Raw Sequencing Reads Research Articles

Upper Tract Microbiome Modifications After Lung Transplantation and Its Impact in CLAD

<h3>Purpose</h3> Survival after lung transplantation is limited in large part due to the high incidence of chronic lung allograft dysfunction (CLAD). Infection is a recognized risk factor for the development of CLAD, and both acute infection and chronic lung allograft colonization with microorganisms increase the risk for CLAD. The aim of our study was to analyze nasopharyngeal (NP) microbiome composition and modifications following lung transplantation, with a focus on its relationship with CLAD. <h3>Methods</h3> This was a longitudinal study in which we analyzed bacterial microbiome in NP swab samples in 68 lung transplanted (LT) patients the day of the transplant, when patients were discharged, at 2-5 months and 12 months after lung transplantation. We also analysed NP swab samples from 10 healthy subjects. CLAD was assessed after 2 years of follow-up. DNA was extracted from the NP swabs. To create the amplicon library, the hyper-variable region (V4) of the 16S ribosomal RNA gene was amplified by standard PCR. Amplicons were purified and sequenced in barcoded pools using Illumina MiSeq technology. Raw sequence reads were demultiplexed by using idemp. The resulting single-end reads were processed using the Dada2 pipeline obtaining an amplicon sequence variant table to which taxonomy was assigned. Alpha diversity, including Shannon and Observed indices, and beta diversity metrics were calculated. We compared these metrics and particular taxa relative abundances between samples of LT patients at different time points and samples of healthy subjects. <h3>Results</h3> From the 68 studied patients, 13 developed CLAD during the two years of follow-up. We observed a significant decrease in the Observed index at the day of the discharge and 2-5 months after transplant when compared with healthy subjects. One year after transplant, CLAD patients had higher Shannon diversity than LT patients who did not develop the dysfunction. On the other hand, a lower beta diversity was observed between healthy subjects and patients with no CLAD. Besides, we detected an increased genus in NP swab samples associated with no CLAD also observed in NP samples from healthy subjects. <h3>Conclusion</h3> Nasopharyngeal overall microbiome composition, as well as relative abundances of particular taxa of non-CLAD patients, was closest to the one from healthy subjects suggesting a better outcome of the transplant if better microbiome normalization.

Development of a versatile resource for post-genomic research through consolidating and characterizing 1500 diverse wild and cultivated soybean genomes

BackgroundWith advances in next-generation sequencing technologies, an unprecedented amount of soybean accessions has been sequenced by many individual studies and made available as raw sequencing reads for post-genomic research.ResultsTo develop a consolidated and user-friendly genomic resource for post-genomic research, we consolidated the raw resequencing data of 1465 soybean genomes available in the public and 91 highly diverse wild soybean genomes newly sequenced. These altogether provided a collection of 1556 sequenced genomes of 1501 diverse accessions (1.5 K). The collection comprises of wild, landraces and elite cultivars of soybean that were grown in East Asia or major soybean cultivating areas around the world. Our extensive sequence analysis discovered 32 million single nucleotide polymorphisms (32mSNPs) and revealed a SNP density of 30 SNPs/kb and 12 non-synonymous SNPs/gene reflecting a high structural and functional genomic diversity of the new collection. Each SNP was annotated with 30 categories of structural and/or functional information. We further identified paired accessions between the 1.5 K and 20,087 (20 K) accessions in US collection as genomic “equivalent” accessions sharing the highest genomic identity for minimizing the barriers in soybean germplasm exchange between countries. We also exemplified the utility of 32mSNPs in enhancing post-genomics research through in-silico genotyping, high-resolution GWAS, discovering and/or characterizing genes and alleles/mutations, identifying germplasms containing beneficial alleles that are potentially experiencing artificial selection.ConclusionThe comprehensive analysis of publicly available large-scale genome sequencing data of diverse cultivated accessions and the newly in-house sequenced wild accessions greatly increased the soybean genome-wide variation resolution. This could facilitate a variety of genetic and molecular-level analyses in soybean. The 32mSNPs and 1.5 K accessions with their comprehensive annotation have been made available at the SoyBase and Ag Data Commons. The dataset could further serve as a versatile and expandable core resource for exploring the exponentially increasing genome sequencing data for a variety of post-genomic research.

