Metabarcoding of protozoa and helminth in black-necked cranes: a high prevalence of parasites and free-living amoebae

Parasites and free-living amoebae (FLA) are common pathogens that pose threats to wildlife and humans. The black-necked crane (Grus nigricollis) is a near-threatened species and there is a shortage of research on its parasite diversity. Our study aimed to use noninvasive methods to detect intestinal parasites and pathogenic FLA in G. nigricollis using high-throughput sequencing (HTS) based on the 18S rDNA V9 region. A total of 38 fresh fecal samples were collected in Dashanbao, China, during the overwintering period (early-, middle I-, middle II-, and late-winter). Based on the 18S data, eight genera of parasites were identified, including three protozoan parasites: Eimeria sp. (92.1%) was the dominant parasite, followed by Tetratrichomonas sp. (36.8%) and Theileria sp. (2.6%). Five genera of helminths were found: Echinostoma sp. (100%), Posthodiplostomum sp. (50.0%), Euryhelmis sp. (26.3%), Eucoleus sp. (50.0%), and Halomonhystera sp. (2.6%). Additionally, eight genera of FLA were detected, including the known pathogens Acanthamoeba spp. (n = 13) and Allovahlkampfia spp. (n = 3). Specific PCRs were used to further identify the species of some parasites and FLA. Furthermore, the 18S data indicated significant changes in the relative abundance and genus diversity of the protozoan parasites and FLA among the four periods. These results underscore the importance of long-term monitoring of pathogens in black-necked cranes to protect this near-endangered species

GPLEXUS: Enabling genome-scale gene association network reconstruction and analysis for very large-scale expression data

The accurate construction and interpretation of gene association networks (GANs) is challenging, but crucial, to the understanding of gene function, interaction and cellular behavior at the genome level. Most current state-of-the-art computational methods for genome-wide GAN reconstruction require high-performance computational resources. However, even high-performance computing cannot fully address the complexity involved with constructing GANs from very large-scale expression profile datasets, especially for the organisms with medium to large size of genomes, such as those of most plant species. Here, we present a new approach, GPLEXUS (http://plantgrn.noble.org/GPLEXUS/), which integrates a series of novel algorithms in a parallel-computing environment to construct and analyze genome-wide GANs. GPLEXUS adopts an ultra-fast estimation for pairwise mutual information computing that is similar in accuracy and sensitivity to the Algorithm for the Reconstruction of Accurate Cellular Networks (ARACNE) method and runs ∼1000 times faster. GPLEXUS integrates Markov Clustering Algorithm to effectively identify functional subnetworks. Furthermore, GPLEXUS includes a novel \u27condition-removing\u27 method to identify the major experimental conditions in which each subnetwork operates from very large-scale gene expression datasets across several experimental conditions, which allows users to annotate the various subnetworks with experiment-specific conditions. We demonstrate GPLEXUS\u27s capabilities by construing global GANs and analyzing subnetworks related to defense against biotic and abiotic stress, cell cycle growth and division in Arabidopsis thaliana. © The Author(s) 2013

A Pruning Method for Deep Convolutional Network Based on Heat Map Generation Metrics

With the development of deep learning, researchers design deep network structures in order to extract rich high-level semantic information. Nowadays, most popular algorithms are designed based on the complexity of visible image features. However, compared with visible image features, infrared image features are more homogeneous, and the application of deep networks is prone to extracting redundant features. Therefore, it is important to prune the network layers where redundant features are extracted. Therefore, this paper proposes a pruning method for deep convolutional network based on heat map generation metrics. The ‘network layer performance evaluation metrics’ are obtained from the number of pixel activations in the heat map. The network layer with the lowest ‘network layer performance evaluation metrics’ is pruned. To address the problem that the simultaneous deletion of multiple structures may result in incorrect pruning, the Alternating training and self-pruning strategy is proposed. Using a cyclic process of pruning each model once and retraining the pruned model to reduce the incorrect pruning of network layers. The experimental results show that proposed method in this paper improved the performance of CSPDarknet, Darknet and Resnet

