Structural diversity of biologically interesting datasets: a scaffold analysis approach

Varun Khanna
Shoba Ranganathan
Structural diversity of biologically interesting datasets: a scaffold analysis approach
R E S E A R C H A R T I C L E Open Access
Background: The recent public availability of the human metabolome and natural product datasets has revitalized "metabolite-likeness" and "natural product-likeness" as a drug design concept to design lead libraries targeting specific pathways. Many reports have analyzed the physicochemical property space of biologically important datasets, with only a few comprehensively characterizing the scaffold diversity in public datasets of biological interest. With large collections of high quality public data currently available, we carried out a comparative analysis of current day leads with other biologically relevant datasets. Results: In this study, we note a two-fold enrichment of metabolite scaffolds in drug dataset (42%) as compared to currently used lead libraries (23%). We also note that only a small percentage (5%) of natural product scaffolds space is shared by the lead dataset. We have identified specific scaffolds that are present in metabolites and natural products, with close counterparts in the drugs, but are missing in the lead dataset. To determine the distribution of compounds in physicochemical property space we analyzed the molecular polar surface area, the molecular solubility, the number of rings and the number of rotatable bonds in addition to four well-known Lipinski properties. Here, we note that, with only few exceptions, most of the drugs follow Lipinski's rule. The average values of the molecular polar surface area and the molecular solubility in metabolites is the highest while the number of rings is the lowest. In addition, we note that natural products contain the maximum number of rings and the rotatable bonds than any other dataset under consideration.Conclusions: Currently used lead libraries make little use of the metabolites and natural products scaffold space. We believe that metabolites and natural products are recognized by at least one protein in the biosphere therefore, sampling the fragment and scaffold space of these compounds, along with the knowledge of distribution in physicochemical property space, can result in better lead libraries. Hence, we recommend the greater use of metabolites and natural products while designing lead libraries. Nevertheless, metabolites have a limited distribution in chemical space that limits the usage of metabolites in library design.Figure 1The number of non-redundant fingerprint features as a function of ECFP fingerprint order. Fingerprints of orders 2, 4, 6 and 8 for datasets comprising drugs, metabolites, toxics, natural products, leads, NCI and ChEMBL are presented.

Background

An established idea of similarity-based virtual screening is that similar structures tend to have similar properties [1]. Diversifying the compound library collection for in silico and in vitro high-throughput screening without compromising biological activity remains an active research area. Chemical space is enormous but mostly biologically insignificant [2] and therefore, uninteresting from a drug design perspective. Given the large number of currently available chemical compounds in one of the largest public databases, PubChem [3], it is impossible and irrational to screen all known compounds for potential ligands. One key methodology, fragment-based virtual screening (FBVS) or fragment-based drug discovery (FBDD), is an emerging area to identify novel, small molecules for preclinical studies. In FBDD, the starting points are small low molecular weight, drug-like fragments. Examples of such fragments are ring systems, functional groups, side chains, linkers and fingerprints.

Over the past decade, substructures contributing to drug-like or lead-like properties have governed library design [4]. In one of the pioneering works to understand the distribution of common fragments in drugs, Bemis and Murcko [5] fragmented a drug dataset (taken from the Comprehensive Medicinal Chemistry database) into rings, linkers, frameworks and side chains. Using twodimensional topological graph-based molecular descriptors, they found 2506 different frameworks for a set of 5120 drug compounds, with the top 32 accounting for the topologies of 50% of the database compounds. They concluded a skewed distribution of molecular frameworks in drugs. Metabolite-likeness is increasingly being used as filter to design lead libraries similar to metabolites with better absorption, distribution, metabolism, elimination and toxicology (ADMET) properties [6]. Many recent studies have compared chemical space occupied by compounds of pharmaceutical interest [7][8][9][10][11][12]. Grabowski and Schneider [7] studied the molecular properties and chemotype diversity of drugs, pure natural products (NPs), and natural product derived compounds. Following the approach described by Bemis and Murcko [5], they virtually dissected the molecules into frameworks, corresponding to scaffolds and sidechains. The drug dataset was ranked most structurally diverse, followed by marine and plant derived NPs, respectively. However, in contrast to the observation of Bemis and Murcko, that only 32 frameworks form the basis of nearly 50% of the compounds in CMC drug database, they found that 160 graph-based frameworks are needed to explain the chemotype of 50% of the compounds in the Collection of Bioactive Reference Analogues (COBRA) dataset [13] which contains drug-like reference molecules for ligand-based library design. In the same year, Siegel and Vieth [8] examined a set of 1386 marketed drugs and found that 15% of the drugs are embedded within other larger drugs, differing by one or more chemical fragments while 30% of drugs contain other drugs as building blocks. Recently, Franco et al. [9] analyzed scaffold diversity of 16 datasets of active compounds, targeting five protein classes, using an entropy-based information metric. They found that compounds targeted to the vascular endothelial growth factor receptor kinase, followed by compounds targeted to HIV reverse transcriptase and phosphodiesterase V, are maximally diverse. On the other hand, molecules in the glucocorticoid receptor, neuraminidase and glycogen phosphorylase β datasets are least diverse. Singh et al. [10] employed multiple criteria to compare libraries of drugs, small molecules and NPs, in terms of physicochemical properties, molecular scaffolds and fingerprints. The degree of overlap between libraries was assessed using the R-NN curve technique and the biologically relevant chemical space occupied by various compound datasets delineated. Hert et al. [11] compared a comprehensive dataset of 26 million compounds (i.e. a representative sample of the full chemical space) with 25810 purchasable screening compounds, metabolites, and natural product dataset. They found that almost 1300 ring systems present in NPs are missing in current day screening or lead libraries and suggest introducing bias in screening libraries towards molecules that are likely to bind protein targets. Khanna and Ranganathan [12] compared current day drugs with toxics and metabolites and found that drugs are more similar to toxics than to metabolites in physicochemical property space distribution.

As discussed above, there are many studies analyzing the scaffolds and physicochemical properties of the various chemical datasets. However, none of the studies contains a comprehensive comparison of the compounds obtained from publically available datasets of human metabolites, toxics, drugs, natural products and currently used lead libraries. In addition, we believe that inclusion of the experimental compounds from National Cancer Institute open database and the recently released ChEMBL database would enhance our analysis and prove useful in recognizing fragments in biologically interesting compounds.

In this study, we aim to answer questions such as 1) What is the physicochemical property space distribution of compounds for the datasets under comparison? 2) Are there any pharmaceutically relevant scaffolds or fragments present in metabolites and natural products that are missing in current lead libraries? 3) Are there any preferred or frequently occurring fragments and scaffolds in these datasets? 4) What is the percentage similarity of the scaffolds and fragments found in drugs to those found in other datasets?

We found patterns of commonly occurring fragments using extended connectivity functional class fingerprint (FCFP_4; details in Methods section). FCFP is a variant of extended connectivity atom type (ECFP) fingerprint, differing from the latter in the assignment of initial code [14]. The highly specific initial atoms types in ECFP fingerprints are replaced with more general atom types, with functional meaning in the FCFP fingerprints. For example, a single initial code is assigned for all halogen atoms in the FCFP fingerprints as they can often substitute each other functionally. In accord with their definition, ECFP fingerprints are a better choice to measure diversity. Therefore, we used ECFP fingerprints for diversity analysis while the more generic FCFP fingerprints were selected for Tanimoto analyses.

Results and discussion

Five different types of pharmaceutically relevant public molecular datasets were selected for this study: drugs, human metabolites, toxics, natural products and a sample of currently used lead compounds. Furthermore, we have also considered two popular small molecule databases viz. National Cancer Institute (NCI) database and ChEMBL database (details in the Methods section). Our results are presented in three sections, viz. preliminary analysis (measuring diversity and Tanimoto similarity), calculating physicochemical properties and scaffold analysis.

