Differences among Unique Nanoparticle Protein Corona Constructs: A Case Study Using Data Analytics and Multi-Variant Visualization to Describe Physicochemical Characteristics

: Gold nanoparticles (AuNPs) used in pharmaceutical treatments have been shown to effectively deliver a payload, such as an active pharmaceutical ingredient or image contrast agent, to targeted tissues in need of therapy or diagnostics while minimizing exposure, availability, and accumulation to surrounding biological compartments. Data sets collected in this ﬁeld of study include some toxico- and pharmacodynamic properties (e.g., distribution and metabolism) but many studies lack information about adsorption of biological molecules or absorption into cells. When nanoparticles are suspended in blood serum, a protein corona cloud forms around its surface. The extent of the applications and implications of this formed cloud are unknown. Some researchers have speculated that the successful use of nanoparticles in pharmaceutical treatments relies on a comprehensive understanding of the protein corona composition. The work presented in this paper uses a suite of data analytics and multi-variant visualization techniques to elucidate particle-to-protein interactions at the molecular level. Through mass spectrometry analyses, corona proteins were identiﬁed through large and complex datasets. With such high-output analyses, complex datasets pose a challenge when visualizing and communicating nanoparticle-protein interactions. Thus, the creation of a streamlined visualization method is necessary. A series of user-friendly data informatics techniques were used to demonstrate the data ﬂow of protein corona characteristics. Multi-variant heat maps, pie charts, tables, and three-dimensional regression analyses were used to improve results interpretation, facilitate an iterative data transfer process, and emphasize features of the nanoparticle-protein corona system that might be controllable. Data informatics successfully highlights the differences between protein corona compositions and how they relate to nanoparticle surface charge.

Introduction
When nanoparticles (NPs) enter a biological fluid, such as serum, proteins and peptides adsorb onto the particle's surface, forming a nanoparticle-protein corona (NPPC) complex [1][2][3]. The protein cloud develops a new surface on the nanoparticle, impacting the interactions between the nanoparticle and the surrounding environment. The NPPC complex assumes the characteristics of a core-shell particle system, where the pristine particle is the "core" and the corona is the "shell" [4]. This creates a new interface between the pristine particle and the surrounding biological fluid. The formation of a protein corona has been shown to change the particle's biological effects [5][6][7][8]. In addition to this change in the particle's characteristics, the adsorbed proteins have the potential to undergo conformational changes. Changes in either set of properties are especially important for nanomaterial drug products [9][10][11][12][13]. The cloak of proteins around particles in biological fluids has been shown to enhance or diminish a particle's specificity to a desired location, incite or inhibit its ability to permeate into a cell, and heighten or mitigate a biochemical response. When a protein corona forms, it is more likely to distribute and become available due to the nano-bio interface that forms around the surface of the nanoparticle. This protein cloud can change the biodistribution of the nanoparticle system depending on the biomolecular composition formed around the surface. Consequently, if the drug is reliant on specific proteins binding to the surface of the nanoparticle, then the particle could be translocated into an unintended location if non-targeted proteins form around the particle.
Research efforts in the field of nanotoxicology have strived to relate (or correlate) particle properties to adverse health effects. There now exists a substantial amount of published literature on this topic; however, there is no general consensus on identifying physicochemical characteristics as predictors of toxicological responses. Many different nanoparticles, such as anatase titania, alpha-quartz silica, and gold colloids, have distinct crystal structures as opposed to amorphous materials of similar chemical composition. Notably, crystal structure has been associated with increased cytotoxicity in mammalian cell populations [14][15][16][17]. Nanoparticle size has been implicated as a main driver in systemic distribution [15,[18][19][20]. Surface charge is related to cellular uptake mechanisms [21][22][23][24]. However, for every paper published that demonstrates a direct or proportional relationship between particle properties and an induced biological affect, there are others that report a lack of association [25][26][27][28].
Toxicologists who study engineered nanomaterials and nanomedicines have been interested in both complex mixtures in environmentally-and physiologically-relevant conditions in order to decipher the role of physiochemical properties in induced mechanisms of toxic action. These analytical interrogations require multi-level data collected on germane parameters. In the NPPC research space, some of these parameters include particle composition, size, and charge; corona source, composition, and mass; and NPPC activity, toxicity, and ADME properties. New data-gathering methods, such as the inclusion of mass spectrometry techniques and resultant data, produce collections of large sets that are sometimes complicated, multifaceted, and often non-comparable. Scientific data visualization is a powerful methodological tool for enabling the understanding of multi-dimensional data sets. The purpose of this study was to demonstrate the utility of data visualization as a method for identifying trends in NPPC physicochemical properties among six unique samples. Data visualization has been shown to play a crucial role by aiding scientists and practitioners to interpret underlying patterns in their data and applying the new knowledge to pose the next set of research questions [29].
Specific to nanotoxicology research, understanding the composition of the protein corona associated with gold nanoparticles (AuNPs) has the potential to improve nano-enabled drug product development [30,31]. Gold nanoparticles are important because they offer the opportunity to advance pharmaceutical treatments by delivering direct payloads to target tissues. For example, in Ghosh et al., hybrid AuNP polymers were transfected vectors into kidney cells [9,[32][33][34][35][36][37]. Quantitative and discovery proteomics of the gold nanoparticle protein corona have enhanced physicochemical characterization [38][39][40][41]. The results presented in this manuscript serve two purposes. First, the data sets expand upon the available data in the protein corona field to assist with its exploitation in pharmaceutical drug development. Second, the data visualization highlights the varying interpretations that can be gained after displaying the same data in different forms. Therefore, the visualization and presentation of results is of relevance to toxicologists and pharmacologists alike.

Materials and Methods
The approach to analyze large sets of proteomic data relevant for nanotoxicological studies needs to be streamlined. The approach used in this study includes data collection, storage, and organization, Figure 1. The flow of data from collection to interpretation. After the conclusion of an experiment, data are collected. Within the first stage, sets of data are stored and organized locally or on the cloud in spreadsheets. Second, data are normalized relative to controls. Third, descriptive statistics are used to demonstrate mean and standard deviation of data. Fourth, utilizing statistical analysis, patterns are identified and categorized. Fifth, data management software is used to represent patterns gathered from statistical analysis in the form of figures and charts. Lastly, data are communicated to scientists and non-scientists through publications.

Protein Corona Sample Preparation
In this study, three different nanoparticle coatings were subjected to two different serum types and incubated for 24 hours. Gold nanoparticles of 30 nm were purchased from NanoHybrids (Austin, TX, USA). Particles were placed in aqueous dispersions with one of the following coatings: polyethylene glycol (PEG), carboxylic acid (COOH), or amine (NH3). The primary particle size of each nanoparticle was measured as 30 ± 2 nm using transmission electron microscopy (TEM). The different coatings provided a variety of charges among the coatings, with the PEG producing a neutrally charged surface, COOH producing a negatively charged surface, and NH3 producing a positively charged surface. This variance assisted the determination of the effects of surface charge on the formation of the protein corona. The serum types used included fetal bovine, termed serumtype 1 (ST1), and equine, termed serum-type 2 (ST2; Equitech-Bio Inc., Kerrville, TX, USA). Particles were incubated with serum at a 1:1 ratio at 37 °C for 24 hours. Coronas were separated from particles in two steps. First, the particle-corona suspension was centrifuged to remove excess proteins not adhered to particle surfaces. Second, the proteins were removed from the particle surface using a lysis buffer of 8 M urea at 95 °C for 5 minutes and then centrifuged to separate the proteins from the nanoparticles. Figure 2 shows the representative TEM of the gold nanoparticles before serum incubation ( Figure 2A) versus the gold nanoparticle after 24-hour incubation with serum-type 1 ( Figure 2B). Briefly, samples were deposited on 200 mesh carbon coated copper grid (EMS, Hatfield, PA, USA), left to dry at room temperature for 5 minutes, and washed with PBS twice. TEM images were collected using a JEM-1010 (JEOL Inc., Peabody, MA, USA) microscope at 60 kV with a spot size of 2 [42,43]. To determine the average corona diameter, 100 nanoparticles imaged in TEM micrographs were measured using Cell Sens version 1.13 from Olympus Corp (Center Valley, PA, USA). The hydrodynamic diameter was determined through dynamic light scattering (DLS), along with the polydispersity index (PdI), where NPPC with serum-type 1 (ST1) was determined to be highly monodisperse in the particle population as indicated by a low PdI of 0.144, whereas NPPC with serum-type 2 (ST2) was determined to be moderately monodisperse in particle population as indicated by a PdI of 0.483. Figure 1. The flow of data from collection to interpretation. After the conclusion of an experiment, data are collected. Within the first stage, sets of data are stored and organized locally or on the cloud in spreadsheets. Second, data are normalized relative to controls. Third, descriptive statistics are used to demonstrate mean and standard deviation of data. Fourth, utilizing statistical analysis, patterns are identified and categorized. Fifth, data management software is used to represent patterns gathered from statistical analysis in the form of figures and charts. Lastly, data are communicated to scientists and non-scientists through publications.

