Background
==========

Health care providers compete for managed care contracts based on cost-effectiveness and quality of care \[[@B1]-[@B4]\]. Information technology (IT) provides a cost-effective way to document productivity, performance measures, cost, and quality of care. Since IT has dropped in cost over time, physician practices are now turning to it to meet these needs. Information technology for this study is defined as computer software used to store, transport, or communicate information \[[@B2],[@B5]-[@B7]\].

The health care organizations that succeed in the 21^st^ century will be those that improve quality and reduce cost. These juxtaposed objectives most likely will be reached through improved handling of information \[[@B2],[@B8],[@B9]\]. The Committee on Quality of Health Care in America reported that most clinical information remains in paper form \[[@B9]\]. This committee made several recommendations for improving quality, including moving clinical information to an electronic format by the end of the decade.

Information technology selection in health care has often been performed in a rather informal way, resulting in the purchase of \"white elephants\" \[[@B10]\]. The systems may not perform as planned and may cause additional work for medical staff. The systems are often purchased or developed in pieces without consideration to the overall business strategy \[[@B1]\].

To date, few publications have documented the selection process and the resulting impact of the IT on the health care organization. Most papers give anecdotal descriptions, often by vendors, but lack client perceptions of the information system\'s value \[[@B1],[@B2],[@B7],[@B11]-[@B14]\]. Even at the hospital level, only a few client perceptions of IT adoption have been reported \[[@B15]-[@B19]\]. The number of available papers that examine IT selections within physician practices is even smaller than those papers addressing hospital selections \[[@B3],[@B20]\]. However, many physicians are transitioning from paper to electronic formats for billing records, medical charts, etc. This study aims to understand the process for selecting IT for physicians\' practices and the perceptions of the IT after it is implemented. *The primary objective of this research was to identify the relationship (if any) between the IT selection process and the office staff\'s perceptions of the it\'s impact on practice activities.*

Methods
=======

To address the research objective, a literature review was completed; an expert panel was formed and consulted; a conceptual model was developed; a telephone interview survey was designed; an exploratory factor analysis was performed; and finally, a logistics regression analysis was performed. The conceptual model for this study was not based on one single overriding pre-established theory (Figure [1](#F1){ref-type="fig"}). Rather, it was drawn from a body of literature as well as from the observations of an expert panel regarding technology selection and how it facilitates or impedes practice activities \[[@B1]-[@B3],[@B11],[@B12],[@B16],[@B21]-[@B42]\]. The expert panel included physicians, health services researchers, informatics researchers, and health care industry consultants.

![Conceptual Model.](1472-6947-2-4-1){#F1}

The telephone survey was conducted with 407 physician practices in Oregon \[[@B2]\]. The survey elements were based on the literature review and on the feedback from the expert panel. The survey addressed the following descriptive research questions:

Q1: Who selects IT for a physician practice (e.g., administrators, clinicians, computer specialists)?

Q2: What selection steps are used?

Q3: What factors influence the purchase?

Q4: Which IT features are selected?

Q5: Who (within the practice) customizes the IT?

Q6: Is time given to learn the IT?

Q7: What are the clinical and office staff members\' perceptions of this IT\'s impact on several office activities (e.g., scheduling, communication, quality reporting)?

The design of the telephone survey was reviewed by the Human Subjects Research Review Committee at Portland State University.

Sample
------

Providence Health System in Portland, Oregon provided a database of practices (n = 933) for this study. These practices all served Providence Health System in some capacity -- e.g., as primary care physicians or specialists. Eligible practices had acquired software within the past five years but not within the past six months. Practices with software older than five years were disqualified because it was unlikely that the decision makers (if present) would recall the details of the selection process. Practices with software selected within the last six months were dropped because new software often requires a learning time period. The original sample of 933 contained 70 practices that had no computers and 35 that had software purchased only in past six months or more than five years ago. In total, 11.1% of the original sample were excluded.

Of the remaining eligible practices (n = 828), 407 completed the telephone survey, representing a response rate of 49.2%. If a qualified respondent at a practice was not reached after at least three attempts (n = 269) or the respondents declined the interview (n = 152), the practice was counted as a nonrespondent. Qualified respondents were involved with software selection or software customization for the practice. Seven practices gave partial interviews and were also counted as nonrespondents. These respondents had to leave in the middle of the interview to address urgent clinic needs. Although these respondents were rescheduled, they were not reached to complete the interviews. Additionally, one respondent gave many \"don\'t know\" responses. The interviewer wrote in the comment section for this office that the respondent was not qualified for the study and should be dropped. Thus, in total, seven partial interviews, and one unqualified interview were dropped from the sample, reducing the total number of offices in the study to 399. The respondents and participating practices are summarized in Table [1](#T1){ref-type="table"}.

::: {#T1 .table-wrap}
::: {.caption}
######

Description of respondents and participating practices
:::

                                                             **Frequency (n = 399)**
  ---------------------------------------------------------- -------------------------
  *Role in practice*
   Administrator/office manager, finance manager, etc.       78.9%
   Billing or scheduling staff                               9.0%
   Physician, physician\'s assistant or nurse practitioner   4.5%
   Other staff members                                       3.8%
   Information system managers                               3.0%
   Nurses and medical assistants                             0.8%

  *Type of practice*
   Various specialties                                       55.7%
   Primary care                                              32.6%
   Primary care and various specialties                      11.7%

  *Practice size*
   Single practitioner                                       46.3%
   2--10 practitioners                                       41.3%
   More than 10 practitioners                                12.4%

  *Practice Ownership*
   Private                                                   83.1%
   Health system owned                                       16.9%
:::

Second interviews were gathered for 189 of the 407 responding practices. Since almost half of the responding offices represented single practitioners, many of these smaller offices had only one eligible participant.

Telephone survey
----------------

The survey questions were developed based on the literature review and discussions with an expert panel. Since many of the respondents were not familiar with technical IT terms, care was taken to present the survey in a \"respondent friendly\" format.

Thirteen college student interviewers and two supervisors conducted the interviews using a telephone interviewing software package, Computer Assisted Survey Execution System. A program was written to provide the interviewers with precise dialogue, questions, and precoded responses. As the interview progressed, the interviewer entered the responses into a personal computer.

Since the study objective included capturing the perceived impacts of IT, we attempted to record perceptions from two representatives from each practice: the decision maker and a primary user (see [Additional File 1: \"Physician Practice Software Telephone Survey, Dialog and Questions\"](#S1){ref-type="supplementary-material"}). The initial interview that included questions related to the selection process and perceived impacts of the IT lasted approximately 15--25 minutes. The respondent was asked to describe a recent IT purchase (at least six months old). For each practice, the respondent indicated whether a person in a specific role -- e.g., an administrator -- was involved or not involved in selection, and involved or not involved in software customization. Customization in this study referred to providing input to the software vendor for writing software specific to the practice.

During the interview we read the respondents a list of selection steps. For each step, the respondent answered \"yes\" or \"no\" as to whether it was performed. During the interview the respondents were read several potential factors that might have influenced the purchase. For each one they rated the statement on a 1-to-6 scale of importance, (ranging from \"no importance\" to \"very high importance\"). Finally, we asked the respondents to react to 12 statements describing potential impacts of the IT on selected practice activities. The statements were intentionally not grouped by any particular theme. The respondents rated each impact statement on a 1-to-5 scale of agreement (\"strongly disagree\", \"slightly disagree\", \"neither agree or disagree\", \"slightly agree\", \"strongly agree\") or selected \"not applicable.\"

The second interview with a primary user of the software included mainly the perceived impact questions, and lasted 7--10 minutes. At the completion of the initial interview, each respondent was offered a summary of the results.

Statistical evaluation
----------------------

The data from all interviews were first descriptively evaluated, primarily by computing frequencies of responses for each question. Factor analysis (principal components) revealed four latent factors related to the respondent\'s perceived impacts of the IT on four practice activities: scheduling, financial analysis, communication, and medical documentation \[[@B2]\]. Therefore, four subscales were created. The scheduling, financial analysis, communication subscales each included two items, and the medical documentation subscale included three items. Responses of \"not applicable\" were coded as missing. For each subscale the mean of the items was computed.

Diagnostic plots of the four practice activity subscales suggested that an explanatory model might be best approached using logistic regression, which relaxes the assumption of normality. The four subscales were recoded to dichotomous variables corresponding to *agree* or *not agree.* If the mean score (of 2--3 impact statements) for a practice activity was greater than 3.0, the respondent was scored as \"1\" for *agree.* If the mean score for a practice activity was 3.0 (\"neither agree or disagree\") or less, the respondent was scored as \"0\" for *not agree.* Each of the four practice activity subscales became the dependent variable in a predictive model. The independent variables entered into the models included the demographic and selection variables.

Multiple logistic regression
----------------------------

We attempted four predictive models, one for each of the newly created dichotomous subscales. Only respondents who found the impact statements relevant were included in the predictive models. Multiple logistic regression revealed relationships between the selection process and the perceptions related to the scheduling, financial analysis, and communication processes. Variables that achieved a significance level of p \< .05 were retained in the models. For the perceptions related to medical documentation, no significant selection variables survived the analysis. This was most likely due to the small number of practices with electronic medical records (n = 89) and aggregating all types of electronic medical record (EMRs) regardless of type and number of functions. It is also possible that the decision to purchase an EMR is often made outside the practice -- e.g., a large health system offers EMRs to the practices. For 11 of the 89 practices that had EMRs, the decision was made by a large health system. Data from these practices were not included in the predictive models, thus reducing the number of available practices with EMRs to 78.

A summary of the models is presented in this paper. The complete analysis and models are available elsewhere \[[@B2]\]. The predictive models were built using a model building data set (299 randomly selected interviews). The models were then tested with a testing data set (the remaining 100 interviews). One-hundred interviews were needed to insure adequate statistical power. As a check for cross-validation, the accuracy with which the models predicted the perceived impact subscale values using the model building data set was compared to the accuracy achieved with the testing data set. Using the parameters established with the model building data set, agreement (or not agreement) to a perceived impact subscale was predicted for the testing data set.

