Improvement of alignment accuracy utilizing sequentially conserved motifs

Saikat Chakrabarti firstname.lastname@example.com
National Centre for Biological Sciences (TIFR)
560065BangaloreIndia

Nitin Bhardwaj
Department of Chemical Engineering
Indian Institute of Technology
MumbaiIndia

Prem A Anand
International Institute of Information Technology (MSIT)
HyderabadIndia

Ramanathan Sowdhamini
National Centre for Biological Sciences (TIFR)
560065BangaloreIndia

BMC Bioinformatics

Improvement of alignment accuracy utilizing sequentially conserved motifs
10.1186/1471-2105-5-167BioMed Central Page 1 of 12 (page number not for citation purposes) Software Ramanathan Sowdhamini* -email@example.com * Corresponding author
Background: Multiple sequence alignment algorithms are very important tools in molecular biology today. Accurate alignment of proteins is central to several areas such as homology modelling, docking studies, understanding evolutionary trends and study of structure-function relationships. In recent times, improvement of existing progressing programs and implementation of new iterative algorithms have made a significant change in this field.Results:We report an alignment algorithm that combines progressive dynamic algorithm, local substructure alignment and iterative refinement to achieve an improved, user-interactive tool. Large-scale benchmarking studies show that this FMALIGN server produces alignments that, aside from preservation of functional and structural conservation, have accuracy comparable to other popular multiple alignment programs.Conclusions:The FMALIGN server allows the user to fix conserved regions in equivalent position in the alignment thereby reducing the chance of global misalignment to a great extent. FMALIGN is available at

Background

The advent of large genome projects has led to an explosion of sequence data in public databases. Analysis of protein families, understanding their evolutionary trends and detection of remote homologues are now the primary objectives. Genome annotation and analysis tools like fold prediction, homology modelling, protein-ligand docking and clustering algorithms rely heavily on accurate multiple alignments to provide a genome-wide perspective.

The most popular approach for multiple sequence alignment has been the progressive alignment method [1]. A multiple alignment is built up gradually by aligning the closest sequences first and successively adding in more distant relatives. A number of alignment programs imply this algorithm, for example MULTALIGN [2], MULTAL [3] and CLUSTALX [4]. They employ a global alignment algorithm to construct an alignment over the entire length of the sequences and differ mainly in the procedure employed to determine the order of alignment of the sequences. The most common usage is the sequential branching method to identify two closest sequences first and subsequently align the next closest sequence to those already aligned. MULTALIGN [2] constructs a guided tree using UPGMA [5] method. A consensus method is then used to align larger and larger groups of sequences according to the branching order of the tree. CLUSTALX uses the alternative neighbour-joining algorithm [6] to construct the tree. In contrast to the above global method, PIMA [7] uses a local dynamic programming algorithm to align only the most conserved motifs. In addition, numerous new alignment algorithms have recently been developed which offer fresh approaches to the multiple alignment problem. A common point of interest has been the appli-cation of iterative strategies to refine and improve the initial alignment. A local alignment approach is implemented in the DIALIGN program [8,9] to construct multiple alignments based on segment-to-segment comparison rather than residue-to-residue comparison using an iterative strategy to improve alignment accuracy. Alignment programs like MATCH-BOX [10] utilize statistical similarity measures to delineate sequentially conserved regions and the final alignments are derived by those of the conserved "box" regions. The regions outside the Cartoon representation of the flowchart of FMALIGN methodology Figure 1 Cartoon representation of the flowchart of FMALIGN methodology. Coloured parts are meant to be important segments of protein sequences.

Six members from globin family are selected and aligned by FMALIGN utilizing sequentially conserved regions Figure 2 Six members from globin family are selected and aligned by FMALIGN utilizing sequentially conserved regions. The structureannotated JOY [24] representation of the alignment shows high conservation of structural features retained in this alignment. Capital and small letters represent solvent-buried and solvent-exposed residues, respectively. Helices and β-strands are represented in red and blue colours, respectively. Concensus secondary structures are marked. Hydrogen bonding patterns are also annotated: hydrogen bond to main chain and side chain are indicated in bold and underlined, respectively. Residues with positive-φ conformation are italised.

"boxes" are not aligned. METAMEME [11] is a motif based search engine that aligns motif regions found in the target sequences. DbClustal [12] combines the advantages of both local and global alignment algorithm in a traditional tree-based progressive alignment. Starting from ClustalW [22], which is a global alignment program, local alignment data or anchor points are incorporated in the dataset. The global alignment is then weighted towards, but not constrained, to the locally conserved segments and the alignment is not subject to iterative refinements. MACAW [13] provides a user interactive interface to select conserved segments from the alignment but these segments are not utilized further to refine the resulting alignment.

In this paper, we present an alignment algorithm that combines the properties of both progressive alignment methods and iterative refinement algorithms. This algorithm offers the user the selective advantage of guiding the course of alignment by simultaneous inputs of multiple conserved motif regions that in turn guarantees retention of structure/function in the final alignment. FMALIGN is an alignment server that provides the user to obtain a con-trol over the alignment by providing important conserved regions as input to the alignment program to achieve a more structurally relevant and functionally useful alignment of protein sequences. It employs the sequential branching method to identify the closest pair of sequences and subsequently includes the next closest sequences to generate a guided tree using UPGMA [5], which in turn dictates the sequential order of the alignment. FMALIGN also considers the local similarity of the sequences in the conserved motif regions; as the name implies, it allows local conserved regions of the sequences to be fixed and aligns the rest based on normal progressive alignment. The chances of global misalignment are thereby reduced and the possibility of obtaining overall better alignment is increased. The FMALIGN server also offers an iterative refinement option where a routine (FINDMOTIF) identifies more conserved regions in the derived alignment and allows the user to provide fresh 'equivalences' to obtain an overall better alignment. Benchmarking studies on difficult alignments, examined in BAliBASE [14], show promising prospect for the FMALIGN server to be an useful alignment algorithm.

SPS for reference 1 for all three categories

Implementation: methodology and description of FMALIGN server

The algorithm of FMALIGN (Fixed Motif ALIGNment) program combines three main criteria: (a) Progressive global alignment method, (b) substructure (local) conserved region fixation and (c) iterative refinement of alignment with identification of more conserved regions. The procedure involves three steps: (i) identification and fixing of the specified sequential conserved regions. This alignment method requires specific regions of the sequences to be aligned as anchor and these anchors are generally meant for sequentially conserved parts which do not undergo many changes. (ii) derivation of the progressive multiple sequence alignment guided by the tree. During this step, excluding the fixed anchored regions, the rest of the sequences are divided into several sub-segments that are aligned employing a dynamic alignment algorithm in a sequential order from N to C-terminus. The phylogenetic guide tree has been derived using Unweighted Pair Group Method with Arithmetic Mean (UPGMA) [5] which is the simplest method of tree construction. The UPGMA method was adopted since the whole length of the protein is divided into several segments considering them as ultrametric After all the sub-segment alignments are per-formed, the aligned parts as well as the selected, fixed motif regions are combined to produce the full-length alignment for a group of proteins under the hierarchical cluster. This process continues until all the protein sequences are aligned multiply. (iii) The iterative refinement of alignment subsequent to the identification of more motifs. In this step, more conserved regions are identified by observing the amino acid exchanges in the resultant alignment derived from the previous iteration. These conserved regions are then used as anchors together with previously identified motifs and the whole process is repeated until an optimal alignment having maximum number of conserved regions and alignment score is obtained. The algorithm thus combines the progressive dynamic algorithm for global multiple alignment and selected conserved regions or local alignments. Once the primary alignment is derived, the second step can be repeated by including more conserved regions from this alignment as motifs to derive a better alignment. Figure 1 shows a cartoon representation of the methods in a flowchart diagram.

FMALIGN server can accept amino acid patterns for multiple motifs provided by the user. It also provides option SPS scores for reference 1 for V2 (20% -40% sequence identity). to the user to search motifs within their sequences using FINDMOTIF routine or to obtain them for the alignment by providing a link to SMoS [15], a structural motif database for aligned protein superfamilies. The FINDMOTIF routine in the server provides sequential conserved regions for a set of proteins on the basis of sequence similarity and a 20 × 20 substitution matrix by consulting large number of structure-based sequence alignments of homologous families [16]. Amino acid exchange scores at every alignment position are assigned the same as the element in this matrix for all possible pairs of proteins and averaged over the number of pairs. Contiguous alignment positions with an average amino acid exchange score over 50 (for homologues) or 40 (for superfamilies) are recognised as motifs. FMALIGN also offers an option for the user to refine the derived alignment by generating more sequential conserved regions through FINDMOTIF option. The inter-motif regions are aligned by normal progressive alignment using standard substitution matrices like BLOSUM62. The gap penalties used in this version of FMALIGN are all maintained according to standard multiple alignment parameters.

Alignment scores

To assess the performance of FMALIGN in comparison to other programs, Sum-of-Pair-score (SPS) and Column-Score (CS) alignment scoring scheme [14] are applied on FMALIGN derived alignments to assess the quality of alignments compared to BAliBASE reference alignments. SPS and CS are calculated such that the score increases with the number of sequences aligned accurately and is used to determine the extent to which the programs succeed in aligning some, if not all, of the sequences in an alignment correctly [14]. The scores used to measure the performance of the various alignment programs may not be appropriate for all the datasets. Therefore, for each reference test the most suitable scoring function have been selected according to the nature of the benchmarking.

