The study of RNA expression is the fastest growing area of genomic research. However, despite the dramatic increase in the number of sequenced transcriptomes, we still do not have accurate estimates of the number and expression levels of non-coding RNA genes. Non-coding transcripts are often overlooked due to incomplete genome annotation. In this study, we use annotation-independent detection of RNA reads generated using a reverse transcriptase with low structure bias to identify non-coding RNA. Transcripts between 20 and 500 nucleotides were filtered and crosschecked with non-coding RNA annotations revealing 111 non-annotated non-coding RNAs expressed in different cell lines and tissues. Inspecting the sequence and structural features of these transcripts indicated that 60% of these transcripts correspond to new snoRNA and tRNA-like genes. The identified genes exhibited features of their respective families in terms of structure, expression, conservation and response to depletion of interacting proteins. Together, our data reveal a new group of RNA that are difficult to detect using standard gene prediction and RNA sequencing techniques, suggesting that reliance on actual gene annotation and sequencing techniques distorts the perceived architecture of the human transcriptome. Standard RNA sequencing pipelines use general gene annotation sets from databases like GENCODE  permits the sequencing of full-length noncoding RNAs  series GSE99065  includinAll datasets were analyzed using the same computational pipeline. Fastq files were checked for quality using FastQC and trimmed using Cutadapt  and Trimhttps://github.com/GaspardR/snakemake_blockbuster.The Blockbuster tool was used for read cluster detection . To prepTo optimize the expression quantification of small and mid-size non-coding RNAs, gene expression estimation in TPM was produced with the CountCorrector pipeline CoCo (v 0.2.1p4) (parameters: -c both –strand 1 –paired) which corrects read assignment for embedded and multimapped genes . The anne-value above 0.01 were filtered out. The overlap of the read clusters with the RNAcentral annotation was checked using the intersect tool from bedtools (parameters: -loj -s).Each cluster sequence was aligned against each non-coding RNA sequence within the RNAcentral annotation (v10)  with nhmEach cluster sequence was run through the following non-coding RNA predictors: snoscan  paramet paramet (defaultTurboFold II  was usedData for the ChIP-Seq of Pol II were obtained from : suppleml-glutamine (Wisent) as recommended by ATCC. For the DKC1 knockdown, cells were seeded in six-well plates (350 000 cells/well) 4 hours prior to transfection with 10 nM DKC1 targeting siRNA using Lipofectamine 2000 (Invitrogen). Two replicates were performed using siRNA 1, targeting the third DKC1 exon (GAAUCCAAAGUUGCUAAGU) and two replicates were generated with siRNA 2, targeting the fourth DKC1 exon (ACACCUCUUGCAUGUGGUU). The cells were trypsinized and collected 72 h post transfection, pelleted and resuspended in 1 ml TRIzol (Ambion) and kept at −80°C until RNA extraction. Eight normal tissue samples: three prostate, two breast and three ovary (samples were obtained from FRSQ tissue bank (The ovarian adenocarcinoma SKOV3ip1 cell line was grown in DMEM/F12 (50/50) medium supplemented with 10% fetal bovine serum and 2 mM sue bank ) tissue Total RNA was extracted from transfected cells and their control (mock transfection), as well as from the tissue samples, with Qiagen's RNEasy minikit as previously described , with onIn order to prepare TGIRT-Seq libraries, 5 μg of each RNA sample, to which a 1 ul spike-in of ERCC EXfold mix 1 was added (ThermoFisher Scientific), were ribo-depleted using a RiboZero Gold (Human/Mouse/Rat) kit (Illumina). 50 ng of each RNA sample was then fragmented using the NEBNext Magnesium RNA Fragmentation Module (New England Biolabs) at 94°C for 2 or 3 min (depending on RIN) and treated with T4 polynucleotide kinase (Epicentre) to remove 3′ phosphates and 2′,3′-cyclic monophosphates, which impede TGIRT template switching . RNAs wePhastCons100way score table for the hg38 assembly was obtained from the UCSC table browser ,43. The de novo transcript annotation. As shown in To identify non-annotated ncRNA genes, ribodepleted non-fragmented RNA extracted from the ovarian cancer cell line SKOV3IP1 was sequenced using TGIRT-seq Figure , which ee-value < 0.01) to known ncRNA biotypes. Of these 69 NA_RNA, 41 displayed homology to tRNA, 20 to snoRNA, snRNA, 7SL RNA or rRNA and eight exhibited homology to antisense RNA or lncRNA. Surprisingly, 32 NA_RNAs had no significant similarity with any known transcript in RNAcentral indicative of potentially new classes of ncRNA. In parallel to the sequence homology searches, family-specific structure prediction was performed using Turbofold  sequence similarity analysis, (ii) family-specific structure prediction, (iii) function prediction and (iv) the identification of overlap with repeated elements Figure . Sequencurbofold , which murbofold  for H/ACurbofold  for tRNAurbofold ,51 identurbofold    Figure . This is) Figure . The con) Figure . InteresThe NA_snoRNAs are generally poorly conserved outside primates and have a low PhastCons100way score compared to known snoRNAs . UnsurprThe ITS and ETS-RNA groups both have a poor sequence conservation level, which corroborates the known high sequence variability of these pre-rRNA spacer regions . While mTo further confirm the identity of the NA_tRNA, we compared their expression mechanism and folding pattern to that of known tRNAs. Five of the 8 NA_RNAs overlapping a Pol III ChIP-Seq peak as discussed above are classified as tRNAs, tRNA fragment or pre-tRNA. The strongest enrichments of RNAPIII ChIP-Seq peaks were observed within a cluster of tRNAs at the beginning of chromosome 1 that holds 4 NA_tRNAs residing near six previously annotated tRNAs Figure . Three oIndeed, even though Turbofold has shown that 6 of the 12 NA_tRNAs can properly fold in a three-leaf clover structure, they all have imperfections when compared with the canonical tRNA model, explaining why none were predicted as being tRNAs by tRNAscan-SE. For example, cluster_5 has an anticodon stem of 7 bp, longer than the expected 5 and has a very small anticodon loop . It is pTo further validate these tRNAs, we analysed datasets of alternative RNA-Seq methodologies that are specialized in the detection of tRNAs to see if they detect our NA_tRNAs. We analysed datasets from the hydro-tRNASeq methodology , the YAMAlthough they were not aligned to specific tRNA genes and were too small to be considered as tRNAs, 27 of the NA_RNAs share high sequence similarity with tRNAs elsewhere in the genome and many of them are overlapping tRNA gene repeats identified by RepeatMasker, and so we classified them as tRNA fragments (tRFs) Figure . To assetRFs are known to come from different portions of their tRNA gene and so they have been divided in different groups in the literature such as 5′ and 3′ halves, 5′ and 3′ fragments, and internal fragments ,63. To dThe three C/D and seven H/ACA snoRNAs identified by snoGPS  and snosThe four ETS-RNA NA_RNAs and four ITS-RNA NA_RNAs are held within four respective copies of the 45S rDNA . The seqe-value < 0.01 mostly due to the short cluster size (<30 nts). In nine cases, the RNA shared sequence similarity with short portions of tRNAs, although they are too short to be full-size functional tRNAs. Other unknown NA_RNAs share sequence similarity with a small portion of an annotated gene but they are too short to form a functional copy of this gene. For example, cluster_273 displayed similarity to the 3′ third of the U12 minor spliceosomal RNA gene and cluster_739 displayed similarity to the 5′ half of the SNORA33 H/ACA snoRNA gene. Interestingly, extension of these fragment sequences shows poor sequence similarity with the reference RNA. Fourteen other RNA in the group did not share sequence similarity with any known gene but were in the size range of miRNAs or piRNAs , a component of the NoRC chromatin remodeling complex . These NTogether the results presented here indicate that the annotation of the human genome is far from complete and underscore the value of direct analysis of RNA-Seq alignment profiles. Clearly, the number of non-annotated genes is not limited to the 111 genes identified here since we used stringent filters on our TGIRT-seq datasets, non-exhaustive sequencing techniques and did not consider sporadically expressed low abundance RNA. Deeper sequencing in different tissues and cell lines, and relaxed filters will uncover many additional yet to be annotated genes. The annotation-independent analysis used in this study might also be of value for identifying biomarkers expressed only in diseased tissues. These types of biomarkers by definition would not be annotated in normal genomes.https://www.ncbi.nlm.nih.gov/geo/) under the accession number GSE126797.Breast, prostate and ovarian as well as DKC1 depleted and lipofectamine SKOV3ip1 fragmented ribodepleted TGIRT-Seq datasets were deposited to the NCBI Gene Expression Omnibus (GEO; gkaa028_Supplemental_FilesClick here for additional data file.


