Bioinformatics Analysis Pipeline Research Articles

Using nanopore sequencing to identify bacterial infection in joint replacements: a preliminary study.

This project investigates if third-generation genomic sequencing can be used to identify the species of bacteria causing prosthetic joint infections (PJIs) at the time of revision surgery. Samples of prosthetic fluid were taken during revision surgery from patients with known PJIs. Samples from revision surgeries from non-infected patients acted as negative controls. Genomic sequencing was performed using the MinION device and the rapid sequencing kit from Oxford Nanopore Technologies. Bioinformatic analysis pipelines to identify bacteria included Basic Local Alignment Search Tool, Kraken2 and MinION Detection Software, and the results were compared with standard of care microbiological cultures. Furthermore, there was an attempt to predict antibiotic resistance using computational tools including ResFinder, AMRFinderPlus and Comprehensive Antibiotic Resistance Database. Bacteria identified using microbiological cultures were successfully identified using bioinformatic analysis pipelines. Nanopore sequencing and genomic classification could be completed in the time it takes to perform joint revision surgery (2-3h). Genomic sequencing in this study was not able to predict antibiotic resistance in this time frame, this is thought to be due to a short-read length and low read depth. It can be concluded that genomic sequencing can be useful to identify bacterial species in infected joint replacements. However, further work is required to investigate if it can be used to predict antibiotic resistance within clinically relevant timeframes.

Deep mining of antibody phage-display selections using Oxford Nanopore Technologies and Dual Unique Molecular Identifiers

Antibody phage-display technology identifies antibody-antigen interactions through multiple panning rounds, but traditional screening gives no information on enrichment or diversity throughout the process. This results in the loss of valuable binders. Next Generation Sequencing can overcome this problem. We introduce a high accuracy long-read sequencing method based on the recent Oxford Nanopore Technologies (ONT) Q20 + chemistry in combination with dual unique molecular identifiers (UMIs) and an optimized bioinformatic analysis pipeline to monitor the selections. We identified binders from two single-domain antibody libraries selected against a model protein. Traditional colony-picking was compared with our ONT-UMI method. ONT-UMI enabled monitoring of diversity and enrichment before and after each selection round. By combining phage antibody selections with ONT-UMIs, deep mining of output selections is possible. The approach provides an alternative to traditional screening, enabling diversity quantification after each selection round and rare binder recovery, even when the dominating binder was > 99% abundant. Moreover, it can give insights on binding motifs for further affinity maturation and specificity optimizations. Our results demonstrate a platform for future data guided selection strategies.

ChEC-seq2: an improved chromatin endogenous cleavage sequencing method and bioinformatic analysis pipeline for mapping in vivo protein-DNA interactions.

Defining the in vivo DNA binding specificity of transcription factors (TFs) has relied nearly exclusively on chromatin immunoprecipitation (ChIP). While ChIP reveals TF binding patterns, its resolution is low. Higher resolution methods employing nucleases such as ChIP-exo, chromatin endogenous cleavage (ChEC-seq) and CUT&RUN resolve both TF occupancy and binding site protection. ChEC-seq, in which an endogenous TF is fused to micrococcal nuclease, requires neither fixation nor antibodies. However, the specificity of DNA cleavage during ChEC has been suggested to be lower than the specificity of the peaks identified by ChIP or ChIP-exo, perhaps reflecting non-specific binding of transcription factors to DNA. We have simplified the ChEC-seq protocol to minimize nuclease digestion while increasing the yield of cleaved DNA. ChEC-seq2 cleavage patterns were highly reproducible between replicates and with published ChEC-seq data. Combined with DoubleChEC, a new bioinformatic pipeline that removes non-specific cleavage sites, ChEC-seq2 identified high-confidence cleavage sites for three different yeast TFs that are strongly enriched for their known binding sites and adjacent to known target genes.