Results and discussion

Benchmarking at family level FMALIGN derived alignments retain high degree of conservation in secondary structures. Six members from the globin family were selected comprising a wide range of sequence identity between them (7% to 61%). Conserved regions for six aligned globin sequences were identified by the FINDMOTIF routine starting from CLUSTALX [4] SPS scores for reference 1 for V3 (>35% sequence identity). In order to evaluate the FMALIGN server, the results have been compared with 10 other alignment programs and objective criteria were employed to assess the quality of an alignment. We selected the BAliBASE benchmark alignment database [14] to compare the performance of the FMALIGN alignment server. The BAliBASE benchmark alignment database contains 142 reference alignments, divided into five reference sets each containing at least 12 representative alignments. Performance of FMALIGN is checked on all five reference sets provided in BAliBASE datasets.

Reference 1 alignments consist of a small number of equidistant sequences of similar length, i.e. the percent residue identity (% ID) between any two sequences is within a specified range and no large extensions or insertions have been introduced. Reference 2 contains alignments of a family of closely related sequences with >25% ID, plus up to three 'orphan' sequences (distant members of the family with <20% ID, sharing a common fold). It is designed to evaluate program accuracy according to two criteria: (i) the stability of the family alignment when orphans are introduced into the sequence set and (ii)the quality of the alignment of the orphan sequences.

Reference 3 checks the ability of the programs to correctly align equidistant divergent families into a single alignment. The reference alignments consist of up to four families, with <25% ID between any two sequences from different families.

Reference 4 and 5 contain alignments of upto 20 sequences including N/C-terminal extensions (upto 400 residues) and insertions (upto 100 residues), respectively.

Reference 1: a small number of approximately equidistant sequences

This dataset is designed to study the effect of sequence length and percentage identity on the performance of the alignment program and provides a basis for the remaining tests. The overall performance of FMALIGN server for this Alignment scores (SPS) are compared for reference 2. dataset is comparable to the two best performing alignment programs, like PRRP [17] and CLUSTALX [4], in all three categories (VI, V2, and V3) based on sequence identity and alignment lengths (short, medium and long) as shown in Figure 3, 4, 5.

Reference 2: a related family with divergent, orphan sequences

It is possible to assess the performance of the methods to align divergent 'orphan' sequences (10-20% ID with the family and between orphans) with a family of highly related (>25% ID) sequences using this data set. It is also interesting to observe the disruption of the family alignment due to the introduction of orphans. Figure 6 shows SPS for the alignment of a single orphan against a closely related family. The global alignment programs again perform better than the local ones in this test. However, CLUSTALX and SAGA [18] now rank above PRRP. The performance of FMALIGN server is significantly better than other programs for all the three length categories.

Reference 3: families of related sequences

This allows the assessment of the programs to correctly align approximately equidistant divergent families (<20% ID) composed of highly related sequences (>25% ID) into a single multiple alignment. Figure 7 shows the scores for the programs in the order. The iterative strategies of PRRP [17] and SAGA [18] perform better in this test than the traditional progressive alignment methods. However, FMA-LIGN performs better than the other progressive methods, with the global methods generally ranking higher than the local methods.

Reference 4: N/C-terminal extensions

This dataset includes large N/C-terminal extensions to investigate whether the programs are capable of aligning the core blocks flanking the extensions. No large internal insertions are introduced at this stage. Mostly local alignment strategies out-perform the global methods. PILEUP (Wisconsin Package v.8; Genetics Computer Group, Madison, WI) is the only program based on a global alignment method which does reasonably well compared to other global methods. Performance of FMALIGN is comparable to the best three methods (DIALIGN [8], SB_PIMA [19] and PILEUP) as shown in Figure 8.   Alignment scores (CS) are compared for reference 4 and 5.

Figure 8

Alignment scores (CS) are compared for reference 4 and 5.

Reference 5: internal insertions

In contrast to reference 4, in this dataset the insertions are internal to the homologous domains and not at the N/Cterminus as overhangs. FMALIGN also performs well in this category and results are comparable to the other better performing global alignment methods like PRRP and CLUSTALX as shown in Figure 8.

Benchmarking at superfamily level Utilization of structural motifs to improve alignment accuracy

The performance of FMALIGN has been tested on a dataset of representative superfamilies of proteins belonging to different structural classes in PASS2 [20] and SMoS [15] databases. The structural motifs from the SMoS database have been utilized as conserved regions for the member of the superfamily (average sequence identity less than 30%). All the proteins of a superfamily have been aligned. Each alignment is assigned a quality score by averaging the amino acid exchange score of each column over the length of the alignment (see method for details). The alignments derived from FMALIGN have been compared against the CLUSTALX-derived sequence alignment as well as the sequence-structure alignment derived from COMPARER [21]. Figure 9 shows an equivalent or better accuracy of the FMALIGN server compared to CLUSTALX and COMPARER-derived alignment. This indicates that FMALIGN is particularly efficient for specific sets of sequences for which the degree of conservation is known. The initial results also indicate that FMALIGN can perform very well and provide an alignment which is very similar or better than a structurally derived alignment.

Utilization of sequence motifs to improve alignment accuracy

Representative superfamily alignments belonging to different structural classes from the PASS2 [20] superfamily alignment database are taken for a benchmarking test (Table 1). These superfamily alignments are of different lengths (short, medium, and long) and possess an average sequence identity that ranges from 10% to 26%. Sequential conserved regions or motifs are identified for each superfamily alignment and utilized in the FMALIGN server to realign the superfamily members. Similarly, the superfamily members are also aligned by the two best performing multiple alignment methods, CLUSTALW [22] and T-Coffee [23]. These alignments are then compared against the structure-based COMPARER alignments provided by PASS2 database using the same alignment scoring scheme (Sum-of-Pair score) of the BAliBASE benchmarking database. FMALIGN-derived alignments

Comparison of alignment accuracy on the basis of sequence similarity.

Figure 9

Comparison of alignment accuracy on the basis of sequence similarity.  [4] and sequence-structure alignment, COMPARER [21].

performed better for most of the cases compared to the other two methods as shown in Figure 10.

Conclusions

FMALIGN server provides a web interface for pairwise and multiple sequence alignments of proteins. FMALIGN provides an alignment by combining progressive dynamic algorithm, local substructure alignment and iterative refinement presenting an improved, user interactive alignment procedure. FMALIGN server allows the user to fix conserved regions in equivalent positions amongst the sequences to be aligned leading to alignments that are reliable and biologically more meaningful. Additional options for the users to choose substitution matrices and gap penalty values may be incorporated in future.

All the alignments provided in BAliBASE are realigned by FMALIGN and the resulting scores (both SPS and CS) are calculated using a standard program kindly provided by the authors of BAliBASE. The inter-motif regions are aligned in FMALIGN by normal progressive alignment using standard substitution matrices like BLOSUM62. The gap penalties used in this version of FMALIGN are all maintained according to standard multiple alignment parameters. Benchmarking at the superfamily level has also been done utilizing both structural and sequential conserved regions. The dataset is wide enough to include proteins from different structural classes and of different length. The average sequence identity for this dataset was less than 30% since the proteins are related at the superfamily level. The performance of FMALIGN has been tested against one of the best performing multiple sequence alignment (T-Coffee) as well as structure based alignment tool (COMPARER). Studies on large and different datasets revealed an overall better performance of FMALIGN server for all categories in BAliBASE benchmarking database. It works especially well at lower sequence identity range, such as superfamily level, where no two proteins are more than 25% identical to each other in comparison to other popular methods like CLUSTALX and T-Coffee. It is well-known that automatic multiple sequence alignments at poor sequence identity are often subject to careful manual validations and improvements to avoid offsets of critical functionally important residues. FMALIGN can sensitively address this issue to avoid manual intervention subsequent to final alignment. FMA-LIGN-derived alignments also show a high conservation of secondary structural elements and provide better align- Figure 10 Comparison of alignment accuracy. FMALIGN derived alignments of the PASS2 [20] superfamily members are compared against the same derived by CLUSTALW [22] and T-Coffee [23].

Comparison of alignment accuracy

ments for comparative modelling. The implementation of this alignment algorithm can be used to include new members into an existing protein superfamily with the help of motif regions that provide a reliable approach to connect protein sequences with their structural homologues within a particular superfamily. FMALIGN is available via the following URL http://caps.ncbs.res.in/ FMALIGN/Home.html

Figure 3
3SPS for reference 1 for all three categories. a) SPS scores are shown for VI (<25% sequence identity) (b) for V2 (20%-40% sequence identity), and (c) V3 (>35% sequence identity). Alignments of different length categories are shown by different line plot.

Figure 4
4SPS scores for reference 1 for V2 (20% -40% sequence identity). Alignments of different length categories are shown by different line plot.

Figure 5
5SPS scores for reference 1 for V3 (>35% sequence identity). Alignments of different length categories are shown by different line plot. alignment with default parameters and aligned by the FMALIGN server. The resultant alignment shows high degree of secondary structural conservation despite the difficulty of aligning a set of sequences having very wide range of sequence identity (Figure 2).

Figure 6
6Alignment scores (SPS) are compared for reference 2. Alignments of different length categories are shown by different line in the plot.

