Reconstructing the transmission dynamics of rubella in Japan, 2012-2013.

BACKGROUND: Japan experienced a nationwide rubella epidemic from 2012 to 2013, mostly in urban prefectures with large population sizes. The present study aimed to capture the spatiotemporal patterns of rubella using a parsimonious metapopulation epidemic model and examine the potential usefulness of spatial vaccination. METHODOLOGY/PRINCIPAL FINDINGS: A metapopulation epidemic model in discrete time and space was devised and applied to rubella notification data from 2012 to 2013. Employing a piecewise constant model for the linear growth rate in six different time periods, and using the particle Markov chain Monte Carlo method, the effective reproduction numbers were estimated at 1.37 (95% CrI: 1.12, 1.77) and 1.37 (95% CrI: 1.24, 1.48) in Tokyo and Osaka groups, respectively, during the growing phase of the epidemic in 2013. The rubella epidemic in 2012 involved substantial uncertainties in its parameter estimates and forecasts. We examined multiple scenarios of spatial vaccination with coverages of 1%, 3% and 5% for all of Japan to be distributed in different combinations of prefectures. Scenarios indicated that vaccinating the top six populous urban prefectures (i.e., Tokyo, Kanagawa, Osaka, Aichi, Saitama and Chiba) could potentially be more effective than random allocation. However, greater uncertainty was introduced by stochasticity and initial conditions such as the number of infectious individuals and the fraction of susceptibles. CONCLUSIONS: While the forecast in 2012 was accompanied by broad uncertainties, a narrower uncertainty bound of parameters and reliable forecast were achieved during the greater rubella epidemic in 2013. By better capturing the underlying epidemic dynamics, spatial vaccination could substantially outperform the random vaccination.

Bayesian Inference of Forces Causing Cytoplasmic Streaming in Caenorhabditis elegans Embryos and Mouse Oocytes.

Cellular structures are hydrodynamically interconnected, such that force generation in one location can move distal structures. One example of this phenomenon is cytoplasmic streaming, whereby active forces at the cell cortex induce streaming of the entire cytoplasm. However, it is not known how the spatial distribution and magnitude of these forces move distant objects within the cell. To address this issue, we developed a computational method that used cytoplasm hydrodynamics to infer the spatial distribution of shear stress at the cell cortex induced by active force generators from experimentally obtained flow field of cytoplasmic streaming. By applying this method, we determined the shear-stress distribution that quantitatively reproduces in vivo flow fields in Caenorhabditis elegans embryos and mouse oocytes during meiosis II. Shear stress in mouse oocytes were predicted to localize to a narrower cortical region than that with a high cortical flow velocity and corresponded with the localization of the cortical actin cap. The predicted patterns of pressure gradient in both species were consistent with species-specific cytoplasmic streaming functions. The shear-stress distribution inferred by our method can contribute to the characterization of active force generation driving biological streaming.

Elevated ß-catenin pathway as a novel target for patients with resistance to EGF receptor targeting drugs.

There is a high death rate of lung cancer patients. Epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs) are effective in some lung adenocarcinoma patients with EGFR mutations. However, a significant number of patients show primary and acquire resistance to EGFR-TKIs. Although the Akt kinase is commonly activated due to various resistance mechanisms, the key targets of Akt remain unclear. Here, we show that the Akt-ß-catenin pathway may be a common resistance mechanism. We analyzed gene expression profiles of gefitinib-resistant PC9M2 cells that were derived from gefitinib-sensitive lung cancer PC9 cells and do not have known resistance mechanisms including EGFR mutation T790M. We found increased expression of Axin, a ß-catenin target gene, increased phosphorylation of Akt and GSK3, accumulation of ß-catenin in the cytoplasm/nucleus in PC9M2 cells. Both knockdown of ß-catenin and treatment with a ß-catenin inhibitor at least partially restored gefitinib sensitivity to PC9M2 cells. Lung adenocarcinoma tissues derived from gefitinib-resistant patients displayed a tendency to accumulate ß-catenin in the cytoplasm. We provide a rationale for combination therapy that includes targeting of the Akt-ß-catenin pathway to improve the efficacy of EGFR-TKIs.

Atom environment kernels on molecules.