Anaplasma phagocytophilum (Foggie) and a high diversity of Wolbachia strains were detected in quill mites via targeted PCR screens. Here, we use an unbiased 16S rRNA gene amplicon sequencing approach to determine other bacteria that potentially impact quill mite biology.The microbiome is an integral component of many animal species, potentially affecting behavior, physiology, and other biological properties. Despite this importance, bacterial communities remain vastly understudied in many groups of invertebrates, including mites. Quill mites (Acariformes: Syringophilidae) are a poorly known group of permanent bird ectoparasites that occupy quills of feathers and feed on bird subcutaneous tissue and fluids. Most of the known species have strongly female‐biased sex ratio, and it was hypothesized that this is caused by endosymbiotic bacteria. Previously, Wolbachia, we found Spiroplasma as potential symbiont of quill mites. Consistently, high Spiroplasma titers were only found in individuals of two mite species associated with finches of the genus Carduelis, suggesting a history of horizontal transfers of Spiroplasma via the bird host. Furthermore, there was evidence for Spiroplasma negatively affecting Wolbachia titers. We found no evidence for the previously reported Anaplasma in quill mites, but detected sequences of high similarity to the potential pathogens Brucella and Bartonella at low abundances. Other amplicon sequence variants (ASVs) could be assigned to a diverse number of bacterial taxa, including several that were previously isolated from bird skin. Further, many frequently found ASVs were assigned to taxa that show a very broad distribution with no strong prior evidence for symbiotic association with animals. We interpret these findings as evidence for a scarcity of resident microbial associates (other than inherited symbionts) in quill mites.We performed 16S rRNA gene amplicon sequencing of 126 quill mite individuals from eleven species parasitizing twelve species (four families) of passeriform birds. In addition to  We found a diverse, but relatively uniform set of bacterial taxa within quill mites that include arthropod endosymbionts, potential pathogens, and bird associated bacteria. The importance of most of these microbes for quill mite biology is unclear, but abundances and distribution patterns suggest that Spiroplasma and Wolbachia are the most important quill mite associates. These obligatory bird ectoparasites live and reproduce inside the quills of feathers where they feed on subcutaneous fluids . Quill mite dispersion has been observed on the same individual (from infected to uninfected feathers), between individuals of the same species  and occasionally by transfer between gregarious bird species Casto, , 1971. TWolbachia and Spiroplasma are among the most commonly associated taxa with quill mites. Other taxa include bacteria that were previously found in association with arthropods and bacteria with a very broad distribution. Strikingly, neither quill mite taxonomy nor bird host taxonomy significantly influences bacterial composition in quill mites. Furthermore, we find that despite the detection of Bartonella and Brucella, quill mites do not seem to be major pathogen vectors in birds.To address these questions, we here assess the bacterial composition of 126 quill mite individuals encompassing eleven species with a more unbiased 16S rRNA gene amplicon sequencing approach. We find that the symbionts 22.1A summary of collected quill mite species and their bird hosts can be found in Table 2.2The V4 hypervariable region of 16S rRNA gene was amplified using PCR primers V4F (GATCAGCAGCCGCGGTAATA) (developed in this study) and V4R (GGACTACCAGGGTATCTAA)  were determined with Kruskal–Wallis rank sum tests, and p‐values were adjusted to these multiple comparisons to control for the false discovery rate . After processing of reads , 912 ASVs were retained. Among the most abundant bacterial genera found in quill mites were Wolbachia positive and negative a1 samples could be observed . Furthermore, six bacterial families were found to be differentially abundant between quill mite species with a Kruskal–Wallis test . None of these taxa seemed dominant in any sampled group (based on mite or bird taxonomy), and the 20 most abundant families made up similar proportions of the total ASVs across samples  harbor seven core gut microbial taxa, five of which are present in other corbiculate bees, and two that are not found anywhere else  and revealed that feathers of these birds harbor bacteria commonly occurring in the soil and phyllosphere , as well as potential plant pathogens  , human skin bacteria (Corynebacterium), or ubiquitous taxa with no strong evidence for symbiotic associations with arthropods . These taxa might constitute true associates, but we cannot exclude the possibility that they originate from contaminating sources.It is important to consider that contaminants from reagents and kits may significantly impact microbiome composition estimates, especially when using low biomass samples such as quill mites  resident symbionts of quill mites, (b) environmentally acquired, transient bacteria, or (c) contaminants from reagents and materials. Each of these options comes with a number of assumptions that can be tested with our data.In summary, we found a diverse range of bacteria associated with quill mites. The lack of differentiation between different mite species or between species collected from different bird hosts leads us to conclude that there are no strong associations with typical gut bacteria as observed in other arthropods. However, we cannot exclude that we missed such potential associates due to the limited amount of DNA that can be extracted from the minute hosts.4.2Brucella and Bartonella. Brucella is the agent of brucellosis, which is considered to be the most widespread zoonotic infection  suggests its finding is of potential importance in understanding this pathogen's dynamics.Due to their ectoparasitic life style with occasional host switching, quill mites have the potential to transmit bacteria between their hosts. Here, we detected two pathogenic microbes that might be important in that respect: e Young, . HoweverBartonella are gram‐negative bacteria that are typically transmitted by blood‐sucking arthropods and are infectious in mammalian hosts (Billeter, Levy, Chomel, & Breitschwerdt, Bartonella incidence in birds (Ebani, Bertelloni, & Mani, Bartonella spp. is broader than previously reported and here we expand the list of potential sources for this zoonotic infection. However, Bartonellaceae can be symbiotic in other hosts, such as honey bees and ants (Segers, Kešnerová, Kosoy, & Engel, Bartonella‐like symbionts were recently found in a number astigmatid mites (Kopecký, Nesvorná, & Hubert, Bartonella detected here might be quill mite symbionts, rather than pathogens. With our data, it is not possible to rule out either possibility.Spiroplasma and Wolbachia in quill mites. Both of these are common across a range of arthropod species (Anbutsu & Fukatsu, Spiroplasma was so far not reported from quill mites, Wolbachia was previously detected and our findings confirm that this is a common symbiont of quill mites (Glowska et al., Wolbachia is most abundant in mites parasitizing birds of the genera Turdus, Erithacus, and Fringilla, whereas Spiroplasma is most strongly associated with mites parasitizing Carduelis. One reason for this may be that some taxa are more susceptible than others for endosymbiosis with certain bacteria, and this phylogenetic effect has been reported for other host taxa as well (Gerth, Saeed, White, & Bleidorn, Spiroplasma abundances were only found in two investigated mite species that are specialized parasites of two bird species of the genus Carduelis (Figure Spiroplasma titers, which may be a result of genuine low titer infections or stem from contamination via simultaneously processed libraries (e.g., through index hopping, Kircher, Sawyer, & Meyer, Spiroplasma titers, the bird host phylogeny seems to be the best predictor for a Spiroplasma infection. One interpretation of this pattern is a history of horizontal transmission of the symbiont via the bird hosts. Horizontal transfers have been inferred from phylogenetic data for Wolbachia and Spiroplasma previously (Gerth, Röthe, & Bleidorn, Finally, we found the symbionts s Figure . A numbeSpiroplasma presence leads to reduced Wolbachia titers, although this is based on a small sample size for samples that are both Wolbachia and Spiroplasma positive (N = 11, Figure Spiroplasma and Wolbachia titers seem to be positively correlated (Figure Drosophila, for example, Spiroplasma seem to limit the proliferation of Wolbachia (Goto, Anbutsu, & Fukatsu, Spiroplasma and Wolbachia titers in very few of the 126 investigated quill mites (Figure Interestingly, we found that d Figure c. It is d Figure , and thus Figure c.5Spiroplasma and Wolbachia are the most important quill mite associates.We found a diverse, but relatively uniform set of bacterial taxa within quill mites that includes arthropod endosymbionts, pathogens, and bird‐associated bacteria. The importance of most of these microbes for quill mite biology is unclear, but abundances and distribution patterns suggest that None declared.EG, ZF, and MG involved in study conception and design. EG involved in collecting and identification of the mite material, funding acquisition, project administration, supervision, writing review and editing and contributed resources. ZF and MD involved in acquisition of data. MG analyzed the data. EG and MG interpreted the data and wrote the paper.None required.


ADAR mediated RNA editing is widespread in humans in which millions of A-to-I changes modify thousands of primary transcripts. RNA editing has pivotal roles in the regulation of gene expression or modulation of the innate immune response or functioning of several neurotransmitter receptors. Massive transcriptome sequencing has fostered the research in this field. Nonetheless, different aspects of the RNA editing biology are still unknown and need to be elucidated. To support the study of A-to-I RNA editing we have updated our REDIportal catalogue raising its content to about 16 millions of events detected in 9642 human RNAseq samples from the GTEx project by using a dedicated pipeline based on the HPC version of the REDItools software. REDIportal now allows searches at sample level, provides overviews of RNA editing profiles per each RNAseq experiment, implements a Gene View module to look at individual events in their genic context and hosts the CLAIRE database. Starting from this novel version, REDIportal will start collecting non-human RNA editing changes for comparative genomics investigations. The database is freely available at  RNA editing refers to a group of non-transient epitranscriptome modifications altering primary RNA transcripts through the insertion/deletion of specific nucleotides or base substitutions . The deaSince Inosine mimics the properties of guanosine (G), it is commonly recognised as G by transcription and translation machineries (other than sequencing enzymes). As a consequence, A-to-I RNA editing can increase the transcriptome and proteome diversity, generate or destroy splice sites, alter codon identity or base-pairing interactions within higher-order RNA structures. Several evidences indicate that ADAR mediated RNA editing plays pivotal functional roles, tuning gene expression ,6 or modRNA editing has a great and promising therapeutic potential . Indeed,https://gtexportal.org/home/). Currently, REDIportal is the unique and comprehensive resource for human A-to-I editing. Indeed, other competing databases such as RADAR  systems  or the Recoding Editing Index (REI) or the expression of ADAR genes, that are expected to facilitate the RNA editing investigations. Now users can browse and visualize editing sites through our embedded and updated JBrowse or explore them in their genic context by our novel Gene View functionality.With this release, REDIportal officially starts collecting RNA editing in non-human organisms, providing annotations for 107 094 A-to-I events from mouse nascent RNAseq data .http://srv00.recas.ba.infn.it/atlas/claire.html)  database (re.html) , making https://www.ncbi.nlm.nih.gov/gap/) (http://ncbi.github.io/sra-tools/). In all cases in which WGS data (DNAseq) were available, they were downloaded from dbGaP in sra format and converted in raw fastq by fastq-dump. RNAseq reads were aligned onto the human genome (hg19/GRCh37) using STAR (version 2.5) (We downloaded 9642 GTEx RNAseq experiments from the database of Genotypes and Phenotypes (dbGaP) (ov/gap/)  under thion 2.5)  providinion 2.5)   . DNAseq ion 2.5)   . All aliion 2.5) .de novo detection of RNA editing events was performed at the CINECA HPC Data Center  consuming ∼30 millions of CPU hours running an optimized version of our REDItools package whose algorithm scales almost linearly with the number of available cores  G ANNOVAR , RefSeq  ANNOVAR  and UCSC ANNOVAR  storing Hg19 and hg38 annotated positions were finally loaded in the MySQL TABLE1 of REDIportal Figure .All positions were also used to interrogate 9642 RNAseq samples to extract RNA editing levels as well as the number of reads supporting As and Gs per site. These values were collected in the MySQL TABLE2 