Survey of Mississippi Mosquito Blood Meals for Vertebrate Host Identification

ABSTRACT Exploring particular mosquito and vertebrate relationships provide insight to potential transmission of several agents of disease. In the current study, the relationship between white-tailed deer (Odocoileus virginianus) and mosquitoes was explored by identifying blood meals within mosquitoes captured throughout Mississippi between June and September of 2013 and 2017. We captured 72 bloodfed mosquitoes between 2 collection years, with a majority of specimens identified as Culex erraticus or Psorophora mathesoni. Seventy-nine percent (26/33) of blood meals in Cx. erraticus originated from the white-tailed deer. These findings implicate mosquitoes may primarily be feeding on white-tailed deer in rural areas of Mississippi.

DeePVP: Identification and classification of phage virion protein using deep learning

The poor annotation of phage virion protein (PVP) is the bottleneck of many areas of viral research, such as viral phylogenetic analysis, viral host identification and antibacterial drug design. Because of the high diversity of the PVP sequences, the PVP annotation remains a great challenging bioinformatic task. Based on deep learning, we present DeePVP that contains a main module and an extended module. The main module aims to identify the PVPs from non-PVP over a phage genome, while the extended module can further classify the predicted PVP into one of the ten major classes of PVP. Compared with the state-of-the-art tools that can distinguish PVP from non-PVP, DeePVP's main module performs much better, with an F1-score 9.05% higher in the PVP identification task. Compared with PhANNs, a tool that can further classify the predicted PVP into a specific class, the overall accuracy of DeePVP's extended module is approximately 3.72% higher in the PVP classification task. Two application cases on the genome of mycobacteriophage PDRPxv and Escherichia phage HP3 show that the predictions of DeePVP are much more reliable and can better reveal the compact PVP-enriched region, which may be conserved during the viral evolution process, over the phage genome.

Role of bile acids in inflammatory liver diseases

Bile acids and their signaling pathways are increasingly recognized as potential therapeutic targets for cholestatic and metabolic liver diseases. This review summarizes new insights in bile acid physiology, focusing on regulatory roles of bile acids in the control of immune regulation and on effects of pharmacological modulators of bile acid signaling pathways in human liver disease. Recent mouse studies have highlighted the importance of the interactions between bile acids and gut microbiome. Interfering with microbiome composition may be beneficial for cholestatic and metabolic liver diseases by modulating formation of secondary bile acids, as different bile acid species have different signaling functions. Bile acid receptors such as FXR, VDR, and TGR5 are expressed in a variety of cells involved in innate as well as adaptive immunity, and specific microbial bile acid metabolites positively modulate immune responses of the host. Identification of Cyp2c70 as the enzyme responsible for the generation of hydrophilic mouse/rat-specific muricholic acids has allowed the generation of murine models with a human-like bile acid composition. These novel mouse models will aid to accelerate translational research on the (patho)physiological roles of bile acids in human liver diseases .

An Unsupervised Algorithm for Host Identification in Flaviviruses

Early characterization of emerging viruses is essential to control their spread, such as the Zika Virus outbreak in 2014. Among other non-viral factors, host information is essential for the surveillance and control of virus spread. Flaviviruses (genus Flavivirus), akin to other viruses, are modulated by high mutation rates and selective forces to adapt their codon usage to that of their hosts. However, a major challenge is the identification of potential hosts for novel viruses. Usually, potential hosts of emerging zoonotic viruses are identified after several confirmed cases. This is inefficient for deterring future outbreaks. In this paper, we introduce an algorithm to identify the host range of a virus from its raw genome sequences. The proposed strategy relies on comparing codon usage frequencies across viruses and hosts, by means of a normalized Codon Adaptation Index (CAI). We have tested our algorithm on 94 flaviviruses and 16 potential hosts. This novel method is able to distinguish between arthropod and vertebrate hosts for several flaviviruses with high values of accuracy (virus group 91.9% and host type 86.1%) and specificity (virus group 94.9% and host type 79.6%), in comparison to empirical observations. Overall, this algorithm may be useful as a complementary tool to current phylogenetic methods in monitoring current and future viral outbreaks by understanding host–virus relationships.

