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BACKGROUND: Reef-building corals, the foundation of tropical coral reefs, are vulnerable to climate change e.g. ocean acidification and elevated seawater temperature. Coral microbiome plays a key role in host acclimatization and maintenance of the coral holobiont's homeostasis under different environmental conditions, however, the response patterns of coral prokaryotic symbionts to ocean acidification and/or warming are rarely known at the metatranscriptional level, particularly the knowledge of interactive and persistent effects is limited. Using branching Acropora valida and massive Galaxea fascicularis as models in a lab system simulating extreme ocean acidification (pH 7.7) and/or warming (32 °C) in the future, we investigated the changes of in situ active prokaryotic symbionts community and gene expression of corals under/after (6/9 d) acidification (A), warming (H) and acidification-warming (AH) by metatranscriptome analysis with pH8.1, 26 °C as the control. RESULTS: A, H and AH increased the relative abundance of in situ active pathogenic bacteria. Differentially expressed genes (DEGs) involved in virulence, stress resistance, and heat shock proteins were up-regulated. Many DEGs involved in photosynthesis, carbon dioxide fixation, amino acids, cofactors and vitamins, auxin synthesis were down-regulated. A broad array of new DEGs involved in carbohydrate metabolism and energy production emerged after the stress treatment. Different response patterns of prokaryotic symbionts of massive G. fascicularis and branching A. valida were suggested, as well as the interactive effects of combined AH and persistent effects. CONCLUSIONS: The metatranscriptome-based study indicates that acidification and/or warming might change coral's in situ active prokaryotic microbial diversity and functional gene expression towards more pathogenic and destabilized coral-microbes symbioses, particularly combined acidification and warming show interactive effects. These findings will aid in comprehension of the coral holobiont's ability for acclimatization under future climate change.

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Genome of Mycetocola spongiae MSC19T, a novel marine sponge-associated Actinobacteria isolated from the Mariana Trench sponge Cacospongia mycofijiensis, was sequenced. The genome has one circular chromosome of 3,196,754 bp, with an average GC content of 66.43 mol%, and 2887 coding sequences. Gene annotation shows that M. spongiae MSC19T possesses series of genes related to adaptation to deep-sea environmental stresses including cold shock, heat shock, osmotic stress and oxidative stress. Genes encoding for heavy metal resistance, multidrug resistance and multiple natural product biosynthesis which are crucial for survival in the extreme environment are also detected in the genome. The potentials to synthesize kinds of vitamins and eukaryotic-like proteins indicates the possible nutrient exchange and mutual recognization between M. spongiae MSC19T and its sponge host. The genome provides insights into the stress resistance and ecological fitness of bacterial symbionts in the deep-sea sponge holobionts.

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The coral symbiont Symbiodinium plays important roles in the adaptation of coral to environmental changes. However, coral-Symbiodinium symbiotic associations are not well-understood in the South China Sea (SCS) whilst considering environmental factors and host taxa. In this study, next-generation sequencing of the internal transcribed spacer region 2 (ITS2) marker gene was used to explore the symbiotic associations between Symbiodinium and five typical coral species across tropical and subtropical reef regions of the SCS. The results showed that Acropora sp., Galaxea fascicularis, Platygyra lamellina, and Sarcophyton glaucum exhibited distinct Symbiodinium compositions between tropical and subtropical reef regions, whereas Porites lutea had stable Symbiodinium compositions. More heterogeneous Symbiodinium compositions among different coral species were observed in the tropical region, but there were no statistically significant differences in Symbiodinium compositions among different coral species in subtropical reef regions. There was a correlation between the Symbiodinium compositions and environmental factors, except for the composition of P. lutea. Symbiodinium subclades D1, D2, C71, C71a, C21, C3b, and C161 were primarily explained by the seawater temperature, nitrate, ammonia, and phosphate. Several host-specific Symbiodinium subclades (e.g., C15, C15.6, and C91) were observed in P. lutea as well. The findings of this study demonstrate the relationship of Symbiodinium diversity with coral hosts and the environment are helpful for elucidating the adaptation of corals to global climate change and anthropogenic disturbance.

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OBJECTIVE: In order to provide reliable data for strategies development on prevention, a meteorological factors-based predicating model for malaria forecast was studied. METHODS: Data on malaria occurrence and climate changes from 1994 to 1999 in counties in Yunnan province was collected and analyzed with software packages of FoxPro 6.0 and Excel 5.0. The forecasting model for malaria occurrence was established, using the Neural Network Toolbox of Matlab 6.1 software package. In the studies of forecasting model, data of malaria and meteorological factors from 1994 to 1999 in Honghe state in Yunnan province was chosen. The meteorological factors included average monthly pressure, air temperature, relative humidity, monthly maximum air temperature, minimum air temperature, rainfall, rainday, evaporation and sunshine hours in the study. The established forecasting model was also tested and verified. RESULTS: The BP network model was established according to data of diseases and meteorological factors from Honghe state in Yunnan province. After training the neural network for 100 times, the error of performance decreased from 3.23608 to 0.035862. Verified by fact data of malaria, the efficiency of malaria forecasting was 84.85%. CONCLUSION: Neural network model was effective for forecasting malaria. It showed advantages as: strong ability for analysis, lower claim for data, convenient and easy to apply etc. Neural network model might be used as a new method for malaria forecasting.

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