High-throughput Sequencing Datasets Research Articles

Assessing the de novo assemblers: a metaviromic study of apple and first report of citrus concave gum-associated virus, apple rubbery wood virus 1 and 2 infecting apple in India

BackgroundThe choice of de novo assembler for high-throughput sequencing (HTS) data remains a pivotal factor in the HTS-based discovery of viral pathogens. This study assessed de novo assemblers, namely Trinity, SPAdes, and MEGAHIT for HTS datasets generated on the Illumina platform from 23 apple samples, representing 15 exotic and indigenous apple varieties and a rootstock. The assemblers were compared based on assembly quality metrics, including the largest contig, total assembly length, genome coverage, and N50.ResultsMEGAHIT was most efficient assembler according to the metrics evaluated in this study. By using multiple assemblers, near-complete genome sequences of citrus concave gum-associated virus (CCGaV), apple rubbery wood virus 1 (ARWV-1), ARWV-2, apple necrotic mosaic virus (ApNMV), apple mosaic virus, apple stem pitting virus, apple stem grooving virus, apple chlorotic leaf spot virus, apple hammerhead viroid and apple scar skin viroid were reconstructed. These viruses were further confirmed through Sanger sequencing in different apple cultivars. Among them, CCGaV, ARWV-1 and ARWV-2 were recorded from apples in India for the first time. The analysis of virus richness revealed that ApNMV was dominant, followed by ARWV-1 and CCGaV. Moreover, MEGAHIT identified novel single-nucleotide variants.ConclusionsOur analyses highlight the crucial role of assembly methods in reconstructing near-complete apple virus genomes from the Illumina reads. This study emphasizes the significance of employing multiple assemblers for de novo virus genome assembly in vegetatively propagated perennial fruit crops.

Neutrophil Extracellular Traps: Potential Thrombotic Markers and Therapeutic Targets in Colorectal Cancer.

Neutrophil extracellular traps (NETs) are promising promoters in venous thromboembolism (VTE). In the present study, we have investigated the potential thrombogenic role of NETs in colorectal cancer (CRC). A total of 583 patients with gastrointestinal malignancies who were diagnosed with or without VTE by extremities arteriovenous ultrasound and computed tomography were enrolled. The incidence of VTE in CRC was as high as 17.53%. In serological ELISA experiments, Cit-H3, MPO and cfDNA were significantly overexpressed in CRC patients with VTE compared to CRC patients without VTE and healthy individuals. Neutrophils from CRC patients with VTE produced appreciable amounts of NETs after stimulation with phorbol-12-myristate-13-acetate, which were lacking in CRC patients without VTE and healthy individuals. CfDNA was positively correlated with plasmin-α2-antiplasmin complex and tissue plasmin activator inhibitor-1 complex, and Cit-H3 was positively correlated with plasmin-α2-antiplasmin complex, suggesting that NETs are associated with increased fibrinolytic activity. We screened some NETs-related genes by analysing several high-throughput sequencing datasets of VTE and NETs. FCGR1A was identified as the optimal target gene by pan-cancer expression analysis and survival analysis. FCGR1A was significantly overexpressed in the peripheral blood of CRC patients without VTE compared to healthy individuals and showed a positive correlation with cfDNA. Neutrophil-derived NETs were significantly reduced by FCGR1A inhibitor exposure. These findings indicate that NETs are actively involved in VTE in CRC. NETs are promising thrombotic marker and therapeutic target in CRC to prevent the thrombotic consequences of cancer.

Theoretical framework for the difference of two negative binomial distributions and its application in comparative analysis of sequencing data.

High-throughput sequencing (HTS) technologies have been instrumental in investigating biological questions at the bulk and single-cell levels. Comparative analysis of two HTS data sets often relies on testing the statistical significance for the difference of two negative binomial distributions (DOTNB). Although negative binomial distributions are well studied, the theoretical results for DOTNB remain largely unexplored. Here, we derive basic analytical results for DOTNB and examine its asymptotic properties. As a state-of-the-art application of DOTNB, we introduce DEGage, a computational method for detecting differentially expressed genes (DEGs) in scRNA-seq data. DEGage calculates the mean of the sample-wise differences of gene expression levels as the test statistic and determines significant differential expression by computing the P-value with DOTNB. Extensive validation using simulated and real scRNA-seq data sets demonstrates that DEGage outperforms five popular DEG analysis tools: DEGseq2, DEsingle, edgeR, Monocle3, and scDD. DEGage is robust against high dropout levels and exhibits superior sensitivity when applied to balanced and imbalanced data sets, even with small sample sizes. We utilize DEGage to analyze prostate cancer scRNA-seq data sets and identify marker genes for 17 cell types. Furthermore, we apply DEGage to scRNA-seq data sets of mouse neurons with and without fear memory and reveal eight potential memory-related genes overlooked in previous analyses. The theoretical results and supporting software for DOTNB can be widely applied to comparative analyses of dispersed count data in HTS and broad research questions.

