Numerous reports have suggested that infectious agents could play a role in neurodegenerative diseases, but specific etiological agents have not been convincingly demonstrated. To search for candidate agents in an unbiased fashion, we have developed a bioinformatic pipeline that identifies microbial sequences in mammalian RNA-seq data, including sequences with no significant nucleotide similarity hits in GenBank. Effectiveness of the pipeline was tested using publicly available RNA-seq data and in a reconstruction experiment using synthetic data. We then applied this pipeline to a novel RNA-seq dataset generated from a cohort of 120 samples from amyotrophic lateral sclerosis patients and controls, and identified sequences corresponding to known bacteria and viruses, as well as novel virus-like sequences. The presence of these novel virus-like sequences, which were identified in subsets of both patients and controls, were confirmed by quantitative RT-PCR. We believe this pipeline will be a useful tool for the identification of potential etiological agents in the many RNA-seq datasets currently being generated. As detailed below, there are numerous reports suggesting that microbes could play a role in neurodegenerative diseases. Microbial sequences are routinely identified in human RNA-sequencing (RNA-seq) data (Infection has been proposed to play a role in multiple neurodegenerative diseases . After dchromosome 9 open reading frame 72 (C9ORF72)] account for 70% of fALS , a hexanucleotide repeat expansion (HRE) occurs that can form RNA with highly stable parallel G-quadruplex structures (G4 RNA). How neurodegeneration occurs from HRE in c9ALS is not well understood, but putative mechanisms include reduction of C9ORF72 expression, production of poly-dipeptides as a result of repeat-associated non-AUG translation of repeat sequences, and the formation of RNA foci that may sequester RNA binding proteins (C9orf72 loss-of-function mouse model (Studies on ALS primarily come from European populations and within these populations four genes [TAR DNA-binding protein 43 (TDP-43), fused in sarcoma/translocated liposarcoma (FUS), superoxide dismutase 1 (SOD1), Cutibacterium acnes, Corynebacterium sp, Fusobacterium nucleatum, Lawsonella clevelandesis, and Streptococcus thermophilus in CSF , and central nervous system (CNS) from ALS patients. For example, bacteria that have been detected include The biological role that these alternative microbiotas play in ALS is also unclear. ALS patients may have a compromised blood brain barrier or blood spinal cord barrier function . It has Numerous studies have looked for biomarkers of ALS , neuroinAlthough other pipelines have used reads that do not map to the host genome (unmapped reads) for microbial identification and quantification, these pipelines cannot be used for discovery as they rely on existing databases of microbial genomes  and MetaWhere MetaShot stops at providing reads assigned to taxonomical categories, we map reads back to contigs and provide proper library normalization for statistical quantification. A similar pipeline known as IMSA  with known bacterial or viral infections. We also examined the differences in microbial identification between polyA and total RNA recovery in multiple tissues, and investigated the effects of globin depletion of blood samples. We then used our pipeline on a novel ALS blood dataset (termed “Our Study”) as well as on five other published ALS datasets from blood or spinal cord samples, analyzed each dataset individually, and analyzed across datasets for changes in microbiota. Although we did not identify any microbes enriched in the blood of ALS patients, we did identify and validate a novel virus-like sequence, demonstrating the potential of the bioinformatic pipeline we have established.C9ORF72 mutant carriers (C9A), 30 symptomatic C9ORF72 ALS cases (C9S), and 30 symptomatic C9ORF72-negative ALS cases (SYM). PAXgene blood RNA tubes were collected at Mayo Clinic Jacksonville and at University of Miami. All 120 RNA samples selected for RNA-seq were received and processed at Mayo Clinic Jacksonville using PAXgene blood RNA kit following manufacturer’s recommendations (Qiagen). Blood RNA was of high quality, assessed in an Agilent Bioanalyzer (Agilent), with RNA integrity values ranging from 7.4 to 9.8, with a median value of 8.7. RNA samples were then sent to The Jackson Laboratory for globin depletion, library preparation and sequencing of total blood RNA.A total of 120 RNA whole blood samples constitute Our Study, which included 30 healthy controls , 30 presymptomatic In vitro transcription was then performed at 42°C for 4 h followed by purification using RNeasy mini kit (Qiagen).Due to the abundance of large hemoglobin RNA transcripts present in the blood, a globin depletion step, using the Ambion GLOBINclear kit (AM1980), was performed before sequencing of the blood RNA samples in order maximize coverage on nonglobin genes. In brief, one microgram of total RNA was used as starting material, and specific biotinylated oligos were used to capture globin mRNA transcripts. The capture oligos were hybridized with total RNA samples at 50°C for 30 min. Streptavidin magnetic beads were then used to bind to the biotinylated capture oligos hybridized to globin mRNA by incubating at 50°C for 30 min. The magnetic streptavidin beads-biotin complex were then captured to the side of the tubes by a magnet, and the resulting supernatant is free of globin mRNA. The globin depleted (GD) RNA was further purified by RNA binding beads and finally eluted in elution buffer. The resulting RNA free of >95% globin mRNA transcripts was then processed for NGS. Of note, to assess the efficiency of the globin RNA depletion, 10% of the samples processed were selected randomly and amplified using a Target-Amp Nano labeling kit (Epicentre). Samples were normalized to 100 ng input and reverse transcribed. First strand cDNA was generated by incubating at 50°C for 30 min with first strand premix and Superscript III. This was followed by second strand cDNA synthesis through DNA polymerase by incubating at 65°C for 10 min and at 80°C for 3 min. Due to the large number of samples, the globin depletion step was performed in two batches. We provided guidelines on how samples would be divided among the batches and also for how samples would be grouped in the sequencing runs in order to minimize technical variability. The Jackson Laboratory personnel were blinded to the identity of the samples.RNA-seq of total blood RNA  was performed in an Illumina HiSeq4000 with >70 million read pairs per sample (100 bp read lengths). Raw reads were then sent back to us for bioinformatics analyses.A total of 500 ng of total blood RNA was used for reverse transcription polymerase chain reaction (RT-PCR), using the high-capacity complementary DNA Transcription Kit with random primers (Applied Biosystems). Quantitative real-time PCR (qRT-PCR) was performed using SYBR GreenER qPCR SuperMix (Invitrogen). Samples were run in triplicate, and qRT-PCRs were run on a QuantStudio 7 Flex Real-Time system (Applied Biosystems).List of primers and their sequences in this study:Primers targeting the novel RNA-dependent RNA polymerase (RDRP) contig from our studyRDRP forward 5′-GCTGTCAAATCGGTTTCCAAC-3′;RDRP reverse 5′-CTGCCTTCGTCATCTTGGAG-3′.Primers targeting highly expressed control regionsGAPDH forward 5′-GTTCGACAGTCAGCCGCATC-3′;GAPDH reverse 5′-GGAATTTGCCATGGGTGGA-3′.https://github.com/Senorelegans/MysteryMiner. We have also deposited a frozen and cite-able version of the software with doi:10.5281/zenodo.4598807 and available at https://zenodo.org/record/4598807#.YEphh5NKjKp. All data in this study were processed identically using the pipeline.See pipeline description in results for an overview of the pipeline; see bioinformatics Supplementary File S1 for a more detailed description of the analysis pipeline, versions, and statistical quantification. For downloading the pipeline and detailed instruction for running the pipeline please read the README at t-test was calculated using normalized coverage (NC) for contigs or binned normalized coverage (BNC) for species/genus, etc. The number of contigs or genus/species is used to obtain an False discovery rate (FDR) corrected (using the Benjamini/Hochberg method) adjusted P-value  via statsmodels in Python. Cutoff for statistical significance is less than an q-value of 0.05 unless otherwise stated.To assess statistical differences between conditions, a two tailed Student’s https://github.com/Senorelegans/MysteryMiner.Our novel pipeline, Mystery Miner, is written as a Nextflow pipeline. Below is a short overview of the Mystery Miner pipeline . A more Raw reads were first checked for quality using FastQC then trimmed to remove both adaptor contamination and low quality basecalls using Trimmomatic. Trimmed reads were then mapped to the host genome using STAR for a fast first-pass followed by a second pass with bowtie2 for sensitivity. Unmapped reads were retained for contig assembly. Filtering out host reads made downstream assembly faster and required less memory. We assembled contigs from unmapped reads with the SPAdes assembler (with “-rna” setting). This assembler was chosen for its recent use in studies of microbial diversity (We then identified contigs that lacked detectable nucleotide similarity to any GenBank entry but showed similarity at the amino acid level using BLASTX (“dark biome”). Furthermore, contigs with no BLASTN or BLASTX hits were labeled as “double dark biome” . Every contig in the “regular biome” and “dark biome” were then queried against the Joint Genome Institute Server for additional taxonomic information. As Mystery Miner is an agnostic tool, it cannot distinguish between true tissue or cell-associated microbes and experimentally introduced contamination.t-test was calculated through NC or BNC, using the number of contigs or genus/species to obtain an FDR corrected adjusted P-value  using statsmodels in Python.For quantification, we mapped the nonhost reads using Bowtie2 to the contigs obtained from SPAdes. Next, we mapped reads to contigs using samtools mpileup (default mapq score) to calculate the amount of reads over each base pair in a contig. We then calculated coverage on the contigs by summing all of the counts for each base pair in a contig and dividing by the length of the contig. We then calculated NC by library size using the number of mapped reads to the host genome. This gave us NC for a contig or BNC for multiple contigs within a species/genus, etc. To assess statistical differences between conditions, a Student’s in vitro model of Chlamydia trachomatis infection from  and total RNA (six samples) were sequenced 1 and 24 h postinfection (hpi). Using the Mystery Miner pipeline, out of 5.32 × 106 reads from all of the samples, 6.04 × 105 reads remained unmapped (∼11.34%) after trimming and mapping to the host genome (Supplementary File S2). From these nonhost reads, 3257 contigs were assembled and 1199 of these