After carefully pruning and filtering the datasets, all the datasets were clustered (see Methods section) to avoid biased results due to overrepresentation of similar molecules.

Preliminary analysis 1.1 Diversity analysis

In order to compare the diversity of features (fragments) present in each dataset, we have plotted the total number of non-redundant fingerprint features calculated, using ECFP fingerprints, up to order 8 ( Figure 1). Our results indicate that overall, the ChEMBL dataset generates the maximum number of fragments and is highly diverse, while the metabolite dataset is the least diverse. From Figure 1a, we note that initially toxics outnumber other molecular datasets in generating features. This could be due to the high heteroatom content in toxics, resulting in large numbers of ECFP features generated during the first iteration step of fingerprinting. Similarly, the NCI dataset contains a large number of features during the initial iteration step of fingerprint feature generation. Metabolites, on the other hand, produce the least number of features, which suggests a limited occupancy of chemical space. Drugs were moderately diverse throughout and we find an increase in fragment diversity with increasing order of fingerprints.

Tanimoto analysis

The Tanimoto similarity coefficient compares two molecules, A and B, having N A as the number of features in A, N B as the number of features in B, and N AB as the number of features common to both A and B as given in equation 1. This value is usually reported in the binary form, represented as T b , and reported for simple comparisons between molecules. However, the Tanimoto coefficient can also encompass nonbinary data [15]; for example, if a fingerprint encodes not just the fragment incidences but also the frequencies of occurrence, as in the case of comparison between two compound datasets. In this case, the Tanimoto coefficient (T nb ) is given by equation 2 where x i A, x i B are the number of times the ith fragment occurs in A and B, respectively, summed over n elements of each fingerprint.
T b = N AB N A + N B − N AB (1) T nb = i=n i=1 x i A.x i B i=n i=1 (x i A) 2 + i=n i=1 (x i B) 2 − i=n i=1 x i A.x i B(2)
We extend this concept to compare different datasets used in this study. To calculate how similar two datasets are, we first calculated the Scitegic Pipeline Pilot connectivity fingerprints, FCFP_4 (details in the Methods section) for all the datasets. Subsequently, the sum of squares of the frequency of fingerprint features was calculated over the n elements for each dataset. Finally, the common features present in both datasets were counted and their frequencies multiplied, to determine T nb .

For the five different datasets described in the Methods section, as well as the two reference datasets, NCI and ChEMBL, the Tanimoto coefficient values are shown in Table 1. We note that the FCFP fingerprint patterns (of order 4; FCFP_4) found in drugs are most similar to toxics (FCFP_4: 0.91) than to any other dataset, except for the fingerprint patterns found in reference datasets. On the other hand, drugs are least similar to metabolites (FCFP_4: 0.72). These observations are consistent with our earlier study on smaller datasets [12]. We also note that ChEMBL contains more druglike fragments than any other biologically relevant fragment type present in this study (FCFP_4: 0.94). Further, we note that the fragments found in metabolites are least similar to the fragments present in NPs and lead dataset. Additionally, with the increasing order of fingerprints (FCFP_6 and so on), although the number of fragments generated increases, the Tanimoto similarity coefficient values fall slightly for all the datasets compared (data not shown). This suggests an inverse relationship between the size of the fragment and the probability of its occurrence in two separate datasets, i.e. the larger the fragment, the less likely that it will be found in the two datasets being compared.

Physicochemical property analysis 2.1 Lipinski's properties for "rule of five" (Ro5) compliance

Ro5 has dominated drug design since 1997 and therefore, we believe it would be useful to analyze these datasets for compliance with the Ro5 test. Ro5 predicts passive and oral absorption based on log P, molecular weight, hydrogen bond donors and hydrogen bond acceptors. We report in Table 2, the percentage of molecules "failing" the Ro5 test, i.e. at least not meeting one condition of the Ro5 test. The results are comparable for both kinds of datasets, showing that randomly selected subsets are representative of the clustered datasets. Also, for the clustered datasets, initially, over 25% of drugs do not adhere to Ro5 while 68% of the metabolites are outside Lipinski's universe. However, by removing lipids from metabolites we note that the percentage of molecules failing Ro5 test drops to 20% indicating that majority of the lipids do not follow Lipinski's rule. Further, we found that similar to drugs, only 26.5% of the toxics fail the Ro5 test. Lipinski's rule was originally designed to estimate bioavailability of compounds rather than toxicity. Therefore, the above result suggests that empirical rules such as Ro5 can be supplemented with toxicity information in order to reduce high attrition rates during drug discovery programs as has been reported in the literature [16,17]. Further, we found that only 16% of NPs failed Lipinski's test. Many other related studies on NPs have reported similar results [7,18]. Grabowski and Schneider [7] analyzed pure natural products (isolated exclusively from plants and terrestrial microorganisms) from MEGAbolite and Interbioscreen, natural products derivatives (isolated and synthesized natural products and derivatives from natural sources like plants, fungi, bacteria and sea organisms) from BioSpecs and marine natural products (isolated from sponges (41%), Coelenterates (21%), marine microorganisms and phytoplankton (10%)) from the literature. They found that 18% of the pure natural products, 30% of the marine natural products and only 8% of the natural product derived compounds violate Lipinski's rule, averaging 18.7%. While Grabowski and Schneider have reported results very similar to ours, Ganesan [18] analyzed a focused set of 24 natural products that were the starting point for marketed drugs in the 25-year period from 1981-2006 and found that 50% of these failed Lipinski's rule. In general, NPs do not necessarily abide by Lipinski's rule because they are thought to enter the human body not by passive diffusion but by more complex mechanisms such as active transportation, and so are not expected to comply with the rules for bioavailability. The probable explanation of our results could be the manner in which the NP dataset is pooled at the ZINC database. ZINC is a public database for commercially available compounds and NPs present in ZINC are pre-filtered to cover more drug-like space, contributing towards Ro5-like characteristics. Lead molecules on the other hand also did well in the Ro5 test as only 19.5% of the molecules violated one or more than one condition of the Lipinski's rule. This is in accordance with the lead-likeness concept proposed earlier [19] which states that leads should be simple, low molecular weight molecules and thus, should fall well within Lipinski's universe. Further, our results indicate that, NCI compounds follow Lipinski's rule more strictly than compounds present in ChEMBL dataset.

Lipinski's properties as boxplots

Box plots for Lipinski properties for random subsets are available from Figure 2. We find that the mean value for the molecular weight in the metabolite dataset is relatively low when compared to the other datasets such as drugs, leads and natural products. We also observe that the lead dataset is well within Lipinski's universe and covers a fair amount of drug space. Further, we find a noticeable difference in lipophilicity values of metabolites as compared to drugs and leads. The mean value of lipophilicity (measured as AlogP) suggests that metabolites prefer a hydrophilic environment. Our results are comparable to the recent study using similar datasets [6]. In this study, lipophilicity (measured by a similar parameter, clogD) for drugs, metabolites and library compounds showed that the distribution of library compounds is similar to that of drugs, but differ markedly from metabolites and that metabolites are more hydrophilic than both drugs and library compounds.

Other physicochemical properties

For a comprehensive study on the physicochemical property space distribution, we computed four more common whole molecule descriptors: the molecular polar surface area, the number of rotatable bonds, the molecular solubility and the number of rings (details in the Methods section). Distributions of these physicochemical properties as box plots are available from Figure 3. We note that metabolites show relatively higher solubility, higher molecular polar surface area but lower complexity (less rings, less rotatable bonds and lower molecular weight) compared to drugs. Further, our results indicate that, in general, NCI molecules are also low molecular weight compounds with less complexity and slightly higher solubility than drug molecules. In addition, we note that a large part of the ChEMBL database contain drug-like compounds with a biasness towards higher molecular weight and more complex molecules than drugs.

