Parapoxvirus in the family Poxviridae causes pseudocowpox in cattle worldwide and presents a zoonotic concern. Most poxviruses produce diseases of similar clinical signs in affected animals, which are impossible to differentiate clinically or by serology. It is, therefore, vital to use molecular assays to rapidly identify the causative agents of poxvirus infections. This study aimed to detect, diagnose, and characterize the causative agent of pox-like skin lesions in a cattle herd in Zambia, initially suspected to be infected with Lumpy Skin Disease virus.Pseudocowpox virus (PCPV) of the genus We used a High-Resolution Melting (HRM) analysis assay to detect the PCPV genome and sequenced the major envelope protein (B2L gene) for comparative sequence and phylogenetic analysis.Our field investigations showed cattle presenting atypical skin lesions and high morbidity within the herd. The laboratory diagnosis, based on the HRM assay revealed PCPV DNA in the samples. Phylogenetic and comparative sequence analyses confirmed PCPV in the samples and revealed genomic differences between samples collected in 2017 and 2018 from the same farm.Our work is the first documented report of PCPV in Zambia. It shows the strength of molecular methods to diagnose pox-like infections in cattle and discriminate between diseases causing similar clinical signs. This rapid and accurate diagnosis improves the response time for more accurate veterinary interventions. Parapoxvirus (PPV) within the family Poxviridae ; goatpox virus (GTPV), sheeppox virus (SPPV) and lumpy skin disease virus (LSDV) [genus Capripoxvirus]; orf virus (ORFV), pseudocowpox virus (PCPV) and bovine papular stomatitis virus (BPSV) [genus Parapoxvirus].We tested the extracted DNA using the HRM assay for the simultaneous detection and differentiation of eight poxviruses.. The metThe reaction contained 200 nM of each primer , fourteen ORFV, and five BPSV.Sequences were aligned using the muscle (codon) option in MEGA 7. The aligned sequence file was saved in FASTA format, then converted to a nexus format using Seaview. We then performed the Bayesian phylogenetic inference with BEAST. First, a BEAST file was produced from the nexus file with the BEAUti module using the TN93 + G nucleotide substitution and a UPGMA starting tree. The Markov Chain Monte Carlo method was run with BEAST, for 10,000,000 generations with a sample taken each 10,000 generations. The TRACER program was used to inspect the log files and determine the optimum number of burn-in based on the Effective Sample Sizes (ESS > 200).TreeAnnotator was used to generate the Maximum Clade Credibility (MCC) after discarding the 2% burn-in. The tree was visualized with the associated meta-data using the ggtree package in R version 3.5.2 .We found approximately one hundred forty animals at the farm presenting with different stages of infection, from recent to healed lesions, and collected skin nodule samples from eight clinically affected cattle with fresh lesions.The animals had nodular lesions, scabs, and dermatitis on the teats, udder, and other parts of the body, including the sternum, limbs, muzzle, and in skin folds Fig. . Some anThe epidemiological history revealed that the farmer had been taking the cattle to graze in the Lukenge swamps since 2011 where they interacted with other neighboring cattle. They first observed skin lesions in 2014 which the farmworkers treated with Oximic plus long-acting (Oxyject 20% plus Sodium diclofenac 0.5%). Since 2014, the farmer reported a loss of approximately 300 animals from a herd of 1900. Because the farm is in an epidemic area for ECF, it was not possible to rule out the involvement of this disease in the high mortalities reported by the farmer. In 2017, as the disease worsened, the farmer finally consulted the veterinary extension services for further investigation.We detected PCPV DNA in eight out of eight samples using the HRM Assay. Figure We successfully amplified and partially sequenced the major envelope protein (B2L gene) in eight out of eight samples. All eight sequences clustered within the PCPV group in the phylogenetic tree Fig. , confirmInterestingly, sequences from samples collected in December 2017 produced a separate cluster from those collected in April 2018 during the follow-up investigation. The only exception was the sequence of sample Mary 2018, which was closely related to, but different from the 2017 PCPVs Fig. . The seqIn this study, we have detected PCPV DNA in a herd of cattle in Zambia from samples collected based on suspicion of LSD using an HRM assay for differential diagnosis of poxvirus infections. Sequencing and the subsequent phylogenetic analyses confirmed that the DNA in these samples belong to PCPV. In the phylogenetic reconstruction, all Zambian PCPVs clustered with previously published sequences of cattle PCPVs.Although PCPV in cattle is reportedly present worldwide –19, therOur findings highlight the relevance of molecular methods for differential diagnosis and the management of pox diseases in ruminants. This robust HRM assay has enabled differentiation between LSDV and PCPV in a single test and identified PCPV, which otherwise would have gone unnoticed.In the phylogenetic analyses, PCPVs from samples collected in December 2017 clustered independently from those samples taken from the same farm in April 2018, showing an evolution of the virus during the persistence of the infection on the farm. A close inspection of the sequence alignments showed up to 9 amino acid changes in the partial B2L sequence of PCPVs in samples collected in April 2018 as compared to those collected in December 2017. Interestingly, the analysis of the PCPV B2L gene in samples collected from the same animal at these two-time points showed that two amino acid mutations occurred.Such genomic changes in the PCPV could suggest either reinfection of the herd or genomic changes of the virus during persistent infections in the herd. A previous report revealed a similar evolution in the ATPase gene of the ORF virus during the persistence of the virus in a sheep herd in Ethiopia . Such alOur field investigations suggested that skin lesions were observed on some cattle in this herd since 2014. However, the farmer consulted local veterinary services only when the health conditions of cattle had worsened. The affected cattle had lesions that were not only confined to the teats and udder, but, were present on other parts of the animal, including the limbs, mouthparts, ventrum, and skin folds, suggesting a severe form of the disease. PCPV has previously been isolated from atypical sites apart from the teats and udder , 19, 21.Nevertheless, it came to our attention that the farmworkers previously had sores mainly on their hands and forearms, and sometimes the face. Those lesions cleared within a few weeks without treatment. This observation is consistent with previous reports showing that PCPV lesions in humans usually resolve quickly . It is aThis first detection and characterization of PCPV demonstrates the power of molecular methods for the differential diagnosis of pox diseases in cattle. The correct identification of the causative agent of pox-like lesions in cattle is essential to allow for proper veterinary intervention, including vaccination of non-infected surrounding herds in the case of LSD. Similarly, strict hygiene measures are needed to prevent transmission to humans when PCPV infection is detected. Country-wide surveillance may be crucial to investigate the prevalence of PCPV infections among the cattle population and identify infection risks for other animals and humans in Zambia.


