Bioinformatics Analysis Pipeline Research Articles

Rapid Lineage Assignment of Severe Acute Respiratory Syndrome Coronavirus 2 Cases through Automated Library Preparation, Sequencing, and Bioinformatic Analysis

The coronavirus disease 2019 (COVID-19) pandemic has provided a stage to illustrate that there is considerable value in obtaining rapid, whole-genome-based information about pathogens. This article describes the utility of a commercially available, automated severe acute respiratory syndrome associated coronavirus 2 (SARS-CoV-2) library preparation, genome sequencing, and a bioinformatics analysis pipeline to provide rapid, near-real-time SARS-CoV-2 variant description. This study evaluated the turnaround time, accuracy, and other quality-related parameters obtained from commercially available automated sequencing instrumentation, from analysis of continuous clinical samples obtained from January 1, 2021, to October 6, 2021. This analysis included a base-by-base assessment of sequencing accuracy at every position in the SARS-CoV-2 chromosome using two commercially available methods. Mean turnaround time, from the receipt of a specimen for SARS-CoV-2 testing to the availability of the results, with lineage assignment, was <3 days. Accuracy of sequencing by one method was 100%, although certain sites on the genome were found repeatedly to have been sequenced with varying degrees of read error rate.

TriTrypDB: An integrated functional genomics resource for kinetoplastida.

Parasitic diseases caused by kinetoplastid parasites are a burden to public health throughout tropical and subtropical regions of the world. TriTrypDB (https://tritrypdb.org) is a free online resource for data mining of genomic and functional data from these kinetoplastid parasites and is part of the VEuPathDB Bioinformatics Resource Center (https://veupathdb.org). As of release 59, TriTrypDB hosts 83 kinetoplastid genomes, nine of which, including Trypanosoma brucei brucei TREU927, Trypanosoma cruzi CL Brener and Leishmania major Friedlin, undergo manual curation by integrating information from scientific publications, high-throughput assays and user submitted comments. TriTrypDB also integrates transcriptomic, proteomic, epigenomic, population-level and isolate data, functional information from genome-wide RNAi knock-down and fluorescent tagging, and results from automated bioinformatics analysis pipelines. TriTrypDB offers a user-friendly web interface embedded with a genome browser, search strategy system and bioinformatics tools to support custom in silico experiments that leverage integrated data. A Galaxy workspace enables users to analyze their private data (e.g., RNA-sequencing, variant calling, etc.) and explore their results privately in the context of publicly available information in the database. The recent addition of an annotation platform based on Apollo enables users to provide both functional and structural changes that will appear as 'community annotations' immediately and, pending curatorial review, will be integrated into the official genome annotation.

Next Generation Sequencing for the Analysis of Parvovirus B19 Genomic Diversity.

Parvovirus B19 (B19V) is a ssDNA human virus, responsible for an ample range of clinical manifestations. Sequencing of B19V DNA from clinical samples is frequently reported in the literature to assign genotype (genotypes 1-3) and for finer molecular epidemiological tracing. The increasing availability of Next Generation Sequencing (NGS) with its depth of coverage potentially yields information on intrinsic sequence heterogeneity; however, integration of this information in analysis of sequence variation is not routinely obtained. The present work investigated genomic sequence heterogeneity within and between B19V isolates by application of NGS techniques, and by the development of a novel dedicated bioinformatic tool and analysis pipeline, yielding information on two newly defined parameters. The first, α-diversity, is a measure of the amount and distribution of position-specific, normalised Shannon Entropy, as a measure of intra-sample sequence heterogeneity. The second, σ-diversity, is a measure of the amount of inter-sample sequence heterogeneity, also incorporating information on α-diversity. Based on these indexes, further cluster analysis can be performed. A set of 24 high-titre viraemic samples was investigated. Of these, 23 samples were genotype 1 and one sample was genotype 2. Genotype 1 isolates showed low α-diversity values, with only a few samples showing distinct position-specific polymorphisms; a few genetically related clusters emerged when analysing inter-sample distances, correlated to the year of isolation; the single genotype 2 isolate showed the highest α-diversity, even if not presenting polymorphisms, and was an evident outlier when analysing inter-sample distance. In conclusion, NGS analysis and the bioinformatic tool and pipeline developed and used in the present work can be considered effective tools for investigating sequence diversity, an observable parameter that can be incorporated into the quasispecies theory framework to yield a better insight into viral evolution dynamics.

Identification of Plant Transcription Factor DNA-Binding Sites Using seq-DAP-seq.