Archaeal community from the Northern Hangzhou Bay to the East China Sea: biogeography, ecological processes, and functional potential

IntroductionArchaeal communities play a crucial role in marine ecosystems, yet our understanding of their ecological and functional traits remains incomplete. This study focuses on northern Hangzhou Bay to fill gaps in knowledge regarding the biogeography and functionality of archaeal groups.MethodsWe utilized a high-throughput sequencing dataset based on the 16S rRNA gene to characterize the archaeal community, aiming to identify biogeographic patterns and assess the influence of environmental factors on community structure.ResultsThe predominant phyla identified were Woesearchaeota, Thaumarchaeota, Euryarchaeota, and Crenarchaeota. Archaeal community structure in sediments showed a geographical pattern along the environmental gradient, influenced by factors such as salinity, ammonium, total phosphorus, pH, and total nitrogen content. Network analysis revealed nonrandom co-occurrence patterns, with associations changing along the salinity gradient. Additionally, this study directly proved the existence of dispersal limitation in this strongly connected marine ecological system through null model analyses. Variation in the archaeal community was attributed to both environmental constraints and stochastic processes due to dispersal limitation. Furthermore, our results revealed that the key biogeochemical functions of the archaeal community also exhibited a clear salinity gradient, the functional differences appear to be influenced by salinity, and the critical roles of archaeal diversity were highlighted.DiscussionAll these findings enhance our understanding of microbial ecology and element transformation in estuarine environments. The highlighted roles of archaeal diversity and the influence of environmental factors on community structure and function underscore the complexity of marine microbial ecosystems.

Simulated High Throughput Sequencing Datasets: A Crucial Tool for Validating Bioinformatic Pathogen Detection Pipelines.

The validation of diagnostic assays in plant pathogen detection is a critical area of research. It requires the use of both negative and positive controls containing a known quantity of the target pathogen, which are crucial elements when calculating analytical sensitivity and specificity, among other diagnostic performance metrics. High Throughput Sequencing (HTS) is a method that allows the simultaneous detection of a theoretically unlimited number of plant pathogens. However, accurately identifying the pathogen from HTS data is directly related to the bioinformatic pipeline utilized and its effectiveness at correctly assigning reads to their associated taxa. To this day, there is no consensus about the pipeline that should be used to detect the pathogens in HTS data, and results often undergo review and scientific evaluation. It is, therefore, imperative to establish HTS resources tailored for evaluating the performance of bioinformatic pipelines utilized in plant pathogen detection. Standardized artificial HTS datasets can be used as a benchmark by allowing users to test their pipelines for various pathogen infection scenarios, some of the most prevalent being multiple infections, low titer pathogens, mutations, and new strains, among others. Having these artificial HTS datasets in the hands of HTS diagnostic assay validators can help resolve challenges encountered when implementing bioinformatics pipelines for routine pathogen detection. Offering these purely artificial HTS datasets as benchmarking tools will significantly advance research on plant pathogen detection using HTS and enable a more robust and standardized evaluation of the bioinformatic methods, thereby enhancing the field of plant pathogen detection.

Genome-wide detection of somatic mosaicism at short tandem repeats.

Somatic mosaicism has been implicated in several developmental disorders, cancers, and other diseases. Short tandem repeats (STRs) consist of repeated sequences of 1-6bp and comprise >1 million loci in the human genome. Somatic mosaicism at STRs is known to play a key role in the pathogenicity of loci implicated in repeat expansion disorders and is highly prevalent in cancers exhibiting microsatellite instability. While a variety of tools have been developed to genotype germline variation at STRs, a method for systematically identifying mosaic STRs is lacking. We introduce prancSTR, a novel method for detecting mosaic STRs from individual high-throughput sequencing datasets. prancSTR is designed to detect loci characterized by a single high-frequency mosaic allele, but can also detect loci with multiple mosaic alleles. Unlike many existing mosaicism detection methods for other variant types, prancSTR does not require a matched control sample as input. We show that prancSTR accurately identifies mosaic STRs in simulated data, demonstrate its feasibility by identifying candidate mosaic STRs in Illumina whole genome sequencing data derived from lymphoblastoid cell lines for individuals sequenced by the 1000 Genomes Project, and evaluate the use of prancSTR on Element and PacBio data. In addition to prancSTR, we present simTR, a novel simulation framework which simulates raw sequencing reads with realistic error profiles at STRs. prancSTR and simTR are freely available at https://github.com/gymrek-lab/trtools. Detailed documentation is available at https://trtools.readthedocs.io/.

