FASTQ Files Research Articles

Comparison of different microbiome analysis pipelines to validate their reproducibility of gastric mucosal microbiome composition.

Microbiome analysis has become a crucial tool for basic and translational research due to its potential for translation into clinical practice. However, there is ongoing controversy regarding the comparability of different bioinformatic analysis platforms and a lack of recognized standards, which might have an impact on the translational potential of results. This study investigates how the performance of different microbiome analysis platforms impacts the final results of mucosal microbiome signatures. Across five independent research groups, we compared three distinct and frequently used microbiome analysis bioinformatic packages (DADA2, MOTHUR, and QIIME2) on the same subset of fastQ files. The source data set encompassed 16S rRNA gene raw sequencing data (V1-V2) from gastric biopsy samples of clinically well-defined gastric cancer (GC) patients (n = 40; with and without Helicobacter pylori [H. pylori] infection) and controls (n = 39, with and without H. pylori infection). Independent of the applied protocol, H. pylori status, microbial diversity and relative bacterial abundance were reproducible across all platforms, although differences in performance were detected. Furthermore, alignment of the filtered sequences to the old and new taxonomic databases (i.e., Ribosomal Database Project, Greengenes, and SILVA) had only a limited impact on the taxonomic assignment and thus on global analytical outcomes. Taken together, our results clearly demonstrate that different microbiome analysis approaches from independent expert groups generate comparable results when applied to the same data set. This is crucial for interpreting respective studies and underscores the broader applicability of microbiome analysis in clinical research, provided that robust pipelines are utilized and thoroughly documented to ensure reproducibility.IMPORTANCEMicrobiome analysis is one of the most important tools for basic and translational research due to its potential for translation into clinical practice. However, there is an ongoing controversy about the comparability of different bioinformatic analysis platforms and a lack of recognized standards. In this study, we investigate how the performance of different microbiome analysis platforms affects the final results of mucosal microbiome signatures. Five independent research groups used three different and commonly used bioinformatics packages for microbiome analysis on the same data set and compared the results. This data set included microbiome sequencing data from gastric biopsy samples of GC patients. Regardless of the protocol used, Helicobacter pylori status, microbial diversity, and relative bacterial abundance were reproducible across all platforms. The results show that different microbiome analysis approaches provide comparable results. This is crucial for the interpretation of corresponding studies and underlines the broader applicability of microbiome analysis.

Robust discrimination between closely related species of salmon based on DNA fragments.

Closely related species of Salmonidae, including Pacific and Atlantic salmon, can be distinguished from one another based on nucleotide sequences from the cytochrome c oxidase sub-unit 1 mitochondrial gene (COI), using ensembles of fragments aligned to genetic barcodes that serve as digital proxies for the relevant species. This is accomplished by exploiting both the nucleotide sequences and their quality scores recorded in a FASTQ file obtained via Next Generation (NextGen) Sequencing of mitochondrial DNA extracted from Coho salmon caught with hook and line in the Gulf of Alaska. The alignment is done using MUSCLE (Muscle 5.2) (Edgar in Nat Commun 13:6968, 2022), applied to multiple versions of each fragment perturbed according to the nucleobase identification error probabilities underlying the quality scores. The Damerau-Levenshtein distance was used to determine the genetic barcode of the candidate species that is closest to each aligned, perturbed fragment. The "votes" that the sampled fragments cast for the different candidate species are then pooled and converted into identification probabilities, using weights determined by the entropy of the fragment-specific identification probability distributions. This novel approach to quantify the uncertainty associated with measurements made using NextGen Sequencing can be applied to discriminate closely related species, hence to value-assignment for reference materials supporting determinations of the authenticity of seafood, for example, NIST Reference Materials 8256 and 8257 (Coho salmon) (Ellisor et al., 2021).

Pre-processing of paleogenomes: mitigating reference bias and postmortem damage in ancient genome data

We investigate alternative strategies against reference bias and postmortem damage in low coverage paleogenomes. Compared to alignment to the linear reference genome, we show that masking known polymorphic sites and graph alignment effectively remove reference bias, but only starting from raw read files. We next study approaches to overcome postmortem damage: trimming, rescaling, and our newly developed algorithm, bamRefine (github.com/etkayapar/bamRefine and zenodo.org/records/14234666), masking reads only at positions possibly affected by PMD. We propose graph alignment coupled with bamRefine as a simple strategy to minimize data loss and bias, and urge the community to publish FASTQ files.