Alignment scores (CS) are compared for reference 3.

Figure 7
7Alignment scores (CS) are compared for reference 3. Alignments of different length categories are shown by different line in the plot.

Table 1 :
1List of superfamilies used for FMALIGN benchmarking.Superfamily code
Superfamily name
Structural
Class

Average length of
proteins

Average sequence
identity(%)

02.01.001
Globin-like
All α
142
15.2
02.01.023
Putative DNA-binding domain
All α
76
10.0
02.01.050
Cytochromes
All α
122
22.0
02.01.060
ACP-like
All α
80
23.5
02.01.101
SAM/Pointed domain
All α
84
13.3
02.02.027
C2 domain(Calcium/lipid-binding domain, CaLB)
All β
75
11.4
02.02.042
Galactose-binding domain-like
All β
185
12.6
02.02.058
ISP domain
All β
136
23.4
02.02.094
Acid proteases
All β
217
20.4
02.02.152
Hedgehog/intein (Hint) domain
All β
177
16.9
02.03.018
Phosphatidylinositol-specific phospholipase C (PI-PLC)
α and β
286
13.6
02.03.059
Ferredoxin reductase-like, C-terminal NADP-linked
domain

α and β
132
21.6

02.03.073
Thiamin diphosphate-binding fold (THDP-binding)
α and β
236
12.1
02.03.148
"Helical backbone" metal receptor
α and β
320
14.4
02.04.010
Chromo domain-like
α and β
68
26.3
02.04.088
Regulatory domain in the amino acid metabolism
α and β
90
12.2
02.04.218
Ribosome inactivating proteins (RIP)
α and β
253
22.1
02.07.017
Leech antihemostatic proteins
α and β
47
19.1

Conserved motifs are identified for superfamilies like, Cytochromes (code: 02.01.050), ACP-like (02.01.060), C2 domain (Calcium/lipid-binding domain, CaLB) (code: 02.02.027), ISP domain (code: 02.02.058), Phosphatidylinositol-specific phospholipase C (PI-PLC) (code: 02.03.018), Esterase/acetylhydrolase (02.03.054), Chromo domain-like (code: 02.04.010) and BPTI-like (02.07.023). These motifs are utilized to realign the sequences by FMALIGN. Alignments are scored using sequence similarity scores based on a 20x20 substitution matrix. FMA-LIGN derived alignments are compared against CLUSTALX
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AcknowledgementsR.S. is a Senior Research Fellow funded by the Wellcome Trust Grant. We would also like to acknowledge National Centre for Biological Sciences (TIFR) for financial support.Authors' contributions S.C. had carried out the benchmarking, was involved in the development of the server and has written the first draft of the manuscript. N.B. had written the initial part of the code and P.A. has written the latter part of the code and developed a web server. R.S. had initiated the idea, was involved in discussions and in the critical reading of the manuscript.List of abbreviationsFMALIGN Fixed Motif ALIGNmentSPS Sum-of-Pairs Score
Progressive sequence alignment as a prerequisite to correct phylogenetic trees. D F Feng, R F Doolittle, J Mol Evol. 25Feng DF, Doolittle RF: Progressive sequence alignment as a prerequisite to correct phylogenetic trees. J Mol Evol 1987, 25:351-360.

A strategy for the rapid multiple alignment of protein sequences. Confidence levels from tertiary structure comparisons. G J Barton, J E Sternberg, J Mol Biol. 198Barton GJ, Sternberg JE: A strategy for the rapid multiple align- ment of protein sequences. Confidence levels from tertiary structure comparisons. J Mol Biol 1987, 198:327-337.

A flexible method to align large numbers of biological sequences. W R Taylor, J Mol Evol. 28Taylor WR: A flexible method to align large numbers of bio- logical sequences. J Mol Evol 1988, 28:161-169.

The CLUSTAL_X windows interface: flexible strategies for multiple sequence alignment aided by quality analysis tools. J D Thompson, T J Gibson, F Plewniak, F Jeanmougin, D G Higgins, Nucleic Acids Res. 25Thompson JD, Gibson TJ, Plewniak F, Jeanmougin F, Higgins DG: The CLUSTAL_X windows interface: flexible strategies for mul- tiple sequence alignment aided by quality analysis tools. Nucleic Acids Res 1997, 25:4876-4882.

P H Sneath, R R Sokal, Numerical Taxonomy Freeman. San Francisco, CASneath PH, Sokal RR: Numerical Taxonomy Freeman San Francisco, CA; 1973.

The neighbor-joining method: a new method for reconstructing phylogenetic trees. N Saitou, M Nei, Mol Biol Evol. 4Saitou N, Nei M: The neighbor-joining method: a new method for reconstructing phylogenetic trees. Mol Biol Evol 1987, 4:406-425.

Pattern-induced multi-sequence alignment (PIMA) algorithm employing secondary structure-dependent gap penalties for use in comparative protein modelling. R F Smith, T F Smith, Protein Eng. 5Smith RF, Smith TF: Pattern-induced multi-sequence alignment (PIMA) algorithm employing secondary structure-depend- ent gap penalties for use in comparative protein modelling. Protein Eng 1992, 5:35-41.

DIALIGN: finding local similarities by multiple sequence alignment. B Morgenstern, W R Atchley, K Hahn, A Dress, 14BioinformaticsMorgenstern B, Atchley WR, Hahn K, Dress A: DIALIGN: finding local similarities by multiple sequence alignment. Bioinformat- ics 1998, 14:290-294.

Fast and sensitive multiple alignment of large genomic sequences. M Brudno, M Chapman, B Gottgens, S Batzoglou, B Morgenstern, BMC Bioinformatics. 466Brudno M, Chapman M, Gottgens B, Batzoglou S, Morgenstern B: Fast and sensitive multiple alignment of large genomic sequences. BMC Bioinformatics 2003, 4:66.

Simultaneous and multivariate alignment of protein sequences: correspondence between physicochemical profiles and structurally conserved regions (SCR). E Depiereux, E Feytmans, Protein Eng. 4Depiereux E, Feytmans E: Simultaneous and multivariate align- ment of protein sequences: correspondence between physi- cochemical profiles and structurally conserved regions (SCR). Protein Eng 1991, 4:603-613.

Meta-MEME: Motifbased Hidden Markov Models of Biological Sequences. W N Grundy, T L Bailey, C P Elkan, M E Baker, Comput Appl Biosc. 13Grundy WN, Bailey TL, Elkan CP, Baker ME: Meta-MEME: Motif- based Hidden Markov Models of Biological Sequences. Com- put Appl Biosc 1997, 13:397-406.

DbClustal: rapid and reliable global multiple alignments of protein sequences detected by database searches. J D Thompson, F Plewniak, J-C Thierry, O Poch, Nucl Acids Res. 28Thompson JD, Plewniak F, Thierry J-C, Poch O: DbClustal: rapid and reliable global multiple alignments of protein sequences detected by database searches. Nucl Acids Res 2000, 28:2919-2926.

A workbench for multiple alignment construction and analysis. G D Schuler, S F Altschul, D J Lipman, Proteins. 9Schuler GD, Altschul SF, Lipman DJ: A workbench for multiple alignment construction and analysis. Proteins 1991, 9:180-190.

A comprehensive comparison of multiple sequence alignment programs. J D Thompson, F Plewniak, O Poch, Nucleic Acids Res. 27Thompson JD, Plewniak F, Poch O: A comprehensive compari- son of multiple sequence alignment programs. Nucleic Acids Res 1999, 27:2682-2690.

SMoS: a database of structural motifs of protein superfamilies. S Chakrabarti, K Venkataramanan, R Sowdhamini, Protein Eng. 16Chakrabarti S, Venkataramanan K, Sowdhamini R: SMoS: a data- base of structural motifs of protein superfamilies. Protein Eng 2003, 16:791-793.

A structural basis for sequence comparisons. An evaluation of scoring methodologies. M S Johnson, J P Overington, J Mol Biol. 233Johnson MS, Overington JP: A structural basis for sequence comparisons. An evaluation of scoring methodologies. J Mol Biol 1993, 233:716-738.

Significant improvement in accuracy of multiple protein sequence alignments by iterative refinement as assessed by reference to structural alignments. O Gotoh, J Mol Biol. 264Gotoh O: Significant improvement in accuracy of multiple protein sequence alignments by iterative refinement as assessed by reference to structural alignments. J Mol Biol 1996, 264:823-838.

SAGA: sequence alignment by genetic algorithm. C Notredame, D G Higgins, Nucleic Acids Res. 24Notredame C, Higgins DG: SAGA: sequence alignment by genetic algorithm. Nucleic Acids Res 1996, 24:1515-1524.

Pattern-induced multi-sequence alignment (PIMA) algorithm employing secondary structure-dependent gap penalties for use in comparative protein modelling. R F Smith, T F Smith, Protein Eng. 5Smith RF, Smith TF: Pattern-induced multi-sequence alignment (PIMA) algorithm employing secondary structure-depend- ent gap penalties for use in comparative protein modelling. Protein Eng 1992, 5:35-41.

PASS2: a semi-automated database of protein alignments organised as structural superfamilies. V Mallika, A Bhaduri, R Sowdhamini, Nucleic Acids Res. 30Mallika V, Bhaduri A, Sowdhamini R: PASS2: a semi-automated database of protein alignments organised as structural superfamilies. Nucleic Acids Res 2002, 30:284-288.