contigs were identified as regular biome (Supplementary File S3). An additional 27 contigs had no BLASTN hit. Of these, we identified two dark biome (BLASTX identified) and no double dark biome (no BLASTX or BLASTN hit) contigs (Supplementary Files S4 and S5).To confirm that Mystery Miner is able to recover and quantify known bacterial infections from sequencing data, we utilized an C. trachomatis (BNC by species) in 24 hpi samples compared with 1 hpi samples in both polyA (q = 0.004) and total RNA (q = 0.0005). In addition to C. trachomatis, we identified six additional bacterial species and one viral species (Alphapapillomavirus 7) in the samples .Using Mystery Miner we successfully identified, and found significantly elevated levels, of  samples , includiin vitro model of severe acute respiratory syndrome coronavirus (SARS-CoV)-1 or -2 infection  after trimming and mapping to the host genome (Supplementary File S2). From these nonhost reads, 42,816 contigs were assembled, of which 1346 regular biome, 27 dark biome, and 7 double dark biome contigs passed the filtering steps (Supplementary Files S2–S5).Out of the 2.81 × 10i.e., SARS-CoV-2 was significantly elevated in COV2). Similar results were seen in the 12 h samples but the 4 h samples did not have sufficient viral reads to detect either SARS-CoV virus (Supplementary File S7). To simulate a novel virus, we ran Mystery Miner on versions of the BLASTN and BLASTX databases obtained before SARS-CoV-2 was discovered and were able to properly identify SARS-CoV-2 as a bat-related CoV  are commensals that are part of the representative bacteria list from the Human microbiome project  at 1000× coverage with no fold change differences between groups. For nonhuman organisms, we took the first 10 kb of the nucleotide sequence for the organism and generated reads at coverage levels of 1000×, 100×, 10×, 1×, 0.1×, and 0.01×. Last, we combined the 1000× coverage human reads separately with each level of coverage for nonhuman organisms and ran Mystery Miner .Chlamydia species (59% sequence identity of the first 10 kb) and the rest of the commensal bacteria, we were able to successfully assemble and correctly identify each species at every level of coverage.We found sequences below 1× coverage did not assemble, suggesting that this is our limit of detection . For the SARS strains, we successfully identified both strains at 1000× coverage but found that with lower coverage levels, SARS-CoV-1 was identified as a SARS-related CoV. This ambiguity is likely due to the 73% nucleotide sequence identity . For the six commensals, we chose the first three species to have fold change differences of 1.8, 1.5, and 1.3 . For SARS, we found that at 1× coverage, the twofold difference of SARS-CoV-2 was correctly called significant (q = 5.14 e−10), but the ambiguously identified SARS-related CoV contig was not called significant (q = 0.489). At 1000× coverage, we found that the correctly identified SARS-CoV-1 contig was falsely called significant (q = 0.0028), this is likely due to ambiguous read mapping from the closely related SARS-CoV-2 sequence, as mentioned above. We found similar results for each coverage level (from 1× to 1000×) for the rest of the organisms and will subsequently use values from 1× coverage as that is the lowest level of detection. For Chlamydia, we found Mystery Miner successfully called C. trachomatis significant (q = 3.57 e−10) and C. pneumoniae not significant (q = 0.709). For the commensals with FC differences, we successfully called each one significant [M. indolicus (q = 6.92 e−7), P. melaninogenica (q = 4.91 e−5), F. alocis (q = 0.017); Along with identification, we looked at Mystery Miners ability to quantify fold change differences between groups (A and B) using the synthetic minibiome. For the four pathogenic organisms, we selected one sequence from each kingdom to have a twofold difference , in blood but not in colon . We found similar amounts of significant BNC by species for polyA RNA versus total RNA in blood (29) and in colon (26). We then looked at significant BNC by genus and found double the amount in blood (14) compared with colon (7), with only one significant genus (Actinomyces) found in both comparisons. We did not find any significant differences in coverage when we looked at the species, genus or superkingdom level for viruses (Supplementary File S8). We conclude that library enrichment with total RNA compared with polyA RNA increases bacterial recovery and diversity in blood.In order to compare effects of library enrichment or depletion, we compared recovered pathogens in a dataset that has polyA enrichment or rRNA depleted total RNA from blood or colonic tissue  and viruses . We also found significant differences in BNC by species  or genus  primarily from Escherichia coli with elevated levels in globin-depleted blood RNA. We did not find any significant differences when we looked for viruses at the species or genus level (Supplementary File S9).We next looked at a RNA-seq dataset from whole blood with GD versus nonglobin depleted (NGD) total RNA (C9ORF72 negative patients (SYM), C9ORF72 positive ALS symptomatic patients (C9S), and C9ORF72 positive asymptomatic individuals (C9A).We used Mystery Miner on our novel RNA-seq dataset of GD and rRNA depleted total blood RNA from 120 individuals. These samples were from four subject groups including healthy control participants (CTL), ALS symptomatic 9 reads. Approximately 2.7% (2.34 × 108) of the reads did not map to the human genome. From these nonhost reads 2,976,988 contigs were assembled and 17,047 BLASTN contigs (regular biome) were identified. A total of 25,815 contigs had no BLASTN hit and after filtering we identified 2,980 dark biome (BLASTX identified) and 859 double dark biome (no BLASTX or BLASTN hit) contigs (Supplementary Files S2–5).The entire dataset contains a combined 8.64 × 10alpha proteo-bacteria, Actinobacteria, Firmicutes, and Bacteroidetes as the most highly represented taxonomies, consistent with other blood biome studies . A phylogeny based solely on viral RDRP protein sequences places the RDRP contig closest to single-stranded (+) viruses of the Barnavirus, Sobemovirus, and Polerovirus genera . However, given the absence of detectable similarity in this contig to other (non-RDRP) viral proteins of these genera, the relationship of the contig sequence to other virus groups is unclear, which supports the view that this contig represents a novel viral sequence.Within the dark biome contigs, we noted numerous contigs with a region of protein sequence similarity to RDRP from multiple RNA viruses, including the velvet tobacco mottle virus  and three with zero coverage (predicted absent). We found typical levels for detection of a virusin the samples with high coverage and detected no signal above background in samples with zero coverage . We concWe next sought to explore whether similar results would be obtained from other ALS datasets. To this end, we examined five other publicly available ALS datasets, consisting of two that used total RNA from blood . As we oWe then looked at the total overlap of genus or species to determine if there are similarities in recovered microbial or viral sequences between datasets. For genus in the regular bacteriome, our dataset had the highest number of unique genus (185), followed by In addition to comparing datasets using taxonomy, we also compared contigs between datasets for nucleotide similarity (>70%) using LAST (Supplementary File S1 for methods). We found that in general, datasets in the regular biome with the largest amount of contigs have the most overlap. Unsurprisingly, in the dark biome we observed less overlap by nucleotide similarity and that our RDRP contig does not share nucleotide similarity with contigs from any dataset. In addition, we also compared the nucleotide similarity of double dark biome contigs and found there is not a large percentage of similar contigs between datasets (Supplementary File S13).FUS mutation to controls, we found three significant differences in BNC by species in the regular bacteriome  and one significant difference in BNC by genus in the dark bacteriome (Photobacterium). In ALS patients with mutations in SOD1 compared with controls, we found two species significantly different in the regular bacteriome (Hydrogenophaga crassostreae and Sphingomonas hengshuiensis). We did not find anything significant in sALS, or in ALS patients with TARDBP mutations with regards to genera/species count (regular or dark biome or viruses) for Gagliardi. We found no significant statistical differences between ALS and control samples for genus/species count of viruses/bacteria in the regular/dark biome for any of the remaining ALS datasets.Finally, we looked for differences in ALS versus control samples for each dataset. In the Gagliardidataset, when we compared ALS patients with the vs. ALS; or controls vs. controls; Since our dataset and many others had few to no significant comparisons for ALS versus control groups within each dataset, a meta-analysis between datasets using this criteria would be difficult. As a second pass analysis we created a less stringent filtering method in order to compare the presence of microbes for each group between datasets (ALS Sobemovirus from controls) with the rest coming from When we looked at ALS or control contigs in the regular bacteriome, the highest number of unique genus or species was from Chlamydia and SARS-CoV reads from RNA-seq datasets from intentionally infected cells. Interestingly, we also identified Mycoplasma reads in the Chlamydia dataset, suggesting that Mystery Miner may also be able to identify unsuspected bacterial infections or contamination. Next, we created a synthetic minibiome of two different Chlamydia species and SARS strains, along with six representative bacteria from the human microbiome to investigate the sensitivity of Mystery Miner with regards to species and strain detection and quantification of small fold changes at low coverage. We find that the pipeline is able to recover and quantify significant fold changes for the bacterial species but has difficulty distinguishing reads that come from highly related sequences. We also use published data to investigate the difference of polyA versus total RNA recovery of bacterial species in multiple tissues. Perhaps surprisingly, we did not see a consistent difference in the recovery of bacterial reads between the two types of RNA-seq libraries, considering that bacterial transcripts are not expected to be polyadenylated. However, it is well-recognized that polyA selection is imperfect, and libraries constructed from polyA-selected RNA routinely contain transcripts thought not to be polyadenylated . We also found increased recovery of bacterial species with globin RNA depletion in blood. This could be the result of an effective increase in read depth for bacteria when not sequencing globin, or an increase in contamination from the globin depletion step. We stress that our bioinformatic