The National Ecological Observatory Network's soil metagenomes: assembly and basic analysis.

The largest dataset of soil metagenomes has recently been released by the National Ecological Observatory Network (NEON), which performs annual shotgun sequencing of soils at 47 sites across the United States. NEON serves as a valuable educational resource, thanks to its open data and programming tutorials, but there is currently no introductory tutorial for accessing and analyzing the soil shotgun metagenomic dataset. Here, we describe methods for processing raw soil metagenome sequencing reads using a bioinformatics pipeline tailored to the high complexity and diversity of the soil microbiome. We describe the rationale, necessary resources, and implementation of steps such as cleaning raw reads, taxonomic classification, assembly into contigs or genomes, annotation of predicted genes using custom protein databases, and exporting data for downstream analysis. The workflow presented here aims to increase the accessibility of NEON's shotgun metagenome data, which can provide important clues about soil microbial communities and their ecological roles.

Large-Scale Integrative Analysis of Soybean Transcriptome Using an Unsupervised Autoencoder Model.

Plant tissues are distinguished by their gene expression patterns, which can help identify tissue-specific highly expressed genes and their differential functional modules. For this purpose, large-scale soybean transcriptome samples were collected and processed starting from raw sequencing reads in a uniform analysis pipeline. To address the gene expression heterogeneity in different tissues, we utilized an adversarial deconfounding autoencoder (AD-AE) model to map gene expressions into a latent space and adapted a standard unsupervised autoencoder (AE) model to help effectively extract meaningful biological signals from the noisy data. As a result, four groups of 1,743, 914, 2,107, and 1,451 genes were found highly expressed specifically in leaf, root, seed and nodule tissues, respectively. To obtain key transcription factors (TFs), hub genes and their functional modules in each tissue, we constructed tissue-specific gene regulatory networks (GRNs), and differential correlation networks by using corrected and compressed gene expression data. We validated our results from the literature and gene enrichment analysis, which confirmed many identified tissue-specific genes. Our study represents the largest gene expression analysis in soybean tissues to date. It provides valuable targets for tissue-specific research and helps uncover broader biological patterns. Code is publicly available with open source at https://github.com/LingtaoSu/SoyMeta.

Streamlined processing and analysis of 16S rRNA amplicon sequencing data with OCMS_16S and OCMSlooksy

16S rRNA gene sequencing is a cost-effective method for profiling the bacterial component of a microbiome. Nevertheless, processing and analysis of the resulting sequencing data is often constrained by the availability of dedicated bioinformaticians - creating a bottleneck for biological interpretation. Multiple visualisation and analysis tools now exist for downstream analysis of 16S rRNA data. These tools are designed with biological scientists in mind and therefore consist of a graphical user interface that interacts with taxonomic counts tables to perform tasks such as alpha- and beta-diversity analysis and differential abundance. However, generating the input to these applications still relies on bioinformatics experience, creating a disconnect between data processing and data analysis. We aimed to bridge the gap between data processing and data analysis. To do this we have created two tools - OCMS_16S and OCMSlooksy - that perform data processing and data visualisation/analysis, respectively. OCMS_16S is a cgat-core based pipeline that wraps DADA2 functionality in order to facilitate processing of raw sequence reads into tables of amplicon sequence variant (ASV) counts using a simple command line interface. OCMSlooksy is an RShiny application that takes an OCMS_16S-generated SQLite database as input to facilitate data exploration and analysis. Combining these tools provides a simple, user-friendly workflow to facilitate 16S rRNA gene amplicon sequencing data analysis from raw reads to results.

