HTS Data Research Articles

MDB-66. HIGH-THROUGHPUT SMALL MOLECULE DRUG SCREENING, TRANSCRIPTOMICS AND INTEGRATIVE AI IDENTIFY TARGETABLE MYC-DEPENDENT THERAPEUTIC VULNERABILITIES IN HIGH-RISK MEDULLOBLASTOMA

Abstract INTRODUCTION MYC-amplified Group 3 medulloblastomas (MBGroup3-MYC) do not respond to conventional up-front therapies and are almost universally fatal (<5% survival). MYC is not directly targetable. Thus, the systematic development of therapies which target complementary MYC-dependencies offers clear potential to rationally re-design effective therapeutic strategies. METHODS Isogenic cell-based models with regulable shRNA MYC-knockdown were developed in three independent MYC-amplified backgrounds (D283Med; D425Med; HD-MB03). These were utilised to i) define the MYC-dependent transcriptome/proteome in MBGroup3, through analysis in conjunction with our primary tumour cohort, and ii) as the basis of an automated MYC-dependent high-throughput drug screen (HTS; >500 clinically-actionable compounds). Bespoke integrative AI tools and bioinformatic analysis, and subsequent siRNA/pharmacodynamic validation, identified and validated candidate MYC-dependent therapeutic vulnerabilities. Prioritised drugs were advanced to pre-clinical trials across two in vivo MBGroup3 models (GTML/Trp53KI/KI (GEMM; MYCN-driven) and GMYC (GEMM-derived allografts; MYC-driven)) to evaluate candidate efficacy and toxicity. RESULTS shRNA-mediated MYC knockdown could be maintained for 28 days without escape in all models and, as anticipated, was associated with cell cycle abrogation and elevated caspase-3/-7 activity. HTS across all models revealed that MYC expression confers differential dependencies to small molecule inhibitors, with 82 compounds spanning 11 major drug classes demonstrating MYC-dependent activity. AI-based integration of HTS data with model and primary patient MYC-dependent transcriptome data shortlisted multiple druggable molecular targets which, in turn, were independently validated. To date, we have progressed alisertib (AURKAi), volasertib (PLK1i) and prexasertib (CHK1i) to in vivo pre-clinical trials. Promisingly, both alisertib and prexasertib demonstrated efficacy within MBGroup3-MYC models. CONCLUSIONS Integrated human tumour and model-based screening approaches have enabled identification and validation of critical MYC co-dependencies, targetable using established small-molecules, to inhibit MBGroup3 tumour growth. Their further evaluation, including integration into established and/or rationally-designed combinations, will be essential next steps in their (pre-)clinical development.

Optimising high-throughput sequencing data analysis, from gene database selection to the analysis of compositional data: a case study on tropical soil nematodes

Optimising high-throughput sequencing data analysis, from gene database selection to the analysis of compositional data: a case study on tropical soil nematodes

Construction of relatedness matrices in autopolyploid populations using low-depth high-throughput sequencing data

Key messageAn improved estimator of genomic relatedness using low-depth high-throughput sequencing data for autopolyploids is developed. Its outputs strongly correlate with SNP array-based estimates and are available in the package GUSrelate.High-throughput sequencing (HTS) methods have reduced sequencing costs and resources compared to array-based tools, facilitating the investigation of many non-model polyploid species. One important quantity that can be computed from HTS data is the genetic relatedness between all individuals in a population. However, HTS data are often messy, with multiple sources of errors (i.e. sequencing errors or missing parental alleles) which, if not accounted for, can lead to bias in genomic relatedness estimates. We derive a new estimator for constructing a genomic relationship matrix (GRM) from HTS data for autopolyploid species that accounts for errors associated with low sequencing depths, implemented in the R package GUSrelate. Simulations revealed that GUSrelate performed similarly to existing GRM methods at high depth but reduced bias in self-relatedness estimates when the sequencing depth was low. Using a panel consisting of 351 tetraploid potato genotypes, we found that GUSrelate produced GRMs from genotyping-by-sequencing (GBS) data that were highly correlated with a GRM computed from SNP array data, and less biased than existing methods when benchmarking against the array-based GRM estimates. GUSrelate provides researchers with a tool to reliably construct GRMs from low-depth HTS data.