Scaffold or cyclic system analysis

It is quite informative to study the molecular frameworks while comparing different datasets of chemical compounds. Since the publication of Bemis and Murcko [5], many attempts have been made to explore the chemical space occupied by bioactive scaffolds [20] as scaffold hopping remains an active area under research [21]. In this study, we define scaffolds as the core structure of the molecule after removing side chains but not the linkers, similar to the definition of atomic frameworks used by Bemis and Murcko. A detailed analysis of the total number of non-redundant scaffolds present in the different datasets is available in Table 3. The percentage of singletons (scaffolds occurring only once) relative to the total number of scaffolds in a dataset has also been reported. In addition, we have tabulated the proportion of non-redundant scaffolds containing aromatic and non-aromatic rings. The drug dataset generates the largest proportion of non-redundant scaffolds (50.0%) relative to the dataset size, followed by the toxics (42%), ChEMBL (33.4%), leads (32%) and NCI dataset (28%). Exceptionally low number of scaffolds in metabolites (14.3%) and natural products (21.2%) suggest lower scaffold diversity in these datasets. The higher scaffold diversity in drugs could be attributed to the fact that drugs are derived from various biologically relevant compounds. The drug scaffold diversity is probably also due to the patenting requirements, to position functionality in the same way as an existing drug but outside of its patent space, that is often achieved by a minor change in the scaffold. Similarly, a large number of scaffolds in the toxic compound set is indicative of the high diversity of compounds with toxicity potential. Further, we note that distribution of scaffolds in all the datasets in highly skewed with large number of them occurring only once (singletons). In fact, almost 70% of the scaffolds in drugs, toxics, NCI and ChEMBL dataset occur only once. We also found that natural products comprise maximum number of recurring scaffolds (100 -% of singletons = 64%) followed by metabolites (38.9%) and leads (35.7%) suggesting that the compounds in these datasets revolve around certain preferred types of scaffolds. Our results agree with the recent study using similar natural product and drug dataset [10]. In their study, authors found high scaffold diversity in drugs (39.7%) while low diversity in natural products (17.9%) which is in accordance with our results. By counting the number of aromatic rings in non-redundant scaffolds, we note that metabolites contain least number of aromatic rings (only 47.3% contain one or more aromatic rings in a scaffold) as compared to other datasets. 85% of the drugs on the other hand have scaffolds with aromatic rings. Furthermore, we note that 97.4% of the scaffolds found in lead dataset contain aromatic rings. There seems to be a bias towards aromatic ring containing scaffolds in presently used lead libraries. The top five scaffolds and their relative percentages based on the total number of scaffolds found in each dataset are shown in Figure 4. Benzene is the most abundant scaffold system in all the datasets, particularly in metabolites (over 36%). Apart from metabolites, toxics (15%) and NCI compounds (13%) also contain benzene in high percentages. Drugs and leads, on the other hand contain benzene in moderate amounts (10% and 7% respectively). While benzene is the most common scaffold type in NP (2.2%) and ChEMBL datasets (3.4%), the relative abundance of benzene in these datasets is far lower than that in the other datasets. Following benzene, pyridine is the second most commonly occurring scaffold type in the top five scaffolds. It is found in four out of seven datasets: metabolites (5.2%), drugs (1%), leads (1%), and NCI (1.2%). We also note that steroid derivatives are largely present in drugs and NPs. Similarly, most of the fused large scaffolds are found in NPs (four of the top five scaffolds) followed by drugs and the ChEMBL dataset. Metabolites, on the other hand, seem to prefer smaller, less complex systems. Likewise, toxics and lead compounds also have few complex fused systems. Other commonly occurring scaffold systems are purine and purine derivatives (found mainly in metabolites and ChEMBL dataset), imidazole and biphenyls.  Frequency of occurrence for non-redundant scaffolds (relative to the dataset size) and number of aromatic ring containing scaffolds (relative to the total number of non-redundant scaffolds) have been reported.

In Table 4, we tabulate the percentages of non-redundant shared scaffolds between pairs of different datasets. From Table 4 we note that drugs and metabolites share 6% of the total non-redundant scaffolds whereas NPs, leads and toxics share overall 2.4%, 1.4% and 7.5% of scaffolds with drugs, respectively. It is interesting to note that metabolites and leads do not share as many scaffolds (0.3%) as drugs and metabolites (6%). Due to the uneven size of the datasets, we have also reported the contribution of each dataset to the set of shared scaffolds. We find that of the total 296 non-redundant scaffolds found in metabolites (Table 3), 123 (42%) are shared by drugs whereas only 68 (23%) are shared by the lead dataset. This suggests that lead compounds need further optimization to become more metabolitelike. Similarly, there seems to be little overlap between the scaffolds of presently used lead libraries and NPs (2.1%). Since metabolites and NPs are recognized by at least one protein in the biosphere, they seem to be appropriate candidates in lead library design. Our results however, indicate that neither metabolites nor NP scaffolds are being sampled enough while designing lead libraries. In addition, we note that over 7% of scaffolds are shared between drugs and toxics while metabolites and toxics share over 6% of the scaffolds, suggesting the recurrence of common scaffolds between these datasets. Compounds in the NCI and ChEMBL datasets are quite diversified; however, the NCI dataset clearly contains   more toxic scaffolds than the ChEMBL dataset. Furthermore, we note that large part of the drug scaffold space is present in NCI (45%) and ChEMBL (72%) implying that these datasets cover good amount of drug-like compounds. We also note that a large part of metabolite scaffold space is present in natural product (47%), NCI (78%) and ChEMBL (73%) datasets. We expect that lead libraries biased towards molecules that biological targets have evolved to recognize, would yield better hits rates, than unbiased or universal libraries. Metabolites and NPs could potentially provide suitable lead molecules. Consequently, we further analyzed these datasets for the type of scaffolds that are currently missing in lead libraries. In fact, we note a very slight overlap in the scaffold space of lead libraries and these datasets as discussed above. We therefore, suggest that with the optimum coverage of biologically relevant scaffold space, hit rates in high throughput screening experiments can be improved. We report a set of scaffolds that occur in NPs ( Figure 5) and metabolites ( Figure 6), with a minimum Tanimoto similarity of 0.9 to the scaffolds found in drugs, which are actually missing in currently used lead datasets.

Conclusions

In this study, we have carried out a detailed analysis of commonly occurring fragments in various datasets of biological interest. Dataset comparison using the Tanimoto coefficient shows that drugs and toxics share a large number of topological fragments whereas drugs are least similar to metabolites than to any other dataset studied. However, in scaffold analysis we found that drugs and metabolites share 6% of the total nonredundant scaffolds, i.e. over 42% of the metabolite scaffolds are present in drugs, whereas only 23% of the metabolite scaffolds are represented in current leads. This shows that although drugs and metabolites share many scaffolds, they largely differ in topological fragment space. Further, we conclude that current lead libraries do not cover much of metabolite scaffold space.

Library design is a multi-class optimization problem. It often presents a trade-off between several factors, including diversity and ADMET properties. Since metabolites and NPs are already optimized by millions of years of evolution to bind to at least one biological macromolecule therefore, it is highly likely that libraries designed based on the scaffolds and fragments occurring in metabolite and NP space will result in molecules with better ADMET properties. Hence, the use of metabolites and NPs while designing lead libraries would be beneficial. However, metabolites occupy a limited space in chemical universe that limits their usage in library design.