of REDIportal Figure .https://github.com/a2iEditing/RNAEditingIndexer) .For each aligned RNAseq experiment, FeatureCounts  a softw. The AEIIndexer) , while t .Finally, a custom script was used to collect main statistics for each GTEx sample and generate the MySQL TABLE3 of REDIportal Figure .As in the previous release, REDIportal allocates all 15 683 855 sites in two main MySQL tables. TABLE1 includes individual sites and their annotations, while TABLE2 stores RNA editing levels per RNAseq, tissue and body site. Statistics and RNA editing metrics per sample are instead stored in the novel TABLE3 MySQL table. It comprises the RNAseq run accession (according to dbGAP), the DNAseq run accession (if available), the organism name, the data source (GTEx or SRA), the body site and its status , the tissue type (bulk or single cell), the number of detected A-to-I events as well as the number of hyper-editing sites, the AEI and REI indices, and the expression of ADAR genes . TABLE3 contains also further statistics such as the distribution of editing sites over the three ALU, REP NON ALU and NON REP groups, the fraction of synonymous or non-synonymous events in protein coding regions, the distribution of editing sites across the gene structure, the fraction of edited and hyper-edited positions, and the distribution of RNA editing levels. In addition, it includes the fraction of edited and unedited genes, and the distribution of all variants detected per sample as a sort of quality check of the prediction.REDIportal provides RNA editing details for 9642 RNAseq samples from 549 individuals across 31 tissues and 54 body sites. By means of the HPC REDItools based pipeline, each sample contains 60 873 edited events on average. The mean number of A-to-I events in hyper-edited reads per sample, instead, is 26 942. The highest number of events was detected in the cerebellar hemisphere and cerebellum . By conthttps://pypi.python.org/pypi/MySQL-python/1.2.5) and mxTextTools (http://www.egenix.com/products/python/mxBase/mxTextTools/) as external modules for MySQL connections and high-performance text manipulation, respectively.The novel REDIportal inherits the layout from the previous version and all web pages are developed in Bootstrap, CSS and JavaScript. Server side operations to query MySQL tables and retrieve data are performed in Python (v2.7) and require MySQLdb  ‘GeAdditionally, the ‘RNA Editing Indices’ panel as well as the ‘Transcriptome Coverage’ panel provide external buttons that enable the recovery of further details. The button ‘REI details’ allows the browsing and filtering of known RNA editing recoding events Figure  and someRNA editing sites can also be retrieved by a dedicated Application Programming Interface (API). Differently from the web-browsing approach, API does not leverage as much bandwidth and can also be interfaced with third-party programs able to manage outputs in JSON format. API outputs can be displayed in the web-browser or in the commands shell.RNA editing is emerging as a relevant epitranscriptome phenomenon involved in a variety of cellular functions. Although the massive transcriptome sequencing has accelerated the research in this field, different aspects of the RNA editing biology need to be elucidated. To facilitate the investigation of A-to-I RNA editing we have updated our just rich REDIportal catalogue distributing to the scientific community >15 millions of A-to-I changes in 9642 human RNAseq samples from the GTEx project. In contrast with the previous release in which RNA editing events were called from a known list of A-to-I changes from only six tissues (18 RNAseq experiments) , the verThe current REDIportal release allows searches at sample level, provides overviews of RNA editing profiles per each RNAseq experiment, implements a Gene View module to look at individual events in their genic context, hosts the CLAIRE resource  and collAs a comprehensive catalogue of A-to-I RNA editing, REDIportal is in active development and thanks to two PRACE European projects for computing resources, its short-term goal will be the inclusion of A-to-I sites from all TCGA samples as well as events from RNAseq data produced in consortia for neurological and neurodegenerative disorders (CommonMind and PsychEncode) or RNAseq experiments from the Nonhuman Primate Reference Transcriptome Resource (NHPRTR) . Finallyhttp://srv00.recas.ba.infn.it/. REDItools is an open source software available in the GitHub repository (https://github.com/BioinfoUNIBA/REDItools).REDIportal is an open source database available through the web page gkaa916_Supplemental_FileClick here for additional data file.


Accurate RNA secondary structure information is the cornerstone of gene function research and RNA tertiary structure prediction. However, most traditional RNA secondary structure prediction algorithms are based on the dynamic programming (DP) algorithm, according to the minimum free energy theory, with both hard and soft constraints. The accuracy is particularly dependent on the accuracy of soft constraints . With the elongation of the RNA sequence, the time complexity of DP-based algorithms will increase geometrically, as a result, they are not good at coping with relatively long sequences. Furthermore, due to the complexity of the pseudoknots structure, the secondary structure prediction method, based on traditional algorithms, has great defects which cannot predict the secondary structure with pseudoknots well. Therefore, few algorithms have been available for pseudoknots prediction in the past. The ATTfold algorithm proposed in this article is a deep learning algorithm based on an attention mechanism. It analyzes the global information of the RNA sequence via the characteristics of the attention mechanism, focuses on the correlation between paired bases, and solves the problem of long sequence prediction. Moreover, this algorithm also extracts the effective multi-dimensional features from a great number of RNA sequences and structure information, by combining the exclusive hard constraints of RNA secondary structure. Hence, it accurately determines the pairing position of each base, and obtains the real and effective RNA secondary structure, including pseudoknots. Finally, after training the ATTfold algorithm model through tens of thousands of RNA sequences and their real secondary structures, this algorithm was compared with four classic RNA secondary structure prediction algorithms. The results show that our algorithm significantly outperforms others and more accurately showed the secondary structure of RNA. As the data in RNA sequence databases increase, our deep learning-based algorithm will have superior performance. In the future, this kind of algorithm will be more indispensable. RNA is an indispensable biopolymer that plays diverse biological roles in regulating translation , where the horizontal coordinates i and vertical coordinates j, indicated the position of the base in the RNA sequence. Using this matrix, the actual base pairing condition of various RNA secondary structures can be fully displayed. Furthermore, this matrix can also be used to predict the RNA secondary structures with or without pseudoknots for the various problems of the traditional RNA secondary structure prediction methods mentioned in this article. Subsequently, a more sophisticated model called ATTfold was proposed to solve the problems.ATTfold was a deep learning model constructed by combining the attention mechanism with the hard constraints of the RNA secondary structure. Its overall structure was divided into three parts, namely, Encoder-Decoder-Constrain. The specific process is shown in Therefore, the RNA secondary structure formed by this bases pairing scoring matrix was not a real and effective structure. To obtain a true and effective RNA secondary structure from the output of this network, the constraints that the RNA sequence must comply with when forming its secondary structure should be added, which are also called hard constraints. Lastly, after the bases pairing scoring matrix was hard-constrained, the corresponding bases pairing matrix was obtained. Each position in the matrix contained only two numbers 0 or 1, where 0 meant that i, j bases were not paired, whereas 1 meant that they were paired.The ATTfold model was unable to directly recognize and calculate characters  of the RNA sequence or the pairing structure. Therefore, they should be encoded and represented, respectively. The RNA sequence character encoding is presented in To obtain the structure information of the pseudoknot and to facilitate our calculation of the loss function and model accuracy in deep learning, the corresponding symmetric matrix (N*N) of the known RNA sequence structure was encoded, since encoding the dot bracket representation of the traditional method no longer met our needs. The horizontal coordinates and vertical coordinates indicated the absolute position of each base in the RNA sequence. Each row and column, which represented the specific position of the i base and j base pairing, was at most 1, while the rest were 0. In this way, RNA secondary structures with and without pseudoknots were well displayed at the same time.The entire Encoder-Decoder network structure is displayed in Regarding the Decoder, its input was a symmetric matrix formed by processing the output of the Encoder and Position-Embedding. The symmetric matrix passed through three different Convolutional Neural Networks, finally forming the symmetric bases pairing scoring matrices (B*N*N). Similarly, the data should still be normalized in the process of processing. The matrix (similar image) data were processed, so Batch-Normalization (BN) . Later, all the values at the top, bottom, left, and right were set to 0, for which the distance to the diagonal was set to 3, while the remaining values were set to 1. In this way, the condition of constraint 2 was satisfied, and the specific form was defined as Constraint_2 in For constraint 3, even though the above two constraints were performed on the bases pairing scoring matrix and the sparse of matrix was made on a large scale, there was still a one-to-many bases pairing situation. In this case, the DP algorithm was not suitable, even though it was able to obtain the optimal solution, since this article focused on the prediction problem of long sequences. To facilitate the calculation, constraint 3 was made through the following simplified operations:Sn represented the number of pairs in each base, and the meaning in the matrix indicated the sum of the values of each row or column. From constraint 3, Sn ≤ 1 was observed. Therefore, it was transformed into the above formula, which well-expressed the effect of constraint 3. So far, the three hard constraints have been successfully transformed into a single constraint problem. For the optimization algorithm for a single constraint problem, the Lagrange Multiplier Method was utilized to solve this problem perfectly  was the main part of the entire function, and the degree of fit between the predicted structure and the real structure was obtained. Furthermore, part 2 was constraint 3, while part 3 was the L1 regularization penalty. The goal of this article was to find the maximum fit between the predicted and the real structures, and it was inconvenient for us to optimize it. Therefore, to optimize the maximum fit using the gradient descent method, formula (6) was used as our final objective function. The optimization process is shown in The secondary structure formed by the prediction matrix obtained for the first time is far from the real secondary structure. With a large amount of labeled data, we also need to optimize each parameter in the ATTfold model through the backpropagation algorithm. Finally, the network trained through the training set, with the help of the validation set, finds a set of parameters for the optimal result as the parameters of the final model. Lastly, the final prediction matrix obtained by ATTfold can be closer to the real secondary structure. During this process, in order to correspond to the final prediction matrix and to better optimize the algorithm, we also converted the real RNA secondary structure into the corresponding matrix form as the final label.As introduced above, a database including 37,149 RNA sequences and their real structures was used as the experimental database in this article. It contains eight RNA families, including 5S_rRNA, tRNA, group_1_intron, 16S_rRNA, tmRNA, SRP, RNaseP, and telomerase. Through data set analysis, the length distribution of the RNA sequence was particularly uneven, due to the wide variety of RNA families contained in the data set. Typically, 16S_rRNA was the family with the greatest impact, but its RNA sequence length was too long, along with a relatively large amount of data, eventually leading to the above problems. As shown in In this chapter, the prediction results of the ATTfold method were elaborated and compared with four traditional classic algorithms [namely, RNAfold  were used, namely, sensitivity (SEN) and positive predictive value (PPV). SEN referred to the number of true paired bases predicted by ATTfold, while PPV indicated the number of true paired bases predicted by ATTfold. To have a more comprehensive evaluation, the new performance matrix composed of SEN and PPV was quoted, and named