Christophe Lambert

High-throughput sequencing (HTS) is an agnostic virus detection technology that can replace or supplement the conventional adventitious virus assays used for safety testing of biologics. Transcriptomic HTS is particularly suitable for the detection of replicating viruses. We have evaluated the sensitivity of virus detection by transcriptomic HTS using targeted and non-targeted bioinformatics analysis of infected cells spiked into a background of uninfected cells, mimicking material representing a test sample from product manufacturing. Eight laboratories tested a mix of Raji cells (expressing Epstein-Barr virus [EBV] RNAs without virus production) and EBV-negative Ramos cells at ratios ranging from 0.5 to 0.00005. Seven laboratories performed the analysis using different HTS protocols, while the eighth laboratory quantified EBV RNA recovery in all extracted samples by quantifying EBNA-1 by RT-ddPCR assay. All participants detected EBV transcripts at a 0.001 ratio with the targeted analysis and a 0.01 ratio with the non-targeted analysis (i.e., one infected cell in 100 uninfected cells), with four laboratories achieving detection at the 0.0001 ratio for both analysis. The results of the spiking studies demonstrated the capabilities of transcriptomic HTS for adventitious virus detection in cell lines used for the production of biologics, and highlight that optimization in the workflow can improve HTS virus detection. IMPORTANCE: High-throughput sequencing (HTS) is currently recognized as a powerful technology for broad virus detection. While HTS can be considered an alternative method to replace or supplement the conventional adventitious virus detection assays for testing biologics, its routine implementation has been challenging due to the complexities of the HTS workflow, including the matrix of the test sample, selection of HTS methodology (viromic, genomic, or transcriptomic), and variability in the protocols (from sample preparation to bioinformatic analysis). The study demonstrated the performance and capabilities of the transcriptomic HTS approach for the detection of virus-infected cells using different workflows presented by seven laboratories. The study indicated that regardless of the protocol used, all participants detected viral RNA at the 0.01 ratio; however, certain HTS protocols demonstrated better sensitivity of viral detection.

Chronic obstructive pulmonary disease (COPD) is an etiologically complex disease characterized by acute exacerbations and stable phases. We aimed to identify biological functions modulated in specific COPD conditions, using whole blood samples collected in the AERIS clinical study (NCT01360398). Considered conditions were exacerbation onset, severity of airway obstruction, and presence of respiratory pathogens in sputum samples. With an integrative multi-network gene community detection (MNGCD) approach, we analyzed expression profiles to identify communities of correlated genes. The approach combined different layers of gene interactions for each explored condition/subset of samples: gene expression similarity, protein-protein interactions, transcription factors, and microRNAs validated regulons. Heme metabolism, interferon-alpha, and interferon-gamma pathways were modulated in patients at both exacerbation and stable-state visits, but with the involvement of distinct sets of genes. An important gene community was enriched with G2M checkpoint, E2F targets, and mitotic spindle pathways during exacerbation. Targets of TAL1 regulator and hsa−let−7b − 5p microRNA were modulated with increasing severity of airway obstruction. Bacterial infections with Moraxella catarrhalis and, particularly, Haemophilus influenzae triggered a specific cellular and inflammatory response in acute exacerbations, indicating an active reaction of the host to infections. In conclusion, COPD is a complex multifactorial disease that requires in-depth investigations of its causes and features during its evolution and whole blood transcriptome profiling can contribute to capturing some relevant regulatory mechanisms associated with this disease. In this work, we explored multi-network modeling that integrated diverse layers of regulatory gene networks and enhanced our comprehension of the biological functions implicated in the COPD pathogenesis.

Background: Multi-locus sequence typing (MLST) is a standard typing technique used to associate a sequence type (ST) to a bacterial isolate. When the output of whole genome sequencing (WGS) of a sample is available the ST can be assigned directly processing the read-set. Current approaches employ reads mapping (SRST2) against the MLST loci, k-mer distribution (stringMLST), selective assembly (GRAbB) or whole genome assembly (BIGSdb) followed by BLASTn sequence query. Here we present STRAIN (ST Reduced Assembly IdentificatioN), an R package that implements a hybrid strategy between assembly and mapping of the reads to assign the ST to an isolate starting from its read-sets. Results: Analysis of 540 publicly accessible Illumina read sets showed STRAIN to be more accurate at correct allele assignment and new alleles identification compared to SRTS2, stringMLST and GRAbB. STRAIN assigned correctly 3666 out of 3780 alleles (capability to identify correct alleles 97%) and, when presented with samples containing new alleles, identified them in 3730 out of 3780 STs (capability to identify new alleles 98.7%) of the cases. On the same dataset the other tested tools achieved lower capability to identify correct alleles (from 28.5 to 96.9%) and lower capability to identify new alleles (from 1.1 to 97.1%). Conclusions: STRAIN is a new accurate method to assign the alleles and ST to an isolate by processing the raw reads output of WGS. STRAIN is also able to retrieve new allele sequences if present. Capability to identify correct and new STs/alleles, evaluated on a benchmark dataset, are higher than other existing methods. STRAIN is designed for single allele typing as well as MLST. Its implementation in R makes allele and ST assignment simple, direct and prompt to be integrated in wider pipeline of downstream bioinformatics analyses.