Definition of general topological equivalence in protein structures -a procedure involving comparison of properties and relationships through simulated annealing and dynamic programming. A Sali, T L Blundell, J Mol Biol. 212Sali A, Blundell TL: Definition of general topological equiva- lence in protein structures -a procedure involving compari- son of properties and relationships through simulated annealing and dynamic programming. J Mol Biol 1990, 212:403-428.

CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position specific gap penalties and weight matrix choice. J D Thompson, D G Higgins, T J Gibson, Nucleic Acids Res. 22Thompson JD, Higgins DG, Gibson TJ: CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position specific gap penalties and weight matrix choice. Nucleic Acids Res 1994, 22:4673-4680.

T-Coffee: A novel method for fast and accurate multiple sequence alignment. C Notredame, D G Higgins, J Heringa, J Mol Biol. 302Notredame C, Higgins DG, Heringa J: T-Coffee: A novel method for fast and accurate multiple sequence alignment. J Mol Biol 2000, 302:205-217.

JOY: protein sequence-structure representation and analysis. K Mizuguchi, C M Deane, T L Blundell, M S Johnson, J P Overington, Bioinformatics. 14Mizuguchi K, Deane CM, Blundell TL, Johnson MS, Overington JP: JOY: protein sequence-structure representation and analysis. Bioinformatics 1998, 14:617-623.


Association of physical performance and biochemical profile of mice with intrinsic endurance swimming
2016

Wen-Ching Huang
Graduate Institute of Sports Science
National Taiwan Sport University
33301TaoyuanTaiwan

Yi-Ju Hsu
Graduate Institute of Sports Science
National Taiwan Sport University
33301TaoyuanTaiwan

Wei Li
Department of Neurosurgery
Taipei Medical University-Wan Fang Hospital
11696TaipeiTaiwan

Ying-Ju Chen firstname.lastname@example.com.-j.c..
Department of Food and Nutrition
Department of Food and Nutrition
Providence University
43301Taichung City-C.H.)Taiwan

Salu Dist
Providence University
200 Chung-Chi Rd43301Taichung CityTaiwan

Chi-Chang Huang
Graduate Institute of Sports Science
National Taiwan Sport University
33301TaoyuanTaiwan

Association of physical performance and biochemical profile of mice with intrinsic endurance swimming

Int. J. Med. Sci
13201610.7150/ijms.16421Received: 2016.06.08; Accepted: 2016.08.22;892 I In nt te er rn na at ti io on na al l J Jo ou ur rn na al l o of f M Me ed di ic ca al l S Sc ci ie en nc ce es s Research Paper  Corresponding authors: Graduate Institute of Sports Science, National Taiwan Sport University, No. 250, Wenhua 1st Rd., Guishan Township, Taoyuan County 33301, Taiwan (C. http://ivyspring.com/terms for terms and conditions.CongenitalExercise performanceEndurance swimmingForelimb gripAmmonia
We aimed to investigate the potential mediators and relationship affecting congenital exercise performance in an animal model with physical activity challenge from physiological and biochemical perspectives. A total of 75 male ICR mice (5 weeks old) were adapted for 1 week, then mice performed a non-loading and exhaustive swimming test and were assigned to 3 groups by exhaustive swimming time: low exercise capacity (LEC) (<3 hr), medium exercise capacity (MEC) (3-5 hr), and high exercise capacity (HEC) (>5 hr). After a 1-week rest, the 3 groups of mice performed an exhaustive swimming test with a 5% and 7.5% weight load and a forelimb grip-strength test, with a 1-week rest between tests. Blood samples were collected immediately after an acute exercise challenge and at the end of the experiment (resting status) to evaluate biochemical blood variables and their relation with physical performance. Physical activity, including exhaustive swimming and grip strength, was greater for HEC than other mice. The swimming performance and grip strength between groups were moderately correlated (r=0.443, p<0.05). Resting serum ammonium level was moderately correlated with endurance with a 7.5% weight load (r=-0.447, p<0.05) and with lactate level (r=0.598, p<0.05). The pulmonary morphology of the HEC group seemed to indicate benefits for aerobic exercise. Mice showed congenital exercise performance, which was significantly correlated with different physical challenges and biochemical variable values. This study may have implications for interference in intrinsic characteristics

Introduction

A heterogeneous stock was produced from the same 8 founder strains, the Collaborative Cross (CC) inbred strains, which include A/J, C57BL/6J, 129S1/SvImJ, NOD/ShiLtJ, NZO/H1LtJ, CAST/EiJ, PWK/PhJ, and WSB/EiJ [1]. The genetically heterogeneous diversity outbred mice showed a broad range of phenotypes, heterozygosity, and genetic buffering. The advantages of outbreeding are normal levels of heterozygosity, similarity to the genetic condition in humans, and substantially increased mapping resolution [2]. The outbred mice showed good adaption, healthy resistance, reproductive ability, and easy management. They are usually used in studies of toxicological assessment, embryology and physiology [3][4][5].

For sport science with animal experiments, exercise performance is usually evaluated by two exercise models: treadmill running and exhaustive swimming test. The treadmill test forces experimental animals to run according to an increased gradient of speed or slope by a negative motivation, electric shock, until exhaustion [6]. The exhaustive swimming Ivyspring International Publisher test involves a loading equivalent to body weight on the tail and is based on the animal's survival instinct [7]. The period of physical activity from the beginning to exhaustion is considered exercise performance.

In recent years, many studies have focused on the effects of different nutrient supplements on physical fatigue or related physiology. Outbred rodents such as Sprague-Dawley rats and ICR and ddY mice are widely used in sport science [8][9][10][11][12]. Biochemical variables including lactate, ammonia, glucose, and creatine kinase have been evaluated for fatigue status. During or after intensive exercise, lactate and ammonia levels are accumulated to cause deleterious effects and result in fatigue [13]. Simultaneously, the muscle needs a great quantity of energy, and lactate level is increased with anaerobic glycolysis. The accumulated lactate level further reduces pH value, which could affect the enzyme activity of glycolysis and muscle contraction via calcium ion release [14]. The immediate ammonia level produced from deamination of AMP to AIP via the purine nucleotide cycle is greatest during intensive exercise, when the rate of ATP utilization may exceed that of ATP production. It could cause peripheral and central fatigue because of muscle phosphocreatine depletion, proton accumulation in muscle, muscle glycogen depletion, reduced blood glucose concentration, and increased ratio of specific amino acids in plasma [15][16].

The intrinsic capacity of rodents can be artificially bred to produce stable inherited lines for basic studies with different purposes. Using a treadmill running protocol, Koch et al. found that the founder population could be divided by high and low exercise capacity, with endurance capacity greater for the high-capacity than low-capacity line by 1.7 times after 6 generations of breeding [17]. As well, artificial selection for high voluntary activities could show significant behavioral differences due to differences in neurotransmitter-associated metabolites in brain regions that affect exercise and motivational state [18]. In the low-capacity line, a combination of risk factors, including female sex and overweight, promoted myocardial stiffness and fibrosis sufficient to cause diastolic dysfunction [19]. In another report, left ventricular myocardial and cardiomyocyte morphology, contractility, and intracellular Ca 2+ handling in both systole and diastole, as well as mean blood pressure, were compromised in rats bred for low aerobic capacity during progression from adult to old age [20]. The high-capacity line did not differ from the low-capacity line in arteriolar branching geometry of the heart and skeletal muscle. Therefore, endothelial tolerance for shear stress and/or coupling of myocardial muscle fiber contractile function were suggested to be increased because of allowing higher flow rate per unit volume muscle or more efficient use of oxygen and nutrients [21].

In established high-or low-capacity animal lines, previous reports addressed the physiological or pathological differences but not biochemical variables and correlations by intensity of exercise. In our previous studies, we found that the same batch of outbred mice exhibited a great variety in exercise performance, so we evaluated the individual mouse exercise capacity before supplement intervention. We have limited reports to reveal the causes or physiological correlations of congenital exercise performance. Therefore, the primary aim of our investigation was to test whether exhaustive swimming and treadmill running tests could be used to distinguish intrinsic exercise capacity in mice. The secondary aim was to determine whether biochemical characteristics were associated with different physical activity intensity in mice with different capacities. This study could provide insights for researchers in sport science who use an animal model to study interference in intrinsic exercise differences. Nutrient supplements or interventions could substantially affect exercise performance or physiology.

Methods

Animal and experimental designs

Male outbred ICR mice (5 weeks old) were purchased from BioLASCO (A Charles River Licensee Corp., Yi-Lan, Taiwan). All animals were maintained under 23±2°C room temperature, 55±10% humidity, and provided a standard laboratory diet (#. 5001; PMI Nutrition International, Brentwood, MO, USA), with distilled water ad libitum. Mice were adapted to the environment for at least 1 week before experiments. The experimental animals were raised in the animal room of National Taiwan Sport University (NTSU) and all procedures were approved by the Institutional Animal Care and Use Committee of NTSU (IACUC-10103).