F1-score (Yonemoto et al., To show the fitting effect of ATTfold on the RNA secondary structures of different families more clearly, each family in the test set was evaluated separately. In the end, four families had better scoring effects–5S_rRNA, tRNA, Telomerase, and tmRNA. The fitting effect of ATTfold on these four families is presented in As observed in 5S_rRNA, the scores of the four traditional algorithms increased by 22% on average, while the score of each algorithm increased by 15% on average in tRNA, and these four traditional methods reached a relatively high score. Therefore, the traditional method is mainly used to predict whether the relative position base is matched but does not accurately predict the exact base that it is matched with. Accordingly, the secondary structure formed by the paired base predicted in this way is quite different from the actual secondary structure. In contrast, our ATTfold model better predicted the precise position of base-to-base pairing. Similarly, the RNA secondary structure closer to the real state was also obtained.Apart from the above two short RNA sequence families, The prediction of the RNA secondary structure has gradually fallen into a bottleneck in traditional algorithm research over the past 40 years. In addition, few methods can be used to predict the RNA secondary structure with pseudoknots at present due to the complexity of traditional algorithms. With the rapid development of deep learning and machine learning, the bottleneck problems can be solved one after another based on the corresponding traditional optimization algorithms. The problem of the RNA secondary structure prediction with pseudoknots is no exception. In this article, a new type of deep learning prediction model named ATTfold was proposed based on the attention mechanism to predict the RNA secondary structure with pseudoknots. Our method was mainly divided into three parts, namely, Encoder-Decoder-Constrain. The Encoder part encoded the RNA sequence through a transformer model on the basis of the attention mechanism to obtain the hidden features. The Decoder part decoded the hidden features through a convolutional neural network to form an unconstrained base pairing score matrix. The Constrain part constrained the bases pairing scoring matrix, which was a condition with which the RNA sequence must comply with to form a secondary structure and was referred to as the hard constraint. In the end, the real and effective RNA secondary structure was predicted.Comparatively speaking, the ATTfold method exhibited the following innovative points. First, it used the transformer model with a dominant position in the NLP direction to extract the hidden features of the RNA sequence. Furthermore, combined with the convolutional neural network, the two-dimensional matrix was decoded to form a pairing matrix. Second, the hard constraints were simplified, and the algorithm was optimized through the Lagrange Multiplier Method. Compared with the DP algorithm, the computational complexity of the algorithm was greatly reduced. Finally, the most important thing was how to predict RNA sequences with pseudoknots. Different from the traditional dot bracket representation method, whose one-dimensional structure only expresses the structure without pseudoknots, more different types of brackets should be added to express the structure with pseudoknots (Wang et al., According to the above results, under the training of tens of thousands of data, ATTfold was greatly improved compared with the traditional algorithms, but it also revealed some problems. When predicting short RNA sequences without pseudoknots, the average score was above 90%. For long RNA sequences with pseudoknots, the effect of our algorithm was inferior to short sequence prediction, even though it was superior to the traditional algorithms. Two causes may be responsible for this result. First, the most important issue for deep learning is the volume of data. Short RNA sequences account for over 80% of the total RNA data volume, and a long time is required to train against these short RNA sequences when training the network; by contrast, the proportion of long RNA sequences is very small. Second, the secondary structure of the long RNA sequences is more complicated, and it is more difficult for the models to extract hidden features. Consequently, more data are needed to fit the long RNA sequences well.Collectively, findings in this study show that the new deep learning prediction model ATTfold, constructed based on the attention mechanism, has been qualitatively improved compared with the traditional algorithm, which can learn similar hidden features through RNA sequences of different families thereby predicting the secondary structure of different RNA families. Moreover, with the discovery of more long RNA sequences and their base pairing structures in the future, the overall performance of the ATTfold algorithm for multiple RNA families will be further improved. However, as far as the current data volume is concerned, although our algorithm has greatly improved the secondary structure prediction of long RNA sequences based on traditional algorithms, its accuracy is still relatively low. Therefore, when there are not enough long RNA sequences with known structures, more effective methods are warranted for prediction.The original contributions presented in the study are included in the article/supplementary materials, further inquiries can be directed to the corresponding author/s.YL and LD conceived and directed the project. YW designed the study and collected the raw data. YW and YG wrote the article and coded the procedure. HZ and SW revised the article critically for important intellectual content. HZ and ZL reviewed the data and the article. All the authors helped in drafting and reviewing the article before approving it for publication.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.


Penicillium digitatum is a widespread pathogen among Rutaceae species that causes severe fruit decay symptoms on infected citrus fruit (known as citrus green mold). The employment of fungicides can effectively control the citrus green mold, significantly reducing agricultural economic loss. In this study, we found that the X33 antifungal extract produced by Streptomyces lavendulae strain X33 inhibited the hyphae polarization of P. digitatum. Additionally, physiological and proteomic analysis strategies were applied to explore the inhibitory mechanism of the X33 antifungal extract of the S. lavendulae strain X33 on the mycelial growth of P. digitatum. A total of 277 differentially expressed proteins, consisting of 207 upregulated and 70 downregulated, were identified from the comparative proteomics analysis. The results indicated that the X33 antifungal extract induced mitochondrial membrane dysfunction and cellular integrity impairment, which can affect energy metabolism, oxidative stress, and transmembrane transport. The improved alkaline phosphatase activity and extracellular conductivity, increased H2O2 and malondialdehyde contents, and inhibition of energy, amino acid, and sugar metabolism indicated that the oxidative stress of P. digitatum is induced by the X33 antifungal extract. These findings provided insight into the antifungal mechanism of the X33 antifungal extract against P. digitatum by suggesting that it may be an effective fungicide for controlling citrus postharvest green mold. Penicillium digitatum, Penicillium italicum, and Geotrichum citri-aurantii during the postharvest storage phase , which exhibited powerful biocontrol effects on P. digitatum by secreting an antifungal substance similar to ε-polylysine  in P. digitatum stimulated by active substances via iTRAQ approach and explore the intrinsic molecular mechanism of the X33 antifungal extract on P. digitatum.In practice, protein expression changes in response to the X33 antifungal extract of pathogens are crucial for explaining the inhibition mechanism of the X33 antifungal extract. Recently, numerous techniques with aim to explore the physiological mechanism and identifying the expression of related proteins in response to environment stress, including two-dimensional polyacrylamide gel electrophoresis , isobariS. lavendulae strain X33   medium  at 28°C. Spores was diluted to achieve a concentration of approximately 107 spores/mL with a blood cell counter. Under sterile conditions, 1 mL of spore suspension was transformed into 50 mL of PDA medium and poured into a plate for condensation, and an Oxford cup was placed in the center of the medium. Then, 200 μL  of each X33 antifungal extract was added to each cup, which were subsequently cultivated at 28°C for 48 h. After incubation, the transparent zone across the Oxford cup was caused by the antifungal effect of the X33 metabolite. The diameter of the zone of inhibition  was determined using calipers in triplicate and stated as mean ± standard error.X33 antifungal extract is a mixture (the main component is ε-three poly-L-lysine) that isolated from fermentation supernatant of 2013163) . The acteviously . P. digip < 0.05 and fold-changes of >1.2 or <0.83. The protein functional annotation and classification was performed by using the Blast2GO program and Kyoto Encyclopedia of Genes and Genomes (KEGG) database1.Fresh spores were cultured under shaking in potato dextrose broth (PDB) for 3 days at 28°C and 160 rpm. Diverse content (0 and 1.2 mg/mL) of the X33 antifungal extracts were devoted into the fermentation cultures at 48 h. After vacuum filtration, the hyphae were washed with deionized water and collected. The harvested mycelia were quickly frozen in liquid nitrogen for iTRAQ analysis. Samples preparation was conducted twice independently with three biological repeats as described previously . ProteinP. digitatum was estimated according to previous study  Co., Ltd., Shanghai, China] and an AKP kit  according to the manufacturer’s instructions  of the X33 antifungal extract were added to the culture mediums. After centrifuging at 12,000 rpm for 10 min, the mycelium was washed with sterile water and dried on filter paper for measuring the ergosterol contents. The determination of ergosterol was measured using the way described previously , according to the method described previously  and malondialdehyde (MDA) contents. The MDA content was estimated by using thiobarbituric acid approach as described previously , according to the manufacturer’s instructions  on P. digitatum were in the ranges of 30.82 ± 0.90, 35.94 ± 0.53, and 40.01 ± 1.02 mm, respectively  analysis and KEGG enrichments were processed that systematic evaluated the biological functions and meaning of these proteins in life activities. As a result, 235 proteins were sub-categorized into 95 hierarchically structured GO classifications. Additionally, 152 proteins identified for differential metabolic pathways by KEGG were sub-categorized into 61 classifications.P. digitatum induced by X33 antifungal extract treatment may be involved in diverse metabolic and cellular processes.According to the GO enrichment analyses of DEPs, majority of upregulated DEPs were contributed to transmembrane transporter activities participated in the molecular function category. Categorization of the identified proteins based on cellular components implied that the most abundant proteins were part of the intrinsic components of membranes, integral components of membranes, and membrane parts. A certain amount of proteins was belonging to the biological process. The most abundant subclasses of proteins were transmembrane, organic anion, and ion transmembrane transporters . For thep < 0.05; p < 0.05), most of which were involved in the “pentose and glucuronate interconversions (map00040)” pathway. Pathways related to “alpha-Linolenic acid metabolism (map00592),” “fatty acid degradation (map00071),” “biosynthesis of unsaturated fatty acids (map01040),” “Peroxisome (map04146),” “propanoate metabolism (map00640),” and “valine, leucine and isoleucine degradation (map00280)” were also significantly enriched.Result from KEGG pathway enrichment analysis indicated that upregulated proteins were impressively enriched in five pathways . A stable level of extracellular conductivity was observed in the control, while the conductivity impressive generally improved with the prolonged treatment duration. Meanwhile, a significant difference in the extracellular conductivity was observed among the treatment groups. It can be concluded that the addition of the X33 antifungal extract improved the extracellular conductivity of P. digitatum cells.Results from quantitative proteomics analysis indicated that the X33 antifungal extract inhibited the cell growth of eability . As illuP. digitatum suspensions with treatment  of the X33 antifungal extract rapidly increased in the first hour of exposure, followed by a moderate ascending trend in the subsequent stage (p < 0.05) than in the control (0.033 U/g prot). The increased AKP activity represents increased damage to the cell walls of P. digitatum.The cell wall is a flexible structure that offers protection to the cell and is, thus, essential for filamentous fungal permeability. AKP is an enzyme produced in the cytoplasm that permeates into the periplasmic space . Generalnt stage . After 8S. lavendulae strain X33 on ergosterol synthesis, the P. digitatum mycelium was treated