From physicochemical properties analysis, we note that there is a need to diversify present day lead libraries in order to optimize the coverage of chemical space. We also note that with the exception of few compounds, most of the drug molecules follow Lipinski's rule whereas over 68% of metabolites are outside Lipinski's universe. On a closer examination of metabolites, we found that the compounds that do not follow Lipinski rule are mainly lipids and large molecules. Further, we note that lipid-free metabolite dataset contains low molecular weight and less complex molecules as compared to other datasets. Our studies on scaffolds systems suggest that drugs are most diverse (50% scaffolds relative to the dataset size) and prefer aromatic to non-aromatic ring-containing scaffolds. Metabolites, on the other hand, have a very narrow distribution of scaffolds (only 14.3% scaffolds relative to the dataset size) of which 38.9% recur. The exceptionally low number of cyclic systems in metabolites implies lower scaffold diversity in metabolites. Further, we confirm earlier reports of skewed distribution of scaffolds, with many more singletons than recurring scaffolds.

Methods

Preparation of datasets

Five different types of biologically relevant molecular datasets have been considered in this study. Beside these, the contents of public databases like NCI and ChEMBL were also analyzed. Table 5 presents a summary of all the databases used in this study. The drug dataset was assembled by merging molecules obtained from the DrugBank [22] and a subset of Kyoto Encyclopedia of Genes and Genomes database (KEGG DRUG) [23]. Drug-Bank is a comprehensive resource on drugs and includes over 1350 FDA-approved small drugs. KEGG is a bioinformatics resource and currently provides 19 databases; we used the KEGG DRUG subset as it contains all the drugs approved in the USA and Japan. It not only contains prescription drugs but also "over-the-counter" (OTC) drugs. Similarly, for metabolite dataset we used the Human Metabolome Database (HMDB) [24], HumanCYC [25] database and BiGG [26] database. HMDB contains information on nearly 8,000 metabolites found in the human body. HumanCYC is a bioinformatics database that combines human metabolic pathway and genome information, providing KEGG, PubChem and ChEBI identifiers for the metabolites present in this database. BiGG stores manually annotated human metabolic network information, with links to KEGG metabolites.

Likewise, for the toxics dataset, compounds from various public sources were integrated to make a single dataset focusing largely on carcinogenic molecules. The Distributed Structure-Searchable Toxicity (DSSTox) Carcinogenic Potency Database [27] contains experimental results and carcinogenicity information for 1547 substances tested against different species. Contrera et al. [28] published a dataset of 282 human pharmaceuticals obtained from FDA database for carcinogenicity studies on mouse and rat. They reported 125 (44% of the above 282) of the positive chemicals that were used in this study. Toxicology Excellence for Risk Assessment (TERA) is an independent non-profit organization dedicated to the public health. Since 1996, TERA has maintained an International Toxicity Estimate for Risk database [29] which provides chronic human risk assessment data from organization around the world for over 650 chemicals [30]. Finally,~1000 molecules with medium and high toxicity were downloaded from the Super-Toxic database [31]. The dataset for NPs was obtained from the ZINC database [32]. These molecules can be searched under the subset tab, as "Meta subsets". For Figure 5 A set of scaffolds present in NPs but are missing in lead dataset. The Tanimoto distance of these scaffolds with the closest counterparts in drugs is also reported.

Khanna and Ranganathan Journal of Cheminformatics 2011, 3:30 http://www.jcheminf.com/content/3/1/30 Figure 6 A set of scaffolds present in metabolites but are missing in lead dataset. The Tanimoto distance of these scaffolds with the closest counterparts in drugs is also reported. Cleaning and processing of the datasets

We followed a standard cleaning procedure (see additional file 1) to obtain a non-redundant dataset in each category. Finally, clustering was performed to address the issue of possible overrepresentation of the chemical space, which might bias the analysis results towards similar molecules [6]. Clusters were generated, using the Cluster "Clara" algorithm embedded in the Pipeline Pilot (PP) software [37] by employing an atom type fingerprint as a chemical descriptor and Euclidean distance was the distance metric selected. Cluster centers served as the representatives for clusters containing more than one molecule while singletons were directly used as cluster centers. This resulted in 30% decreases of each dataset. Upon further analysis, we found that clustered metabolite set contains lipids in large numbers. In order to remove the bias towards lipids and large molecules, we filtered out lipids resulting in 2072 molecules in the "lipid-free" metabolite dataset, used for analysis in this study.

To simplify the analysis, we randomly selected 2000 compounds from each of the clustered datasets and lipid-free metabolite dataset in case of metabolites. The majority of the analysis was carried out using the clustered datasets and lipid-free metabolite dataset, except for preliminary analysis, where these randomly selected molecules were used and in the case of Ro5 test, where both datasets were compared.

Molecular descriptors

All the descriptors were calculated using PP. Beside the four Lipinski properties: molecular weight, the number of hydrogen bond acceptors, AlogP (a hydrophobicity measure) and the number of hydrogen bond donors [4], other descriptors such as molecular polar surface area (MPSA), molecular solubility (MS), the number of rings (NR) and the number of rotatable bonds (NRB) were also computed. AlogP was calculated using the Ghose-Crippen method [38] which takes into account the group's contribution to Log P. MPSA is defined as the sum over all the polar atoms. This descriptor is correlated with drug transport capabilities and is important in penetrating the blood-brain barrier. The NRB is a direct measure of the flexibility of molecules thus related to MPSA. Binary descriptors (ECFP_4 and FCFP_4) were calculated using a structural property calculator embedded in PP. Initially, each atom is assigned a code based on its properties and connectivity. With increasing iteration, each atom code is combined with the code of its immediate neighbours to produce the next order code. This process is repeated until the desired number of iterations has been achieved, typically to four iterations, generating ECFP_4, or FCFP_4 fingerprints.

Cyclic systems

In addition to examining the physicochemical properties, each dataset was also explored for the frequent scaffold systems. We used an inbuilt PP protocol to identify the most common fragments, by setting "Frag-mentType" to MurckoAssemblies and adjusting "Max-FragSize" parameter at the required level.

Additional material

Additional file 1: Supplementary figure S1. Flowchart adapted for the overall methodology.

Figure 2
2Box plots for the Lipinski physicochemical properties: (a) molecular weight, (b) the number of hydrogen bond acceptors, (c) AlogP and (d) the number of hydrogen bond donors.

Figure 3
3Box plots for other significant physicochemical properties: (a) molecular polar surface area, (b) the number of rotatable bonds, (c) molecular solubility and (d) the number of rings.

Figure 4
4Top 5 scaffolds derived from A. drugs, B. metabolites, C. toxins, D. natural products, E. leads, F. NCI and G. ChEMBL. The extent of occurrence of the scaffold relative to the total number of scaffolds in the dataset (as %) are listed.

percentage of shared scaffolds is given in the brackets, along with percentages of shared scaffolds from each contributing dataset. D: Drugs, M: Metabolites, T: Toxics, P: Natural Products, L: Leads, N: NCI, C: ChEMBL.

