Read Counts Research Articles

Systematic evaluation of the Precision ID GlobalFiler™ NGS STR panel v2 using single-source samples of various quantity and quality and mixed DNA samples

Massively parallel sequencing (MPS) techniques were developed approximately 15 years ago. Meanwhile, several MPS kits for forensic identification, phenotypic information, ancestry, and mitochondrial DNA analysis have been developed and their use has been established. Sequencing short tandem repeats (STRs) has certain advantages over the currently used length-based genotyping methods, which are based on PCR amplification followed by capillary electrophoresis (CE). MPS is more discriminative and includes the possibility of testing high numbers of targets (> 100), different types of markers [STRs and single nucleotide polymorphisms (SNPs)], as well as the use of smaller amplicons (< 300 bp). This study evaluated in 24 experimental runs the Precision ID GlobalFiler™ NGS STR panel v2 from ThermoFisher, which targets 31 autosomal STRs, amelogenin, and three Y-markers (one STR, SRY, and Yindel). Single-source samples were used in 18 experimental runs, for systematic evaluation. These included assessing library preparation benchmark conditions, limited DNA input, as well as testing repeatability, number of samples per run, and degraded DNA samples. Full profiles were consistently obtained from as little as 50 pg DNA input. Using the optional recovery PCR method improved outcomes for samples with low DNA input. Full profiles were also obtained from severely degraded DNA samples with degradation indices (DI) of > 60. In addition, six experimental runs were performed testing various two-person mixtures with mixture ratios ranging from 1:20 to 20:1. Major and minor contributors were distinguishable by their read counts (coverage), because less DNA input yielded lower read counts, analogous to the traditional CE technology, where less DNA produces lower peak heights. Mixture ratios of approximately 1:1 were indistinguishable, while a greater imbalance, i.e., higher mixture ratios, made the mixture more distinguishable between major and minor contributors. Based on this information, the highest success rate of correctly deconvoluted four-allelic loci was from mixtures with 1:3 ratios. At higher mixture ratios, the drop-out rate of the minor contributor increased, reducing the number of four-allelic loci.

Major changes in AD brain gene expression caused by the T9861C mtDNA mutation

AbstractBackgroundEvidence of mitochondrial dysfunction in Alzheimer’s Disease (AD) has accumulated over the years. We have previously identified a mutation in mitochondrial DNA (mtDNA) that is strongly associated with AD. This T9861C mutation changes a phenylalanine into a leucine at amino acid position 219 of cytochrome c oxidase subunit 3, resulting in a significant reduction of cytochrome oxidase activity. We report the effect of this mutation on the expression of genes involved in mitochondrial function in AD brains.MethodTotal RNA sequencing with ribosome depletion was performed on frozen archival tissue from parietal cortex of three control and eight sporadic AD (four AD, and four AD+ (AD patients possessing the T9861C mtDNA mutation)) brains. Reads were aligned to a transcriptome reference (Ensemble, GCRh38) containing all annotated mRNA and noncoding RNA using kallisto bus (v0.48). Read counts were calculated for genes using bustools (v0.41). Count data were analyzed using RNAfuzzyApp. Read count normalization was performed using TMM and differential gene expression was determined using edgeR. Pathway enrichment was performed using Gene Ontology (GO).ResultThe heatmap shown identifies a total of 472 deferentially expressed genes (DEGs, p value &lt;0.05, FDR &lt;0.05) in the comparison of control vs AD vs AD+ samples. Principal component analysis shows that gene expression in AD samples is significantly different from that in controls and that a more striking difference exists between the AD+ samples and both control and AD samples. The 472 DEGs can be grouped into five clusters. These clusters impact a variety of mitochondrial functions related to synaptic signaling, cell junctions, neuron projections, the HIF‐1 signaling pathway, voltage‐gated channel activity and carbohydrate metabolism.ConclusionThe presence of the T9861C mutation in the brains of AD patients has a significant effect on expression of the brain transcriptome in the parietal cortex. There is a general and robust decrease in expression of the majority of DEGs relative to control and non‐mutation containing AD brains. There is also a smaller number of DEGs that are upregluated compared to control and non‐mutation containing AD brains. These gene expression changes impact mitochondrial function resulting in neuronal failure and neurodegeneration.

FedExosome: profiling AD‐related mitochondrial DNA and microRNAs isolated from neuronal‐enriched peripheral blood packages

AbstractBackgroundAlzheimer’s disease (AD) is a complex disease characterized by impaired memory, diminished cognition, and specific brain pathology (i.e., accumulation of amyloid beta plaques and tau tangles). AD progression is often difficult to detect with the earliest clinical symptoms appearing decades after key neuropathological changes have occurred. Accurate diagnosis early, between initial disease onset and clinical manifestation, could reveal critical aspects of pathogenesis and inform the development of effective therapeutic strategies to prevent neuronal death. Evidence suggests that early alterations in the AD brain can propagate to local and distal cells through the biological packages secreted by neurons. These neuronal‐enriched extracellular vesicles (NEEVs) can cross the blood‐brain barrier and are capable of mediating systemic inflammation in response to CNS‐sourced stress through their mitochondrial DNA (mtDNA) and microRNA (miRNA) cargo. Circulating NEEVs may therefore serve as an easily accessible, early indicator of the forthcoming neuropathological changes in the AD brain. Here, we develop a higher throughput workflow for profiling mtDNA and miRNA in plasma NEEVs and apply the workflow to clinical samples collected longitudinally from participants in the Texas Alzheimer’s Research and Care Consortium.MethodHuman plasma samples were processed using a two‐step method involving (1) precipitation of total exosomes with ExoQuick (SBI) and (2) immunoprecipitation‐based enrichment of NEEVs with a biotinylated antibody against a well‐established neuronal surface marker (CD171). Captured NEEVs were stained and analyzed using flow cytometry as well as nanoparticle tracking analysis (NTA). DNA and RNA were then extracted for mtDNA quantification and miRNA sequencing.ResultThe protocol modifications implemented in this project were compared based on particle size, distribution, and purity as well as absolute mtDNA load and spike‐in normalized miRNA read counts. These data not only demonstrate the feasibility of profiling the mtDNA and miRNA from 500µL of plasma NEEVs but have also resulted in a higher throughput workflow for processing TARCC patient samples.ConclusionThis work represents the first successful attempt to simultaneously quantify mtDNA and sequence miRNA in the relatively small subpopulation of plasma EVs that originate from neurons. Future studies will harness the protocol developed herein to assess the biomarker potential of NEEVs in the TARCC cohort.

Extracellular Vesicles from Human Adipose Tissue-Derived Mesenchymal Stem Cells Suppress RANKL-Induced Osteoclast Differentiation via miR122-5p.