Identification and bioinformatics analysis of genes associated with pyroptosis in spinal cord injury of rat and mouse

The mechanism of spinal cord injury (SCI) is highly complex, and an increasing number of studies have indicated the involvement of pyroptosis in the physiological and pathological processes of secondary SCI. However, there is limited bioinformatics research on pyroptosis-related genes (PRGs) in SCI. This study aims to identify and validate differentially expressed PRGs in the GEO database, perform bioinformatics analysis, and construct regulatory networks to explore potential regulatory mechanisms and therapeutic targets for SCI. We obtained high-throughput sequencing datasets of SCI in rats and mice from the GEO database. Differential analysis was conducted using the “limma” package in R to identify differentially expressed genes (DEGs). These genes were then intersected with previously reported PRGs, resulting in a set of PRGs in SCI. GO and KEGG enrichment analyses, as well as correlation analysis, were performed on the PRGs in both rat and mouse models of SCI. Additionally, a protein–protein interaction (PPI) network was constructed using the STRING website to examine the relationships between proteins. Hub genes were identified using Cytoscape software, and the intersection of the top 5 hub genes in rats and mice were selected for subsequent experimentally validated. Furthermore, a competing endogenous RNA (ceRNA) network was constructed to explore potential regulatory mechanisms. The gene expression profiles of GSE93249, GSE133093, GSE138637, GSE174549, GSE45376, GSE171441_3d and GSE171441_35d were selected in this study. We identified 10 and 12 PRGs in rats and mice datasets respectively. Six common DEGs were identified in the intersection of rats and mice PRGs. Enrichment analysis of these DEGs indicated that GO analysis was mainly focused on inflammation-related factors, while KEGG analysis showed that the most genes were enriched on the NOD-like receptor signaling pathway. We constructed a ceRNA regulatory network that consisted of five important PRGs, as well as 24 miRNAs and 34 lncRNAs. This network revealed potential regulatory mechanisms. Additionally, the three hub genes obtained from the intersection were validated in the rat model, showing high expression of PRGs in SCI. Pyroptosis is involved in secondary SCI and may play a significant role in its pathogenesis. The regulatory mechanisms associated with pyroptosis deserve further in-depth research.

Endotype-driven Co-module mechanisms of danhong injection in the Co-treatment of cardiovascular and cerebrovascular diseases: A modular-based drug and disease integrated analysis

Ethnopharmacological relevanceCardiovascular and cerebrovascular diseases are the leading causes of death worldwide and interact closely with each other. Danhong Injection (DHI) is a widely used preparation for the co-treatment of brain and heart diseases (CTBH). However, the underlying molecular endotype mechanisms of DHI in the CTBH remain unclear. Aim of this studyTo elucidate the underlying endotype mechanisms of DHI in the CTBH. Materials and methodsIn this study, we proposed a modular-based disease and drug-integrated analysis (MDDIA) strategy for elucidating the systematic CTBH mechanisms of DHI using high-throughput transcriptome-wide sequencing datasets of DHI in the treatment of patients with stable angina pectoris (SAP) and cerebral infarction (CI). First, we identified drug-targeted modules of DHI and disease modules of SAP and CI based on the gene co-expression networks of DHI therapy and the protein–protein interaction networks of diseases. Moreover, module proximity-based topological analyses were applied to screen CTBH co-module pairs and driver genes of DHI. At the same time, the representative driver genes were validated via in vitro experiments on hypoxia/reoxygenation-related cardiomyocytes and neuronal cell lines of H9C2 and HT22. ResultsSeven drug-targeted modules of DHI and three disease modules of SAP and CI were identified by co-expression networks. Five modes of modular relationships between the drug and disease modules were distinguished by module proximity-based topological analyses. Moreover, 13 targeted module pairs and 17 driver genes associated with DHI in the CTBH were also screened. Finally, the representative driver genes AKT1, EDN1, and RHO were validated by in vitro experiments. ConclusionsThis study, based on clinical sequencing data and modular topological analyses, integrated diseases and drug targets. The CTBH mechanism of DHI may involve the altered expression of certain driver genes (SRC, STAT3, EDN1, CYP1A1, RHO, RELA) through various enriched pathways, including the Wnt signaling pathway.