High-throughput sequencing (HTS) has demonstrated capabilities for broad virus detection based upon discovery of known and novel viruses in a variety of samples, including clinical, environmental, and biological. An important goal for HTS applications in biologics is to establish parameter settings that can afford adequate sensitivity at an acceptable computational cost (computation time, computer memory, storage, expense or/and efficiency), at critical steps in the bioinformatics pipeline, including initial data quality assessment, trimming/cleaning, and assembly (to reduce data volume and increase likelihood of appropriate sequence identification). Additionally, the quality and reliability of the results depend on the availability of a complete and curated viral database for obtaining accurate results; selection of sequence alignment programs and their configuration, that retains specificity for broad virus detection with reduced false-positive signals; removal of host sequences without loss of endogenous viral sequences of interest; and use of a meaningful reporting format, which can retain critical information of the analysis for presentation of readily interpretable data and actionable results. Furthermore, after alignment, both automated and manual evaluation may be needed to verify the results and help assign a potential risk level to residual, unmapped reads. We hope that the collective considerations discussed in this paper aid toward optimization of data analysis pipelines for virus detection by HTS.

COPD patients have increased risk of developing pneumonia, which is associated with poor outcomes. It can be symptomatically indistinguishable from exacerbations, making diagnosis challenging. Studies of pneumonia in COPD have focused on hospitalised patients and are not representative of the ambulant COPD population. Therefore, we sought to determine the incidence and aetiology of acute exacerbation events with evidence of pneumonic radiographic infiltrates in an outpatient COPD cohort. One hundred twenty-seven patients with moderate to very severe COPD aged 42–85 years underwent blood and sputum sampling over one year, at monthly stable visits and within 72 h of exacerbation symptom onset. 343 exacerbations with chest radiographs were included. 20.1% of exacerbations had pneumonic infiltrates. Presence of infiltrate was highly seasonal (Winter vs summer OR 3.056, p = 0.027). In paired analyses these exacerbation events had greater increases in systemic inflammation. Bacterial detection rate was higher in the pneumonic group, with Haemophilus influenzae the most common bacteria in both radiological groups. Viral detection and sputum microbiota did not differ with chest radiograph appearance. In an outpatient COPD cohort, pneumonic infiltrates at exacerbation were common, and associated with more intense inflammation. Bacterial pathogen detection and lung microbiota were not distinct, suggesting that exacerbations and pneumonia in COPD share common infectious triggers and represent a continuum of severity rather than distinct aetiological events. Trial registration Number: NCT01360398 .

Background: Alterations in the composition of the lung microbiome associated with adverse clinical outcomes, known as dysbiosis, have been implicated with disease severity and exacerbations in COPD.Objective: To characterise longitudinal changes in the lung microbiome in the AERIS study (Acute Exacerbation and Respiratory InfectionS in COPD) and their relationship with associated COPD outcomes.Methods: We surveyed 584 sputum samples from 101 patients with COPD to analyse the lung microbiome at both stable and exacerbation time points over 1 year using high-throughput sequencing of the 16S ribosomal RNA gene. We incorporated additional lung microbiology, blood markers and in-depth clinical assessments to classify COPD phenotypes.Results: The stability of the lung microbiome over time was more likely to be decreased in exacerbations and within individuals with higher exacerbation frequencies. Analysis of exacerbation phenotypes using a Markov chain model revealed that bacterial and eosinophilic exacerbations were more likely to be repeated in subsequent exacerbations within a subject, whereas viral exacerbations were not more likely to be repeated. We also confirmed the association of bacterial genera, including Haemophilus and Moraxella, with disease severity, exacerbation events and bronchiectasis.Conclusions: Subtypes of COPD have distinct bacterial compositions and stabilities over time. Some exacerbation subtypes have non-random probabilities of repeating those subtypes in the future. This study provides insights pertaining to the identification of bacterial targets in the lung and biomarkers to classify COPD subtypes and to determine appropriate treatments for the patient.Trial registration number: Results, NCT01360398.

Several nucleic-acid based technologies have recently emerged with capabilities for broad virus detection. One of these, high throughput sequencing, has the potential for novel virus detection because this method does not depend upon prior viral sequence knowledge. However, the use of high throughput sequencing for testing biologicals poses greater challenges as compared to other newly introduced tests due to its technical complexities and big data bioinformatics. Thus, the Advanced Virus Detection Technologies Users Group was formed as a joint effort by regulatory and industry scientists to facilitate discussions and provide a forum for sharing data and experiences using advanced new virus detection technologies, with a focus on high throughput sequencing technologies. The group was initiated as a task force that was coordinated by the Parenteral Drug Association and subsequently became the Advanced Virus Detection Technologies Interest Group to continue efforts for using new technologies for detection of adventitious viruses with broader participation, including international government agencies, academia, and technology service providers.

φCbK is a B3 morphotype bacteriophage of the Siphoviridae family that infects Caulobacter crescentus , the preeminent model system for bacterial cell cycle studies. The last 4 decades of research with φCbK as a genetic and cytological tool to study the biology of the host warrant an investigation of the phage genome composition. Herein, we report the complete genome sequence of φCbK and highlight unusual features that emerged from its annotation. The complete genome analysis of the φCbK phage provides new insight into its characteristics and potential interactions with its Caulobacter crescentus host, setting the stage for future functional studies with φCbK.