The measurement of molecular similarity is an essential part of various machine learning tasks in chemical informatics. Graph kernels provide good similarity measures between molecules. Conventional graph kernels are based on counting common subgraphs of specific types in the molecular graphs. This approach has two primary limitations: (i) only exact subgraph matching is considered in the counting operation, and (ii) most of the subgraphs will be less relevant to a given task. In order to address the above-mentioned limitations, we propose a new graph kernel as an extension of the subtree kernel initially proposed by Ramon and Gärtner (2003). The proposed kernel tolerates an inexact match between subgraphs by allowing matching between atoms with similar local environments. In addition, the proposed kernel provides a method to assign an importance weight to each subgraph according to the relevance to the task, which is predetermined by a statistical test. These extensions are evaluated for classification and regression tasks of predicting a wide range of pharmaceutical properties from molecular structures, with promising results.

Extension and verification of the SEIR model on the 2009 influenza A (H1N1) pandemic in Japan.

In order to understand the evolution of the 2009 influenza A (H1N1) pandemic within local regions of Japan, we studied the significance of regional migration between these regions. For this purpose, we have employed an extended SEIR model to describe the immigration of infected people and the stochastic variation of the infectious efficiency. We then applied a data assimilation technique in order to study how the agreement of the simulation results with the observed data depends on the presence/absence of immigration and the degree of variation of the infectious efficiency. Reproducibility is evaluated by log-likelihood values. The log-likelihood does not indicate the significance of immigration. Although there are multiple waves in the time course of the number of reported infected individuals, these waves could be explained by the stochastic nature of infectious events.

Enhancement of collective immunity in Tokyo metropolitan area by selective vaccination against an emerging influenza pandemic.

Vaccination is a preventive measure against influenza that does not require placing restrictions on social activities. However, since the stockpile of vaccine that can be prepared before the arrival of an emerging pandemic strain is generally quite limited, one has to select priority target groups to which the first stockpile is distributed. In this paper, we study a simulation-based priority target selection method with the goal of enhancing the collective immunity of the whole population. To model the region in which the disease spreads, we consider an urban area composed of suburbs and central areas connected by a single commuter train line. Human activity is modelled following an agent-based approach. The degree to which collective immunity is enhanced is judged by the attack rate in unvaccinated people. The simulation results show that if students and office workers are given exclusive priority in the first three months, the attack rate can be reduced from [Formula: see text] in the baseline case down to 1-2%. In contrast, random vaccination only slightly reduces the attack rate. It should be noted that giving preference to active social groups does not mean sacrificing those at high risk, which corresponds to the elderly in our simulation model. Compared with the random administration of vaccine to all social groups, this design successfully reduces the attack rate across all age groups.

Epidermal growth factor receptor tyrosine kinase defines critical prognostic genes of stage I lung adenocarcinoma.

PURPOSE: To identify stage I lung adenocarcinoma patients with a poor prognosis who will benefit from adjuvant therapy. PATIENTS AND METHODS: Whole gene expression profiles were obtained at 19 time points over a 48-hour time course from human primary lung epithelial cells that were stimulated with epidermal growth factor (EGF) in the presence or absence of a clinically used EGF receptor tyrosine kinase (RTK)-specific inhibitor, gefitinib. The data were subjected to a mathematical simulation using the State Space Model (SSM). "Gefitinib-sensitive" genes, the expressional dynamics of which were altered by addition of gefitinib, were identified. A risk scoring model was constructed to classify high- or low-risk patients based on expression signatures of 139 gefitinib-sensitive genes in lung cancer using a training data set of 253 lung adenocarcinomas of North American cohort. The predictive ability of the risk scoring model was examined in independent cohorts of surgical specimens of lung cancer. RESULTS: The risk scoring model enabled the identification of high-risk stage IA and IB cases in another North American cohort for overall survival (OS) with a hazard ratio (HR) of 7.16 (P = 0.029) and 3.26 (P = 0.0072), respectively. It also enabled the identification of high-risk stage I cases without bronchioalveolar carcinoma (BAC) histology in a Japanese cohort for OS and recurrence-free survival (RFS) with HRs of 8.79 (P = 0.001) and 3.72 (P = 0.0049), respectively. CONCLUSION: The set of 139 gefitinib-sensitive genes includes many genes known to be involved in biological aspects of cancer phenotypes, but not known to be involved in EGF signaling. The present result strongly re-emphasizes that EGF signaling status in cancer cells underlies an aggressive phenotype of cancer cells, which is useful for the selection of early-stage lung adenocarcinoma patients with a poor prognosis. TRIAL REGISTRATION: The Gene Expression Omnibus (GEO) GSE31210.