Abstract PR002: Single-cell spatial analysis reveals microenvironmental features that contribute to immune discrepancies between adult and pediatric nasopharyngeal carcinomas

Abstract Background: Nasopharyngeal carcinoma (NPC) is an EBV-positive malignancy with high immune infiltrates but also high immunosuppressive activities. Pediatric NPC only constitutes 0.1-1% of the total NPC incidences and exhibits superior treatment responses and prognosis compared to adult NPC. Clinically, we consider that the differences in the tumor landscape between adult and pediatric NPC patients are the vital driver of such immune discrepancies. Therefore, we apply scRNA-seq and Visium spatial sequencing to primary pediatric NPC tissues and paired peripheral blood samples, and integrate our data with multiple independent adult NPC scRNA-seq cohorts. Furthermore, we establish a bioinformatics analysis pipeline that can calculate spatial cellular compositions and biological activities. Here, we demonstrate that low lipid metabolism, disrupted interactions between NPC cells and regulatory T cells (Tregs)/CD8+ T cells, and high infiltration of central stem memory T cells, contribute to more potent and enduring anti-tumor immunity in pediatric NPC patients, and these microenvironmental features might serve as new immunotherapeutic vulnerabilities. Method: 5' scRNA-seq coupled with TCR/BCR profiling was performed on 5 primary and treatment-naïve pediatric NPC tissues with 5 paired peripheral blood samples. Visium spatial-seq was performed on 4 paired pediatric and 7 adult NPC tissues. We incorporated our scRNA-seq cohort with three independent adult NPC cohorts. Our computational framework was integrated with existing analysis packages, including Seurat, Spacexr, and SpaCET, to analyze single-cell-referenced spatial data. The key results in our study were validated by multiplex IHC staining and flow cytometry on patient samples. Results: We established a multi-center NPC scRNA spatial cohort containing 503,021 cells from 69 samples, and 15,222 spatial spots from 11 samples. We developed a computational framework to calculate spatial co-localization and biological activities. We unveiled that lipid metabolism was elevated in the tumor-T cell core in adult NPC. Strong co-localizations between NPC cells and Tregs/CD8+ T cells were found in adult NPC, with spatially enriched CD70-CD27 and LGALS9-TIM3 interactions. This finding was corroborated by multiplex IHC staining of KRT19+ NPC cells, CD4+/FOXP3+ Tregs, and CD8+/PD-1/TIM-3+ exhausted T cells. We also identified and characterized novel IL7R+ central stem memory T cells in pediatric NPC tissues and blood, with strong resilience to immunosuppressive cues, including TGF-β, PEG2, and cyclic AMP, and could lead to enduring immunity and anti-PD-1/PD-L1 responses. Conclusion: We demonstrate that higher T-cell immunosuppression and exhaustion caused by aberrant lipid metabolism, tumor-T interactions, and a lower age-associated central stem memory T-cell pool, are vital drivers of immune discrepancies in adult and pediatric NPC patients. Thus, screening and targeting these molecular microenvironmental characteristics might help stratify immunotherapy responders and generate therapeutic benefits in NPC patients. Citation Format: Lanqi Gong, Yu Zhang, Grace Guan, Beibei Ru, Peng Jiang. Single-cell spatial analysis reveals microenvironmental features that contribute to immune discrepancies between adult and pediatric nasopharyngeal carcinomas [abstract]. In: Proceedings of the AACR-NCI-EORTC Virtual International Conference on Molecular Targets and Cancer Therapeutics; 2023 Oct 11-15; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2023;22(12 Suppl):Abstract nr PR002.

Polycystin-2-dependent transcriptome reveals early response of autosomal dominant polycystic kidney disease.

Autosomal dominant polycystic kidney disease (ADPKD) is caused by mutations in polycystin genes, Pkd1 and Pkd2, but the underlying pathogenic mechanisms are poorly understood. To identify genes and pathways that operate downstream of polycystin-2 (PC2), a comprehensive gene expression database was created, cataloging changes in the transcriptome immediately following PC2 protein depletion. To explore cyst initiation processes, an immortalized mouse inner medullary collecting duct line was developed with the ability to knock out the Pkd2 gene conditionally. Genome-wide transcriptome profiling was performed using RNA sequencing in the cells immediately after PC2 was depleted and compared with isogenic control cells. Differentially expressed genes were identified, and a bioinformatic analysis pipeline was implemented. Altered expression of candidate cystogenic genes was validated in Pkd2 knockout mice. The expression of nearly 900 genes changed upon PC2 depletion. Differentially expressed genes were enriched for genes encoding components of the primary cilia, the canonical Wnt pathway, and MAPK signaling. Among the PC2-dependent ciliary genes, the transcription factor Glis3 was significantly downregulated. MAPK signaling formed a key node at the epicenter of PC2-dependent signaling networks. Activation of Wnt and MAPK signaling, concomitant with the downregulation of Glis3, was corroborated in Pkd2 knockout mice. The data identify a PC2 cilia-to-nucleus signaling axis and dysregulation of the Gli-similar subfamily of transcription factors as a potential initiator of cyst formation in ADPKD. The catalog of PC2-regulated genes should provide a valuable resource for future ADPKD research and new opportunities for drug development.NEW & NOTEWORTHY Autosomal dominant polycystic kidney disease (ADPKD) is the most common inherited kidney disease. Mutations in polycystin genes cause the disease, but the underlying mechanisms of cystogenesis are unknown. To help fill this knowledge gap, we created an inducible cell model of ADPKD and assembled a catalog of genes that respond in immediate proximity to polycystin-2 depletion using transcriptomic profiling. The catalog unveils a ciliary signaling-to-nucleus axis proximal to polycystin-2 dysfunction, highlighting Glis, Wnt, and MAPK signaling.