Protein Corona Sample Preparation
In this study, three different nanoparticle coatings were subjected to two different serum types and incubated for 24 h. Gold nanoparticles of 30 nm were purchased from NanoHybrids (Austin, TX, USA). Particles were placed in aqueous dispersions with one of the following coatings: polyethylene glycol (PEG), carboxylic acid (COOH), or amine (NH 3 ). The primary particle size of each nanoparticle was measured as 30 ± 2 nm using transmission electron microscopy (TEM). The different coatings provided a variety of charges among the coatings, with the PEG producing a neutrally charged surface, COOH producing a negatively charged surface, and NH 3 producing a positively charged surface. This variance assisted the determination of the effects of surface charge on the formation of the protein corona. The serum types used included fetal bovine, termed serum-type 1 (ST1), and equine, termed serum-type 2 (ST2; Equitech-Bio Inc., Kerrville, TX, USA). Particles were incubated with serum at a 1:1 ratio at 37 • C for 24 h. Coronas were separated from particles in two steps. First, the particle-corona suspension was centrifuged to remove excess proteins not adhered to particle surfaces. Second, the proteins were removed from the particle surface using a lysis buffer of 8 M urea at 95 • C for 5 min and then centrifuged to separate the proteins from the nanoparticles. Figure 2 shows the representative TEM of the gold nanoparticles before serum incubation ( Figure 2A) versus the gold nanoparticle after 24-h incubation with serum-type 1 ( Figure 2B). Briefly, samples were deposited on 200 mesh carbon coated copper grid (EMS, Hatfield, PA, USA), left to dry at room temperature for 5 min, and washed with PBS twice. TEM images were collected using a JEM-1010 (JEOL Inc., Peabody, MA, USA) microscope at 60 kV with a spot size of 2 [42,43]. To determine the average corona diameter, 100 nanoparticles imaged in TEM micrographs were measured using Cell Sens version 1.13 from Olympus Corp (Center Valley, PA, USA). The hydrodynamic diameter was determined through dynamic light scattering (DLS), along with the polydispersity index (PdI), where NPPC with serum-type 1 (ST1) was determined to be highly monodisperse in the particle population as indicated by a low PdI of 0.144, whereas NPPC with serum-type 2 (ST2) was determined to be moderately monodisperse in particle population as indicated by a PdI of 0.483.

Mass Spectrometry Data Collection
To identify and quantify the composition within each corona, proteins were removed from the particle surfaces after incubation. Once proteins were isolated, samples underwent tryptic digestion and were analyzed on liquid chromatography tandem mass spectrometry (LC-MS/MS) [44]. Proteins were identified and quantified using Progenesis QI for proteomics v4.1 (Nonlinear Dynamics, Waters Corporation, Milford, MA, USA).

Data Flow
Using tool sets available in Microsoft Excel (Redmond, WA, U.S. 2016), the following method was used to analyze data. Excel was used to compute graphical representation formulas that produced a series of graphs, charts, and tables. Visualized data summarized the results obtained from human-defined clustering. Here, seven different graphical representations were used: conditional formatting, two-dimensional (2D) stacked column charts, 2D pie-of-pie charts, histograms, and combo charts (of scatter and bar). After LC-MS/MS raw data collection, empirical values were stored and organized into clusters based upon characteristics, such as nanoparticle coating, NPPC size, serum-type, or protein abundance, mass, or identification. Subsequent analyses involved the clustering of protein data based on molecular or biological functions, such as transcription activity, actin-binding tendencies, or detoxification initiation.

Results
The most commonly used type of figure in proteomic studies is a table that displays high outputs of data in a tabular format. Although this may be the most comprehensive method that lists the most parametric outputs, it is technically not a visualization, nor is it conducive to quick but meaningful interpretation. Interpreting the results of such tables requires time, explanations, and a substantial amount of previous experience in proteomic analyses. Table 1 presents a traditional tabular format of the proteomic data set collected in this study. Information from the table ranges from the number of peptides to the molecular function of for each corona protein found. Any information can be added to the table, which increases the versatility of the data, but it is not a representation of the findings that is easily visualized. Trends and correlations are difficult to quickly obtain from a table, so may not be the best format to report data where multiple parameters are required to identify patterns. Tabular format is also limited by the number of rows and columns in a readable print-out. Overall, this method displays rapid information like sample name, type, and other simple parameters in the experiment.

Mass Spectrometry Data Collection
To identify and quantify the composition within each corona, proteins were removed from the particle surfaces after incubation. Once proteins were isolated, samples underwent tryptic digestion and were analyzed on liquid chromatography tandem mass spectrometry (LC-MS/MS) [44]. Proteins were identified and quantified using Progenesis QI for proteomics v4.1 (Nonlinear Dynamics, Waters Corporation, Milford, MA, USA).

Data Flow
Using tool sets available in Microsoft Excel (Redmond, WA, USA, 2016), the following method was used to analyze data. Excel was used to compute graphical representation formulas that produced a series of graphs, charts, and tables. Visualized data summarized the results obtained from human-defined clustering. Here, seven different graphical representations were used: conditional formatting, two-dimensional (2D) stacked column charts, 2D pie-of-pie charts, histograms, and combo charts (of scatter and bar). After LC-MS/MS raw data collection, empirical values were stored and organized into clusters based upon characteristics, such as nanoparticle coating, NPPC size, serum-type, or protein abundance, mass, or identification. Subsequent analyses involved the clustering of protein data based on molecular or biological functions, such as transcription activity, actin-binding tendencies, or detoxification initiation.

