samples 6 mm-thick were mounted in a brass cylindrical sample holder with TBS tissue freezing medium (EMS, Washington, PA, USA). The frozen samples were placed in a sample holder in a cryo-ultra microtome (Reichert Ultracut E/FC4D) and cut at a specimen temperature of 100°C. These samples were first planed with a glass knife, after which the surface was planed with a diamond knife (Histo no trough, 8 mm 45°C; Drukker International, The Netherlands). After planing, the samples were placed in a dedicated cryo-preparation chamber (CT 1500 HF, Oxford instruments, UK). All the samples in the cryo-preparation chamber were freeze dried for 3 min at −90°C and 8 × 10−4 Pa, and subsequently sputtered with a 10-nm layer of Pt. The samples were cryo-transferred into the field emission scanning microscope (Jeol 6300F, Japan) on the sample stage at −190°C. All images were recorded digitally (Orion, 6 E.L.I. sprl, Belgium) at a scan rate of 100 s (full frame) at the size of 2,528 × 2,030, 8 bit. The images were optimized and resized for publication by use of Adobe Photoshop CS.
To quantify the micrographic images, files were opened with Image J software developed at the National Institute of Health, USA (http://www.rsb.info.nih.gov/ij/). The surface area of three hundred cortical and xylem cells was measured as shown by the insets in Figs. 7a and 7b. One hundred cells per field of view were measured in three replicates. Similarly, cell wall thickness of 150 cells was quantified as shown by the inset in Fig. 7c. Fifty cells per field of view were measured in three replicates. Standard deviations of three measurements were determined.
Isolation of cell wall material and analysis of the cellulose content
Stem samples from one high expresser of the CBM29 and CBM2b-1-2 series, one pGreen7k vector control, and one wild-type control plant were ground to a fine powder in liquid nitrogen. For each isolation, 1 g of this stem material was extracted in a 50 mmol L−1 Tris[HCl], pH 7.2 solution containing 1% SDS, for 3 h at room temperature (RT) with continuous shaking. The cell wall material CWM was spun down by centrifugation at 13,000 rpm for 15 min. Subsequently, the residue was washed with water, ethanol, and acetone, then air-dried. The different cell wall materials (10 mg) were hydrolysed in 1 mL 2 mol L−1 TFA. The TFA-insoluble cellulose was spun down at 13,000 rpm for 15 min, and the pellet was suspended in 67% sulfuric acid. The suspension was heated and diluted appropriately to determine the cellulose content colorimetrically, using anthrone as colouring agent (Updegraff 1969). The acid hydrolysis was performed in quadruplicate.
Results
Expression of bacterial CBM29 and CBM2b-1-2 genes in tobacco
To detect expression of the introduced CBM genes in the tobacco plant, we performed Northern blot analysis with total RNA. Figure 1 shows representatives of three classes of transcript expression (high, low, and none) of the three CBM modules as revealed by the Northern analysis. Ten of the CBM29-1-2 plants were classified as high expressers, five as low expressers, and two as none expressers. For the CBM29-2 plants, ten lines were classified as high expressers, seven as low expressers, and two as none expressers (note that none expressers may include plants with very low RNA expression, which could not be detected). For the CBM2b-1-2 plants, six each were classified as high and low expressers whereas four plants were classified as none expressers. Preliminary attempts at purifying CBM proteins using affinity purification with the hexahistidine tag were not successful (data not shown). Similarly, Western detection of a hexahistidine epitope-tagged fungal elicitor protein ECP2 that was infiltrated into the apoplast of leaves did not succeed in the tomato plants but succeeded in Arabidopsis thaliana (van Esse et al. 2006). The results indicated that the histidine tag was cleaved in the tomato plants but not in A. thaliana. These observations have since led to the idea that the hexahistidine epitope tags have cleavage sites recognized by proteases, which are specific for the cell walls of the solanaceous species.
Fig. 1Transcript analysis of CBM29 and CBM2b-1-2 genes in transgenic tobacco leaves. A differential transcript expression pattern is shown in the upper panel with the representative of each class in the three transgenic lines CBM29-2 (a), CBM29-1-2 (b) and CBM2b-1-2 (c). Line 14 in aline 5 in both b and c, represent high expressers. Lines 9, 11 and 1 represent low expressers. Line 17 in a, line 6 in b and line 14 in c represent none expressers. The lower panel shows RNA blots for the 18S ribosomal RNA internal control for each of the series
We corroborated this notion by incubating a protein ladder, consisting of five different proteins of known sizes, each containing an N-terminal RGS·(HIS)6–tag, in protein extracts of wild-type tobacco (Compier 2005). The tagged proteins of the size marker that were incubated in tobacco extract without EDTA and protease inhibitor were not detectable on a Western blot whereas the markers incubated in extract supplemented with EDTA and protease inhibitor were detected. This suggested that extracts from tobacco contained proteases that could remove the hexahistidine epitope tag.