TrieDedup: a fast trie-based deduplication algorithm to handle ambiguous bases in high-throughput sequencing

BackgroundHigh-throughput sequencing is a powerful tool that is extensively applied in biological studies. However, sequencers may produce low-quality bases, leading to ambiguous bases, ‘N’s. PCR duplicates introduced in library preparation are conventionally removed in genomics studies, and several deduplication tools have been developed for this purpose. Two identical reads may appear different due to ambiguous bases and the existing tools cannot address ‘N’s correctly or efficiently.ResultsHere we proposed and implemented TrieDedup, which uses the trie (prefix tree) data structure to compare and store sequences. TrieDedup can handle ambiguous base ‘N’s, and efficiently deduplicate at the level of raw sequences. We also reduced its memory usage by approximately 20% by implementing restrictedDict in Python. We benchmarked the performance of the algorithm and showed that TrieDedup can deduplicate reads up to 270-fold faster than pairwise comparison at a cost of 32-fold higher memory usage.ConclusionsThe TrieDedup algorithm may facilitate PCR deduplication, barcode or UMI assignment, and repertoire diversity analysis of large-scale high-throughput sequencing datasets with its ultra-fast algorithm that can account for ambiguous bases due to sequencing errors.

Clinical Significance of Upregulation of EZH1 Expression in Hepatocellular Carcinoma Tissues.

The incidence and mortality of hepatocellular carcinoma (HCC) are increasing. It is urgent to develop more effective HCC biomarkers for diagnosis and treatment. This project intends to verify the expression of enhancer of zeste 1 polycomb repressive complex 2 subunit (EZH1) and its mechanism in HCC. This study integrates global microarray and high-throughput sequencing datasets, combined with internal immunohistochemistry, to analyze the expression and prognostic value of EZH1 in HCC. Functional enrichment analysis was conducted to investigate transcriptional targets, which were achieved by intersecting HCC over-expressed genes, EZH1 co-expressed genes and putative transcriptional targets. The relationship between EZH1 and anticancer drugs was detected by drug sensitivity analysis. In this study, 84 datasets from 40 platforms (3,926 HCC samples and 3,428 non-cancerous liver tissues) were included to show the high expression of EZH1 in HCC. Immunohistochemistry with 159 HCC samples and 62 non-HCC samples confirmed the high expression level. HCC patients with high EZH1 expression had worse survival prognoses. Gene ontology and Reactome analysis revealed that metabolism-related pathways, including autophagy, are critical for HCC. Interestingly, as one of the EZH1 potential transcriptional targets, autophagy-related 7 (ATG7) appeared in the above pathways. ATG7 was positively correlated with EZH1, upregulated in HCC, and mediated poor prognosis. Upregulation of EZH1 was found to be in contact with HCC anti-tumor drug resistance. The upregulation of EZH1 expression can promote the occurrence of HCC and lead to poor clinical progression and drug resistance; these effects may be mediated by regulating ATG7.

Abstract A023: Assessing the reproducibility crisis in vaginal microbiome studies for clinical applications in endometrial cancer

Abstract Recent associations between vaginal microbiota and gynecological cancer have led to high-throughput sequencing datasets identifying diagnostic biomarkers for Endometrial Cancer (EC). However, lack of bioinformatics standards results in inconsistent independent studies with poor reproducibility, making them clinically inapplicable. This study leverages publicly available amplicon sequence data to address the reproducibility of EC microbiome studies. We implemented bioinformatics pipelines from five studies to reproduce and measure the replicability of published results across cohorts using metrics such as alpha diversity, beta diversity and differentially expressed taxa. We further evaluated separability between benign and cancer patients at different taxonomic levels (phylum, class, order, family, and genus) within and across datasets using boosted tree classifiers and Area Under the Curve (AUC) as the performance metric. While we reproduced the general trend of observing higher microbial diversity in EC compared to healthy controls, we found irregularities in two cohorts. Using beta diversity distance metrics, we identified that histology alone explains less than 3% of the variance in all cohorts. Three microbiome differential abundance methods were used in the five studies. While they all agree on a decrease in the Lactobacillus genus in EC patients, there is no consensus on other taxa associated with EC. We also found that separability between benign and cancerous conditions is highest at the class level, having an AUC score of 0.86. In subsequent steps, we will perform an integrative analysis to identify an EC vaginal microbiome predictive signature that is preserved across all five cohorts, benefiting screening programs. Citation Format: Dollina D. Dodani, Aline Talhouk. Assessing the reproducibility crisis in vaginal microbiome studies for clinical applications in endometrial cancer [abstract]. In: Proceedings of the AACR Special Conference on Endometrial Cancer: Transforming Care through Science; 2023 Nov 16-18; Boston, Massachusetts. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(5_Suppl):Abstract nr A023.