with several concentrations  of the antifungal metabolite from S. lavendulae strain X33. By comparison with the control group, it can be acknowledged that improved concentration of the X33 antifungal extract would cause a significantly decline of ergosterol formation in P. digitatum mycelium  production and antioxidant defense decides the level of oxidative stress . H2O2 isng trend . When thS. lavendulae strain X33 on pyruvate biosynthesis, the P. digitatum hyphae were induced by diverse contents  of the X33 antifungal extract. As a result, the improving content of the X33 antifungal extract caused an obvious decline in pyruvate accumulation in P. digitatum (P. digitatum hyphae experienced a 41.41% reduction compared to the control group (2.75 ± 0.18 mg/g FW). The reduction in pyruvate indicated the metabolic disturbance induced by the X33 antifungal extract treatment.Pyruvate is an intermediate involved in the basic metabolism of the whole organism and plays an important role in the metabolic relationships of sugar, fat, and amino acids. To evaluate the effect of igitatum . When trStreptomyces spp.). Previously, we screened a S. lavendulae strain that inhibited the growth of P. digitatum and isolated an active X33 antifungal extract. The present study highlights the protein expression and physiological analysis of P. digitatum to investigate the molecular mechanism of the X33 antifungal substance.Green mold decay is a serious disease in fruit, particularly citrus fruits . RecentlP. digitatum increased in a dose-dependent manner, indicating significant inhibition of the growth of P. digitatum by the X33 antifungal extract. In addition to the antifungal agents thymol . The current knowledge suggested that the ZOI of s thymol , chitosas thymol , clove es thymol , and tras thymol , this st survive . If causP. digitatum between the X33 antifungal extract treatment and control groups. This may be related to the modulations in membrane permeability regulated by the synthesis of ergosterol, which is a lipid molecule widely listed in the cell membrane and regulates membrane-dependent properties. In addition, reducing the accumulation of ergosterol is frequently lead to the modulations in cell wall. Induced by X33 antifungal extract, ergosterol is unable to maintain cell wall integrity for P. digitatum, which caused leakage of cell compounds and, ultimately, hyphae growth restriction or cell death. The changes in mycelial morphology can be connected to the internalization of the X33 antifungal extract into the cell, which interferes with metabolic processes by destroying proteins and enzymes derived in the low viability spores.The subcellular level analyses showed a clear difference in the hyphae of 731318940), aldose 1-epimerase (gi| 859269367), and phosphopyruvate hydratase (gi| 901823486), are upregulated by X33 antifungal extract treatment  (gi| reatment . PGK is reatment .859265321) and pyruvate carboxylase (gi| 859268618), were significantly downregulated in P. digitatum cells treated with the X33 antifungal extract , ATP synthase subunit C (gi | 121713168), ATP synthase subunit H (gi| 859270273), nicotinamide adenine dinucleotide (NADH) dehydrogenase 12 kda subunit (gi| 358373893), NADH dehydrogenase (gi| 525588153), NADH-ubiquinone oxidoreductase 20.9 kDa subunit (gi| 952549797), and acyl carrier protein (gi| 995934404)  . In a pr5934404)  found thsubunits . MoreoveP. digitatum scavenges excess ROS through ROS scavenging enzymes . Interestingly, in the present study, SOD [Cu-Zn] (gi| 145237624) and peroxidase (POD) (gi| 859264108) were downregulated, while catalase (CAT) (gi| 584410839) was upregulated in cells treated with the X33 antifungal extract  and C-4 methylsterol oxidase (gi| 859268431) were upregulated, which are involved in inositol phosphate metabolism and fatty acid metabolism (145258080), 3-β-hydroxysteroid dehydrogenase (gi| 859260572), and acyl-CoA dehydrogenase (gi| 859263266) were downregulated by 1.07- to 1.72-fold. These results imply that the X33 antifungal extract probably affects the integrity of plasma membranes of P. digitatum cells through disturbing the biosynthesis of membrane lipid biosynthesis.Accumulation of ROS is able to affects the normal cellular and membrane functions through oxidizing lipids, nucleic acids, proteins, and carbohydrates . Phosphatabolism . In contTo further verify this point, the physiological indexes closely involving in the membrane structure and function were further assayed, such as the extracellular conductivity values, the lipid peroxidation, and the ergosterol accumulation. The enhancement of MDA, a key marker involved in lipid peroxidation, also supported this viewpoint, as its levels significantly increased after the addition of the X33 antifungal extract. The significant increase in the extracellular conductivity values further confirmed the hypothesis that addition of X33 antifungal extract damages membrane integrity by disturbing the biosynthesis of membrane lipid biosynthesis. Based on the results of the above DEPs levels and physiological parameters, membrane structure may be the main target of X33 antifungal extract.859266290), endoglucanase (gi| 859262625), 1,3-β-glucanosyltransferase (gi| 859262943), and β-glucosidase (gi| 859260053), which are important cell wall-related proteins, were overexpressed. A previous study reported a similar phenomenon wherein limonene treatment caused a compensatory response to cell wall damage through the overexpression of several genes, which participate in the cell wall integrity signaling pathway.The cell wall is the first barrier of filamentous fungi resist environmental pressures and is crucial for maintaining the normal growth of fungal . In the p < 0.05; G. citri-aurantii (A significant increase in AKP activity induced by constant X33 antifungal extract exposure time aurantii . This fiP. digitatum after treatment with the X33 antifungal extract, which performs the novel concept that this extract disturbs the TCA cycle, glycolysis, oxidative phosphorylation, affects the contents of various cellular constitutes, and stimulates cell integrity damage, mitochondrial dysfunction, and oxidative stress of strain P. digitatum. These findings not only discovered underlying mechanism for action mode of the X33 antifungal extract on P. digitatum, but also provides impressive guidance for the investigation of novel fungicides to reduce the postharvest decay of fruits.This study presents a proteomics analysis of The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.S-HL conducted the experiments and wrote the manuscript. PL, EY, and XZ participated in the research. BZ and XW planned, analyzed, and interpreted the data, and wrote the manuscript. All authors contributed to the article and approved the submitted version.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.


Long non-coding RNAs (lncRNAs) are emerging as novel biomarkers for a variety of chronic conditions including autoimmune disease. PAXgene Blood RNA tubes are routinely used in clinical research and molecular diagnostic development to capture RNA profiles in peripheral whole blood. While the stability of mRNA expression profiles captured using PAXgene tubes has been documented previously, no previous work has determined the stability of lncRNA expression profiles observed in PAXgene tubes stored at − 80 °C. Here we sought to determine the effects on lncRNA expression profiles following − 80 °C storage of total RNA templates, cDNA synthesized using fresh or frozen total RNA template, and the impact of freeze–thaw cycles on both total RNA and cDNA obtained from PAXgene tubes.We find that storage of whole blood in PAXgene tubes, total RNA and cDNA for up to 1 year at − 80 °C or up to ten total RNA or cDNA freeze–thaw cycles do not significantly alter lncRNA expression profiles compared to baseline. As monthly expression profiles were determined, some month to month lncRNA expression variability was observed. However, all monthly observations fell within the 95% confidence interval calculated at baseline. Long non-coding RNAs (lncRNAs) play pivotal roles in gene regulation, protein synthesis, sex chromosome compensation and telomere maintenance –5. Work RNA was isolated from peripheral whole blood collected into PAXgene Blood RNA tubes  according to the manufacturer’s supplied protocol using an automated QIAcube system (Qiagen). RNA samples were further purified, concentrated, and eluted in RNase-free water using the RNEasy MinElute Cleanup Kit . cDNA synthesis was performed using the Superscript III First-Strand Synthesis System with oligo-dT as primer . cDNA samples were purified and eluted in elution buffer  using the QiaQuick PCR Purification Kit  prior to qRT-PCR measurements. Total RNA and cDNA were assessed for quantity and purity using a Nanodrop 1000 spectrophotometer (ThermoFisher) prior to storage at − 80 °C in 1.5 mL Eppendorf tubes. RNA integrity number (RIN) values were obtained using the RNA 6000 Nano Total RNA Kit and a Bioanalyzer 2100 instrument . All above-mentioned steps were performed according to the manufacturer’s protocols. Freeze–thaw studies were performed by thawing RNA samples and incubating the sample for 10 min at room temperature (21 °C) and re-freezing the sample at − 80 °C for 30 min.GAPDH using the Custom DNA oligos were synthesized  that target specific lncRNA and mRNA genomic loci  values as depicted in Additional file To further assess the quality of each RNA template, cDNA was synthesized from each of the RNA samples at baseline, after storage for 1 year or after a series of five or ten freeze–thaw cycles. cDNA synthesized from each of these RNA templates did not exhibit significant differences in lncRNA expression values Fig. a. As an Since the beginning of this century, the number of documented RNA classes, and variety of noncoding transcripts has grown significantly. In the mid-2000s, the FANTOM3 project identified ~ 35,000 non-coding transcripts from ~ 10,000 distinct loci . RecentlMCCC1-AS1 in our analysis, the monthly fluctuations observed among other lncRNAs resulted in overlapping 95% confidence intervals. Future studies seeking to develop novel assays based on mRNA or lncRNA expression values should examine expression differences over time, particularly for transcripts with lower overall expression values, to ensure that any technical variation or intrinsic stability does not adversely impact the accuracy, reproducibility or interpretability of assay results.We find that PAXgene tubes maintain the stability of lncRNA expression profiles when stored at − 80 °C for up to 1 year. We also find that storing total RNA or cDNA synthesized from fresh total RNA template or total RNA stored at − 80 °C for 1 year does not result in significant changes in lncRNA expression. While some month to month variation between datapoints is observed, we believe that these changes are inherent in the technical processing of samples and can disproportionately affect qRT-PCR targets of lower abundance. Additionally, while not directly examined in our work, previous studies have shown that expression measurements using shorter target amplicon lengths (70-250 bp) are less susceptible to variations in RNA integrity than larger PCR amplification products . With thWhile the characteristics of each respective target should be assessed individually, one limitation of this study was the relatively small number of lncRNA and mRNA targets evaluated. Three laboratory technicians performed PAXgene RNA isolations, synthesized cDNA, and performed gene expression studies. This was intentional to simulate multiple operators that could potentially process PAXgene tubes in a clinical setting. PCR primers used in this study were printed onto 384-well plates robotically. Additional liquid handling steps could be implemented to further minimize operator to operator technical variation.GAPDH throughout our investigation. While GAPDH is widely accepted as an appropriate housekeeping gene, depending upon the use case, incorporation of other housekeeping genes could further reduce operator to operator or monthly expression variability. While single RIN assessments were performed for each of the experimental conditions in Fig. lncRNA and mRNA expression was normalized to Additional file 1:Table S1. Genomic locations for qRT-PCR targets used in the study. Additional file 2:Figure S1. RNA sequencing data for annotated lncRNAs differentially expressed among healthy controls and MS patients.Additional file 3:Figure S2. Schematic representation of the lncRNA stability study.Additional file 4:Figure S3. Monthly lncRNA expression analysis over the course of 1 year.Additional file 5:Table S2. Summary of quality metrics across all total RNA samples isolated from PAXgene Blood RNA tubes.Additional file 6:Figure S4. Summary of total RNA yield from PAXgene Blood RNA tubes processed during the 1 year study.Additional file 7:Figure S5. Schematic representation of freeze-thaw experiments.Additional file 8:Figure S6. Digitally captured full size electropherogram of total RNA samples processed for RNA Integrity Number analysis.Additional file 9:Figure S7. Summary of lncRNA and mRNA expression values and representative amplification plots for mRNAs and lncRNAs.Additional file 10:Figure S8. Long term cDNA storage and freeze-thaw cycles do not alter lncRNA or mRNA expression.


