Host genetic background rather than diet-induced gut microbiota shifts of sympatric black-necked crane, common crane and bar-headed goose

IntroductionGut microbiota of wild birds are affected by many factors, and host genetic background and diet are considered to be two important factors affecting their structure and function.MethodsIn order to clarify how these two factors influence the gut microbiota, this study selected the sympatric and closely related and similar-sized Black-necked Crane (Grus nigricollis) and Common Crane (Grus grus), as well as the distantly related and significantly different-sized Bar-headed Goose (Anser indicus). The fecal samples identified using sanger sequencing as the above three bird species were subjected to high-throughput sequencing of rbcL gene and 16S rRNA gene to identify the feeding types phytophagous food and gut microbiota.ResultsThe results showed significant differences in food diversity between black-necked cranes and Common Cranes, but no significant differences in gut microbiota, Potatoes accounted for approximately 50% of their diets. Bar-headed Geese mainly feed on medicinal plants such as Angelica sinensis, Alternanthera philoxeroides, and Ranunculus repens. Black-necked cranes and Common Cranes, which have a high-starch diet, have a similar degree of enrichment in metabolism and synthesis functions, which is significantly different from Bar-headed Geese with a high-fiber diet. The differences in metabolic pathways among the three bird species are driven by food. The feeding of medicinal plants promotes the health of Bar-headed Geese, indicating that food influences the functional pathways of gut microbiota. Spearman analysis showed that there were few gut microbiota related to food, but almost all metabolic pathways were related to food.ConclusionThe host genetic background is the dominant factor determining the composition of the microbiota. Monitoring the changes in gut microbiota and feeding types of wild birds through bird feces is of great reference value for the conservation of other endangered species

Mitochondrial genome analysis, phylogeny and divergence time evaluation of the Strix aluco (Aves, Strigiformes, Strigidae)

BackgroundDivergence time analysis can provide a reference for the evolution process of species and is also the basis for other further studies. In this study, a complete mitochondrial genome of the Strix aluco was reported for the first time, with a total length of 18,632 bp. There were 37 genes, including 22 tRNAs, 2 rRNAs, 13 protein-coding genes (PCGs), and 2 non-coding control regions (D-loop). This study provides a reference for the evolution history of the S. aluco.New informationThe second-generation sequencing of the complete mitochondrial genome of the S. aluco was conducted using the Illumina platform, and then Tytoninae was used as the out-group, PhyloSuite software was applied to build the ML-tree and BI-tree of the Strigiformes, and finally, the divergence time tree was constructed using Beast2.6.7 software, the age of Miosurnia diurna fossil-bearing sediments (6.0~9.5 Ma) was set as the internal correction point. The common ancestor of the Strix was confirmed to have diverged during the Pleistocene(2.58~0.01Ma). The dramatic uplift of the Qinling Mountains in the Middle Pleistocene and the climate oscillation of the Pleistocene together caused Strix divergence between the northern and southern parts of mainland China. The isolation of glacial-interglacial rotation and glacier refuge was the main reason for the divergence of the common ancestor of the Strix uralensis and the S. aluco during this period

Comprehensive Analysis of Metabolome and Transcriptome in Fruits and Roots of Kiwifruit

Kiwifruit (Actinidia chinensis) roots instead of fruits are widely used as Chinese medicine, but the functional metabolites remain unclear. In this study, we conducted comparative metabolome analysis between root and fruit in kiwifruit. A total of 410 metabolites were identified in the fruit and root tissues, and of them, 135 metabolites were annotated according to the Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway. Moreover, 54 differentially expressed metabolites (DEMs) were shared in root and fruit, with 17 DEMs involved in the flavonoid pathway. Of the 17 DEMs, three flavonols (kaempferol-3-rhamnoside, L-Epicatechin and trifolin) and one dihydrochalcone (phloretin) showed the highest differences in the content level, suggesting that flavonols and dihydrochalcones may act as functional components in kiwifruit root. Transcriptome analysis revealed that genes related to flavonols and dihydrochalcones were highly expressed in root. Moreover, two AP2 transcription factors (TFs), AcRAP2-4 and AcAP2-4, were highly expressed in root, while one bHLH TF AcbHLH62 showed extremely low expression in root. The expression profiles of these TFs were similar to those of the genes related to flavonols and dihydrochalcones, suggesting they are key candidate genes controlling the flavonoid accumulation in kiwifruit. Our results provided an insight into the functional metabolites and their regulatory mechanism in kiwifruit root