The miR-25802/KLF4/NF-κB signaling axis regulates microglia-mediated neuroinflammation in Alzheimer’s disease

Microglia-mediated neuroinflammation plays a critical role in the occurrence and progression of Alzheimer’s disease (AD). In recent years, studies have increasingly explored microRNAs as biomarkers and treatment interventions for AD. This study identified a novel microRNA termed miR-25802 from our high-throughput sequencing dataset of an AD model and explored its role and the underlying mechanism. The results confirmed the miRNA properties of miR-25802 based on bioinformatics and experimental verification. Expression of miR-25802 was increased in the plasma of AD patients and in the hippocampus of APP/PS1 and 5 × FAD mice carrying two and five familial AD gene mutations. Functional studies suggested that overexpression or inhibition of miR-25802 respectively aggravated or ameliorated AD-related pathology, including cognitive disability, Aβ deposition, microglial pro-inflammatory phenotype activation, and neuroinflammation, in 5 × FAD mice and homeostatic or LPS/IFN-γ-stimulated EOC20 microglia. Mechanistically, miR-25802 negatively regulates KLF4 by directly binding to KLF4 mRNA, thus stimulating microglia polarization toward the pro-inflammatory M1 phenotype by promoting the NF-κB-mediated inflammatory response. The results also showed that inhibition of miR-25802 increased microglial anti-inflammatory M2 phenotype activity and suppressed NF-κB-mediated inflammatory reactions in the brains of 5 × FAD mice, while overexpression of miR-25802 exacerbated microglial pro-inflammatory M1 activity by enhancing NF-κB pathways. Of note, AD-associated manifestations induced by inhibition or overexpression of miR-25802 via the NF-κB signaling pathway were reversed by KLF4 silencing or upregulation. Collectively, these results provide the first evidence that miR-25802 is a regulator of microglial activity and establish the role of miR-25802/KLF4/NF-κB signaling in microglia-mediated neuroinflammation, suggesting potential therapeutic targets for AD.

Immune-Related Genes and Immune Cell Infiltration Characterize the Maturation Status of Arteriovenous Fistulas: An Integrative Bioinformatics Study and Experimental Validation Based on Transcriptome Sequencing.

Arteriovenous fistula (AVF) is the preferred vascular access for hemodialysis, but the low maturation rate is concerning. Immune cells' impact on AVF maturation lacks bioinformatics research. The study aims to investigate the potential predictive role of immune-related genes and immune cell infiltration characteristics in AVF maturation. We analyzed the high-throughput sequencing dataset to identify differentially expressed genes (DEGs). Then, we performed enrichment analyses (GO, KEGG, GSEA) on immune-related genes and pathways in mature AVF. We focused on differentially expressed immune-related genes (DEIRGs) and constructed a PPI network to identify hub genes. These hub genes were validated in other databases and experiments, including qPCR and immunohistochemistry (IHC). The immune cell infiltration characteristics in native veins, failed AVFs, and matured AVFs were analyzed by cibersortX. Partial experimental validation was conducted using clinical samples. Our results showed that immune-related genes and signaling pathways are significantly enriched in mature AVF. We validated this in other databases and ultimately identified three hub genes (IL1B, IL6, CXCR4) in combination with experiments. Significant differences in immune cell infiltration characteristics were observed among native veins, failed AVFs, and matured AVFs. Immune cell infiltration analysis revealed that accumulation of CD4+ T cells, dendritic cells, mast cells and M2 macrophages contribute to AVF maturation. These immune-related genes and immune cells have the potential to serve as predictive factors for AVF maturation. We partially validated this experimentally. From a bioinformatics perspective, our results have identified, for the first time, a set of immune-related genes and immune cell infiltration features that can characterize the maturation of AVF and significantly impact AVF maturation. These features hold potential as predictive indicators for AVF maturation outcomes.