A rat liver cell atlas reveals intrahepatic myeloid heterogeneity

The large size and vascular accessibility of the laboratory rat (Rattus norvegicus) make it an ideal hepatic animal model for diseases that require surgical manipulation. Often, the disease susceptibility and outcomes of inflammatory pathologies vary significantly between strains. This study uses single-cell transcriptomics to better understand the complex cellular network of the rat liver, as well as to unravel the cellular and molecular sources of inter-strain hepatic variation. We generated single-cell and single-nucleus transcriptomic maps of the livers of healthy Dark Agouti and Lewis rat strains and developed a factor analysis-based bioinformatics analysis pipeline to study data covariates, such as strain and batch. Using this approach, we discovered transcriptomic variation within the hepatocyte and myeloid populations that underlie distinct cell states between rat strains. This finding will help provide a reference for future investigations on strain-dependent outcomes of surgical experiment models.

Using combined single-cell gene expression, TCR sequencing and cell surface protein barcoding to characterize and track CD4+ T cell clones from murine tissues.

Single-cell gene expression analysis using sequencing (scRNA-seq) has gained increased attention in the past decades for studying cellular transcriptional programs and their heterogeneity in an unbiased manner, and novel protocols allow the simultaneous measurement of gene expression, T-cell receptor clonality and cell surface protein expression. In this article, we describe the methods to isolate scRNA/TCR-seq-compatible CD4+ T cells from murine tissues, such as skin, spleen, and lymph nodes. We describe the processing of cells and quality control parameters during library preparation, protocols for multiplexing of samples, and strategies for sequencing. Moreover, we describe a step-by-step bioinformatic analysis pipeline from sequencing data generated using these protocols. This includes quality control, preprocessing of sequencing data and demultiplexing of individual samples. We perform quantification of gene expression and extraction of T-cell receptor alpha and beta chain sequences, followed by quality control and doublet detection, and methods for harmonization and integration of datasets. Next, we describe the identification of highly variable genes and dimensionality reduction, clustering and pseudotemporal ordering of data, and we demonstrate how to visualize the results with interactive and reproducible dashboards. We will combine different analytic R-based frameworks such as Bioconductor and Seurat, illustrating how these can be interoperable to optimally analyze scRNA/TCR-seq data of CD4+ T cells from murine tissues.

Diagnostic genome sequencing improves diagnostic yield: a prospective single-centre study in 1000 patients with inherited eye diseases

PurposeGenome sequencing (GS) is expected to reduce the diagnostic gap in rare disease genetics. We aimed to evaluate a scalable framework for genome-based analyses ‘beyond the exome’ in regular care...

TheiaEuk: a species-agnostic bioinformatics workflow for fungal genomic characterization.