Researchers are increasingly interested in cell therapy using mesenchymal stem cells (MSCs) as an alternative remedy for osteoporosis, with fewer side effects. Thus, we isolated and characterized extracellular vesicles (EVs) from human adipose tissue-derived MSCs (hMSCs) and investigated their inhibitory effects on RANKL-induced osteoclast differentiation. Purified EVs were collected from the supernatant of hMSCs by tangential flow filtration. Characterization of EVs included typical evaluation of the size and concentration of EVs by nanoparticle tracking analysis and morphology analysis using transmission electron microscopy. hMSC-EVs inhibited RANKL-induced differentiation of bone marrow-derived macrophages (BMDMs) into osteoclasts in a dose-dependent manner. F-actin ring formation and bone resorption were also reduced by EV treatment of osteoclasts. In addition, EVs decreased RANKL-induced phosphorylation of p38 and JNK and expression of osteoclastogenesis-related genes in BMDMs treated with RANKL. To elucidate which part of the hMSC-EVs plays a role in the inhibition of osteoclast differentiation, we analyzed miRNA profiles in hMSC-EVs. The results showed that has-miR122-5p was present at significantly high read counts. Overexpression of miR122-5p in BMDMs significantly inhibited RANKL-induced osteoclast differentiation and induced defects in F-actin ring formation and bone resorption. Our results also revealed that RANKL-induced phosphorylation of p38 and JNK and osteoclast-specific gene expression was decreased by miR122-5p transfection, which was consistent with the results of hMSC-EVs. These findings suggest that hMSC-EVs containing miR122-5p inhibit RANKL-induced osteoclast differentiation via the downregulation of molecular mechanisms and could be a preventive candidate for destructive bone diseases.

Detection of KMT2A Partial Tandem Duplications ( KMT2A-PTDs) in Healthy Donors Using Next Generation Sequencing

Introduction KMT2A ( MLL) fusions are relevant biomarkers in myeloid malignancies. KMT2A-PTD (also called MLL-PTD, partial tandem duplication), a non-fusion gene rearrangement, also occurs and serves as an important biomarker for Menin-inhibitors. Traditionally, KMT2A-PTDs are detected by RT-qPCR (quantitative real-time PCR). Here we report the detection of KMT2A-PTDs in whole blood samples taken from healthy donors using the Oncomine TM Myeloid Assay GX v2, which is a next-generation sequencing (NGS) based assay that enables efficient detection of multiple genetic alterations in parallel. KMT2A-PTD positive clinical research samples were used as a control in this study. This study was initiated based on reports from various healthcare professionals who observed several healthy blood donors identified with KMT2A-PTD using the Oncomine TM Myeloid Assay GX v2. As a result, we conducted tests on healthy controls and compared them with clinical research samples known to have myeloid malignancies to explore this genomic alteration further. Methods We sequenced 20 whole blood samples (total 104 replicates) from healthy donors at 3 different sites of Thermo Fisher Scientific TM (South San Francisco, CA; Guilford, CT; Carlsbad, CA). These whole blood samples were received from Stanford Blood Center or purchased from Discovery Life Sciences. As a control, we also sequenced 20 clinical research samples (total 85 replicates) that are known to have myeloid malignancies in both Sonora Quest Laboratories and at Thermo Fisher Scientific TM South San Francisco site. These samples were run on the Ion Torrent TM Genexus TM Software 6.6 and analyzed using the Oncomine TM Myeloid Assay GX v2 with fusion profiling, where the assay design targets 6 different KMT2A-PTD variants. Results The data set has mean read length 90 - 120bp and total mapped fusion reads 20,000 - 30,000. We have found evidence of KMT2A-PTDs in confirmed positive samples from myeloid malignancies (including: AML, acute myeloid leukemia; MPN, myeloproliferative neoplasms; MDS, myelodysplastic syndromes; CML, chronic myeloid leukemia; CMML, chronic myelomonocytic leukemia; or JMML, juvenile myelomonocytic leukemia) and healthy donor samples. The read counts of PTDs detected in the healthy donor samples are on average about 1/3 of the read counts of PTDs detected in the myeloid positive samples, but the distributions overlap. Approximately 33% of myeloid positive samples have PTD read counts higher than the maximum read counts detected in any of the healthy donor samples. The sequences of KMT2A-PTDs found in both healthy donor samples and myeloid samples were further analyzed in BLAT (BLAST-Like Alignment Tool) to confirm that they match the specific exons on the KMT2A gene. Based on this data, only samples with myeloid malignancies have &amp;gt;2000 reads that support KMT2A-PTDs with this assay. Conclusions We describe the detection of KMT2A-PTDs in a range of clinical research samples, including healthy controls as well as samples with myeloid malignancies. KMT2A-PTDs were detected in healthy control samples at lower levels, however, one third of myeloid samples showed significantly higher KMT2A-PTD read counts compared to healthy controls. This intriguing finding warrants further investigation, as it opens up the possibility of conducting a prospective study to follow up on these individuals and observe if they develop myeloid malignancies. Moreover, this finding presents an opportunity for conducting retrospective studies to explore whether healthy controls previously identified with this alteration several years ago after blood donation were subsequently recorded in the health national system with myeloid malignancies. (For research use only. Not for use in diagnostic procedures.)

Transposable Elements Are Differentially Expressed in MDS Stem Cells in a Disease-Stage-Specific Manner