Lossless and reference-free compression of FASTQ/A files using GeneSqueeze

As sequencing becomes more accessible, there is an acute need for novel compression methods to efficiently store sequencing files. Omics analytics can leverage sequencing technologies to enhance biomedical research and individualize patient care, but sequencing files demand immense storage capabilities, particularly when sequencing is utilized for longitudinal studies. Addressing the storage challenges posed by these technologies is crucial for omics analytics to achieve their full potential. We present a novel lossless, reference-free compression algorithm, GeneSqueeze, that leverages the patterns inherent in the underlying components of FASTQ files to solve this need. GeneSqueeze’s benefits include an auto-tuning compression protocol based on each file’s distribution, lossless preservation of IUPAC nucleotides and read identifiers, and unrestricted FASTQ/A file attributes (i.e., read length, number of reads, or read identifier format). We compared GeneSqueeze to the general-purpose compressor, gzip, and to a domain-specific compressor, SPRING, to assess performance. Due to GeneSqueeze’s current Python implementation, GeneSqueeze underperformed as compared to gzip and SPRING in the time domain. GeneSqueeze and gzip achieved 100% lossless compression across all elements of the FASTQ files (i.e. the read identifier, sequence, quality score and ‘ + ’ lines). GeneSqueeze and gzip compressed all files losslessly, while both SPRING’s traditional and lossless modes exhibited data loss of non-ACGTN IUPAC nucleotides and of metadata following the ‘ + ’ on the separator line. GeneSqueeze showed up to three times higher compression ratios as compared to gzip, regardless of read length, number of reads, or file size, and had comparable compression ratios to SPRING across a variety of factors. Overall, GeneSqueeze represents a competitive and specialized compression method for FASTQ/A files containing nucleotide sequences. As such, GeneSqueeze has the potential to significantly reduce the storage and transmission costs associated with large omics datasets without sacrificing data integrity.

CWL-Based Analysis Pipeline for Hi-C Data: From FASTQ Files to Matrices.

Over a decade has passed since the development of the Hi-C method for genome-wide analysis of 3D genome organization. Hi-C utilizes next-generation sequencing (NGS) technology to generate large-scale chromatin interaction data, which has accumulated across a diverse range of species and cell types, particularly in eukaryotes. There is thus a growing need to streamline the process of Hi-C data analysis to utilize these data sets effectively. Hi-C generates data that are much larger compared to other NGS techniques such as chromatin immunoprecipitation sequencing (ChIP-seq) or RNA-seq, making the data reanalysis process computationally expensive. In an effort to bridge this resource gap, the 4D Nucleome (4DN) Data Portal has reanalyzed approximately 600 Hi-C data sets, allowing users to access and utilize the analyzed data. In this chapter, we provide detailed instructions for the implementation of the common workflow language (CWL)-based Hi-C analysis pipeline adopted by the 4DN Data Portal ecosystem. This reproducible and portable pipeline generates standard Hi-C contact matrices in formats such as .hic or .mcool from FASTQ files. It enables users to output their own Hi-C data in the same format as those registered in the 4DN Data portal, facilitating comparative analysis using data registered in the portal. Our custom-made scripts are available on GitHub at https://github.com/kuzobuta/4dn_cwl_pipeline .

Prediction of antimicrobial susceptibility of pneumococci based on whole-genome sequencing data: a direct comparison of two genomic tools to conventional antimicrobial susceptibility testing.

Determination of antimicrobial resistance (AMR) in pneumococcal isolates is important for surveillance purposes and in a clinical context. Antimicrobial susceptibility testing (AST) of pneumococci is complicated by the need for exact minimal inhibitory concentrations (MICs) of beta-lactam antibiotics. Two next-generation sequencing (NGS) analysis tools have implemented the prediction of AMR in their analysis workflow, including the prediction of MICs: Pathogenwatch (https://pathogen.watch/) and AREScloud (OpGen). The performance of these tools in comparison to phenotypic AST following EUCAST guidelines is unknown. A total of 538 Streptococcus pneumoniae isolates were used to compare both tools with phenotypic AST for penicillin, amoxicillin, cefotaxime/ceftriaxone, erythromycin, trimethoprim-sulfamethoxazole, and tetracycline. Disk diffusion was performed for all isolates, and broth microdilution was performed for isolates with reduced beta-lactam susceptibility. Demultiplexed FASTQ files from Illumina sequencing, covering the whole genome of pneumococci, were used as input for the NGS tools. Categorical agreement (CA), major error (ME), and very major error (VME) rates were calculated. For beta-lactam antibiotics, CA was high (>94%) associated with none or only one ME and VME (<1%). For erythromycin and tetracycline, CA was >93% for predictions by AREScloud, while for Pathogenwatch, this ranged around 88%. For trimethoprim-sulfamethoxazole, CA was for both tools <86%. High VME rates were observed for erythromycin and tetracycline, higher for Pathogenwatch (53.6% and 47.0%, respectively) compared to AREScloud (14.3% and 19.1%, respectively). Both tools performed excellently despite the complexity of predicting beta-lactam resistance in pneumococci. Further optimization and validation are needed for non-beta-lactams since high (very) major error rates were observed.