De novo genome assembly and functional annotation for Fusarium langsethiae

BackgroundFusarium langsethiae is a T-2 and HT-2 mycotoxins producing species firstly characterised in 2004. It is commonly isolated from oats in Northern Europe. T-2 and HT-2 mycotoxins exhibit immunological and haemotological effects in animal health mainly through inhibition of protein, RNA and DNA synthesis. The development of a high-quality and comprehensively annotated assembly for this species is therefore essential in providing the molecular understanding and the mechanism of T-2 and HT-2 biosynthesis in F. langsethiae to help develop effective control strategies.ResultsThe F. langsethiae assembly was produced using PacBio long reads, which were then assembled independently using Canu, SMARTdenovo and Flye. A total of 19,336 coding genes were identified using RNA-Seq informed ab-initio gene prediction. Finally, predicting genes were annotated using the basic local alignment search tool (BLAST) against the NCBI non-redundant (NR) genome database and protein hits were annotated using InterProScan. Genes with blast hits were functionally annotated with Gene Ontology.ConclusionsWe developed a high-quality genome assembly of a total length of 59 Mb and N50 of 3.51 Mb. Raw sequence reads and assembled genome is publicly available and can be downloaded from: GenBank under the accession JAFFKB000000000.All commands used to generate this assembly are accessible via GitHub: https://github.com/FadyMohareb/fusarium_langsethiae.

SASCRiP: A Python workflow for preprocessing UMI count-based scRNA-seq data

In order to reduce the impact of technical variation inherent in single-cell RNA sequencing (scRNA-seq) technologies on biological interpretation of experiments, rigorous preprocessing and quality control is required to transform raw sequencing reads into high-quality, gene and transcript counts. While hundreds of tools have been developed for this purpose, the vast majority of the most widely used tools are built for the R software environment. With an increasing number of new tools now being developed using Python, it is necessary to develop integrative workflows that leverage tools from both platforms. We have therefore developed, SASCRiP (Sequencing Analysis of Single-Cell RNA in Python), a modular single-cell preprocessing workflow that integrates functionality from existing, widely used R and Python packages, and additional custom features and visualizations, to enable preprocessing of scRNA-seq data derived from technologies that use unique molecular identifier (UMI) sequences in a single Python analysis workflow. We describe the utility of SASCRiP using datasets derived from peripheral blood mononuclear cells sequenced using droplet-based, 3′-end sequencing technology. We highlight SASCRiP’s diagnostic visualizations and fully customizable functions, and demonstrate how SASCRiP provides a highly flexible, integrative Python workflow for preparing unprocessed UMI count-based scRNA-seq data for subsequent downstream analyses. SASCRiP is freely available through PyPi or from the GitHub page.

The microbiome of the buffalo digestive tract

Buffalo is an important livestock species. Here, we present a comprehensive metagenomic survey of the microbial communities along the buffalo digestive tract. We analysed 695 samples covering eight different sites in three compartments (four-chambered stomach, intestine, and rectum). We mapped ~85% of the raw sequence reads to 4,960 strain-level metagenome-assembled genomes (MAGs) and 3,255 species-level MAGs, 90% of which appear to correspond to new species. In addition, we annotated over 5.8 million nonredundant proteins from the MAGs. In comparison with the rumen microbiome of cattle, the buffalo microbiota seems to present greater potential for fibre degradation and less potential for methane production. Our catalogue of microbial genomes and the encoded proteins provides insights into microbial functions and interactions at distinct sites along the buffalo digestive tract.

A mixture model for determining SARS-Cov-2 variant composition in pooled samples.

Despite of the fast development of highly effective vaccines to control the current COVID-19 pandemics, the unequal distribution and availability of these vaccines worldwide and the number of people infected in the world lead to the continuous emergence of Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2) variants of concern. Therefore, it is likely that real-time genomic surveillance will be continuously needed as an unceasing monitoring tool, necessary to follow the spread of the disease and the evolution of the virus. In this context, new genomic variants of SARS-CoV-2, including variants refractory to current vaccines, makes genomic surveillance programs tools of utmost importance. Nevertheless, the lack of appropriate analytical tools to quickly and effectively access the viral composition in meta-transcriptomic sequencing data, including environmental surveillance, represent possible challenges that may impact the fast adoption of this approach to mitigate the spread and transmission of viruses. We propose a statistical model for the estimation of the relative frequencies of SARS-CoV-2 variants in pooled samples. This model is built by considering a previously defined selection of genomic polymorphisms that characterize SARS-CoV-2 variants. The methods described here support both raw sequencing reads for polymorphisms-based markers calling and predefined markers in the variant call format. Results obtained using simulated data show that our method is quite effective in recovering the correct variant proportions. Further, results obtained by considering longitudinal data from wastewater samples of two locations in Switzerland agree well with those describing the epidemiological evolution of COVID-19 variants in clinical samples of these locations. Our results show that the described method can be a valuable tool for tracking the proportions of SARS-CoV-2 variants in complex mixtures such as waste water and environmental samples. http://github.com/rvalieris/LCS. Supplementary data are available at Bioinformatics online.