Introduction Transposable elements (TEs) are widely spread across the human genome. Although TEs have been implicated in the pathogenesis of hematological malignancies, their expression in myelodysplastic syndromes (MDS) has not yet received extensive analysis. In the present study, we performed RNA sequence analysis of bone marrow stem and progenitor cells from patients with MDS and secondary acute myeloid leukemia (AML) and profiled differentially expressed TEs. Methods Bone marrow samples were obtained from each patient at the time of diagnosis. Normal bone marrow (NBM) samples from four healthy individuals were purchased from Lonza and processed in the same manner. After separation of CD34 + cells using magnetic beads, we purified lineage marker-negative (Lin -) CD34 + CD38 - stem cells and Lin - CD34 + CD38 + progenitor cells by cell sorting. The sorted cells were subjected to RNA-seq analysis using oligo-dT primers, which can capture poly-adenylated TE transcripts. Each read was aligned to the reference genome, then the annotation of the transcripts was performed using RepeatMasker, a program that screens DNA sequences for interspersed repeats and low-complexity DNA sequences (https://www.repeatmasker.org). Read counts for individual TEs were aggregated to the level of TE class, family, and subfamily according to hierarchies defined by the human RepeatMasker (repClass, repFamily, and repName, respectively). To generate raw counts of individual TE regions in RepeatMasker, the bedtools map function (version 2.27.1) was used with bed files of each sample. Normalization and significant expression differences were detected using DESeq2 (version 2.2.1). Differentially expressed TEs were defined using the cutoff of adjusted P values (q &amp;lt; 0.05). DESeq2 normalized counts were obtained from read counts by considering sequence depth, gene length, and RNA composition. Normalized counts were z-score scaled, and then subjected to uniform manifold approximation and projection (UMAP). Results Thirty-two patients were included in the present study. According to the 2016 revision to the World Health Organization classification (Arber DA, et al. Blood. 2016; 127: 2391-2405), 13 patients (40.6%) were diagnosed with MDS with multilineage dysplasia (MDS-MLD), 15 patients (46.9%) with MDS with excess blasts (MDS-EB), and 4 patients (12.5%) with AML with myelodysplasia-related changes (AML-MRC). We found that 26 and 17 repName TEs were significantly up-regulated in stem and progenitor cells compared with their NBM counterparts, and the majority of them belonged to LTRs, followed by DNA transposons. Since each LTR has multiple individual regions where similar sequences are scattered, LTR expression was further distinguished by the genomic locations of individual LTRs. Comparison of MDS-MLD, MDS-EB, and AML/MRC stem and progenitor cells to NBM counterparts revealed that more LTRs were differentially expressed in stem cells than in progenitor cells. Among them, many of the up-regulated LTRs were shared among different disease types (MDS-MLD, MDS-EB, and AML-MRC) in both stem and progenitor cells. AML-MRC had the largest number of differentially up- and down-regulated LTRs in stem cells. MDS-EB had the largest number of differentially up-regulated LTRs in progenitor cells, while only a small portion of LTRs were down-regulated in progenitor cells. LTRs at individual regions were differentially expressed in a more disease-specific manner, particularly in stem cells. Accordingly, UMAP plots that visualized the expression profiles of LTRs at individual regions showed that AML-MRC stem cells form a unique cluster, discriminating AML-MRC from MDS. To further understand the transcriptional reprogramming of LTRs during the disease progression from MDS to secondary AML, we analyzed the expression of differentially expressed LTRs at individual regions specific to AML-MRC by K-means clustering. They were subdivided into four clusters, of which Clusters 1, 3, and 4 showed gradual upregulation during disease progression, while Cluster 2 showed comparable upregulation at MDS and AML-MRC stages. Conclusions Our results suggest that specific LTRs behave in a disease-status and cell-type-specific manner in MDS and during leukemic transformation, providing fundamental information for understanding the pathogenesis of MDS.

Rapid Clinical Mutation Screening for AML Using the Genexus Platform

Introduction: The promise of “precision medicine” is using therapeutic agents matched to the inferred biology of a patient's particular disease. In acute myeloid leukemia this approach demands fast and accurate assessment of the mutation and cytogenetic landscape of each AML case. The NCI Myeloid Malignancies Molecular Analysis for Therapy Choice program (“myeloMATCH”) is a precision medicine clinical trial initiative across the NCI National Clinical Trials Network for patients with newly diagnosed acute myeloid leukemia and myelodysplasia. In myeloMATCH the goal is to have cytogenetic and mutation assays performed within 72 hours of central lab receipt, allowing for a rapid assignment of the appropriate clinical trial from the myeloMATCH trial portfolio. Here we report analytic performance of rapid genomic profiling laboratory developed test based on the Thermo Fisher Genexus Oncomine Myeloid Panel GXv2 (NCI Myeloid Assay version 2, or “NMAv2”) which performs rapid sequencing of SNVs, indels, and gene fusions, capturing 1,661 hotspot variants (45 genes) and 779 fusions (35 genes) with a turnaround time of &amp;lt;48 hours from specimen receipt to clinical reporting. Methods: The validation testing was performed by two harmonized laboratories; NCI Molecular Characterization laboratory (MoCha) and Fred Hutch Molecular Oncology (MO). We conducted analytical validation and harmonization experiments for CLIA/CAP and FDA regulatory compliant use in this trial across both accredited clinical laboratories with harmonized workflows. Samples are extracted with AllPrep per manufacture protocol. Library preparation with the Oncomine Myeloid Assay GX v2 and the sequencing reactions are performed on the automated Ion Torrent Genexus System. Sequencing data generated by NMAv2 was analyzed using Ion Torrent Genexus system version 6.6.2.1. The default manufacturer workflow has been customized using a custom designed hotspot browser extensible data (BED) file, sequence variant baseline (SVB) file, fusion target file, torrent variant caller (TVC) version 5.18 and Oncomine extended filter chain (OEFC) version 5.16. In NMA, the default 2.5%, and 3% VAFs were set up for calling hotspot and non-hotspots SNV/indel, 1% VAF for FLT3-ITD and 100 read counts for all fusion, except 2000 read counts for KMT2A-PTD. Results: The validation set included 148 unique samples and comprised of 76 AML/MDS patients (49 peripheral blood, 27 bone marrow aspirate), 51 healthy donor blood, 6 healthy donor bone marrow, 7 cancer cell lines, 5 normal HapMap cell lines, and 3 contrived materials encompassing 364 known mutations (143 SNPs, 132 indels, 27 fusions). 180 runs were completed for this validation (106 at FH and 74 at MoCha) with 89.44% success rate in passing run level metrics. Results of the validation testing are detailed in Table 1. High reproducibility in detecting all reportable variants was observed, with overall &amp;gt;99% mean positive percentage agreement (PPA) across both labs (Figure 1). The limit of detection for each variant type was &amp;lt;0.06% for hotspot SNVs, 0.07% for non-hotspot SNVs, 0.51% for hotspot indels, ~1% for non-hotspot indels, 0.23% for FLT3-ITDs, and 50 minimum reads at 0.1% tumor content for fusions. 99.39% concordance was observed by testing 86 blinded validation sample set with analytically validated orthogonal assays. Conclusions: The NMAv2 has high specificity, sensitivity, accuracy and reproducibility (inter-lab and intra-lab) with sequencing results generated within 48 hours of assay initiation. As such, the assay is well suited for use to rapidly categorize the genomic alteration of AML to support clinical care in patients with active myeloid malignancies, including those patients potentially enrolled in the myeloMATCH program. Table 1: Results of validation experiment results for the NMAv2 Figure 1: Harmonization results for the NMAv2 between MoCha and MO labs, Right Panel = Indel; Left panel = SNV

Gene Expression Machine Learning Models Classify Pediatric AML Subtypes with High Performance