Table 1
1Tanimoto similarity values using circular connectivity fingerprint descriptors for different datasets under study.The upper half of the diagonal contains non-binary Tanimoto similarity values calculated using the FCFP_4 fingerprint.Datasets
Drugs Metabolites Toxics NPs Leads NCI ChEMBL

Drugs
1
0.72
0.91 0.85 0.78 0.92
0.94

Metabolites
1
0.73 0.58 0.49 0.67
0.63

Toxics
1
0.80 0.75 0.94
0.84
NPs
1
0.76 0.84
0.88

Leads
1
0.85
0.88

NCI
1
0.90

ChEMBL
1

Table 2
2Comparision of the number of molecules failing Lipinski's "rule of five" (Ro5) in clustered and randomly selected datasets.Dataset
Total no. of
molecules
(in clustered dataset)

% of molecules failing Ro5 in clustered
datasets

% of molecules failing Ro5 in randomly selected
subset

Drugs
3788
25.7
23.0

Metabolites
6124
68.0
20.0*

Toxics
2166
26.5
21.5

NPs
61972
16.2
15.0

Leads
67983
19.8
19.5
NCI
161336
19.5
15.5

ChEMBL
379827
36.4
36.0

*Metabolite dataset excluding lipids and large molecules (details in the Methods section)

Table 3
3Scaffold analysis of various clustered datasets under study.Dataset
Occurrence of scaffolds (% relative to
dataset size)

No. of singletons (% relative to number
of scaffolds)

Aromatic scaffolds (% relative to number
of scaffolds)

No.
%
No.
%
No.
%

Drugs
1874
50.0
1411
75.3
1588
85.0

Metabolites
296
14.3
181
61.1
140
47.3

Toxics
905
42.0
689
76.1
656
72.3
NPs
13151
21.2
6053
46.0
11776
90.0

Leads
21621
32.0
13819
64.0
21057
97.4

NCI
44324
28.0
31880
72.0
36778
83.0

ChEMBL
126843
33.4
87750
69.2
119419
94.1

Table 4
4Scaffolds shared between pairs of clustered datasets.

Table 5
5Databases used in this study lead dataset, we merged two independent screening sets obtained from BioNET[33] and Maybridge database[34]. The molecules in these two databases are well diversified and we integrated them to form a dataset of lead compounds as found in pharmaceutical collections. Further, we included molecules from NCI open database[35]. The latest September 2003 release of the database stores 260071 organic compounds tested by NCI for anticancer activity. Since many of the compounds are experimental, have not been tested for human consumption and covers high diversity therefore, we believe it would be good choice to include this dataset in our study. One other public dataset, ChEMBL[36]  was used as the reference dataset for biologically interesting molecules. ChEMBL is a chemogenomics data resource with over 8000 targets and about 622,884 bioactive compounds.All datasets are current as of 10-November-2010.Datasets
Number of molecules
Clustered dataset
Reference

Drugs
DrugBank
1372
3788
[22]

KEGG drugs
7057
[23]

Metabolites
HMDB
7888
6124, 2072*
[24]

HumanCYC
984
[25]
BiGG
730
[26]

Toxics
DSSTox
582
2166
[27]

FDA Carcinogenicity
125
[28]

ITER
514
[30]

SuperToxic
1097
[31]

NPs
ZINC NP database
89425
61972
[32]

AcknowledgementsVK is grateful to Macquarie University for the award of MQRES research scholarship.Authors' contributions VK curated the datasets and conducted the analysis work; SR directed the study and both authors prepared and approved the manuscript.Competing interestsThe authors declare that they have no competing interests.
*Metabolite dataset excluding lipids and large molecules (details in the Methods section). *Metabolite dataset excluding lipids and large molecules (details in the Methods section).

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Increased Preventive Effect on Colon Carcinogenesis by Use of Resistant Starch (RS3) as the Carrier for Polysaccharide of Larimichthys Crocea Swimming Bladder
2014

Lian-Hong Chen
College of Life Science and Technology
Southwest University for Nationalities
610041ChengduChina

Jia-Le Song
Department of Food Science and Nutrition
Pusan National University
609-735BusanKorea

Yu Qian
Department of Biological and Chemical Engineering
Chongqing University of Education
400067ChongqingChina

Xin Zhao
Department of Biological and Chemical Engineering
Chongqing University of Education
400067ChongqingChina

Hua-Yi Suo
College of Food Science
Southwest University
400715ChongqingChina

Jian Li email@example.com.
College of Life Science and Technology
Southwest University for Nationalities
610041ChengduChina

Increased Preventive Effect on Colon Carcinogenesis by Use of Resistant Starch (RS3) as the Carrier for Polysaccharide of Larimichthys Crocea Swimming Bladder

Int. J. Mol. Sci
15201410.3390/ijms15010817Received: 1 December 2013; in revised form: 20 December 2013 / Accepted: 30 December 2013 /International Journal of Molecular Sciences † These authors contributed equally to this work. * Author to whom correspondence should be addressed; E-Mail: firstname.lastname@example.com;polysaccharideLarimichthys croceaC57BL/6 micecolon carcinogenesisresistant starch
The preventive effect of polysaccharide of Larimichthys crocea swimming bladder (PLCSB) and the increase of this effect by use of resistant starch (RS3) as the carrier for PLCSB on azoxymethane (AOM) and dextran sulfate sodium (DSS)-inducing colon carcinogenesis in C57BL/6 mice has been studied. RS3 microspheres carrying PLCSB (RS3 + PLCSB) were produced and evaluated as a potentially improved colon carcinogenesis therapy for this study. The body weight, colon length, and colon weight of mice were determined, and colonic tissues were histologically observed. The serum levels of proinflammatory cytokines and the inflammation and apoptosis-related genes in colonic tissue were also tested. The PLCSB or RS3 + PLCSB significantly suppressed AOM and DSS-induced body weight loss, colon length shortening and decreased the colon weight to length ratio. PLCSB or RS3 + PLCSB reduced the levels of the serum pro-inflammatory cytokines IL-6, IL-12, TNF-α, and IFN-γ to a greater extent compared with the control mice, and the levels of RS3 + PLCSB were more close to the normal mice than PLCSB treated OPEN ACCESS Int. J. Mol. Sci. 2014, 15   818    mice. Histopathological examination of sections of colon tissues showed that the RS3 + PLCSB group recovered well from colon carcinogenesis; however, the tissue sections of the stachyose + starch could reduce the necrosis degree. PLCSB significantly induced apoptosis in tissues of mice (p < 0.05) by up-regulating Bax, caspase-3, and caspase-9, and down-regulating Bcl-2. The expression of genes associated with inflammation-related NF-κB, iNOS, and COX-2 genes, was significantly down-regulated, and IκB-α was up-regulated (p < 0.05). These results suggest that PLCSB is a potent preventive against in vivo colon carcinogenesis and that PLCSB with an RS3 carrier could increase the preventative effect in mice.

Introduction

As one of the main commercial fishes in the coastal waters of China, Larimichthys crocea swimming bladder contains rich protein, microelements and vitamins. Traditional medicine considers that it has good curative effect against diseases including amnesia, insomnia, dizziness, anepithymia and postpartum weakness [1]. Modern scientific research also suggests that Larimichthys crocea swimming bladder serves to remove free radicals and ward against cancers [2]. Swimming bladders are important balancing organs and the amount of polysaccharide in it constitutes as much as 10% of its weight. Polysaccharide is an important type of functional material. It has been proven that polysaccharide in swimming bladders can accelerate the healing of cuts and prevent infection as well as thrombus [3]. In vitro and in vivo experiments have proved that polysaccharide in lentinus edodes, lucid ganoderma and spirulina seaweed serves to prevent and cure colon cancer [4,5].

Resistant starch (RS) is the sum of starch and starch degradation products that are not absorbed in the small intestine since it is resistant to enzymatic digestion [6]. RS3 is a retrograded starch, formed on cooling in processed foods, including cooled cooked potato, bread and cornflakes [7]. RS appears to have a number of physiological effects, including weight control, prevention of diabetes, lipid reduction and promotion of inorganic salt absorption [8]. Functional polysaccharides would take effect in the infected part of colon cancer after being absorbed by the human body and some polysaccharides would be lost in the digestion process. Resistant starch would not be digested and absorbed in the stomach; it would be decomposed after entering the intestinal tract and utilized to wrap up functional material to decrease the loss of functional components in the digestion process, thus allowing more functional material to take effect after entering the intestinal tract. Therefore, the healthcare and cure effect of functional materials would be improved [9].