TRANSCRIPTOME SEQUENCING OF LEPISANTHES FRUTICOSA TO DISCOVER SSR MARKERS

Lepisanthes fruticosa (ceri Terengganu) is one of the important underutilized fruit plants with high value of bioactive compounds and pharmacological properties. Current studies have focused mainly on the bioactive compounds which are essential for functional food and pharmaceutical applications. However, studies on the diversity and conservation of L. fruticosa are still scarce since genomic and genetic resources for this plant species are still lacking. In this study, RNA sequencing of L. fruticosa leaf was carried out using Illumina HiSeq to identify potential unigenes and simple sequence repeats (SSRs). A total of 52,657 unigenes were identified from about 91,043,356 million raw sequence reads. Mining of SSRs from these unigenes have predicted a total of 23,958 SSRs which was approximately 45.58% of total unigenes obtained. Dinucleotide repeats motif was the highest (21.48%) and the next were trinucleotide repeats motif (14.65%). A total of 4,620 SSRs ranging from 12 to 116 bp were selected for experimental validation. Bioinformatic analysis via GO and KEGG platforms showed that a total of 1,861 (40.28%) SSR-containing unigenes matched to Gene Ontology (GO) terminology and 48 biochemical pathways. The SSR-containing unigenes of L. fruticosa were involved in various cell functions and a majority of their functions were associated with purine and thiamine metabolism. In addition. A majority of SSR-containing unigenes were involved in organic and heterocylic compounds bindings, indicating an active event of biosynthesis process of secondary metabolites in L. fruticosa. SSR markers obtained from this study provides new genetic information that can be utilized to facilitate future characterization of L. fruticosa accessions at molecular levels.

Salivary MicroRNA Signature for Diagnosis of Endometriosis.

Background: Endometriosis diagnosis constitutes a considerable economic burden for the healthcare system with diagnostic tools often inconclusive with insufficient accuracy. We sought to analyze the human miRNAome to define a saliva-based diagnostic miRNA signature for endometriosis. Methods: We performed a prospective ENDO-miRNA study involving 200 saliva samples obtained from 200 women with chronic pelvic pain suggestive of endometriosis collected between January and June 2021. The study consisted of two parts: (i) identification of a biomarker based on genome-wide miRNA expression profiling by small RNA sequencing using next-generation sequencing (NGS) and (ii) development of a saliva-based miRNA diagnostic signature according to expression and accuracy profiling using a Random Forest algorithm. Results: Among the 200 patients, 76.5% (n = 153) were diagnosed with endometriosis and 23.5% (n = 47) without (controls). Small RNA-seq of 200 saliva samples yielded ~4642 M raw sequencing reads (from ~13.7 M to ~39.3 M reads/sample). Quantification of the filtered reads and identification of known miRNAs yielded ~190 M sequences that were mapped to 2561 known miRNAs. Of the 2561 known miRNAs, the feature selection with Random Forest algorithm generated after internally cross validation a saliva signature of endometriosis composed of 109 miRNAs. The respective sensitivity, specificity, and AUC for the diagnostic miRNA signature were 96.7%, 100%, and 98.3%. Conclusions: The ENDO-miRNA study is the first prospective study to report a saliva-based diagnostic miRNA signature for endometriosis. This could contribute to improving early diagnosis by means of a non-invasive tool easily available in any healthcare system.

QuasiFlow: a Nextflow pipeline for analysis of NGS-based HIV-1 drug resistance data.