Bambu and its applications in the discovery of active molecules against melanoma

Purpose or objectiveMelanoma is one of the most dangerous forms of skin cancer and the discovery of novel drugs is an ongoing effort. Quantitative Structure Activity Relationship (QSAR) is a computational method that allows the estimation of the properties of a molecule, including its biological activity. QSAR models have been widely employed in the search for potential drug candidates, but also for agrochemicals and other molecules with applications in different branches of the industry. Here we present Bambu, a simple command line tool to generate QSAR models from high-throughput screening bioassays datasets. MethodsThe tool was developed using the Python programming language and relies mainly on RDKit for molecule data manipulation, FLAML for automated machine learning and the PubChem REST API for data retrieval. As a proof-of-concept we have employed the tool to generate QSAR models for melanoma cell growth inhibition based on HTS data and used them to screen libraries of FDA-approved drugs and natural compounds. Additionally, Bambu was compared to QSAR-Co, another automated tool for QSAR model generation. Resultsbased on the developed tool we were able to produce QSAR models and identify a wide variety of molecules with potential melanoma cell growth inhibitors, many of which with anti-tumoral activity already described. The QSAR models are available through the URL http://caramel.ufpel.edu.br, and all data and code used to generate its models are available at Zenodo (https://doi.org/10.5281/zenodo.7495214). Bambu source code is available at GitHub (https://github.com/omixlab/bambu-v2). In the benchmark, Bambu was able to produce models with higher accuracy, recall, F1 and ROC AUC when compared to QSAR-Co for the selected datasets. ConclusionsBambu is an free and open source tool which facilitates the creation of QSAR models and can be futurely applied in a wide variety of drug discovery projects.

First report of pothos latent virus infecting upland cotton (Gossypium hirsutum) in the United States.

Pothos latent virus (PoLV) is a virus with isometric virions and a positive-sense RNA genome, approximately 4.4 kb in size, currently classified in the genus Aureusvirus, family Tombusviridae (Martelli et al. 1998; Rubino et al. 1995). After its original discovery from hydroponic-grown pothos plants (Scindapsus aureus) in Italy (Sabanadzovic et al. 1995), additional PoLV isolates were reported from pigeonpea (Cajanus cajan) and lisianthus (Eustoma grandiflorum) in India and Taiwan, respectively (Chen et al. 2016; Kumar et al. 2001). PoLV has not been previously reported on the American continent. During 2019, we carried out a state-wide, RT-PCR-based survey for cotton leafroll dwarf virus (CLRDV), as previously described (Aboughanem-Sabanadzovic et al. 2019). Plants exhibiting symptoms reported associated with CLRDV (Avelar et al. 2019) were collected from cotton fields throughout Mississippi. Samples consisted of individually bagged, six inch-long, apical portions collected from five to twelve cotton plants per field. At the end of the season, the total RNAs extracted from a subset of CLRDV-infected samples using a Spectrum RNA extraction kit (Sigma, St Louis, MO), were randomly selected for additional characterization by Illumina 150 nt paired-end high-throughput sequencing at the UIUC Core Sequencing Facility (University of Illinois, Urbana, IL). De novo assembly of 46 to 60 million raw reads/sample was performed by metaSPAdes (Nurk et al. 2017). In addition to several CLRDV-specific contigs, analyses of 184,173 contigs assembled from a sample collected in Clay County (lab code CL-112) revealed a large contig # 63556 of 4298 nt in size with identities ranging from 90.5% to 94.3% with three PoLV genome sequences available in GenBank, suggesting that an isolate of this virus (PoLV-cot; GenBank OP584699) was coinfecting the sample along with CLRDV. Sequence analyses showed that contig #63556 represents approx. 97-98% of the entire PoLV-cot genome. To verify HTS data, specific primers (PoLV-F 5'ACATATATCAGAGAGAGCTCAGGTC3' and PoLV-R 5'GCTCCCATGACAGACCTCACT3') were designed on conserved sequences of all four PoLV genomes and used in a single-tube RT-PCR. The initial tests on RNAs from CL-112 and six other samples from the same field confirmed PoLV-cot infections in the original and an additional cotton plant. Sanger sequencing of the two 294 bp-long RT-PCR products revealed >99% nt mutual identity and 97.5-99% with PoLV isolates. However, none of the additional 226 cotton samples collected in 2019 across the state of Mississippi and 12 samples collected in the same field in 2020 tested positive for PoLV-cot. At this moment, it is not clear whether the PoLV infections originated from infected seeds or, more likely, from soil-borne inoculum. Indeed, several aureusviruses are known to be transmitted by soil either involving vectors belonging to the fungal genera Olpidium and/or Polymyxa (i.e., cucumber leaf spot virus, maize white line mosaic virus), or in a vectorless manner (Rochon et al. 2012). Previous studies on this virus demonstrated low-rate experimental transmission through the soil with no apparent involvement of specific vectors (Chen et al. 2016; Kumar et al. 2001; Sabanadzovic et al. 1995). In summary, results of our study indicate an original report of PoLV on the North American continent, along with description of a new host. Possible impact of PoLV-cot on the cotton industry, or any other susceptible crop in the US, is yet to be understood. Funding: This work has been partially supported by financial support from Cotton Inc, Cotton Foundation, USDA-ARS 58-6066-9-033 and 2020 MAFES-SRI grants. NAS and SS acknowledge partial support from the National Institute of Food and Agriculture, US Department of Agriculture, Hatch Projects Numbers 7001412 and1021494, respectively. The author(s) declare no conflict of interest. 1. Aboughanem-Sabanadzovic, N., et al. 2019. Plant Dis 103: 1798. 2. Avelar, S., et al. 2019. Plant Dis 103: 592. 3. Chen, Y-K., et al. 2016. J Phytopath 164: 650. 4. Kumar, P.L., et al. 2001. Plant Dis 85: 208. 5. Martelli, G.P., et al. 1998. Arch Virol 143: 1847. 6. Nurk, S., et al. 2017. Genome Res 27: 824. 7. Rochon, D., et al. 2012. Virus Taxonomy: Ninth Report of the International Committee on Taxonomy of Viruses. Amsterdam, NL, Elsevier Academic Press, pp 1111-1138. 8. Rubino, L., et al. 1995. J Gen Virol 76: 2835. 9. Sabanadzovic, S., et al.1995. Eur J Plant Pathol 101:171.