Results
The most commonly used type of figure in proteomic studies is a table that displays high outputs of data in a tabular format. Although this may be the most comprehensive method that lists the most parametric outputs, it is technically not a visualization, nor is it conducive to quick but meaningful interpretation. Interpreting the results of such tables requires time, explanations, and a substantial amount of previous experience in proteomic analyses. Table 1 presents a traditional tabular format of the proteomic data set collected in this study. Information from the table ranges from the number of peptides to the molecular function of for each corona protein found. Any information can be added to the table, which increases the versatility of the data, but it is not a representation of the findings that is easily visualized. Trends and correlations are difficult to quickly obtain from a table, so may not be the best format to report data where multiple parameters are required to identify patterns. Tabular format is also limited by the number of rows and columns in a readable print-out. Overall, this method displays rapid information like sample name, type, and other simple parameters in the experiment. Table 1. Proteins found in the corona of the AuNP-Amine-ST1 sample. The most common method to display high outputs of data into a figure is a traditional table. Although this may be the most comprehensive method, it is not a visualization, nor is it simple for non-scientists to interpret. For the functional data, only the most common function is noted. Bar graphs are an essential figure used for comparisons between different samples or parameters within a sample set. There are a limitless number of parameters that can be compared using a bar graph, but this method is limited to plots using a single x-(independent variable) and a single y-axis (dependent variable). Often, double y-axes add additional variable to the system, as long as the x-axis is constant between differing y-value parameters. Figure 3 shows two different bar charts that compare each protein from the corona against the normalized relative abundance. The chart presents the amount of each identified protein in each of the NPPC samples. With this visualization technique, each protein, serum-type, or nanoparticle surface coating is compared. For instance, the gold nanoparticle with the NH 3 coating incubated in serum-type 2 had the most MARCS_RAT (Myristoylated alanine-rich C-kinase substrate) protein. In contrast, ACDA1_METTE (Acetyl-CoA decarbonylase/synthase complex subunit alpha 1) protein was the most abundant component in the corona in the COOH coated gold nanoparticle incubated in the same serum-type 2. This method quantitatively compares protein abundance among multiple parameters. Bar graphs are an essential figure used for comparisons between different samples or parameters within a sample set. There are a limitless number of parameters that can be compared using a bar graph, but this method is limited to plots using a single x-(independent variable) and a single y-axis (dependent variable). Often, double y-axes add additional variable to the system, as long as the xaxis is constant between differing y-value parameters. Figure 3 shows two different bar charts that compare each protein from the corona against the normalized relative abundance. The chart presents the amount of each identified protein in each of the NPPC samples. With this visualization technique, each protein, serum-type, or nanoparticle surface coating is compared. For instance, the gold nanoparticle with the NH3 coating incubated in serum-type 2 had the most MARCS_RAT (Myristoylated alanine-rich C-kinase substrate) protein. In contrast, ACDA1_METTE (Acetyl-CoA decarbonylase/synthase complex subunit alpha 1) protein was the most abundant component in the corona in the COOH coated gold nanoparticle incubated in the same serum-type 2. This method quantitatively compares protein abundance among multiple parameters. (Top) Each protein is represented by its own normalized abundance percentages. For example, MARCS_RAT appears in all NPPC samples, but seems to be the most abundant protein in the amine-coated AuNP with serum-type 2 shell. (Bottom) Each NPPC is represented with its own normalized abundance percentages. For example, all NPPC samples contain multiple proteins, but amine-coated AuNP with serum-type 2 shell seems to be the most diverse. Figure 4 includes six pie graphs and comprehensively illustrates the total protein composition within each sample. Samples are categorized by nanoparticle coating and serum type. This method distinguishes the most abundant proteins among each of the coating-serum combinations using the average normalized abundance. Simultaneously, the figures depict the variations among each of the coating-serum combinations. This enables the comprehensive comparison of the average normalized abundance of each sample and the contrast of the protein composition between each combination [45][46][47][48][49][50]. Beginning with the three pie graphs representing samples incubated in serum-type 1, the corona from the NH3-coated particle is primarily composed of CALL_DROME (Calmodulin-related protein 97A) and MED22_CHICK (Mediator of RNA polymerase II transcription subunit 22). Similarly, the most abundant proteins in the PEG-ST1 combination are CALL_DROME and MED22_CHICK; however, these proteins are less abundant compared with NH3-ST1. These first two pie graphs implicate a commonality among the protein corona compositions specifically for serum- Figure 3. Protein abundance percent in sample. (Top) Each protein is represented by its own normalized abundance percentages. For example, MARCS_RAT appears in all NPPC samples, but seems to be the most abundant protein in the amine-coated AuNP with serum-type 2 shell. (Bottom) Each NPPC is represented with its own normalized abundance percentages. For example, all NPPC samples contain multiple proteins, but amine-coated AuNP with serum-type 2 shell seems to be the most diverse. Figure 4 includes six pie graphs and comprehensively illustrates the total protein composition within each sample. Samples are categorized by nanoparticle coating and serum type. This method distinguishes the most abundant proteins among each of the coating-serum combinations using the average normalized abundance. Simultaneously, the figures depict the variations among each of the coating-serum combinations. This enables the comprehensive comparison of the average normalized abundance of each sample and the contrast of the protein composition between each combination [45][46][47][48][49][50]. Beginning with the three pie graphs representing samples incubated in serum-type 1, the corona from the NH 3 -coated particle is primarily composed of CALL_DROME (Calmodulin-related protein 97A) and MED22_CHICK (Mediator of RNA polymerase II transcription subunit 22). Similarly, the most abundant proteins in the PEG-ST1 combination are CALL_DROME and MED22_CHICK; however, these proteins are less abundant compared with NH 3 -ST1. These first two pie graphs implicate a commonality among the protein corona compositions specifically for serum-type 1. However, in the COOH-ST1 sample, whereas MED22_CHICK is the most abundant protein in the sample, CALL_DROME is one of the least. This observation indicates the COOH coating (negative charged surface) may alter the protein composition to a higher degree than that of the NH 3 (positively charged surface) and PEG (neutrally charged surface) coatings. The three pie graphs representing samples incubated in serum-type 2 illustrate a more distributed composition for NH3-and PEG-coated nanoparticles. In both the NH3-ST2 and PEG-ST2 samples, MED22_RAT, CALL_DROME, and MED22_CHICK proteins are equally abundant. This observation indicates that these proteins may play a significant role in particle absorption in serumtype 2. However, in the COOH-ST2 sample, the CALL_DROME protein is by far the most abundant, with MARCS_RAT present in a small amount. As with the ST1-incubated samples, the COOH coating (negative charged surface) forms a different protein corona (in terms of composition) than that of the NH3 (positively charged surface) and PEG (neutrally charged surface) coatings.
The two most common proteins found in all coronas were CALL_DROME, (calmodulin-related protein 97A) and MARCS_CHICK (myristoylated alanine-rich C-kinase substrate). CALL_DROME regulates a range of normal cellular functions such as inflammation, metabolism, apoptosis, and immune response. The protein specifically functions as a calcium-binding messenger that modifies interactions with different target proteins like phosphatases or kinases. When calcium binds to calmodulin, a large structural change occurs. Electrophiles are needed to sequester the readily available electrons that influence the binding affinity for the positively-charged surface functionalization on the nanoparticles. MARCS is a common protein found in the cytoskeleton that performs a series of important functions including binding actin, calmodulin, and synapsin. This protein is in a class that strongly impacts cell motility, secretion, and shape. This class of proteins also participates in cell cycle regulation, neural development, and transmembrane transport. They play a part in sequestering acidic membrane phospholipids like PIP2, which is vital in protein signaling.
Both proteins play important roles in the maintenance of normal cellular functions in eukaryotic cells. However, in the presence of nanoparticles, a large portion of these proteins adsorb onto particles' surfaces. When these proteins adsorb onto nanoparticles surfaces, the structure changes, and function is lost. It is important to know which proteins bind to the surface and lose structural and functional capabilities, especially when nanoparticles are used in therapeutic applications.
Similar to the bar chart in Figure 3, the pie graphs in Figure 4 visually compare the abundance of each protein for all six nanoparticle-protein corona samples. The difference between the two figures is that the bar chart is used for quantitative analyses whereas the pie graph lends itself to qualitative analyses. The latter method best displays the different constituents of each sample so that trends in protein content with either serum-type or nanoparticle surface coating are noticeable. One trend demonstrated by the pie graphs is the increased abundance of protein MED22_CHICK in the The three pie graphs representing samples incubated in serum-type 2 illustrate a more distributed composition for NH 3 -and PEG-coated nanoparticles. In both the NH 3 -ST2 and PEG-ST2 samples, MED22_RAT, CALL_DROME, and MED22_CHICK proteins are equally abundant. This observation indicates that these proteins may play a significant role in particle absorption in serum-type 2. However, in the COOH-ST2 sample, the CALL_DROME protein is by far the most abundant, with MARCS_RAT present in a small amount. As with the ST1-incubated samples, the COOH coating (negative charged surface) forms a different protein corona (in terms of composition) than that of the NH 3 (positively charged surface) and PEG (neutrally charged surface) coatings.
The two most common proteins found in all coronas were CALL_DROME, (calmodulin-related protein 97A) and MARCS_CHICK (myristoylated alanine-rich C-kinase substrate). CALL_DROME regulates a range of normal cellular functions such as inflammation, metabolism, apoptosis, and immune response. The protein specifically functions as a calcium-binding messenger that modifies interactions with different target proteins like phosphatases or kinases. When calcium binds to calmodulin, a large structural change occurs. Electrophiles are needed to sequester the readily available electrons that influence the binding affinity for the positively-charged surface functionalization on the nanoparticles. MARCS is a common protein found in the cytoskeleton that performs a series of important functions including binding actin, calmodulin, and synapsin. This protein is in a class that strongly impacts cell motility, secretion, and shape. This class of proteins also participates in cell cycle regulation, neural development, and transmembrane transport. They play a part in sequestering acidic membrane phospholipids like PIP2, which is vital in protein signaling.
Both proteins play important roles in the maintenance of normal cellular functions in eukaryotic cells. However, in the presence of nanoparticles, a large portion of these proteins adsorb onto particles' surfaces. When these proteins adsorb onto nanoparticles surfaces, the structure changes, and function is lost. It is important to know which proteins bind to the surface and lose structural and functional capabilities, especially when nanoparticles are used in therapeutic applications.
Similar to the bar chart in Figure 3, the pie graphs in Figure 4 visually compare the abundance of each protein for all six nanoparticle-protein corona samples. The difference between the two figures is that the bar chart is used for quantitative analyses whereas the pie graph lends itself to qualitative analyses. The latter method best displays the different constituents of each sample so that trends in protein content with either serum-type or nanoparticle surface coating are noticeable. One trend demonstrated by the pie graphs is the increased abundance of protein MED22_CHICK in the samples incubated in ST1 compared to those incubated with ST2. A benefit of this method is the ability to easily see parts of the whole, i.e., the abundance of each protein in all samples. This method is limited by the sample size: if there are too many different proteins, it may be difficult to differentiate one protein from another. With a smaller sample size, this method works to compare individual samples.
The Pie-of-Pie graph, shown in Figure 5, enables the visualization of proteins identified by their molecular function and subsequent protein composition. This Figure Supports interpretation as it relates to biological relevance and assist in the design of future toxicological studies by narrowing the scope of pathway analyses. The first pie graph illustrates four primary functions categorized as transcription, actin-binding, toxin activity, and other. Presenting data in this manner does not compromise space by expending excessive detail on smaller groups that may not be relevant to the study (i.e., the use of the 'other' category combines lesser functions into a single wedge in the entire pie). Pie-of-Pie is not limited to just two graphs; the data can be extended into many other pie graphs that detail the composition of other functional groups noted in the molecular function graph, i.e., actin-binding and toxin activity. The second pie graph depicts the individual proteins that comprise the most abundant transcription functional group proteins identified in the NH 3 -ST1 combination. In this sample, the MED22_CAEEL protein is the most abundant among the transcription proteins (73%).  In some datasets, a figure needs to compare multiple parameters that are related in an experiment. In Figure 6, the abundance of protein is compared with the average NPPC complex diameter. Here, the x-axis is the serum-type and the y-axis is the normalized abundance of protein. The color blocks within each column represent the different nanoparticle surface functionalization/charges. When comparing the columns, the gold nanoparticles coated with PEG seem to have absorbed more proteins from serum-type 2 when compared with serum-type 1. Conversely, the gold nanoparticles coated with amine and carboxyl functional grouped absorbed more proteins from serum-type 1 when compared with serum-type 2. In addition to assessing the amount of proteins absorbed onto particle surfaces, this plot indicates the differences in NPPC size relative to serum type (as indicated by white diamonds markers). On average, the particle diameter of NPPC complexes was larger when incubated in serum-type 2 (109 nm) compared to when particles were incubated in serum-type 1 (79 nm). Since the ST1-incubated particles contain more proteins but have a smaller diameter, we concluded that incubation with ST1 creates a more densely packed protein corona shell. TEM images shown in Figure S1 provide visual evidence of this phenomenon.  . The hybridized abundancy figure demonstrates multiple parameter results, including coating composition, serum type, abundance of proteins for each sample, average corona size for that serum, and protein coating abundancy distribution in each serum type. This method is the most comprehensive by displaying multiple variables in one figure. This method is recommended to be used for summaries of bigger picture data to compare multiple parameters. Figure 6. The hybridized abundancy figure demonstrates multiple parameter results, including coating composition, serum type, abundance of proteins for each sample, average corona size for that serum, and protein coating abundancy distribution in each serum type. This method is the most comprehensive by displaying multiple variables in one figure. This method is recommended to be used for summaries of bigger picture data to compare multiple parameters.
In bottom-up proteomics methodology, the use of high-performance liquid chromatography (HPLC) is essential to separate peptides before analyzing with mass spectrometry. The most common separation method is reverse phase chromatography, where the HPLC separation column is packed with C18 (a separation column packed with particles composed of 18 carbon atoms), or another non-polar stationary phase. The mobile phase is a gradient from polar to non-polar solvents. In Figure S2, the peptides analyzed in all samples were plotted against retention time to show the polarity distribution of the peptides. Most of the peptides eluted earlier on in the gradient, i.e.,~36-56 min, indicating that most of the peptides that compose this protein corona are relatively polar. Data can be used to compare the polarity of peptides in each corona when plotted. Sample number or peptide abundance are not limitations with this figure, but a discrepancy can be made with the bins for the histogram. When analyzing multiple samples, the bins would need to be consistent to be comparable. The data from this study show that most peptides that comprise all protein coronas in all six samples are in the polar region.
The last data visualization method used in this study is a heat map ( Table 2). The table ranks the abundance from lowest (red) to highest (green) of all six samples. Given the comprehensive nature of the heat map, this figure enables the inclusion of multiple parameters, including protein abundance, nanoparticle coating, and serum-type. The color gradient creates a visual focus on the most abundant proteins in each sample type, while also comparing each protein against the other coating-serum combinations. Table 2 shows that CALL_DROME, MED22_CAEEL, and MED22_HUMAN can be quickly identified as the most abundant proteins among all the samples. Together, these three proteins may play a large role in the formation of the protein corona. Table 2. Heat map of the average normalized abundance of each serum/coating combination, illustrated through color scale with red as the least abundant, and green as the most. The map conveys the abundancy of protein-type or functional group in a sample for multiple coating or serum. However, this method does not consider the variations in incubation time. ST1 refers to serum-type 1 and ST2 refers to serum-type 2.