BIOM-53. LOW-PASS WHOLE GENOME SEQUENCING OF CEREBROSPINAL FLUID IDENTIFIES TUMOR AGNOSTIC TP53 ABERRATIONS IN LEPTOMENINGEAL METASTATIC DISEASE

Abstract Leptomeningeal metastatic disease (LMD) is the spread of cancer cells into the cerebrospinal fluid (CSF)-filled spaces surrounding the central nervous system (CNS). Once LMD occurs, patients endure devastating neurologic symptoms and survive for only a few weeks to months. The clinical diagnosis of LMD has risen as patient survival from non-CNS metastatic disease improves. Unfortunately, detection of LMD even in symptomatic patients is &amp;lt;50% using the current gold standard – CSF cytology. Therefore, a strong clinical need exists for the sensitive and early detection of LMD. Here, we sought to develop a minimally invasive liquid biopsy approach to diagnose LMD using next-generation sequencing. Because copy number variations (CNVs) are common in cancer and increase after chemoradiation, we hypothesized that CNV detection in CSF may offer a means to detect LMD since patients have already undergone treatment for the underlying disease. Specifically, we sought to develop low-pass whole genome sequencing (lpWGS) of cell-free DNA in CSF to detect CNVs associated with LMD. A novel lpWGS algorithm was developed using FASTQ files trimmed to 30 million total paired reads. The genome was partitioned into one million continuous base pair segments excluding problematic areas yielding 2,445 regions across 22 autosomes. Control samples were used to model the read depth for each region to establish the euploid state. Read depth across the genome for all samples was 0.6X. CSF samples from lung cancer, breast cancer, and melanoma patients with LMD demonstrated prominent evidence of monosomies and trisomies throughout the genome. Lung cancer exhibited frequent amplifications (&amp;gt;4-fold). Notably, we observed 17p monosomy in CSF regardless of cancer type which indicates TP53 haploinsufficiency may support LMD progression, a conjecture supported by a codeletion of TP53 in one patient. lpWGS detects LMD in solid tumor cancers and may also identify novel mechanisms for LMD progression.

A WFS1 variant disrupting acceptor splice site uncovers the impact of alternative splicing on beta cell apoptosis in a patient with Wolfram syndrome

Aims/hypothesisWolfram syndrome 1 (WS1) is an inherited condition mainly manifesting in childhood-onset diabetes mellitus and progressive optic nerve atrophy. The causative gene, WFS1, encodes wolframin, a master regulator of several cellular responses, and the gene’s mutations associate with clinical variability. Indeed, nonsense/frameshift variants correlate with more severe symptoms than missense/in-frame variants. As achieving a genotype–phenotype correlation is crucial for dealing with disease outcome, works investigating the impact of transcriptional and translational landscapes stemming from such mutations are needed. Therefore, we sought to elucidate the molecular determinants behind the pathophysiological alterations in a WS1 patient carrying compound heterozygous mutations in WFS1: c.316-1G>A, affecting the acceptor splice site (ASS) upstream of exon 4; and c.757A>T, introducing a premature termination codon (PTC) in exon 7.MethodsBioinformatic analysis was carried out to infer the alternative splicing events occurring after disruption of ASS, followed by RNA-seq and PCR to validate the transcriptional landscape. Patient-derived induced pluripotent stem cells (iPSCs) were used as an in vitro model of WS1 and to investigate the WFS1 alternative splicing isoforms in pancreatic beta cells. CRISPR/Cas9 technology was employed to correct ASS mutation and generate a syngeneic control for the endoplasmic reticulum stress induction and immunotoxicity assays.ResultsWe showed that patient-derived iPSCs retained the ability to differentiate into pancreatic beta cells. We demonstrated that the allele carrying the ASS mutation c.316-1G>A originates two PTC-containing alternative splicing transcripts (c.316del and c.316–460del), and two open reading frame-conserving mRNAs (c.271–513del and c.316–456del) leading to N-terminally truncated polypeptides. By retaining the C-terminal domain, these isoforms sustained the endoplasmic reticulum stress response in beta cells. Otherwise, PTC-carrying transcripts were regulated by the nonsense-mediated decay (NMD) in basal conditions. Exposure to cell stress inducers and proinflammatory cytokines affected expression levels of the NMD-related gene SMG7 (>twofold decrease; p<0.001) without eliciting a robust unfolded protein response in WFS1 beta cells. This resulted in a dramatic accumulation of the PTC-containing isoforms c.316del (>100-fold increase over basal; p<0.001) and c.316–460del (>20-fold increase over basal; p<0.001), predisposing affected beta cells to undergo apoptosis. Cas9-mediated recovery of ASS retrieved the canonical transcriptional landscape, rescuing the normal phenotype in patient-derived beta cells.Conclusions/interpretationThis study represents a new model to study wolframin, highlighting how each single mutation of the WFS1 gene can determine dramatically different functional outcomes. Our data point to increased vulnerability of WFS1 beta cells to stress and inflammation and we postulate that this is triggered by escaping NMD and accumulation of mutated transcripts and truncated proteins. These findings pave the way for further studies on the molecular basis of genotype–phenotype relationship in WS1, to uncover the key determinants that might be targeted to ameliorate the clinical outcome of patients affected by this rare disease.Data availabilityThe in silico predicted N-terminal domain structure file of WT wolframin was deposited in the ModelArchive, together with procedures, ramachandran plots, inter-residue distance deviation and IDDT scores, and Gromacs configuration files (doi/10.5452/ma-cg3qd). The deep-sequencing data as fastq files used to generate consensus sequences of AS isoforms of WFS1 are available in the SRA database (BioProject PRJNA1109747).Graphical