After 1-week adaption, all 75 mice performed the exhaustive swimming exercise with 0%, 5%, 7.5% body-weight loading on the tail and a grip strength test, with a 1-week rest interval between each physical activity. After the 0% loading weight load evaluation, mice were assigned to 3 groups by exhaustive swimming time: low exercise capacity (LEC) (< 3 hr), medium exercise capacity (MEC) (3-5 hr), and high exercise capacity (HEC) (>5 hr). Each group comprised an equal proportion of about 24-27 mice for the following tests. One week after the last grip strength test, 8 mice randomly chosen from each group were killed. Blood was collected, and organs including the heart, lung, liver, muscle, epididymal adipose tissue (EAT), and brown adipose tissue (BAT) were collected and weighed for further biochemical and pathological analysis.

Exhaustive swimming exercise

A weight equivalent to 0%, 5%, and 7.5% body weight was attached to the root of the mouse tail by lead fish sinkers. The tank was maintained at 28°C during the swimming process and the endurance for each mouse was measured as swimming time recorded from the beginning of the time to exhaustion, defined by observing uncoordinated movements and failure to return to the surface within 7 s. The time of floating, struggling and making necessary movements was assessed until possible drowning and exhaustion. The other details of the exhaustive swimming exercise were previously described [7,9].

Forelimb grip strength

The low-force testing system (Model-RX-5, Aikoh Engineering, Nagoya, Japan) was used to measure forelimb grip strength. The amount of tensile force was measured by the force transducer equipped with a metal bar (2 mm diameter, 7.5 cm length), which was gripped by mice. The maximal force (grams) recorded by the low-force system was considered grip strength. The detailed procedures were previously described [7].

Blood biochemical variables

The biochemical variables lactate, ammonia, and glucose were evaluated for fatigue status in previous studies [7,9]. We evaluated these variables for possible association with congenital exercise performance. Blood samples were immediately collected from resting mice by means of a submandibular duct method in fed mice. For the effects of physical challenge on these variables, blood samples were immediately collected from the mice after the 15-min swimming test without weight loading. The plasma was analyzed for lactate, ammonia, glucose, and creatine kinase levels by use of an auto-analyzer (Hitachi 7060, Hitachi, Tokyo) after centrifugation at 1,500 × g, 4°C for 10 min.

Oral glucose tolerance test (OGTT)

The mice fasted for 16 hr before oral glucose challenge. The glucose (2.5 g/kg) was administered by oral gavage and blood samples were collected from the tail vein at 0, 15, 30, 60, and 120 min for glucometer analysis (ACCU-CHEK Comfort, Roche).

Histology

The mice were euthanized with CO2 in an appropriate chamber by trained personnel at the end of experiments. The blood was collected, and tissues or organs related to physical activities or energy homeostasis were weighed and immersed in 10% buffered formalin. Paraffin-embedded samples were sectioned into 4-μm-thick slides and underwent hematoxylin and eosin (H&E) staining as previously described [22]. Samples were photographed by using a light microscope equipped with a CCD camera (BX-51, Olympus, Tokyo).

Statistical analysis

All data are expressed mean ± SEM and were analyzed by use of SAS 9.0 (SAS Inst., Cary, NC, USA). The Cochran-Armitage test for dose-effect trend analysis was evaluated by use of SAS v9.0. The performance in the exhaustive swimming exercise with 0%, 5%, and 7.5% loading equivalent to body weight, grip strength and biochemical variables were assessed by one-way ANOVA. The correlation between exhaustive swimming exercise (0%, 5%, and 7.5% loading), grip strength, and biochemical variables was analyzed by Pearson's product-moment correlation coefficient (r). P<0.05 was considered statistically significant.

Results

Exercise performance, definition, general characteristics

After 1-week adaption, all mice performed the exhaustive swimming exercise with 0% body-weight loading. The endurance of individual mice exhibited normal contribution and significant difference according to number of mice and endurance time (Fig.  1a). We ranked mice by individual swimming time (Fig. 1b). Most mice were exhausted within 10 hr, and the mean exhaustive swimming time was about 4 hr. However, a few mice exhibited extreme endurance capacity, more than 10 hr. Therefore, we divided all mice into 3 experimental groups (LEC, MEC, and HEC) according to swimming endurance time: <3 hr, 3-5 hr, and > 5 hr, respectively. Each group contained 24-27 mice for further physical tests (Table 1). To monitor the growth curve and nutritional supplement, we recorded the body weight and daily intake of each group. The 3 groups did not differ in weight, diet or water intake at each week (Table 2). Therefore, the difference in exercise performance would not be affected by nutritional supplementation and might be affect by other physiological factors.

Physical activity test

The physical activities in this experiment included an exhaustive swimming exercise with 0%, 5%, and 7.5% body weight loading and a grip strength test. Each physical test was separated by a 1-week rest for animal recovery. With 0% body-weight loading, the 3 groups differed in endurance (p<0.05) (Fig. 2a). We progressively increased the body weight loading to 5% and 7.5%. With 5% body weight loading, the endurance of the HEC group was greater than the LEC group by 4.48-fold (78.73±20.76 vs 14.35±1.71 min; p=0.006) (Fig. 2b). With 7.5% body-weight loading, the endurance of the LEC and MEC groups was lower than the HEC group by 52% and 36%, respectively (3.72±0.25 and 4.95±0.41 vs 7.75±0.92 min) (p<0.001 and p=0.005) (Fig. 2c). In addition, the HEC group showed greater grip strength than the LEC group, by 14.7% (p<0.001) (Fig. 2d).  Baseline BW (g)

Biochemical variables

We examined fatigue-related biochemical indicators including lactate, ammonia, and glucose in the 3 groups to evaluate the fatigue status after an exhaustive exercise measured with the effects of functional nutrient supplements in previous studies [7,9]. During resting conditions, lactate level did not differ between the groups (p>0.05, Fig. 3a). Ammonia level was lower, by 15.3%, for the HEC than LEC group (p=0.0378, Fig. 3b). Glucose level did not differ among the groups (p>0.05, Fig. 3c). After a 15-min swimming challenge without loading (Fig. 4), lactate level was higher for the LEC group than MEC and HEC groups (p trend<0.05). Ammonia level was lower, by 15.6%, for the HEC than LEC group (p trend <0.05). Creatine kinase level was lower, by 30.5%, for the HEC than LEC group (p trend <0.05). Lactate, ammonia, and creatine kinase levels were significantly decreased after physical challenge depending on capacity (p < 0.05).

OGTT for glucose level

After the administration of glucose (2.5 g/kg) by oral gavage, blood was collected for glucose measurement (Fig. 3d). During resting, glucose level did not differ among the groups (p>0.05). At 15 and 30 min, the level was higher for the LEC group than MEC and HEC groups (p<0.05). At 60 min, the level was higher for the LEC and MEC groups than HEC group (p<0.05). At 120 min, the level was still higher for the LEC than HEC group (p<0.05).

Tissue weight and morphological examination

Eight mice were sacrificed for morphological examination of weight of physical activity-related tissues at the end of the experiment. The groups did not differ in appearance of physical-activity related tissues (p>0.05; Table 3) and the liver, lung and muscle did not differ among the groups (Fig. 4). However, the alveolus space was slightly greater for the HEC group than the other groups (Fig. 4). Table 3. Organ weight (g) in the 3 mouse groups.

Correlation between physical activities and biochemical variables

In terms of swimming endurance with different loading (0%, 5%, and 7.5%), 0% weight-loading endurance was moderately correlated with 5% and 7.5% weight-loading endurance (r=0.615, p=0.001; r=0.542, p=0.006; Fig. 5a and 5b) and 5% and 7.5% endurance were moderately correlated (r=0.606,  p=0.002, Fig. 5c). Furthermore, grip strength was moderately correlated with 0% weight-loading endurance (r=0.443, p=0.006, Fig. 5d) but not 5% and 7.5% endurance (r=0.163, p=0.166; r=0.099, p=0.409; data not shown). Ammonia level was moderately negatively correlated with 7.5% loading (r=-0.447, p=0.029; Fig. 6a) and negatively but not significantly with 5%, 7.5%, and grip strength (r=-0.391, -0.156, and -0.198; p>0.05, data not shown). Lactate and ammonia level showed a moderate positive correlation at resting status (r=0.598, p=0.002; Fig. 6b).

Comment

We previously found that the factors affecting mouse swimming endurance include weight loading, water temperature, and strain [7,9]. In this case, the factors were consistent with strain and water temperature, and congenital exercise performance showed a significant difference within the same strain according to different equivalent body-weight loading. The physiological or genetic factors may play important roles to cause this consistent difference.

The relationship between endurance capacity and voluntary activity level has been studied with mixed results. The variation in activity levels may rely on motive differences for being active as well as physical ability to increase for certain durations or particular intensities within and among species. In previous studies, 10 inbred strains were used to investigate the relationship between voluntary activities and treadmill endurance and showed that the 2 distribution patterns are not concordant and may be caused by different genetic contributions to these two phenotypes [23]. By using artificial selection/breeding for intrinsic aerobic endurance of genetically heterogeneous N:NIH rats, the high-capacity line showed 1.7-fold higher exhaustive time than the low-capacity line at generation 6 [17]. Hence, exercise capacity and voluntary activities could be affected by heritable factors [24].