Possessing structural homology with their active enzyme counterparts but lacking catalytic activity, pseudoenzymes have been identified for all major enzyme groups. Caspases are a family of cysteine‐dependent aspartate‐directed proteases that play essential roles in regulating cell death and inflammation. Here, we discuss the only human pseudo‐caspase, FLIP(L), a paralog of the apoptosis‐initiating caspases, caspase‐8 and caspase‐10. FLIP(L) has been shown to play a key role in regulating the processing and activity of caspase‐8, thereby modulating apoptotic signaling mediated by death receptors (such as TRAIL‐R1/R2), TNF receptor‐1 (TNFR1), and Toll‐like receptors. In this review, these canonical roles of FLIP(L) are discussed. Additionally, a range of nonclassical pseudoenzyme roles are described, in which FLIP(L) functions independently of caspase‐8. These nonclassical pseudoenzyme functions enable FLIP(L) to play key roles in the regulation of a wide range of biological processes beyond its canonical roles as a modulator of cell death. FLIP(L) is to date the only true pseudo‐caspase identified in the human proteome. As a classical pseudoenzyme, it functions as a regulator of its active homologs, the apoptosis‐initiating caspases, caspase‐8 and caspase‐10. Additionally, FLIP(L) functions independently of these caspases. These nonclassical pseudoenzyme functions enable FLIP(L) to play key roles in the regulation of a wide range of biological processes beyond its canonical roles as a modulator of cell death. AIM2interferon‐inducible protein AIM2AKTprotein kinase BAPCadenomatous polyposis coliASCapoptosis‐associated speck‐like protein containing CARDATGautophagy‐related genesBcl‐2B‐cell lymphoma 2BCLAF1Bcl‐2 associated transcription factor 1BidBH3‐interacting domain death agonistCD95cluster of differentiation 95DAMPsdamage‐associated molecular patternDDdeath domainDEDdeath effector domainDRdeath receptorERKextracellular signal‐regulated kinaseFADDFAS‐associated death domain proteinHINhematopoietic interferon‐inducible nuclear antigensIFNinterferonIKKIκB kinaseILinterleukinIRF3interferon regulatory transcription factor 3ITF2immunoglobulin transcription factor‐2JNKc‐Jun N‐terminal kinaseLC3microtubule‐associated protein 1A/1B‐light chain 3MAPmitogen‐activated proteinMEKK1MAP/ERK kinase kinase 1MKK7MAP kinase kinase 7MLKLmixed lineage kinase domain‐like proteinNESnuclear export signalNF‐κBnuclear factor kappa‐light‐chain‐enhancer of activated B cellsNGFnerve growth factorNLRP3NACHT, LRR, and PYD domains‐containing protein 3NLSnuclear localization sequencePERKprotein kinase RNA‐like endoplasmic reticulum kinasePYDpyrin domainRAF‐1RAF proto‐oncogene serine/threonine‐protein kinaseRIPK1receptor‐interacting serine/threonine‐protein kinase 1SGLT1sodium/glucose cotransporter 1TIP49TBP‐interacting protein 49TLRsToll‐like receptorTNFtumor necrosis factorTNFRtumor necrosis factor receptorTRADDTNFR1‐associated death domainTRAF1TNF receptor‐associated factorTRAILTNF‐related apoptosis‐inducing ligandWntWingless‐related integration siteInitially identified over 50 years ago, pseudoenzymes have recently been the subject of increased interest as the originally unanticipated extent of their biological importance has become apparent It is thought most likely that pseudoenzymes evolved as a result of gene duplication events, with one of the duplicated genes no longer having the selective pressure of maintaining an active catalytic site It is estimated that pseudoenzymes comprise ~ 10% of proteomes, a further indication of their biological importance CFLAR gene (located on chromosome 2q33.1), c‐FLIP has been shown to be involved in regulating a number of key cellular functions, including apoptosis, necroptosis, and autophagy. c‐FLIP is expressed in humans as a number of splice variants, namely FLIP(L) (FLIP long), FLIP(S) (FLIP short), and FLIP(R) (FLIP Raji). Each of these splice variants possesses two tandem N‐terminal protein–protein interaction domains termed death effector domains (DEDs). These DEDs facilitate homotypic interactions with other proteins bearing DEDs, most importantly procaspase‐8 and procaspase‐10 and FADD. While each possesses tandem N‐terminal DEDs, c‐FLIP splice forms differ at their C terminus FLIP(S) is a 26‐kDa protein comprised of two DEDs followed by a 20 amino acid unstructured sequence, which plays an important role in its ubiquitination and subsequent proteasomal degradation.FLIP(R) is a 24‐kDa protein notable for its restricted expression pattern, being found mainly in T cells. Like FLIP(S), it is comprised of two DEDs, although its C‐terminal region is different due to alternative splicing.FLIP(L) is a 55‐kDa protein, the C terminus of which possesses a region which is highly similar to the equivalent regions of procaspase‐8 and procaspase‐10. FLIP(L) differs from these caspases; however, in that this region is catalytically inactive hence marking it as a classic pseudoprotease.Encoded by the CFLAR gene is located within the same chromosomal region (2q33.1) as the genes encoding its paralogs caspase‐8 (CASP8) and caspase‐10 (CASP10) CFLAR and its paralogs has shed further light on its evolutionary origins. Across a variety of species, including humans, it is apparent that CFLAR has a unique and independent evolutionary path, which is distinct from those of CASP8 and CASP10. Furthermore, it was noted that in humans, the molecular evolutionary rate of CFLAR is 1.7× faster than that of CASP8The CFLAR, CASP8, and CASP10 is seen in rodents. While CASP8 and CASP10 are mostly conserved throughout vertebrate evolution, CASP10 is not present in a subset of rodents. While present in the guinea pig and squirrel, this gene is absent in both the rat and the mouse CASP10 gene occurred in and around the cretaceous period, when the divergence of these rodent clades is thought to have taken place An interesting point of note in the evolution of 315 and Y360 in humans), respectively. Comparative analysis of the pseudo‐caspase domain in FLIP(L) has further identified the importance of a leucine residue (L413 in humans). Conserved across vertebrates, this leucine is able to form a distinct tertiary structure triad with the aforementioned conserved arginine and tyrosine residues At the protein level, this evolutionary divergence manifests itself most obviously in the pseudo‐caspase domain of FLIP(L), where amino acid substitutions have resulted in loss of catalytic activity. In both caspase‐8 and caspase‐10, the catalytic activity is imparted by a catalytic dyad composed of histidine and cysteine residues. In comparison, in FLIP(L), these residues are absent, being replaced by arginine (R) and tyrosine (Y) residues  can be roughly subdivided into those in which FLIP(L) forms complexes via its tandem DEDs with FADD and procaspase‐8 and/or procaspase‐10 and those where it acts independently of FADD and its genetic paralogs.One of the most fundamental and widely studied roles of FLIP(L) is its modulation of signaling of tumor necrosis factor (TNF) receptor superfamily members TNF‐related apoptosis‐inducing ligand R1 (TRAIL‐R1)/death receptor 4 (DR4), TRAIL‐R2/DR5, and Fas/cluster of differentiation 95 (CD95) within the extrinsic apoptotic pathway 374, breaking the linking region between the large and small catalytic subunits. This results in the generation of two fragments, a p43/41 subunit and a p12 subunit, although it is important to note that the cleaved small subunits from each member of the homodimer remain in the complex and are essential for forming the active site of the intermediate partially processed enzyme.Interdimer cleavage between 2 proximal dimers occurs at Asp210, Asp216, or Asp223 located between DED2 and the large catalytic subunit and at Asp384 in the small catalytic subunit. These intradimer cleavage events yield the apoptotically active enzyme, a heterotetramer comprised of two p10 and two p18 subunits, with two active sites The second stage is intradimer cleavage at either AspIn order to be fully processed, procaspase‐8 must form homodimers, with this dimerization initially occurring via their tandem DEDs and then via their caspase domains, which align in an antiparallel fashion to create two catalytically active sites Fig. . FollowiThe first rate‐limiting step requires the proximity of two procaspase‐8 homodimers in adjacent DISCs, which is why receptor ‘superclustering’ is needed for efficient activation of apoptosis signaling Through their tandem DEDs, all FLIP splice forms are capable of forming heterodimers with procaspase‐8 Fig. . Crucial. In fact, the FLIP(L)/procaspase‐8 heterodimer is more efficient at catalyzing this first rate‐limiting activation step than the procaspase‐8 homodimer. This appears to be related to a lower activation energy for the formation of the heterodimer’s active site 210, Asp216, or Asp223 between DED2 and the large catalytic subunit of caspase‐8. Moreover, there is a lack of a suitable caspase‐8 cleavage site between FLIP(L)’s DED2 and its large pseudocatalytic subunit. Therefore, further processing (intradimer cleavage) of the FLIP(L)/procaspase‐8 heterodimer is prevented and the heterodimeric enzyme remains bound at the DISC, where it can cleave local substrates (adjacent dimers and RIPK1), but cannot propagate an apoptotic signal FLIP(L)/procaspase‐8 heterodimer‐mediated cleavage of adjacent corecruited procaspase‐8 homodimers and other FLIP(L)/procaspase‐8 heterodimers occurs between the large and small catalytic subunits in a similar manner to that described above for the homodimers (Step 1: interdimer cleavage)Apoptosis inhibition: High levels of