approach alone cannot distinguish between contamination and the true existence of microbial sequences in human tissue.We have created Mystery Miner to search for and quantify known and unknown microbes in RNA-seq datasets as a tool for researchers to study dysbiosis and identify infectious agents. We validated the pipeline by recovering and quantifying 9 reads. This dataset was generated from whole blood total RNA that was depleted for both ribosomal and globin transcripts. It encompasses samples from controls, participants with a C9ORF72 hexanucleotide expansion (symptomatic and presymptomatic), and C9ORF72 negative ALS patients. We found no statistical difference in microbial sequence read coverage between controls and any class of ALS patients, examining either individual contigs or using various taxonomical binning approaches. We also did not detect any batch effects or obvious age or sex biases in the recovery of microbial reads . Overall, we found no evidence of blood microbial sequences contributing to either C9ORF72 negative ALS or symptomatic patients harboring the C9ORF72 hexanucleotide expansion. However, ALS is a CNS disease, so our findings in these blood samples do not necessarily preclude a role for microbes in this disease.We then used Mystery Miner on a novel ALS blood dataset (Our Study) consisting of 8.64 × 10A unique aspect of Mystery Miner is that it tracks nonhuman reads that do not have significant BLASTN hits in GenBank. We were intrigued by the identification of a large contig (>5 kb) in the dark biome of our ALS dataset that showed protein sequence similarity to RDRPS, the essential replicase of RNA viruses. To validate that this virus-like sequence was not an artifact of contig assembly or a contaminant introduced during library construction or sequencing, we used RT-PCR of the original patient samples to demonstrate that this sequence was present in positive samples identified through the RNA-seq analysis and not detectable in negative samples. We cannot extrapolate from this specific example to determine what fraction of the “dark” and “double dark” sequences represent true novel microbial sequences present in human blood, but we note that analysis of human cell-free blood DNA has reported the identification of thousands of novel bacterial sequences , and in the case of the SOD1 patients the excess microbial reads were in the control samples. In the absence of additional information  it is difficult to conclude that these sequence/sample correlations are meaningful. Finally, we compared identified microbial sequences in the control and ALS samples across the datasets and did not identify any genera/species that were preferentially recovered in either sample type.We then analyzed several publicly available ALS datasets for statistically significant differences between recovered microbial sequences in ALS and control samples. Only one dataset  upon which the analysis is based. This limited sequence signal could preclude identification of rarer microbes. Perhaps more problematic is the possibility that contaminating sequences obscure true tissue-associated microbial sequences. Any candidate microbes identified using Mystery Miner that correlate with human phenotypes will necessarily require independent validation. Despite these limitations, we believe Mystery Miner will be a useful tool for future researchers investigating known and unknown microbes that could contribute to disease, as our analyses have shown it to be sensitive to bacterial/viral agents in sequencing data.Raw sequencing data for Our Study dataset is available in the NCBI Sequence Read Archive under the accession number PRJNA715316.https://github.com/Senorelegans/MysteryMiner. Supplementary material available at figshare: https://doi.org/10.25387/g3.13315181.All other datasets are publicly available, and all of the code used in this article is available at


Spermatogenesis is a prolonged and highly ordered physiological process that produces haploid male germ cells through more than 40 steps and experiences dramatic morphological and cellular transformations. The ubiquitin proteasome system (UPS) plays central roles in the precise control of protein homeostasis to ensure the effectiveness of certain protein groups at a given stage and the inactivation of them after this stage. Many UPS components have been demonstrated to regulate the progression of spermatogenesis at different levels. Especially in recent years, novel testis-specific proteasome isoforms have been identified to be essential and unique for spermatogenesis. In this review, we set out to discuss our current knowledge in functions of diverse USP components in mammalian spermatogenesis through: (1) the composition of proteasome isoforms at each stage of spermatogenesis; (2) the specificity of each proteasome isoform and the associated degradation events; (3) the E3 ubiquitin ligases mediating protein ubiquitination in male germ cells; and (4) the deubiquitinases involved in spermatogenesis and male fertility. Exploring the functions of UPS machineries in spermatogenesis provides a global picture of the proteome dynamics during male germ cell production and shed light on the etiology and pathogenesis of human male infertility. Spermatogenesis, which occurs within seminiferous tubules of male testes, ensures sustainable production of male gametes, the spermatozoa . GeneralIn mammals, this continuous spermatogenic program, from As spermatogonia to elongated spermatozoa, involves more than 40 steps and extends for months  . In all cell types, precise control of the effective window of a given protein relies largely on multiple machineries that regulate its abundance via tightly and precisely controlled protein synthesis and degradation. In recent years, genome-wide RNA-sequencing (RNA-seq) of purified male germ cells or isolated single cells has provided fruitful knowledge on the transcriptional regulations during mammalian spermatogenesis ,8. HowevThe ubiquitination-proteasome system (UPS) plays crucial roles in the regulation of protein degradation, and in some cases, protein activity ,10 Figu. UbiquitProteasomes, comprised of a 20S core particle (CP) and one or two regulatory particles (RPs), are central executors to the UPS and protein degradation . The 20SHowever, the proteolytic activity and specificity of proteasomes are significantly increased by adding RPs to the 20S proteasome . The RPsEvents of proteasome degradation are common and crucial to spermatogenesis . First oDrosophila, nearly one third of the 20S CP subunits possess alternative isoforms, the majority of which are solely expressed in testis . Ho. Ho44]. Cullin-RING Ligases (CRLs) family of multi-subunit E3 ligase complexes is the largest E3 ligase family . In CRL bona fide substrate of β-TrCP1, regulating metaphase to anaphase progression in both mitosis and meiosis via its inhibitive effects on the anaphase promoting complex/cyclosome, or APC/C [The CRL1 complex, or SCFβ-TrCP, is the most known E3 ubiquitin ligase and is expressed ubiquitously in all cell types. This E3 complex comprises the scaffold protein CUL1, the adaptor protein SKP1 (S phase kinase-associated protein), the substrate receptor β-TrCP and the E2-interacting protein ROC1/2. There are two paralogs of β-TrCP in mammals, β-TrCP1 (BTRC) and β-TrCP2 (FBXW11) . β-TrCP1or APC/C . While tor APC/C . Double or APC/C . Snail1 or APC/C . Deficieor APC/C . Cul4a knockout testes [Ddx4-Cre results in complete loss of male germ cells [Similar to the SCF complex, CRL4 complex is made up of the scaffold protein CUL4A/B, the adaptor protein DDB1 (DNA damage binding protein 1), the substrate receptor  and ROC1/2 . CUL4A it testes ,46. In ct testes . Althougt testes . Diminist testes ,102. Strrm cells . DCAF12 rm cells . DCAF12 rm cells . IncreasC. elegans or Drosophila. In C. elegans, CUL2 is highly expressed in germline and participates in multiple developmental processes in complex with distinct substrate receptors [C. elegans gonads, resulting in premature meiotic spermatocytes [Besides SCF and CRL4 complexes, CRL2 and CRL3 also exhibit certain roles in spermatogenesis in eceptors . The Leueceptors . In testeceptors ,105. NorDrosophila has four CUL3 isoforms and one of them is expressed exclusively in male germ cells (CUL3testis), playing an essential role during post-meiotic differentiation. CUL3testis combines with its adaptor protein Klhl10 and targets dBruce, a caspase inhibitor for degradation [radation ,50. Thisradation . Besides the CRLs family, many other E3 ubiquitin ligases also participate in the regulation of spermatogenesis. In addition to be regulated by the SCF-EMI1 ubiquitination pathway, APC/C itself is an E3 ubiquitin ligase complex and mediates poly-ubiquitination of substrates for proteasomal degradation . During SIAH1A confers cell growth arrest in somatic cells, while in male germ cells, it plays an important role to promote spermatogenesis progression beyond MI . Siah1a HEI10 is an E3 ubiquitin ligase important for the regulation of homologous recombination during pachytene . FollowiRNF8, or RING finger protein 8, is essentially a DNA damage response (DDR) protein that ubiquitinates histone H2A and H2AX to facilitate downstream recruitment of DDR factors to DSB-flanking chromosomes . In pachUBR2 also targets histone H2A for ubiquitination conjugation. UBR2 primarily localizes to the unsynapsed chromatin regions where it transfers ubiquitin from its E2 enzyme HR6B to histone H2A, mediating meiotic silencing of unsynapsed chromatin (MSUC) . FurtherDeubiquitinases (DUBs) are cysteine proteases, which remove and recycle ubiquitin. Till now, more than 100 DUB enzymes have been identified in human . DUBs paUSP7 localizes preferentially to the XY body in early pachytene spermatocytes but gradually decreases its level as meiosis progresses, which is highly concurrent with the expression manner of SCML2, a testis-specific polycomb protein . BiochemUSP8 is expressed in both brain and testis and interacts with MSJ1, STAM2, EEA1 and VSP54 . In mousDrosophila, USP14 deficiency impairs spermatid individualization, during which syncytial spermatids are separated into individual cells [USP2 is restrictedly expressed in elongating spermatids, possessing speculative function during post-meiotic differentiation. USP2 deficiency in mice leads to defective sperm whose motility is highly vulnerable to environmental changes . Besidesal cells . USP14 dal cells . Mammalian USP9X is the functional orthologue of drosophila deubiquitinating enzyme fat facets (Faf), which interacts with and impedes the degradation of a DEAD-box RNA helicase Vasa, a highly-conserved marker for germ cells . USP9X iUSP26 has been considered as a potential infertility gene due to its restricted expression in mammalian testis and X-chromosome-localization  [USP26 associated with non-obstructive azoospermia or asthenozoospermia, suggesting a causal relationship with human male infertility. However, most of the identified USP26 polymorphisms