The clinical incidence of antimicrobial-resistant fungal infections has dramatically increased in recent years. Certain fungal pathogens colonize various body cavities, leading to life-threatening bloodstream infections. However, the identification and characterization of fungal isolates in laboratories remain a significant diagnostic challenge in medicine and public health. Whole-genome sequencing provides an unbiased and uniform identification pipeline for fungal pathogens but most bioinformatic analysis pipelines focus on prokaryotic species. To this end, TheiaEuk_Illumina_PE_PHB (TheiaEuk) was designed to focus on genomic analysis specialized to fungal pathogens. TheiaEuk was designed using containerized components and written in the workflow description language (WDL) to facilitate deployment on the cloud-based open bioinformatics platform Terra. This species-agnostic workflow enables the analysis of fungal genomes without requiring coding, thereby reducing the entry barrier for laboratory scientists. To demonstrate the usefulness of this pipeline, an ongoing outbreak of C. auris in southern Nevada was investigated. We performed whole-genome sequence analysis of 752 new C. auris isolates from this outbreak. Furthermore, TheiaEuk was utilized to observe the accumulation of mutations in the FKS1 gene over the course of the outbreak, highlighting the utility of TheiaEuk as a monitor of emerging public health threats when combined with whole-genome sequencing surveillance of fungal pathogens. A primary result of this work is a curated fungal database containing 5,667 unique genomes representing 245 species. TheiaEuk also incorporates taxon-specific submodules for specific species, including clade-typing for Candida auris (C. auris). In addition, for several fungal species, it performs dynamic reference genome selection and variant calling, reporting mutations found in genes currently associated with antifungal resistance (FKS1, ERG11, FUR1). Using genome assemblies from the ATCC Mycology collection, the taxonomic identification module used by TheiaEuk correctly assigned genomes to the species level in 126/135 (93.3%) instances and to the genus level in 131/135 (97%) of instances, and provided zero false calls. Application of TheiaEuk to actual specimens obtained in the course of work at a local public health laboratory resulted in 13/15 (86.7%) correct calls at the species level, with 2/15 called at the genus level. It made zero incorrect calls. TheiaEuk accurately assessed clade type of Candida auris in 297/302 (98.3%) of instances. TheiaEuk demonstrated effectiveness in identifying fungal species from whole genome sequence. It further showed accuracy in both clade-typing of C. auris and in the identification of mutations known to associate with drug resistance in that organism.

NUDT21 alters glioma migration through differential alternative polyadenylation of LAMC1.

Gliomas and their surrounding microenvironment constantly interact to promote tumorigenicity, yet the underlying posttranscriptional regulatory mechanisms that govern this interplay are poorly understood. Utilizing our established PAC-seq approach and PolyAMiner bioinformatic analysis pipeline, we deciphered the NUDT21-mediated differential APA dynamics in glioma cells. We identified LAMC1 as a critical NUDT21 alternative polyadenylation (APA) target, common in several core glioma-driving signaling pathways. qRT-PCR analysis confirmed that NUDT21-knockdown in glioma cells results in the preferred usage of the proximal polyA signal (PAS) of LAMC1. Functional studies revealed that NUDT21-knockdown-induced 3'UTR shortening of LAMC1 is sufficient to cause translational gain, as LAMC1 protein is upregulated in these cells compared to their respective controls. We demonstrate that 3'UTR shortening of LAMC1 after NUDT21 knockdown removes binding sites for miR-124/506, thereby relieving potent miRNA-based repression of LAMC1 expression. Remarkably, we report that the knockdown of NUDT21 significantly promoted glioma cell migration and that co-depletion of LAMC1 with NUDT21 abolished this effect. Lastly, we observed that LAMC1 3'UTR shortening predicts poor prognosis of low-grade glioma patients from The Cancer Genome Atlas. This study identifies NUDT21 as a core alternative polyadenylation factor that regulates the tumor microenvironment through differential APA and loss of miR-124/506 inhibition of LAMC1. Knockdown of NUDT21 in GBM cells mediates 3'UTR shortening of LAMC1, contributing to an increase in LAMC1, increased glioma cell migration/invasion, and a poor prognosis.

The diagnosis of hereditary cancer syndromes with atypical manifestation: clinical cases

Background: Germinal pathogenic variants are the cause of the development of hereditary cancer syndromes (HCS). Various genetic tests are used for HCS detect, from the «frequent» mutations of one or several genes analysis to the full-length gene sequence, next-generation sequencing (NGS) based panel, whole exome (WES) or whole genome sequencing (WGS).There are some HCS cases with atypical clinical manifestations and the family history does not allow one to suspect a specific HCS and limit oneself to the study of only one or a few genes. Conducting research using NGS to assess the selected sample of cancer patient’s genetic characteristics has revealed atypical HCS cases.Aim: To present the WGS diagnosis results for two atypical hereditary tumor syndromes cases.Materials and methods: DNA isolation was performed using Qiagen DNA Isolation kit. WGS for all samples was performed at DNBSEQ-T7 (MGI) and DNBSEQ-G400 (MGI) sequencing platforms using PCR-free protocol with average sample coverage 30x. A standard bioinformatics analysis pipeline was implemented for all the samples data processing.Potential clinically relevant variants were validated using Sanger sequencing. For all patients was received signed a written consent.Results: In the first case report, a pathogenic variant in the TP53 gene was identified: c. 637C > T, p. Arg213Ter, rs397516436, and Li – Fraumeni syndrome was confirmed. In the second case, we detected two pathogenic variants carrier — BRCA2: c. 6644_6647del, p. Tyr2215SerfsTer13, rs80359616 and MSH2: c. 1906G > C, p. Ala636Pro, rs63750875 associated with hereditary breast and ovarian cancer and hereditary colorectal cancer (Lynch syndrome).Conclusion: NGS, including WGS makes it easier to identify all clinically significant germline variants associated with hereditary cancer syndromes in cancer patients, as well as to trace their segregation in relatives.