The identification of genome-wide transcription factor binding sites(TFBS) is a critical step in deciphering gene and transcriptional regulatory networks. However, determining the genome-wide binding of specific TFs or TF complexes remains a technical challenge. DNA affinity purification sequencing (DAP-seq) and modifications such as sequential DAP-seq (seq-DAP-seq) are robust in vitro methods for mapping individual TF or TF complex binding sites in a genome-wide manner. DAP-seq protocols use a genomic DNA (gDNA) library from any target organism with or without amplification, allowing the determination of TF binding on naked or endogenously modified DNA, respectively. As a first step, the gDNA is fragmented to ~200bp, end-repaired, and sequencing adaptors are added. This gDNA library can be used directly or an amplification step may be performed to remove DNA modifications such as cytosine methylation. DNA libraries are then incubated with an affinity-tagged TF or TF- complex immobilized on magnetic beads. The TF or TF complex of interest is generally produced using recombinant protein expression and purified prior to DNA affinity purification. After incubation of the DNA library with the immobilized TF of interest, multiple wash steps are performed to reduce non-specific DNA binding and the TF-DNA complexes eluted. The eluted DNA is PCR-amplified and sequenced using next-generation sequencing. The resulting sequence reads are mapped to the corresponding reference genome, identifying direct potential bound regions and binding sites of the TF or TF complex of interest. Predictive TFBS models are generated from the bound regions using downstream bioinformatics analysis pipelines. Here, we present a detailed protocol outlining the steps required for seq-DAP-seq of a heterooligomeric TF complex (Fig. 1) and briefly describe the downstream bioinformatics pipeline used to develop a robust TFBS model from sequencing data generated from a DAP-seq experiment.

Identification of a Novel FAM135B Cancer Susceptibility Mutation in a Family with Multiple Cases of MLL-Rearranged AML

Background: Familial aggregation of malignancies, including leukemia, is rarely reported due to the scattered cases, weakened bonding among generations, and low birth rates in modern society. The emerging demands for predicting and preventing cancer development are outgrowing the progress in the studies of the mechanism underlying the propensity of malignancies. Results: Here we reported a family with 4 cases of acute leukemia (AL) and four solid tumors (2 for lung cancer, 1 for renal clear cell carcinoma, and one for rectal adenocarcinoma). Besides, one member in the family was diagnosed with intraspinal cavernous hemangioma, and another one was diagnosed with congenital heart disease (transposition of the great arteries). 2 out of 4 AL patients were cousins and diagnosed with acute monocytic leukemia (AML-M5 with MLL-rearrangement) in our center. Whole genome sequencing was performed on two patients with AML, two patients with solid tumors, one obligated carrier, and three healthy members. Using an in-house bioinformatic data analysis pipeline, we identified 18 single nucleotide variants (SNVs) and four insertions/deletions (Indel) in all the affected members and one obligated carrier while absent in healthy members. Genotyping of these variants in 57 family members confirmed that 18/22 variants co-segregated with the malignant phenotype of this family. By screening for these 18 variants in 13 AL pedigrees, we narrow our cancer-predisposing candidate list to one variant, hitting FAM135B (family with sequence similarity 135 member B; NM_015912: exon13:c.A2621G:p.E874G), which is recurrent in another AML pedigree with two cases in two consecutive generations. This mutation resides in a highly conserved locus among species and is predicted to be deleterious. FAM135B is predicted to encode serine esterase, and the role of FAM135B in AML was not documented. Intriguingly, all the affected familial AML cases from two pedigrees were diagnosed with AML-M5 with MLL-rearrangement. Moreover, this mutation is recurrent in sporadic adult AML cohort (6/467), among which half (3/6) were AML-M5, two M3, and one M2. Functionally, we found that overexpression of FAM135B promotes the proliferation and colony formation of AML cells. Consistently, downregulation of FAM135B nearly abolished the colony-forming capacity of AML cells and impaired cell proliferation. Further studies on the role of FAM135B in AML development are under investigation. Conclusion: We identify a novel cancer-predisposing gene, FAM135B, and its mutation E874G occurred in both familial and sporadic AML cases, especially AML-M5 with MLL-rearrangement.