Abbreviation NH 3 -ST2 NH 3 -ST1 PEG-ST2 PEG-ST1 COOH-ST2 COOH-ST1
In bottom-up proteomics methodology, the use of high-performance liquid chromatography (HPLC) is essential to separate peptides before analyzing with mass spectrometry. The most common separation method is reverse phase chromatography, where the HPLC separation column is packed with C18 (a separation column packed with particles composed of 18 carbon atoms), or another nonpolar stationary phase. The mobile phase is a gradient from polar to non-polar solvents. In Figure S2, the peptides analyzed in all samples were plotted against retention time to show the polarity distribution of the peptides. Most of the peptides eluted earlier on in the gradient, i.e., ~36-56 min., indicating that most of the peptides that compose this protein corona are relatively polar. Data can be used to compare the polarity of peptides in each corona when plotted. Sample number or peptide abundance are not limitations with this figure, but a discrepancy can be made with the bins for the histogram. When analyzing multiple samples, the bins would need to be consistent to be comparable. The data from this study show that most peptides that comprise all protein coronas in all six samples are in the polar region.
The last data visualization method used in this study is a heat map ( Table 2). The table ranks the abundance from lowest (red) to highest (green) of all six samples. Given the comprehensive nature of the heat map, this figure enables the inclusion of multiple parameters, including protein abundance, nanoparticle coating, and serum-type. The color gradient creates a visual focus on the most abundant proteins in each sample type, while also comparing each protein against the other coating-serum combinations. Table 2 shows that CALL_DROME, MED22_CAEEL, and MED22_HUMAN can be quickly identified as the most abundant proteins among all the samples. Together, these three proteins may play a large role in the formation of the protein corona. Table 2. Heat map of the average normalized abundance of each serum/coating combination, illustrated through color scale with red as the least abundant, and green as the most. The map conveys the abundancy of protein-type or functional group in a sample for multiple coating or serum. However, this method does not consider the variations in incubation time. ST1 refers to serum-type 1 and ST2 refers to serum-type 2.

Discussion
The use of graphical methods for categorical data has permeated the peer-reviewed literature in many Science, Technology, Engineering, and Mathematics (STEM) related fields [51][52][53][54]. Some fields, such as those in the 'omics arena, require advanced computational methods in order to organize complex quantitative data sets [51]. Other analytical fields, such as statistical sciences, have contributed to the development of a data visualization industry; major players can be described as solution providers of either visual analytics or reporting tools [55,56]. Designing effective graphics is generally regarded as art informed by large sets of empirical data [57][58][59].
When relating (or correlating) particle properties to adverse health effects, it is important to define the parametric space in which the experimental data set belongs. In toxicological data, the entire space can be defined as the biological (or biochemical) effects of an exogenous toxicant (or endogenous toxin) on an in vivo or in vitro test system. However, an investigator can also define a specific space, such as the composition of the NPPC construct. In the former, the mass spectrometry data used in this paper reveals only a small number of critical attributes needed to define the possible health effects of gold nanoparticle exposure on human health. In the latter, the mass spectrometry data visualized herein defines the composition of the NPPC construct completely and lends itself to interpretation that promotes the next set of pathway-specific toxicity studies.
Specific to 'omics data, modern nanotoxicology experimental designs incorporate genomics, proteomics, and metabolomics into the already tedious process of nanomaterial physicochemical characterization, dose-response relationships, exposure dosimetry, and mechanistic analyses [60][61][62][63]. For example, genomic data provide detailed information about specific cellular pathways triggered after exposure. Such data include peptide sequence, length, and density. Metabolomics data aid in assembling biotransformation reaction schemes of degraded xenobiotics and include not only metabolites, but also information about the parent molecules [64,65]. Similarly, proteomics data contribute to the characterization of normal cell and tissue physiological processes [66,67]. This paper focused on the different ways in which data collected from proteomic mass spectrometry analyses can be presented, visualized, and communicated. The proteins analyzed are those that form a protein corona shell around a gold nanoparticle core. Our results show that the composition, density, and size of the protein corona varies when the nanoparticle zeta potential changes, as well as when the serum-type changes. Therefore, it is reasonable to expect that the protein corona formed on the surface of nanomaterial drug products (i.e., nano-drugs, nano-additives, or nano-carriers) may differ between drug products and between individual patients.
A suite of visualization techniques was used to communicate the results. Data visualization refers to the techniques used to organize large data sets and communicate results [51,59]. Some of these massive data sets are measured in gigabytes (109 bytes) and terabytes (1012 bytes) [65,68]. Until recently, most toxicological data sets have been held to a manageable sample size (with one variable measured over five or more doses, time points, and replicates) due to limitations in metrology, computational power, and resources [69]. Advanced data analytics and multi-variant visualization have not pervaded the toxicology literature. Today, proteomic data are becoming a routine endpoint in toxicological assessments [60][61][62][63]66,67,70]. Therefore, the inclusion of this type of data has created the opportunity for more comprehensive analysis, and thus the critical need to visualize the variability of multiple parameters and endpoints in a single graphical representation [71].
Adding multiple independent variables (such as dose, time, and sample-type) to toxicological experimental designs presents challenges when reporting and communicating data. This is not unique to input variables; increasing the number of output variables or endpoints also complicates the reporting process. The advent of 'omics techniques, such as multiplexing, microarray analyses, and RNAseq, facilitate the collection of multiple endpoints within a single biological test system. The plethora of data collected at the completion of these types of studies present challenges with data storage, cleaning, normalization, organization, processing, statistical analyses, pattern recognition, modeling, and communication [72,73].

Conclusions
Relating descriptive to functional analyses, the health effects of the protein corona can be determined using mass spectrometry tools only if meaningful interpretation can be visualized. A successful flow of information that bridges data collection, visualization, and results communication is needed to enable a higher degree of user interaction. Further developments in this work flow will include extension to more sophisticated software and the use of novel statistical analysis tools.


Identification and analysis of key genes associated with ulcerative colitis based on DNA microarray data

Abstract This study aimed to identify genes that may play a role in development of ulcerative colitis (UC) and gain insight into its pathogenesis. Gene expression profiling data, including samples collected from 13 early-stage UC (EUC), 8 advanced-stage UC (AUC), and 5 control subjects, were downloaded from the Gene Expression Omnibus database under the accession number of GSE9452. Differentially expressed genes (DEGs) were identified in EUC and AUC compared with controls. DEGs for EUC and AUC, as well as AUC-specific DEGs were subjected to pathway enrichment analysis. Random Walk with Restart (RWR) was used to identify DEGs that are critical in UC based on a protein-to-protein interaction (PPI) network and the inflammatory bowel disease (IBD) pathway downloaded from the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. IL17 and transforming growth factor beta TGF-β) expression levels in colonic tissue from patients with UC and normal colonic mucosa from healthy adults were analyzed by immunohistochemistry (IHC). A total of 3511 and 911 DEGs were identified in AUC and EUC, respectively. The overlapping DEGs and the AUC-specific DEGs were both enriched in pathways related to immunity, such as antigen processing and presentation. AUC-specific DEGs were related to cell migration, such as ECM-receptor interaction. Following DEG prioritization, TLR4 and STAT1 were linked with EUC, AUC, and CD. The upregulated gene TGFB increased the number of Th17 cells, as verified by IHC. Furthermore, PIK3R1, CREBBP, and STAT1 were part of high-degree nodes in the PPI sub-network. The upregulated gene TGFB may regulate IL17 expression in UC. PIK3R1 may participate in immunity and CREBBP may interact with STAT1 in the development and progression of UC.

Introduction
Inflammatory bowel disease (IBD), which comprises ulcerative colitis (UC) and Crohn disease (CD), is a chronic and idiopathic inflammatory disorder of the gastrointestinal tract. [1] CD may affect any part of the gastrointestinal tract, but mainly affects the distal ileum and the colon. In contrast, UC usually starts in the rectum and extends through part of or the entire colon. [2] Because of its early onset, irregular course of disease, unpredictable prognosis, and lack of a definitive cure, IBD imposes a considerable global public health burden. [3] In Asia, the incidence of IBD, especially UC, is increasing in parallel with rapid socioeconomic development and increasingly westernized lifestyle. [4,5] Therefore, it is essential to gain further insight into the pathogenesis of IBD, in order to develop effective drugs and therapies for the treatment of IBD.
Although the precise etiology of IBD is not yet fully understood, evidence has demonstrated that the etiology of this disease is multifactorial. [6] A widely accepted hypothesis is that both CD and UC result from the interaction of genetic and environmental factors including an overactive, poorly controlled mucosal immune response, and defects in mucosal barrier function. [7] Numerous studies have shown that the dynamic balance between microbes, especially commensal flora, and host defense responses at the mucosal barrier have a crucial role in the development and pathogenesis of chronic IBD. [6,8] Strober et al [9] reviewed the cytokine responses mediating intestinal inflammation in IBD and demonstrated that anticytokine agents, such as antitumor necrosis factor (TNF)-a therapy, are likely to be useful in the treatment of IBD. Genetic factors play an important role in IBD pathogenesis. Fujimoto et al [10] revealed that the UC-associated gene RNF186 maintained gut homeostasis through controlling protein homeostasis in the colonic epithelia and may be a candidate marker for the diagnosis of UC. In addition, Majumdar et al [11] showed that altered expression of TNFAIP3 was associated with disease severity in UC. Furthermore, a study recommended the use of p53 and p21 in the large bowel mucosa as valuable biomarkers of inflammatory-related carcinogenesis in UC. [12] However, the molecular mechanism leading to development and progression of IBD remains to be elucidated.
Olsen et al [13] reported a diagnosable preinflammatory state in patients diagnosed with UC and noted the usefulness of random forest modeling of genome-wide gene expression in distinguishing quiescent and active UC colonic mucosa versus CD colonic mucosa and controls. Another study using the same microarray data for gene selection revealed that changes in the expression of a select set of genes could serve as an early marker of UC. [14] In this study, we downloaded the same microarray data and reanalyzed it using different methods. Differentially expressed genes (DEGs) were identified by comparing the expression profiles in early-stage UC (EUC) and advanced-stage UC (AUC) with controls. We also performed pathway enrichment analysis on overlapping DEGs in EUC and AUC as well as AUC-specific DEGs, and the algorithm Random Walk with Restart (RWR) was applied to prioritize critical DEGs in UC based on a protein-to-protein interaction (PPI) network and the IBD pathway downloaded from a public database. Immunohistochemical (IHC) analysis was used to detect expression of IL17 and transforming growth factor beta (TGF-b) in colonic tissues from UC patients and normal colonic mucosa from healthy volunteers. We aimed to identify critical genes involved in UC and to gain insight into the molecular mechanisms responsible for UC onset and progression. Understanding the molecular mechanisms of UC will be an important step in gaining further insight into its pathogenesis and guiding future in-depth functional studies as well as treatment of UC.