FLIP(L) will result in a predominance of FLIP(L)/procaspase‐8 heterodimers; therefore, the lack of procaspase‐8 homodimers will prevent sufficient processing of procaspase‐8 to its apoptosis active heterotetramer.Apoptosis activation: Low levels of FLIP(L) will result in a predominance of procaspase‐8 homodimers; in this scenario, the FLIP(L)/caspase‐8 heterodimer can promote the rate‐limiting first cleavage step of adjacent procaspase‐8 homodimers and thereby promote apoptosis induction.In recent years, a number of models of DISC assembly have been proposed We have recently defined the stoichiometry of FLIP(L):procaspase‐8 at the DISC at which these two opposing functions operate Similar to its action in regulating the TRAIL‐R1/DR4, TRAIL‐R2/DR5, and Fas/CD95 receptors, FLIP(L) also plays an important role in the downstream regulation of the TNFR1. Like the aforementioned receptors, TNFR1 is a member of the TNF receptor superfamily. Upon activation by its cognate ligand TNF‐α, formation of the TNFR1 complex I occurs. Initially, the adaptor protein TNFR1‐associated death domain protein (TRADD) is recruited followed by RIPK1, TRAF2, and cellular inhibitor of apoptosis proteins‐1/2. This complex is subsequently capable of activating both nuclear factor kappa‐light‐chain‐enhancer of activated B cells (NF‐κB) and mitogen‐activated protein (MAP) kinase signaling pathways in a manner dependent on RIPK1 ubiquitination by the linear ubiquitin chain assembly complex CFLAR/c‐FLIP is an NF‐κB target gene, the levels of FLIP expression are enhanced by TNFR1 complex I, which therefore acts to modulate the signaling output from complex II Similar to the DISC, FLIP(L), and in particular the ratio of procaspase‐8 homodimers to procaspase‐8/FLIP(L) heterodimers, plays a key role in determining signaling from TNFR1 complex II. In order for apoptosis‐inducing levels of caspase‐3/7 activity to be generated, sufficient numbers of procaspase‐8 homodimers must be recruited to the complex. In instances where procaspase‐8/c‐FLIP heterodimers are in greater abundance, this activation will be impeded. Moreover, as Procaspase‐8/FLIP heterodimers formed at TNFR1 complex II can also modulate necroptosis, a pro‐inflammatory and caspase‐independent form of cell death In addition to its roles when functioning as part of a complex with caspase‐8, FLIP(L) also has nonclassical pseudoenzyme functions that are independent of caspase‐8 Fig. .Inflammasomes are intracellular multimeric protein complexes primarily responsible for the processing of the inflammatory caspase, procaspase‐1, in response to a range of damage‐ and pattern‐associated molecular patterns (DAMPs and PAMPs) Inflammasomes are classified based on the protein upon which their scaffold structure is formed FLIP(L) has been shown to directly influence the NLRP3 inflammasome: Through its pseudo‐caspase domain, FLIP(L) is capable of interacting directly with both NLRP3 and procaspase‐1, thereby modulating the activity of the inflammasome Although procaspase‐8 has also been shown to modulate the inflammasome, these functions, which are specific for the long FLIP splice form, are not reported to be caspase‐8‐dependent and are therefore examples of FLIP(L) acting in a nonclassic pseudoenzyme manner APC/APC gene or amplification of β‐catenin being an initiating event in the majority of colorectal cancers Independently of procaspase‐8, FLIP(L) has also been implicated in playing a role in the Wnt signaling pathway. This signaling cascade is induced following the binding of Wnt proteins to the N‐terminal region of Frizzled receptors FLIP(L) has been reported to inhibit β‐catenin ubiquitination, leading to increases in cytosolic β‐catenin and its translocation to the nucleus, where it promotes activation of its target genes More recently, it has been reported that FLIP(L) could interact via its DED with the nuclear protein TBP‐interacting protein 49 (TIP49) and that this interaction was crucial for the ability of FLIP(L) to modulate Wnt signaling Another process in which the pseudo‐caspase FLIP(L) has a role to play is in autophagy. Interestingly, in modulating this cellular function, FLIP(L) can act both independently or in concert with its other genetic paralog, procaspase‐10.Autophagy is a degradation process in which cytoplasmic proteins are packaged into autophagosomes that in turn are delivered to lysosomes, where an autolysosome is formed, and the packaged cytoplasmic material degraded FLIP(L) has been shown to directly bind Atg3 and impede autophagy by preventing binding of Atg3 to LC3. This in turn prevents the formation of autophagosomes FLIP(L) has also been reported to play a role in the modulation of NF‐κB signaling. It has been reported that FLIP(L), recruited following activation of the Fas DISC, was capable of interacting with both TRAF‐1 and TRAF‐2, as well as with kinases RIPK1 and RAF proto‐oncogene serine/threonine‐protein kinase (Raf‐1) 376, yielding p43‐FLIP, led to enhanced/more effective RIPK1 recruitment. This activation of caspase‐8 and cleavage of FLIP(L) was further implied to be associated with migration of FLIP(L) and caspase‐8 to lipid rafts Additionally, FLIP(L) has been demonstrated to be involved in the regulation and activation of T cells An alternative mechanism of FLIP regulation of NF‐κB signaling was reported in which p22‐FLIP, a nonclassical cleavage product formed by cytoplasmic c‐FLIP and procaspase‐8 heterodimers, directly binds to and modulates the activity of the IκB kinase (IKK) complex, a critical upstream of regulator of the NF‐κB signaling pathway As noted above, following ligation of TNFR1 by TNF‐α, NF‐κB activation by complex I causes upregulation of FLIP(L) expression levels. FLIP(L) was subsequently shown to be essential for TNF‐α‐induced MAPK activation, mediating these effects through its interactions with Raf‐1 in a Ras‐independent manner It has been reported that FLIP(L) plays a role in both determining ER morphology and modulating the functions of ER mitochondria‐associated membranes Using hepatocellular carcinoma cells, it was recently reported that when FLIP(L) was overexpressed, aerobic glycolysis was accelerated It has been reported that in HeLa cells, FLIP(L) was capable of enhancing motility and also adhesion to extracellular matrix proteins Interferon (IFN) regulatory transcription factor 3 (IRF3) is a transcription factor that promotes expression of IFNα, IFN β, and other interferon‐stimulated genes, which in turn moderate cellular responses to, for instance, viral infection In an embryonic mouse model, it was observed that FLIP(L) was significantly expressed in neurons, with these levels subsiding later in development CASP12 gene encodes a truncated protein due to premature stop codon, so it is probably more correctly classified as a pseudogeneTo our knowledge, FLIP(L) is the only true pseudoenzyme of the caspase enzyme family in humans. In the majority of humans, the The authors declare no conflict of interest.PS and TS wrote the manuscript and prepared figures. CJS and DBL edited and revised the manuscript.


Wolbachia is a maternally transmitted bacterium that lives inside arthropod cells. Historically, it was viewed primarily as a parasite that manipulates host reproduction, but more recently it was discovered that Wolbachia can also protect Drosophila species against infection by RNA viruses. Combined with Wolbachia’s ability to invade insect populations due to reproductive manipulations, this provides a way to modify mosquito populations to prevent them transmitting viruses like dengue. In this review, we discuss the main advances in the field since Wolbachia’s antiviral effect was discovered 12 years ago, identifying current research gaps and potential future developments. We discuss that the antiviral effect works against a broad range of RNA viruses and depends on the Wolbachia lineage. We describe what is known about the mechanisms behind viral protection, and that recent studies suggest two possible mechanisms: activation of host immunity or competition with virus for cellular resources. We also discuss how association with Wolbachia may influence the evolution of virus defense on the insect host genome. Finally, we investigate whether the antiviral effect occurs in wild insect populations and its ecological relevance as a major antiviral component in insects. Wolbachia pipientis is a maternally transmitted alphaproteobacterium that lives obligatorily within the cytoplasm of arthropod cells , Chikungunya virus (CHIKV), Yellow Fever virus (YFV), Zika virus (ZIKV) and West Nile virus (WNV)  . Wolbachtibility –15, and tibility , 17.Wolbachia antiviral effect was discovered 12 years ago, current research gaps, and potential future developments. First, we address the generality of the antiviral effect and how it depends on Wolbachia lineage and on virus identity. Second, we discuss the possible mechanisms of antiviral protection. Third, we discuss how association with Wolbachia may influence the evolution of virus defense on the insect host genome. Finally, we discuss the virus blocking ecological relevance by addressing if it occurs in wild insect populations.In this Mini Review, we discuss the main advances in the field since the Wolbachia protects flies and mosquitoes against RNA viruses , and Drosophila C virus (DCV). However, DCV is not commonly found in wild Drosophila populations .The level of protection against viruses varies among the host , 42. It tificial . For exanogaster , 45, pro aegypti Table 1.a strain . All theWolbachia are carried out using only the adult stage. So far, only Graham et al. , while in mosquitoes some studies use oral infection besides microinjection. Although methods such as microinjection allow greater viral dose precision, we know that in nature insects acquire many pathogens by feeding , interfering with virus replication. The presence of the bacterium in host cells leads to cellular stress, including oxidative stress that activates host immune pathways  , CNPq , and a Newton Advanced Fellowship from the Royal Society.