For cross-validation, the accuracy levels were compared using a z-test for proportions. As seen in Table [2](#T2){ref-type="table"}, the scheduling and financial analysis models had non-significant (p \> .05) drops in accuracy. This suggests that the models may be generalized to other physician offices with similar demographics. Since the accuracy level dropped dramatically for the communication model, this model did not \"cross-validate.\" The observations made in this study accurately describe the idiosyncrasies of this sample used to build the communication model, but may not accurately describe other samples of physician offices.

::: {#T2 .table-wrap}
::: {.caption}
######

Cross-Validation Summary
:::

  **Practice Activity**   **Model Building Data Accuracy**   **Testing Data Accuracy**   **p**
  ----------------------- ---------------------------------- --------------------------- -------
  Scheduling              73%(n = 136)                       65%(n = 43)                 .437
  Financial Analysis      86%(n = 166)                       73%(n = 56)                 .059
  Communication           90%(n = 89)                        66%(n = 35)                 .003
  Medical Documentation
:::

Once the results were completed, the expert panel was reconvened to provide insight in interpreting the results. In the sections that follow, the descriptive results, comparison of the decision maker vs user, and each cross-validated model are summarized and discussed.

Results and Discussion
======================

Most administrators were involved in the selection (68%) and customization (63%) processes (Table [3](#T3){ref-type="table"}). Clinical staff members were also very involved in selection (62%) but not as involved in customization (33%).

::: {#T3 .table-wrap}
::: {.caption}
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Selection process
:::

  ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
  **Who selects (Q1)? And Who customizes (Q5)? At least 1 of the following**                                      **Selection Frequency (n = 399)**                                **Customization Frequency (n = 399)**
  --------------------------------------------------------------------------------------------------------------- ---------------------------------------------------------------- ---------------------------------------
  Administrators: office manager, financial manager, or medical director                                          68%                                                              63%

  Clinical staff members: a physician, physician\'s assistant, nurse practitioner, nurse or medical technicians   62%                                                              33%

  Computer consultant from outside the practice                                                                   48%                                                              39%

  Office staff members: billing clerk, scheduler, receptionist, or secretary                                      42%                                                              42%

  Representative from: health system, insurance company or patients                                               18%                                                              18%

  Computer specialist within the practice                                                                         17%                                                              13%

  **What selection steps are used (Q2)?**                                                                         **Frequency (n = 399)**

  Performed cost comparisons                                                                                85%

  Viewed software demonstration                                                                                81%

  Issued a RFP (Request For Proposal) or RFI                                                                      79%

  Compared software options with the best in the field                                                            78%

  Conducted prior user interviews                                                                                76%

  Performed a needs assessment                                                                                75%

  Developed selection criteria                                                                                73%

  Reviewed your long term business plan                                                                           60%

  Made a site visit                                                                                47%

  Developed a decision analysis                                                                                35%

  Formed a selection committee                                                                                21%

  **What factors influence the purchase (Q3)?**\                                                                  **Frequency (n = 399) Rated \"high or very high importance\"**
  **Influenzing Statements**

  The software appeared easy to use.                                                                              80%

  Software appeared to improve one or more of the business processes in the practice process.                     79%

  The software provided the most value for cost.                                                                  73%

  The software would help the practice perform processes needed to reach our long term business strategy.         66%

  The vendor had many sites and was responsive to our needs during the selection process.                         55%

  There were strong testimonies from prior users.                                                                 47%

  The software was already in use by other sites affiliated with this practice.                                   41%

  Software was compatible with existing practice systems in the practice.                                         36%
  ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
:::

Eighty percent or more of the practices performed cost comparisons and/or viewed software demonstrations. The frequencies for the steps the practices took in selecting software are depicted in descending order in Table [3](#T3){ref-type="table"}.

Seventy percent or more of the practices stated that \"ease of use,\" \"improving a business process,\" and \"most value for cost\" were important factors influencing the purchase (Table [3](#T3){ref-type="table"}). The frequency of factors receiving either \"high\" or \"very high importance\" is also presented in descending order in Table [3](#T3){ref-type="table"}.

The practices typically chose commercial packages that cost less than \$50,000. (Note: these data were collected during the fall, 1996). Information related to IT cost, customization level, and the number of users is presented in Table [4](#T4){ref-type="table"}. There were four basic software packages considered in this study. The type of package, associated computer activities, and frequencies are presented in Table [4](#T4){ref-type="table"}. The results indicate that more than 85% of the practices used the software for managed care or practice management activities. Fewer than half of the practices used the software for communication activities. Only 23% of the practices accessed a completed patient record with the software.

::: {#T4 .table-wrap}
::: {.caption}
######

Selected IT
:::

  **Details of IT**                        **Frequency (n = 399)**
  ---------------------------------------- ------------------------------------------------------------------------ -------------------------
  *Software cost*
   Given to the practice (\$0)             6%
   Less than \$10,000                      48%
   \$10--50,000                            38%
   \$More than \$50,0                      8%

  *Customization level*
   Commercial package (no customization)   49%
   Commercial package + customization      42%
   Completely custom package               9%

  *Number of users*
   Only 1 user                             23%
   2--5 users                              44%
   More than five users                    8%

  **Software Category**                    **Computer Activities** Which IT features are selected (Q4)?             **Frequency (n = 399)**

  Electronic Medical Record                Access and complete patient records using computerized patient records   23%

  Managed Care                             Track incoming and outgoing referrals                                    48%
                                           Track patient enrollment                                                 44%
                                           Capitation accounting                                                    32%
                                           Query database                                                           38%
                                           Statistical reporting on utilization and outcomes                        46%
                                           Follow clinical guidelines                                               24%
                                           **At least one managed care activity**                                   **85%**

  Communication                            Email or telemedicine to external colleagues                             15%
                                           Email within the practice                                                20%
                                           Remote link with other information systems                               17%
                                           Access to internet                                                       8%
                                           Electronic data interchange (EDI)                                        14%
                                           Online literature searches                                               8%
                                           **At least one communication activity**                                  **45%**

  Practice Management                      Billing and collections                                                  78%
                                           Appointment scheduling                                                   50%
                                           Accounting spreadsheets                                                  51%
                                           **At least one practice management activity**                            **92%**
:::

Ninety percent of the respondents felt the software had impacted their billing process (Table [5](#T5){ref-type="table"}). The first column in Table [5](#T5){ref-type="table"} lists the theme of the impact statement. The middle column is the proportion of respondents who rated the software -- meaning the impact statement was *relevant* to their software. For those who found the impact statement relevant, the last column depicts the proportion who slightly or strongly agreed with the impact statement. For example, in Table [5](#T5){ref-type="table"}, 74% of the respondents felt the software affected the accuracy of their practice documents. Of those, 85% of the respondents agreed that practice documents were more accurate since the software was implemented.

::: {#T5 .table-wrap}
::: {.caption}
######

What are the clinical and office staffs members perceptions of this it\'s impact on office activities (Q7)?
:::

  **Impact Themes**                                          **\"Relevant Proportion (n = 399)**   **For \"relevant\" responders only, the \"agreed\" proportion**
  ---------------------------------------------------------- ------------------------------------- -----------------------------------------------------------------
  Improved billing process                                   90%                                   89%
  More accurate documents                                    74%                                   85%
  Improved ability to analyze managed care costs             65%                                   85%
  Improved scheduling process                                58%                                   76%
  Improved access to patient information at multiple sites   55%                                   83%
  Reduced malpractice costs                                  50%                                   53%
  Improved referral process                                  50%                                   68%
  Reduced time for recording patient information             47%                                   77%
  Improved communication                                     44%                                   76%
  Improved documented quality                                38%                                   78%
  Quicker lab results                                        19%                                   60%
  Access to more journals                                    15%                                   38%
:::

Comparison of decision-maker vs user
------------------------------------

The primary respondents agreed with users on their perceptions of the software\'s impact on scheduling and financial analysis activities (p \< .001). For the scheduling model, Phi was .359, with a maximal Phi of .778. For the financial analysis model, Phi was .418 with a maximal Phi of .920. Since the primary respondent was reasonably knowledgeable about the perceived impacts of the software, we did not include the user data in the remainder of the cross-validated models. The user provided only a few demographics and the perceived impact data, while the primary respondent provided the selection data as well as the perceived impact data.

Predicting the impact of the software on scheduling activities
--------------------------------------------------------------

For the scheduling model, five selection variables as a group predicted with 73% accuracy the subscale of whether the respondents on average would agree with the following two impact statements:

\"The software has improved the scheduling of patients for routine, preventive and urgent appointments.\"

\"The software has improved the referral process in sending and receiving referrals quickly.\"

The statistically significant (p \< .05) predictors are presented in Table [6](#T6){ref-type="table"} along with the expected response by the respondent and the results of the multiple logistic regression analysis. The second column of the table contains the coefficient (or weighting value of B). The Wald statistic (Bj/standard error) gives a measure of significance of B for the predictor variable.

::: {#T6 .table-wrap}
::: {.caption}
######

Scheduling Model
:::

  **Predictor**                                                        **Coeff. (Bj)**   **Wald Statistic**   **p**    **Odds Ratio**   **Respondent\'s Reaction to Scheduling Statements**
  -------------------------------------------------------------------- ----------------- -------------------- -------- ---------------- -----------------------------------------------------
  Software with electronic medical record features.                    1.36              3.91                 0.0481   3.89             More likely to agree
  The practice compared software options with the best in the field.   1.36              5.94                 0.0148   3.88             More likely to agree
  Software with practice management features.                          0.53              3.34                 0.0676   1.70             More likely to agree
  Importance of prior user testimony                                   0.33              4.74                 0.0295   1.39             More likely to agree
  The respondent personally selected the software.                     -1.61             8.22                 0.0041   0.20             Less likely to agree
:::

Looking at the odds ratios in Table [6](#T6){ref-type="table"}, the likelihood of agreement with the scheduling subscale is almost four times (odds ratio, OR = 3.89) as great when practices selected EMR packages than if they did not select EMR packages. At first this finding was surprising. Many EMRs, however, have automatic recall features when the patient should be called or sent a reminder for a health check. Similarly, the likelihood of agreement was almost four times (OR = 3.88) as great when the practice compared the software options with the best in the field than if it did not perform this step.