Convolutional Neural Networks are a family of algorithms that can classify data based on learned patterns. Here we present MuStARD an application of Convolutional Neural Networks that can learn patterns associated with user-defined sets of genomic regions, and scan large genomic areas for novel regions exhibiting similar characteristics. We demonstrate that MuStARD is a generic method that can be trained on different classes of human small RNA genomic loci, without need for domain specific knowledge, due to the automated feature and background selection processes built into the model. We also demonstrate the ability of MuStARD for inter-species identification of functional elements by predicting mouse small RNAs (pre-miRNAs and snoRNAs) using models trained on the human genome. MuStARD can be used to filter small RNA-Seq datasets for identification of novel small RNA loci, intra- and inter- species, as demonstrated in three use cases of human, mouse, and fly pre-miRNA prediction. MuStARD is easy to deploy and extend to a variety of genomic classification questions. Code and trained models are freely available at  Several small non-coding RNA molecule families such as microRNA (miRNA), small nucleolar RNA (snoRNA), small nuclear RNA (snRNA), piwi-interacting RNA (piRNA), short hairpin RNA (shRNA), small interfering RNA (siRNA), promoter-associated short RNAs (PASRs), termini-associated short RNAs (TASRs)4, transcription initiation RNAs (tiRNAs)5, and others, now populate the functional expression map of known genomes. The plethora of functional small non-coding RNA classes supports the idea of a highly interconnected transcriptomic landscape and highlights the necessity of computational approaches that can effectively identify them against the enormous background variability of eukaryotic genomes. Along with our deeper understanding of well-established organisms, the total number of sequenced genomes has been increasing hand in hand with fast pace. NCBI currently lists just over 7,000 eukaryotic sequenced genomes, of which almost 50 have fully assembled genomes, and approximately 1,000 have some assembled chromosomes. The experimental annotation of newly sequenced genomes is a much slower and piecemeal process that benefits greatly from the availability of computational techniques that can guide and assist the annotation.Since the human genome was first sequenced about two decades ago6 starting in parallel with the sequencing of the first human genome. Small non-coding RNAs, with their shorter length, lack of coding three nucleotide periodicity pattern, and often small number of known examples per class, offer a tougher challenge for computational methods. A common approach for in silico identification of putative small non-coding RNA genomic loci has been the use of sequence homology between molecules from well annotated species, such as humans, and the new species in question. These methods, while efficient when homology is high, are bound to preferentially annotate a subset of loci, biased towards extra-conserved molecules. However, a large number of small non-coding RNAs are more evolutionary constrained. For example, an estimated 40% of human miRNAs have developed recently in evolutionary history and can only be found in other primates7.Computational methods for genomic annotation have a history at least as long as full genome sequencing, with computational identification of exons and protein coding genes8. A large drawback of such methods is their dependence on expert-defined features and background sets, which tend to produce methods that perform well in evaluations closely matching their training biases, but fail to produce robust classification in more realistic conditions, such as when ‘scanning’ a large genomic region. The second large drawback of these methods is that they are, by design, focused on one specific class or subclass of small non-coding RNA molecules. For example, a method tailored for pre-miRNA prediction is not suitable for snoRNA prediction and vice versa. This issue leads to an unbalanced development of methods towards specific families and ignores others that may not be populous or well-researched enough to warrant the attention of in silico method developers. For example, as mentioned above, pre-microRNA prediction is a well-researched field with over thirty computational methods published in the past decade or so, while in contrast snoRNA prediction displays a distinct paucity of options, with methods becoming obsolete and unusable after more than a decade10 and the rate of identification severely slowing down in new species11.To avoid the constraints and biases of homology based identification of small non-coding RNA loci, there has been a steady development of algorithms that aim at modelling characteristics of a specific class or subclass, and then evaluating proposed regions of a genome for their potential to encode a small non-coding RNA of this class. For example, over thirty computational methods aiming at pre-microRNA identification have been developed to date, with no tool significantly outperforming all others on benchmarked datasetsMachine-learning System for Automated RNA Discovery), a flexible Deep Learning framework that utilizes raw sequence, conservation, and folding data to identify genomic loci with similar characteristics of a given set of regions , raw sequence and conservation (MuStARD-mirSC), raw sequence and secondary structure (MuStARD-mirSF), secondary structure and conservation (MuStARD-mirFC), secondary structure only (MuStARD-mirF) and sequence only (MuStARD-mirS). For the combination of raw sequence, secondary structure and conservation, we have trained an additional model after disabling the class weights option in Keras (MuStARD-mirSFC-U model).The same pipeline was used to create three snoRNA detection models, one for detecting the C/D box snoRNA subspecies (MuStARD-snoSFC-U-CDbox), one for H/ACA box (MuStARD-snoSFC-U-HACAbox) and one for detecting all types of snoRNAs (MuStARD-snoSFC-U).Detailed information related to the network architecture and training scheme can be found in 13. Fly genome (version 5.32) was downloaded from FlyBase14. Pre-miRNA sequences were downloaded from miRBase v22.115. Basewise conservation scores, based on phyloP algorithm, of 99 and 59 vertebrate genomes with human and mouse respectively were downloaded from the UCSC genome repository16. For genome scanning tests, targets were extended by + /− 5,000 bp and the resulting regions were merged in the case of strand specific overlaps. The regions were assessed by a moving window of width 100 and step 5. Any prediction overlapping the target by at least 50% was considered a positive. A full explanation of the production of Training Sets, and the Methodology of comparisons can be found in Human GRCh38) and mouse (GRCm38) genomes and corresponding gene and snoRNA annotations were downloaded from Ensembl v93 repository and mousMuStARD is developed in python utilizing tensorflow and Keras for the Deep Learning aspect, R for visualizing the performance and Perl for file processing, reformatting and module connectivity. Full list of dependencies can be found on MuStARD’s gitlab page.MuStARD is able to execute either on CPU or GPU depending on the underlying hardware configuration by taking into advantage tensorflow’s flexibility. The framework has been designed to maintain a minimal memory footprint thus allowing the execution even on personal computers. Running time heavily depends on input dimensionality, number of instances in the training set, learning rate and GPU availability. On a GPU (NVIDIA GeForce GTX 1050Ti) it took approximately 5 minutes to train a model on 30,000 positive and negative sequences.MuStARD operates directly on genomic intervals in BED format, in the cases of both the training and prediction modules. For example, regarding small RNA-Seq datasets, MuStARD does not directly process aligned reads. Instead, users need to provide a bed file with small RNA-Seq enriched regions to be scanned with MuStARD. Essentially, sequencing depth is not crucial as our algorithm works after a ‘peak calling’ step that can be as simple as a bedtools merge command followed by a bedtools coverage filtering.For the mouse liver small RNA-Seq dataset, we scanned 14,552 intervals  with a total size of 3.9mbp, mean interval size of 268 bp and standard deviation of 50.3 bp. The MuStARD-mirSFC-U model was used with a sliding window step of 10 bp and the total running time was 36 minutes (CPU usage only).In the case of the fly embryo dataset, we scanned 1,638 intervals (819 peaks) with 526 kb total size, 321 bp mean size with standard deviation of 172 bp. We used MuStARD-mirSF model with a sliding window step of 10 bp and total running time of 3.8 minutes.The average running time of MuStARD per peak, based on the mir-SFC-U model, was 0.17 seconds, while based on the mir-SF model was 0.13 seconds. Running times were normalized on 321 bp average interval size.17 and the fact that pre-miRNAs have highly conserved regions corresponding to the mature miRNA sequences. Surprisingly, the non-balanced model (MuStARD-mirSFC-U) performs best out of all model combinations including the balanced three input model. Since MuStARD-mirSFC-U outperforms all other models, we will only report results for this model in the following evaluations. For snoRNAs, the equivalent best performing model is MuStARD-snoSFC-U. Detailed explanation of the training scheme can be found in We compared the performance of MuStARD on all combinations of input data for the pre-miRNA prediction dataset  on drosophila using the top 819 small RNA-Seq enriched regions  in human, it needs to increase the score threshold at a level that reduces the sensitivity below 16%  containing 1,227 annotated mouse pre-miRNAs. The inter-species prediction correctly identified pre-miRNAs with a small number of false positives, at a rate of 1/260kbp. Figure In order to evaluate our method on another difficult and realistic task, we mined all pre-miRNAs that were annotated in previous versions of miRBase (version 14 to 22.1) but have since been retracted from it. Extensive details can be found in 24 a state-of-the-art snoRNA prediction software developed specifically to identify each of these two categories against background . SnoRNAs are a class of small RNAs with widely varying structure, sequence, and conservation patterns. We experimentally trained a model on all snoRNAs as well as two additional models for the most populous snoRNAs sub-families, the H/ACA and C/D box. H/ACA box snoRNAs have a secondary structure consisting of hairpins and single stranded regions. In contrast, C/D box snoRNAs have a stem-box structure that is much more variable than H/ACA box. In addition, our ‘all snoRNA’ dataset includes snoRNAs beyond these two sub-families. For the two sub-families we were also able to benchmark against snoReportnd Table . We obsend Table . These rWe present here a flexible Deep Learning framework that can be used to identify small RNA genomic loci based on the sequence, conservation, and secondary structure characteristics of the class. A model can be easily trained, without any changes on the code, to identify any class of small RNA loci provided enough examples of the class exist. Training of the model does not require expertly curated features specific to the RNA class. In contrast to highly specific methods that rely on extraction of hundreds of features, our method operates directly on raw sequence, conservation scores, and a simple linear folding representation. Despite the simplicity of the inputs, and the generality of the method, it manages to convincingly outperform all state of the art methods that have been each developed and trained on one single class of RNAs specifically.An important aspect of our method is the ability, for the first time, to scan large genomic regions, or even several thousand sequencing peaks, at an