Gut Virome Analysis of Cameroonians Reveals High Diversity of Enteric Viruses, Including Potential Interspecies Transmitted Viruses

Yinda, Claude Kwe; Vanhulle, Emiel; Conceição-Neto, Nádia; Beller, Leen; Deboutte, Ward; Shi, Chengying; Ghogomu, Stephen Mbigha; Maes, Piet; Ranst, Marc Van; Matthijnssens, Jelle

doi:10.1128/msphere.00585-18

Cited by 68 publications

(93 citation statements)

References 95 publications

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“…There are two possible reasons for limited worldwide HPeV15 detection: (i) silent transmission due to HPeV underdiagnosis explained by the lack of HPeV routine testing in clinical settings and lack of awareness among clinicians 1 , or (ii) localized geographical circulation of the virus in parts of Africa and Asia. However, in Africa, none of the studies conducted between 2002 and 2016 for HPeV detection in stool samples detected HPeV15 strains, neither in healthy nor in children with gastrointestinal symptoms [23][24][25][26] , except for one study from Ghanaian children in 2007-2008 11 . Nevertheless, these studies highlight the high genetic diversity of HPeVs in Africa, which include rare types like HPeV16, HPeV17 or the new type HPeV18 11,23,25,26 .…”

Section: Discussionmentioning

confidence: 99%

“…However, in Africa, none of the studies conducted between 2002 and 2016 for HPeV detection in stool samples detected HPeV15 strains, neither in healthy nor in children with gastrointestinal symptoms [23][24][25][26] , except for one study from Ghanaian children in 2007-2008 11 . Nevertheless, these studies highlight the high genetic diversity of HPeVs in Africa, which include rare types like HPeV16, HPeV17 or the new type HPeV18 11,23,25,26 . The studies that detected HPeVs in respiratory samples in African countries (Tunisia, Kenya and Gabon) did not perform type identification and therefore it is not possible to know whether type 15 was detected in these samples [27][28][29][30] .…”

Section: Discussionmentioning

confidence: 99%

“…In recent years, NGS has boosted identification of unsuspected and novel viruses and is becoming the standard for the discovery of viral pathogens 33,34 . It has been successfully used to identify or characterize full-genomes of new HPeVs like a HPeV1 variant 35 , type 4 31 , types 5 and 6 32 , type 7 36 , type 16 26 , type 17 37 , types 18 and 19 38 or to detect HPeVs in clinical specimens 25,26,[39][40][41] . Thanks to NGS, we were able to detect a mixed infection of HPeV15 and EV-A71 when using this technique to characterize the full genome sequence of EV-A71 13 .…”

Section: Discussionmentioning

confidence: 99%

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Identification and molecular characterization of the first complete genome sequence of Human Parechovirus type 15