Comparison of the Concordance of Allergic Diseases between Monozygotic and Dizygotic Twins: A Cross-Sectional Study Using KoGES HTS Data.

Several epidemiological studies have demonstrated that genetic and environmental factors contribute to the development of allergic diseases. However, there is limited information on these factors in the Korean population. This study investigated the importance of genetic and environmental factors in allergic diseases, such as allergic rhinitis, asthma, allergic conjunctivitis, or atopic dermatitis, by comparing the disease incidence in Korean adult monozygotic and dizygotic twins. This cross-sectional study utilized data from 1296 twin pairs, including 1052 monozygotic and 244 dizygotic twins, aged over 20 years, from the Korean Genome and Epidemiology Study (2005-2014). The study utilized binomial and multinomial logistic regression models to compute odds ratios of disease concordance. The concordance rate (92%) of the presence or absence of atopic dermatitis in monozygotic twins was slightly higher than that in dizygotic twins (90.2%), which only had a borderline significance (p = 0.090). The concordance rates of other allergic diseases within monozygotic twins were lower compared to dizygotic twins (asthma, 94.3% vs. 95.1%; allergic rhinitis, 77.5% vs. 78.7%; allergic conjunctivitis, 90.6% vs. 91.8%), of which the differences were not statistically significant. Monozygotic twins had a higher proportion of cases in which both siblings had allergic diseases than dizygotic twins (asthma, 1.1% vs. 0.0%; allergic rhinitis, 6.7% vs. 3.3%; atopic dermatitis, 2.9% vs. 0.0%; allergic conjunctivitis, 1.5% vs. 0.0%), of which the differences were also not statistically significant. In conclusion, our results appear to indicate the relative importance of environmental factors over genetic factor in the development of allergic diseases in Korean adult monozygotic twins.

Predicting the Likelihood of Molecules to Act as Modulators of Protein-Protein Interactions.