LegumeIP: An Integrative Platform for Comparative Genomics and Transcriptomics of Model Legumes

In this chapter, we introduce the latest development of LegumeIP: a platform of comparative genomics and transcriptomics, and then describe some practical usages of the LegumeIP for studying gene functions, molecular mechanisms underpinning the plant-rhizobia interactions, and genome evolution with respect to nitrogen fixing in several agriculturally important model legume species. LegumeIP currently hosts large-scale genomics and transcriptomics data that include (i) genomic sequences of three model legumes, Medicago truncatula, Glycine max (soybean), Lotus japonicus, and two reference plant species, Arabidopsis thaliana and Poplar trichocarpa, with the annotation based on UniProt, InterProScan, Gene Ontology, and KEGG (Kyoto Encyclopedia of Genes and Genomes) databases, comprising a total of 222,217 protein-coding gene sequences; (ii) large-scale compendium gene expression data sets compiled from various tissues of multiple species. These include 104 microarray data sets from L. japonicus, 156 microarray data sets from M. truncatula gene atlas database, and 14 RNA-seq data sets from G. max. These data are further compiled centering on four tissues: nodules, flowers, roots, and leaves being shared by all species; (iii) systematic synteny analysis among M. truncatula, G. max, L. japonicus, and A. thaliana; (iv) reconstruction of gene family and gene family-wide phylogenetic analysis across the five hosted species; and (v) genome-wide reconstruction of gene coexpression networks. The usefulness of this platform in facilitating molecular research of legume species is demonstrated by two case studies, in which SymRK (symbiosis receptor-like kinase) genes for symbiosis analysis and nitrogen-fixation-related genes in M. truncatula were identified through integrative analysis of gene expression and constructed coexpression networks provided by the LegumeIP platform. The LegumeIP is freely available at http://plantgrn.noble.org/LegumeIP/

Gut fungi of black-necked cranes (Grus nigricollis) respond to dietary changes during wintering

Abstract Background Migratory birds exhibit heterogeneity in foraging strategies during wintering to cope with environmental and migratory pressures, and gut bacteria respond to changes in host diet. However, less is known about the dynamics of diet and gut fungi during the wintering period in black-necked cranes (Grus nigricollis). Results In this work, we performed amplicon sequencing of the trnL-P6 loop and ITS1 regions to characterize the dietary composition and gut fungal composition of black-necked cranes during wintering. Results indicated that during the wintering period, the plant-based diet of black-necked cranes mainly consisted of families Poaceae, Solanaceae, and Polygonaceae. Among them, the abundance of Solanaceae, Polygonaceae, Fabaceae, and Caryophyllaceae was significantly higher in the late wintering period, which also led to a more even consumption of various food types by black-necked cranes during this period. The diversity of gut fungal communities and the abundance of core fungi were more conserved during the wintering period, primarily dominated by Ascomycota and Basidiomycota. LEfSe analysis (P  2) found that Pyxidiophora, Pseudopeziza, Sporormiella, Geotrichum, and Papiliotrema were significantly enriched in early winter, Ramularia and Dendryphion were significantly enriched in mid-winter, Barnettozyma was significantly abundant in late winter, and Pleuroascus was significantly abundant in late winter. Finally, mantel test revealed a significant correlation between winter diet and gut fungal. Conclusions This study revealed the dynamic changes in the food composition and gut fungal community of black-necked cranes during wintering in Dashanbao. In the late wintering period, their response to environmental and migratory pressures was to broaden their diet, increase the intake of non-preferred foods, and promote a more balanced consumption ratio of various foods. Balanced food composition played an important role in stabilizing the structure of the gut fungal community. While gut fungal effectively enhanced the host’s food utilization rate, they may also faced potential risks of introducing pathogenic fungi. Additionally, we recongnized the limitations of fecal testing in studying the composition of animal gut fungal, as it cannot effectively distinguished between fungal taxa from food or soil inadvertently ingested and intestines. Future research on functions such as cultivation and metagenomics may further elucidate the role of fungi in the gut ecosystem