Single-Cell RNA Sequencing of Sickle Cell Reticulocytes to Identify Beta-Globin Genotypes and Associated Gene Expression Differences

Background: Sickle cell disease (SCD) is a genetic condition typically caused by a single point mutation in both copies of the beta-globin (HBB) gene, resulting in sickle (S) hemoglobin (HbS) production instead of adult (A) hemoglobin (HbA). GPH101 (nulabeglogene autogedtemcel) is an investigational gene-edited autologous hematopoietic stem cell-based therapy in clinical development for SCD that is designed to directly correct the underlying mutation, thereby decreasing HbS production and restoring HbA expression. Previous allogeneic hematopoietic stem cell transplant data, mostly in the context of using HLA-matched related donors, has demonstrated a competitive advantage for homozygous (AA) or heterozygous (AS) hemoglobin due to ineffective erythropoiesis of SS erythroid progenitors. At early timepoints following infusion of nulabeglogene autogedtemcel, red blood cells (RBCs) expressing corrected HbA cannot be distinguished from HbA in transfused blood. Because RBCs are enucleated, tracking of gene editing at the genomic level is not possible. However, reticulocytes are immature RBCs that still contain some RNA and could be used to evaluate allelic correction. We sought to develop a single-cell RNA sequencing (scRNAseq) method to enable enumeration of potential gene-editing outcomes in peripheral reticulocytes. Methods: Reticulocytes were sorted from peripheral blood from healthy donors (AA), donors with sickle cell trait (AS), and donors with sickle cell disease (SS) by fluorescence-activated cell sorting (FACS) based on a Live/CD235a+/CD45-/CD71+/TO+ phenotype and mixed at given ratios before application to the 10x genomics workflow for scRNAseq. Individual mRNA molecules were tagged with a unique molecular identifier (UMI) to enable transcript quantification. A custom bioinformatics analysis pipeline was developed to identify HBB variants within the scRNAseq data to identify individual reticulocytes expressing either normal HBB, sickle HBB, or both. Results: Genotypes of individual cells could be clearly distinguished from scRNAseq data using a custom bioinformatics pipeline. Additionally, differential mRNA expression profiling between AA and SS samples was performed and previously known results from bulk RNAseq studies were produced, including upregulation of HbA2 subunit delta and alpha hemoglobin stabilizing protein. An examination of cell populations underlying these expression differences will be presented. Conclusion: Our data demonstrate the utility of scRNAseq for evaluating differential HBB editing outcomes in erythroid progenitors from patients treated with nulabeglogene autogedtemcel to support clinical development. Further evaluation of the clinical utility of the scRNAseq pipeline is planned.

Rapid genome sequencing for pediatrics.

The advancements made in next-generation sequencing (NGS) technology over the past two decades have transformed our understanding of genetic variation in humans and had a profound impact on our ability to diagnose patients with rare genetic diseases. In this review, we discuss the recently developed application of rapid NGS techniques, used to diagnose pediatric patients with suspected rare diseases who are critically ill. We highlight the challenges associated with performing such clinical diagnostics tests in terms of the laboratory infrastructure, bioinformatic analysis pipelines, and the ethical considerations that need to be addressed. We end by looking at what future developments in this field may look like and how they can be used to augment the genetic data to further improve the diagnostic rates for these high-priority patients.

Genomic Characterization of Antimicrobial-Resistant Salmonella enterica in Duck, Chicken, and Pig Farms and Retail Markets in Eastern China.

ABSTRACTAntimicrobial-resistant Salmonella enterica poses a significant public health concern worldwide. However, the dissemination of Salmonella enterica among food animals in eastern China has not been fully addressed. Here, we demonstrated the antimicrobial resistance (AMR) patterns and the whole-genome characterization of 105 S. enterica isolates from 1,480 fecal samples and anal swabs collected from 22 different farms (chickens, ducks, and pigs) and two live animal markets located in Zhejiang and Fujian Provinces in eastern China in 2019. The prevalence of isolates in duck farms (19.17%, 23/120) was statistically significantly higher (P < 0.001) than that in chicken farms (6.61%, 37/523) and pig farms (3.50%, 7/200). Among these isolates, 75.26% (79/105) were multidrug resistant, with the highest rates of resistance to tetracycline (76.20%) and ampicillin (67.62%) and the lowest resistance rate to meropenem (0.00%). The serotypes were consistent with sequence types and were closely related to the sampling animal species and sites. S. enterica serotype Kentucky (20.95%, 22/105) was the most frequent serotype and harbored more AMR patterns and genes than others. Furthermore, IncFII(S) and IncHI2 were the most prevalent replicons. A total of 44 acquired AMR genes were found. Among those genes, aac(6′)-Iaa, blaTEM-1B, floR, dfrA14, fosA7, mph(A), qnrS1, sul1, tet(A), and ARR-3 were the dominant AMR genes mediating the AMR toward aminoglycosides, β-lactams, phenicol, trimethoprim, fosfomycin, macrolide, quinolone, sulfonamides, tetracycline, and rifampin, respectively. The consistency of acquired AMR genes with AMR phenotypes for ampicillin, ceftiofur, ceftazidime, meropenem, sulfamethoxazole-trimethoprim, and tetracycline was >90%. Together, our study highlights the application of whole-genome sequencing to assess veterinary public health threats.IMPORTANCE Public health is a significant concern in China, and the foodborne pathogen Salmonella, which is spread via the animal-borne food chain, plays an important role in the overall disease burden in China annually. The development of advanced sequencing technologies has introduced a new way of understanding emerging pathogens. However, the routine surveillance application of this method in China remains in its infancy. Here, we applied a pool of all isolates from the prevalence data in Zhejiang and Fujian for whole-genome sequencing and combined these data with the cutting-edge bioinformatic analysis pipeline for one-step determination of the complete genetic makeup for all 105 genomes. The illustrated method could provide a cost-effective approach, without labor-intensive laboratory characterization, for predicting serotypes, genotypes, plasmid types, antimicrobial resistance genes, and virulence genes, and thus would provide essential knowledge for emerging pathogens. Our findings and perspectives are essential for delivering updated knowledge on foodborne pathogens in an understudied region in China.