The practices that selected practice management software were 1.70 times more likely to agree that the software had improved the scheduling and referring of patients than practices that selected other types of software. This finding was expected since these packages typically include a scheduling module. Additionally, practices that considered \"prior user testimony\" important in the selection process were 1.39 times more likely to agree with the scheduling subscale than those practices that did not consider prior user testimony as an important influence.

Finally, a respondent who had personally selected the software was less likely to agree with the impact statements (OR = 0.20). The members of the expert panel felt this was a symptom of \"unmet expectations.\" The members of the selection team knew how the software was supposed to perform and were likely disappointed when it didn\'t live up to the vendor promises. These respondents had also probably seen the \"Cadillac\" performers and realized that their software had only achieved \"Chevrolet\" status. Another explanation is that these practices failed to fully implement the software or to adapt clinic workflows to fully utilize the software.

In summary, practices that selected EMR or practice management software, that made software comparisons, or that considered prior user testimony as important were more likely to have perceived improvements in the scheduling process than were other practices.

Predicting the impact of the software on financial analysis activities
----------------------------------------------------------------------

For the financial analysis model, five selection variables as a group predicted with 86% accuracy the subscale of whether the respondents on average would agree with the following two impact statements:

\"The software has created a more accurate and timely billing process.\"

\"The software has improved the practice\'s ability to track and analyze costs and revenues associated with managed care contracts.\"

The most dramatic increase in odds of agreement (OR = 8.2) occurred when the practice reduced the workload to allow time to learn the software, Table [7](#T7){ref-type="table"}. However, only 36% of the 399 practices reported that reduced workloads were provided during the implementation phase. According to the survey conducted by Ambosa et al. \[[@B21]\], expecting medical staff to learn new software while caring for a full load of patients is a common reason for failure.

::: {#T7 .table-wrap}
::: {.caption}
######

Financial Analysis Model
:::

  **Financial Analysis Model Predictor**                    **Coeff. (Bj)**   **Wald Statistic**   **p**    **Odds Ratio**   **Respondent\'s Reaction to Financial Analysis Statements**
  --------------------------------------------------------- ----------------- -------------------- -------- ---------------- -------------------------------------------------------------
  Time to learn (reduced workload to learn the software).   2.1               5.44                 0.0197   8.20             More likely to agree
  Software with managed care features.                      1.52              7.74                 0.0054   4.59             More likely to agree
  Importance of \"value for cost\' purchase influence.      0.69              8.04                 0.0046   2.00             More likely to agree
  Importance of compatibility purchase influence.           -0.41             5.89                 0.0152   0.66             Less likely to agree
  The cost of the software.                                 -1.4              6.74                 0.0094   0.25             Less likely to agree
:::

The odds of agreement were increased by more than a factor of four (OR = 4.59) for each increase in managed care activities the software contained. Since most managed care software packages are marketed to assist the practice in documenting costs associated with managed care contracts, this finding was expected.

Practices that considered value an important consideration were twice (OR = 2.0) as likely to agree with the financial analysis subscale. By contrast, practices that considered compatibility an important influence were less likely (OR = 0.66) to agree with financial analysis subscale. At first the compatibility result was surprising. However, 51% of these practices were first-time buyers, and usually buying billing software, so compatibility was not a critical consideration. Ninety-one percent of first-time buyers who rated compatibility as low-to-no importance agreed with the financial analysis subscale. It is also possible that practices with existing good financial analysis processes (and little room to improve) rated compatibility as important but disagreed that the new software had improved the existing good process.

The finding that less expensive packages related to more satisfied buyers was interesting (OR = 0.25). There were many good financial packages available for less than \$10,000 in 1996. Practices that spent less than \$10,000 bought software packages with few, but very functional, features. Those practices that spent more than \$10,000 were purchasing complex systems, perhaps for multiple sites. Financial analysis may just have been a small module of these multi-purpose packages.

In summary, practices that considered value important, that did not consider compatibility important, that selected managed care software, that spent less than \$10,000, or that provided learning time during implementation were more likely to perceive that the software had improved the financial analysis process than were other practices.

Observations from both models
-----------------------------

In looking over the predictors for the two cross-validated models (scheduling and financial analysis), some predictors naturally belong in one model or the other -- e.g., practice management software in the scheduling model and managed care software in the financial analysis model. The themes in the scheduling model center around software features (emr and practice management software, comparison of software options) and usability (prior user testimony and personal selection by respondent). The themes in the financial analysis model include cost (software cost, value), software features (managed care software and compatibility), and learning time. This might suggest that the respondents for the financial analysis model had differing roles in the practice than the respondents for the scheduling model. In both of these models, 79% of the respondents were administrators.

Since all types of administrators (e.g., office managers, finance managers) were grouped together, it was impossible to identify the primary role of administrator who responded. The differences in the models also suggest that the predictors of success differ by the types of activities the software is intended to perform.

It might appear odd that some predictors (e.g., learning time) did not carry through to both models. It is likely that the type and complexity of software package contributed to the learning demands on the office. Many of the respondents who agreed with the financial analysis subscale chose managed care software that bundled together many activities (tracking incoming and outgoing referrals, patient enrollment, capitation accounting, and/or utilization reporting). For practices learning this type of software, protected learning time was an important predictor of success. For practices implementing practice management software (scheduling, billing, and/or accounting spreadsheets), the learning demand was less. This naturally suggests that the decision to reduce the workload while learning a software package should consider the number and complexity of the tasks to be learned.

Limitations and research opportunities
--------------------------------------

The respondents for this study primarily represented practices that serve Providence Health System in Oregon. These practices served either as managed care providers or as fee-for-service providers. The only practices excluded were pure HMO providers -- e.g., Kaiser Permanente. The pure HMO practices were excluded because it was unclear whom to interview regarding software selections. Often these practices are given software directly from the organization. Eighty-seven percent of these practices in this study had 10 practitioners or less. Only 17% of these practices had in-house computer specialists assisting with software selection. The results of this study may not generalize to large practices that often have in-house computer specialists assisting with selection. A future study could include a nationwide survey of all types of physician practices, regardless of managed care status, ownership, specialty, or size.

This study is retrospective in nature, requiring the respondents to recall a software purchase that occurred several months, perhaps more than a year, earlier. In an \"ideal study design,\" a questionnaire should be distributed to practices that have recently made selections. Another questionnaire addressing the impact on the practice could be sent at a pre-defined follow-up period -- e.g., six months after implementation. This \"ideal study design\" would be difficult to conduct without a sufficient list of practices that have recently purchased software. Perhaps software manufacturers and vendors could provide lists of recent clients (with permission) to interested researchers.

The cross-sectional survey design of this study captured the technical aspects of the selection process (e.g., who was involved, what steps that were taken). Although the respondents were given a few \"open-ended\" questions, most provided little additional information. There could have been additional selection steps, influences, and impacts. It is also possible that the observed changes in impact were related to variables we didn\'t attempt to measure -- e.g., ability and desire of management to implement new technologies and to change existing practice activities. Focus groups might be more effective at capturing underlying management expertise. Another very time-invasive approach would be to conduct a series of case studies, documenting the decision-making process over time. This research would need support from practices for observers to remain on-site during the selection process. This format would also promote a more well-rounded, multiple perspectives evaluation. The current study relies on perceptive responses (primarily from office managers) to measure many variables, including impact variables. Their perceptions were related to business-related practice activities. Only 5.3% of the respondents were clinicians. It is likely that expanding this study to include more clinician responses would reveal perceptions related to other processes -- e.g., medical documentation or treatment processes.

The subscales (related to practice activities) were formed from responses to only two to three original impact questions. A stronger design of these practice activities impacts would include several questions related to each activity. Given the exploratory nature of this current research, this limitation could not have been foreseen. However, the results of this study open doors for more confirmatory types of studies to design survey instruments that measure software impact with underlying practice activity constructs. This study does not attempt to demonstrate cause and effect. It would be important to have respondents rate existing practice activities (before purchasing software) to control for a \"ceiling effect\" -- practices with existing good processes have little room to improve. If such a trial were designed, it would also need to control for the type of IT and the needs of the buyer.

To move toward a more direct measure of impact would require the practices to closely measure performance and behavior. For example, in this study, the respondent is asked if the practitioners have an improved ability to consult professional literature online. A direct measurement method would determine the number of online literature consultations before and after the software installation.

Conclusions
===========

The results of this research describe the software selection process as it occurs in physician practices. Using a telephone interview survey gave the researcher (and other interviewers) direct contact with the decision makers in each practice. The results of this study also describe how software is perceived to affect several practice activities.

The objective of this study was to identify relationships (if any) between the IT selection process and the office staff\'s perceptions of the IT\'s impact on practice activities. The results of the multiple logistic regression models confirmed relationships between the selection process and the perceived impacts related to the scheduling and financial analysis activities. The results of this study demonstrated a relationship (not cause and effect) between the selection process and the user perception of software usefulness.

Although many of the relationships were expected (e.g., performing software comparisons, interviewing prior users, and selecting certain software features improved perceptions about practice activities), perhaps one of the most important predictors of improvement was reducing the workload during implementation. Despite the importance of this predictor, only 36% of the practices performed this step in this study. If more practices had performed this step, it might have carried even more weight in the analysis. From a practical standpoint, many of the offices selected and implemented IT but expected the staff to learn the software while caring for a full load of patients. Investigators from a previous study by Ambroso et al. \[[@B21]\] cite this expectation as a common reason for IT failure.

One of the secondary findings of this research is that the purchasers of the software (often office managers) had perceptions about the software\'s use similar to those of users (who were not involved in the selection process). This finding supports the use of a single-survey-response study design for understanding perceived impacts related to software\'s impacts on business-related practice activities.