Next-generation sequencing (NGS) enables reliable detection of resistance mutations in minority variants of human immunodeficiency virus type 1 (HIV-1). There is paucity of evidence for the association of minority resistance to treatment failure, and this requires evaluation. However, the tools for analyzing HIV-1 drug resistance (HIVDR) testing data are mostly web-based which requires uploading data to webservers. This is a challenge for laboratories with internet connectivity issues and instances with restricted data transfer across networks. We present QuasiFlow, a pipeline for reproducible analysis of NGS-based HIVDR testing data across different computing environments. Since QuasiFlow entirely depends on command-line tools and a local copy of the reference database, it eliminates challenges associated with uploading HIV-1 NGS data onto webservers. The pipeline takes raw sequence reads in FASTQ format as input and generates a user-friendly report in PDF/HTML format. The drug resistance scores obtained using QuasiFlow were 100% and 99.12% identical to those obtained using web-based HIVdb program and HyDRA web respectively at a mutation detection threshold of 20%. QuasiFlow and corresponding documentation are publicly available at https://github.com/AlfredUg/QuasiFlow. The pipeline is implemented in Nextflow and requires regular updating of the Stanford HIV drug resistance interpretation algorithm. Supplementary data are available at Bioinformatics Advances online.

Using RNA-Seq for Transcriptome Profiling of Botrylloides sp. Regeneration.

The decrease in sequencing costs and technology improvements has led to the adoption of RNA-sequencing to profile transcriptomes from further non-traditional regeneration model organisms such as the colonial ascidian Botrylloides leachii. The relatively unbiased way in which transcripts are identified and quantified makes this technique suitable to detect large-scale changes in expression, and the identification of novel transcripts and isoforms. Of particular interest to many researchers is the discovery of differentially expressed transcripts across different treatment conditions or stages of regeneration. This protocol describes a workflow starting from processing raw sequencing reads, mapping reads, assembly of transcripts, and measuring their abundance, creating lists of differentially expressed genes and their biological interpretation using gene ontologies. All programs used in this protocol are open-source software tools and freely available.

The investigation of intraspecific characteristics and comparative analyses of the complete mitochondrial genome of Stegobium paniceum (Linnaeus, 1758) (Coleoptera: Ptinidae) assembled from public NGS raw reads of the black truffle, Tuber melanosporum.

Due to the rapid development of NGS technologies, a huge amount of NGS raw reads have been accumulated in public repositories, such as the Short Read Archive of NCBI. We successfully rescued the complete mitochondrial genome of Stegobium paniceum, a drug store beetle, from public NGS raw reads of truffle generated from the whole genome project. The circular mitogenome of S. paniceum is 15,474 bp long including 13 protein-coding genes, two ribosomal RNA genes, 22 transfer RNAs, and a single large non-coding region of 803 bp. Intraspecific transfer RNAs structure and sequence variations were investigated and simple sequence repeats identified from three S. paniceum mitochondrial genomes were compared showing their diversities as fundamental data to utilize them in various aspects including developing efficient molecular markers in the family, Ptinidae. Phylogenetic analysis of 23 Bostrichoidea mitochondrial genomes presented better species identification based on phylogenetic analyses and the optimal options for constructing phylogenetic trees based on Bostrichoidea mitochondrial genomes. Our results present not only utilization of public NGS raw read sequences but also intraspecific features of S. paniceum mitochondrial genomes and comparative analysis of Bostrichoidea mitochondrial genomes in various aspects.

Nipah virus vector sequences in COVID-19 patient samples sequenced by the Wuhan Institute of Virology

We report the detection of Nipah virus in an infectious clone format, a BSL4-level pathogen and CDC-designated Bioterrorism Agent, in raw RNA-Seq sequencing reads deposited by the Wuhan Institute of Virology (WIV) produced from five December 2019 patients infected with SARS-CoV-2. Research involving Nipah infectious clones has never been reported to have occured at the WIV. These patient samples have been previously reported to contain reads from several other viruses: Influenza A, Spodoptera frugiperda rhabdovirus and Nipah. Previous authors have interpreted the presence of these virus sequences as indicative of co-infections of the patients in question by these pathogens or laboratory contamination. However, our analysis shows that NiV genes are encapsulated in synthetic vectors, which we infer was for assembly of a NiV infectious clone. In particular, we document the finding of internal N, P-V-W-C and L protein coding sequences as well as coverage of the G and F genes. Furthermore, the format of Hepatitis D virus ribozyme and T7 terminator downstream of the 5-prime end of the NiV sequence is consistent with truncation required at the end of the genome for a full length infectious clone. This indicates that research at WIV was being conducted on an assembled NiV infectious clone. Contamination of patient sequencing reads by an infectious NiV clone of the highly pathogenic Bangladesh strain could indicate a significant breach of BSL-4 protocols. We call on WIV to explain the purpose of this research on infectious clones of Nipah Virus, the full chronology of this work, and to explain how and at what stage of sample preparation this contamination occurred.