In the present study, the anticancer effect of polysaccharide of Larimichthys crocea swimming bladder (PLCSB) was tested. RS3 was used as a carrier for PLCSB and its preventative effect on colitis was further examined. The levels of inflammatory cytokines were used to determine the preventative effects on azoxymethane (AOM) and dextran sulfate sodium (DSS)-induced colon carcinogenesis in mice.

Colon tissue histology was also used to determine the preventative effects in vivo. The mRNA and protein expression levels were determined for clarifying the preventative effects of PLCSB and RS3 + PLCSB.

Results and Discussion

Body Weight and Colon Observations

Body weight is an important marker for colon cancer in mice; the body weight of AOM and DSS-induced colon cancer mice were lower than normal mice [10]. The normal mice had a normal diet and their body weight increased during the nine weeks of experiments. The body weights of the control mice with AOM and DSS-induced colon cancer were significantly decreased after six weeks. As shown in Figure 1, following the initiation of AOM and DSS-induced colon cancer, the body weights of the mice in the PLCSB and RS3 + PLCSB groups were significantly lower compared with those of the normal mice after four weeks, but higher than AOM and DSS-induced colon cancer control mice. RS3 + PLCSB treated mice could alleviate the weight loss compare to the PLCSB treated mice.

The total colonic length was significantly shorter in the AOM and DSS-treated mice (control group and sample treated group) compared with the normal mice as shown in Table 1 (p < 0.05). The normal mice had the longest colon length at 80.33 ± 3.86 mm and the colon length of control mice was the shortest (72.14 ± 5.21 mm). The total colonic length was longer in the RS3 + PLCSB-treated mice (77.31 ± 2.58 mm) than in the PLCSB-treated mice (75.47 ± 3.21 mm). AOM and DSS-induced colon carcinogenesis mice model is a staple research model for colon cancer. The increase in the ratio of colon weight to colon length was a consequence of apparent mucosal thickening and the formation of neoplasia [11]. The significant shortening of the colonic length in AOM and DSS-treated mice indicates that AOM and DSS contributed to the process of cancer changes in the colon. In particular, the colon weight and weight/length ratio were reduced slightly in the sample treatment groups compared to the control mice.

Effect of PLCSB and RS3 + PLCSB on Serum Levels of IL-6, IL-12, TNF-α and IFN-γ

The IL-6 level of normal mice was 46.2 ± 8.9 pg/mL; however, in control mice the IL-6 level was significantly increased to 243.1 ± 18.6 pg/mL. The levels of IL-6 in mice fed with PLCSB and RS3 + PLCSB were 167.8 ± 17.5 and 112.6 ± 12.3 pg/mL, respectively ( Figure 2). Control mice showed the highest IL-12 level in 741.3 ± 71.3 pg/mL, PLCSB and RS3 + PLCSB reduced the levels in 608.7 ± 55.2 and 474.3 ± 61.4 pg/mL, and the level of normal mice was lowest (324.7 ± 45.8 pg/mL). The TNF-α levels in the normal, control, PLCSB and RS3 + PLCSB treated mice were 43.7 ± 5.3, 81.2 ± 7.1, 67.2 ± 6.5 and 55.4 ± 5.7 pg/mL, respectively. The serum IFN-γ level in the mice in the RS3 + PLCSB treated group (51.4 ± 2.8 pg/mL) was significantly lower compared with those in the control (71.3 ± 5.5 pg/mL) and PLCSB treated group (58.7 ± 4.3 pg/mL), the level of normal mice was 34.7 ± 3.7 pg/mL. Inflammation is a critical component of tumor progression; cancers arise from infection and inflammation [12]. Cytokines are recognized to perform a major role in the immunopathogenesis of inflammatory bowel disease and the promotion of neoplastic transformation [11]. It is well known that the increased pro-inflammatory cytokines (IL-6, IL-12, TNF-α, and IFN-γ) amplify the inflammatory cascade and result in intestinal tissue damage in patients, as well as in animal models [13,14]. Cytokine receptors and the inflammatory cytokines IL-6, IL-12, IFN-γ and TNF-α play pathogenic roles in colon disease, lower levels of these cytokines were indicative of improved anticancer effects [15,16]. IL-6 is regarded as an important tumor-promoting factor in various types of human cancer; an increased expression of IL-6 is found in patients with colon cancer, where IL-6 levels are elevated in the serum of patients and in tumor tissue [17]. IL-6 expression is regulated through the activation of several transcription factors such as NF-κB, C/EBPβ or AP-1. The regulation of IL-6 expression through these transcription factors enables a rather unspecific up-regulation of this cytokine during nearly every type of inflammation-related cancer [18]. IL-12 through IFN-ã-dependent induction of the antiangiogenic factors interferon-inducible protein (IP) 10 and monokine induced by gamma interferon (MIG) contributes to tumor eradication [19]. Observations by Popivanova et al. [20] have revealed the crucial involvement of TNF-α in the initiation of chronic inflammation-mediated colon carcinogenesis; blocking of TNF-α reversed carcinoma progression, even after colon carcinoma was established. Drugs targeting TNF-α may be useful for the treatment of cancers, particularly those arising from chronic inflammation. In this study, we observed that the colonic levels of IL-6, IL-12, TNF-α, and IFN-γ in the AOM and DSS-induced colon cancer mice were markedly decreased by PLCSB and RS3 + PLCSB treatment. Based on this study, PLCSB showed a strong colon carcinogenesis preventive effect, and the PLCSB carried by RS3 could increase the anticancer effect.

Effects of PLCSB and RS3 + PLCSB on Histological Changes

The H&E staining assay was used to evaluate the therapeutic effects of PLCSB and RS3 + PLCSB in AOM and DSS-induced colon cancer in mice ( Figure 3). Normal mice did not show evidence of colonic inflammation, injury, or neoplasms. Nodular or polypoid colonic tumors were observed macroscopically in the colons of control group mice treated with AOM and DSS. The colonic tissues of mice receiving AOM and DSS evidenced mild to severe inflammation, characterized by crypt damage and inflammatory cell infiltration. The aforementioned mucosal thickening in the mice receiving AOM and DSS, as mentioned above, appeared to be attributable to the burden of colonic neoplasms. These phenomena did not distinctly differ between the PLCSB and RS3 + PLCSB fed groups. However, tissue sections from RS3 + PLCSB-treated AOM and DSS-induced colon cancer mice had more intact surface epithelium, normal colon cells and less inflammatory and neoplastic reactions than those in the PLCSB treated mice. Histopathological analysis is an important clinical standard used to diagnose colon cancer [21]. In addition, the histopathological examination of mouse colon sections is reported as an effective method to check the hepatoprotective activity against AOM and DSS-induced colon cancer in the mouse model [10]. From the sections examined in the present study, PLCSB and RS3 + PLCSB were observed to exert a preventive effect against AOM and DSS-induced colon cancer, and RS3 + PLCSB showed better anticancer effect than PLCSB treatment alone.