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Zika virus, have recently emerged as one of the most important threats to human health. The use of metagenomics-based approaches has already proven valuable to aid surveillance of arboviral infections, and the ability to reconstruct complete viral genomes from metatranscriptomics data is key to the development of new control strategies for these diseases. Herein, we used RNA-based metatranscriptomics associated with Ion Torrent deep sequencing to allow for the high-quality reconstitution of an outbreak-related Zika virus (ZIKV) genome , with extended 5′-UTR and 3′-UTR regions, using a newly-implemented bioinformatics approach. Besides allowing for the assembly of one of the largest complete ZIKV genomes to date, our strategy also yielded high-quality complete genomes of two arthropod-infecting viruses co-infecting C6/36 cell lines, namely: Alphamesonivirus 1 strain Salvador  and Aedes albopictus totivirus-like (4618 nt); the latter likely represents a new viral species. Altogether, our results demonstrate that our bioinformatics approach associated with Ion Torrent sequencing allows for the high-quality reconstruction of known and unknown viral genomes, overcoming the main limitation of RNA deep sequencing for virus identification.Arboviruses, including the  Aedes genus play an important role in the transmission of viral etiological agents of emerging human infections, including Dengue virus (DENV), Zika virus (ZIKV), Chikungunya virus (CHYKV) and Yellow fever virus (YFV) . Si country c. IndeedSimilarly, for the previous Zika virus strains derived from the outbreak in 2015, the viral strain assembled in this work clustered together with other strains with Asian genotype d. ComparZika virus, with the new assembly strategy, we were also able to reconstruct two large contigs showing similarities to viral sequences. One contig of 20,194 nt showed a sequence similarity at the nucleotide level  with viruses from Alphamesonivirus genus while one contig with 4618 nt presented a similarity at the protein level  with viruses from the Totiviridae family, which likely represents a new viral species. Phylogenetic analysis based on the nucleotide sequence revealed that the Alphamesonivirus identified in C6/36 cell lines is a new strain of a previously identified Alphamesonivirus 1 from the Mesoniviridae family, a group of single-stranded RNA viruses that have been described in mosquitoes (A. albopictus mosquitoes. It was named Alphamesonivirus 1 strain Salvador. Phylogenetic analysis based on RdRp protein of contig related to the Totiviridae family suggested its relationship to a newly proposed family, Totivirus-like, which is different from totiviruses that have Fungi as natural hosts, and have been described in a vast range of organisms (Aedes albopictus totivirus-like.Notably, besides the identification of contigs showing sequence similarity to the squitoes a 43]. T. TZika vrganisms b [44,45,Totivirus and Alphamensonivirus assembled genomes, 16,319 and 40,521 respectively, representing 0.09% and 0.22% of the library. Furthermore, RNA density along viral genomes showed continuous coverage, suggesting that they were correctly assembled (Totivirus-like sequence, experimental validation was performed by RT-PCR, and we were able to detect the viral genome in the same C6/36 population we used to perform deep sequencing. We also retrieved a fragment of its genome through Sanger sequencing in order to compare with the contiguous sequence obtained through metatranscriptomics (For both of the two new viruses identified, we observed an ORF structure consistent with their closely related viruses c,d. In assembled c,d. To ciptomics . We obseZika virus genome, including 5′ and 3′ UTR regions, that would not be possible using standard approaches. Furthermore, we also assembled the full genomes of two unknown viruses co-infecting the stock, Alphamesonivirus and the newly described Totivirus-like, highlighting the potential of our strategy for monitoring viral contaminations. Therefore, our work highlights the importance of the development of new unbiased metagenomic approaches for the identification of viral sequences.Altogether, our results indicate that our strategy based on RNA deep sequencing using Ion Torrent technology allows the confident reconstitution of known and unknown viruses. Using this strategy, we were able to reconstruct the complete genome of the outbreak-related


Coronavirus disease COVID-19) is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) 9 is caus. In addiNatural products have been increasingly applied for healing, actively attracting the attention of biomedical research and technology -8. HowevInterestingly, the biological activities of propolis are attributed to a variety of chemical constituents such as phenolic acids, phenolic acid esters, flavonoids, terpenoids, artepillin C, caffeic acid, chrysin, galangin, quercetin, apigenin, kaempferol, pinobanksin 5-methyl ether, pinobanksin, pinocembrin, and pinobanksin 3-acetate . Propoliin vitro anti-HSV-1 activity of Brazilian green PE and its efficacy against cutaneous HSV-1 infection in a mouse model  collected from temperate climate exerts potent and broad-spectrum antiviral activity against a diverse panel of viruses such as herpes simplex virus type 1 (HSV-1), herpes simplex virus type 2 (HSV-2), influenza virus types A and B, and human immunodeficiency virus (HIV) . In 2016se model . In brie sources . Concern sources . Propoli sources .in vitro and in vivo studies verified that ethanol extracts of Brazilian propolis exhibited antiviral activity against the influenza virus  activities of propolis extracts were investigated. Briefly, Dakahlia propolis  has shown a high antiviral activity against infectious bursal disease virus, high antibacterial activity against Escherichia coli, and high antifungal activity against Candida albicans , at a dose of 45 drops three times a day for 14 days. After 12 days of treatment, the patient's general clinical condition improved significantly, and she recovered, with a negative RT-PCR test result. To the best of our knowledge, this is the first reported case indicating the therapeutic potential of propolis in SARS-CoV-2 infection. The relevance of this therapeutic intervention based on a single case report is debatable because the clinical effectiveness could have been influenced by several factors, including the patient's response to the use of other adjuvant therapies.The patient had no past history of chronic diseases including kidney disease, hypertension, endocrine disorders, or neurological and musculoskeletal diseases. Moreover, she reported smoking but no alcohol intake. In addition, her routine blood test results were within the normal reference range at hospital admission. However, the reverse transcription-polymerase chain reaction (RT-PCR) assay for SARS-CoV-2 of a nasopharyngeal swab sample yielded a positive result. The patient was advised by the medical team to stay at home. At home, the patient maintained a healthy diet and adequate hydration. She also reported that she had started consuming the non-alcoholic preparation of Brazilian green propolis (EPP-AFin vitro activity against the SARS-CoV-2 virus, and more than 300 active clinical trials are underway to identify effective medical treatments for this infection (in vitro and in vivo studies (On the basis of these facts, what is the suggested course of action? First, it is clear that there are no medical therapies that can improve the outcomes of SARS-CoV-2-infected patients . To datenfection , includinfection ,29. Seconfection . Considenfection . Third, nfection ,16,28,29nfection . This menfection . In this studies . In thisOverall, there is a global effort to fighting this threat on several fronts in order to restore economic collapse, health systems, lifestyle habits, and safe living ,37. Unti