Meta-analysis of larvae of the black soldier fly (Hermetia illucens) microbiota based on 16S rRNA gene amplicon sequencing.

Black soldier fly larvae (BSFL) belong to the most widely reared insects as an alternative protein source at industrial scale. Bacteria in the larval gut can provide benefits for the animal, though some bacteria can also be pathogenic for the insect. Accurate characterization of the BSFL microbiota is important for the production of BSFL in terms of yield and microbiological safety. In this study, 16S ribosomal RNA gene sequence data sets from 11 studies were re-analysed to gain better insights in the BSFL gut microbiota, potential factors that influence their composition, and differences between the gut and the whole larvae microbiota. A core gut microbiota was found consisting of members of Enterococcus, Klebsiella, Morganella, Providencia, and Scrofimicrobium. Further, the factors ‘Study’, ‘Age’ and ‘Feed’ (i.e. rearing substrate of the larvae) significantly affected the microbiota gut composition. When compared to whole larvae, a significantly lower diversity was found for gut samples, suggesting that the larvae harboured additional microbes on their cuticle or in the insect body. Universal choices in insect sample type, primer selection and bio-informatics analysis pipeline can strengthen future meta-analyses and improve our understanding of the BSFL gut microbiota towards the optimization of insect rearing conditions and substrates.

Comprehensive analysis of pathways in Coronavirus 2019 (COVID-19) using an unsupervised machine learning method

The World Health Organization (WHO) introduced “Coronavirus disease 19” or “COVID-19” as a novel coronavirus in March 2020. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) requires the fast discovery of effective treatments to fight this worldwide crisis. Artificial intelligence and bioinformatics analysis pipelines can assist with finding biomarkers, explanations, and cures. Artificial intelligence and machine learning methods provide powerful infrastructures for interpreting and understanding the available data. On the other hand, pathway enrichment analysis, as a dominant tool, could help researchers discover potential key targets present in biological pathways of host cells that are targeted by SARS-CoV-2. In this work, we propose a two-stage machine learning approach for pathway analysis. During the first stage, four informative gene sets that can represent important COVID-19 related pathways are selected. These “representative genes” are associated with the COVID-19 pathology. Then, two distinctive networks were constructed for COVID-19 related signaling and disease pathways. In the second stage, the pathways of each network are ranked with respect to some unsupervised scorning method based on our defined informative features. Finally, we present a comprehensive analysis of the top important pathways in both networks. Materials and implementations are available at: https://github.com/MahnazHabibi/Pathway.

Predictive Modelling in Clinical Bioinformatics: Key Concepts for Startups.

Clinical bioinformatics is a newly emerging field that applies bioinformatics techniques for facilitating the identification of diseases, discovery of biomarkers, and therapy decision. Mathematical modelling is part of bioinformatics analysis pipelines and a fundamental step to extract clinical insights from genomes, transcriptomes and proteomes of patients. Often, the chosen modelling techniques relies on either statistical, machine learning or deterministic approaches. Research that combines bioinformatics with modelling techniques have been generating innovative biomedical technology, algorithms and models with biotech applications, attracting private investment to develop new business; however, startups that emerge from these technologies have been facing difficulties to implement clinical bioinformatics pipelines, protect their technology and generate profit. In this commentary, we discuss the main concepts that startups should know for enabling a successful application of predictive modelling in clinical bioinformatics. Here we will focus on key modelling concepts, provide some successful examples and briefly discuss the modelling framework choice. We also highlight some aspects to be taken into account for a successful implementation of cost-effective bioinformatics from a business perspective.

A reappraisal of microbiome dysbiosis during experimental periodontitis.