Targeting protein-protein interactions (PPIs) by small molecule modulators (iPPIs) is an attractive strategy for drug therapy, and some iPPIs have already been introduced into the clinic. Blocking PPIs is however considered to be a more difficult task than inhibiting enzymes or antagonizing receptor activity. In this paper, we examine whether it is possible to predict the likelihood of molecules to act as iPPIs. Using our in-house iterative stochastic elimination (ISE) algorithm, we constructed two classification models that successfully distinguish between iPPIs from the iPPI-DB database and decoy molecules from either the Enamine HTS collection (ISE 1) or the ZINC database (ISE 2). External test sets of iPPIs taken from the TIMBAL database and decoys from Enamine HTS or ZINC were screened by the models: the area under the curve for the receiver operating characteristic curve was 0.85-0.89, and the Enrichment Factor increased from an initial 1 to as much as 66 for ISE 1 and 57 for ISE 2. Screening of the Enamine HTS and ZINC data sets through both models results in a library of ∼1.3 million molecules that pass either one of the models. This library is enriched with iPPI candidates that are structurally different from known iPPIs, and thus, it is useful for target-specific screenings and should accelerate the discovery of iPPI drug candidates. The entire library is available in Table S6.

Comparison of the Concordance of Cardiometabolic Diseases and Physical and Laboratory Examination Findings between Monozygotic and Dizygotic Korean Adult Twins: A Cross-Sectional Study Using KoGES HTS Data.

This study investigated the contribution of genetic and environmental factors to cardiometabolic diseases (CMDs) by comparing disease concordance in monozygotic and dizygotic twins. This cross-sectional study analyzed 1294 (1040 monozygotic and 254 dizygotic) twin pairs (>20 years) based on the Korean Genome and Epidemiology Study data (2005-2014). The odds ratios of disease concordance were calculated using binomial and multinomial logistic regression models. The occurrence of CMDs (hypertension, hyperlipidemia, type 2 diabetes, cerebral stroke, transient ischemic attack, and ischemic heart disease) and related physical and laboratory levels did not differ between the monozygotic and dizygotic twin groups. The odds for concordance of the presence/absence of CMDs and the likelihood of incident CMD within monozygotic twins were comparable to that of dizygotic twins. The absolute differences in hemoglobin A1c, insulin, low- and high-density lipoprotein cholesterol, total cholesterol, triglycerides, and systolic blood pressure were lower in monozygotic twins than in dizygotic twins. Absolute differences in fasting glucose and diastolic blood pressure did not differ between groups. Although baseline levels of several laboratory parameters related to CMD showed a strong likelihood of heritability in monozygotic twins, CMD phenotype appears to be largely affected by environmental factors.

Preliminary Analysis of the Presence of Bacterial Azurin Coding Gene in CRC Patients and Correlation with the Microbiota Composition.

Azurin, a bacterial cupredoxin firstly isolated from the bacterium Pseudomonas aeruginosa, is considered a potential alternative therapeutic tool against different types of cancer. In this work we have explored the relationship possibly existing between azurin and colorectal cancer (CRC), in light of the evidence that microbial imbalance can lead to CRC progression. To this aim, the presence of azurin coding gene in the DNA extracted from saliva, stool, and biopsy samples of 10 CRC patients and 10 healthy controls was evaluated by real-time PCR using primers specifically designed to target the azurin coding gene from different bacterial groups. The correlation of the previously obtained microbiota data with real-time PCR results evidenced a "preferential" enrichment of seven bacterial groups in some samples than in others, even though no statistical significance was detected between controls and CRC. The subset of azurin gene-harbouring bacterial groups was representative of the entire community. Despite the lack of statistical significance between healthy and diseased patients, HTS data analysis highlighted a kind of "preferential" enrichment of seven bacterial groups harbouring the azurin gene in some samples than in others.

Comparison of the Coincidence of Osteoporosis, Fracture, Arthritis Histories, and DEXA T-Score between Monozygotic and Dizygotic Twins: A Cross-Sectional Study Using KoGES HTS Data.

We explored the genetic and environmental inter-relationships among osteoporosis, fracture, arthritis, and bone mineral density concordance in monozygotic twins compared to those in dizygotic twins. This cross-sectional research assessed data of 1032 monozygotic and 242 dizygotic twin pairs aged >20 years included in the Healthy Twin Study data of the Korean Genome and Epidemiology Study between 2005 and 2014. Outcomes of interest included illness concordance and absolute differences in dual-energy X-ray absorptiometry (DEXA) T-scores. We found comparable concordances of osteoporosis, fractures, osteoarthritis, and rheumatoid arthritis between monozygotic and dizygotic twins. Medical histories of osteoporosis, fractures caused by accident or falling, osteoarthritis, and rheumatoid arthritis were not distinct between monozygotic and dizygotic twins. Accidental fracture occurrence in both monozygotic twins showed significantly lower odds than that in dizygotic twins. Genetic influence on liability to fracture risk might thus be maintained. DEXA T-scores for bone mineral density indicated more comparable tendencies within monozygotic twin pairs than within dizygotic ones, suggesting the relative importance of genetic contribution to bone mineral density. The relative importance of genetic factors in bone mineral density is sustained between monozygotic twins; overt disease expression of osteoporosis, fractures, or arthritis may be affected by environmental factors.