acceptably low false discovery rate. Machine learning methods can only learn variation that is presented to them. When looking for extremely rare events, such as small RNA genomic loci, it becomes evident that large genomic regions will need to be scanned, and the background variation will be enormous. However, most negative loci have extremely low potential of being confused for small RNA loci. A static classification between real loci and randomly selected background is prone to overestimate the predictive power of evaluated methods. Some methods have attempted to create ‘harder’ negative sets by including sequences that have characteristics similar to the predicted class. This approach implies that the researcher already knows the major characteristics of the predicted class, and that these characteristics will remain stable for each training model. In our case neither of these prerequisites were true.We initially prototyped our method with a small set of negatives, four for each real training example, randomly selected from regions within the coding and intronic regions of mRNAs, and long non-coding RNAs. We quickly realized that while our method could separate between these categories easily, it still produced a large amount of false positives in the more realistic large region scanning evaluation. Training several models using different, but equal, background sets showed us variability in the number and range of identified false positives. However, we noticed that a number of false positives appeared consistently in several of the trained models. These ‘hard cases’ of background variation are the ones that have sequence, conservation, and folding characteristics closest to the real training examples and are thus harder to differentiate. We decided to attempt an iterative enrichment technique for the training background in which ‘hard cases’ that confuse our models consistently are added into the training set for a second round of training. This method achieved a great leap in performance when evaluated in completely independent data. Importantly, this automatic iterative method does not rely on an expert user to select the characteristics of importance. The ‘hard cases’ are identified by the training model itself and will fit to whatever positive set it is training on. The enriched background for pre-miRNA and sno-RNA models is radically different, representing the differences of these classes between them, and allowing the models to be easily and accurately trained on any positive set of small RNA loci.Using a number of pre-miRNA prediction algorithms for region scanning was time consuming and arduous labor. To calculate hundreds of features on regions spanning less than one percent of the human genome, all other algorithms (with miRBoost being the sole exception) required to group the scanning region into smaller batches of 2000 sequences in order to parallelize the analysis into a computer cluster (MetaCentrum-CERIT). Even so, the computing time for each single batch was approximately 4 days. In contrast, our algorithm was able to scan the mouse benchmark dataset that includes several million base pairs in a few hours on a single CPU.We have demonstrated that our method can be used for cross-species prediction of small RNAs. As a proof of concept we trained models on human pre-miRNAs and snoRNAs and then identified their counterparts in mouse, a pair of well annotated species that have considerable evolutionary distance. The pre-miRNAs we correctly identified on the mouse genome were enriched in evolutionary conserved pre-miRNAs in human . That said, the majority (70%) of our predicted pre-miRNAs are not homologous to human pre-miRNAs and would not be easily identified by a simple homology search.gitlab.com/RBP_Bioinformatics/mustard.The method presented here can be generalized for any class of small RNAs on any species. We chose to highlight two examples (pre-miRNAs and sno-RNAs) that differ radically. Where pre-miRNAs have high levels of conservation and fold into characteristic hairpin structures, sno-RNAs show a much wider size distribution (118.8 mean / 59.1 sd vs 81.9 mean / 16.9 sd) and have a variety of subclasses with variable secondary structure and evolutionary patterns, making their identification harder. Thousands of known pre-miRNA sequences against a few hundred sno-RNAs reduce the size of the training set, adding a level of difficulty to the task. It follows that several methods for pre-miRNA identification have been developed to date, while sno-RNA identification methods have not been developed in the past decade. The need for modern, easy to use, easy to train, methods becomes self-evident, especially for RNA classes with fewer members, for which no new development is performed. It is beyond the scope of this paper to develop models for each class of small RNAs, but using our openly available method researchers can easily produce such models for their own RNAs of interest. MuStARD has been specifically designed to automate this process and facilitate ease-of-use by simplifying the input requirements. Regions of the targeted small RNA class can be loaded as a bed file, and MuStARD handles all pre-processing steps such as sequence and evolutionary conservation extraction as well as secondary structure calculation. Additionally, the iterative training module provides an interface for the automatic selection of background genomic loci that optimally represent the negative set, specifically tailored for the specific small RNA class. MuStARD can be easily applied on any small RNA identification problem that would not be easily identifiable by using older methods. Extensive documentation and tutorials on using MuStARD for novel RNA class predictions are available along with the MuStARD code repository at To conclude, we have developed a method that is easy to train and deploy for any class of small RNA genomic loci. Using the novel iterative background selection our method can choose the background ‘hard cases’ specific for each training, boosting performance. We show that our method outperforms class specific methods, both in accuracy, and computational performance. We achieved cross species identification of small RNAs beyond homology, and also highlighted a realistic use case in the identification of pre-miRNAs out of small RNA-Seq peaks.Supplementary Table S1Supplementary Table S2Supplementary Table S3Supplementary Table S4Supplementary Table S5Supplementary Table S6Supplementary Table S7Supplementary Table S8Supplementary Information


We have introduced a variation of the qRT-PCR method: human-specific glyceraldehyde 3-phosphate dehydrogenase (GAPDH) qRT-PCR, which allows quantification of metastasis in xenograft models without the requirement of overexpressed exogenous genes. This makes the method easily amenable to many xenograft models without alteration of the cancer cells. We determined that the method is able to detect a few human cells within abundant mouse lung tissue. Further, the human-specific GAPDH qRT-PCR is more sensitive and correlates with histological analysis in terms of determining relative metastatic burden, suggesting that human-specific GAPDH qRT-PCR could be used as a primary method for quantification of disseminated human cells in murine xenograft models.Metastasis is the primary cause of cancer-related mortality. Experimental models that accurately reflect changes in metastatic burden are essential tools for developing treatments and to gain a better understanding of disease. Murine xenograft tumor models mimic the human scenario and provide a platform for metastasis analyses. An   We describe a quantitative reverse-transcriptase polymerase chain reaction (qRT-PCR)-based method for quantifying metastasis in xenograft models. Human-specific glyceraldehyde 3-phosphate dehydrogenase (GAPDH) qRT-PCR allows quantification of a few disseminated human cells in murine tissue, without the requirement of overexpressed exogenous genes. The method is more sensitive and correlates with histological analysis. Favored among researchers are murine tumor models of metastasis where cancer cells are orthotopically implanted, allowing for tumor-specific preferences for dissemination to distant sites, such as lung, brain, liver, and bone.Metastasis is the predominant cause of cancer-related mortality.ex vivo to quantify lung metastasis in dissected lung tissue. Both MRI and BLI require specific costly equipment and facilities, which are prohibitive for some researchers. Hence, there is a need for methods that use equipment and facilities common in molecular biology laboratories.There are numerous quantitative tools to measure metastasis in animal models, some of which are designed to mimic the clinical setting and allow for metastasis quantification in a live animal. These methods include small-animal magnetic resonance imaging (MRI), positron emission tomography (PET), and computed tomography (CT) and bioluminescence imaging (BLI) of live animals.ex vivo methods have been developed for metastasis quantification amenable to most molecular biology laboratories. Flow cytometry analysis can be used for lung metastasis quantification. For example, cancer cells engineered to express green fluorescent protein (GFP) can be detected by flow cytometry and relative percentages of GFP+ cells determined in harvested tissues.A number of Stereological analysis estimates metastasis volumes in mouse lungs by hematoxylin and eosin (H&E) staining of thin sections of cryostat-embedded lungs.ex vivo method gaining favor is based on the amplification of tumor-specific transcripts using quantitative reverse-transcriptase polymerase chain reaction (qRT-PCR). This method gives an assessment of the overall metastatic burden of the total tissue, independent of uneven distribution of metastasis in the tissue. qRT-PCR has been shown to be a sensitive method, detecting 10 cancer cells per 0.5−2 μg of lung tissue.An alternative The widespread use of qRT-PCR as a technique for evaluating murine metastasis is thus limited by the requirement of a highly expressed gene that is unique to the cancer cells and the lack of comparison between metastasis quantification by qRT-PCR and a well-accepted method . Herein, we describe our method of quantifying expression of an abundant human and mouse glyceraldehyde 3-phosphate dehydrogenase (GAPDH) transcript by qRT-PCR utilizing validated human-specific primers. We demonstrate that human-specific GAPDH qRT-PCR is highly sensitive, discriminating between transcripts of human and mouse origin with high specificity. The method reliably detects 100 human cells in a mouse lung lobe (∼70 mg tissue). Human-specific GAPDH qRT-PCR was successfully utilized to detect increased metastatic burden imparted by aldehyde dehydrogenase 1A3 (ALDH1A3) expression in MDA-MB-231 breast cancer cells and demonstrated that MDA-MB-231 cells are more metastatic than MDA-MB-436 cells. A direct comparison of lung metastasis quantified using human-specific GAPDH qRT-PCR versus histological quantification of H&E-stained thin sections and found the two methods highly correlative, with the former being more sensitive.Herein, we compare two methods for quantification of lung metastasis in a murine xenograft tumor model: the gold-standard histological-based method of quantifying percent metastasis of sectioned and stained fixed lung tissue versus a qRT-PCR-based method of quantifying levels of human cancer cell-specific transcripts in RNA extracted from murine lung tissue. We have introduced a variation of the qRT-PCR method that will allow accurate and sensitive quantification of metastasis without the requirement of OE of exogeneous genes in the context of xenograft models . In To compare the two methods of metastasis quantification, we established a tumor xenograft model with previously demonstrated differing levels of lung metastasis to better test the sensitivity of the methods. We utilized MDA-MB-231 