A high-resolution shape fitting and simulation demonstrated equatorial cell surface softening during cytokinesis and its promotive role in cytokinesis.

Different models for animal cell cytokinesis posit that the stiffness of the equatorial cortex is either increased or decreased relative to the stiffness of the polar cortex. A recent work has suggested that the critical cytokinesis signaling complex centralspindlin may reduce the stiffness of the equatorial cortex by inactivating the small GTPase Rac. To determine if such a reduction occurs and if it depends on centralspindlin, we devised a method to estimate cortical bending stiffness with high spatio-temporal resolution from in vivo cell shapes. Using the early Caenorhabditis elegans embryo as a model, we show that the stiffness of the equatorial cell surface is reduced during cytokinesis, whereas the stiffness of the polar cell surface remains stiff. The equatorial reduction of stiffness was compromised in cells with a mutation in the gene encoding the ZEN-4/kinesin-6 subunit of centralspindlin. Theoretical modeling showed that the absence of the equatorial reduction of stiffness could explain the arrest of furrow ingression in the mutant. By contrast, the equatorial reduction of stiffness was sufficient to generate a cleavage furrow even without the constriction force of the contractile ring. In this regime, the contractile ring had a supportive contribution to furrow ingression. We conclude that stiffness is reduced around the equator in a centralspindlin-dependent manner. In addition, computational modeling suggests that proper regulation of stiffness could be sufficient for cleavage furrow ingression.

Bayesian experts in exploring reaction kinetics of transcription circuits.

MOTIVATION: Biochemical reactions in cells are made of several types of biological circuits. In current systems biology, making differential equation (DE) models simulatable in silico has been an appealing, general approach to uncover a complex world of biochemical reaction dynamics. Despite of a need for simulation-aided studies, our research field has yet provided no clear answers: how to specify kinetic values in models that are difficult to measure from experimental/theoretical analyses on biochemical kinetics. RESULTS: We present a novel non-parametric Bayesian approach to this problem. The key idea lies in the development of a Dirichlet process (DP) prior distribution, called Bayesian experts, which reflects substantive knowledge on reaction mechanisms inherent in given models and experimentally observable kinetic evidences to the subsequent parameter search. The DP prior identifies significant local regions of unknown parameter space before proceeding to the posterior analyses. This article reports that a Bayesian expert-inducing stochastic search can effectively explore unknown parameters of in silico transcription circuits such that solutions of DEs reproduce transcriptomic time course profiles. AVAILABILITY: A sample source code is available at the URL http://daweb.ism.ac.jp/~yoshidar/lisdas/.

A state space representation of VAR models with sparse learning for dynamic gene networks.

We propose a state space representation of vector autoregressive model and its sparse learning based on L1 regularization to achieve efficient estimation of dynamic gene networks based on time course microarray data. The proposed method can overcome drawbacks of the vector autoregressive model and state space model; the assumption of equal time interval and lack of separation ability of observation and systems noises in the former method and the assumption of modularity of network structure in the latter method. However, in a simple implementation the proposed model requires the calculation of large inverse matrices in a large number of times during parameter estimation process based on EM algorithm. This limits the applicability of the proposed method to a relatively small gene set. We thus introduce a new calculation technique for EM algorithm that does not require the calculation of inverse matrices. The proposed method is applied to time course microarray data of lung cells treated by stimulating EGF receptors and dosing an anticancer drug, Gefitinib. By comparing the estimated network with the control network estimated using non-treated lung cells, perturbed genes by the anticancer drug could be found, whose up- and down-stream genes in the estimated networks may be related to side effects of the anticancer drug.

Effect of collagen hydrolysates from salmon and trout skins on the lipid profile in rats.

The effect of collagen hydrolysates from fish skins on lipid profile was assessed in rats administered chum salmon or rainbow trout collagen peptide. Single oral administration of soybean oil with or without one of either type of fish collagen peptide demonstrated that rat plasma triglycerides were significantly decreased 2 h later after the intake of oil and peptide mixtures (p < 0.05). The free and peptide forms of hydroxyproline derived from fish collagen peptides were statistically higher than those of zero time after oral administration. To test the effect of fish collagen peptides on continuous administration, rats were fed an AIN-93G purified diet containing 0.17% fish collagen peptide. The peptide groups had lower levels of plasma total lipids and triglycerides compared with the control group. However, the body, liver, and fat weights of rats were not significantly different between groups. These results suggest that fish collagen hydrolysates affect lipid absorption and metabolism in rats and may be useful in suppressing the transient increase of plasma triglycerides.

