Search

ABSTRACT Endosymbionts can strongly affect bacterial microbiota in pests. The white-backed planthopper Sogatella furcifera, a notorious pest in rice, is usually co-infected with Cardinium and Wolbachia, but the effects of these endosymbionts together or individually on the host microbiome and fecundity are unclear. Here, we established three S. furcifera lines (Cardinium and Wolbachia double-infected, Cardinium single-infected, and both-uninfected lines) backcrossed to a common nuclear background and found that single and double infections reduced bacterial diversity and changed bacterial community structure across nymph and adult stages and across adult tissues. The endosymbionts differed in densities between adults and nymphs as well as across adult tissues, with the distribution of Cardinium affected by Wolbachia. Both the single infection and particularly the double infection reduced host fecundity. Lines also differed in levels of metabolites, some of which may influence fecundity (e.g., arginine biosynthesis and nicotinamide metabolism). Cardinium in the single-infected line upregulated metabolic levels, while Wolbachia in the double-infected line appeared to mainly downregulate them. Association analysis pointed to possible connections between various bacteria and differential metabolites. These results reveal that Cardinium by itself and in combination with Wolbachia affect bacterial microbiota and levels of metabolites, with likely effects on host fecundity. Many of the effects of these metabolically limited endosymbionts that are dependent on the hosts may be exerted through manipulation of the microbiome. IMPORTANCE Endosymbionts can profoundly affect the nutrition, immunity, development, and reproduction of insect hosts, but the effects of multiple endosymbiont infections on microbiota and the interaction of these effects with insect host fitness are not well known. By establishing S. furcifera lines with different endosymbiont infection status, we found that Cardinium and the combined Cardinium + Wolbachia infections differentially reduced bacterial diversity as well as changing bacterial community structure and affecting metabolism, which may connect to negative fitness effects of the endosymbionts on their host. These results established the connections between reduced bacterial diversity, decreased fecundity and metabolic responses in S. furcifera.

• An improved method considering correlated stochastic variables is proposed for PLF. • A twice-permutation technique is proposed to ensure the desired correlations. • Singular value decomposition extends the scope of the method. As the increasing integration of large-scale renewable energy sources in power systems, the stochastic characteristics of loads and renewable energy systems become much more complex and impacts power systems much more than ever. Probabilistic load flow analysis is a powerful tool to discover the stochastic characteristics of power systems. There are two important issues for probabilistic load flow analysis based on Monte Carlo simulation: (i) How to generate random samples with the specific distribution and correlation; and (ii) how to make the simulation method to work well even when the correlation matrices are not positive definite. In order to handle the two issues, Nataf transformation combined with Latin hypercube sampling and singular value decomposition method is proposed for solving probabilistic load flow problems with correlated variables in this paper. By using the singular value decomposition (SVD), the proposed method works well even when the correlation matrices are not positive definite. And the twice-permutation technique based on SVD ensures that the samples have the desired correlations. The investigation on modified IEEE 14-bus system and modified IEEE 118-bus system shows that the proposed method is accurate and efficient. [ABSTRACT FROM AUTHOR]