Comparison of the Differences in State-Trait Anxiety Inventory Scores and Insomnia Histories between Monozygotic and Dizygotic Twins: A Cross-Sectional Study Using KoGES HTS Data.

The heritability of anxiety and its association with insomnia have been suggested. This study investigated the coincidence of anxiety and insomnia in monozygotic twins compared to dizygotic twins. The Korean Genome and Epidemiology Study 2005–2014 was used. The ≥20-year-old cohort population was composed of 1300 twin participants. A total of 980 monozygotic twins and 232 dizygotic twins were compared for the concordance for the history of insomnia in both twin pairs (coincidence of insomnia) and the difference in state of anxiety and trait of anxiety scores. The odds ratios (ORs) for the coincidence of insomnia in monozygotic twins compared to dizygotic twins were analyzed using multiple logistic regression analysis. The estimated values (EV) of the difference of state and trait of anxiety scores were analyzed using a linear regression model. The coincidence of insomnia was not high in monozygotic twins compared to dizygotic twins. The difference in the state of anxiety score was comparable between monozygotic twins and dizygotic twins. However, the difference in anxiety scores was higher in dizygotic twins than in monozygotic twins. The monozygotic twin group did not demonstrate higher coincidence of insomnia or the state of anxiety than the dizygotic twin group. However, the monozygotic twin group indicated higher coincidence of the trait of anxiety than the dizygotic twins. The current results implied the potential contribution of heritable factors for the trait of anxiety.

Simulated Leaching of Foliar Applied Copper Bactericides on the Soil Microbiome Utilizing Various Beta Diversity Resemblance Measurements.

ABSTRACTCopper bactericides are routinely used to control Xanthomonas perforans (XP), causal agent of bacterial spot of tomato. Given the widespread tolerance to copper in XP strains in FL, USA, nanotechnology-based elemental composites have gained interest for their potential applications in agriculture in part due to their enhanced antimicrobial properties and toxicity to copper-tolerant strains. However, little is known about the potential impact of conventional copper bactericides as well as nano-based elemental composites on soil microbial communities, as determined by high-throughput sequencing of the 16S rDNA. We compared the effects of 2 and 200 μg/mL of core-shell (CS), a metallic copper composite, and a conventional copper bactericide + mancozeb (Cu+Man) on the soil microbiome. These treatments were compared to three controls, the microbial profile of the soil prior to application of copper products, a water application, and spiking the soil with a soilborne phytobacterium, Ralstonia solanacearum (RS). The RS treatment was included to determine if downstream analysis could detect the artificial inoculation. Utilizing multiple β diversity measurements, each emphasizing various tenets of ecology, provided a greater perspective of the effects the treatments had on the microbiome. Analysis of HTS data revealed that the two treatments containing field applied rates of metallic copper, CS 200 and Cu+Man, had the largest impact on the soil microbiome at seven-days posttreatment compared to water. However, we simulated field applied rates of CS 200 entering the soil by treating soil with CS 2 and determined this concentration had a negligible effect on the soil microbiome.IMPORTANCE Nanotechnology-based elemental composites have gained popularity for their potential applications in plant disease management due to their enhanced antimicrobial properties. However, little is known about their potential impact on the environment. Foliar applications of nano metallic composites upon leaching into the soil have the potential to impact soil microbial populations that in turn influence soil health. Utilizing multiple β diversity measurements, high-throughput sequencing analysis revealed that field applied rates of metallic copper (200 μg/mL) from an advanced copper composite (core-shell [CS]) and a conventional copper bactericide in combination with mancozeb had the largest impact on the soil microbiome compared to water and nontreated control. To simulate leaching from the leaf surface, a lower concentration (2 μg/mL) of CS was also applied to the soil and had a negligible effect on the soil microbiome. Thus, field applied rates of CS may have a minimal effect on soil microbial communities.

First report of Grapevine enamovirus 1 in Vitis vinifera cultivar 'Meunier' in France.