Enterobacteriaceae and Veillonellaceae families, while the beneficial genera Faecalibacterium and Oscillospira were significantly reduced. SCFA quantification revealed a significant increase of fecal acetate and propionate in GSD subjects, but with a beneficial role probably reduced due to unbalanced bacterial interactions; nutritional values correlated to bacterial genera were significantly different between experimental groups, with nearly opposite cohort trends.A life-long dietary intervention can affect the substrates’ availability for gut fermentation in metabolic diseases such as the glycogen-storage diseases (GSD). Besides drug consumption, the main treatment of types GSD-Ia and Ib to prevent metabolic complications is a specific diet with definite nutrient intakes. In order to evaluate how deeply this dietary treatment affects gut bacteria, we compared the gut microbiota of nine GSD-I subjects and 12 healthy controls (HC) through 16S rRNA gene sequencing; we assessed their dietary intake and nutrients, their microbial short chain fatty acids (SCFAs) via gas chromatography and their hematic values. Both alpha-diversity and phylogenetic analysis revealed a significant biodiversity reduction in the GSD group compared to the HC group, and highlighted profound differences of their gut microbiota. GSD subjects were characterized by an increase in the relative abundance of  The evidence of interplay between intestinal commensal bacteria and host physiological functions has hugely grown over the last years, shedding new light on clinical research on pathological conditions . Among tGSD are a group of hereditary metabolic disorders caused by the deficiency of one of the enzymes involved in glycogen metabolism. Glycogen is primarily stored in liver and muscle, and disorders of glycogen degradation may affect both tissues ,4. GSD tGSD-I results in a defect in the glucose-6-phosphatase system, which is required for the hydrolysis of glucose-6-phosphate into glucose and inorganic phosphate ,8, impaiTwo main subtypes of GSD-I are recognized: type Ia (GSD-Ia), due to a defect of the catalytic subunit glucose-6-phosphatase-α in the endoplasmic reticulum, and responsible for 80% of cases of GSD-I , and typThe primary aim of the dietary treatment is not only avoiding hypoglycemia, but also achieving a good metabolic control , minimizSince nutritional intake is one of the most relevant factors influencing the gut microbiota’s composition , it is rThe aim of our study was thus to compare dietary macronutrient intake, gut microbial biodiversity and microbial metabolite production in patients with GSD-Ia/Ib and healthy subjects, in order to better evaluate and characterize diet or disease-related microbiome differences.p = 0.0176). Within the entire dataset, 3/21 resulted obese , 4/21 overweight , 14/21 normal weight . Overall mean BMI values for the enrolled subjects were 26.8 ± 4.8 for GSD patients and 21.6 ± 2.9 for healthy controls (HC) , antihypertensive drugs , triglyceride lower-drugs , salicylates , granulocyte-colony stimulating factor  and multivitamin and calcium with vitamin D .Three GSD-Ib patients were reported to be neutropenic and to have IBD.Fasting blood samples of GSD patients were analyzed for total cholesterol, triglycerides, insulin, glucose, uric acid, liver enzymes and lactate . GSD patThe daily energy intakes and the diet macronutrient compositions of enrolled subjects are reported in p = 0.0468) and carbohydrate intakes , but a lower lipid intake  was observed. No significant differences were observed for proteins. As expected from the dietary recommendations, sugar consumption was reduced in the GSD group (p = 0.0013), whereas the starch intake was higher in GSD (mean ± SD: 110.27 g ± 44.80) compared to HC (180.94 g ± 62.81) (p = 0.004). Total fiber intake (p = 0.0148) and soluble fiber intake (p = 0.0227) were higher in GSD patients, whereas no significant differences were detected for the insoluble fraction.Compared to HC, GSD group showed a significantly higher daily energy , samples were subsampled to 50,000 reads each by random picking. After quality filtering processes, we obtained a mean count of 40,988.261 reads per sample  for the entire dataset, average 1654 OTUs per sample).p = 0.02; observed species, p = 0.02; Shannon, p = 0.002; Faith’s phylogenetic diversity, p = 0.03). As shown in p = 0.004 and p = 0.01). A clear difference among HC and GSD subjects was highlighted in beta-diversity as well B. Both uWe found several significant differences in taxas’ relative abundances among the two groups across all phylogenetic levels. p = 0.001). Several dominant families were also significantly diverse in the two cohorts: Ruminococcaceae (p = 0.002), Veillonellaceae (p = 0.030) and Enterobacteriaceae (p = 0.006) (Ruminococcaceae was more abundant among controls (40.3% vs. 15% GSD), both Veillonellaceae and Enterobacteriaceae were much higher among GSD patients . As shown in Ruminococcus , Faecalibacterium  and Oscillospira . In total, 1596 OTUs out of were classified as Enterobacteriaceae at the family level, and 792 of them were annotated as Escherichia coli. GSD patients were found significantly increased in Escherichia coli compared to HC .At the phylum level A, differ= 0.006) B. Note, p values are reported in All relative abundances and significant p = 0.0159). In particular, the concentration of acetate and propionate were higher in patients , whereas the concentration of butyrate was similar in the two groups (p = 0.8381). No significant differences were found for the branched-chain fatty acids iso-valerate and iso-butyrate between the two groups  in GSD group (o groups .p = 0.0094); in particular, tryptophan (p = 0.017), glutathione (p = 0.009) and beta-alanine (p = 0.0004); and for lipid metabolism, alpha-linolenic acid especially (p = 0.025). Intriguingly and counterintuitively, the starch and sucrose metabolism pathways were significantly reduced in GSD subjects . At a broad functional level (level 2 KEGG), the functional analysis predicted an enrichment in genes encoding enzymes for amino acid metabolism . On the other side, we observed Faecalibacterium and Oscillospira (significantly decreased in GSD subjects) to be negatively related to acetate concentrations . The obtained data revealed positive correlations between the Correlations of nutritional parameters to bacteria abundances revealed several divergent relationships between the two cohorts, as shown in the Odoribacter and Parabacteroides genera  only in GSD patients, whereas a milder, opposite trend characterized the HC group. In GSD patients, starch intake positively correlated with Veillonella, Citrobacter and Akkermansia genera  and negatively with Coprococcus and Clostridium genera . The latter two genera, in particular, showed an opposite trend with nutritional values between the two groups (correlated positively to HC and negatively to GSD patients).Fiber intake showed a strong positive correlation to Our study investigated the impact of the life-long cornstarch-rich diet characterizing the treatment of GSD patients by integrating gut microbiota, microbial metabolites and nutritional data. The identification of bacterial metabolism is crucial for the understanding of a possible microbial role in metabolic diseases. For this reason, we highlighted the importance of short chain fatty acids in gut microbiota characterization as bacteria cooperate and feed one on another’s products (cross-feeding). To our knowledge, this is the first study evaluating the impact of the cornerstone diet of GSD-I on gut microbial cross-feeding and metabolites production. During the last decade, a healthy gut microbiota has been typically characterized by members of the phyla Bacteroidetes and Firmicutes, and their genera are believed to be the main responsible bacteria for positive biodiversity in the human gut , as theiEnterobacteriaceae family, have been suggested to exert pro-inflammatory activity both locally, at the gastrointestinal mucosa level, and systemically /405; the QUICKI (quantitative insulin sensitivity index) was calculated as follows: 1/[log10 insulin (μUI/mL) + log10 basal blood glucose (mg/dL)]; HOMAβ (Homeostatic Model Assessment of β-cell function) was calculated as follows: 20 × basal insulin (μUI/mL)/[basal blood glucose (mmol/L) -3.5] ; the TygThe study was conducted at the Pediatric Department of San Paolo Hospital in Milan, with the previous approval by Ethics Committee of San Paolo Hospital in Milan ; written informed consent was obtained from each enrolled subject.Fecal DNA extraction was performed using the Spin stool DNA kit , according to manufacturer’s instructions. For each sample, 25 ng of extracted DNA was used to construct the sequencing library.The V3–V4 hypervariable regions of the bacterial 16S rRNA gene were amplified with a two-step barcoding approach according to the Illumina 16S Metagenomic Sequencing Library Preparation . Briefly, DNA samples were amplified with dual-index primers using a Nextera XT DNA Library Preparation Kit (Illumina) and library concentration and quantification were determined using a KAPA Library Quantification Kit  and Agilent 2100 Bioanalyzer System , respectively. The libraries were pooled and sequenced with a MiSeq platform (Illumina) for 2 × 250 base paired-end reads and a total of 2.5 Gbases raw reads were obtained.The obtained 16S rRNA gene paired sequences were merged using Pandaseq   metrics through the QIIME pipeline; statistical evaluation among alpha-diversity indices was performed by a non-parametric Monte Carlo-based test, using 9999 random permutations. Weighted and unweighted UniFrac distances and Permanova (adonis function) in the R package vegan   quantification was performed by gas chromatography. Concentrations of acetic, propionic, iso-butyric, butyric and iso-valeric acids were assessed as previously described .Briefly, analyses were performed using a Varian model 3400 CX Gas chromatograph fitted with FID detector, split/splitless injector and a SPB-1 capillary column . Quantification of the SCFAs was obtained through calibration curves of acetic, propionic, iso-butyric, butyric, and iso-valeric acid in concentrations between 0.25 and 10 mM . Results are expressed as mg/g of wet weight of feces.All comparisons were performed using MATLAB software .Comparisons of the two groups were performed using Student’s t-test for normally distributed variables and Wilcoxon test for non-normally distributed variables. For evaluating differences in relative abundances of bacterial groups, a Mann–Whitney U-test was performed. Due to the small number of samples, no correction methods have been applied.p-values < 0.05 were considered as significant for each analysis.Correlations between taxa and nutritional values and SCFA quantities were assessed through Spearman correlation and the associated linear regression model.