Identification of Genes Related to Cold Tolerance and Novel Genetic Markers for Molecular Breeding in Taiwan Tilapia (Oreochromis spp.) via Transcriptome Analysis.

Simple SummaryIn this study, we investigated the brain, gill, liver, and muscle transcriptomic responses of Taiwan tilapia towards cold stress. Some key genes and molecular markers involved in cold biological pathways were screened through differential expression. Among them, energy-related metabolic pathways and nucleotide genotypes were highly correlated with cold tolerance traits. This suggested that single nucleotide polymorphism (SNP) genetic variation can be used as a molecular marker to assist the selection and verification of cold-tolerant populations. Our study results will accelerate the understanding of different farmed tilapia tolerance mechanisms to environmental temperature changes and provide insights for the molecular breeding of cold-tolerant Taiwan tilapia species.Taiwan tilapia is one of the primary species used in aquaculture practices in Taiwan. However, as a tropical fish, it is sensitive to cold temperatures that can lead to high mortality rates during winter months. Genetic and broodstock management strategies using marker-assisted selection and breeding are the best tools currently available to improve seed varieties for tilapia species. The purpose of this study was to develop molecular markers for cold stress-related genes using digital gene expression analysis of next-generation transcriptome sequencing in Taiwan tilapia (Oreochromis spp.). We constructed and sequenced cDNA libraries from the brain, gill, liver, and muscle tissues of cold-tolerance (CT) and cold-sensitivity (CS) strains. Approximately 35,214,833,100 nucleotides of raw sequencing reads were generated, and these were assembled into 128,147 unigenes possessing a total length of 185,382,926 bp and an average length of 1446 bp. A total of 25,844 unigenes were annotated using five protein databases and Venny analysis, and 38,377 simple sequence repeats (SSRs) and 65,527 single nucleotide polymorphisms (SNPs) were identified. Furthermore, from the 38-cold tolerance-related genes that were identified using differential gene expression analysis in the four tissues, 13 microsatellites and 37 single nucleotide polymorphism markers were identified. The results of the genotype analysis revealed that the selected markers could be used for population genetics. In addition to the diversity assessment, one of the SNP markers was determined to be significantly related to cold-tolerance traits and could be used as a molecular marker to assist in the selection and verification of cold-tolerant populations. The specific genetic markers explored in this study can be used for the identification of genetic polymorphisms and cold tolerance traits in Taiwan tilapia, and they can also be used to further explore the physiological and biochemical molecular regulation pathways of fish that are involved in their tolerance to environmental temperature stress.

Reproducible RNA-seq data processing and analysis tools in the cloud.

Discovering heterogeneous biological processes underlying Alzheimer's Disease (AD) is a key to prioritizing potential drug candidates. Analysis of RNA sequencing (RNA-seq) data can improve our understanding of these processes by revealing gene expression patterns associated with AD. Analyzing this data requires processing large volumes of sequencing files, as well as downstream analysis in a secure compute environment. To ensure reliable results, it is important to execute software consistently so that results can be reproduced in different environments. To help address these challenges, we have developed reproducible bioinformatic tools for raw data processing and analysis in the Amazon Web Services (AWS) cloud compute environment. We have implemented a RNA-seq processing pipeline in common workflow language (CWL). Raw sequencing reads in the form of Fastq or Bam files are aligned to the reference genome using the STAR read aligner and gene counts are quantified (https://github.com/Sage-Bionetworks-Workflows/dockstore-workflow-rnaseq). In addition, we have developed an R package for gene count normalization (https://github.com/Sage-Bionetworks/sageseqr). To enable execution of these tools, we provide an analytical workspace in a secure AWS compute environment (https://adknowledgeportal.synapse.org/Analytical%20Workspace). We have utilized these tools to reprocess data from several RNA-seq studies that are available through the AD Knowledge Portal (adknowledgeportal.org) as the RNAseq Harmonization Study. As new datasets are generated, they can be processed with a consistent software environment to enable cross-study analysis. By enabling reproducible data processing, users can perform similar RNA-seq experiments without needing to implement new pipelines. The development of reproducible RNA-seq processing and analysis tools provides a valuable resource for the AD research community. While we have demonstrated execution of these tools in the cloud, they may also be executed in diverse environments such as high performance compute clusters. Our tools will remain stable resources for reproducible processing of RNA-seq datasets under evolving infrastructures.