Grapevine enamovirus 1 (GEV-1) is a member of the genus Enamovirus in the family Solemoviridae. GEV-1 was first described in 2017 in a few grapevine cultivars in Brazil (Silva et al. 2017) and subsequently in China (Ren et al. 2021). We first identified GEV-1 using high throughput sequencing (Illumina, NOVASeq SP, TruSeq mRNA stranded 2*150 bp) of ribosomal RNA depleted total RNAs extracts using RNeasy Plant mini kit) (Qiagen) from a Vitis vinifera 'Meunier' leaf sample collected in a more than 20 year old commercial vineyard in the Champagne region of France in 2019. Analyses of the 47,573,330 total reads were performed using CLC Genomics Workbench 12.0 software (Qiagen) as previously described (Hily et al. 2018). The GEV-1 genome, determined only from the HTS data (isolate GEV-1-Fr; GenBank accession No. MW760844), is 6 262 nucleotides (nt) long and fully covered with 5,706 reads (mapping parameters of 0,5 in length and 0,7 in similarity fractions using CLC). Compared with the previously determined sequences (NC_034836 and KX645875) from Brazil, the GEV-1-Fr sequence contain a few indels, including a deletion of 9 nt in the 5' untranslated region (UTR), an insertion of 3 nt located in the overlapping region of the open reading frame (ORF)1 and ORF2, and a single nt insertion in the non-coding region between ORF2 and ORF3. These indels also exist within the sequence of isolate SD-CG from China (MT536978). However, GEV-1-Fr contains a unique 45 nt insertion in the 3'-UTR, although this needs to be verified using standard assays. Overall, GEV-1-Fr exhibits 88.7, 89.1 and 93.3 % identity at the nt level with isolates from Brazil (NC_034836, KX645875) and China (MT536978), respectively. The GEV-1-infected 'Meunier' grapevine showed symptoms of light chlorotic patterns on the leaves that were probably due to the presence of other co-infecting viruses, including Grapevine fanleaf virus, Grapevine Pinot gris virus, Grapevine rupestris stem pitting-associated virus and Grapevine fleck virus. The detection of GEV-1 was further confirmed in the 'Meunier' grapevine via RT-PCR using newly designed primer pairs Fwd_GEV_5600: GCAAGGAGCAGCCCTATAATGCT and Rev_GEV_6075: CTAGTCGATACGATCTATAGGCGAGG that amplified a 474 bp fragment of ORF5. We also designed a TaqManTM assay in OFR5 with the following primers and probe; Fwd_GEV_5662: ACAAGTGCCYGTTTCCATAG, Probe_GEV_5724-FAM: TTTACCGAGGACTATGACGCCGC, Rev_GEV_5772: CACCGGCTCCATAACCATT. Among all the samples from different grapevine cultivars and geographic regions in France that were tested with the TaqMan assay (N=188), only the original 'Meunier' plant from Champagne was positive for GEV-1. To our knowledge, this is the first report of GEV-1 in France and in European vineyards in general. Although many aspects of the virus biology are yet to be elucidated, our results expand its geographical range. New GEV-1 detection primers can be developed, considering its genetic diversity, to facilitate its detection and further define its evolutionary history. Compared to the original sequences (NC_034836 and KX645875) in Brazil a few indels have been identified, including a deletion of 9nt located in the 5' untranslated region (UTR), an insertion of 3nt located in the overlapping region of the open reading frame (ORF)1 and ORF2 and a single nucleotide insertion in the non-coding region between ORF2 and ORF3. All indels were already described in the Chinese sequence (MT536978). However, this new GEV-1-Fr isolate is the only one that displays a 45nt insertion in the 3'-UTR. Overall, GEV-1-Fr exhibits 88.7, 89.1 and 93.3 % identity with isolates from Brazil (NC_034836, KX645875) and China (MT536978), respectively. No specific symptoms were observed in the GEV-1-infected 'Meunier' grapevine other than light chlorotic patterns on the leaves that were probably due to the presence of other virus, as this plant was co-infected with grapevine fanleaf virus (GFLV), grapevine Pinot gris virus (GPGV), grapevine rupestris stem pitting-associated virus (GRSPaV) and grapevine fleck virus (GFkV). The detection of GEV-1 was further confirmed via RT-PCR using newly designed primer pairs located in the 'aphid transmission protein' producing a 474 nt amplicon; Fwd_GEV_5600: GCAAGGAGCAGCCCTATAATGCT; Rev_GEV_6075: CTAGTCGATACGATCTATAGGCGAGG. GEV-1 was detected in all cuttings (N=15) obtained from the original plant. We also designed a tool for a TaqManTM-based detection in the same genome region as mentioned above; Fwd_GEV_5662: ACAAGTGCCYGTTTCCATAG; Probe_GEV_5724-FAM: TTTACCGAGGACTATGACGCCGC; Rev_GEV_5772: CACCGGCTCCATAACCATT. Among all the samples from different grapevine cultivars and geographic regions in France that were tested with the TaqMan assay (N=188), only the original 'Meunier' plant from Champagne was found positive for GEV-1 in gapevine in France.