Sarbecovirus, within Betacoronavirus. Our method achieves 100% accurate classification of the COVID-19 virus sequences, and discovers the most relevant relationships among over 5000 viral genomes within a few minutes, ab initio, using raw DNA sequence data alone, and without any specialized biological knowledge, training, gene or genome annotations. This suggests that, for novel viral and pathogen genome sequences, this alignment-free whole-genome machine-learning approach can provide a reliable real-time option for taxonomic classification.The 2019 novel coronavirus  has spread to 184 countries with over 1.5 million confirmed cases. Such major viral outbreaks demand early elucidation of taxonomic classification and origin of the virus genomic sequence, for strategic planning, containment, and treatment. This paper identifies an intrinsic COVID-19 virus genomic signature and uses it together with a machine learning-based alignment-free approach for an ultra-fast, scalable, and highly accurate classification of whole COVID-19 virus genomes. The proposed method combines supervised machine learning with digital signal processing (MLDSP) for genome analyses, augmented by a decision tree approach to the machine learning component, and a Spearman’s rank correlation coefficient analysis for result validation. These tools are used to analyze a large dataset of over 5000 unique viral genomic sequences, totalling 61.8 million bp, including the 29 COVID-19 virus sequences available on January 27, 2020. Our results support a hypothesis of a bat origin and classify the COVID-19 virus as  Coronaviridae now contains four genera . While  species , 7–9. Ph species , 10–12.  species , 10, 13.Like other RNA viruses, coronavirus genomes are known to have genomic plasticity, and this can be attributed to several major factors. RNA-dependent RNA polymerases (RdRp) have high mutation rates, reaching from 1 in 1000 to 1 in 10000 nucleotides during replication , 14, 15.Sarbecovirus) and lineage C (sub-genus Merbecovirus) of Betacoronavirus, respectively [The highly pathogenic human coronaviruses, Severe Acute Respiratory Syndrome coronavirus (SARS-CoV) and Middle East respiratory syndrome coronavirus (MERS-CoV) belong to lineage B  of Betacoronavirus. From phylogenetic analysis of the RdRp protein, spike proteins, and full genomes of the COVID-19 virus and other coronaviruses, it was found that the COVID-19 virus is most closely related to two bat SARS-like coronaviruses, bat-SL-CoVZXC21 and bat-SL-CoVZC45, found in Chinese horseshoe bats Rhinolophus sinicus [Sarbecovirus) Betacoronaviruses [RaTG13 [RaTG13 and two bat SARS-like CoVs (ZC45 and ZXC21) are also supported in alignment-based phylogenetic analyses [Sarbecovirus strains, the hypothesis that the COVID-19 virus originated from bats is deemed very likely [From analyses employing whole genome to viral protein-based comparisons, the COVID-19 virus is thought to belong to lineage B  [Flavivirus genus to species classification using 2D CGR as numerical representation [Betacoronavirus, while its genomic similarity to the sub-genus Sarbecovirus supports a possible bat origin.All analyses performed thus far have been alignment-based and rely on the annotations of the viral genes. Though alignment-based methods have been successful in finding sequence similarities, their application can be challenging in many cases , 46. It LDSP-GUI  provideson (CGR)  as numeron (CGR) , 51, 52.entation  and MLDSa priori biological knowledge, by a simultaneous processing of the geometric space of all relevant viral genomes. The main contributions are:This paper demonstrates how machine learning using intrinsic genomic signatures can provide rapid alignment-free taxonomic classification of novel pathogens. Our method delivers accurate classifications of the COVID-19 virus without Identifying intrinsic viral genomic signatures, and utilizing them for a real-time and highly accurate machine learning-based classification of novel pathogen sequences, such as the COVID-19 virus;A general-purpose bare-bones approach, which uses raw DNA sequences alone and does not have any requirements for gene or genome annotation;The use of a “decision tree” approach to supervised machine learning , for successive refinements of taxonomic classification.A comprehensive and “in minutes” analysis of a dataset of 5538 unique viral genomic sequences, for a total of 61.8 million bp analyzed, with high classification accuracy scores at all levels, from the highest to the lowest taxonomic rank;Coronaviridae and the known sub-genera of the genus Betacoronavirus.The use of Spearman’s rank correlation analysis to confirm our results and the relatedness of the COVID-19 virus sequences to the known genera of the family BetacoronavirusRaTG13 from the GISAID platform, and two additional sequences  from the NCBI, were downloaded on January 27, 2019. All of the available viral sequences were downloaded from the Virus-Host DB . Virus-Host DB covers the sequences from the NCBI RefSeq  and GenBank . All sequences shorter than 2000 bp and longer than 50000 bp were ignored to address possible issues arising from sequence length bias. Accession numbers for all the sequences used in this study can be found in The Wuhan seafood market pneumonia virus (COVID-19 virus/SARS-CoV-2) isolate Wuhan-Hu-1 complete reference genome of 29903 bp was downloaded from the National Center for Biotechnology Information (NCBI) database on January 23, 2020. All of the available 28 sequences of COVID-19 virus and the bat k-mer (oligomers of length k) based numerical representation known as Chaos Game Representation (CGR) [k-mer value 7 is used for all the experiments. The value k = 7 achieved the highest accuracy scores for the HIV-1 subtype classification [MLDSP  and MLDSon (CGR) . The k-mfication  and thisfication . A pairwfication  as a disfication  by applyfication . MoDMap3fication . A 10-fofication  and neigfication  phylogenk-mer is calculated in each genome. Due to differences in genome length between species, proportional frequencies are computed by dividing each k-mer frequency by the length of the respective sequence. To determine whether there is a correlation between k-mer frequencies in COVID-19 virus genomes and specific taxonomic groups, a Spearman’s rank correlation coefficient test is conducted for k = 1 to k = 7.In this paper, MLDSP-GUI is augmented by a decision tree approach to the supervised machine learning component and a Spearman’s rank correlation coefficient analysis for result validation. The decision tree parallels the taxonomic classification levels, and is necessary so as to minimize the number of calls to the supervised classifier module, as well as to maintain a reasonable number of clusters during each supervised training session. For validation of MLDSP-GUI results using CGR as a numerical representation, we use Spearman’s rank correlation coefficient –61, as fAdenoviridae, Anelloviridae, Caudovirales, Geminiviridae, Genomoviridae, Microviridae, Ortervirales, Papillomaviridae, Parvoviridae, Polydnaviridae, Polyomaviridae, and Riboviria). We used MLDSP-GUI with CGR as the numerical representation at k = 7. The maximum classification accuracy of 94.9% is obtained using the Quadratic SVM model. The respective MoDMap3D is shown in As shown in Betaflexiviridae, Bromoviridae, Caliciviridae, Coronaviridae, Flaviviridae, Peribunyaviridae, Phenuiviridae, Picornaviridae, Potyviridae, Reoviridae, Rhabdoviridae, and Secoviridae), see k = 7 as the numerical representation was used for the classification of the dataset in Test-2. The maximum classification accuracy of 93.1% is obtained using the Quadratic SVM model. The respective MoDMap3D is shown in Coronaviridae for all 29 sequences  is designed to classify the COVID-19 virus among the families of the Riboviria realm. We completed the dataset pre-processing using the same rules as in Test-1 and obtained a dataset of 2779 sequences placed into the 12 families  . The maximum classification accuracy of 100% is obtained using the Linear Discriminant model for Test-3b. All six classification models trained on 60 sequences were used to classify the 29 COVID-19 virus sequences. All of our machine learning-based models predicted the label as Betacoronavirus for all 29 sequences (The third test (Test-3a) is designed to classify the COVID-19 virus sequences at the genus level. We considered 208 navirus) . MLDSP-Gequences . To veriequences . This seBetacoronavirus genus, there now is a question of its origin and relation to the other viruses of the same genus. To examine this question, we preprocessed our dataset from our third test to keep the sub-clusters of the Betacoronavirus with at least 10 sequences (Test-4). This gives 124 sequences placed into four clusters  , followed by Merbecovirus (0.0746), Embecovirus (0.0914), and Nobecovirus (0.0916). The three closest sequences based on the average distances from all COVID-19 virus sequences are RaTG13 (0.0203), bat-SL-CoVZC45 (0.0418), and bat-SL-CoVZXC21 (0.0428).Given confirmation that the COVID-19 virus belongs to the covirus) . The maxSarbecovirus (47 sequences) and COVID-19 virus (29 sequences) clusters and achieved separation of the two clusters visually apparent in the MoDMap3D, see BetacoronavirusRaTG13 sequence collected from a bat host.For Test-6, we classified Alphacoronavirus, Betacoronavirus, Deltacoronavirus, Gammacoronavirus) of the family Coronaviridae. The CGR plots visually suggest and the pairwise distances confirm that the genomic signature of the COVID-19 virus Wuhan-Hu-1  for alignment-based methods to compare a large number of sequences that are too different in their composition. Alignment-free methods have been used successfully in the past to address the limitations of the alignment-based methods –51. The Coronaviridae , see Betacoronavirus, see Betacoronavirus as the largest cluster and it can be argued that the higher accuracy could be a result of this bias. To avoid bias, we did an additional test removing the smallest cluster Gammacoronavirus and limiting the size of remaining three clusters to the size of the cluster with the minimum number of sequences i.e. 20 with Test-3b. MLDSP-GUI obtains 100% classification accuracy for this additional test and still predicts all of the COVID-19 virus sequences as Betacoronavirus. These tests confirm that the COVID-19 virus sequences are from the genus Betacoronavirus.Test-1 starts at the highest available level and classifies the viral sequences to the 11 families and Riboviria realm . There iaviridae . Test-3aBetacoronavirus for sub-genus classification. Test-4 