Effects of PLCSB and RS3 + PLCSB on Apoptosis-Related Gene Expression of Bax, Bcl-2 and Caspases

To elucidate the mechanisms underlying the colon carcinogenesis preventive effect by PLCSB and RS3 + PLCSB, expression of Bax, Bcl-2, and caspase-3 and caspase-9 in colon, tissues were measured by RT-PCR and Western blot analysis. As shown in Figure 4, expression of pro-apoptotic Bax and anti-apoptotic Bcl-2 showed significant changes in the presence of PLCSB and RS3 + PLCSB. These results suggest that the PLCSB and RS3 + PLCSB induced apoptosis in the colon tissues via a Bax and Bcl-2 dependent pathway. The mRNA expression levels of caspase-3 and caspase-9 were very low in untreated control mice, but significantly increased after the mice were fed with PLCSB and RS3 + PLCSB; mRNA and protein expressions of caspase-3 and caspase-9 was gradually elevated with RS3 + PLCSB. More specifically, apoptosis induction by RS3 + PLCSB was related to up-regulation of Bax, caspase-9, and caspase-3, and down-regulation of Bcl-2 in terms of mRNA and protein expression. The effects of RS3 + PLCSB were greater compared with those of the PLCSB.

Apoptosis is a fundamental cellular event, and understanding its mechanisms of action will help harness this process for use in tumor diagnosis and therapy [22]. In a healthy cell, the anti-apoptotic protein Bcl-2 is expressed on the outer mitochondrial membrane surface [23]. Because the Bax and Bcl-2 genes are mainly expressed during apoptosis, we determined that these genes regulate apoptotic activity. Apoptosis results from activation of caspase family members that act as aspartate-specific proteases [24]. Caspases form a proteolytic network within the cell whereby upstream initiator caspases are activated early in the apoptotic process (caspase-9) and in turn activate other downstream caspases (caspase-3). Cytochrome c and procaspase-9 processing is highly dependent on caspase-3, placing this caspase in a central position as a regulator of essential apoptotic pathways in cancer cells [25]. Caspase-3 was also reported to play a role as an amplifier of apoptotic signals (i.e., by cleaving Bcl-2) [26].

Effects of PLCSB and RS3 + PLCSB on Inflammation-Related Gene Expression of NF-κB, IκB-α, iNOS and COX-2

The next experiments investigated whether the anticancer actions of PLCSB and RS3 + PLCSB were associated with inhibited expression of the inflammation-related genes NF-κB, IκB-α, iNOS, and COX-2. As shown in Figure 5, mRNA and protein expressions of NF-κB and IκB-α was reduced in colon tissues fed with PLCSB and RS3 + PLCSB. PLCSB and RS3 + PLCSB significantly modulated the expression of genes associated with inflammation. mRNA and protein expressions of NF-κB was decreased while IκB-α mRNA and protein levels were increased. Additionally, mRNA and protein expression of COX-2 and iNOS were gradually decreased in the presence of the PLCSB dependent on use of RS3 as the carrier for PLCSB. Our findings indicate that PLCSB may help prevent cancer in the early stages by increasing anti-inflammatory activities. Overall, the results of this experiment showed that RS3 + PLCSB had a stronger anti-inflammatory effect on colon cancer than PLCSB.

Additionally, anticancer mechanisms underlying the effect of PLCSB and RS3 + PLCSB on AOM and DSS-induced colon cancer involve the induction of apoptosis by increasing the number of apoptotic bodies, regulating mRNA and protein expression of Bax and Bcl-2, and promoting anti-inflammatory effects by down-regulating iNOS and COX-2 gene expression. COX-2 has been suggested to play an important role in colon carcinogenesis, and NOS, along with iNOS, may be a good target for the chemoprevention of colon cancer [27]. Researchers have revealed the crucial involvement of the IκB kinase β/NF-κB (IKKβ/NF-κB) in colon carcinogenesis induced by combined treatment with AOM and DSS [20]. NF-κB is one of the most ubiquitous transcription factors, and regulates the expression of genes required for cellular proliferation, inflammatory responses, and cell adhesion [28]. NF-κB is present in the cytosol where it is bound to the inhibitory protein IκB-α. Following its induction by a variety of agents, NF-κB is released from IκB-α and translocates to the nucleus where it binds to the IκB binding sites in the promoter regions of target genes [29]. These mechanisms could be involved in the anticancer effects of PLCSB and RS3 + PLCSB in colon cancer, and RS3 + PLCSB could increase the anticancer effects in AOM and DSS-induced colon cancer.

Experimental Section

Polysaccharide Larimichthys crocea Swimming Bladder (PLCSB) Preparation

Wild Yellow Sea Larimichthys crocea were purchased from Shandong Province in China. Swimming bladder of Larimichthys crocea (1 kg) was dried by freeze-drying, and the dried samples were crushed. Three liters petroleum ether was added into swimming bladder of Larimichthys crocea and then reflux extraction was performed twice (1 h each time) at 60 °C to remove the protein; the residuals were gathered after filtration. Three liters absolute ethyl alcohol was then added and reflux extraction was performed for 3 h; the residuals without protein were filtrated and gathered. Finally, three liters water was added and the residuals were extracted at 60 °C for 2 h; filter liquid was collected. The crude polysaccharide of Larimichthys crocea swimming bladder was gained after evaporating [30].

Animals

Seven-week-old male C57BL/6 mice (n = 40) were purchased from the Experimental Animal Center of Chongqing Medical University (Chongqing, China). They were maintained in a temperature-controlled facility (temperature 23 ± 1 °C, relative humidity 50% ± 5%) with a 12 h light/dark cycle. The mice had unlimited access to a standard mouse chow diet and water.

Azoxymethane (AOM) and Dextran Sulfate Sodium (DSS)-Induced Colorectal Carcinogenesis Model

The mice were divided into four groups (n =10 each). The normal group mice received no treatment during the experimental period. The control group mice received no treatment during the first four weeks. The PLCSB group mice were fed with a mouse diet containing 1% PLCSB for four weeks; the RS3 + PLCSB group mice were fed with a mouse diet containing RS3 + PLCSB starch microspheres for four weeks, the mice diet also contained 1% PLCSB. Then the control, PLCSB and RS3 + PLCSB group mice were administered single intraperitoneal injections of 10 mg/kg bw azoxymethane (AOM, Sigma Co., St. Louis, MO, USA). After the injection for one week and four weeks, the mice received 2.5% dextran sulfate sodium (DSS, M w 30,000-50,000, MP Bio., Solon, OH, USA) in their drinking water for one week. In the period of colon carcinogenesis induction, the PLCSB and RS3 + PLCSB group mice were continually fed with the sample diet. The body weight was recorded daily, and the colon length and weight were measured after dissection [10].

Analysis of Inflammation-Related Cytokines in Serum by Enzyme-Linked Immunosorbent Assay (ELISA)

For the serum cytokine assay, blood from the inferior vena cava was collected in a tube and centrifuged at 1100× g, 4 °C for 10 min. The serum was aspirated and assayed as described below. Concentrations of inflammatory-related cytokines IL-6, IL-12, TNF-α, and IFN-γ in serum were measured by ELISA according to the manufacturer's instructions (Biolegend, San Diego, CA, USA). Briefly, biotinylated antibody reagent was added to 96-well plates, then supernatants of homogenized serum were added and the plates were incubated at 37 °C in CO 2 for 2 h. After washing with PBS, streptavidin-horseradish peroxidase (HRP) solution was added and the plate was incubated for 30 min at room temperature. The absorbance was measured at 450 nm using a microplate reader (iMark; Bio-Rad, Hercules, CA, USA) [6].

Histological Analysis

The distal colons from each animal were subjected to histological examination. The colon tissues were fixed in 10% neutral-buffered formalin, dehydrated in ethanol and embedded in paraffin. Colon tissue sections (4 μm) were then cut and stained with hematoxylin and eosin (H&E) [31].