IGF2BP1 promotes multiple myeloma with chromosome 1q gain via increasing CDC5L expression in an m6A-dependent manner

Multiple myeloma (MM) patients with chromosome 1q gain (1q+) are clinically and biologically heterogeneous. The underlying molecular mechanisms are still under investigation, while the identification of targets for effective therapy of this subgroup of MM patients is urgently needed. We aimed to investigate the clinical significance and the regulatory mechanisms of insulin-like growth factor 2 messenger RNA (mRNA) binding protein 1 (IGF2BP1), a N6-methyladenosine (m6A) reader, in MM patients with 1q+. We found that MM patients with 1q+ exhibit a significantly higher level of IGF2BP1 mRNA than controls, while higher IGF2BP1 expression predicted a worse prognosis in MM patients with 1q+. IGF2BP1 overexpression promoted cell proliferation and G1-to-S phase transition of the cell cycle in NCI-H929 cells. Through comprehensive in silico analyses of existing public datasets and in-house generated high-throughput sequencing datasets, along with in vitro experiments, we identified CDC5L as a target of IGFBP1, which can bind to the m6A sites of CDC5L mRNA to up-regulate its protein abundance. Higher CDC5L expression also predicted a worse prognosis of MM patients with 1q+. Moreover, both knockdown and mutation of CDC5L attenuated the pro-proliferative effect of IGF2BP1. Furthermore, IGF2BP1 inhibitor BTYNB effectively inhibited CDC5L expression in MM cells with 1q+ and suppressed the proliferation of these cells in vitro and in vivo. Therefore, IGF2BP1 acts as a post-transcriptional enhancer of CDC5L in an m6A-dependent manner to promote the proliferation of MM cells with 1q+. Our work identified a novel IGF2BP1-CDC5L axis and provided new insight into developing targeted therapeutics for MM patients with 1q+.

IGF2BP1 Promotes Multiple Myeloma with Chromosome 1q Gain Via Increasing CDC5L Expression in an m6A-Dependent Manner

Background: Multiple myeloma (MM) patients with chromosome 1q gain (1q+) are clinically and biologically heterogeneous. The underlying molecular mechanisms are still under investigation, while identification of targets for effective therapy of this subgroup of MM patients is urgently needed. We aimed to investigate the clinical significance and the regulatory mechanisms of insulin-like growth factor 2 messenger RNA (mRNA) binding proteins 1 (IGF2BP1), a N6-methyladenosine (m6A) reader, in MM patients with 1q+. Methods: Comprehensive in silico analyses of existed public datasets (MMRF CoMMpass study) and in-house generated high-throughput sequencing datasets (RNA-sequencing, methylated RNA immunoprecipitation sequencing, and crosslinking immunoprecipitation sequencing), along with routine molecular and cellular in vitro assays, as well as in vivo animal model studies, were conducted. Results: IGF2BP1 displays a high expression level in MM patients with 1q+ and its high expression predicts poor prognosis in these patients. IGF2BP1 overexpression promoted cell proliferation and G1-to-S phase transition of the cell cycle in H929 cells. Combinatory analyses of MeRIP-seq, RNA-seq, CLIP-seq and patients' survival data identified CDC5L as a target of IGF2BP1, which can bind the m6A sites of CDC5L mRNA to upregulate its protein abundance. Higher CDC5L expression also predicted worse prognosis of MM patients with 1q+. Moreover, both knockdown and mutation of CDC5L attenuated the pro-proliferative effect of IGF2BP1. Furthermore, IGF2BP1 inhibitor BTYNB effectively inhibited CDC5L expression in MM cells with 1q+, and suppressed the proliferation of these cells in vitro and in vivo. Conclusions: IGF2BP1 acts as a post-transcriptional enhancer of CDC5L in an m6A manner to promote the proliferation of MM cells with 1q+. Our work identified a novel IGF2BP1-CDC5L axis and provided new insight in developing targeted therapeutics for MM patients with 1q+.