Computational Analysis of HTS Data and Its Application in Plant Pathology.

High-throughput sequencing is a basic tool of biological research, and it is extensively used in plant pathology projects. Here, we describe how to handle data coming from a variety of sequencing experiments, focusing on the analysis of Illumina reads. We describe how to perform genome assembly and annotation with DNA reads, correctly analyze RNA-seq data to discover differentially expressed genes, handle amplicon sequencing data from microbial communities, and utilize small RNA sequencing data to predict miRNA sequences and their putative targets.

Highly Accurate Filters to Flag Frequent Hitters in AlphaScreen Assays by Suggesting their Mechanism.

AlphaScreen is one of the most widely used assay technologies in drug discovery due to its versatility, dynamic range and sensitivity. However, a presence of false positives and frequent hitters contributes to difficulties with an interpretation of measured HTS data. Although filters do exist to identify frequent hitters for AlphaScreen, they are frequently based on privileged scaffolds. The development of such filters is time consuming and requires deep domain knowledge. Recently, machine learning and artificial intelligence methods are emerging as important tools to advance drug discovery and chemoinformatics, including their application to identification of frequent hitters in screening assays. However, the relative performance and complementarity of the Machine Learning and scaffold-based techniques has not yet been comprehensively compared. In this study, we analysed filters based on the privileged scaffolds with filters built using machine learning. Our results demonstrate that machine-learning methods provide more accurate filters for identification of frequent hitters in AlphaScreen assays than scaffold-based methods and can be easily redeveloped once new data are measured. We present highly accurate models to identify frequent hitters in AlphaScreen assays.

A nuclear phylogenomic study of the angiosperm order Myrtales, exploring the potential and limitations of the universal Angiosperms353 probe set.

To further advance the understanding of the species-rich, economically and ecologically important angiosperm order Myrtales in the rosid clade, comprising nine families, approximately 400 genera and almost 14,000 species occurring on all continents (except Antarctica), we tested the Angiosperms353 probe kit. We combined high-throughput sequencing and target enrichment with the Angiosperms353 probe kit to evaluate a sample of 485 species across 305 genera (76% of all genera in the order). Results provide the most comprehensive phylogenetic hypothesis for the order to date. Relationships at all ranks, such as the relationship of the early-diverging families, often reflect previous studies, but gene conflict is evident, and relationships previously found to be uncertain often remain so. Technical considerations for processing HTS data are also discussed. High-throughput sequencing and the Angiosperms353 probe kit are powerful tools for phylogenomic analysis, but better understanding of the genetic data available is required to identify genes and gene trees that account for likely incomplete lineage sorting and/or hybridization events.

Occurrence and distribution of microbial pollutants in coastal areas of the Adriatic Sea influenced by river discharge

The transport of a variety of pollutants from agricultural, industrial and urbanised areas makes rivers major contributors to the contamination of coastal marine environments. Too little is known of their role in carrying pathogens to the coast. We used DNA-based metabarcoding data to describe the microbial community composition in seawater and sediment collected in front of the estuary of the Tronto, the Chienti and the Esino, three Italian rivers with different pollution levels that empty into the north-central Adriatic Sea, and to detect and measure within these communities the relative abundance of microbial pollutants, including traditional faecal indicators and alternative faecal and sewage-associated pollutants. We then applied the FORENSIC algorithm to distinguish human from non-human sources of microbial pollution and FAPROTAX to map prokaryotic clades to established metabolic or other ecologically relevant functions. Finally, we searched the dataset for other common pathogenic taxa. Seawater and sediment contained numerous potentially pathogenic bacteria, mainly faecal and sewage-associated. The samples collected in front of the Tronto estuary showed the highest level of contamination, likely sewage-associated. The pathogenic signature showed a weak but positive correlation with some nutrients and strong correlations with some polycyclic aromatic hydrocarbons. This study confirms that rivers transport pathogenic bacteria to the coastal sea and highlights the value of expanding the use of HTS data, source tracking and functional identification tools to detect microbial pollutants and identify their sources with a view to gaining a better understanding of the pathways of sewage-associated discharges to the sea.