is designed to classify Betacoronavirus into the four sub-genera , see Sarbecovirus. This suggests substantial similarity between the COVID-19 virus and the Sarbecovirus sequences. Test-5 and Test-6 , they are still distinguishable from known species. Therefore, these results suggest that the COVID-19 virus may represent a genetically distinct species of Sarbecovirus. All the COVID-19 virus sequences are visually seen in MoDMap3D generated from Test-5 (see RaTG13 (distance: 0.0203), followed by the bat-SL-CoVZC45 (0.0418), and bat-SL-CoVZX21 (0.0428). To confirm this proximity, UPGMA and neighbor-joining phylogenetic trees are computed from the PCC-based pairwise distance matrix of sequences in Test-6, see Figs RaTG13 sequence closest to the COVID-19 virus sequences, followed by the bat-SL-CoVZC45 and bat-SL-CoVZX21 sequences. This closer proximity represents the smaller genetic distances between these sequences and aligns with the visual sequence relationships shown in the MoDMap3D of Sequences become very similar at lower taxonomic levels (sub-genera and species). Test-4, Test-5, and Test-6 investigate within the genus st-6 see  are desist-5 see  as a clost-5 see  visuallyBetacoronavirus genus  by a quantitative analysis based on the Spearman’s rank correlation coefficient tests. Spearman’s rank correlation coefficient [ρ value provides the degree of correlation between the two groups and their k-mer frequencies. The COVID-19 virus was compared to all genera under the Coronaviridae family and the k-mer frequencies showed the strongest correlation to the genus Betacoronavirus, and more specifically Sarbecovirus. The Spearman’s rank tests corroborate that the COVID-19 virus is part of the Sarbecovirus sub-genus, as shown by CGR and MLDSP. When analyzing sub-genera, it could be hard to classify at lower k values due to the short oligonucleotide frequencies not capturing enough information to highlight the distinctions. Therefore despite the Spearman’s rank correlation coefficient providing results for k = 1 to k = 7, the higher k-mer lengths provided more accurate results, and k = 7 was used.We further confirm our results regarding the closeness of the COVID-19 virus with the sequences from the fficient –61 testsAttributes of the COVID-19 virus genomic signature are consistent with previously reported mechanisms of innate immunity operating in bats as a host reservoir for coronaviruses. Vertebrate genomes are known to have an under-representation of CG dinucleotides in their genomes, otherwise known as CG suppression , 83. ThiWith human-to-human transmission confirmed and concerns for asymptomatic transmission, there is a strong need for continued intervention to prevent the spread of the virus , 62–64. MLDSP-GUI is an ultra-fast, alignment-free method as is evidenced by the time-performance of MLDSP-GUI for Test-1 to Test-6 given in Sarbecovirus, within Betacoronavirus.The results of our machine learning-based alignment-free analyses using MLDSP-GUI support the hypothesis of a bat origin for the COVID-19 virus and classify COVID-19 virus as sub-genus ab initio, using raw DNA sequence data alone and without the need for gene or genome annotation. We use this method to provide evidence for the taxonomic classification of the COVID-19 virus as Sarbecovirus, within Betacoronavirus, as well as quantitative evidence supporting a bat origin hypothesis. Our results are obtained through a comprehensive analysis of over 5000 unique viral sequences, through an alignment-free analysis of their two-dimensional genomic signatures, combined with a “decision tree” use of supervised machine learning and confirmed by Spearman’s rank correlation coefficient analyses. This study suggests that such alignment-free approaches to comparative genomics can be used to complement alignment-based approaches when timely taxonomic classification is of the essence, such as at critical periods during novel viral outbreaks.This study provides an alignment-free method based on intrinsic genomic signatures that can deliver highly-accurate real-time taxonomic predictions of yet unclassified new sequences, S1 File(PDF)Click here for additional data file.


Psychosocial costs, or quality of life costs, account for psychological distress, pain, suffering and other negative experiences associated with cancer. They contribute to the overall economic burden of cancer that patients experience. But this category of costs remains poorly understood. This hinders opportunities to make the best cancer control policy decisions. This study explored the psychosocial cost burden associated with cancer, how studies measure psychosocial costs and the impact of this burden.A systematic literature review of academic and grey literature published from 2008 to 2018 was conducted by searching electronic databases, guided by the Institute of Medicine’s conceptualization of psychosocial burden. Results were analyzed using a narrative synthesis and a weighted proportion of populations affected was calculated. Study quality was assessed using the Ottawa‐Newcastle instrument.A total of 25 studies were included. There was variation in how psychosocial costs were conceptualized and an inconsistent approach to measurement. Most studies measured social dimensions and focused on the financial consequences of paying for care. Fewer studies assessed costs associated with the other domains of this burden, including psychological, physical, and spiritual dimensions. Fourty‐four percent of cancer populations studied were impacted by psychosocial costs and this varied by disease site (38%‐71%). Two studies monetized the psychosocial cost burden, estimating a lifetime cost per case ranging from CAD$427753 to CAD$528769. Studies were of varying quality; 60% of cross‐sectional studies had a high risk of bias.‐making. More support is needed to address the psychosocial cost burden faced by patients and their families.Consistency in approach to measurement would help to elevate this issue for researchers and decision makers. At two‐thirds of the total economic burden of cancer, economic evaluations should account for psychosocial costs to better inform decision This information is crucial to help set future health budgets, to help aid in the allocation of scarce resources and, ultimately, supports decision‐making for cancer control systems.The rising costs of cancer treatment and supportive careGenerally, the economic burden of cancer care has been described as three broad categories: direct costs, indirect costs, and psychosocial costsThe psychosocial burden of cancer refers to the myriad ways that cancer and the cancer experience impact patients and their families, including caregivers. This burden is conceptualized as resulting from impacts on four domains: physical, psychological, spiritual, and social well‐being Figure . A recenIllness and disease are responsible for a wide variety of deteriorations in quality of life that are frequently referred to as psychosocial costs… Disease may bring about personal catastrophes that are not reflected in the direct and indirect economic costs that are usually estimated for a specific disease… These include, but are not limited to, undesired changes in life plans, anxiety, reduced self‐esteem and feeling of well‐being, and other emotional problems… psychosocial costs are a significant, and very likely quite large, component of the total burden of illness. To ignore them, or misrepresent them, can result in an underestimate of the impact of disease and bias the decision‐making process.Psychosocial costs represent the additional cost to individuals’ well‐being that is associated with cancer. A seminal definition describes psychosocial costs as follows:In other literature, psychosocial costs have been described as an intangible cost due to the challenges of assigning a monetary value.These challenges with the definition of psychosocial costs have resulted in a mixed approach to measurement. Some studies approach measurement of these costs using generic assessments of quality of life that may not capture the full realm of psychosocial burden according to the four key domains, namely: physical, psychological, spiritual, and social well‐being. While some of this impact may in fact be captured in generic quality of life assessments, it is unclear whether and to what extend the generic tools are able to capture the full breadth and impact of the psychosocial burden of illness. There is also a varied approach to measurement of psychosocial costs. The inconsistent approach to examining these costs in the literature creates missed opportunities for leveraging these learnings to inform decision‐making and resource allocation. Furthermore, despite recognition of the additional burden caused by psychosocial costs raised in the Hosdgeson and Meiners 1982 paper, the methods to advance to measurement and use of psychosocial cost data have been scare, particular in the health economics literature.Understanding the scope and scale of the cancer burden borne by patients and families is important to properly and accurately measure the overall economic burden of cancer to society. It is also required if we are to take seriously our commitments and value placed on patient‐centered care within the cancer control system. Moreover, this information is necessary to inform decision‐making around best models of care to support patients and the patient experience throughout the cancer trajectory. It is also critical for monitoring the potential impact—even inadvertent—of shifts in cost out of the system that may result in an economic burden for patients.The objective to this review was to answer the following research questions: (a) what is the psychosocial cost burden associated with cancer and how do studies measure psychosocial costs? and (b) what is the prevalence and impact of this burden?22.1The review was registered on PROSPERO CRD42019133975.In this review, we drew on the definition of psychosocial burden from the Institute of Medicine’s conceptual framework. We aimed to understand and describe the estimated costs associated with this burden, either in monetary terms or when defined as intangible costs. Psychosocial consequences of cancer can constitute a cost to patients and caregivers that may or may not have been monetized. These consequences also have potential to lead to direct economic impacts for patients and their families, which also may or may not be monetized. This review intentionally casts its scope broadly to capture all these potential conceptualizations of psychosocial costs, aligned with the definition of psychosocial costs