Intrinsic endurance capacities have been evaluated by a treadmill running exercise in rats [17], and we used another aerobic exercise model, swimming, to distinguish intrinsic capacities. The capacities of the 3 mouse groups showed similar normal distribution (Fig. 1a) and ranking individuals by swimming time (Fig. 2b) showed results similar to that reported by Koch et al. [17]. The aerobic swimming model could also be another way to evaluate intrinsic endurance. As we showed, the outbred ICR strain mice showed intrinsic differences in exercise capacity in the founder population without artificial breeding (Fig. 1). After designating 3 groups as LEC, MEC, and HEC according to exhaustive swimming capacity with 0% weight loading (Table 1), these groups were challenged by 5% and 7.5% weight-loading tests, defined as moderate-and high-intensity exercise. The HEC group showed greater exercise endurance than the LEC group with 5% and 7.5% loading (Fig. 2). Therefore, intrinsic exercise endurance may not be affected by different exercise intensities. As well, grip strength was greater for the HEC than LEC group (Fig. 2d). Muscle strength can be improved by resistance training via muscular and neuromuscular adaption [25]. Therefore, the HEC mice may have better neuromuscular adaption or coordination to recruit more motor units for greater force production than the LEC group. However, further studies could investigate physiological differences. In the current study, we chose the ICR strain because this strain is widely used to evaluate nutrient supplements or interventions against fatigue or for exercise physiology [26][27][28][29][30]. Therefore, the individual capacity of experimental animals could influence the real effects caused by different interventions.

HEC mice could have greater glucose tolerance and maintain better homeostasis than the MEC and LEC groups (Fig. 3d). They could have better mechanisms to regulate glucose utilization for energy needs. The results were consistent with previous studies showing an association of high running capacity in rats and greater glucose uptake and oxidation as compared to low running capacity [31]. Bioinformatics analysis of skeletal muscle gene expression revealed that many of the genes upregulated in high-capacity rats were related to oxidative energy metabolism [32]. Biochemical variables including lactate, ammonia, and creatine kinase have been used to evaluate the physiological status of fatigue [7,9,33,34]. We found resting ammonia level significantly different among the 3 mouse groups (Fig. 3b); ammonia content was lower in the HEC than LEC group. After the same 15-min swimming challenge, lactate, ammonia, and creatine kinase levels were lower in the HEC than LEC group, showing significant capacity-dependent effects (Fig.  4). In a previous study, rats with low capacity promptly became fatigued and had slow metabolic recovery after stimulated maximal muscle contractions [35]. Our current findings could also illuminate physiological metabolite production that could be affected by intrinsic capacities.

In previous study, artificial selection of rats for high or low exercise endurance showed that the low-capacity line was heavier than the high-capacity line by 16% to 20% [17]. We found no difference in body weight between mice with different exercise capacities (Table 2), and the proportion of adipose tissue, including EAT and BAT, did not differ ( Table  3). The difference in weight and fat tissue proportion between previous studies and the current study may have been due to artificial breeding over several generations to establish the high-and low-capacity rats in the previous study. In the previous study, the low-capacity rats showed dysregulated muscle lipid metabolism, such as greater lipid accretion, reduced triglycerides lipase activity, greater level of non-esterified fatty acid, and triglyceride concentrations, as compared to high-capacity rats [36]. Our animals were a founder population, not artificially bred. Therefore, the genes related to lipid metabolism dysregulation were not evaluated in our case, but the weight and composition did not differ from previous results.

We found that only lung tissue showed a slight difference among groups, with greater alveolus space in the HEC group than in the LEC and MEC groups. Hemoperfusion and diffusion capacity may be elevated for oxygen being carried by red blood cells. In related reports, the pulmonary oxygen uptake was differentially affected by age and by exercise performance [37,38]. In those morphological observations, the pulmonary development could be caused by the genetic variations of these 3 groups and could also provide different viewpoints to understand the developmental process under capacity evolution in future study.

The correlations among physiological biochemistry, physical activities, and capacity were little studied in previous studies. We found greater correlation with the HEC group than other groups and the capacity of the HEC group would not be affected by different intensities (Fig. 6a-c). The ammonia concentration was negatively correlated with physical activity with 7.5% weight loading and ammonia and lactate levels were positively correlated (Fig. 7). In a previous correlation study, grip strength could be related to the bone mineral density of the lumbar spine, femoral neck, and total body [39] and was reported as a predictor of nutrition status [40]. Aerobic capacity and grip strength are moderated by a different energy metabolism system. However, we found that grip strength was significantly correlated with intrinsic aerobic capacity (Fig. 6d). For future studies, individual exercise capacity could be evaluated by grip strength or ammonia concentration (Fig. 7a) before experimental intervention.

Here, we aimed to investigate the potential mediators and relationship that affects congenital exercise performance in an animal model with physical activity challenge from physiological and biochemical perspectives. Mice performed a non-loading and exhaustive swimming test and then were assigned to 3 groups by low, medium and high exercise capacity (LEC, MEC, HEC). Physical activity, including exhaustive swimming time and grip strength, was greater for HEC than other mice. Swimming performance and grip strength were moderately correlated between groups. Resting serum ammonium level was moderately correlated with performance with a 7.5% weight load and with lactate level (p<0.05). The pulmonary morphology of the HEC group seemed to indicate benefits for aerobic exercise.

Mice showed congenital exercise performance, which was significantly correlated with different physical challenges and biochemical variables. This study may have implications for interference in intrinsic characteristics.

Conclusions

We demonstrated an intrinsic difference in exercise performance in outbred mice, which have been widely used in sport science for physiological research. The intrinsic differences might cause experimental variations or improper interpretations of the effects of interventions or supplements. We provide correlation findings for physical activities and basic biochemical variables for related references. In practical application, researchers could investigate the effect of interventions on experimental animals without influences caused by intrinsic differences in physical activities or physiology. However, these intrinsic differences in exercise performance also leave many questions to answer. In the future, these populations with different exercise capacities could be studied in terms of genomics and proteomics to provide a better understanding of the physiological differences in exercise performance.

2 Figure. 1
21.6±0.5 30.5±0.5 33.1±0.5 35.0±0.5 36.5±0.5 7.3±0.2 7.7±0.4 MEC 28.7±0.3 30.7±0.3 33.6±0.3 35.7±0.4 36.5±0.4 7.7±0.4 8.5±0.5 HEC 28.5±0.4 30.8±0.4 33.9±0.4 36.1±0.4 37.1±0.5 6.9±0.4 7.8±1.Swimming exercise capacity in mice. a Capacity shown as endurance time by number of mice. b After capacity ranking with 0% loading, exercise capacity by defined LEC, MEC, and HEC mice. LEC, low exercise capacity (<3 hr); MEC, medium exercise capacity (3-5 hr); HEC, high exercise capacity (>5 hr).

Figure. 2
2Swimming time with different loadings and grip strength. The 3 capacity groups were challenged with different weight loading from 0% (a), 5% (b) and 7.5% (c). d Grip strength. Data are mean±SD from experiments. Bars with different letters (a, b, c) are significantly different at p < 0.05.

Figure. 3
3Effect of different exercise capacity on serum levels of lactate (a); ammonia (b); glucose (c) during resting conditions. d The glucose tolerance was evaluated by oral glucose tolerance test (OGTT). Data are mean±SD from experiments. Bars or data points with different letters (a, b, c) are significantly different at p < 0.05.

epididymal adipose tissue; BAT, brown adipose tissue.

Figure. 4
4Effect of the different exercise capacity on serum levels of lactate (a); ammonia (b); glucose (c) and CK (d) after a 15-min swimming exercise challenge without loading. The glucose tolerance was evaluated by OGTT (d). Data are mean±SD from experiments. Bars with different letters (a, b) are significantly different at p < 0.05.

Figure. 5
5Sections of the liver (top), muscle (middle) and lung (bottom) from LEC, MEC and HEC groups (H&E stain).

Figure. 6
6Correlation of different physical activities for mice with different exercise capacity.

Figure. 7
7Correlation of ammonia level and 7.5% weight loading (a) and lactate level (b) for mice with difference exercise capacity.

Table 1 .
1Definition of groups and number of mice.Groups
Range of swimming time
Amounts
LEC
<3 hours
24
MEC
3-5 hours
27
HEC
>5 hours
24

LEC, low exercise capacity; MEC, medium exercise capacity; HEC, high exercise
capacity

Table 2 .
2General characteristics of growth and diet during experiments.
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AcknowledgementsThe corresponding author Huang Chi-Chang acknowledges the Ministry of Science and Technology (MOST) of Taiwan, the successor to the NationalScienceCouncil (grant no. MOST-104-2628-H-179-001-MY3), for financial support. The authors thank Laura Smales (BioMedEditing, Toronto, Canada) for her careful editing of the manuscript, Miss Jou-Chi Tang for providing technical assistance in animal experiments, and Dr. Chin-Chao Chiu, a pathological veterinarian, for interpreting the pathological data.Authors' contributionsWen-Ching Huang, Ying-Ju Chen and Chi-Chang Huang designed the experiments. Wen-Ching Huang, Yi-Ju Hsu, and Li Wei carried out the laboratory experiments. Wen-Ching Huang, Li Wei, and Chi-Chang Huang analyzed the data, interpreted the results, prepared figures, and wrote the manuscript. Chi-Chang Huang and Ying-Ju Chen contributed reagents, materials and analysis platforms. All authors read and approved the final manuscript.Competing InterestsThe authors have declared that no competing interest exists.
Status and access to the Collaborative Cross population. C E Welsh, D R Miller, K F Manly, J Wang, L Mcmillan, G Morahan, R Mott, F A Iraqi, D W Threadgill, F P De Villena, Mamm Genome. 23Welsh CE, Miller DR, Manly KF, Wang J, McMillan L, Morahan G, Mott R, Iraqi FA, Threadgill DW, de Villena FP.. Status and access to the Collaborative Cross population. Mamm Genome. 2012;23:706-12.