Microarray data
The gene expression dataset GSE9452 [13] was obtained from the NCBI Gene Expression Omnibus database (http://www.ncbi.nlm. nih.gov/geo/), which is based on a GPL570 [HG-U133_Plus_2] Affymetrix Human Genome U133 Plus 2.0 Array platform. A total of 26 samples are included in this dataset, [13] including 13 UC samples without macroscopic signs of inflammation defined as the EUC group, 8 UC samples with macroscopic signs of inflammation defined as the AUC group, and 5 non-IBD control subjects.

Data preprocessing and screening of DEGs
Data preprocessing was performed using robust multiarray analysis [15] implemented in R. DEGs in the EUC and AUC groups were identified by comparison with the normal controls using the Linear Models for Microarray data (Limma) package. [16] The threshold for the identification of DEGs was set at jlog 2 fold change j ≥ 0.5 and a P-value <.01.

Pathway enrichment analysis of DEGs
Overlapping DEGs in the EUC and AUC groups as well as AUCspecific DEGs were selected for pathway enrichment analysis.
Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis was implemented for the overlapping DEGs and the AUC-specific DEGs using the online tool, the Database for Annotation, Visualization, and Integrated Discovery. [17] The threshold for significance was set at P < .01. KEGG network analysis was performed with the plugin Enrichment Map (http:// baderlab.org/Software/EnrichmentMap), a Cytoscape [18] plugin for functional enrichment visualization.

PPI network construction
The online database resource Search Tool for the Retrieval of Interacting Genes (STRING) can provide comprehensive coverage and ease of access to both predicted and experimental PPI data. [19] Moreover, interactions identified in STRING include a confidence score, allowing calibration of accuracy and coverage. [19] Genes with documented interactions with the DEGs were extracted from the STRING database. Subsequently, the PPI network was constructed based on DEG-DEG, gene-gene, and gene-DEG interactions. The interaction pairs with a PPI combined score >0.7 were selected for subsequent construction of a new network.

Random walk with restart to prioritize DEGs
A random walk is defined as an iterative walker's transition from a single node to a randomly chosen neighbor starting at a given source node, s. [20] In this study, we applied the RWR algorithm in the dent package, [21] which calculated a priority score for every node based on the steady state probability. The equation for RWR is defined as: where r is the restart probability, W the column-normalized adjacency matrix of the graph, and p t is the vector of size equivalent to the number of nodes in this graph where the ith element holds the probability of being at node i at time step t. In the present study, RWR analysis was performed using a custom R program in the total DEG PPI network with 48 IBD genes set as the seed node. These 48 IBD genes were extracted from the IBD pathway (pathway ID, hsa05321) which was downloaded from the KEGG pathway database. [22] We set the restart probability parameter at 0.7, as recommended by Wu et al. [23] DEGs were ranked according to the values (probability scores) in the steadystate probability vector. The probability scores of DEGs were log 10 transformed and regarded as node attributes and displayed by Cytoscape. [19] Higher scores indicated the nodes were more closely connected with IBD genes. The top 100 genes with the highest scores were used to construct the PPI subnetwork.

Tissue collection and IHC detection
Colonic mucosal biopsies were obtained from 4 patients diagnosed with UC who visited the Department of Gastroenterology at the First Affiliated Hospital of Zhengzhou University, along with normal colonic mucosa from 6 healthy adult volunteers. All experimental procedures were authorized by the Ethics Committee of the First Affiliated Hospital of Zhengzhou University and performed in accordance with the appropriate ethical standards. Written informed consent was obtained from all patients prior to obtaining samples.
Paraffin-embedded specimens were sectioned at 6-mm thickness, and then used for IHC analysis. Samples were dewaxed in xylene and dehydrated in ethanol. Sections were sequentially subjected to antigen retrieval in citrate buffer and heated in a microwave oven for 13 minutes and then cooled at room temperature. The endogenous peroxidase activity was quenched with 3% H 2 O 2 for 10 minutes and the samples were washed 3 times in PBS (3 minutes each wash). The samples were flooded with 10% bovine serum albumin (BSA) for 60 minutes at room temperature to reduce background staining. After washing, sections were incubated with the rabbit-derived monoclonal antibody anti-IL17 or anti-TGF-b (1:100 dilution, Abgent, San Diego, CA) overnight at 4°C. Samples were washed 3 times in PBS (3 minutes each wash) and incubated with antirabbit IgG biotinylated secondary antibody for 60 minutes at 37°C. Samples were washed 3 times with PBS (3 minutes each wash). Antibody staining was visualized in 3,3 0 -diaminobenzidine tetrahydrochloride (DAB) according to the manufacturer's instructions.
Counterstaining was performed with hematoxylin. The sections were dehydrated through a graded alcohol series for 30 seconds, cleared with xylene, and then mounted with neutral balsam. Images at 200Â magnification were obtained, and the integrated optical density (IOD) was determined by Image Pro Plus (IPP) software to quantify protein expression. Mean density (IOD/ area) was calculated.

Statistical analysis
The data were analyzed using SPSS 17.0 software (SPSS Inc., Chicago, IL). The Student t test was used to assess the significance of differences among groups. A P-value of <.05 was defined as statistically significant.

DEG screening
As shown in Fig. 1, 3511 DEGs were identified in AUC compared with controls, including 1512 upregulated genes and 1999 downregulated genes. Comparison of EUC with healthy controls identified 911 DEGs, including 433 upregulated genes and 478 downregulated genes. Most of the DEGs identified in EUC were also differentially expressed in AUC. We found that 93.3% (404/ 433) of genes upregulated in EUC were also upregulated in AUC and 95.8% (458/478) of genes downregulated in EUC were also downregulated in AUC.

KEGG enrichment analysis
The results of KEGG enrichment analysis of the overlapping DEGs and the AUC-specific DEGs using Database for Annotation, Visualization and Integrated Discovery are shown in Fig. 2.
The overlapping DEGs and the AUC-specific DEGs were both enriched in the pathways related to immunity. For example, the overlapping DEGs were related to the intestinal immune network for IgA production. The AUC-specific DEGs were enriched in cytokine-cytokine receptor interactions, natural killer (NK) cell mediated cytotoxicity, and the B cell receptor signaling pathway. Furthermore, the AUC-specific DEGs were enriched in the pathways related to cell migration, such as extracellular matrix (ECM)-receptor interaction, and focal adhesion. The overlapping DEGs and the AUC-specific DEGs were simultaneously enriched in cell adhesion molecules (CAMs) and antigen processing and presentation pathways.

DEG prioritization and IBD pathways
The probability scores of DEGs ranged from ÀInf to À1.79 after log 10 transformation. Higher scores indicate closer connection with IBD genes; the DEGs with the top 15 scores were connected to the IBD pathway, as shown in Fig. 3. [24] All 15 genes were found to be differentially expressed in AUC, and 4 of the 15 were also differentially expressed in EUC, including signal transducer and activator of transcription 4 (STAT4), STAT1, toll-like receptor 4 (TLR4), and RAR-related orphan receptor C (RORC). Most of the 15 genes could be found in CD branches. Furthermore, these genes were linked with branches related to increasing the frequency of Th17 and regulatory T (Treg) cells, such as the upregulated gene TGFB.

PPI subnetwork of DEGs
In order to gain more insight into key genes in UC, we performed a full screen of the top 100 genes with higher probability scores and other DEGs in the PPI subnetwork to better understand their functions. As shown in Fig. 4, the subnetwork had 665 nodes and 1892 edges. Furthermore, the top 10 DEGs with higher node degrees were extracted and are presented in Table 1, for example, phosphoinositide-3-kinase, regulatory subunit 1 (PIK3R1) (degree = 104), CREB binding protein (CREBBP) (degree = 92), STAT1 (degree = 87), CD44 molecule (CD44) (degree = 81), and Janus kinase 1 (JAK1) (degree = 80).

IHC detection analysis
The mean age of the 4 UC patients was 48 years (range, 20-60 years) and the ratio of males to females was 1:1. The results of IHC analysis of IL17 and TGF-b expression levels are shown in Fig. 5A and B. Positive expression of TGF-b protein was detected in IBD samples and its expression was rare in normal controls. High levels of expression of IL17 were identified in IBD samples. TGF-b expression was comparatively higher in disease samples than in healthy samples (Fig. 5B). Overall, the expression levels of IL17 and TGF-b in the IBD specimens were found to be upregulated compared with normal controls (P < .05).