Periodontitis is a chronic inflammatory disease associated with the presence of dysbiotic microbial communities. Several studies interrogating periodontitis pathogenesis have utilized the murine ligature-induced periodontitis (LIP) model and have further examined the ligature-associated microbiome relying on 16S rRNA-based sequencing techniques. However, it is often very challenging to compare microbial profiles across studies due to important differences in bioinformatic processing and databases used for taxonomic assignment. Thus, our study aim was to reanalyze microbiome sequencing datasets from studies utilizing the LIP model through a standardized bioinformatic analysis pipeline, generating a comprehensive overview of microbial dysbiosis during experimental periodontitis.We conducted a reanalysis of 16S rDNA gene sequencing datasets from nine published studies utilizing the LIP model. Reads were grouped according to the hypervariable region of the 16S rDNA gene amplified (V1-V3 and V4), preprocessed, binned into operational taxonomic units and classified utilizing relevant databases. Alpha- and beta-diversity analyses were conducted, along with relative abundance profiling of microbial communities. Our findings revealed similar microbial richness and diversity across studies and determined shifts in microbial community structure determined by periodontitis induction and study of origin. Clear variations in the relative abundance of bacterial taxa were observed starting on day 5 after ligation and onward, consistent with a distinct microbial composition during health and experimental periodontitis. We also uncovered differentially represented bacterial taxa across studies, dominating periodontal health and LIP-associated communities. Collectively, this reanalysis provides a unified overview of microbial dysbiosis during the LIP model, providing new insights that aim to inform further studies dedicated to unraveling oral host-microbial interactions.

NGS4THAL, a One-Stop Molecular Diagnosis and Carrier Screening Tool for Thalassemia and Other Hemoglobinopathies by Next-Generation Sequencing

Thalassemia is one of the most common genetic diseases and a major health threat worldwide. Accurate, efficient, and scalable analysis of next-generation sequencing (NGS) data is much needed for its molecular diagnosis and carrier screening. We developed NGS4THAL, a bioinformatics analysis pipeline analyzing NGS data to detect pathogenic variants for thalassemia and other hemoglobinopathies. NGS4THAL realigns ambiguously mapped NGS reads derived from the homologous Hb gene clusters for accurate detection of point mutations and small insertions/deletions. It uses a combination of complementary structural variant (SV) detection tools and an in-house database of control data containing specific SVs to achieve accurate detection of the complex SV types. Detected variants are matched with those in HbVar (A Database of Human Hemoglobin Variants and Thalassemia Mutations), allowing recognition of known pathogenic variants, including disease modifiers. Tested on simulation data, NGS4THAL achieved high sensitivity and specificity. For targeted NGS sequencing data from samples with laboratory-confirmed pathogenic Hb variants, it achieved 100% detection accuracy. Application of NGS4THAL on whole genome sequencing data from unrelated studies revealed thalassemia mutation carrier rates for Hong Kong Chinese and Northern Vietnamese that were consistent with previous reports. NGS4THAL is a highly accurate and efficient molecular diagnosis tool for thalassemia and other hemoglobinopathies based on tailored analysis of NGS data and may be scaled for population carrier screening.

Abstract 2801: Detection of biallelic loss of DNA repair genes in formalin-fixed, paraffin embedded (FFPE) tumor samples using a novel tumor-only sequencing panel with error correction

Abstract Background: Loss-of-function (LOF) mutations in DNA damage response (DDR) tumor suppressor genes are compensated for by functional redundancies, exposing synthetic lethal (SL) interactions and opportunities for targeted therapy. Patient selection for SL-based therapy may be improved by assessment of gene-specific loss of heterozygosity (LOH) and biallelic LOF, neither of which is routinely reported by existing targeted sequencing panels. The Synthetic Lethal Interactions for Precision Diagnostics (SNiPDx) targeted sequencing panel features a novel bioinformatic analysis pipeline that enables accurate genome-wide determination of allele-specific copy number, estimation of tumor ploidy and purity, and detection of single nucleotide and indel variants in target genes focused on DDR pathways, all from tumor-only samples. Here we describe the development and accuracy of SNiPDx for detection of LOH and bi-allelic LOF genetic alterations in FFPE samples. Methods: Genomic DNA (&amp;gt;50 ng) was extracted from FFPE samples of multiple solid tumor types (n = 43). Next-generation sequencing was performed on anchored multiplex PCR libraries, constructed using probes that incorporate unique molecular identifiers and span 26 genes and 5,000 genome-wide common germline single-nucleotide polymorphisms (SNPs). Unmatched non-tumor samples (n = 24) were used to generate a reference baseline dataset. The FACETS algorithm, optimized to account for differential DNA fragmentation across samples, was used to assess copy number imbalance in heterozygous SNPs and to quantify tumor purity. Allele fractions at each heterozygous SNP were used to estimate allelic imbalances across chromosomal regions. A reference dataset was derived from matched FFPE tumor samples by whole genome sequencing (WGS) and analysis of sequence data using 3 complementary algorithms. Allele-specific copy number analysis and tumor purity estimation from SNiPDx and WGS data were compared. Results: Copy number was evaluable in 605 genes from 24 matched tumor samples that passed quality control filters. Median sequencing depth across samples by SNiPDx and WGS were 1346x and 18.6x, respectively. LOH detection by SNiPDx was reproducible (100%) across 170 genes from 7 samples sequenced and analyzed in duplicate. A strong correlation was observed between sample purity estimates by WGS and SNiPDx (Pearson’s r = 0.81, p &amp;lt; 0.001). Compared with WGS-derived calls, the sensitivity and specificity of LOH detection by SNiPDx were 95% and 90%, respectively, rising to 97% and 91% in regions with LOH agreement by all 3 WGS algorithms, and to 99% and 97% in diploid regions with no subclonal alterations. b The SNiPDx panel is a novel clinical test for biallelic loss in FFPE tumor-only samples with high accuracy as validated through concordance with a WGS-derived dataset. Citation Format: Dominik Glodzik, Pier Selencia, Ryan Rogge, Ian M. Silverman, Michael Zinda, Maria Koehler, Robert D. Daber, Verity Johnson, Jorge S. Reis-Filho, Victoria Rimkunas. Detection of biallelic loss of DNA repair genes in formalin-fixed, paraffin embedded (FFPE) tumor samples using a novel tumor-only sequencing panel with error correction [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2801.

