Background Learning the exact demographic characteristics of a neighborhood in which a public library serves, assists the collection development librarian in building an appropriate collection. Gathering that demographic information can be a lengthy process, and then formatting the information for the neighborhood in question becomes arduous. As society ages and the methods for health care evolve, people may take charge of their own health. With this prospectus, public libraries should consider creating a consumer health collection to assist the public in their health care needs. Using neighborhood demographic information can inform the collection development librarians as to the dominant age groups, sex, and races within the neighborhood. With this information, appropriate consumer health materials may be assembled in the public library.
Background
Learning the exact demographic characteristics of a neighborhood in which a public library serves, assists the collection development librarian in building an appropriate collection. Gathering that demographic information can be a lengthy process, and then formatting the information for the neighborhood in question becomes arduous.As society ages and the methods for health care evolve, people may take charge of their own health. With this prospectus, public libraries should consider creating a consumer health collection to assist the public in their health care needs. Using neighborhood demographic information can inform the collection development librarians as to the dominant age groups, sex, and races within the neighborhood. With this information, appropriate consumer health materials may be assembled in the public library.Methods In order to visualize the demographics of a neighborhood, the computer program ArcView GIS (geographic information systems) was used to create maps for specified areas. The neighborhood data was taken from the U.S. Census Department's annual census and library addresses were accumulated through a free database. After downloading the census block information from   the data was manipulated with ArcView GIS and queried to produce maps displaying the requested neighborhood demographics to view in respect to libraries.
Methods
In order to visualize the demographics of a neighborhood, the computer program ArcView GIS (geographic information systems) was used to create maps for specified areas. The neighborhood data was taken from the U.S. Census Department's annual census and library addresses were accumulated through a free database. After downloading the census block information from   the data was manipulated with ArcView GIS and queried to produce maps displaying the requested neighborhood demographics to view in respect to libraries.Results ArcView GIS produced maps displaying public libraries and requested demographics. After viewing the maps the collection development librarian can see exactly what populations are served by the library and adjust the library's collection accordingly.
Results
ArcView GIS produced maps displaying public libraries and requested demographics. After viewing the maps the collection development librarian can see exactly what populations are served by the library and adjust the library's collection accordingly.Conclusions ArcView GIS can be used to produce maps displaying the communities that libraries serve, spot boundaries, be it "man-made or natural," that exist prohibiting customer service, and assist collection development librarians in justifying their purchases for a dedicated consumer health collection or resources in general.
Conclusions
ArcView GIS can be used to produce maps displaying the communities that libraries serve, spot boundaries, be it "man-made or natural," that exist prohibiting customer service, and assist collection development librarians in justifying their purchases for a dedicated consumer health collection or resources in general.

Background Libraries have the objective to build collections that support the communities they serve. To build a viable collection the collection development librarian, outreach/marketing librarian, and others, must determine exactly what populations reside in the neighborhood, in respect to race, spoken language, educational level, and age groups. One method collection development librarians use to gather demographic information is to physically visit the communities and integrate into the neighborhoods. They may attend events within the community in order to analyze the attendees or walk around the neighborhood to get a feel for the community. Another method for collection development is to perform an informal survey with people that visit the library and learn about their preferences and/or what they like to read or look for on the Internet. Using census information can be a third way to gather community/neighborhood information. The US Census Bureau takes a census of the United States every 10 years and publishes the results on the Internet. Taking the Census information and transforming it into a graphical format provides an objective view of the communities surrounding a library. One way to visualize the census data is through the use of GIS, Geographic Information Systems. GIS are databases arranged by spatial coordinates that, when programmed, can produce maps [ 1 ]. The US Geological Survey office defines GIS as "a computer system capable of capturing, storing, analyzing, and displaying geographically referenced information; that is, data identified according to location [ 2 ]." Many disciplines have used GIS and many large academic libraries even support GIS by establishing a GIS department and employing a librarian to specialize in GIS information. In the field of library science articles have been published describing the establishment of GIS departments in libraries and what GIS librarians do [ 3 - 7 ], but little has been published describing how a library has used GIS. Governments use GIS to visualize land use planning, tax appraisal, utility and infrastructure planning and more [ 8 ]. Businesses have used GIS for real estate analysis, marketing, and demographic analysis [ 8 ]. Based on this approach, a research project was developed to test the use of GIS in collection development for a large metropolitan public library system. Building a collection for a library has become a sophisticated art. Johnson states that "collection building consists of four steps: identifying the relevant items, assessing the item to decide if it is appropriate for the collection and evaluating its quality, deciding to purchase, and preparing an order [ 9 ]." Each step in the process of collection development produces a layer of difficulties. In order to get to these steps a collection development librarian must be aware of their audiences. To name a few necessary pieces of information, the librarian must know the predominate age of their clients, their reading genre, their reading level, their culture, and their preferred language [ 9 ]. After one has gathered community information then the decision of what to purchase for use by the community, can be initiated and carried out [ 10 ]. This paper reports the steps taken to use GIS in assisting a large metropolitan public library system in visualizing neighborhoods that surround branch libraries in order to make an educated decision on whether a dedicated consumer health collection should be established to support the community. The objective of the project was to determine if GIS could be used to improve collection development.
Background
Libraries have the objective to build collections that support the communities they serve. To build a viable collection the collection development librarian, outreach/marketing librarian, and others, must determine exactly what populations reside in the neighborhood, in respect to race, spoken language, educational level, and age groups. One method collection development librarians use to gather demographic information is to physically visit the communities and integrate into the neighborhoods. They may attend events within the community in order to analyze the attendees or walk around the neighborhood to get a feel for the community. Another method for collection development is to perform an informal survey with people that visit the library and learn about their preferences and/or what they like to read or look for on the Internet. Using census information can be a third way to gather community/neighborhood information. The US Census Bureau takes a census of the United States every 10 years and publishes the results on the Internet. Taking the Census information and transforming it into a graphical format provides an objective view of the communities surrounding a library. One way to visualize the census data is through the use of GIS, Geographic Information Systems. GIS are databases arranged by spatial coordinates that, when programmed, can produce maps [ 1 ]. The US Geological Survey office defines GIS as "a computer system capable of capturing, storing, analyzing, and displaying geographically referenced information; that is, data identified according to location [ 2 ]."Many disciplines have used GIS and many large academic libraries even support GIS by establishing a GIS department and employing a librarian to specialize in GIS information. In the field of library science articles have been published describing the establishment of GIS departments in libraries and what GIS librarians do [ 3 - 7 ], but little has been published describing how a library has used GIS.Governments use GIS to visualize land use planning, tax appraisal, utility and infrastructure planning and more [ 8 ]. Businesses have used GIS for real estate analysis, marketing, and demographic analysis [ 8 ]. Based on this approach, a research project was developed to test the use of GIS in collection development for a large metropolitan public library system.Building a collection for a library has become a sophisticated art. Johnson states that "collection building consists of four steps: identifying the relevant items, assessing the item to decide if it is appropriate for the collection and evaluating its quality, deciding to purchase, and preparing an order [ 9 ]." Each step in the process of collection development produces a layer of difficulties. In order to get to these steps a collection development librarian must be aware of their audiences. To name a few necessary pieces of information, the librarian must know the predominate age of their clients, their reading genre, their reading level, their culture, and their preferred language [ 9 ]. After one has gathered community information then the decision of what to purchase for use by the community, can be initiated and carried out [ 10 ].This paper reports the steps taken to use GIS in assisting a large metropolitan public library system in visualizing neighborhoods that surround branch libraries in order to make an educated decision on whether a dedicated consumer health collection should be established to support the community. The objective of the project was to determine if GIS could be used to improve collection development.Methods After comparing large library systems in major metropolitan areas, (Chicago, Los Angeles, and New York City) Chicago was selected as a convenient sample to be mapped with GIS because of its size, number of public libraries, their geographic distribution within the urban area, and ease of access to data. Compared to other large cities, the New York Public Library system has 86 libraries and Los Angeles has 67 public libraries [ 11 ]. The city of Chicago has 77 public libraries with one central library. With the selection of the city of Chicago, the next step was to create a map of the metropolitan area. ESRI ArcGIS v9.0 geographic information software (GIS) [ 12 ] was selected as the GIS application. The software permits data manipulation and mapping capabilities. There are four components of ArcGIS that work together to give a high level of functionality to the program: ArcReader, ArcView, ArcEditor and ArcInfo. By inserting data files into ArcEditor the datum may convert into a graphical representation. Once a base map has been created with geographic boundaries, locations may be established to show distances within the map. In order to create the city map of Chicago, data files were obtained from the U.S. Census Bureau website. The Census Bureau produces TIGER (Topologically Integrated Geographic Encoding and Referencing) Files available for free to the public [ 13 ]. These files contain geographic structures, such as streets, highways, and addresses, for tabulation and dissemination. To set the boundary lines of metropolitan Chicago, Cook County (the county in which the city of Chicago resides) was selected as the source for the data file [ 14 ]. This TIGER file contained the street map for the city of Chicago. The next step required mapping distances between neighborhoods and neighborhood demographics. In order to achieve this, files were obtained from the U.S. Census Bureau website [ 15 ] which distributes the 2000 Census statistics in TIGER File format. Downloading the files for Census blocks in Cook County provided the necessary data to build a visual representation of neighborhoods. The final piece of information needed to build a map showing neighborhoods in respect to public library locations, was to map the public libraries within Chicago. Because the base map of Cook County was a street map, addresses of public libraries were entered into ArvView to mark all the public libraries within the city of Chicago. The state of Illinois provides a database available on the Internet that permits searching types of libraries and locations of libraries,  . The output of this search was imported into Excel, cleaned, and converted into a dBase file. The dBase file was imported into ArcView to coordinate the library addresses to the street addresses of the base map. This enabled the software to place an image for each public library in the city map of Chicago. With the city of Chicago and its public libraries now in a GIS application, the location of libraries was displayed in respect to neighborhoods and distances from library to library (See Figure  1 ). Because the Census data was used to build the map, ArcView can be queried to show demographics of the city. This allows useful data manipulation in relation to points of interest, in this case, public libraries. Thus, ArcView was queried to show the distribution of all men and women within the city of Chicago as well as the breakdown of their age groups. The software application produced a graphic which used color schemes with a map legend. If a librarian wanted to compare the number of men in their 40s to women in their 40s, ArcView can map those fields with a well-structured query. ArcGIS was also capable of creating maps that display census ethnic groups. For example, Figure  2  shows a high percentage of African Americans in the southern neighborhoods of the city as compared to the northern part of the city. With maps such as these, a collection development librarian can graphically see the demographic breakdown of neighborhoods and build collections according to its clientele [ 16 ]. Figure 1 Location of public libraries in the city of Chicago Figure 2 Density of African American population in southern part of Chicago