List of Abbreviations
=====================

EMR: Electronic Medical Record

IT: Information Technology

OR: Odds Ratio

Competing Interests
===================

None Declared.

Author comments on prior presentation of results
================================================

The results of this study were presented at the Portland International Conference on Management of Engineering and Technology, Portland Oregon, 1997 and 1999. The results were also presented at the Institute for Operations Research and Management Science, Philadelphia, Pennsylvania, 1999. The references for the conference proceedings are listed below.

**Eden K,** Kocoaglu, D. Information Technology Selection Process and Perceived Impacts in Physician Practices. In *Technology and Innovation Management.* Portland State University, PICMET conference proceedings, 1999, pp. 562--568. Executive summary presented in proceedings, Portland International Conference on Management of Engineering and Technology, Portland, Oregon, 1999, pp. 392--394.

**Eden K,** Kocaoglu, D. Selection of Information Technology in the Health Care Industry. Presented at Institute for Operations Research and the Management Sciences conference. Philadelphia, Pennsylvania, November, 1999.

**Eden K,** Kocaoglu D. Selection and Implementation of Information Technology in the Health Care Industry. Preliminary results presented at the Portland International Conference on Management of Engineering and Technology, published in proceedings, Portland, Oregon, 1997, pp. 199--202.

Pre-publication history
=======================

The pre-publication history for this paper can be accessed here:

<>

Supplementary Material
======================

::: {.caption}
###### Additional file 1

Scripted telephone survey, \"Physician Practice Software Telephone Survey, Dialog and Questions\", by K.B. Eden,

The file contains the script, questions and pre-coded responses, variables names (in left margins, that appear as: \>xxxx\<), and several logical statements (e.g., goto, if, etc.) to lead the interviewer through the interview.
:::

::: {.caption}
######

Click here for file
:::

Acknowledgments
===============

I would like to thank Dundar Kocaoglu, PhD, Nancy Perrin, PhD, Mara Tableman, PhD, Wayne Wakeland, PhD, Laurie Skokan, PhD, Dr. Robert Eder, PhD and Bruce Bayley, PhD, for providing direction on this study. I would also like to thank Nancy Perrin, PhD, Mark Helfand, MD, MPH, William Hersh, MD, FACP, Joan Ash, PhD, John Beekman, PhD, Jane Beekman and the BioMed Central reviewers for critically reviewing earlier versions of this manuscript and providing thoughtful feedback. I would like to acknowledge Gary Miranda for his careful editing and suggestions to make this manuscript \"reader friendly.\" This project would not have been possible without the support and funding from Providence Health System and the use of the Portland State University Regional Research Institute. Finally, I would like to thank my husband, Kevin, and my children, Matthew and Erika, who were very supportive during this project.


Background
==========

Coiled-coil domains are protein oligomerization motifs, which consist of two or more alpha helices that twist around one another to form a supercoil \[[@B1]\]. Peptides with the capacity to form coiled coils are characterized by a heptad repeat pattern in which residues in the first and fourth position are hydrophobic, and residues in the fifth and seventh position are predominantly charged or polar. This pattern can be used by computational methods to predict coiled-coil domains in amino acid sequences \[[@B2],[@B3]\].

Coiled-coil proteins can be grouped into two general classes. The first class is comprised of short coiled-coil domains of six or seven heptad repeats, also called leucine zippers. They are frequently found as homo- and hetero-dimerization domains in transcription factors (e.g. \[[@B4]\]). The second class is defined by long coiled-coil domains of several hundred amino acids, which are found in a variety of proteins involved in structuring cellular processes \[[@B1]\].

One of the three main classes of cytoskeletal proteins, the intermediate-filament proteins, represents a well-characterized group of coiled-coil proteins. A subgroup of intermediate-filament proteins are the nuclear lamins, which are involved in attaching chromatin to the nuclear envelope and have recently been implicated in inherited human diseases \[[@B5],[@B6]\]. Besides structural proteins of the cytoskeleton, the motor proteins that interact with them also contain coiled-coil motifs. Myosin, the actin motor protein, has an extended coiled-coil domain necessary for the assembly of the muscle thick filaments \[[@B7]\]. Kinesin and dynein, two microtubule motor proteins, also contain coiled-coil domains \[[@B8],[@B9]\].

The coiled-coil motif has also been identified in a variety of proteins associated with centromeres, centrosomes, the nuclear matrix, and chromatin \[[@B10]-[@B14]\]. TPR, a protein associated with the inner filaments of the nuclear pore complex is an extended coiled-coil protein \[[@B15]\]. The filamentous structures associated with both the outer and inner surface of the nuclear pore complex suggest that additional unknown coiled-coil proteins might be involved in its assembly. Several large coiled-coil proteins were also found associated with the cytoplasmic surface of the Golgi apparatus, and two of them have been shown to function in the docking of vesicles to the Golgi cisternae \[[@B16]-[@B18]\]. AKAPs are adapter proteins that attach protein kinase PKA pathways to cytoskeletal elements. At least one AKAP, AKAP 450 is a large coiled-coil protein attaching PKA to the centrosome \[[@B19]\].

Despite the multitude of cellular functions in which coiled-coil proteins participate, the general theme is the association of proteins with the solid-state components of the cell. In some cases, as in the attachment of PKA, this function appears to be crucial for the spatial regulation of signal transduction. Importantly, mutations in individual coiled-coil proteins lead to specific developmental or even behavioral phenotypes. The Drosophila mutations *Mushroom body defect* (a gene involved in the proliferation of neuronal precursor cells) and *quick-to-court* (a mutation that causes elevated sexual behavior) lie in two tissue-specifically expressed coiled-coil proteins of unknown function \[[@B20],[@B21]\]. In humans and mouse models, specific point mutations in nuclear lamins are the cause for autosomal Emery-Dreifuss muscular dystrophy \[[@B22],[@B23]\].

In contrast to animals and yeast, only a small number of long coiled-coil proteins have been identified from plants. MFP1 contains an extended coiled-coil domain and is located at the nuclear periphery of tobacco suspension culture cells \[[@B24],[@B25]\]. The carrot protein NMCP1 is also located at the nuclear rim and has been shown to migrate to the spindle poles in dividing carrot suspension culture cells \[[@B26],[@B27]\]. The cellular function of these plant proteins is not known.

Several candidates for myosins and kinesins have been identified from plants and their function as motor proteins is under investigation \[[@B28],[@B29]\]. Besides these few examples, nothing is presently known about plant long coiled-coils and their potential function in anchoring and structuring of different cellular events.

In the early nineties, several groups reported lamin-like proteins in different plant species \[[@B30]-[@B32]\]. A purification protocol adapted from animal lamins and applied to pea nuclei was used to purify four proteins between 49 and 66 kD \[[@B31]\]. These proteins were recognized by antibodies against mammalian intermediate filament proteins, and by antibodies against a peptide derived from lamin B. By immunofluorescence and immunogold labeling, these antigens were located mostly in the internal nuclear matrix, and not predominantly at the nuclear rim. Similar results were obtained by other investigators \[[@B32]\]. Although these early reports were promising, no lamin ortholog has been identified molecularly from plants. In a recent search of all publicly available plant sequences, including the full *Arabidopsis* genome, no homologs of lamins were found (\[[@B33]\] and Meier, unpublished results). Similarly, despite earlier reports of lamin-like proteins in yeast \[[@B34]\], the fully sequenced *S. cerevisiae* genome \[[@B35]\] also contains no lamin genes. It is therefore likely that non-animal eukaryotes have a distinct set of nuclear envelope proteins that functionally replace the lamins.

Here, we have used a tomato protein containing long coiled-coil domains to search the Arabidopsis genome for related sequences. We have identified seven novel Arabidopsis proteins with extended coiled-coil domains, which form a protein family distinct from other Arabidopsis long coiled-coil proteins. They are characterized by four novel motifs, which are highly conserved in sequence and position, and which were not found in proteins outside of this family. In addition, a rice homolog was identified, indicating conservation between dicots and monocots.

Results
=======

LeFPP, a novel plant coiled-coil protein
----------------------------------------

A yeast two-hybrid screen was performed to identify interaction partners of the tomato nuclear envelope-associated protein LeMAF1 \[[@B36]\]. A prey plasmid (pAD-LeFPP) was identified that led to activation of the two reporter genes *HIS* and *LacZ* after retransformation with the pBD-LeMAF1 bait. We tested the specificity of this interaction by co-transforming pAD-LeFPP with two additional bait plasmids, pBD-AtMAF1 and pBD-AtRanGAP1. AtMAF1 is one of three Arabidopsis homologs of LeMAF1 (Patel and Meier, unpublished results), and has 45% amino acid identity with LeMAF1. AtRanGAP1 is one of the two Arabidopsis Ran GTPase activating proteins. Its N-terminus has 28% amino acid identity with LeMAF1 and 30% with AtMAF1 (Fig. [1C](#F1){ref-type="fig"}; \[[@B33],[@B37]\]). Fig. [1A](#F1){ref-type="fig"} shows that pAD-LeFPP leads to activation of the *HIS* gene in combination with pBD-LeMAF1 (sector a) and with pBD-AtMAF1 (sector b), but not with pBDAtRanGAP1 (sector c) or the binding domain vector pBD-GAL4 alone (sector d). This demonstrates that the observed interaction is specific for MAF1-like proteins.