Genes and regulatory mechanisms associated with experimentally-induced bovine respiratory disease identified using supervised machine learning methodology

Bovine respiratory disease (BRD) is a multifactorial disease involving complex host immune interactions shaped by pathogenic agents and environmental factors. Advancements in RNA sequencing and associated analytical methods are improving our understanding of host response related to BRD pathophysiology. Supervised machine learning (ML) approaches present one such method for analyzing new and previously published transcriptome data to identify novel disease-associated genes and mechanisms. Our objective was to apply ML models to lung and immunological tissue datasets acquired from previous clinical BRD experiments to identify genes that classify disease with high accuracy. Raw mRNA sequencing reads from 151 bovine datasets (n = 123 BRD, n = 28 control) were downloaded from NCBI-GEO. Quality filtered reads were assembled in a HISAT2/Stringtie2 pipeline. Raw gene counts for ML analysis were normalized, transformed, and analyzed with MLSeq, utilizing six ML models. Cross-validation parameters (fivefold, repeated 10 times) were applied to 70% of the compiled datasets for ML model training and parameter tuning; optimized ML models were tested with the remaining 30%. Downstream analysis of significant genes identified by the top ML models, based on classification accuracy for each etiological association, was performed within WebGestalt and Reactome (FDR ≤ 0.05). Nearest shrunken centroid and Poisson linear discriminant analysis with power transformation models identified 154 and 195 significant genes for IBR and BRSV, respectively; from these genes, the two ML models discriminated IBR and BRSV with 100% accuracy compared to sham controls. Significant genes classified by the top ML models in IBR (154) and BRSV (195), but not BVDV (74), were related to type I interferon production and IL-8 secretion, specifically in lymphoid tissue and not homogenized lung tissue. Genes identified in Mannheimia haemolytica infections (97) were involved in activating classical and alternative pathways of complement. Novel findings, including expression of genes related to reduced mitochondrial oxygenation and ATP synthesis in consolidated lung tissue, were discovered. Genes identified in each analysis represent distinct genomic events relevant to understanding and predicting clinical BRD. Our analysis demonstrates the utility of ML with published datasets for discovering functional information to support the prediction and understanding of clinical BRD.

RNA-Seq Data Analysis Pipeline for Plants: Transcriptome Assembly, Alignment, and Differential Expression Analysis.

In this chapter, we describe methods for analyzing RNA-Seq data, presented as a flow along a pipeline beginning with raw data from a sequencer and ending with an output of differentially expressed genes and their functional characterization. The first section covers de novo transcriptome assembly for organisms lacking reference genomes or for those interested in probing against the background of organism-specific transcriptomes assembled from RNA-Seq data. Section 2 covers both gene- and transcript-level quantifications, leading to the third and final section on differential expression analysis between two or more conditions. The pipeline starts with raw sequence reads, followed by quality assessment and preprocessing of the input data to ensure a robust estimate of the transcripts and their differential regulation. The preprocessed data can be inputted into the de novo transcriptome flow to assemble transcripts, functionally annotated using tools such as InterProScan or Blast2Go and then forwarded to differential expression analysis flow, or directly inputted into the differential expression analysis flow if a reference genome is available. An online repository containing sample data has also been made available, as well as custom Python scripts to modify the output of the programs within the pipeline for various downstream analyses.

Single-Cell Proteogenomic Sequencing Allows Early Detection of Relapse Clone with CN-LOH at FLT3-ITD Locus from Initial Diagnosis in AML