can’t disrupt its enzymatic function [USP26 is indispensable for mouse fertility in both sexes [USP26 mutation on male fertility dependent on the genetic background of mice [USP26 mutants in DBA/2, rather than C57BL/6 background exhibit impaired spermatogenesis with obvious deficiency and malformation of spermatozoa, resulting in infertile or sub-fertile males. These results implicate that strain-specific genetic components interact with USP26 mutations to interfere spermatogenesis; such components may also exist in human but further investigations are needed.n males) ,150,151.function . In addith sexes . It has  of mice . USP26 mUCHL3 expression exhibits differentiation-dependent pattern during spermatogenesis with minimal amount in spermatogonia but to a sequentially increasing extent in meiotic pachytene spermatocytes and post-meiotic spermatids . BesidesUCHL1 shares high sequence similarity with UCHL3, however, its distribution pattern in male germ cells is totally different, indicating distinct function during spermatogenesis . PredomiBesides the DUBs in USP and UCH families, CYLD (cylindromatosis) is also found to be required for spermatogenesis by regulating early wave of germ cell apoptosis mainly in spermatogonia, a requisite process to eliminate excessive germ cells and thereby maintain the balance between germ cells and Sertoli cells . CYLD diFor a prolonged and highly ordered physiological process such as spermatogenesis, the precise control of protein homeostasis to ensure the functioning of certain protein groups at a given stage and the inactivation of them after this stage (actively or passively) is critical for the step-by-step progression. In this review, we summarized the biochemical and physiological functions of the UPS components in male spermatogenesis, including the diverse proteasomes that target ubiquitinated proteins for proteolysis, the E3 ubiquitin ligases that tag their substrates for ubiquitination, as well as the DUBs that remove ubiquitin chains from the substrates. We propose further investigation on the underlying mechanisms of UPS-mediated protein homeostasis during spermatogenesis, in order to reveal the unique protein degradation events in male germ cells, interpret the specificity of testis-associated proteasome isoforms in targeting different groups of substrates, and clarify the pathogenesis of human male infertility related to UPS dysfunction. However, most related studies do not take the big picture of the UPS into consideration. For example, some studies only investigate an E3 and sometimes including its substrates but do not analyze the following degradation events, or some other studies focus on a proteasome isoform but omit its substrate specificity and the biochemical uniqueness. The main reason should be the complexity of both the UPS and the spermatogenic procedure, and one way to improve it might be focusing on a certain protein in spermatogenesis, to systematically investigate its expression pattern transcriptionally and translationally, its E3 and DUB, the ubiquitination cascade and the proteasome isoform that proteolyzes it. The other way is to investigate the proteomic alterations during each step of spermatogenesis with advanced techniques, such as quantitative MS and ubiquitination-related MS. Since the regulation of protein ubiquitination and degradation is always specific and precise to a narrow time window, the significance of a single degradation event might be underestimated, if the result is drawn with male germ cells of mixed stages. Due to the development of a WIN18,446/RA-based protocol to synchronize spermatogenesis into a “tsunami” , it is pAround 8–12% of couples worldwide suffer from infertility and male factors contribute to up to 20–30% of the cases independently . Causes


Although their RNA abundance can be evaluated by Northern blotting and primer extension, the nature  of these RNAs cannot be revealed by these methods. Moreover, mRNA sequencing (mRNA-seq) is not able to distinguish To elucidate the abundance, source loci, and sequence nature of SINE RNAs, we established a deep sequencing method, designated as melRNA-seq (medium-length RNA-seq), which can determine whole-length RNA sequences. Total RNA samples were treated with 5′ pyrophosphohydrolase (RppH), which allowed ligation of an RNA adaptor to the 5′ end of intact SINE RNAs. Similarly, another adaptor was ligated to the 3′ end, followed by reverse transcription, PCR amplification, size selection, and single-end deep sequencing. The analysis of two biological replicates of RNAs from mouse spermatogonia showed high reproducibility of SINE expression data both at family and locus levels.This new method can be used for quantification and detailed sequence analysis of medium-length non-coding RNAs, such as rRNA, snRNA, tRNAs, and SINE RNAs. Further, its dynamic range is much wider than Northern blotting and primer extension. Many non-coding RNAs play various important roles in cell functions such as gene regulation, chromatin organization, RNA splicing and translation. Sequencing methods for regulatory small non-coding RNAs , namely miRNAs, siRNA, and piRNAs, have been well established based on ligation of an adaptor to both the 5′ and 3′ ends of RNA molecules. On the other hand, there is no established sequencing method for non-coding RNAs of medium length such as snRNAs and tRNAs.bona fide SINE RNAs from these longer transcripts. Small RNA-seq is not applicable either, since it only captures RNAs with 5′-monophosphate and 3′-hydroxyl ends, and SINE RNAs have a 5′-triphosphate (or capped) end which cannot ligate with an adaptor. In recent years, some methods for the sequencing and computational analysis of SINE transcripts have been developed [Small interspersed elements (SINEs) are a class of retrotransposons widely distributed in eukaryotes that produce medium-length RNAs  via RNA polymerase III (Pol III) transcription. SINEs proliferated by retrotransposition during evolution, and therefore a large number of divergent copies are present in each genome. The human Alu SINE and rodent B1 and B2 SINEs have been shown to play a role in regulation of gene expression. Although the abundance of SINE RNAs can be analyzed by Northern blotting and primer extension, the nature  of these RNAs cannot be revealed by these methods. Regarding mRNA sequencing (mRNA-seq), many SINE copies reside in mRNA and long non-coding RNAs, which precludes distinguishing To elucidate the nature of SINE RNA sequences, we established a deep sequencing method, designated as melRNA-seq (medium-length RNA-seq), which can determine whole-length RNA sequences up to 600 nucleotides Fig. . Herein,Brain tissue was dissected from adult male mice (C57BL6/J). The spermatogonia were collected from testes at postnatal day 7 by fluorescence-activated cell sorting using an anti-EpCam antibody as described previously .Tissue and cell samples were dissolved in Isogen (Nippon gene), and total RNAs were prepared by Direct-zol RNA prep kit (Zymo Research). Although other RNA preparation methods are also applicable, we suggest avoiding RNA degradation to capture full-length RNAs. RNA (500 ng) was treated with 25 U of RNA 5′ pyrophosphohydrolase  for 1 h at 37 °C in a 50-μl reaction mixture containing 1× Thermopol buffer (New England Biolab). This procedure freed 5′ cap or triphosphate into monophosphate, which can be later ligated by an RNA ligase. The reaction was stopped by phenol-chloroform extraction and ethanol precipitation. Sequencing libraries were constructed by using NEBNext Small RNA Library Prep kit (New England Biolab) according to manufacturer’s instructions. PCR products were run on a 6% native polyacrylamide gel, and a gel region corresponding to 240–380 bp (an insert size of 113–253 bp) was extracted. DNAs were eluted in 300 μl TE, ethanol precipitated, and dissolved in 20 μl of 0.1× TE. Library concentrations were determined by real-time RCR using the KAPA library quantification kit (KAPA Biosystems) on a StepOnePlus (Thermofisher).The libraries were denatured in sodium hydroxide, neutralized in HT1 buffer (Illumina), diluted to 10 pM, mixed with the phiX174 control library (Illumina) (70% melRNA-seq libraries and 30% control), and sequenced on a MiSeq with MiSeq Reagent Kit v2 (Illumina) in the 300-bp single-end mode. About 6 million reads were yielded for each library. The sequences obtained by this method correspond to the sequences of the original RNA transcripts. Note that the libraries made by this method are also compatible with paired-end sequencing by MiSeq, HiSeq, and NovaSeq.https://cutadapt.readthedocs.io) with the option -a AGATCGGAAGAGCACACGTCT, and reads that did not contained the adaptor sequence were discarded. The retained reads were analyzed by repeatmasker (https://www.repeatmasker.org). Reads matched to the sense strand of a SINE sequence with “position in query start” and “position in repeat start” both ≤5 were retained. This procedure selected for RNA reads generated by transcription starting within 5 bp from the start nucleotide of the respective consensus sequences. The retained reads (about 78% of the reads containing a SINE sequence) were mapped onto the mouse reference genome (mm10) by hisat2 [For sequence analysis, the adaptor sequence was removed from the obtained reads by cutadapt  in the libraries prepared from brain RNAs. The 5S rRNA (121 nucleotides in length) is transcribed by Pol III and thus has a triphosphate at the 5′ end , whereasbona fide SINE transcripts. Under these criteria, many reads (about 78% of reads containing a SINE sequence) were identified as SINE transcripts  around them likely due to the degradation of mRNAs of their host genes , and its applicability to studies on other non-coding RNAs as described below.We used a MiSeq 300-cycles kit for 300-bp single-end sequencing. Therefore, RNAs of ≥300 nucleotides could not be analyzed. However, 600-bp single-end sequencing (MiSeq) and 250-bp paired-end sequencing  allow analysis of these RNAs, including human Alu SINE RNAs .In the repeatmasker outputs, various snRNAs and tRNAs were also identified. Thus, the method offers an opportunity for the analysis of the expression of tRNAs, snRNAs, snoRNAs, etc. However, we note that additional steps may be required to ligate aminoacylated tRNAs and the adaptor.Herein, we present a new method for the quantification and sequence analysis of medium-length non-coding RNAs, such as rRNA, snRNA, and SINE RNAs. Like mRNA-seq and small RNA-seq, its dynamic range is much better than Northern blotting. Moreover, whereas quantification by Northern blotting depends on the degree of homology to the sequence(s) of the probe(s) used, melRNA-seq is independent. Moreover, source SINE loci could be identified by the method. Despite the source loci from “multiple hit” reads cannot be uniquely determined, repeatmasker allows identifying their subfamilies. As data produced by this method are highly reproducible, its applicability to many types of medium-length RNAs is guaranteed.