ISeq: an integrated tool to fetch public sequencing data.

High-throughput sequencing technologies [next-generation sequencing (NGS)] are increasingly used to address diverse biological questions. Despite the rich information in NGS data, particularly with the growing datasets from repositories like the Genome Sequence Archive (GSA) at NGDC, programmatic access to public sequencing data and metadata remains limited. We developed iSeq to enable quick and straightforward retrieval of metadata and NGS data from multiple databases via the command-line interface. iSeq supports simultaneous retrieval from GSA, SRA, ENA, and DDBJ databases. It handles over 25 different accession formats, supports Aspera downloads, parallel downloads, multi-threaded processes, FASTQ file merging, and integrity verification, simplifying data acquisition and enhancing the capacity for reanalyzing NGS data. iSeq is freely available on Bioconda (https://anaconda.org/bioconda/iseq) and GitHub (https://github.com/BioOmics/iSeq).

Amplicon-based long-read sequencing is useful for confirming zygosity of DSG2 variants associated with Japanese ARVC

Abstract Background Arrhythmogenic right ventricular cardiomyopathy (ARVC) is an inherited cardiomyopathy mainly caused by desmosomal gene variants. Previously, we reported that DSG2 variants were the most common in 153 Japanese ARVC probands (DSG2 multiple, n=62 and single, n=4; PKP2, n=28; others, n=7). Among the multiple DSG2 variant carriers, 22 probands were compound heterozygous carriers, and 25 were homozygous, which were confirmed by genetic testing for family members (Figure A). On the other hand, 88% of their family members with single DSG2 variant carriers were not diagnosed with ARVC, which meant that recessive DSG2 variants were the major cause of ARVC in Japan. However, zygosities in remaining 15 probands with multiple DSG2 variants were unknown due to the lack of genome data from their parents. Purpose This study aimed to clarify the zygosities of multiple DSG2 variants using amplicon-based long-read sequencing. Methods Genomic DNA was extracted from whole blood using standard protocols. The range containing multiple variants was amplified by long-range PCR. Sequencing libraries were prepared using ONT Ligation Sequencing Kit. Each library was loaded onto a flow cell for sequencing on ONT GridION Mk1 running MinKNOW control software. FASTQ files were aligned to hg38 using minimap2. Variants were called using Clair3 and phased using WhatsHap. Integrative Genomics Viewer (IGV) was used for visualization of the phased variants. Results We successfully phased all the multiple DSG2 variants found in the 15 proband. A typical result of amplicon-based LRS was shown in Figure B. It was IGV screen shot of reads at DSG2 containing c.874C&amp;gt;T (p.R292C) and c.1481A&amp;gt;C (p.D494A). The reads colored in red belonged to one haplotype, while the ones in blue belonged to the other. Each of the two variants was on different color reads, indicating they were compound heterozygous variants. Using LRS, we confirmed all the 15 probands had compound heterozygous variants in DSG2. Overall, the 62 probands (40.5%) with DSG2 variants were recessive variant carriers (Figure A). Conclusions Japanese ARVC was mainly caused by autosomal recessive variants in DSG2. Clarifying the zygosity of causative variants is crucial for the diagnosis in recessive diseases including Japanese ARVC. Amplicon-based LRS was helpful for phasing the multiple DSG2 variants directly without genetic testing of the parents.Figure AFigure B

SWQC: Efficient sequencing data quality control on the next-generation sunway platform