Characterization of new cristamonad species from kalotermitid termites including a novel genus, Runanympha

Cristamonadea is a large class of parabasalian protists that reside in the hindguts of wood-feeding insects, where they play an essential role in the digestion of lignocellulose. This group of symbionts boasts an impressive array of complex morphological characteristics, many of which have evolved multiple times independently. However, their diversity is understudied and molecular data remain scarce. Here we describe seven new species of cristamonad symbionts from Comatermes, Calcaritermes, and Rugitermes termites from Peru and Ecuador. To classify these new species, we examined cells by light and scanning electron microscopy, sequenced the symbiont small subunit ribosomal RNA (rRNA) genes, and carried out barcoding of the mitochondrial large subunit rRNA gene of the hosts to confirm host identification. Based on these data, five of the symbionts characterized here represent new species within described genera: Devescovina sapara n. sp., Devescovina aymara n. sp., Macrotrichomonas ashaninka n. sp., Macrotrichomonas secoya n. sp., and Macrotrichomonas yanesha n. sp. Additionally, two symbionts with overall morphological characteristics similar to the poorly-studied and probably polyphyletic ‘joeniid’ Parabasalia are classified in a new genus Runanympha n. gen.: Runanympha illapa n. sp., and Runanympha pacha n. sp.

Identify phage hosts from metaviromic short reads based on deep learning and Markov chain model

Phages - viruses that infect bacteria and archaea - are dominant in the virosphere and play an important role in the microbial community. It is very important to identify the host of a given phage fragment from metavriome data for understanding the ecological impact of phage in a microbial community. State-of-the-art tools for host identification only present reliable results on long sequences within a narrow candidate host range, while there are a large number of short fragments in real metagenomic data and the taxonomic composition of a microbial community is often complicated. Here, we present a method, named HoPhage, to identify the host of a given phage fragment from metavirome data at the genus level. HoPhage integrates two modules using the deep learning algorithms and the Markov chain model, respectively. By testing on both the artificial benchmark dataset of phage contigs and the real virome data, HoPhage demonstrates a satisfactory performance on short fragments within a wide candidate host range at every taxonomic level. HoPhage is freely available at http://cqb.pku.edu.cn/ZhuLab/HoPhage/.

Highly Pathogenic Avian Influenza Viruses at the Wild–Domestic Bird Interface in Europe: Future Directions for Research and Surveillance

Highly pathogenic avian influenza (HPAI) outbreaks in wild birds and poultry are no longer a rare phenomenon in Europe. In the past 15 years, HPAI outbreaks—in particular those caused by H5 viruses derived from the A/Goose/Guangdong/1/1996 lineage that emerged in southeast Asia in 1996—have been occuring with increasing frequency in Europe. Between 2005 and 2020, at least ten HPAI H5 incursions were identified in Europe resulting in mass mortalities among poultry and wild birds. Until 2009, the HPAI H5 virus outbreaks in Europe were caused by HPAI H5N1 clade 2.2 viruses, while from 2014 onwards HPAI H5 clade 2.3.4.4 viruses dominated outbreaks, with abundant genetic reassortments yielding subtypes H5N1, H5N2, H5N3, H5N4, H5N5, H5N6 and H5N8. The majority of HPAI H5 virus detections in wild and domestic birds within Europe coincide with southwest/westward fall migration and large local waterbird aggregations during wintering. In this review we provide an overview of HPAI H5 virus epidemiology, ecology and evolution at the interface between poultry and wild birds based on 15 years of avian influenza virus surveillance in Europe, and assess future directions for HPAI virus research and surveillance, including the integration of whole genome sequencing, host identification and avian ecology into risk-based surveillance and analyses.