RT-PCR Assay

Total RNA from colon tissue cells was isolated using Trizol reagent (Invitrogen, Carlsbad, CA, USA) according to the manufacturer's recommendations. The RNA was digested with RNase-free DNase (Roche, Basel, Switzerland) for 15 min at 37 °C and purified using an RNeasy kit (Qiagen, Hilden, Germany) according to the manufacturer's protocol. cDNA was synthesized from 2 μg of total RNA by incubation at 37 °C for l h with avian myeloblastosis virus reverse transcriptase (GE Healthcare, Little Chalfont, UK) with random hexanucleotides according to the manufacturer's instruction. Sequences of primers used to specifically amplify the genes of interest were as follows: 5'-AAGCTGAGCGAGTGT CTCCGGCG-3' (forward) and 5'-CAGATGCCGGTTCAGGTACTCAGTC-3' (reverse) for Bax; 5'-CTCGTCGCTACCGTCGTGACTTGG-3' (forward) and 5'-CAGATGCCGGTTCAGGTACTCAG TC-3' (reverse) for Bcl-2; 5'-CAAACTTTTTCAGAGGGGATCG-3' (forward) and 5'-GCATACTG TTTCAGCATGGCA-3' (reverse) for caspase-3; 5'-GGCCCTTCCTCGCTTCATCTC-3' (forward) and 5'-GGTCCTTGGGCCTTCCTGGTAT-3' (reverse) for caspase-9; 5'-CACTTATGGACAACTATGAG GTCTCTGG-3' (forward) and 5'-CTGTCTTGTGGACAACGCAGTGGAATTTTAGG-3' (reverse) for NF-κB; 5'-GCTGAAGAAGGAGCGGCTACT-3' (forward) and 5'-TCGTACTCCTCGTCTTTCA TGGA-3' (reverse) for IκB-α; 5'-AGAGAGATCGGGTTCACA-3' (forward) and 5'-CACAGAACTGAGG GTACA-3' (reverse) for iNOS; 5'-TTAAAATGAGATTGTCCGAA-3' (forward) and 5'-AGATCACCT CTGCCTGAGTA-3' (reverse) for COX-2. GAPDH was amplified as an internal control gene with the following primers: 5'-CGGAGTCAACGGATTTGGTC-3' (forward) and 5'-AGCCTTCTCCATGGTC GTGA-3' (reverse). Amplification was performed in a thermal cycler (Eppendorf, Hamburg, Germany). The polymerase chain reaction (PCR) products were separated in 1.0% agarose gels and visualized with ethidium bromide staining [32].

Western Blot Analysis

Total colon tissue protein was obtained with RIPA buffer as described [33]. Protein concentrations were determined with a Bio-Rad protein assay kit (Bio-Rad Laboratories Inc., Hercules, CA, USA). For the western blot analysis, aliquots of the lysate containing 30-50 μg protein were separated by sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) and then electrotransferred onto a nitrocellulose membrane (Schleicher and Schuell, Keene, NH, USA). The membranes were subjected to immunoblot analysis and the proteins were visualized by an enhanced chemiluminescence (ECL) method (GE Healthcare). The cell lysates were separated by 12% SDS-PAGE, transferred onto a polyvinylidene fluoride membrane (GE Healthcare), blocked with 5% skimmed milk and hybridized with primary antibodies (diluted 1:1000). The antibodies against Bax, Bcl-2, caspase-3, caspase-9, NF-κB, IκB-α, iNOS and COX-2 were obtained from Santa Cruz Biotechnology Inc. (Santa Cruz, CA, USA), then incubated with the horseradish peroxidase-conjugated secondary antibody (Santa Cruz Biotechnology Inc. (Santa Cruz, CA, USA) for 1 h at room temperature. The blots were washed three times with PBS-T and then developed by enhanced chemiluminescence (Amersham Life Science, Arlington Heights, IL, USA).

Statistical Analysis

Data are presented as the mean ± SD. Differences between the mean values for individual groups were assessed with a one-way ANOVA with Duncan's multiple range test. Differences were considered significant when p < 0.05. SAS version 9.1 (SAS Institute Inc., Cary, NC, USA) was used for statistical analyses.

Conclusions

In summary, the colon carcinogenesis preventive effect of PLCSB and RS3 + PLCSB were evaluated by various in vivo experimental methods, including basic observations in mice (body weight, colon weight and colon length), serum cytokine assay, tissue RT-PCR, and Western blot assays. Mice consumed a mouse diet that included PLCSB or RS3 + PLCSB. PLCSB is partially decomposed by digestion prior to entering the colon; therefore, PLCSB is only partly absorbed in the colon, which reduces its efficacy. By contrast, PLCSB combined with RS3 is not absorbed in the stomach. After it enters into the colon, RS3 is broken down by intestinal bacteria, and PLCSB is released. As indicated by the experimental results, combining RS3 with PLCSB enables the full efficacy of PLCSB to be utilized. Compared with the direct use of PLCSB, this method produces better preventative effects against colon cancer.

Figure 1 .
1Changes in the body weight of AOM and DSS-induced colon cancer mice during the experiment.

Figure 2 .
2Serum IL-6, IL-12, TNF-α, and IFN-γ levels of mice with AOM and DSS-induced colon cancer treated with PLCSB and RS3 + PLCSB. PLCSB, polysaccharide of Larimichthys crocea swimming bladder; RS3, resistant starch 3; a-d Mean values with different letters over the bars are significantly different (p < 0.05) according to Duncan's multiple range test.

Figure 3 .
3Microscopic observations of the effects of PLCSB and RS3 + PLCSB on histological damage in the colon tissues of mice with AOM and DSS-induced colon cancer (magnification, 40×). PLCSB, polysaccharide of Larimichthys crocea swimming bladder; RS3, resistant starch 3.

Figure 4 .
4Effects of PLCSB and RS3 + PLCSB on mRNA and protein expression of Bax, Bcl-2, caspase-3 and caspase-9 in AOM and DSS-induced colon cancer mice. PLCSB, polysaccharide of Larimichthys crocea swimming bladder; RS3, resistant starch 3.

Figure 5 .
5Effects of PLCSB and RS3 + PLCSB on mRNA and protein expression of NF-κB, IκB-α, iNOS and COX-2 in AOM and DSS-induced colon cancer mice. PLCSB, polysaccharide of Larimichthys crocea swimming bladder; RS3, resistant starch 3.

Table 1 .
1Effects of PLCSB and RS3 + PLCSB treatment on colon lengths and colon weights of AOM and DSS-induced colon cancer mice.Group
Colon length (mm)
Colon weight (g)
Colon weight/length (mg/cm)
Normal
80.33 ± 3.86 a
0.23 ± 0.03 d
28.63 ± 7.77 d
Control
72.14 ± 5.21 d
0.47 ± 0.08 a
65.15 ± 15.36 a
PLCSB
75.47 ± 3.21 c
0.35 ± 0.02 b
46.38 ± 6.23 b
RS3 + PLCSB
77.31 ± 2.58 b
0.30 ± 0.03 c
38.80 ± 11.63 c

PLCSB, polysaccharide of Larimichthys crocea swimming bladder; RS3, resistant starch 3; a-d Mean values
with different letters in the same column are significantly different (p < 0.05) according to Duncan's
multiple-range test.

© 2014 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/).
AcknowledgmentsThis research project was supported by National Science and Technology Program of China (No.2012BAD13B06) and Chongqing Science Technology Commission (No.cstc2011jjA80001), People's Republic of China.Author ContributionsXin Zhao made contribution to conception, design, and give final approval of the version to be submitted; Lian-Hong Chen, Xin Zhao, Jia-Le Song did the experiments and Lian-Hong Chen, Jia-Le Song, Yu Qian made contribution to acquisition of data, analysis and interpretation of data; Hua-Yi Suo and Jian Li made contribution to revise the manuscript.Conflicts of InterestThe authors declare no conflict of interest.
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