The challenges of the genome-based identification of antifungal resistance in the clinical routine.

The increasing number of chronic and life-threatening infections caused by antimicrobial resistant fungal isolates is of critical concern. Low DNA sequencing cost may facilitate the identification of the genomic profile leading to resistance, the resistome, to rationally optimize the design of antifungal therapies. However, compared to bacteria, initiatives for resistome detection in eukaryotic pathogens are underdeveloped. Firstly, reported mutations in antifungal targets leading to reduced susceptibility must be extensively collected from the literature to generate comprehensive databases. This information should be complemented with specific laboratory screenings to detect the highest number possible of relevant genetic changes in primary targets and associations between resistance and other genomic markers. Strikingly, some drug resistant strains experience high-level genetic changes such as ploidy variation as much as duplications and reorganizations of specific chromosomes. Such variations involve allelic dominance, gene dosage increments and target expression regime effects that should be explicitly parameterized in antifungal resistome prediction algorithms. Clinical data indicate that predictors need to consider the precise pathogen species and drug levels of detail, instead of just genus and drug class. The concomitant needs for mutation accuracy and assembly quality assurance suggest hybrid sequencing approaches involving third-generation methods will be utilized. Moreover, fatal fast infections, like fungemia and meningitis, will further require both sequencing and analysis facilities are available in-house. Altogether, the complex nature of antifungal resistance demands extensive sequencing, data acquisition and processing, bioinformatic analysis pipelines, and standard protocols to be accomplished prior to genome-based protocols are applied in the clinical setting.

Colored stains: Microbial survey of cellulose-based and lignin rich papers

Over the centuries, various types of paper have been produced, each characterized by a different ratio of natural macromolecules, mainly lignin and cellulose. Handmade paper has a higher content of cellulose with respect to the early machine-made paper, where lignin is the other important component. Microorganisms are able to colonize and deteriorate both types. They can release on their surfaces pigments and colorants which produced anesthetic stains. To better understand the microbiota colonizing these stains, 17 samples were analyzed, from both handmade and machine-made paper surfaces, as well as library and archive environments. Combination of microbiological and high-throughput sequencing (HTS) approaches were applied. The culture-dependent methodology comprised: isolation, DNA identification, hydrolytic and paper staining assays. The HTS was performed by MinION platform and for the mycobiome a more suitable bioinformatics analysis pipeline, MetONTIIME based on QIIME2 framework, was applied. The paper model staining assay permitted the direct recognition of colorizing isolates which in combination with sequencing data evidenced a complex microbial community able to stain the two types of paper. Staining abilities were confirmed by frequently isolated and detected fungi as well as newly discovered ones Roussoella euonymi and Achaetomium. We have also evidenced the staining ability of several bacteria.

A framework for real-time monitoring, analysis and adaptive sampling of viral amplicon nanopore sequencing.

The ongoing SARS-CoV-2 pandemic demonstrates the utility of real-time sequence analysis in monitoring and surveillance of pathogens. However, cost-effective sequencing requires that samples be PCR amplified and multiplexed via barcoding onto a single flow cell, resulting in challenges with maximising and balancing coverage for each sample. To address this, we developed a real-time analysis pipeline to maximise flow cell performance and optimise sequencing time and costs for any amplicon based sequencing. We extended our nanopore analysis platform MinoTour to incorporate ARTIC network bioinformatics analysis pipelines. MinoTour predicts which samples will reach sufficient coverage for downstream analysis and runs the ARTIC networks Medaka pipeline once sufficient coverage has been reached. We show that stopping a viral sequencing run earlier, at the point that sufficient data has become available, has no negative effect on subsequent down-stream analysis. A separate tool, SwordFish, is used to automate adaptive sampling on Nanopore sequencers during the sequencing run. This enables normalisation of coverage both within (amplicons) and between samples (barcodes) on barcoded sequencing runs. We show that this process enriches under-represented samples and amplicons in a library as well as reducing the time taken to obtain complete genomes without affecting the consensus sequence.

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.