Methods
After comparing large library systems in major metropolitan areas, (Chicago, Los Angeles, and New York City) Chicago was selected as a convenient sample to be mapped with GIS because of its size, number of public libraries, their geographic distribution within the urban area, and ease of access to data. Compared to other large cities, the New York Public Library system has 86 libraries and Los Angeles has 67 public libraries [ 11 ]. The city of Chicago has 77 public libraries with one central library.With the selection of the city of Chicago, the next step was to create a map of the metropolitan area. ESRI ArcGIS v9.0 geographic information software (GIS) [ 12 ] was selected as the GIS application. The software permits data manipulation and mapping capabilities. There are four components of ArcGIS that work together to give a high level of functionality to the program: ArcReader, ArcView, ArcEditor and ArcInfo. By inserting data files into ArcEditor the datum may convert into a graphical representation. Once a base map has been created with geographic boundaries, locations may be established to show distances within the map.In order to create the city map of Chicago, data files were obtained from the U.S. Census Bureau website. The Census Bureau produces TIGER (Topologically Integrated Geographic Encoding and Referencing) Files available for free to the public [ 13 ]. These files contain geographic structures, such as streets, highways, and addresses, for tabulation and dissemination. To set the boundary lines of metropolitan Chicago, Cook County (the county in which the city of Chicago resides) was selected as the source for the data file [ 14 ]. This TIGER file contained the street map for the city of Chicago.The next step required mapping distances between neighborhoods and neighborhood demographics. In order to achieve this, files were obtained from the U.S. Census Bureau website [ 15 ] which distributes the 2000 Census statistics in TIGER File format. Downloading the files for Census blocks in Cook County provided the necessary data to build a visual representation of neighborhoods.The final piece of information needed to build a map showing neighborhoods in respect to public library locations, was to map the public libraries within Chicago. Because the base map of Cook County was a street map, addresses of public libraries were entered into ArvView to mark all the public libraries within the city of Chicago. The state of Illinois provides a database available on the Internet that permits searching types of libraries and locations of libraries,  . The output of this search was imported into Excel, cleaned, and converted into a dBase file. The dBase file was imported into ArcView to coordinate the library addresses to the street addresses of the base map. This enabled the software to place an image for each public library in the city map of Chicago.With the city of Chicago and its public libraries now in a GIS application, the location of libraries was displayed in respect to neighborhoods and distances from library to library (See Figure  1 ). Because the Census data was used to build the map, ArcView can be queried to show demographics of the city. This allows useful data manipulation in relation to points of interest, in this case, public libraries. Thus, ArcView was queried to show the distribution of all men and women within the city of Chicago as well as the breakdown of their age groups. The software application produced a graphic which used color schemes with a map legend. If a librarian wanted to compare the number of men in their 40s to women in their 40s, ArcView can map those fields with a well-structured query. ArcGIS was also capable of creating maps that display census ethnic groups. For example, Figure  2  shows a high percentage of African Americans in the southern neighborhoods of the city as compared to the northern part of the city. With maps such as these, a collection development librarian can graphically see the demographic breakdown of neighborhoods and build collections according to its clientele [ 16 ].Results Fifteen maps were created showing a breakdown of each demographic with the city's public libraries. Each map provided information regarding gender, age, and race of the clientele surrounding the libraries. These maps used a combination of color schemes to graphically represent differences between groups. The maps displayed different age groups divided by ranges of age (30–39, 40–49, 50–64, 65 and up). The age ranges presented in the maps varied by query and census information. The groups analyzed were: women, men, white women (group age 40–49), African American women (group age 40–49), Asian (men/women, Latinos (men/women). The color schemes used represent the number of people that live within the census block as to the specified GIS query. In Figure  2 , the numbers, 0–992 in the legend are the African Americans residing within the census block. Hence the red blocks have a large population (403–992) of African Americans as compared to the dark green areas. Figure  3  represents a query for African American women in their 40s. The results produced a map showing that 26–59 African American women in their 40s live within the shaded red blocks. Figure 3 African American women in their 40s in proximity to public libraries In this project ArcGIS was used to build maps of neighborhoods in the city of Chicago, display public libraries and spatially represent distances from libraries to populations. The US Census demographic information was incorporated into ArcGIS and queried to produce 15 different maps. Queries were built to display neighborhood demographics through colors showing the percentages of the population(s). Each query produced a map for a specific age or race. Once the map was produced the public libraries were added to the map and analysis was made regarding collection needs according to the neighborhood population surrounding the library. To decide if a public library should establish a consumer health collection, a map showing the breakdown of women in their 40s was built. As in Figure  4 , the map shows a very high percentage of women 40–49 close to the Uptown Branch Library. The collection development librarian can now assess the libraries existing collection for health related materials. If there are very few resources then the collection development librarian should begin to purchase consumer health materials. If the library has a strong consumer health collection already then building maps to show the racial breakdown of the neighborhood could lead the collection development librarian to purchase culturally sensitive health materials. Figure 4 High density neighborhoods with all women in their 40s in proximity to public libraries
Results
Fifteen maps were created showing a breakdown of each demographic with the city's public libraries. Each map provided information regarding gender, age, and race of the clientele surrounding the libraries. These maps used a combination of color schemes to graphically represent differences between groups. The maps displayed different age groups divided by ranges of age (30–39, 40–49, 50–64, 65 and up). The age ranges presented in the maps varied by query and census information. The groups analyzed were: women, men, white women (group age 40–49), African American women (group age 40–49), Asian (men/women, Latinos (men/women). The color schemes used represent the number of people that live within the census block as to the specified GIS query. In Figure  2 , the numbers, 0–992 in the legend are the African Americans residing within the census block. Hence the red blocks have a large population (403–992) of African Americans as compared to the dark green areas. Figure  3  represents a query for African American women in their 40s. The results produced a map showing that 26–59 African American women in their 40s live within the shaded red blocks.In this project ArcGIS was used to build maps of neighborhoods in the city of Chicago, display public libraries and spatially represent distances from libraries to populations. The US Census demographic information was incorporated into ArcGIS and queried to produce 15 different maps. Queries were built to display neighborhood demographics through colors showing the percentages of the population(s). Each query produced a map for a specific age or race. Once the map was produced the public libraries were added to the map and analysis was made regarding collection needs according to the neighborhood population surrounding the library.To decide if a public library should establish a consumer health collection, a map showing the breakdown of women in their 40s was built. As in Figure  4 , the map shows a very high percentage of women 40–49 close to the Uptown Branch Library. The collection development librarian can now assess the libraries existing collection for health related materials. If there are very few resources then the collection development librarian should begin to purchase consumer health materials. If the library has a strong consumer health collection already then building maps to show the racial breakdown of the neighborhood could lead the collection development librarian to purchase culturally sensitive health materials.Discussion By creating maps showing the demographic breakdown of the city, a public library can build, adjust, or verify its collection in respect to the neighborhoods it supports [ 17 ]. It is easier to define the characteristics of the library's service area clientele by making visual representations (maps) of neighborhoods surrounding a library. The neighborhood information may then be used to justify additional resource purchases, a change in collection policy and even a change in library services to fit the culture of the neighborhood. By using GIS, libraries will be capable of finding specific populations within the library's neighborhood to support or serve thus taking some of the guesswork out of collection development [ 17 , 18 ]. Collections may be tailored specifically to the neighborhood by using the subjective data provided with GIS. Many studies have been conducted analyzing what people search for on the Internet and who is searching. Studies have found that women in their 40s search for and utilize health information from the Internet more than men [ 19 - 24 ]. Using this information to assist in collection development, a librarian would need to know how large the population of women in their 40s is in the neighborhood the library serves. Using the map created by ArcGIS showing the distribution of women in their 40s in respect to library locations can assist the collection development librarian in purchasing appropriate materials. Using the maps showing the race of women in their 40s would also assist the librarian in purchasing appropriate materials. For instance, if most women are African American then the collection development librarian would purchase materials supporting race-related health needs such as Sickle Cell Anemia, Coronary Heart Disease, Diabetes and others (See Figure  2  and  3 ). Recommendations Using ArcGIS can assist in defining the exact make-up of the population that a library serves. By turning the census information into a graphic the library is provided a chance to see how far (physical distance) their services may reach. Library systems that have branch libraries may have one collection development librarian who buys for each library. By using ArcGIS the central library can analyze the communities surrounding each library to purchase materials appropriately without having to spend time in each library learning the environment. Purchase decisions may now be justified with hard data from ArcGIS. For example, if ArcGIS shows that the largest population surrounding a library speaks a minority language, materials should be purchased in the dominant language to best meet the needs of the community. Limitations There is a fairly high learning curve to use ESRI Arcview GIS. There are five programs in ESRI Arcview and each program has separate features that must be used within the specific program to be imported into the primary map. Learning what each program does and then how to incorporate the data into the map requires hours of reading. Acquiring the necessary data to build a map requires background study of the geographic area in order to assure that the map is configured correctly. Upon attaining all necessary data for the map, one must build queries to manipulate the data according to the information need, and then configure the map accordingly. While GIS presents a new technique to use in performing collection development, there was not a way to test the differences from a collection built using GIS information and the current methods of collection development. In addition, research has discovered that knowing the service area radius of the library doesn't mean that people within the service area will actually use or belong to the neighborhood library [ 17 , 25 ]. When using GIS one must take into account geographic barriers to library access such as major highways, and railways. People are often reluctant to cross these barriers and may elect to visit another library further away with no geographic obstructions [ 17 , 25 , 26 ]. These barriers must be represented in the GIS map to fully display the areas libraries serve. The TIGER files contain some geographic structures but most will have to be hand coded. Thus, all neighborhoods should be examined in order to assure the marking of geographic barriers. While GIS can take the census information and demonstrate the population surrounding a library, the census does not describe the specific users of the library [ 25 ]. To build a map showing a library's service area, patron addresses (information now protected by the Patriot Act) would need to be coded into GIS. Using patron zip codes of residence encompasses too much or too little area and there is no correlation between US Postal Service ZIP Codes and US Census Bureau Geography [ 27 ]. Thus, mapping or defining areas through zip codes is extremely difficult. Without patron address information, there can be no clear realization of exactly how far people will travel to a library, and if people cross-neighborhoods to use a certain library [ 18 , 25 ].