An Integrated Mass Spectrometry-Based Glycomics-Driven Glycoproteomics Analytical Platform to Functionally Characterize Glycosylation Inhibitors.

Cancer progression is linked to aberrant protein glycosylation due to the overexpression of several glycosylation enzymes. These enzymes are underexploited as potential anticancer drug targets and the development of rapid-screening methods and identification of glycosylation inhibitors are highly sought. An integrated bioinformatics and mass spectrometry-based glycomics-driven glycoproteomics analysis pipeline was performed to identify an N-glycan inhibitor against lung cancer cells. Combined network pharmacology and in silico screening approaches were used to identify a potential inhibitor, pictilisib, against several glycosylation-related proteins, such as Alpha1-6FucT, GlcNAcT-V, and Alpha2,6-ST-I. A glycomics assay of lung cancer cells treated with pictilisib showed a significant reduction in the fucosylation and sialylation of N-glycans, with an increase in high mannose-type glycans. Proteomics analysis and in vitro assays also showed significant upregulation of the proteins involved in apoptosis and cell adhesion, and the downregulation of proteins involved in cell cycle regulation, mRNA processing, and protein translation. Site-specific glycoproteomics analysis further showed that glycoproteins with reduced fucosylation and sialylation were involved in apoptosis, cell adhesion, DNA damage repair, and chemical response processes. To determine how the alterations in N-glycosylation impact glycoprotein dynamics, modeling of changes in glycan interactions of the ITGA5–ITGB1 (Integrin alpha 5-Integrin beta-1) complex revealed specific glycosites at the interface of these proteins that, when highly fucosylated and sialylated, such as in untreated A549 cells, form greater hydrogen bonding interactions compared to the high mannose-types in pictilisib-treated A549 cells. This study highlights the use of mass spectrometry to identify a potential glycosylation inhibitor and assessment of its impact on cell surface glycoprotein abundance and protein–protein interaction.

A multi-platform reference for somatic structural variation detection

Accurate detection of somatic structural variation (SV) in cancer genomes remains a challenging problem. This is in part due to the lack of high-quality, gold-standard datasets that enable the benchmarking of experimental approaches and bioinformatic analysis pipelines. Here, we performed somatic SV analysis of the paired melanoma and normal lymphoblastoid COLO829 cell lines using four different sequencing technologies. Based on the evidence from multiple technologies combined with extensive experimental validation, we compiled a comprehensive set of carefully curated and validated somatic SVs, comprising all SV types. We demonstrate the utility of this resource by determining the SV detection performance as a function of tumor purity and sequence depth, highlighting the importance of assessing these parameters in cancer genomics projects. The truth somatic SV dataset as well as the underlying raw multi-platform sequencing data are freely available and are an important resource for community somatic benchmarking efforts.