Pediatric acute myeloid leukemia (pAML) encompasses over 20 molecular subtypes driven by unique genetic alterations, including hallmark chromosomal rearrangements and less frequently, point mutations or tandem duplications. Collectively, many of these pAML molecular categories are enriched in pediatric populations and are not represented by current classification systems, including the recently updated WHO or ICC. While fusion detection from RNA-Seq-based approaches is robust, many fusion negative subtypes would need to be defined by expression-based approaches as mutation calling from RNA-Seq data is less developed. Nevertheless, this could be challenging for subtypes with similar transcriptional profiles, such as those with shared HOX expression patterns, including NPM1, NUP98r, UBTF, DEK::NUP214, and KMT2A-PTD. To aid in the appropriate molecular classification of pAML, which is crucial for prognosis, we developed and compared three gene expression-based classifiers. A total of 1707 pAML gene expression profiles were mapped and analyzed from three distinct sources (St. Jude = 659; TARGET = 168; AAML1031 = 880). Raw read count data was normalized and scaled to obtain a relative expression value in transcripts per million (TPM), which served as input for feature selection, model training, and testing. Ground truth labels for all 1707 samples were obtained through multi-omics analysis, including whole genome sequencing, to identify fusions and mutations. For validation purposes, the data was stratified by subtypes and split 70/30 into training (n=1187) and testing (n=520). Three machine learning models were selected: random forest, XGboost, and linear support vector machine (SVM). Each sample had gene expression TPM data for 60,754 transcripts, out of which 20,004 transcripts related to protein-coding genes were incorporated for feature selection. Feature selection was performed using a median absolute deviation (MAD) algorithm to select the 3000 transcripts with the highest variability. Top variable 3000 genes were selected to allow for adequate tuning of the number of predictors. Each model was independently trained using stratified cross-validation and Monte-Carlo search for hyperparameter tuning. The best model was selected based on the Matthews Correlation Coefficient (MCC). Each model was tested on the hold-out TPM set with z-score normalization. On the hold-out testing set (n=520), the linear SVM model outperformed the random forest and XGboost models on five performance metrics across all subtypes (sensitivity=0.9577; precision=0.9577; specificity=0.9978; F1=0.96; accuracy=0.9958). The random forest (sensitivity=0.9154; precision=0.9154; specificity=0.9955; F1=0.92; accuracy=0.9915) and XGboost models (sensitivity=0.9231; precision=0.9231; specificity=0.9960; F1=0.92; accuracy=0.9923) also performed well across all subtypes. Although feature selection was shared across all three models, performance within each subtype varied between models. The linear SVM model demonstrated strong performance overall, driven by high specificity in classifying the KMT2Ar subgroup (n=127) and equal sensitivity across the GLIS-rearranged (n=14), GATA1 (n=10), BCL11B (n=7), CBFB::MYH11 (n=60), CEBPA (n=33), and RUNX1::RUNX1T1 (n=78) subtypes. The primary difference in performance between models is the high false positive rate for KMT2Ar and NPM1 (n=55) in the random forest and XGboost models. A preliminary hypothesis for this might be due to the large representation of KMT2Ar and NPM1 in the training data (24.85% and 10.78%, respectively). Synthetic upsampling (SMOTE) for the training dataset (n=1369) counteracts bias towards the majority classes and increases performance for the random forest (sensitivity=0.9327; precision=0.9327; specificity=0.9965; F1=0.93; accuracy=0.9933), XGboost (sensitivity=0.9404; precision=0.9404; specificity=0.9969; F1=0.94; accuracy=0.9940) and linear SVM (sensitivity=0.9615; precision=0.9615; specificity=0.9980; F1=0.96; accuracy=0.9962) models. Conjointly, these models demonstrate the utility and effectiveness of a machine learning approach for classifying pAML samples from transcriptome sequencing data, which may have broad clinical and research utility, especially for fusion negative subtypes.

Single-Cell Genomics Details the Maturation Block in BCP-ALL and Identifies Therapeutic Vulnerabilities in DUX4-Rearranged Cases

B cell progenitor acute lymphoblastic leukemia (BCP-ALL) is the most common childhood malignancy. It is initiated by multiple genetic alterations, causing a maturation arrest and accumulation of abnormal progenitor B cells. Current treatment protocols with chemotherapy have led to favorable outcomes, but are associated with significant toxicity and risk of side effects, highlighting the necessity for highly effective, less toxic, targeted drugs that also show efficacy in children experiencing relapse. We have used multimodal single-cell sequencing to delineate the transcriptional, epigenetic, and immunophenotypic characteristics of 23 childhood BCP-ALLs, including the BCR::ABL1-positive, ETV6::RUNX1-positive, high hyperdiploid, and recently discovered DUX4-rearranged subtypes. In total, 188,546 single cells derived from 23 BCP-ALLs and 9 normal bone marrow samples were profiled using the 10X Genomics platform. Cellranger multi and atac count (10X Genomics) were used to generate read count matrices. Further analysis was performed using Seurat and Signac. The in-house developed program SingleCellProjections was used to create a normal bone marrow reference graph onto which the BCP-ALL data was projected. Projection of the ALL cells along the normal B cell differentiation axis, revealed a diversity in the maturation block between the different BCP-ALL subtypes. Whereas the BCR::ABL1-, ETV6::RUNX1-positive and high hyperdiploidy cells mainly showed similarities to normal pro-B cells, the DUX4-rearranged ALL cells also displayed a transcriptional signature resembling mature B cells. In addition, the blast population in DUX4-rearranged ALLs showed multilineage priming toward non-hematopoietic cells, myeloid and T cell lineages, but also an activation of PI3K/AKT signaling that sensitized the cells to PI3K inhibition. Finally, we show that chimeric antigen receptor T cell therapy targeting the upregulated myeloid receptor CD371 (CLL-1), effectively eliminates DUX4-rearranged ALL cells. Our results provide a detailed characterization of BCP-ALL at the single-cell level and reveal therapeutic vulnerabilities in the DUX4-rearranged subtype with important implications for the understanding of ALL biology and new therapeutic strategies.

A Comprehensive Genomic Profiling of Myeloid Malignancies Demonstrates Mutational Spectrum of DNA Variants Including FLT3-ITDs, and Gene Fusions