RNA-based therapies have been promising method for treating all kinds of diseases, and four siRNA-based drugs and two mRNA-based drugs have been approved and are on the market now. However, none of them is applied for cancer treatment. This is not only because of the complexity of the tumor microenvironment, but also due to the intrinsic obstacles of RNAs. Until now, all kinds of strategies have been developed to improve the performance of RNAs for cancer therapy, especially the nanoparticle-based ones using biogenic materials. They are much more compatible with less toxicity compared to the ones using synthetic polymers, and the most widely studied biogenic materials are oligonucleotides, exosomes, and cell membranes. Particular characteristics make them show different capacities in internalization and endosomal escape as well as specific targeting. In this paper, we systematically summarize the RNA-based nano-delivery systems using biogenic materials for cancer therapy, and we believe this review will provide a valuable reference for researchers involved in the field of biogenic delivery and RNA-based therapies for cancer treatment. This may be attributing their superiorities over other macromolecular drugs such as protein- and DNA-based treatments. Compared with protein-based therapeutics, RNA therapeutics are more cost-effective, and they can be manufactured rapidly on a large scale. In the meantime, these treatments have been demonstrated to display greater therapeutic efficiency than DNA drugs because they can act in the cytoplasm, whereas DNA therapeutics require entry into the nucleus . mRNA-ban, 2020) . All theAlthough RNA drugs designed for cancer treatment have been examined in clinical trials for several decades, none has been successfully approved for clinical application. This is attributable to the complicated pathophysiological environments of cancers including the dense tumor stroma, collapsed blood vessels, immunosuppression, multidrug resistance, and hypoxia, which can promote tumor progression and prevent therapeutics from entering tumor tissue , as wellTo date, multiple chemical modifications have been reported to improve the stability and reduce the immunogenicity of RNA therapies . In addivia membrane fusion mechanism. Although oligonucleotide assemblies seem to show lower targeting ability than exosomes and cell membranes, the high programmability makes them with precisely controlled structures and functional sites for ligand attachments to induce enhanced tumor accumulation, cellular internalization, and endosome escape  to achieve optimal delivery effects by improving the tumor accumulation and cytosol release of RNAs . In addiDNA nanotechnology was proposed in the 1980s by Nadrian Seeman, but no construct was successfully built until 1998. Subsequently, various techniques have been developed to prepare DNA self-assemblies with assorted sizes, shapes, and structures . FurtherAlthough the internalization efficiency can be improved by tuning the characteristics of DNA nanosystems, most of them are endocytosed via non-specific pathways such as scavenger receptor- , clathriIn addition to targeting, the applicability of DNA nanostructures is also limited by the labile “soft-matter” constructs in harsh physiological environments. Moreover, physiological environments that contain a high amount of DNase and low concentration of salt can induce the disintegrity of DNA nanostructures. To improve the stability, the spermidine/DNA nanoprism complex was developed . The resCompared with DNA nanotechnology, RNA nanotechnology emerged somewhat later in the 1990s . Howevervia electrostatic interaction is also reported and confirmed to improve the stability of RNA assemblies  which is programmed with therapeutic siRNA targeting cell adhesion molecule (EpCAM) for drug resistance reversal and EpCAM aptamer for specific guidance was reported by Chen and co-workers. After entering targeted cells with the help of the aptamer, the PRS can be digested by Dicer enzymes in the cytoplasm, thus releasing EpCAM siRNA to induce a synergistic effect with sorafenib (SF)  Chen HY. Anothersemblies . The mecsemblies . In addisemblies . In partsemblies . This ag3WJ, b3WJ, and c3WJ  from the phi29 DNA packaging motor system was widely engineered and applied for RNA targeted delivery by Guo group . The pRNand c3WJ . This un of pRNA   which exhibit good intrinsic targeting properties, the membranes of the human non-small cell lung carcinoma cell H1299 have been coated on siRNA/Poly (β-amino ester) (PBAE) core through a co-extrusion method . The coaAs mentioned above, red blood cell membrane (RBCm) can provide long systematic circulation, and they have also been confirmed to display no toxicity or immunogenicity in autologous applications . But becOther cell membranes derived from platelets , endoplaConcerning RNAs therapy, it has been proposed and researched for decades. However, the intrinsic barriers in stability, immunogenicity, specific targeting, and endosomal escape make them not easy to realize clinical transformation. The approved RNA-drugs using nanoparticles shed new light on innovative design of therapeutics, but other vehicles are still needed to reduce immunogenicity while improving specific targeting and endosomal escape of RNAs. In this review, we summarized the RNA-delivery systems using biogenic materials for cancer treatment. These materials display excellent biocompatibility and biodegradability, resulting in low immunogenicity. In addition, their specifically intrinsic properties result in differences in therapeutic efficiency and many other biological functions among RNA delivery systems. For example, the oligonucleotides formed DNA and RNA NPs can deliver RNAs via simple Watson-Crick pairing and Hoogsteen hydrogen bonding with no need for additional positively charged transfection agents or complicated preparation technology. In addition, their unique programmable and predictable structure confers NPs with tunable size and shape, which can be optimized to induce high internalization efficiency as well as endosomal escape. In addition, they can also be programmed with functional sites for ligand attachments to ensure tumor-specific delivery. Concerning exosomes and cell membranes, they both exhibit homologous tropism for specific RNAs delivery. In addition, the biocompatibility with endosomal membranes also confers them with endosomal escape capacity via a fusion-based mechanism. In a word, all described biogenic materials can significantly reduce the immunogenicity and improve specific targeting and endosomal escape of RNAs.in vivo behavior of RNA nanoparticles using different delivery vehicles. Especially, the relatively unstable oligonucleotides assemblies which lack covalent binding or crosslinking may dissociate at ultra-low concentrations in animal and human circulation systems after systemic injection. Although we can ensure their specific delivery to tumor sites in vitro or even in vivo, but for clinical applications, additional approaches for the selective uptake of RNAs by specific organs and diseased cells are desired. In addition, biogenic materials with high purity and large-scale production are basic requirements. However, none of these biogenic materials meets these requirements, especially exosomes. In addition, the contents of exosomes such as nucleic acids and proteins participate in both physiological and pathological processes. Their ambiguous roles make them so they cannot be applied for RNA delivery for sure. For example, the most contentious MSC-derived exosomes, many reports have confirmed their therapeutic effect for cancer treatment, but some studies demonstrate their significant roles in the growth, progression, and metastasis of tumors. Therefore, it is important to clarify the biological function of these materials before their clinical use. In summary, all biogenic materials have their strengths and weaknesses, and additional efforts are required to develop ideal carriers for RNA delivery, especially carriers with clinical applicability.Without question, there are some issues and challenges that need to be addressed and tackled before biogenic material-based systems are used for RNAs delivery in clinic. For example, it is important to better understand the


Cyanophages are viruses that target cyanobacteria and directly control their abundance via viral lysis. Cyanobacteria are known to cause large blooms in water bodies, substantially contributing to oxygen depletion in bottom waters resulting in areas called dead zones. 2 fixation, which are known to form large blooms as well as resistant resting cells known as akinetes. Here, we investigated cyanophage diversity and community structure plus cyanobacteria in dead zone sediments. We sampled surface sediments and sequenced DNA and RNA, along an oxygen gradient—representing oxic, hypoxic, and anoxic conditions—in one of the world’s largest dead zones located in the Baltic Sea. Cyanophages were detected at all stations and, based on partial genome contigs, had a higher alpha diversity and different beta diversity in the hypoxic-anoxic sediments, suggesting that cyanobacteria in dead zone sediments and/or environmental conditions select for specific cyanophages. Some of these cyanophages can infect cyanobacteria with potential consequences for gene expression related to their photosystem and phosphate regulation. Top cyanobacterial genera detected in the anoxic sediment included Dolichospermum/Anabaena, Synechococcus, and Cyanobium. RNA transcripts classified to cyanobacteria were associated with numerous pathways, including anaerobic carbon metabolism and N2 fixation. Cyanobacterial blooms are known to fuel oxygen-depleted ecosystems with phosphorus , and our cyanophage data indicate the potential for viral lysis of cyanobacteria which might explain the high nutrient turnover in these environments.Up to 20% of prokaryotic organisms in the oceans are estimated to die every day due to viral infection and lysis. Viruses can therefore alter microbial diversity, community structure, and biogeochemical processes driven by these organisms. Cyanophages are viruses that infect and lyse cyanobacterial cells, adding bioavailable carbon and nutrients into the environment. Cyanobacteria are photosynthesizing bacteria, with some species capable of NIMPORTANCE Cyanophages are viruses that target cyanobacteria and directly control their abundance via viral lysis. Cyanobacteria are known to cause large blooms in water bodies, substantially contributing to oxygen depletion in bottom waters resulting in areas called dead zones. Our knowledge of cyanophages in dead zones is very scarce, and so far, no studies have assembled partial cyanophage genomes and investigated their associated cyanobacteria in these dark and anoxic sediments. Here, we present the first study using DNA and RNA sequencing to investigate in situ diversity of cyanophages and cyanobacteria in dead zones. Our study shows that dead zone sediments contain different cyanophages compared to oxic sediments and suggest that