Genome‐wide identification of catfish antimicrobial peptides: A new perspective to enhance fish disease resistance

AbstractAntimicrobial peptides (AMPs), also known as host defence peptides, are evolutionarily ancient defensive weapons that can combat infections caused by pathogens. Fish‐derived AMPs have shown microbicidal properties, host immunomodulatory responses, and are induced by a variety of factors, especially pathogenic infection and abiotic stress, which may activate downstream signalling pathways to initiate the expression of antimicrobial peptide genes. Large‐scale genome sequencing has been applied in many aquatic animals, providing databases for the systematic identification of putative AMPs in these species. Recently, the whole genome and the proteome of channel catfish (Ictalurus punctatus) were released; by taking advantage of the antimicrobial peptide database and combining the proteome of catfish, we established a bioinformatic analysis pipeline to identify catfish AMP repertoire. This review introduces an effective method for the identification and development of putative AMPs based on protein datasets, and summarizes the structural properties, immunomodulation and molecular responses in vivo of catfish AMPs. In addition, the evaluation of the antibacterial activity of synthetic or isolated AMPs in vitro, and the applications of AMPs in fish via genetic engineering and clustered regularly interspaced short palindromic repeats associated proteins system (CRISPR/Cas9) were presented, which supports antimicrobial research and aquaculture therapy. The summary and comparison of these AMPs will enhance our understanding and their cross‐species applications. Combined with current genetic engineering and genome editing techniques, it is possible to promote the development of immune processes to protect valuable aquatic animals from infectious diseases.

Analysis of the methods and quality assurance of metagenomic next-generation sequencing to detect the microbial cfDNA from blood samples in China

Objective: To investigate the methods and quality assurance of metagenomic next-generation sequencing (mNGS) to detect the microbial cfDNA (mcfDNA) from blood samples in different laboratories across China. Methods: In October 2020, questionnaires about detecting mcfDNA in blood samples with mNGS were distributed to 80 laboratories across the country. The questionnaire included four parts: pre-analysis, during analysis, post-analysis, and carrying out of performance validation for mNGS. (1) Pre-analysis: the requirements for samples quality, such as collection, storage, the transportation conditions of samples; (2) During analysis: the extraction workflows of mcfDNA, the quality requirements of the library, the application of the sequencing platforms and the bioinformatics analysis pipelines; (3) Post-analysis: the standard of interpretation results for mNGS; (4) Carrying out of performance validation: the minimum detection limit for various pathogens. All laboratories are required to fill in the questionnaire according to the actual situation. The feedback data were summarized and analyzed. Results: The 80 laboratories included 20 medical centers and 60 independent medical laboratories. There were 80.0% (64/80) of laboratories indicated that both plasma and serum samples were used to detect mcfDNA in blood, and the rest of the laboratories (16/80, 20.0%) only used plasma samples. The sequencing platforms used by mNGS laboratories involved in the survey included illumina (49), Beijing Genomics Institute (16), Ion Torrent (13) and Nanopore sequencing (2). There were 87.5% (70/80) of laboratories used the integrated analysis tools built by the third-party laboratories, and other laboratories (12.5%, 10/80) independently built the analysis platform by open-source software. The interpretation criteria of mNGS results varied between laboratories, among which the normalized number of pathogen-specific sequences, relative abundance, genome coverage rate, and the detection of the microorganism in the negative control were the main factors considered by laboratories. Most laboratories (76.3%, 61/80) had carried out the performance validation for the mcfDNA mNGS workflows. The limit of detection of the laboratories-developed mNGS workflows for Gram-positive bacteria, Gram-negative bacteria, fungi, parasites, and other pathogens were mainly distributed at 10-100 copies/ml, DNA virus was mainly distributed at 500-1 000 copies/ml. Conclusions: The mNGS workflows of various laboratories are very different. In order to ensure timely and accurate testing results, every laboratory needs to actively optimize the mNGS testing procedures, improve quality assurance measures, and carry out performance validation before mNGS is widely used in clinical settings.

A Rapid and Affordable Screening Tool for Early-Stage Ovarian Cancer Detection Based on MALDI-ToF MS of Blood Serum

Ovarian cancer is a worldwide health issue that grows at a rate of almost 250,000 new cases every year. Its early detection is key for a good prognosis and even curative surgery. However, current medical examination methods and tests have been inefficient in detecting ovarian cancer at the early stage, leading to preventable death. So far, new screening tests based on molecular biomarker analysis techniques have not resulted in any substantial improvement in early-stage diagnosis and increased survival. Thus, whilst there remains clear potential to improve outcomes through early detection, novel approaches are needed. Here, we postulated that MALDI-ToF-mass-spectrometry-based tests can be a solution for effective screening of ovarian cancer. In this retrospective cohort study, we generated and analyzed the mass spectra of 181 serum samples of women with and without ovarian cancer. Using bioinformatics pipelines for analysis, including predictive modeling and machine learning, we found distinct mass spectral patterns composed of 9–20 key combinations of peak intensity or peak enrichment features for each stage of ovarian cancer. Based on a scoring algorithm and obtained patterns, the optimal sensitivity for detecting each stage of cancer was 95–97% with a specificity of 97%. Scoring all algorithms simultaneously could detect all stages of ovarian cancer at 99% sensitivity and 92% specificity. The results further demonstrate that the matrix and mass range analyzed played a key role in improving the mass spectral data quality and diagnostic power. Altogether, with the results reported here and increasing evidence of the MS assay’s diagnostic accuracy and instrument robustness, it has become imminent to consider MS in the clinical application for ovarian cancer screening.