IntroductionFLT3-ITD is one of the most common and clinically relevant mutations in acute myeloid leukemia (AML). A high FLT3-ITD allelic ratio (AR) (>0.5) is strongly associated with poor prognosis. FLT3-ITDs are heterogeneous mutations, varying in sizes and locations with some patients having multiple FLT3-ITDs. Unfortunately, conventional clinical techniques are often not adequate in measuring these characteristics at sufficient resolution. The current study aimed to characterize an AML case with 2 FLT3-ITDs at diagnosis, map the multi-omic evolution of AML clones, and understand genetic patterns underlying AML relapse using single-cell proteogenomic sequencing (ScPGseq).MethodsLeukemic samples from a 46-year old female patient diagnosed with de novo AML obtained at initial diagnosis and relapse were archived and used in this study. Based on the 2017 ELN risk stratification, the patient was classified into a low-risk group due to normal karyotype/low FLT3-ITD AR/NPM1+. The patient achieved complete remission (CR) after induction chemotherapy in combination with midostaurin. The patient relapsed 2 months after 4 cycles of consolidation, with a CR duration of 6 months. After relapse, the disease was refractory despite reinduction and salvage therapies, even with gilteritinib (Fig A). ScPGseq was performed using the Mission Bio's AML panel and antibody oligoconjugates for 16 cell surface proteins. Raw sequencing reads were processed using Mission Bio's Tapestri pipeline and results were exported from Tapestri Insights (v3.0.2). All downstream computational and statistical analyses were performed using R and Python.ResultsConsistent with clinical sequencing (R 2 = 0.997) and PCR, ScPGseq identified 5 mutations in FLT3 (21bp and 39bp ITDs and D835Y), NPM1, and WT1 at diagnosis (2367 cells). At relapse, 1 FLT3-ITD (21bp) and mutations in NPM1 and WT1 were detected while the other FLT3-ITD (39bp) and FLT3 D835Y were absent (2611 cells). Clonal analyses of mutation patterns identified 4 AML clones at diagnosis (C1-4) and 1 at relapse (C3R; Fig B). Two closely related clones, C3 and C3R (WT1+/NPM1+/21bp FLT3-ITD+) differed in zygosity of 21bp FLT3-ITD, where nearly all cells in C3R carries homozygous FLT3-ITD. SNP array confirmed the presence of copy neutral loss of heterozygosity (CN-LOH) in chr13q.Analysis of 16 cell surface proteins along with clonal information identified 3 clusters including one devoted to non-leukemic fractions (i.e., no mutations) (top) and another nearly exclusively consisting of mutant cells from diagnosis, which we termed “monocyte-like signature” (right bottom). The other cluster was composed of nearly all mutant cells from the relapse sample and about 1/3 of mutant cells from diagnosis, which we termed “immature myeloid cell signature” (left) (Fig C).When investigating further, C3 cells with immature myeloid cell signature (98/287 cells, 34.1%) exhibited much higher allelic burden of (mean 73% vs. 56%, adj. p < 1.1e-6) and were significantly enriched with homozygous 21bp FLT3-ITDs compared to C3 cells with monocyte-like signature (189/287 cells, 65.9%) (41/98 cells, 42% vs. 11/189 cells, 5.8%, adj. p = 2.7e-13). No other mutations showed similar patterns. It indicates that C3R existed from the initial diagnosis at low frequency, instead of losing heterozygosity during relapse. Without the single cell proteogenomic analyses, C3R cells, present in <5% cells at the initial diagnosis could have not been detected.The multi-omic information elucidates the complete clonal history of this AML (Fig D). Starting with WT1 and NPM1 mutations (C1), 3 FLT3 mutations (2 ITDs and D835Y) were subsequently acquired in 3 subclones (C2-4). A subset of C3 cells (WT1+/NPM1+/21bp FLT3-ITD+) further gained CN-LOH in chr13q (C3R at diagnosis). When treated, AML cells without homozygous 21bp FLT3-ITD were cleared. Cells with homozygous 21bp FLT3-ITD survived/escaped from the treatment and became the dominant clone at relapse (C3R at relapse).ConclusionThe current study demonstrates that ScPGseq allows1) simultaneous and comprehensive analyses of multiple FLT3-ITDs at the single-cell level2) early detection of relapse clone with subclonal homozygous 21bp FLT3-ITD from the initial diagnosis, which explains one of the mechanisms of relapse in AML cases with low FLT3-ITD AR3) multi-omic clonal analyses, which further refine clonal models relying only on mutation profiles. [Display omitted] DisclosuresMinden: Astellas: Consultancy. Kim: Pfizer: Honoraria, Research Funding; Bristol-Meier Squibb: Research Funding; Novartis: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Paladin: Honoraria, Research Funding.