Fernandez-Garcia

Simon‐Lorière

Kébé

et al. 2020

Sci Rep

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Using a metagenomics approach, we have determined the first full-length genome sequence of a human parechovirus type 15 (HPeV15) strain, isolated from a child with acute flaccid paralysis and co-infected with EV-A71. HPeV15 is a rarely reported type. To date, no full-length genome sequence of HPeV15 is available in the GenBank database, where only limited VP1 sequences of this virus are available. Pairwise comparisons of the complete VP1 nucleotide and deduced amino acid sequences revealed that the study strain belongs to type 15 as it displayed 79.6% nucleotide and 93.4% amino acid identity with the HPeV15 prototype strain. Comparative analysis of available genomic regions and phylogenetic analysis using the P2 and P3 coding regions revealed low nucleotide identity to HPeV reference genomes. Phylogenetic and similarity plot analyses showed that genomic recombination events might have occurred in the UTRs and nonstructural region during HPeV15 evolution. The study strain has high similarity features with different variants of HPeV3 suggesting intertypic recombination. Our data contributes to the scarce data available on HPeVs in Africa and provides valuable information for future studies that aim to understand the evolutionary history, molecular epidemiology or biological and pathogenic properties of HPeV15. Human parechoviruses (HPeVs) are non-enveloped viruses that belong to one of the four species of genus Parechovirus within the Picornaviridae family. HPeV positive-sense single-stranded RNA genome is about 7300 bases in length, flanked by 5′ and 3′ untranslated regions (UTRs). The genome encodes a polyprotein which is cleaved by the viral protease (3 C) to produce the mature structural (VP0, VP3 and VP1) and nonstructural (2A-C, 3A-D) proteins 1. HPeVs can be transmitted through the fecal-oral and respiratory routes 1,2. HPeV infections are common throughout the world and most often cause asymptomatic or mild gastrointestinal or respiratory symptoms 1,2. However, syndromes such as encephalitis, aseptic meningitis, acute flaccid paralysis (AFP), sepsis and myocarditis have been increasingly recognized in primary HPeV infection in neonates and infants 2. After EVs, HPeVs are considered the second most common cause of central nervous system (CNS) viral infections 2. However, HPeV infections are currently underdiagnosed given that routine diagnostic assays are generally lacking in clinical settings 1,3. Following HPeV intestinal replication, infectious viruses are shed in feces where viruses can be detected by PCR and virus isolation 3. However, HPeVs are known for poor adaptation to cell cultivation systems and not all types replicate in cell lines routinely used for enterovirus (EV) detection 4,5. Because specific types could be missed or underdiagnosed using viral isolation, molecular identification methods are highly recommended for the routine detection of HPeVs in clinical samples 3,4. To date, 19 different HPeV types (HPeV1 to 19) have been described based on sequence similarity within their VP1 capsid pro...

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Identification and molecular characterization of the first complete genome sequence of Human Parechovirus type 15

Fernandez-Garcia

Simon‐Lorière

Kébé

et al. 2020

Sci Rep

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“…Particularly, metagenomic NGS (mNGS) allows unbiased detection of all microbes and viruses in a sample, showing potential for timely detection of rare or novel infectious etiologies, as well as for surveillance of foodborne and waterborne viruses [20][21][22][23][24][25][26]. However, the use of mNGS as a potential surveillance tool requires a deeper understanding of what is "normal" diversity in humans [27], as well as wildlife [28,29] and farm animals [30]. Characterizing species-specific metagenomes could potentially be used to provide a surveillance baseline for early detection and for tracking of movements of pathogens across different hosts, and has been promoted by projects like the global virome project [31].…”

mentioning

confidence: 99%

Virus Metagenomics in Farm Animals: A Systematic Review

Kwok

Nieuwenhuijse

Phan

et al. 2020

Viruses

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A majority of emerging infectious diseases are of zoonotic origin. Metagenomic Next-Generation Sequencing (mNGS) has been employed to identify uncommon and novel infectious etiologies and characterize virus diversity in human, animal, and environmental samples. Here, we systematically reviewed studies that performed viral mNGS in common livestock (cattle, small ruminants, poultry, and pigs). We identified 2481 records and 120 records were ultimately included after a first and second screening. Pigs were the most frequently studied livestock and the virus diversity found in samples from poultry was the highest. Known animal viruses, zoonotic viruses, and novel viruses were reported in available literature, demonstrating the capacity of mNGS to identify both known and novel viruses. However, the coverage of metagenomic studies was patchy, with few data on the virome of small ruminants and respiratory virome of studied livestock. Essential metadata such as age of livestock and farm types were rarely mentioned in available literature, and only 10.8% of the datasets were publicly available. Developing a deeper understanding of livestock virome is crucial for detection of potential zoonotic and animal pathogens and One Health preparedness. Metagenomic studies can provide this background but only when combined with essential metadata and following the "FAIR" (Findable, Accessible, Interoperable, and Reusable) data principles.Viruses 2020, 12, 107 2 of 22 flocks and wild birds, suggesting that pre-existing biosecurity measurements could not keep up with the rate of livestock intensification [16]. In 2007-2010, a large-scale Q fever outbreak was reported in the Netherlands, affecting more than 3500 human cases and resulting in a huge economic loss [17,18]. A steep increase in the number of goat farms most likely was the driver for the increased prevalence of Coxiella burnetii infections, with animal abortion waves that had gone unnoticed. The policy of voluntary reporting abortion outbreaks to the Animal Health Service hindered the timely detection of the circulation of Q fever, and therefore early interventions. These examples indicate that zoonotic risks in the livestock industry should be carefully managed and adapted to livestock intensification. The One Health approach has been coined for advocating collaboration between multiple stakeholders including veterinarians, clinicians, epidemiologists, virologists, microbiologists, ecologists, and policy makers to prevent and control EIDs through the human-animal-environment interface [19]. Surveillance of livestock and the surrounding environment is a hallmark of early detection but is currently targeted to known risks.Advances in Next-Generation Sequencing (NGS) technologies and rapid development of bioinformatics and computational tools offer new opportunities for EID surveillance in quality and in scale. Particularly, metagenomic NGS (mNGS) allows unbiased detection of all microbes and viruses in a sample, showing potential for timely detection of rare or novel infectio...

