Integrated intracellular metabolic profiling and pathway analysis approaches reveal complex metabolic regulation by Clostridium acetobutylicum

BACKGROUND: Clostridium acetobutylicum is one of the most important butanol producing strains. However, environmental stress in the fermentation process usually leads to a lower yield, seriously hampering its industrialization. In order to systematically investigate the key intracellular metabolites that influence the strain growth and butanol production, and find out the critical regulation nodes, an integrated analysis approach has been carried out in this study. RESULTS: Based on the gas chromatography-mass spectrometry technology, the partial least square discriminant analysis and the pathway analysis, 40 metabolic pathways linked with 43 key metabolic nodes were identified. In-depth analysis showed that lots of amino acids metabolism promoted cell growth but exerted slight influence on butanol production, while sugar metabolism was favorable for cell growth but unfavorable for butanol synthesis. Besides, both lysine and succinic acid metabolism generated a complex effect on the whole metabolic network. Dicarboxylate metabolism exerted an indispensable role on cell growth and butanol production. Subsequently, rational feeding strategies were proposed to verify these conclusions and facilitate the butanol biosynthesis. Feeding amino acids, especially glycine and serine, could obviously improve cell growth while yeast extract, citric acid and ethylene glycol could significantly enhance both growth and butanol production. CONCLUSIONS: The feeding experiment confirmed that metabolic profiling combined with pathway analysis provided an accurate, reasonable and practical approach to explore the cellular metabolic activity and supplied a basis for improving butanol production. These strategies can also be extended for the production of other important bio-chemical compounds. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12934-016-0436-4) contains supplementary material, which is available to authorized users

A Dimensionality Reduction-Based Multi-Step Clustering Method for Robust Vessel Trajectory Analysis

The Shipboard Automatic Identification System (AIS) is crucial for navigation safety and maritime surveillance, data mining and pattern analysis of AIS information have attracted considerable attention in terms of both basic research and practical applications. Clustering of spatio-temporal AIS trajectories can be used to identify abnormal patterns and mine customary route data for transportation safety. Thus, the capacities of navigation safety and maritime traffic monitoring could be enhanced correspondingly. However, trajectory clustering is often sensitive to undesirable outliers and is essentially more complex compared with traditional point clustering. To overcome this limitation, a multi-step trajectory clustering method is proposed in this paper for robust AIS trajectory clustering. In particular, the Dynamic Time Warping (DTW), a similarity measurement method, is introduced in the first step to measure the distances between different trajectories. The calculated distances, inversely proportional to the similarities, constitute a distance matrix in the second step. Furthermore, as a widely-used dimensional reduction method, Principal Component Analysis (PCA) is exploited to decompose the obtained distance matrix. In particular, the top k principal components with above 95% accumulative contribution rate are extracted by PCA, and the number of the centers k is chosen. The k centers are found by the improved center automatically selection algorithm. In the last step, the improved center clustering algorithm with k clusters is implemented on the distance matrix to achieve the final AIS trajectory clustering results. In order to improve the accuracy of the proposed multi-step clustering algorithm, an automatic algorithm for choosing the k clusters is developed according to the similarity distance. Numerous experiments on realistic AIS trajectory datasets in the bridge area waterway and Mississippi River have been implemented to compare our proposed method with traditional spectral clustering and fast affinity propagation clustering. Experimental results have illustrated its superior performance in terms of quantitative and qualitative evaluation

Trade-induced atmospheric mercury deposition over China and implications for demand-side controls

Mercury (Hg) is of global concern because of its adverse effects on humans and the environment. In addition to long-range atmospheric transport, Hg emissions can be geographically relocated through economic trade. Here, we investigate the effect of China’s interregional trade on atmospheric Hg deposition over China, using an atmospheric transport model and multiregional input-output analysis. In general, total atmospheric Hg deposition over China is 408.8 Mg yr-1, and 32% of this is embodied in China’s interregional trade, with the hotspots occurring over Gansu, Henan, Hebei, and Yunnan provinces. Interprovincial trade considerably redistributes atmospheric Hg deposition over China, with a range in deposition flux from −104% to +28%. Developed regions, such as the Yangtze River Delta (Shanghai, Jiangsu, and Zhejiang) and Guangdong, avoid Hg deposition over their geographical boundaries, instead causing additional Hg deposition over developing provinces. Bilateral interaction among provinces is strong over some regions, suggesting a need for joint mitigation, such as the Jing-Jin-Ji region (Beijing, Tianjin, and Hebei) and the Yangtze River Delta. Transferring advanced technology from developed regions to their developing trade partners would be an effective measure to mitigate China’s Hg pollution. Our findings are relevant to interprovincial efforts to reduce trans-boundary Hg pollution in China

Coexistence of Histologically Confirmed Hashimoto's Thyroiditis with Different Stages of Papillary Thyroid Carcinoma in a Consecutive Chinese Cohort

Purpose. To determine the relationship between Hashimoto's thyroiditis (HT) and all stages of papillary thyroid carcinoma (PTC) with or without local lymph node metastasis (LNM). Methods:. We conducted a retrospective study of thyroidectomies from 2008–2013 in First Affiliated Hospital of Nanjing Medical University. We categorized patients according to the presence of histopathologically proven HT. The prevalence of mPTC (maximum diameter ≤ 10 mm) and crPTC (clinical relevant PTC) and local LNM rates were compared. Results:. We evaluated 6,432 consecutive thyroidectomies. In total, 1,328 specimens were confirmed as HT. The prevalence of PTC in this HT cohort was 43.8%, significantly higher than non-HT group. After adjustment of gender and age, the prevalence of PTC was still higher in HT group. HT was a risk factor for PTC in multivariate analysis with odds ratio 2.725 (95% CI, 2.390–3.109) (P < 0.001). However, no correlation was found between HT and LNM of PTC. Conclusion:. HT was associated with an increased prevalence of all stages of PTC, independent of tumor size, gender, and age. In contrast, locally advanced disease defined by LNM was unrelated to HT. These data suggest an association of HT with low risk PTC and a potential protective immunologic effect from further disease progression

Effect of combined use of L carnitine and hemodialysis on clinical efficacy and quality of life of uremic patients

Purpose: To investigate the clinical efficacy of combined use of L-carnitine and hemodialysis in the treatment of uremic patients, and its effect on their quality of life. Methods: A total of 160 uremic patients who were admitted to Lujiang County People's Hospital from November 2018 to August 2020 were selected and divided equally into dialysis group (hemodialysis), and combined group (L-carnitine + hemodialysis). Some clinical indices and parameters, including safety profile, National Institutes of Health Stroke Scale (NIHSS) score, CD4+ and CD4+/CD8+, and plasma protein levels were evaluated for both study groups. Results: Patients treated with L-carnitine + hemodialysis in the combined group resulted in significantly higher clinical treatment effectiveness than those in the dialysis group (p &lt; 0.05). However, safety profiles were comparable in the two groups (p &gt; 0.05). No significant difference in NIHSS score between the two groups before treatment, but L-carnitine + hemodialysis led to a better NIHSS score than in the dialysis group after treatment (p &lt; 0.05). However, there were better levels of CD4+ and CD4+/CD8+ in the combined group than in the dialysis group (p &lt; 0.05). Similarly, although pre-treatment plasma protein levels in the two groups were comparable, there were significantly lower plasma protein levels in the combined group than in the dialysis group post-treatment (p &lt; 0.05). Conclusion: The combination of L-carnitine and hemodialysis in the treatment of uremia patients improves the clinical management of the patients, enhances their quality of life, and shows good safety profile. However, further clinical trials should be carried out prior to the application of the combined treatment in clinical practice