Parameter estimation of in silico biological pathways with particle filtering towards a petascale computing.

The aim of this paper is to demonstrate the potential power of large-scale particle filtering for the parameter estimations of in silico biological pathways where time course measurements of biochemical reactions are observable. The method of particle filtering has been a popular technique in the field of statistical science, which approximates posterior distributions of model parameters of dynamic system by using sequentially-generated Monte Carlo samples. In order to apply the particle filtering to system identifications of biological pathways, it is often needed to explore the posterior distributions which are defined over an exceedingly high-dimensional parameter space. It is then essential to use a fairly large amount of Monte Carlo samples to obtain an approximation with a high-degree of accuracy. In this paper, we address some implementation issues on large-scale particle filtering, and then, indicate the importance of large-scale computing for parameter learning of in silico biological pathways. We have tested the ability of the particle filtering with 10(8) Monte Carlo samples on the transcription circuit of circadian clock that contains 45 unknown kinetic parameters. The proposed approach could reveal clearly the shape of the posterior distributions over the 45 dimensional parameter space.

A comparative study of the effects of erabu sea snake (Laticauda semifasciata) lipids, green tea extract and conjugated linoleic acid on the swimming endurance of mice.

The aim of this study was to assess the interaction between physical and biochemical parameters in mice fed 1 % sea snake lipids (SSL) and compare these with animals fed diets containing 0.2 % green tea extract (GTE) or 0.5 % conjugated linoleic acid (CLA). The swimming times of the SSL group were significantly increased at Weeks 12 and 16 (p<0.001), and those of the GTE group, at Week 12 (p<0.005), but not those of the control or CLA group, compared with those at Week 0. The increase tended to be significant in the SSL group compared with the control group at Week 12 (p=0.09). Both the SSL and GTE groups had significantly longer swimming times than the CLA group at Weeks 12 and 16 (p<0.001). After 5 minutes of swimming exercise, the SSL group exhibited significantly lower levels of plasma and muscle lactates (p<0.01), and plasma non-esterified fatty acid (NEFA) (p<0.001) than the control group. There were no significant differences in any of plasma glucose, muscle and liver glycogens, muscle lactate dehydrogenase (LDH), carnitine palmitoyltransferase (CPT), or monocarboxylate transporter 1 (MCT1) between SSL and control groups. The results suggest that the intake of 1 % SSL improved endurance more than the intake of 0.2 % GTE or 0.5 % CLA in mice. This action may involve the promotion of lactate oxidation for utilization.

Effects of yogurt supplemented with fish oil on plasma lipid and glucose concentrations, and liver lipid contents in mice.

The aim of this study is to clarify the effects of yogurt supplemented with fish oil on plasma lipid and glucose concentrations, and hepatic lipid contents in mice. Male Crlj:CD-1 (ICR) mice were fed five experimental diets for 12 weeks. The experimental diets were as follows: without yogurt and fish oil (control diet); 10% (w/w) yogurt without fish oil [10% FO(-)]; 10% yogurt with fish oil [10% FO(+)]; 30% yogurt without fish oil [30% FO(-)]; 30% yogurt with fish oil [30% FO(+)]. Plasma triacylglycerol concentrations in the 10% FO(+) and 30% FO(-) groups were significantly lower than that in the control diet group (p < 0.05 and p < 0.05, respectively). Plasma total cholesterol and phospholipid concentrations were significantly lower in the 30% FO(+) group than in the control diet group (p < 0.005). Concentrations tended to be lower with supplementation with fish oil. Plasma glucose concentrations in the 10% FO(+) and 30% FO(+) groups were significantly lower than those in the control diet group (p < 0.005 and p < 0.01, respectively). Hepatic triacylglycerol and total cholesterol contents in the 30% FO(+) group were significantly lower than those in the control diet group (p < 0.05 and p < 0.005, respectively). These results suggest that plasma triacylglycerol and glucose concentrations are effectively decreased by supplementation of yogurt with fish oil.