Sequencing data quality control can significantly prevent low-quality data from impacting downstream applications in bioinformatics. The enormous growth of biological sequencing data in recent years introduces new challenges to the efficiency of quality control processes and motivates the need for fast implementations on modern compute systems. The powerful next-generation heterogeneous Sunway platform holds significant potential for addressing this challenge. However, there are currently no dedicated quality control applications that can fully utilize its computational power. To bridge this gap, we introduce SWQC, a novel quality control application specifically designed for the Sunway platform. We present an efficient distributed FASTQ I/O framework for Sunway-based workstations and supercomputers to take advantage of fast SSDs and the parallel file system. In order to support both process-level and thread-level (CPE-level) parallelism to leverage the computational power, we refactor and optimize all standard quality control modules for the heterogeneous Sunway architecture. When using a single node, SWQC achieves speedups between 2 and 40 over highly optimized quality control applications executed on a high-end 48-core AMD server. Additionally, when using 16 nodes, SWQC achieves parallel efficiencies of 70% (for reading and writing a single file) and 95% (for reading one file and writing split files) compared to a single node. Overall, SWQC is able to perform quality control operations for a 140GB FASTQ file within only 70 s using a single Sunway node. It is publicly available at https://github.com/RabbitBio/SWQC.

Identification of mutations in canine oral mucosal melanomas by exome sequencing and comparison with human melanomas

Oral mucosal melanomas (OMMs) are aggressive neoplasms commonly found in dogs but rare in humans. Utilizing whole exome sequencing (WES), which focuses on protein-coding regions to reveal mutation profiles, we conducted a comparative analysis of canine OMM and human melanomas. This study involved DNA extraction, exome enrichment, and sequencing from three canine OMM cell lines (CMGD-2, CMGD-5, TLM-1), five canine OMM frozen samples, a human OMM cell line (MEMO), and a human commercial skin melanoma cell line (SK-MEL-28). The sequencing and subsequent analysis of FASTQ files yielded final variant files, leading to the identification of mutations. Our findings revealed a total of 500 mutated genes in canine OMM, including significant ones such as EP300, FAT4, JAK3, LRP1B, NCOR1, and NOTCH1. Notably, 82 shared mutations were identified between human melanomas and canine OMM genomes. These mutations were categorized based on the gene functions. The identification of these mutations provides critical insights that can pave the way for the development of novel therapeutic strategies for both canine and human OMM, offering hope for more effective treatments in the future.

A retrospective whole genome sequencing of SARS-CoV-2 isolate from respiratory sample of an individual from Ota – Ogun State, Nigeria

Severe acute respiratory syndrome coronavirus – 2 (SARS-CoV-2) which was responsible for the COVID-19 pandemic has now been considered an endemic virus, that will continually produce sporadic outbreaks in different communities around the globe. Although, there are several surveillance studies on SARS-CoV-2 globally, including Nigeria, there is still an important need to understand the uniqueness of the strains of the virus that was circulating immediately after the pandemic, to add to the global database of information that will aid vaccine production and future preparedness against another SARS - related CoV pandemic.Towards the end of the pandemic, between February – August 2022, SARS-CoV-2 was detected in a surveillance project in Ota – Ogun State Nigeria. We carried out a retrospective whole genome sequencing (WGS) analysis of SARS-CoV-2 in the previously reported positive sample, at Inqaba biotech in South Africa. RNA extraction was carried out using the Quick – RNA viral kit (Zymo) and library preparation was done by NEBNext ARTIC SARS-CoV-2 FS Library Prep kit (Illumina) according to the manufacturer's instruction. The WGS was carried out on the NextSeq500 platform by Illumina. The fastq file containing the unassembled raw sequence reads were submitted to NCBI SARS-CoV-2 resources, and a BioProject accession number PRJNA1076330 was issued. The DRAGEN Targeted Microbial, GISAID-CoVsurver mutation, BLAST and MAFFT applications were used for analysis.The spike (s) gene of the study sequence possessed seven mutations (G142D, A163V, V213G, D614G, H655Y, N679K, P681H) and shared 99.4 % identity with those of the Wuhan reference sequence WIV04. The non-structural proteins (NSP) – 7,8,9,10 and 16 shared 100 % identity with bat/Yunnan/RaTG13 (a SARS- related CoV found in bats) sequence on GISAID. Although, the study sequence was obtained from an asymptomatic individual, the S mutations observed are known to be related to virulence, antigenic shift, enhanced transmissibility and host change. Thus, upon successful exploitation of this asymptomatic variant, it may possibly be transformed to a potential candidate for SARS-CoV-2 vaccine in future.