Mitochondrial genome analysis, phylogeny and divergence time evaluation of Strix aluco (Aves, Strigiformes, Strigidae)

BackgroundPrior research has shown that the European peninsulas were the main sources of Strix aluco colonisation of Northern Europe during the late glacial period. However, the phylogenetic relationship and the divergence time between S. aluco from Leigong Mountain Nature Reserve, Guizhou Province, China and the Strigiformes from overseas remains unclear. The mitochondrial genome structure of birds is a covalent double-chain loop structure that is highly conserved and, thus, suitable for phylogenetic analysis. This study examined the phylogenetic relationship and divergence time of Strix using the whole mitochondrial genome of S. aluco.New informationIn this study, the complete mitochondrial genome of Strix aluco, with a total length of 18,632 bp, is reported for the first time. A total of 37 genes were found, including 22 tRNAs, two rRNAs, 13 protein-coding genes and two non-coding control regions. Certain species of Tytoninae were used as out-group and PhyloSuite software was applied to build the ML-tree and BI-tree of Strigiformes. Finally, the divergence time tree was constructed using BEAST 2.6.7 software and the age of Miosurnia diurna fossil-bearing sediments (6.0–9.5 Ma) was set as internal correction point. The common ancestor of Strix was confirmed to have diverged during the Pleistocene (2.58–0.01 Ma). The combined action of the dramatic uplift of the Qinling Mountains in the Middle Pleistocene and the climate oscillation of the Pleistocene caused Strix divergence between the northern and southern parts of mainland China. The isolation of glacial-interglacial rotation and glacier refuge was the main reason for the divergence of Strix uralensis and S. aluco from their common ancestor during this period. This study provides a reference for the evolutionary history of S. aluco

Automatic Verification Flow Shop Scheduling of Electric Energy Meters Based on an Improved Q-Learning Algorithm

Considering the engineering problem of electric energy meter automatic verification and scheduling, this paper proposes a novel scheduling scheme based on an improved Q-learning algorithm. First, by introducing the state variables and behavior variables, the ranking problem of combinatorial optimization is transformed into a sequential decision problem. Then, a novel reward function is proposed to evaluate the pros and cons of the different strategies. In particular, this paper considers adopting the reinforcement learning algorithm to efficiently solve the problem. In addition, this paper also considers the ratio of exploration and utilization in the reinforcement learning process, and then provides reasonable exploration and utilization through an iterative updating scheme. Meanwhile, a decoupling strategy is introduced to address the restriction of over estimation. Finally, real time data from a provincial electric energy meter automatic verification center are used to verify the effectiveness of the proposed algorithm

Automatic Verification Flow Shop Scheduling of Electric Energy Meters Based on an Improved Q-Learning Algorithm

Considering the engineering problem of electric energy meter automatic verification and scheduling, this paper proposes a novel scheduling scheme based on an improved Q-learning algorithm. First, by introducing the state variables and behavior variables, the ranking problem of combinatorial optimization is transformed into a sequential decision problem. Then, a novel reward function is proposed to evaluate the pros and cons of the different strategies. In particular, this paper considers adopting the reinforcement learning algorithm to efficiently solve the problem. In addition, this paper also considers the ratio of exploration and utilization in the reinforcement learning process, and then provides reasonable exploration and utilization through an iterative updating scheme. Meanwhile, a decoupling strategy is introduced to address the restriction of over estimation. Finally, real time data from a provincial electric energy meter automatic verification center are used to verify the effectiveness of the proposed algorithm

Image_1_Host genetic background rather than diet-induced gut microbiota shifts of sympatric black-necked crane, common crane and bar-headed goose.TIF