![Yeast two-hybrid interaction of LeFPP with MAF1. (A)Yeast colonies co-expressing bait and prey plasmids were streaked on dropout plates without leucine and tryptophane (LT) or dropout plates without leucine, tryptophane, and histidine (LTH). (a) pBD-LeMAF1/pAD-LeFPP; (b) pBD-AtMAF1/pAD-LeFPP; (c) pBD-AtRanGAP1/pAD-LeFPP; (d) pBD-GAL4/pAD-LeFPP. (B) Quantification of β-Galactosidase activity in yeast strains harboring combinations of bait and prey plasmids. (1) p53/pSV40 (positive control); (2) pLaminC/pSV40 (negative control); (3) pBD-LeMAF1/pAD-LeFPP; (4) pBD-GAL4/pAD-LeFPP. Units, relative β-Galactosidase units (see Methods). (C) Sequence alignment between LeMAF1, AtMAF1, and the N-terminal domain of AtRanGAP1. Identical amino acids are shaded in black.](1471-2164-3-9-1){#F1}

Fig. [1B](#F1){ref-type="fig"} shows the quantification of β-Galactosidase activity in the pBD-LeMAF1/pAD-LeFPP strain. Lanes one and two are the positive and negative control provided with the kit, respectively, lane three shows the activity of the pBD-LeMAF1/pAD-LeFPP strain and lane 4 the activity of the pBD-GAL4/pAD-LeFFP strain. Although yeast two-hybrid interactions can not be easily quantified, the very high level of β-Galactosidase expressed by the pBD-LeMAF1/pAD-LeFPP stain suggests a comparably strong interaction in the yeast two-hybrid screen.

The 1927 bp pAD-LeFPP cDNA was sequenced and was shown to contain an uninterrupted open reading frame in frame with the GAL4 activation domain. The conceptual translation of this sequence leads to a protein of 582 amino acids (aa) with a calculated molecular weight of 64.4 kDa and a pI of 4.7. The first methionine is at position 71, and does not have a plant start ATG consensus sequence \[[@B38]\], suggesting that the cDNA is not full length.

A BLAST search with the pAD-LeFPP open reading frame indicated that it represents a novel protein with weak similarity to filament-like proteins from animals and yeast (see below). Secondary structure prediction algorithms showed that it is organized almost entirely in alpha-helical domains and contains extensive stretches of coiled-coil domains (Fig. [2A](#F2){ref-type="fig"} and Fig [3](#F3){ref-type="fig"}). The protein was named LeFPP for tomato filament-like plant protein. RNA blot analysis showed a single species of mRNA of ca. 2.4 kb. It is present in tomato leaves, fruits, flowers, light-grown seedlings, and dark-grown seedlings (Fig. [2B](#F2){ref-type="fig"}).

![LeFPP is a ubiquitously expressed coiled-coil protein. (A) Non full-length amino acid sequence of LeFPP (GenBank accession number AF405309). The Lysine in position 1 is the first amino acid encoded by the LeFPP cDNA in frame with the GAL4 activation domain. The coiled-coil regions are boxed. The residues correlating to motifs I, II, III, and IV are indicated by asterisks below the sequence (see text). (B) RNA blot analysis of LeFPP RNA accumulation in different tomato tissues. Approximately equal amounts of total RNA (20 μg) were loaded per lane, as confirmed by ethidium-bromide staining (data not shown). yl, young leaves; yf, young fruits; fl, fully open flowers; sd, light-grown seedlings; sd, dark-grown seedlings. The positions of RNA size markers are indicated on the right.](1471-2164-3-9-2){#F2}

![Comparison of predicted secondary structure of FPP proteins. All proteins are drawn to scale. The numbers on the scales represent amino acids. Alpha helices are indicated as gray bars (A); coiled-coil domains are indicated as white bars (\*). The positions of the four conserved sequence motifs are indicated as I, II, III, and IV on the scales. I^\*^ indicates the variant of motif I found in OsFPP.](1471-2164-3-9-3){#F3}

A family of LeFPP-like proteins in Arabidopsis
----------------------------------------------

A BLAST search was performed with the LeFPP amino acid and DNA sequence. In a protein BLAST, sequences with highest scores represented a number of uncharacterized open reading frames in the Arabidopsis genome, followed by sequences for animal filament-like proteins such as myosin, kinesin, ankyrin, etc. While the e-values for the Arabidopsis ORFs were between 2e-56 and 1e-16, the best e-value for a non-plant sequence was 0.003 for an unknown human open reading frame. The Arabidopsis \"hits\" represented seven unique genes, some of which had repeated entries in GenBank (see Table [1](#T1){ref-type="table"}).

::: {#T1 .table-wrap}
::: {.caption}
######

Genomic organization and features of the Arabidopsis *FPP* gene family.
:::

  ---------------------------------------------------------------------------------------------------------------------------------
  Name     GenBank Accession\   Alias^b^    Chr. \#\         Size ORF (aa)\        pI    EST^c^       library
           (TAIR name^a^)                   (Map pos. (Mb)   (Size protein (kDa)
  -------- -------------------- ----------- ---------------- --------------------- ----- ------------ -----------------------------
  AtFPP1   AGG51666             T5M16.17    1                779                   4.7   AV524363     Seedlings, aboveground

           (AT1G77580)                      (28.9)           (87.8)                                   org.

                                                                                APZL64f04F   Mixed tissues

  AtFPP2   AAF16547             T26F17.2    1                629                   4.9   AV524363     Seedlings, aboveground org.

           (AT1G21812)          F8K7.24     (7.65)           (71.1)

  AtFPP3   AAF27033             T12H1.24    3                603                   5.3   AV547438     Roots

           (AT3G05270)          T15D2.30    (1.5)            (68.3)                      T22555       Mixed tissues

  AtFPP4   AAG12549             F6F9.12     1                1024                  5.2   AV528396     Seedlings, aboveground org.

           (AT1G19835)          F14P1.46    (6.8)            (114.5)

                                F6F9.17                                                  AV556649     Green siliques

                                                                                AV546174     Roots

                                                                                AV547135     Roots

  AtFPP5   CAB81521             T19K4.250   4                981                   6.3   RAFL16-63-   unknown

           (AT4G36120)                      (16.3)           (110.4)                     O05^d^

  AtFPP6   AAG51782             T6B12.1     1                1054                  4.8   AA042379     Hypocotyls

           (AT1G47900)                      (17.2)           (118.5)                     N96153       Mixed tissues

  AtFPP7   AAC23780             F26B6.1     2                886                   5.7   W43775       Hypocotyls

           (AT2G23360)          T20D16.1    (10.24)          (99.9)
  ---------------------------------------------------------------------------------------------------------------------------------

^a^The Arabidopsis Information Resource. ^b^Additional early annotation names of the same protein. ^c^GenBank accession numbers. ^d^RIKEN Arabidopsis Full-Length cDNA <>
:::

A translated BLAST search identified one additional \"hit\" in the rice BAC H0212B02 of chromosome 4 (GenBank accession number AL442007). The \"hit\" lay between the annotated open reading frames H0212B02.17 and H0212B02.18 in a region of DNA not predicted to have coding capacity. However, running GenScan \[[@B39]\] on this part of the BAC, an open reading frame of 1074 amino acids was identified on the opposite strand that contained the \"hit\". The predicted coding region begins at position 114,922 of the H0212B02 DNA sequence, ends at position 107,317, and contains seven predicted introns. This rice open reading frame was named OsFPP and was included in the sequence and structural analysis shown in Fig. [3](#F3){ref-type="fig"} and Fig. [4](#F4){ref-type="fig"}.

![Four novel sequence motifs define a sub family of plant coiled-coil proteins. (A) Sequence alignment of motifs I, II, III, and IV. In all four alignments, amino acid residues conserved among all dicot sequences are indicated in black. Amino acids in the rice sequence that match these conserved residues are also indicated in black. All residues are matched by the rice sequence in motifs II, III, and IV, while 6 residues are exchanged in the rice sequence in motif I. Numbers on the left indicate the position of the first residue of each motif in the amino acid sequence of the respective protein. (B) Percent amino acid identity between FPP proteins.](1471-2164-3-9-4){#F4}

Fig. [3](#F3){ref-type="fig"} shows a comparison of the secondary structure of LeFPP with the seven Arabidopsis ORFs and the rice ORF. All open reading frames are characterized by extended alpha-helical and coiled-coil domains. The extension of the coiled-coil domains shown in Fig. [3](#F3){ref-type="fig"} is derived from the \"coiled-coil\" analysis software in the DNASTAR sequence analysis software package, and was confirmed in all cases by the MultiCoil algorithm \[[@B2]\]. In addition, MultiCoil predicted that all coiled-coil regions have a high probability to form dimers and a low probability to form trimers. The seven Arabidopsis ORFs were named AtFPP1 -- AtFPP7 (Fig. [3](#F3){ref-type="fig"} and Table [1](#T1){ref-type="table"}). They fall into two groups according to size and extension of coiled-coil domains. AtFPP1, AtFPP2, and AtFPP3 are shorter (603 aa to 779 aa) and contain two extended coiled-coil domains connected by a short non-coiled-coil linker. AtFPP4 to AtFPP7 are longer (866 aa to 1054 aa) and contain a longer N-terminal and a shorter C-terminal coiled-coil domain, separated by a long non-coiled-coil linker domain (Fig. [3](#F3){ref-type="fig"}). The rice ORF (OsFPP) resembles structurally more the second group of Arabidopsis ORFs, with two shorter stretches of coiled-coil domains separated by a long linker.

Novel sequence motifs define a subfamily of plant coiled-coil proteins
----------------------------------------------------------------------

Alignment of all nine amino acid sequences showed the presence of four novel sequence motifs, which are 100% conserved between the tomato and Arabidopsis proteins (Fig. [4A](#F4){ref-type="fig"}). Motifs II, III, and IV are also 100% conserved in OsFPP, while motif I is present, but slightly less conserved in the monocot sequence. Their location in each protein is indicated in Fig. [3](#F3){ref-type="fig"}. Their position with respect to the arrangement of the coiled-coil domains is strikingly conserved, despite differences in spacing between the four motifs due to the different length of the proteins and the coiled-coil domains.

Motifs I and II always correlate with the N-terminal cluster of coiled-coil domains, with motif I being located at the beginning and motif II at the end of this cluster. Motif III is always located in the linker between the N-terminal and C-terminal coiled-coil domains. Motif IV is always located at the very end of the most C-terminal coiled-coil domain. This positioning is conserved even in the rice sequence, where the C-terminal coiled-coil domain is extremely short (Fig. [3](#F3){ref-type="fig"}).