Association studies in outbred mice in a new era of full-genome sequencing. B Yalcin, J Flint, Mamm Genome. 23Yalcin B, Flint J. Association studies in outbred mice in a new era of full-genome sequencing. Mamm Genome. 2012;23:719-726.

Evaluation of the genotoxicity of extracts of Houttuynia cordata Thunb. C K Kang, D S Hah, C H Kim, E Kim, J S Kim, Am J Chin Med. 40Kang CK, Hah DS, Kim CH, Kim E, Kim JS. Evaluation of the genotoxicity of extracts of Houttuynia cordata Thunb. Am J Chin Med. 2012;40:1019-32.

Estimation of mouse fetal weight by ultrasonography: application from clinic to laboratory. T M Nguyen, H Nakamura, A Wakabayashi, T Kanagawa, S Koyama, T Tsutsui, T Hamasaki, T Kimura, Lab Anim. 46Nguyen TM, Nakamura H, Wakabayashi A, Kanagawa T, Koyama S, Tsutsui T, Hamasaki T, Kimura T. Estimation of mouse fetal weight by ultrasonography: application from clinic to laboratory. Lab Anim. 2012;46:225-30.

Down-regulation of hydrogen sulfide biosynthesis accompanies murine interstitial cells of Cajal dysfunction in partial ileal obstruction. X Guo, X Huang, Y S Wu, D H Liu, H L Lu, Y C Kim, W X Xu, PLoS One. 748249Guo X, Huang X, Wu YS, Liu DH, Lu HL, Kim YC, Xu WX. Down-regulation of hydrogen sulfide biosynthesis accompanies murine interstitial cells of Cajal dysfunction in partial ileal obstruction. PLoS One. 2012;7:e48249.

Ganoderma tsugae Hepatoprotection against Exhaustive exercise-induced liver injury in rats. C C Huang, W C Huang, S C Yang, C C Chan, W T Lin, Molecules. 18Huang CC, Huang WC, Yang SC, Chan CC, Lin WT. Ganoderma tsugae Hepatoprotection against Exhaustive exercise-induced liver injury in rats. Molecules. 2013;18:1741-54.

Triterpenoid-Rich Extract from Antrodia camphorata Improves Physical Fatigue and Exercise Performance in Mice. Evid Based Complement Alternat Med. C C Huang, M C Hsu, W C Huang, H R Yang, C C Hou, 364741Huang CC, Hsu MC, Huang WC, Yang HR, Hou CC. Triterpenoid-Rich Extract from Antrodia camphorata Improves Physical Fatigue and Exercise Performance in Mice. Evid Based Complement Alternat Med. 2012;2012:364741.

Effects of oat β-glucan on endurance exercise and its anti-fatigue properties in trained rats. C Xu, J Lv, Y M Lo, S W Cui, X Hu, M Fan, Carbohydr Polym. 92Xu C, Lv J, Lo YM, Cui SW, Hu X, Fan M. Effects of oat β-glucan on endurance exercise and its anti-fatigue properties in trained rats. Carbohydr Polym. 2013;92:1159-65.

Pumpkin (Cucurbita moschata) fruit extract improves physical fatigue and exercise performance in mice. S Y Wang, W C Huang, C C Liu, M F Wang, C S Ho, W P Huang, C C Hou, H L Chuang, C C Huang, Molecules. 17Wang SY, Huang WC, Liu CC, Wang MF, Ho CS, Huang WP, Hou CC, Chuang HL, Huang CC. Pumpkin (Cucurbita moschata) fruit extract improves physical fatigue and exercise performance in mice. Molecules. 2012;17:11864-76.

Effect of Conclevan on endurance capacity in mice. N Ikarashi, Y Fukazawa, T Toda, M Ishii, W Ochiai, M Usukura, K Sugiyama, Biol Pharm Bull. 35Ikarashi N, Fukazawa Y, Toda T, Ishii M, Ochiai W, Usukura M, Sugiyama K. Effect of Conclevan on endurance capacity in mice. Biol Pharm Bull. 2012;35:231-238.

Effects of citrulline supplementation on fatigue and exercise performance in mice. K Takeda, M Machida, A Kohara, N Omi, T Takemasa, J Nutr Sci Vitaminol. 57Takeda K, Machida M, Kohara A, Omi N, Takemasa T. Effects of citrulline supplementation on fatigue and exercise performance in mice. J Nutr Sci Vitaminol. 2011;57:246-50.

Bioactivity-guided fractionation for anti-fatigue property of Acanthopanax senticosus. L Z Huang, B K Huang, Q Ye, L P Qin, J Ethnopharmaco. 133Huang LZ, Huang BK, Ye Q, Qin LP. Bioactivity-guided fractionation for anti-fatigue property of Acanthopanax senticosus. J Ethnopharmaco. 2011;133:213-19.

Metabolic factors in fatigue. K Sahlin, Sports Med. 13Sahlin K. Metabolic factors in fatigue. Sports Med. 1992;13:99-107.

Lactic acid and exercise performance: culprit or friend. S P Cairns, Sports Med. 36Cairns SP. Lactic acid and exercise performance: culprit or friend? Sports Med. 2006;36:279-291.

Exercise-induced hyperammonemia: peripheral and central effects. E W Banister, B J Cameron, Int J Sports Med. 11Banister EW, Cameron BJ. Exercise-induced hyperammonemia: peripheral and central effects. Int J Sports Med. 1990;11:S129-142.

Effects of diets supplemented with branched-chain amino acids on the performance and fatigue mechanisms of rats submitted to prolonged physical exercise. G Falavigna, Alves De Araújo, Jjr, M M Rogero, I S Pires, R G Pedrosa, Martins Ejr, Alves De Castro, I Tirapegui, J , Nutrients. 4Falavigna G, Alves de Araújo JJr, Rogero MM, Pires IS, Pedrosa RG, Martins EJr, Alves de Castro I, Tirapegui J. Effects of diets supplemented with branched-chain amino acids on the performance and fatigue mechanisms of rats submitted to prolonged physical exercise. Nutrients. 2012;4:1767-80.

Artificial selection for intrinsic aerobic endurance running capacity in rats. L G Koch, S L Britton, Physiol Genomics. 5Koch LG, Britton SL. Artificial selection for intrinsic aerobic endurance running capacity in rats. Physiol Genomics. 2001;5:45-52.

Selection for increased voluntary wheel-running affects behavior and brain monoamines in mice. R P Waters, R B Pringle, G L Forster, K J Renner, J L Malisch, Garland Tjr, J G Swallow, Brain Research. 1508Waters RP, Pringle RB, Forster GL, Renner KJ, Malisch JL, Garland TJr, Swallow JG. Selection for increased voluntary wheel-running affects behavior and brain monoamines in mice. Brain Research. 2013;1508:9-22.

Overweight female rats selectively breed for low aerobic capacity exhibit increased myocardial fibrosis and diastolic dysfunction. V G Demarco, M S Johnson, L Ma, L Pulakat, I Mugerfeld, M R Hayden, M Garro, W Knight, S L Britton, L G Koch, J R Sowers, Am J Physiol Heart Circ Physiol. 302DeMarco VG, Johnson MS, Ma L, Pulakat L, Mugerfeld I, Hayden MR, Garro M, Knight W, Britton SL, Koch LG, Sowers JR. Overweight female rats selectively breed for low aerobic capacity exhibit increased myocardial fibrosis and diastolic dysfunction. Am J Physiol Heart Circ Physiol. 2012;302:1667-1682.

Intrinsic aerobic capacity sets a divide for aging and longevity. L G Koch, O J Kemi, N Qi, S X Leng, P Bijma, L J Gilligan, J E Wilkinson, H Wisløff, M A Høydal, N Rolim, P M Abadir, E M Van Grevenhof, G L Smith, C F Burant, O Ellingsen, S L Britton, U Wisløff, Circ Res. 109Koch LG, Kemi OJ, Qi N, Leng SX, Bijma P, Gilligan LJ, Wilkinson JE, Wisløff H, Høydal MA, Rolim N, Abadir PM, van Grevenhof EM, Smith GL, Burant CF, Ellingsen O, Britton SL, Wisløff U. Intrinsic aerobic capacity sets a divide for aging and longevity. Circ Res. 2011;109:1162-1172.

Vascularity of myocardium and gastrocnemius muscle in rats selectively bred for endurance running capacity. P E Beighley, M Zamir, R J Wentz, L G Koch, S L Britton, E L Ritman, Physiol Genomics. 45Beighley PE, Zamir M, Wentz RJ, Koch LG, Britton SL, Ritman EL. Vascularity of myocardium and gastrocnemius muscle in rats selectively bred for endurance running capacity. Physiol Genomics. 2013;45:119-125.