Discussion
By creating maps showing the demographic breakdown of the city, a public library can build, adjust, or verify its collection in respect to the neighborhoods it supports [ 17 ]. It is easier to define the characteristics of the library's service area clientele by making visual representations (maps) of neighborhoods surrounding a library. The neighborhood information may then be used to justify additional resource purchases, a change in collection policy and even a change in library services to fit the culture of the neighborhood. By using GIS, libraries will be capable of finding specific populations within the library's neighborhood to support or serve thus taking some of the guesswork out of collection development [ 17 , 18 ]. Collections may be tailored specifically to the neighborhood by using the subjective data provided with GIS.Many studies have been conducted analyzing what people search for on the Internet and who is searching. Studies have found that women in their 40s search for and utilize health information from the Internet more than men [ 19 - 24 ]. Using this information to assist in collection development, a librarian would need to know how large the population of women in their 40s is in the neighborhood the library serves. Using the map created by ArcGIS showing the distribution of women in their 40s in respect to library locations can assist the collection development librarian in purchasing appropriate materials. Using the maps showing the race of women in their 40s would also assist the librarian in purchasing appropriate materials. For instance, if most women are African American then the collection development librarian would purchase materials supporting race-related health needs such as Sickle Cell Anemia, Coronary Heart Disease, Diabetes and others (See Figure  2  and  3 ).Recommendations Using ArcGIS can assist in defining the exact make-up of the population that a library serves. By turning the census information into a graphic the library is provided a chance to see how far (physical distance) their services may reach. Library systems that have branch libraries may have one collection development librarian who buys for each library. By using ArcGIS the central library can analyze the communities surrounding each library to purchase materials appropriately without having to spend time in each library learning the environment. Purchase decisions may now be justified with hard data from ArcGIS. For example, if ArcGIS shows that the largest population surrounding a library speaks a minority language, materials should be purchased in the dominant language to best meet the needs of the community.
Recommendations
Using ArcGIS can assist in defining the exact make-up of the population that a library serves. By turning the census information into a graphic the library is provided a chance to see how far (physical distance) their services may reach. Library systems that have branch libraries may have one collection development librarian who buys for each library. By using ArcGIS the central library can analyze the communities surrounding each library to purchase materials appropriately without having to spend time in each library learning the environment. Purchase decisions may now be justified with hard data from ArcGIS. For example, if ArcGIS shows that the largest population surrounding a library speaks a minority language, materials should be purchased in the dominant language to best meet the needs of the community.Limitations There is a fairly high learning curve to use ESRI Arcview GIS. There are five programs in ESRI Arcview and each program has separate features that must be used within the specific program to be imported into the primary map. Learning what each program does and then how to incorporate the data into the map requires hours of reading. Acquiring the necessary data to build a map requires background study of the geographic area in order to assure that the map is configured correctly. Upon attaining all necessary data for the map, one must build queries to manipulate the data according to the information need, and then configure the map accordingly. While GIS presents a new technique to use in performing collection development, there was not a way to test the differences from a collection built using GIS information and the current methods of collection development. In addition, research has discovered that knowing the service area radius of the library doesn't mean that people within the service area will actually use or belong to the neighborhood library [ 17 , 25 ]. When using GIS one must take into account geographic barriers to library access such as major highways, and railways. People are often reluctant to cross these barriers and may elect to visit another library further away with no geographic obstructions [ 17 , 25 , 26 ]. These barriers must be represented in the GIS map to fully display the areas libraries serve. The TIGER files contain some geographic structures but most will have to be hand coded. Thus, all neighborhoods should be examined in order to assure the marking of geographic barriers. While GIS can take the census information and demonstrate the population surrounding a library, the census does not describe the specific users of the library [ 25 ]. To build a map showing a library's service area, patron addresses (information now protected by the Patriot Act) would need to be coded into GIS. Using patron zip codes of residence encompasses too much or too little area and there is no correlation between US Postal Service ZIP Codes and US Census Bureau Geography [ 27 ]. Thus, mapping or defining areas through zip codes is extremely difficult. Without patron address information, there can be no clear realization of exactly how far people will travel to a library, and if people cross-neighborhoods to use a certain library [ 18 , 25 ].
Limitations
There is a fairly high learning curve to use ESRI Arcview GIS. There are five programs in ESRI Arcview and each program has separate features that must be used within the specific program to be imported into the primary map. Learning what each program does and then how to incorporate the data into the map requires hours of reading. Acquiring the necessary data to build a map requires background study of the geographic area in order to assure that the map is configured correctly. Upon attaining all necessary data for the map, one must build queries to manipulate the data according to the information need, and then configure the map accordingly.While GIS presents a new technique to use in performing collection development, there was not a way to test the differences from a collection built using GIS information and the current methods of collection development.In addition, research has discovered that knowing the service area radius of the library doesn't mean that people within the service area will actually use or belong to the neighborhood library [ 17 , 25 ]. When using GIS one must take into account geographic barriers to library access such as major highways, and railways. People are often reluctant to cross these barriers and may elect to visit another library further away with no geographic obstructions [ 17 , 25 , 26 ]. These barriers must be represented in the GIS map to fully display the areas libraries serve. The TIGER files contain some geographic structures but most will have to be hand coded. Thus, all neighborhoods should be examined in order to assure the marking of geographic barriers.While GIS can take the census information and demonstrate the population surrounding a library, the census does not describe the specific users of the library [ 25 ]. To build a map showing a library's service area, patron addresses (information now protected by the Patriot Act) would need to be coded into GIS. Using patron zip codes of residence encompasses too much or too little area and there is no correlation between US Postal Service ZIP Codes and US Census Bureau Geography [ 27 ]. Thus, mapping or defining areas through zip codes is extremely difficult. Without patron address information, there can be no clear realization of exactly how far people will travel to a library, and if people cross-neighborhoods to use a certain library [ 18 , 25 ].Conclusions Budget constraints will add a need for justification of purchases for a library's collection. Using GIS may provide one method to justify additions to collections. As health care changes and individuals take more control of their health, libraries need to have materials that provide valid information in an age and language appropriate format and be culturally sensitive. Branch libraries respond to the demands of the neighborhood [ 25 ] and knowing what those demands are will make an effective library [ 17 ]. GIS can assist collection development librarians located in a central library buy for branch libraries, by saving them time, and answering demographic questions about the population a library serves. Knowing details about its communities will make a more functional library within a neighborhood. To verify the use of GIS for collection development, an analysis of a library's collection should be conducted and compared with information provided from a GIS application to see if the collection satisfies the library's neighborhood.