Because grinding of the plant material and protein extraction were performed at 4°C, and the protein extraction buffer contained EDTA and protease inhibitor, removal of the RGS·(HIS)6-tag from the fusion proteins had probably occurred already in planta, most probably extracellularly. It should be noted that with the presence of the NTP303 signal peptide, the fusion proteins would be secreted into the apoplast. Cleavage of the RGS·(HIS)6-tag was, therefore, likely to occur after translocation into the extracellular space.
CBM29-1-2 and CBM2b-1-2 have different binding patterns in the stem
Immunofluorescence detection of CBM29-1-2 and CBM2b-1-2 binding to stems of the wild-type tobacco plant was performed order to determine, a priori, the particular tissues of the stem where the introduced CBM29-1-2 module would bind. Indirect immunofluorescence micrographs (Fig. 2) revealed that CBM2b-1-2 binding is restricted to the secondary cell walls of stem vascular tissue—xylem cells and phloem fibres (Fig. 2a)—whereas CBM29-1-2 indiscriminately binds to every tissue of the stem, from the epidermis through the cortex and the vascular tissue to the pith parenchyma in the innermost part (Fig. 2b). In the control (no CBM) weak autofluorescence was observed in the xylem vessel cell walls (Fig. 2c). This ex-situ evidence for unselective binding of the CBM29-1-2 to both the primary and the secondary cell walls of the mature stem of the tobacco plant thus complements earlier evidence for its binding to a predominantly primary-walled maize coleoptile (McCartney et al. 2004). It should also be noted that tobacco is a dicotyledonous plant whereas maize is monocotyledonous, and that there are many differences between the polysaccharide composition of the walls of these species. Both results thus confirm the binding promiscuity of the tandem CBM29-1-2, as previously characterized (Charnock et al. 2002; Freelove et al. 2001). We also conclude that the CBM2b-1-2 ligands are present in the vascular tissue of the stem.
Fig. 2Micrographs, obtained by indirect immunofluorescence microscopy, showing binding of CBM2b-1-2 (a), CBM29-1-2 (b), and control (no CBM) (c) to transverse sections of wild-type tobacco stem. The CBM29-1-2 binds indiscriminately to every tissue of the stem, from the epidermis through the cortex and the vascular tissue to the pith parenchyma in the innermost part whereas CBM2b-1-2 binding is restricted to the vascular tissues. ct cortical parenchyma, xy xylem cells, ph pith. Arrow head indicates the epidermis. Arrow indicates the phloem fibres. Scale bar = 100 μm
Reduced stem elongation and prolonged juvenility are observed for CBM29-1-2 transformants
An indication of reduced stem elongation in the transgenic plants expressing the tandem CBM29-1-2 was first apparent after the third week of transfer to the greenhouse. The different stem elongation of the tandem CBM29-1-2-expressing plants and the control plants became significant (P < 0.05) at weeks seven and eight, when other plants had reached their final height (Table 1, Fig. 3). The tandem CBM29-1-2-expressing plants continued to increase in height until the eleventh week, however, when they reached their final height, an average of 58 cm. There was, as a result, no significant difference between the heights of the tandem CBM29-1-2 lines at maturity and the heights of the single CBM29-2 lines, the pGreen vector control, and the wild-type control plants. There was no significant difference, at any time, between stem elongation of transgenic plants expressing CBM29-2 and the control plants.
Table 1Plant height (cm) of transgenic lines CBM 29-1-2, CBM 29-2, empty pGreen7k control, and wild-type controlPlant lineWeeks after transplant3456789CBM29-1-29.5 ± 1.316.1 ± 2.227.0 ± 3.837.7 ± 5.543.1 ± 5.3a50.5 ± 5.4a56.6 + 6.7CBM29-29.3 ± 1.316.9 ± 1.927.5 ± 3.240.9 ± 5.050.7 ± 4.157.8 ± 4.163.8 + 4.7PGreen7k9.5 ± 1.617.3 ± 2.330.1 ± 3.844.6 ± 5.257.6 ± 7.163.1 ± 5.865.4 + 5.6WT Ctrl9.7 ± 1.317.1 ± 2.331.5 ± 3.750.6 ± 7.363.6 ± 7.168.4 ± 5.568.8 + 5.6The values are means and standard deviations for six transgenic lines (high expressers) and six control plants. Each line has three individual plants (three replicates)aPlant height significantly different from that of controlsFig. 3Reduced stem elongation in time, and delayed development of CBM 29-1-2 transgenic tobacco plants. The picture was taken at week eight
In addition to reduced stem elongation it was also observed that flower development was delayed in the tandem CBM29-1-2-expressing plants. We observed that at week eight, when most plants had already flowered (Fig. 3) and set fruit (data not shown), the tandem CBM29-1-2-expressing plants were just developing flower buds (Fig. 3). The flower buds eventually developed into normal flowers and also set fruits. Most plants, except the CBM29-1-2 plants, had also lost many leaves, because of faster ageing (Fig. 3).