Bayesian learning of biological pathways on genomic data assimilation.

MOTIVATION: Mathematical modeling and simulation, based on biochemical rate equations, provide us a rigorous tool for unraveling complex mechanisms of biological pathways. To proceed to simulation experiments, it is an essential first step to find effective values of model parameters, which are difficult to measure from in vivo and in vitro experiments. Furthermore, once a set of hypothetical models has been created, any statistical criterion is needed to test the ability of the constructed models and to proceed to model revision. RESULTS: The aim of our research is to present a new statistical technology towards data-driven construction of in silico biological pathways. The method starts with a knowledge-based modeling with hybrid functional Petri net. It then proceeds to the Bayesian learning of model parameters for which experimental data are available. This process exploits quantitative measurements of evolving biochemical reactions, e.g. gene expression data. Another important issue that we consider is statistical evaluation and comparison of the constructed hypothetical pathways. For this purpose, we have developed a new Bayesian information-theoretic measure that assesses the predictability and the biological robustness of in silico pathways. AVAILABILITY: The FORTRAN source codes are available at the URL http://daweb.ism.ac.jpyoshidar/GDA/ SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Effects of herring roe on plasma lipid, glucose, insulin and adiponectin levels, and hepatic lipid contents in mice.

We previously reported that lipids extracted from salted herring roe product (Kazunoko), which contains large amounts of cholesterol, phosphatidylcholine and n-3 polyunsaturated fatty acids (PUFAs) such as eicosapentaenoic acid (EPA, 20:5n-3) and docosahexaenoic acid (DHA, 22:6n-3), decreased plasma lipid and glucose concentrations in mice. The aim of this study was to evaluate the effects of Kazunoko containing large amounts of protein on lipid and glucose metabolism in mice. Male Crlj:CD-1 (ICR) mice were fed three experimental diets containing lyophilized Kazunoko for 12 wk. The experimental diets were as follows: without Kazunoko (control diet); 1% Kazunoko (1% Kazunoko diet); and 4% Kazunoko (4% Kazunoko diet). Plasma total cholesterol, phospholipid and glucose concentrations tended to be lower in the 1% and 4% Kazunoko diet groups than in the control diet group. There were significant differences in plasma glucose concentration between the control and 4% Kazunoko diet groups (p<0.05). Plasma adiponectin concentrations in mice fed the 4% Kazunoko diet were also significantly higher than in those fed the control diet (p<0.05), but there were no marked differences in plasma insulin concentration among the three dietary groups. Hepatic total cholesterol and phospholipid contents tended to be lower in the 4% Kazunoko diet group than in control diet group. Plasma and hepatic n-3/n-6 ratios in the 1% Kazunoko diet and 4% Kazunoko diet groups were significantly higher when compared with those of the control diet group (p<0.005 and p<0.0005, respectively). These results suggest that ingestion of Kazunoko influences lipid and glucose metabolism in mice fed the Kazunoko diets, as compared with animals fed the control diet.

Statistical inference of transcriptional module-based gene networks from time course gene expression profiles by using state space models.

MOTIVATION: Statistical inference of gene networks by using time-course microarray gene expression profiles is an essential step towards understanding the temporal structure of gene regulatory mechanisms. Unfortunately, most of the current studies have been limited to analysing a small number of genes because the length of time-course gene expression profiles is fairly short. One promising approach to overcome such a limitation is to infer gene networks by exploring the potential transcriptional modules which are sets of genes sharing a common function or involved in the same pathway. RESULTS: In this article, we present a novel approach based on the state space model to identify the transcriptional modules and module-based gene networks simultaneously. The state space model has the potential to infer large-scale gene networks, e.g. of order 10(3), from time-course gene expression profiles. Particularly, we succeeded in the identification of a cell cycle system by using the gene expression profiles of Saccharomyces cerevisiae in which the length of the time-course and number of genes were 24 and 4382, respectively. However, when analysing shorter time-course data, e.g. of length 10 or less, the parameter estimations of the state space model often fail due to overfitting. To extend the applicability of the state space model, we provide an approach to use the technical replicates of gene expression profiles, which are often measured in duplicate or triplicate. The use of technical replicates is important for achieving highly-efficient inferences of gene networks with short time-course data. The potential of the proposed method has been demonstrated through the time-course analysis of the gene expression profiles of human umbilical vein endothelial cells (HUVECs) undergoing growth factor deprivation-induced apoptosis. AVAILABILITY: Supplementary Information and the software (TRANS-MNET) are available at http://daweb.ism.ac.jp/~yoshidar/software/ssm/.