Conclusions
Budget constraints will add a need for justification of purchases for a library's collection. Using GIS may provide one method to justify additions to collections. As health care changes and individuals take more control of their health, libraries need to have materials that provide valid information in an age and language appropriate format and be culturally sensitive. Branch libraries respond to the demands of the neighborhood [ 25 ] and knowing what those demands are will make an effective library [ 17 ]. GIS can assist collection development librarians located in a central library buy for branch libraries, by saving them time, and answering demographic questions about the population a library serves. Knowing details about its communities will make a more functional library within a neighborhood. To verify the use of GIS for collection development, an analysis of a library's collection should be conducted and compared with information provided from a GIS application to see if the collection satisfies the library's neighborhood.List of Abbreviations GIS = geographical information systems
List of Abbreviations
GIS = geographical information systems


Background Chronic ethanol exposure has been shown to result in changes in neuronal cyto-architecture such as aberrant sprouting and alteration of neurite outgrowth. In PC12 cells, chronic ethanol treatment produces an increase in Nerve Growth Factor (NGF)-induced neurite outgrowth that appears to require the epsilon, but not delta, isoform of Protein Kinase C (PKC). Neurites contain a core of microtubules that are formed from polymerization of free-tubulin. Therefore, it would be expected that an increase in neurite outgrowth would correlate with an increase in microtubule content. We examined the effect of chronic ethanol exposure on microtubule content in PC12 cells and the role of PKC epsilon and delta in ethanol's effect on microtubule levels.
Background
Chronic ethanol exposure has been shown to result in changes in neuronal cyto-architecture such as aberrant sprouting and alteration of neurite outgrowth. In PC12 cells, chronic ethanol treatment produces an increase in Nerve Growth Factor (NGF)-induced neurite outgrowth that appears to require the epsilon, but not delta, isoform of Protein Kinase C (PKC). Neurites contain a core of microtubules that are formed from polymerization of free-tubulin. Therefore, it would be expected that an increase in neurite outgrowth would correlate with an increase in microtubule content. We examined the effect of chronic ethanol exposure on microtubule content in PC12 cells and the role of PKC epsilon and delta in ethanol's effect on microtubule levels.Results Chronic ethanol exposure of wild-type and vector control PC12 cells resulted in a significant increase in microtubule content and a corresponding decrease in free tubulin. There was also a significant increase in microtubule content in PC12 cells expressing a dominate-negative inhibitor of epsilon PKC; cells which have previously been shown to have no ethanol-induced increase in neurite outgrowth. In contrast, ethanol had no effect on microtubule content in PC12 cells expressing a dominate-negative inhibitor of delta PKC.
Results
Chronic ethanol exposure of wild-type and vector control PC12 cells resulted in a significant increase in microtubule content and a corresponding decrease in free tubulin. There was also a significant increase in microtubule content in PC12 cells expressing a dominate-negative inhibitor of epsilon PKC; cells which have previously been shown to have no ethanol-induced increase in neurite outgrowth. In contrast, ethanol had no effect on microtubule content in PC12 cells expressing a dominate-negative inhibitor of delta PKC.Conclusion These results suggest that chronic ethanol exposure alters the relative ratio of free tubulin to microtubule-associated tubulin, an important component of the cytoskeleton. Further, the data from the PKC dominant-negative cell lines suggest that the effects of ethanol on microtubule content do not correlate with the effects of ethanol on neurite outgrowth. The delta isoform of PKC appears to be necessary for the ethanol-induced increase in microtubule content. These studies demonstrate an effect of chronic ethanol exposure which may contribute to previously documented alterations of neuronal cyto-architecture.
Conclusion
These results suggest that chronic ethanol exposure alters the relative ratio of free tubulin to microtubule-associated tubulin, an important component of the cytoskeleton. Further, the data from the PKC dominant-negative cell lines suggest that the effects of ethanol on microtubule content do not correlate with the effects of ethanol on neurite outgrowth. The delta isoform of PKC appears to be necessary for the ethanol-induced increase in microtubule content. These studies demonstrate an effect of chronic ethanol exposure which may contribute to previously documented alterations of neuronal cyto-architecture.

Background Chronic ethanol exposure has been shown to cause damage to the adult and developing nervous system [ 1 , 2 ]. For example,  in vivo  chronic ethanol has been shown to cause aberrant sprouting of hippocampal neurites in developing rats [ 3 ], increase the length of dendrites in cerebellar Purkinje neurons [ 4 ], the size of synaptic terminals of cerebellar granule cells [ 5 ], and the number of dendritic spines on hippocampal dentate granule neurons in adult rats [ 6 ]. Furthermore,  in vitro  ethanol enhances neurite outgrowth in cultured rat cerebellar neurons [ 7 ]. Contrary to the enhancement of neurite outgrowth, other studies have shown that chronic ethanol exposure inhibits the growth of dendrites in CA1 hippocampal neurons and cerebellar Purkinje cells  in vivo  and inhibits chick spinal cord neurite formation  in vitro  [ 8 , 9 ]. However, the mechanisms underlying this alteration of dendrite formation induced by ethanol exposure remain unknown. PC12 cells have been used as a cell culture model system to study the underlying mechanisms of ethanol's alteration of neurite outgrowth [ 10 , 11 ]. PC12 cells are a rat chromaffin cell line that differentiate into neuronal-like cells in the presence of Nerve Growth Factor (NGF) [ 12 ]. Using these cells, chronic ethanol has been shown to enhance NGF-induced neurite outgrowth [ 10 , 11 ]. Thus, PC12 cells have proven to be a valuable system for studying the mechanisms underlying ethanol-induced enhancement of neurite outgrowth. Nerve growth factor-induced neurite outgrowth in PC12 cells involves an induction of microtubule assembly [ 13 , 14 ]. Microtubules are formed from α and β tubulin proteins, which form head to tail protofilaments [ 15 ]. Studies have shown that Protein Kinase C (PKC) activation enhances the polymerization of tubulin to form microtubules [ 16 - 19 ]. Schultz et al. [ 20 ] have also demonstrated that microtubules containing phosphorylated tubulin are more stable than those containing unphosphorylated tubulin, although it remains unclear whether tubulin phosphorylation is the cause or the result of microtubule stabilization. PKC also modulates the activity of several microtubule associated proteins, including those involved in microtubule polymerization and vesicle transport [ 21 - 24 ]. Specific isoforms of PKC have also been implicated in mediating NGF-induced neurite outgrowth. Using both antisense oligonucleotides and specific inhibitors of PKC delta, Corbit et al. [ 25 ] have demonstrated that this isoform of PKC is required for NGF-induced neurite outgrowth. Other studies have found that in PC12 cells which over-express PKC epsilon, there is an enhancement of NGF-induced neurite outgrowth, while PC12 cells which over-express a dominant negative inhibitor of PKC epsilon show an inhibition of neurite outgrowth [ 26 , 27 ]. Thus, both the epsilon and delta isoforms of PKC have been implicated in modulation of NGF-induced neurite outgrowth. It is possible that the enhancement of NGF-induced neurite outgrowth produced by chronic ethanol may be due to ethanol's known alterations of PKC signaling. Chronic ethanol has multiple effects on PKC, including altered PKC subcellular localization following chronic exposure [ 28 , 29 ]. Messing et al. [ 30 ] have shown that chronic ethanol exposure actually increases total cellular content of PKC delta and epsilon (membrane associated and cytosolic) in PC12 cells. However, other studies have shown that membrane-associated PKC activity is down-regulated following chronic ethanol exposure [ 31 ]. Thus, while total cellular content of PKC may increase with chronic ethanol exposure, membrane-associated PKC may be down-regulated. Interestingly, Hundle et al. [ 32 ] have shown, using PC12 cells which over-express an inhibitory fragment of either delta or epsilon PKC, that PKC epsilon is required for ethanol's enhancement of neurite outgrowth. In this study, we examined the effect of chronic ethanol exposure on the neuronal microtubule cytoskeleton using PC12 cells as a model system. Here we show that chronic ethanol exposure increases microtubule content, while decreasing free-tubulin content. Thus, it appears that ethanol enhances microtubule polymerization in PC12 cells. We also investigated the role of microtubule polymerization in mediating ethanol's effects on neurite outgrowth using PC12 cells which over-express an inhibitory fragment of either delta or epsilon PKC. Importantly, it is the PKC epsilon isoform which is required for ethanol's enhancement of neurite outgrowth. Here, we found that the PKC delta isoform, but not the PKC epsilon isoform, is required for the enhancement of microtubule polymerization following treatment with chronic ethanol. Thus, it appears that neurite outgrowth does not correlate with enhanced microtubule polymerization in PC12 cells.