Group A rotaviruses in Chinese bats: genetic composition, serology, and evidence for bat-to-human transmission and reassortment

Bats are natural reservoirs for many pathogenic viruses, and increasing evidence supports the notion that bats can also harbor group A rotaviruses (RVAs), important causative agents of diarrhea in children and young animals. Currently, 8 RVA strains possessing completely novel genotype constellations or genotypes possibly originating from other mammals have been identified from African and Chinese bats. However, all the data were mainly based on detection of RVA RNA, present only during acute infections, which does not permit assessment of the true exposure of a bat population to RVA. To systematically investigate the genetic diversity of RVAs, 547 bat anal swabs or gut samples along with 448 bat sera were collected from five South Chinese provinces. Specific reverse transcription-PCR (RT-PCR) screening found four RVA strains. Strain GLRL1 possessed a completely novel genotype constellation, whereas the other three possessed a constellation consistent with the MSLH14-like genotype, a newly characterized group of viruses widely prevalent in Chinese insectivorous bats. Among the latter, strain LZHP2 provided strong evidence of cross-species transmission of RVAs from bats to humans, whereas strains YSSK5 and BSTM70 were likely reassortants between typical MSLH14-like RVAs and human RVAs. RVA-specific antibodies were detected in 10.7% (48/448) of bat sera by an indirect immunofluorescence assay (IIFA). Bats in Guangxi and Yunnan had a higher RVA-specific antibody prevalence than those from Fujian and Zhejiang provinces. These observations provide evidence for cross-species transmission of MSLH14-like bat RVAs to humans, highlighting the impact of bats as reservoirs of RVAs on public health.IMPORTANCE Bat viruses, such as severe acute respiratory syndrome (SARS), Middle East respiratory syndrome (MERS), Ebola, Hendra, and Nipah viruses, are important pathogens causing outbreaks of severe emerging infectious diseases. However, little is known about bat viruses capable of causing gastroenteritis in humans, even though 8 group A viruses (RVAs) have been identified from bats so far. In this study, another 4 RVA strains were identified, with one providing strong evidence for zoonotic transmission from bats to humans. Serological investigation has also indicated that RVA infection in bats is far more prevalent than expected based on the detection of viral RNA.status: publishe

GPLEXUS: enabling genome-scale gene association network reconstruction and analysis for very large-scale expression data

The accurate construction and interpretation of gene association networks (GANs) is challenging, but crucial, to the understanding of gene function, interaction and cellular behavior at the genome level. Most current state-of-the-art computational methods for genome-wide GAN reconstruction require high-performance computational resources. However, even high-performance computing cannot fully address the complexity involved with constructing GANs from very large-scale expression profile datasets, especially for the organisms with medium to large size of genomes, such as those of most plant species. Here, we present a new approach, GPLEXUS (http://plantgrn.noble.org/GPLEXUS/), which integrates a series of novel algorithms in a parallel-computing environment to construct and analyze genome-wide GANs. GPLEXUS adopts an ultra-fast estimation for pairwise mutual information computing that is similar in accuracy and sensitivity to the Algorithm for the Reconstruction of Accurate Cellular Networks (ARACNE) method and runs ∼1000 times faster. GPLEXUS integrates Markov Clustering Algorithm to effectively identify functional subnetworks. Furthermore, GPLEXUS includes a novel ‘condition-removing’ method to identify the major experimental conditions in which each subnetwork operates from very large-scale gene expression datasets across several experimental conditions, which allows users to annotate the various subnetworks with experiment-specific conditions. We demonstrate GPLEXUS’s capabilities by construing global GANs and analyzing subnetworks related to defense against biotic and abiotic stress, cell cycle growth and division in Arabidopsis thaliana