Introduction Myeloid malignancies are a group of clonal hematopoietic disorders including acute myeloid leukemia (AML), myeloproliferative neoplasms (MPN), myelodysplastic syndromes (MDS), chronic myeloid leukemia (CML), chronic myelomonocytic leukemia (CMML), and juvenile myelomonocytic leukemia (JMML). These diseases are heterogenous and genetically complex and the number of genetic alterations associated with them continues to grow. To address the need for comprehensive genetics research we developed the Oncomine TM Myeloid Assay and the Oncomine TM Myeloid Assay GX v2, which both detect genomic alterations associated with myeloid malignancies including 45 DNA genes and &amp;gt;30 fusion driver genes with the ability to detect &amp;gt;700 fusion isoforms. Our panel includes genetic alterations in AML, MPN, and MDS such as FLT3-Internal Tandem Duplications (ITDs), IDH1/2, CEBPA, CALR, and TP53. Methods Here we describe the genomic profiles of 8503 clinical research samples (including AML, MPN, MDS, CML, CMML, or JMML). A total of 4723 samples were run on the Ion GeneStudio TM S5 System and analyzed using the Oncomine TM Myeloid Research workflow on Ion Reporter TM 5.18. A total of 3780 samples were run on the Ion Torrent TM Genexus Software 6.6 and analyzed using the Oncomine TM Myeloid Assay GX v2. Results The turnaround time (the time between starting the run and NGS data report) was 23-25 hours and the hands-on time was around 1 hour, which enabled fast and accurate profiling of myeloid malignancies, and facilitated the timely availability of results. The success rate of the samples was 100%. Frequency of relevant mutations by gene Some genes had a higher rate of genetic mutations. TET2 has the highest percentage of detection (12.6%, 1068 positive sample/8503 total number of samples). Other important genes with the highest percentage of detection included ASXL1 9.3% (787/8503), DNMT3A 7.8% (665/8503), TP53 7.5%, (640/8503), SRSF2 6.9% (587/8503), JAK2 6.6%, (562/8503), RUNX1 5.2% (446/8503), SF3B1 5.0% (423/8503), IDH2 4.0% (337/8503), NRAS 3.8% (318/8503), FLT3 3.4% (292/8503), ABL1 3.2% (273/8503), BCOR 2.7% (229/8503), NPM1 2.4% (201/8503), and IDH1 1.0% (170/8503). Overall, 4797 (56.4%) of samples had a mutation in at least one gene in the panels, and the average number of mutations per samples was 2.3 within these samples. The most co-occurring gene pairs include ASXL1 and TET2, as well as SRSF2 and TET2, both are detected in more than 2% co-occurring gene pairs. Variant allele frequency by gene We observed a substantial difference between the variant allele frequency (VAF) of relevant DNA mutations (SNP + Insertions + Deletions) in different genes. ANKRD26 has a median VAF of 66%, whereas MYD88 has a median VAF of only 7%. (Fig.1) Mutation spectrum of FLT3ITD variants We observed FLT3-ITD in 3% of samples. We observed the VAF of FLT3-ITD variants averaged around 0.32. Interestingly, the length of the FLT3-ITD duplication is multi-modal, with the highest peak at around 34 bp. The maximum duplication length detected is 151 bp. Mutation spectrums of fusions Of the 8480 samples with RNA fusion profiling, 710 were fusion positive (8.4%). ABL1 is the most common fusion driver (432 samples, 5.1%), with 88% of these being BCR- ABL1. Other common fusion drivers include KMT2A (171 samples, 2.0%; 29% are KMT2A-MLLT3 and 26% are KMT2A-MLLT1), MYH11 (126 samples, 1.5%; all are CBFB-MYH11), RARA (117 samples, 1.4%; all are PML-RARA), RUNX1 (95 samples, 1.1%; 56% are RUNX1-RUNX1T1). A total of 17 distinct driver genes, 49 distinct gene pairs and 110 distinct gene isoforms were observed, including rare fusions (for example, ZMYM2-FGFR1, KAT6A-CREBBP, etc) that are not detectable by traditional methods like qPCR (quantitative polymerase chain reaction) or FISH (fluorescence in-situ hybridization). The read count of the fusion positive samples ranges from 21 to 113,000. (Fig.2) Conclusions The Oncomine Myeloid Assay is a fast, accurate, robust, and reproducible solution for comprehensive genomic profiling of myeloid malignancies. We describe the mutational spectrum of DNA variants and RNA fusions in a range of clinical research samples, exploring the VAF, the abundance of mutations in different genes, and the properties of observed gene fusions. (For research use only. Not for use in diagnostic procedures.)

Development of novel parameters for pathogen identification in clinical metagenomic next-generation sequencing.

Introduction: Metagenomic next-generation sequencing (mNGS) has emerged as a powerful tool for rapid pathogen identification in clinical practice. However, the parameters used to interpret mNGS data, such as read count, genus rank, and coverage, lack explicit performance evaluation. In this study, the developed indicators as well as novel parameters were assessed for their performance in bacterium detection. Methods: We developed several relevant parameters, including 10M normalized reads, double-discard reads, Genus Rank Ratio, King Genus Rank Ratio, Genus Rank Ratio*Genus Rank, and King Genus Rank Ratio*Genus Rank. These parameters, together with frequently used read indicators including raw reads, reads per million mapped reads (RPM), transcript per kilobase per million mapped reads (TPM), Genus Rank, and coverage were analyzed for their diagnostic efficiency in bronchoalveolar lavage fluid (BALF), a common source for detecting eight bacterium pathogens: Acinetobacter baumannii, Klebsiella pneumoniae, Streptococcus pneumoniae, Staphylococcus aureus, Hemophilus influenzae, Stenotrophomonas maltophilia, Pseudomonas aeruginosa, and Aspergillus fumigatus. Results: The results demonstrated that these indicators exhibited good diagnostic efficacy for the eight pathogens. The AUC values of all indicators were almost greater than 0.9, and the corresponding sensitivity and specificity values were almost greater than 0.8, excepted coverage. The negative predictive value of all indicators was greater than 0.9. The results showed that the use of double-discarded reads, Genus Rank Ratio*Genus Rank, and King Genus Rank Ratio*Genus Rank exhibited better diagnostic efficiency than that of raw reads, RPM, TPM, and in Genus Rank. These parameters can serve as a reference for interpreting mNGS data of BALF. Moreover, precision filters integrating our novel parameters were built to detect the eight bacterium pathogens in BALF samples through machine learning. Summary: In this study, we developed a set of novel parameters for pathogen identification in clinical mNGS based on reads and ranking. These parameters were found to be more effective in diagnosing pathogens than traditional approaches. The findings provide valuable insights for improving the interpretation of mNGS reports in clinical settings, specifically in BALF analysis.

Differential gene expression between viruliferous and non-viruliferous Schizaphis graminum (Rondani)

An experiment was performed to measure the effect of Cereal Yellow-Dwarf Virus (CYDV), strain CYDV-RPV, on gene expression in its insect vector, greenbug aphid (Schizaphis graminum (Rondani)). RNA was sampled in three replicates from four treatments (biotypes B and H with or without carried CYDV), at 0, 1, 2, 3, 5, 10, 15 and 20 days from the introduction of carrier and virus-free greenbugs to uninfected wheat cv. ‘Newton’. Illumina paired-end sequencing produced 1,840,820,000,000 raw reads that yielded 1,089,950,000 clean reads, which were aligned to two greenbug, Trinity transcriptome assemblies with bowtie2. Read counts to contigs were analyzed with principal components and with DESeq2 after removing contaminating contigs of wheat or microbial origin. Likelihood ratio tests with one transcriptome showed that CYDV influenced gene expression about seven-fold less than time or biotype, which were approximately equal. With the other transcriptome, virus, time, and biotype were about equally important. Pairwise comparisons of virus to no virus for each timepoint yielded estimates of fold-change that comprised expression profiles for each contig when ordered by timepoint. Hierarchical clustering separated expression profiles into 20 groups of contigs that were significantly differentially expressed for at least one timepoint. Contigs were also sorted by timepoint of maximally differential expression between virus and no virus. All contigs that were significantly differentially expressed at FDR = 0.05 were annotated by blast searches against NCBI nr and nt databases. Interesting examples of up-regulation with virus included a lysosomal-trafficking regulator, peptidylprolylisomerase, RNA helicase, and two secreted effector proteins. However, carried virus did not consistently change aphid gene expression overall. Instead there was complex interaction of time, biotype, host response, and virus.