Discussion
Gene expression profiling in disease can uncover the underlying gene changes contributing to the disease and promote the identification of targets for therapeutic intervention. In the current study, we analyzed the DEGs using gene expression profiles for EUC and AUC in comparison with healthy controls. The results showed that 93.3% (404/433) of the genes upregulated in EUC were also upregulated in AUC and 95.8% (458/478) of the genes downregulated in EUC were also downregulated in AUC. The overlapping DEGs in AUC and EUC and the AUC-specific DEGs were both enriched in pathways related to immunity, such as NK cell mediated cytotoxicity, and antigen processing and presentation. In addition, the AUCspecific DEGs were also enriched in pathways related to cell migration, such as ECM-receptor interaction. Following DEG prioritization, we found that the DEGs TLR4, STAT4, STAT1, and RORC were linked with EUC, AUC, and CD. Moreover, the upregulated gene TGFB was involved in increasing the frequency of Th17 cells, as verified by IHC analysis. Furthermore, PIK3R1, CREBBP, and STAT1 were part of higher-degree nodes in the PPI subnetwork. Available evidence suggests that the adaptive immune response plays a major role in the pathogenesis of IBD. [25,26] An abnormal immune response against the microorganisms of the intestinal flora is considered to be responsible for IBD in genetically susceptible individuals. [6] Not only T helper cell type (Th) 1 and Th2 immune responses, but also other subsets of T cells, namely Th17 and Treg cells, are likely to play an important role in IBD. [25,27] Yu et al [28] demonstrated that NK cells, collectively referred to as innate lymphoid cells, provide a crucial link between the innate and adaptive immune responses to infection. Additionally, the work of Bär et al [29] demonstrated that CD and UC appear to modulate the major histocompatibility complex class I-and II-related pathways for the presentation of exogenous antigens in intestinal epithelial cells. In the present study, we found that the overlapping DEGs and the AUC-specific DEGs were both enriched in pathways related to immunity, such as antigen processing and presentation pathways. In addition, AUC-specific DEGs were related to the NK cell-mediated cytotoxicity pathway, in accord with previous studies. However, Tanida et al [30] had reported that mucosal addressin CAM-1 plays an essential role in IBD development by mediating the migration as well as accumulation of lymphocytes into the gut interstitium, causing mucosal injury. A study indicated ECM remodeling as a key event and an active participant in sustaining intestinal inflammation in IBD. [31] Evidence shows that the involvement of adhesion molecules play an important role in the adherence of lymphocytes and macrophages to endothelial cells maintaining the chronic inflammation in UC patients. [32] In our study, both the overlapping DEGs and the AUC-specific DEGs were enriched in CAMs. The AUC-specific DEGs were enriched in pathways related to cell migration, such as ECM-receptor interaction and focal adhesion. In this context, our observations indicate that immunity and focal adhesion and ECM-receptor interaction are key components of UC which may increase in the AUCs, maintaining inflammation and tissue damage. The recent discovery and characterization of Th17 cells and their signature cytokines (IL-17) represents a hallmark in T-cell immunobiology by providing a novel pathway connecting adaptive and innate immunity. [25] High transcript levels of IL-17A have been found in both CD and UC mucosa, compared with healthy mucosa. [33,34] In addition, Hundorfean et al [35] had revealed that IL17 is pathogenic in IBD (especially in CD) and that cytokines can directly contribute to long-term intestinal inflammation through complex mechanisms, such as proinflammatory effects on the IEC barrier. However, the TGFB family of cytokines are multifunctional proteins that control proliferation, differentiation, adhesion, and other functions in many cell types. [36] Treg cells are defined as T cells able to suppress the proliferation of Th0 cells which can differentiate into Th1, Th2, or Th17 cells. Treg cells can exert their function via producing the antiinflammatory cytokines TGF-b and IL-10, preventing both the activation and function of T cells that have escaped other mechanisms. [25] Marafini et al [37] demonstrated that TGF-b1, which can inhibit pathogenic responses in the gut, is highly expressed in both CD and UC. In accordance with these findings, our study clearly showed that the upregulated gene TGFB was linked with branches related to increasing the frequency of Th17 and Treg cells. Furthermore, the expression levels of IL17 and TGF-b were found to be upregulated in UC specimens compared with healthy specimens, by IHC analysis. A recent study showed that the joint expression of IL-17A, IL-17F, IL-21, RORC, and www.md-journal.com TGF-b was significantly predictive of Rachmilewitz endoscopic index in UC patients. [38] IL17 is increased in active UC and serum IL-17A levels of treatment-naive patients with UC is shown to reflect clinical disease severity at the onset of the disease. [39,40] Additionally, Feng et al [41] reported that IL-17 increased accompany with TGF-b in UC patients. On this basis, we postulate that the upregulated gene TGF-b may regulate the function of Th17 cells, affecting the expression of in the progression of IBD. IL-17 and TGF-b levels may be valuable in the clinical management of patients with UC; the underlying mechanisms warrant further investigation using larger sample sizes. PIK3R1 can phosphorylate the inositol ring of phosphatidylinositol and is responsible for regulating a diverse range of cellular functions, such as proliferation and migration. [42] Cahill et al [43] demonstrated that the important isoform PI3-Kg plays a crucial role in intestinal inflammation and dysregulation of the PI3K signaling pathway in innate and adaptive immune cells as well as in the intestinal epithelium, perhaps leading to inflammatory disorders including IBD. Moreover, in the current study, PIK3R1 had the highest node degree in the PPI subnetwork. In this context, we suggest that PIK3R1 might play a critical role in the development and progression of UC via immune response involvement.
In addition, CREBBP has histone acetyltransferase activity and is involved in transcriptional coactivation. STAT1 is a signal transducer and transcriptional activator which mediates cellular responses to interferons and other cytokines. Zhang et al [44] showed that the promoter-bound STAT1 dimer could interact with the histone acetyltransferase CREBBP and that acetylation  of histones is crucial for STAT1-mediated transcriptional activation. Moreover, Neurath showed that several IBD susceptibility loci contain genes that encode proteins involved in cytokine induction, chemokine receptor signaling, and Th cell responses including STAT1. [45] Furthermore, in our study, we found that STAT1 expression was altered in both EUC and AUC, as well as CD, and CREBBP had a higher node degree in the PPI subnetwork. On this basis, we suggest that CREBBP might play an essential role in the onset and progression of IBD by interacting with STAT1. This study has some limitations. Notably, the number of samples included in the microarray data was relatively small. Additionally, data from only 4 UC cases were used for validation of the expression of IL17 and TGF-b, which reduced the power of this study. Further studies using a larger sample size are required. Moreover, the molecular functions of other potentially significant genes were analyzed mostly using bioinformatic methods. Thus, the biological roles of these genes should be investigated and determined by further experimental studies.
In conclusion, we performed gene expression profile analysis to identify critical genes in IBD. Immunity and cell movement appear to be key components of UC and cell migration ability may increase in the AUC, causing lesion metastases. The upregulated gene TGFB may regulate IL17 expression in UC. PIK3R1 may be involved in the onset and progression of UC by participating in immunity. CREBBP may interact with STAT1 in the development and progression of IBD. However, extensive further work is necessary. Advances in understanding the molecular mechanism of IBD will aid in the development of novel targeted therapies with greater efficacy.


extfieldsofstudy: Chemistry Medicine

text:
The discovery of hydrogen bonds in DNA and a re-evaluation of the 1948 Creeth two-chain model for its structure

We recall the experimental approaches involved in the discovery of hydrogen bonds in deoxyribonucleic acid (DNA) made 70 years ago by a team of scientists at University College Nottingham led by J.M. Gulland, and in relation to previous studies. This discovery proved an important step in the elucidation of the correct structure for DNA made by J.D. Watson and F.H.C. Crick, as acknowledged in ‘The Double Helix’. At that time of the discovery, however, it was impossible to delineate between inter- and intra-chain hydrogen bonds. We also consider in the light of more recent hydrodynamic theory a tentative model for DNA proposed by Gulland's and D.O. Jordan's PhD student J.M. Creeth in his PhD thesis of 1948, with the correct prediction of two chains with a sugar-phosphate backbone on the exterior and hydrogen-bonded bases between the nucleotide bases of opposite chains in the interior. Our analysis shows that his incorporation of alternating breaks in the two-chain structure was not necessary to explain the viscosity data on scission of hydrogen bonds after titrating to high or low pH. Although Creeth's model is a depiction of DNA structure alone, he could not know whether the hydrogen bonding was intermolecular, although this was subsequently proved correct by others. The mechanisms by which replicative processes occurred were of course unknown at that time, and so, he could not have realised how closely his tentative model resembled steps in some viral replicative mechanisms involving the molecule of life that he was working on.

Introduction
The elucidation of the double-helix structure for DNA was one of the most exciting and momentous discoveries of the 20th century. The correct model brilliantly constructed by J.D. Watsona postdoctoral Research Fellowand F.H.C. Cricka PhD studentwith two sugar-phosphate chains running antiparallel 'space group C2' to each other and with paired pyrimidine-purine nucleotide bases held together by hydrogen bonds was, as acknowledged by Watson and Crick, dependent on the data of others [1].
In this short article, written to commemorate the 70th anniversary of the discovery of hydrogen bonds in DNA by scientists at the University of Nottingham and part of the meeting held on 10 November 2017 to commemorate this [2], we review the experiments undertaken on highly purified calf-thymus DNA that led to the hydrogen bond discovery communicated in three papers published in the Journal of the Chemical Society, with the definitive study based on hydrodynamic experiments by J.M. Creeth, J.M. Gulland and D.O. Jordan [3,4]. This discovery also built on the contributions of other groups, most notably from Stockholm, Uppsala and Oxford. Reference has been made previously to the discovery by the Nottingham team, but this has generally focused on the contributions of Gulland [5], or Jordan and Gulland [6,7]. The discovery is acknowledged in the latest version of J.D. Watson's classical book The Double Helix [8], including the important role played by Creeth. We take a fresh look at the hydrogen bond discovery by the Nottingham team in the light of contributions from scientists elsewhere. Furthermore, in his PhD thesisnow fully available online [4] -Creeth proposed a two-chain model for DNA that appears to be not too dissimilar from the final and correct structure worked out by Watson and Crick some 5 years later. The existence of this model was made aware to one of us (S.E.H.)a former postdoctoral Research Fellowduring a tea-room conversation with him in the Department of Medicine laboratories at the University of Bristol in 1982. 'Tucked away in the corner of my thesis was a structure which proved not too dissimilar from the final structure worked out at Cambridge'.
In this study, we take a look at the hydrogen bond discovery by the Nottingham team and how it built on the contributions from scientists elsewhere. We also re-examine in the light of modern hydrodynamic theory the proposed structure of Creeth [4] with its correct prediction of two chains with the sugar-phosphate backbone on the exterior and hydrogen bonds between the nucleotide bases of opposite chains in the interior, and consider how close he camebut how far he wasfrom the final structure worked out by Watson and Crick. Using more recently developed hydrodynamic theory, we now show that his incorporation of alternating breaks in his two-chain structure was not necessary to explain the viscosity data on scission of the hydrogen bonds on titrating to high or low pH.