The amino acid identities between the eight dicot proteins are in the range of 20% to 50% (Fig. [4B](#F4){ref-type="fig"}), with the rice sequence showing between 13% and 26% identity with the dicot sequences. The Arabidopsis protein with the greatest similarity to LeFPP is AtFPP1 (40% identity).

Table [1](#T1){ref-type="table"} shows an overview of the genomic organization of *AtFPP1* through *AtFPP7* and of the features of the predicted proteins. The genes are not clustered, and no two of them are arranged in tandem array. Except for chromosome 5, at least one member of this gene family is present on each chromosome. ESTs have been identified for all genes, indicating that they are expressed (Table [1](#T1){ref-type="table"}). Consistent with the ubiquitous expression pattern found for *LeFPP*, *AtFPP* expression covers a variety of tissues and developmental stages including shoots, hypocotyls, roots, and siliques.

In order to identify more long coiled-coil proteins in the Arabidopsis genome, the complete list of annotated ORFs was searched with the keyword \"myosin\" to identify ORFs that had been annotated as myosin-like proteins. Because of the long stretches of conserved heptad repeats, BLAST searches with large coiled-coil proteins usually produce significant e-values with at least some of the large number of myosin heavy-chain sequences in GenBank, frequently leading to this annotation.

The 40 ORFs identified this way were analyzed by MultiCoil and 37 were found to contain coiled-coil domains of at least 200 amino acids. 3 ORFs showed no coiled-coil motifs and were discarded. AtFPP2 and AtFPP5 had been annotated as \"myosin-like\" too and were therefore identified in this search. They were discarded to avoid duplication. None of the remaining 35 new coiled-coil proteins contained any of the conserved motifs identified in the FPP family.

The 35 new sequences were aligned with the seven AtFPP sequences in a CLUSTAL analysis. Fig. [5](#F5){ref-type="fig"} shows the results represented as a phylogenetic tree. The AtFPP family clearly forms a family of sequences separate from the other coiled-coil proteins (boxed in Fig. [5](#F5){ref-type="fig"}). The three \"short\" AtFPPs, AtFPP1, AtFPP2, and AtFPP3 form a sub-family, as do the four \"long\" AtFPPs AtFPP4, AtFPP5, AtFPP6, and AtFPP7 (see Fig. [3](#F3){ref-type="fig"}). Interestingly, the two most closely related *AtFPP* genes, *AtFPP1* and *AtFPP2* are located in comparable positions on two duplicated segments of chromosome 1, indicating that they might be derived from a recent duplication event.

![Phylogenetic tree derived from a CLUSTAL analysis of the full ORF sequences of Arabidopsis coiled-coil proteins. The seven AtFPP sequences (boxed) were aligned together with 35 additional Arabidopsis coiled-coil proteins that had been annotated as \"myosin-like\". The length of each pair of branches measures the distance between sequences. Units indicate the number of computed residue substitution events. Dotted lines have no unit length and were included for alignment purposes. ORFs were identified by their TAIR gene names, accessible through <>.](1471-2164-3-9-5){#F5}

Discussion
==========

A novel class of plant coiled-coil proteins has been identified that are conserved in dicots and monocots. Seven members of the FPP protein family are present in the Arabidopsis genome. They are characterized by the presence of two clusters of coiled-coil domains separated by a linker domain of variable size. Four highly conserved sequence motifs were identified, which are a signature feature of this protein family and which are also conserved in the homologs LeFPP and OsFPP from tomato and rice, respectively. The tomato homolog of the FPP family binds in a yeast two-hybrid assay to a nuclear envelope-associated protein, indicating that they might function in nuclear envelope biology.

Besides the presence of extended coiled-coil domains, the FPP family members have no sequence similarity to the vertebrate lamins. They also do not show significant sequence similarity to other vertebrate nuclear envelope-associated proteins such as LAP1, LAP2, MAN1, and emerin (GenBank accession numbers A55649, S55255, Q9WU40, and XP_048410, respectively). None of these proteins appears to have a convincing ortholog in the Arabidopsis genome (data not shown), consistent with the hypothesis that the composition of nuclear envelope-associated proteins differs significantly between the plant and animal kingdom.

Interestingly, a recent report about the protein composition of the nuclear envelope of the highly divergent eukaryote *Trypanosoma brucei* has identified a coiled-coil nuclear envelope protein (TbNUP-1) in this organism that also has no similarity with lamins \[[@B40]\]. TbNUP-1 is a 350 kDa protein with a striking repeat structure. Its full sequence is presently not known but a partial open reading frame of 268 amino acids has been constructed \[[@B40]\]. The near-perfect 144 amino acids repeats contain two coiled-coil domains each. Pairwise alignments of TbNUP-1 with AtFPP1 through ATFPP7 showed no significant sequence similarities beyond the typical low degree of similarity generally observed between coiled-coil proteins (data not shown). The TbNUP-1 open reading frame was also used for a BLAST search of the full Arabidopsis genome. Only comparably weak similarities with other \"myosin-like proteins\" were found.

The four conserved sequence motifs identified in the FPP family appear to be unique to this group of coiled-coil proteins and are neither shared by other coiled-coil open reading frames present in the Arabidopsis genome nor by coiled-coil proteins such as lamins from other organisms. Their role in the structure or function of the FPP proteins is presently not known, but their high degree of conservation in the tomato and rice sequences imply that they are of relevance. Several functional small conserved motifs have been identified in other families of long coiled-coil proteins. The nuclear lamins contain short, conserved sequences flanking the lamin rod domain, which are phosphorylated by cdc2 kinase \[[@B41]\]. The 90 amino acid PACT domain, which immediately follows the coiled-coil domain of the related proteins pericentrin and AKAP450 and two uncharacterized coiled-coil open reading frames from Drosophila and *Schizosaccharomyces pombe*, confers centrosomal localization \[[@B19]\]. A conserved domain of ca. 50 amino acids was found in several Golgi-associated large coiled-coil proteins. It has been named GRIP domain \[[@B42]\] or Golgi localization domain (GLD) \[[@B18]\] and is sufficient to specify Golgi targeting in mammalian cells. Nearly all intermediate filament proteins exhibit a highly conserved amino acid motif (YRKLLEGEE) at the C-terminal end of their central alpha-helical rod domain which has been shown to be crucial for the formation of authentic tetrameric complexes and for the control of filament width \[[@B43]\].

No sequences with similarity to the consensus sequences of motifs II, III, and IV were found in the databases that might indicate their potential function. However, a sequence with some similarity to motif I was identified in the hemaglutinin-esterase fusion glycoprotein from influenza virus C (GenBank accession number S07412). Alignment of this sequence (amino acids 39 to 74) with the central conserved residues of motif I: A\--GWEK\-\--E\-\--LK\--L\-\-\-\-\-\-\-\-\-\-\--R\--H-D (\-- indicating non-conserved residues) shows a good match of identical and conserved amino acid residues: A\--GFEK\-\--D\-\--LK\--I\-\-\-\-\-\-\-\--R\--H-E. The fusion glycoproteins contain a coiled-coil domain and are involved in fusing the viral envelope with the host cell membrane. The conformational change of the protein necessary for membrane fusion is driven by ionic interactions between residues of the coiled-coil domain, and Glu74, the last amino acid aligning to motif I, is involved in this process \[[@B1]\]. In light of their putative interaction with the nuclear envelope-associated protein MAF1, it will be interesting to investigate if FPPs are associated with membrane systems of the plant cell.

Conclusions
===========

A family of novel long coiled-coil proteins has been identified from plants. They are characterized by two clusters of long coiled-coil domains separated by a non-coiled-coil linker and by four novel sequence motifs. Seven members of the *filament-like plant protein* (*FPP*) family are present in the Arabidopsis genome, and one homolog each has been identified from tomato and rice. Thus, this family of proteins appears to be conserved among higher plants, but we have not found convincing homologs from yeast or animals by sequence similarity searches. Tomato FPP was originally isolated in a yeast two hybrid screen with MAF1, a small plant-specific protein associated with the nuclear envelope \[[@B36]\]. Because the full Arabidopsis genome does not appear to contain genes for lamins, it is of particular interest to investigate which other long coiled-coil proteins might functionally replace lamins in the plant kingdom. The FPP family represents an exciting group of candidates for such proteins.

Materials and methods
=====================

Yeast two-hybrid screen
-----------------------

Total RNA from young tomato leaves (*Lycopersicon esculentum*) was isolated as described by Wanner and Gruissem \[[@B44]\]. mRNA was purified using the mRNA purification kit (Pharmacia Biotech, Piscataway, NJ). To construct the yeast two-hybrid library the cDNA synthesis kit, the Gigapack III gold packaging extract and the HybriZAP two-hybrid predigested vector kit (all Stratagene, La Jolla, CA) were used according to the protocol of the manufacturer. The size of the primary library was determined to be 1.5 × 10^6^ plaque forming units. The complete cDNA of LeMAF1 (GenBank accession number AF118113) was cloned as an EcoRI/XhoI fragment between the EcoRI and SalI sites of the bait vector pBD-GAL4 (Statagene). Approximately one million primary transformants were screened for growth on histidine dropout plates. All steps of the library screening including the transformation, the isolation of clones, and the verification of the interactions, and the β-Galactosidase assays were performed as described by the manufacturer. pBD-AtRanGAP1 \[[@B33]\] and pBD-AtMAF1 were both constructed by cloning an EcoRI fragment containing the complete open reading frames into the EcoRI site of pBD-GAL4. The AtMAF1 open reading frame was isolated by PCR from Arabidopis genomic DNA using the 5\' primer 5\'-TCC ATG GCC GAA ACC GAA-3\' and the 3\' primer 5\'-CTA AGT TCA CTT CGA ACT GCT C-3\', which had been designed to match the Arabidopsis MAF1 homolog on the P1 clone MGG4 of chromosome 5 (GenBank accession number AB008267).