Molecular mechanisms underpinning quantitative resistance to Phytophthora sojae in Glycine max using a systems genomics approach.

Expression of quantitative disease resistance in many host-pathogen systems is controlled by genes at multiple loci, each contributing a small effect to the overall response. We used a systems genomics approach to study the molecular underpinnings of quantitative disease resistance in the soybean-Phytophthora sojae pathosystem, incorporating expression quantitative trait loci (eQTL) mapping and gene co-expression network analysis to identify the genes putatively regulating transcriptional changes in response to inoculation. These findings were compared to previously mapped phenotypic (phQTL) to identify the molecular mechanisms contributing to the expression of this resistance. A subset of 93 recombinant inbred lines (RILs) from a Conrad × Sloan population were inoculated with P. sojae isolate 1.S.1.1 using the tray-test method; RNA was extracted, sequenced, and the normalized read counts were genetically mapped from tissue collected at the inoculation site 24h after inoculation from both mock and inoculated samples. In total, more than 100,000 eQTLs were mapped. There was a switch from predominantly cis-eQTLs in the mock treatment to an almost entirely nonoverlapping set of predominantly trans-eQTLs in the inoculated treatment, where greater than 100-fold more eQTLs were mapped relative to mock, indicating vast transcriptional reprogramming due to P. sojae infection occurred. The eQTLs were organized into 36 hotspots, with the four largest hotspots from the inoculated treatment corresponding to more than 70% of the eQTLs, each enriched for genes within plant-pathogen interaction pathways. Genetic regulation of trans-eQTLs in response to the pathogen was predicted to occur through transcription factors and signaling molecules involved in plant-pathogen interactions, plant hormone signal transduction, and MAPK pathways. Network analysis identified three co-expression modules that were correlated with susceptibility to P. sojae and associated with three eQTL hotspots. Among the eQTLs co-localized with phQTLs, two cis-eQTLs with putative functions in the regulation of root architecture or jasmonic acid, as well as the putative master regulators of an eQTL hotspot nearby a phQTL, represent candidates potentially underpinning the molecular control of these phQTLs for resistance.

Metabarcoding read abundances of orchid mycorrhizal fungi are correlated to copy numbers estimated using ddPCR

Summary Quantifying the abundances of fungi is key to understanding natural variation in mycorrhizal communities in relation to plant ecophysiology and environmental heterogeneity. High‐throughput metabarcoding approaches have transformed our ability to characterize and compare complex mycorrhizal communities. However, it remains unclear how well metabarcoding read counts correlate with actual read abundances in the sample, potentially limiting their use as a proxy for species abundances. Here, we use droplet digital PCR (ddPCR) to evaluate the reliability of ITS2 metabarcoding data for quantitative assessments of mycorrhizal communities in the orchid species Neottia ovata sampled at multiple sites. We performed specific ddPCR assays for eight families of orchid mycorrhizal fungi and compared the results with read counts obtained from metabarcoding. Our results demonstrate a significant correlation between DNA copy numbers measured by ddPCR assays and metabarcoding read counts of major mycorrhizal partners of N. ovata, highlighting the usefulness of metabarcoding for quantifying the abundance of orchid mycorrhizal fungi. Yet, the levels of correlation between the two methods and the numbers of false zero values varied across fungal families, which warrants cautious evaluation of the reliability of low‐abundance families. This study underscores the potential of metabarcoding data for more quantitative analyses of mycorrhizal communities and presents practical workflows for metabarcoding and ddPCR to achieve a more comprehensive understanding of orchid mycorrhizal communities.

PSIX-20 Differential Gene Expression in Peripheral White Blood Cells of Fertile and Sub-Fertile Beef Heifers

Abstract Infertility in beef heifers remains a major economic loss to the cow-calf production industry. Identifying beef heifers with superior genetic potential for improved fertility would increase profitability. Therefore, the study aimed to identify differences in transcriptome profile from peripheral white blood cells (pWBCs) of beef heifers with varying reproductive potential. To accomplish this, Angus-Simmental crossbred heifers were subjected to an estrus synchronization and fixed-time artificial insemination (AI) protocol (7-Day CO-Synch + CIDR). Fourteen days after AI, all heifers were exposed to fertile bulls for a 60-day natural breeding season. Depending on the presence or absence of conceptus 120 days post-AI, heifers were classified as fertile (pregnant heifers) or sub-fertile (non-pregnant by AI or bull-breeding). Pregnancies were terminated, and all animals in both groups were cycling when pWBCs were isolated. Total RNA was extracted from the PWBCs and subjected to the library preparation and sequencing on the Nova-Seq platform. The read counts were obtained after data filtering and quality control using FastQC v0.11.9 and MultiQC v1.12 and alignment to the Ensemble’s ARS UCD1.2 Bos taurus genome reference using STAR aligner v2.7.5 for fertile (n = 8) and sub-fertile (n = 5) beef heifers. The filtered data were subjected to differential expression analysis using DESeq2. We identified 300 significantly differentially expressed genes (DEGs) with P-value ≤ 0.05 and absolute (log2 fold change) ≥ 0.5. Among them, 34 genes including FKBP5, ADCY3, REPS1, LAMP1, CTSH, ADGRE5 were identified with padj &amp;lt; 0.05. DEGs were over-represented for pathways such as response to thyroid stimulus, immune response, response to lipid, neutrophil degranulation, and calcium ion binding. Some of these genes were reported as fertility-related, while the other genes are novel and have the potential to predict heifers with varying reproductive potential. A detailed understanding of the underlying biological mechanisms of the top genes and a follow-up study with a higher sample size at different time points could validate the potential candidates identified in this study.