these viruses are able to affect cyanobacterial photosystem and phosphate regulation. Furthermore, cyanophage-controlled lysis of cyanobacteria might also increase the turnover of carbon, phosphorus, and nitrogen in these oxygen-free environments at the bottom of the sea. As e formed  and conse formed , 26 and e formed  and mighOne of the largest dead zones in the world is located in the Baltic Sea , which hin situ cyanophages and cyanobacteria in dead zone sediments. To investigate this aim, we sampled the top 0- to 2-cm sediment surface from four stations along a 60- to 210-m water depth gradient in one of the largest dead zones in the world . These stations included oxic (station A), hypoxic (D), near-anoxic (E), and anoxic (F) sediment (2S), and station E higher concentrations of nitrous oxide (N2O)  . DNA andSiphoviridae cyanophages (x), Podoviridae cyanophages (y), and Myoviridae cyanophages (z) (according to the cyanophage taxonomic classifications)  were assembled from the coassembly metagenome data with an average genome size of 5,076 bp . These partial cyanophage genome sequences were used in network analyses using vConTACT , which ccations) . See Fig10.1128/mSphere.00208-21.1FIG S1FIG S1, PDF file, 1.3 MB.Network showing all virus reference nodes (noncyanophages as black and cyanophages as green) alongside identified cyanophage contigs (blue nodes). Download Copyright © 2021 Broman et al.2021Broman et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 10.1128/mSphere.00208-21.5DATA SET S1Data Set S1, XLSX file, 0.2 MB.Constructed cyanophage contigs classified by first detecting virus contigs using VirSorter and then classified further as cyanophages based on protein content using vConTACT. The values in the table show normalized mapped bp values: bp mapped on contig/contig length (in kb)/metagenome bases (in Mb). The NCBI NT classifications of the contigs are also shown in the table. Download Copyright © 2021 Broman et al.2021Broman et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the P = 0.081), 3.51 ± 0.43 , and 3.11 ± 0.39  compared to 2.37 ± 0.11 at station A .Cyanobacterial alpha diversity (Shannon’s H) in the sediment was significantly higher in the oxic station A (Shannon’s H 4.17 ± 0.02) than in the hypoxic-anoxic stations D, E, and F ) compared to the hypoxic-anoxic stations D, E, and F , while the photosystem II had a different number of RNA transcripts for each station  The first sheet shows GeTMM values for proteins classified to reference cyanobacterial species in the UniProtKB/Swiss-Prot database. (2) The second sheet shows GeTMM values for proteins and their KEGG KO and KEGG modules classifications. (3) The third sheet shows the results of the edgeR analysis. Only statistically significant proteins are shown (FDR < 0.05). The table shows logFC (fold change) values for pairwise comparisons (sample 1 versus sample 2), and values of >0 denote more RNA transcripts in sample 1. Values higher in sample 1 are denoted by red cells, while values higher in sample 2 are denoted by green cells. Note that there were no differences between stations D and E. Download Copyright © 2021 Broman et al.2021Broman et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the RNA polymerase was present at all stations in the cyanobacterial RNA data ; also, t2 fixation (genes nifDEHKNU) were present at all stations (nifU RNA transcripts was higher at station A than at D and F (FDR < 0.05), and nifH RNA transcripts were higher at station E than at station A (FDR < 0.05). Genes related to assimilatory nitrate reduction and uptake (nitrate reductase narB and nitrate transporters nrtABCDP) had together more RNA transcripts at the oxic station A than at the hypoxic-anoxic stations  than at stations D, E, and F  were correlated with the relative abundance of several cyanophages, possibly due to viral infection. On the bacterial genome, CRISPR  loci consist of virus-specific DNA repeats with adjacent cas genes. Upon viral infection, the virus DNA is sampled and inserted into a CRISPR locus and transcribed into crRNA, which is then used to guide Cas endonuclease proteins to cleave invading virus DNA . In more detail, the cyanobacterial photosynthesis data set was dominated by psbA genes at all stations except the anoxic station F. It is therefore possible that changes in cyanobacterial photosynthesis regulation could have partly been due to viral infection by cyanophages. Our results indicate that prokaryote-virus interactions are an important factor to consider in future studies looking at prokaryotic photosystem genes or protein counts. Similarly, cyanophages were also found to carry genes for the phosphorus regulon phoH. Even though the function of the translated protein is still not fully understood   in our study indicated that akinetes or fermentative metabolism could have been possible. Interestingly, the relative proportion of classified reads attributed to Anabaena was higher in the RNA-seq than in the DNA data, and this might be explained by the following: (i) Anabaena is known to produce akinetes  . The preakinetes  and (ii)nditions . The preediments , 46, resxogenous ) or akinbstrates . In contakinetes . Howeverentation . Our mRN2-fixation genes are being transcribed by cyanobacteria in dead zones. We detected the highest number of N2-fixation-related RNA transcripts in the oxic station A, possibly due to there being a higher relative abundance of cyanobacteria at that station than at the hypoxic-anoxic stations. The sampled hypoxic-anoxic stations have low or no nitrate and nitrite  where ammonium might have been scarce. However, further controlled studies are needed to investigate if cyanobacterial N2 fixation is an important metabolic process in dead zone sediments. We were also able to detect RNA transcripts classified to genes coding for photosynthesis at all sampled stations. The highest number of photosynthesis RNA transcripts was detected at the oxic station A, possibly due to cyanobacteria having the highest relative abundance at that station. Accumulation of RNA transcripts attributed to the photosystem in darkness has previously been observed for the strain Synechocystis sp. strain PCC 6803  and akinete-forming Dolichospermum/Anabaena. Cyanobacterial RNA transcripts in sediment were classified to, e.g., anaerobic glycolysis and N2 fixation. Considering that large amounts of cyanobacteria sinking to dead zone sediments are known to fuel the benthic ecosystems with phosphorus , our cyanophage data indicate the potential for viral lysis of cyanobacteria, and this might sustain the high nutrient turnover in these heavily eutrophied environments.We have here shown that cyanophages and cyanobacteria are present in one of the largest dead zones in the world. Cyanophages were detected at all stations and had a different beta diversity in the oxic sediment than in the hypoxic and anoxic sediments, suggesting that cyanobacteria and/or the environment select for specific cyanophages. Moreover, our results show that these cyanophages can infect cyanobacteria, affecting the photosystem and phosphate regulation. Top prevalent cyanobacteria detected include genera not capable of forming akinetes (2 until being transferred and stored at −80°C.Sediment was collected using a modified box corer  during 2DNA and RNA were extracted from thawed and homogenized sediment with the DNeasy PowerMax soil kit (Qiagen) and RNeasy PowerSoil kit (Qiagen), with 10 g and 2 g input material, respectively. Extracted RNA was DNase treated with the Turbo DNA-free kit (Invitrogen) and rRNA depletion using the RiboMinus transcriptome isolation kit . Multiplexed libraries were prepared with the ThruPLEX DNA-seq (Rubicon Genomics) and TruSeq RNA Library Prep v2  kits for DNA and RNA, respectively. Paired-end 2- by 150-bp sequencing was conducted at the Science for Life Laboratory, Stockholm, Sweden, on the Illumina NovaSeq platform . The sequencing yielded on average 41 million (range 32 to 52) and 82 million (range 74 to 89) read-pairs for each DNA and RNA sample, respectively.NC_001422.1). Reads were quality trimmed using Trimmomatic 0.36  were classified against the NCBI RefSeq genome database (downloaded 1 March 2019) using Kraken2 2.0.7 with a paired-end setup . The DNAgene length corrected trimmed mean of M-values (GeTMM)  . Unique  (GeTMM)  by linkihttps://de.cyverse.org). Contigs classified as “Pretty sure” and “Quite sure” by VirSorter were used for further analyses. The contigs were classified for taxonomy using BLAST 2.10.1+  using the software PAST. Differences in RNA transcripts for annotated proteins between stations were tested with the R package edgeR 3.24.3  using the software PAST 3.25 . ShannonR 3.24.3  by usingR 3.24.3 . False-dPRJNA531756 (The raw sequencing data supporting the conclusions of this article are available in the NCBI BioProject repository, NA531756 .


It is of interest to document the moelcular docking analysis of SARS-CoV-2 linked RNA dependent RNA polymerase (RdRp) with compounds from Plectranthus amboinicus. Hence, we report the binding features of rutin, Luteolin, Salvianolic acid A, Rosmarinic acidand p-Coumaric acid with the target protein SARS-CoV-2 linked RNA dependent RNA polymerase (RdRp) for further consideration. The new strain of coronavirus SARS-CoV-2 (Severe Acute Respiratory Syndrome) is the infectious disease COVID-19 . RdRp isThe three-dimensional structure of the protein RdRp of SARS-CoV-2 (PDB ID: 6M71) was downloaded from the Protein Data Bank (www.rcsb.org/pdb). This structure is solved  with 2.9Thirty compounds from the Plectranthus amboinicus plant were gleaned from literature. The compound structures were downloaded in .sdf format from the database of PubChem compounds (www.pubchem.ncbi.nlm.nih.gov/). All the compounds were translated to .Pdb formatby using the online smiles converter. The energy of all ligands was minimized and translated to the PDBQT file format.The grid box around the binding pocket is positioned using a standard protocol . PyRx haThe Lipinski filters (http:/www.scfbio-iitd.res.in / software / drugdesign / lipinski.jsp) were used to measure the drug likeness of the compounds from the docking calculation. Four of the five parameters defined for drug likeness are molecular mass, cLogP,hydrogen donor and acceptor and molar refractive index .It is of interest to document the moelcular docking analysis of SARS-CoV-2 linked RNA dependent RNA polymerase (RdRp) with compounds (Table 1 - see PDF) from Plectranthus amboinicus. Hence, we report the binding features of rutin, Luteolin, Salvianolic acid A,Rosmarinic acid and p-Coumaric acid with the target protein SARS-CoV-2 linked RNA dependent RNA polymerase (RdRp) for further consideration (Table 2 - see PDf). The interactions between the targeted protein and the ligands were analysed using the Pymol MolecularVisualization Tools as shown in We report the binding features of rutin, Luteolin, Salvianolic acid A, Rosmarinic acid and p-Coumaric acid with the target protein SARS-CoV-2 linked RNA dependent RNA polymerase (RdRp) for further consideration.