Fasudil, a Rho-kinase inhibitor, protects against excessive endurance exercise training-induced cardiac hypertrophy, apoptosis and fibrosis in rats. T J Ho, C C Huang, C Y Huang, W T Lin, Eur J Appl Physiol. 112Ho TJ, Huang CC, Huang CY, Lin WT. Fasudil, a Rho-kinase inhibitor, protects against excessive endurance exercise training-induced cardiac hypertrophy, apoptosis and fibrosis in rats. Eur J Appl Physiol. 2012;112:2943-2955.

Genetic influence on daily wheel running activity level. J T Lightfoot, M J Turner, M Daves, A Vordermark, S R Kleeberger, Physiol Genomics. 19Lightfoot JT, Turner MJ, Daves M, Vordermark A, Kleeberger SR. Genetic influence on daily wheel running activity level. Physiol Genomics. 2004;19:270-276.

Endurance capacity of mice selectively bred for high voluntary wheel running. T H Meek, B P Lonquich, R M Hannon, Garland Tjr, J Exp Biol. 212Meek TH, Lonquich BP, Hannon RM, Garland TJr. Endurance capacity of mice selectively bred for high voluntary wheel running. J Exp Biol. 2009;212:2908-2917.

The adaptations to strength training: morphological and neurological contributions to increased strength. J P Folland, A G Williams, Sports Med. 37Folland JP, Williams AG. The adaptations to strength training: morphological and neurological contributions to increased strength. Sports Med. 2007;37:145-168.

Polysaccharide of radix pseudostellariae improves chronic fatigue syndrome induced by poly I:C in mice. R Sheng, X Xu, Q Tang, D Bian, Y Li, C Qian, X He, X Gao, R Pan, C Wang, Y Luo, Y Xia, Y Dai, Evid Based Complement Alternat Med. 840516Sheng R, Xu X, Tang Q, Bian D, Li Y, Qian C, He X, Gao X, Pan R, Wang C, Luo Y, Xia Y, Dai Y. Polysaccharide of radix pseudostellariae improves chronic fatigue syndrome induced by poly I:C in mice. Evid Based Complement Alternat Med. 2011;2011:840516.

Korean mistletoe (Viscum album coloratum) extract improves endurance capacity in mice by stimulating mitochondrial activity. H Y Jung, A N Lee, T J Song, H S An, Y H Kim, K D Kim, I B Kim, K S Kim, B S Han, C H Kim, K S Kim, J B Kim, J Med Food. 15Jung HY, Lee AN, Song TJ, An HS, Kim YH, Kim KD, Kim IB, Kim KS, Han BS, Kim CH, Kim KS, Kim JB. Korean mistletoe (Viscum album coloratum) extract improves endurance capacity in mice by stimulating mitochondrial activity. J Med Food. 2012;15:621-628.

Anti-fatigue effect of Zizania caudiflora (Turczaninow) Nakai. N H Kim, P D Moon, S C Pak, H M Kim, H J Jeong, Am J Chin Med. 40Kim NH, Moon PD, Pak SC, Kim HM, Jeong HJ. Anti-fatigue effect of Zizania caudiflora (Turczaninow) Nakai. Am J Chin Med. 2012;40:111-120.

Perinatal exercise improves glucose homeostasis in adult offspring. L G Carter, K N Lewis, D C Wilkerson, C M Tobia, S Y Ngo Tenlep, P Shridas, M L Garcia-Cazarin, G Wolff, F H Andrade, R J Charnigo, K A Esser, J M Egan, R De Cabo, K J Pearson, Am J Physiol Endocrinol Metab. 303Carter LG, Lewis KN, Wilkerson DC, Tobia CM, Ngo Tenlep SY, Shridas P, Garcia-Cazarin ML, Wolff G, Andrade FH, Charnigo RJ, Esser KA, Egan JM, de Cabo R, Pearson KJ. Perinatal exercise improves glucose homeostasis in adult offspring. Am J Physiol Endocrinol Metab. 2012;303:1061-1068.

Chicken essence improves exercise performance and ameliorates physical fatigue. W C Huang, C I Lin, C C Chiu, Y T Lin, W K Huang, H Y Huang, C C Huang, Nutrients. 6Huang WC, Lin CI, Chiu CC, Lin YT, Huang WK, Huang HY, Huang CC. Chicken essence improves exercise performance and ameliorates physical fatigue. Nutrients. 2014;6:2681-2696.

Low intrinsic running capacity is associated with reduced skeletal muscle substrate oxidation and lower mitochondrial content in white skeletal muscle. D A Rivas, S J Lessard, M Saito, A M Friedhuber, L G Koch, S L Britton, B B Yaspelkis, J A Hawley, Am J Physiol Regul Integr Comp Physiol. 300Rivas DA, Lessard SJ, Saito M, Friedhuber AM, Koch LG, Britton SL, Yaspelkis BB 3rd, Hawley JA. Low intrinsic running capacity is associated with reduced skeletal muscle substrate oxidation and lower mitochondrial content in white skeletal muscle. Am J Physiol Regul Integr Comp Physiol. 2011;300:835-843.

Gene expression centroids that link with low intrinsic aerobic exercise capacity and complex disease risk. R Kivelä, M Silvennoinen, M Lehti, R Rinnankoski-Tuikka, T Purhonen, T Ketola, K Pullinen, M Vuento, N Mutanen, M A Sartor, H Reunanen, L G Koch, S L Britton, H Kainulainen, FASEB J. 24Kivelä R, Silvennoinen M, Lehti M, Rinnankoski-Tuikka R, Purhonen T, Ketola T, Pullinen K, Vuento M, Mutanen N, Sartor MA, Reunanen H, Koch LG, Britton SL, Kainulainen H. Gene expression centroids that link with low intrinsic aerobic exercise capacity and complex disease risk. FASEB J. 2010;24:4565-74.

Resveratrol protects against physical fatigue and improves exercise performance in mice. R E Wu, W C Huang, C C Liao, Y K Chang, N W Kan, C C Huang, Molecules. 18Wu RE, Huang WC, Liao CC, Chang YK, Kan NW, Huang CC. Resveratrol protects against physical fatigue and improves exercise performance in mice. Molecules. 2013;18:4689-4702.

Whey Protein Improves Exercise Performance and Biochemical Profiles in Trained Mice. W C Chen, W C Huang, C C Chiu, Y K Chang, C C Huang, Med Sci Sports Exerc. 46Chen WC, Huang WC, Chiu CC, Chang YK, Huang CC. Whey Protein Improves Exercise Performance and Biochemical Profiles in Trained Mice. Med Sci Sports Exerc. 2014;46:1517-1524.

Rats bred for low aerobic capacity become promptly fatigued and have slow metabolic recovery after stimulated, maximal muscle contractions. S Torvinen, M Silvennoinen, H Piitulainen, J Närväinen, P Tuunanen, O Gröhn, L G Koch, S L Britton, H Kainulainen, PLoS One. 748345Torvinen S, Silvennoinen M, Piitulainen H, Närväinen J, Tuunanen P, Gröhn O, Koch LG, Britton SL, Kainulainen H. Rats bred for low aerobic capacity become promptly fatigued and have slow metabolic recovery after stimulated, maximal muscle contractions. PLoS One. 2012;7:e48345.

Dysregulation of muscle lipid metabolism in rats selectively bred for low aerobic running capacity. F J Spargo, S L Mcgee, N Dzamko, M J Watt, B E Kemp, S L Britton, L G Koch, M Hargreaves, J A Hawley, Am J Physiol Endocrinol Metab. 292Spargo FJ, McGee SL, Dzamko N, Watt MJ, Kemp BE, Britton SL, Koch LG, Hargreaves M, Hawley JA. Dysregulation of muscle lipid metabolism in rats selectively bred for low aerobic running capacity. Am J Physiol Endocrinol Metab. 2007;292:1631-1636.

Update in the understanding of respiratory limitations to exercise performance in fit, active adults. J A Dempsey, D C Mckenzie, H C Haverkamp, M W Eldridge, Chest. 134Dempsey JA, McKenzie DC, Haverkamp HC, Eldridge MW. Update in the understanding of respiratory limitations to exercise performance in fit, active adults. Chest. 2008;134:613-22.

Assessing exercise limitation using cardiopulmonary exercise testing. M K Stickland, S J Butcher, D D Marciniuk, M Bhutani, Pulm Med. 824091Stickland MK, Butcher SJ, Marciniuk DD, Bhutani M. Assessing exercise limitation using cardiopulmonary exercise testing. Pulm Med. 2012;2012:824091.

Association between lean mass and handgrip strength with bone mineral density in physically active postmenopausal women. R V Marin, M A Pedrosa, L D Moreira-Pfrimer, S M Matsudo, M Lazaretti-Castro, J Clin Densitom. 13Marin RV, Pedrosa MA, Moreira-Pfrimer LD, Matsudo SM, Lazaretti-Castro M. Association between lean mass and handgrip strength with bone mineral density in physically active postmenopausal women. J Clin Densitom. 2010;13:96-101.

The use of hand grip strength as a predictor of nutrition status in hospital patients. A Flood, A Chung, H Parker, V Kearns, O Sullivan, T A , Clin Nutr. 33Flood A, Chung A, Parker H, Kearns V, O'Sullivan TA. The use of hand grip strength as a predictor of nutrition status in hospital patients. Clin Nutr. 2014;33:106-114.