There was no morphological or developmental change in the transgenic plants expressing the tandem CBM2b-1-2 gene compared with the pGreen7k vector control. The average plant height of the tandem CBM2b-1-2 series at maturity was 71 cm, which was comparable with the 75 cm average height for their pGreen7k vector control plants. We also did not observe a particular trend in plant development with regard to stem elongation, leaf abscission, or flower formation.
Cellular alterations are observed in transgenic plants
The cellular basis for the CBM29-1-2 plant phenotypes was examined by histological examination of the transgenic tobacco stems. The examination revealed a prominent layer of collapsed cells in the cortex of the stems of the tandem CBM29-1-2-expressing plants (Figs. 4a, 5a). We also observed that this morphological alteration is consistently more severe in the high expressers than in the low expressers. This alteration was also observed in the high expressers of the single CBM29-2, although it was much less severe (Fig. 4c). For the CBM2b-1-2 stems, irregularly shaped cells were observed in the cortex of the high expressers (Fig. 5c). The micrographs of both the empty vector control and untransformed wild-type control looked the same; hence the pGreen7k micrograph was taken to represent the controls (Fig. 4b). Altered cell enlargement across the tissues in the CBM29s-expressing stems was also observed. This was particularly true for the tandem CBM29-1-2-expressing plants (compare, for example, Figs. 5a and 5d with 5b/c and 5e/f, respectively). The altered cell size phenotype is widespread in the high and low CBM29-1-2-expressing plants. This is not observed, however, in the single CBM29-2-expressing stems, in which only the high expressers had noticeably larger cells in the epidermal, cortex, and xylem tissues than those of the controls. Similarly, there was an indication of larger xylem cells in the outermost layer of the CBM2b-1-2 stems than in controls (compare Figs. 5e and 5f). There was, however, no clear indication of altered enlargement of the stem pith parenchyma cells of the transgenic plants.
Fig. 4Cross sections of transgenic and control stems, showing collapsed cortical cells and enlarged cortical and xylem cells of the CBM29-1-2 stems. Sections of CBM29-1-2 (a), empty Pgreen7k control (b), CBM29-2 stem (c), and CBM2b-1-2 (d) were stained with toluidine blue. The boxed area indicates tissues shown with higher magnification in Fig. 5. ep epidermis, ct cortex, xy xylem, ph pithFig. 5 Magnification (10×) of cross sections of CBM29-1-2 stem (a, d), control stem (b, e), and CBM2b-1-2 stem (c, f). Micrographs show collapsed cortical cells (a), enlarged cortical and xylem cells of the CBM29-1-2 stems (a, d), and irregularly shaped stem cortical cells of the CBM2b-1-2 stems (c). Asterisks indicate irregular cortical cells in the CBM2b-1-2 transformants; in the CBM29-1-2 transformants these cells are more apparent and are not highlighted. ep epidermis, ct cortex, xy xylem, pf phloem fibre
Cryo-scanning electron microscopy (cryo-SEM) of tobacco stems
To establish that the transgenic stems indeed had larger cells than those of the controls we made cryo-SEM examinations of stem samples. Figure 6a shows the collapsed and enlarged cells of the cortex in the sections of transgenic line 5 of CBM29-1-2, as previously observed by light microscopic examination. The cortical cells of the control plant were smaller and the collapsed phenotype was not apparent (Fig. 6b). The micrographs also showed that xylem cells were indeed larger in the CBM29s-expressing stems than in the controls (compare transgenic line 5, Fig. 6c and the empty vector control, Fig. 6d). The micrographs also indicated that the cell walls of the xylem cells were thinner in the CBM29s-expressing stems (Fig. 6c) than in stems of controls (Fig. 6d). Similar micrographs were obtained for the CBM2b-1-2 series, which confirmed that the radial surface area and cell wall diameter of their stem xylem cells were larger and thinner, respectively, than those of the controls (data not shown). These notable observations were later substantiated by quantification. Figure 7a shows that almost 50% and 40% of the stem cortical cells of the wild-type and pGreen7k control plants, respectively, were grouped in the small class (50–100 μm2), whereas fewer than 20% of the stem cortical cells of the CBM29s-expressing plants were grouped in the small