Easy analysis of bacterial whole-genome sequencing data for clinical microbiologists using open-source Galaxy platform: Characterization of ESBL-producing Enterobacterales from bloodstream infections

ObjectivesClinical microbiologists require easy-to-use open access tools with graphical interfaces to perform bacterial whole-genome sequencing (WGS) in routine practice. This study aimed to build a bioinformatics pipeline on the open-source Galaxy platform, facilitating comprehensive and reproducible analysis of bacterial WGS data in a few steps. We then used it to characterize our local epidemiology of ESBL-producing Enterobacterales isolated from patients with bacteremia. MethodsWe built a bioinformatics pipeline consisting of the following sequential tools: Fastp (input data trimming); FastQC (read quality control); SPAdes (genome assembly); Quast (quality control of genome assembly); Prokka (gene annotation); Staramr (ResFinder database) and ABRicate (CARD database) for antimicrobial resistance (AMR) gene screening and molecular strain typing. Paired-end short read WGS data from all ESBL-producing Enterobacterales strains isolated from patients with bacteremia over one year were analysed. ResultsThe Galaxy platform does not require command line tools. The bioinformatics pipeline was constructed within one hour. It only required uploading fastq files and facilitated systematization of de novo assembly of genomes, MLST typing, and AMR gene screening in one step. Among the 66 ESBL-producing strains analysed, the two most frequent ESBL genes were blaCTX−M-15 (62.1%) and blaCTX−M-27 (13.6%). ConclusionsThe open-access Galaxy platform provides a graphical interface and easy-to-use tools suitable for routine use in clinical microbiology laboratories without bioinformatics specialists. We believe that this platform will facilitate fast and low-cost analysis of bacterial WGS data, especially in resource-limited settings.

Cost-efficient PCR based DNA barcoding of marine invertebrate specimens with NovaSeq amplicon sequencing.

The marine environment harbors high biodiversity; however, it is poorly understood. Nucleotide sequence data of all marine organisms should be accumulated before natural and/or anthropogenic environmental changes jeopardize the marine environment. In this study, we report a cost-effective and easy DNA barcoding method. This method can be readily adopted without using library preparation kits. It includes multiplex PCR of short targets, indexing PCR, and outsourcing to a sequencing service using the NovaSeq system. We targeted four mitochondrial genes [cytochrome c oxidase subunit I (COI), COIII, 16S rRNA (16S), and 12S rRNA (12S)] and three nuclear genes [18S rRNA (18S), 28S rRNA (28S), internal transcribed spacer 2 (ITS2)] in 95 marine invertebrate specimens, which were primarily annelids. The primers, including adapters and indices for NovaSeq sequencing, were newly designed. Two PCR runs were conducted. The 1st PCR amplified specific loci with universal primers and the 2nd added sequencing adapters and indices to the 1st PCR products. The gene sequences obtained from the FASTQ files were subjected to BLAST search and phylogenetic analyses. One run using 95 specimens yielded sequences averaging 2816bp per specimen for a total length of six loci. Nuclear genes were more successfully assembled compared with mitochondrial genes. A weak but significantly negative correlation was observed between the average length of each locus and success rate of the assembly. Some of the sequences were almost identical to the sequences obtained from specimens collected far from Japan, indicating the presence of potentially invasive species identified for the first time. We obtained gene sequences efficiently using next-generation sequencing rather than Sanger sequencing. Although this method requires further optimization to increase the success rate for some loci, it is used as a first step to select specimens for further analyses by determining the specific loci of the targets.

Abstract We018: The Molecular Mechanism of Coronary Collateral Growth Induced by Repetitive Ischemia by Single Cell-RNA Sequencing

Coronary heart diseases (CHD) are the leading cause of mortality and morbidity in the United States. A well-developed coronary collateral circulation ameliorates the consequences of CHD, reducing the incidence of sudden death and infarct sizes following coronary occlusion. Stimulation of coronary collateral growth (CCG) is also an alternative therapeutic approach for patients with intractable angina pectoris. The importance of CCG is undisputable, but the process and mechanism underlying the CCG is unclear. We developed a mouse model of CCG by repetitive ischemia (RI) in adults, and the CCG was validated with contrast echo to measure the coronary blood flow 3D images of micro-CT to quantitate the CCG. In this study, we use single cell-RNA sequencing (scRNA-Seq) to analyze the molecular signaling of CCG at different time points during the RI process. We digested the hearts from RI days 5, 10, and 17 and control (naïve) mice, harvested non-cardiomyocytes, performed scRNA-Seq with 10x Genomics and sequenced with Next-Seq 500 (Illumina).The fastq files are converted to a gene count matrix using kallisto|bustools with refdata-gex-mm10-2020-A employed as the reference mouse genome. The Kallisto bustools output is then converted in R to files compatible for uploading to Cellenics, an open-source platform for single-cell RNA-Seq analysis. Dimensionality reduction is done with Principal Components Analysis (PCA) with 30 principal components. We will use UMAP to visualize cell clusters. Cellenics uses scType to annotate the cell populations automatically when building clusters of cell populations. Our preliminary data show dynamic kinetics of non-cardiomyocytes induced by RI over the timeline of CCG and different cell populations of non-cardiomyocytes and EC expression profiling varied in response to RI throughout CCG. We will validate the critical signaling in CCG and explore the potential targets to induce CCG.

