Necessary Sequencing Depth and Clustering Method to Obtain Relatively Stable Diversity Patterns in Studying Fish Gut Microbiota

The 16S rRNA gene is one of the most commonly used molecular markers for estimating bacterial diversity during the past decades. However, there is no consistency about the sequencing depth (from thousand to millions of sequences per sample), and the clustering methods used to generate OTUs may also be different among studies. These inconsistent premises make effective comparisons among studies difficult or unreliable. This study aims to examine the necessary sequencing depth and clustering method that would be needed to ensure a stable diversity patterns for studying fish gut microbiota. A total number of 42 samples dataset of Siniperca chuatsi (carnivorous fish) gut microbiota were used to test how the sequencing depth and clustering may affect the alpha and beta diversity patterns of fish intestinal microbiota. Interestingly, we found that the sequencing depth (resampling 1000-11,000 per sample) and the clustering methods (UPARSE and UCLUST) did not bias the estimates of the diversity patterns during the fish development from larva to adult. Although we should acknowledge that a suitable sequencing depth may differ case by case, our finding indicates that a shallow sequencing such as 1000 sequences per sample may be also enough to reflect the general diversity patterns of fish gut microbiota. However, we have shown in the present study that strict pre-processing of the original sequences is required to ensure reliable results. This study provides evidences to help making a strong scientific choice of the sequencing depth and clustering method for future studies on fish gut microbiota patterns, but at the same time reducing as much as possible the costs related to the analysis.</p

TINGKAT KEMAMPUAN SISWA TERHADAP MATERIRNMENGHARGAI PENINGGALAN SEJARAHRNKELAS IV SDN GUGUS MONTASIK

Banda Ace

Spatiotemporal Genotype Replacement of H5N8 Avian Influenza Viruses Contributed to H5N1 Emergence in 2021/2022 Panzootic

Since 2020, clade 2.3.4.4b highly pathogenic avian influenza H5N8 and H5N1 viruses have swept through continents, posing serious threats to the world. Through comprehensive analyses of epidemiological, genetic, and bird migration data, we found that the dominant genotype replacement of the H5N8 viruses in 2020 contributed to the H5N1 outbreak in the 2021/2022 wave. The 2020 outbreak of the H5N8 G1 genotype instead of the G0 genotype produced reassortment opportunities and led to the emergence of a new H5N1 virus with G1's HA and MP genes. Despite extensive reassortments in the 2021/2022 wave, the H5N1 virus retained the HA and MP genes, causing a significant outbreak in Europe and North America. Furtherly, through the wild bird migration flyways investigation, we found that the temporal-spatial coincidence between the outbreak of the H5N8 G1 virus and the bird autumn migration may have expanded the H5 viral spread, which may be one of the main drivers of the emergence of the 2020-2022 H5 panzootic.IMPORTANCESince 2020, highly pathogenic avian influenza (HPAI) H5 subtype variants of clade 2.3.4.4b have spread across continents, posing unprecedented threats globally. However, the factors promoting the genesis and spread of H5 HPAI viruses remain unclear. Here, we found that the spatiotemporal genotype replacement of H5N8 HPAI viruses contributed to the emergence of the H5N1 variant that caused the 2021/2022 panzootic, and the viral evolution in poultry of Egypt and surrounding area and autumn bird migration from the Russia-Kazakhstan region to Europe are important drivers of the emergence of the 2020-2022 H5 panzootic. These findings provide important targets for early warning and could help control the current and future HPAI epidemics.</p

Nearly a decade-long repeatable seasonal diversity patterns of bacterioplankton communities in the eutrophic Lake Donghu (Wuhan, China).

Uncovering which environmental factors govern community diversity patterns and how ecological processes drive community turnover are key questions related to understand the community assembly. However, the ecological mechanisms regulating long-term variations of bacterioplankton communities in lake ecosystems remain poorly understood. Here we present nearly a decade-long study of bacterioplankton communities from the eutrophic Lake Donghu (Wuhan, China) using 16S rRNA gene amplicon sequencing with MiSeq platform. We found strong repeatable seasonal diversity patterns in terms of both common (detected in more than 50% samples) and dominant (relative abundance &gt;1%) bacterial taxa turnover. Moreover, community composition tracked the seasonal temperature gradient, indicating that temperature is a key environmental factor controlling observed diversity patterns. Total phosphorus also contributed significantly to the seasonal shifts in bacterioplankton composition. However, any spatial pattern of bacterioplankton communities across the main lake areas within season was overwhelmed by their temporal variabilities. Phylogenetic analysis further indicated that 75%-82% of community turnover was governed by homogeneous selection due to consistent environmental conditions within seasons, suggesting that the microbial communities in Lake Donghu are mainly controlled by niche-based processes. Therefore, dominant niches available within seasons might be occupied by similar combinations of bacterial taxa with modest dispersal rates throughout different lake areas

Host-microbiota interactions and responses to grass carp reovirus infection in Ctenopharyngodon idellus

Gut microbiota could facilitate host to defense diseases, but fish-microbiota interactions during viral infection and the underlying mechanism are poorly understood. We examined interactions and responses of gut microbiota to grass carp reovirus (GCRV) infection in Ctenopharyngodon idellus, which is the most important aquaculture fish worldwide. We found that GCRV infection group with serious haemorrhagic symptoms (G7s) showed considerably different gut microbiota, especially with an abnormally high abundance of gram-negative anaerobic Cetobacterium somerae. It also showed the lowest (p < 0.05) alpha-diversity but with much higher ecological process of homogenizing dispersal (28.8%), confirming a dysbiosis of the gut microbiota after viral infection. Interestingly, signaling pathways of NOD-like receptors (NLRs), toll-like receptors (TLRs), and lipopolysaccharide (LPS) stimulation genes were significantly (q-value < 0.01) enriched in G7s, which also significantly (p < 0.01) correlated with the core gut microbial genera of Cetobacterium and Acinetobacter. The results suggested that an expansion of C. somerae initiated by GCRV could aggravate host inflammatory reactions through the LPS-related NLRs and TLRs pathways. This study advances our understanding of the interplay between fish immunity and gut microbiota challenged by viruses; it also sheds new insights for ecological defense of fish diseases with the help of gut microbiota

The stigma phenotype of <i>lesr</i> mutant.

<p>The stigma phenotype of <i>lesr</i> mutant.</p

Sequencing data quality.

<p>Sequencing data quality.</p

Genetic analysis and gene mapping of a low stigma exposed mutant gene by high-throughput sequencing - Fig 3

<p><b>SNP-index graphs of H-pool (A), L-pool (B) and Δ(SNPindex) graph (C) fromSLAF-seq analysis.</b> X-axis represents the position of 12 rice chromosomes and Y-axis represents the SNP-index. A candidate gene (<i>LESR</i>) location was identified in rice chromosome10 (8.61 to 9.69 Mb or 12.76 to 15.35 Mb) with the criteria that A the SNP-index in H-pool was near 0, B SNP-index in L-pool was near 1, and C theΔ(SNP-index) was over the confidence value (P < 0.05).</p

The difference between 93S and <i>lesr</i> in the localization interval and validation of candidate genes' markers.

<p>The difference between 93S and <i>lesr</i> in the localization interval and validation of candidate genes' markers.</p

Polymorphism markers used for genetic mapping of <i>LESR</i>.

<p>Polymorphism markers used for genetic mapping of <i>LESR</i>.</p