Levels of plasma insulin, leptin and adiponectin, and activities of key enzymes in carbohydrate metabolism in skeletal muscle and liver in fasted ICR mice fed dietary n-3 polyunsaturated fatty acids.

The aim of this study was to clarify the mechanisms related to plasma glucose concentration in mice fed a diet rich in n-3 polyunsaturated fatty acids (n-3 PUFAs). Male Crlj:CD-1 (ICR) mice were fed experimental diets containing 6% lard (LD), 6% fish oil (FO) or 4.1% lard plus 1.5% docosahexaenoic acid ethyl ester and 0.4% eicosapentaenoic acid ethyl ester (DE) for 12 weeks. There were no marked differences in plasma glucose and insulin concentration changes on glucose tolerance test between the three dietary groups. At the end of the feeding trial, plasma glucose concentration was significantly lower in fasted mice in the FO group than in those in the LD group (P<.005). Plasma adiponectin concentration was significantly higher in the FO group than in the LD group (P<.05). Hexokinase, phosphofructokinase, glucose-6-phosphate dehydrogenase and glycerophosphate dehydrogenase activities in skeletal muscle tended to be lower in the FO group than in the LD group, while there were no differences in glucokinase and phosphofructokinase activities in liver between the three dietary groups. However, hepatic glycerophosphate dehydrogenase activity was 53-fold and 4.2-fold higher in the FO group than in the LD and DE groups, respectively (P<.0005 and P<.05, respectively). These results suggest that the reduction in plasma glucose concentration in mice fed n-3 PUFAs is mainly caused by acceleration of glucose uptake and glycerol synthesis in the liver rather than in the skeletal muscle.

Predicting differences in gene regulatory systems by state space models.

We propose a statistical strategy to predict differentially regulated genes of case and control samples from time-course gene expression data by leveraging unpredictability of the expression patterns from the underlying regulatory system inferred by a state space model. The proposed method can screen out genes that show different patterns but generated by the same regulations in both samples, since these patterns can be predicted by the same model. Our strategy consists of three steps. Firstly, a gene regulatory system is inferred from the control data by a state space model. Then the obtained model for the underlying regulatory system of the control sample is used to predict the case data. Finally, by assessing the significance of the difference between case and predicted-case time-course data of each gene, we are able to detect the unpredictable genes that are the candidate as the key differences between the regulatory systems of case and control cells. We illustrate the whole process of the strategy by an actual example, where human small airway epithelial cell gene regulatory systems were generated from novel time courses of gene expressions following treatment with(case)/without(control) the drug gefitinib, an inhibitor for the epidermal growth factor receptor tyrosine kinase. Finally, in gefitinib response data we succeeded in finding unpredictable genes that are candidates of the specific targets of gefitinib. We also discussed differences in regulatory systems for the unpredictable genes. The proposed method would be a promising tool for identifying biomarkers and drug target genes.

Effect of Erabu sea snake (Laticauda semifasciata) lipids on the swimming endurance of mice.

BACKGROUND/AIM: This study was designed to investigate the effect of Erabu sea snake (Laticauda semifasciata) lipids on the swimming endurance of mice. METHOD: Twelve-week-old male Crlj: CD-1 (ICR) mice were fed one of three experimental diets containing 6% lard, fish oil or sea snake lipids for 16 weeks. Swimming exercise was conducted in an acrylic plastic tank filled with 25 cm of water maintained at 23 degrees C. Every 4 weeks, the mice were made to perform swimming exercises with loads attached to their tails, corresponding to approximately 1 or 2% of their body weights. RESULTS: The group fed the sea snake lipid diet exhibited significantly improved swimming endurance compared with the lard diet group (p < 0.05); however, this result was not observed in the fish oil diet group. In the sea snake lipid diet group, plasma and muscle lactates were significantly lower, and plasma glucose and muscle glycogen were significantly higher than in the lard diet group (p < 0.05). CONCLUSION: These results suggest that the intake of sea snake lipids enhanced the swimming endurance of the mice by delaying the accumulation of lactate during swimming exercise.