Alus in 3′ UTRs, AU-rich elements, and RNA-binding proteins. SGs and processing bodies (P-bodies) transiently interact through a docking mechanism to allow the exchange of RNA species. However, the types of RNA species exchanged, and the mechanisms and outcomes of exchange are still unknown. Specialized RNA-binding proteins, including adenosine deaminase acting on RNA (ADAR1-p150), with an affinity toward inverted repeat Alus, and Tudor staphylococcal nuclease (Tudor-SN) are specifically recruited to SGs under OS along with an RNA transport protein, Staufen1 (STAU1), but their precise biochemical roles in SGs and SG/P-body docking are uncertain. Here, we critically review relevant literature and propose a hypothetical mechanism for the processing and decay of oxidized-RNA in SGs/P-bodies, as well as the role of ADAR1-p150, Tudor-SN, and STAU1.Reactive oxygen species (ROS) generated under oxidative stress (OS) cause oxidative damage to RNA. Recent studies have suggested a role for oxidized RNA in several human disorders. Under the conditions of oxidative stress, mRNAs released from polysome dissociation accumulate and initiate stress granule (SG) assembly. SGs are highly enriched in mRNAs, containing inverted repeat (IR)  Cells constantly generate reactive oxygen species as byproducts of oxidative phosphorylation. The endogenous ROS generation can be regulated under many conditions . In addiExcessive reactive oxygen species cause both physical and chemical damage to RNA, including strand breaks , inductiThese chemically modified and oxidized adducts in mRNA lead to the generation of short polypeptides due to premature translation termination  and stalE. coli cells under OS  preferentially binds to oxidized RNA and reduces the levels of 8-oxo-G containing RNA, thereby increasing the viability of HeLa cells under OS . TruD -mediated stress, and viral infections . They reolysomes  and largolysomes . Severalolysomes . Howeverolysomes  along wiolysomes . Cell siolysomes . SGs indolysomes . SGs areolysomes , etc. Tholysomes .in vitro and possibly contribute to SG formation . It is a reversible process of a homogeneous mixture into a dilute and condensed phase . There iormation . RNA notormation .It is well known that acute and destructive oxidative stress can lead to cell death/apoptosis. Arsenite treatment, heat shock, and hypoxia-induced OS and SGs formed under these conditions were shown to inhibit apoptosis . MoreoveRecent evidence suggests that SG-enriched ATF4 mRNA translates efficiently within SGs  and U2OSAbnormality and deficiency of SGs are implicated in various human diseases such as promoting cancer cell survival and tumor progression  and in tAlu, antisense Alu, or without any Alu in their 3′ UTR diffuse throughout the cytoplasm  tails from human cells . The aveytoplasm . Other sytoplasm . Analysiytoplasm . Resultsytoplasm . Additioytoplasm . HoweverInterestingly, the heat-shock proteins Hsp70 and Hsp90 and their respective mRNAs that are preferentially expressed under cellular stress are excluded from SGs . The mec+ mRNA + mRNA . However+ mRNA .via the LLPS process and the protein’s activity was inhibited. Deb1p condensation led to translational repression of mRNAs with structurally complex 5′ UTRs (Mounting evidence suggests that long UTRs in mRNAs are indispensable for the mRNAs to be recruited into SGs via LLPS. Recent studies demonstrated that tandem binding of 3′ UTR of p53 mRNA by TIA-1 (an abundant SG protein) efficiently enhanced phase separation and formed a potential site for SG assembly . Deb1p i 5′ UTRs . Interes 5′ UTRs . These sin vivo  tails . P-bodiein vivo . Importain vivo , suggestin vivo , but thein vivo , indicatAlus accumulated in SGs form double-stranded structures in the mRNA 3’ UTR regions which may provide higher stability and become targets for ADARs (Recent findings demonstrate that the p150 isoform of adenosine deaminase acting on RNA (ADAR1) and the dsRNA-binding protein Tudor staphylococcal nuclease (Tudor-SN) are specifically recruited and colocalized in SGs upon OS induction . A specior ADARs . Weissbaor ADARs , suggestMED13, STAT3, and F11R transcripts which contain IR Alus were found to have mostly elevated A–I editing levels  induce ADAR1 . On the In vitro studies revealed that Tudor-SN specifically binds to and cleaves runs of I–U and U–I rich regions in dsRNA  is a multifunctional protein with major implications in SG assembly, gene regulation, and pre-mRNA splicing . Additioin dsRNA , demonstin dsRNA . As discin dsRNA . While ain dsRNA , the invin dsRNA . This suAlu repeats in their 3′ UTRs and prevents nuclear retention and promotes export of the mRNAs to the cytoplasm  (Alus and selectively exports hyperedited transcripts.Staufen 1 (STAU1) is a double-stranded RNA-binding protein associated with polysomes and recruited to SGs upon OS. It is always present in SGs during their assembly and dissolution; however, it is not required for SG formation ytoplasm . Furtherytoplasm . Elbarbanse mRNA . Recent (lncRNA) . These sAlus and STAU1 depletion prevents export of these RNAs to PBs, resulting in stabilization and accumulation of SGs. In contrast, overexpression of STAU1 resulted in inhibition of SG accumulation perhaps due to STAU1-mediated rapid export of mRNA from SGs to PBs. Endogenous STAU1 was barely detected in PBs under resting conditions, though upon stress induction it was recruited to PBs ” within living HeLa cells by staining with 8-OHG-specific antibody . It is le in SGs . These f2O2-induced SGs or 8-OHG-containing RNAs in ORBs. It is likely that highly structured RNA species are preferentially oxidized. In vivo and in vitro studies have demonstrated that noncoding structural RNAs such as tRNA and rRNA molecules having complex double-stranded structures are oxidized to a greater extent in their native conformation compared to their denatured forms  tail, and this forms the basis for mRNA decay in PBs. Apart from ADAR1 and Tudor-SN, Staufen1 (STAU1) is also recruited to SG upon OS induction. STAU1 is a natural transporter of mRNA molecules, and it can bind to the 3′UTR of Tudor-SN processed mRNA and transport it from SGs to PBs where it is degraded .• We speculate that oxidized RNAs are recognized and sequestered in the SG-mediated degradation process. First, ADAR1-p150 may preferentially recognize and edit oxidatively damaged mRNA and that these RNAs will be sorted and routed specifically to PBs for degradation, while normal mRNAs may be released from SGs for reinitiation of translation upon stress removal. Second , oxidized RNAs may contain oxidatively damaged nucleobases that behave like edited bases and trigger selected cleavage by Tudor-SN-like activities .We have proposed a hypothetical model to describe how under various physiological stress conditions (e.g. oxidative stress) a specific set of RNA molecules are damaged by oxidative stress insults and traffic to SGs for sequestration. From there, a subset of these RNAs is shuttled to PBs for degradation. This process protects cells from adverse consequences of RNA oxidation and enhances cell survival. This hypothesis is captured in the following key points:


Human enteric viruses such as norovirus (NoV) and hepatitis A virus (HAV) are some of the most important causes of foodborne infections worldwide. Usually, infection via fish consumption is not a concern regarding these viruses, since fish are mainly consumed cooked. However, in the last years, raw fish consumption has become increasingly common, especially involving the use of seabass and gilthead seabream in dishes like sushi, sashimi, poke, and carpaccio. Therefore, the risk for viral infection via the consumption of raw fish has also increased. In this study, a virologic screening was performed in 323 fish specimens captured along the Portuguese coast using a tetraplex qPCR optimised for two templates (plasmid and in vitro transcribed RNA) to detect and quantify NoV GI, NoV GII and HAV genomes. A difference of approximately 1-log was found between the use of plasmid or in vitro transcribed RNA for molecular-based quantifications, showing an underestimation of genome copy-number equivalents using plasmid standard-based curves. Additionally, the presence of NoV genomic RNA in a pool of seabass brains was identified, which was shown to cluster with a major group of human norovirus sequences from genogroup I (GI.1) by phylogenetic analysis. None of the analysed fish revealed the presence of NoV GII or HAV. This result corroborates the hypothesis that enteric viruses circulate in seawater or that fish were contaminated during their transportation/handling, representing a potential risk to humans through raw or undercooked fish consumption. Enteric viruses represent a major risk to human health, being responsible for numerous outbreaks worldwide. The best characterised foodborne viral agents are human norovirus (NoV) and hepatitis A virus (HAV), which cause the most significant part of foodborne-associated illness globally. According to Centers for Disease Control and Prevention (CDC) and the European Centre for Disease Prevention and Control (ECDC), NoV-associated foodborne infections are one of the most frequently reported in the United States and European Union (EU), being, for instance, associated with 457 outbreaks, and, most importantly with 11,125 cases of illness in 2019  only in the EU . It is eDomestic and restaurants settings are described as the most common places associated with NoV/HAV outbreaks , and theUsually, since fish are mainly consumed cooked, their ingestion is not a concern regarding a possible contamination with the most common foodborne-associated viruses. However, in recent times raw fish consumption has become increasingly common, and the use of gilthead seabream, Atlantic horse mackerel, and seabass in dishes like sushi, sashimi, poke, and carpaccio has increased. Tthese eating habits create concerns regarding the safety of consuming raw fish since some may be contaminated with several types of potentially pathogenic microorganisms, including bacteria, viruses, and parasites ,7,8. AltThe existing literature suggests that in the assessment of a qPCR assay several factors should be considered, namely, the effect of using different templates to generate standard curves for the absolute quantification of RNA viruses, which can be made from either a plasmid, a synthetic oligonucleotide, or an in vitro transcribed RNA . The advSparus aurata) and seabass (Dicentrarchus labrax), two of the most extensively wild-caught and farmed species in aquaculture, as well as sardine (Sardina pilchardus) and Atlantic horse mackerel (Trachurus trachurus), two of the most consumed fish species in Portugal.Therefore, in this study, we developed a tetraplex qPCR assay to detect and quantify NoV GI, NoV GII, and HAV genomes, using mengovirus as an internal control. This method was implemented either using plasmid or an in vitro transcribed RNA for standard curve construction, which allowed for NoV and HAV genomic quantification to be evaluated by comparing the two standards curve types in terms of efficiency, sensitivity, and detection limit. These tetraplex qPCR assays were used to perform a virological screening in fish captured and farmed along the Portuguese coast, focusing on four of the most economically important species from the Atlantic coast. These included gilthead seabream  was used as process control since this virus is not naturally present in food matrices . Moreove in food . Mengovi in food . Total RTo determine the optimal input for the control process virus, different amounts of mengovirus were used to spike one soft and internal tissue (liver) and one hard and external tissue (gills) to assess the limit of viral genome detection.Additionally, before total RNA extraction, all the samples were spiked with 25 µL of mengovirus suspension to indirectly estimate losses of the target viruses, which can occur at several stages during processing. The recovery rate