High throughput sequencing from Angolan citrus accessions discloses the presence of emerging CTV strains

BackgroundCitrus industry is worldwide dramatically affected by outbreaks of Citrus tristeza virus (CTV). Controls should be applied to nurseries, which could act as diversity hotspots for CTV. Early detection and characterization of dangerous or emerging strains of this virus greatly help to prevent outbreaks of disease. This is particularly relevant in those growing regions where no dedicated certification programs are currently in use.MethodsDouble-stranded RNA extracted from Citrus spp. samples, collected in two locations in Angola, were pooled and submitted to a random-primed RNA-seq. This technique was performed to acquire a higher amount of data in the survey, before the amplification and sequencing of genes from single plants. To confirm the CTV infection in individual plants, as suggested by RNA-seq information from the pooled samples, the analysis was integrated with multiple molecular marker amplification (MMM) for the main known CTV strains (T30, T36, VT and T3).ResultsFrom the analysis of HTS data, several assembled contigs were identified as CTV and classified according to their similarity to the established strains. By the MMM amplification, only five individual accessions out of the eleven pooled samples, resulted to be infected by CTV. Amplified coat protein genes from the five positive sources were cloned and sequenced and submitted to phylogenetic analysis, while a near-complete CTV genome was also reconstructed by the fusion of three overlapping contigs.ConclusionPhylogenetic analysis of the ORF1b and CP genes, retrieved by de novo assembly and RT-PCR, respectively, revealed the presence of a wide array of CTV strains in the surveyed citrus-growing spots in Angola. Importantly, molecular variants among those identified from HTS showed high similarity with known severe strains as well as to recently described and emerging strains in other citrus-growing regions, such as S1 (California) or New Clade (Uruguay).

BCO App: tools for generating BioCompute Objects from next-generation sequencing workflows and computations

The BioCompute Object (BCO) standard is an IEEE standard (IEEE 2791-2020) designed to facilitate the communication of next-generation sequencing data analysis with applications across academia, government agencies, and industry. For example, the Food and Drug Administration (FDA) supports the standard for regulatory submissions and includes the standard in their Data Standards Catalog for the submission of HTS data. We created the BCO App to facilitate BCO generation in a range of computational environments and, in part, to participate in the Advanced Track of the precisionFDA BioCompute Object App-a-thon. The application facilitates the generation of BCOs from both workflow metadata provided as plaintext and from workflow contents written in the Common Workflow Language. The application can also access and ingest task execution results from the Cancer Genomics Cloud (CGC), an NCI funded computational platform. Creating a BCO from a CGC task significantly reduces the time required to generate a BCO on the CGC by auto-populating workflow information fields from CGC workflow and task execution results. The BCO App supports exporting BCOs as JSON or PDF files and publishing BCOs to both the CGC platform and to GitHub repositories.

BCO App: tools for generating BioCompute Objects from next-generation sequencing workflows and computations

The BioCompute Object (BCO) standard is an IEEE standard (IEEE 2791-2020) designed to facilitate the communication of next-generation sequencing data analysis with applications across academia, government agencies, and industry. For example, the Food and Drug Administration (FDA) supports the standard for regulatory submissions and includes the standard in their Data Standards Catalog for the submission of HTS data. We created the BCO App to facilitate BCO generation in a range of computational environments and, in part, to participate in the Advanced Track of the precisionFDA BioCompute Object App-a-thon. The application facilitates the generation of BCOs from both workflow metadata provided as plaintext and from workflow contents written in the Common Workflow Language. The application can also access and ingest task execution results from the Cancer Genomics Cloud (CGC), an NCI funded computational platform. Creating a BCO from a CGC task significantly reduces the time required to generate a BCO on the CGC by auto-populating workflow information fields from CGC workflow and task execution results. The BCO App supports exporting BCOs as JSON or PDF files and publishing BCOs to both the CGC platform and to GitHub repositories.