RNA isolation and RNA blotting
------------------------------

Total RNA from tomato leaves (5--15 mm), fruits (3--8 mm in diameter), whole flowers, and light-grown and dark-grown tomato seedlings was isolated with the Trizol Reagent from Gibco BRL (Gaithersburg, MD). Total RNA (20 μg each) was separated on a formaldehyde gel, blotted to a nitrocellulose membrane, and hybridized with a radioactively labeled fragment corresponding to bp 1140 -- 1926 of the LeFPP cDNA, essentially as described by \[[@B45]\].

Database searches, secondary structure prediction, and sequence comparison
--------------------------------------------------------------------------

Sequences with similarity to LeFPP were identified by conducting BLAST (Basic Local Alignment Search Tool; \[[@B46]\]) searches for similarity to sequences contained in GenBank. GenBank data were accessed through NCBI <>. Arabidopsis genome data were accessed through the TAIR database <>. Arabidopsis ESTs were accessed through the TIGR *Arabidopsis thaliana* database <>. Multiple sequence alignments were performed with the DNASTAR protein alignment protocol (DNASTAR, Madison, WI) using the CLUSTAL algorithm. Parameters for CLUSTAL alignments were: Pairwise alignment: Ktuple, 1; gap Penalty, 3; windows, 5; diagonals saved, 5. Multiple alignment: gap penalty, 10; gap length penalty, 10. The PAM250 weight table was used. Percent identities and phylogenetic relationships were derived from the CLUSTAL output using the MEGALIGN software package in DNASTAR. The secondary structures of the proteins, α-helical, and coiled-coil regions were analyzed using DNASTAR PROTEAN software and the coiled-coil regions were confirmed with the MultiCoil algorithm \[[@B2]\]. Coiled-coil proteins from Arabidopsis were identified by searching the lists of complete-genome ORF annotations <> for the keywords \"myosin\" (40 hits) and \"coiled-coil\" (0 hits). Arabidopsis ORFs for CLUSTAL analysis were downloaded either using their GenBank accession numbers or, if not yet available, by searching the TAIR database with the respective gene name.


Background
==========

The potential adaptive significance of transposable elements (TEs) to the host genomes in which they reside is a topic that has been hotly debated by molecular evolutionists for more than two decades. While the biological importance of TEs seemed self-evident to those scientists involved in their initial discovery \[e.g., \[[@B1],[@B2]\]\], the subsequent realization that TEs could be maintained in populations even while imparting slight selective disadvantage to their hosts \[e.g., \[[@B3]-[@B5]\]\] drew into question the presumption of adaptive significance. However, even if TEs can be maintained in populations on a day-to-day basis without providing selective advantage, it does not preclude the possibility that the insertion of TEs in or near genes may, in some instances, be of adaptive advantage.

If TE insertion variants have contributed to adaptive gene evolution, such variants might be expected to be in high frequency or fixed in populations and species. Initial surveys of natural populations of *Drosophila melanogaster* showing that TE insertion alleles are in uniformly low frequency seemed to negate the adaptive hypothesis \[[@B6]\]. However, the sporadic discovery of degenerate TEs or TE fragments as critical components of functional genes in both plants and animals was sufficient to keep the adaptive hypothesis alive throughout the pre-genomic era \[[@B7]-[@B11]\].

The current availability of the complete or nearly complete sequence of select genomes representing a variety of species is providing an unprecedented opportunity to examine the frequency and distribution of TEs in eukaryotic genomes. The results have been dramatic. TEs not only comprise a significant fraction of nearly all eukaryotic genomes thus far sequenced, they have been found to be components of the regulatory and/or coding regions of a surprisingly large number of genes \[e.g., \[[@B12]\]\]. For example, a recent genomic analysis of 13,799 human genes revealed that approximately 4% harbored retrotransposon sequences within protein-coding regions \[[@B13]\]. Similar results have been recently reported for the nematode *Caenorhabditis elegans*\[[@B14]\]. Here we analyze the polymorphism of two LTR retrotransposon / host gene associations across geographically widespread *D. melanogaster* populations and a representative population of the *D. melanogaster* sibling species, *Drosophila mauritiana*.

Results
=======

We have initiated a genomic analysis of LTR retrotransposons present in the *Drosophila melanogaster* genome \[e.g., \[[@B15]\]\]. Of particular interest is identification of genes harboring TEs and determining if these insertion alleles are in high frequency or fixed among natural populations as would be expected from the adaptive hypothesis. We report here the results of an analysis of two LTR retrotransposon-containing genes located on the second chromosome of the sequenced *D. melanogaster y; cn bw sp* strain. These two genes present an interesting contrast in that one of them, *Chitinase 3 (Cht3)*, is located within constitutive heterochromatin (Genbank accession: AE002743) while the other, *cathD*, is located in a euchromatic region of the chromosome (Genbank accession: AE003839). Our findings demonstrate that while the euchromatic *cathD* insertion variant was not detected in any of the natural populations examined, the insertion variant present in the heterochromatic *Cht3* gene was found to be apparently fixed throughout the species. These results are consistent with the view that the presence of TEs in constitutive heterochromatin may have relevance to the expression of heterochromatic genes \[e.g., \[[@B16],[@B17]\]\].

Genomic analysis of the sequenced y; *cn bw sp* strain of *Drosophila melanogaster* identified a full-length *Burdock* LTR retrotransposon located just 3\' to the *cathD* gene and a 359bp LTR fragment (complete LTR is 659 bp) of an *Antonia* LTR retrotransposon \[[@B15]\] located within an intron of the *Cht3* gene (Figure [1](#F1){ref-type="fig"}). A set of PCR primers were designed to amplify regions of both genes and retrotransposon sequences. Appropriate pairs of gene and element primers were used to detect the presence or absence of the respective retrotransposon inserts associated with each gene in strains representing 12 geographically dispersed populations of *D. melanogaster*. The results presented in Figure [2](#F2){ref-type="fig"} and Table [1](#T1){ref-type="table"} demonstrate that while the *Burdock* insertion located just 3\' to *cathD* gene is not present in any of 12 strains representing a geographically diverse sampling of natural populations, the *Antonia* LTR fragment located in the intron of the heterochromatic *Cht3* gene is fixed in all 12 strains tested.

![**Genomic structure of the *Cht3 and cathD genes* in the *Drosophila melanogaster* genome.(A)** Chromosome 2 illustrating location of *Cht3* and *cathD* genes (red lines) in reference to constitutive heterochromatin (in blue) \[[@B34]\]. Numbers above each red line refer to Flybase cytogenetic placement. (Chromosome not drawn to scale). **(B & C)** Green arrows represent Flybase-predicted gene regions with corresponding identification. Yellow blocks depict ESTs concordant to the predicted gene region. Blue boxes are predicted exon regions. Red boxes denote LTR position and internal arrows indicate orientation of retroelement. The black line and numbers represent position along the genomic clone sequence which is identified below the figure. Black arrows indicate direction and location of forward (f) or reverse(r) PCR primers. **(B)** An *Antonia* LTR fragment (359nt) is inserted in an intron of *Cht3* in 12 geographically distinct *Drosophila melanogaster* strains. **(C)** A full-length *Burdock* retroelement, only present in the sequenced y; *cn bw sp* strain, overlaps the predicted exon boundaries of the *cathD* gene by 6nt.](1471-2148-2-5-1){#F1}

::: {#T1 .table-wrap}
::: {.caption}
######

Presence or absence of retroelement sequence associated with *cathD* and *Cht3* genes in strains representing 12 natural populations of *D. melanogaster*.
:::

  Georgaphic area   Strain          *cathD / Burdock*   *Cht3 / Antonia*
  ----------------- --------------- ------------------- ------------------
  Lab stock         *y; cn bw sp*   \+                  \+
  Americas          Athens          \-                  \+
                    California      \-                  \+
                    Antilles        \-                  \+
  Europe            Germany         \-                  \+
                    Italy           \-                  \+
  Africa            Capetown        \-                  \+
                    Cotonake        \-                  \+
                    Dimonika        \-                  \+
                    Kenia           \-                  \+
                    Niamey          \-                  \+
                    Swaziland       \-                  \+
  Asia              India           \-                  \+

Males and females from each strain were tested. (+) indicates presence of retroelement sequence, (-) indicates absence of retroelement sequence.
:::

It is formally possible that the presence of the *Antonia* LTR within the *Cht3* intron was the result of a chance fixation event prior to the expansion of *D. melanogaster* around the world. Thus, to further test the adaptive hypothesis we compared the level of sequence divergence within the LTR and its flanking intronic sequence between the two sibling species *Drosophila melanogaster* and *Drosophila mauritiana*. If the LTR-containing intron is under stabilizing selection, a lower than neutral rate of substitution would be expected. A total of 685 bp of the *Cht3* intron was sequenced. This region spans 264 bp of the 359 bp *Antonia* LTR fragment. The sequence of this region in a *D. melanogaster* (Dimonika, Africa) and *D. mauritiana* (Mauritius, Africa) strain was aligned with the homologous region in the sequenced *D. melanogaster y; cn bw sp* strain (Figure [3](#F3){ref-type="fig"}). The two *melanogaster* strains were 100% identical. The *melanogaster* sequences were found to be only 1.3% (9 substitutions/685 nucleotide sites) diverged from that of *D. mauritiana*. This value is significantly less than half of the expected 4.3 % (± 2.7) divergence based on the *Drosophila* neutral substitution rate of 0.016 (± 0.005) substitutions/site/million year \[[@B18]\] over the estimated 2.7 million years separating the two species \[[@B19]\].

To directly compare the substitution rate for the *Cht3* intron with that of another *Drosophila* gene intron, we randomly selected intron 1 of the *Drosophila* alcohol dehydrogenase (*Adh*) gene. *Adh* is a widely studied *Drosophila* gene and it has been sequenced in several *Drosophila* species including *D. melanogaster*, accession X60793 \[[@B20]\] and *D. mauritiana*, accession M19264 \[[@B21]\]. The sequence divergence between *D. melanogaster* and *D. mauritiana* in the *Adh* intron 1 (7.9%, Figure [4](#F4){ref-type="fig"}), is higher than that for the LTR containing *Cht3* intron (1.3%). These results strongly suggest that conservative selection has been operating on the LTR containing intron associated with the *Drosophila Cht3* gene over the past 2.7 million years.