State of knowledge up to 1947
Prior to 1947, there had been growing interest in DNA (and indeed RNA) as it was considered this might be a substance which was associated with genes or inheritance [9]. Debate of the role of DNA in genes and heredity began in 1944 with the identification of DNA as the 'transforming principle' in pneumococci (see ref. [10]). Furthermore, the first proposal that macromolecular DNA was related to macromolecular microsomal (ribosomal) RNA, which in turn produces cytoplasmic enzymes, was made in 1947 by Boivin and Vendrely [11]. However, during the 1940s, there remained a core of researchers who retained the view that it was the protein component of chromosomes, with its 20 or so amino acids (which could potentially offer the required wide variation in combinations), that was still likely to turn out to be the mechanism of heredity [12,13]. This idea and the 'tetranucleotide' theory that underpinned it were not finally overturned in favour of nucleic acid until 1950 when Erwin Chargaff reported that the four bases in DNA could be present in varying amounts depending on the species from which the DNA was derived [14].
The DNA molecule was known to be polymeric with a high molecular mass [15,16]. It was also known to consist of polymeric deoxyribose linked together in unbranched chains by phospho-diester bondsas opposed to glycosidic bonds found in other polysaccharides and glycoconjugatestogether with pyrimidine 'bases' namely thymine (T) and cytosine (C), and the purine bases adenine (A) and guanine (G). Working on highly purified calf-thymus DNA, Signer et al. [16] showed that these molecules had a molecular mass (molar mass) of between 0.5 and 1.0 MDa. Furthermore, measurements on the optical anisotropy of solutions had suggested that the pyrimidine and purine bases were situated in planes perpendicular to the longitudinal axis of the DNA molecule [16]. This was reinforced by X-ray diffraction studies by Astbury and Bell [17] and Bell [18] at Leeds, who determined the spacing between the bases to be ∼0.34 nm.
Step one: non-degraded high-purity DNA Of utmost importance when performing structural evaluations on nucleic acids is the molecular integrity of the material being studied [19]. So, the first paper, by Gulland et al. [20], focused on the production of DNA from calf thymus of the highest degree of integrity and purity. This involved avoidance of the application of high acid or high alkali in the extraction process. A mild extraction procedure was therefore employed in which only neutral solutions and temperatures near 0°C were used, improving on an earlier extraction procedure by Bang [21] and later by Hammersten in Stockholm [22], to give a product that was believed to be protein-free. This was based on (i) negative Biuret and Sakaguchi tests and (ii) total nitrogen determinations matching closely only the calculated nitrogenous base content [23]. It was particularly important to ensure (and verify) removal of protein (as Gulland presumably recognised), as it was still believed by some at that time (including A. Mirsky) to be the basis of heredity; it was suggested that protein could still be present, albeit in low amounts, but remain undetected due to insensitive protein determination methods.
The purity and sample integrity were checked using the still fledging but powerful technique of sedimentation velocity in the analytical ultracentrifuge. An oil-turbine-driven instrument developed in Uppsala, Sweden by T. Svedberg was used at a rotor speed of 60 600 rpm. The Schlieren (refractive index gradient) optical system was employed which revealed a single hyper-sharp boundary ( Figure 1). That study by R. Cecil and A.G. (Sandy) Ogston [24] in the Department of Biochemistry at the University of Oxford was also reported in the same journal as the Nottingham papers. From measurements of the sedimentation coefficient (from the sedimentation rate) and the translational diffusion coefficient (from the spreading of the sedimenting boundary), an estimate for the (weight average) molecular mass of 0.82 MDa was obtained using the sedimentation-diffusion equation of Svedberg and Pedersen after correction for non-ideality [25].
The second paper in the series, by Gulland et al. [26], involved electrometric (potentiometric) titration experiments using hydrogen and glass electrodes applied to solutions of the mildly isolated and highly purified calf-thymus DNA. The acid-base properties of preparations of DNA had been the subject of many previous investigations, but these had given conflicting results: this was largely ascribed by Gulland et al. [26] to the different degrees of degradation of the samples studied. The importance of avoiding potentially degradative and disruptive conditions in the extraction and purification procedure was stressed again: studies on poor-quality material which had been damaged or degraded were of limited use in connection with the nucleic acid structure.
Step two: titrometric studies Using the samples with the requisite high quality prepared by Gulland et al. [20] and starting with solutions at pH 6-7, Gulland et al. [26] followed the effects of progressive addition of either acid or alkali on, respectively, the amino groups (on the pyrimidines T and C and the purine A) or (what they believed) the enolic hydroxyl groups of T and the purine G. They observed unusual behaviour with pK a values for the groups at more extreme pH values than expected: the groups failed to titrate as they would have done in free solutionindicating the possibility that, in the DNA molecule, the titratable groups may have been blocked from doing so by the formation of hydrogen bonds.
Step three: viscometric and streaming birefringence measurements The final step was made by Creeth et al. [3] who used a technique known as capillary viscometry, which provides a measure of the size of the DNA molecule in solutionand how the size can change. Earlier work had shown a reduction in viscosity (resistance to flow) on the addition of acid or alkali [27], but that reduction had been interpreted in terms of depolymerisation and had been on DNA of questionable quality (see footnote to ref. [26], and also see ref. [23]). The new study [3] was performed on the same mildly isolated high-quality and molecularly intact DNA used for the titration studies. The direction of sedimentation is from left to right. Adapted from Cecil and Ogston [24]. The peak to the left is the air/solvent meniscus. Reproduced with permission from the Royal Society of Chemistry.
A Frampton [28,29] capillary viscometer was employed, which provides the advantage over conventional Ostwald capillary viscometers in being able to measure viscosity at different hydrostatic pressures. This is important for solutions of highly elongated macromolecules which could exhibit full or partial alignment under shear and hence 'shear thinning', a form of non-Newtonian behaviour, effects which become more marked as the pressure or shear/flow rate is increased [30].
The parameter that was measured (in a controlled temperature bath) was the relative viscosity, η r , defined by [28] where η r is the viscosity of the solution and η o is the viscosity of the solvent. These are apparent values corresponding to particular applied pressures (or equivalent shear rates). High (apparent) relative viscosities were observed at a series of hydrostatic pressures, which increased as the pressure was dropped, confirming non-Newtonian behaviour. The dramatic way the high relative viscosity (resistance to flow) of DNA solutions dropped is shown in Figure 2 for two hydrostatic pressures at the same extremes of pH, namely below 5.6 and above 11.9 observed by electrometric titration, confirming the existence of hydrogen bonds [3]. These observations were reinforced by streaming birefringence measurements, which involved mechanical stirring of a solution placed in a small cell on the optical stage of a polarising microscope. The high birefringence caused by the alignment of long polymeric molecules at neutral pH was completely lost at the same extremes of pH observed using viscometry, again through disruption of the hydrogen bonds.
Gulland's team concluded that hydrogen bonds exist between neighbouring nucleotides, and that these are disrupted by high-acid or high-alkali conditions to give units of much smaller size [3]: The critical pH values are coincident with those in which a liberation of amino and hydroxyl groups has been observed and it is considered that the two phenomena are related and are due to the fission of the hydrogen bonds postulated as linking the purine-pyrimidine hydroxyl groups and some of the amino-groups.
However, as to which base linked with which was not known. Furthermoreas pointed out by Booth and Hey [6] there was also no decision at this stage that hydrogen-bonded bases on neighbouring chains led to a Starting from neutral pH (6-7), either acid was added down to a pH of below 3.5, or alkali was added to a pH of over 12. Plot I, under an applied hydrostatic pressure of 3000 dyn cm −2 ; Plot II, applied pressure 7000 dyn cm −2 . Also shown are the very different profiles for preparations of material after (III) alkaline pretreatment ( ) and (IV) acid pretreatment ( ), and also for a sample provided by Professor Caspersson ( ). Redrawn from Creeth et al. [3].
double-stranded structure. Initially, in the concluding remarks of the Creeth et al.'s paper, it was left open as to whether the bonding was between adjacent chains or within a single chain [3]. Indeed, it was concluded that the data could be explained by either one (or both) of the following two scenarios: 1. rupture of hydrogen bonds between adjacent chains producing units of lower molecular mass but greater asymmetry; 2. rupture of hydrogen bonds within a single chain, followed by rolling up to a more compact conformation.
In Gulland's view [19], hydrogen bonds led to aggregates or 'micelles' consisting of several chains, a view repeated by Jordan [31]. However, Creeth in his PhD thesis which followed soon after [4] produced his own two-chain model for DNA, which turned out to be not too far away from the true structure. Although the work was undertaken at University College Nottingham, because this was before the College received its Royal Charter, PhD's were officially external degrees of the University of London.
The hydrogen bond discovery in the light of previous work the issue of sample integrity The Nottingham team led by Gulland were by no means the first to isolate and study the physico-chemical properties of DNA preparations in solution. As we have seen, by 1924 the Hammersten-Bang procedure for preparing relatively highly purified DNA had been established, and Hammersten had shown how the viscosity of DNA preparations changed in the presence of added acid. Further progress was made by Hollaender and Emmons in 1941 [32]: these researchers had also observed that preparations in water formed a heavy viscous solution, which appeared birefringent under streaming flow. They also observed ultraviolet radiation-induced depolymerisation of sodium thymonucleate. Despite reservations later expressed by Gulland et al. [26] on the quality of the DNA being used, Tennent and Vilbrandt at Wisconsin [27,33] had also characterised a DNA preparation using viscometry and analytical ultracentrifigation and estimated a chain length for the molecule of ∼5000 Å. In 1943, Villbrandt and Tennent [27] followed up and extended Hammarsten's observations on DNA titrations by using both acid and base and forward and back titrations. However, for comparable concentrations, their relative viscosities η r were an order of magnitude lower than those of Creeth et al. [3] and instead of observing a rapid fall in η r at pH <5.6 or >10.9, they observed a maximum in η r and a gradual reduction in η r as the pH was changed in either direction from neutrality: they interpreted the data in terms of a simple depolymerisation at fission points randomly along the chain. Creeth et al. [3] were able to successfully repeat the Villbrandt and Tennent profile by pretreating the original sodium salt of calf-thymus DNA with the 'harsh' conditions of either strong alkali at pH 12.5 or with strong acid at pH 3.5 and then precipitation of ethyl alcohol at pH 7.0. In this way, they were able to explain the anomalous results seen by these and other researchers before. The data for comparison are shown here in Figure 2. Villbrandt and Tennent were unable to see the effects that Creeth et al. [3] had seen because of the lower quality of their DNA sample (see footnote to ref. [26]).