MetaBakery: a Singularity implementation of bioBakery tools as a skeleton application for efficient HPC deconvolution of microbiome metagenomic sequencing data to machine learning ready information.

In this study, we present MetaBakery (http://metabakery.fe.uni-lj.si), an integrated application designed as a framework for synergistically executing the bioBakery workflow and associated utilities. MetaBakery streamlines the processing of any number of paired or unpaired fastq files, or a mixture of both, with optional compression (gzip, zip, bzip2, xz, or mixed) within a single run. MetaBakery uses programs such as KneadData (https://github.com/bioBakery/kneaddata), MetaPhlAn, HUMAnN and StrainPhlAn as well as integrated utilities and extends the original functionality of bioBakery. In particular, it includes MelonnPan for the prediction of metabolites and Mothur for calculation of microbial alpha diversity. Written in Python 3 and C++ the whole pipeline was encapsulated as Singularity container for efficient execution on various computing infrastructures, including large High-Performance Computing clusters. MetaBakery facilitates crash recovery, efficient re-execution upon parameter changes, and processing of large data sets through subset handling and is offered in three editions with bioBakery ingredients versions 4, 3 and 2 as versatile, transparent and well documented within the MetaBakery Users' Manual (http://metabakery.fe.uni-lj.si/metabakery_manual.pdf). It provides automatic handling of command line parameters, file formats and comprehensive hierarchical storage of output to simplify navigation and debugging. MetaBakery filters out potential human contamination and excludes samples with low read counts. It calculates estimates of alpha diversity and represents a comprehensive and augmented re-implementation of the bioBakery workflow. The robustness and flexibility of the system enables efficient exploration of changing parameters and input datasets, increasing its utility for microbiome analysis. Furthermore, we have shown that the MetaBakery tool can be used in modern biostatistical and machine learning approaches including large-scale microbiome studies.

Metagenomics insights into the microbial resistome and virulome composition of Kampala’s wastewater

Background Antimicrobial-resistant (AMR) infections represent a major global health threat, causing approximately 700,000 deaths each year directly due to AMR-related issues worldwide. In Africa, 42.6% of countries lack sufficient data on AMR, highlighting a crucial gap in our reports. Consequently, there's a pressing need for thorough AMR surveillance data. Urban sewage, harboring a diverse array of microbes from sizable and mostly healthy populations, offers an excellent sampling opportunity. This study set out to identify and assess the microbes present in urban sewage in Kampala, while also analyzing the microbial resistome and virulome associated with urban sewage. Methods Samples were gathered from two wastewater treatment facilities, capturing data from both wet and dry seasons to reflect population behavior across seasons. DNA was extracted from these samples and underwent shotgun metagenomics sequencing. The resulting FastQ files were analyzed using a tailored metagenomics approach to identify microbial profiles, antibiotic-resistant genes, and virulence factors. Results In the pathobiome examined, Pseudomonas psychrophila, a fish pathogen, was the most prevalent, while Klebsiella pneumoniae was the least prevalent. Analysis identified 23 resistant genes, primarily conferring resistance to tetracyclines. Additionally, 29 virulence factors were identified, with a predominant association with bacterial motility. Notably, all of these virulence factors were found within Pseudomonas aeruginosa strain PAO1. Conclusion The utilization of shotgun metagenomics in sewage analysis is crucial for ongoing monitoring of microbial diversity and antimicrobial resistance. This approach uncovers intricate details that would be challenging or costly to obtain through conventional methods like PCR and culture-based techniques.