![**Nucleotide alignment of the 659 bp intron 1 of the *Adh* gene in *Drosophila melanogaster* and *Drosophila mauritiana*.** Sequences obtained through GenBank for *D.melanogaster* (accession: X60793, \[[@B20]\] and *D.mauritiana* (accession: M19264, \[[@B21]\]). Sequences were aligned using MacVector (See Materials and Methods for details).](1471-2148-2-5-4){#F4}

Discussion
==========

For many years, constitutive heterochromatin was considered to be of little or no functional significance \[[@B22]\]. This view seemed to be supported by early molecular studies showing that heterochromatin consists almost exclusively of highly repeated and middle repetitive DNA \[e.g., \[[@B23],[@B24]\]\]. The middle repetitive fraction was viewed as the descendent of once active TEs that had the misfortune of inserting into transcriptionally inert heterochromatin at some point in their evolutionary history \[e.g., \[[@B6],[@B20]\]\]. The view of heterochromatin as a genetic wasteland gradually changed with the mapping of a number of functionally important *Drosophila* genes to constitutive heterochromatin \[e.g., \[[@B24]-[@B31]\]\]. Reexamination of *Drosophila* constitutive heterochromatin revealed that long stretches of highly repetitive DNA are interrupted by \"islands\" of retrotransposon sequences \[e.g., \[[@B32],[@B33]\]\]. *Drosophila* genes in heterochromatin are typically associated with these islands of retrotransposons \[[@B2],[@B31],[@B34]-[@B36]\]. It has been suggested that transposable elements inserted into heterochromatin may locally alter chromatin structure \[e.g., \[[@B16]\]\]. Our results suggest that in at least some instances, the association of heterochromatic genes with transposable element sequences may be of adaptive significance.

Conclusions
===========

The results presented here are consistent with the hypothesis that a 359 bp fragment of the *Antonia* retrotransposon located within the intron of the heterochromatic *Drosophila melanogaster Cht3* gene may be of adaptive evolutionary significance. Further genomic and molecular analyses will be required to assess the general importance of LTR retrotransposon sequences to the evolution of heterochromatic gene structure and function.

Materials and Methods
=====================

Gene Region Annotation
----------------------

BLASTS of sequenced DNA turned up several instances of genes proximal to an LTR retrotransposon. Sequence retrieval was initiated via BLASTN searches (default parameters- \[[@B37]\]) against the BDGP <> and GenBank <> databases using LTRs from previously identified *Drosophila* retroelements as queries \[[@B15]\]. Results with E-values \< e^-10^ were annotated on the corresponding clone, whereupon visual inspection of several annotations confirmed the presence of retroelements proximal to known genes. Selected genes were BLASTed against NCBI\'s EST database and mapped along with predicted transcript structures from Flybase <>. Chromosomal location of clones was also determined from Flybase.

PCR
---

*D. melanogaster* strains from Dimonika, Niamey, Swaziland, Kenia, Capetown, Cotonake, and India were obtained from Charles F. Aquadro, Cornell University. Germany, Italy, and Antilles strains were obtained from Nikolaj Junakovic, Universitá la Sapienza, Rome, Italy. California and Athens strains are from Daniel Promislow, University of Georgia. *D. melanogaster y; cn bw sp* strain was obtained from the Bloomington, IN, stock center. The *D. mauritiana* (241.0) strain was provided by the Bowling Green, OH, *Drosophila* stock center.

PCR primers were designed with MacVector 7.0 <> and synthesized by Integrated DNA Technologies (Coralville, LA) (Table [2](#T2){ref-type="table"}). Three PCR reactions were performed per strain, per gene. For all PCR reactions, 1.0 μl of a single fly DNA prep \[[@B38]\] was used and amplification was performed in a Hot Top equipped Robocyler Gradient 96 (Stratagene, La Jolla, CA). 10 μl of product was separated on a 1% agarose gel in 0.5× TBE running buffer containing 0.25 μg mL^-1^ ethidium bromide. Gel images were visualized by UV transillumination.

::: {#T2 .table-wrap}
::: {.caption}
######

Primers used for PCR analysis.
:::

  Primer                 Sequence                            Position 1
  ---------------------- ----------------------------------- ---------------
  *cht3(f)*              5\'-TGATGCCATACTCCTACTCCGTAAC-3\'   27910:27934
  *cht3(f2)*             5\'-ATGAAAAACGGATGGACAGCC-3\'       18549:18569
  *cht3(r)*              5\'-CATTCCTGTTTGCCAACCCC-3\'        28395:28376
  *Antonia LTR(f)*       5\'-TTAAGCGAACGTCGGAGAC-3\'         21299:21317
  *Antonia LTR(r)*       5\'-CCACTAGAAGGGTGAAAACTGC-3\'      21570:21549
  *cathD(f)*             5\'-GGTGAAGCCGCCATTTTACG-3\'        220780:220799
  *cathD(r)*             5\'-CGCCCAGCACAAACTTGATTAC-3\'      221240:221219
  *Burdock LTR(f)*       5\'-TGACCGACGCTTCTAATCTTCC-3\'      221638:221659
  *Burdock element(r)*   5\'-GGTTGGCAGTATGGGAACTTAGTG-3\'    221918:221895

^1^ Position is relative to location of first nucleotide within clone, AE002743 for *cht3* or AE003839 for *cathD*.
:::

### Cht3 PCR

The PCR products for primer set *cht3(f)* and *cht3(r)* and primer set *Antonia LTR(f)* and *Antonia LTR(r)* were amplified in a 25 μl reaction containing 3 mM MgCl~2~, 10X PCR buffer supplied by Pierce (Rockford, IL), 5% DMSO, 0.2 mM dNTPs, 0.5 μM of each primer, and 0.5 U of Taq DNA polymerase supplied by Pierce \[Rockford, IL\]. The program consisted of an initial incubation at 94°C for 3 min for 1 cycle, a 30 cycle extension at 94°C for 30 sec, 56°C for *cht3(f)/cht3(r)* primer set or 57°C for *Antonia LTR(f)/Antonia LTR(r)* primer set for 30 sec, 72°C for 1 min 30 sec, and a 1 cycle final extension of 72°C for 5 min. The PCR products for primer set *cht3(f2)* and *LTR(r)* were amplified in a 25 μl reaction containing Expand Long Template PCR System 10X PCR buffer \#1 supplied by Roche (Indianapolis, IN), 0.35 mM dNTPs, 0.32 μM of each primer, and 1.3 U of Expand Long Template PCR System DNA polymerase mix supplied by Roche (Indianapolis, IN). The program consisted of an initial incubation at 94°C for 3 min for 1 cycle, a 30 cycle extension at 94°C for 30 sec, 52°C for 30 sec, 68°C for 3 min, and a 1 cycle final extension at 68°C for 5 min.

### cathD PCR

The reaction mix and program used for all sets of primers are the same as those described for primer set *cht3(f)* and *cht3(r)* and primer *set Antonia LTR(f)* and *Antonia LTR(r)* in the *Cht3* PCR (above). The annealing temperature for primer set *cathD(f)* and *cathD(r)* is 58°C, for primer set *Burdock LTR(f)* and *Burdock element(r)* is 59°C, and for primer set *cathDff*) and *Burdock element(r)* is 56°C.

Sequencing
----------

PCR products of the *Cht3* intron were sequenced in the Molecular Genetics Instrumentation Facility at the University of Georgia. Sequences were aligned with Mac Vector 7.0 and compared to the published y; *cn bw sp* strain. Substitutions and insertion/deletion sites (indels) were summed for each sequence product and compared to the expected divergence based upon the neutral substitution rate. The expected number of polymorphisms between *D. melanogaster* and *D. mauritiana* was calculated based on the *Drosophila* neutral substitution rate of .016 (± 0.005) substitutions per site/million years \[[@B18]\] on 685 bp over a divergence time of 2.7 million years \[[@B19]\].

![**PCR analysis testing for the presence of an LTR retroelement feature in two genes, *Cht3* and *cathD*, across three representative *Drosophila* strains.** A negative image is presented for visual clarity. Three PCR reactions were performed per strain, per gene. M = 1 kb ladder, M2 = 0.1 kb ladder. **(A)** An *Antonia* LTR fragment is fixed in the intron of the heterochromatic *Cht3* gene in all 12 tested strains (only three shown). *Cht3-G = cht3* primers (f+r), expected product= 488 bp. L = *Antonia* LTR primers (f+r), expected product= 272 bp. G~2~/L = *cht3(f2) + Antonia* LTR (r) primers, expected product= 3022 bp. **(B)** A full-length Burdock LTR retrotransposon is found to be associated with cathD only in the sequenced y; *cn bw sp* strain. *cathD -- G = cathD* primers (f+r), expected product = 461 bp. L = *Burdock* primers (f+r), expected product = 280 bp. G/L = cathD(f) and *Burdock* element (r), expected product= 1139 bp.](1471-2148-2-5-2){#F2}

![**Nucleotide alignment of a 685 bp Cht3 intron fragment in *D.melanogaster* and *D.mauritiana*.** Cht3 intron sequence from the *Drosophila melanogaster y; cn bw sp* strain (accession AE002743). The Antonia LTR stretches from bp 1 -- 264, where a black diamond (♦) indicates the end of LTR sequence. Strains representing the *D. melanogaster*, Africa (Dimonika) population and a strain representing the *D. mauritiana*, Mauritius population were sequenced. Sequences were aligned using MacVector (See Materials and Methods for details).](1471-2148-2-5-3){#F3}

Note added in proof
-------------------

The two Cht3 intron fragments descibed in Figure 3 have the following provisional accession numbers in GenBank:

D. melanogaster, Africa - AY081055

D. mauritiana - AY081054

Acknowledgements
================

E.W.G supported through an NIH Genetics Training Grant. This work supported by a National Institutes of Health grant to J.F.M.