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“…To verify the performance of the proposed model on the real metagenomic dataset, we collected the virome dataset from the wastewater (accession number in NCBI: SRR5192446) and two virome datasets from the human gut (accession number in NCBI: SRR7892426 and ERR2868024) [22,23].…”

Section: Datasetsmentioning

confidence: 99%

DeephageTP: A Convolutional Neural Network Framework for Identifying Phage-specific Proteins from metagenomic sequencing data

Chu

Guo

Cui

et al. 2020

Preprint

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Background: Bacteriophage (phage) is the most abundant and diverse biological entity on the Earth. This makes it a challenge to identify and annotate phage genomes efficiently on a large scale. Portal (portal protein), TerL (large terminase subunit protein) and TerS (small terminase subunit protein) are the three specific proteins of the tailed phage. Here, we develop a CNN (convolutional neural network)-based framework, DeephageTP, to identify these three proteins from metagenome data. The framework takes one-hot encoding data of the original protein sequences as the input and extracts the predictive features in the process of modeling. The cutoff loss value for each protein category was determined by exploiting the distributions of the loss values of the sequences within the same category. Finally, we tested the efficacy of the framework using three real metagenomic datasets. Result: The proposed multiclass classification CNN-based model was trained by the training datasets and shows relatively high prediction performance ( A ccuracy : Portal, 98.8%; TerL, 98.6%; TerS, 97.8%) for the three protein categories, respectively. The experiments using the independent mimic dataset demonstrate that the performance of the model could become worse along with the increase of the data size. To address this issue, we determined and set the cutoff loss values (i.e., TerL: -5.2, Portal: -4.2, TerS: -2.9) for each of the three categories, respectively. With these values, the model obtains high performance in terms of Precision in identifying the TerL and Portal sequences (i.e, ~ 94% and ~ 90%, respectively) from the mimic dataset that is 20 times larger than the training dataset. More interestingly, the framework identified from the three real metagenomic datasets many novel phage sequences that are not detectable by the two alignment-based methods (i.e., DIAMOND and HMMER). Conclusion: Compared to the conventional alignment-based methods, our proposed framework shows high performance in identifying phage-specific protein sequences with a particular advantage in identifying the novel protein sequences with remote homology to their known counterparts in public databases. Indeed, our method could also be applied for identifying the other protein sequences with the characteristic of high complexity and low conservation. The DeephageTP is available at https://github.com/chuym726/DeephageTP .

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Gut Virome Analysis of Cameroonians Reveals High Diversity of Enteric Viruses, Including Potential Interspecies Transmitted Viruses

Cited by 68 publications

References 95 publications

Identification and molecular characterization of the first complete genome sequence of Human Parechovirus type 15

Identification and molecular characterization of the first complete genome sequence of Human Parechovirus type 15

Virus Metagenomics in Farm Animals: A Systematic Review

DeephageTP: A Convolutional Neural Network Framework for Identifying Phage-specific Proteins from metagenomic sequencing data

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