P-426 Comprehensive study of the endometrial early/mid secretory phase during natural cycles reveals conserved molecular dynamics involved in the window of implantation

Abstract Study question Is there a common transcriptomic signature in the endometrial early/mid secretory phase of healthy women in natural cycles? Summary answer We observed a conserved transcriptomic regulation of the endometrium around the window of implantation (WOI), consistent across donors, and not influenced by the biopsy procedure. What is known already Extensive research has been conducted on the gene expression changes that characterize the WOI, aiming to identify signals that indicate its onset. However, much of this work has utilized whole-transcriptome techniques and has involved IVF patients, who require luteal phase support. The few studies performed in natural cycles often relied on restricted methods, such as qPCR that examines only a few targets simultaneously. As such, an extensive transcriptomics study in volunteer donors in true natural cycles is lacking. This may provide deeper insights into the physiological changes during the WOI in young women driven by the endogenous effects of progesterone. Study design, size, duration 34 healthy donors were enrolled. Each donor provided one endometrial sample, on a day between LH + 2 and LH + 9 during their natural cycle. A minimum of two and a maximum of five donors were allocated to each day. Ovulation was detected via ecographic inspection and hormonal determination of LH/E2/P4. LH + 0 was established when the LH peak was detected (&amp;gt;2.8 times basal levels), together with a subsequenc decrease in E2 (-30%) and increase in P4 (&amp;gt;1.6ng/ml) Participants/materials, setting, methods When the biopsy was of sufficient size, we separated it in two parts and extracted RNA separately to assess concordance between different areas of the same biopsy. RNAseq libraries were prepared with NEBNextUltraII Directional RNA LibraryPrep and sequenced on a NovaSeq6000. Fastq files were aligned with STAR to GRCh38. Principal component analyses (PCA) were created with plotPCA, gene expression time course analysis was performed with DEseq2, and pseudotime analysis was performed using the slingshot package. Main results and the role of chance Of the 34 donors, we obtained good quality RNAseq data (minimum 20 million reads) from 41 samples, with 27 donors having one sample sequenced, while 2 independent samples from the same biopsy were obtained from 7 volunteers. Analysing same-biopsy concordance via PCA revealed a great degree of similarity between all the pairs, which clustered close to each other. Samples from different donors collected on the same day also showed a good clustering, apart from a single day LH + 4 sample. Pseudotime analysis revealed a distinct trajectory in transcriptomic profiles changing progressively from day LH + 2 to LH + 9. This analysis clearly delineated three phases: LH + 2 to LH + 5, LH + 6 to LH + 7, and LH + 8 to LH + 9. Accordingly, we categorized these into pre-receptive, receptive and post receptive groups, and conducted a time course experiment. We identified a total of 3,311 genes with varying expression across these three timepoints, primarily associated with progesterone signalling, metabolism and cell-to-cell communication. Based on their expression patterns, we isolated a subset of 538 genes which increased their expression during the receptive phase only. These genes could represent novel markers for a more in-depth characterization of the WOI. Limitations, reasons for caution The samples analysed are in bulk, and the genes identified could be expressed either in the stroma or in the epithelial compartment. These findings are not applicable to artificial cycles, as the absence of the corpus luteum and varying levels of exogenous progesterone administered may change the transcriptomic signatures. Wider implications of the findings Our study revealed good concordance of whole transcriptome profiles of donor’s endometria according to cycle progression. This may lead to the identification of novel markers involved in the regulation of the WOI that may not have been found in previous studies performed on artificial cycles. Trial registration number not applicable

The endogenous association among MMP2/miR-1248/Circ_0087558/miR-643/ MAP2K6 axis can contribute to brain metastasis in basal-like subtype of breast cancer

Brain metastasis in basal-like breast cancer poses a significant challenge in cancer management due to its aggressive nature and limited treatment options. This study conducted a comprehensive analysis to explore the potential role of circular RNAs (circRNAs) as members of endogenous networks in developing breast cancer brain metastasis.Here, we utilized RNA sequencing data from primary breast cancer and brain metastasis tissue with basal-like subtype (n = 11). After quality controlling and preprocessing of fastq files, gene expression of mRNA and circRNAs were extracted from matched samples and normalized. Then, we employed the weighted gene co-expression network analysis approach to identify brain metastasis-associated circRNA modules (SpearmanCorrelation>0.5, P−value<0.05). Moreover, we found five protein-coding genes of PHLDA1, SLC12A2, MMP2, RGP1, and MAP2K6, significantly upregulated in brain metastatic tissues compared to primary breast cancer (FDR<0.05). These genes were enriched in the "GnRH signaling pathway" and "Fluid shear stress and atherosclerosis" pathways (FDR<0.05).Next, to explore the potential interactions between circRNAs and protein-coding genes, we reconstructed a competing endogenous RNA (ceRNA) network using mutual miRNAs between the circRNA module and upregulated mRNAs. Notably, we could detect two axes of circ_0087558/miR-604/MMP2 and MMP2/miR-1248/Circ_0087558/miR-643/MAP2K6 in ceRNA network.In conclusion, the identified circRNA-miRNA-mRNA axes might be therapeutic targets or diagnostic biomarkers for this challenging subtype of breast cancer. However, due to the small number of samples, further experimental validations are essential.