provided by Hodgson 1982.A detailed search strategy was developed and informed by the Institute of Medicine’s conceptual framework for the psychosocial burden of cancer,We aimed to include all evidence on the psychosocial cost burden associated with cancer in all populations. We included pediatric and adult cancers to understand whether there were important differences in the cost estimates, the approaches to measurement used across studies as well as the extent to which this issue was measured across different patient populations. For pediatric cancers, the caregiver’s/parent’s costs, reflect a main source of economic burden associated with these types of cancers. In addition, and importantly, patient and caregiver costs are ultimately borne and shared at the household‐level. For these reasons, both adult and pediatric populations were considered within scope of this review.The titles and abstracts of all retrieved papers were independently screened for eligibility by two individuals and, subsequently, the full text articles of the remaining articles, using pre‐specified inclusion criteria. Articles were included if they were published between the years 2008 and 2018, were a primary research study that focused on measuring or describing the psychosocial cost burden associated with cancer, and were conducted in a population of patients at any point along the cancer care continuum . Articles were excluded if they were not written in English. Consistency in the screening process between individuals was ensured by having each person independently review between 20 and 50 papers and then compare their results with each other, discussing and resolving any discrepancies and consulting with the research team as needed.A form was developed to guide the data extraction process. The form captured information on study characteristics  measurement/definition of outcomes, key demographic characteristics of the research population , dimensions of the psychosocial burden  and its measurement, and key findings.We assessed the risk of bias in the cross‐sectional and cohort studies using the Ottawa‐Newcastle quality appraisal tool.The results were first analyzed using a narrative synthesis that focused on distilling the main dimensions of the psychosocial cost burden that were the focus of the studies, how they were measured, the factors that contributed to the psychosocial cost burden, and notable differences in this burden between cancer populations. Studies were categorized according to how they conceptualized the psychosocial costs burden, including costs associated with physical, spiritual, social, and psychological well‐being Figure . SubsequThis systematic review is reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta‐analyses (PRISMA) guidelines (online appendix). This was a systematic review, so ethics approval was not required.33.1There were 883 studies retrieved in the search of the academic databases; an additional 11 papers were identified from references lists. No relevant studies were found in the search of the grey literature. After screening and full text review, 25 papers met the inclusion criteria and were included for analysisThe Ottawa‐Newcastle tool was used to assess the risk of bias in cross‐sectional studies (20/26 studies) , financial strain or stress (n = 4), or financial toxicity (n = 3) reported by patients and their families. There was also wide variation in the tools used to measure these outcomes, with no gold standard tool acknowledged and few validated tools used to measure the outcomes  costs of cancer. There was inconsistency in the terminology, definitions, and measurement of outcomes related to financial consequences associated with cancer. Most studies measured the negative consequences to patients and families in terms of the The burden on patients associated with psychological and physical consequences of cancer and their impact on patients’ well‐being was investigated in six studies and were described in terms of quality of life costs. In these studies, quality of life costs were explicitly conceptualized as the cost to patients of lost opportunities for social engagement reduced social functioning and the impact on their intrinsic value of health.A minority of studies (n = 3) focused on the impact of psychological distress and disorders such as stress, anxiety, and depression and their relationship with negative financial outcomes. These studies conceptualized the psychosocial cost burden in terms of the increased risk of experiencing negative outcomes associated with declines in one’s psychological well‐being. Different tools were used in each study, none of which was validated Table .There were no studies that measured the psychosocial cost burden of cancer in a holistic way, accounting for all four of the dimensions, and none of the studies explicitly measured the costs associated with the spiritual dimension of this burden.3.3The proportion of the study populations reporting psychosocial costs associated with cancer varied between studies, from 16% of patients reporting financial distress in a study of individuals with breast, esophageal, kidney, lung, pancreatic, prostate, sarcoma, testicular, or uterine cancersFigure Several studies found this burden was associated with an increased risk of facing negative outcomes including: treatment and medication non‐adherence,Two modeling studies estimated the cost of the psychosocial burden associated with cancer as health‐related quality of life costs, measured using the SF‐12 and the HUI.4To the authors’ knowledge, this is the first systematic review to synthesize studies on the psychosocial cost burden associated with cancer. The review suggests that approximately 44% of individuals affected by cancer are impacted by psychosocial costs and that this burden has far reaching implications, significantly increasing the likelihood of poorer health, clinical, and economic outcomes for patients and caregivers. The estimated lifetime costs of the psychosocial burden were approximately CDN $428689 using conservative measures, representing two‐thirds of the total economic burden associated with a cancer diagnosis. This review highlights that this burden is likely too great to ignore, yet it remains a neglected topic in the published literature and potentially also in clinical settings.Most studies in this review focused narrowly on a selection of constructs that related to the psychological and social domains of the psychosocial burden of cancer and, in particular, the financial impact of illness. This focus on financial impacts of cancer is likely a result of most research originating from the US where the impoverishing consequences of cancer are well documented.4.1This review confirms that more research on this topic is needed, particularly outside the United States. This review supports a call to action for a research agenda to investigate psychosocial costs more comprehensively in all settings given that we know that the psychosocial burden of cancer transcends borders.This review also confirms that there is an inconsistent approach to the measurement of the psychosocial cost burden associated with cancer. Most studies used customized or purpose‐built measures, resulting in a range of tools in use in practice. In part, this may be due to the lack of a gold standard or validated approach for measurement. This has limited opportunities for comparison within and between cancer populations and across settings and this potentially hampers efforts to elevate the importance of this issue. Furthermore, it is well known that some sub‐populations are more at risk of facing an economic burden associated with cancer due to out‐of‐pocket,‐making. First, these existing tools potentially underestimate the psychosocial costs associated with cancer as they do not account for all dimensions of the psychosocial burden. Second, if these costs are to be included in economic evaluations of cancer interventions to better reflect the potential costs and benefits associated with cancer care, it is important to gain a better understanding of how to accurately capture and incorporate psychosocial costs to ensure they are not inadvertently double‐counted as both costs and benefits . Finally, the tendency in economic evaluations is to adopt a health care system perspective to best inform health system decision‐making.The sole method used in the literature for monetizing the psychosocial costs was to calculate the cost per quality adjusted life year. Two generic tools, the SF‐124.2This study has limitations. We only included studies published in the last 10 years to ensure that, to the extent possible, the costs and policy contexts reflected contemporary circumstances. We also limited our inclusion criteria to English studies only. It is possible that we missed relevant studies; however, key experts were engaged to ensure seminal papers were not missed. The study quality of most studies was low. This limits the extent to which conclusions can be drawn but emphasizes the need for a research agenda to improve the measurement, breadth, and quality of studies on the psychosocial costs of cancer across settings.5The prevalence of the psychosocial cost burden found in this review supports a need for greater attention to address these costs, account for them in the context of economic evaluations and improve the psychosocial support programs available to cancer patients and their families. Elevating the importance of psychosocial costs is needed to drive efforts to develop more routine approaches to measurement as well as efforts to use these data to support decision making to lessen the economic burden faced by cancer patients and their families. Advancing our understanding of the psychosocial costs of cancer will help to meet commitments to patient‐centred care and research by improving our understanding of the full breadth of costs borne by patients and areas where supportive care can best mitigate this burden.This review was conducted by the Canadian Partnership Against Cancer (the Partnership). The Partnership is the steward of the Canadian Strategy on Cancer Control which provides a roadmap to improve equity in the cancer system and to deliver world‐class cancer care to everyone in Canada, while focusing on a sustainable healthcare system for the future. The results of this review will inform efforts to strengthen supports available to cancer patients, aligned with the key priorities and actions outlined in the Strategy.Figure S1 Risk of biasClick here for additional data file.Data S1. Supporting Information.Click here for additional data file.