Background
Chronic ethanol exposure has been shown to cause damage to the adult and developing nervous system [ 1 , 2 ]. For example,  in vivo  chronic ethanol has been shown to cause aberrant sprouting of hippocampal neurites in developing rats [ 3 ], increase the length of dendrites in cerebellar Purkinje neurons [ 4 ], the size of synaptic terminals of cerebellar granule cells [ 5 ], and the number of dendritic spines on hippocampal dentate granule neurons in adult rats [ 6 ]. Furthermore,  in vitro  ethanol enhances neurite outgrowth in cultured rat cerebellar neurons [ 7 ]. Contrary to the enhancement of neurite outgrowth, other studies have shown that chronic ethanol exposure inhibits the growth of dendrites in CA1 hippocampal neurons and cerebellar Purkinje cells  in vivo  and inhibits chick spinal cord neurite formation  in vitro  [ 8 , 9 ]. However, the mechanisms underlying this alteration of dendrite formation induced by ethanol exposure remain unknown.in vivoin vitroin vivoin vitroPC12 cells have been used as a cell culture model system to study the underlying mechanisms of ethanol's alteration of neurite outgrowth [ 10 , 11 ]. PC12 cells are a rat chromaffin cell line that differentiate into neuronal-like cells in the presence of Nerve Growth Factor (NGF) [ 12 ]. Using these cells, chronic ethanol has been shown to enhance NGF-induced neurite outgrowth [ 10 , 11 ]. Thus, PC12 cells have proven to be a valuable system for studying the mechanisms underlying ethanol-induced enhancement of neurite outgrowth.Nerve growth factor-induced neurite outgrowth in PC12 cells involves an induction of microtubule assembly [ 13 , 14 ]. Microtubules are formed from α and β tubulin proteins, which form head to tail protofilaments [ 15 ]. Studies have shown that Protein Kinase C (PKC) activation enhances the polymerization of tubulin to form microtubules [ 16 - 19 ]. Schultz et al. [ 20 ] have also demonstrated that microtubules containing phosphorylated tubulin are more stable than those containing unphosphorylated tubulin, although it remains unclear whether tubulin phosphorylation is the cause or the result of microtubule stabilization. PKC also modulates the activity of several microtubule associated proteins, including those involved in microtubule polymerization and vesicle transport [ 21 - 24 ].Specific isoforms of PKC have also been implicated in mediating NGF-induced neurite outgrowth. Using both antisense oligonucleotides and specific inhibitors of PKC delta, Corbit et al. [ 25 ] have demonstrated that this isoform of PKC is required for NGF-induced neurite outgrowth. Other studies have found that in PC12 cells which over-express PKC epsilon, there is an enhancement of NGF-induced neurite outgrowth, while PC12 cells which over-express a dominant negative inhibitor of PKC epsilon show an inhibition of neurite outgrowth [ 26 , 27 ]. Thus, both the epsilon and delta isoforms of PKC have been implicated in modulation of NGF-induced neurite outgrowth.It is possible that the enhancement of NGF-induced neurite outgrowth produced by chronic ethanol may be due to ethanol's known alterations of PKC signaling. Chronic ethanol has multiple effects on PKC, including altered PKC subcellular localization following chronic exposure [ 28 , 29 ]. Messing et al. [ 30 ] have shown that chronic ethanol exposure actually increases total cellular content of PKC delta and epsilon (membrane associated and cytosolic) in PC12 cells. However, other studies have shown that membrane-associated PKC activity is down-regulated following chronic ethanol exposure [ 31 ]. Thus, while total cellular content of PKC may increase with chronic ethanol exposure, membrane-associated PKC may be down-regulated. Interestingly, Hundle et al. [ 32 ] have shown, using PC12 cells which over-express an inhibitory fragment of either delta or epsilon PKC, that PKC epsilon is required for ethanol's enhancement of neurite outgrowth.In this study, we examined the effect of chronic ethanol exposure on the neuronal microtubule cytoskeleton using PC12 cells as a model system. Here we show that chronic ethanol exposure increases microtubule content, while decreasing free-tubulin content. Thus, it appears that ethanol enhances microtubule polymerization in PC12 cells. We also investigated the role of microtubule polymerization in mediating ethanol's effects on neurite outgrowth using PC12 cells which over-express an inhibitory fragment of either delta or epsilon PKC. Importantly, it is the PKC epsilon isoform which is required for ethanol's enhancement of neurite outgrowth. Here, we found that the PKC delta isoform, but not the PKC epsilon isoform, is required for the enhancement of microtubule polymerization following treatment with chronic ethanol. Thus, it appears that neurite outgrowth does not correlate with enhanced microtubule polymerization in PC12 cells.Results Chronic ethanol exposure increases microtubule content in PC12 cells For the following studies, we used 100 mM ethanol for four days as a chronic exposure; a dose and duration used by previous researchers to demonstrate ethanol's enhancement of neurite outgrowth [ 10 ]. This is a concentration of ethanol which can easily be achieved by chronic alcoholics (0.46 g/dl). Figure  1  demonstrates that following a 96 hour exposure to 100 mM ethanol, there was approximately a 13% increase in polymerized microtubules compared to control cells (t 9  = 5.2; p < 0.001; n = 10). Similarly, there was a significant decrease (about 15%) in free-tubulin concentration (t 9  = 5.7; p < 0.001; n = 10) following 96 hours of ethanol exposure. There was no effect on total tubulin expression following four days of chronic ethanol exposure (t 9  = 0.034; data not shown). Chronic ethanol exposure in PKC dominant-negative PC12 cells We next used PC12 cells which over-express the first variable domain of PKC epsilon or delta, which acts as an isozyme specific inhibitor of PKC epsilon or delta [ 32 ], respectively, to investigate the role of PKC in ethanol's enhancement of microtubule polymerization (Figure  2 ). Interestingly, in the cells which express the inhibitor of PKC epsilon (DNE cells), we found that chronic ethanol exposure significantly increased microtubule content (t 5  = 7.15; p < 0.001; n = 6) and decreased tubulin content (t 5  = 3.4; p < 0.01; n = 6) (similar to control PC12 cells). In the cells which express the inhibitor of PKC delta (DND cells), there was no significant effect on microtubule (t 5  = 0.02; n = 6) or tubulin (t 5  = 0.44; n = 6) content. Ethanol had no effect on total tubulin content in either the DNE (t 5  = 0.2) or DND cells (t 5  = 0.013). There was no difference between the control vector transfected PC12 cells and wild-type PC12 cells, thus these groups were combined and expressed in Figure  1 . Importantly, for both the DNE and DND experiments, these experiments were replicated in two different sub-clones of transfected PC12 cells. In other words, we had two strains of dominant-negative PKC epsilon cells (DNE1 and DNE4) and two strains of dominant-negative PKC cells (DND21 and DND24). Results were similar between the two epsilon lines and between the two delta lines.
Results
Chronic ethanol exposure increases microtubule content in PC12 cells For the following studies, we used 100 mM ethanol for four days as a chronic exposure; a dose and duration used by previous researchers to demonstrate ethanol's enhancement of neurite outgrowth [ 10 ]. This is a concentration of ethanol which can easily be achieved by chronic alcoholics (0.46 g/dl). Figure  1  demonstrates that following a 96 hour exposure to 100 mM ethanol, there was approximately a 13% increase in polymerized microtubules compared to control cells (t 9  = 5.2; p < 0.001; n = 10). Similarly, there was a significant decrease (about 15%) in free-tubulin concentration (t 9  = 5.7; p < 0.001; n = 10) following 96 hours of ethanol exposure. There was no effect on total tubulin expression following four days of chronic ethanol exposure (t 9  = 0.034; data not shown).
Chronic ethanol exposure increases microtubule content in PC12 cells
For the following studies, we used 100 mM ethanol for four days as a chronic exposure; a dose and duration used by previous researchers to demonstrate ethanol's enhancement of neurite outgrowth [ 10 ]. This is a concentration of ethanol which can easily be achieved by chronic alcoholics (0.46 g/dl). Figure  1  demonstrates that following a 96 hour exposure to 100 mM ethanol, there was approximately a 13% increase in polymerized microtubules compared to control cells (t 9  = 5.2; p < 0.001; n = 10). Similarly, there was a significant decrease (about 15%) in free-tubulin concentration (t 9  = 5.7; p < 0.001; n = 10) following 96 hours of ethanol exposure. There was no effect on total tubulin expression following four days of chronic ethanol exposure (t 9  = 0.034; data not shown).Chronic ethanol exposure in PKC dominant-negative PC12 cells We next used PC12 cells which over-express the first variable domain of PKC epsilon or delta, which acts as an isozyme specific inhibitor of PKC epsilon or delta [ 32 ], respectively, to investigate the role of PKC in ethanol's enhancement of microtubule polymerization (Figure  2 ). Interestingly, in the cells which express the inhibitor of PKC epsilon (DNE cells), we found that chronic ethanol exposure significantly increased microtubule content (t 5  = 7.15; p < 0.001; n = 6) and decreased tubulin content (t 5  = 3.4; p < 0.01; n = 6) (similar to control PC12 cells). In the cells which express the inhibitor of PKC delta (DND cells), there was no significant effect on microtubule (t 5  = 0.02; n = 6) or tubulin (t 5  = 0.44; n = 6) content. Ethanol had no effect on total tubulin content in either the DNE (t 5  = 0.2) or DND cells (t 5  = 0.013). There was no difference between the control vector transfected PC12 cells and wild-type PC12 cells, thus these groups were combined and expressed in Figure  1 . Importantly, for both the DNE and DND experiments, these experiments were replicated in two different sub-clones of transfected PC12 cells. In other words, we had two strains of dominant-negative PKC epsilon cells (DNE1 and DNE4) and two strains of dominant-negative PKC cells (DND21 and DND24). Results were similar between the two epsilon lines and between the two delta lines.
