Detection of Porcine Respirovirus 1 (PRV1) in Poland: Incidence of Co-Infections with Influenza A Virus (IAV) and Porcine Reproductive and Respiratory Syndrome Virus (PRRSV) in Herds with a Respiratory Disease

Porcine respirovirus 1 (PRV1) is also known as porcine parainfluenza virus 1 (PPIV1). The prevalence and the role of PRV1 infections for pig health is largely unknown. In order to assess the PRV1 prevalence in Poland, nasal swabs and oral fluids collected from pigs from 30 farms were examined with RT real-time PCR. Additionally, IAV and PRRSV infection statuses of PRV1-positive samples were examined. The results showed that the virus is highly prevalent (76.7% farms positive) and different patterns of PRV1 circulation in herds with mild–moderate respiratory disease were observed. Co-infections with IAV and PRRSV were infrequent and detected in 8 (23.5%) and 4 (11.8%) out of 34 PRV1-positive nasal swab pools from diseased pens, respectively. In one pen PRV1, IAV, and PRRSV were detected at the same time. Interestingly, PRV1 mean Ct value in samples with co-infections was significantly lower (29.8 ± 3.1) than in samples with a single PRV1 infection (32.5 ± 3.6) (p < 0.05), which suggested higher virus replication in these populations. On the other hand, the virus detection in pig populations exhibiting respiratory clinical signs, negative for PRRSV and IAV, suggests that PRV1 should be involved in differential diagnosis of respiratory problems.

The Use of PCR for Respiratory Virus Detection on the Diagnosis and Treatment Decision of Respiratory Tract Infections in Iraq

Diseases of the respiratory system are a common cause of antibiotic prescription in Iraq and worldwide. Technology has been recently used for its diagnosis, such as the Film Array Respiratory Panel. This study aims to identify the correlation between the diagnosis and treatment of respiratory tract infections with the result of polymerase chain reaction (PCR) for respiratory viruses. A descriptive, cross-sectional, retrospective study included 134 patients treated at Alkharama Hospital and the Private Hospital in Baghdad, Iraq, in the period from January 2020 to March 2020 For all cases, the results of the panel and the treatment received by the patients were analysed. 58% received antibiotic treatment upon admission, 13% combined treatment (antibiotic + antiviral), 27% received symptomatic treatment, and 2% were treated with the first-instance antiviral. After the result, 38% continued with antibiotics, 30% with antibiotics and antivirals, 13.8% with antivirals and 18.2% with symptomatic treatment. Despite the worldwide alarm over antimicrobial resistance, patients continue to be treated with antibiotics due to a situation that is influenced by several factors.

Improvement of field-deployable metagenomic virus detection by a simple pretreatment method.

As both the current COVID-19 pandemic and earlier pandemics have shown, animals are the source for some of the deadliest viral pathogens, which can spread to humans. Therefore, early detection at the point of incidence is crucial to both prevent and understand the threats posed to human health from pathogens in animal reservoirs. Often, the exact genetic nature of these zoonotic pathogens is unknown and advanced laboratory facilities do not exist in most field settings and therefore the development of methods for unbiased metagenomic and point of incidence detection is crucial in order to identify novel viral pathogens in animals with zoonotic and pandemic potential. Here we addressed some of these issues by developing a metagenomic Nanopore next-generation sequencing (mNGS) method for nucleic acids extracted from clinical samples from patients with SARS-CoV-2. To reduce the non-RNA viral genetic components in the samples, we used DNase pretreatment in a syringe followed by filtration and found that these pretreatments increased the number of SARS-CoV2 reads by > 500-fold compared with no pretreatment. The simple protocol, described here, allows for fast (within 6 hours) metagenomic detection of RNA viruses in biological samples exemplified by SARS-CoV-2 detection in clinical throat swabs. This method could also be applied in field settings for point of incidence detection of virus pathogens, thus eliminating the need for transport of infectious samples, cold storage and a specialized laboratory.

NeoRdRp: A comprehensive dataset for identifying RNA-dependent RNA polymerase of various RNA viruses from metatranscriptomic data

RNA viruses are distributed in various environments, and most RNA viruses have been recently identified by metatranscriptome sequencing. However, due to the high nucleotide diversity of RNA viruses, it is still challenging to identify their sequences. Therefore, this study generated a dataset of RNA-dependent RNA polymerase (RdRp) domains essential for all RNA viruses belonging to Orthornavirae. Also, the collected genes with RdRp domains from various RNA viruses were clustered by amino acid sequence similarity. For each cluster, a multiple sequence alignment was generated, and a hidden Markov model (HMM) profile was created if the number of sequences was greater than five. Using the 1,467 HMM profiles, we detected RdRp domains in the RefSeq RNA virus sequences, combined the hit sequences with the RdRp domains, and reconstructed the HMM profiles. As a result, 2,234 HMM profiles were generated from 12,316 RdRp domain sequences, and the dataset was named NeoRdRp. Additionally, using the UniProt dataset, we confirmed that almost all NeoRdRp HMM profiles could specifically detect RdRps in Orthornavirae. Furthermore, we compared the NeoRdRp dataset with two previously reported RNA virus detection methods to detect RNA virus sequences from metatranscriptome sequencing data. Our methods can identify most of the RNA viruses in the datasets; however, some RNA viruses were not detected, similar to the other two methods. The NeoRdRp can be improved by repeatedly adding new RdRp sequences and can be expected to be widely applied as a system for detecting various RNA viruses from metatranscriptome data.