class. An opposite trend was observed in the large class (150–200 μm2)—fewer than 20% of the cortical cells of the two control stems were grouped compared with more than 30 and 40% of the stems expressing the single CBM29-2 and the tandem CBM29-1-2, respectively. We thus infer that the cortical cells of the CBM29-2-expressing stems are larger than those of the control stems. Similar quantitative data were obtained from the xylem cells depicted in Fig. 7b, which shows that only the xylem cells of the CBM29-1-2-expressing stems are larger than those of the control stems. There was no difference between the size of the xylem cells of the single CBM29-2 stems and those of the controls. Similarly, we quantified the thickness of the cell walls of the xylem cells of the CBM29s-expressing stems and the pGreen7k control stems as presented in Fig. 7c. Approximately 45% of the cell walls of the xylem cells of the CBM29-1-2-expressing stem were grouped in the thin class (1.2–2 μm) whereas fewer than 10% of the stem xylem cell walls of the single CBM29-2, the pGreen7k control, and the wild-type control plants were grouped in this class. More than 60% of the xylem cell walls of the two control stems were grouped in the thick class (2.8–3.6 μm) whereas approximately 22 and 0% of the stem xylem cell walls of the single CBM29-2 and the tandem CBM29-1-2, respectively, were grouped in this class. We can also infer from this result that the thickness of the cell walls of the xylem cells of the CBM29-2-expressing stems (particularly of the tandem CBM29-1-2) are thinner than those of the pGreen7k and the wild-type control stems.
Fig. 6Scanning electron micrographs of transgenic tobacco stems to show details of larger cells and thinner cell walls of the CBM 29-1-2 stems compared with the pGreen7k control. CBM29-1-2 (a) and (c), and empty pGreen7k control (b) and (d). a and b show the cortex tissue whereas c and d show the xylem tissue. pf phloem fibre, xy xylem, ct cortex. Arrow heads indicate collapsed layer of cortical cellsFig. 7Cell size distribution in the cortex (a), cell size distribution in the xylem (b), and cell wall thickness of xylem cells (c) in the stems of transgenic and the wild-type tobacco plants. Insets show how the surface area and wall thickness measurements were taken. Surface areas of 300 cells were measured, 100 cells per field of view. Cell wall diameters of 150 cells were measured, 50 cells per field of view. Error bars represent standard deviation. Grey bars represent wild-type plants. Diagonal bars represent empty pGreen7k transformants. Open bars represent single CBM29-2 transformants. Horizontal brick bars represent double CBM29-1-2 transformants
A similar trend was observed for the xylem cells of the CBM2b-1-2 stems, which were larger than those of the pGreen7k control plants (data not shown). Similarly, the cell walls of the xylem cells of the CBM2b-1-2-expressing stems were thinner than those of the pGreen7k control stems (data not shown).
Analysis of cellulose content
A colorimetric assay was performed on samples of crystalline cellulose obtained from four separate acid hydrolyses to determine the cellulose content in the stem cell walls of the CBM29s and CBM2b-1-2 transformants and the control plants. There were no substantial differences between levels of cellulose in the stem cell walls of the transgenic tobacco lines expressing the single CBM29-2, the tandem CBM29-1-2, the tandem CBM2b-1-2, the empty vector control, and the wild-type control plants (data not shown). Hence, it can be concluded that use of these CBMs for cell wall modification did not interfere with the extent of cellulose biosynthesis.
Discussion
It has been suggested that networks of plant cell wall polysaccharides can affect the properties of cellulose fibres. We hypothesized that expression of promiscuous CBMs in tobacco plants might lead to modification of these networks, although they might also affect plant development in more general terms. In this study, expression of two promiscuous Piromyces CBM29-containing proteins was targeted at tobacco cell walls to provide baseline information on the possibility of using CBMs for in-planta modification of the properties of cellulose fibre for industrial applications. In parallel, we also targeted the expression of the less promiscuous tandem CBM2b-1-2 protein of the C. fimi xylanase 11A, with a view to comparing its effect with that of the more promiscuous CBM29-1-2.