EP376: NGS detection of NUDT15 6-bp insertion and UGT1A1 (TA) repeat polymorphism on a preventative genomics assay

Application of Next-Generation Sequencing (NGS) for pharmacogenetic analysis provides cost-effective and high-throughput alternatives to other genotyping methodologies, making it a method of choice for pharmacogenetic testing. However, the nature of short-read sequencing technologies may carry challenges when it comes to the detection of specific complex structures/variants such as large insertion/deletions (indels), or repeat polymorphisms. These limitations can be overcome by the design of sequencing probes and optimized NGS library preparation process, as well as customized bioinformatic analysis pipelines. UGT1A1 is an important pharmacogene that impacts response to multiple drugs, including the HIV treatment, Atazanavir. The TA repeat polymorphism, rs3064744, is identified across various geographic, ethnic and racial populations and causes reduction in gene activity, resulting in toxicity and dosage effects. NUDT15 plays an important role in the metabolism of thiopurines. The rs746071566 microsatellite polymorphism, c.38GAGTCG[4] (p.13GV[4]), plays a critical role in thiopurine-induced adverse reactions and has been identified as a commonly observed variant in pan-ethnic populations. The current study evaluated the performance of a targeted NGS capture and bioinformatics platform to identify the promoter (TA)7 repeat polymorphism UDP glucuronosyl-transferase UGT1A1, as well as a 6 bp insertion NUDT15 variant. Both variants were analyzed as a part of a 455 gene panel utilized for a preventative genomics assay. The study consisted of 413 validation samples analyzed for UGT1A1 (rs3064744), and 186 analyzed for NUDT15 (rs746071566). The specimens were retrieved from various sources: DNA from Coriell Repository, and DNA isolated from whole blood and saliva by the Maxwell RSC Blood DNA Kit (Promega, Madison, Wisconsin) on the automated Maxwell RSC 48 Instrument (Promega). Sequencing was performed using custom-designed targeted capture and Fast Hybridization Target Enrichment workflow (Twist Bioscience, South San Francisco, CA). The samples were prepared using 50 ng of input DNA and the Twist EF Library Prep Kit 2.0 + UDIs and Fast Hyb reagents (Twist Bioscience), and run in a format of 12 samples per pool and 36 per single run on MiniSeq System (Illumina, San Diego, CA). Selected samples were confirmed using Sanger sequencing. The data was analyzed using the Edico Dragen server (Illumina), and in-house developed proprietary pipeline. For the purpose of this study, the reference ranges were defined for UGT1A1 as *1 AT[7], *28 AT[8], *36 AT[6] and *39 AT[9]. The genotypes frequency detected for UGT1A1 rs3064744 were as follow: AT[7]/AT[6] 0.97%, AT[7]/AT[7] 62%, AT[7]/AT[8] 24%, AT[7]/AT[9] 0.97%, AT[8]/AT[8] 11.8%, AT[9]/AT[9] 0.24%; and for NUDT15 rs746071566 GGAGTC[1]/GGAGTC[1] 99.5%, GGAGTC[1]/GGAGTC[2] 0.5% and GGAGTC[1]/GGAGTC[0] 0%. This data is comparable to the frequency published in the gnomAD database. Additionally, Sanger confirmation analysis performed for selected Coriell samples yielded 100% concordance between NGS and Sanger sequencing data. As previously reported, ∼14% of variants (eg, indels, small CNVs, and complex changes) identified during NGS analysis can impose technical difficulties resulting in decreased detection rates. Both of the variants investigated in this study (UGT1A1 rs3064744 and NUDT15 rs746071566) belong to this group of challenging variants, and thus require specific consideration during the analytical and bioinformatic design and validation. The NGS capture presented in this study was iteratively designed to optimize the ability to sequence across these regions. Performance data for these variants is dependent on wet laboratory procedure and bioinformatic variant assembly because standard VCF calling software packages do not account for repetitive elements. The in-house developed bioinformatic script accurately translated the output of the VCF pipeline into the genotype calls. The comparability of the obtained genotype frequencies with the published data in gnomAD further confirmed the accuracy of the assay performance. Analysis of the samples using Sanger sequencing further confirmed data received on NGS analysis.