cells, with or without ALDH1A3 OE,Notably, others have reported that quantitative assessment of the metastatic load is possible by counting the average number of micrometastases in ten histological thin sections within the middle of the lung.Our histological metastasis quantification established a reference for later comparison with the qRT-PCR method and also confirmed that the MDA-MB-231 cells had metastasized to the lungs in these specimens and that ALDH1A3 OE increased metastasis D. With tHigh sensitivity in qRT-PCR-based methods of lung metastasis quantification is more likely achievable if detecting abundant cancer cell-unique transcripts. GAPDH is among the most abundantly expressed transcripts; hence, if human-specific primers to GAPDH that discriminate between human and mouse transcripts in qRT-PCR are successfully designed, then it could be a highly sensitive method for detection of disseminated human cancer cells in murine xenograft models. Further, it would preclude the requirement of introduction of a foreign gene and be amenable to all xenograft models, eliminating the need for a cell line-specific overexpressed gene .in silico analysis of the specificity of the human GAPDH primers against the human and mouse transcriptome  Primer-Basic Local Alignment Search Tool (Primer-BLAST), we designed primers to unique regions of the human GAPDH transcript that would generate amplicons of 86 bp for the human GAPDH transcript . The pricriptome . The in To confirm the specificity of the primers, we used the human GAPDH primers in qRT-PCR experiments against RNA isolated from cultured MDA-MB-231 cells and naive NOD/SCID mouse lung (harvested from a mouse that had no exposure to human cells). We similarly used the primers designed against mouse GAPDH with the two different templates. Serial dilutions of the cDNA templates in the qPCR reactions revealed that our human GAPDH primers were highly specific to the template of human origin (MDA-MB-231 cells) A. There Having identified human GAPDH primers with high specificity for transcripts of human origin, we next determined the detection and specificity limit of the human GAPDH primers for human GAPDH transcripts in the context of abundant mouse transcripts. For this analysis, we added known amounts of serially diluted MDA-MB-231 and MDA-MB-436 cells  into tubes containing a naive NOD/SCID lung lobe. We then purified the total RNA and performed qRT-PCR on the samples using the human-specific GAPDH primers and nonspecific mouse GAPDH primers as a reference gene. The resulting CT values demonstrate the high specificity of the human-specific GAPDH primers; in the context of abundant mouse RNA, the human GAPDH transcript was detectable when as few as 100 MDA-MB-231 or MDA-MB-436 cells were spiked into the mouse lung lobe sample A. The stHaving established the dynamic range and specificity of the human-specific GAPDH qRT-PCR, we next quantified the lung metastasis in the left lobe of the harvested lung from the MDA-MB-231 xenograft experiment  and end-point analyses , are also effective for detecting/quantifying metastasis, each having its own strengths and limitations. Stereological and histological analyses by H&E staining closely mimic the clinical pathological practice and are therefore commonly used to quantify lung metastasis, with the latter being the gold standard.,qRT-PCR has proven to be a sensitive measure to quantitatively detect cancer cell transcripts (exogenously introduced or overexpressed in a specific cancer cell line) within the mouse lung tissue.It is noteworthy that qPCR of Alu elements, with over 50,000 copies in the human genome, overcomes the copy number limitation associated with gene-specific PCR.To our knowledge, no other groups have utilized human-specific primers designed to unique regions of abundant human transcripts to quantify lung metastasis in murine xenograft tumor models utilizing RNA samples. We demonstrate that human-specific GAPDH qRT-PCR is highly sensitive and an accurate method of detecting xenografted cancer cells that have disseminated in mice. Importantly, this method precludes the requirement of the introduction of a foreign gene or a gene that may be overexpressed in only some cancer cell lines  of the minced tissue for RNA extraction.We propose that qRT-PCR analysis can be performed in lieu of the other well-accepted metastasis quantification models. Importantly, the demonstration here that human-specific GAPDH qRT-PCR correlates highly with the gold-standard histological method is critical if the qRT-PCR method is to be used with confidence over other accepted metastasis quantification methods. Furthermore, our inclusion here of the ALDH1A3 OE model and MDA-MB-231 versus MDA-MB-436 models was to test the capacity of the qRT-PCR method for discriminating between different conditions with varying metastatic burden. Both the human-specific GAPDH qRT-PCR method and the histological method similarly indicated that ALDH1A3 OE in MDA-MB-231 cells increased lung metastasis (p < 0.05). Now that we have validated this method, we could use this method to answer other metastasis questions. For example, we could utilize human-specific GAPDH qRT-PCR to determine if ALDH1A3 OE will enable MDA-MB-436 cells to significantly metastasize to the lungs. This is currently unknown. Hence, these comparative method analyses will be valuable for other researchers considering qRT-PCR as a primary method of metastasis quantification. qRT-PCR as a method of metastasis burden in murine xenograft models may be best suited for research groups with limited access to certain types of equipment, with time and budget constraints.In conclusion, the data presented suggest that human-specific GAPDH qRT-PCR provides an efficient, sensitive, and cost-effective method of detecting human tumor-derived mRNA within murine tissues. The protocol should be considered as a primary method of metastasis quantification when utilizing murine xenograft models.2 incubator. MDA-MB-231 cells were cultured in Dulbecco’s modified Eagle’s medium , supplemented with 10% fetal bovine serum  and 1× antibiotic-antimycotic , and cultured at. MDA-MB-436 cells were grown in Leibovitz’s medium , supplemented with 10% FBS, 1× AA, 10 μg/mL human insulin (Sigma), and 16 μg/mL L-glutathione (Invitrogen). ALDH1A3 OE and control MDA-MB-231 clones were previously generatedMDA-MB-231 and MDA-MB-436 cells were obtained from the American Type Culture Collection and cultured at 37°C, 5% CO6 cells of either ALDH1A3 OE or vector control MDA-MB-231 cells or with 5 × 106 MDA-MD-436 cells . The cells were injected within the two groups, alternating up to 5 animals, to minimize any variability due to time of injection. All of the animals were injected within the same hour. All tumor measurements were completed at the same time (within 1 h). Tumor measurements were always completed by the same person  to minimize variability due to slight differences in measuring techniques. The animals had ear holes for individual identification. No animals were excluded from the experiment, as all animals bore tumors at the first measurement time point. Mice bearing MDA-MB-231 of MDA-MB-436 tumors were euthanized in in a CO2 chamber under full anesthesia (unconscious) by isoflurane inhalation on day 49 (MDA-MB-231 experiment) or 74 (MDA-MB-436 experiment) post-cancer cell implantation. The lungs were then harvested. All animal experiments comply with the standards set by the Canadian Council on Animal Care (CCAC) and were performed according to a protocol approved by Dalhousie University’s Committee on Laboratory Animals (protocol 19-013). Notably, as per the CCAC guidelines, no tumor exceeded the 17-mm diameter in any direction and were all less than 10% of the animal’s body weight. The harvested samples for metastasis quantification were assigned numbers and blinded.A xenograft model was utilized as previously described.Methods for mouse dissection have been previously published in detail;2O, and 46.1 g Na acetate-3H2O) for 24 h for fixation. The tissue was then rinsed 3 times in 70% ethanol and stored in 70% ethanol until paraffin embedding. For infiltration (embedding), tissues were first dehydrated, cleared with xylene, and infiltrated  were captured on the Zeiss Axio Imager Z1 with color and monochrome camera at 2.5× magnification. To capture the entire lung section, each slide had 4−12 images captured (depending on size of tissue). Images were stitched together using Adobe Photoshop and then imported into the ImageJ program for metastasis quantification. Each metastatic colony on the lung was freehand circled and pixelated, then the entire area of the lung was freehand circled and pixelated, and percent metastasis was calculated . The tot,The harvested left lung lobe  was placAdditionally, a naive mouse lung lobe was spiked with increasing numbers of MDA-MB-231 cells to generate a standard curve for the qRT-PCR-based method. Six tubes containing minced lung tissue and 1 mL of TRIzol were prepared, and then 0; 10; 100; 1,000; 100,000; or 1,000,000 MDA-MB-231 or MDA-MB-436 cells were added to the tubes in 100 μL of PBS.g for 10 min for phase separation. The top clear aqueous layer (350 μL) was harvested and combined with 350 μL of 70% ethanol (1:1) and applied to PureLink RNA columns . RNA was then purified following the manufacturer’s protocol and incorporated the addition of DNase  to eliminate DNA contamination. RNA should be stored at −80°C. RNA concentrations were measured on a SpectraMax M2 spectrophotometer (Molecular Devices). Absorbance (A) values at 230, 260, and 280 nm were determined and are also an assessment of purity. The RNA samples should have an A260/A280 of ∼2.0 and an A260/A230 of 2.0−2.2. Higher A260/A230 ratios may indicate organic compound  contamination, and those samples should not be used. RNA samples should also be assessed by gel to visualize the 28-s rRNA and 18-s rRNA  as recommended (400 μL) was added to the tubes. The tubes were vortexed for another 10 min or until tissue was dissolved. The tubes were centrifuged at 10,000 × 8-s rRNA  or by miRNA was converted to cDNA using iScript  following the manufacturer’s protocol. Briefly, 0.25 μg of RNA of each sample was added to 2 μL of 5× iScript reaction mix, and RNase-free water was added to 10 μL of total reaction volume and mixed with a pipette tip. The reactions (in 8-strip PCR tubes) were incubated in a thermal cycler following the manufacturer’s protocol . Notably, due to potential low levels of residual DNase contamination, it is important to store cDNA at −20°C, avoid long-term storage beyond 1 month, and keep cDNA samples on ice when being utilized in qRT-PCR reactions.Before use in qRT-PCR reactions, the cDNA samples were diluted 1/10 in the 8-strip PCR tubes. With the use of a multichannel (helps reduce variability between samples due to pipetting error), 4 μL was dispensed into a 384-well qPCR plate (Bio-Rad). Subsequently, the SsoAdvanced Universal SYBR Green Supermix  Master Mix containing 5 μL of the SsoAdvanced Universal SYBR Green Supermix and 1 μL of target primers (from 4 μM stock containing forward and reverse primers) was diluted to a final working concentration of 0.4 μM in the qPCR reaction, according to the manufacturer’s protocol. 10 μL of total reaction was loaded per well in duplicate. The sequences for the human-specific and nonspecific mouse GAPDH primers were generated using the NCBI BLAST . The qRT10 of the arbitrary set starting material amounts of each sample.To test the specificity of the primers and determine the efficiency of the primers shown in , we geneAll statistical analyses were performed using GraphPad Prism 8. In all cases where two experimental conditions are compared, an unpaired Student’s t test was performed. The data were assessed for normality by performing the D’Agostino & Pearson test using GraphPad Prism. If the data were not normally distributed, then a Mann-Whitney test was performed.