IntroductionGut microbiota of wild birds are affected by many factors, and host genetic background and diet are considered to be two important factors affecting their structure and function.MethodsIn order to clarify how these two factors influence the gut microbiota, this study selected the sympatric and closely related and similar-sized Black-necked Crane (Grus nigricollis) and Common Crane (Grus grus), as well as the distantly related and significantly different-sized Bar-headed Goose (Anser indicus). The fecal samples identified using sanger sequencing as the above three bird species were subjected to high-throughput sequencing of rbcL gene and 16S rRNA gene to identify the feeding types phytophagous food and gut microbiota.ResultsThe results showed significant differences in food diversity between black-necked cranes and Common Cranes, but no significant differences in gut microbiota, Potatoes accounted for approximately 50% of their diets. Bar-headed Geese mainly feed on medicinal plants such as Angelica sinensis, Alternanthera philoxeroides, and Ranunculus repens. Black-necked cranes and Common Cranes, which have a high-starch diet, have a similar degree of enrichment in metabolism and synthesis functions, which is significantly different from Bar-headed Geese with a high-fiber diet. The differences in metabolic pathways among the three bird species are driven by food. The feeding of medicinal plants promotes the health of Bar-headed Geese, indicating that food influences the functional pathways of gut microbiota. Spearman analysis showed that there were few gut microbiota related to food, but almost all metabolic pathways were related to food.ConclusionThe host genetic background is the dominant factor determining the composition of the microbiota. Monitoring the changes in gut microbiota and feeding types of wild birds through bird feces is of great reference value for the conservation of other endangered species.</p

Table_2_Host genetic background rather than diet-induced gut microbiota shifts of sympatric black-necked crane, common crane and bar-headed goose.DOCX

IntroductionGut microbiota of wild birds are affected by many factors, and host genetic background and diet are considered to be two important factors affecting their structure and function.MethodsIn order to clarify how these two factors influence the gut microbiota, this study selected the sympatric and closely related and similar-sized Black-necked Crane (Grus nigricollis) and Common Crane (Grus grus), as well as the distantly related and significantly different-sized Bar-headed Goose (Anser indicus). The fecal samples identified using sanger sequencing as the above three bird species were subjected to high-throughput sequencing of rbcL gene and 16S rRNA gene to identify the feeding types phytophagous food and gut microbiota.ResultsThe results showed significant differences in food diversity between black-necked cranes and Common Cranes, but no significant differences in gut microbiota, Potatoes accounted for approximately 50% of their diets. Bar-headed Geese mainly feed on medicinal plants such as Angelica sinensis, Alternanthera philoxeroides, and Ranunculus repens. Black-necked cranes and Common Cranes, which have a high-starch diet, have a similar degree of enrichment in metabolism and synthesis functions, which is significantly different from Bar-headed Geese with a high-fiber diet. The differences in metabolic pathways among the three bird species are driven by food. The feeding of medicinal plants promotes the health of Bar-headed Geese, indicating that food influences the functional pathways of gut microbiota. Spearman analysis showed that there were few gut microbiota related to food, but almost all metabolic pathways were related to food.ConclusionThe host genetic background is the dominant factor determining the composition of the microbiota. Monitoring the changes in gut microbiota and feeding types of wild birds through bird feces is of great reference value for the conservation of other endangered species.</p

Table_1_Host genetic background rather than diet-induced gut microbiota shifts of sympatric black-necked crane, common crane and bar-headed goose.DOCX

IntroductionGut microbiota of wild birds are affected by many factors, and host genetic background and diet are considered to be two important factors affecting their structure and function.MethodsIn order to clarify how these two factors influence the gut microbiota, this study selected the sympatric and closely related and similar-sized Black-necked Crane (Grus nigricollis) and Common Crane (Grus grus), as well as the distantly related and significantly different-sized Bar-headed Goose (Anser indicus). The fecal samples identified using sanger sequencing as the above three bird species were subjected to high-throughput sequencing of rbcL gene and 16S rRNA gene to identify the feeding types phytophagous food and gut microbiota.ResultsThe results showed significant differences in food diversity between black-necked cranes and Common Cranes, but no significant differences in gut microbiota, Potatoes accounted for approximately 50% of their diets. Bar-headed Geese mainly feed on medicinal plants such as Angelica sinensis, Alternanthera philoxeroides, and Ranunculus repens. Black-necked cranes and Common Cranes, which have a high-starch diet, have a similar degree of enrichment in metabolism and synthesis functions, which is significantly different from Bar-headed Geese with a high-fiber diet. The differences in metabolic pathways among the three bird species are driven by food. The feeding of medicinal plants promotes the health of Bar-headed Geese, indicating that food influences the functional pathways of gut microbiota. Spearman analysis showed that there were few gut microbiota related to food, but almost all metabolic pathways were related to food.ConclusionThe host genetic background is the dominant factor determining the composition of the microbiota. Monitoring the changes in gut microbiota and feeding types of wild birds through bird feces is of great reference value for the conservation of other endangered species.</p