Chronic ethanol exposure in PKC dominant-negative PC12 cells
We next used PC12 cells which over-express the first variable domain of PKC epsilon or delta, which acts as an isozyme specific inhibitor of PKC epsilon or delta [ 32 ], respectively, to investigate the role of PKC in ethanol's enhancement of microtubule polymerization (Figure  2 ). Interestingly, in the cells which express the inhibitor of PKC epsilon (DNE cells), we found that chronic ethanol exposure significantly increased microtubule content (t 5  = 7.15; p < 0.001; n = 6) and decreased tubulin content (t 5  = 3.4; p < 0.01; n = 6) (similar to control PC12 cells). In the cells which express the inhibitor of PKC delta (DND cells), there was no significant effect on microtubule (t 5  = 0.02; n = 6) or tubulin (t 5  = 0.44; n = 6) content. Ethanol had no effect on total tubulin content in either the DNE (t 5  = 0.2) or DND cells (t 5  = 0.013). There was no difference between the control vector transfected PC12 cells and wild-type PC12 cells, thus these groups were combined and expressed in Figure  1 . Importantly, for both the DNE and DND experiments, these experiments were replicated in two different sub-clones of transfected PC12 cells. In other words, we had two strains of dominant-negative PKC epsilon cells (DNE1 and DNE4) and two strains of dominant-negative PKC cells (DND21 and DND24). Results were similar between the two epsilon lines and between the two delta lines.Discussion Cells maintain a balance between free tubulin in the cytoplasm and tubulin which is polymerized into microtubules of the cytoskeleton. In this study, we find that chronic ethanol exposure increases microtubule content while decreasing free tubulin content in PC12 cells. Ethanol appears to be enhancing polymerization of tubulin into microtubules. While there was no increase in total tubulin within the cells, there was a change in the proportion of tubulin in the polymerized (microtubule) versus non-polymerized state. We initially hypothesized that the increase in microtubule content was due to ethanol's enhancement of neurite outgrowth. Presumably, neurite outgrowth is not due to simple stability of microtubules, but to an increase in dynamic microtubule growth. Microtubules are particularly abundant along the axons of nerve cells and multiple labs have show that chronic ethanol exposure increases NGF-induced neurite outgrowth in PC12 cells [ 10 , 11 ]. Therefore, since the cells that were exposed to ethanol have increased neurite outgrowth, this could be reflected as an increase in microtubles. However, it is not clear that there would necessarily be a concomitant decrease in free tubulin content or what effect of ethanol may have on microtubule stability, per se. Our data from experiments utilizing PC12 cells which express dominant negative inhibitors of PKC do not seem to support the idea that the increased microtubule content reflects increased neurite outgrowth. Hundle et al. [ 32 ] have demonstrated the specificity of these inhibitory fragments by measuring PMA-induced translocation of PKC epsilon and delta to the particulate fraction of the cells using Western blotting with isoform specific antibodies. They show (see Figure  2  of Hundle et al. [ 32 ]) that PKC epsilon, but not PKC delta, translocation was specifically inhibited in the cells expressing the epsilon inhibitory fragment. Further, PKC delta, but not PKC epsilon, was specifically inhibited in the cells expressing the delta inhibitory fragment. Thus demonstrating that the delta and epsilon inhibitory fragments selectively inhibit PMA-induced translocation of their corresponding PKC isozymes. Hundle et al [ 32 ] has shown, using these same cells, that chronic ethanol exposure enhances NGF-induced neurite outgrowth in control cells and cells expressing a dominant-negative inhibitor of PKC delta (DND cells) but not in cells expressing the inhibitor of the epsilon isoform of PKC (DNE cells). While it was not a goal of the current study to measure neurite outgrowth, daily observation of the cells was that neurite outgrowth was most dramatic in wild-type cells, slightly less so in the DND cells, and very limited in DNE cells (C. Reiter-Funk, personal observation). The work of Hundle et al [ 32 ] suggests that PKC epsilon but not PKC delta is involved in the effect of ethanol on neurite outgrowth. Therefore, we would have predicted that ethanol would cause an increase in microtubule content in DND cells but not in the DNE cells which do not have increased neurite outgrowth. However, we found that chronic ethanol enhances polymerization of tubulin into microtubules in dominant-negative PKC epsilon cells but not dominant-negative delta cells. Thus it appears that the delta isoform is involved in ethanol's enhancement of microtubule polymerization. Further, our data suggest that neurite outgrowth does not correlate with enhanced microtubule polymerization in PC12 cells. Interestingly, PKC delta has previously been shown to be involved in NGF-induced neurite outgrowth in PC12 cells [ 25 ]. It should be noted however, that our experimental paradigm varied slightly from that of Hundle et al [ 32 ]. For example, we allowed our cells to differentiate for 4 days prior to beginning ethanol. Therefore, apparent discrepancies of our findings with those of Hundle et al [ 32 ] could be related to these differences. Based on our data, we speculate that ethanol's enhancement of microtubule polymerization may involve phosphorylation of tubulin by PKC. However, it should be noted that we have, thus far, not directly measured phosphorylation of tubulin. Studies have shown that chronic ethanol increases expression of delta and epsilon PKC in PC12 cells [ 30 ] and PKC activation enhances tubulin polymerization into microtubules [ 16 - 19 ]. Alternatively, chronic ethanol could be acting to alter important microtubule associated proteins. Many of these proteins, including Microtubule-associated Proteins (MAPS) are important for promoting microtubule assembly [ 34 , 35 ] and it has been shown that PKC phosphorylation mediates the assembly-promoting activity of these proteins [ 21 , 22 , 36 , 37 ]. Further studies are required to determine the mechanism of ethanol's enhancement of microtubule formation and the apparent role of delta PKC.
Discussion
Cells maintain a balance between free tubulin in the cytoplasm and tubulin which is polymerized into microtubules of the cytoskeleton. In this study, we find that chronic ethanol exposure increases microtubule content while decreasing free tubulin content in PC12 cells. Ethanol appears to be enhancing polymerization of tubulin into microtubules. While there was no increase in total tubulin within the cells, there was a change in the proportion of tubulin in the polymerized (microtubule) versus non-polymerized state.We initially hypothesized that the increase in microtubule content was due to ethanol's enhancement of neurite outgrowth. Presumably, neurite outgrowth is not due to simple stability of microtubules, but to an increase in dynamic microtubule growth. Microtubules are particularly abundant along the axons of nerve cells and multiple labs have show that chronic ethanol exposure increases NGF-induced neurite outgrowth in PC12 cells [ 10 , 11 ]. Therefore, since the cells that were exposed to ethanol have increased neurite outgrowth, this could be reflected as an increase in microtubles. However, it is not clear that there would necessarily be a concomitant decrease in free tubulin content or what effect of ethanol may have on microtubule stability, per se.Our data from experiments utilizing PC12 cells which express dominant negative inhibitors of PKC do not seem to support the idea that the increased microtubule content reflects increased neurite outgrowth. Hundle et al. [ 32 ] have demonstrated the specificity of these inhibitory fragments by measuring PMA-induced translocation of PKC epsilon and delta to the particulate fraction of the cells using Western blotting with isoform specific antibodies. They show (see Figure  2  of Hundle et al. [ 32 ]) that PKC epsilon, but not PKC delta, translocation was specifically inhibited in the cells expressing the epsilon inhibitory fragment. Further, PKC delta, but not PKC epsilon, was specifically inhibited in the cells expressing the delta inhibitory fragment. Thus demonstrating that the delta and epsilon inhibitory fragments selectively inhibit PMA-induced translocation of their corresponding PKC isozymes.Hundle et al [ 32 ] has shown, using these same cells, that chronic ethanol exposure enhances NGF-induced neurite outgrowth in control cells and cells expressing a dominant-negative inhibitor of PKC delta (DND cells) but not in cells expressing the inhibitor of the epsilon isoform of PKC (DNE cells). While it was not a goal of the current study to measure neurite outgrowth, daily observation of the cells was that neurite outgrowth was most dramatic in wild-type cells, slightly less so in the DND cells, and very limited in DNE cells (C. Reiter-Funk, personal observation). The work of Hundle et al [ 32 ] suggests that PKC epsilon but not PKC delta is involved in the effect of ethanol on neurite outgrowth. Therefore, we would have predicted that ethanol would cause an increase in microtubule content in DND cells but not in the DNE cells which do not have increased neurite outgrowth. However, we found that chronic ethanol enhances polymerization of tubulin into microtubules in dominant-negative PKC epsilon cells but not dominant-negative delta cells. Thus it appears that the delta isoform is involved in ethanol's enhancement of microtubule polymerization. Further, our data suggest that neurite outgrowth does not correlate with enhanced microtubule polymerization in PC12 cells. Interestingly, PKC delta has previously been shown to be involved in NGF-induced neurite outgrowth in PC12 cells [ 25 ]. It should be noted however, that our experimental paradigm varied slightly from that of Hundle et al [ 32 ]. For example, we allowed our cells to differentiate for 4 days prior to beginning ethanol. Therefore, apparent discrepancies of our findings with those of Hundle et al [ 32 ] could be related to these differences.Based on our data, we speculate that ethanol's enhancement of microtubule polymerization may involve phosphorylation of tubulin by PKC. However, it should be noted that we have, thus far, not directly measured phosphorylation of tubulin. Studies have shown that chronic ethanol increases expression of delta and epsilon PKC in PC12 cells [ 30 ] and PKC activation enhances tubulin polymerization into microtubules [ 16 - 19 ]. Alternatively, chronic ethanol could be acting to alter important microtubule associated proteins. Many of these proteins, including Microtubule-associated Proteins (MAPS) are important for promoting microtubule assembly [ 34 , 35 ] and it has been shown that PKC phosphorylation mediates the assembly-promoting activity of these proteins [ 21 , 22 , 36 , 37 ]. Further studies are required to determine the mechanism of ethanol's enhancement of microtubule formation and the apparent role of delta PKC.Conclusion Our studies demonstrate that chronic ethanol alters the relative ratio of free versus microtubule-associated tubulin content in PC12 cells, resulting in an increase in microtubule content and a corresponding decrease in free tubulin. This alteration was found to occur in wild type cells, as well as those expressing a dominant-negative inhibitor of epsilon PKC but not in cells expressing a dominant-negative inhibitor of delta PKC. These ethanol-induced changes could be important during activity-dependent remodeling of synapses or developmental growth of axons and dendrites which may lead to cognitive dysfunction.