of mengovirus was calculated using the following formula: % recovery control virus =  × 100.A total of 323 fishes were analysed (post-mortem) in this study. These were either (i) wild specimens caught along the coast of Peniche, Figueira da Foz and Algarve, (ii) available at supermarkets, (iii) discarded from fish markets, or (iv) farmed in the Algarve and Setúbal region , Table 1Approximately 2.0 g of fish tissue was chopped using a sterile razor blade and homogenised in 10 mL of TNE buffer  using a Precellys Evolution Homogenizer . The homogenates were centrifuged 10 min at 2000 rpm at 4 °C to remove particulate debris, and the supernatant used for total RNA extraction, carried out from 250 µL of clarified supernatant, using NZYol , as described by the supplier. RNA was dissolved in 30 µL of DEPC-water, and the concentration and purity of the obtained RNA extracts determined using a NanoDrop 1000 spectrophotometer. RNA extracts were stored at −80 °C until further use. For qPCR reactions, the RNA was converted to cDNA as described in For each target, a distinct set of primers/probes  was usedFor the construction of the standard curves based on plasmid DNA reference templates, three recombinant plasmids were artificially synthesised  harbouring partial sequences of the junction of open reading frames 1 and 2 (ORF1/ORF2) of NoV GI and GII, and the 5′ noncoding region (5′NCR) of HAV. These same plasmids served as a starting point to obtain PCR products from which the in vitro transcribed RNA of each targeted genomic region was subsequently synthesised. Amplicons corresponding to sections of recombinant plasmid DNA were amplified by conventional PCR, and the success of the amplification process was confirmed by their visualisation on 2% agarose gels, followed by their purification with NZYGelpure kit . The primers used to obtain these PCR fragments were chosen to include a T7 RNA polymerase-specific promoter upstream of the viral coding sequence. The fragments were then used as a template for in vitro transcription with the NZY T7 High Yield RNA Synthesis kit . The obtained RNA was purified with NZY RNA isolation kit  and treated with DNase I solution to prevent DNA contamination. RNA concentrations and purity were estimated using a NanoDrop 1000 spectrophotometer. Total RNA was converted to cDNA as previously described and used to construct the quantification standard curves.7 to 10 genome equivalents. Each target was either tested individually or as a mixture.Standard curves were constructed (one for each template) using 10-fold serial dilutions ranging from 10Single and multiplex qPCR reactions for detecting NoV GI, NoV GII, HAV, and mengovirus were carried out in a total volume of 20 μL using SensiFAST™ Probe No-ROX amplification mix . The concentrations of Nov GI, NoV GII, HAV, and mengovirus forward/reverse primers and probes , as wellAdenoviridae), infectious pancreatic necrosis virus , infectious hematopoietic necrosis virus , viral haemorrhagic septicaemia virus , viral nervous necrosis virus  and Hepatitis E virus .All single and multiplex assays for the detection of NoV GI, NoV GII, HAV, and mengovirus genomes were tested for cross-reactivity with other viruses available including, adenovirus type 5  for viral quantification, positive wastewater samples previously shown to contain NoV GI, NoV GII, and HAV genomes (our work—not published), were used to validate the qPCR assays developed. The quantification of genome equivalents was carried out by qPCR using either plasmid or in vitro transcribed RNA standard-based curves. RNA was extracted from these samples with QIAamp Viral RNA Mini Kit  according to the manufacturer’s instructions, using 140 µL of each sample and an elution volume of 80 µL (double elution 2 × 40 µL). RNA concentration and purity were estimated using a NanoDrop 1000 spectrophotometer. RNA was converted to cDNA as previously described in ® VR-3234SD™, Quantitative Synthetic Norovirus G2 (II) RNA ATCC® VR3235SD™, and Quantitative Synthetic Hepatitis A virus DNA VR-3257SD™. NoV GI and HAV detection was carried out in a total volume of 25 μL using NZYTaq II 2× Green Master mix . For NoV GI, each 25 µL reaction volume included 0.4 µM of each primer (Three nested-PCR (nPCR) protocols targeting NoV GI, NoV GII, and HAV genomes were developed to further characterise by phylogenetic analysis the positive samples previously obtained by the qPCR protocols. All nPCR assays were optimised using synthetic templates purchase from American Type Culture Collection , including Quantitative Synthetic Norovirus G1 (I) RNA ATCCh primer  and 5 µLh primer  and 5 µLFor NoV GII detection, the NZYTaq II 2× Green Master mix was not efficient, being used the Platinum™ SuperFi II Green PCR Master Mix  in a total volume of 20 μL. The thermal cycling conditions for PCR and nPCR included 1 cycle at 98 °C for 30 s, followed by 35 cycles at 98 °C for 10 s, 60 °C for 10 s and 72 °C for 30 s, and a 10 min final extension step at 72 °C.https://blast.ncbi.nlm.nih.gov/Blast.cgi (accessed on 31 March 2021)).All amplification steps were performed on a Doppio VWR thermocycler , and their success was confirmed by amplicon visualisation on 1.5% agarose gels. Additionally, non-template (negative) controls were used in each run. PCR products were sequenced in both directions using Sanger’s method  sequences used for the preparation of the different sequence datasets were selected among those deposited in the GenBank database, on the proviso that they would be representative of (i) each of the previously described species with (ii) a significant sequence overlap with the sequences obtained during this study to maximise the number of unambiguously aligned nt positions in each sequence alignment. For phylogenetic analysis, multiple alignments of nt sequences were constructed with the iterative G-INS-I method as implemented in MAFFT vs. 7 , followeThe generation of a standard curve for qPCR quantification of RNA viruses can be constructed based on the use of serial dilutions of several possible templates, including a viral genome, a structurally equivalent template prepared from in vitro transcribed RNA, or a plasmid harbouring the targeted sequence in the form of dsDNA. In an ideal situation, standard curves for qPCR analysis should be constructed using serial dilutions of cDNA prepared from RNA extracted from a viral suspension. However, since logistic limitations deter virus isolation/propagation in our laboratory, plasmids and in vitro transcribed RNA molecules prepared from a synthetic template were chosen as the starting model for standard-curve construction.3 and 10 genome copies, respectively. For HAV, the detection limit was 102 genome copies in the single reaction, increasing 1-log in the multiplex format  values between samples using the plasmid and the in vitro transcribed RNA standard-based curve . In term9 genome copies/µL, gave similar multiplex qPCR quantification results  of NoV GI genome was disclosed, corresponding to a pool of seabass brain tissue from specimens obtained from Peniche fish market . Similarly, using in vitro transcribed RNA as a standard curve, this sample was also positive with a Cq value of 32.89 corresponding to 4.64 × 104 genome copies of NoV GI. None of the 323 fish specimens analysed revealed the presence of NoV GII or HAV genomes.The analysis of the 323 fish specimens using plasmid as a standard curve for NoV and HAV detection and quantification, revealed one pool where the presence of 1.55 × 10The positive sample for NoV GI obtained by qPCR was further confirmed by conventional nPCR and characterised by Sanger sequencing of the open reading frame 1 (ORF1)-ORF2 junction region, the most conserved region of the norovirus genome . The norNoV and HAV are the leading cause of foodborne viral infections, being responsible for considerable economic and health burdens globally. Moreover, of all the viruses associated with foodborne infections, NoV and HAV are the most important viral pathogens regarding the severity of the associated illnesses and their common occurrence worldwide . They ar9 genome copies/µL, and in all the performed extractions, its recovery rate was consistently above 10%.In the present study, a virologic screening of the two above-mentioned enteric viruses targeted mainly four fish species highly consumed in Europe and caught/farmed along the Portuguese coast. For that purpose, we used a multiplex qPCR assay designed for the detection/quantification of NoV and HAV genomes, considering two different approaches as standard curves: an external in vitro synthesised RNA and a plasmid. Furthermore, this assay also included a control virus since losses of the targeted viral genomes can occur at several stages during sample preparation and nucleic acid extraction ,25. All Multiplex qPCR-based methods have been designed to detect and quantify several targets in a single reaction. However, they tend to display lower detection sensitivity when compared to standard singleplex qPCR . These lThe standard curve in an absolute qPCR assay is generated by amplifying serial dilutions of a standard DNA, which can be a plasmid, a PCR amplicon, a synthesised oligonucleotide, a genomic DNA, or a cDNA. Among the various types of the standard template, using a plasmid is one of the most common options due to its high stability and preparation reproducibility. However, plasmids could adopt several conformational structures, namely a supercoiled form, which can suppress qPCR assay compared to other templates ,28. In tInterestingly, in this study, NoV GI genomic RNA was detected in one out of a total of 400 pools (50 pools of fish × 8 organs). Although seafood contamination by these viruses is well described ,33,34,35As expected, the phylogenetic analysis of the norovirus sequence detected in this study revealed that it clustered with a major group containing norovirus sequences from genogroup I, in particular GI.1 , since ihttps://wwwn.cdc.gov/norsdashboard/ (accessed on 2 April 2021)). In this system regarding the years between 2007 and 2018, norovirus outbreak data showed that fish as a vehicle of transmission of NoV infection, without accounting for person-to-person transmission, were responsible for 15 outbreaks with 180 infected people and four hospitalisations. Moreover, fish contamination could be due to the presence of enteric viruses in the surrounding water. In fact, waterborne infections due to noroviruses were responsible for 55 outbreaks, 3330 illnesses and 33 hospitalisations ) between 2007 and 2018 in the United States, whereas in the EU, in 2019, were responsible for 11 outbreaks and 26 hospitalisations [The detection of NoV genomes in fish samples can indicate that the water where the analysed fish swam was contaminated with human faeces, probably due to sewage pollution. Despite the fact that the most common categories linked to outbreaks of NoV and HAV are fresh vegetables, fruit, and shellfish since they are consumed raw or undercooked, any type of food could be implicated in an outbreak . InformaIn conclusion, we developed a multiplex qPCR method to identify and quantify NoV and HAV genomes based on two different standard-curve construction approaches. Our data suggest that the one based on in vitro transcribed RNA is a better solution for RNA virus quantification in biological samples. Furthermore, detectingthe human pathogenic NoV GI genome in fish brains supports the hypothesis that these viruses circulate in seawater. On the other hand, while foodborne infection control might be very straightforward by simply cooking the food before consumption, raw or undercooked fish consumption as a common trend makes this control very difficult. Thereby, the monitorisation of these pathogens to properly assess human health risks associated with raw or undercooked fish consumption could be of utmost importance since NoV and HAV are characterised by a high rate of transmission, which makes them even more challenging to control.