Our results reveal that the tandem CBM29-1-2 modified the tobacco cell wall structure with consequent altered cellular and organ morphology. The tandem CBM29-1-2 modules had more effect than the single CBM29-2. Expression of the tandem CBM29-1-2 modules in the tobacco cell wall affected plant development, as reflected by reduced stem elongation rate, enlarged xylem, and collapsed cortical cells. The stem phenotype of the CBM29-1-2-expressing plants was more of delayed development of the whole plant rather than mere elongation defects of the stem. We suggest that the delayed development (prolonged juvenility) was as a result of reduced rate of anabolism in the tandem CBM29-1-2-expressing plant. It could be that structural remodelling of the cell wall has affected the normal developmental cues that signal the cells to synthesize new cell wall materials. It is conceivable that when the cell walls are held loosely continuously as a result of the continuous presence of CBM29-1-2, the rate of wall extension will outpace biosynthetic rate. Consequently, excessive thinning of the cell walls may occur (Kutschera 1990), which may have led to the eventual collapse of, particularly, the primary walled cells (and not so much of the secondary-walled xylem cells). Thus, because of the extensive loosening of their cell walls and consequent altered expansion, the cortical cells may have yielded to tissue pressure within the stem. The altered cell enlargement, which led to the collapse of the cortical cells of the tandem CBM29-1-2-expressing stems, is proposed as the cellular basis for the reduced stem elongation. We have not noticed that the increased size of the xylem cells led to thicker stems. It is possible the gain in the size of the xylem cells is neutralized by the collapse of the cortical cells.
The explanation given above is based on the assumption that the CBMs do not bind to polymers until they have reached the wall. It should be noted, however, that by placing a signal peptide at the amino terminus of the CBMs we have targeted them through the secretory pathway, which includes the site of hemicellulose biosynthesis (the Golgi apparatus) (Denecke et al. 1990; Vitale and Denecke 1999). It is, therefore, possible the CBMs had already bound to hemicellulose before their deposition in the cell wall. As a result, the hemicellulose content, length, and/or branching pattern might have changed; this, in turn, could affect their interaction with cellulose microfibrils, leading to structural alteration of the cell wall.
The expression of the CBM2b-1-2 modules in the tobacco cell wall altered the cellular morphology of the transgenic plants, as was apparent from the irregular shape of the cortical cells and altered cell enlargement of the xylem cells of the stems. The deformation of the cortical cells of the CBM2b-1-2 expressing plants is, however, less pronounced than that of the cortical cells of the CBM29-1-2 transformants. The more obvious phenotypic changes in the CBM29-1-2-expressing plants is consistent with the result of the immunolabelling study with the proteins, which revealed that CBM29-1-2 binds to polysaccharides that are integral to both primary and secondary cell wall structure. CBM2b-1-2, in contrast, binds only to xylan and non-crystalline cellulose. As xylan is mainly present in the secondary cell wall, it is not strange to see the effect of CBM2b-1-2 most pronounced in the secondary walled xylem. The finding that CBM2b-1-2 did not label the cortex (Fig. 2a) suggests that the deformation of the cortical cells is an indirect effect of CBM2b-1-2. The cortical parenchyma has no secondary walls, suggesting that the walls are much weaker here; consequently, this tissue might collapse when challenged by excessive expansion of flanking tissues. The expression of the CBM2b-1-2 only had effect on cellular morphology and not on plant morphology whereas the expression of the CBM29-1-2 in tobacco led to altered structural morphology and altered development of the transgenic plants. The reduced stem elongation phenotype of the CBM29-1-2 tobacco plants is, however, in contrast with the report by Safra-Dassa et al. (2006) of enhanced growth of transgenic potato plants expressing a bacterial CBM3. These contrasting results might be because of the different specificities of the CBMs, because we have used double CBMs in our investigation as opposed to the single CBM they used. That the experiments were performed on different plant species may also have contributed to the different observations.
There are reports attributing cell wall phenotypes in mutant or transgenic plants to abnormal wall assembly in general, or depletion of the cellulose content in particular (Szyjanowicz et al. 2004; Taylor et al. 1999, 2000; Tsabary et al. 2003; Turner and Somerville 1997). In all these instances the plants had deformed walls in vascular cells and reduced growth as a result of cellulose depletion. Our results reveal that wall deformation (cortical cells) can also occur without a marked reduction in cellulose content.
To summarise, the results of our investigation suggest that expression of the promiscuous tandem CBM29-1-2 can alter cell morphology to a larger extent than that of more specific CBM CBM2b-1-2, although the actual effect is unpredictable. Use of CBMs for in-planta cell wall modification thus still needs further investigation.

carbohydrate-binding modules
cell expansion
cell wall modification
cellulose–hemicellulose networks
plant growth and development
promiscuous polysaccharide recognition