PSII-6 Computational Tools and Resources for Analysis and Exploration of Single-Cell Rnaseq Data in Agriculture

Abstract The agriculture genomics community has numerous data submission standards available but little experience describing and storing single-cell (e.g., scRNAseq) data. Other single-cell genomics infrastructure efforts, such as the Human Cell Atlas Data Coordination Platform (HCA DCP), have resources that could benefit our community. For example, the HCA DCP is integrated with Terra, a cloud-native workbench for computational biology developed by Broad, Verily, and Microsoft that houses tools for scGenomics analysis. We will describe a pilot-scale project to determine if our current metadata standards for livestock and crops can be used to ingest scRNAseq datasets in a manner consistent with HCA DCP standards and if established resources (e.g., Terra) can be used to analyze the ingested data. Currently, the most comprehensive data ingestion portal for high throughput sequencing datasets from plants, fungi, protists, and animals/humans is Annotare (located at EMBL-European Bioinformatics Institute), ensures that sufficient metadata are collected to enable re-analysis and dissemination via the Single Cell Expression Atlas (SCEA). Annotare supports user-directed annotation and processing of their data, as well as search tools via the SCEA and transferred to the Galaxy analysis space. For animal datasets, another EMBL-EBI portal, the FAANG portal, has been developed that provides bulk and scRNAseq data access. scRNAseq data/metadata can be submitted to FAANG using a semi-automated process. We have extended this tool for scRNAseq data so that files can be validated using the HCA DCP metadata and data validation service. These files are also incorporated using EMBL-EBI’s HCA DCP ingestion service and transferred to Terra for further analysis. Once incorporated, datasets will augment the DCP resource for the scientific community. In an extension of these efforts, we have also created a Shiny-based web application, called Shiny-PIGGI, for the single cell-level transcriptomic study of pig immune tissues and peripheral blood mononuclear cells, which will be an important resource for improved annotation of porcine immune genes and cell types. The Shiny-PIGGI (https://shinypiggi.ansci.iastate.edu) is implemented completely in R, runs on any modern web browser, and requires no programming. This tool thus increases accessibility through eliminating technical training requirements for using Seurat object and related R packages commonly used in scRNAseq analysis. Our main goal was to develop an interactive web application that allows users such as animal scientists and immunologists to visualize and analyze biological datasets. This tool will be demonstrated at the meeting. We intend to further build upon these existing tools to construct a scientist-friendly data resource and analytical ecosystem to facilitate single cell-level genomic analysis through data ingestion, storage, retrieval, re-use, visualization, and comparative annotation across agricultural species.

Characterization of the complete genome of oat sterile dwarf virus: a fijivirus occurring in the temperate climate zone of Europe.

Oat sterile dwarf virus (OSDV) is a fijivirus whose genome segments 7 to 10 were sequenced earlier. In the current study, the complete genome was sequenced. To confirm the genome ends, rapid amplification and sequencing of cDNA ends were performed. The complete OSDV genome consists of 10 double-stranded RNA (dsRNA) segments with a total size of 28,686 bp. The sense strand sequence of all segments has the terminal consensus sequence motif 5'-AACGA(5-7)… U(6-8)(A/U)GUC-3', in which the length of the stretches of A and U varies, being slightly shorter for segments 1-4 and longer for segments 5-10. The 3' end of segment 3 is …UGUC, not AGUC as in the other segments. Segments 5, 7, and 10 contain two small ORFs, while each of the other segments contains one long ORF. ORF7-2 and ORF9 are slightly longer than annotated before. Phylogenetic analysis based on amino acid sequences of the RNA-directed RNA polymerase (RdRP) placed OSDV between the plant fijiviruses and Nilaparvata lugens reovirus (NLRV), an insect fijivirus that does not replicate in plants. OSDV RdRP shares 48-49% sequence identity with other plant-infecting fijivirus RdRPs and 30% identity with that of NLRV. OSDV has earlier been reported in several Northern and Central European countries. The sequencing of the complete genome serves as a reference for identifying all segments in future high-throughput sequencing datasets, enabling the investigation of the molecular epidemiology and evolution of OSDV.