Feasibility and Accuracy of Nanopore Transcriptome Sequencing for Pediatric Acute Leukemia Lineage Classification in a Resource-Constrained Setting

Introduction: Gene expression profiling (GEP) has the potential to offer alternative approaches to conventional testing methodologies (flow cytometry, immunohistochemistry, karyotyping, and FISH) for cancer diagnostics. Possible advantages of this approach include unified protocols, automated analyses obviating high-end technical expertise, and perhaps lower training, capital, and skilled human resource requirements in comparison with complex traditional testing. These factors are of particular value in resource-constrained settings, where even leukemia lineage classification is unavailable or inconsistent in many locations, with critical implications for risk stratification and treatment selection. The goals of this study were to (a) ascertain and subsequently improve the accuracy of a lineage classifier in a different context than the pre-testing stage, and (b) determine feasibility of mRNA sequencing for cancer classification in a low-resource setting. The approach utilized PCR-amplified full-length cDNA sequencing using Oxford Nanopore Technologies (ONT) PCR-cDNA barcoding kit (SQK-PCB109) and MinION Mk1b. Methods: One hundred and ten pediatric acute leukemia specimens were collected and sequenced at Indus Hospital &amp; Health Network (IHHN), Karachi, Pakistan and analyzed at the University of North Carolina at Chapel Hill (UNC), USA. Specimens (isolated peripheral blood and / or bone marrow mononuclear cells), frozen in liquid nitrogen or Zymo DNA/RNA shield at -80 oC, were acquired from IHHN's pediatric biorepository. Specimen-associated data included clinical history, CBC, flow cytometry, FISH and karyotyping results from routine clinical testing at the IHHN's ISO 15189-accredited clinical laboratory. The study site did not have prior experience with NGS and did not require external hands-on training for this project. 100 ng of extracted RNA from each of 48 B-ALL, 30 T-ALL, 31 AML, and 1 mixed phenotype acute leukemia specimens was used to prepare libraries. Sequencing data were transferred to UNC and analyzed with an acute leukemia lineage classification algorithm. Results: Following delivery of reagents and equipment, a 6-week protocol optimization and self-training phase utilized open-source protocols and training videos. The next runs to sequence 110 specimens were completed in 4 months by 2 technical staff at the study site. Using a probability threshold for confident calling (&amp;gt;0.8), established during the development phase at UNC, 91 of 110 specimens (82.7%) were called with high confidence. Of the confident calls, 85 were correctly classified (42 B-ALL, 21 T-ALL, and 22 AML), representing an accuracy of 93.4%. The read N50 of confidently classified, concordant specimens ranged from 270 to 745. The total number of reads ranged from 10,000 to &amp;gt;900,000, while the number of aligned reads ranged from 5,000 to 1.5 million. On the other hand, 6 (6.6%) confidently called specimens were discordant. Of these, the mixed phenotype specimen was classified as AML, but mixed phenotype acute leukemia is not included in our training dataset. One misclassified T-ALL specimen had a read count below 10,000 - a threshold demonstrated in the development phase to be optimal for correct classification. Two additional misclassified samples had very low diversity of transcripts express, which is a quality indicator in development. Read counts of these specimens ranged from 22,000 to 311,000, while aligned reads were between 10,000 and 293,000. Further investigation of these discordant calls is ongoing. Low confidence calls (17% of total), with a classification probability between less than 0.8, were either concordant (13) or discordant (6). Further investigation and retesting of these specimens to ascertain reproducibility of low confidence calls has not been performed yet. Conclusions: Taken together, the approach tested - with automated analysis, limited space, training and technical staffing requirements - was feasible at the study site. It provides proof of concept for a low-cost, ONT sequencing based GEP approach for acute leukemia diagnostics whereby testing can be performed in peripheral laboratories while analysis can be centralized, even across continents. Next steps include (a) incorporating these data into a new training model with updated quality control parameters for the next validation run, and (b) developing the genomic subtype classification results.

Inhibition of Topors Ubiquitin Ligase Augments the Efficacy of DNA Hypomethylating Agents through DNMT1 Stabilization

DNA hypomethylating agents (HMAs) are commonly used to treat myeloid malignancies including myelodysplastic syndrome (MDS) and acute myeloid leukemia (AML). However, their therapeutic effects are not satisfactory. We performed CRISPR-Cas9 knockout (KO) screening to identify molecular targets that enhance the efficacy of HMAs. MDS-L and MOLM-13 cells expressing Cas9 were infected with an sgRNA lentiviral library containing 12,409 sgRNAs targeting 1,383 epigenetic factors and exposed to low-dose HMAs, decitabine (DAC) or azacitidine (AZA), for 14 days. The read counts of sgRNA against TOPORS which encodes a ubiquitin/SUMO E3 ligase, were reproducibly decreased during culture specifically in the presence of HMAs in both screening. To validate the screening results, we performed competitive growth assays by co-culturing parental cells and TOPORS-KO cells using MOLM-13, MDS-L, SKK-1, and SKM-1 cells. In all cell lines, GFP-positive TOPORS-KO cells decreased over time only in the presence of HMAs. We established single-cell clones of TOPORS-KO MOLM-13 and MDS-L cells using two different sgRNAs. The cell growth of all TOPORS-KO clones was dramatically suppressed only in the presence of DAC. Mice transplanted with TOPORS-KO cells showed significantly longer survival when treated with DAC. Next, we analyzed Topors-deficient mice and no significant changes were observed in the peripheral blood or in the percentage of bone marrow (BM) hematopoietic stem and progenitor cells. Topors−/− BM cells successfully restored hematopoiesis in lethally irradiated recipient mice at levels comparable to those of WT BM cells after transplantation, suggesting that Topors is dispensable for normal hematopoiesis. Topors−/−MLL/AF9 leukemic cells showed comparable growth in the absence of DAC but significantly inferior growth in the presence of DAC compared to WT MLL/AF9 leukemic cells. We transplanted them with support BM cells into lethally irradiated recipient mice and treated with DAC. Topors−/− leukemic cells significantly decreased during treatment compared with WT leukemic cells. Enhanced cytotoxic effects of TOPORS KO combined with HMAs was closely associated with increased apoptosis. Cell cycle analysis revealed that among TOPORS-KO cells, those in the G2/M phase accumulate and those in the S phase are reduced upon DAC treatment. RNA sequencing on cells treated with DAC showed that gene sets associated with chromosome segregation and mitosis are significantly downregulated in TOPORS-KO cells. Addback experiment revealed that TOPORS RING finger domain, which mediates ubiquitination, is responsible for HMA resistance. To identify TOPORS ubiquitination substrates, we performed second CRISPR-Cas9 screening with the same library used as the first screening, using TOPORS-KO MDS-L and MOLM-13 clone cells. Both screening showed that UHRF1-DNMT1 axis is implicated in the augmented sensitivity to DAC. Consistently, DNMT1 remained more stable in TOPORS-KO cells than WT cells after DAC exposure not only in human MDS/AML cell lines but also in murine leukemic cells. HMAs incorporated into DNA trap DNMT1 and form DNA-DNMT1 crosslinks, which causes apoptosis and mitotic defects. DNMT1-DNA crosslink formation triggers the prominent SUMOylation of DNMT1 followed by ubiquitination, which promotes the resolution of crosslinks via proteasomal degradation and cell fitness. Mass analysis of ubiquitinated peptides and immunoprecipitation with Nanobody against DNMT1 demonstrated that TOPORS loss causes accumulation of SUMOylated DNMT1 due to inefficient ubiquitination, suggesting that TOPORS works as SUMO-targeted ubiquitin ligase for DNMT1. In patient samples treated with AZA (n=23), treatment resistance significantly correlated with TOPORS expression levels. Furthermore, an ubiquitination inhibitor TAK-243 as well as a SUMOylation inhibitor TAK-981 showed synergistic effect with HMAs through DNMT1 stabilization. These results suggested that they are likely to be promising therapeutic agents in clinical practice. Our findings unveil a novel mechanism of resistance to HMAs and provide an attractive therapeutic strategy for myeloid malignancies that interferes with resolution of DNA-DNMT1 crosslinks by targeting DNMT1 post-translational modification.