Conclusion
Our studies demonstrate that chronic ethanol alters the relative ratio of free versus microtubule-associated tubulin content in PC12 cells, resulting in an increase in microtubule content and a corresponding decrease in free tubulin. This alteration was found to occur in wild type cells, as well as those expressing a dominant-negative inhibitor of epsilon PKC but not in cells expressing a dominant-negative inhibitor of delta PKC. These ethanol-induced changes could be important during activity-dependent remodeling of synapses or developmental growth of axons and dendrites which may lead to cognitive dysfunction.Methods Materials Wild type PC12 cells were a gift from Nicholas Pantazis, Ph.D (University of Iowa). PC12 cell lines expressing dominant negative inhibitors of delta or epsilon PKC were a gift from Robert Messing, M.D (University of California, San Francisco). RPMI, Dulbecco's Modified Eagle Medium, and PenStrep were purchased from Gibco, NGF from R&D Systems, horse and fetal bovine serum was purchased from Hyclone, and laminin was purchased from Invitrogen. The microtubule assay kit was purchased from Cytoskeleton, Inc (#BK038). Cell Culture PC12 cells were cultured in RPMI medium supplemented with 10% horse serum, 5% fetal bovine serum, and 1% PenStrep. Medium for the dominant-negative PC12 cells also included G418 (250 μg/ml) for selection purposes. The dominant-negative lines are cells that have been transfected to express isozyme specific dominant-negative inhibitors of delta or epsilon PKC and have been used to show specific PKC isozyme involvement in multiple ethanol effects [ 32 , 33 ]. These cell lines stably express the fragments δV1 or εV1, which are derived from the first variable domains of delta PKC or epsilon PKC, respectively. There was also a cell line transfected with vector alone, which served as a control. Dr. Messing has shown that these fragments can function as isozyme specific inhibitors [ 32 , 33 ]. The cells were maintained in an incubator at 37°C in 5% CO 2 . For all experiments, PC12 cells were plated into six-well, laminin-coated (5 μg/ml) plates at a density of 270,000 cells/well. The cells were differentiated into neuronal-like cells with Nerve Growth Factor (25 ng/ml) for four days prior to addition of any chronic drug exposure. For chronic exposures, ethanol (100 mM) was added to the culture media (along with NGF) for 96 hours and plates (control and ethanol) were wrapped in parafilm to prevent ethanol evaporation. Media were changed every other day. Microtubule assay Measurement of microtubule and free-tubulin contents in cells are well-established assays. The materials are available as a commercially kit (Cytoskeleton, Inc). Microtubules are very sensitive to changes in temperature; therefore, all equipment and buffers were warmed to 37°C before use (unless otherwise indicated). To prevent free tubulin from polymerizing onto existing microtubules during the assay, lysis buffer was added at a ratio of 10 volumes of buffer to 1 volume of cell pellet. Cells were homogenized via syringe trituration and incubated for 10 minutes in lysis buffer (contents listed below). 10 μL of cell homogenates were saved for protein measurement using the Bradford Assay. Homogenized cells were then centrifuged at 100,000 × g for 30 minutes to separate microtubules from free-tubulin. The polymerized microtubules settle in the pellet, while the free-tubulin remains in the supernatant. Following centrifugation, the supernatant (free-tubulin) was removed and frozen until Western blot analysis. The pellet was resuspended in ice-cold water containing CaCl 2  (200 μM) and incubated for one hour. CaCl 2  acts to enhance microtubule depolymerization. Thus, the microtubules remaining in the pellet were depolymerized to free-tubulin. The samples were then centrifuged at 14,000 × g (4°C) for 10 minutes. The supernatant (containing free-tubulin representing the original microtubules) was collected and frozen. Tubulin concentrations in both fractions were measured using Western blotting as described below. Buffer contents Lysis buffer : LMS1 solution containing GTP (100 mM) + ATP (100 mM) + protease inhibitor cocktail (10 μM) + Okadaic Acid (100 nM). LMS1 : PIPES (100 mM) containing MgCl 2  (5 mM) + EGTA (1 mM) + glycerol (30%)+ Nonidet P40 (0.1%) + Triton X-100 (0.1%) + Tween 20 (0.1%) + beta-mercapto-ethanol (0.1%) + Antifoam (0.001%). Protease inhibitor cocktail : Pepstatin (1 μg/ml) + Leupeptin (1 μg/ml) + benzamidine (10 μg/ml) + tosyl arginine methyl ester (500 μg/ml). Measurement of tubulin concentrations Western blot analysis was used to determine tubulin content. Samples for Western blot were boiled with electrophoresis sample buffer (containing 25% glycerol, 5% beta-mercaptoethanol, 2% SDS, and 0.01% bromophenol blue in 62.5 mM Tris-HCl, pH 6.8). Equal amounts of protein (25 μg) were separated by SDS-PAGE using a 12% polyacrylamide gel. Molecular weight standards were also loaded. The separated proteins were transferred to nitrocellulose membranes using an electroblotting apparatus. The membrane was blocked [Tris-Buffered Saline (TBS) containing 4% nonfat dry milk and 0.05% Tween-20] for 20 minutes at room temperature. Membranes were then incubated in primary antibody (500 ng/ml; monoclonal anti-beta Tubulin; Cytoskeleton, Inc) overnight at 4°C. Following incubation in the primary antibody, membranes were washed with TBS/0.05% Tween-20 and incubated in secondary antibody (1:1000 dilution; HRP conjugated anti-mouse antibody; Santa Cruz). Membranes were again washed and tubulin bands were visualized by Enhanced Chemiluminescence (ECL) using standard luminol reagents captured using a BioRad Gel Doc 1000. The computer analysis software Molecular Analyst (version 2.1; BioRad) was used to quantify tubulin concentrations (relative density using the Volume Analysis setting with equal sized rectangular boxes set around individual bands). All blots were very clean and no filtering was needed. Data expression and statistical analysis Tubulin concentrations in both the free-tubulin and microtubule fractions were expressed as a percent control relative density and presented as mean (+/-SEM). The data represent multiple replicates of the same experiment (n values listed in figure legends). Student's t-tests were used to make comparisons between control and ethanol-treated PC12 cells. Each individual band analyzed was an independent extract from the same experiment, with the experiment being replicated 5 times. Each replication of the experiment was composed of 2 separate wells of cells for each treatment condition. For each of the 5 replications, the 2 control bands were averaged and each sample, including the controls, from that replication normalized to the average. The data was combined after all 5 replications.
Methods
Materials Wild type PC12 cells were a gift from Nicholas Pantazis, Ph.D (University of Iowa). PC12 cell lines expressing dominant negative inhibitors of delta or epsilon PKC were a gift from Robert Messing, M.D (University of California, San Francisco). RPMI, Dulbecco's Modified Eagle Medium, and PenStrep were purchased from Gibco, NGF from R&D Systems, horse and fetal bovine serum was purchased from Hyclone, and laminin was purchased from Invitrogen. The microtubule assay kit was purchased from Cytoskeleton, Inc (#BK038).
Materials
Wild type PC12 cells were a gift from Nicholas Pantazis, Ph.D (University of Iowa). PC12 cell lines expressing dominant negative inhibitors of delta or epsilon PKC were a gift from Robert Messing, M.D (University of California, San Francisco). RPMI, Dulbecco's Modified Eagle Medium, and PenStrep were purchased from Gibco, NGF from R&D Systems, horse and fetal bovine serum was purchased from Hyclone, and laminin was purchased from Invitrogen. The microtubule assay kit was purchased from Cytoskeleton, Inc (#BK038).Cell Culture PC12 cells were cultured in RPMI medium supplemented with 10% horse serum, 5% fetal bovine serum, and 1% PenStrep. Medium for the dominant-negative PC12 cells also included G418 (250 μg/ml) for selection purposes. The dominant-negative lines are cells that have been transfected to express isozyme specific dominant-negative inhibitors of delta or epsilon PKC and have been used to show specific PKC isozyme involvement in multiple ethanol effects [ 32 , 33 ]. These cell lines stably express the fragments δV1 or εV1, which are derived from the first variable domains of delta PKC or epsilon PKC, respectively. There was also a cell line transfected with vector alone, which served as a control. Dr. Messing has shown that these fragments can function as isozyme specific inhibitors [ 32 , 33 ]. The cells were maintained in an incubator at 37°C in 5% CO 2 . For all experiments, PC12 cells were plated into six-well, laminin-coated (5 μg/ml) plates at a density of 270,000 cells/well. The cells were differentiated into neuronal-like cells with Nerve Growth Factor (25 ng/ml) for four days prior to addition of any chronic drug exposure. For chronic exposures, ethanol (100 mM) was added to the culture media (along with NGF) for 96 hours and plates (control and ethanol) were wrapped in parafilm to prevent ethanol evaporation. Media were changed every other day.