Identification and Analysis of Hub Genes and Biological Pathways Involved in Alzheimer’s Disease (AD) Using Transcriptomics Dataset

Alzheimer’s disease (AD) is an irreversible and progressive neurodegenerative disorder. The brain mechanisms involved in this disease remain largely unknown. Hence, this study used the integrated bioinformatics approach to analyze a high throughput sequencing dataset (GSE162873) in order to identify the potential biomarkers involved in the pathophysiology of this disease. DESeq2 package was used for the identification of differentially expressed genes (DEGs) from both healthy and diseased patients. DAVID, a web-based bioinformatics resource, was used to perform functional enrichment analysis. StringApp plugin in Cytoscape was utilized to construct the protein-protein interaction (PPI) networks, whereas hub genes were identified through cytoHubba. MCODE was used to perform module analysis, ClueGO to evaluate the KEGG pathways enriched in modules, and miRNet platform for the interaction analysis of miRNAs and hub genes. Drug-genes interaction analysis was performed using DGIdb resource to find out the related drugs. A total of 652 DEGs were screened which were significantly enriched in GO terms. KEGG pathways analysis showed that PI3K-Akt signaling, hippo signaling, MAPK signaling, TGF-beta signaling, and sphingolipid signaling were significantly enriched pathways. A total of 12 hub genes were found to be significantly interacting with miR-603, miR-10b-5p, miR-124-3p, and miR-1-3p, and some FDA approved drugs. The current study provided an insight into the molecular mechanisms of AD and identified some potential biomarker genes, their pathways, miRNAs, and drugs which might be useful for diagnostic and therapeutic purposes.

Identification and validation of key biomarkers and potential therapeutic targets for primary open-angle glaucoma.

Primary open-angle glaucoma (POAG) is a prevalent cause of blindness worldwide, resulting in degeneration of retinal ganglion cells and permanent damage to the optic nerve. However, the underlying pathogenetic mechanisms of POAG are currently indistinct, and there has been no effective nonsurgical treatment regimen. The objective of this study is to identify novel biomarkers and potential therapeutic targets for POAG. The mRNA expression microarray datasets GSE27276 and GSE138125, as well as the single-cell high-throughput RNA sequencing (scRNA-seq) dataset GSE148371 were utilized to screen POAG-related differentially expressed genes (DEGs). Functional enrichment analyses, protein-protein interaction (PPI) analysis, and weighted gene co-expression network analysis (WGCNA) of the DEGs were performed. Subsequently, the hub genes were validated at a single-cell level, where trabecular cells were annotated, and the mRNA expression levels of target genes in different cell clusters were analyzed. Immunofluorescence and quantitative real-time PCR (qPCR) were performed for further validation. DEGs analysis identified 43 downregulated and 32 upregulated genes in POAG, which were mainly enriched in immune-related pathways, oxidative stress, and endoplasmic reticulum (ER) stress. PPI networks showed that FN1 and DUSP1 were the central hub nodes, while GPX3 and VAV3 were screened out as hub genes through WGCNA and subsequently validated by qPCR. Finally, FN1, GPX3, and VAV3 were determined to be pivotal core genes via single-cell validation. The relevant biomarkers involved in the pathogenesis of POAG, may serve as potential therapeutic targets. Further studies are necessary to unveil the mechanisms underlying the expression variations of these genes in POAG.

Weighted Gene Co-Expression Network Analysis of Immune Infiltration in Nonalcoholic Fatty Liver Disease.

Immune cell infiltration is an important component of nonalcoholic fatty liver disease (NAFLD) pathogenesis. This study aimed to explore novel genes associated with immune infiltration in the progression of NAFLD. CIBERSORT was used to evaluate the abundance of immune infiltration in the human NAFLD via a high-throughput sequencing dataset. Further weighted gene co-expression network analysis (WGCNA) was performed to search for the susceptibility gene module and hub genes associated with differential immune cells. The expression of hub genes in different liver non-parenchymal cell clusters and NAFLD-associated hepatocellular carcinoma (HCC) was also explored. Four hub genes (ITGBL1, SPINT1, COL1A2, and THBS2) were ultimately identified, which may be associated with immune infiltration, fibrosis progression, and activity score. The receiver operating characteristic curve (ROC) analysis suggested that these genes had good predictive value for NASH and advanced fibrosis. A single-cell analysis showed that COL1A2 was highly expressed in hepatic stellate cells (HSCs), especially in the later stage, while SPINT1 was highly expressed in cholangiocytes (Cho). In addition, ITGBL1, COL1A2, and THBS2 might be associated with transforming from nonalcoholic steatohepatitis (NASH) to HCC. Our findings identified several novel genes that might be related to immune infiltration in NAFLD. These genes may serve as potential markers for the assessment of immune infiltration as well as therapeutic targets for NAFLD. More studies are needed to elucidate the biological mechanism of these genes in the occurrence and development of NAFLD.