Multiplexed Functional Assessment of Glanzmann Thrombasthenia Variants

High throughput genome and exome sequencing tests are used to identify disease-causing genetic variants, including those that cause inherited platelet disorders (IPDs) like Glanzmann Thrombasthenia (GT). However, most variants identified in clinical tests are classified as variants of unknown significance (VUS), lacking sufficient evidence to guide informed clinical decision-making. This frequent result of a VUS complicates clinical diagnosis and can be a source of uncertainty, distress, and inequity for patients. Functional studies that demonstrate a damaging effect of variants are considered strong evidence of pathogenicity but are missing for most variants. However, individually testing the function of all possible missense changes in IPD genes is impractical. Multiplexed assays of variant effect (MAVEs) have been developed for select diseases to simultaneously measure the functional effect of all possible nucleotides in a specific genomic region. However, MAVEs have typically been employed in cell lines, and often utilize over-expression systems, which don't account for the proper cellular and genomic context needed to assess variants in IPDs. In this study, we use CRISPR/CAS9 mediated homology directed repair (HDR) in CD34+ cells to create MKs with GT mutations in the gene ITGA2B and show that they model GT patient platelet phenotypes. We then implement a MAVE in CD34+ derived MKs to evaluate the impact on αIIb expression of a library of all possible single nucleotide changes for 12 nucleotides surrounding a type I GT variant. This was done by introducing a pooled library of HDR donors containing a mixture of test variants into CD34+ cells. This resulted in a pool of endogenously edited MKs, each containing one of 32 different nucleotide changes. These MKs were sorted into CD42+ and CD41+ or CD41- populations and analyzed using high throughput sequencing to assess variant enrichment. Analysis of sequencing reads indicated distribution of variant containing reads across all 12 nucleotides and incorporation of all 48 different HDR donors (32 changes, 12 identity replacements). Numerous indels were also identified. Our results identified two distinct clusters of variants: those supporting a damaging effect on expression, and those likely having no effect on expression. Notably, we observed that all nonsense variants and indels resulting in a stop codon clustered in the CD41- enriched population, whereas all silent variants clustered in the CD41+ enriched population. To classify variants more precisely, we used an expectation-maximization (EM) algorithm based on the distribution of read counts between the CD41+ and CD41- populations. This identified 12 missense, 3 nonsense, and 30 indel variants as abnormal, compared to 24 missense and 10 silent variants as normal with respect to CD41 expression. Agreement of our results with computational assessments of variant effects supported the findings. In conclusion, we developed an approach for the multiplexed assessment of GT variants, enabling the simultaneous evaluation of the functional effect of at least 32 different variants within a specific region of ITGA2B. This opens the door to assessing the functional impact of all possible nucleotide changes across the entire ITGA2B coding region and lays the foundation for similar investigations in other IPD genes.

Combining Air Sampling and DNA Metabarcoding to Monitor Plant Pathogens

Monitoring the air for airborne plant pathogens is an increasingly common method for the management of economically important plant diseases. In Alberta, Canada, several commodity clusters, including dry bean, canola, potato, and wheat, currently support air monitoring research programs for airborne pathogens of interest. In this study, we assessed the feasibility of monitoring for these, and more, plant fungal pathogens simultaneously using two different sampler types (cyclone versus rotation impaction) and by metabarcoding the ITS1 region using the Illumina sequencing platform. We collected air samples from four geographically distant sites across Alberta and monitored four crop types in southern Alberta. Overall, we found weak, but statistically significant, effects of geographic location and crop type on the aeromycobiota community composition. A few common taxa, such as Ramularia, Alternaria, and Epicoccum, constituted the vast majority of reads across all samples. Nevertheless, in each sample, we identified many plant pathogens of interest and organisms that previous research has found antagonistic to those pathogens, highlighting the utility of these approaches in understanding the pathobiome. In assessing the real-world implications of read counts, we discovered that they were only weakly correlated with spore counts quantified by qPCR. The two types of samplers collected different community profiles, reinforcing the importance of carefully considering which sampler type to use in monitoring programs. Taken together, our results show promise for the future of monitoring the air pathobiome, although much more work is required to understand the relationship of airborne communities to their in-field impact on disease development. [Formula: see text] Copyright © 2023 His Majesty the King in Right of Canada, as represented by the Minister of Agriculture and Agri-Food Canada. This is an open access article distributed under the CC BY-NC-ND 4.0 International license .

Exploring Tomato Fruit Viromes through Transcriptome Data Analysis.

This study delves into the complex landscape of viral infections in tomatoes (Solanum lycopersicum) using available transcriptome data. We conducted a virome analysis, revealing 219 viral contigs linked to four distinct viruses: tomato chlorosis virus (ToCV), southern tomato virus (STV), tomato yellow leaf curl virus (TYLCV), and cucumber mosaic virus (CMV). Among these, ToCV predominated in contig count, followed by STV, TYLCV, and CMV. A notable finding was the prevalence of coinfections, emphasizing the concurrent presence of multiple viruses in tomato plants. Despite generally low viral levels in fruit transcriptomes, STV emerged as the primary virus based on viral read count. We delved deeper into viral abundance and the contributions of RNA segments to replication. While initially focused on studying the impact of sound treatment on tomato fruit transcriptomes, the unexpected viral presence underscores the importance of considering viruses in plant research. Geographical variations in virome communities hint at potential forensic applications. Phylogenetic analysis provided insights into viral origins and genetic diversity, enhancing our understanding of the Korean tomato virome. In conclusion, this study advances our knowledge of the tomato virome, stressing the need for robust pest control in greenhouse-grown tomatoes and offering insights into virus management and crop protection.