Cell Culture
PC12 cells were cultured in RPMI medium supplemented with 10% horse serum, 5% fetal bovine serum, and 1% PenStrep. Medium for the dominant-negative PC12 cells also included G418 (250 μg/ml) for selection purposes. The dominant-negative lines are cells that have been transfected to express isozyme specific dominant-negative inhibitors of delta or epsilon PKC and have been used to show specific PKC isozyme involvement in multiple ethanol effects [ 32 , 33 ]. These cell lines stably express the fragments δV1 or εV1, which are derived from the first variable domains of delta PKC or epsilon PKC, respectively. There was also a cell line transfected with vector alone, which served as a control. Dr. Messing has shown that these fragments can function as isozyme specific inhibitors [ 32 , 33 ].The cells were maintained in an incubator at 37°C in 5% CO 2 . For all experiments, PC12 cells were plated into six-well, laminin-coated (5 μg/ml) plates at a density of 270,000 cells/well. The cells were differentiated into neuronal-like cells with Nerve Growth Factor (25 ng/ml) for four days prior to addition of any chronic drug exposure. For chronic exposures, ethanol (100 mM) was added to the culture media (along with NGF) for 96 hours and plates (control and ethanol) were wrapped in parafilm to prevent ethanol evaporation. Media were changed every other day.Microtubule assay Measurement of microtubule and free-tubulin contents in cells are well-established assays. The materials are available as a commercially kit (Cytoskeleton, Inc). Microtubules are very sensitive to changes in temperature; therefore, all equipment and buffers were warmed to 37°C before use (unless otherwise indicated). To prevent free tubulin from polymerizing onto existing microtubules during the assay, lysis buffer was added at a ratio of 10 volumes of buffer to 1 volume of cell pellet. Cells were homogenized via syringe trituration and incubated for 10 minutes in lysis buffer (contents listed below). 10 μL of cell homogenates were saved for protein measurement using the Bradford Assay. Homogenized cells were then centrifuged at 100,000 × g for 30 minutes to separate microtubules from free-tubulin. The polymerized microtubules settle in the pellet, while the free-tubulin remains in the supernatant. Following centrifugation, the supernatant (free-tubulin) was removed and frozen until Western blot analysis. The pellet was resuspended in ice-cold water containing CaCl 2  (200 μM) and incubated for one hour. CaCl 2  acts to enhance microtubule depolymerization. Thus, the microtubules remaining in the pellet were depolymerized to free-tubulin. The samples were then centrifuged at 14,000 × g (4°C) for 10 minutes. The supernatant (containing free-tubulin representing the original microtubules) was collected and frozen. Tubulin concentrations in both fractions were measured using Western blotting as described below.
Microtubule assay
Measurement of microtubule and free-tubulin contents in cells are well-established assays. The materials are available as a commercially kit (Cytoskeleton, Inc). Microtubules are very sensitive to changes in temperature; therefore, all equipment and buffers were warmed to 37°C before use (unless otherwise indicated). To prevent free tubulin from polymerizing onto existing microtubules during the assay, lysis buffer was added at a ratio of 10 volumes of buffer to 1 volume of cell pellet. Cells were homogenized via syringe trituration and incubated for 10 minutes in lysis buffer (contents listed below). 10 μL of cell homogenates were saved for protein measurement using the Bradford Assay. Homogenized cells were then centrifuged at 100,000 × g for 30 minutes to separate microtubules from free-tubulin. The polymerized microtubules settle in the pellet, while the free-tubulin remains in the supernatant. Following centrifugation, the supernatant (free-tubulin) was removed and frozen until Western blot analysis. The pellet was resuspended in ice-cold water containing CaCl 2  (200 μM) and incubated for one hour. CaCl 2  acts to enhance microtubule depolymerization. Thus, the microtubules remaining in the pellet were depolymerized to free-tubulin. The samples were then centrifuged at 14,000 × g (4°C) for 10 minutes. The supernatant (containing free-tubulin representing the original microtubules) was collected and frozen. Tubulin concentrations in both fractions were measured using Western blotting as described below.Buffer contents Lysis buffer : LMS1 solution containing GTP (100 mM) + ATP (100 mM) + protease inhibitor cocktail (10 μM) + Okadaic Acid (100 nM). LMS1 : PIPES (100 mM) containing MgCl 2  (5 mM) + EGTA (1 mM) + glycerol (30%)+ Nonidet P40 (0.1%) + Triton X-100 (0.1%) + Tween 20 (0.1%) + beta-mercapto-ethanol (0.1%) + Antifoam (0.001%). Protease inhibitor cocktail : Pepstatin (1 μg/ml) + Leupeptin (1 μg/ml) + benzamidine (10 μg/ml) + tosyl arginine methyl ester (500 μg/ml).
Buffer contents
Lysis buffer : LMS1 solution containing GTP (100 mM) + ATP (100 mM) + protease inhibitor cocktail (10 μM) + Okadaic Acid (100 nM).LMS1 : PIPES (100 mM) containing MgCl 2  (5 mM) + EGTA (1 mM) + glycerol (30%)+ Nonidet P40 (0.1%) + Triton X-100 (0.1%) + Tween 20 (0.1%) + beta-mercapto-ethanol (0.1%) + Antifoam (0.001%).Protease inhibitor cocktail : Pepstatin (1 μg/ml) + Leupeptin (1 μg/ml) + benzamidine (10 μg/ml) + tosyl arginine methyl ester (500 μg/ml).Measurement of tubulin concentrations Western blot analysis was used to determine tubulin content. Samples for Western blot were boiled with electrophoresis sample buffer (containing 25% glycerol, 5% beta-mercaptoethanol, 2% SDS, and 0.01% bromophenol blue in 62.5 mM Tris-HCl, pH 6.8). Equal amounts of protein (25 μg) were separated by SDS-PAGE using a 12% polyacrylamide gel. Molecular weight standards were also loaded. The separated proteins were transferred to nitrocellulose membranes using an electroblotting apparatus. The membrane was blocked [Tris-Buffered Saline (TBS) containing 4% nonfat dry milk and 0.05% Tween-20] for 20 minutes at room temperature. Membranes were then incubated in primary antibody (500 ng/ml; monoclonal anti-beta Tubulin; Cytoskeleton, Inc) overnight at 4°C. Following incubation in the primary antibody, membranes were washed with TBS/0.05% Tween-20 and incubated in secondary antibody (1:1000 dilution; HRP conjugated anti-mouse antibody; Santa Cruz). Membranes were again washed and tubulin bands were visualized by Enhanced Chemiluminescence (ECL) using standard luminol reagents captured using a BioRad Gel Doc 1000. The computer analysis software Molecular Analyst (version 2.1; BioRad) was used to quantify tubulin concentrations (relative density using the Volume Analysis setting with equal sized rectangular boxes set around individual bands). All blots were very clean and no filtering was needed.
Measurement of tubulin concentrations
Western blot analysis was used to determine tubulin content. Samples for Western blot were boiled with electrophoresis sample buffer (containing 25% glycerol, 5% beta-mercaptoethanol, 2% SDS, and 0.01% bromophenol blue in 62.5 mM Tris-HCl, pH 6.8). Equal amounts of protein (25 μg) were separated by SDS-PAGE using a 12% polyacrylamide gel. Molecular weight standards were also loaded. The separated proteins were transferred to nitrocellulose membranes using an electroblotting apparatus. The membrane was blocked [Tris-Buffered Saline (TBS) containing 4% nonfat dry milk and 0.05% Tween-20] for 20 minutes at room temperature. Membranes were then incubated in primary antibody (500 ng/ml; monoclonal anti-beta Tubulin; Cytoskeleton, Inc) overnight at 4°C. Following incubation in the primary antibody, membranes were washed with TBS/0.05% Tween-20 and incubated in secondary antibody (1:1000 dilution; HRP conjugated anti-mouse antibody; Santa Cruz). Membranes were again washed and tubulin bands were visualized by Enhanced Chemiluminescence (ECL) using standard luminol reagents captured using a BioRad Gel Doc 1000. The computer analysis software Molecular Analyst (version 2.1; BioRad) was used to quantify tubulin concentrations (relative density using the Volume Analysis setting with equal sized rectangular boxes set around individual bands). All blots were very clean and no filtering was needed.Data expression and statistical analysis Tubulin concentrations in both the free-tubulin and microtubule fractions were expressed as a percent control relative density and presented as mean (+/-SEM). The data represent multiple replicates of the same experiment (n values listed in figure legends). Student's t-tests were used to make comparisons between control and ethanol-treated PC12 cells. Each individual band analyzed was an independent extract from the same experiment, with the experiment being replicated 5 times. Each replication of the experiment was composed of 2 separate wells of cells for each treatment condition. For each of the 5 replications, the 2 control bands were averaged and each sample, including the controls, from that replication normalized to the average. The data was combined after all 5 replications.
Data expression and statistical analysis
Tubulin concentrations in both the free-tubulin and microtubule fractions were expressed as a percent control relative density and presented as mean (+/-SEM). The data represent multiple replicates of the same experiment (n values listed in figure legends). Student's t-tests were used to make comparisons between control and ethanol-treated PC12 cells.Each individual band analyzed was an independent extract from the same experiment, with the experiment being replicated 5 times. Each replication of the experiment was composed of 2 separate wells of cells for each treatment condition. For each of the 5 replications, the 2 control bands were averaged and each sample, including the controls, from that replication normalized to the average. The data was combined after all 5 replications.Authors' contributions CKR-F conceived and performed the experiments and participated in writing the manuscript. DPD participated in the design of the study and writing of the manuscript. Both authors read and approved the final manuscript.
Authors' contributions
CKR-F conceived and performed the experiments and participated in writing the manuscript. DPD participated in the design of the study and writing of the manuscript. Both authors read and approved the final manuscript.