The Creeth two-chain model for the structure of DNA
Shortly after the Journal of the Chemical Society publications [3,20,26], Creeth's PhD thesis appeared [4] in which, on page 85, he presented a diagram showing his two-chain model for the structure, interpreted from the viscosity, birefringence and titration results. This model is shown in Figure 3a, alongside the correct Watson-Crick two-chain double-helix model [1] for comparison (Figure 3b). A putative ball-and-stick model for the Creeth structure constructed by the authors is shown in Figure 4.
The Creeth structure from page 85 of his thesis, and shown here as Figure 3a, has two chains and is a long linear molecule with the sugar-phosphate backbone clearly and correctly on the outside of the molecule. The constituent chains are united down their common length by hydrogen bonding between facing amino and hydroxyl groups of opposite chain bases correctly on the inside of the molecule.
All these features are consistent with the correct Watson-Crick structure produced 5 years later. Creeth's model, although close, falls short on two main grounds: 1. The existence of alternating breaks with some unpaired bases in the separate chains. 2. The chains are shown as linear, not in a double helical structure.
Creeth also estimated inaccurately the number of hydrogen bonds to be a maximum of two per four phosphorus atoms. Also neither he, nor Gulland or Jordan knew of the equivalence of A with T, and C with G, or of the correct tautomeric keto forms, although Gulland was aware of the work of A.E. Mirsky, who had provided evidence that the total molecular proportions of purines = total molecular proportions of pyrimidines in earlier DNA preparations studied (see ref. [34]).
The breaks in the chain were considered consistent with the reduction in viscosity on scission of the hydrogen bonds [4]: The action of acids and alkalis on this model is to sever the hydrogen bonds uniting the individual polynucleotide chains which are thus liberated. Being relatively small and flexible they do not interact to form the network characteristic of the micellar state, and the solution is not very viscous.
Retrospective view of the viscosity data: there was no need to include the alternating breaks in the chains We now include a retrospective analysis by ourselves of the original Creeth data. Evidence for long linear molecules due to non-Newtonian or shear thinning behaviour of solutions resulting from molecular alignment under shear was clearly seen from both the streaming birefringence and from the effects of hydrostatic pressure on measured relative viscosities: this is evident from Figure 2 (similar profiles are given in Figure 3 in ref. [3])  and the difference between curves I (3000 dyn cm −2 ) and II (7000 dyn cm −2 ): the birefringence largely disappears at high and low pH after the hydrogen bonds have been disrupted by titration. The non-Newtonian effects made it very difficult at the time for Gulland, Creeth or Jordan to comment further on the structure, but with more recent hydrodynamic theory (see, for example, ref. [35]) it is possible to show, in hindsight, that the viscosity behaviour is at least consistent with a halving of the molecular mass on titration.
Firstly, we utilise a dataset of relative viscosity η r versus hydrostatic pressure under neutral pH conditions (for a concentration of 0.243% or 2.43 × 10 −3 g/ml) in Figure 4 of ref. [3]. Extrapolation of this dataset to zero pressure yields a value for the η r which is close to Newtonian: a value of η r ∼ 150 is obtained ( Figure 5 and Table 1). We can obtain an estimate for [η] using the approximate relation of Solomon and Ciuta [36]: which negates the need for a concentration extrapolation. This relationnot available in 1947yields a value for [η] of ∼7000 ml/g. As a check we can use a dataset obtained at a lower concentration (0.12 × 10 −3 g/ml) from Graph 3/2 of Creeth's PhD thesis [4]. From the much smaller variation of η r with pressure, a zero pressure value of η r ∼ 2 is obtained ( Figure 5), reassuringly leading to a similar estimate for [η] of ∼7000 ml/g. Finally, also from Figure 4 of ref. [3], we can estimate a zero pressure extrapolated value of η r of ∼10 at 2.43 mg/ml [yielding a drop in the intrinsic viscosity to ∼1500 ml/g ( Figure 5)]. Secondly, we can use the Mark-Houwink-Kuhn-Sakurada (MHKS) relation (see ref. [35] and references cited therein) linking intrinsic viscosity for a homologous series of polymers to see if a dissociation of the double-stranded model to give two complete rather than two broken chains is consistent with the data. The MHKS relation is where κ η is a constant for a homologous polymer series and a is the MHKS scaling coefficient. For (nondraining) spheres a = 0, for random coils a = 0.5-0.7 and for rods a = 1.8 (see refs [35,37]). For a rod-shaped molecule, the expected reduction in intrinsic viscosity corresponding to a halving of the molar mass as the two chains come apart would from eqn (3) lead to a maximum reduction of 3.5× (corresponding to a = 1.8), bringing the intrinsic viscosity down from 7000 to ∼2000 ml/g. Bearing in mind that the  single-chain molecules will be considerably more flexible than their double-chained counterpartsand hence their viscosities will be lower to a certain degree, this plus a halving in molecular mass through hydrogen bond disruption would not be an implausible explanation of the results. We stress that this is our modern calculation, not a review of Creeth's from his thesis.
Other recent work has shown that single-chain nucleic acids are much more flexiblelower persistence lengths of ∼2 nm [30] than the more rigid double helical structure of ∼40 nm [38].
So, the two-chain hydrogen-bonded model for DNA given in Creeth's PhD thesis could, on the basis of our calculation, have accounted for the viscosity data available at the time and without the need for alternating breaks in the chains. The second scenariothat the action of titrating with acid or alkali could also lead to a fission of intra-chain hydrogen bonds between bases along the same chainis also consistent with the drop in viscosity and loss of birefringence, and Creeth repeats this in his thesis. Although the molecular mass would then remain the same, the removal of the bonds could encourage the chain to take a more flexible, less-extended conformation resulting in a lowering of viscosity and loss of birefringence.
In his thesis [4], Creeth concludes ( page 91) 'the macromolecular structure of sodium deoxyribonucleate isolated by mild methods is that of a very long, very asymmetric rigid particle. Further, it has been shown that this condition is in all probability due to the presence of hydrogen bonds'.
Besides the breaks, also missing from the Creeth two-chain model is, of course, the double helix and the correct pairing of the bases. In 1952, Watson and Crick were given access to the high-resolution X-ray diffraction imagethe famous 'Photo 51' of Rosalind Franklin and R.G. Gosling (see ref. [8]), with the characteristic cross and intensity pattern of a double helix with 'space group C2'two chains running antiparallel with each other. This image was generated from 'Signer' DNA [16], of similar high purity to that from the Nottingham group. Florence Bell and W.T. Astbury did not have access to this high-quality DNA when they published their diffraction images in 1939 [17,18]. It is interesting to speculate that if the Nottingham team had made available their high-purity DNA, could the Leeds team have produced images of the same quality as Rosalind Franklin's. In 1946, Gulland and Astbury both attended the same meetinga meeting of the Society of Experimental Biology in Cambridge, and both contributed to the Proceedings published a year later [23,34,39]this may have been an opportunity lost. Later, the Leeds team were able to produce a high enough quality image: in 1951, E. Beighton, a researcher in Astbury's laboratory, produced a clear diffraction pattern with the characteristic helical cross (see ref. [40]), almost a year before the Kings College London Group produced 'Photo 51'. But, by then, it was too late as the Nottingham team had all but gone: Creeth had finished his PhD and had become a research Fellow at the Courtauld Institute in London before moving to the Department of Physical Chemistry in Wisconsin [41]. His supervisor Doj Jordan moved to take up an appointment at the University of Adelaide, and tragically Masson Gulland was killed in a train crash in October 1947 [6], not long after the publication of the hydrogen bond finding.
The Nottingham team also would not have had access to the findings of Chargaff et al. on the equivalence of the basesnot published until 1950. And finally, the Nottingham team were working on the assumption that the thymine and guanine bases were in the enolic hydroxyl tautomeric form (-COH=N-), a commonly held view at that time, and included in J.N. Davidson's classic text [42] used by Watson. As famously recalled by J.D. Watson in his book [8], he and Crick had been struggling to get the T-A and C-G base pairs to fit into the helical structure. J. Donohue, a Guggenheim Fellow sharing an office with them at the Cavendishand an expert on tautometric formswas able to point out to Watson that Davidson was wrong, and the T and G bases were primarily in the keto form (-CO-NH-) at physiological pH's [8]. This enabled Watson and Crick to complete their model, with the bases hydrogen bonded in their correct tautomeric forms (Figure 6), allowing a regular stacking of bases within the antiparallel double helical frame of the sugar-phosphate backbone and at the correct spacing.

Concluding remarks
To the modern-day molecular biologist, it is not hard to recognise that the diagram drawn by Creeth [4] in his 1948 PhD thesis (Figure 3a) bears similarities to sketches that might be drawn today for the design of PCR ( polymerase chain reaction) experiments using multiple annealing primers. It also resembles current textbook diagrams depicting mechanisms by which some viruses integrate into host chromosomes during their replication cycles using staggered cuts in the duplex and the resulting production of short single-stranded segments (e.g. bacteriophage Musee ref. [43]) or indeed the mechanisms by which some viruses replicate their nucleic acids (e.g. double-stranded DNA synthesis generated from retroviral single-stranded RNA, [44]). Of course, in 1947, the processes of DNA replication (or indeed PCR amplification) were not yet discovered; the correct semi-conservative replication model of Watson and Crick was not proposed until 1953 [45] and not confirmed until 1958 using analytical ultracentrifugation [46]. Nor was it yet known in 1947 what the mechanism of heredity was. So, although Creeth's model is a depiction of DNA structure alone, he could not have realised how it resembles what we now understand about steps in some replicative mechanisms for the molecule of life that he was working on.
In Mike Creeth's own words, looking back after his retirement [47]: 'In hindsight, we had been given not just a glimpse, but a good view of that particular bonding that is nothing less than the key to life on this planet.' He, and his distinguished supervisors Gulland and Jordan (Figure 7), and fellow students Threlfall and Taylor, helped pave the way for the final discovery by Watson and Crick of the structure of the macromolecule that is the key to life, but, characteristically, was too modest to say that.